Macro-Level Determinants of Local Government Interaction

Abstract

Empirical and theoretical research on government competition and collaboration identifies several important macro-level characteristics that can affect these forms of interaction between local governments within the same large jurisdiction. These characteristics are fragmentation of governments, fiscal dispersion of governments, sorting of population by governments, and decentralized fiscal responsibility between state and local governments. This study presents indices to measure these characteristics and examines how metropolitan regions in the United States with populations greater than one million are distributed on these indices. The study also examines how these regions compare on conditions that are likely to motivate sales tax competition between municipal governments.

Keywords

fragmentation local government metropolitan regions macro-level conditions state–local relations

One predominant and obvious feature of metropolitan regions everywhere is that they consist of numerous local governments that interact with each other in many ways. Compared with rural regions that have fewer local governments with less jurisdictional overlap and contiguity, most local governments in metropolitan regions cannot function without affecting or being affected by the actions and decisions of their neighbors or other local governments in the region. Local governments can interact directly with each other through inter-local agreements and other forms of collaboration. They can interact indirectly by competing with each other for economic resources that generate revenues and by copying or learning from each others’ behavior. Local governments can also directly conflict with each other through lawsuits, threats, and other strategic maneuvers to force outcomes that are contrary to the interests of other governments. Compared with rural regions, these conditions are more likely to exist in metropolitan areas.

We know from the study of collaboration and competition between governments in metropolitan regions that these forms of interactions are likely to vary by their organizational structure and population size and the flow of resources in the region. Some argue that greater fragmentation of local government, in which local governing functions are distributed across many governments, hinders intergovernmental collaboration to solve regional-level problems (Downs 1994; Lowery 2000) and alters the form of collaborative arrangements (Feiock 2009). In conjunction with other macro-level features, such as decentralization of power and authority from higher to lower levels of government, fragmentation of government has been associated with more intensive inter-local government competition (Boyne 1996; Brennan and Buchanan 1980; Campbell 2004) and greater disparity and segregation of populations (Howell-Moroney 2008; Tiebout 1956; Weiher 1991).

In this research note, we construct a set of five indices that measure five metropolitan and/or state-level conditions, which are closely linked to both competition and collaboration among governments at the local level. The macro-level conditions are fragmentation of local governments, dispersion of fiscal responsibility among local governments, state–local decentralization of fiscal responsibility, sorting of population across local governments, and the prevalence of highly mobile and taxable resources. The conditions measured by our first four indices have been extensively documented in research on fiscal federalism, governance, regionalism, and related subjects. Previous studies either estimate or use them as controls for the impact of these conditions on local government interaction and behavior (see Boyne 1992; Hendrick, Jimenez, and Lal 2011 and Yeung 2009 for reviews of this research). The last index that measures the prevalence of mobile and taxable resources has not yet been measured in prior research. However, its connection to competitive behavior, especially with respect to sales taxes, is well-recognized (Rork 2003).

All five indices are calculated for the largest 51 metropolitan regions in the United States (population greater than one million as of 2000) to determine whether any of them have combined characteristics that are particularly conducive or adverse to competitive and collaborative interactions between local governments. The five indices are constructed using data from the U.S. Census of Governments (2007), the decennial U.S. Census, and other sources. Three of the indices are also comprised of other commonly used indices, such as the Hirschman-Herfindahl Index (HHI) and the coefficient of variation, which have often been used in prior empirical research to measure component variables of the macro-level conditions. It is important to recognize that our indices characterize conditions of metropolitan areas or states that affect the behavior of all local governments within each region or state. These indices do not measure the conditions of individual governments.

Although the primary unit of analysis in this study is the metropolitan region, theory tells us that some state-level conditions (e.g., the operational and financial responsibilities and authority of state government relative to local governments) can greatly affect government interaction at the local level. Thus, a comprehensive assessment of macro-level conditions that affect local government interaction will require consideration of state-level attributes and qualities that reflect state–local relationships. Therefore, two of our indices use component variables that represent qualities at the state–level rather than the regional level.

The primary purpose of constructing these indices is to create comprehensive measures of these macro-level conditions that better reflect their attributes and qualities than what is found in most prior research. Most research that incorporates one or more of these conditions measures each of them with one or two component variables only, which does not capture their full range of attributes and qualities. In addition, we know from theory and observation that, although closely related, the attributes and qualities of many of these conditions are distinct, yet research often uses them interchangeably. Most studies also recognize no more than a few of these conditions when assessing the impacts of state and regional-level conditions on local government behavior. Knowing how large metropolitan regions in the United States compare on all conditions will provide important context for future research on local government interaction and relative financial behavior.

Macro-Level Conditions That Affect Interaction Between Governments

Many studies claim that fragmentation of government increases the transaction costs of developing and maintaining intergovernmental agreements (see Feiock 2002, 2009 for reviews of this literature). Other research focuses on examining how fragmentation increases spillover effects between governments and finds that positive spillovers limit the incentives for collaboration and negative spillovers increase conflict between governments (Carruthers and Ulfarsson 2002; Howell-Moroney 2008). Whatever the mechanism, the impact of fragmentation of government on intergovernmental behavior is well-recognized, but fragmentation of government is defined and measured in many ways (Boyne 1992; Hamilton, Miller, and Paytas 2004; Hendrick, Jimenez, and Lal 2011; Yeung 2009).

