WHY DO COMMUNITIES MOBILIZE AGAINST
GROWTH: GROWTH PRESSURES, COMMUNITY
STATUS, METROPOLITAN HIERARCHY, OR
STRATEGIC INTERACTION?
MAI THI NGUYEN
University of North Carolina-Chapel Hill
ABSTRACT: Findings from this study challenge the conventional wisdom about the motivations
for local growth control. Using data of California ballot box growth controls merged with city
level demographic and housing data from the U.S. Census Bureau, logit models are estimated to
test four hypotheses for why communities mobilize against growth. Of the four hypotheses, growth
pressures, community status, metropolitan hierarchy, and strategic interaction, the only hypothesis
that was strongly supported by the logistic regression analyses was strategic interaction. Support
for the strategic interaction hypothesis reveals that jurisdictions located in regions where growth
control policies are more abundant have a higher probability of mobilizing against growth. In other
words, jurisdictions’ growth control policies influence the growth decisions made by neighboring
jurisdictions within the same region. One of the most surprising findings in the logistic regression
analyses is that low-income suburbs are significantly more likely to mobilize against growth than
high-income suburbs. These results refute the commonly held belief that growth control is strictly a
concern of elite communities and suggest that residents of low-income suburbs may be turning to
the ballot box to control growth because their communities are the locations of choice for noxious
land uses.
A
s the pressures on municipal budgets mounted and local public infrastructure became inadequate to support rapid rates of growth, the 1970s ushered in a new, more ardent era of growth
control and land use regulations. In the early periods of the movement, residents of communities
across the United States lamented the rising costs of growth, not only to their pocketbooks, but
also to the deterioration of their quality of life and character of their communities. Residents
charged that they were shouldering the costs of expansion, in the form of higher property and
sales taxes, increased housing prices, greater commute times, overcrowded schools, decreased air
quality, and large capital outlays (Degrove, 1995; Fulton, 1999). These trends sparked a movement to control growth that began in places like Ramapo, New York and Petaluma, California
and thousands of local communities located in between.
As the growth control movement gained momentum and the range of growth control tools
communities used to slow down the pace of growth diversified, skeptics began to question the
true motivations behind local growth control. Evidence that growth controls denied the entry of
low-income and minority populations from living in certain communities, such as Mount Laurel,
Direct Correspondence to: Mai Thi Nguyen, Department of City & Regional Planning, University of North CarolinaChapel Hill, 313 New East Building, Campus Box 3140, Chapel Hill, NC 27599-3140. E-mail: [email protected].
JOURNAL OF URBAN AFFAIRS, Volume 31, Number 1, pages 25–43.
C 2009 Urban Affairs Association
Copyright All rights of reproduction in any form reserved.
ISSN: 0735-2166.
26 I JOURNAL OF URBAN AFFAIRS I Vol. 31/No. 1/2009
New Jersey, raised skepticism regarding their intent (South Burlington County, NAACP v. Mt.
Laurel, 450 A. 2nd 390 (1983)). Claims of exclusionary land use and growth control policies are
often directed at wealthy suburban communities that are predominately white, suggesting that
these “elite” communities have manipulated the local planning and political process to control
the type of development, and consequently, the type of people that move into the neighborhood
(Frieden, 1979). Those who hold this argument contend that individuals in these communities
are more likely to be politically active because they have the prerequisite skills and resources
(McCarthy & Zald, 1977; Jenkins, 1983; Burbank, Heying, & Andranovich, 2000). Others suggest
that these elite communities are more homogeneous and therefore share common values and
concerns regarding growth and development (Guest & Oropesa, 1984). These commonalities
enable them to mobilize as a collective to maintain their advantaged economic status within the
economically stratified metropolitan system, which is the main tenet of the metropolitan hierarchy
thesis (Logan, 1978).
There have been other explanations for communities’ mobilization efforts in favor of growth
control. Some scholars suggest that communities adopt growth control policies for fear of spillover
growth from neighboring jurisdictions. This causes them to adopt growth control policies when
their neighbors have already done so. This reaction to neighboring jurisdictions’ policy making is
considered “strategic interaction” (Brueckner, 1995, 1998). Whereas the metropolitan hierarchy
thesis is considered to be an inward looking strategy, the strategic interaction perspective asserts
that communities are outward looking and pay attention to the growth control activities that
neighboring localities participate in.
One technique for controlling growth is the direct democracy process whereby citizens can
place initiatives and referenda on the ballot for a vote. Because it requires citizen mobilization in
order to qualify and adopt ballot box measures, the direct democracy process may reveal more
about citizen concerns stemming from growth issues than growth control policies adopted by
other means (i.e., by local governmental or planning processes). Such “ballot-box zoning” as it
has been called among urban planners is particularly popular in California. Although the ability to
qualify land-use or ballot box growth controls (hereafter, BBGCs) has been around in California
since 1973, it was not until 1986 that a noticeable number of local jurisdictions employed this
method (Fulton et al., 2002).
Although there has been considerable research related to growth control, there have been few
studies that have examined why communities mobilize politically in favor of growth control.
Moreover, there is scant research on the use of the ballot box to control growth. Using a database
of BBGC measures in California, this study examines four explanations for why citizens mobilize
against growth: growth pressures, community status, metropolitan hierarchy, and strategic interaction. Each of these four explanations will be discussed in detail in section two of this article.
The data and methods employed in this study are explained in section three. Next, a comparison
of differences in characteristics between cities that have mobilized against growth and cities
that have not is provided in section four. Section five will highlight key findings in the logistic
regression analyses. The final section of the article provides a summary discussion of the overall
findings.
GROWTH CONTROL MOBILIZATION: EXAMINING FOUR EXPLANATIONS
Growth Pressures
A seemingly obvious explanation for why residents support growth control policies is that
their community has experienced rapid rates of growth. Surprisingly, there has been inconclusive
evidence to show that jurisdictions that have strong support for growth control are actually
I Why Do Communities Mobilize Against Growth? I 27
growing at a faster pace than other localities. Lewis and Neiman’s (2002) survey of local officials
and planners revealed that cities in the San Francisco Bay Area, which had the slowest housing
growth, had the largest number of growth management policies. They attribute the popularity of
growth controls in the Bay Area to the “unease” of local citizens regarding growth. This suggests
that perception, rather than reality about growth is what drives antigrowth sentiments. Several
studies have found that indeed actual local rates of growth are not a robust predictor of support
for growth control, but rather perceptions about growth are much better (Baldassare, 1981, 1985;
Baldassare & Wilson, 1996). Furthermore, extant research suggests that residents’ perceptions of
growth do not match real rates of growth (Baldassare, 1981). These findings reveal that, although
many residents will vehemently attest to the damaging ramifications of rapid growth, there may
be some inconsistencies between residents’ perceptions of growth within their local jurisdiction
and actual growth rates.
