The Japanese Journal of American Studies, No. 19 (2008)

City-County Separation and Consolidation in
the United States:
The Impact on Urban Growth

Masaharu YASUOKA*

CITY-COUNTY SEPARATION: ADVANTAGES AND DISADVANTAGESAs of 2002, governmental units in the United States consisted of a federal
government, fifty state governments and 87,525 local governments.1 Of these
local governments, 3,034 county governments manage activities of statewide
concern at the local level. The basic functions of the counties include property tax assessment and collection, law enforcement, elections, and roadmaintenance, along with record keeping of land transactions, births anddeaths, and so on. In addition to these traditional functions, the responsibilities of county governments are expanding to include health care management, hospitals, pollution control, mass transit, industrial development, social
services, and consumer protection.
Like the counties, cities, interchangeably known as municipalities, aregeneral purpose units of local governments, and according to the 2002 Census of Governments, there are 19,429 municipalities in the United States.
However, in terms of the relationship with state governments, municipal governments are more self-governing than county governments. As comparedwith counties, which are expected to complement the administrative activities of state governments, cities have greater decision-making authority and
Copyright © 2008 Masaharu Yasuoka. All rights reserved. This work may be used,with this notice included, for noncommercial purposes. No copies of this work may
be distributed, electronically or otherwise, in whole or in part, without permission
from the author.
*Associate Professor, Kobe University

177

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MASAHARU YASUOKA

discretion, with more powerful elected officials in both the executive and thelegislative branches.
Since there are so many small local jurisdictions in the United States, it hasoften been argued that the consolidation of a county and proximate municipalities into a single countywide government would better serve the areawide
administrative needs of residents across fragmented jurisdictions. Nevertheless, the responses of voters to city-county consolidation proposals are usually less than positive. During the 1990s, when a number of consolidationplans were proposed, voters rejected the merger of Des Moines and PolkCounty, Iowa; Spokane and Spokane County, Washington; and Knoxvilleand Knox County, Tennessee.2 As of 2006, the number of completely consolidated governments in the United States was not more than eleven.3 The
list includes Anchorage, Alaska; San Francisco, California; Denver, Colorado; Kansas City, Kansas; and Honolulu, Hawaii.
In contrast to these proposed city-county consolidations, of the fifty American states, Virginia is the only state with a statewide practice of city-county
separation. In the state of Virginia, all cities are completely independent of
their neighboring counties with regard to territory, population, tax base, service authority, and so on.4 Jack D. Edwards summarizes the advantages anddisadvantages of city-county separation as follows.5 The advantages of thissystem are (1) clear-cut lines of responsibility, (2) simplicity in tax and governmental structures, and (3) limit on the number of governments. He discusses the following disadvantages: (1) the probability of duplication in
public services, especially in the case of small cities and adjacent counties,(2) disincentive to county-city cooperation, and (3) the tax base competition
between counties and cities. The issue of tax base after city annexation isparticularly important. In many cases of annexation in other states, there is nochange in the ability of the county to tax all the citizens and maintain its services. However, in Virginia, due to the city-county separation, the entire tax
base that is annexed by a city is subtracted from the county. As a result, therelationship between cities and counties tends to be competitive and contentious.6
S. J. Malenski discusses the advantages of city-county consolidation incontrast to city-county separation on the basis of these cases.7 The first advantage is the reduction of the abovementioned intergovernmental conflicts.
The second is the broadening of the revenue base. The third advantage islong-range regional planning and problem solving. The fourth is the recognition of the communities of interest. The fifth is the so-called “economies ofscale,” and the last advantage he suggests has to do with general economic

CITY-COUNTY SEPARATION AND CONSOLIDATION IN THE UNITED STATES

179

benefits.
Although the advantages and disadvantages of both independent and consolidated cities have been discussed by state and local politicians, there islittle empirical research to assess the validity of these assertions. What kindof impact do the differences in the types of cities have on policy outputs?
This paper explores the relationships between some of the socioeconomic
indicators and the types of cities (independent/consolidated/other, usual cities) based on 195 cities in the United States, each with a population greaterthan 100,000. Through this statistical analysis, I would like to attempt to explain the impact of city-county separation – which is one of the most important factors that structure urban policies in Virginia – in quantitative terms, bycomparing with cases of consolidated cities.
A STATISTICAL ANALYSIS OF THE IMPACT OF CITY-COUNTY SEPARATION
To what extent do the socioeconomic indicators differ between independent cities and non-independent cities? By using the data compiled in theCounty and City Data Book 1994, first of all, I would like to compare themeans of population change (percent), median household income percent
change, poverty population percentage, retail trade sales percent change,
crime rate, unemployment rate, and housing ownership rate among independent, consolidated, and other cities.8
Of the 195 cities with a population greater than 100,000 in 1990, ten wereindependent cities (eight cities in Virginia, plus St. Louis, and Baltimore),and fourteen were consolidated city-county governments. Six cities in thedata-set are ones that have no county government.9
Table 1 displays the mean differences of each variable among different
types of cities. As for the median income change, both independent cities andconsolidated cities show a higher mean than conventional dependent cities.
The mean of consolidated cities in this indicator is slightly greater than thatof independent cities. With regard to the population change, dependent cities
show a higher mean than both independent and consolidated cities. In termsof population change, consolidated cities rank the lowest among all types ofcities. As for the percentage of poverty population, the difference of themeans of these three types of cities appears to be negligible. Standard deviations of this variable remain slightly above 5, while most of the standard deviations of the other variables exceed 20, except for the unemployment rate
and owner occupancy rate.