Generally speaking, the concept of fragmentation refers to regions that have many small, local jurisdictions that are distinct and independent of each other, but it is often conceptualized as having vertical and horizontal dimensions. On the horizontal dimension, it describes regions with widely dispersed populations among all local jurisdictions (e.g., local governments per capita) or jurisdictions at the same level (e.g., percent of population in central city). On the vertical dimension, it characterizes regions with many overlapping local governments (e.g., ratio of special purpose to general purpose governments) (Berry 2008).

Both of these conceptualizations view fragmentation in terms of number of local governments and how populations are organized within metropolitan areas, which is the focus of the first index developed here. Alternatively, fragmentation has been defined as the distribution of fiscal responsibility (spending) and authority (revenue generation) on both the vertical and horizontal dimension (Campbell 2004; Dolan 1990). This conceptualization of fragmentation as the dispersion of fiscal responsibility among local governments in a region is the focus of the second index developed here.

The decentralization of authority and responsibility of local governments with respect to state government is the focus of the third index. In conjunction with local government fragmentation, the impact of state–local decentralization on competition between local governments is well-recognized (Brennan and Buchanan 1980; see Musso 1998, for a review of this theoretical literature). In this case, decentralization of state–local relations increases the capacity of governments to act independently, adapt, and be competitive, but it is also expected to increase the costs of intergovernmental cooperation at the local level (Basolo 2003).

Based on Tiebout’s (1956) arguments that competition between governments will result in allocative efficiency (the extent to which governments supply the level and quality of services preferred by citizens), many studies claim that metropolitan regions that are fragmented and competitive will have populations that are sorted by jurisdiction on socioeconomic characteristics (Dowding, John, and Biggs 1994; Howell-Moroney 2008). Regions in which the populations of jurisdictions are homogeneous internally but heterogeneous relative to each other will reduce the common ground on which governments can forge and sustain agreements, which increases conflict between governments and the costs of collaboration (Amirkhanyan 2009; Feiock et al. 2010; Lubell et al. 2002). Population sorting among census designated places in a region is the fourth index developed here.

In conjunction with fragmentation and state–local decentralization, competition between local governments is also increased by the mobility of capital and taxable resources within a region. Tiebout’s arguments about competition between governments are based on the ability of revenue generating households and businesses to move to new jurisdictions, thereby increasing taxable resources within the jurisdiction (Breton 1995; Feiock 2002; Oates 2005). Competition is generated between governments because they value the fiscal productivity of these mobile resources (Peterson 1981; Schneider 1989).

Compared with property taxes, however, tax base resources that generate sales taxes are more mobile and desirable by local governments because of the ability to export the burden of financing services to non-residents (Hendrick, Wu, and Jacob 2007; Sjoquist, Walker, and Wallace 2005). Thus, competition between local governments for sales tax generating enterprises should be higher in regions where more local governments have greater authority to tax sales transactions within their jurisdiction (Feiock, Steinacker, and Park 2009) and where sales taxes are more important to the revenue of the government. This last macro-level condition is measured using an index of the prevalence of sales taxes among municipalities within a region.

Macro-Level Indices: Metropolitan and State-Based Measures

The indices that represent fragmentation, fiscal dispersion, and population sorting are constructed from component variables that are measured for individual governments and then aggregated at the metropolitan level. The index that represents fiscal decentralization of state–local relations is constructed from component variables that can be measured only at the state level. Thus, the value of this index will be the same for all metropolitan regions in the same state (e.g., Sacramento, San Diego, San Francisco, and Los Angeles), and metropolitan regions that have municipal governments in more than one state will have one index for each state.¹ The index that represents prevalence of sales taxes is constructed from variables that vary predominately by state law that determines local taxation privileges but can be measured at the metropolitan level similar to the first three macro-level conditions. Table 1 shows the component variables that are used to construct each index and a brief description of what the variables represent.

Table 1.

Indices of Macro-Level Conditions Affecting Governmental Interactions.

	Representation
Fragmentation Index (metro level)
1. Total number of local governments	Size of region
2. Total local governments per capita	Political fragmentation
3. Total local governments per square mile	Spatial fragmentation
4. HH Index of percent of different types of local government	Range of local governments
5. Percent of population not in central city	Suburban domination
6. Percent of special purpose to general purpose governments	Special purpose domination
Fiscal Responsibility Dispersion Index (metro level)
7. HH Index of percent spending by each local government of total local spending	Distributed across all local governments
8. HH Index of percent spending across types of local government	Distributed across types of governments
9. HH Index of percent spending by municipalities of total municipal spending	Distributed across municipalities
10. Percent of total local spending that is not by central city	Not concentrated in central city
11. Percent total local spending that is not by counties	Not concentrated in counties
12. Percent special purpose spending of total local spending	Distributed to special purpose
Population Sorting Index (metro level)
13. Index of population heterogeneity, education	Sorting by education
14. Coefficient of variation, percent white population	Sorting by race
15. Coefficient of variation, percent Hispanic population	Sorting by Hispanic ethnicity
16. Index of population heterogeneity, household income	Sorting by household income
17. Coefficient of variation, percent poverty population	Sorting by poverty
Fiscal Decentralization Index (state level)
18. Percent local spending of total state plus local spending	Local share of spending responsibility
19. Percent local revenue of state plus local revenue minus state aid to local government	Local share of revenue responsibility
20. Percent of local revenues coming from state government (R)	State aid to local government
Prevalence of Sales Taxes (metro level, vary by state)
21. Percent of municipalities in region with general sales tax	Pervasiveness of sales taxes across governments
22. Median percent general sales tax of own-source revenue (in municipalities with general sales tax)	Importance of sales taxes in the governments
23. Whether state distributes sales to municipalities based on point of sale

Note. R: Distribution is reversed to be consistent with direction of other variables in the index. HH Index = Hirschman-Herfindahl Index.

