Geographic structure of metropolitan segregation in the United States

Immigrant suburbanisation and the shifting geographic structure of metropolitan
segregation in the United States
Author(s): Chad R Farrell
Source: Urban Studies , Vol. 53, No. 1 (JANUARY 2016), pp. 57-76
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Urban Studies
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Urban Studies
2016, Vol. 53(1) 57–76
Urban Studies Journal Limited 2014
Reprints and permissions:
DOI: 10.1177/0042098014558537
Immigrant suburbanisation and the
shifting geographic structure of
metropolitan segregation in the
United States
Chad R Farrell
University of Alaska Anchorage, USA
This study investigates immigrant suburbanisation trends over the past decade in the metropolitan USA, focusing on how suburbanisation affects the residential segregation of foreign-born
populations. Using 2000–2012 data from the decennial census and American Community Survey,
it tracks the suburban settlement patterns of 17 country-of-origin groups. It uses a methodological
approach that decomposes metropolitan segregation into within and between city/suburb components. The findings indicate that most immigrant groups rapidly suburbanised during the 2000s,
though there remain large differences in suburbanisation rates among country-of-origin groups.
Immigrant suburbanites tend to be less segregated from US-born whites than are their coethnic
counterparts in large cities. Suburbanisation continues to blur the city/suburb divide, which now
accounts for a small share of the segregation experienced by most groups. At the metro level, suburbanisation is associated with lower levels of immigrant segregation even after controlling for relevant metropolitan characteristics. These findings are consistent with spatial assimilation, though
trends over time suggest a more complicated picture. While immigrants are gaining access to the
suburbs, most groups experienced increasing segregation at the same time they were rapidly suburbanising. This is due to increasing segregation within the suburbs, which often offset segregation
declines occurring within large cities. These findings underscore the importance of understanding
the underlying and often countervailing city/suburb contributions to metropolitan segregation.
enclave, immigration, metropolitan, neighbourhood, segregation, suburb
Received August 2014; accepted October 2014
Over the past 50 years, two demographic
forces have profoundly changed the structure and composition of metropolitan
America: suburbanisation and immigration
(Alba and Nee, 2003; Iceland, 2009;
Corresponding author:
Chad R Farrell, Department of Sociology, University of
Alaska Anchorage, 3211 Providence Drive (SSB 372),
Anchorage, AK 99508-4614, USA.
Email: [email protected]
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Jackson, 1987; Singer et al., 2008). As immigrants have become more geographically
dispersed throughout the USA, there has
been increasing scholarly interest in immigrant settlement patterns in new destinations
outside of traditional gateways (Hall, 2013;
Lichter et al., 2010; Singer, 2005). Some of
these new destinations include smaller cities
and rural counties, but most of them are
found in suburbia. Reversing a pattern of
urban concentration in previous eras, immigrants often bypass large cities altogether
for settlement in the suburbs. In 1980, about
four out of ten immigrants were suburbanites. By 2010, over half of the foreign-born
population was suburbanised and this figure
rises to 61% in large metropolitan areas
(Wilson and Singer, 2010).
Immigrant suburbanisation has contributed to the increasing ethnoracial diversity of
the suburban ring. During the 2000s,
Hispanics alone accounted for nearly half of
suburban population growth in the largest
100 metropolitan areas and the majority of
‘melting pot suburbs’ are concentrated in
immigrant-rich metropolitan areas in
California, Texas and Florida (Frey, 2011).
How are these newcomers incorporated into
the residential mix of suburbia? This is a
crucial question given that residential segregation continues to have implications for
racial and ethnic disparities in education,
employment and health (Iceland, 2009;
Massey and Denton, 1993; White and Glick,
2009). Thus, it is important to identify
whether our diversifying suburban rings are
indeed integrating or instead fragmenting
into a mix of homogeneous neighbourhoods
like we see in many of our largest cities.
Existing research on suburbanisation and
residential inequality often relies on panethnic ethnoracial groupings, thereby treating
Latino or Asian populations as monoliths
(Clark, 2006; Farrell, 2008; Lichter et al.,
2010; Massey and Denton, 1988). This is
problematic given the cultural, linguistic and
geographic diversity within their ranks, and
this is doubly so for the foreign-born segments of these populations (Kim and White,
2010). This study falls in line with other
recent work (Hall, 2013; Iceland, 2009;
Lobo et al., 2007) that forgoes the panethnic
approach by looking at settlement patterns
of immigrants from 17 different countriesof-origin. In addition to considering a wide
array of groups, it uses a novel methodological approach that decomposes metropolitan
segregation into within and between city/
suburb components. It is structured around
four guiding questions: (1) Are immigrant
suburbanites less segregated than their
urban counterparts? (2) To what degree does
suburbanisation affect immigrant segregation in metropolitan areas? (3) Does the
impact of suburbanisation hold after taking
other relevant metropolitan characteristics
into account? (4) Are these findings consistent across country-of-origin groups?
The residential incorporation of
immigrant groups
Assimilation or stratification?
Residential attainment is a crucial bellwether
for immigrant incorporation into a host
society. This is the fundamental assumption
of the spatial assimilation perspective, which
focuses on the residential patterns of immigrants and their offspring (Massey, 1985;
Rosenbaum and Friedman, 2007). The logic
of the perspective is straightforward. Upon
entry into the USA, immigrant groups will
tend to settle in segregated residential areas
within large cities. The reason for this clustering is both cultural and economic.
Newcomers seek support and a sense of
belonging and they can find it in the social
networks and institutions that an ethnic
enclave provides. There are also economic
benefits to residing in an enclave, including
access to ethnic labour markets and entry
58 Urban Studies 53(1)
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into occupational niches. Over time, the perspective predicts, group members that
become more acculturated and socioeconomically mobile will become more residentially dispersed as their social networks and
housing options expand (Charles, 2007;
Johnston et al., 2002; Massey and Denton,
1988; South et al., 2008). A major component of this spatial dispersion involves suburbanisation and greater residential contact
with the US-born population. Seen in this
