Separate, Unequal, and Ignored? Interjurisdictional Competition and the Budgetary Choices of Poor and Affluent Municipalities

Benedict S. Jimenez is assistant

professor at Northeastern University in

Boston. His research focuses on urban pub-

lic fi nance and management. He received

the 2013 Paul A. Volcker Junior Scholar

Research Award from the American Political

Science Association for his research on

municipal fi scal retrenchment and recovery.

His studies on the roles of citizens and

strategic management in city fi scal retrench-

ment have been published in the Journal

of Public Administration Research

and Theory and American Review of

Public Administration.

E-mail: [email protected]

246 Public Administration Review • March | April 2014

Public Administration Review,

Vol. 74, Iss. 2, pp. 246–257. © 2014 by

The American Society for Public Administration.

DOI: 10.1111/puar.12186.

Benedict S. JimenezNortheastern University

Th e fundamental value underlying the design of a fragmented system of local governance is consumer sovereignty. Th is system functions as a market-like arrangement providing citizen-consumers a choice of jurisdictions that off er diff erent bundles of public services and taxes. However, the same choice also can facilitate class-based population sorting, creating regions where fi s-cally wealthy jurisdictions coexist with impoverished ones. Some argue that the public market enhances the power of all consumers, whether poor or rich. Even if the poor are concentrated in some jurisdictions, they can exercise their voice to ensure that their government responds to their service needs. But does the voice of the poor matter as much as the voice of the rich in determining service levels in the local public market? Comparing the budgetary choices in poor and affl uent municipalities, this article shows that in highly fragmented regions, some municipal services are provided the least in communities where they are needed the most.

No other country in the world—not even those with bigger populations—has as many fi scally and administratively autonomous

local governments as the United States. According to the Census of Governments, there were 89,004 local governments in the country in 2012. Th ese govern-ments—counties, municipalities, townships, special authorities, and school districts—play an important role in promoting the quality of life of citizens by pro-viding critical public services such as infrastructure, education, health, and public welfare, among many.

Th e literature on local public fi nance suggests two contrasting outcomes of this highly fragmented system of local governance.1 On the one hand, the local public market model suggests that a fragmented system approximates a market-like arrangement that provides citizens a choice of jurisdictions that satisfy their specifi c tax and service preferences (Tiebout 1956). Th e ability of citizens to compare and choose among numerous local government-producers spurs competition among jurisdictions to attract fi scally desirable residents, forcing those governments to

produce public services at the lowest possible cost in order to reduce local tax burdens (Bestley and Case 1995; Brennan and Buchanan 1980; Schneider 1989). Competition generated by fragmentation, in this strand of the literature, is a positive-sum game in which groups of citizens benefi t without making someone else worse off .

On the other hand, the choice available in the local public market can lead to the sorting of population by economic class, creating spatially segregated commu-nities with diff ering fi scal wealth and unequal capacity to deliver local public services (Hill 1974; Lowery 2000; Schneider and Logan 1981, 1982). Some communities are able to control taxable resources in a region (DeHoog, Lowery, and Lyons 1991), enabling their rich residents to enjoy higher levels of services relative to the tax costs (Schneider 1987). In other communities, limited local fi scal wealth can lead to the suboptimal provision of public services in the absence of aid from higher-level governments or access to debt markets (Schneider and Logan 1981). In this literature, competition is a zero-sum game that creates winners and losers among suburban communities.

Not all agree with the conclusion that only some communities—the affl uent ones—benefi t from frag-mented local governance. Even if the poor are highly concentrated in some jurisdictions, the argument goes, they can still exercise their voice to ensure that their local government spends more for services that they need the most (see Ostrom 1983). Th e mar-ket, whether public or private, enhances consumer sovereignty. Th e poor and the rich may be spatially separated, and the fi scal wealth of their communi-ties may be unequal, but the public market aff ords residents a voice in local policy making, limiting the ability of local offi cials to act independently of the wishes of citizen-consumers. But does the voice of the poor matter as much as the voice of the rich in deter-mining the level of services in the local public market? Or are the poor not only separate and unequal but also ignored?

Separate, Unequal, and Ignored? Interjurisdictional Competition and the Budgetary Choices of Poor and Affl uent Municipalities

Separate, Unequal, and Ignored? Interjurisdictional Competition and the Budgetary Choices of Poor and Affl uent Municipalities 247

in fewer jurisdictions in a region leads to higher expenditures (for comprehensive reviews of the literature, see Hendrick, Jimenez, and Lal 2011; Jimenez and Hendrick 2010).3

Th e conclusion that fragmentation improves local fi scal discipline as evidenced by smaller budgets is not without controversy. Some argue that spending is a very rough and imperfect measure of pro-ductive effi ciency of local governments (see Ostrom 1983). Others point out that lower spending in fragmented systems only refl ects the underprovision of welfare programs by governments that fear attracting the poor (Dowding, John, and Biggs 1994).

Government Fragmentation, Spatial Segregation, and Fiscal InequalityA contending set of arguments points out that government frag-mentation can produce negative consequences for some groups of citizens. Representative of this view is the social stratifi cation and government inequality thesis (or SSGI) developed by Hill (1974) and Neiman (1976), which suggests that population sorting in highly fragmented regions creates communities that have limited fi scal wealth to support the service needs of their residents.

Th e social stratifi cation part of the thesis suggests that residential choice is not only based on citizens’ service preferences but also fueled by “lifestyle” considerations, specifi cally, the desire to live in socially homogenous communities (Weiher 1991).4 Th ere is stronger evidence in the literature pointing to the spatial separation of races in more fragmented regions, with some studies demonstrating seg-regation by income or other sociodemographic characteristics (see Bischoff 2008; Burns 1994; Eberts and Gronberg 1981; Heikkila 1996; Lewis and Hamilton 2011; Miller 1981; Morgan and Mareschal 1999; Stein 1987; Weiher 1991).5 Research has shown that some municipal governments employ exclusionary zoning, prohibit multifamily housing, and reject public housing projects to preserve the racial and social homogeneity of municipal enclaves (Massey and Denton 1993). To recruit new residents with desir-able socioeconomic backgrounds, Weiher (1991) argued, enclaves provide cues to prospective home buyers about the social character of their communities.

Th e government inequality part of the SSGI thesis suggests that spa-tial economic segregation leads to the mismatch of service needs and fi scal capacity in municipalities. Hill argued that “municipal seg-regation of class and status groups tends to divorce fi scal resources from public needs in the metropolis” (1974, 1557). Th e poor and minorities are isolated in jurisdictions with limited fi scal wealth, while rich white households escape to their municipal enclaves.

