



































 
 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 
 

Residential Segregation 
 
 

Jesus Campos 
Department of Sociology 

California State University, Los Angeles 
 

 
This study explores the influences of socioeconomic factors on residential 
integration.  Data was collected through an internet questionnaire and 
respondents were gathered by snowball technique.  Residential integration 
is measured by the percentage of White individuals living in the 
respondent's specific ZIP Code based off information from the U.S. 
Census Bureau.  The author proposes that an increase in socioeconomic 
status will lead to an increase in residential integration.  Despite having 
mixed results, a significant positive correlation was found between one's 
level of education and residential integration, supporting a part of the 
author's original hypothesis. 
 

 
INTRODUCTION 

 
1Studying the residential patterns of the 
various peoples within the United States 
will almost certainly lead the reader to a 
well known set of facts: those of us 
living in this country are not evenly 
distributed and tend to cluster based on a 
number of demographic factors. Though 
this statement may come off as a 
common sense, , it nonetheless poses a 
difficult social challenge for the United 
States: How and/or why does a nation 
which pronounces itself as diverse and 
accepting continue to exhibit such a 
large degree of residential segregation. 
The implications here mean that U.S. 
residents may not be free (at least in a 
                                                 
Jesus Campos is a graduate student in the 
Department of Sociology. His paper was 
originally submitted to SOC. 410 Advanced 
Statistics, taught by Dr. Hyojoung Kim in Winter 
2011. 

structural sense) to settle down wherever 
they  wish. In a sense, it would appear as 
if  modern U.S. society has become 
tolerant of those of varying 
ethnicities/skin colors/outwardly 
appearances only in its public space (i.e. 
the workplace; schools; any place 
sufficiently distant from our own 
neighborhood), but  has yet to conquer 
the unseen barrier that tends to keep our 
living spaces distinctly segregated from 
one another. Perhaps most critical, 
however, is the issue of whether or not 
such residential segregation, if 
continued, will perpetually prevent the 
populace of this country from achieving 
true and complete social unity.  
 
LITERATURE REVIEW 
 

In trying to explain the causes of 
residential segregation, two prominent 
theories have arisen. The first, known as 



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CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

spatial assimilation, assumes that 
economic factors in of themselves will 
always produce such uneven 
distribution, even if we were to presume 
no discrimination based on physical 
and/or identity traits. In essence, this 
theory proposes that, since the free-
market model of the United States 
produces a variation in the costs of 
housing and living from one area to the 
next, it is a given consequence that those 
with more money will be the ones with 
access to the costlier housing, regardless 
of other factors. The second theory, 
known as place stratification,  factors in 
the potential consequences of other non-
economic variables, such as race or 
ethnicity. Even factors such as religion 
or nationality, not readily apparent to the 
naked eye, may serve to create an 
atmosphere of non-acceptance or 
outright discrimination, effectively 
keeping people away from certain 
residential areas. In this theory, 
economics tends to lose its importance, 
since it presumes that two individuals of 
different traits, even if in the same social 
class, would likely not settle down in the 
same residential area (Iceland and 
Wilkes 2006). 

 The issue is more complex than 
this, however. As will be further 
discussed later, research has shown that 
residential segregation also varies 
between Whites and various individual 
race/ethnicities. For example, there 
appears to be less spatial segregation 
between Whites and Asians than 
between Whites and Blacks or even 
Whites and Latinos (Massey and Fong 
1990).  Depending on one’s point of 
view, this is to be attributed either to a 
greater ethnic acceptance by Whites for 
Asians than for other races (i.e. place 

stratification), or to economic 
differences (caused by educational and 
occupational differences) between these 
minorities that has led one minority to 
achieve a generically higher status (i.e. 
spatial assimilation).  

Unfortunately, there is no 
consensus on what exactly constitutes 
socioeconomic status. In their attempt to 
define socioeconomic status, Massey 
and Denton include a person’s objective 
level of both wealth and education, as 
well as the more subjective perceived 
status of the person by the rest of society 
(Massey and Denton 1985). These 
concepts can be further divided into 
various sub-dimensions. For example, 
wealth is often tied to income, and 
perceived status can be seen as a 
combination of such factors as the extent 
of social networks and type of 
occupation. Though we typically view 
these dimensions as achievable by 
anyone dedicated enough to pursue 
them, Massey and Denton point out that 
U.S. society has been plagued by the 
unequal achievement of these factors 
largely as a result of discrimination 
based on any number of traits, such as 
race, class, or gender. As such, they 
conclude that it becomes somewhat 
necessary to include such ascribed 
characteristics as race and gender when 
determining socioeconomic status, as 
such factors can have considerable 
impact on patterns of spatial integration. 
(Massey and Denton, 1985).  