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MASAHARU YASUOKA
Table 1
INCCHANG

INDEP
CONSO
OTHER
POPCHAN
INDEP
CONSO
OTHER
POVFAMIL
INDEP
CONSO
OTHER
RSALECHA INDEP
CONSO
OTHER
CRIMERAT INDEP
CONSO
OTHER
UNEMPLOY INDEP
CONSO
OTHER
OWNEROCC INDEP
CONSO
OTHER

N
10
14
162
10
14
164
10
14
164
10
14
158
10
14
164
10
14
164
10
14
164

Means
80.7700
82.3286
74.9840
11.2000
6.592918.4085
12.2800
13.1286
11.999453.0200
39.9286
44.9101
8989.408595.938428.156.85005.8571
6.747652.5100
48.557154.0085

Standard Deviation
9.444323.5828
21.6166
24.4241
13.1856
25.9179
5.79135.8551
5.962022.7168
20.0392
24.8725
3343.222146.193601.84
1.47591.77012.446310.042210.60879.7469

Independent cities show more significant changes in retail sales than doother types of cities. For this variable, the mean of consolidated cities islower than that of dependent cities. As for the crime rate, the mean of independent cities is higher than that of consolidated and other cities. The difference of the means in unemployment appears to be insignificant between independent and other cities, while consolidated cities show the lowest mean
among all the types of cities. Finally, among these three types of cities, consolidated cities show the lowest rate of housing ownership. For independent
cities, the rate of housing ownership is lower than that of other cities, buthigher than that of consolidated cities.
The result of a t-test of the dependent and non-dependent cities samples inTable 2 reveals that the two-tailed significance levels of income change,
population change, and housing ownership rate are low enough to reject thenull hypothesis. The two-tailed significance levels are not low enough toprove that two population variances are significantly different; however, we
can observe that while independent cities appear to show greater population
and retail sales growth than consolidated cities, they also show a higher crime

CITY-COUNTY SEPARATION AND CONSOLIDATION IN THE UNITED STATES

181

rate and unemployment rate. It might be possible to assume that independent
cities appear to be economically more successful than consolidated cities;
however, socially speaking, they show more problems than consolidated cities.
Table 2
Independent Samples Test
Levene’s Test for
Equality of Variances

F

INCCHANG

Equal
variancesassumed

Sig.
.572

.450

Equal
variancesnotassumed
POPCHANEqual
variancesassumed

9.543

.002

Equal
variancesnotassumedPOVFAMIL

Equal
variancesassumed

.011

.918

Equal
variancesnotassumed
RSALECHA

Equal
variancesassumed

.261

.610

Equal
variancesnotassumedCRIMERAT

Equal
variancesassumed

.556

.457

Equal
variancesnotassumedUNEMPLOY Equal
variancesassumed

.831

.363

Equal
variancesnotassumedOWNEROCC Equal
variancesassumedEqual
variancesnotassumed

1.211

.272

t-test for Equality of Means

t

Sig.
Mean
Std. Error(2-tailed) Difference Differencedf

95% ConfidenceInterval of the Mean
Lower

Upper

2.833

191

.005

11.8709

4.18993.606520.13532.986

44.475.005

11.8709

3.97573.860919.8809-2.505193.013-12.1247

4.8404-21.6716-2.5777-3.34661.054.001-12.1247

3.6238-19.3707-4.8787

1.075

193

.284

1.2554

1.1674-1.04713.55801.076

42.167.288

1.2554

1.1665-1.0983

3.6092-.016

187.987 -7.4643E-02

4.7524-9.44989.3005-.018

49.523.986 -7.4643E-02

4.1432-8.39858.2492

1.296

193

.197

889.5311

686.5229-464.5198

2243.5819

1.492

48.900.142889.5311

596.1487-308.5364

2087.5986

.256

193

.798

.1202

.4686

-.80401.0444.287

47.206

.776

.1202

.4190

-.7227.9631-3.096193.002-6.0569

1.9563-9.9154-2.1985-2.81339.036.008-6.0569

2.1529-10.4114-1.7025

.2299( 187)
P= .001-.2527( 187)
P= .000.3932( 187)
P= .000-.0621( 187)
P= .396.0314( 187)
P= .668-.3137( 187)
P= .000.0379( 187)
P= .605

POPCHANPOVFAMILRSALECHACRIMERATUNEMPLOYOWNEROCC

TYPE2-.0553( 187)
P= .449.2225( 187)
P= .002-.1858( 187)
P= .010-.2046( 187)
P= .005.5618( 187)
P= .000-.4407( 187)
P= .000

1.0000
(
0)
P= .