What residents actually fear may be ANY impending growth, not actual growth that their
locality has experienced in the past. At a time in which acronyms such as NIMBY (Not In My
Backyard), BANANA (Build Absolutely Nothing Anywhere Near Anyone), and LULU (Locally
Unwanted Land Use) have become commonplace in the vocabulary of public planners and
developers, it should not come as a surprise that residents resist growth even though it has not
actually occurred.
Some have suggested that regional growth rates are better predictors of growth control adoption
than local growth rates (Levine, Glickfeld, & Fulton, 1996). This might explain why studies have
not found a link between local rates of growth and support for growth control. It may also indicate
that residents are aware of growth that is occurring in the region and fearful that this growth might
change their local jurisdiction.
Although the above studies found little evidence for the relationship between prior growth and
support for local growth control, there have been a few studies that did indeed find a positive link.
Protash and Baldassare’s (1983) results from a study of northern California cities showed that past
rates of population growth were the strongest predictors of growth control policy adoption, even
controlling for other variables such as percentage of white-collar employees, percentage owneroccupied housing, perceived local concern for growth, and antigrowth philosophy. Similarly,
Donovan, Neiman, and Brumbaugh (1994) found that previous rates of housing construction are
a positive indicator of growth regulation adoption in seven urbanized areas in Southern California
(excluding Los Angeles and San Diego). Although these studies measure growth differently
(population versus housing construction), the results do provide some evidence that the adoption
of growth control policies is not completely predicated on some irrational fear of growth by
residents, but on actual rates of growth.
These conflicting results still leave uncertainty as to whether communities that are implementing growth policies are doing so for the reason purported: rapid growth. Moreover, are growth
policies adopted as a reaction to actual rates of growth or are they regulations that keep out
unwanted future growth? If communities are not experiencing real growth rates and a decline in
quality of life that is associated with it, then why is there such strong resentment toward growth?
Is it based on inaccurate perceptions of growth? Vogel and Swanson (1989) suggest that debates
over growth are ill-informed and not based on solid empirical evidence. One might also argue
that there are other reasons (e.g., exclusion) for why residents desire to keep out growth. Some
of these explanations are explored next.
Community Status
The most commonly expressed concerns regarding growth pressures revolve around issues
such as increased taxes, traffic congestion, overcrowded schools, and demands on other public
28 I JOURNAL OF URBAN AFFAIRS I Vol. 31/No. 1/2009
infrastructure. Critics of growth control suggest that there are other factors to raise questions
about the true intent behind growth controls. One of the most infamous land use court cases
involved the wealthy community of Mount Laurel, New Jersey. The New Jersey court ruled that
Mount Laurel’s large-lot zoning was discriminatory toward low-income populations because it
inhibited the ability of developers to build affordable housing (South Burlington County, NAACP
v. Mt. Laurel, 336 A.2d 713 (1975)). It was evident to the courts that low- and moderate-income
households were priced out of the market for homes due to inflated housing prices created by
large lot zoning. To rectify this situation, the New Jersey courts ordered Mount Laurel to provide
its “fair share” of affordable housing (South Burlington County, NAACP v. Mt. Laurel, 450 A.
2nd 390 (1983)).
Mount Laurel was apparently not alone in their use of snob-zoning and land-use regulations to
keep out undesirable growth. During the same period, but on opposite coasts, the growth control
movement was spreading rapidly in the San Francisco Bay Area. Frieden (1979), in his seminal
work, was one of the first to argue that wealthy suburbanites had coopted the environmental
movement for exclusionary purposes. In his discussion of the Marin County’s antigrowth actions
he states,
It means closing the gates on people who may want to move in and, where possible, even to
people who may want to visit; turning to state and federal governments for help in paying
the costs of exclusivity; and maintaining a moral righteousness while providing a better living
environment for the established residents (Frieden, 1979, p. 37).
Accounts of wealthy communities throughout the country utilizing land use regulations and
growth control strategies to keep out unwanted types of development (and arguably “undesirable” neighbors) has raised concerns about the disproportionately negative consequences and
costs resulting from growth control policies (Advisory Commission on Regulatory Barriers to
Affordable Housing, 1991). Moreover, it is widely believed that communities with more socioeconomic resources are better able to use political and social institutions in order to be selective about
what type of growth and development is allowable. An examination of the political participation
literature will elucidate why this might be so.
Borrowing from Hirschman’s (1970) work on the rational actions of consumers, Lyons,
Lowery, and DeHoog (1992) examine citizens’ responses to municipal dissatisfaction and suggest
that there are four categories of responses: exit, voice, loyalty, and neglect. “Voice” actions are the
most relevant to this study of BBGCs and therefore, will be discussed here. According to Lyons
and his colleagues, voice can represent a number of activities that individuals use to attempt
to alter the existing condition of the community, which usually involves making one’s preferences/concerns heard or recognized by those who make policy decisions. Examples of voice include writing letters to local officials, signing petitions, attending city council meetings, voting, or
picketing.
Why do citizens respond with voice? Lyons et al.’s study of two Kentucky counties found
that individuals who became more dissatisfied over time with their local government, had higher
levels of investment in their community, and were homeowners versus renters, were more likely
to exhibit voice behaviors when they were dissatisfied. Furthermore, their results revealed that
education and income increase the likelihood of a voice response.
Compared to other political participation studies at the individual level, these findings, especially those related to the relationship between political participation (i.e., voice) and education,
income, investment, and homeownership status, are quite consistent (see, for example, Public
Policy Institute of California, 2002). Fischel’s (2001) homevoter hypothesis also emphasizes the
importance of homeownership as an economic investment that encourages residents to voice. He
I Why Do Communities Mobilize Against Growth? I 29
argues that for most individuals, their home is the largest asset they will ever have, and therefore
they will spend a lot of time and energy in protecting its value.
In a comprehensive review of the political participation literature, Sharp (2003) examines the
variations in participation within politics and public affairs among different groups. She explains
there are significant differences in who is most likely to vote. Her review finds that whites, from
more privileged socioeconomic backgrounds, are much more likely to vote than minorities (who
are typically more socioeconomically disadvantaged). Socioeconomic and racial biases also exist
in the levels of citizen-initiated contacts with public officials and interest group participation.