Although the population threshold of one million is somewhat arbitrary, this research measures these five indices only for the 51 largest metropolitan regions in the United States. Research on collaboration and competition recognizes that the size of a region can fundamentally affect how governments interact and that it is not often meaningful to compare large and small metropolitan areas on many factors (Miller and Lee 2009). Thus, the analysis is limited to the largest regions to control for size, but all indices could easily be adapted to include smaller regions.

The data used to generate these measures come primarily from the U.S. Census Bureau 2000 and 2010 decennial census and U.S. Census of Governments 2007. The decennial census supplies data on population, square miles, and all socioeconomic features of the population sorting index. Unfortunately, the U.S. Census Bureau ceased to collect socioeconomic data for the entire population as part of the decennial census after 2000, which is why the population sorting index is calculated for 2000 and not 2010.² The Census of Governments data are used to determine the number and types of local governments in each region and their spending. It is also used to determine the total spending, revenue, and state aid of all local governments in each state. One advantage of Census of Governments data is that all financial values are reported for governments in the same way, which make the data comparable across governments. One disadvantage is that some of the characteristics reported by the financial data may not be entirely accurate for particular governments.³

Metropolitan regions are defined here using counties within Metropolitan Statistical Areas (MSA) or within Combined Metropolitan Statistical Areas (CMSA) as designated in 2000 by the U.S. Census Bureau. To ensure that the regions had cohesive population concentrations, counties were removed from the MSA or CMSA if they had populations less than 50,000 and/or are not contiguous with other counties in the region. This means that some of the regions examined here will be more urban and cohesive than what is documented by the U.S. Census Bureau, but these changes are expected to better reflect the attributes and qualities from Table 1 in large regions. These adjustments are described in the online Appendix A.

Both the Census of Governments and the decennial census define metropolitan regions based on county boundaries and also link all other local governments to counties only and not to any other smaller common unit of analysis on which measures of these factors can be based.⁴ This is not always ideal with respect to maintaining cohesive population concentrations when a county is large spatially and its density becomes much lower away from the central city such as in western United States. In addition, some special purpose governments may cross county boundaries, but this problem is minimized because the primary unit of analysis is the metropolitan area and not the county.

Fragmentation Index

The variables used to construct both the fragmentation and fiscal responsibility dispersion indices are calculated based on the work of Boyne (1992) and Hendrick, Jimenez, and Lal (2011) who identify and organize numerous variables that are used to measure these concepts in empirical research. Both authors distinguish between structural fragmentation variables, some of which are combined to create the fragmentation index here, and financial fragmentation variables that are combined to create the fiscal responsibility dispersion index.

As noted by these authors, the most common measure of structural fragmentation is total number of local governments (variable 1 in Table 1), which represents the size of the region. Others have noted, however, that this value should be standardized by population (variable 2) to present a more accurate measure of fragmentation with respect to the organization of population and voters (Dolan 1990; Stansel 2006). In fact, some studies have shown that the correlation between number of local governments in a region and other characteristics, such as total spending by local governments, changes direction when the former is standardized by population. The total number of local governments also can be standardized by land area, which represents the spatial fragmentation of governments in a region (variable 3).

Structural fragmentation can also be measured as the distribution or concentration of different types of local governments in a region (variable 4). This variable and others that are components of the fiscal responsibility dispersion index are based on the Hirschman-Herfindahl Index (HHI) and represent the distribution of government characteristics within a metropolitan region. This index measures the concentration of qualities in a distribution where p_i represents the proportion or share of some quality i among N total items. The following equation shows the HHI calculation for the percent of different types of governments.

HHI a = \sum_{i = 1}^{N} p_{i}^{2} .

There are five different types of local government (N), i represents each type of local government, and “a” is the metropolitan region. All HHI indicators in Table 1 are also reversed by subtracting from 1 to reflect dispersion or fragmentation rather than concentration and then standardized so that values range from 0 to 1. The percentage of the population that is not in the central city (variable 5)⁵ represents the extent to which the population in the region is located in the suburbs, and the percentage of special purpose governments (variable 6) measures the degree to which the region is dominated by overlapping, special purpose governments.

The value of combining these component variables to create one index of fragmentation is apparent from examining how the 51 metropolitan regions compare on these component variables. Specifically, the picture of fragmentation among the regions changes dramatically for each variable. With respect to total number of local governments, Chicago and New York rank the highest, but their rank declines to 11th and 35th, respectively, for the number of local governments per capita. Chicago is ranked 16th and 29th on the HHI index of percent different types of local government and percent population not in the central city. Correlations of percent special purpose governments (vertical fragmentation of local government) and the other measures of fragmentation (horizontal) for all 51 regions range from −.51 to .30, and correlations among all other fragmentation variables range from .02 to .83. Although all these component variables have been linked to fragmentation of local governments in metropolitan regions within prior research (Boyne 1992; Hendrick, Jimenez, and Lal 2011), the change in rankings among governments for these variables and the range of correlations demonstrate that most variables are representing different characteristics of this macro-level condition.