.2299
( 187)
P= .001

POPCHAN

Table 3.0048
( 187)
P= .947-.4755( 187)
P= .000.5903( 187)
P= .000.4901( 187)
P= .000-.5172( 187)
P= .000

1.0000(0)
P= .

-.4407( 187)
P= .000-.2527( 187)
P= .000.0798( 187)
P= .275.1974( 187)
P= .006-.3063( 187)
P= .000-.2528( 187)
P= .000

1.0000(0)
P= .

-.5172( 187)
P= .000.5618( 187)
P= .000.3932( 187)
P= .000

POVFAMIL RSALECHA(Coefficient/(D.F.)/Two-tailed Significance)“.” is printed if a coefficient cannot be computed

1.0000(0)
P= .

INCCHANG

INCCHANG

Zero Order Partials.0280( 187)
P= .702-.3619( 187)
P= .000.1862( 187)
P= .0101.0000(0)
P= .

-.2528( 187)
P= .000.4901( 187)
P= .000-.2046( 187)
P= .005-.0621( 187)
P= .396.0109( 187)
P= .881-.2677( 187)
P= .000

1.0000
(
0)
P= .

.1862
( 187)
P= .010-.3063( 187)
P= .000.5903( 187)
P= .000-.1858( 187)
P= .010.0314( 187)
P= .668-.0070( 187)
P= .924

1.0000(0)
P= .

-.2677( 187)
P= .000-.3619( 187)
P= .000.1974( 187)
P= .006-.4755( 187)
P= .000.2225( 187)
P= .002-.3137( 187)
P= .000

CRIMERAT UNEMPLOY OWNEROCC

1.0000(0)
P= .

-.0070( 187)
P= .924.0109( 187)
P= .881.0280( 187)
P= .702.0798( 187)
P= .275.0048( 187)
P= .947-.0553( 187)
P= .449.0379( 187)
P= .605

TYPE2

182
MASAHARU YASUOKA

To examine the relationships among these variables, I would like to use apartial correlation analysis, holding constant the effects of city-county separation (independent cities) and city-county consolidation.

CITY-COUNTY SEPARATION AND CONSOLIDATION IN THE UNITED STATES

183

Table 4Controlling for TYPE 2
INCCHANGPOPCHAN POVFAMIL RSALECHA CRIMERAT UNEMPLOY OWNEROCC

INCCHANG

1.0000
(
0)
P= .

.2325
( 186)
P= .001-.2530( 186)
P= .000.3917( 186)
P= .000-.0632( 186)
P= .389.0310( 186)
P= .673-.3137( 186)
P= .000

POPCHAN.2325( 186)
P= .001

1.0000(0)
P= .

-.4412( 186)
P= .000.5689( 186)
P= .000-.2034( 186)
P= .005-.1855( 186)
P= .011.2224( 186)
P= .002

POVFAMIL-.2530( 186)
P= .000-.4412( 186)
P= .000

1.0000(0)
P= .

-.5193( 186)
P= .000.4902( 186)
P= .000.5903( 186)
P= .000-.4755( 186)
P= .000

RSALECHA.3917( 186)
P= .000.5689( 186)
P= .000-.5193( 186)
P= .000

1.0000(0)
P= .

-.2560( 186)
P= .000-.3082( 186)
P= .000.1986( 186)
P= .006

CRIMERAT-.0632( 186)
P= .389-.2034( 186)
P= .005.4902( 186)
P= .000-.2560( 186)
P= .000

1.0000
(
0)
P= .

.1859
( 186)
P= .011-.3619( 186)
P= .000

UNEMPLOY.0310( 186)
P= .673-.1855( 186)
P= .011.5903( 186)
P= .000-.3082( 186)
P= .000.1859( 186)
P= .0111.0000(0)
P= .

-.2676( 186)
P= .000-.3137.2224-.4755( 186)( 186)( 186)
P= .000
P= .002
P= .000(Coefficient/(D.F.)/Two-tailed Significance).1986( 186)
P= .006-.3619( 186)
P= .000-.2676( 186)
P= .000

1.000
(
0)
P= .

OWNEROCC“ . “ is printed if a coefficient cannot be computed

As Table 3 indicates, type 2, which is a dummy variable that assigns 1 forindependent cities and 0 for other types of cities, has no significant correlations with any other variables. Therefore, as indicated in Table 4, if the effectof independent cities is controlled, the correlation coefficients will not bechanged substantially. In Table 1, the population change in independent cities
is lower than that of other cities. In Table 3, the population change and type2 variables are negatively correlated (-.0553), although the correlation is notstatistically significant.

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MASAHARU YASUOKA
Table 5Model Summarya,bVariables

Model
1EnteredRemoved

c,d

.

TYPE2

R

R SquareAdjusted
R Square

.049

.002

-.003

Std. Errorof theEstimate
25.0838a.

Dependent Variable: POPCHAN
Method: Enterc.
Independent Variables: (Constant), TYPE2
d.
All requested variables entered.
b.

