Advances in Applied Sociology
2014. Vol.4, No.2, 40-52
Published Online February 2 014 in SciRes (
Understanding Poverty Rate Dynamics in Moderately Poor
Urban Neighborhoods: A Competitive Perspective
Chunhui Ren1*, Hazel Morrow-Jones2
1University of Texas at Austin, Austin, USA
2The Ohio State University, Columbus, USA
Received November 23rd, 2013; revised December 23rd, 2013; accepted December 30th, 2013
Copyright © 2014 Chunhui Ren, Hazel Morrow-Jones. This is an open access article distrib uted und er the Crea-
tive Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any me-
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This study introduces a framework to model moderate-to-high poverty transition in urban neighborhoods
using their relative competitive positions within metropolitan areas. Relative competitive position is
measured by a variety of neighborhood attributes, including resident and neighborhood characteristics,
locational attributes, among others. The model was estimated using the decennial census, using tracts
from 1990 and 2000 as proxies for neighborhoods. Results indicate that the competitive model works well
as a method to evaluate neighborhood poverty transition. Neighborhoods with relatively unfavorable
competitive positions within a metropolitan area experience more poverty growth and therefore are likely
to have more concentrated poverty in the future. Based on the results, several recommendations are made
to intervene. These include promoting public transit, immigrant assimilation programs, among others.
Keywords: Housing; Community; Minority; Neighborhood; Poverty
If the poor population were evenly distributed, poor individ-
uals would only have to cope with their low incomes. But in
reality, poor people tend to live near other poor people in
neighborhoods with high poverty rates. The concentration of
low-income people leads to many social problems, such as per-
sistent unemployment, poor school performance, and welfare
dependency. Therefore, poor individuals not only have to deal
with their personal financial difficulties, but to “suffer from the
negative effects brought about by the harsh social environment
which is caused by poverty and is causing poverty as well”
(Jargowsky, 2003). The deleterious consequences of high-
poverty neighborhoods have been discussed and studied by
many urban researchers and include, for example negative ef-
fects on property values, child development, and political activ-
ity (Cohen & Dawson, 1993; Oreopoulos, 2003; Kingsley &
Petit, 2003).
Many urban researchers focus on how to improve the condi-
tions of high-poverty neighborhoods to get them out of the
high-poverty status (see for example, Fogarty, 1977; Wilson,
1987; Massey & Eggers, 1990; Kasarda, 1993; Galster et al.,
2003; Ellen & O’Regan, 2007; Rosenthal, 2007). Relatively
fewer studies have systematically modelled the process in
which healthy neighborhoods decline into high-poverty areas.
The basic proposition of this study is that the best way to
fight concentrated poverty is to discover the dynamics of lower-
poverty neighborhoods so that policy inventions can be made to
impede the poverty concentration process. In this study, poverty
dynamics in moderately poor urban neighborhoods are modeled
from a competitive perspective. That is, we see neighborhood
poverty change as a result of inter-neighborhood competition,
as those neighborhoods with disadvantaged positions are more
likely to experience further declines and fall into higher-poverty
categories, while those with more favorable positions show
stronger ability to resist further poverty concentration.
In this study, we introduce a competitive conceptual frame-
work to explore poverty dynamics patterns in moderate-poverty
neighborhoods in metropolitan America during the 1990s. We
begin with a brief discussion of past literature on neighborhood
poverty and then introduce the conceptual framework for the
analysis. We offer a first test of the framework using data from
the Neighborhood Change Database (NCDB) and OLS regres-
sion. We conclude with the major findings, their policy impli-
cations and an agenda for future research based on the model.
Literature Review and the Competitive Model
Most of the earlystudies on concentrated poverty are cross-
sectional. Their efforts were focused on different geographic
areas varying in their levels of poverty (Massey, Eggers, &
Denton, 1990; Eggers & Massey, 1992; Hughes, 1989; Mincy
et al., 1990). More recently, urban scientists began to pay atten-
tion to the dynamic nature of neighborhood poverty issues and
started to explore factors that are predictive of a neighbor-
hood’s future course of poverty transition. In general, they ap-
proach urban poverty dynamics from the following perspec-
*Corresponding author.
Job Market Competition
Neighborhood poverty status is typically defined by the pro-
portion of low-income population in the neighborhood. There-
fore, the most intuitive mechanism for measuring poverty tran-
sition is through people’s income level changes as a result of
job market outcomes. Three groups of characteristics are found
to affect neighborhood residentsearning potential. Some
neighborhood resident characteristics affect people’s competi-
tive ability through straightforward market processes. These
include age, educational attainments, work experiences, lan-
guage barriers, and others (Massey & Eggers, 1990; Kasarda,
1993; Galster & Mincy, 1993). These factors give a neighbor-
hood’s residents advantages or disadvantages based on pure
market mechanisms. Some other factors distort market rational-
ity and put specific groups in disadvantaged positions and make
their neighborhoods more vulnerable to increased poverty con-
centration, such as racial discrimination in labor markets. A
neighborhood’s geographical location is also an important fac-
tor in determining its residentsaccess to employment oppor-
tunities. These include, but not limited to, the distance to em-
ployment centers, the access to public transit (Galster & Mincy,
Housing Stock Filtering
The basic tenet of filtering models is that as the housing
stock of a neighborhood ages, more affluent households move
out in search of more appealing residential options and their
vacancies are often filled by less affluent in-movers. As this
process continues, neighborhoods experience declines in their
economic status (Metzger, 2000). According to this perspective,
the age of a neighborhood’s housing stock is the key to under-
standing its subsequent course of economic status change.
Many urban researchers include in their models neighborhood
age indicators to capture the effect of the filtering process
(Brueckner, 1977; Galster et al., 2003; Rosenthal, 2007; Ellen
& O’Regan, 2007). But the empirical evidence is far from con-
sistent. Based on data in the 1950s and 1960s, Brueckner (1977)
found a positive correlation between a neighborhood’s down-
ward income succession and the proportion of old housing units.
Recent evidence, however, suggests that old housing stock is
not necessarily correlated with poverty growth, since as a
neighborhood gets older, it is more likely to become the target
for urban redevelopment, which will attract more affluent
households and therefore elevate the neighborhood’s economic
status in future (Ellen & O’Regan, 2007; Rosenthal, 2007).