light, suburbanisation is integral to spatial
assimilation (Clark, 2007). If true, immigrant suburbanites should be less segregated
than their urban counterparts and higher
rates of suburbanisation at the metropolitan
level should be associated with lower levels
of immigrant segregation.
One issue with the spatial assimilation
perspective as it was originally formulated is
the implicit assumption that the process of
assimilation works in the same fashion
across immigrant groups. Portes and Zhou
(1993) introduced a segmented assimilation
approach to address this limitation. It recognises that immigrant groups face different
opportunities and obstacles in a host country depending on their race, national origin,
refugee status and access to ethnic networks
and resources. This helps account for the
fact that detailed ancestry groups often exhibit disparate segregation patterns and trajectories (Fong and Hou, 2009), even within
the same metropolitan area (Lee et al.,
2014). Owing to in-group preferences (Clark,
2002; Fong and Chan, 2010) or reliance on
ethnic housing markets (Zhou, 1992), certain
groups may be more likely to seek out clustered ethnic neighbourhoods. Other more
suburbanised groups may rely on ‘heterolocal’ ethnic networks that need not be tethered to a segregated enclave (Zelinsky and
Lee, 1998). As such, the segmented assimilation model predicts that country-of-origin
groups will have divergent rates of suburban
settlement and that group segregation
patterns will vary within the suburban ring
as well.
Another underlying assumption of the
spatial assimilation perspective is much less
applicable in the current era because of
changing immigrant settlement patterns.
Rather than opting for residence in the
dense urban core, many immigrants have
responded to the decentralisation of jobs by
settling directly in the suburbs (Liu and
Painter, 2011). How are they received? The
place stratification perspective points to several barriers to spatial assimilation, some of
which include white racial attitudes, preferences and behaviours. White discomfort
with racially diverse neighbourhoods is welldocumented, though there is some debate
about whether this is ultimately based on
racial (or nativist) animus or class-based
concerns (Charles, 2006; Ellen, 2000; Harris,
1999; Krysan et al., 2009). Either way,
neighbourhood demographic dynamics are
consistent with white flight and avoidance of
burgeoning immigrant populations
(Crowder et al., 2011). Perhaps this helps
account for rapid white population growth
in homogeneous exurban areas distant from
diversifying inner suburban rings (Frey,
The place stratification perspective also
identifies institutional barriers to residential
integration (Massey and Denton, 1993).
Housing audits and analyses of mortgage
data indicate that minority households continue to face discrimination in housing and
lending (Pager and Shepherd, 2008). A
recent housing audit carried out by the US
Department of Housing and Urban
Development found that black, Hispanic
and Asian testers seeking information about
rental units were told about and shown
fewer units than white testers (Turner et al.,
2013). Asian home seekers experienced the
highest levels of discrimination; they were
told about and shown 15.5% and 18.8%
fewer properties, respectively, than their
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white counterparts (Turner et al., 2013). In
addition to subtle forms of housing discrimination, purportedly race-neutral zoning regulations could also serve to segregate
immigrants. Rothwell and Massey (2009)
find that anti-density zoning leads to
increased racial segregation by essentially
pricing out many minority residents. These
regulations are particularly prevalent in
highly suburbanised metropolitan areas with
fragmented local governments. The place
stratification perspective predicts that institutional practices, legal and illegal, limit the
housing choices of foreign-born racial minorities and will increase segregation as a
result. This segregation could be manifested
in a widening residential city/suburb divide
between immigrants and native whites or to
increased segregation within the suburban
ring itself.
Immigrant segregation and
While there is a great deal of research on
immigrant suburbanisation (Frey, 2011;
Singer et al., 2008; Wilson and Singer, 2010)
and immigrant segregation (Hall 2013;
Iceland, 2009; Iceland and Scopilliti, 2008;
Lieberson, 1980), there are relatively few
studies that systematically analyse the interplay between the two. Using methods similar
to those used in the present study, Fischer et
al. (2004) decompose neighbourhood segregation by race, class, and lifecycle into its
geographic components for the entire USA.
They find that neighbourhood segregation
between the foreign-born and US-born population increased by two-thirds between
1960 and 2000. Much of this increase was
due to increasing immigrant concentrations
in certain metropolitan areas and to increasing segregation within city and suburban
communities. Their decomposition does not
separate out the relative contributions of
segregation shifts within cities compared
with that occurring within suburbs, but it
clearly shows that the differences between
cities and suburbs is playing a small and
decreasing role. By 2000, the city/suburb
divide between the foreign-born and USborn population accounted for just 4% of
total immigrant segregation in the US.
Rather than focusing on immigrants in
the aggregate, Cutler et al. (2008) measure
immigrant segregation trends for specific
nativity groups between 1910 and 2000.
They identify segregation declines in the
early part of the century but, consistent with
Fischer et al. (2004), these declines had
reversed by 1970. As the spatial assimilation
perspective would predict, they find that segregation increases in recent decades are due
largely to an influx of new immigrants that
are of lower socioeconomic status and less
proficient with the English language than
previous waves. Additionally, they contend
that suburbanisation plays a role in this
increasing segregation. Because immigrants
rely on public transit more than the native
population, they are forced to residentially
cluster around public transit hubs in heavily
automobile-dependent (i.e. suburbanised)
metropolitan areas. Thus, the increasingly
sprawled nature of metropolitan areas has
driven immigrants to segregate, though the
authors do not address whether or to what
degree this is occurring in the suburbs.
Alba et al. (1999) find disparate segregation patterns across groups residing in the
cities and suburbs of New York and Los
Angeles. Mexicans in both metropolitan
areas are less segregated in the city than they
are in the suburban ring while Cubans exhibit the opposite pattern. High levels of suburban segregation are particularly high in
Los Angeles, with Chinese, Indians and
Vietnamese all registering lower segregation
levels in the city than in its outskirts. Logan
et al. (2002) also set their sights on Los
Angeles and New York. The thrust of their