Historically speaking, within the 
United States, the dominant WASP 
(White Anglo Saxon Protestant) group 
has constantly expected minority groups 
to eventually assimilate into the 
dominant group (Hirsch 1942; 
Woolston, 1945; Gordon; 1964). The 



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CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

desire to one day establish a 
homogenous national culture, albeit 
reflecting the traits of the dominant 
group, is considered to be the driving 
force behind such an overwhelming 
expectation. The residential aspect of the 
assimilation processes reflects the 
tendency of a minority group to integrate 
proximally (i.e. in terms of distance) 
with the dominant group. This becomes 
difficult, however, as several decades 
and generations of systematic ethnic and 
racial segregation has created a strong 
barrier to such spatial integration, 
creating somewhat of a paradox (the 
dominant group expects minorities to 
assimilate, yet still discriminate against 
and resist any encroaches by minority 
members). (Massey and Denton 1988) 

From the perspective of the 
minority individual, Fong and Wilkes 
identified several factors that minorities 
consider when determining ideal 
neighborhoods: health; crime rates; 
quality of local schools; the perceived 
socioeconomic status of the area; and, 
quite interestingly, level of interaction 
with the dominant group (Fong and 
Wilkes 1999). Although all factors are 
important, the basis of residential 
integration seems to imply that the 
perceived status (i.e. social class) of the 
area in question would be the main 
factor for where minorities choose to 
settle down and reside in, since this 
would have a direct effect on the degree 
of interaction and integration with the 
dominant Anglo population (Mullan and 
Massey, 1984). As such, Massey and 
Denton were actually able to record that 
increases in the social status of 
minorities (as a result of choice of 
neighborhood) often resulted in an 
increased likelihood of contact with the 

dominant group (Massey and Denton 
1985; Beshers, Lauman, & Bradshaw, 
1964). This leads Gordon to simply 
declare residential integration as a means 
for minorities to increase their 
socioeconomic status (Gordon 1961). 

This does not mean, however, 
that all minority group members choose 
to move into neighborhoods consisting 
mostly of dominant group members. 
Even within the segregated communities 
of the minority groups there exist 
hierarchies which allow local minority 
residents to achieve greater wealth and 
status relative to those around them. 
Some residents may prefer to buy homes 
within their segregated community than 
to rent homes outside of them in order to 
heighten their status within these 
minority hierarchies (Fong and Shibuya, 
2000). Nevertheless, minorities are still 
aware that the areas outside of their 
somewhat isolated neighborhood 
hierarchies provide an opportunity for 
even greater social ascension, with the 
well-documented difference in the 
quality of schools between white and 
minority neighborhoods being a prime 
example (Massey and Denton 1988). In 
this regard, attempts by minorities to 
integrate into Anglo-dominant 
neighborhoods may be seen as nothing 
more than rational decisions made to 
improve their lives and that of their 
families.  

Though many studies have hinted 
at a positive connection between 
socioeconomic status and degree of 
residential segregation, race seems to 
produce variation among the patterns of 
residential integration (Fong 1965; 
Massey and Fong 1990; Massey and 
Bitterman 1985). According to Massey 
and Fong, Asians appear to be the 



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CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

quickest in residentially integrating into 
dominant Anglo communities, followed 
by Latinos. Blacks have the lowest 
degree of integration, though in no way 
does this mean they are incapable of 
reaching a high level of socioeconomic 
status (Massey and Fong 1990). Instead, 
the implication here may revolve around 
the level of acceptance by the Anglo 
community towards each minority 
group. Logan, Alba, and Leung also 
observed that dominant Anglo 
acceptance (in regards to residential 
integration) also vary by region (Logan, 
Alba, and Leung 1996). Consequently, 
though we may presume that higher 
socioeconomic status should lead to 
increased opportunities for residential 
mobility, it must be realized that the 
perceived status of any minority may be 
arbitrary to the dominant Anglo group; it 
is they who have the power to restrict 
residential integration to minority group 
members (with the White flight 
phenomenon serving as an ideal 
example). 

The question remains as to which 
of these two leading theories is more 
accurate? Does one trump the other, or is 
it possible that both theories can coexist 
side by side? If ascribed factors such as 
race and gender are to be included into 
socioeconomic status, then this would 
seemingly eliminate the need to measure 
residential segregation based on these 
factors alone (i.e. place stratification). 
However, the possibility exists that a 
measure of using socioeconomic status 
(including both ascribed and achieved 
features) may very well yield a different 
set of results than a measure using 
ascribed features alone.   
 
 

HYPOTHESIS 
 

I propose that an increase in 
socioeconomic status will lead to 
increased residential integration. My 
ultimate intention is to examine the 
proposed causes behind a person’s 
existing social class and their degree of 
residential integration. I’d like to 
determine if higher levels of a minority 
individual’s socioeconomic dimensions 
(i.e. education and wealth) are in fact 
correlated to their residential placement 
within society.  

Second, I would like to analyze 
the effect of secondary independent 
variables on residential integration:  

• Sex, an ascribed visible feature, 
is viewed by place stratification 
theory as having an impact on 
residential placement (though 
marital status may have a greater 
role than sex alone). I 
hypothesize that minority males 
are more likely than minority 
females to reside in areas that are 
predominantly white.  
 

• Race/ethnicity, being the most 
prominent ascribed feature, 
ultimately determines (according 
to place stratification) residential 
placement as a result of dominant 
group acceptance or rejection. I 
hypothesize that non-Whites 
respondents are more likely to 
reside in areas that are not 
predominantly White. 

 
• Religion, though not easily 

discernible, also has the potential 
to affect patterns of integration. 
(i.e. existence of predominantly 
Jewish neighborhoods; Catholic 



RESIDENTIAL SEGREGATION by Jesus Campos   16 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

and Protestant divides). I 
hypothesize that non-Christians 
and non-Jews are more likely to 
find themselves in areas that are 
less predominantly white than 
those who are Christians or Jews. 