Social Externalities
Another strand of theories to explain neighborhood poverty
growth, as summarized by Rosenthal (2007), is based on neigh-
borhood social externalities. This argument suggests that cer-
tain types of households tend to behave in ways that generate
social capital or social costs for the neighborhood. These in turn
play an essential role in affecting householdsdecisions about
moving into or out of the neighborhood. For example, house-
holds with high educational attainments are expected to gener-
ate social capital, which attracts higher status residents and
therefore enhance the neighborhood’s future economic status.
Low-income families serve as an opposite example. Therefore,
age alone can’t explain a neighborhood’s subsequent courses of
poverty transition, since the life-cycle stages are accelerated or
delayed by the social capital and social costs associated with
that neighborhood (Rosenthal, 2007).
The change in neighborhood poverty status results from the
decisions and activities of the actors involved in the neighbor-
hood process. Those decisions and activities are mainly based
on economic, racial and cultural considerations on the part of
residents. The important question that arises is: do they make
their decisions solely based on the current status of the neigh-
borhood attributes? Early empirical studies usually take this as
an assumption (Massey, Eggers, & Dent on, 1990; Mincy et al.,
1990). As a consequence, their results are, to some degree,
cross-sectional approximations of the dynamic process (Galster
& Mincy, 1993). But in reality, people’s expectations play an
essential role in determining decisions about residential loca-
tion, financial investments, housing upkeep efforts, and others
(Galster, 2001). More recently, increasing attention has been
paid to the expectation side of neighborhood poverty transition
processes. Ellen (2000) proposes a new hypothesis to explain
neighborhood racial transition, which attributes the key driving
force of racial transition to white people’s racially-based pre-
dictions on future neighborhood quality. This explanation of
racial transition can be easily applied to understand neighbor-
hood poverty change as well. Rosenthal’s (2007) model also
takes the expectation factors into account with the housing age
and externality theories. As the presence of social-capital re-
lated attributes reinforce people’s positive expectation about
future neighborhood quality, the consequent in-migration of
more affluent households enhances the neighborhood’s eco-
nomic status. The presence of social-cost related attributes be-
haves in the opposite way.
Threshold Effect s
Threshold effects are another non-negligible aspect of neigh-
borhood poverty succession that urban researchers have long
been aware of. Typically, as the level of one neighborhood
attribute exceeds certain threshold values, the behavior of the
actors in the neighborhood might change in an exponential way
rather than a simple linear way. For example, as a neighbor-
hood’s negative attributes surpass certain critical values, they
might lead to rapid poverty growth either by generating accele-
rated out-migration of non-poor residents or by causing rapid
increases in negative behavior of the stayers. Crime and poor
school quality are examples of typically perceived negative
neighborhood attributes (Downs, 1994). Increases in the share
of low-status residents and racial minorities are believed to
have substantially contributed to “middle-class flight” in the
1960s. Similarly, as the level of a beneficial behavior exceeds
certain threshold values, the subsequent positive changes may
upgrade the neighborhood’s economic status. For instance, the
housing-upgrading behavior of a household depends, to a great
extent, on the number or proportion of other households who
choose to do so. Once this surpasses a certain critical level,
more households will follow in pursuit of capital gains through
increased neighborhood property value (Quercia & Galster,
A New Conceptual Framework
The conceptual framework for the research is based on the
literature cited above and focuses on a competitive perspective.
It is assumed that neighborhood poverty dynamics are natural
outcomes of inter-neighborhood competitions within the same
region. The competition occurs in two ways. It can be either in
the form of competing for employment opportunities on the
part of neighborhood residents, or in the form of competing for
more affluent households on the part of the neighborhoods
themselves. Thus, according to this competitive perspective, the
neighborhood poverty transition can be viewed as being caused
by two processes. One is the incumbent income changes of the
neighborhood residents, resulting from changes in employment
outcomes. Another process is the migration of households with
various levels of economic status.
Therefore, based on those two poverty change mechanisms,
we propose the concepts of income-related competitive disad-
vantage/advantage and mobility-related competitive disadvan-
tage/advantage to explain a neighborhood’s subsequent course
of poverty transition. Within a given region, the former is used
to measure a neighborhood’s competitive position in terms of
its residentsability to maintain and improve their earning po-
tential. And the latter is used to assess a neighborhood’s com-
petitive position in terms of its locational attractiveness, or in
other words, a neighborhood’s ability to maintain and attract
relatively higher status households.
The concept of income-related competitive disadvantage/
advantage can be understood within the framework of job mar-
ket competition. People living in a neighborhood with a favora-
ble competitive position as compared with other neighborhoods
in the same region, are less likely to experience income decline,
which therefore enhances the neighborhood’s ability to resist
negative poverty transition, and vice versa. Mobility-related
competitive disadvantage/advantage, on the other hand, meas-
ures a neighborhood’s relative attractiveness to more affluent
households. Households make residential location decisions
according to their evaluation of various neighborhood attributes.
The evaluation, however, is not based on intrinsic characteris-
tics of these attributes, but on a comparison of them in compet-
ing areas (Galster, 2001). In other words, what determines a
neighborhood’s attractiveness is not the absolute value of its
attributes but its relative position in the large scale geographic
region. Therefore, the relativistic evaluation leads to inter-
neighborhood competition where neighborhoods are placed in
different competitive positions according to their relative levels
of competitive features.
The concept of mobility-related competitive disadvantage/
advantage is also closely related to the aforementioned expecta-
tion aspect of poverty transition. That is, neighborhood poverty
change can be seen as a function of people’s residential deci-
sions based on their expectation about future neighborhood
quality. As social externality theories suggest, some neighbor-
hood characteristics such as dilapidated housing stock, high
crime rates and other bad behaviors of the residents, weaken
people’s perceived prospect of the neighborhood and cause
middle-class flight. While other characteristics such as high
homeownership rates and the presence of a large proportion of
highly educated residents, may reinforce people’s confidence
and therefore make the neighborhood more appealing to afflu-
ent households (DiPasquale & Glaeser, 1999). The age and
physical conditions of the housing stock function in a similar
way. Newer housing stock is usually perceived to be associated
with neighborhood upgrading which attracts households with
relatively higher economic status, while older housing stock
generally confirms people’s negative expectation on future
neighborhood quality and therefore causes neighborhood to
decline. In addition, the factors on which the expectation is
based may go beyond the current conditions and the geograph-
ical boundaries of the neighborhood. For example, the past
pattern of racial composition change is found to be an important
predictor of future neighborhood racial transition (Ellen, 2000).
