Advances in Applied Sociology
2011. Vol.1, No.1, 1-12
Copyright © 2011 SciRes. DOI:10.4236/aasoci.2011.11001
Income, Homeownership and Residential Assorting among
Latinos in the US
Antwan Jones
Department of Sociology, The George Washington University, Washington DC, USA.
Received November 13th, 2011; revised December 15th, 2011; accepted December 28th, 2011.
This study examines the 2000 Public Use Microdata 5% Sample to ascertain the relationship between income,
residential assorting and homeownership for those of Latino origin (N = 46,024). Using logistic and ordinary
least squares regression, I find that income has a disproportionate impact on the odds of owning a home and how
ethnically similar the area Hispanics live for all five ethnic groups. In addition, income has a significant impact
on the extent of ethnic clustering in the area Hispanics live. I conclude that income is a highly significant me-
diator of the relationship between ethnicity and both homeownership and residential assorting. The results illus-
trate that ethnic differences in income also relate to ethnic differences in residential assorting, an extension to the
emerging literature that racial differences in income influence racial differences i n residential location.
Keywords: Clustering, Dissimilarity, Homeownership, Income, Latinos, Segregation
Introduction
While over two-thirds (67.4%) of Americans owned a home
in 2009, less than half of minorities in the US were homeown-
ers (US Census Bureau, 2010). Specifically, 48.4% of Hispan-
ics owned a home in 2009, compared to 41.2% in 1994 (US
Census Bureau, 2010). This growth is the direct result of local-
ized policy programs implemented in geographical regions
where Hispanics are centrally located (Eggers & Burke, 2002;
Finkel & Kennedy, 2002).
Even though there has been a moderate increase in the num-
ber of Hispanic homeowners (Kochhar et al., 2009), certain eth-
nic groups within the pan-Latino culture still have higher home-
ownership rates and higher percent increases in homeownership
than other Latino groups. Specifically , more than half (52.2%) of
the Cubans in the Unites States in 2002 owned a home, compared
to 45.9% for Mexicans, 37.2% for Puerto Ricans and 31.9%
Central/South Americans (Bureau of Labor Statistics, 2002).
This inter-ethnic disparity in homeownership could be driven
by differential economic factors. For instance, Cubans may be
more likely to emigrate with higher levels of disposable income
compared to other Latinos (Saiz, 2003). As a result, Cubans
may be more likely that other Hispanic groups to benefit from
programs geared toward homeownership because of this higher
level of collateral (Ratner, 2002). As such, surmising that high
Latino homeownership rates are due to the aid of public policy
programs may be confounding the ethnic differences in home-
ownership. Moreover, most of the housing programs neglect the
differences in neighborhood structure and characteristics that
facilitate or impede certain ethnic groups from gaining entrance
into neighborhoods.
The focus of this paper is to test whether isolating socioeco-
nomic and sociodemographic differences in homeownership among
Hispanics will lead to a normalization of rates of homeowner-
ship among Latinos. Using various individual and areal charac-
teristics, this paper addresses two research questions: 1) Can
interethnic differences in homeownership and residential as-
sorting be explained by income? and 2) How does income at-
tainment differentially impact certain ethnic group’s access to
homes and neighborhoods?
This paper uniquely contributes to the literature base on home-
ownership in three ways: First, this study uses a large sample of
Latinos from the US Bureau of the Census (N = 46,024) which
enables that creation of a more accurate picture of Latino home-
ownership. Other studies rely on surveys that tend not to over-
sample Latinos and as such, their sample size is relatively small
(Flippen, 2001). This study will use a robust dataset with an
ample number of cases to test specific hypotheses regarding an
ethnic minority group.
Second, this research looks at residential assorting in two
complimentary ways: Latinos are residentially positioned in so-
ciety via both segregation and self-segregation. Prior studies are
limited in residential assorting, as they tend to focus on segre-
gation and its sole impact on homeownership (Crowder et al.,
2006). Yet, we know for Latinos, enclaves provide an environ-
ment that yields positive benefits (Portes & Rumbaut, 1996).
Third, this research builds on an emerging literature that sug-
gests differences in income across races influences residential
assorting by surmising that this trend also occurs within ethnic
groups as well (T hernstrom & Thernstrom, 1997). To date, there
have been no studies that explore this phenomenon within eth-
nic groups such as Asians and Latinos (McConnell & Marcelli,
2007). This research, which focuses on Latinos, uses this gap in
the literature as its prime contributio n.
Income and Ethnicity
It is well documented that the black-white inequality in in-
come is severe in the United States (Oliver & Shapiro, 1995).
Race and ethnicity both share paramount roles in human capital
and income generation as well as in inequality (Keister, 2000;
Oliver & Shapiro, 1995; Spilerman, 2000; Wolff, 1998). These
roles are adequately seen through spatial assimilation theory:
because of a racial-ethnic hierarchy, individuals’ opportunities
to accumulate income are often limited independent of other
characteristics such as level of education (Oliver & Shapiro,
1995). Racial-ethnic categories (and even color of skin) place

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2
individuals in this hierarchy, with Whites ranked above non-
Whites and among non-Whites ranked Asian, Hispanic, and
Black in order (Oliver & Shapiro, 1995). These social posi-
tions offer differential opportunities and constraints in the ac-
cumulation of income.
Examples of the structural effect of the ethnic hierarchy in-
clude but are not limited to the following four aspects. First,
minorities are more likely to have lower incomes. The ethnic
hierarchy in the US sorts individuals to the capital-intensive
primary sector and labor intensive secondary sector of the labor
market and subjects them to institutional or other forms of dis-
crimination (Doeringer & Piore, 1971).
Second, minority workers are strictly placed in the ethnic
queue for a job (Lieberson, 1980; Reskin & Roos, 1990) and
bottom positions in the ethnic hierarchy are the last to be con-
sidered. Thus, coupled with being tracked into secondary sector
occupations, minorities are also being considered last for those
positions.
Third, minorities are more likely to live in segregated neigh-
borhoods. Segregation creates unfavorable lending institution
policies and housing prices in dual housing markets (Alba &
Logan, 1991; Massey & Denton, 1993). Minorities are subject
to higher mortgage interest rates and their houses depreciate in
segregated neighborhoods (Williams et al., 2005).