study is not toward immigrant segregation
60 Urban Studies 53(1)
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per se but instead the related issue of ethnic
neighbourhoods. They present evidence that
many Cubans, Mexicans, Chinese and
Vietnamese opt for suburban neighbourhoods with large concentrations of
Clark and Blue (2004) also do not look
directly at immigrant segregation but do
assess black, Asian and Hispanic segregation
from whites in a small sample of immigrant
gateway cities. They find all groups tend to
be less segregated in the suburbs, though the
suburban advantage tends to be smaller (and
occasionally reversed) for Latinos and
Asians than it is for Blacks. In a subsequent
study of 16 large metropolitan areas, Clark
(2007) finds further evidence of Asian and
Latino integration in the suburbs relative to
cities. Lichter et al. (2010) analyse Hispanic
settlement patterns in a much larger sample
of places. Consistent with the previous two
studies, they find that in 2000 Hispanics were
generally less segregated in suburban places
than they were in central cities. However,
Hispanic suburbanites tend to be highly segregated in a subset of new destinations where
the growing Hispanic population is disproportionately foreign-born. In fact, some of
the most segregated communities in America
are found in the suburban ring of these emerging Hispanic immigrant destinations.
Previous research on immigrant settlement patterns suffers from a number of limitations. These include focusing on overly
broad panethnic groups, relying on limited
metropolitan sample sizes, and leaving the
impact of immigrant suburbanisation unexamined. The present study seeks to contribute
to the literature on immigrant suburbanisation and segregation in a number of ways.
First, it will provide data on the most recent
immigrant segregation trends occurring in
the USA for a large sample of metropolitan
areas. Second, it will look at the settlement
patterns of 17 country-of-origin groups,
thereby producing a more refined picture of
immigrant segregation than many studies
provide. Third, it will decompose immigrant
segregation into its constituent geographic
components and assess the relative city/suburb contributions to shifting settlement patterns across metropolitan neighbourhoods.
Data and methods
This study incorporates decennial census
data from 2000 Summary File 3 and data
from the 2008–2012 American Community
Survey (ACS) 5-year estimates. I have
selected the 17 largest country-of-origin
groups from the foreign-born population, all
of which had estimated national populations
of 500,000 or more in the 2008–2012 ACS.
These country-of-origin groups include
immigrants hailing from five Asian countries
(China, India, Korea, Philippines, Vietnam),
five Latin American countries (Colombia, El
Salvador, Guatemala, Honduras, Mexico),
four Caribbean countries (Cuba, Dominican
Republic, Haiti, Jamaica), two European
countries (Germany, United Kingdom) and
Canada. The US-born segment of the nonHispanic white population will serve as the
reference group. This follows the convention
in most segregation research and reflects the
reality that native whites are the largest and
most suburbanised ethnoracial group in the
USA. Furthermore, they are the group least
likely to embrace residential integration
(Charles, 2006) and most likely to move in
the face of a proximate and growing immigrant population (Crowder et al., 2011).
This makes native whites a particularly pertinent reference group to consider when
investigating immigrant incorporation in the
Metropolitan areas are defined according
to the 2009 Office of Management and
Budget specifications. The analyses include a
sample of 263 metropolitan areas with at
least 1000 foreign-born residents in both
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2000 and 2008–2012 and at least one principal
city of 50,000 or more in 2010. Group-specific
analyses result in varying metro sample sizes
because they are limited to metropolitan areas
in which the group population size is 1000 or
more. Census tracts serve as the proxies for
metropolitan neighbourhoods in this study,
and all tract data were extracted using the
Social Explorer online research tool (Social
Explorer, 2014).1 Census tracts are excluded if
their group quarters populations make up
more than one-quarter of total population.
This results in a total of 57,752 tracts making
up 263 metropolitan areas. Tracts located outside of principal cities of 50,000 or more are
coded as suburban. Thus, suburban tracts will
be found in both suburban municipalities and
in unincorporated areas. In cases where a
tract crosses principal city boundaries it is
assigned city/suburb status based on where
the majority of the tract population is located.
Measuring and decomposing segregation
While many studies of immigrant segregation
use the dissimilarity index (Hall, 2013; Iceland,
2009; Iceland and Scopilliti, 2008), I opt for
Theil’s (1972) information theory index (H)
because it is decomposable into geographic
components. This will allow me to assess the
degree to which overall metropolitan segregation is a function of neighbourhood differences
within cities, within suburbs, or between cities
and suburbs. In order to estimate H it is first
necessary to calculate an entropy index (E),
which reflects the degree to which US-born
white and foreign-born populations co-reside
in a given geographic unit:
E = Xn
r =1
Qr ln 1
where Qr refers to a nativity group’s proportion of a particular geographic area. In the
two-group framework used here the denominator for Qr includes the immigrant group in
question combined with the native white
population; it excludes other nativity groups
and US-born nonwhites. This two-group E
reaches its maximum of 0.693 (natural log of
2) when both groups are of equal size. It
reaches its minimum value of zero when only
one group is present. Entropy indices are
calculated for each metropolitan area and all
of its census tracts.
H is interpreted as the difference between
the entropy of a metropolitan area and the
weighted average entropy of its constituent
census tracts. High levels of segregation occur
when tracts have much lower entropy scores
than their metropolitan contexts, as would be
the case in a metropolitan area featured by
immigrant enclaves that are spatially separated
from white residential areas. Lower segregation
occurs when the level of overall entropy in a
metropolitan area is reflected in most of its
neighbourhoods, a situation in which immigrant groups are residentially intermingled with
US-born white population. H is calculated
using the formula:
H =
i= 1
T ð Þ E Ei
E ð2Þ
where ti is the total two-group population of
tract i and T is the total two-group metropolitan population. At its extremes, H can
indicate maximum residential integration
(H = 0) or maximum residential segregation (H = 1).
H is decomposable into three geographic
components, reflecting the degree to which
metropolitan segregation is due to the
unequal distribution of immigrants and
native whites (1) within principal cities, (2)