Income-transitions in adjacent areas also affect people’s resi-
dential decisions as suggested by border models (Leven et al.,
As Figure 1 illustrates, the theoretical framework of this
study proposes that neighborhood poverty change can be ac-
counted for by a neighborhoods relative competitive position
measured by its characteristics in the light of income-related
competitive disadvantage/advantage and mobility-related com-
petitive disadvantage/advantage. It should be noted that the
effects of those two types of characteristics are not completely
exclusive of each other. That is, some income-related factors
also contribute to poverty change by causing selective migra-
tion and vice versa. The grouping is primarily based on their
expected major poverty-generating mechanisms as suggested
by previous studies. As Figure 1 demonstrates, solid lines in-
dicate major effects while dotted lines indicate minor effects. In
the quantitative analysis, a set of explanatory variables, selected
on the basis of the above concepts, will be tested as to their
ability to explain poverty change.
Data, Study Area, and Variables
The primary source of data used in this study is the Neigh-
borhood Change Database (NCDB) developed by Geolytics in
conjunction with the Urban Institute. It contains data from four
decennial census years: 1970, 1980, 1990 and 2000. The unique
feature of this data set is that the tract boundaries from all four
census years are standardized based on their census 2000 loca-
tions. This feature provides convenience for longitudinal stu-
dies. Many empirical studies have been designed to take ad-
vantage of NCDB’s longitudinal data structure. Ellen and
O’Regan (2007) use NCDB to compare neighborhood econom-
ic status dynamic patterns across the three decades (1970-2000)
and find that several unique patterns occurred in the 1990s,
such as significant economic gains in the most impoverished
neighborhoods. Kingsley and Petit (2003) use NCDB to ex-
amine the 1970-2000 trends of poverty concentration in US
metropolitan areas. They also notice the flow of economic gains
into high-poverty neighborhoods during the 1990s and point
out that “the poverty reduction occurred as the net result of
different types of neighborhoods moving in and out of the high-
poverty category rather than the incumbent changes in high-
poverty areas”.
Although the NCDB has a great advantage for longitudinal
studies, it also has limitations that users should be aware of
(Tatian, 2003). First of all, since many tracts have changed
boundaries over the four decennial census years, the forced
standard boundaries are bound to lead to inaccurate information.
Secondly, because of the changes in the way certain data are
collected and tabulated, some variables are defined differently
by different census years. Finally, to provide convenience for
cross-decade comparison, the selection of variables for the
NCDB emphasizes those variables available in all census years.
Figure 1.
The conceptual framework.
Therefore, some variables available only in certain census years
are not included. Since our study only focuses on the 1990-
2000 Period and use variables that were defined consistently in
both of these two census years, we do not expect to see signifi-
cant biases and inaccuracies.
REIS is the acronym for the Regional Economic Information
System created by the Bureau of Economic Analysis (BEA) to
make available employment information by detailed industrial
file and at different geographic levels. In this study, all the em-
ployment-related variables are calculated based on REIS.1
Study Areas
This study is based in the 100 largest metropolitan areas at
the time of the 1990 census as measured by the size of popula-
tion. A metropolitan area typically has one or several popula-
tion centers surrounded by suburban counties. The Census Bu-
reau defines several types of metropolitan areas. There are
stand-alone Metropolitan Statistical Areas, known as standard
MSAs, Consolidated Metropolitan Statistical Areas (CMSAs)
and Primary Metropolitan Statistical Areas (PMSAs). PMSAs
are component units of CMSAs. The metropolitan areas defined
in this study include both MSAs and PMSAs. CMSAs are ex-
cluded because they are too large to be representative of unified
housing and labor markets (Jargowsky, 2003), and therefore
can not ensure competitive relationship between component
neighborhoods. Appendix A shows a list of the 100 largest
MSAs and PMSAs by population size.
Unit of Analysis
Consistent with previous work (Fogarty, 1977; Galster &
Mincy, 1993; Galster et al., 2003; Kingsley & Pettit, 2003; Jar-
gowsky, 2003; Rosenthal, 2007; Ellen & O’Regan, 2007), cen-
sus tracts are used as proxies for urban neighborhoods. “census
tracts are usually delineated by bounding features such as roads
or rivers, and are supposed to have relatively homogeneous
attributes with regard to economic, social and housing stock
characteristics” (Jargowsky, 2003). Some urban scientists sug-
gest that using block groups might be a better choice (Schuler
et al., 1992). However many important socioeconomic variables
are not available at the block group level. Also, using census
tracts is consistent with most of the previous studies, making it
easier to conduct historical comparison (Ellen, 2000). Therefore,
although not perfect, census tracts are the best approximations
to neighborhoods available for the quantitative analysis. The
terms “neighborhood” and “census tract” are used interchange-
ably in the remainder of this paper.
The Dependent Variable
The poverty rate is defined by the US Census Bureau as a
percentage of the residents living below the Census Bureau
Poverty Line. That is, a person is considered poor if he or she
lives in a family with an income less than the Census Bureau
Poverty Standard which is meant to reflect the costs of buying
life necessities in the area and adjusted for inflation and family
size (Jargowsky, 2003).
There has been no academic consensus on what poverty rate
cutoffs should be used to classify neighborhood into different
categories. Scholars use varying cut off values, depending on
specific emphases of the research. Jargowsky and Bane (1990),
Kasarda (1993) and Jargowsky (1997), for example, used a
40% poverty rate as the threshold to distinguish “ghettos” from
mixed-income neighborhoods and this cutoff is chosen primar-
ily based on the experienced researchersobservations of phys-
ical appearance in the neighborhoods. Galster and colleagues
(2003) used the 20% poverty rate as the cut off between mod-
erate-poverty and high-poverty neighborhoods and the 40%
poverty as the cut off between high-poverty and extremely
high-poverty neighborhoods. Those cut off values are chosen
because historical studies indicate exacerbated social problems
once poverty rates exceed such threshold values (Galster et al.,
In this study, we employ 10% and 20% poverty rates as the
cutoff values to distinguish moderate-poverty neighborhoods
from low-poverty and high-poverty neighborhoods2. If a census
tract has between 10% and 20% residents living below the
Census Bureau Poverty Level, it’s classified as a moderate-
There is an inconsistency with regard to MSA boundaries between R
and NCDB. To deal with this problem, REIS MSAs are broken down into
counties and reorganized to match the boundaries of NCDB.
Low-poverty, high-poverty and extremely high-
poverty n eighborhoods are
defined as those with below 10% poverty rates, between 20% and 40%
poverty rates, and above 40% poverty rates. Refer to appendix table B for
descriptive statistics of these different poverty-level tracts.
poverty neighborhood. The dependent variable in this study is
defined as the absolute difference in neighborhood poverty rate
between 1990 and 2000.