Fiscal Responsibility Dispersion Index

Component variables for this index as shown in Table 1 measure whether service delivery and spending for local services are concentrated within a few governments or dispersed over many governments at the same level or overall within the region. For instance, a metropolitan region in which the central city delivers a large portion of all local services provided in the region is more concentrated than one in which service delivery is dispersed among many local governments. With respect to different levels of local government, a region in which the counties or general purpose governments dominate the provision and production of local services is more concentrated than one in which provision and production are spread out among many local governments or special purpose governments, respectively.

The HHI variables percent spending by each local government (variable 7), percent spending across types of local governments (variable 8), and percent spending by municipalities (variable 9) use the standardized and reversed HHI to measure the dispersion of spending across all local governments, across the five types of governments, and across all municipalities in the region, respectively.⁶ The dispersion of spending across municipalities is examined separately because these local governments are the most common and powerful general purpose governments in metropolitan regions in terms of zoning and development. Central cities are removed from the calculation of percent spending by each local government and municipality because central city spending is so much higher than other local governments in a region and dominates the HHI calculations greatly.⁷ Variables 10 and 11 measure the extent to which local spending in the region is not concentrated in the central city or counties, respectively, and variable 12 measures whether local spending is distributed by special purpose rather than general purpose governments. Percent spending by special purpose governments and governments that are not counties measure dispersion on a vertical dimension, but the other component variables measure this macro-level condition on a horizontal dimension.

Similar to the fragmentation index, many of the components variables used to construct the fiscal responsibility dispersion index have been used by prior research to measure what has been described as fragmentation of spending (or revenues) and even decentralization of spending (or revenues) (Yeung 2009). No combined index has been developed to measure all of the qualities of this macro-level condition. Compared with the fragmentation index, however, the correlations among the component variables for this index are much stronger indicating that these variables may be measuring similar features.

Population Sorting Index

The condition of population sorting is assessed for five different characteristics as reported in the 2000 decennial census. These characteristics are education, race, Hispanic population, household income, and poverty and are similar to those used by Stein (1987) to test Tiebout’s population sorting hypothesis. Stein notes that the distribution of populations on income alone is not sufficient to determine residential sorting of population into jurisdictions within metropolitan areas. As is done here, he creates a composite index of population sorting from his six socioeconomic variables, but his data do not include municipalities with population less than 5,000, which are likely to be more homogeneous than larger municipalities.⁸

Data for 2000 is used to construct this index because that is the last decennial census in which these characteristics were measured for all governments at the same time. It also does not make sense to measure this macro-level condition for more than one level of local government in the same region. Thus, these component variables are measured for all municipalities and census designated places (CDP) only. Unlike municipalities, CDPs are not incorporated, but both jurisdictions represent boundaries within which regional populations are most likely to segregate (Lewis 1996).⁹ Furthermore, variation in state law regarding incorporation results in some states, such as Florida, having a lot of CDPs that would be municipalities in other states, and many CDPs incorporate at a later date.

Because education (variable 13) and household income (variable 16) are reported only at the ordinal level, sorting of population by these characteristics are measured using an index of heterogeneity or dispersion that is usually applied at the block or neighborhood level of metropolitan regions (Pack and Pack 1977). The formula for this population sorting indicator (PSI), which is shown below, uses the HHI calculated across categories of the population characteristic within the places of the entire region.

PSI a = \frac{\sum_{i = 1}^{N} | HHI i - HHI a |}{N} .

In the above equation, i represents place (CDP or municipality), “a” is the metropolitan region, and N is the number of census places. The HHI components are calculated as proportions of a cumulative frequency distribution of each ordinal category.

The other three population characteristics—percent white, percent Hispanic, and percent poverty (variables 14, 15, and 17, respectively)—are measured using the coefficient of variation (α/µ) for all places because of the availability of interval data for these characteristics.¹⁰ Although they are calculated very differently, the component variables that are based on the coefficient of variation have similar means and standard deviations to the variables that are based on the PSI. It is also noteworthy that all these standard deviations are somewhat higher than the standard deviations reported by Stein (1987), indicating that there is greater variation in sorting across the smaller set of metropolitan regions examined here.

Decentralization of State–Local Fiscal Relations Index

This index is used to measure the discretion and responsibility of local governments over the provision and production of state and local services combined relative to state government. There are many ways of measuring this condition across the 50 states (Stephens and Wikstrom 2000; Zimmerman 1995), but these approaches have some common features. They all include an assessment of whether the source of revenue and responsibility for state and local expenditures combined is state or local government. Table 1 shows the three component variables that comprise this index. Variable 18 calculates local spending as a percentage of total local plus state spending and represents the local share of total spending responsibility in each state. Variable 19 represents the local government share of state plus local revenue responsibility and calculates local revenue as a percentage of state plus total local revenue minus the revenue that state governments transfer to local governments. Finally, variable 20 calculates the percentage of local revenue that is transferred from state to local government and represents state aid to local governments.