Coefficientsa
Unstandardized
Coefficients
B

Std. Error(Constant)

16.766

1.844

TYPE2

-5.566

8.144

Model
1

a.

Standardized
Coefficients
Beta
-.049

t

Sig.

9.091

.000

-.684

.495

Dependent Variable: POPCHAN

Table 5 presents a regression using population change as its dependent
variable and the dummy for Type 2 (independent cities) as an independent
variable. This shows a negative effect on population change by independent
cities, although the effect is not statistically significant. This finding seems tocorrespond with the conventional understanding that since annexation is difficult under the city-county separation system, city-county separation could
have a negative effect on population growth.10

-.2527( 187)
P= .000.3932( 187)
P= .000-.0621( 187)
P= .396.0314( 187)
P= .668-.3137( 187)
P= .000

POVFAMILRSALECHACRIMERATUNEMPLOYOWNEROCC.2225( 187)
P= .002-.1858( 187)
P= .010-.2046( 187)
P= .005.5618( 187)
P= .000-.4407( 187)
P= .000

1.0000
(
0)
P= .

.2299
( 187)
P= .001

POPCHAN.0656-.1180( 187)( 187)
P= .370
P= .106(Coefficient/(D.F.)/ Two-tailed Significance)“.” is printed if a coefficient cannot be computed.2299( 187)
P= .001

POPCHAN

TYPE3

1.0000(0)
P= .

INCCHANG

INCCHANG

Zero Order Partials.0463( 187)
P= .527-.4755( 187)
P= .000.5903( 187)
P= .000.4901( 187)
P= .000-.5172( 187)
P= .000

1.0000(0)
P= .

-.4407( 187)
P= .000-.2527( 187)
P= .000

POVFAMIL-.0584( 187)
P= .425.1974( 187)
P= .006-.3063( 187)
P= .000-.2528( 187)
P= .000

1.0000(0)
P= .

-.5172( 187)
P= .000.5618( 187)
P= .000.3932( 187)
P= .000

RSALECHA

Table 6.0018
( 187)
P= .980-.3619( 187)
P= .000.1862( 187)
P= .0101.0000(0)
P= .

-.2528( 187)
P= .000.4901( 187)
P= .000-.2046( 187)
P= .005-.0621( 187)
P= .396

CRIMERAT-.1048( 187)
P= .151-.2677( 187)
P= .000

1.0000
(
0)
P= .

.1862
( 187)
P= .010-.3063( 187)
P= .000.5903( 187)
P= .000-.1858( 187)
P= .010.0314( 187)
P= .668

UNEMPLOY-.1198( 187)
P= .101

1.0000(0)
P= .

-.2677( 187)
P= .000-.3619( 187)
P= .000.1974( 187)
P= .006-.4755( 187)
P= .000.2225( 187)
P= .002-.3137( 187)
P= .000

OWNEROCC

1.0000(0)
P= .

-.1198( 187)
P= .101-.1048( 187)
P= .151.0018( 187)
P= .980-.0584( 187)
P= .425.0463( 187)
P= .527-.1180( 187)
P= .106.0656( 187)
P= .370

TYPE3

CITY-COUNTY SEPARATION AND CONSOLIDATION IN THE UNITED STATES
185

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MASAHARU YASUOKA
Table 7

Controlling for TYPE 3
INCCHANGPOPCHAN POVFAMIL RSALECHA CRIMERAT UNEMPLOY OWNEROCC

INCCHANG

1.0000
(
0)
P= .

.2398
( 186)
P= .001-.2565( 186)
P= .000.3986( 186)
P= .000-.0623( 186)
P= .396.0385( 186)
P= .600-.3087( 186)
P= .000

POPCHAN

.2398( 186)
P= .001

1.0000(0)
P= .

-.4388( 186)
P= .000.5598( 186)
P= .000-.2058( 186)
P= .005-.2006( 186)
P= .006.2113( 186)
P= .004

POVFAMIL-.2565( 186)
P= .000-.4388( 186)
P= .000

1.0000(0)
P= .

-.5159( 186)
P= .000.4905( 186)
P= .000.5991( 186)
P= .000-.4739( 186)
P= .000

RSALECHA.3986( 186)
P= .000.5598( 186)
P= .000-.5159( 186)
P= .000

1.0000(0)
P= .

-.2532( 186)
P= .000-.3147( 186)
P= .000.1921( 186)
P= .008

CRIMERAT-.0623( 186)
P= .396-.2058( 186)
P= .005.4905( 186)
P= .000-.2532( 186)
P= .000

1.0000
(
0)
P= .

.1874
( 186)
P= .010-.3644( 186)
P= .000

UNEMPLOY.0385( 186)
P= .600-.2006( 186)
P= .006.5991( 186)
P= .000-.3147( 186)
P= .000.1874( 186)
P= .0101.0000(0)
P= .

-.2838( 186)
P= .000-.3087.2113-.4739( 186)( 186)( 186)
P= .000
P= .004
P= .000(Coefficient/(D.F.)/Two-tailed Significance).1921( 186)
P= .008-.3644( 186)
P= .000-.2838( 186)
P= .000

1.0000(0)
P= .