Fourth, these constraints further pass across generations. The
disadvantages of minorities are replicated and deepened through
intergenerational transfers of income in the form of inheritance
(Spilerman, 2000). Blacks and Hispanics have a lower inci-
dence and lesser amounts of parental intergenerational transfers
(Smith, 1995), which directly affects the amount of income and
indirectly affects income through the investment in children’s
college education (Conley, 1999). These constraints face non-
Whites even though their human capital is comparable with
Whites, resulting in their lower rate of income accumulation
(Hao, 2006). Because income is accumulated along the life
cycle, differential rates of income accumulation lead to increas-
ing disparities in income that follow throughout the life cycle.
Differential rates also lead to increasing disparities in other im-
portant things in life, suc h as property purchases, inheritance an d
credit (Hao, 2004).
Residential Segregation and Self-Segregation
Given that income provides access to resources that affect
homeownership, it is also feasible that income, along with his-
torical, geographical and migration patterns, impact general
residential assorting of minorities. Residential assorting in this
research is defined as the percent distribution of ethnic group’s
presence at a given neighborhood level. Two key archetypes of
a high percent distribution of Latino groups are residential seg-
regation and self-segregation. Both have differential impacts on
homeownership and income operates differently in these two
types of neighborhood types. However, income may have a
greater positive effect in self-segregated areas rather than insti-
tutionalized segregated residential areas.
Residential Segregation
Segregation is generally defined as the practice of separating
people of different races, classes, or ethnic groups, in various
different social contexts such as schools, housing, and public or
commercial facilities. When discussing residential segregation,
it is important to slightly alter this definition. Residential seg-
regation can be defined as the practice of separating and grouping
people of different racial or socioeconomic groups in the con-
text of housing (Massey, 1989). This separation can be gener-
ally conceptualized in one of two forms: denial of access into a
neighborhood or partitioning members of a group into a confined,
geographic entity.
According to some, residential segregation is more pronounced
in areas where there are more rented apartments that homes
(Massey, 1989). This is an important observation, as homeown-
ership represents an important form of household income, both
through the status (i.e., wealth creation) and social psychology-
cal benefits it embodies and through the forced savings, infla-
tion protection and numerous tax benefits its confers (Henretta,
1979; Kain & Quigley, 1972). Income inequality is a basic
component of place stratification theory—those with the great-
est income also tend to be those at the top of the hierarchy and
tend to reside outside of segregated neighborhoods (Iceland,
2004; Haan, 2005). Essentially, income creates a division, and
those with more income are able to not only actively decide
where they want to live, but, as a result, they inevitably decide
where those with little income can live.
There is little research on the effect of residential segregation
on Hispanics with regards to homeownership. Flippen (2001)
states that these shortcomings need to be addressed because of
three facts: 1) Hispanic-White segregation has increased with
time in the United States; 2) Hispanic segregation is lower than
corresponding figures for African Americans, even though they
are the largest group; and 3) There is a divergent segregation
experience between those Hispanics who enter the country and
those Hispanics who are second- and third-generation immi-
grants in the United States.
Previous research on the impact of segregation on home-
ownership for Hispanics has drawn three conclusions: First, this
type of ethnic assorting negatively affects homeownership at all
levels of socioeconomic status. Flippen (2001) was able to as-
certain this conclusion by comparing homeownership within
segregated areas with regards to Blacks and Whites. Second,
there is an intergenerational link in ethnic residential segrega-
tion and this intergenerational link decreases the propensity to
live in an ethnically diverse neighborhood. Borjas (1998) found
that the probability that a respondent lived in an ethnically seg-
regated neighborhood in 1992 would be 49.5 percentage points
higher if their parents also lived in an ethnically segregated
neighb orh ood. Th i rd, re si de ntial se g regati on ha s a se mi -sta bi lizin g,
yet negative affect on housing tenure and resilience of neigh-
borhood change, that is, people who live in segregated areas
tend to stay in these areas, which then create little room for the
neighborhood to change. Painter et al. (2001) finds that al-
though Latinos have a higher likelihood of residing in their
neighborhood for more than ten years, they also have lower
homeownership probabilities. In addition, if one is a recent im-
migrant into this segregated area, this probability decreases further
by 12 percentages points (Painter et al., 2001). What the litera-
ture generally shows is a negative outcome for Hispanics that
live in segregated areas with regards to homeownership.
Residential Self-Segregation
The notion of self-segregation has a longstanding presence in
the literature but in the public sphere, Tatum’s (1999) boo k, Why
are all the black kids sitting together in the cafeteria? and other
conversations about race, sparked public concern over the issue
of self-segregation. Self-segregation, loosely defined, is the
tendency for people of one ethnic group to gravitate to- wards
members of that ethnic group. The assumption embodied in
self-segregation is that people are making individual, rational
choices that are independent of social structures that may force

A. JONES 3
the individuals in close arrangements otherwise. In the Latino
context, self-segregation occurs in the form of enclaves. An en-
clave is a spatial residential concentration of immigrants from a
given ethnic group.
There is much information on enclaves and the enclave labor
market; however, there are only a few empirical studies that ad-
dress the enclave impact on individual homeownership. Gener-
ally, the function of enclaves is to incorporate recent immigrants
into mainstream society without assimilation (South et al.,
2005). Rosenbaum (1992) looks at the case of New York City
and finds that enclaves allow for privileged information of
housing vacancies before the unit even becomes vacant. In
addition, rental turnover occurs within the White-Puerto Rican
context when units are of low quality. Puerto Rican-White
turnover occurs when the rental units are in higher quality
neighborhoods.
Krivo (1995) posits that household crowding and housing
costs play a role in the low homeownership rates of Mexicans,
Puerto Ricans and Cubans. However, the strongest predictors of
homeownership were the ethnic composition of one’s neighbors
and the composition of the immigrant’s household. Income is
used as a control variable and not as a focus variable, which is a
recurring, problematic issue in research on homeownership and
neighborhood composition. Regardless, once the data are post-
stratified by ethnicity and national origin, Krivo finds that for
Cubans, the regression has little explanatory power for these
groups. Krivo surmises that because of the nature of the immi-
grant enclaves (i.e., in Florida) having a strong influence on the
property value and ethnic composition of one’s neighbors, these
predictors that were once significant cease to be significant in
another model because the metric has changed.
Within the enclave, the property value and neighborhood
composition has a different meaning. Property value must in-
clude spillover effects, and neighborhood composition must also
include absence of assimilation (Logan et al., 2002). These two
things are implied when measuring Latino groups. As such, if
researchers were to operationalize the variables that would con-
ceptualize the nature of segregation in an enclave, they might
find significance in the predictors for the Cuban case that Krivo
could not.