between cities and suburbs, and (3) within
the suburban ring. The geographic decomposition of H can be expressed as:
H = HC 3 S +
HC +
HS ð3Þ
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where TC, EC, and HC and TS, ES, and HS
are the total two-group metropolitan population, entropy and segregation of principal
cities and the suburban ring, respectively.
The first geographic component, HC 3 S,
refers to segregation between city and suburb, and the remaining two components
refer to segregation occurring within principal cities and within the surrounding suburbs. This sort of geographic decomposition
has been used to analyse the city/suburb contributions to segregation among panethnic
ethnoracial groups (Farrell, 2008; Fischer,
2008; Fischer et al., 2004; Reardon et al.,
2000), but it has not yet disentangled the
geographic components of segregation for
specific immigrant groups.2
Multivariate models
The multivariate analyses will incorporate a
technique developed by Massey and Denton
(1989) to combine segregation scores for all
groups and time points across metropolitan
areas (see also Hall, 2013; Iceland and
Scopilliti, 2008). I use generalised linear
models with robust standard errors to
account for the clustering of observations
within metro areas. The full model includes
group-specific suburbanisation rate and
population size, dummy variables for year
and country-of-origin, and a series of metropolitan controls that have been shown to
correlate with segregation (Farley and Frey,
1994; Lee et al., 2008; Logan et al., 2004).
These include region, population size and
the overall suburbanisation of the metropolitan area. Additionally, I control for metro
demographic composition by including percentage of the population that is black, foreign-born, recent immigrants (arrived in the
past decade) and of retirement age. The
availability of housing is captured by calculating the vacancy rate and the percentage of
housing units built in the most recent
decade. Functional specialisation as a
military, university or manufacturing centre
is represented in the models with variables
measuring the percentage of the labour force
in the Armed Forces, percentage of the population enrolled in college, and the percentage of the employed labour force engaged in
manufacturing. Finally, reliance on public
transit is operationalised as the percentage
of employed workers that use public
Patterns of immigrant suburbanisation and
segregation, 2008–2012
Table 1 documents the rapid suburbanisation of the foreign-born population in the
sample of 263 metropolitan areas.
According to the 2008–2012 ACS estimates,
about half (50.9%) of the foreign-born population resides in the suburbs, compared to
70% of the US-born white population. Since
2000, the rate of suburban growth among
immigrants was nearly four times that of the
US-born population and more than 12 times
that of native whites. All of the immigrant
groups originating from outside Europe and
Canada suburbanised at a higher rate than
the US-born population, with Indians,
Salvadorans, Guatemalans and Hondurans
experiencing the largest relative gains.
Despite this suburban growth, all the groups
except Cubans are less suburbanised than
the native white population. Apart from
Cubans, Colombians exhibit high rates of
suburbanisation and Canadians, Germans
and British have suburbanisation levels that
mirror the total US-born population. On the
other end of the spectrum, Chinese,
Dominican, Guatemalan and Mexican
immigrants have the lowest rates of
Are immigrant suburbanites less segregated than their urban counterparts? Figure
1 provides segregation indices for the city
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and suburban segments of each group’s population for metropolitan areas meeting
group-specific size criteria. The H indices
are weighted by group size so they reflect the
segregation levels experienced by the average
group member. Overall, immigrants are
slightly more segregated from whites in cities
than they are in suburbs. However, different
patterns emerge when breaking up the analysis by country-of-origin. Among most
Asian immigrants, city segregation levels
tend to be higher than those in the suburbs,
consistent with the spatial assimilation perspective. However, Vietnamese suburbanites
are more segregated than their city counterparts and in fact are more segregated than
all other Asian groups regardless of city or
suburban location.
Among those with Caribbean origins,
Cubans demonstrate a pattern similar to the
Vietnamese. Not only are Cuban suburbanites more segregated from whites than
Cuban city-dwellers, but they are more segregated than any of the immigrant groups
originating outside the Caribbean. Despite
their high levels of segregation, the other
Caribbean groups conform to the spatial
assimilation predictions of lower suburban
than city segregation. Jamaicans in particular evince a large city/suburb gap in segregation levels consistent with spatial
assimilation. The average Jamaican resident
of a principal city is more segregated than
any other immigrant group member while
the average Jamaican suburbanite experiences lower segregation levels than any of
the other Caribbean group members and
most immigrants of Latin American origins.
Latin American immigrants generally exhibit segregation levels somewhere between
Table 1. Suburbanisation of metropolitan immigrants by country-of-origin, 2000 and 2008–2012.
2000 suburban
2012 suburban
Per cent
2000 %
2012 %
suburban Difference
US-born 112,038,582 125,110,641 11.7 61.1 62.8 1.6
White 90,434,371 93,225,902 3.1 68.4 70.0 1.6
Foreign-born 13,220,874 18,379,476 39.0 46.4 50.9 4.5
China 549,217 853,840 55.5 38.7 42.7 4.0
India 530,273 1,016,405 91.7 55.8 59.2 3.3
Korea 399,537 534,039 33.7 50.6 54.1 3.5
Philippines 595,620 868,506 45.8 47.3 52.4 5.1
Vietnam 401,740 574,875 43.1 42.9 49.4 6.5
Cuba 573,319 708,144 23.5 68.6 71.0 2.4
Dom. Rep. 209,924 314,751 49.9 31.5 38.1 6.6
Haiti 199,808 316,408 58.4 50.0 58.8 8.8
Jamaica 244,525 333,734 36.5 46.4 52.7 6.3
Latin America
Colombia 287,737 397,399 38.1 59.3 65.2 5.9
El Salvador 329,801 587,995 78.3 41.9 51.7 9.8
Guatemala 174,720 340,701 95.0 39.0 46.1 7.1
Honduras 107,673 213,536 98.3 41.2 47.5 6.3
Mexico 3,407,635 4,709,798 38.2 41.7 46.2 4.6
Canada 412,118 418,909 1.6 61.2 62.7 1.6
Germany 353,945 314,961 -11.0 60.7 62.4 1.7
UK 354,069 365,874 3.3 60.7 62.5 1.9
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that occurring for Asian and Caribbean
immigrants. Colombians are the least segregated and Hondurans are the most segregated among Latin Americans, but all
groups are more segregated in the cities than
they are in the suburbs. Canadians,
Germans, and British are by far the least
segregated country-of-origin groups. They
display city/suburb differences consistent
with spatial assimilation, though the city/
suburb gap is very small compared with the
other groups.
Decomposing metropolitan immigrant
While the results in Figure 1 are useful, they
do not give a sense of how suburbanisation,
city segregation and suburban segregation