OWNEROCC“.” is printed if a coefficient cannot be computed

Table 6 presents simple correlations among variables, including a dummy
variable Type 3 that assigns 1 for consolidated cities and 0 for other types ofcities. As in the case of independent cities, overall correlations between consolidated cities and other socioeconomic variables are low and insignificant.
Therefore, if the effect of city-county consolidation is controlled, as shown in
Table 7, correlation coefficients will remain almost unaffected. However,generally speaking, correlation coefficients between type 3 and other variables are higher than those between type 2 and other variables. Like independent cities, consolidated cities show a weak negative effect on population
change (-.1180) and owner occupancy rate (-.1198). As Table 1 demonstrates,
consolidated cities show the lowest mean of population change and owneroccupancy rate among all types of cities.
The only statistically significant effect of consolidated cities is their impact
on owner occupancy rate. Table 8 is the result of a linear regression, using

CITY-COUNTY SEPARATION AND CONSOLIDATION IN THE UNITED STATES

187

owner occupancy rate as a dependent variable, and a dummy variable forconsolidated cities as an independent variable. It shows that consolidated cities have a negative impact that deducts 4.836% from a base owner occupancy
rate level of 53.393%. It makes the mean of the owner occupancy rate of
consolidated cities around five percent lower than the average (the mean of
the owner occupancy rate of 195 cities is 53.0456%).
Table 8Model Summarya,bVariables
ModelEnteredRemoved1

TYPE3c,d.

R

R SquareAdjusted
R Square

.123

.015

.010

Std. Errorof theEstimate
10.1567a.

Dependent Variable: OWNEROCC
Method: Enterc.
Independent Variables: (Constant), TYPE3
d.
All requested variables entered.
b.

Coefficientsa
Unstandardized
Coefficients
B

Model
1
a.

Std. Error(Constant)

53.393

.755

TYPE3

-4.836

2.818

Standardized
Coefficients
Beta
-.123

t

Sig.

70.724.000-1.716.088

Dependent Variable: OWNEROCC

The difference between independent and consolidated cities lies in retailsales change and unemployment rate. While independent cities are positively
correlated with retail sales change (.0798) and unemployment (.0109), consolidated cities are negatively correlated with these variables (retail sales
change: -.0584; unemployment: -.1048). These differences seem to be reflected in the differences in means shown in Table 1. While independent cities show the highest means for these two variables among the three city categories, consolidated cities show the lowest means.
As mentioned earlier, generally speaking, previous studies have focusedon the advantages and disadvantages of city-county consolidation only interms of the reduction of jurisdictional conflict, the economies of scale, andthe broadened revenue base; however, thus far, they have paid little attention

188

MASAHARU YASUOKA

to the socioeconomic impact of consolidation. As an exception, S. J. Makielski suggests that consolidation is likely to attract business and industry owing
to the aggressive attitude of the strengthened government.11 However, as hehimself admits, “it is difficult to assess accurately the validity of the claim.”
The comparison of means and the partial correlation analysis used here show
a mixed result for this thesis. The negative effect of consolidated cities on
unemployment rates appears to support this claim. However, at the sametime, consolidated cities have negative correlations with population change,
retail sales change, and owner occupancy rate. Therefore, it appears to be difficult to state that consolidated cities are improving their economic conditions.
On the other hand, compared to consolidated cities, independent cities
have weaker correlations with every variable. As in the case of consolidated
cities, the results of correlational analyses are mixed in terms of economic
development. Independent cities have positive correlations with incomechange and retail sales change, which are reflected in their higher means forthese variables, as compared to dependent cities. However, they have negative correlations with population change and the owner occupancy rate, andpositive correlations with the poverty population rate, crime rate, and unemployment rate. Therefore, considering these data, one may state that while
independent cities are relatively successful in economic terms, they are notsucceeding in improving the socioeconomic status of residents.
CITY GROWTH COMPARED WITH COUNTY GROWTH
In the former sections, the effects of both city-county separation and citycounty consolidation were examined based on the city data. What can we say
about the differences in population, income, and retail sales growth between
cities and their counties, then? In this section, I would like to examine theeffect of city-county consolidation and city-county separation on the growth
gap between cities and counties. New variables are created by dividing a city
growth percentage by its central county growth percentage.12 The county thathas the largest population and is the nearest to the central city is selected. For
example, Atlanta is surrounded by a number of counties, but Fulton County
has the largest population; therefore, it is selected. In the case of independent
cities, the nearest neighboring county is chosen. Isle of Wight County is selected as a central county for Chesapeake, Norfolk, Portsmouth, and Virginia
Beach. As for Washington, D.C., Fairfax County, which has the largest population, is selected.

