Measuring Metropolitan Government Structure and Its Impact on
Income Distribution in Metropolitan Areas
Anja Kurki
Department of Government and Politics
University of Maryland
3140 Tydings Hall
College Park, MD 20742
Phone: (301) 405-8269
E-mail: [email protected]
Paper prepared for presentation at the American Political Science Association Annual Meeting in
Atlanta, Georgia, September 2-5, 1999.
Abstract
This paper provides a preliminary analysis of the importance of metropolitan area government
structure on socioeconomic inequality in approximately 70 metropolitan areas in the United
States. This study particularly focuses on meaningful classification of metropolitan areas
according to their government structure and its possible relationship to measures of inequality,
such as the Gini coefficient, the dissimilarity of poor index and the isolation of poor index.
Cluster analysis and data from the 1987 Census of Governments are applied in order to classify
metropolitan areas according to their government structure and OLS regression analysis is used to
test if the created test variable ‘metropolitan area government structure’ has a significant
relationship to socioeconomic inequality in metropolitan areas. The results show that government
structure should not be ignored when income inequality and dissimilarity of poor are considered.
However, due to the small size of the data set and the explorative nature of the classification of
the metropolitan areas, these results must be considered preliminary.
Key words: metropolitan area government structures, income inequality, social inequality
Introduction
This paper has two goals. The first one is to offer a sensible classification of metropolitan
areas according to their government structure. The second goal, closely related to the first one, is
to test if different metropolitan government structures are systematically related to different levels of
socioeconomic inequality in metropolitan areas. The models applied are cross sectional and thus the
more methodologically appropriate research question “do changes in metropolitan government
structure cause changes in economic inequality” cannot be answered in the framework of this paper.
However, the models presented in this paper will provide a preliminary analysis concerning the
importance of government structure when inequalities in metropolitan areas are studied.
The structure of the paper is as follows. First the theoretical background concerning
inequality and its potential consequences, as well as research themes related to the often assumed
importance of government structures are discussed. Second, classification of metropolitan areas
according to their government structure is explored in a more detailed manner. Then regression
models testing the created test variables are examined and finally the results are briefly discussed.
Theory
Why metropolitan level analysis?
There are several reasons why a study of the impact of government structures on socioeconomic
inequalities could use entire metropolitan areas as the level of analysis. In the United States an
increasing percentage of the population lives in metropolitan statistical areas (MSAs). In 1970, 69
percent and in 1990, 77 percent of the U.S. population lived in metropolitan statistical areas.
Increasing numbers of metropolitan area residents are located in suburbs (in 1990 about 45% of the
U.S. population lived in suburbs while about 37% resided in central cities). Using metropolitan
statistical areas as the level of analysis will include most of the population in the United States, and
will also make it possible to test the importance of different governance structures: several types of
metropolitan government structures exist.
Perhaps more importantly, central cities and suburbs do not form two isolated entities. They
are dependent on each other and form a regional economy with common housing, land, and labor
markets (on the exact relationship between central cities and suburbs see for instance Hill et al. 1995,
Bingham and Kalich 1996, Savitch et al. 1993, Voith 1998). If the premise that metropolitan areas
form a regional or metropolitan wide economy is accepted1, problems that central cities and suburbs
are experiencing are regional problems. The specific way different markets, such as housing or labor
markets, function depends in part on the government structure of the metropolitan area. For instance,
in a fragmented metropolitan area housing markets may further segregation based on race and
income, because the political jurisdictions may form identifiable areas which can be represented by
differences in housing prices. Poverty, crime, and segregation based on race and income will not only
increase direct costs for the city and the surrounding suburbs but may also have a lasting impact on
a region’s reputation and attractiveness for economic investments. (Hill et al 1995.)
A final reason for using metropolitan areas as the unit of analysis is availability of data. Many
relevant variables measuring how well an average individual is succeeding socioeconomically in the
United States are available for the metropolitan statistical areas. The official standards for
metropolitan statistical areas used by the Bureau of the Census at least partly capture the idea of
regional economy. The definition of a metropolitan area and particularly standards considering
combination of adjacent metropolitan statistical areas are not only based on the size of population and
population density measures, but also consider dependence in the form of work related commuting
(State and Metropolitan Area Data Book 1997-98).
Why would government structure matter?
Metropolitan area government structures are a result of historical processes and vary widely from one
metropolitan area to another. Therefore it is surprising that theorizing about the importance of
metropolitan government structures for service provision, economic development, and equity has
mostly reflected two points of view. The first one emphasizes the importance of fragmented
metropolitan areas, capitalist markets, competition between jurisdictions, economic growth, the role
of individuals, and exit as a political choice. (Tiebout 1956, Ostrom et al 1961, Peterson 1981). The
second approach emphasizes the importance of a unitary metropolitan area government as a way to
increase not only efficiency but also equity of service allocation, economic development, and
redistribution of resources within metropolitan areas (Lyons and Lowery 1989b, Hawkins et al
1991). Next I will discuss the importance of different government structures in metropolitan areas
and how different government structures can influence individuals’ opportunities for socioeconomic
advancement.
The main idea is that different kinds of metropolitan governance structures will create,
together with capitalist markets and a political system, systematic differences in individuals’ life
chances/opportunities and consequently in socioeconomic advancement. Different government
structures will also affect how the existing distribution of opportunities can be changed. Some
government structures may be better suited for exit, while others may be more suitable for political
participation. The historical process of creating different institutions for local governance may help
us to understand the relationships between types of government structures, politics, and regional
economy. According to Miller (1981) and Burns (1994) the movements for incorporated
municipalities and special districts were and are often organized by business actors, while Feiock and
Carr (1997) point out the importance of academic and public policy actors in city-county
consolidation attempts.
To put it briefly and generally, the government structure of the metropolitan area will
influence the functioning of markets and political processes and vice versa. For instance, it has been
argued that a metropolitan area with numerous independent jurisdictions creates a less restricted
environment for markets to work. It has also been pointed out that such a metropolitan area may
experience greater economic development while increasing differences in individuals’ socioeconomic
advancement. (Logan and Schneider 1982.) It is not enough to theorize and hypothesize about the
general differences between more or less unified governance structures. It is also important to
understand how certain institutions, such as special districts, could influence the distribution of
opportunities. The increasing popularity of special districts emphasizes the interactive connection
between economic and political systems. Special districts can be seen either as a result of how the
capitalist system changes political arrangements, or of how political actors can use markets for their
own benefit.
Fragmented Metropolitan Areas
Fragmentation in the context of metropolitan areas refers to a government structure that includes
several independent jurisdictions. A fragmented metropolitan area government structure results from
a historical process in which communities around the core city were incorporated, often as a response
to an annexation threat (Burns 1994, Miller 1981). As a result, fragmentation in metropolitan areas
seems to be particularly related to ‘old’ metropolitan areas that have used all available land for
development. A fragmented metropolitan area structure is often discussed from two opposite points
of view: public choice (broadly defined) and the social stratification-government inequality approach.
While the public choice approach sees metropolitan fragmentation to be useful because it induces
competition among local jurisdictions, the social stratification-inequality approach sees jurisdictional
fragmentation as harmful, because it induces or furthers class and race-based segregation.
According to the public choice approach, a fragmented metropolitan area forms an
environment in which free markets, individual choice, and responsible local governments (due to their
small size and ‘closeness’ to citizens) will thrive. The fragmented government structure offers
alternatives for both citizens and businesses to choose from, and forces local governments to compete
against each other. The competition is assumed to be beneficial, improving efficiency in both
economic development and service production. Economies of scale, management of negative
spillover effects, and decreases in transaction costs could be achieved by voluntary cooperation such
as creating special districts. (Petersen 1981, Tiebout 1956, Ostrom et al 1961, Ostrom 1983.) The
focus is clearly on economic growth and efficiency, rather than equality.
The focus, according to the social stratification-inequality approach, should be on equality
instead of efficiency, when fragmented metropolitan areas are considered. Segregation based on
income and race is not based on individuals’ free choices or preferences over publicly provided
services, as the public choice approach would claim. Individuals’ choices are restricted by many
different factors, the most important ones being wealth, race and exclusionary policies created by
independent political jurisdictions (Logan and Schneider 1982, see also Ostrom’s criticism 1983).