Individuals with higher socioeconomic status are more likely to contact public officials regarding
matters that affect the community and they are more often involved in neighborhood organizations.
Although the extant literature on political participation shows a racial and socioeconomic status
bias toward involvement in local civic activities, it is not clear whether this same bias extends
to ballot initiatives regulating growth. Only recently have studies examined this specific issue
and found mixed results. In a study of open space ballot measures, Romero and Lisero (2002)
find some support for a socioeconomic status bias. Their results indicate that factors influencing
the proposal of an open space ballot initiative include: smaller population, greater proportion
of whites, and higher median household incomes. Surprisingly, they also find that places that
were less sprawling (as measured by population density) were significantly more likely to place
open space measures on the ballot, prompting them to conclude that real land use patterns do not
shape growth control policies. Howell-Moroney (2004) challenged Romero and Lisero’s findings,
arguing that their flawed methodology led to erroneous conclusions about the effect of real land
use patterns influencing open space ballot measures. Howell-Moroney retested Romero and
Lisero’s hypotheses using data from 350 municipalities in the Delaware Valley in Pennsylvania
and New Jersey. Consistent with Romero and Lisero, they found that wealthier communities do
place open space measures on the ballots more often. In contrast, Howell-Moroney’s study finds
that places that are experiencing sprawl are also more, not less, likely to propose an open space
ballot initiative, therefore suggesting that land use patterns do promote growth control politics.
The voluminous literature on political participation and the more recent literature on openspace ballot measures suggest that higher status communities are more likely to both participate
in political and civic activities and also control growth.
Metropolitan Hierarchy
The political participation literature, as discussed above, has provided substantial evidence to
show a clear race and class bias in who participates in local public affairs. While the emphasis
within this body of work has been on individual self-interest and action, Logan (1978) posits an
alternative explanation for why groups or organizations might utilize the political system to shape
growth or spatial distribution. He believes that it is not merely class and status among individual
actors (i.e., their background characteristics) that shapes spatial distribution, but rather that people
and organizations, in territorially defined spaces, mobilize around their shared interests. One
shared interest that binds individuals within a shared physical and political boundary, according to
Logan, is the desire to maintain or improve their locality’s status advantage within the metropolitan
system. Similarly, Hill’s (1974) social status-government inequality (SSGI) thesis posits that
advantaged homogeneous communities manipulate government powers to consciously segregate
and benefit their community at the expense of neighboring communities.
Both Logan’s and Hill’s work underscore the importance of a jurisdiction’s relative status within
the metropolitan government structure and the metropolitan context that motivates collective
action. It is not merely an individual’s background or social status, but the collective social
standing of the jurisdiction relative to other places within the metropolitan area. The reason that
30 I JOURNAL OF URBAN AFFAIRS I Vol. 31/No. 1/2009
residents are concerned about their jurisdiction’s standing within the metropolitan hierarchy is
because their economic welfare is intricately linked to the locality where they live. Also, the local
level is the political unit in which groups have the most effective influence on policies that shape
their well-being.
Prior research has found considerable support for the relationship between a community’s
relative socioeconomic status and the ability to mobilize. These studies have found that higher
status communities are more capable of mobilizing because they are more homogeneous and,
therefore, share similar values, preferences, or needs (Guest & Oropesa, 1984). Furthermore,
similarities in background often translate into less variation in political ideology (Burbank
et al., 2000). This has been found to be true for support of the environmental movement as well
(Neiman & Loveridge, 1981). The prevailing belief is that upper strata communities, whether
through self-selection or because they are more homogeneous, share similar values and goals and
are, therefore, more able to develop coalitions of common interests.
Another reason why higher status communities are better equipped to mobilize is because
they have the resources to do so. They have more money, labor power, and skills to coordinate
for effective cooperation (McCarthy & Zald, 1977; Jenkins, 1983; Burbank et al., 2000). Guest
and Oropesa (1984) maintain, “The well-to-do are apparently quite able to protect and enhance
their communities . . . they are successful in employing whatever technique is at their disposal”
(p. 839).
Much of the literature on local political participation follows a rational choice logic, even
if not explicitly stated, which implies that people get involved in local politics because “they
hope to bring about policies that benefit them” (Rosenstone & Hansen, 1993, p. 101). The studies
reviewed in this section provide evidence that communities at the top of the metropolitan hierarchy
are better able to mobilize for two main reasons: (1) their members share many commonalities
(values, preferences, norms) and (2) these communities have the capital, labor, and power to
do so. The metropolitan hierarchy thesis posits that it is in the best interest of communities to
maintain or improve their economic position relative to other competing localities, within the
same region.
There has been very little empirical research that examines the metropolitan hierarchy thesis.
Among the few studies that have examined this issue, there have been varied results. Some
studies find no support for this claim (Gottdiener & Neiman, 1981), while others find weak
support (Medler & Mushkatel, 1979; Protash & Baldassare, 1983; Donovan & Neiman, 1992).
Strategic Interaction
Although the metropolitan hierarchy perspective emphasizes the utilization of political institutions and processes for the status advancement of localities, there is little discussion about
how cities interact with or react to one another. Moreover, how do the regulatory actions or
policy decisions of one locality influence the action of other localities, particularly neighboring
jurisdictions? Brueckner (1995) introduces a model of strategic interaction that addresses this
question. He contends that growth decisions must be considered within a regional context and
that the growth decisions made by one city affect other cities within the region. Thus, he suggests
that cities are not only inward, but also outward looking when decisions about growth are made.
Brueckner (1995) states, “. . . use of [growth] controls is widespread and policies appear to be
chosen conditional on the choices of other cities . . . a change in the objective function of one
city is likely to affect the choices made by all through strategic interactions . . .” (p. 396). Strategic interaction refers to “policy interdependence” among local government units (Brueckner,
1998).
I Why Do Communities Mobilize Against Growth? I 31
Although Peterson (1981) did not focus on the interaction between local jurisdictions, the
notion that city government actions are motivated by economic competition between places was
a major theme in his work. The crux of Peterson’s analysis of local government processes is that
cities participate in activities only if there is potential for economic benefit. Peterson identifies
three policy arenas: allocational, developmental, and redistributive. He believes that all cities
must have allocational activities because these are what he calls “housekeeping” or necessary
activities, such as providing police and fire services or street cleaning. Among the other two
policy arenas, Peterson argues that cities are more likely to participate in activities within the
developmental rather than the redistributive policy arena. Examples of developmental projects
include infrastructure improvements or downtown redevelopment. These types of projects are
designed to stimulate economic growth and improve the tax base of the city.