CITY-COUNTY SEPARATION AND CONSOLIDATION IN THE UNITED STATES

189

Table 9 displays the mean differences of the city/county ratios of each variable among the different types of cities. As for the city/county median income change ratio (INCRATIO), while most of the cities indicate that counties grow faster than cities, there appears to be little gap between cities andcounties, in the case of independent (TYPE 2.00) and consolidated cities(TYPE 1.00). With regard to the city/county population ratio (POPRATIO),
there appear to be significant differences depending on the types of cities. In
the case of usual, dependent cities, the increase in population is greater incities than in their neighboring counties (city/county ratio = 1.5514). However, in consolidated cities, the increase in population is slightly slower than
it is in their counties. In the case of independent cities, as indicated by thenegative ratio, the increase in population is higher in the counties than in thecities. This is consistent with the previous arguments in this article. As for thecity/county poverty population rate ratio (POVRATIO), the poverty rates of
independent cities are more than twice that of their neighboring counties(2.5771), while the poverty rates of other types of cities are only one and ahalf times as high as that of their counties. A higher poverty rate compared tothat of counties appears to be one of the characteristics of independent cities.
For example, in 1990, the poverty rate of Charlottesville (10.0%) was morethan double that of Albemarle County (4.8%), exactly as indicated by themeans in this table.13 Finally, with regard to the city/county retail sales
change ratio (SALRATIO), the difference among the three types of cities appears to be negligible. All three types of cities display slightly slower growththan the counties.

TYPE

TYPE

TYPE

TYPE

INCRATIOPOPRATIOPOVRATIOSALRATIO

.00
1.00
2.00
Total
.00
1.00
2.00
Total
.00
1.00
2.00
Total
.00
1.00
2.00
Total

N
169
14
10
193
171
14
10
195
171
14
10
195
165
14
10
189

Mean
.9509
1.00301.0145.9580
1.5514.9667-.10441.42451.35311.21182.57711.4057
.9042
.9986
.9465
.9134

Table 9Std.
Deviation.1283
2.67E-02
.1480
.1259
8.3679.3993
2.26667.8586.7026.6556
1.6909.8185.4960
6.28E-02
.4240
.4734

Descriptives

Std. Error
9.9E-03
7.1E-03
4.7E-02
9.1E-03
.6399
.1067
.7168
.5628
5.4E-02.1752.5347
5.9E-02
3.9E-02
1.7E-02.1341
3.4E-02

Lower
Bound
.9314
.9876
.9086
.9401
.2882
.7362
-1.7259.3146
1.2470.8333
1.36751.2901
.8279
.9624
.6432
.8455

Upper
Bound
.9704
1.01851.1204.9759
2.8146
1.19731.51702.53451.45911.5904
3.78681.5213.9804
1.03491.2498.9813

95% ConfidenceInterval for Mean
Minimum
.29
.94
.77
.29
-32.67.13-5.67-32.67
.10
1.00
.43
.10
-2.00
.80
.34
-2.00

Maximum
1.26
1.07
1.23
1.26
83.00
1.89
2.80
83.00
6.05
3.47
5.15
6.05
2.58
1.06
1.67
2.58

190
MASAHARU YASUOKACITY-COUNTY SEPARATION AND CONSOLIDATION IN THE UNITED STATES

191

Table 10ANOVA
Sum ofSquares
INCRATIO

df

Mean
Square

F
2.199

.114

.233

.792

12.265

.000

.280

.756

BetweenGroups

6.9E-022

3.4E-02

WithinGroups2.973

190

1.6E-02

3.041

192

29.0652

14.53211952.0

192

62.25011981.0

194

14.7222

7.361

115.233192.600

129.955

194

.127

2

6.3E-0242.010186.22642.137

188

Total
POPRATIO Between
GroupsWithinGroupsTotal
POVRATIO Between
GroupsWithinGroupsTotal
SALRATIO Between
GroupsWithinGroupsTotal

Sig.

In order to examine whether the three population means are significantly
different statistically, an analysis of variance technique (ANOVA) is used. In
the ANOVA, the observed significance level is obtained by comparing the
calculated F value to the F distribution. As Table 10 shows, the two-tailed
significance levels of the population change ratio and retail sales change ratio
are not low enough to reject the null hypothesis that all groups have the samemean in the population. As explained above, although the mean differencesof the population change ratio appears to be plausible, they are not statistically significant enough. From Table 10, we can see that the mean differences of the income change ratio and poverty rate ratio are statistically significant.
To examine the relationships between the types of cities and these city/
county ratio variables, I would again like to use a partial correlation technique, holding constant the effects of city-county separation and city-county
consolidation.

192

MASAHARU YASUOKA
Table 11
Zero Order Partials
INCRATIO POPRATIO POVRATIO SALRATIO

TYPE2

TYPE3

INCRATIO

1.0000(0)
P= .

-.0474( 187)
P= .517-.1380( 187)
P= .058.2813( 187)
P= .000.1039( 187)
P= .155.0986( 187)
P= .177

POPRATIO-.0474( 187)
P= .5171.0000(0)
P= .

-.0094( 187)
P= .897.0059( 187)
P= .936-.0474( 187)
P= .518-.0186( 187)
P= .800

POVRATIO-.1380( 187)
P= .058-.0094( 187)
P= .897

1.0000(0)
P= .

-.3038( 187)
P= .000.3358( 187)
P= .000-.0695( 187)
P= .342SALRATIO.2813( 187)
P= .000.0059( 187)
P= .936-.3038( 187)
P= .000

1.0000
(
0)
P= .