combine to create broader patterns of metropolitan segregation for these groups. For
example, consider Colombians. Despite relatively high levels of city segregation, the bulk
of Colombian segregation at the metro level
might be coming from settlement patterns
outside the city given that they are one of
the most suburbanised immigrant groups. A
geographic decomposition of H can help to
identify the city/suburb contributions to
overall metropolitan segregation.
Figure 2 accomplishes this by displaying
2008–2012 decomposed metropolitan segregation levels for immigrant groups by country-of-origin. As is the case in cities and
suburbs (see Figure 1) Canadian-born residents have the lowest overall levels of metropolitan segregation from the US-born
.28 .30
.45 .47
.14 .15 .14
.42 .43 .44
.13 .13 .13
)H( noitagergeS
.28 .30
.45 .47
.14 .15 .14
.42 .43 .44
.13 .13 .13
)H( noitagergeS
Figure 1. Immigrant segregation by country-of-origin in cities and suburbs, 2008–2012.
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whites, followed closely by those from
Germany and the UK. Caribbean origins
are represented on the right side of the figure, with Dominicans, Haitians, Cubans and
Jamaicans occupying the four most segregated spots.3 Though less segregated than
the Caribbean groups, Hondurans,
Guatemalans and Salvadorans are still twice
as segregated from the native white population as the average foreign-born resident.
The segregation level for Mexicans is notably lower than the other Latin American
groups. Filipinos are the least segregated
group originating from Asia, with Chinese
and Vietnamese immigrants experiencing the
highest segregation levels among Asian
Suburbanisation levels for the various
nativity groups are also found in Figure 2.
There is some evidence for a desegregating
effect of suburbanisation, as there is a
modest but statistically significant negative
correlation between group-specific suburbanisation rates and H indices across groups
and eligible metros (r = 20.252; p\0.001;
N = 1428). However, it is clear that suburbanisation does not necessarily lead to
greater residential integration. For example,
Columbians are more than twice as segregated as the Canadian/European contingent
despite being more suburbanised.
Furthermore, metropolitan Chinese immigrants are only slightly more segregated than
Colombians despite very low suburbanisation levels. While Dominicans fit the spatial
assimilation logic of high segregation and
low suburbanisation, the Cuban and Haitian
experience shows that suburbanisation and
segregation can go hand-in-hand. The shading in the bars gives some sense of the geographic contributions to segregation.
Consistent with their high levels of
.133 .137 .140
.271 .290 .290
.325 .360
.466 .479
.503 .505 .515
% suburban
Segregation (H)
Within city
Between city/suburb
Within suburb
% suburban
Figure 2. Immigrant segregation and suburbanisation by country of origin, 2008–2012.
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suburbanisation, Cuban and Haitian segregation in metropolitan areas is largely a
function of segregation occurring within the
suburbs. Residential differences between the
urban core and suburban periphery appear
relatively small for most groups, though a
discernible city/suburb divide is present for
Chinese, Jamaicans and Dominicans.
Table 2. Geographic decomposition of metropolitan immigrant segregation by country-of-origin, 2008–
Geographic components
Per cent
Metro H Within
Foreign-born 263 0.223 0.087 0.028 0.108
Per cent share 100.0 39.2 12.4 48.4 50.9
China 119 0.325 0.154 0.049 0.122
% share 100.0 47.4 15.0 37.6 42.7
India 122 0.290 0.108 0.012 0.170
% share 100.0 37.3 4.0 58.6 59.6
Korea 96 0.290 0.128 0.012 0.150
% share 100.0 44.3 4.0 51.7 54.5
Philippines 97 0.271 0.127 0.010 0.134
% share 100.0 46.9 3.8 49.3 52.3
Vietnam 101 0.360 0.159 0.025 0.176
% share 100.0 44.0 7.0 49.0 49.8
Cuba 41 0.505 0.134 0.003 0.367
% share 100.0 26.5 0.7 72.8 72.0
Dom. Rep 37 0.567 0.277 0.094 0.197
% share 100.0 48.7 16.6 34.7 37.8
Haiti 25 0.515 0.197 0.026 0.292
% share 100.0 38.3 5.0 56.8 59.4
Jamaica 45 0.503 0.226 0.058 0.220
% share 100.0 44.9 11.4 43.7 53.1
Latin America
Colombia 48 0.309 0.103 0.012 0.195
% share 100.0 33.3 3.8 62.9 66.4
El Salvador 67 0.445 0.197 0.018 0.229
% share 100.0 44.4 4.1 51.6 51.9
Guatemala 75 0.466 0.229 0.029 0.207
% share 100.0 49.1 6.3 44.6 45.8
Honduras 48 0.479 0.231 0.028 0.220
% share 100.0 48.3 5.7 46.0 47.8
Mexico 220 0.374 0.184 0.025 0.164
% share 100.0 49.2 6.8 44.0 46.2
Canada 99 0.133 0.048 0.004 0.081
% share 100.0 36.4 2.7 61.0 63.4
Germany 100 0.140 0.053 0.003 0.084
% share 100.0 37.7 2.1 60.2 63.1
UK 88 0.137 0.050 0.003 0.083
% share 100.0 36.6 2.5 60.9 63.1
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Table 2 provides a more precise decomposition of the H indices for country-of-origin groups. This table includes the withincity, between city/suburb and within-suburb
components that combine to make up overall metropolitan segregation as measured by
the H index. It also includes each component’s percent share of H to give a sense of
its relative contribution to metropolitan segregation. A comparison of the Dominican
and Cuban components of segregation provides a useful illustration of the utility of
such a geographic decomposition. These two
groups experience high levels of segregation
but the geographic structure of that segregation varies markedly. Within-suburb residential differences account for the largest
share of Cuban segregation (72.8%), followed by within-city differences (26.5%) and
a small city/suburb component (0.7%). By
contrast, nearly half (48.7%) of Dominican
segregation is due to within-city differences,
just one-third (34.7%) is due to withinsuburb differences, and the remaining
16.6% is due to the city/suburb divide. To
put it a different way, eliminating residential
segregation from native whites in the suburban ring would make Cubans as residentially integrated as immigrants from the
UK. Doing the same for Dominican suburbanites would have less of an overall impact,
reducing Dominican segregation by onethird or so but still leaving them as segregated as Mexican immigrants.
The shifting geographic structure of
immigrant segregation in the 2000s
How has immigrant segregation changed
during the last decade? Table 3 documents
shifts in the segregation index H in metropolitan areas meeting the group-specific
population thresholds for 2000 and 2008–
2012. I will focus on the weighted results but
the unweighted indices are also provided in
the table. As a whole, the foreign-born
population became slightly less segregated
from US-born whites during the 2000s,
though it is quickly apparent that countryof-origin plays an important role in the
trends. Among Asian origin groups, segregation increased across the board, with
Indians and Koreans experiencing the largest absolute and relative gains. Slight
declines in already high levels of segregation
were most common among Caribbean
groups. Cubans are the outlier in in this