Earlier studies testing the fi scal inequality argument compared cen-tral cities with suburban municipalities and generally found that the sorting out process leaves central cities with a disproportionate share of needy populations (Downs 1994), without adequate improve-ments in their revenue-raising capacities (Ladd and Yinger 1991). Still, there was some debate in the early empirical literature on the severity of municipal fi scal disparity. Hill (1974) found that the number of cities per capita had a signifi cant direct relationship with intermunicipal income inequality. However, this fi nding is likely to be a statistical artifact because Hill’s measure of interjurisdictional income variation naturally increases as the number of jurisdictions

To answer this question, this article examines the budgetary policies of a subset of local governments in the United States: municipal governments. It explores how interjurisdictional competition is related to expenditures for diff erent types of services in municipali-ties with high concentrations of poor and affl uent families. Briefl y, the empirical analysis suggests that in the local public market, some municipal services are provided the least in communities where they are needed the most.2 Th e study is organized as follows: Th e next section reviews the theoretical and empirical literature on the posi-tive and negative outcomes of government fragmentation in order to develop testable hypotheses. Succeeding sections present the empiri-cal model, measures, and estimation strategy and discuss the results and their implications on both theory and policy.

Theoretical and Empirical LiteratureGovernment Fragmentation, Citizen Welfare, and Fiscal DisciplineTh e foundational argument for the welfare-enhancing and fi scal disciplinary eff ects of local government fragmentation was laid out by Tiebout (1956), who described the fragmented metropolis as a marketplace in which residents shop for tax and service packages, and local governments function as fi rms that off er such packages. Residents who are unsatisfi ed with their local government can vote with their feet and relocate to a jurisdiction that best satisfi es their tastes for specifi c services and the attendant tax costs. Tiebout argued that “[t]he greater the number of communities and the greater the variance among them, the closer the consumer will come to fully realizing his preference position” (1956, 418).

Beyond preference satisfaction, others extended Tiebout’s market model, arguing that interjurisdictional competition improves fi scal discipline in government. In the Leviathan hypothesis, Brennan and Buchanan concluded that “the potential for fi scal exploitation varies inversely with the number of competing governmental units” (1980, 180). Th e greater the number of jurisdictions, the more diffi cult it is for local governments to maximize revenues and exploit citizens through excessive tax rates. Schneider (1989) linked interjurisdic-tional competition with the control of budget maximization by local bureaucrats. Competition lessens the information advantage of bureaucrats by increasing information available to buyers about the true cost of public services; buyers can then shop for other jurisdic-tions with more effi ciently run governments (Schneider 1989). On the other hand, Bestley and Case (1995) pointed to the process of “yardstick competition,” in which voters rely on neighboring juris-dictions as benchmarks for evaluating the fi scal performance of their own government and unseat incumbents who are fi scally profl igate and corrupt.

Empirical studies tend to support the claim that local government fragmentation reduces the size of public budgets. Two types of studies can be found in the literature: fragmentation studies that examine the eff ects of the number of local governments in a region and fi scal dispersion studies that assess the eff ects of market share or the distribution of revenue or expenditure responsibilities across diff erent governments. In the fragmentation track, more studies fi nd that a greater number of municipalities and other general-purpose governments leads to lower spending or revenues. Th ere are fewer studies that examine the eff ects of fi scal dispersion, but most show that the concentration of expenditure or revenue responsibilities


Although Peterson’s (1981) City-Limits model did not distin-guish among types of cities, its main arguments can be adapted to develop testable hypotheses about how interjurisdictional competition can infl uence service decisions in poor and affl uent municipalities. Peterson argued that municipalities use expenditure policies strategically with the end goal of promoting local economic well-being. He distinguished among three general categories of expenditure policies according to their benefi t–cost ratios and their impact on the local economy. Benefi ts refer to the specifi c serv-ice provided, while the cost refers to the tax bill. Developmental services help municipalities attract mobile capital, promote local economic growth and expand the local tax base. With greater revenues, the average taxpayer can receive higher benefi ts relative to the taxes he or she pays. Redistributive services involve the transfer of income from the well-off to low-income, often non-taxpaying residents, thus increasing the burden for the more productive mem-bers of the community without improving local economic competi-tiveness. Allocational services refer to the housekeeping functions of government that benefi t all members of a community. Although they have neutral eff ects on the local economy, these services are characterized by lower benefi t–cost ratio because they do not help generate revenues.

Following Peterson (1981), competition for economic development will force poor jurisdictions to invest more in development-oriented services in order to attract mobile high-income households and businesses and spend less for redistributive services. Poor residents need more redistributive services, but responding to residents’ needs requires higher taxation, which, in turn, drives away fi rms and high-income groups. If governments in poorer jurisdictions respond in this manner, then competition results in the underprovision of municipal services directly benefi ting poor households. Other scholars suggested similar outcomes from intense interjurisdictional competition. For example, Oates argued that “[i]n an attempt to keep taxes low to attract business investment, local offi cials may hold spending below those levels for which marginal benefi ts equal marginal costs, particularly for those programs that do not off er direct benefi ts to local businesses” (1972, 143). Finally, in regard to allocative services, because they have neutral eff ects on economic growth, Peterson’s (1981) arguments suggest that it is limited fi scal

wealth, rather than interjurisdictional compe-tition, that will force poor municipalities to spend less for these services. It is hypothesized that

Hypothesis 1: Interjurisdictional competi-tion is positively associated with develop-mental expenditures in municipalities with high concentration of poor families.

Hypothesis 2: Interjurisdictional competi-tion is negatively correlated with redistrib-utive expenditures in municipalities with high concentration of poor families.

Hypothesis 3: Interjurisdictional competi-tion is not systematically associated with allocational expenditures in municipalities with high concentration of poor families.

in a metropolitan area grows (see Ostrom 1983). Neiman’s (1976, 1979) study of one metropolitan area found that poor municipali-ties have weaker residential property tax base, but he argued that such municipalities still benefi t by having high-value industrial-manufacturing property. It is unlikely, however, that a majority of poor jurisdictions across the country have a high concentration of business properties (Hill 1976).

Studies by Schneider and Logan (1981, 1982) provided strong evidence of fi scal inequality among suburban municipalities. In one study, Schneider and Logan (1982) found that high-income families are concentrated in suburban communities with higher fi scal wealth, whereas poorer families tend to be excluded from such jurisdictions. Th is is a refl ection of strategic fi scal behavior in which affl uent households attempt to control taxable resources in a region to reduce the tax costs of local services that they receive (Schneider 1987; see also DeHoog, Lowery, and Lyons 1991). In another study, Schneider and Logan (1981) grouped suburban municipalities according to the concentration of specifi c income groups and found that poorer jurisdictions have lower tax bases and higher expendi-tures (mostly for social services) and fi nanced mainly through higher intergovernmental aid and debt, whereas affl uent jurisdictions enjoy higher service levels relative to their residents’ tax rates. Th ey con-cluded that “the real advantage of suburban governmental fragmen-tation and class segregation accrues only to a very small minority of households” (1981, 34).

Strategic Budgetary Choices in Segregated CommunitiesResearch suggests that competition in the local public market cre-ates winners and losers among suburban communities. Ostrom (1983) questioned the conclusion that only some income groups are advantaged by the local market and argued that even the poor could benefi t from decentralized local governance. She argued that if the poor and minorities could form their own communities, they would have more opportunities to provide input and infl uence local budg-etary decision making, ensuring that their government provided the services that they needed. Government consolidation could dilute the infl uence of the poor and minorities. Ostrom argued that “[t]he more local government is centralized, the easier it is for those who already have power to increase their relative stakes and payoff s” (1983, 107). But are governments in poor jurisdictions located in highly fragmented regions really responsive to the needs of their residents? Competition in the local public market is likely to produce a diff erent result.