.0166
( 187)
P= .821.0510( 187)
P= .485

TYPE2

.1039
( 187)
P= .155-.0474( 187)
P= .518.3358( 187)
P= .000.0166( 187)
P= .8211.0000(0)
P= .

-.0669( 187)
P= .361.0986-.0186-.0695( 187)( 187)( 187)
P= .177
P= .800
P= .342(Coefficient/(D.F.)/Two-tailed Significance).0510( 187)
P= .485-.0669( 187)
P= .361

1.0000
(
0)
P= .

TYPE3

“.” is printed if a coefficient cannot be computed

Table 12Controlling for.. TYPE2 TYPE3
INCRATIO POPRATIO POVRATIO SALRATIOINCRATIO

1.0000(0)
P= .

-.0407( 185)
P= .580-.1805( 185)
P= .013.2776( 185)
P= .000

POPRATIO-.0407( 185)
P= .580

1.0000
(
0)
P= .

.0058
( 185)
P= .937.0078( 185)
P= .915POVRATIO-.1805( 185)
P= .013.0058( 185)
P= .937

1.0000(0)
P= .

-.3267( 185)
P= .000

.2776
.0078
-.3267( 185)( 185)( 185)
P= .000
P= .915
P= .000(Coefficient/(D.F.)/Two-tailed Significance)

1.0000(0)
P= .

SALRATIO“.” is printed if a coefficient cannot be computed

CITY-COUNTY SEPARATION AND CONSOLIDATION IN THE UNITED STATES

193

Table 11 represents the correlations among the ratio variables and dummy
variables for independent cities (TYPE2) and consolidated cities. Onceagain, the correlation coefficients among these variables and the city typevariables are generally low. Hence, the difference between Table 12, which
controls the effect of independent and consolidated cities, and Table 11 appears to be negligible. The strongest correlation between these variables and
TYPE 2 is the poverty rate ratio, at .3358. As we have seen above, this positive correlation between independent cities and the higher city/county poverty rate ratio appears to be reflected in the mean difference of this variable.
Both TYPE 2 and TYPE 3 have weak positive correlations with the incomechange ratio. The means of the income change ratio of both types of cities arearound one, which implies that there is no difference in the degree of incomechange between cities and counties. On the other hand, the mean for theother types of cities is .9509, which shows that the income change rate in cities is slightly slower than that in counties. We might be able to state that citycounty separation and consolidation slightly improves the speed of the income change of cities as compared to that of counties.

Table 13Model Summarya,bVariables
ModelEnteredRemoved1

TYPE2c,d.

R

R SquareAdjusted
R Square

.334

.111

.107

Std. Errorof theEstimate.7736

a.

Dependent Variable: POVRATIO
Method: Enterc.
Independent Variables: (Constant), TYPE2
d.
All requested variables entered.
b.

Coefficientsa
Unstandardized
Coefficients
B

Model
1

a.

Std. Error(Constant)

1.342

057

TYPE2

1.235

.251

Dependent Variable: POVRATIOStandardized
Coefficients
Beta
.334

t

Sig.

23.603

.000

4.916

.000

194

MASAHARU YASUOKA

Table 13 presents a regression using the city/county poverty rate ratio as its
dependent variable and the dummy for TYPE 2 (independent cities) as anindependent variable. This shows that independent cities have a positive effect on the city/county poverty rate ratio. It indicates that independent cities
have a positive impact that adds 1.235% to the base city/county poverty rate
ratio level of 1.342%. As a result, the city/county poverty rate ratio of independent cities is almost double that of the average cities, as discussed before.
In this section, we examined the effect of city-county separation, using
city/county ratio data. However, except for the poverty rate, the difference
among the three types of cities appears to be negligible. With regard to theclearly higher percentage of the poverty population of independent cities ascompared to their neighboring counties, this finding appears to again lead toour observation that independent cities are not succeeding in improving thesocioeconomic status of residents.
CONCLUSIONIn drawing conclusions from these statistical analyses, we can observe that
there is no evidence strong enough to support the hypothesis that city-county
consolidation has a positive effect on economic development. At the sametime, there is little proof that the practice of city-county separation has anegative effect on the economic performances of cities. The types of cities
appear to be weak indicators of socioeconomic variables. However, the socioeconomic characteristics of the residents of independent cities describedabove may lead to fiscal problems in the following manner. Due to the higher
percentages of poverty populations and unemployment rates, cities have tocollect more taxes per capita than counties, in order to support their financial
burden. However, they receive less overall state aid per capita than counties,
mainly because, unlike the counties whose roads are constructed and maintained by the state, they have to construct and maintain their own roads.14
Therefore, as compared to counties, cities are more likely to experiencehigher levels of fiscal stress relative to counties.15 They cannot utilize the resources of their neighboring counties, which generally have a smaller dependent population. Further, independent cities find it difficult to grow territorially because any annexation attempts by cities could be opposed by theirneighboring counties for the reasons mentioned above.16 The lower population growth of independent cities in relation to that of dependent cities, which
was identified in this research, appears to support this hypothesis.