Explanatory Variables
As discussed in the conceptual framework, this research in-
tends to use a neighborhood’s relative competitive position in
the metropolitan area to explain the variation in future poverty
rate change. Most prior work calculates a neighborhood’s rela-
tive position in terms of a certain attribute by using a ratio of
the value of the neighborhood to the average value of the MSA
(Rosenthal, 2007; Ellen & O’Regan, 2007). In this study, how-
ever, the measure of a neighborhood’s position relative to the
entire MSA holds less explanatory power, because neighbor-
hoods in different economic status categories usually do not
have a direct competitive relationship. Therefore, in this study,
a neighborhoods relative competitive status in terms of a cer-
tain attribute is measured by using a ratio of the neighborhood
value to the average value of all neighborhoods of the same
poverty category in the MSA. For example, a moderate-poverty
neighborhood’s relative competitive position measured by ho-
meownership rate is calculated by a ratio of the neighborhood
homeownership rate to the average homeownership rate of all
moderate-poverty neighborhoods in the MSA. All neighbor-
hood-level variables used in this study are measured this way
except for dummy variables. The purpose of this measurement
is to ensure that neighborhoods within a metropolitan area form
a direct competitive relationship with each other, which is the
fundamental idea of this study.
Based on the conceptual framework and past empirical stu-
dies, a set of explanatory variables are selected to measure a
neighborhood’s relative competitive position within the MSA
(See Table 1 for the detailed descriptions of the variables, and
table B in the Appendix section for their descriptive statistics).
In the model, the dependent variable is measured by decadal
changes 1990-2000, and most explanatory variables are in be-
ginning-of-period values while some of them are changes in the
previous decade. The idea is that the beginning-of-period val-
ues of a neighborhood’s attributes are predictive of its future
course of poverty transition in the next decade. Dummy va-
riables are added to test possible threshold effects of the se-
lected neighborhood attributes.
Discussion of Model Results
Originally there are 17,986 NCBD census tracts that have a
moderate poverty status. After excluding 10,172 inappropriate
tracts, the reported results are based on 7814 moderate poverty
census tracts in the 100 largest US metropolitan statistical areas.
The excluded tracts include 1) those not located in the 100
largest MSAs (in rural areas or small MSAs); 2) those with 0
population or 0 poverty-status determined population in 1980
and 2000; 3) those with a population less than 100 in 1990; 4)
those with 0 families in 1990; 5) those with 10% or more of
their populations in mental hospital or juvenile institutions; 6)
those with 40% or more of their populations in military institu-
tions, college dormitories, other institutions and non-institu-
tional quarters.
Table 2 shows model results based on the Ordinary Least
Square regression (OLS). The R-squared statistic is 0.202,
which is a typically low value for models predicting neighbor-
hood poverty change. Almost all the estimated coefficients are
significant with the expected signs. This is very rare compared
with previous studies, indicating that using a neighborhood’s
relative competitive position to predict its future course of po-
verty transition generally works well.
Income-Related Neighborhood Attributes
Consistent with historical findings (Scott, 1988; Galster &
Mincy, 1993), low educational attainment in a neighborhood is
found to be a negative characteristic that puts the neighbor-
hood’s residents in an unfavorable position in employment
markets. One possible explanation for the positive coefficient
on Foreign90 is that new immigrants still suffer from a lan-
guage barrier and the lack of middle-class social connection.
Although some prior studies suggest that there might be posi-
tive effects due to their support network based on homogeneous
backgrounds, such effects are found to be minor in moderately
poor areas. A relatively higher proportion of female-headed
families also makes the neighborhood more likely to experience
poverty growth. It is not possible to tell with the available data
if this affects poverty rate change through welfare dependency
or by giving women additional difficulty in job market compe-
tition or in some other manner. The variable NoCar90 is used
to test the effect of automobile ownership on people’s competi-
tive position in employment markets. As it turns out, in mod-
erate-poverty neighborhoods, the relative lack of transportation
means significantly limits people’s access to job opportunities
(Galster & Mincy, 1993). Finally, a neighborhood that has a
relatively larger portion of married-couple familie s i s less li kely
go through poverty growth, which indic ates marriage ’s positive
role in resisting economic hardship, be it financially or psycho-
Two variables are included to test a neighborhood’s loca-
tional disadvantages or advantages in its residentsaccess to
employment opportunities. Both Access-To-Local-Job90 and
Distance-To-CBD90 turn out to be significant, but the influ-
ence of the distance to CBD is much greater than the proximity
to local jobs. This indicates that in an age of advanced trans-
portation technologies, whether or not a neighborhood is lo-
cated in a job-rich community is no longer a crucial factor in
determining its residentsaccess to job opportunities, and
people depend more on more distant metropolitan employment
opportunities than local ones. Those findings are also consistent
with the significant positive coefficient on NoCar90, which
implies the importance of automobile ownership.
Mobility-Related Neighborhood Attributes
As discussed earlier, when it comes to the effect of old
housing stock on neighborhood poverty status change, con-
flicting evidence is present in the urban literature. The results
from this research indicate that a moderate-poverty neighbor-
hood is expected to experience less poverty growth if it has a
larger proportion of old housing units or a larger proportion of
new housing units. So evidence is found to support Ellen and
O’Regan (2007) and Rosenthal’s (2007) proposal that as a
neighborhood becomes older, its chance of receiving major
reinvestment increases, which will elevate its economic status
in the next decade. The coefficient of the variable vacancy90
Table 1 .
The de s crip tio ns of t he v a riab le s.