Reliance on own-source revenues to fund local services forces municipalities to compete with each other for high-income taxpayers. Schneider argued that municipalities “must be careful to protect their tax base from erosion caused by the out-migration of the taxable resources possessed by higher-income individuals” (1987, 50–51). Th is suggests that in highly fragmented regions, both poor and affl uent municipalities will be more responsive to the service demands of fi scally desirable and mobile actors rather than those of poor households.

Reliance on own-source rev-enues to fund local services

forces municipalities to compete with each other for high-income

taxpayers.

In highly fragmented regions, both poor and affl uent munici-

palities will be more respon-sive to the service demands of fi scally desirable and mobile

actors rather than those of poor households.


of poor or affl uent income groups. Th e location quotient is “the ratio of the number of local families in an income group to the number that would be in the [municipality] if the group were distrib-uted among [municipalities] according to their population sizes” (Schneider and Logan 1981, 21). Th e formula used here is as follows:

Location quotient = xi ________

Xn(yi /Yn)

where x is the number of poor (or affl uent) families in municipality i, X is the total number of poor (or affl uent) families in metropoli-tan area n, y is the number of families in municipality i, and Y is the total number of families in metropolitan area n. Th e denominator is simply what the number of poor (or affl uent) families in a munici-pality would have been assuming that the poor (or affl uent) were distributed across jurisdictions based on jurisdictions’ shares in the total number of families in a metropolitan area.

Key to the location quotient is the defi nition of income groups, specifi cally, “poor” and “affl uent” families. Identifying poor families is straightforward; it is based on the federal poverty threshold, which was $17,029 for a family of four in 1999.7 Because the poverty income threshold has no exact equivalent in the income categories listed in the 2000 decennial census, this research uses the nearest category of $19,999 as the family poverty ceiling. While there is a federally defi ned poverty threshold, there is no offi cial threshold for affl uence. Following Coulton et al. (1996), the fl oor for affl uence is set at twice the median income for a family of four in 1999. Th e nearest census category of double the median income of $50,046 is $100,000.

Measures of Budgetary ChoicesTh e outcome variables are the direct general expenditures of munici-pal governments for developmental, redistributive, and allocational services. To ensure comparability with previous research, spending is standardized by population. However, spending is also standardized by income to test for robustness. Following Schneider (1989), devel-opmental services include sewerage, waste management, utilities, highways, transit, and transportation. Redistributive expenditures cover health, hospitals, public welfare, and housing and community development. Allocational services include fi nance, judicial, general staff , and public buildings. Education can be viewed as either a redistributive or a developmental service, whereas police and fi re protection can be classifi ed as either developmental or allocational services. Because of the diffi culty of categorizing these services, they are not included in the analysis.

Measures of Interjurisdictional CompetitionFollowing previous studies, interjurisdictional competition is measured as the degree of fragmentation of municipal governments and the dispersion of expenditure and revenue responsibilities across jurisdictions. Th ese variables are measured at the metropolitan level and thus will be constant for municipalities located in the same metropolitan area.

Fragmentation is measured as the number of municipal govern-ments in a metropolitan area standardized by the total incorporated population. Th is variable is considered in the literature as a meas-ure of political fragmentation (Hendrick, Jimenez, and Lal 2011). Fiscal dispersion is measured by the Hirschman-Herfi ndahl Index (HHI) for total direct general expenditures and total tax revenues.

How will competition infl uence budgetary choices in affl uent com-munities? Th e City-Limits model is not clear on whether affl uent communities will need to spend more for developmental services. Th ere is reason to believe that the pressure to spend for developmen-tal programs is not as intense in affl uent communities as in poorer jurisdictions. Because of the existing fi scal wealth in the community, rich residents might reject new growth, preferring to preserve the local quality of life that attracted them to live in such communities in the fi rst place, and avoid growth-related problems such as excessive traffi c and rapid loss of open space (Bollens 1990). In other affl uent juris-dictions, there can be continued demand for developmental services, but residents can be more selective in supporting projects (Schneider 1989). Th us, in richer municipalities, interjurisdictional competition is not an overwhelming force that determines budgetary choices for developmental services.

Peterson postulated that “local government expenditures are cor-related with indicators of a community’s fi scal resources” (1981, 47). Th e implication, it seems, is that rich communities will spend more for redistribution precisely because they have the capacity to do so.6 Yet Peterson clarifi ed that increasing taxation to support higher redistributive spending will lead to out-migration of fi scally desir-able actors. As for allocational spending, Peterson’s argument that it is not important for economic competition remains applicable in the case of affl uent communities. It is hypothesized that

Hypothesis 4: Interjurisdictional competition has no system-atic relationship with developmental expenditures in munici-palities with high concentration of affl uent families.

Hypothesis 5: Interjurisdictional competition is negatively associated with redistributive expenditures in municipalities with high concentration of affl uent families.

Hypothesis 6: Interjurisdictional competition has no system-atic relationship with allocational expenditures in municipali-ties with high concentration of affl uent families.

Empirical Model, Measures, and Data SourcesIdentifying Segregated MunicipalitiesTh e hypotheses are tested using cross-sectional data for all incorpo-rated municipalities across all 362 metropolitan statistical areas in the United States. Only municipalities located in metropolitan areas are included because the metropolitan region is considered as an integrated economy that more aptly represents the local market for public goods and thus is the appropriate level to measure competi-tion among jurisdictions. Th e data are from the 2002 Census of Governments and the 2000 decennial census.A critical issue is how to identify communities with high concentrations of specifi c income groups. Diff erent measures have been used to assess income homo-geneity of the population, but most of these measures use the census tract as the unit of analysis and are aggregated at the metropolitan level (for a survey of these measures, see Massey and Denton 1988). Unfortunately, census tract–based homogeneity measures cannot be aggregated at the municipal level because the boundaries of census tracts are fi xed at the county level.

As an alternative, this research uses Schneider and Logan’s (1981) location quotient to identify municipalities with high concentrations


Estimation StrategySeparate regressions are estimated for affl uent and poor municipalities to assess whether interjurisdictional competition is associated with the budgetary choices of these segregated communities. Consistent with Schneider and Logan (1981), poor municipalities are defi ned as those with a poor location quotient that is greater than 2, which means that the municipality has twice the number of poor families if such families were distributed among jurisdictions according to their share of the metropolitan population. Affl uent municipalities include those with an affl uent location quotient greater than 2.

ResultsSpatial Economic Segregation and Fiscal Disparities in Metropolitan AreasIt is the rich rather than the poor who are the most segregated in the country. Of the 9,007 incorporated municipalities in the sample with complete data, 11 percent or 957 jurisdictions have very high concentrations of affl uent families, whereas only 4 percent or 323 jurisdictions are classifi ed as being very poor. A large major-ity of municipalities in the sample, 86 percent or 7,727, contain a heterogeneous mix of income groups (as indicated by poor and affl uent location quotients of less than 2). Th ese fi ndings are similar to the results of Schneider and Logan’s (1981) analysis of 1970 data, although they focused only on 31 metropolitan areas.