 
• Legal status, such as whether the 

respondent is a U.S. citizen, 
resident, or other perhaps even 
undocumented, may play a key 
role amongst certain groups, 
particularly Latinos and Asians, 
regarding their willingness to 
integrate into dominant Anglo 
communities. I hypothesize that 
as legal status increases (using an 
ordinal scale to be described 
later) so does residential 
integration into predominantly 
white neighborhoods. 

 
• Generation, though not 

applicable in all cases, may affect 
residential patterns. Some 
respondents of certain origin (i.e. 
Latinos and Asians) may be so 
far removed from their 
immigrant ancestors that they no 
longer see their racial or ethnic 
minority status as an impediment 
towards integration (i.e. they may 
feel sufficiently comfortable 
around dominant Anglo groups, 
or perhaps are fully assimilated 
and are now Anglicized). I 
hypothesize that for those 
respondents whose families come 
from immigrant backgrounds, the 
more generations the family has 
resided in the United States 
(using an ordinal measurement to 
be described later) the higher the 

percentage of Whites living in 
the respondent’s neighborhood.   

 
• Marital status may affect a 

person’s willingness to 
residentially integrate? For 
example, a single minority 
woman may become more 
willing to integrate into a 
dominant Anglo neighborhood 
once married. I hypothesize that 
married minority women are 
more likely to live in areas with a 
larger percentage of Whites than 
unmarried minority women. 
 

• Home ownership may potentially 
add to a person’s status. 
Furthermore, individuals who are 
seeking to purchase a home may 
tend to seek out neighborhoods 
based much more so on the 
demographic characteristics of 
the area than those who are 
seeking to rent or lease 
(presumably since renting or 
leasing is often viewed as 
temporary).  
 

• Job type may play a role in 
determining one’s socioeconomic 
status, as certain different 
societies attach greater prestige 
to some occupations than they do 
to others. 

 
SURVEY PROCEDURES 
 

All members of a focus group 
completed the survey in about five 
minutes. All were satisfied with the 
instructions within the survey, as well as 
with the overall appearance of the 
questionnaire. However, some members 



RESIDENTIAL SEGREGATION by Jesus Campos   17 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

raised concerns about the response 
categories to some of the questions. For 
example, the questions measuring 
income and savings were originally 
open-ended; the respondent was 
instructed to write a numerical figure to 
the nearest thousand. However, the focus 
group felt that this could be rather 
difficult for the respondent to calculate 
in the course of a few minutes. As such, 
I decided to introduce an ordinal scale in 
intervals of ten thousand dollars.  

The focus group also raised 
concerns in the distinction of the 
response options between two questions: 
one which asked for race and the other 
for ethnicity. The race question 
originally allowed a respondent to 
choose between White (including White 
Hispanic); Black (including Black 
Hispanic); Asian (including Indian and 
Pacific Islander); and Native American. 
The group felt the options were too 
vague, and perhaps even insulting. In 
expanding the options, the resulting list 
was almost identical to the existing 
options already in place for the question 
regarding ethnicity. As such, I decided to 
combine race and ethnicity into a single 
question asking for the respondent’s 
race/ethnicity (though this has forced me 
to consolidate two separate secondary 
independent variables into one).  

Lastly, the group raised concerns 
about determining socioeconomic status. 
For example, the group raised the 
possibility of how home ownership can 
affect perceived status, persuading me to 
include such a question. The group also 
assisted me in expanding the options for 
the question regarding occupation, and 
also assisted in determining perceived 
various occupation options listed in the 
questionnaire.  

An online questionnaire, posted 
on the online survey site 
zoomerang.com, was utilized to measure 
the variables. Posting the questionnaire 
online allows the researcher to 
potentially reach a large number of 
people for a minimal amount of effort 
(as opposed to telephone surveys, which 
can be time-consuming). Furthermore, 
online surveys are comparable to mailed 
surveys in regards to minimizing any 
potential social desirability effects. 
Lastly, online survey sites allow a 
researcher to collect and analyze results 
rather quickly and efficiently. 
Zoomerang, the website used to create 
and post this questionnaire, gives users a 
fairly large number of options in creating 
a web-based survey (including survey 
templates, response types, etc). The 
website then creates a specific internet 
address for the survey, allowing 
respondents to reach the survey by either 
imputing the address or linking to it.   

Zoomerang considers a complete 
questionnaire as one in which 
respondents have clicked on the DONE 
button located at the bottom of the 
questionnaire page. Thus complete 
questionnaires can include those in 
which not every question was answered. 
Zoomerang also keeps track of what are 
referred to as views, which include both 
the number of surveys marked as 
complete (as previously defined) as well 
as those surveys that were opened but 
subsequently closed without clicking the 
DONE icon. Such ‘incomplete’ visits are 
not included in the tally of complete 
questionnaires. The site does not allow 
one to distinguish between those 
incomplete surveys in which a person 
simply viewed the survey and decided 
not to respond, and those in which a 



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CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

survey was completed but the 
respondent did not click DONE (for 
whatever reason) or accidently closed 
the page. It is also impossible to 
ascertain whether or not some 
respondents who simply ‘viewed’ the 
questionnaire the first time came back to 
complete one later on.  