Hypotheses
On the basis of the above review, the following hypotheses
will be tested about the impact of income, homeownership and
residential assorting for Latinos:
1) Because income is generally salient in homeownership,
increasing the amount of income will increase the likelihood of
a Latino person owning a home.
2) Because income is generally salient in residential assorting,
increasing the amount of income will increase the likelihood of
a Latino person owning a home in a non-segregated neighbor-
hood. However, low income will increase the likelihood of a
Latino person to rent a home in a se gr ega te d n eighborhood.
3) While income is important in residential outcomes, ethnic-
ity also plays a role in shaping how and where individuals re-
side, as not all ethnic groups own homes or live in areas uni-
formly similar. As such, income will have differential impacts
with regard to ethnic status. Thus, for some groups, income will
matter more in dictating whether or not they own a home and
whether or not they live in segregated or self-segregated areas.
Specifically, Cubans will be the most impacted by income’s
influence on residential outcomes than other Latino groups.
Likewise, compared to other Latino groups, Puerto Ricans will
be the least impacted by income’s influence on residential out-
comes. Data and Methods
For this research, I will be analyzing the 2000 Public Use
Microdata Sample (PUMS) files. There are fifty files, each cor-
responding to the fifty states. The PUMS files contain records
representing five-percent samples of the occupied and vacant
housing units in the US and the people in the occupied units.
People living in group quarters also are included. The PUMS
files can be used to answer many questions about housing units
and households in the United States. The interviews cover core
questions that are repeated each decade. The PUMS files pro-
vide data on apartments, single-family homes, mobile homes,
vacant homes, family composition, income, housing and neigh-
borhood quality, housing costs, equipment, fuels, size of hous-
ing unit, and recent movers. The overall sample size is 46,024
persons of Latino origin who identified themselves as house-
holder or head of household and who lived in identifiable Pub-
lic Use Microdata Areas (PUMAs).
The PUMS files contain PUMAs in order to maintain the
confidentiality of the PUMS data. The 5-percent state-level
files contain PUMAs having a minimum population of 100,000.
Each state is separately identified and may be comprised of one
or more PUMAs. Large metropolitan areas may be subdivided
into PUMAs. PUMAs and do not cross sta te lines. PUMAs also
are defined for places of residence on April 1, 1995 and places
of work.
The two dependent variables are homeownership propensity
(whether or not the respondent owns a home) and residential
assorting (the extent to which the respondent lives in a segre-
gated area). Homeownership propensity is a dummy variable,
based on if the respondent owns a home (which is the reference
category). Residential assorting is measured by two segregation
indices: evenness and clustering. Conceptually, evenness mea-
sures the percentage of a group’s population that would have to
change residence for each neighborhood to have the same per-
centage of that group as the metropolitan area overall (Massey
& Denton, 1989). To measure evenness, the index of dissimi-
larity, Id, will be used. The range of this index is from 0 (com-
plete integration) to 1 (complete segregation).
Clustering measures the extent to which areal units inhabited
by minority members adjoin one another, or cluster, in space
(Massey & Denton, 1993; Wilkes & Iceland, 2004). The US
Bureau of the Census has two distinct measures of clustering:
spatial proximity, which is the average of intragroup proximi-
ties for the minority and majority populations weighted by the
proportions each group represents of the total population, and
relative clustering, which compares the average distance between
minority members with the average distance between majority
members (Wilkes & Iceland, 2004). For this research, relative
clustering is used. This index equals 0 when minority members
display the same amount of clustering as the majority, is posi-
tive when minorities display greater clustering than the majority,
and is negative when minorities display less clustering than the
majority. Thus, evenness measures people (capturing segrega-
tion) and clustering measures space (capturing self-segregation).
Ethnicity is the main independent variable and is captured by
six categories: Mexican (53.76% of the sample), Puerto Rican
(11.70%), Cuban (5.96%), South American (a conglomeration of
several ethnicities tied to countries in South America as well as
Central America, all accounting for 14.48% of the sample),
Spaniard (origin is from the country of Spain, accounting for

A. JONES
4
0.51% of the sample) and Other (13.58%). Other refers to peo-
ple who listed a religion as their ancestry but listed Hispanic as
their ethnicity. Because of the relatively high percentage of those
falling in the “Other” category, they will be kept in the analysis.
There are two blocks of control variables that will be simul-
taneously controlled for in the model. In this study, demo-
graphic variables shall be defined as those variables to which
ethnicity is not inextricably linked. Age is one control variable
that facilitates the relationship between income, homeowner-
ship and cost of home. Young adults may be more likely to not
have income and rent instead of own (Skaburskis, 1996). In ad-
dition, young adults may be more likely to live in highly segre-
gated areas. Age, in the regression models, will be centered, in
order to limit any collinearity present in the data.
Education, a standard indicator of socioeconomic status, is also a
control va riable—the assi milation model expects more highly edu-
cated people to be more likely to accumulate income and own a
home (Borjas, 1998) Highly educated persons should live in more
diverse environments. Education, as a categorical variable, will also
be centered in the regression analysis. Ethnic employment status is
also a determinant of income, homeownership and residential as-
sortment. Specifically, those individuals who are employed are
more likely to own a home, have income and have live in
non-segregated areas. Employment status is a dummy variable.
Gender is a control variable used in the model s. The literat ure sug-
gests that on average, me n are more likely to accrue income and be
homeowners than women (Reskin & Roos, 1990). Thus, men are
used as th e contr ast c ategor y.
Marital status is another demographic variable related to the
dependent variab les. Non-family households and cohabitors may
be less permanent and therefore less able to commit themselves
to owning a home (Wu et al., 2004). In addition, divorced cou-
ples will be less likely to own a home, as they are more apt to
sell the house after marital dissolution. This is also true for sin-
gle-parent families. As such, it is expected to find more stable
family units engaging in homeownership and income than un-
stable family types. Marital status is conceptualized as a dummy
variable indicating if the respondent has ever been married.
Region of residence is the final demographic variable included
in the analysis. Persons in the southern region of the United
States are more prone to own homes, as there is more residen-
tial assorting of homes in the South than the other regions
(Walker et al., 1997). In addition, those in the southern region
would be more likely to live in segregated areas than other
regions. Thus, the South is used as the contrast categ ory.