In recent decades, there has been much contention about the economic benefits of development
and growth for cities (Downs, 1994; Burchell, Downs, Mukherji, & McCann, 2005). For example,
a report by the Sierra Club challenges the widely held philosophy that “growth is good” by
analyzing the multitude of ways in which sprawl or rapid growth takes an economic toll on
society (Sierra Club, 1998). The Sierra Club also provides insights into how taxpayers subsidize
sprawl through a number of federal, state, and local programs that provide incentives and monetary
benefits for sprawling development (Sierra Club, 2000). Sharing similar opinions about the fiscal
disadvantages of growth, Fodor (1999) argues that growth actually induces higher taxes. This
is evidenced by bigger cities having higher tax rates. Fodor explains the reason for this is that
as cities develop and population increases, cities must provide the necessary infrastructure and
public services (i.e., police and fire protection, schools, trash collection), which usually involve
large up-front capital outlays.
The skepticism over the monetary gains from growth has been especially strong in California
after the passage of Proposition 13 in 1978. The passage of Proposition 13 altered the tax
structure of local jurisdictions, causing a massive reduction in revenues from property tax and
elevating the importance of sales tax in providing revenue for necessary public services. Under
Proposition 13, property tax is capped at 1% of assessed value and properties are only reassessed
at market value once the property is sold. If the property is not sold, the assessed value of the
property can increase no more than 2% annually. In addition, the only way to change the property
tax rate is by a two-thirds approval by citizens (Fulton, 1999). The limitations on how much
local government can garner from property tax and their inability to increase rates in times of
fiscal necessity creates challenges to raising revenue. As a consequence, many California cities
view growth policies that restrict residential or nonretail development as more economically
advantageous to municipalities1 and they tend to deflect residential development to neighboring
jurisdictions.
The California example is consistent with the logic of the strategic interaction perspective as
posited by Brueckner (1995, 1998). The argument holds that jurisdictions adopt growth-restricting
policies as a response to competing jurisdictions’ (i.e., their neighbors’) growth restricting activities due to the need to be economically competitive and to guard against unwanted growth
that spills over from other jurisdictions. Using a spatial lag model to test for strategic interaction among 173 California cities that had adopted at least one growth control measure by 1988,
Brueckner finds that there is “convincing evidence of strategic interaction” (p. 462). In addition,
cities that have higher populations, education and skill levels, property values, and are more
liberal, tend to have more stringent growth restrictions.
These four hypotheses span the interdisciplinary literature on the motivations for growth
control. There has been no study to date that tests the relative merits of these explanations. The
following sections will discuss the data, methods, and analyses used in this study to determine
which of the four hypotheses better explains why communities mobilize against growth.
32 I JOURNAL OF URBAN AFFAIRS I Vol. 31/No. 1/2009
DATA AND METHODS
The ballot measure data employed in this study was obtained from city elections or clerks offices
throughout California. A list of local ballot measures from 1986 to 2000 was compiled from two
sources. A handbook published by the California Association of Realtors (CAR) provided a list of
local land use ballot measures from 1986 to 1992 (California Association of Realtors, 1992). At
the date in which the CAR handbook data stopped, the editor of a monthly newsletter specializing
in planning and development issues in California titled California Planning & Development
Reports (CP&DR) documented all land use related ballot measures up to the year 2000. Together,
these two sources provided the name, date, and location of the ballot measure. For most of the
ballot measures that were identified in the CAR, there was enough information to determine if
the measure attempted to restrict growth. Unfortunately, the CP&DR list did not always provide
sufficient information about the contents of the ballot measure, just that it related to land use. In
cases in which the CAR information was not detailed enough and for all ballot measures on the
CP&DR list, a copy of the sample ballot was requested from city and county clerks offices. Of
the 262 sample ballots requested, 222 were received, which was an 85% success rate of retrieval.2
“Ballot box growth controls” or BBGCs are defined as any ballot measure that attempts to restrict
the rate, distribution, timing, or sequencing of growth.
Once the BBGCs were identified, the variable “Qualify” was created. Qualify is dichotomous
and is coded “1” if a city has qualified one or more BBGCs between 1986 and 2000 and “0”
otherwise. Due to the fact that majority of cities that used the ballot box to control growth did
so only once throughout the study period, a determination was made to create a dichotomous
variable.3 The analysis includes 108 cities that qualified at least one BBGC measure and 314
cities that did not, which totals 422 cities used in the analysis.4 This database was merged with
city level demographic and housing data from the 1980 and 1990 Censuses (U.S. Census Bureau,
1980, 1990).
The Qualify variable is a measure of citizen mobilization in favor of BBGCs. In order for a
measure to be placed on a city-wide ballot in a general election in California, a petition must be
signed by 10% of registered voters. In a special election, signatures from 15% of the registered
voters population must be garnered (Gordon, 2004). Thus, in order for a measure to appear on
the ballot for a vote, it generally involves a substantial education and mobilization campaign
involving significant time and financial resources.
Logistic regression analyses are employed to determine what factor best explains citizen
mobilization in favor of growth control. Four logit models are estimated to determine the relative
effect of four explanations for why citizens qualify BBGCs: community status, growth pressures,
metropolitan hierarchy, and strategic interaction. The set of variables for each of these hypotheses
is included in the model additively.5 After each set of variables is included in the model, a loglikelihood ratio test is conducted to determine whether the addition of the variables creates a
better fitting model. If the log-likelihood test statistic indicates that the inclusion of the variables
does not improve the fit of the model, then these variables will not be included in subsequent
models.
Community Status variables6 include: suburb, residential stability, homeownership, and median
household income (which is categorized into three levels: Low Income, Middle Income, and High
Income).7 All of these variables are expected to be positively related to Qualify. Three interaction
variables for suburb by median household income (e.g., low income × suburb, middle income ×
suburb, and high income × suburb) are also included in the community status model. The omitted
category is high income × suburb. The interaction variable is included in the model because there
is a growing recognition that there are a variety of different types of suburbs (Orfield, 1997;
Lee & Leigh, 2005). For example, “inner-ring” suburbs closer to urban centers tend to be older,
lower income, and experiencing encroaching blight whereas, “exurban” suburbs that are located
I Why Do Communities Mobilize Against Growth? I 33
on the fringe of the metropolitan area are newer, wealthier, and more racially homogenous. Thus,
high-income suburbs compared to low-income or middle-income suburbs may have different
growth control needs. Although the interaction variable does not capture the full complexity of
the differences between types of suburbs and their growth interests, it attempts to determine if
there are variations in growth mobilization between suburbs at three income levels.