Resident Characteristic s
Proportion of Residents Aged
25+ Who Complete 0 - 8 Years of School i n Tract in 1990/The Counterpart in
MSA in 1990
Proportion of Fore i gn
-Born Resi dents in Tract in 1990/The Counterpart in MSA in 1990 +
Proportion of Marrie d
-Couple Familie s in Tract in 1990/The Counterpart in MSA in 1990
Proportion of Female
-Headed Families in Tract in 1990/The Counterpart in MSA in 1990 +
Proportion of Househol ds Without a C ar in Tract i n 1990/The C ount erpart in MSA in 1990
-Population Ratio in County in 199 0/The Counterpart in MSA in 1990
The Tract’ s Distance to CBD in 1990/T he Counterpart in MSA in 19 90
Housing Stock Ch arac te ri st ic s
Proportion of Housing Units Aged 50+ in Tract in 1990/The C ount erpart in MSA in 1990
Proportion of Housing Units Aged 5
in Tract in 1990/The Counterpart in MSA in 1990
Housing Vacancy Rate i n Tract in 1990/The Counterpart in
MSA in 1990 +
Social Externalitie s
Tract Pove rt y Rate in 1990/The Counte rpart in MSA in 1990
(Tract Pove r t y Rate in 1990/The Count erpart in MSA in 1990)
- (Tract Poverty Rate in 1980/The Counterpart
in MSA in 198 0)
The Tract’ s Distance to t he Neare st Tract with 20%+ Poverty Rate in 1990/The Counterpart in MSA in 19 90
The Tract’ s Distance to t he Neare st Tract with 40%+ Poverty Rate in 1990/The Counterpart in
MSA in 1990 +
Proportion of African
-American Residents in Tract in 1990/The C ount erpart in MSA in 1990 +
(Proportion of African
-American Residents in Tract in 1990/The Coun t erpart in MSA in 1990)
(Proportion of African-American Residents in Tract in 1980/The Counter part in MSA in 1980) +
Proportion of Hispanic Residents in Tract in 1990/The Count erpart in MSA i n 1990
Tract Population Density in 1990/T he Counterpart in MSA i n 1990
Proportion of Families
with an Annual Income $60000+ in Tract in 1990/T he Counterpart in MSA in 1990
Proportion of Residents Aged 16+ in Executives, Managers or Administrators in Tract in 1990/The Counte r part
in MSA in 199 0
Homeow nership Rate i n
Tract in 1990/The Counterpart in MSA in 1990
Proportion of Residents Aged 25+ with a College Degree/The Counterpart in MSA in 1990
Proportion of Househol ds Who Have R es i ded in the Tract for More T han 5 Years/The Counterpart
in MSA
in 1990
The Control Variable
Proportion of Families with Own Chil dren in Tract in 1990/The Counterpa rt in MSA in 19 90
Dummy Var i abl es
The Variable Poverty90 Exceeds One Standard Deviation Above Its Mean
The Variable Poverty90 Exceeds Two Standard Deviation Above Its Me an
The Variable Black90 Exceeds One Standard Deviation Above Its Mean
The Variable Black90 Exceeds Two Standard Deviation Above Its Me
an +
Note: aFamilies with of children are known to be more sensitive to the neighborhood environment, and therefore are expected to have a relatively higher likelihood
of moving out if theres a sign of decline. So the variable Propwithchild90is created to control for this effect.
exhibits a significantly positive coefficient. So the expected
negative effect of vacant housing units is confirmed. In mod-
erate-poverty areas, neighborhoods with higher housing vacan-
cy rates appear less appealing to relatively affluent households
and those neighborhoods are more likely to slip into concen-
trated poverty.
When it comes to social externality effects, Pop-Density90
proves to have a positive impact on poverty rate increase. So
neighborhoods with relatively higher population density are in a
disadvantaged position in the competition for more affluent
Table 2.
Results of moderate poverty tracts.
(Adjusted R-squared = 0.202; N = 7814)
Explanatory Variables Coefficient Std-Erro r T-value
Constant 0.0179 0.0144 1.2444
Resident Characteristics
Lowedu90 0.0699*** 0.0154 4.5498
Foreign90 0.0951*** 0.0130 7.3233
Married90 0.0628*** 0.0197 3.1898
Female-Headed90 0.0728*** 0.0211 3.4515
Nocar90 0.1092*** 0.0144 7.5605
Place Characteristics
Access-To-Local-Job90 0.0385*** 0.0113 3.4109
Distance-To-CBD90 0.0661*** 0.0189 3.5041
Housing Stock Charac te ri stic s
ProOldHou90 0.0465*** 0.0117 3.9688
ProNewHou90 0.0559*** 0.0119 4.6846
Vacancy90 0.0644*** 0.0114 5.6677
Social Externalitie s
Poverty90 0.2262*** 0.0172 13.1856
Poverty80-90 0.0749*** 0.0116 6.4587
Distance-To-20Pov90 0.0897*** 0.0155 5.7894
Distance-To-40Pov90 0.0898*** 0.0201 4.4730
Black90 0.1376*** 0.0218 6.3235
Black80-90 0.0693*** 0.0112 6.1720
Hispanic90 0.0083 0.0122 0.6797
Pop-Density90 0.0748*** 0.0107 6.9659
HighIncome90 0.031* 0.0168 1.8439
Highpro90 0.0697*** 0.0153 4.5482
Homeowner90 0.0969*** 0.0168 5.7823
Highedu90 0.1071*** 0.0212 5.0617
Move5years90 0.0515*** 0.0168 3.0657
The Control V ariable
Propwithchild90 0.0114 0.0150 0.7599
Dummy Var i abl es
Poverty90-Dummy-1 0.0132 0.039 0.3383
Poverty90-Dummy-2 0.1257 0.0774 1.6251
Black90-Dummy-1 0.0439 0.054 0.8133
Black90-Dummy-2 0.166** 0.0732 2.2698
Note: Al l estimates are produced by SP SS 13, using standardized sc ores. *P < 0.1, **P < 0.05, ***P < 0.01.
residents. Following Rosenthals approach (2007), we use den-
sity as an indirect measure for the level of criminal activities.
Density can also serve as a proxy for many other poverty-
related factors. For example, while higher-density neighbor-
hoods are expected to frighten away wealthier famili es because
of their perceived higher crime rates, it is also plausible that
people might be actually deterred by the higher tax rates, and
poorer quality schools of central city communities since higher-
density neighborhoods are more likely to be located in central
city areas. With limited data sources, it is not possible to diffe-
rentiate between these possible poverty-generating mechan-
On the social capital side, all four variables exhibit signifi-
cantly negative coefficients as expected. Neighborhoods with
relatively higher proportions of high-income families, college
graduates and residents with high-status jobs, have an advan-
taged competitive position in the metropolitan area, and there-
fore are likely to experience less poverty growth. The social
capital created by these variables makes a neighborhood more
appealing and works as an attractor for affluent families. On the
other hand, they are also supposed to help the incumbent
neighborhood residents thrive by providing positive role mod-
els and increasing social interaction in the community (Rosen-
thal, 2007; Ellen & O’Regan, 2007). As the most prominent
social capital generator as proved by numerous past studies
(Brueckner, 1977; Carter et al., 1998; Galster et al., 2003; Ellen
& O’Regan, 2007; Rosenthal, 2007), homeownership’s role in
resisting poverty growth is confirmed in this study. The varia-
ble Move5Years90 represents the proportion of the households
who have resided in the neighborhood for more than 5 years.