In tandem with demographic variables, sociodemographic vari-
ables shall be defined as those variables with which ethnic- ity
shares some relationship. Immigration status (immigrated before
1975 or after 1975) is an aspect of assimilation (Alba, 1992). This
measure operationalizes the spatial assimilation model, which
would anticipate less desirable spatial outcomes, and thus less
proximity to non-Hispanic whites, for recent immigrants. The
cut-off date of 1975 is literature-consistent (Jones-Correa, 1998).
Language is also a sociodemographic determinant: bilingual
persons who speak English poorly are the most likely to live in
residential enclaves (Chandrasekhar, 2004). English language
ability, meaning speaking English only at home and speaking
English well, is an important aspect of assimilation. Nativity, in
tandem with language, is another factor that affects whether one
lives in an ethnically similar area. Group members born in the
United States are expected to be less likely to live in ethnic
neighborhoods than immigrants; among immigrants, the most
recent arrivals are expected to be most likely to live in residen-
tial enclaves (Chandrasekhar 2004).
To examine the relationship between ethnicity, income and
homeownership, logistic regression is used. To examine the re-
lationship between ethnicity, income and residential assorting,
ordinary least squares regression is used. To model each of the
three dependent variables, each dependent variable will have
three models. The first model for each dependent variable will
include income and ethnicity. The subsequent model for each
dependent variable will add to the previous model demographic
and sociodemographic control variables. The final model for
each dependent variable will add to the second model interact-
tion terms between income and ethnicity.
Results
Descriptive Statistics
Table 1 presents descriptive statistics for the characteristics
of respondents, both as the sample as a whole and stratified by
each individual Latino ethnic group. Significant differences are
marked on the table. Approximately 45 percent of the respon-
dents in the sample are homeowners, with higher percentages
coming from Cubans (57.45%) and Spaniards (57.81%). Puerto
Ricans and South Americans have the lowest percentage of
homeowners, with 34.7 and 34.4 percent respectively. Across
all ethnicities, there seems to suggest a high level of segrega-
tion, the lowest value on the dissimilarity index being 0.5957
and the highest value being 0.6940. With the relative clustering
index, all values are positive, indicating Latinos display greater
clustering than the Whites. The average family income for the
sample is around $38,000. Cubans and Spaniards have consid-
erably higher average family incomes ($42,000 and $51,000
respectively), while Puerto Ricans have considerably lower av-
erage family incomes ($33,000).
Demographic controls. The average age of the respondents
in the sample is 43 years. Spaniards and Cubans have consid-
erably higher mean ages, 48 and 53 respectively. Cubans and
Spaniards also have higher educational attainment relative to
the overall average. Cubans, on average, were high school gra-
duates, and Spaniards have an average of one or more years of
college with no degree. The overall average i s twelfth grade with
no diploma. Approximately 62% of the sample is employed,
with higher percentages in Mexicans (65%) and South Ameri-
cans (66%) and lower percentages in Cubans (52%) and Puerto
Ricans (55%). Generally, at least half of those in each ethnicity
are employed. Over two-thirds of the sample is male. Even more
outstanding, 72% of Mexicans in the sample are male, indicat-
ing that there are significantly more males than female repre-
sentation relative to the other ethnic groups. Over half of the
sample (58%) is married, again with a significant difference in
the proportion of married Mexicans (63%). Lastly, 35 percent
of this sample lives in the Southern part of the United States.
Ethnic variation exists in the data, as 74% of Cubans residing in
the South, reinforcing the great geographic concentration that
Cubans have in the South, particularly in Florida where there
are Cuban enclaves (Borjas, 1998). Among the remaining eth-
nicities, this characteristic is reversed, with 22% being the low-
est percentage of residents in the South (Puerto Ricans) and 37
percent being the highest (“Other” group).
Sociodemographic controls. The overall percentage of peo-
ple who immigrated after 1975 is 44%. South Americans had
the highest percentage of respondents who immigrated after
1975 (72%) while Mexicans approached the overall proportion
of respondents immigrating after 1975 (44%). The remainder of
the ethnicities showed less than two-fifths of the respondents

A. JONES 5
Table 1.
Descriptive statistics of variables stratified by ethnicity.
Overall Mean Mexicans Puerto RicansCubans South Americansa Spaniards Otherb
Dependent Variables
Homeownership 45.52%c 48.65% 34.96% 57.45% 34.04% 57.81% 48.75%
Residential Assorting
Segregation (Spatial Dissimilarity) 0.66 0.66 0.70 0.60 0.69 0.64 0.65
Self-Segregation (Spatial Clustering) 0.59 0.43 1.25 0.41 0.80 0.75 0.49
Mediator Variable
Family Earnings $37667.01 $37511.09 $33439.71 $42140.75 $39107.19 $50943.21 $37922.40
Demographic Control Variables
Age 42.72 41.22 43.80 53.47 41.72 47.86 43.87
Education 7.84 7.28 8.57 8.85 8.35 10.19 8.40
Employment Status (Employed) 61.97%c 64.72% 55.01% 52.39% 65.72% 62.45% 57.25%
Gender (Male) 66.52%c 72.16% 50.75% 67.66% 62.07% 64.98% 62.08%
Marital Status (Marr ied) 58.14%c 63.03% 42.80% 56.87% 56.70% 51.05% 54.32%
Region (South ) 35.44%c 34.89% 22.42% 74.37% 30.55% 29.11% 37.22%
Sociodemographic Control Variables
Immigrant Status (after 1975) 43.94%c 43.87% 25.13% 35.11% 72.41% 27.00% 34.58%
Language Ability (Bilingual) 57.20%c 54.41% 70.11% 53.77% 60.60% 54.85% 55.07%
Language Proficiency (English Well) 73.69%c 71.81% 85.97% 62.89% 67.13% 89.45% 81.69%
Nativity (US Born) 36.64%c 41.94% 41.39% 12.76% 7.17% 48.52% 53.01%
Proportion 100.00% 53.76% 11.70% 5.96% 14.48% 0.51% 13.58%
N 46,024 24,744 5383 2743 6665 237 6252
Source: 2000 US Census Public Use Microdata Sample. a“South Am ericans” al so includes Latinos of Central American Origin; bPeople who listed a religion as their ancestry
and listed their ethnicity as Latino were placed in this category; cA Chi-Square test of proportional differences between ethnicities have been found to be sign ificant at the
0.001 le vel.
immigrated after 1975. Over half of the sample (57%) is bilin-
gual. Mexicans and Cubans fall very close to the overall per-
centage (54% for both); however, Puerto Ricans and South
Americans have the highest percentage of bilingual persons (70
and 61 percent respectively). In the sample, 74% of the re-
spondents spoke English well. Cubans and South Americans
have over three-fifths of their respondents who speak English
well (63 and 67 percent respectively), while Puerto Ricans and
Spaniards have close to all of their respondents who speak Eng-
lish well (86 and 90 percent respectively). Lastly, only 37% of
the sample was born in the United States. Close to half of the
Spaniard and Other Latinos were born in the United States (49
versus 53 percent) while South Americans and C ubans have o nly
a small minority of US born respondents (7 versus 13 percent).