Variables measuring growth pressures are: Population Change (1980–1990), White Change
(1980–1990),8 Kid Change (1980–1990), and Travel (1980). Rapid population change is believed
to encourage growth control policies within cities. The change in the population of children in a
city is a proxy for school overcrowding. The expectation is that greater increases in the number of
children in the jurisdiction will place greater strains on the schools, and therefore induce greater
mobilization for growth control policies. It is uncertain how White Change will affect Qualify.
The political participation literature suggests that whites participate in the political process at
a significantly higher rate than non-whites, and that therefore cities that have greater white
population growth or stable levels of white populations are expected to be more likely to use the
ballot box to pursue their growth interests. Alternatively, cities that are losing white population
and gaining minority population may feel threatened by the changing racial composition and may
be more likely to qualify growth controls in order to reduce growth and consequently slow down
demographic change.
Metropolitan Hierarchy is a rank ordering of city median incomes by quintiles within the
Metropolitan Statistical Area. Extant studies suggest that this variable should be positively related
to the qualification of BBGCs. The final independent variable of interest, Strategic Interaction,
is a ratio calculated by dividing the number of other cities, within the same county, which have
proposed at least one ballot measure during the period of study by the total number of cities in
the county. It is predicted that as the ratio increases (i.e., more strategic interaction), cities are
more likely to qualify growth control policies at the ballot box.
Control variables include population size (natural log),9 age structure of population (Kids and
Seniors), and four regional dichotomous variables.10 The reference category for the regional
variables is the San Francisco Bay Area and the other categories are Los Angeles Region, Central
Valley Region, and Other Region. For further details about independent and dependent variables,
see Table 1.
Before turning to the results from the study, some potential limitations of the study must be
addressed. First, this study only examines BBGCs and not growth controls that emerge through
other political and administrative processes. Thus, this study cannot be generalizable to states
that eschew the direct democracy process. Second, even though other states allow for local ballot
initiatives, local jurisdictions in California routinely resort to the ballot box to control and manage
land use. Local jurisdictions’ heavy reliance on the ballot box may make the politics of growth in
California unique. Finally, the passage of Proposition 13 in 1978 may have created perverse fiscal
incentives for local jurisdictions in California to control growth.11 Considering this, there are
24 states that allow for the initiative process (see www.iandrinstitute.org) and a growing number
of local and state initiatives throughout the nation relating to growth and development, especially
in regards to open space protection (Myers, 1999; Myers & Puentes, 2001; Romero & Lisero,
2002; Howell-Moroney, 2004). Therefore, the lessons learned from this study may be relevant to
these places.
COMPARING CITIES THAT QUALIFIED BALLOT BOX GROWTH
CONTROLS TO CITIES THAT DID NOT
Our discussion of the results begins with a look at the differences in characteristics between
cities that qualified BBGCs between 1986 and 2000 and cities that did not. Table 2 shows
descriptive statistics for these two types of cities. In addition, t-tests were conducted to compare
34 I JOURNAL OF URBAN AFFAIRS I Vol. 31/No. 1/2009
TABLE 1
Variable Descriptions
Variables
Description
Dependent Variables
Qualify
One or more BBGC qualified (1986–2000) = 1, else = 0
Community Status
Suburb
White (%)
Residential stability (%)
Homeownership (%)
Low income
Middle income
High income∗
Low income × Suburb
Middle income × Suburb
High income × Suburb∗
Suburb = 1, else = 0
Percentage of White in population 1980
Percentage of population living in same house between 1985–1990
Percentage of owner-occupied housing 1980
Cities with median incomes below $41,263 (2005$)
Cities with median incomes between $41,263 & $54,251 (2005$)
Cities with median incomes higher than $54,251 (2005$)
Interaction of low-income cities by suburban status
Interaction of middle-income cities by suburban status
Interaction of high-income cities by suburban status
Growth Pressures:
Population change 1980–1990 (%)
White change 1980–1990 (%)
Kid change 1980–1990 (%)
Travel 1980
Metropolitan Hierarchy
Metropolitan hierarchy
Strategic Interaction
Strategic interaction
Controls
Kids (%)
Seniors (%)
Population (LN)
Region (4 dummy variables)
Percentage of population change 1980–1990
Percentage of White population change 1980–1990
Percentage of change in <18 years old in population 1980–1990
Average travel time to work for workers 16+ who did not work at home
1980
Rank of 1980 Median Household Income (by quintile) within MSA
Number of other cities in county that qualified BBGCs/Total number of
cities in county
Percentage of population <18 years old in 1980
Percentage of population 65+ years old in 1980
Natural log of 1980 population
San Francisco Bay Area∗ , Los Angeles region, Central Valley region,
other region
Note: ∗ Denotes reference category.
mean differences in characteristics between cities. Only city characteristics with mean differences
that were statistically significantly at p < 0.05 are considered to be significant.
Among the community status variables, all were significant except for homeownership. As
expected, cities qualifying BBGCs were more likely to be suburban, had larger white populations,
and higher incomes. Sixty-seven percent of cities that qualified BBGCs were suburban compared
to only 48% of cities not qualifying BBGCs. Cities qualifying BBGCs had, on average, 8.25%
more whites residing in their jurisdiction than cities that did not. Median household income in
cities qualifying BBGCs was $47,494, a difference of roughly $5,800 more than cities without
growth control ballot measures. The only result that was the opposite of expectations was that
cities qualifying BBGCs had lower residential stability than cities that did not. Overall, the results
from the bivariate analyses support the notion that higher-status cities are more likely to mobilize
in favor of growth control.