This index differs from the indices developed by Stephens (1974) and later updated by Stephens and Wikstrom (2000) and Zimmerman (1995) in that it measures only the distribution of revenues and spending between state and local governments and shared revenues. It does not include structural or functional information that is examined by one or more of these other indices including the distribution of personnel between state and local government and shares of specific services that are delivered by each sector. However, the correlation between the index developed here and the centralization index developed by Stephens for 1995 (Stephens and Wikstrom 2000) for all 50 states is −.71 indicating that the two indices do coincide.¹¹

Prevalence of Sales Taxes Index

As an indicator of competitive pressure, the mobility of governments’ financial resources cannot be measured directly but must be inferred from conditions that are likely to correlate with competition for mobile tax capital (Feiock, Steinacker, and Park 2009). With respect to sales taxes levied by local governments, one can look at the percentage of local governments with sales taxes and the extent to which governments rely on them. The more pervasive local sales taxes are among governments within a metropolitan region that levy a sales tax and the more that these governments rely on this source of revenue, the greater the competition between them to attract sales tax generating enterprises and transactions.

The importance of sales taxes to the revenue structure of local governments depends ultimately on whether state law allows local governments to levy such a tax, and state laws often grants different sales tax privileges to local governments. Continuing with the focus on municipalities that has been used with other component variables, this index is constructed from two variables that are measured for municipalities only. These variables are percent of the municipalities in a region with a general sales tax (variable 21) and median percent sales tax of own-source revenue (variable 22). They are not measured for other local governments because competition is most likely to occur between governments of the same type, and relative to counties, municipalities are smaller and most likely to compete for enterprises that generate sales tax.

With respect to the prevalence of sales taxes and competition for sales taxes, it is also important to determine whether the state distributes some portion of the state sales taxes to municipal governments based on the point of sale. Unfortunately, U.S. Census Bureau data do not reveal this information. If the state distributes sales taxes to local governments in this manner, then the competitive effects are similar to having the privilege of levying a local sales tax and will enhance the effects of sales tax competition (Lewis and Barbour 1999). Whether or not state government distributes state sales taxes to municipal governments (variable 23) is reported for states based on a survey conducted by the Committee on Tax and Finance for the Florida Senate in 2006.¹² This information is reported here separately rather than being incorporated into the index of the prevalence of sales taxes.

Comparison of Metropolitan Areas on Indices

Online Appendix B shows the calculations for all variables in Table 1 that are components of the macro-level indices, except the variables that comprise the prevalence of sales taxes that are reported in Table 3, for all 51 metropolitan regions. With the exception of the percent of municipalities with general sales taxes (variable 21) and median percent of general sales taxes (variable 22), the Z scores for all variables are calculated to normalize them before summing component variables to create a composite index for each macro-level condition. With respect to the prevalence of sales taxes, its two component variables are simply multiplied together and divided by 100 to produce this index.

Our assumption for all indices is that the component variables are additive and equally weighted, which may not be correct, but that is the procedure used most commonly by the indices cited here (e.g., Stein 1987; Stephens 1974). There is also no basis for assuming that the component variables have a more complex relationship with each other or that some of the components should be weighted more than others, so the simplest conceptualization and functional form is used to calculate these indices. In addition, the indices do not represent true latent variables in which the unobserved concepts (macro-level conditions) determine the observed, component variables. Rather, concepts such as fragmentation and fiscal dispersion are determined by theoretical definitions about what constitutes a fragmented or fiscally dispersed regional system (Boyne 1992). Thus, internal consistency and high correlations between component measures of an index may not be relevant criteria for judging these indices and their components.¹³ However, Cronbach’s alpha tests of the consistency of component variables within the composite index were conducted for fragmentation, fiscal dispersion, population sorting, and fiscal decentralization. These tests reported alpha values of .56, .82, .62, and .83, respectively, which show the composite variables for fragmentation and population sorting to be less reliable than the composite variables for fiscal dispersion and decentralization.

Table 2 shows the composite indices calculated for fragmentation, fiscal dispersion, and population sorting for all 51 metropolitan regions. The table shows that, for the most part, fragmented regions tend to be older, industrial regions in the Midwest and eastern portions of the United States. By comparison, the distributions of fiscal dispersion of metropolitan regions and population sorting do not seem concentrated in particular regions of the United States.

Table 2.

Sum of Z-Scores of Indicators of Fragmentation, Fiscal Dispersion, and Population Sorting.