In sum, Cubans are the most advantaged group in the sample.
Cubans enjoy higher than average rates of homeownership and
higher mean family earnings, as well as lower than average de-
grees of segregation. Puerto Ricans are the opposite. They en-
joy lower than average rates of homeownership and lower mean
family earnings, as well as higher than average degrees of seg-
regation. However, Cuban’s demographic profile in the sample
seem most similar to Mexicans which may indicate a statistical
nonsignificant difference between the two.
Homeownership
Table 2 presents the odds ratios for modeling homeowner-
ship for this Latino sample. Model 1 seeks to answer the ques-
tion of whether there are real ethnic differences in who is likely
to own a home. According to the model, which only includes the
variables capturing ethnicity, there are substantive ethnic dif-
ferences in homeownership. Cubans are about 75% more likely
to own a home compared to Mexicans. Puerto Ricans, South
Americans and Spaniards are all less likely to own a home
compared to Mexicans (34%, 49% and 17% respectively). For
the Latino group “Other”, individuals are 50% more likely to
own a home compared to Mexicans.
Model 2 adds family earnings with ethnicity to explore whe-
ther ethnic differences in homeownership are attenuated when
considering family earnings. What is evident is that while fam-
ily earnings affects the parameter estimates for ethnicity, family
earnings alone does not explain the ethnic differences in home-
ownership. Two interesting results emerge from Model 2. First,
with the addition of family earnings, Spaniards actually become
more likely (35%) to own a home compared to Mexicans net of
income. Recall that in Model 1, Spaniards were 49% less likely,
compared to Mexicans. In addition, the highly significant 50%

A. JONES
6
Table 2.
Odds Ratios for Homeownership.
Model 1 Model 2 Model 3 Model 4
Ethnicitya
Cuban 1.75*** 1.46*** 0.68*** 0.70***
Puerto Rican 0.66*** 0.58*** 0.47*** 0.47***
South American 0.51*** 0.50*** 0.51*** 0.51***
Spaniard 0.83*** 1.35* 0.91 0.89
Other 1.50*** 1.02 0.85*** 0.84***
Family Earningsbc 1.23*** 1.16*** 1.18***
Demographic Characteristics
Agec 1.05*** 1.05***
Educationc 1.05*** 1.05***
Employment Status (Employed) 1.23*** 1.23***
Gender (Ma l es) 1.25*** 1.25***
Marital Status (Ever Married) 1.95*** 1.95***
Region (South) 1.79*** 1.79***
Sociodemographic Characteristics
Immigration Status (after 1975) 0.62*** 0.62***
Language A bility (Bilingual) 1.09* 1.09*
Language Proficiency (English Well) 1.70*** 1.70***
Nativity (US B orn) 0.88*** 0.88***
Interactions
Family Earnings*Cubanabc 1.02
Family Earnings*Puerto Ricanabc 0.98*
Family Earnings*South Americanabc 0.97*
Family Earnings*Spaniardabc 0.97
Family Earnings*Otherabc 0.97*
R2GSC 0.02 0.17 0.32 0.32
Source: 2000 US Census Public Use Microdata Sample. *p < 0.05; **p < 0.01; ***p < 0.001; aMexican is the contrast category; bFamily earnings are in increm ents of $10,000;
cThese variables are cent ered around the overall mean. For specific values, see Table 1.
greater likelihood that “Others” had in Model 1 disappears with
the addition of income. In all other ethnicities, the same results
remain and the parameter estimates do not change greatly. In
addition, family earnings have a significant, independent effect
on homeownership above and beyond ethnicity. That is, for
each $10,000 increase in family income, a Latino’s likelihood
of owning a home is enhanced by 23% regardless of one’s eth-
nic affiliation. According to Model 2, the ethnic differences in
homeownership cannot be fully explained by the amount of
family earnings that a Latino person brings into the household.
Model 3 adds the demographic and sociodemographic char-
acteristics to explore the possibility that ethnic differences in
homeownership may be related to these controls, and as such,
the effect of ethnicity could be attenuated by the addition of these
variables. However, with adding these characteristics in the mo-
del, the only ethnicity that loses statistical significance is Span-
iards, net of these control variables. In Model 2, the odds ratio
was marginally significant (p < 0.05) with the addition of fam-
ily earnings. The effects that completely attenuate Spaniard’s
advantage over Mexicans (i.e., the contrast group) are family
earnings and gender. That is, males with high family incomes
are equally likely to own a home, regardless of whether they are
Spaniard or Mexican. This result is quite significant: in the biva-
riate case, there were clear differences homeownership rates for
Spaniards (57.81%) and Mexican s (48.65%) and t here were clear
differences in family earnings for these two groups (over $50,000
for Spaniards and over $35,000 for Mexicans). Thus, Spaniards
were clearly advantaged compared to Mexicans. However,
Model 3 indicates that selectivity along gender lines erases this
large disadvantage that Mexicans have compared to Spaniards.
Even though Spaniards lose statistical significance in Model
3, the “Other” group (i.e., persons who listed a religion as an
ancestry but Latino as their ethnicity) gains significance in Mo-
del 3. This ethnic group is about 15% less likely to own a home
compared to Mexicans. By adding gender and language profi-
ciency, this ethnic group’s likelihood becomes statistically rel-
evant compared to Mexicans. That is, controlling for gender
and language proficiency (net of family earnings) set Mexicans
apart from “Others” in their likelihood of owning a home. Fu-
ture research should delve into understanding homeownership
from this population of Latinos.
For Cubans, an interesting reversal of odds emerges when
adding demographic and sociodemographic characteristics. That
is, when controlling for these variables, Cubans have a decreased
likelihood of owning a home relative to Mexicans. Consider in
Model 2, where Cubans were 46% more likely to own a home
compared to Mexicans, net of family earnings. In Model 3, Cu-
bans are now 32% less likely to own a home relative to Mexi-
cans, net of family earnings and demographic and sociodemo-
graphic controls. Further analysis indicates that age and immi-

A. JONES 7
gration status are the main contributors to this reversal. That is,
Cubans older than the average age of the sample (42.72) who
also migrated after 1975 are at the highest risk of not owning a
home, possibly due to the dual disadvantage of entering into the
workforce at a later age and being a recent immigrant. The
combination of two factors account for the now reversed pro-
pensity to own a home.