The growth pressures variables provided mixed results in the bivariate analyses. First, there
were no statistically significant differences in population change and travel time to work between
I Why Do Communities Mobilize Against Growth? I 35
TABLE 2
Descriptive Statistics by Cities Qualifying and Cities not Qualifying BBGCs
All Cities
(N = 422)
Mean
Suburba
White (%)
Residential stability (%)
Homeownership (%)
Median income (2005$)
Pop change 1980–1990 (%)
White change 1980–1990 (%)
Kid change 1980–1990 (%)
Travel 1980
Metropolitan hierarchy
Strategic interaction
Population
Kids (%)
Seniors (%)
San Francisco Bay Areaa
Los Angeles regiona
Central Valley regiona
Other regiona
0.47
71.43
45.60
60.26
43,148
33.63
−8.79
−1.08
20.10
3.02
0.24
42,425
27.66
11.59
0.22
0.35
0.20
0.22
SD
0.50
21.95
9.30
13.94
18,732
43.15
6.55
3.20
5.27
1.37
0.21
160,235
6.37
6.02
0.42
0.48
0.40
0.42
Cities w/BBGCs
(N = 108)
Mean
0.67
77.56
43.87
60.50
47,494
36.69
−7.33
−2.02
22.30
3.13
0.39
84,355
25.62
11.23
0.40
0.37
0.07
0.17
SD
0.47
14.77
8.94
14.86
16,144
51.62
5.21
2.88
4.67
1.29
0.20
304,479
6.38
6.89
0.49
0.49
0.25
0.37
No BBGCs
(N = 314)
Mean
0.48
69.31
46.19
60.17
41,653
32.57
−9.28
−0.75
19.34
2.98
0.19
28,003
28.36
11.71
0.15
0.34
0.25
0.24
SD
0.50
23.58
9.36
13.63
19,342
39.86
6.89
3.25
5.26
1.40
0.19
45,018
6.22
5.70
0.37
0.48
0.43
0.43
Mean
Difference
0.19∗∗
8.25∗∗∗
−2.32∗
0.33
5,841∗∗
4.12
1.95∗∗
−1.27∗∗∗
2.96
0.15
0.20∗∗∗
56,352
−2.74∗∗∗
−0.48
0.25∗∗∗
0.03
−0.18∗∗∗
−0.07
Note: a Values for this variable represent the percentage of cases that were coded “1.”
∗
t-test is significant at p < 0.05.
∗∗
t-test is significant at p < 0.01.
∗∗∗
t-test is significant at p < 0.001.
cities qualifying BBGCs and cities not qualifying. Contrary to predictions, cities qualifying
growth controls had a faster decline in their kid population than cities that did not. Finally, cities
that qualified BBGCs had slower rate of decline in white population compared to cities that did
not qualify. The average rate of decline in white population was roughly 2% slower in cities
qualifying BBGCs. These results suggest that there appears to be very little evidence that local
growth pressures are influencing the mobilization of growth controls.
In regards to the metropolitan hierarchy variable, there was very little difference in metropolitan
hierarchy status between cities with qualifying growth controls and cities not qualifying. In
contrast, there was a significant difference in strategic interaction. Cities qualifying growth
control ballot measures had a 0.20 higher ratio value of strategic interaction than cities that did
not. Considering that the range for this measure is between 0 and 1, this is a large difference.
This result indicates that jurisdictions located near other growth controlling jurisdictions are more
likely to qualify growth control measures.
There were also significant regional differences in BBGC activity. The San Francisco Bay
Area and Los Angeles Region qualified the largest proportion of growth control ballot measures,
40% and 37% respectively. Cities qualifying growth controls are also more likely to be located in
the San Francisco Bay Area than cities not qualifying growth controls. Cities that do not resort to
the ballot box to control growth have a greater probability of being located in the Central Valley
Region than cities that do. These regional differences reveal that growth control mobilization is
clustered in distinct regions throughout the state. Figure 1 provides a map of the distribution of
BBGCs qualified throughout the state of California between 1986 and 2000.
36 I JOURNAL OF URBAN AFFAIRS I Vol. 31/No. 1/2009
FIGURE 1
California Ballot Box Growth Controls Qualified, 1986–2000
I Why Do Communities Mobilize Against Growth? I 37
RESULTS: WHICH HYPOTHESIS BEST PREDICTS GROWTH
CONTROL MOBILIZATION?
The results from the logistic regression analyses provide some interesting insight into why
communities mobilize against growth. The first logistic regression model (Model 1) regresses
Qualify on community status and control variables. The results from the community status logit
model do not show a clear link between community status and support for growth control. Results
shown in Model 1 reveal that the only variable that performed as expected was White (see Table 3).
Cities with larger white populations were more likely to qualify BBGCs. Contrary to expectations,
cities with greater residential stability were significantly less likely to attempt to control growth
using the ballot box. Neither Suburb nor Homeownership is significant in the Community Status
model. The most surprising finding from Model 1 is that low-income suburbs are over 12 times
more likely to qualify BBGCs than high-income suburbs. These results suggest that the prevailing
belief that high status communities are more inclined to use the political process to control growth
may no longer be true. The findings from Model 1 reveal that lower-income suburbs with a larger
white population that have moved to their neighborhoods more recently have a greater probability
of using the ballot box to dictate local growth outcomes.
TABLE 3
Logistic Regression Models Predicting the Likelihood of Qualifying BBGCs
Model 1
Community
Status
Odds Ratio
Suburb = 1
White (%)
Residential stability (%)
Homeownership (%)
Low incomea
Middle incomea
Low income × Suburbb
Middle income × Suburbb
Pop change 1980–1990 (%)
White change 1980–1990 (%)
Kids change 1980–1990 (%)
Travel 1980
Metropolitan hierarchy
Strategic interaction
Population (LN)
Kids (%)
Seniors (%)
LA regionc
Central Valley regionc
Other regionc
Constant
n
-2 log-likelihood ratio
Pseudo R 2
Model 2
Growth
Pressures
Odds Ratio
Model 3
Metropolitan
Hierarchy
Odds Ratio
0.380
1.038∗∗∗
0.939∗∗∗
1.030
0.512
0.938
12.718∗∗
2.385
0.287
1.033∗∗
0.930∗∗
1.011
0.742
1.246
14.885∗∗
2.733
1.004
1.092∗∗∗
0.794∗∗∗
1.067
0.296
1.036∗∗
0.932∗∗
1.017
0.592
0.850
15.021∗∗
2.553
1.005
1.095∗∗∗
0.783∗∗∗
1.059
0.839
1.972∗∗∗
0.931
0.947
0.532∗
0.103∗∗∗
0.353∗∗
0.002∗∗
422
323.060
0.324
2.185∗∗∗
0.939
0.987
0.753
0.170∗∗
0.424
0.001∗∗
422
297.602
0.402
2.188∗∗∗
0.937
0.979
0.828
0.214∗∗
0.517
0.001∗∗
422
296.793
0.404
Note: a Reference Category = High Income.
b
Reference Category = High Income × Suburb.
c
Reference Category = San Francisco Bay Area.