Residential segregation and unequal economic development are considered to be harmful, because
they will impact the well-being of local jurisdictions and communities as well as the whole
metropolitan area.
Fragmented metropolitan areas with independent jurisdictions can enhance the unequal
distribution of individual opportunities in a metropolitan area, because individuals’ opportunities are
closely related to their residential location. Urban sprawl, together with independent local jurisdictions
have contributed to persistent differences in residential communities over time. The flight of mostly
white middle class residents to suburbs has concentrated poorer people in the cities. This together
with structural changes in the economy (decreasing manufacturing jobs, an increase in service jobs
more often located in suburbs) has caused large differences in jurisdictions’ tax bases and
consequently in service provision.
Once inequality has been established, competition between jurisdictions may have the
tendency to further existing inequalities. Jurisdictions with stronger tax bases, better services, and
good reputations attract more economic development and better off individuals. Jurisdictions with
weaker tax bases may have to accept projects funded by federal grants and aid, and may end up, for
instance, with a larger share of subsidized housing. Fierce competition between local governments
may result in unequal and uncoordinated economic development in a metropolitan area. (Goetz and
Kaysen 1993.)
Both approaches have gained empirical support in testing the so-called sorting hypothesis
which states that individuals with similar characteristics will concentrate in the same political
jurisdiction. While the public choice approach emphasizes individual choice and income, the social
stratification-government inequality thesis highlights the importance of factors which limit choice,
such as race and discrimination. In addition, the public choice approach has tested the efficiency
assumption that local government competition could limit local government inefficiency (See for
instance Logan and Schneider 1982, Lowery and Lyons 1989, Dowding and John 1996, Hoyt 1990,
Eberts and Gronberg 1981, Percy and Hawkins 1992 ).
Unified Government (City-County Consolidation)
The need for unified government can be backed up with several economic and political arguments
which are often related to the idea of competition and cooperation. Supporters of unified government
often see lack of coordination and cooperation as costly: a larger government unit could take
advantage of economies of scale related to service production and decrease transaction costs.
However, it has been pointed out that other means for achieving these goals exist, such as the
creation of special districts for service production (Feiock and Carr 1997). Therefore, not
surprisingly, many supporters of unified government also see it as a means to more equitable local
governance (Williams 1971, Marando 1974, Logan and Schneider 1982, Hawkins et al. 1991).
Because jurisdictional fragmentation is assumed to further inequalities, a unified metropolitan
government is seen as a solution. I will briefly lay out the most important points arguing that a
unified government could influence individuals’ opportunities.
Basic services such as police, fire, garbage collection, and infrastructure maintenance are
important for individuals’ health, safety, and socioeconomic success. Run-down neighborhoods do
not attract business and do not often offer opportunities for self-improvement. Supporters of a
unified government often assume that centralized service production could decrease the differences
in quality of services provided to all individuals living in the jurisdiction. This point of view does not
acknowledge differences in political influence among constituencies within the unified government.
The idea behind ‘better and more equally distributed services’ is the common sense belief that unified
metropolitan government would have a stronger tax base and consequently is able to provide
more/better quality services. The question is whether this is actually the case. The assumed benefits
from economies of scale and decreased transaction costs might be partly offset by the cost of larger,
more bureaucratic government (Bolmquist and Parks 1995, Miller et al. 1995).
In addition to service production, a unified government could equalize economic development
and decrease residential segregation based on race or income. It has been argued that in fragmented
metropolitan areas numerous independent jurisdictions are engaged in unhealthy competition
concerning economic development. The process of unplanned economic development may increase
the existing differences between different jurisdictions: well-developed jurisdictions with strong tax
bases can choose what kind of economic development is needed, if any. A unified government, the
argument goes, would have a stronger position in planning and coordinating economic development,
and could distribute it more equally.
A unified government could also have an active role in decreasing residential segregation.
Harmonization of zoning, abandoning restrictive architectural demands, and more equal distribution
of public as well as subsidized housing could create more diverse neighborhoods. As forcefully
argued by Wilson (1987, 1996), Jargowsky (1997) and Sugrue (1996) residential segregation is
clearly related to the self-perpetuating nature of urban poverty. Residential location is an extremely
important factor determining individuals’ opportunities in metropolitan areas. Residential location
is related to security, health, availability and location of jobs, and quality of services such as schools
(Downs 1973).
A unified government could potentially create more equal opportunity structures in
metropolitan areas. However, creating a fully unified metropolitan government is not considered
politically feasible, because it would cause a radical redistribution of resources. And even if a unified
government could potentially decrease inequalities, it is very plausible that the existing political
system would leave this potential largely unused. A unified government, by creating access,
simultaneously increases the importance of politics in allocative decisions (Williams 1971). Political
decision making within a unified government can allocate money, services and economic development
(and consequently socioeconomic opportunity) unequally. A unified government structure
emphasizes the importance of voice or political participation. How economic inequality transfers
into political inequality in capitalist democratic systems has been widely theorized and studied (Dahl
1982, 1985; Gaventa 1992).
Special Districts
The number of special districts in metropolitan areas has increased rapidly. In 1992 there were
13,343 special districts in 312 metropolitan areas, the number of districts ranging from 0 (Monroe,
LA) to 665 (Houston, TX). Because special districts can play an important role in service production
in metropolitan areas, their role should be acknowledged when the influence of metropolitan area
government structures on socioeconomic inequalities is considered.
Creation of special districts is often initiated by a business-related actor or, in the case of
service producing districts with taxation rights, by municipalities and cities. According to Foster
(1997) special districts are often created to in order to avoid tax and debt limits created and enforced
by the state. In addition to being politically feasible, creation of special districts has also been backed
up by economic efficiency arguments. Special districts can cover several political jurisdictions and
consequently can take advantage of economies of scale and decrease transaction costs in fragmented
metropolitan areas (Feiock and Carr 1997).
But the efficiency of special districts has also been questioned. Foster (1997) argues that
some types of special districts, particularly those in charge of system maintenance policies, tend to
have higher per capita expenditure than if the services were provided by a general purpose
government. In addition, she argues that metropolitan areas which rely on special districts in service
provision, tend to allocate less money to welfare services than metropolitan areas which have general
purpose governments. This could imply that certain groups of individuals would be worse off in
metropolitan areas where service providing special districts are common. The reality, however, is not
this simple. Some ’regionalizing’ special districts may actually equalize distribution of services, and
allow jurisdictions to receive more services than they could afford by themselves. However, these
regionalizing special districts are often coordinating and financing system maintenance policies, such
as sewage and water treatment, instead of lifestyle policies which would more clearly equalize
opportunity structures. (Williams 1971.)
Need for a More Comprehensive Understanding of Local Governance Structures
It is clear that in reality the U.S. has neither unitary metropolitan governments with wide
redistributive powers nor totally non-coordinated fragmented metropolitan areas; rather, several types
of overlying metropolitan government institutions coexist. Therefore theories and empirical models
focusing on the number of independent jurisdictions in the metropolitan area regardless of their
responsibilities, geographical coverage or history cannot give an adequate picture of metropolitan
area governance, or the influence of governance institutions on persistent differences in metropolitan
areas. An empirical model measuring the influence of metropolitan government structure should not
be based only on the number of independent jurisdictions, but must more fully capture the complex,
layered institutional structure of metropolitan areas.
In this paper, as a first step in the process of creating a better measure for metropolitan area
government structures, I have classified metropolitan areas according to their government structure.
The classification is based on the existence of different government institutions in metropolitan areas,
including the number of municipalities, townships, special districts, and school districts. Based on
the cluster analysis dummy variables measuring metropolitan area government structure were created.
The main task of this paper is to test the popular ideas developed above: government
structures should matter and particularly, more fragmented metropolitan areas might experience not
only higher levels of economic competition and growth, but also higher levels of economic and social
inequalities. Because testing both the economic and inequality aspects would be a far too large project
to accomplish in this paper, I will focus on the potential relationship between metropolitan
government structure and the levels of socioeconomic inequalities metropolitan areas are
experiencing. Hence, the hypotheses to be tested are:
H0 = The government structure is not associated with the level of economic and social inequality in
metropolitan areas.
HA = More fragmented metropolitan areas are associated with higher levels of economic and social
inequality.