Fragmentation of local government		Fiscal dispersion of local government^a		Population sorting of census places
1. Chicago	8.49	Salt Lake City	4.74	St. Louis	7.62
2. Pittsburgh	7.47	St. Louis	4.48	Washington, D.C.	5.70
3. St. Louis	6.16	Chicago	4.35	New York	4.38
4. New York	5.28	Pittsburgh	4.13	Phoenix	3.86
5. Philadelphia	4.73	Atlanta	4.11	Birmingham	3.41
6. Denver	3.84	Greenville	3.81	Memphis	3.33
7. Boston	3.28	Portland	3.56	Chicago	3.11
8. Cincinnati	3.11	Dallas	3.44	Denver	3.07
9. San Francisco	2.66	Seattle	3.32	Louisville	3.02
10. Hartford	2.48	Denver	3.31	San Francisco	2.88
11. Cleveland	2.39	Phoenix	3.30	Oklahoma City	2.45
12. Indianapolis	2.00	Detroit	3.27	Miami	2.42
13. Rochester	2.00	Orlando	3.22	Columbus, OH	2.05
14. Kansas City	1.66	Los Angeles	3.16	Atlanta	1.95
15. Grand Rapids	1.64	Cincinnati	3.11	Houston	1.43
16. Houston	1.58	Birmingham	2.98	Cincinnati	1.20
17. Milwaukee	1.52	Cleveland	2.96	Cleveland	1.19
18. Los Angeles	1.32	Houston	2.94	Philadelphia	0.93
19. Detroit	1.26	Sacramento	2.94	Boston	0.85
20. Seattle	0.95	Columbus, OH	2.85	Dallas	0.83
21. Greenville	0.84	Grand Rapids	2.78	Austin	0.81
22. Sacramento	0.82	Minneapolis	2.71	Las Vegas	0.79
23. Minneapolis	0.74	San Francisco	2.69	Indianapolis	0.64
24. Buffalo	0.58	San Diego	2.65	Kansas City	0.29
25. Louisville	0.49	Miami	2.38	Tampa	0.24
26. Salt Lake City	0.09	Boston	2.35	Hartford	0.20
27. Atlanta	0.03	Milwaukee	2.27	Detroit	0.03
28. Portland	−0.32	Austin	1.39	Charlotte	0.03
29. Columbus, Ohio	−0.51	Philadelphia	1.36	Salt Lake City	−0.05
30. Dallas	−0.61	Kansas City	1.24	Orlando	−0.62
31. Birmingham	−0.74	Louisville	1.21	Los Angeles	−0.80
32. Miami	−0.74	Tampa	0.97	Seattle	−0.94
33. Tampa	−0.82	Hartford	0.67	Pittsburgh	−1.03
34. Orlando	−1.18	Indianapolis	0.41	Greenville	−1.05
35. Oklahoma City	−1.27	Rochester	−0.49	San Antonio	−1.08
36. Phoenix	−1.28	San Antonio	−1.00	Raleigh	−1.21
37. Austin	−1.55	Buffalo	−1.09	Richmond	−1.25
38. San Diego	−1.83	Oklahoma City	−1.26	Nashville	−1.69
39. Richmond	−2.66	New Orleans	−1.34	Sacramento	−2.10
40. New Orleans	−3.08	Charlotte	−2.85	Minneapolis	−2.45
41. San Antonio	−3.49	Las Vegas	−4.68	Milwaukee	−2.50
42. Nashville	−3.55	Jacksonville	−5.30	Baltimore	−2.58
43. Las Vegas	−4.01	Raleigh	−5.55	New Orleans	−2.68
44. Baltimore	−4.04	New York	−5.95	Grand Rapids	−2.82
45. Norfolk	−4.42	Norfolk	−6.83	Greensboro	−3.31
46. Memphis	−4.59	Washington, D.C.	−8.10	Portland	−3.55
47. Jacksonville	−4.88	Nashville	−8.58	San Diego	−3.59
48. Washington, D.C.	−4.93	Baltimore	−10.11	Rochester	−4.49
49. Raleigh	−5.03	Richmond	−10.23	Jacksonville	−5.61
50. Greensboro	−5.83	Memphis	−10.30	Norfolk	−5.87
51. Charlotte	−6.06	Greensboro	−11.39	Buffalo	−7.42

Note. HH Index = Hirschman-Herfindahl Index.

Central cities are removed from these calculations because their size overwhelms the HH indices in the component indicators.

Examining each column of numbers in the table shows that Chicago, Denver, and St. Louis are ranked very high on all three macro-level conditions. Of these regions, Chicago is much larger than the other two in terms of total population and number of governments.¹⁴ The fragmentation of local governments in St. Louis and Pittsburgh and its effect on the organization of service production and metropolitan governance in these regions has been studied in depth (Advisory Commission on Intergovernmental Research, 1988, 1993; Parks and Oakerson 1993). Table 2 shows, however, that Pittsburgh’s population is not highly sorted compared with the other three regions. The table also shows that New York and San Francisco metropolitan regions are highly fragmented and their populations are highly sorted, but the fiscal responsibilities of local governments in these regions are not highly dispersed.

Comparing the pattern of regions at the high end of the three distributions in Table 2 with the pattern at the low end of these distributions, we show that there are more metropolitan regions that are consistently low than high on all distributions. Specifically, there are seven regions in the 30th percentile of fragmentation, fiscal dispersion, and population sorting. These regions are Richmond, New Orleans, Nashville, Baltimore, Norfolk, Jacksonville, and Greensboro. There are also seven regions in the 30th percentile of at least two distributions in Table 2. These regions are San Diego, Las Vegas, Memphis, Washington, D.C., Raleigh, Charlotte, and Buffalo. Notice that the majority of these 14 regions are in the South Atlantic division and the South region more broadly as classified by the U.S. Census Bureau.