group, with an absolute increase in H nearly
equal to that of Koreans. Latin American
groups varied markedly in their segregation
trajectories. Colombians and Salvadorans
experienced gradual gains, Guatemalans and
Hondurans experienced very large absolute
and relative increases, and Mexicans became
less segregated from native whites.
Canadians, Germans and British all became
more segregated during the 2000s, though
their very low 2000 segregation indices should
be taken into account when considering the
percent changes.
What underlying changes in the distribution of immigrant groups have contributed
to these overarching segregation trends?
Figure 3 presents changes in the three geographic components of segregation during
this period. It is clear that there is much
going on under the surface of the overall
shifts in metropolitan segregation depicted
in Table 3. Among Asian origin groups,
increasing segregation within the suburbs
accounted for the lion’s share of the segregation increases. While Chinese, Indian and
Korean immigrants did experience increasing segregation within principal cities,
increases in the within-suburb components
easily outpaced them. A more varied picture
emerges for the Caribbean origin groups.
While Cuban trends mirror that of the Asian
groups (i.e. within-suburb increases), the
appearance of steady segregation declines
for Dominicans, Haitians and Jamaicans
masks two countervailing trends. Namely,
68 Urban Studies 53(1)
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the city/suburb divide accounts for a declining share of segregation as these two groups
suburbanise and diversify the suburban ring.
However, the suburban neighbourhoods in
which Dominicans and Haitians are settling
are becoming more segregated even as their
city neighbourhoods become more residentially integrated with the US-born white
Among Latin American groups, the
suburban-centric shift of segregation
observed among Asian groups continues in
a more dramatic fashion. Guatemalans and
Hondurans in particular experienced large
increases in the within-suburb components,
larger than any of the other groups.
Mexicans, by far the largest immigrant
group in the analysis, experienced more
moderate increases in the within-suburb
components. The desegregation trend for
Mexicans within cities more than compensated for the uptick in the suburbs. Finally,
Canadians, Germans and British experienced increasing segregation within cities
and suburbs, but the suburban increases
were roughly twice the size of the increases
occurring within cities.
Multivariate analyses of immigrant
What is the impact of immigrant suburbanisation after taking into account other relevant metropolitan characteristics? Table 4
-0.040 -0.020 0.000 0.020 0.040 0.060
Dom. Rep.
El Salvador
Within city
Between city/suburb
Within suburb
Figure 3. Changes in the geographic components of immigrant segregation, 2000 and 2008–2012.
Farrell 69
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presents the results of regression analyses
assessing the association between groupand metro-specific variables with immigrant
segregation.4 To review, the units of analysis
are pooled group- and year-specific metropolitan areas. The first model in the table
includes a dummy variable for the year,
indicating a segregation increase of 0.047
units between 2000 and 2008–2012 (see
unweighted results in Table 3). The second
model introduces group-specific population
sizes and suburbanisation rates. The suburbanisation variable is negative and statistically significant, indicating that immigrant
segregation is associated with lower levels of
segregation. This model also includes a
series of dummy variables identifying each
immigrant group in question. Canadians,
the least segregated group, serve as the reference group. After controlling for group size
and suburbanisation, all groups except the
British remain significantly more segregated
than Canadians.
The third model in Table 4 introduces a
host of metropolitan controls. The findings
indicate higher levels of immigrant segregation are likely to be found in larger, suburbanised metropolitan areas with high
vacancy rates, large black populations and
many recent immigrants. The negative coefficient for immigrant group suburbanisation
decreases threefold after controlling for the
metropolitan characteristics. A stepwise
analysis (not shown) reveals that the metropolitan suburbanisation rate accounts for
more than one-third of this change.
Table 3. Immigrant segregation by country-of-origin, 2000 and 2008–2012.
Weighted H Unweighted H
N 2000 2012 Diff. % chg. 2000 2012 Diff. % chg.
Foreign-born 263 0.225 0.223 20.003 21.2 0.118 0.140 0.022 19.1
All groups 1,103 0.348 0.363 0.016 4.3 0.215 0.262 0.047 21.9
China 86 0.306 0.328 0.021 7.0 0.209 0.237 0.028 13.6
India 87 0.231 0.290 0.060 25.8 0.213 0.274 0.061 28.9
Korea 75 0.247 0.291 0.044 17.8 0.178 0.230 0.052 29.4
Philippines 76 0.260 0.272 0.012 4.8 0.190 0.236 0.046 24.1
Vietnam 81 0.331 0.362 0.030 9.2 0.275 0.311 0.036 12.9
Cuba 32 0.467 0.509 0.042 9.1 0.252 0.332 0.079 31.3
Dom. Rep. 20 0.599 0.574 20.025 24.2 0.397 0.435 0.038 9.6
Haiti 18 0.526 0.518 20.009 21.6 0.415 0.431 0.015 3.7
Jamaica 31 0.513 0.509 20.003 20.7 0.346 0.388 0.041 12.0
Latin America
Colombia 32 0.293 0.313 0.020 6.8 0.238 0.285 0.047 19.8
El Salvador 46 0.426 0.447 0.021 4.9 0.311 0.377 0.066 21.1
Guatemala 41 0.409 0.471 0.061 14.9 0.337 0.404 0.067 19.9
Honduras 26 0.425 0.487 0.062 14.6 0.369 0.452 0.082 22.3
Mexico 179 0.385 0.374 20.011 22.8 0.270 0.295 0.025 9.4
Canada 94 0.078 0.132 0.054 69.5 0.083 0.132 0.049 59.8
Germany 95 0.080 0.140 0.059 74.0 0.081 0.141 0.060 73.7
UK 84 0.087 0.137 0.050 57.4 0.086 0.138 0.051 59.5
70 Urban Studies 53(1)
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Table 4. Generalised linear regressions of immigrant segregation on group and metro characteristics,
2000 and 2008–2012.
Coef. SE Coef. SE Coef. SE
Year effecta 0.047 (0.002)*** 0.042 (0.002)*** 0.033 (0.005)***
Group % suburban 20.038 (0.012)** 20.116 (0.019)***
Group pop. size 0.019 (0.003)*** 0.011 (0.003)***
Country of originb
China 0.104 (0.006)*** 0.090 (0.007)***
India 0.126 (0.006)*** 0.113 (0.006)***
Korea 0.094 (0.005)*** 0.079 (0.005)***
Philippines 0.094 (0.007)*** 0.092 (0.007)***
Vietnam 0.174 (0.007)*** 0.154 (0.007)***
Cuba 0.175 (0.013)*** 0.152 (0.014)***
Dom. Rep. 0.292 (0.018)*** 0.246 (0.017)***
Haiti 0.301 (0.016)*** 0.271 (0.015)***
Jamaica 0.249 (0.019)*** 0.214 (0.016)***
Colombia 0.147 (0.009)*** 0.125 (0.009)***
El Salvador 0.223 (0.011)*** 0.209 (0.011)***
Guatemala 0.250 (0.011)*** 0.230 (0.012)***
Honduras 0.288 (0.013)*** 0.258 (0.013)***
Mexico 0.147 (0.008)*** 0.161 (0.008)***
Germany 0.007 (0.004)* 0.000 (0.004)
UK 0.007 (0.003) 0.001 (0.003)
Metro characteristics
Pop. size 0.014 (0.004)***
% suburban 0.099 (0.027)***
% black 0.013 (0.003)***
% foreign-born 0.008 (0.006)
Recent immigrants 0.041 (0.013)**
New construction 20.043 (0.008)***
Vacancy rate 0.018 (0.008)*
Retirement 20.009 (0.013)
Military 20.001 (0.002)
Manufacturing 20.005 (0.008)
University 0.004 (0.010)