In the regression models dummy variables are used to measure different kinds of government
structures. Therefore in the actual statistical model I am mostly interested in the signs and
significance of these dummy variables.
Other factors related to economic inequality
In order to properly address the potential relationship between government structures and
socioeconomic inequality it is important to understand what other factors can influence the levels of
economic inequality in metropolitan areas. The most important factors that should be considered are
economic and social ones. The economic structure of a metropolitan area may influence the level of
socioeconomic inequality. Metropolitan areas with a significant manufacturing base may experience
lower levels of income inequality, because manufacturing jobs are more often unionized and pay
higher salaries, while metropolitan areas with a high percentage of service jobs could have higher
levels of economic inequality. If a metropolitan area is economically successful, it may have a higher
income level that is often assumed to be inversely related to income inequality. In addition,
metropolitan areas with thriving economies are more likely to experience lower unemployment and
stronger pressure to raise salaries. These factors could decrease the level of economic inequality,
particularly if a thriving economy especially helps individuals who previously were unemployed. (See
for instance Chakravorty 1996, Levernier 1999.)
The social factors related to economic inequality that are also closely related to the economic
success of metropolitan areas are social and human capital. It is clear that family structure has an
impact on economic well-being; single-parent families, particularly those with female heads of
household, are often economically worse off. The education level of a metropolitan area population
might not only be related to the economic success of the metropolitan areas, but may also be an
indication about the level of social capital in the area. Similarly, the percentage of poor families can
function both as an indication of economic success and availability of social and economic resources
in metropolitan areas. Another social factor that requires consideration is the position of minorities.
It is a well-known fact that minorities, especially African-Americans and Native Americans, due to
historic and present discrimination, are on average economically worse-off than the white population.
Race is also related to levels of economic and residential segregation in metropolitan areas. Hence
metropolitan areas with a large percentage of minorities may experience greater income inequality.
(Kovandzic et al 1998, Levernier 1999).
In addition to economic and social factors, regional differences must be considered. Regional
differences, most noticeably larger consentration of African-Americans in the South and older
metropolitan areas in the Northeast and the Midwest. Regional dummy variables could also control
for other phenomena such as economic success of metropolitan areas in the Sunbelt.
Methods
The Data
The data set includes 74 metropolitan areas in the United States. The metropolitan areas were
selected based on the availability of data concerning inequality2. The included metropolitan areas
included are listed in Table 1. The main sources of data in this study are the 1987 Census of
Governments and the 1990 Census of Population. In addition, I have used two measures,
dissimilarity and isolation of poor (based on 1990 Census of Population poverty statistics), calculated
by Abramson et al (1995) as my dependent variables.3 The third dependent variable, the Gini
coefficient, was calculated from the 1990 Census data concerning family income.4
**Table 1 about here**
Cluster Analysis
The goal of cluster analysis is to recognize patterns in data. Hence its usefulness as a classification
procedure. Cluster analysis categorizes cases (in this paper metropolitan areas) according to variables
included in the model, increasing objectivity of the classification. Although cluster analysis is a useful
tool in creating categories, it has several potential drawbacks. The results of cluster analysis depend
on the variables included in the model, the format of the variables (unstandardized vs. standardized
variables), the measure of similarity/dissimilarity, and the applied clustering algorithm. Different
measures of similarity/dissimilarity and clustering algorithms can give different results even when the
same data set and variables are used. Therefore it is very important that the results of cluster analysis
are carefully analyzed. In addition, the consistency of the results should be established by using the
same model in two subsets of a larger data set (Aldenderfer and Blashfield 1984).
In the clustering analysis I applied unstandardized variables (numbers of different kinds of
government institutions), the squared Euclidean distance and Euclidean distance as dissimilarity
measures, and the Ward and K-means methods as the clustering algorithms. I used unstandardized
variables, because all grouping variables I am using are in the same scale, i.e. the number of
government institutions in metropolitan areas.
The Ward method can be problematic. It is designed to optimize the minimum variance within
clusters and works very well when multivariate normal distributions are used. However, it can lead
to misclassifications, especially if the grouping variables are correlated within a cluster. (Kaufman and
Rousseeuw 1990). The Ward method is also a hierarchical clustering method and once a case is
assigned to a certain cluster it cannot be reassigned. The K-means method applies Euclidean distance
as a dissimilarity measure. The K-means method divides the data set in original cluster centers and
assigns cases to their closest centers, recalculates the centers and reassigns cases until no changes
could be made that would minimize the variance within each cluster. I applied a K-means method
that updates the cluster centers after the complete data set has been allocated. The K-means method
overcomes the problem of wrong initial assignments which can take place with hierarchical clustering
methods, but can also give misleading results if the initial cluster centers are not appropriate.
The purpose of the cluster analysis was to categorize metropolitan areas according to their
fragmentation. It has been hypothesized that fragmented metropolitan areas can be related to a higher
level of socioeconomic inequality. Independent jurisdictions in metropolitan areas could offer more
choices, be easier to identify and thus further economic and racial segregation. In addition, the
existence of independent jurisdictions may offer opportunities for discriminatory zoning and building
codes. Fragmented metropolitan areas could also experience more competition concerning economic
development that could further ‘quality of life’ differences within metropolitan areas: jurisdictions
with high tax bases may have more choices when service provision and options for economic
development are considered.
In the ideal case metropolitan areas would not be classified only according to their formal
government structure, but the classification would include measures for other factors that may
influence economic inequality, such as the level of economic cooperation between political
jurisdictions in metropolitan areas, the economic success of metropolitan areas over time and more
detailed information concerning metropolitan wide institutions and their policies. However, because
of the lack of easily available data and the purpose of this paper as the first step in exploring how
metropolitan areas could be classified ( if such classification by applying cluster analysis is possible),
I wanted to limit the number of variables to those describing the formal government structure of the
selected metropolitan areas.
The grouping variables for the cluster analysis were created by adding together different kinds
of government organizations from county level data to match the 1990 metropolitan area boundaries
(the Census of Governments 1987). The following variables were selected to measure the government
structure in metropolitan areas: number of municipalities, special districts, special districts with the
right to collect taxes, school districts, townships and total governments.
Cluster analysis applying the Ward method with nonstandardized grouping variables created
four categories of metropolitan areas that can be described as least fragmented, somewhat
fragmented, very fragmented and extremely fragmented (hereafter also called Model 1). The category
of least fragmented metropolitan areas includes 43 cases that have constantly smaller values in all
grouping variables than other categories. The category of somewhat fragmented metropolitan areas
includes 28 cases. It differs from the least fragmented ones particularly by having higher numbers of
school and special districts. The very fragmented metropolitan areas in turn, though having higher
numbers of all government institutions, have especially a higher number of special districts. This
category includes 13 metropolitan areas. The last category, extremely fragmented metropolitan areas,
includes only 5 cases. These cases are characterized by a very high number of government
institutions and high numbers of total governments.
**Tables 2 and 3 about here**
The results were quite different when the K-means method was applied. Instead of creating a clear
classification based on the degree of fragmentation, the cluster analysis identified three levels of
fragmentation, and divided a class ‘more fragmented’ metropolitan areas to two sub-groups; one with
a high number of special districts and one with high numbers of municipalities and townships
(hereafter also called Model 2).
**Tables 4 and 5 about here**
The descriptive statistics tables show that there are substantively significant differences between the
created clusters. Particularly Model 1 concerning government structure seems to capture the levels
of fragmentation more accurately. However, it can be seen from Table 3 that the distinctions between
different categories are not clear-cut. Many clusters are overlapping when it comes to the minimum
and maximum numbers of specific kinds of government institutions. When the K-means method is
applied, metropolitan areas also clustered according to the levels of fragmentation, but perhaps also
more clearly according to certain types of government institutions. The differences in classification
could be at least partly explained by the Ward method’s sensitivity to profile elevation.
I have included the different categories of metropolitan areas as dummy variables in the
regression model. In order to apply both classifications I will have two sets of test variables, Model
1 and Model 2, both including three dummy variables. The category of most fragmented metropolitan
areas in both models is applied as the base line, i.e., taking the value of the intercept. Therefore,
when the results of regressions are interpreted, the null hypothesis will be rejected if the dummy
variables measuring government structure are significant and have negative signs.