Th e HHI was originally used to measure the degree of competition among fi rms in an industry by assessing a fi rm’s market share. Th e HHI formula is as follows:

where S2 is the square of fi rm i’s actual share of the market, which is then summed across N number of fi rms in an industry or system. Th e HHI ranges from 0 to 1, with lower values indicating that market shares are concentrated in a few fi rms. In this study, a lower value for the HHI of spending means that only a small number of city governments have higher shares of total municipal spending in a metropolitan area. Similarly, a lower HHI of tax revenues means that a higher proportion of total municipal tax revenues in a region is collected by a few city governments.

Control VariablesOnly control variables with a strong foundation in the literature are included in the models. Federal and state governments provide aid to local governments for welfare programs and transportation- and infrastructure-related services, among others things. Other than supporting local service provision, studies show that such grants actually stimulate government spending through the fl ypaper eff ect (see Dollery and Worthington 1996).8 Th e models control for inter-governmental revenue as a percentage of own-source revenues.

Sociodemographic controls include population density, which meas-ures the demand for public services (Hendrick, Jimenez, and Lal 2011; Jimenez 2009). Age distribution of population also has diff er-ent impacts on expenditures. A higher proportion of the population age 65 years and older is associated with increased expenditures for specifi c services such as health and hospitals. A younger, school-age population, on the other hand, may mean lower revenue collections, leading to lower expenditures for diff erent services (see Alesina, Baqir, and Easterley 1999; Cutler, Elmendorf, and Zeckhauser 1993).

Economic variables include median family income and the local economic structure. Higher income is expected to induce demand for government services (Oates 1972). Income also proxies for local fi scal capacity.9 Percentage employment in manufacturing and services measure the local economic structure. Alternatively, these variables proxy for the strength of local business interests (Schneider 1989). Another political variable is ethnic heterogeneity,

which Alesina, Baqir, and Easterley (1999) found to have a negative relationship with expenditures for some development-oriented and welfare services.

Because service responsibilities of municipal governments vary across the country, indices measuring the number of developmental, redistributive, and allocational services they provide are included in the analysis (Schneider 1989). Finally, the models include state dummies to control for state-specifi c factors that might aff ect municipal expenditures, such as state fi scal institutions (e.g. tax and expenditure limits), historical factors (e.g. variations in the fi scal responsibilities and home rule powers of municipalities across states), and even cultural factors (e.g. conservative or liberal political cultures) (see Jimenez 2009). Table 1 shows the basic descriptive statistics and data sources.

Table 1 Descriptive Statistics

Variable Obs. Mean SD

Per capita developmental expendituresa 9,008 272.77 986.74Per capita redistributive expendituresa 9,008 45.09 510.73Per capita allocational expendituresa 9,008 128.43 427.04Developmental expenditures per $1,000

incomea,b

9,007 13.84 38.05

Redistributive expenditures per $1,000 incomea,b

9,007 2.47 27.79

Allocational expenditures per $1,000 incomea,b 9,007 6.38 14.73Number of municipalities in MSA per 1,000

incorporated populationa

9,030 0.11 0.11

HHI of direct general expenditures (range 0–1)a 9,030 0.57 0.23HHI of tax revenues (range 0–1)a 9,030 0.59 0.23Development service index (number of such

services, range 0–9)a

9,030 3.20 1.42

Redistributive service index (number of such services, range 0–4)a

9,030 0.75 0.88

Allocational service index (number of such services, range 0–4)a

9,030 2.47 1.33

Intergovernmental revenues or IGR (from federal and state) as % of own-source revenuesa

8,993 17.44 16.50

% of local population 65 and olderb 9,007 14.21 6.56% of local population 18 and youngerb 9,007 27.30 6.01Population 9,009 16,691.36 113,213.20Population density (population per 1,000

square miles)b

9,009 0.77 0.91

Ethnic heterogeneity index = 1 – ∑(Race)2, where Race i denotes the share of popula-tion identifi ed as of race i including white, black, Hispanic, Asian and Pacifi c Islander, and American Indian (range 0–1, with higher values indicating greater population heterogeneity)b

9,030 0.22 0.21

% of employed in manufacturingb 9,007 15.25 8.26% of employed in servicesb 9,007 1.86 7.95Median family income (in $1,000)b 9,009 52.57 23.90

a Raw data from the 2002 Census of Governments. b

Raw data from the 2000 decennial census.


property tax, which is understandable given their richer property tax base.

Municipalities also support local budgets with revenue transfers from the federal and state governments, as well as through issuance of debt. In comparison with other jurisdictions, poor localities are more dependent on intergovernmental transfers. Rather surprisingly, although poor municipalities still have higher debt compared to richer ones, it is the mixed-income jurisdictions that have the high-est debt among the three groups. Affl uent communities are the least dependent on intergovernmental revenue and have the lowest total debt relative to income.

Poor municipalities spend more on average in comparison with mixed-income and affl uent jurisdictions. Communities also have diff erent budgetary priorities, but in general, poor jurisdictions spend the most for developmental, redistributive, and allocational services, whereas affl uent municipalities spend the least. Higher

spending in poor jurisdictions refl ects the greater need for government services among residents in these communities.

Interjurisdictional Competition and Budgetary ChoicesTo support the provision of services to a needy population, poor municipalities must collect higher revenues relative to their residents’ incomes and rely more on aid from

higher-level governments and, to a certain extent, on debt. Th ese revenue sources are unsustainable in the long term. Higher taxes and fees can lead to fi scal out-migration. Overreliance on fi scal transfers subjects local policies to the vagaries of political decisions in federal and state capitols, and any sudden decline in aid will be disastrous for the fi scal condition of poor municipalities. Debt needs to be repaid with interest. For poor jurisdictions, competition for external capital and local economic development is a high-stakes game in

which expenditure policies become crucial.