For Puerto Ricans and South/Central Americans, the coeffi-
cients marginally change from models 2 and 3 but they are still
significant, indicating that for both groups, the likelihood of
owning a home is lower (53% lower for Puerto Ricans and 49%
lower for South/Central Americans) compared to Mexicans. In
addition, family earnings continue to be positive and highly sig-
nificant, indicating that a $10,000 increase in family earnings is
associated with a 16% increase in the likelihood of owning a
home.
Independent effects are observed with demographic and so-
ciodemographic controls and should be acknowledged. All of
the demographic controls are positive and highly significant.
That is, for a ye ar increase in age beyond the mean, having more
education, being employed, being a male, having ever been
married and residing in the South is associated with an increase
in the likelihood of owning a home. However, for sociodemo-
graphic characteristics (i.e., characteristics directly associated
with ethnicity), there is varied effects. Immigrating after 1975
is associated with a 38% decrease in the likelihood of owning a
home for Latinos. Likewise, being born in the US is associated
with a 12% decrease in the likelihood of owning a home for
Latinos. On the reverse, being bilingual is associated with 9%
greater odds of owning a home and speaking English well is
associated with a 70% increase in the odds of owning a home
for Latinos. Thus, while immigration and nativity are detrimen-
tally associated with homeownership, language acts as a posi-
tive force towards homeownership for Latinos.
Because family earnings and ethnicity remain highly signify-
cant in predicting homeownership among Latinos, it is possible
that family earnings and ethnicity interact to predict homeown-
ership. That is, there are ethnic differences in homeownership
and family earnings are positively related to homeownership.
However, the ethnic differences in homeownership could be
based on family earning differentials between ethnic groups. That
is, having a certain amount of income may be more beneficial
in one ethnic group than another in determining homeowner-
ship. Model 4 attempts to answer this question by adding inter-
action terms in order to assess where family earnings illuminate
ethnic-specific differences in homeownership. Indeed, there are
significant moderator effects between family income and eth-
nicity on homeownership. Figure 1 allows for the visual inter-
pretation of the three significant interaction effects seen in
Model 4. Mexicans are the category of contrast so the results
must be interpreted in that regard. As seen in the figure, the
predicted probabilities follow an almost s-shaped pattern, indi-
cating that at extreme higher and lower standard deviations from
mean family earnings, there are larger probability differences in
homeownership between the four ethnicities.
Figure 1.
Predicted probabilities of homeownership based on the interaction of ethnicity and family earnings.

A. JONES
8
To illustrate, at two standard deviations above the mean on
family earnings (SD = 2659.8), Mexicans have a 0.62 chance of
owning a home. The other ethnic groups have about a 0.60
chance. Likewise at two standard deviations below the mean,
Mexicans have a 0.42 chance of owning a home compared to
about a 0.43 chance for Puerto Ricans, South Americans and
“Others”. Thus, the graph illustrates that at high levels of in-
come, these ethnic groups are disadvantaged in owning a home,
but at lower levels of income, these ethnic groups are advan-
taged in owning a home, compared to Mexicans. The implica-
tion of this interaction is paramount: Income has a unique stabi-
lizing effect on homeownership for these ethnic groups. Higher
incomes do not necessarily translate to higher likelihoods of
owning a home for Puerto Ricans, South Americans and Others.
However, important to consider is the relative size of the dif-
ference in the predicted probabilities. Even though the results
are significant, the difference in probabilities at the upper and
lower tails of the distribution is rather small. Thus, while in-
come impacts the ethnic difference in homeownership, the ex-
act magnitude (or how much income impacts the ethnic differ-
ence in homeownership) is still unclear.
Residential Assorting
While understanding who owns a home among Latinos is
important, it is equally important to assess where Latinos are
livi ng and in what mann er. Tables 3 an d 4 present the ordinary
least squares (OLS) regression estimates of residential assorting.
Table 3 focuses on residential assorting via segregation and
Table 4 focuses on residential assorting via self-segregation. To
review, segregation is measured through spatial dissimilarity,
which is an index from 0 (complete integration) to 1 (compete
segregation). The higher the number, the more segregated the
area is. Similarly, self-segregation is measured through spatial
clustering, which is an index where negative scores indicate
that the majority (Whites) is more spatially clustered than the
minority (Latinos) and positive scores indicate that the minority
(Latinos) are more spatially clustered than the majority (Whites).
Tables 3 and 4 also follow the same modeling pat tern as Table 2:
Model 1 is the zero-order model with ethnicity, Model 2 in-
cludes family earnings, Model 3 includes demographic and so-
ciodemographic characteristics and Model 4 includes interact-
tion terms.
Table 3.
OLS regression estimates of residential assorting via segregation (spatial dissimilarity).
Model 1 Model 2 Model 3 Model 4
Ethnicitya
Cuban –0.06*** –0.06*** –0.03*** –0.03***
Puerto Rican 0.04*** 0.04*** 0.03*** 0.03***
South American 0.03*** 0.04*** 0.03*** 0.03***
Spaniard –0.02* –0.01* –0.01 –0.01
Other 0.00*** –0.01*** 0.00 0.00
Family Earningsbc 0.00 0.00** 0.00***
Demographic Characteristics
Agec 0.00*** 0.00***
Educationc 0.00*** 0.00***
Employment Status (Employed) 0.00*** 0.00***
Gender (Ma l es) –0.01*** –0.01***
Marital Status (Ever Marrie d) 0.00** 0.00**
Region (South) –0.07*** –0.07***
Sociodemographic Characteristics
Immigration S t atus (After 1975) 0.00 0.00
Language Ab ility (Bilingual ) 0.02*** 0.02***
Language Proficiency (English Well) –0.03*** –0.03***
Nativity (US Born) –0.01*** –0.01***
Interactions
Family Earnings*Cubanabc 0.00
Family Earnings*Puerto Ricanabc –0.01***
Family Earnings*South Americanabc 0.00
Family Earnings*Spaniardabc 0.00
Family Earnings*Otherabc 0.00
Constant 0.65*** 0.71*** 0.70***
Adjusted R2 0.02 0.05 0.18 0.18
Source: 2000 US Census Public Use Microdata Sample. *p < 0.05; **p < 0.01; ***p < 0.001; aMexican is the contrast category; bFamily earnings are in increm ents of $10,000;
cThese variables are cent ered around the overall mean. For specific values, see Table 1.