Model 1: categories created by applying the Ward method
1. Dummy: Least Fragmented
2. Dummy: Somewhat Fragmented
3. Dummy: Very Fragmented
Model 2: categories created by applying the K-means method
1. Dummy: Least Fragmented
2. Dummy: Fragmented; Municipalities and Townships
3. Dummy: Fragmented; Special Districts
The Regression Model
The level of economic inequality is modeled as follows:
Income inequality = b0 +b1x1 + ... + bnxn + u,
where economic inequality for metropolitan areas is measured by three inequality indices: the Gini
coefficient, the dissimilarity and isolation indices. All the dependent variables take values between
0 and 100. In the case of the Gini coefficient 0 represents no income inequality (X percentage of
population will receive X percentage of the income for all values of X), while 100 would represent
an extreme inequality (one person receiving all the income). The interpretations of the dissimilarity
and isolation of poor indices are somewhat similar. The dissimilarity index shows what percentage
of the poor would have to move in order to gain an even distribution of poor in a metropolitan area
while the isolation index indicates how poor the neighborhood of an average poor person is. For
instance, a value of 75 indicates that 75% of individuals in a certain geographical area are poor
(Abramson et al. 1995).
All dependent variables applied in the regression analysis are measures of income inequality
which are often applied as proxies for more general phenomena such as differences in quality of life
or opportunity structures for individuals. While the Gini coefficient strictly measures income
inequality, the dissimilarity and isolation indices seem to capture the idea of broader socioeconomic
inequality better. Isolation and concentration of the poor, economic and/or racial residential
segregation in metropolitan areas, have had severe consequences for economic and social well-being.
High and extreme poverty neighborhoods may not only be lacking in services such as good quality
schools, but high concentration of poverty is often related to lack of other resources for advancement
such as social capital.
Although the Gini coefficient has been widely applied in inequality studies, it has its
drawbacks. The Gini coefficient fulfills several principles considered to be compelling for inequality
measures such as ‘income scale independence’, ‘principle of population’, and ‘weak principle of
transfer’. According to the income scale independence principle the value of an inequality measure
should not change if all individuals’ incomes increase by the same proportion. The principle of
population simply states that if the number of individuals in the society increases but the distribution
of the income remains the same, the value of the inequality measure should remain the same. The
weak principle of transfers refers to the idea that redistribution form a wealthier individual to a poorer
one, if the redistributed amount of money is less than half of the difference between these individuals’
incomes, should cause a decrease in the value of the inequality measure. However, the Gini coefficient
does not meet the principles of decomposability and strong transfers. The Gini coefficient for a nation
cannot be derived from a subsets of the nation, such as the states in the United States. For instance,
it is possible that the Gini coefficient for every states increases, but the value of the Gini coefficient
for the whole nation decreases. According to the principle of strong transfers the value of the
inequality measure, when income is redistributed between two individuals, should only depend on the
income difference between these two individuals, not their location within the income distribution.
The Gini coefficient is more sensitive to income transfers at the middle of the income distribution than
at the ends of it. (Cowell 1995, Chakravorty 1998.)
Test and Control Variables
The test variable ‘metropolitan government structure’ has been created by applying cluster analysis.
I am going to use two different models of metropolitan government structure. The first cluster
analysis created four categories that can be ordered from the least fragmented to the extremely
fragmented. Three dummy variables will be included (least fragmented, somewhat fragmented and
very fragmented) in the regression models to account for government structure. The second cluster
analysis applying the K-means algorithm also created four clusters, but these clusters cannot be as
clearly ordered according to the level of fragmentation. However, I chose to include three dummy
variables in the model (least fragmented, fragmented: municipalities and townships, fragmented:
special districts) to measure the government structure. Therefore, in both cases the category of most
fragmented metropolitan areas will take the value of the intercept.
It is important to control for other factors that may influence economic inequality in
metropolitan areas. As mentioned above, social, economic and regional differences between
metropolitan areas should be acknowledged. I have included the following independent variables to
control for differences in economic structure and success:
1. %MANUFACTURING: Percent of manufacturing firms that have 20 or more workers (1987).
Individuals working in manufacturing may be receiving higher salaries due to higher levels of
unionization. Because larger firms are more likely to be unionized, I have included this variable
instead of the more commonly used ‘percentage of working force in manufacturing’. I will
hypothesize that this variable is negatively associated with inequality measures.
2. MEDIAN: Median income (1990). A higher level of median income has been assumed to indicate
a higher level of economic success in metropolitan areas. Therefore it is assumed that median income
will be negatively associated with the inequality measures.
3. %UNEMPLOYED: Unemployment level (1990). Unemployment rates will have an impact on the
aggregate level of income and measures of income inequality. The unemployment rate variable
should have a positive relationship with inequality measures.
The following variables were selected to control for social factors influencing economic inequality:
1. %BACHELOR: Percentage of individuals with bachelors degree (1990). This variable is used as
an indicator of the quality of the labor force. Because individuals with higher education levels tend
to receive higher salaries and are also more capable of moving to suburban communities, it is assumed
that this variable will be positively related to the inequality measures.
2. %MINORITIES: Percentage of minorities, calculated here as percentage of nonwhites (1990).
Metropolitan areas with higher percentage of minorities may have higher levels of socioeconomic
inequality due to immigration, ongoing discrimination, historical discrimination and race and income
based residential segregation. Therefore this variable should be positively related to the inequality
measures.
3. %FEMALE: Percentage of poor families that are headed by a female (1990). This variable is also
used as a proxy for the existence of social capital. Single-headed families often have lower levels of
income. The erosion of stable family structure has been associated with many problems faced in
extremely high poverty neighborhoods, such as inner cities in many metropolitan areas in the United
States. Therefore it is assumed that this variable is positively associated with inequality measures.
I have also included three dummy variables to account for regional differences:
1. DSOUTH. A dummy variable including states in West South Central, East South Central and
South Atlantic census regions.
2. DWEST. A dummy variable including states in Pacific and Mountain census regions.
3. DMIDWEST. A dummy variable including states in West North central and East North Central
census regions.
The percentage of poor families and other poverty statistics are often included as control variables.
I decided not use the percentage of poor families as one of my control variables because two of the
inequality indices are based on poverty statistics. However, I included the percentage of poor
families that are headed by a female as a control variable. This could cause model specification
problems if the percentage of female headed poor families is highly correlated to the number of poor
families/individuals. But this is not the case. The correlation coefficient between the percentage of
female headed poor families and the percentage of individuals below the poverty level or the families
below the poverty level is -.25 and -.27 respectively.
Results
The regional dummy variable for the South is somewhat correlated with the government structure
variables assigned to least fragmented metropolitan areas (Pearson’s correlation of .46). In addition,
some of the control variables were also moderately correlated among themselves and with the
regional dummy variable for the South. For instance, as could be expected, median income and
percentage of individuals with Bachelor’s degree and unemployment rates and median income are
moderately correlated (Pearson’s correlation values of .684 and -.578 respectively). The VIF values
do not exceed 10 (often considered as the critical threshold value) for any of the models. However,
auxiliary regressions showed that the dummy variable for South can be almost as well predicted by
other independent variables in the model as the dependent variables implying a potential problem with
multicollinearity. Therefore I ran four different models regarding each dependent variable: two
regressions with Model 1 government structure dummies with and without dummy variables for
regions, and two regressions with Model 2 government structure dummies with and without dummy
variables for regions.
Tables 6 and 7 show the results of six regressions, two for each dependent variable. In Table
6, Model 1 dummy variables for metropolitan government structure are included while Table 7
includes Model 2 dummy variables for the government structure. In both tables the regional dummy
variables were included in the regressions. Tables 8 and 9 show results of regressions which are
otherwise similar to the ones described above, but are lacking the regional dummy variables. Next
I will briefly discuss the results regarding each dependent variable.