Table 3 shows the results of regression models estimating the relationship between measures of interjurisdictional competition and expen-ditures for diff erent types of services in poor municipalities. Standard errors have been clustered by metropolitan area and are robust to heteroskedasticity and possible error corre-lation. Because of the very high correlation (r = .90) between the HHI-based measures, they

are not included in the same models. Th ere is also a partially strong correlation (r = .60) between measures of the age distribution of the population, but running the models with or without one of the

measures does not change the results. Because of space consideration, the correlation matri-ces, as well as the results for state dummies in the regression models, are not presented here but are available from the author upon request.11

Models 1 and 2 show the results for develop-mental expenditures. Th e number of municipal

Table 2 classifi es municipalities into “poor,” “mixed-income,” and “affl uent” jurisdic-tions according to their location quotients and presents information about a number of fi scal indicators for each group of jurisdic-tions. All the fi scal indicators are standard-ized by income and measured as averages for each municipal group. Because of the wide variation in the revenue sources and expendi-ture responsibilities of municipal governments across the country, comparing their performance across multiple fi scal dimensions is inherently problematic. Th us, the information presented in table 2 should be considered preliminary and serves only to provide a general picture of municipal fi scal performance.10

Poor municipalities raise more revenues from own sources relative to their residents’ income. Th is means that families and businesses in poor localities shoulder a heavier burden in terms of supporting their local govern-ments in comparison with residents and fi rms located in other municipalities. On average, governments in poor localities collect $57 in diff erent types of local taxes and fees for every $1,000 in private income, whereas mixed-income jurisdictions collect $48, and affl uent areas collect only $27. As for specifi c revenue sources, poor municipalities depend more on non-property-tax revenues as well as charges and miscellaneous fees in comparison with other jurisdictions. Greater reliance on user fees and charges can lead to better alloca-tion of resources by matching the supply of services with consumer demand. Nevertheless, this revenue source raises equity concerns, especially with regard to service access by underprivileged residents. Indeed, it is ironic that user fees are one of the main sources of revenues in municipali-ties where a sizeable portion of the population has a greater need for government services but can least aff ord to pay for them. In compari-son, affl uent jurisdictions rely more on the

Table 2 Selected Fiscal Indicators (per $1,000 income)

IndicatorsPoor

(N = 323)Mixed-Income

(N = 7,727)Affl uent (N = 957)

Own-source revenues $56.77 $47.63 $27.08Total tax revenues 19.26 19.55 18.05Property tax revenues 8.48 10.50 12.72Other tax revenues 10.78 9.06 5.33Charges and miscellaneous fees 24.23 16.03 6.29Federal and state revenues 18.14 10.38 3.52Total debt 36.28 55.58 23.20Total direct expenditures 71.82 58.85 28.87Developmental expenditures 17.50 14.54 6.92Redistributive expenditures 4.36 2.63 0.60Allocational expenditures 9.65 6.49 4.35

Note: “Poor” municipalities are defi ned as those with a poor location quotient greater than 2, “affl uent” municipalities include those with an affl uent location quotient greater than 2, and “mixed-income” municipalities have poor and af-fl uent location quotients that are less than 2. The fi gures represent averages for each type of municipality. Raw data are from the 2002 Census of Governments, Individual Finance Files.

Overreliance on fi scal transfers subjects local policies to the

vagaries of political decisions in federal and state capitols, and any sudden decline in aid will be disastrous for the fi scal con-dition of poor municipalities.

For poor jurisdictions, compe-tition for external capital and local economic development

is a high-stakes game in which expenditure policies become

crucial.

Families and businesses in poor localities shoulder a heavier

burden in terms of supporting their local governments in com-parison with residents and fi rms located in other municipalities.


fragmentation measure is also negative, but the estimates fail to reach conventional levels of statistical signifi cance. Similar to the results in Schneider (1989), very few control variables are signifi cant in the redistribution models. Th e results show that jurisdictions that off er more redistributive services and those that are more urbanized spend more for social welfare services.

In models 5 and 6, the number of municipal governments has a statistically signifi cant and negative relationship with alloca-tional expenditures, which is contrary to the third hypothesis. Specifi cally, a 10 percent increase in the number of municipal governments per 1,000 people is associated with a decrease in allocational expenditures of $55 to $60 per 10 persons. As for the control variables, poor municipalities that are more dependent on intergovernmental revenues, have a younger population, are more urbanized, and have a smaller service industry spend less for general administration.

Table 4 presents the results for municipalities classifi ed as affl uent. Th e results in models 6–12 confi rm the last three hypotheses that interjurisdictional competition is associated with smaller redistribu-tive budgets in affl uent localities but has no systematic relationships with expenditures for developmental and allocational services. Note that it is the fragmentation measures that are signifi cant in models 8 and 9 or the redistribution models. Specifi cally, the results show that a 10 percent increase in the number of municipal governments per 1,000 people is associated with a decrease in redistributive spending of $10 to $11 per 10 persons.

governments standardized by population has no signifi cant relation-ship with developmental expenditures, but both measures of fi scal dispersion—the HHI for expenditures and tax revenues—are posi-tively associated with developmental spending. In regions where fi scal responsibilities are more equally distributed among numerous munici-palities—in other words, where competition is more intense—poor jurisdictions spend more for developmental services, providing support for the fi rst hypothesis. Specifi cally, a one-unit increase in the HHI for spending is associated with a $91 increase in per capita developmental spending, whereas a one-unit increase in the HHI for tax revenues is associated with a $118 increase per capita.

As for control variables, municipalities that off er more types of developmental services also spend more for this function. Denser jurisdictions spend less for this expenditure category, suggesting that there are savings from economies of scale in consumption. Demand from business groups, specifi cally service-oriented industries, and the fragmentation of the population according to race and ethnicity, are associated with bigger developmental budgets.

Th e dependent variable in models 3 and 4 is redistributive spending. Consistent with the second hypothesis, the greater the competition in the metropolitan area, as measured by higher values for the HHI of spending and taxes, the lower the expenditures for redistributive services. Specifi cally, a one-unit increase in the HHI for spend-ing is associated with an $88 decrease in per capita redistributive spending, whereas a one-unit increase in the HHI for tax revenues is associated with an $85 reduction per capita. Th e sign for the

Table 3 Results When Spending Is Standardized by Population, Poor Municipalities Only

Independent Variables

Per Capita DevelopmentalExpenditures

Per Capita RedistributiveExpenditures

Per Capita AllocationalExpenditures

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE

Number of municipalities in MSA per 1,000 people (log)

–10.41 26.30 –3.56 26.87 –18.40 20.64 –21.11 21.42 –60.16** 25.76 –54.87** 24.66

HHI of direct expenditures 91.37* 48.73 — — –87.99* 46.57 — — 9.90 42.42 — —

HHI of tax revenues — — 117.94** 53.72 — — –84.52* 46.59 — — 48.34 46.98

Developmental service index 67.14*** 7.76 67.29*** 7.76 — — — — — —

Redistributive service index — — — — 123.67*** 37.31 123.82*** 37.40 — — — —

Allocational service index — — — — — — — — 11.15 9.31 11.10 9.27

IGR as % of own-source revenues 0.38 0.64 0.42 0.64 –0.80 0.60 –0.79 0.59 –1.21*** 0.44 –1.17*** 0.44

% population 65 and older 1.20 1.89 1.26 1.87 2.02 1.59 1.94 1.58 –3.28 2.40 –3.30 2.38

% population 18 and younger –1.01 1.66 –0.91 1.66 –0.07 0.94 –0.10 0.95 –7.16** 3.03 –7.10** 3.03

Population density –29.746* 17.78 –30.11* 17.83 27.11** 13.55 27.21** 13.60 –30.94** 14.20 –31.06** 14.09

Ethnic heterogeneity index 108.02** 47.09 105.02** 46.65 –20.41 36.88 –18.46 36.50 35.94 59.10 34.39 58.36

% employed in manufacturing –0.36 1.13 –0.42 1.12 0.30 0.80 0.28 0.81 –1.43 0.94 –1.53 0.93

% employed in services 5.90* 3.14 6.01* 3.12 1.50 1.01 1.41 1.00 10.81*** 3.58 10.86*** 3.58

Median family income ($1,000s) 1.36 1.70 1.36 1.69 0.92 0.88 0.91 0.87 –2.87 2.12 –2.87 2.12

Constant –143.14 106.10 –140.51 104.12 –78.11 68.38 –85.98 69.79 311.84** 156.00 305.94** 153.50

State dummies Yes Yes Yes Yes Yes Yes

N 298 298 297 297 302 302

R2 0.50 0.50 0.31 0.31 0.39 0.39

Notes: Standard errors (SE) have been clustered by metropolitan area and are robust to heteroskedasticity and error correlation. The base state is Colorado. Because of space consideration, results for state dummies are not shown but are available from the author. *** Signifi cant at 1%; ** at 5%; * at 10% (two-tailed tests).