Public Transit 20.007 (0.004)
Northeast 0.007 (0.010)
South 20.010 (0.008)
Midwest 0.007 (0.009)
Intercept 0.215 (0.005)*** 20.045 (0.027) 20.168 (0.081)*
Log likelihood 1676.7 2976.3 3233.7
N 2206 2206 2206
Note: All continuous independent variables (except group and metro suburbanisation rates) are logarithmically
transformed. Robust standard errors appear in parentheses.
p \ 0.05; **p \ 0.01; ***p \ 0.001 a
Reference group is 2000.
Reference group is Canada.
Reference group is West.
Farrell 71
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However, a model excluding metro suburbanisation still results in a decrease in the
group suburbanisation coefficient
(B = 20.088; se = 0.011; p \ 0.001).
Thus, accounting for metropolitan characteristics only strengthens the apparent integrating effect of immigrant suburbanisation.
The coefficient for the year effect remains
statistically significant in the full model,
indicating that the increases in immigrant
segregation between 2000 and 2008–2012
remain largely intact.
Group-specific models
To test whether suburbanisation has a consistent effect across groups, I stratified the
sample and carried out separate regression
analyses for each immigrant group (not
shown). Not all groups had sample sizes
large enough to carry out such an analysis,
so I pooled together Haitians and
Dominicans and excluded the four remaining groups (Cubans, Jamaicans,
Colombians, Hondurans) that had less than
75 cases. In addition to H, I also conducted
regressions that treat the geographic components of H as dependent variables. The suburbanisation coefficients for the metro H
models were negative for all but one of the
groups (Salvadorans) and statistically significantly for five of them: Koreans,
Vietnamese, Guatemalans, Mexicans and
Germans. In the component models, suburbanisation has a significantly negative effect
on the within-city components for all groups
and on the between city/suburb components
for all groups except Filipinos. In the
within-suburb models the effect of suburbanisation flipped, registering strong positive
effects for all groups.
Many of the findings outlined above are consistent with the logic of spatial assimilation.
Most immigrant groups are less segregated
from native whites in the suburbs than they
are in large cities. Furthermore, suburbanisation is associated with lower immigrant segregation even after controlling for relevant
metropolitan characteristics. Immigrant suburbanisation continues to blur the city/suburb divide, which now accounts for only a
sliver of the segregation experienced by most
groups. However, though immigrants are
certainly gaining access to the suburbs, this
is not to say that suburbanisation marks the
end of segregation. To the contrary, most
groups experienced increasing segregation
from native whites in the 2000s at the same
time they were rapidly suburbanising. This is
due primarily to increasing segregation
within the suburbs, which often offset segregation declines within cities. As a result,
within-suburb segregation is accounting for
a larger share of metropolitan segregation.
So, immigrant groups tend to be less segregated in metros where they are highly suburbanised but immigrant suburbanisation is
having an increasingly segregative effect over
time within the suburban ring.
Consistent with the segmented assimilation perspective, some of the findings vary
markedly across immigrant groups. Though
all immigrant groups are suburbanising,
there are large group differences in suburbanisation rates, often among groups coming
from the same region. When comparing city
with suburban segregation, Asian origin
groups like Indians and Vietnamese (along
with Cubans) register suburban segregation
levels comparable with or higher than that
occurring among their coethnics in the city.
These findings are consistent with previous
studies of immigrant settlement patterns
New York and Los Angeles (Alba et al.,
1999; Logan et al., 2002). Additionally, high
levels of suburbanisation are not associated
with residential integration for all groups.
Colombians and Cubans are more highly
suburbanised than Canadians, Germans or
72 Urban Studies 53(1)
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British, but they are also much more segregated. It is worth noting that the four
Caribbean groups are the most highly segregated despite large differences in their
respective suburbanisation levels. This fact
lends credence to the place stratification perspective, underscoring a persistent and pernicious colour line in the USA that transcends
one’s country-of-origin.
One limitation of this study is that the
ACS data do not provide characteristics
such as income, English language proficiency or year of entry by country-of-origin.
Thus, it is impossible to test to what degree
segregation within the suburbs is a function
of the socioeconomic or acculturation characteristics of immigrants, as predicted by the
spatial assimilation perspective. A broader
issue to consider is whether suburbanisation
actually results in upward residential mobility for all immigrant groups. As suburban
poverty rates rise in the USA (Kneebone
and Berube, 2013), certain groups may find
themselves segregated into declining neighbourhoods in the inner suburban ring.
Those with the financial resources to attain
suburban residence outside these struggling
areas may not integrate with the native
white population to the degree implied by
spatial assimilation. They may instead opt
for bustling suburban enclaves distinct from
predominantly white environs in the metropolitan periphery (Li, 1998). Future research
should endeavour to disentangle these two
suburban scenarios.
I wish to thank Carolyn Forner and anonymous
Urban Studies reviewers for their valuable feedback on previous drafts of this paper.
This research received no specific grant from any
funding agency in the public, commercial, or notfor-profit sectors
1. Tracts from 2000 are adjusted to conform to
2010 tract boundaries.
2. The entropy index and Theil’s H are often
used to measure diversity and segregation
involving more than two groups (Farrell and
Lee, 2011; Reardon and Firebaugh, 2002). I
use a simple two-group framework that capitalises on the decomposability of H into its
geographic components.
3. These segregation levels are quite high.
Consider that the weighted black-white metro
H is 0.417 (N = 250). Thus, the average nonHispanic black metropolitan resident is considerably less segregated from white residents
than is the average Caribbean, Honduran,
Guatemalan or Salvadoran immigrant.
4. Metropolitan H has a truncated range of values but histograms reveal that the H scores
approximate a normal distribution. This is
further bolstered by modest skewness and
kurtosis scores (s = 0.563, k = 0.037).
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Students barely have time to read. We got you! Have your literature essay or book review written without having the hassle of reading the book. You can get your literature paper custom-written for you by our literature specialists.