The significantly negative sign of this variable supports the
view that neighborhood stability helps to resist future poverty
The analysis results report a positive correlation between the
relative proportion of African Americans and future neighbor-
hood poverty growth. The past racial transition trend (Black80-
90) proves to be influential as well. Those findings indicate that
relatively high percentages of African-American residents are
still associated with declines in moderate-poverty urban areas
where there is often a relatively large white population. The
findings provide support for Ellen’s (2000) “race-based neigh-
borhood stereotyping hypothesis” that although pure racial
prejudice has lost its popularity, white people still tend to use
race as a measure to estimate future neighborhood quality when
making residential location decisions.
The model results also show that once the relative proportion
of African-American residents exceeds two standard deviations
above the mean, the negative effect of Black90 is significantly
muted. This result confirms the other side of racial effects. That
is, once the percentage of minority residents surpasses certain
thresholds the large share of minorities becomes a neighbor-
hood stabilizer. This supports the idea that African Americans
can take advantage of race-based support networks to cope with
economic hardship (Galster & Mincy, 1993). The relative pro-
portion of Hispanic residents is irrelevant to future poverty
growth in moderate-poverty neighborhoods.
Four variables are created to examine the endogenous effects
of concentrated poverty. Similar to racial variables, Poverty90
and Poverty80-90 are used to test the effects of income tipping
and the past trend of income transition. Distance-To-20Pov90
and Distance-To-40Pov90 are used to evaluate the spill-over or
border effects of concentrated poverty.
Some interesting results are found. Consistent with conven-
tional wisdom, the two distance variables have negative coeffi-
cients which mean that proximity to concentrated poverty con-
tributes to future poverty increase. However, the signs for the
coefficients of both Poverty90 and Poverty80-90 are signifi-
cantly negative. These results suggest that a neighborhood will
go through more poverty growth if it is located closer to other
neighborhoods with high poverty rates, but it will experience
less poverty growth if it has a relatively higher poverty status
It seems unreasonable that affluent households would be at-
tracted to neighborhoods with relatively higher poverty status,
while in the meantime trying to avoid those with proximity to
concentrated poverty. One explanation is that strong policy
intervention has been involved in the neighborhood poverty
transition process during the 1990s. As Ellen and O’Regan
(2007) suggest, numerous anti-poverty initiatives were put into
practice during this period. These include strict enforcement of
anti-discrimination laws in housing markets, welfare reforms,
and other programs designed to promote employment rates
among the working poor. With those anti-poverty efforts, the
presence of a higher proportion of poor individuals actually
increases a neighborhood’s chance of receiving government
assistance and therefore makes it less likely to experience po-
verty growth in the subsequent decade. The two distance va-
riables mainly reflect the effects of natural economic forces.
In summary, our quantitative model works well to explain
the course of poverty transition of moderate-poverty neighbor-
hoods in US metropolitan areas. In general, neighborhoods with
attributes that gives them advantages in the inter-neighborhood
competition, show better ability to resist future poverty increase.
These attributes include a relatively larger proportion of mar-
ried couple families, a relatively larger proportion of home-
owners, and so on. On the other hand, neighborhoods with at-
tributes that gives them disadvantages in the inter-neighborhood
competition, are more subject to future poverty growth. These
attributes include a relatively higher density, relatively larger
African American population and so on.
Conclusions, Discussion and F uture Research
Conclusions and Policy Implications
Concentrated poverty has long been a focus in the urban lite-
rature, but most of the previous studies focus on areas that al-
ready have high concentrations of poverty in the hope of help-
ing these distressed urban areas get out of impoverished condi-
tions. This study is intended to complement the urban poverty
literature by discovering poverty dynamics patterns in mod-
erately poor urban areas, so that policies can be made to inter-
vene in the process by which poverty gets concentrated. A
competitive model is established to explore the course of neigh-
borhood poverty concentration. Several important findings are
revealed and they lead to interesting policy recommendations.
Most of anti-poverty programs are reactive rather than pre-
ventive in nature. That is, they tend to target already poor urban
neighborhoods in the hope of poverty deconcentration instead
of working on moderately poor areas to impede the downward
filtering process. Some urban programs apply a place-oriented
approach, such as the empowerment zone (EZ) which offers tax
breaks and other policy benefits to attract business investments
to upgrade distressed areas. Some programs rely on a people-
oriented perspective and aim to fight concentrated poverty by
helping poor individuals directly, such as community training
programs and the moving to opportunity (MTO) program
(Temkin & Rohe, 1996). All these programs underplayed the
fact that the best way to fight concentrated poverty is to prevent
poverty from getting concentrated. The policy emphasis, there-
fore, should be shifted towards these areas where noticeable
amount of poverty is present but the magnitude is not signifi-
cant enough to cause substantial socials problems and subse-
quent income tipping.
The important finding of the competitive model applied in
this study is that neighborhood poverty status is a dynamic
process instead of a static phenomenon. As a result of intensive
inter-neighborhood competitions, those with unfavorable posi-
tions will keep declining, eventually leading to concentrated
poverty and a stratified metropolis pattern. Also, when it c omes
to poverty rate transition due to people’s mobility patterns,
some neighborhoodsgains of affluent households are others
losses. From a local perspective, some communities may be-
come the winners of the competition and experience growth in
economic status, but for the region as a whole, it is a zero-sum
game at best (Orfield, 2002). Therefore, to prevent the stratifi-
cation of metropolitan patterns and wasteful competition, a
regional perspective is needed when enacting and implementing
public policies, and some type of governing body at the metro-
politan level is required to coordinate local efforts.
The findings of this research also provide a rich set of em-
pirical results needed for enacting specific policies. For exam-
ple, those areas with a large body of residents in disadvantaged
competitive positions should be given more attention by policy
makers, such as neighborhoods with a higher percentage of
female-headed families or a higher percentage of new immi-
grants. Automobile ownership is another significant factor in
explaining neighborhood poverty growth, indicating the impor-
tance of transportation means for gaining access to job oppor-
tunities. A well-developed public transit system might fill this
vacancy and therefore increase an area’s ability to resist pover-
ty growth.
Evidence to support central-city revitalization programs is
found in this study also. Although decentralization has become
the dominant pattern of economic activities, people living in
moderately poor urban areas still have heavy reliance on CBD
jobs. This finding also conforms to what observed by Downs
(1994) who contends that central cities are still desirable loca-
tions for a wide variety of industries.