∗
p < 0.10 ∗ ∗ p < 0.05 ∗∗∗ p < 0.01.
Model 4
Strategic
Interaction
Odds Ratio
0.281
1.031∗∗
0.940∗∗
1.008
0.869
1.279
10.876∗
2.447
1.005
1.087∗∗∗
0.801∗∗∗
1.061
7.114∗∗
2.028∗∗∗
0.930
0.978
1.035
0.296
0.493
0.001∗∗
422
292.364
0.417
38 I JOURNAL OF URBAN AFFAIRS I Vol. 31/No. 1/2009
There are several control variables that are significant in Model 1. First, cities with large
populations are much more likely than cities with smaller populations to qualify growth control
measures at the ballot box. Second, cities located in the San Francisco Bay Area have a greater
probability of qualifying growth control ballot measures than all of the other three regions.
In the next model (Model 2), variables signifying growth pressures are included with the
community status and control variables. When the growth pressure variables are added, the loglikelihood ratio statistics reveal that Model 2 is a significantly better fitting model than Model
1 (p < 0.001), as calculated by the likelihood ratio chi-square test (hereafter, LR chi-square).12
Overall, there was mixed support for the growth pressure hypothesis. Cities with greater white
population increases (or conversely, smaller growth in minority population) are more likely to
qualify BBGCs. Change in kid population, which was included in the model as a proxy for
school overcrowding, was significant, but negative (see Model 2 in Table 3). Therefore, a greater
increase in the number of children in the population reduces the probability that cities will qualify
BBGCs. Although it was originally thought that greater increases in the population of children
was a proxy for school overcrowding and that school overcrowding would increase support for
growth controls, this may not be the case. An alternate explanation might be that growth in the
number of children in these cities is due to the affordability of homes, thereby attracting young
families. Due to the ability of these young families to afford these places, they may not have a
desire to control growth. Yet another explanation may be that growth in children population is not
a good proxy for school overcrowding and is measuring something else altogether. Population
change and travel time to work are not significant. As found in Model 1, low-income suburbs
remain significantly more likely to qualify BBGCs than high-income suburbs.
The inclusion of the Metropolitan Hierarchy variable in Model 3 does not significantly improve
the goodness of fit of the model (p > 0.05). The Metropolitan Hierarchy variable is not significant,
as shown in Table 3. As a result, there is little support for the contention that high-status cities are
more likely than lower-status cities to utilize the political process (e.g., the ballot box) to remain
in the upper echelon of the metropolitan hierarchy. Rather, the robustness of the interaction
variable, low-income by suburb, shows that the types of communities that mobilize politically
around growth control are different than what has been commonly believed. Model 3 reveals that
low-income suburbs are over 15 times more likely than high-income suburbs to qualify growth
control ballot measures.
Due to the results of the goodness of fit test, the metropolitan hierarchy variable is omitted
from Model 4 and the strategic interaction variable is introduced. This model, as indicated by the
LR chi-square statistic is the best fitting model among the four models. Among the community
status variables, white and residential stability remain robust predictors of Qualify. Although the
probability of a low-income suburb qualifying a BBGC is over 10 times greater than a high-income
suburb, the significance level is a bit weaker at p < 0.10. The two growth pressures variables
that are significant are white change and kid change. Furthermore, when the strategic interaction
variable is included in Model 4, it is a strong predictor of qualify. This suggests that cities located
near other growth control cities are more likely to use the ballot box to control growth within
their local jurisdiction. Moreover, cities make decisions based on the growth control decisions
that other cities within their region are making. Among the control variables, population size
remains significant while the regional control variables lose their significance.
DISCUSSION
The results from the logistic regression analyses reveal that growth politics in California is a
complex phenomenon that challenges our current understanding of the motivations behind local
growth control. Focusing specifically on growth controls at the ballot box, this research finds
I Why Do Communities Mobilize Against Growth? I 39
little evidence that elite communities are the drivers of growth control in local communities
throughout California. There was mixed support for the community status hypothesis and no
support for the metropolitan hierarchy hypothesis. What emerged as one of the strongest predictors
of growth control mobilization was racial composition. Local jurisdictions with larger white
populations were significantly more likely to qualify BBGCs. Also, local areas that experienced
more residential instability were more likely to propose growth control ballot measures. One
possible interpretation of these results is that white residents decide to pull up the drawbridge once
they move to new localities. If this is true, this would result in increased residential segregation
between rather than within local jurisdictions.
Most studies have considered suburbs to be monolithic in nature, while this study compares
low-income, middle-income, and high-income suburbs. The results show that suburbs are indeed
not monolithic. Rather, low-income suburbs are much more likely than other types of places to
mobilize in favor of growth control. This is in fact one of the most robust findings in all four of
the logistic regression analyses.
There are several possible explanations for the higher probability of BBGCs in low-income
suburbs. First, there is a greater tendency to site LULUs, such as landfills, cash checking stores,
auto-dealerships, and poorly designed multifamily housing developments in lower status communities. The ballot box may be one tool that low-income suburbs can utilize to ward off LULUs
and protect themselves from these obnoxious developments. Second, residents of high-income
suburbs have greater power to influence decisions and may use different types of political tools,
such as contacting and influencing local officials or city planning staff, instead of the ballot box
to control growth or affect change. Thus, high-income suburbs are less likely to resort to the
ballot box to control growth. Finally, the lower probability of BBGCs in high-income suburbs
may be that the economic costs of entering these types of neighborhoods is prohibitive in and
of itself. In other words, high-income suburbs have de facto growth control due to the high cost
of housing and, therefore there is no need to use the political process to control growth. For all
these reasons, better regional and state policies should be enacted to ensure that there is more
equity in the sitting of LULUs and better regional distribution. A regional governmental entity or
state level government may be best suited to oversee that low-income suburbs are not receiving
an unfair share of unwanted land uses and obnoxious developments.
There was mixed support for the local growth pressure hypothesis. Population change and
travel time to work were not significant predictors of qualifying BBGCs. The one variable that
did predict growth control mobilization was the change in white population. Cities with stable
or growing proportions of whites in their population were more likely to qualify BBGCs. This
is an interesting finding considering that most cities in California are becoming more racially
diverse, thereby losing white population. These findings suggest that cities that are able to retain
or attract white residents are also more likely to utilize the initiative process to control local
growth. One reason for this might be that whites are more civically engaged, better able to
mobilize politically, have greater political participation rates than non-whites and utilize the
ballot box to manage growth within their jurisdiction. Another factor that might explain this is
that whites in jurisdictions that have a stable or growing white population want to maintain the
racial composition of their city by using growth controls to keep out “undesirable” populations.