Since the Basic survey package 
offered by Zoomerang was utilized, a 
maximum of 100 complete surveys were 
allowed. For the purposes of this survey, 
the site was instructed to close the 
survey once this limit was reached. This 
survey was completed by 100 people 
(the maximum allowed), and was viewed 
by 126. Thus, on 26 occasions the 
survey was opened but then closed 
without clicking the DONE icon.  

Though the questionnaire was 
posted online, the researcher is still 
responsible for finding those he or she 
wishes to survey and directing them to 
the questionnaire page online. As such, 
this particular survey was undertaken 
using the snowballing technique, in 
which roughly forty people, including 
friends, family, and classmates, were 
originally asked to participate in the 
survey, and were also asked to spread 
the word to others in their social 
network, with the only limitation being 
that an individual must be an adult and 
could only take the survey once. The 100 
completed survey mark was reached 
roughly over two weeks after the survey 
was activated online. Since the majority 
of those originally asked to participate 
are non-Whites and tend to be of college 
age, this may arguably mirror itself in 
the final results at least in terms of age 
and race/ethnicity.  

 
 

MEASUREMENT OF VARIABLES 
 

Residential integration is to be 
the dependent variable. To determine the 
level of integration for each respondent, 
the percentage of Whites living within 
the respondent’s ZIP Code (given by the 
respondent in the questionnaire) will be 
obtained from the U.S. Census Bureau’s 
official website: 
http://factfinder.census.gov/home/saff/m
ain.html?_lang=en. The data from the 
website is from the 2000 Census. 

The primary independent 
variable is socioeconomic status, to be 
measured amongst the following three 
separate components: 

 
1) Income- Measured by asking 

respondents their level of 
income using an ordinal scale 
with ten thousand dollar 
intervals; 

 
2) Savings- Measured using the 

same ordinal scale as for the 
question regarding income; 

 
3) Level of Education- 

Measured using an ordinal 
scale consisting of the 
various educational degrees 
typically offered in the 
United States. 

 
In addition to these three 

measures of socioeconomic status, I will 
ask the participants to also list the 
following secondary independent 
variables:  

1) sex- obtained from a simple 
dichotomous male/female 
option;  

http://factfinder.census.gov/home/saff/main.html?_lang=en
http://factfinder.census.gov/home/saff/main.html?_lang=en


RESIDENTIAL SEGREGATION by Jesus Campos   19 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

Residential  
Integration 

 

Socioeconomic  
 Status 

(achieved traits only) 
 

 
 

Sex 
Race/Ethnicity 

Religion 
 

Legal Status 
Generation 

Marital Status 
Home Ownership 

Job Type 
(both achieved and ascribed 

traits) 

2) race/ethnicity- obtained from 
a nominal-level list 
consisting of the more 
common race/ethnic 
backgrounds found within the 
United States;  

3)  religion- obtained using 
another nominal list consisting of 
various religious         
backgrounds;  

4)  legal status- determined by an 
ordinal list of possible options in 
descending order; 

5) generation- obtained by 
defining first generation, and 
then asking if respondent to     
mark which generation they 
belong to; 

6) marital status- obtained from 
a list consisting of various 
relationship possibilities; 
7) home ownership-  obtained 
from a dichotomous list of either 
rent/lease or own; 
8) job type- obtained by asking 
respondents to choose their 
occupation from a nominal  
    list; a separate question, 
Employment, will ask 
respondents if they are 
temporarily     
    employed or directly 
employed.  
                            
Though ascribed factors such as 

race and sex can be construed to some as 
being a determinant of socioeconomic 
status, I will keep them separate for the 
purpose of this piece. Thus, since I will 
define socioeconomic status using only 
achieved factors, the distinction between 
the spatial assimilation and place 
stratification theories will be intact (the 
ascribed secondary independent 

variables can be used to test the latter). 
The following diagram is fairly 
representative of the correlations I intend 
to do: 

 
 
DATA CLEANING 
 

In total, 14 distinct variables 
were measured throughout the course of 
the questionnaire. However, many of 
these variables were later recoded, and 
some were eliminated altogether. All 
nominal sets of response categories 
consisting of more than two possible 
options were recoded into various 
dichotomous variables. However, 
original response categories that did not 
illicit a single response were dropped 
entirely from any future analysis. Table 
1 in Appendix II is a summary of the 
cleaning and recoding process and 
includes all the new dichotomous 
variables (with the exception of the 
reference groups, though they too have 
been recoded). Table 1 also summarizes 
the mean and standard deviation of all 
listed variables.  

The nominal Marital Status 
variable originally consisted of three 
separate response categories. However, 
since one of the original response 



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CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

categories did not illicit a single 
response, the remaining two categories 
have automatically become a 
dichotomous variable. The values for 
these two categories were simply 
changed to 0 and 1 to match all other 
dichotomous variables. Similarly, the 
variable Generation, which originally 
consisted of four different response 
options, did not receive a single response 
for the category 3rd Generation. This 
response category, therefore, was 
completely eliminated (with the value 
numbers changed to remain 
consecutive), and the remaining 
categories recoded into dichotomous 
variables. 