CITY-COUNTY SEPARATION AND CONSOLIDATION IN THE UNITED STATES

195

On the other hand, city-county consolidation does not guarantee that theeconomic situations of consolidated cities will improve. While consolidated
cities show the highest mean of income change and the lowest mean of unemployment rate, they represent the highest mean of poverty population and thelowest mean of retail sales change and owner occupancy rate. In this way,consolidations seem to have produced mixed results in terms of economic
impact. According to the case study of Athens-Clarke County, Georgia, eventhe organizational and personnel efficiencies that are often claimed as advantages of a consolidated government appear to be derived from a political climate favoring spending cuts rather than from efficiencies achieved by merging the two governments.17
This research was intended as an investigation of the impact of the practiceof city-county separation on urban policy outputs. In order to explore thissubject further, we must pay attention to the differences between central cities and suburbs for the variables used in this research18. As shown in this paper, the types of cities by themselves do not satisfactorily explain the variances of socioeconomic indexes among cities. Policies are not formulated asa result of city-county separation but by city governments. Therefore, future
research should explore how to integrate the types of cities as an intervening
variable into a series of analytical models. This article can serve as the necessary first step toward building a framework to assess the impact of institutional differences (city-county separation/consolidation) on urban policy
outputs.
NOTES
1

U.S. Census Bureau, 2002 Census of Governments, 6.
Ann O’M Bowman and Richard C. Kearney, State and Local Government, 5th ed. (Boston: Houghton Mifflin, 2004), 343.
3
National League of Cities, “About Cities: City-County Consolidations,” National Leagueof Cities website. http://www.nlc.org/about_cities/cities_101/166.aspx (accessed on September 1, 2007).
4
Kee Ok Park, “State-Local Relations in Virginia: Is It Time for a Change?”(91st annual
meeting of the American Political Science Association, Chicago, September 1995), 1.
5
Jack D. Edwards, Neighbors and Sometimes Friends: Municipal Annexation in Modern
Virginia (Charlottesville, Va: Center for Public Service, University of Virginia, 1992), 29−31.
6Park, “State-Local Relations,” 5.
7
Stanislaw J. Makielski, City-County Consolidation: A Guide for Virginians (Charlottesville, Va: Urban Research Division, Institute of Government, University of Virginia, 1971),
22−29.
8
Population net change (percent) 1980−1992; median household income change (percent)
1979−1989; percentage of families whose income is below the poverty level (1989); retailtrade sales change (percent) 1982−1987; crime rate per 100,000 resident population estimated2

196

MASAHARU YASUOKA

for 1991 by the Federal Bureau of Investigation; civilian unemployed as a percentages of thetotal civilian labor force (1991); percentage of owner occupied in total occupied housing units(1990).
9The eight cities in Virginia are Alexandria, Chesapeake, Hampton, Newport News, Norfolk, Portsmouth, Richmond, and Virginia Beach. The fourteen cities that have consolidated
city-county governments are San Francisco, Calif., Denver, Colo., Honolulu, Hawaii, Nashville, Tenn., Jacksonville, Fla., Columbus, Ga., Indianapolis, Ind., Lexington-Fayette, Ky.,
Baton Rouge, La., New Orleans, La., Boston, Mass., New York, N.Y., Philadelphia, Pa. Six
cities which have no county government are Bridgeport, Hartford, New Haven, Stamford,
Waterbury in Connecticut and Providence in Rhode Island.
10
See David Rusk, Cities without Suburbs (Washington, D.C.: Woodrow Wilson Center
Press, 1993) and Edwards, Neighbors and Sometimes Friends.
11
Makielski, City-County Consolidation, 29.
12County population net change (percent) 1980−1992; median household income change(percent) 1979−1989; percentages of families whose income is below the poverty level (1989);
retail trade sales change 1982−1987.
13
See William H. Lucy, Planning Strategically as If People Matter: A Plan for Charlottesville (Charlottesville, Va: University of Virginia, 1996), Unpublished, Chapter IV.
14
Park, “State-Local Relations”11; Joint Legislative Audit and Review Commission, Virginia General Assembly, Intergovernmental Mandates and Financial Aid to Local Governments (Richmond, VA: Commonwealth of Virginia, 1992), 69−104.
15
ibid., 30.
16
Park, “State-Local Relations,”11.
17
Stephen E. Condrey, “Organizational and Personnel Impacts on Local Government Consolidation: Athens-Clarke County, Georgia,” Journal of Urban Affairs, Vol.16 (1994):
371−83.
18
See William H. Lucy and David L. Phillips, The Post-Suburban Era: Cities, Suburbs, andExurbs 1960 to 1990 and Beyond (Charlottesville, Va: University of Virginia, 1996), Unpublished; William H. Lucy, Confronting Suburban Decline: Strategic Planning for Metropolitan
Renewal (Washington, D.C.: Island Press, 2000); William H. Lucy and David L. Phillips, Tomorrow’s Cities, Tomorrow’s Suburbs (Chicago: Planners Press, American Planning Association, 2006).