Future research should explore this issue and provide more clarity as to the motivation behind
growth control mobilization among whites.
There was absolutely no support for the metropolitan hierarchy thesis, which holds that cities
at the top of the metropolitan economic hierarchy are more likely to control growth than cities
below them. A better explanation for why residents mobilize against growth lies in the strategic
interaction variable, which was a strongly correlated with the qualification of BBGCs. The
significance of the strategic interaction variable highlights the importance of understanding
40 I JOURNAL OF URBAN AFFAIRS I Vol. 31/No. 1/2009
growth dynamics at the regional level and reveals that growth politics and policy at the local level
are inextricably linked to the region. Moreover, it suggests that citizens are aware of the policy
decisions and regulatory actions that are occurring in neighboring jurisdictions.
In a state such as California, where local land use planning functions in the context of home rule
and land use decisions are more parochially self-interested, it is not surprising that residents are
closely monitoring activities in neighboring localities. The lack of support for the metropolitan
status hierarchy thesis and the strong statistical significance for the strategic interaction variable
indicates that residents do not mobilize around common shared values or economic interests, but
rather, they are reacting to the growth control activities of neighboring jurisdictions within the
region and do not want to be the locality that absorbs their neighbors’ unwanted growth. Since
real growth pressures are not driving BBGCs, it may be that perceptions about growth or the ill
effects of growth are spurring residents to qualify BBGCs. Or communities may be emulating
their neighbors because they believe that growth controls have had positive effects. Whatever the
reason behind strategic interaction, the robustness of this measure suggests that growth control
policies may be better implemented at the regional level, rather than the local level, in order to
avoid a race between communities as to which is able to pull up the drawbridge the fastest.
The findings from this study clearly show that the reasons communities support and mobilize in
favor of growth control are multifaceted and complex. The increasing prevalence of local BBGCs
in states such as California is changing the local growth control landscape and challenging our
understanding of community mobilization around growth control. Future research should explore
why low-income suburbs are more likely than other places to use the ballot box to control growth.
Furthermore, there is more room for research in the area of policy interdependence among
local jurisdictions within a region. That is, how much influence do the actions of neighboring
jurisdictions have on growth policy decisions of local communities?
ACKNOWLEDGEMENTS: Research funding was provided by the Public Policy Institute of California and Solimar
Research Group. The author is grateful to Raymond Burby, David Godschalk, and three anonymous reviewers for
comments on earlier drafts of this paper.
ENDNOTES
1 This is often referred to as the “fiscalization of land use.” For more information on this topic, see Misczynski
(1986), Fulton (1999), and Lewis (2001).
2 Upon requesting the sample ballots, it was realized that there were five duplicate measures that were on the
list, and therefore these were omitted. Other reasons for the lack of retrieval of ballot measures involved city
and county clerks’ staff not being able to find copies of sample ballots or not having sufficient staff members
to locate these documents. In general, city and county clerks’ staff attempted to be helpful.
3 We also coded the dependent variable as an ordinal variable with four categories: 0, 1, 2, and 3. The categories
0, 1, and 2 represent the exact number of BBGCs a city qualified throughout the study period. If a city
qualified 3 or more BBGCs, the dependent variable was coded 3. There were 314 cities that had 0 BBGCs,
56 cities had 1, 22 cities had 2, and 30 cities had 3 or more. We ran ordinal logistic regression using this
alternative dependent variable regressed on all of the same independent variable as found in the logistic
models. The results from the ordinal logistic regression models were qualitatively the same as the logistic
regression models. We chose to report the results from the logistic regression analyses because there is greater
familiarity among readers with logistic regression as compared to ordinal logistic regression.
4 California had 422 incorporated cities in 1980 and 456 in 1990. Since some of the variables used in the
logistic regression analyses measure change in sociodemographics between 1980 and 1990, cities that were
established after 1980 were not included in the analyses (i.e., 422 cities are included in the analyses).
I Why Do Communities Mobilize Against Growth? I 41
5 Bivariate correlations were conducted for all explanatory variables to assess possible collinearity. No correlation coefficients exceed 0.70, which is the commonly accepted threshold for variables that may be collinear.
The highest correlation coefficient was −0.607, which is well below the level that would be cause for concern.
6 The original full model included three other community status variables: median housing value, percentage
of college educated, and percentage of persons in professional or managerial occupations. These variables
were highly correlated with the variable median household income, with bivariate correlations of 0.70 and
above, and therefore created problems of multicollinearity. The decision to retain median household income
and omit these three variables in the analysis was based on income being the most frequently used indicator
of social status in the political participation and growth control literatures.
7 When the distribution of independent variables was evaluated, median household income was identified as
being positively skewed. Instead of transforming the variable, such as by taking the log value, three categories
were created to classify median household income. In a prior analysis, the log value of median household
income was included in the model, but it was found to be highly correlated with the interaction variable in the
model. Developing three categories for median household income did not pose problems with multicollinearity.
Not only do the three income categories make sense for quantitative purposes, but conceptually we think of
income in terms of these three categories.
8 The original full model included variables measuring change in Asian population, change in Black population,
and change in Hispanic population, but none of these variables were significant in any of the models. For the
sake of parsimony, they are not included here.
9 The distribution of population city size was positively skewed, and therefore the variable was transformed by
taking the natural log. When a variable is positively skewed, this is a strong indicator that it is exponentially
distributed. When variables that are exponentially distributed are included in linear models, such as logit
models, it is common practice to transform them (Kay & Little, 1987).
10 Percentage of registered voters who are Democrat was included as a control for political ideology in the
original full model, but it was not significant in any of the models and was therefore omitted.
11 For more information about Proposition 13, see Fulton (1999).
12 To determine whether the full model is a better fitting model than the nested model, a likelihood ratio chisquare test is conducted. This test involves subtracting the −2 log likelihood ratio of the restricted model from
the −2 log likelihood ratio of the full model. The difference is distributed as a chi-square (χ 2 ). Significance
values can be determined by examining the χ 2 distribution table and by calculating the difference in degrees
of freedom between the full model and the restricted model. This test is also called a “goodness of fit” test.
The calculations are not shown here, but can be obtained upon request from the author.
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