As for the control variables, models 7 and 8 show that affl uent municipalities that provide more types of developmental services and have higher income spend more for developmental services, whereas those that are more urbanized and have a younger popu-lation spend less for such services. In models 9 and 10, affl uent enclaves that off er more social and other welfare programs conse-quently allocate more resources for such services, whereas those

with higher incomes and a younger and ethnically fragmented population have smaller redistributive budgets. Finally, models 11 and 12 show that intergovernmental transfers, percentage of population 18 years and younger, and population density are nega-tively correlated with allocational expenditures, whereas income and percentage employed in services are positively associated with spending.

Table 4 Results When Spending Is Standardized by Population, Affl uent Municipalities Only








4.14 22.74 6.75 22.09 –10.14*** 3.29 –10.68*** 3.36 1.29 150.50 –5.95 138.11

HHI of direct expenditures 48.89 67.11 — — 10.24 7.08 — — –14.35 51.67 — —HHI of tax revenues — — 17.08 60.42 — — 7.50 6.93 — — –0.72 41.50Developmental service index 78.67*** 10.16 79.12*** 10.18 — — — — — — — —Redistributive service index — — — — 27.27*** 3.21 28.34*** 3.36 — — — —Allocational service index — — — — — — — — 4.04 6.18 3.77 6.15IGR as % of own-source

revenues0.18 2.28 0.16 2.26 0.27 0.17 0.24 0.17 –2.10*** 0.72 –2.08*** 0.72

% population 65 and above 2.93 2.60 2.89 2.59 –0.08 0.29 0.06 0.33 –2.00 2.49 –2.00 2.47% population 18 and below –7.12** 3.17 –7.09** 3.18 –0.89** 0.35 –0.83** 0.35 –9.09*** 2.72 –9.10*** 2.74Population density –39.05** 17.69 –39.98** 17.62 0.61 2.31 0.15 2.37 –39.99*** 9.54 –39.60*** 9.45Ethnic heterogeneity index –76.52 77.58 –77.75 77.76 –18.76** 9.48 –24.92** 11.24 –107.98 92.50 –106.93 92.77% employed in manufacturing 0.71 2.82 0.66 2.83 –0.26 0.39 –0.28 0.39 –2.70 2.09 –2.68 2.10% employed in services 2.43 3.73 2.38 3.71 0.38 0.31 0.50 0.35 5.271* 3.02 5.30* 3.02Median family income

($1,000)1.32** 0.56 1.32** 0.56 –0.07* 0.04 –0.08** 0.04 1.25*** 0.37 1.25*** 0.38

Constant 59.09 166.69 86.00 162.05 8.14 18.79 6.52 19.28 305.06** 132.42 297.50** 133.96State dummies Yes Yes Yes Yes Yes YesN 940 940 938 938 937 937R2 0.24 0.24 0.28 0.27 0.25 0.25


Table 5 Results When Spending Is Standardized by Population, Mixed-Income Municipalities Only








5.76 6.11 7.16 6.32 4.21 4.18 3.84 4.08 –37.28*** 11.17 –36.37*** 11.39

HHI of direct expenditures 35.79** 16.41 — — –20.36** 10.12 — — 16.89 13.08 — —HHI of tax revenues 38.61** 16.36 — — –11.03 9.57 — — 25.40 16.81Developmental service index 68.03*** 2.16 68.16*** 2.17 — — — — — — — —Redistributive service index — — — — 71.41*** 7.33 71.41*** 7.33 — — — —Allocational service index — — — — — — — — 16.12*** 5.68 15.99*** 5.73IGR as % of own-source revenues 0.76** 0.30 0.77** 0.30 0.06 0.17 0.06 0.17 –0.71** 0.30 –0.70** 0.30% population 65 and above 1.45* 0.88 1.46* 0.88 –0.01 0.72 0.00 0.71 –0.99 0.80 –0.97 0.79% population 18 and below –1.88* 0.97 –1.87* 0.97 –1.56** 0.65 –1.54** 0.65 –2.66 1.83 –2.63 1.82Population density –26.02*** 3.84 –26.12*** 3.85 5.31 5.19 5.35 5.20 –11.49** 5.67 –11.52** 5.69Ethnic heterogeneity index –3.75 15.99 –3.96 15.98 –12.62 11.63 –12.26 11.63 –81.17 74.85 –80.84 74.97% employed in manufacturing –1.11*** 0.39 –1.12*** 0.39 –0.21 0.30 –0.22 0.30 –1.89*** 0.66 –1.90*** 0.66% employed in services 4.29** 1.78 4.30** 1.78 0.53 0.35 0.53 0.35 17.49** 7.97 17.50** 7.97Median family income ($1,000) 1.38*** 0.35 1.36*** 0.35 –0.63*** 0.17 –0.63*** 0.17 –0.05 0.36 –0.07 0.36Constant 68.33 61.30 70.81 60.80 130.35** 50.80 123.75** 50.48 305.84* 163.81 303.98* 162.95State dummies Yes Yes Yes Yes Yes YesN 7,680 7,680 7,688 7,688 7,687 7,687R2 0.21 0.21 0.13 0.13 0.10 0.10



public market. In the hope of attracting development from other municipalities, poor municipalities tailor services according to the needs of mobile high-income households and fi rms rather than their underprivileged residents. In line with Peterson’s (1981) City-Limits model, the results show that poor municipalities tend to spend more for developmental services and less for redistributive programs as interjurisdictional competition intensifi es.13

A diff erent way to interpret the fi nding is that municipalities located in more consolidated regions—regardless of the city’s income classifi cation—tend to spend more for redistributive serv-ices. An important question is, other than the weaker competitive pressures in more consolidated metropolitan areas, what other fac-

tors might help explain the higher municipal redistributive spending in these regions? It is possible that public managers and street-level employees in large city bureaucracies, guided by professional norms, exercise their discre-tion to allocate more resources for services benefi ting the poor (see Ostrom 1983; Schneider and Logan 1981). Yet another pos-sibility is that in bigger jurisdictions, redis-

tributive issues have greater chances of being included in the local policy agenda. In more fragmented regions where high-income residents do not share a local government with the poor, some communities can easily ignore demand for more redistribution (Howell-Moroney 2008; Jargowski 2002; Lowery 2000). Th ese are viable arguments, but they need to be explored in greater detail in future research.