Do you struggle with finance? No need to torture yourself if finance is not your cup of tea. You can order your finance paper from our academic writing service and get 100% original work from competent finance experts.

Computer science

Computer science is a tough subject. Fortunately, our computer science experts are up to the match. No need to stress and have sleepless nights. Our academic writers will tackle all your computer science assignments and deliver them on time. Let us handle all your python, java, ruby, JavaScript, php , C+ assignments!


While psychology may be an interesting subject, you may lack sufficient time to handle your assignments. Don’t despair; by using our academic writing service, you can be assured of perfect grades. Moreover, your grades will be consistent.


Engineering is quite a demanding subject. Students face a lot of pressure and barely have enough time to do what they love to do. Our academic writing service got you covered! Our engineering specialists follow the paper instructions and ensure timely delivery of the paper.


In the nursing course, you may have difficulties with literature reviews, annotated bibliographies, critical essays, and other assignments. Our nursing assignment writers will offer you professional nursing paper help at low prices.


Truth be told, sociology papers can be quite exhausting. Our academic writing service relieves you of fatigue, pressure, and stress. You can relax and have peace of mind as our academic writers handle your sociology assignment.


We take pride in having some of the best business writers in the industry. Our business writers have a lot of experience in the field. They are reliable, and you can be assured of a high-grade paper. They are able to handle business papers of any subject, length, deadline, and difficulty!


We boast of having some of the most experienced statistics experts in the industry. Our statistics experts have diverse skills, expertise, and knowledge to handle any kind of assignment. They have access to all kinds of software to get your assignment done.


Writing a law essay may prove to be an insurmountable obstacle, especially when you need to know the peculiarities of the legislative framework. Take advantage of our top-notch law specialists and get superb grades and 100% satisfaction.

What discipline/subjects do you deal in?

We have highlighted some of the most popular subjects we handle above. Those are just a tip of the iceberg. We deal in all academic disciplines since our writers are as diverse. They have been drawn from across all disciplines, and orders are assigned to those writers believed to be the best in the field. In a nutshell, there is no task we cannot handle; all you need to do is place your order with us. As long as your instructions are clear, just trust we shall deliver irrespective of the discipline.

Are your writers competent enough to handle my paper?

Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.

What if I don’t like the paper?

There is a very low likelihood that you won’t like the paper.

Reasons being:

  • When assigning your order, we match the paper’s discipline with the writer’s field/specialization. Since all our writers are graduates, we match the paper’s subject with the field the writer studied. For instance, if it’s a nursing paper, only a nursing graduate and writer will handle it. Furthermore, all our writers have academic writing experience and top-notch research skills.
  • We have a quality assurance that reviews the paper before it gets to you. As such, we ensure that you get a paper that meets the required standard and will most definitely make the grade.

In the event that you don’t like your paper:

  • The writer will revise the paper up to your pleasing. You have unlimited revisions. You simply need to highlight what specifically you don’t like about the paper, and the writer will make the amendments. The paper will be revised until you are satisfied. Revisions are free of charge
  • We will have a different writer write the paper from scratch.
  • Last resort, if the above does not work, we will refund your money.

Will the professor find out I didn’t write the paper myself?

Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.

What if the paper is plagiarized?

We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.

When will I get my paper?

You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.

Will anyone find out that I used your services?

We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.

How our Assignment  Help Service Works

1.      Place an order

You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.

2.      Pay for the order

Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.

3.      Track the progress

You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.

4.      Download the paper

The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.

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