Future Research Directions
This study contributes to the urban poverty literature by ex-
ploring the poverty succession mechanisms in moderate-po-
verty neighborhoods. From a practical perspective, a set of
poverty change predictors are identified, which can serve as the
basis for formulating anti-poverty policies targeting particularly
moderately poor neighborhoods to prevent them from degrad-
ing into high-poverty areas. From a theoretical perspective, this
study enriches the existing urban poverty literature with its
model of explaining neighborhood poverty dynamics through
the inter-neighborhood competition.
It should be noted that this study is an initial step in model-
ing neighborhood poverty change from an inter-neighborhood-
competition perspective. There are several important limitations
that we hope can be overcome by future research.
To begin with, a richer set of explanatory variables might
help to improve the model. For example, many possible poverty-
change indicators are not available in NCDB and REIS, the two
major datasets used in this study. We have no direct measure
for school quality which is probably the most important factor
to consider when a family makes residential choices. Also, the
explanatory power of the model might be significantly ex-
panded if influential factors at larger geographical scale could
be added, such as metropolitan demographic change s and larger-
scale econom i c dynamics.
In addition, we carefully selected explanatory variables in
order to separate the effects of the two poverty-generating me-
chanisms: incumbent income changes and selective migration.
The results were unexpected in that most neighborhood-level
variables affect poverty transition through both of the two
processes. For example, a higher proportion of college gra-
duates generates higher levels of social capital and therefore
elevates the economic status of the neighborhood by attracting
more affluent families. On the other hand, these highly-edu-
cated people themselves are in a favorable position in employ-
ment markets and can also help the rest of the neighborhood
residents prosper through positive role-model effects. A single
city or single metro case study with more detailed information
will work better to distinguish between the effects of these two
Finally, we relied solely on the traditional OLS method to
perform statistical analyses. The accuracy of the results will
certainly be improved if more sophisticated statistical ap-
proaches can be employed, such as using spatial regression
models to take care of spatial autocorrelation and applying
simultaneous equation modeling to explore complex interac-
tions of different neighborhood attributes.
The primary purpose of this study is to provide a new
framework for understanding the dynamics of moderately poor
urban areas and to perform a first test of its usefulness. As more
elements are added by future research, especially with the
newly-released Census 2010 data, we hope the model proposed
in this study will give us a deeper insight into the neighborhood
transition mechanisms.
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Table A.
The 100 largest MSAs.
Rank MSA Na me Population (1990) Population (2000)
1 Los Angeles-Long Beach-Santa Ana, CA 8,803,804 9,463,883
2 New York-Northern New Jersey-Long Island, NY-NJ-PA 8,485,881 9,261,473
3 Chicago-Joliet-Naperville, IL-IN-WI 7,263,100 8,107,644
4 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 4,853,879 5,037,277
5 Detroit-Warren-Livonia, MI 4,256,681 4,429,015
6 Washington-Arlington-Alexandria, DC-VA-MD-WV 3,915,023 4,537,017
7 Houston-Sugar Land-Baytown, TX 3,317,651 4,162,976
8 Boston-Cambridge-Quincy, MA-NH 3,163,429 3,349,729
9 Atlanta-Sandy Springs-Marietta, GA 2,659,377 3,712,871
10 Nassau-Suffolk, NY PMSA 2,588,748 2,740,474
11 Riverside-San Bernardino-Ontario, CA 2,553,696 3,216,638
12 Dallas-Fort Worth-Arlington, TX 2,539,540 3,312,284
13 Minneapolis-St. Paul-Bloomington, MN-WI 2,470,395 2,888,515
14 St. Louis , MO-IL 2,465,930 2,575,551
15 San Die go-Carlsbad-San Marcos, CA 2,411,260 2,682,438
16 Orange County, CA PM SA 2,406,318 2,772,046
17 Baltimore-Towson, MD 2,317,207 2,478,515
18 Pittsburgh, PA 2,229,272 2,190,442
19 Phoenix-Mesa-Glendale, AZ 2,224,932 3,114,937
20 Cleveland-Elyria-Mentor, OH 2,190,554 2,235,084
21 San Francisco-Oakland-Fremont, CA 2,061,150 2,377,318
22 Seattle-Tacoma-Bellevue, WA 1,967,708 2,340,497
23 Tampa-St. Petersburg-Clearwater, FL 1,963,580 2,248,952
24 Miami -Fort Lauderdale-Pompano Beach, FL 1,932,629 2,228,236
25 Newark, NJ PMSA 1,892,972 2,009,948
26 Denver-Aurora-Broomfield, CO 1,614,261 2,073,249
27 San Francisco-Oakland-Fremont, CA 1,601,830 1,729,392
28 Kansas City, MO-KS 1,509,671 1,689,903
29 San Jose-Sunnyvale-Santa Clara, CA 1,486,159 1,670,333
30 Portland-Vancouver-Hillsboro, OR-WA 1,477,750 1,874,313
31 Cincinnati-Middletown, OH-KY-IN 1,449,682 1,555,636
32 Milwaukee-Waukesha-West Allis, WI 1,424,494 1,493,311
33 Virginia Beach-Norfolk-Newport News, VA-NC 1,376,102 1,521,625
34 Indianapolis-Carmel, IN 1,374,677 1,603,105