A variable labeled Job Type 
Status was to be included into the final 
analyses. This variable was to be 
determined by a designation of Lower 
Status and Higher Status based upon the 
respondent’s occupation as given for the 
variable Job Type. However, since the 
variable Job Type contained 18 missing 
answers, the idea was abandoned. The 
actual occupation options for the 
variable Job Type will be used in 
analysis instead (they will be recoded 
into dichotomous variables). 
Furthermore, a variable entitled 
Employment Type was simply 
eliminated, as its analytical justification 
was difficult to establish. 

 
UNIVARIATE ANALYSIS 
 
 The dependent variable, 
consisting simply of the percentage of 
Whites living within the respondent’s 
ZIP Code, yielded a mean value of 
39.36%, with a standard deviation of 
24.57 %. Level of Education produced a 
mode response of a Bachelor’s Degree; 

Level of Income produced a mode 
response of $30,000-$40,000; and Less 
than $10,000 was the mode response for 
Level of Savings. Table 2 in Appendix II 
summarizes the response frequencies for 
the dependent and key independent 
variables.  
 The average age of all 
respondents was 27.8 years with a 
standard deviation of 5.6 years. Of the 
100 total respondents, 47 are females 
and 53 are males. Most of the 
respondents (44) are Roman Catholic 
and most (64) are Latino/Hispanic. 
Furthermore, 64 of the 100 respondents 
are not married and 80 either rent or 
lease (but do not own) their housing. 
Another vast majority of respondents 
(77) were born in the United States, and 
64 respondents come from immigrant 
families and are the first generation to be 
born in this country. In terms of 
occupation types, the Administrative 
option had the most responses with 28.  
 
BIVARIATE ANALYSIS 
 
(Refer to table in Appendix III for all 
Pearson’s values obtained from the 
bivariate analyses) 
 

In comparing the key dependent 
variable (utilizing White Percentage of 
ZIP Code) with the variable Level of 
Education (the first of three key 
independent variables), a Pearson’s 
value of -.561 (significant at the .01 
level) is obtained. This is not what was 
expected, as an interpretation of this 
value means that as the level of 
education a respondent has obtained 
increases, the average White percentage 
of the respondent’s neighborhood (i.e. 
the area encompassed by their ZIP Code) 



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CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

decreases. The relationship between the 
White percentage of the respondent’s 
neighborhood and their level of income 
did not produce a statistically significant 
value. However, there does appear to be 
some association between White 
percentage of ZIP Code and Level of 
Savings. A Pearson’s value of .237 
(significant at the .05 level) was 
obtained in the bivariate analysis of the 
two variables. This implies a positive 
association, meaning that the average 
percent of Whites living in the 
respondent’s neighborhood increases 
alongside one’s amount of savings.  

Other factors that may potentially 
influence the dependent (i.e. the control 
variables) include one’s race/ethnicity, 
religion, legal status, job type, immigrant 
generation (if applicable) marital status, 
home ownership, sex, and age. The 
Pearson’s values obtained from 
comparing the various non-White 
race/ethnicities with the White 
percentage of the neighborhood are 
somewhat mixed. There is a significant 
value (at the .01 significance level) 
between Latino/Hispanic and White 
Percentage of ZIP Code of -.275, 
meaning that the average White 
percentage of the neighborhood 
decreases when moving from non-
Latino/Hispanic to Latino/Hispanic. 
However, the Pearson’s values for the 
remaining race/ethnicities are somewhat 
inconclusive, with none being significant 
even at the .05 level. 

There are only two religions (out 
of six) with Pearson’s values that are 
statistically significant: Atheist/Agnostic 
with a value of -.384 (at the .01 
significance level), and Other Religion 
with a value of .220 (at the .05 level). 
Thus, there is a strong negative 

association between those that are 
Atheist or Agnostic and the average 
percent of Whites living in their 
neighborhoods, whereas there is a 
potential positive association between 
those who responded to belonging to an 
unlisted religion and the dependant 
variable. 
 The Pearson’s value measuring 
the relationship between Legal Status (in 
regards to citizenship) and White 
percentage of ZIP Code is not 
statistically significant (its value is -
.147). This implies a lack of relationship 
between the two variables.  
 Of the seven different 
occupations to receive a response for the 
Job Type variable, only two had any sort 
of statistically significant association 
with the dependant variable, though in 
opposite directions. These two are 
Administrative and Education, with 
Pearson’s values of .565 and -.585, 
respectively (with both values significant 
at the .01 level).  This implies that those 
working in the administrative field are 
more likely to live in areas of higher 
concentrations of Whites, on average, 
than those working in the education 
field. Though retail, food service, and 
public services jobs yielded figures 
denoting negative associations with the 
dependant variable, while business and 
health/medicinal fields yielded positive 
association values, none of these 
occupation types yielded results with 
any acceptable statistical significance 
levels, so no such associations can be 
established. 

The bivariate analyses between 
the dependant variable and the three 
dichotomous variables constituting the 
[immigrant] Generation variable yielded 
results that can be said to be expected in 



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regards to the relationship between the 
two factors. Though the Pearson’s value 
for those who responded to belonging to 
1st Generation yielded a statistically 
insignificant value, the analyses for 2nd 
Generation and Beyond 2nd Generation 
produced values (significant at the .01 
level) of -.338 and .345, respectively. An 
interpretation implies that those who 
belong to the 2nd immigrant generation 
(i.e. the first generation of their family to 
be born in the United States) are likely to 
live in areas with a lesser concentration 
of Whites than those whose families 
have been in this country longer, though 
the exact generation the respondent 
belongs to cannot be determined. 