Table 3 shows the composite index values for fiscal decentralization of state–local relations and the prevalence of sales taxes among municipal governments in all 51 metropolitan regions. It also shows how state sales taxes are distributed to local governments in these regions. All of these variables vary predominately by state government, which means that the values will be the same or very similar for regions in the same state. In all, 12 of the regions in this table are located in multiple states as documented in the left-hand column that identifies the metropolitan region. The next column in the table shows the number of municipalities that are located in the different states of those regions. The values of the fiscal decentralization index and information about state sales taxes distribution in the right-hand column also are shown for each state in regions that exist in multiple states.

Table 3.

State-Based Macro-Level Indices, 2007.

			Prevalence of sales taxes: median % sales tax^a % municipalities with sales tax
	Number of municipalities	Sum of Z-Scores of fiscal decentralization	Index	Rank	Distribution of state sales tax and basis^b
1. Denver, CO	73	4.40	29.0	5	NS
2. Nashville, TN	47	4.28	8.8	19	Population
3. Memphis, TN	42	4.28	10.2	17	Population
4. Tampa, FL	35	4.10	4.5	22	Population
5. Jacksonville, FL	15	4.10	3.4	25	Population
6. Orlando, FL	36	4.10	1.7	27	Population
7. Miami, FL	103	4.10	0.8	31	Population
8. Atlanta, GA	113	3.46	0.0	40	NS^a
9. Houston, TX	116	3.38	15.2	11	NS
10. Dallas, TX	186	3.38	13.9	12	NS
11. San Antonio, TX	49	3.38	13.0	15	NS
12. Austin, TX	48	3.38	12.7	16	NS
13. Las Vegas, NV	5	2.78	3.9	23	NS
14. Chicago, IL/IN/WI	271/30/5	2.68/1.14/−.53	5.8	20	POS^a/CPOS^a/NS^a
15. St. Louis, MO/IL	121/60	2.03/2.68	19.7	7	NS/POS^a
16. Kansas City, MO/KS	57/25	2.03/1.17	19.1	8	NS/NS
17. Seattle, WA	89	1.92	16.0	10	NS
18. Rochester, NY	42	1.69	1.1	30	NS
19. Buffalo, NY	26	1.69	0.0	41	NS^a
20. New York, NY/NJ	188/147	1.69/.92	0.3	33	NS^a/NS^a
21. Indianapolis, IN	81	1.14	0.0	42	CPOS^a
22. Los Angeles, CA	180	1.02	13.3	13	POS^a
23. Sacramento, CA	19	1.02	16.1	9	POS^a
24. San Diego, CA	18	1.02	13.3	14	POS^a
25. San Francisco, CA	87	1.02	9.9	18	POS^a
26. Baltimore, MD	14	1.02	0.0	43	NS^a
27. Washington, D.C., MD/VA	49/19	1.02/.58	1.3	29	NS^a/POS^a
28. Phoenix, AZ	33	0.82	44.2	1	population
29. Portland, OR/WA	33/7	0.79/1.92	2.6	26	NSST^a/NS
30. Birmingham, AL	88	0.63	35.8	2	NS
31. Norfolk, VA	14	0.58	4.8	21	POS^a
32. Richmond, VA	14	0.58	3.6	24	POS^a
33. Salt Lake City, UT	118	0.36	20.6	6	POS^a
34. Raleigh, NC	35	0.32	0.0	39	NS^a
35. Greensboro, NC	43	0.32	0.0	44	NS^a
36. Charlotte, NC/SC	39/9	0.32/.03	0.0	38	NS^a/NS^a
37. Cleveland, OH	136	0.28	0.2	34	^{a c}
38. Columbus, OH	79	0.28	0.1	36	^{a c}
39. Cincinnati, OH/KY	122/36	0.28/−2.18	1.7	28	^{a c}/NS^a
40. Hartford, CT/NY	23	0.16/1.69	0.0	45	NS^a/NS^a
41. Philadelphia, PA/NJ	91/58	0.04/.92	0.0	37	NS^a/NS^a
42. Pittsburgh, PA/NJ	255	0.04/.92	0.0	46	NS^a/NS^a
43. Greenville, SC	40	0.03	0.6	32	NS^a
44. Milwaukee, WI	70	−0.53	0.0	47	NS^a
45. Boston, MA/NH/RI	35/1/8	−0.59/0.3/−0.97	0.0	48	NS^a/NSST^a/NS^a
46. New Orleans, LA	15	−0.80	32.4	4	NS
47. Detroit, MI	108	−0.89	0.0	49	^{a d}
48. Grand Rapids, MI	50	−0.89	0.0	50	^{a d}
49. Oklahoma City, OK	77	−1.18	33.5	3	NS
50. Minneapolis, MN	160	−1.20	0.1	35	NS^a
51. Louisville, KY	148	−2.18	0.0	51	NS^a

Note. POS = point of sale; NS = no share to municipalities; NSST = no state sales tax. a. State does not allow municipalities to levy sales taxes, or state limits the privilege to some governments.

Classification of distribution as of 2005 is based on a survey conducted by Committee on Tax and Finance, FL Senate (2006).

Share multiple taxes combined based on need and other factors.

Share based on population and need.