A. JONES 9
Table 4.
OLS regression estimates of residential assorting via self-segregation (spatial clustering).
Model 1 Model 2 Model 3 Model 4
Ethnicitya
Cuban 0.16** –0.03 0.27*** 0.27***
Puerto Rican 0.84*** 0.82*** 0.74*** 0.74***
South American 0.40*** 0.37*** 0.29*** 0.29***
Spaniard 0.31*** 0.31*** 0.30*** 0.30***
Other 0.05** 0.06** 0.10*** 0.10***
Family Earningsbc 0.00*** 0.00*** 0.01***
Demographic Characteristics
Agec 0.00*** 0.00***
Educationc 0.00 0.00
Employment Status (Employed) –0.01 0.00
Gender (Males) 0.00 0.00
Marital Status ( Ever Married) –0.02 –0.01
Region (South) –0.73*** –0.73***
Sociodemographic Characteristics
Immigration Status (After 1975) 0.05*** 0.05***
Language Ability (Bilingual) 0.05 –0.0 1
Language Proficiency (English Well) 0.00 0.00
Nativity (US Born) –0.09*** –0.09***
Interactions
Family Earnings*Cubanabc 0.00
Family Earnings*Puerto Ricanabc –0.01***
Family Earnings*South Americanabc 0.00
Family Earnings*Spaniardabc 0.00
Family Earnings*Otherabc 0.00
Constant 0.36*** 0.43*** 0.72*** 0.71***
Adjusted R2 0.03 0.09 0.24 0.24
Source: 2000 US Census Public Use Microdata Sample. *p < 0.05; **p < 0.01; ***p < 0.001; aMexican is the contrast category; bFamily earnings are in increments of $1 0,000;
cThese variables are cent ered around the overall mean. For specific values, see Table 1.
Segregation. Model 1 of Table 3 illustrates that there are sta-
tistically significant ethnic differences in the extent of segrega-
tion for Latinos. In this zero-order model, Cubans and Span-
iards, compared to Mexicans, live in less segregated areas. How-
ever, for Puerto Ricans, South Americans and Other Latinos,
these groups live in more segregated areas compared to Mexi-
cans. Model 2 adds family earnings with ethnicity to explore
whether ethnic differences in level of segregation are attenuated
when considering family earnings. What is evident is that while
family earnings affects the parameter estimates for ethnicity,
family earnings alone does not explain the ethnic differences in
levels of segregation. Across Models 1 and 2, the parameter es-
timates for the ethnicity generally do not change. In addition, fa-
mily earnings are insignificant in predicting segregation. This re-
sult is odd, as the literature suggests that income is associated
with place of residence, particularly segregated area s (Edin et al.,
2003). However, inco me may be suppressed in its effect on seg-
regation for Latinos. This explanation is explored in Model 3.
Model 3 adds the demographic and sociodemographic char-
acteristics to explore the possibility that ethnic differences in
segregation may be related to these controls, and as such, the
effect of ethnicity could be attenuated by the addition of these
variables. However, with adding these characteristics in the mo-
del, the effects of Spaniards and “Others” are attenuated. In Model
2, the odds ratio for Spaniards was marginally significant (p <
0.05) and the odds ratio for “Others” was highly significant (p
< 0.001) with the addition of family earnings. The effects that
completely attenuate Spaniard’s disadvantage over Mexicans
(i.e., the contrast group) are the same effects that completely the
Mexican-Other difference in segregation—the sociodemogra-
phic variables of language ability, language proficiency and na-
tivity. Thus, the inclusion of ethnically-related characteristics
completely explains the differences for the level of segregation
in an area for these specific Latino groups.
In Model 3, family earnings gains statistical significance (p <
0.01); however, the magnitude of the coefficient is quite small
(β = 0.0003), indicating that every $10,000 increase in family
earnings is associated with an increase in the level of segrega-
tion for Latinos. Age, education, and employment status together
create this significant income effect in this model. However,
this finding should be taken as tentative—its larger implication
is that large amounts of income are associated with incremental

A. JONES
10
increases in segrega tion.
The demographic and sociodemographic controls also have
unique independent effects. As with the previous table model-
ing homeownership, all of the demographic controls are highly
significant. Positive correlates to segregation are being older,
having education, being employed and being married. Corre-
lates associated with a decrease in segregation are being a male
and living in the South. However, these results should are
tenuous, as most of the coefficients are 0.00, indicating that the
effects, while significant, are also rather small.
Additionally, significant sociodemographic controls are lan-
guage ability, language proficiency and nativity. Recall that
these three variables explained away the ethnicity effect on se-
gregation among Spaniards and “Others”. Being bilingual is as-
sociated with increased segregation, while speaking English
well and being born in the US is associated with lower levels of
segregation. These results highlight the salience of language
and nativity in residential assorting.
Because family earnings and ethnicity remain highly signify-
cant in predicting segregation levels, interaction terms are still
necessary. Model 4 adds interaction terms in order to assess
where family earnings illuminate ethnic-specific differences in
segregation. However, the only significant moderator effect be-
tween family income and ethnicity on segregation is regarding
Puerto Ricans. As seen graphically in Figure 2, the greatest dis-
crepancies in the predicted level of segregation between Puerto
Ricans and Mexicans are at the ends of the family earning dis-
tribution. The level of segregation experienced by Mexicans at
all standard deviations of family earnings is consistent and
almost invariant, indicating that regardless of income, Mexi-
cans remain fairly non-segregated. However, for Puerto Ricans,
there is more variation: at lower levels of income, Puerto Ri-
cans reside in, on average, higher segregated areas compared to
Mexicans. At higher levels of income, Puerto Ricans reside in
non-segregated areas. Thus, income seems to have a more posi-
tive effect for Puerto Ricans in living in non-segregated areas
than for Mexicans.
Self-Segregation. Model 1 of Table 4 shows statistically sig-
nificant ethnic differences in the extent of self-segregation for
Latinos. According to Model 1, all ethnicities are more self-se-
gregated compared to Mexicans. Model 2 adds family earnings
to Model 1. What is evident is that while family earnings af-
fects the parameter estimates for ethnicity , family earnings alone
does not explain the ethnic differences in homeownership. The
interesting finding is that net of income, the difference in level
of self-segregation between Cuban’s and Mexican’s disappears.