Although mixed-income jurisdictions are not the focus of this research, table 5 provides the results for mixed-income jurisdic-tions, which constitute the majority of municipalities in metro-politan areas. Th e results are similar to those in poor localities: intermunicipal competition is associated with higher expenditures for developmental services and lower investments in redistributive and allocational services. Mixed-income municipalities may not exactly be declining jurisdictions, but the need to expand the local revenue base by attracting new development remains a paramount concern.

Table 6 presents the results when the spending variables are stand-ardized by income rather than population. (Because of space consid-eration, the results for other control variables are not shown but are available from the author.) Th e models include the same control variables except that median family income is no longer included and has been replaced by population.12 Th e results are very similar to the fi ndings in the per capita spending models. Th e only major diff erence is that the fragmentation measure is now signifi cantly and positively associated with developmental spending in affl uent municipalities, which is contrary to the fourth hypothesis.

DiscussionTh is research makes two primary contributions to the literature on local public fi nance. First, this article shows that the voices of citizen-consumers do not carry equal weight in the segregated local

Table 6 Results When Spending Is Standardized by Income

Poor Municipalities Only


Developmental Expenditures per$1,000 Income

Redistributive Expenditures per $1,000 Income

Allocational Expenditures per $1,000 Income



Number of municipalities in MSA per 1,000 people (log) 0.75 2.54 1.52 2.54 –2.18 2.16 –2.55 2.24 –3.54** 1.75 –3.16* 1.70HHI of direct expenditures 14.86*** 5.54 — — –6.74* 4.05 — — 2.17 3.40 — —

HHI of tax revenues — — 16.43*** 6.04 — — –7.16* 3.98 — — 4.51 3.97

N 298 298 297 297 302 302

R2 0.28 0.28 0.29 0.30 0.32 0.32

Affl uent Municipalities Only



Number of municipalities in MSA per 1,000 people (log) 1.36*** 0.50 1.40*** 0.49 –0.31* 0.16 –0.27* 0.16 –0.25 0.80 –0.25 0.80HHI of direct expenditures 0.41 1.15 — — 0.64 0.48 — — 0.41 2.01 — —HHI of tax revenues — — –0.11 1.25 — — 0.24 0.37 — — 0.53 1.95N 940 940 938 938 937 937R2 0.39 0.39 0.23 0.23 0.33 0.33

Mixed-Income Municipalities Only



Number of municipalities in MSA per 1,000 people (log) –0.22 0.63 –0.18 0.61 0.32 0.22 0.31 0.21 –1.61*** 0.58 –1.23** 0.48HHI of direct expenditures 1.12 1.19 — — –1.03* 0.57 — — 0.92 1.00 — —HHI of tax revenues — — 1.15 1.38 — — –0.08 0.63 — — 0.67 1.05N 7,680 7,680 7,688 7,688 7,687 7,687R2 0.10 0.10 0.11 0.11 0.06 0.11

Note: Standard errors (SE) have been clustered by metropolitan area and are robust to heteroskedasticity and error correlation. The base state is Colorado. Because of space consideration, results for other control variables and the state dummies are not shown but are available from the author. *** Signifi cant at 1%; ** at 5%; * at 10% (two-tailed tests).

Municipalities located in more consolidated regions— regardless of the city’s income classifi cation—tend to spend

more for redistributive services.


municipal redistributive spending. Still, as Ostrom recognized, “all arguments for funding aspects of urban services by larger units of governments…assume that larger units will actively pursue redistributive policies” (1983, 101). Indeed, developments in the intergovernmental grant system and the redistributive programs of higher governments are raising questions about the future of redis-tribution policy in the country.

Specifi cally, the federal welfare reforms that began in the 1980s, combined with the proliferation of federal waivers in recent years that have allowed states to suspend or modify specifi c requirements of federal grant programs (see Th ompson 2013), have increased state discretion in shaping social welfare policy. Devolution can improve the eff ectiveness of federal programs by allowing states to adjust program rules and administration according to the prevailing local conditions (Th ompson 2013). But devolution has also created a welfare system that is best described as “a patchwork of programs” with rules and benefi ts that vary across states (Caraley 2001–2, 542). Th ere also are questions about the capacity of states to meet increased demand for public assistance during economic downturns. Have welfare reform, federal waivers, and multiyear budget crises forced all government levels to withdraw from redistribution? If

so, how has this aff ected the condition of the increasing number of poor Americans?15

Finally, looking beyond federal and state roles, scholars and policy makers alike are increas-ingly exploring interlocal cooperation as a means to address metropolitan-wide issues in light of the failure of most municipal consoli-dation initiatives across the country. Research informed by the institutional collective action framework has shown that fragmented

systems create opportunities for local governments to voluntarily cooperate to achieve shared policy objectives (Feiock 2007). Others, however, argue that voluntary agreements have focused on the pro-vision of system maintenance services (such as emergency manage-ment services), whereas joint eff orts to address redistributive issues have been sorely lacking (Lowery 2000).

It is possible that fragmentation itself has made it harder to craft a regional response to the issue of local fi scal disparity (see Altshuler et al. 1999; Downs 1994). Lowery (2000) argued that jurisdictional boundaries reinforce communities’ sense of separateness from other communities, providing little incentive for residents and local offi cials to coordinate and address issues related to poverty concen-tration as long as poverty and its consequences only aff ect other neighborhoods (see also Howell-Moroney 2008; Jargowski 2002). How can this constraint be overcome to promote greater coordina-tion among local actors?

ConclusionAlthough this research found that fi scally wealthy communities coex-ist with impoverished jurisdictions in metropolitan America, it is important to emphasize that spatial economic segregation—at least, as measured at the municipal level—is not that severe. Th e results show that diff erent income groups, more or less, are dispersed across a greater number of jurisdictions. Although segregation festers only at the periphery of the metropolis, the plight of the metropolitan

Second, the empirical results also highlight some needed revisions to Peterson’s (1981) model. Specifi cally, not all cities face the same budgetary policy constraints rooted in the imperative of competing for economic growth. Budgetary choices vary across jurisdictions on the basis of their diff ering needs for growth. With a more acute need to build their revenue base, poorer municipalities are aff ected more intensely by competition and have limited budgetary fl exibility, as they need to prioritize developmental services over both redistribu-tive and allocational services. In the case of allocational services, Peterson argued that they “have little eff ect on a city’s economic growth” (1981, 50), implying that competition has no relationship with spending for governmental administration. Yet in poor munici-palities, the results show that allocational budgets tend to decrease as competition intensifi es.