35 Fort Wort h-Arlington, TX PMSA 1,353,764 1,691,352
36 Columbus, OH 1,334,674 1,523,881
37 Sacramento-Arden-Arcade-Roseville, CA 1,333,407 1,540,760
38 San Anto nio-New Braunfels, TX 1,287,621 1,541,284
39 Bergen-Passaic, NJ PMSA 1,277,980 1,372,791
40 Fort La uderdale-Hollywood-Pompano Beach, FL PMSA 1,255,242 1,530,737
41 New Orleans-Metairie-Kenner, LA 1,214,326 1,266,582
42 Orlando-Kissimmee-Sanford, FL 1,213,005 1,632,377
43 Buffalo-Niagara Falls, NY 1,178,011 1,161,926
44 Hartford-West Hartford-E ast Ha rtfo rd, C T 1,133,991 1,167,171
45 Providence-New Bedford-Fall River, RI-MA 1,116,411 1,170,823
46 Charlotte-Gastonia-Rock Hill, NC-SC 1,109,365 1,433,620
47 Salt Lake City, UT 1,070,600 1,326,965
48 Greensboro-High Point, NC 1,006,213 1,200,117
49 Middlesex-Somerset-Hunterdon, NJ PMSA 985,716 1,139,643
50 Rochester, NY 985,477 1,023,027
51 Memphis, TN-MS-AR 985,090 1,122,347
52 Monmouth-Ocean, NJ PMSA 983,890 1,125,219
53 Nashville-Davidson-Murfreesboro-Franklin, TN 971,835 1,218,485
54 Dayton, OH 947,571 946,566
55 Grand Rapids-Wyoming, MI 937,836 1,088,426
56 Oklahoma City, OK 922,673 1,040,958
57 Louisville-Jefferson County, KY-IN 895,404 966,034
58 Jacksonville, FL 894,351 1,094,171
59 West Palm Beach, FL SM SA 860,943 1,127,640
60 Richmond, VA 850,439 978,327
61 Birmingham-Hoover, AL 840,139 921,106
62 Honolul u, HI 818,435 831,910
63 Albany-Schenectady-Troy, NY 812,897 828,822
64 Raleigh-Cary, NC 790,272 1,110,405
65 Greenville-Mauldin-Easley, SC 774,278 899,596
66 Austin-Round Rock-San Marcos, TX 763,024 1,142,566
67 Fresno, CA 754,474 917,162
68 Las Vegas-Paradise, NV 740,120 1,121,269
69 Tulsa, OK 704,606 796,096
70 Oxnard-Thousand Oaks-Ventura, CA 661,917 746,186
71 Tucson, AZ 658,178 832,275
72 Syracuse, NY 637,106 631,198
73 Akron, OH 633,030 670,905
74 Omaha-Council Bluffs, NE-IA 614,525 672,601
75 Toledo, OH 603,579 608,454
76 Gary, IN PMSA 599,556 626,703
77 Allentown-Bethlehem-Easton, PA-NJ 592,210 636,936
78 El Paso, TX 591,248 673,263
79 Harrisburg-Carlisle, PA 587,987 629,401
80 Seattle-Tacoma-Bellevue, WA 579,782 693,485
81 Springfield, MA 568,257 571,903
82 Jersey City, NJ SMSA 552,992 608,811
83 Scranton-W ilkes-Barre, PA 547,267 532,545
84 Albuquerque, NM 541,723 637,528
85 Bakersfield-Delano, CA 537,646 644,418
86 New Haven-Milford, CT 524,742 535,921
87 Baton Rouge, LA 517,085 590,591
88 Wilmington, NC 508,170 579,329
89 Knoxvil l e, TN 496,577 569,133
90 Charle st on, WV 494,596 543,387
91 Youngstown-Warren-Boardman, OH-PA 492,699 482,671
92 North Port-Bradenton-Sarasota, FL 489,487 583,940
93 Wichita, KS 485,269 545,220
94 Stockton, CA 473,856 547,774
95 Worcester, MA 471,667 504,396
96 Mobile, AL 469,485 533,071
97 Little Rock-Nort h Little Rock-Conway, AR 451,305 496,830
98 Bridgeport-Stamford-Norwalk, CT 441,589 457,968
99 Vallejo-Fairfield, CA 434,757 511,287
100 Johnson City, TN 433,915 478,035
Source: The Burea u of Economic Analysis’ Re gional Informati on System (REIS).
Table B.
Descriptive statistics for different poverty-level tracts.
Dependent Variable
Mod-Poverty Tracts High-Poverty Tracts Ex-Poverty Tracts
N Mean STD N Mean STD N Mean STD
Povratechange90-00 7814 0.0106 0.0599 5221 0.0064 0.0797 1946 0.0896 0.1133
Resident Characteristic s
Lowedu90 7814 0.1182 0.0758 5221 0.1973 0.1167 1946 0.2270 0.1152
Foreign90 7814 0.1320 0.1515 5221 0.1695 0.1901 1946 0.1008 0.1445
Married90 7814 0.7322 0.1003 5221 0.5858 0.1385 1946 0.3905 0.1708
Female-Headed90 7814 0.1365 0.0651 5221 0.2355 0.1023 1946 0.3892 0.1494
Nocar90 7814 0.1562 0.1422 5221 0.2987 0.1866 1946 0.5258 0.2056
Place Characteristics
Access-To-Local-Job90 7814 0.9890 0.3088 5221 1.0519 0.3717 1946 1.0667 0.4330
Distance-To-CBD90 7814 14.7510 14.2428 5221 10.1450 12.4403 1946 6.0768 7.6611
Housing Stock Ch arac te ri st ic s
ProOldHou90 7814 0.2314 0.2527 5221 0.3150 0.2553 1946 0.3457 0.2270
ProNewHou90 7814 0.0821 0.1035 5221 0.0546 0.0778 1946 0.0376 0.0605
Vacancy90 7814 0.0841 0.0665 5221 0.1071 0.0674 1946 0.1408 0.0954
Social Externalitie s
Poverty90 7814 0.1416 0.0286 5221 0.2822 0.0567 1946 0.5119 0.1039
Poverty80-90 7814 0.0197 0.0530 5221 0.0480 0.0846 1946 0.1033 0.1112
Distance-To-20Pov90 7814 4.0659 6.7175 5221
Distance-To-40Pov90 7814 10.3539 14.6089 5221 5.4683 12.3336 1946
Black90 7814 0.1578 0.2440 5221 0.3584 0.3521 1946 0.5988 0.3616
Black80-90 7814 0.0289 0.0813 5221 0.0250 0.1027 1946 0.0220 0.1210
Hispanic90 7814 0.1460 0.1911 5221 0.2438 0.2859 1946 0.2042 0.2870
Pop-Density90 7814 9406 15514 5221 14255 20921 1946 16568 23519
HighIncome90 7814 0.1506 0.0994 5221 0.0820 0.0690 1946 0.0353 0.0519
Highpro90 7814 0.1034 0.0427 5221 0.0746 0.0399 1946 0.0553 0.0390
Homeowner90 7814 0.5350 0.2128 5221 0.3974 0.2119 1946 0.2519 0.1882
Highedu90 7814 0.1666 0.1263 5221 0.1132 0.1089 1946 0.0684 0.0883
Move5years90 7814 0.4834 0.1446 5221 0.4840 0.1489 1946 0.4873 0.1468
The Control Variable
Propwithchild90 7814 0.4951 0.1023 5221 0.5546 0.1052 1946 0.6118 0.1265
Note: Al l entries are based on the original dat a, as opposed to the relative measures used in t he quantitative models.