The bivariate analysis between 
the dependent variable and Marital 
Status did not produce a statistically 
significant Pearson’s value, nor did the 
analysis with the variable Age. However, 
the relationship between the dependent 
variable and Home Ownership did 
produce a positive association of .415 
(significant at the .01 level). This means 
that those who own their own housing 
live, on average, in neighborhoods with 
a greater percentage of Whites than 
those who rent or lease. Similarly, the 
association between the dependent 
variable and Sex also produces a 
Pearson’s value that is statistically 
significant (at the .01 level) value. The 
value, .353, implies a positive 
association between the respondent’s sex 
(when moving from female to male) and 
the dependent variable.  

 
OLS REGRESSION ANALYSIS 
 

Though the various Pearson’s 
values allow us to establish a correlation 
between two variables, such an analysis 

does not allow us to measure the effect 
that all possible key and control 
variables have on the dependent variable 
as a whole. Thus, a linear regression 
must be utilized to determine the overall 
influence of all independent variables on 
the dependent variables; in essence, a 
linear regression allows one to measure 
the strength of the model as a whole. For 
this particular model, an OLS regression 
analysis will be utilized since the single 
dependent variable is of continuous 
value (a percentage figure measuring the 
percentage of Whites living in the 
respondent’s ZIP Code).  

In theory, a linear regression 
analysis of the research model would 
include the single dependent variable 
plus the twenty-nine key and control 
independent variables (most of which 
are recoded dichotomous variables). 
However, SPSS excluded six of the 
independent variables (though not key 
ones) during the regression analysis. The 
excluded variables are White; 
Latino/Hispanic; Roman Catholic; 
Deist/Spiritualist; Administrative Job; 
and Beyond 2nd Generation. The reason 
for these exclusions may be related to an 
issue of perfect multicollinearity with 
the variables in question.  Several 
attempts were made to solve this issue 
by recoding some of the existing 
dichotomous variables pertaining to 
Race/Ethnicity and Religion in order to 
compress them into fewer variables. No 
such recode attempts produced a model 
in which no independent variables were 
excluded by SPSS.  

The model which ultimately bore 
the fewest excluded variables (four) was 
one in which four separate religious 
variables (Buddhist/Tao/Shinto; 
Atheist/Agnostic; Deist/Spiritualist; and 



RESIDENTIAL SEGREGATION by Jesus Campos   23 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

Other Religion) were merged into a 
single category known simply as Other 
Religion, and two racial/ethnic variables 
(Multi-Racial/Ethnic and Other 
Race/Ethnicity) were merged into a 
single category known as Other Race. 
The four excluded variables under this 
new model are Latino/Hispanic; Roman 
Catholic; Administrative Job; and 
Beyond 2nd Generation. This new model 
is summarized in Appendix IV as Table 
1. Despite the changes between them, 
the initial and newer models yielded 
similar regression values, with F-test  
values of 11.23 and 11.91, respectively, 
and Adjusted R-squared values of .77 
and .76, respectively. Since this second 
model yielded fewest excluded 
variables, it shall be used to analyze all 
other independent variables.  
 As stated, the linear regression 
analyses for the research model yielded a 
statistically significant (at the .01 level) 
F-test score of 11.91, meaning that at 
least one of the independent variables 
has a significant effect on the dependent 
variable). The Adjusted R-squared value 
of .76 means that the model can explain 
76% of the variance in the dependent 
variable. In regards to specific 
independent variables, three of the 
variables (Income; Other Race; and 2nd 
Generation) produced values significant 
at the .05 level. The variable 2nd 
Generation produced a negative 
relationship, meaning those who are in 
the first generation of their family to be 
born in this country decreases the 
average White percentage of one’s 
neighborhood (a somewhat expected 
outcome). Another expected outcome is 
the positive relationship between one’s 
level of education and the dependent 
variable, meaning that, on average, as 

the respondent’s level of education rises, 
so does the percentage of Whites living 
in their neighborhood (i.e. ZIP Code). 
The positive relationship between the 
dependent variable and the variable 
Other Race may be somewhat confusing. 
Though this variable excludes Whites, 
Latino/Hispanics, and Asians, it is 
difficult to specify as to what race this 
significant association can be attributed 
to.  

 
CONCLUSION 
 

Overall, the regression model 
may be interpreted as having produced 
somewhat ambiguous results. The model 
is strong in that in can explain much of 
the variance in the dependent variable; 
however, the fact remains that only three 
of the over two dozen independent and 
control variables yielded any sort of 
statistically significant influence (with 
only one of the three key variables 
having any sort of significant influence). 
Furthermore, variables that were largely 
expected to produce a significant 
positive relationship (such as Education, 
Savings, and White) failed to do so. 
Similarly, variables that were expected 
to produce negative relationships (most 
notably Latino and Black) also failed to 
do so (with Latino even being 
completely excluded by SPSS from the 
model). Obviously, such outcomes may 
be a result of issues relating to sample 
size, or perhaps more likely to issues 
relating to the use of availability 
sampling. Nevertheless, in taking into 
account the absence of such largely 
expected outcomes, it may perhaps be 
inaccurate to conclude that the overall 
model is indeed a strong one. 