Table 3 shows that metropolitan regions in Colorado (Denver), Tennessee, Florida, Georgia (Atlanta), and Texas are the most fiscally decentralized with respect to state–local relations but that Chicago and St. Louis are also relatively decentralized. The rankings of the index for the prevalence of sales taxes show that these values are highest for specific regions and states in the southern and western portions of the United States. The prevalence of sales taxes is highest in Phoenix, Birmingham, Oklahoma City, New Orleans, Denver, and Salt Lake City, but St. Louis and Kansas City are ranked seventh and eighth on this index. Table 3 also shows that sales taxes are relatively prevalent in metropolitan regions in California, Tennessee, and Texas. A total of 15 regions have index values of zero, which means that none of the municipalities in these regions can levy a general sales tax.

Whether state sales taxes are distributed based on the point of sale must also be factored into considerations of sales tax competition and evaluated in conjunction with the prevalence of sales taxes. Of the states that have metropolitan regions ranked in the top eight of the prevalence of sales taxes index, only Illinois (St. Louis) and Utah (Salt Lake City) distribute their state sales taxes based on point of sale. Thus, these regions and Chicago are more competitive with respect to increasing sales receipts than what is indicated by the sales tax index. California also distributes their state sales taxes based on point of sale, which increases sales tax competition among municipal governments in the four metropolitan regions in this state relative to other regions.

Applying all five indices to all the regions examined here reveal only three regions in which local governments are highly fragmented, fiscally dispersed, sorted by population, fiscally decentralized relative to state government, and competitive with respect to sales taxes. These regions are Chicago, Denver, and St. Louis.¹⁵ There are five regions on the opposite end of these indices that might be described as having relatively low fragmentation, fiscal dispersion, population sorting, fiscal decentralization, and competition. These regions are Richmond, Baltimore, Norfolk, Raleigh, Greensboro, and Charlotte.

Finally, it is important to recognize that these macro-level conditions are not likely to change dramatically over a short period of time because they represent the combined attributes and qualities of many units of government within a larger system rather than the attributes and qualities of individual governments. Comparing the all indices calculated with U.S. Census of Governments data for 2007 and 2002 shows little change in the rankings of metropolitan regions on fragmentation, fiscal responsibility dispersion, and fiscal decentralization.

Discussion and Conclusion

This research note has presented indices to measure five macro-level conditions at the metropolitan and state level that have been identified in many areas of research as being important to the behavior of local governments, especially their interactions (e.g., collaboration and competition) and financial policies (e.g., tax rates and spending). The three macro-level conditions of fragmentation, fiscal responsibility dispersion, and fiscal decentralization and most of the component variables incorporated in the indices developed to measure these conditions have been documented or used in prior research according to meta-analyses conducted by others (Boyne 1992; Hendrick, Jimenez, and Lal 2011; Yeung 2009). These studies show that there is weak agreement on how these complex conditions should be conceptualized and measured. With the exception of fiscal decentralization, these conditions are often measured very simply, and there is little exploration of the performance of composite measures of these conditions.

This research expands the discussion of the three macro-level conditions in these meta-analyses to the conditions of population sorting and prevalence of highly mobile, taxable resources. This research hopes to further agreement on how to conceptualize all five of these conditions, and it documents the distribution of state sales tax to local governments and the basis of taxation for all 50 states, which no prior studies have done. This research also conducts an initial investigation into how the conditions can be measured to identify which large metropolitan regions in the United States are likely to be more competitive or cooperative. These measures indicate that local governments in the Chicago, Denver, and St. Louis regions have very different macro-level pressures that may enhance competition or hinder collaboration compared with local governments in the Richmond, Baltimore, Norfolk, Raleigh, Greensboro, and Charlotte regions.

Collaboration among local governments is being promoted heavily by professional associations, such as National League of Cities, Government Finance Officers Association, and the International City/County Managers Association, as a way to increase the efficiency of local service delivery in this time of fiscal stress and address collective regional problems caused by sprawl and local disparities. Consistent with predictions about the eight regions at the high and low end of the five macro-level conditions, future research might focus on how these conditions affect particular types of collaborative or cooperative arrangements. Such research could build on previous work on the “integrating structures” of regional governance and local service delivery in St. Louis (Parks and Oakerson 1993) and the work of Miller and Lee (2009) who conduct a comparative study of Boston, Minneapolis, Pittsburgh, and St. Louis using surveys. Future research should also realize that competition and collaboration are different but related phenomena that can exist simultaneously rather than two ends of the same continuum.

Although the component variables for each index could be used separately in regression analyses that explain local government behavior in many areas, this is not very efficient and may not be an accurate way of measuring these compound concepts. Precisely because of their complexity, however, the indices developed here will likely require further specification of the macro-level conditions being represented. For instance, prior research indicates that the horizontal and vertical dimensions of fragmentation may have different effects on the financial behavior of local governments relative to each other (Berry 2008; Campbell 2004). In this case, future research should explore whether these two dimensions should be measured separately. Future research could also examine whether any of the component variables of each index matter more to the interaction of governments and, therefore, should be weighted more than the other variables in the index.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Rebecca Hendrick is a professor in the Department of Public Administration at the University of Illinois at Chicago. Her current research focuses on financial management of local governments and fiscal interactions (competition and collaboration) between local governments at a regional level, particularly in the Chicago metropolitan region. Her work appears in public administration, political science, and urban affairs journals.

Yu Shi is a Ph.D. candidate in the Department of Public Administration at the University of Illinois at Chicago. Her research focuses on intergovernmental relations, collective actions, and public debt management. She is currently working on her dissertation, which explores the impact of fiscal federalism on government performance in the United States.

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