In comparison with poor municipalities, competition does not impose considerable policy constraints on affl uent cities. Th ese municipalities can vary their budgets depending on residents’ prefer-ences and not necessarily on the need to compete for economic growth. Given the strong resource base of these communities, resi-dents can spend more for services they prefer or choose lower service levels to further reduce their tax bills (see Schneider and Logan 1982). For example, because preferences vary, there is no clear trend for allocational spend-ing that is supported by the nonsignifi cant fi ndings for measures of competition. Th e results for developmental services are mixed. What is clear is that the primary interest of wealthy residents is to protect their com-munity’s existing fi scal wealth by limiting access of the poor to their enclaves. Th ey do so by off ering very few, if any, redistributive programs. In this case, it can be argued that governments in affl uent jurisdictions are responsive to the demands of their residents.

Beyond theory, the empirical fi ndings point to a number of policy issues that deserve further examination in future research. First, some observers argue that unequal access to public services leads to limited opportunities and poorer life outcomes for low-income individuals (see Altshuler et al. 1999). Th e Great Recession of 2007–9 is likely to have intensifi ed demand for public services especially in metropolitan areas. Analysis by Kneebone and Garr concluded that between 2000 and 2008, “suburbs had overtaken primary cities as home to the largest share of the nation’s poor” (2010, 4). With the transforma-tion of poverty from a central city to a suburban phenomenon, it is important to assess whether the very low redistributive spending at the municipal level poses a threat to the health and safety of poor suburban residents. Do higher-level governments need to intervene with more aid to ensure better access to local social services?

Second, this research indeed shows that poor municipalities receive higher intergovernmental aid in comparison to other types of jurisdictions. In addition to providing local aid, it is also important to note that higher-level governments directly fund and implement major redistributive programs—the budgets for which dwarf that of city redistributive spending.14 Th e crucial role of higher-level governments suggests that care must be taken when interpreting the results here to mean that the overall social safety net infrastruc-ture in the country is inadequate. Th e results here only pertain to

What is clear is that the primary interest of wealthy residents is to protect their community’s existing fi scal wealth by limit-ing access of the poor to their

enclaves.


underclass is surely not a peripheral issue. As Downs argued, although depressed neighborhoods in some municipalities contain only “a small percentage of the U.S. population,” they nevertheless face some of the “most virulent forms” of social and economic prob-lems in the nation, such as childhood poverty and chronic unem-ployment (1994, 4). Th e challenge ahead is to craft institutional and policy responses that both eff ectively address the problem of economic segregation and fi scal disparity and are politically accept-able to diff erent stakeholders in our federal system of governance.

While this research has provided important insights about inter-jurisdictional competition and budgetary choices in municipal governments, its limitations should be noted. Th ere also are some unanswered questions. First, the cross-sectional data preclude any defi nitive statements about causes and eff ects. Second, it is not clear why the fragmentation (also fi scal dispersion) measures are signifi cant in some models but not in others. Although both are generally recognized measures of interjurisdictional competition, the results suggest that the mechanisms linking political fragmentation and fi scal dispersion with government spending operate diff er-ently depending on the service and community types. Th ird, it is also important to assess the relationship between competition and spending for other services, particularly education and public safety. Fourth, future research can use other measures of spatial economic segregation that can be applied at the municipal level to examine whether the fi ndings here are robust to the income classifi cation system used. Finally, analysis of more recent data will provide crucial information about how the Great Recession has reshaped the distribution of poverty, wealth, and public services in metropolitan America.

Notes 1. Th e highly fragmented system of local governments approximates what Ostrom,

Tiebout, and Warren (1961, 831) called a polycentric political system. Research has shown that this polycentric system is associated with diff erent economic and political outcomes (see Jimenez and Hendrick 2010).

2. A variation of this phrase was originally used by Peterson (1981, 60). 3. On the other hand, studies suggest that fragmentation of single-purpose govern-

ments increases the size of the local public sector. 4. In fairness to Tiebout, he recognized this possibility, albeit in a footnote, in

which he wrote, “Not only is the consumer-voter concerned with economic pat-terns, but he desires, for example, to associate with ‘nice’ people” (1956, 418).

5. Note that studies comparing population homogeneity at the community level with overall metropolitan homogeneity (e.g., Eberts and Gronberg 1981) have been criticized because the observed population sorting represents sta-tistical sorting: it is a statistical certainty that population homogeneity at the community level will increase relative to that at the metropolitan level as the number of communities and governments increases (see Dowding, John, and Biggs 1994).

6. Specifi cally, Peterson wrote that “[r]edistributive policies that weaken the local economy are only provided at a level which the city can aff ord” (1981, 50).

7. Th e family size is consistent with Massey and Denton (1988) and Coulton et al. (1996).

8. Th e fl ypaper eff ect refers to the phenomenon whereby money sticks where it hits. Rather than use the grants to reduce local tax burdens, money sticks where it lands—in local governments, where grants are used to expand the local budget.

9. Because local governments in diff erent states rely on diff erent sources of revenues, it is diffi cult to use a single measure of fi scal capacity. I experimented with

own-source revenues per capita as a control, but this did not change the results and, in fact, strengthened the statistical signifi cance of the HHI measures. I opted to exclude this measure in the fi nal spending models because it is endogenous.

10. Because the Bartlett’s test of population heterogeneity is signifi cant—which means that the three groups have unequal variances—I cannot use analysis of variance or ANOVA to compare the means for fi scal indicators for the three groups of municipalities. Th e standard F-test of signifi cance in ANOVA is unre-liable when group population variances are unequal.

11. Numerous other models were estimated to examine how variable operationaliza-tion infl uenced the fi ndings. For example, I removed public transit (which may not be strictly developmental, assuming that it is used mainly by the poor) and health services (which include items that are not purely redistributive) from the spending categories. I also disaggregated intergovernmental revenue by service type. Th e signs and level of statistical signifi cance for measures of interjurisdic-tional competition did not change. Finally, I experimented with other controls such as wages, government employment, government form, central city status, fi scal institutions, and home rule, but these highly correlated with other controls. In particular, high correlation with some state dummies meant that a number of observations were automatically dropped from the analysis. I decided to retain the state dummies because they control for unobserved eff ects, which are unlikely to be strictly exogenous in the spending models.

12. Th e reason for this is rather simple: standardizing the outcome variable by popu-lation (or income) already helps control for population (or income).

13. A potential problem with prioritizing developmental services is that empirical evidence as to the effi cacy of municipal developmental policies in promot-ing local economic growth is inconclusive (see Schneider 1989). More likely, economic growth is a function of the competitive advantages of the region as a whole rather than of individual localities.

14. Examples of redistributive programs by higher-level governments funded through general taxes (the federal and state governments share funding respon-sibility for some of these programs) include Medicaid, Temporary Assistance for Needy Families, and the Supplemental Nutrition Assistance Program, among others. Th ese programs, however, are not directly comparable with redistributive services provided by municipal governments. Finally, one must also consider the role of other local governments such as counties and special districts. Unfortunately, it was not possible to include other local governments’ expenditures because these cannot be disaggregated according to municipal boundaries.

15. Using 100 percent of the federal poverty income threshold, recent analysis by Kneebone and Garr (2010) showed that the number of poor Americans increased by 15.4 percent, or approximately 5.2 million, from 2000 to 2008.

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Documents

Separate, Unequal, and Ignored? Interjurisdictional Competition and the Budgetary Choices of Poor and Affluent Municipalities