 



RESIDENTIAL SEGREGATION by Jesus Campos   24 
 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 
 

 

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Massey, Douglas S.; Eric Fong. Segregation 
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Massey, Douglas S.; Denton, Nancy A. 
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RESIDENTIAL SEGREGATION by Jesus Campos   25 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

APPENDIX I 
 
 

This survey questionnaire is a part of a study attempting to measure the connection 
between socioeconomic status and residential patterns. The questionnaire consists of 
14 questions, and should take roughly 5 minutes to complete. 
 
Please note that the survey is intended to be anonymous – do not write your name or 
any other identifying information on open-ended portions of the questionnaire.  
 
Your participation is greatly appreciated. Thank you. 
 
 
1) Age: ______    
  
2) Sex:   [   ] Male    [   ] Female  
 
3) What ZIP Code do you currently reside in?     _________________________  

 

4) What is your current citizenship/residency status? 

         [   ] Natural-born citizen          
         [   ] Naturalized Citizen 
         [   ] Legal Resident                  
         [   ] Temporary Resident/Visitor   
         [   ] Undocumented Status 
 
5) Marital Status    
 
        [    ] Single                               
        [    ] Divorced/Widowed          
        [    ] Married (including common law)            
 
6) What is the highest level of education you have completed so far? 
 
        [    ] Less than high school 
        [    ] High school diploma/GED 
        [    ] Associate’s Degree 
        [    ] Bachelor’s Degree 
        [    ] Master’s Degree  
        [    ] Doctorate or equivalent 
 
 



RESIDENTIAL SEGREGATION by Jesus Campos   26 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

7) What is your approximate annual income (including that not reported to the 
government) to the nearest thousand? Please include wages from employment, money 
received from tenants, government subsidies (welfare), etc. Do not include income from 
bank and/or government loans that must be repaid.   
 
            [   ] Below $10,000 
            [   ] $10,000-$20,000 
            [   ] $20,000-$30,000 
            [   ] $30,000-$40,000 
            [   ] $40,000-$50,000 
            [   ] $50,000-$60,000 
            [   ] Above $60,000 
 
8) To the best of your knowledge, how much money do you have in savings to the nearest 
thousand? This includes money stored in a bank account, trust fund, mutual fund, stock 
options, real estate, etc.  
 
            [   ] Below $10,000 
            [   ] $10,000-$20,000 
            [   ] $20,000-$30,000 
            [   ] $30,000-$40,000 
            [   ] $40,000-$50,000 
            [   ] $50,000-$60,000 
            [   ] Above $60,000 
 
9) What is your religious affiliation/preference?  
                    
            [   ] Protestant                  [   ] Jewish                          [   ] Muslim  
            [   ] Roman Catholic        [   ] Hindu/Sikh                  [   ] Buddhist/Tao/Shinto             
            [   ] Atheist/Agnostic       [   ] Deist/Spiritualist          [   ] Other 
 
 
10) Do you own or rent/lease your current housing (whether it be a house, apartment, or 
otherwise)? Rent includes any money you pay your parents for housing if you still reside 
with them. 
 
             [   ] Own       [   ] Rent/Lease   
 
11) If you or either side of your family immigrated to the U.S. from abroad, what 
generation do you belong to (using the definition of first generation as those who 
originally immigrated here)? Please leave blank if the question is not applicable to you.        
                 
             [   ] 1st Generation    
             [   ] 2nd Generation      



RESIDENTIAL SEGREGATION by Jesus Campos   27 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

             [   ] 3rd Generation   
             [   ] Beyond 3rd Generation 
 12) What is your current employment status? Temporary employment is employment 
through agencies/staffing services or seasonal employment. 
 
              [   ] Unemployed                   
              [   ] Temporary employment     
              [   ] Direct employment     
 
 
 
 
13) How would you classify the field of your current employment (if you have more than 
one job, please choose the job which provides you with the majority of your income)? 
 
              [   ] Retail                                                [   ] Business   
              [   ] Food Services                                   [   ] Public Service  
              [   ] Administrative                                  [   ] Education  
              [   ] Manufacturing                                  [   ] General Clerical 
              [   ] Health/Medicine                               [   ] Sales (including Insurance) 
              [   ] Repair/Mechanic                             
              [   ] Other (Please specify): ________________________ 
             

14) Which race/ethnic group do you identify yourself as belonging to?   
           
              [   ] Black                                                     [   ] White (non-Hispanic)                  
              [   ] Asian                                                     [   ] Latino/Hispanic                             
              [   ] Native American or Alaskan Native     [   ] Middle Eastern    
              [   ] Indian                                                    [   ] Pacific Islander 
              [   ] Other                                                     [   ] Multi-Ethnic 
 

 

 

 

 

 

 



RESIDENTIAL SEGREGATION by Jesus Campos   28 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

 

 Variable Categories Frequency Mean S.D. 