Gini coefficient
It is clear that the strongest results regarding the importance of the government structure concern the
Gini coefficient measuring income inequality. All models described below explain quite large
proportions of variance of the Gini coefficient. The value of the R2 ranges from .646 to .70
depending on the included test and control variables. In the case of Model 1 (Table 6) all other
dummies except the one measuring ‘somewhat fragmented’ government structure are significant, and
all the dummy variables have negative signs as expected: less fragmented metropolitan areas would
have lower levels of income inequality. It should also be noticed that the significance of the dummy
variables measuring government structure decreases the less fragmented the structure is. This is
interesting, because the categories of very fragmented and extremely fragmented metropolitan areas
do not include many cases and it is possible that an influential case in these categories could easily
skew the results. However, the only case that could be considered as an influential case was El Paso
and deleting the case did not change the results substantially.
Some of the control variables did not turn out to be significant while others have the expected
relationships. For instance, an increase in the median income for a metropolitan area would decrease
the value of the Gini coefficient, while an increase in the unemployment rate would increase the value
of the Gini coefficient. Also, the percent of individuals with bachelors degree is positively and
significantly associated with the dependent variable. This could imply that the income distribution
is even more skewed in metropolitan areas with better educated labor force.
When the Model 2 dummy variables for government structure were included in the regression
models the results were quite similar (Table 7). All dummy variables measuring metropolitan
government structure, except the dummy for ‘fragmented; special districts’, were significant and the
coefficients also had the expected directions. This is interesting, because it is often theoretically
hypothesized that fragmentation in the form of independent general governments is associated with
higher levels of economic and social inequality. As before, variables ‘median income’ and
‘unemployment rate’ were significant and had negative and positive signs respectively.
To get a better sense of the substantial (non)importance of the results I calculated expected
values for the Gini coefficient when all the control variables took their mean values and the values
of dummy variables were alternated to match different combinations of regions and government types.
For instance, when Model 1 (government structure) was applied, the expected value for the Gini
coefficient in the case of extremely fragmented metropolitan areas located in the Northeast is 41.64,
while the expected value for metropolitan areas with the least fragmented metropolitan government
structure would be 40.56. These values would be 40.77 and 39.70 respectively, if the metropolitan
areas would be located in the West. In the case of Model 2 (government structure), the expected
value for extremely fragmented metropolitan areas in the Northeast is 41.62 and for the least
fragmented government structure it is 40.42. If these metropolitan areas would be located in the
West, the values would be 40.72 for extremely fragmented metropolitan areas and 39.52 for the least
fragmented ones. It is difficult to address the statistical or substantive importance of these differences
because of the limited size of the data set at this time.
Because of the possible multicollinearity among independent variables I decided to run the
same regressions without the regional dummy variables. The results of these regressions (Table 8)
were different from the earlier ones. Importantly, when the Model 1 dummy variables are included,
the dummy variable for ‘least fragmented metropolitan areas’ is not significant while the dummy
variable for ‘somewhat fragmented metropolitan areas’ changes to be significant. These changes
could be related to the multicollinearity among the dummy variable for the South, the percentage of
minorities, and the dummy variable for least fragmented metropolitan areas. For instance, the
percentage of minorities turns out to be significant when regional variables are omitted. Otherwise
the results concerning control variables are very similar to those given by the model that included
regional dummy variables. When the Model 2 (Table 9) is applied, all dummy variables measuring
government structure turn out to be significant, even though ‘least fragmented’ and ‘fragmented:
special districts’ only barely so. Again, the results concerning control variables have not changed.
Dissimilarity of Poor
While the regression results concerning the Gini coefficient give somewhat strong support to the idea
that metropolitan area government structures matter when income inequality is studied, the regression
results concerning the dissimilarity of poor index are more mixed. As above, it is possible that
multicollinearity among test and control variables have had an impact on the results, particularly when
the regional dummies are included in the regressions. The regression explained more of the variation
in the dependent variable than I expected. The value of R2 ranges from .544 to .615.
When Model 1 variables together with all control variables are included, none of the dummy
variables measuring government structure turn out to be significant (Table 6). However, at least the
test variables have negative signs as assumed. In addition, surprisingly few control variables are
significant, even though the R2 had a value of .615, implying a possible problem with multicollinearity.
The percentage of individuals with bachelor’s degree and the percentage of poor families that are
headed by a female have positive significant coefficients as expected, while the coefficients for the
median income, the percentage of minorities, and the percentage of firms with more than 20
employees are not significant. The regional dummy variable South is also statistically significant at
the 0.1 level.
The results did not change very much when Model 2 variables were used (Table 7). None of
the dummy variables measuring government structure are significant. The results regarding control
variables have changed. Now the percentage of firms with more than 20 employees is significantly
(barely) and positively related to the dissimilarity of poor index, while both the regional dummies for
South and West are negatively related to the dissimilarity of poor index.
When the regression models were rerun without the regional dummy variables, the results
changed quite radically. When the Model 1 variables were included, the dummy variables for the least
fragmented metropolitan areas and for the somewhat fragmented metropolitan areas turn out to be
statistically significant (Table 8). Both variables had negative coefficients implying that the less
fragmented metropolitan areas would have lower values of dissimilarity of poor. In addition, some
of the control variables – the median income and the percentage of firms with more than 20
employees – also became statistically significant. It is surprising that an increase in median income
would increase the value of the dissimilarity index. It is often assumed that an increase in income is
negatively associated with inequality. However, it is possible that residential segregation is positively
associated with an increase in median income which could explain the positive relationship. All these
changes and the fact that the value of R2 changed slightly (from .615 to .591) are supportive of the
idea that some multicollinearity was present in the earlier model applying Model 1 variables.
The results, when Model 2 variables were included in the regression, are less clear. Only the
intercept (that stands for the extremely fragmented metropolitan areas located in the Northeast) turns
out to be significant. In addition, the median income becomes positively and significantly related to
the dissimilarity of poor, while the percentage of individuals with bachelor’s degree becomes
statistically insignificant.
Again, to address the potential substantive importance of the results I have calculated
expected values for the dissimilarity of poor index when the control variables are kept constant at
their mean values while the dummy variables for government structures and regions take different
values. I decided to use the Model 1 both when the regional dummy variables are included and
omitted. When the regional dummy variables are included in the regression, the expected value for
extremely fragmented metropolitan areas located in the Northeast is 40.24, while for the least
fragmented metropolitan areas it is 37.34. If the metropolitan areas would be located in the West,
the expected values would be 37.34 and 33.84 respectively.
When the dummy variables for regions are omitted, the expected value of the dissimilarity
index is 39.62 for the extremely fragmented metropolitan areas, 37.47 for the very fragmented
metropolitan areas, 34.62 for the somewhat fragmented metropolitan areas and finally, 35.17 for the
least fragmented metropolitan areas.
Isolation of Poor
The regression models explaining ‘isolation of poor’ gave the clearest results regarding the lack of
importance of metropolitan government structures: all four regressions unfailingly showed no
statistically significant association between government structure (regardless of how metropolitan
areas were classified) and the isolation of poor index. However, all the control variables in every
model are statistically significant and have the hypothesized directions, except for the variable
measuring economic structure of the metropolitan area which has a positive sign (% of manufacturing
firms with more than 20 workers). It is possible that manufacturing is more concentrated in older
metropolitan areas in Northeast and Midwest that also have a higher degree of residential segregation.
This could explain the positive relationship between larger manufacturing establishments and the
isolation index. The median income is negatively associated with the isolation of poor while the
unemployment rate, the percentage of individuals with bachelor’s degree, the percentage of
minorities, and the percentage of poor families headed by a female are positively associated with the
isolation index.
Conclusion
The aim of this paper was to classify metropolitan areas according to their government structure in
a meaningful way, and to test if the created categories of metropolitan government structures are
related to socioeconomic inequalities in metropolitan areas. The null hypothesis tested was a simple
one – the government structure is not associated with the level of economic and social inequality
in metropolitan areas – while the alternative hypothesis more specifically claimed that more
fragmented metropolitan government structures are associated with higher levels of socioeconomic
inequality. In this paper I used three different indices as a proxy for socioeconomic inequalities. The
Gini coefficient is a measure of income inequality, while the isolation and dissimilarity of poor indices
that are partially based on the number of poor more closely match the idea of residential segregation
based on income, and consequently more closely capture the idea of high and extreme poverty
neighborhoods.