This finding is significant, as it suggests that net of income,
Cubans are as self-segregated as Mexicans. In addition, family
Figure 2.
Predicted segregation based on the interaction of ethnicity and family
earnings.
earnings have a significant (p < 0.001), independent effect on
homeownership above and beyond ethnicity. However, the mag-
nitude of the coefficient is quite small (β = 0.0039), indicating
that every $10,000 increase in family earnings is associated
with an increase in the level of self-segregation regardless of
one’s ethnic affiliation. However, this finding should be taken
as tentative—its larger implication is that large amounts of in-
come are assoc iated with in cremen tal in creas es in self-s egreg ation .
Model 3 adds the demographic and sociodemographic char-
acteristics to Model 2. Adding these characteristics in the model
creates a significant Cuban effect on self-segregation. That is,
Cubans score on average 0.27 points higher on self-segregation
compared to Mexicans net of demographic and sociodemo-
graphic correlates. That is, controlling for region of residence
(net of family earnings) set Mexicans apart from Cubans in the
extent of self-segregation. This result is expected, as the con-
trast category for region is south, which as discussed earlier is
where Cubans primarily reside. Other ethnicities maintain their
significance and magnitude, indicating that all ethnicities, rela-
tive to Mexicans, are self-segregated.
In Model 3 only four of the additional variables are signify-
cant. Age and immigrati on status are positively associated with
self-segregation. Also, region of residence and being born in the
US are both negatively associated with self-segregation. Further
research should look at how these variables are uniquely related
to the enclave context.
Model 4 adds interaction terms in order to assess where fam-
ily earnings illuminate ethnic-specific differences in self-segre-
gation. However, the only significant moderator effect between
family income and ethnicity on self-segregation is regarding
Puerto Ricans. Figure 3, which is the graphical depiction of the
interaction effect, illuminates an interesting finding—at low
levels of family earnings, Puerto Ricans tend to self-segregate
more than Mexicans. However, at high levels of family earnings
Puerto Ricans tend not to self-segregate as much. This result
speaks to the utility of the enclave in the economic accultura-
tion process. Puerto Ricans are more likely to migrate with low
savings and income (Massey & Sana, 2003), and thus, they use
the informal networks and the enclave to catapult themselves in
to a higher SES standing (via family income) to thus move in
more integrated areas.
Discussion
The current study was conducted to evaluate the effective-
ness of income in attenuating the ethnic difference in home-
ownership and residential assorti ng among Latinos in the United
States. The ethnic differences in income were hypothesized to
impact the ethnic differences i n both who owns a home and how
Figure 3.
Predicted self-segregation indexscore based on the interaction of ethnic-
ity and family earnings.

A. JONES 11
segregated the area is in which Latinos liv e.
The amount of income significantly increased the likelihood
of a Latino person owning a home. Indeed, for all groups ex-
cept for Spaniards, all Latino groups were significantly less
likely to own a home net of demographic and sociodemogra-
phic characteristics, compared to Mexicans. Further, income
has different effects for Puerto Ricans, South Americans and
“Others”. Income has a unique stabilizing effect on homeown-
ership for these ethnic groups. Higher incomes do not necessar-
ily translate to higher likelihoods of owning a home for Puerto
Ricans, South Americans and Others. Thus, income differen-
tially impacts homeownership for each of the ethnic groups.
For residential assorting, there were ethnic differences in se-
gregation and self-segregation. Compared to Mexicans, Cubans
were less segregated and both Puerto Ricans and South Ameri-
cans were more segregated net of controls. For self-segregation,
all ethnic groups were more self-segregated compared to Mexi-
cans, net of controls. In addition, income was positively associ-
ated with segregation and self-se gregation, indicating that higher
incomes were associated with both living in segregated and
se lf-segregated areas. However, whe n looking at ethnicity -income
interactions, only Puerto Ricans were found to have a different
effect on segregation and self-segregation based on income.
That is, at lower levels of income, Puerto Ricans resided in
higher segregated areas compared to Mexicans. At higher levels
of income, Puerto Ricans resided in non-segregated areas. Thus,
income seemed to have a more positive effect for Puerto Ricans
in living in non-segregated areas than for Mexicans.
Likewise for self-segregation, Puerto Ricans tend to self-se-
gregate more than Mexicans at low levels of family earnings
and likely not to self-segregate at high levels of family earnings.
Thus, the enclave effect for Puerto Ricans seemed to be protec-
tive, as Puerto Ricans with low savings and income used the in-
formal networks within the enclave to catapult themselves in to
a higher SES standing (via family income) to then move in
more integrated areas.
Despite these significant findings, the research is not without
its limitations. First, while the literature indicates that income is
significant in understanding homeownership, wealth would have
provided a more nuanced discussion of how socioeconomic
standing impacts homeownership and residential assorting. Sec-
ond, the research used MSA-level indicators of segregation and
self-segregation, but the research would have benefitted from a
more narrow focus on neighborhoods or micropolitan areas. In
this case, inferences regarding residential assorting would have
more focused to the immediate locale and would have made the
arguments posed in this paper stronger. Lastly, a more sensitive
measure of self-segregation is warranted. Spatial clustering mea-
sures areal space but a measure that incorporates other aspects
of an enclave would have provided a more accurate measure,
such as census tract-level language percentage. Since an enclave’s
goal is to incorporate recent immigrants into mainstream soci-
ety without assi milation, language prevalence would speak more
accurately to whether an area is an enclave.
Limitations notwithstanding, this research also contains pow-
erful strengths that should also be mentioned. This research
utilizes a very large, distinct dataset to explore ethnic-based dif-
ferences in income, homeownership and residential assorting.
Et hnic variation on these three phenomena within the pan-Latino
culture has not been robustly explored in the literature. Further,
the research incorporates individual and contextual factors in
understanding the Latino residential experience in the US.
The research strengthens our knowledge of how ethnicity,
income and residence are interconnected for Latinos. Future
research should analyze how these connections are present in
other ethnic groups such as Asians or people of African descent.
Future research should also explore how income impacts types
of segregation for Latinos. Emerging research has found that
hypersegregated areas offer differential impacts on outcomes
such as health compared to segregated or integrated areas
(Jones & Goza, 2009). Lastly, residential assorting only de-
scribes the temporal context in which a person lives. Future re-
search should also use a longitudinal perspective to see how
income impacts residential mobility and neighborhood change.
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