D
V

 Percentage of 
Whites in ZIP Code 

N/A  N/A 39.36 24.57 

IN
D

E
P
E

N
D

E
N

T
 V

A
R

IA
B
L

E
S 

Education Level 1=Less than high school 
2=High School Diploma/GED 
3=Associate’s Degree 
4=Bachelor’s Degree 
5=Master’s Degree 
6=Doctorate or equivalent 

  0 
  10 

10 
63 
17 
0 

 
 
3.87 

 
 
.81 

Income 1=Less than $10,000 
2=$10,000 to $20,000 
3=$20,000 to $30,000 
4=$30,000 to $40,000 
5=$40,000 to $50,000 
6=$50,000 to $60,000 
7=More than $60,000 

21 
11 
11 
28 
24 
5 
0 

 
 
3.43 

 
 
1.68 

Savings 1=Less than $10,000 
2=$10,000 to $20,000 
3=$20,000 to $30,000 
4=$30,000 to $40,000 
5=$40,000 to $50,000 
6=$50,000 to $60,000 
7=More than $60,000 

93 
4 
2 
1 
0 
0 
0 

 
 
1.11 

 
 
.45 

C
O

N
T

R
O

L
 V

A
R

IA
B
L

E
S 

Race/ 
Ethnicity#1 

 

Black/Non-Black 
Asian/Non-Asian 
Latino or Hispanic/Non-Latino or Hispanic 
Other Race-Ethnicity/Non-Other Race-Ethnicity 
Multi-Ethnic/Non-Multi-Ethnic 

5/95 
7/93 
64/36 
5/95 
5/95 

.05 

.07 

.64 

.05 

.05 

.23 

.26 

.48 

.22 

.22 

Religion#2 

 
Roman Catholic/Non-Roman Catholic 
Buddhist, Tao or Shinto/Non-Buddhist, Tao, or Shinto 
Atheist or Agnostic/Non-Atheist or Agnostic 
Deist or Spiritualist/Non-Deist or Spiritualist 
Other/Non-Other 

44/56 
5/95 
16/84 
3/97 
30/70 

.44 

.05 

.16 

.03 

.30 

.45 

.22 

.37 

.17 

.46 

Legal Status 
 

1=Undocumented 
2=Temporary Visitor 
3=Permanent Resident 
4=Naturalized Citizen 
5=Natural-born Citizen 

0 
0 
3 
20 
77 

 
 
4.74 

 
 
.51 

Job Type#3 Retail Job/Non-Retail Job 
Business Job/Non-Business Job 
Food Services Job/Non-Food Services Job 
Public Service Job/Non-Public Service Job 
Administrative Job/Non-Administrative Job 
Health & Medicine Job/Non-Health & Medicine Job 

7/75 
3/79 
3/79 
16/66 
28/54 
6/76 

.09 

.04 

.04 

.20 

.34 

.07 

.28 

.19 

.19 

.40 

.48 

.26 
Generation#4 1st Generation/Non-1st Generation 

2nd Generation/Non-2nd Generation 
12/83 
64/31 

.13 

.67 
.33 
.47 

Marital Status 0=Single 
1=Married 

64 
32 

1.33 
 

.47 
 

Home Ownership 0=Rent/Lease 
1=Own 

80 
20 

1.20 .40 

Sex 0=Female 
1=Male 

47 
53 

.53 .50 

Age   27.80 5.60 

APPENDIX II 
 
TABLE 1:                                                          



RESIDENTIAL SEGREGATION by Jesus Campos   29 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

 

 

 

 

 

 
 

White Percentage of Neighborhood (N=100) 

Percent White 

 

10 10

63

17

0

10

20

30

40

50

60

70

High School 
Diploma/GED

Associate's 
Degree

Bachelor's 
Degree

Master's 
Degree

Level of Educaton (N=100)

21

11 11

28
24

5

0

5

10

15

20

25

30

Income  (N=100)

93

4 2 1
0

10
20
30
40
50
60
70
80
90

100

Savings (N=100)

TABLE 2: 

#Denotes recoded variable in X/Y format, where X has value of 1 and Y has value of 0. 
1 Reference group is White (non-Hispanic). 
2Reference group is Protestant. 
3 Reference group is Education. 
4 Reference group is Beyond 2nd Generation. 
 



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RESIDENTIAL SEGREGATION by Jesus Campos 32 

CALIFORNIA SOCIOLOGY JOURNAL, 2011, VOL. 3 (ISSUE 1: 12-32) 

APPENDIX IV 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

Table 1: Linear Regression 
Independent Variables# Coefficients Standard Error 

Intercept 73.30 56.30 
Education -14.02 5.26 
Income 16.64 5.33* 
Savings -3.72 5.91 
White -71.80 29.76 
Asian -34.66 20.27 
Other Race 37.26 11.56* 
Protestant 32.49 19.43 
Other Religion 4.77 11.51 
Legal Status 1.12 5.80 
Retail Job -34.20 16.44 
Business Job 70.62 32.23 
Food Services Job 11.76 21.58 
Public Service Job -8.55 13.47 
Education Job -27.62 16.54 
Health & Medicine Job 18.17 11.22 
1st Generation -19.79 24.53 
2nd Generation -57.07 17.32* 
Marital Status -3.60 8.93 
Home Ownership (Rent→Own) .87 10.26 
Age .37 1.33 
Sex (Female→Male) -1.32 5.87 

F-test 11.91 
Adjusted R-squared .76 

# Variables excluded by SPSS: Latino/Hispanic; Roman Catholic; Administrative Job; Beyond 2nd Generation 
*p<0.05 