The metropolitan areas were classified by applying a cluster analysis. More specifically, the
metropolitan areas were categorized according to the number of total governments, municipalities,
special districts, special districts with taxation rights, school districts, and townships. Because the
result of a cluster analysis is not only sensitive to the grouping variables, but also to the
similarity/dissimilarity measure and the algorithm used in clustering, I decided to use two different
methods. As a result I have two different classifications: Model 1 that more clearly categorized the
metropolitan areas according to their fragmentation, and Model 2 that made more clear cut
distinctions between the types of government institutions.
The regression analyses gave mixed results and the analyses should be redone with a data set
including all metropolitan areas. The smallness of the data set used for this paper somewhat
undermines the results; partly because some of the categories created by the cluster analyses included
only five cases, and also because of multicollinearity problems which may very well be related to the
small size of the data set. However, even when the drawbacks are known, the preliminary results of
the regression analyses are encouraging.
According to the regression analyses more fragmented metropolitan areas are likely to have
higher Gini coefficient values. Even though these results look statistically more robust than the ones
for the dissimilarity or isolation of poor, the substantive importance can be questioned. After all, the
expected values for the Gini coefficient are quite similar when different government structures are
applied.
When the dissimilarity of poor is used as a dependent variable, the results are more mixed.
The variables measuring government structures are not significant when the regional dummy variables
are included as control variables. However, when the regional dummy variables are omitted (and it
is quite clear that there is a moderate multicollinearity problem among the test and control variables),
the Model 1 dummy variables for least fragmented and somewhat fragmented metropolitan areas
become significant. These variables also have negative signs: less fragmented metropolitan areas tend
to have lower values for the dissimilarity of poor index when variables controlling for unknown
regional factors were omitted. The results are also substantively intriguing: the expected values for
the dissimilarity index varied quite a lot depending on the government structure. The most
disappointing results are related to the isolation of poor index. None of the dummy variables
measuring government structure turn out to be significant in any of the regression models.
Even though it cannot be concluded that the metropolitan government structure as defined
in this paper is clearly related to different forms of socioeconomic inequality, it can be concluded that
it should not be ignored when inequalities in metropolitan areas are explained. A better classification
for the metropolitan areas, a data set including all metropolitan areas, and a more careful model
specification will better reveal if metropolitan government structures have an impact on
socioeconomic inequalities.
APPENDIX 1: TABLES
TABLE 1. Metropolitan areas included in the
study
Metropolitan area
Akron, OH
Albany-Sch-Tro, NY
Albuquerque, NM
Allento-Beth-East, PA
Atlanta, GA
Austin, TX
Bakersfield, CA
Baltimore, MD
Baton Rouge, LA
Birmingham, AL
Buffalo, NY
Charleston-etc, SC
Charlotte-Gastonia, NC
Chattanooga, TN-GA
Chigago, IL
Cincinnati, OH-KY-IN
Cleveland, OH
Columbia, SC
Columbus, OH
Dallas-Fort Wort, TX
Dayton, OH
Denver-Boulder, CO
Detroit, MI
El Paso, TX
Flint, MI
Fort Laud-Hollywood, FL
Fresno, CA
Gary-East Chicago, IN
Grand Rapids, MI
Greensboro-etc, NC
Greenville-etc, SC
Harrisburg, PA
Hartford, CT
Honolunu, HI
Houston, TX
Indianapolis, IN
Jacksonville, FL
Johnson City, TN-VA
Kansas City, MO-KS
Knoxville, TN
Lancaster, PA
Lansing-etc, MI
Las Vegas, NV
Los Angele-Long B, CA
Louisville, KY
Memphis, TN-AR-MS
Miami, FL
GINI
.
40.00
43.00
39.00
41.00
44.00
42.00
40.00
45.00
45.00
42.00
41.00
41.00
44.00
42.00
42.00
42.00
40.00
40.00
42.00
40.00
41.00
.
44.00
42.00
.
44.00
.
38.00
42.00
41.00
38.00
39.00
39.00
43.00
40.00
41.00
43.00
41.00
44.00
37.00
40.00
41.00
.
42.00
45.00
45.00
ISOL
22.90
15.60
22.00
14.30
22.40
24.80
23.70
24.30
29.20
25.20
24.90
23.50
17.00
21.80
28.70
27.60
29.90
20.10
26.20
21.70
23.00
19.50
29.10
35.40
26.70
17.00
29.60
24.90
17.30
15.90
18.10
16.50
25.70
14.00
23.70
19.10
20.00
18.80
19.80
21.20
15.30
23.70
16.10
23.10
25.80
33.40
26.20
DISSIM
39.70
34.00
34.00
34.50
39.60
37.60
32.30
46.00
36.10
35.50
44.80
32.00
31.90
30.10
49.80
43.00
51.40
33.10
43.60
37.30
40.20
39.20
50.10
30.40
40.10
32.00
32.90
43.50
37.50
29.30
30.30
35.70
52.80
33.50
36.00
39.90
32.50
19.60
39.60
27.30
32.70
35.00
28.50
34.90
39.30
42.90
31.30
Metropolitan area
GINI
Milwaukee, WI
40.00
Minneapolis-St Paul, MI 39.00
Mobile, LA
45.00
Nashville-etc, TN
42.00
New Haven-West H, CT 41.00
New Orleans, LA
47.00
New York, NY-NJ
.
Newark, NJ
.
Oklahoma City, OK
42.00
Omaha, NE-IA
40.00
Orlando, FL
40.00
Philadelphia, PA-NJ
42.00
Phoenix, AZ
41.00
Pittsburgh, PA
44.00
Portland, OR-WA
40.00
Providence-etc, RI
41.00
Raleigh-etc, NC
41.00
Richmond, VA
40.00
Riverside-etc, CA
.
Rochester, NY
40.00
Sacramento, CA
40.00
St Louis, MO-IL
41.00
Salt Lake-etc, UT
38.00
San Antonio, TX
43.00
San Diego, CA
41.00
San Francisco-etc, CA
40.00
San Jose, CA
.
Seattle-Everett, WA
.
Springfield-etc, MA-CT 41.00
Stockton, CA
41.00
Syracuse, NY
40.00
Tacoma, WA
.
Tampa-etc, FL
42.00
Tucson, AZ
44.00
Tulsa, OK
43.00
Vallejo-etc, CA
.
Washington, DC-MD-VA 38.00
West Palm-etc, FL
44.00
Wichita, KS
40.00
Wilmington, DE-NJ-MD 43.00
Youngstown-etc, OH
42.00
ISOL
32.30
19.50
32.30
21.10
19.10
35.20
29.40
20.70
21.80
19.90
16.10
25.40
21.70
21.10
14.70
17.10
18.50
23.30
16.40
21.60
18.30
23.90
15.10
30.00
18.10
16.50
12.30
13.10
25.30
22.50
23.20
18.90
19.10
25.90
19.40
10.10
14.00
16.60
18.40
15.50
24.80
DISSIM
55.10
39.90
35.50
33.40
46.00
22.00
43.00
48.20
32.60
40.30
27.20
47.90
36.50
34.40
27.10
36.20
35.30
44.00
24.70
42.00
31.00
43.60
30.00
37.60
31.90
36.00
31.30
29.80
41.60
31.50
41.30
27.80
31.00
34.50
29.20
25.50
38.10
34.40
33.90
34.60
37.10
Note: Please notice that all these cases were included in the
classification of the metropolitan areas. However, because of missing
data the actual number of cases in regression analysis is 73, when the
dependent variable is the Gini coefficient and 74 when the dependent
variable is the isolation or dissimilarity indices.
TABLE 2. Results of the Cluster Analysis, Model 1
Least Fragmented
Somewhat Fragmented
Very Fragmented
Extremely Fragmented
Akron, (OH)
Albuquerque, (NM)
Baltimore, (MD)
Baton Rouge, (LA)
Birmingham, (AL)
Buffalo, (NY)
Charleston, (SC)
Charlotte-Gastonia etc (NC)
Chattanooga, (TN-GA)
Columbia, (SC)
El Paso, (TX)
Flint, (MI)
Fort Lauderdale-Hollywood
(FL)
Gary-Hammond, (IN)
Grand Rapids, (MI)
Greensboro-Winston- etc. (NC)
Honolunu, (HI)
Jacksonville, (FL)
Johnson City-Kingsport-etc.
(TN-VA)
Knoxville, (TN)
Lancaster, (PA)
Lansing-East Lansing, (MI)
Las Vegas, (NV)
Memphis, (TN-AR-MS)
Miami, (FL)
Mobile, (LA)
Nashville, (TN)
New Haven, (CT)
New Orleans, (LA)
Orlando, (FL)
Raleigh-Durham, (NC)
Richmond, (VA)
San Antonio, (TX)
San Jose, (CA)
Springfield, (MA-CT)
Tacoma, (WA)
Tampa, (FL)
Tucson, (AZ)
Vallejo-etc., (CA)
Washington DC
West Palm Beach-etc. (FL)
Wilmington, (DE-NJ-MD)
Youngstown-Warren, (OH)
Allentown etc, (PA)
Atlanta, (GA)
Austin, (TX)
Bakersfield, (CA)
Cincinnati, (OH-KY-IN)
Cleveland, (OH)
Columbus, (OH)
Dayton, (OH)
Fresno, (CA)
Greenville-Spartanburg, (SC)
Harrisburg-etc. (PA)
Hartford-New Britain, (CT)
Indianapolis, (IN)
Louisville, (KY)
Milwaukee, (WI)
New York, (NY-NJ)
Newark, (NJ)
Oklahoma City, (OK)
Phoenix, (AZ)
Providence, (RI)
Rochester, (NY)
Salt Lake City, (UT)
San Diego, (CA)
Stockton, (CA)
Syracuse, (NY)
Tulsa, (OK)
Wichita, (KS)
Albany etc, (NY)
Dallas, (TX)
Denver, (CO)
Detroit, (MI)
Kansas City, (MOKS)
Los Angeles-Long
Beach, (CA)
Minneapolis-St. Paul,
(MN)
Newark, (NJ)
Omaha, (NE-IA)
Portland, (OR-WA)
Sacramento, (CA)
San Francisco, (CA)
Seattle, (WA)
Chicago, (IL)
Houston, (TX)
Philadelphia, (PA)
Pittsburgh, (PA-NJ)
St. Louis, (MO-IL)
Notes: The variables used in the cluster analysis were the number of total governments, school districts, special districts, special districts with right
to tax, municipalities, and townships.
The Ward method with squared Euclidean distance and non-standardized variables was applied.
TABLE 3. Descriptive Statistics for Government Structure (Model 1)
CATEGORIES
N
Least Fragmented
Total Governments
Municipalities
Townships
School Districts
Special Districts
Special Districts with Taxation
43
Somewhat Fragmented
Total Governments
Municipalities
Townships
School Districts
Special Districts
Special Districts with Taxation
28
Very Fragmented
Total Governments
Municipalities
Townships
School Districts
Special Districts
Special Districts with Taxation
13
Extremely Fragmented
Total Governments
Municipalities
Townships
School Districts
Special Districts
Special Districts with Taxation
5
Min.
Max.
Mean
Std. Dev.
4
1
0
0
2
0
152
80
48
37
61
34
70.48
23.13
7.06
9.83
25.74
6.9
35.49
16.16
13.47
10.81
16.40
8.43
112
6
0
3
23
0
282
129
117
102
124
91
190.92
46.53
32.14
39.71
69.53
30.07
56.59
31.20
22.72
22.59
28.64
23.03
236
15
0
19
32
2
450
178
115
129
320
227
340.76
87.07
27.93
71.69
154.92
87.69
64.68
55.11
43.46
32.29
76.02
54.04
633
75
0
45
246
0
844
208
199
220
577
410
728.2
156
89
126.8
353.4
168.2
84.71
51.41
74.77
70.26
129.56
165.39
TABLE 4. Results of the Cluster Analysis, Model 2
Least Fragmented
Fragmented
Akron, (OH)
Albuquerque, (NM)
Austin, (TX)
Bakersfield, (CA)
Baltimore, (MD)
Baton Rouge, (LA)
Birmingham, (AL)
Buffalo, (NY)
Charleston, (SC)
Charlotte-Gastonia etc (NC)
Chattanooga, (TN-GA)
Columbia, (SC)
Dayton, (OH)
El Paso, (TX)
Flint, (MI)
Fort Lauderdale-Hollywood
(FL)
Fresno, (CA)
Gary-Hammond, (IN)
Grand Rapids, (MI)
Greensboro-Winston- etc. (NC)
Greenville-Spartanburg, (SC)
Hartford-New Britain, (CT)
Honolunu, (HI)
Jacksonville, (FL)
Johnson City-Kingsport-etc.
(TN-VA)
Knoxville, (TN)
Lancaster, (PA)
Lansing-East Lansing, (MI)
Las Vegas, (NV)
Memphis, (TN-AR-MS)
Milwaukee, (WI)
Miami, (FL)
Mobile, (LA)
Nashville, (TN)
New Haven, (CT)
New Orleans, (LA)
New York, (NY-NJ)
Oklahoma City, (OK)
Orlando, (FL)
Phoenix, (AZ)
Providence, (RI)
Raleigh-Durham, (NC)
Richmond, (VA)
Salt Lake City, (UT)
San Antonio, (TX)
San Diego, (CA)
San Jose, (CA)
Springfield, (MA-CT)
Stockton, (CA)
Syracuse, (NY)
Tacoma, (WA)
Tampa, (FL)
Tucson, (AZ)
Tulsa, (OK)
Vallejo-etc., (CA)
Washington DC
West Palm Beach-etc. (FL)
Wilmington, (DE-NJ-MD)
Youngstown-Warren, (OH)
Fragmented: Special Districts Fragmented: Townships
Albany etc, (NY)
Allentown etc, (PA)
Atlanta, (GA)
Cincinnati, (OH-KY-IN)
Cleveland, (OH)
Columbus, (OH)
Dallas, (TX)
Detroit, (MI)
Harrisburg-etc. (PA)
Indianapolis, (IN)
Louisville, (KY)
Minneapolis-St. Paul, (MN)
Newark, (NJ)
Portland, (OR-WA)
Rochester, (NY)
Wichita, (KS)
Denver, (CO)
Kansas City, (MO-KS)
Los Angeles-Long Beach,
(CA)
Omaha, (NE-IA)
Sacramento, (CA)
San Francisco, (CA)
Seattle, (WA)
Extremely
Chicago, (IL)
Houston, (TX)
Philadelphia, (PA)
Pittsburgh, (PA-NJ)
St. Louis, (MO-IL)
Notes: The variables used in the cluster analysis were the number of total governments, school districts, special districts, special districts with right
to tax, municipalities, and townships.
The K-means method with Euclidean distance and non-standardized variables was applied.
TABLE 5. Descriptive Statistics for Government Structure (Model 2)
CATEGORIES
N
Least Fragmented
Total Governments
Municipalities
Townships
School Districts
Special Districts
Special Districts with Taxation
60
Fragmented: municipalities & townships
Total Governments
Municipalities
Townships
School Districts
Special Districts
Special Districts with Taxation
16
Fragmented: special districts
Total Governments
Municipalities
Townships
School Districts
Special Districts
Special Districts with Taxation
7
Extremely Fragmented
Total Governments
Municipalities
Townships
School Districts
Special Districts
Special Districts with Taxation
5
Min.
Max.
Mean
Std. Dev.
4
1
0
0
2
0
187
80
56
67
124
91
93.56
25.26
9.0
17.23
38.03
15.76
49.64
17.32
16.12
18.71
28.58
20.22
196
39
0
12
26
0
450
178
117
115
121
89
281.25
83.62
60.25
56.69
75.56
28.75
67.75
46.33
38.76
28.90
30.83
28.36
242
15
0
19
135
64
415
133
33
95
320
227
331.57
68.57
7.0
56.71
206.42
112.51
58.36
47.80
12.19
28.19
61.07
55.91
633
75
0
45
246
0
844
208
199
220
577
410
728.2
156
89
126.8
353.4
168.2
84.71
51.41
74.77
70.26
129.56
165.39
TABLE 6. Determinants of Income Inequality, Model 1 Government Structure (N=74)a
Independent variable
Dependent variables
GINI
Isolation of Poor
Dissimilarity of Poor
CONSTANT
44.774
(17.029)***
2.302
(.360)
-1.663
(-.172)
%MANUFACTURING
-2.599E-02
(.374)
.173
(2.452)***
.152
(1.425)
%BACHELOR
.133
(2.972)***
.503
(4.611)***
.384
(2.322)**
MEDIAN INCOME
-2.417E-04
(-4.608)***
-5.662E-04
(-4.434)***
2.064E-04
(1.068)
%MINORITIES
2.198E-02
(1.299)
.151
(3.686)***
8.789E-02
(1.149)
1.750
(.850)
17.014
(3.432)***
26.738
(3.564)***
.536
(4.517)***
2.018
(6.993)***
.750
(1.716)*
DUMMY: Least fragmented
-1.080
(-1.898)*
-.520
(-.376)
-2.893
(-1.382)
DUMMY: Somewhat fragmented
-1.052
(-1.509)
-1.225
(-.724)
-3.927
(-1.533)
-1.247
(-2.175)**
.504
(.362)
-.979
(-.465)
DUMMY: South
.506
(1.058)
-.624
(-.479)
-3.374
(-1.709)*
DUMMY: West
-.863
(-1.309)
-2.297
(-1.463)
-3.468
(-1.460)
DUMMY: Midwest
-.617
(-1.427)
.879
(.834)
1.121
(.703)
F
15.516
18.312
10.722
R2
.708
.740
.615
%FEMALE
%UNEMPLOYED
DUMMY: Very fragmented
Note: t values are in parenthesis
* = p < .1; ** = p < .05; *** = p < .01
a
N=73 in a model in which the GINI coefficient is the dependent variable
TABLE 7. Determinants of Income Inequality, Model 2 Government Structure (N=74)a
Independent variable
Dependent variables
GINI
Isolation of Poor
Dissimilarity of Poor
CONSTANT
44.860
(17.086)***
1.439
(.221)
-4.243
(-.422)
%MANUFACTURING
-2.993E-02
(-1.031)
.185
(2.561)**
.189
(1.696)*
%BACHELOR
.130
(2.879)***
.502
(4.509)***
.363
(2.107)**
MEDIAN INCOME
-2.413E-04
(-4.506)***
-5.649E-04
(-4.264)***
2.452E-04
(1.197)
%MINORITIES
2.353E-02
(1.426)
.156
(3.927)***
.101
(1.643)
1.842
(.882)
16.527
(3.275)***
23.906
(3.063)***
%UNEMPLOYED
.541
(4.499)***
2.053
(6.937)***
.848
(1.852)*
DUMMY: Least fragmented
-1.197
(-2.147)**
.424
(.341)
-.439
(-.229)
DUMMY: fragmented;
municipalities & townships
-1.029
(-1.676)*
-.599
(-.-320)
-667
(-.230)
DUMMY: fragmented; special
districts
-986
(-1.188)
.652
(.469)
-.913
(-.425)
DUMMY: South
.634
(1.179)
-1.007
(-.768)
-4.346
(-2.143)**
DUMMY: West
-.893
(-1.355)
-2.412
(-1.574)
-4.274
(-1.803)*
DUMMY: Midwest
-.650
(-1.489)
.821
(.760)
.894
(.534)
F
15.478***
17.386***
9.453***
R2
.707
.729
.582
%FEMALE
Note: t values are in parenthesis
* = p < .1; ** = p < .05; *** = p < .01
a
N=73 in a model in which the GINI coefficient is the dependent variable
TABLE 8. Determinants of Income Inequality, Model 1 Government Structure
(N=74)a
Independent variable
Dependent variables
GINI
Isolation of Poor
Dissimilarity of Poor
45.239
(18.532)***
-1.829
(-.327)
-11.850
(-1.383)
%MANUFACTURING
9.384E-04
(.033)
.231
(3.583)***
.208
(2.099)**
%BACHELOR
.183
(4.005)***
.511
(4.886)***
.295
(1.841)*
MEDIAN INCOME
-3.020E-04
(-6.087)***
-5.669E-04
(-4.991)***
3.616E-04
(2.076)**
%MINORITIES
2.874E-02
(1.9)*
.125
(3.615)***
1.527E-02
(.228)
1.771
(.877)
21.324
(4.613)***
33.781
(4.766)***
.457
(3.686)***
2.052
(7.246)***
.989
(2.277)**
-.812
(-1.387)
-1.068
(-.795)
-4.456
(-2.166)**
DUMMY: Somewhat
fragmented
-1.465
(-2.050)**
-2.123
(-1.312)
-5.004
(-2.017)**
DUMMY: Very
fragmented
-1.362
(-2.293)**
-.229
(-.168)
-2.157
(-1.034)
F
16.378***
23.007***
12.710***
R2
.658
.731
.591
CONSTANT
%FEMALE
%UNEMPLOYED
DUMMY: Least
fragmented
Note: t values are in parenthesis
* = p < .1; ** = p < .05; *** = p < .01
a
N=73 in a model in which the GINI coefficient is the dependent variable
TABLE 9. Determinants of Income Inequality, Model 2 Government Structure
(N=74)a
Independent variable
Dependent variables
GINI
Isolation of Poor
Dissimilarity of Poor
CONSTANT
45.878
(18.510)***
-3.972
(-717)
-18.140
(-2.066)**
%MANUFACTURING
-3.702E-03
(-.126)
.242
(3.659)***
.257
(2.448)**
%BACHELOR
.187
(4.042)***
.492
(4.638)***
.238
(1.416)
MEDIAN INCOME
-3.243E-04
(-6.466)***
-5.442E-04
(-4.746)***
4.676E-04
(2.573)**
%MINORITIES
3.445E-02
(2.310)**
.131
(3.860)***
1.657E-02
(.307)
2.468
(1.197)
21.038
(4.453)***
31.139
(4.157)***
.420
(3.346)***
2.053
(6.937)***
1.223
(2.706)***
DUMMY: Least
fragmented
-1.049
(-1.805)*
2.235E-02
(.018)
-1.748
(-.899)
DUMMY: fragmented;
municipalities &
townships
-1.783
(-2.082)**
-.920
(-.498)
-.693
(-.236)
DUMMY: fragmented;
special districts
-1.186
(-1.803)*
.533
(.379)
-1.312
(-.588)
F
15.618***
21.790***
10.680***
R2
.646
.719
.544
%FEMALE
%UNEMPLOYED
Note: t values are in parenthesis
* = p < .1; ** = p < .05; *** = p < .01
a
N=73 in a model in which the GINI coefficient is the dependent variable
Endnotes
1. It is assumed that metropolitan areas form regional economies which are distinct enough from
the national/state economy as a whole. Some authors see an especially distinctive role for large
metropolitan cities and metropolitan areas as growth centers (Pierce 1993, Voith 1998).
2. The author did not want to use all metropolitan areas in the United States in order to test the
reliability of the created classifications. During my dissertation research I will classify
metropolitan areas according to a larger set of variables including land area and population.
3. I apply the dissimilarity and isolation indices calculated by Alan J. Bramson, Mitchell S. Tobin
and Matthew R. VanderGoot in Housing Policy Debate, volume 6, issue 1. They calculated these
indices for the 100 largest metropolitan areas using data from the Under Class Data Base
developed at The Urban Institute. The dissimilarity index can be calculated from
n
D=
ti pi − P
∑ 2TP(1 − P)
i =1
where n is the number of areal units, ti is the total population in the areal unit and pi is the
proportion of poor people of an areal unit of i; T is the population of the whole geographical unit
(i.e. metropolitan area) and P is the proportion of poor in the whole geographical area.
The isolation index can be calculated from the following formula
n
I=
 xi xi 
∑  X ⋅ t 
i =1
i
where n is the number of areal units, xi is the number of poor and ti is the total population of the
areal unit i; X is the number of poor of the whole geographical unit (i.e. metropolitan area).
4.The Gini Index was calculated from the 1990 Census data by using a variable ‘family income’
that included 25 categories. The last category was open-ended. Therefore I assumed a mean
value of $200 000 for the last open-ended category. The formula for calculating the Gini
coefficient was taken from Betz (1972):
Gini Coefficient ' 1 & j (fi % 1 & f i) (Yi %Yi %1)
k
i'1
where k is the number of income categories, fi is the percentage of population in the ith income
category, and Yi is the percentage of income in the ith income category.
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