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Tax Competition among Municipal Governments: Exit vs. Voice

Rebecca Hendrick [email protected] 312-355-0305 Yonghong Wu [email protected] 312-996-5073 Benoy Jacob [email protected] Public Administration (m/c 278) University of Illinois at Chicago 412 South Peoria Street Chicago IL 60607

Paper prepared for delivery at Annual Association of Budgeting and Financial Management Conference, November 10-12, 2005 in Washington, DC.

Tax Competition among Municipal Governments: Exit vs. Voice

ABSTRACT

This is an empirical exploration on municipal tax competition or mimicking. The models estimate local property tax rate, sales tax rate and total revenue burden separately using a spatiallag component that relates each municipality’s tax or revenue burden to that of its neighbors, controlling for other factors affecting municipal tax and revenue burdens. The level of “neighborliness” is measured in two ways — spatial distance between municipalities and a binary indicator of simple contiguity. Based on two commonly used estimators, the results show that tax competition exists for property taxes, but not sales taxes or total revenue burden. This suggests that tax and revenue competition among municipalities in this region is based primarily on visibility and voice rather than mobility and exit mechanism.

TAX COMPETITION AMONG MUNICIPAL GOVERNMENTS: EXIT VS VOICE Arguments for the existence and form of tax competition among local governments can be traced back to Tiebout (1956) and Oates (1972). Stated simply, tax competition exists between governments because of residents’ ability to sort themselves into governing jurisdictions within a region according to their preferences for public services and the costs of services in the form of tax burdens. Thus, competition requires that households be relatively mobile and unrestricted in their movement among jurisdictions. Tax competition is then manifested when jurisdictions independently establish tax rates to maximize the welfare of residents within the region. Over time, the tax and service choices of jurisdictions and households affect the value of tax bases of all jurisdictions within the region (Wilson, 1999). Although these arguments are applied primarily to households, the mechanisms underlying tax competition are easily extended to mobile firms and capital (Oates and Schwab, 1991), all of which affect the varied tax bases on which local governments depend on for their revenue (e.g. income, property, and sales tax) (Rork, 2003). Tax competition based on mobility or exit (Hirschman, 1970) is only one form of strategic interaction among governments. Another form of strategic interaction is “yardstick competition,” as originally proposed by Shleifer (1985) and adapted to governments by Besley and Case (1995). Yardstick competition combines Hirschman’s voice arguments and Salmon’s (1987) external benchmarking in the context of taxation. Salmon’s primary argument in this regard is that citizens (voters) of a jurisdiction will use information about the goods and services of other jurisdictions as a benchmark to evaluate the performance of their own governments. Citizens then use these assessments to pressure their government, through voting or other voice

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methods, to improve its performance in comparison to other jurisdictions. With respect to taxation, these mechanisms will encourage “tax mimicking” in which jurisdictions copy the tax behavior of other jurisdictions in the region (Ladd, 1992; Case et al, 1993). One important elaboration of the voice argument concerns the asymmetry of information between politicians and voters (Besley and Case, 1995). Politicians know more about the costs of services than voters, which allows politicians to set tax rates strategically to influence voters’ opinions. One source of tax information asymmetry is the government’s revenue structure (Wagner, 1976). More complex and diverse revenue structures create a fiscal illusion among taxpayers, or even government officials, that total tax and revenue burdens are less than they actually are, which provides an incentive to raise revenues beyond efficient levels (Dollery and Worthington, 1996). Another source of fiscal illusion and information asymmetry concerning taxation is its visibility. Taxation levels for visible, salient, or direct taxes are more likely to be accurately determined by taxpayers (DiLorenzo, 1982; Sausgruber, Rupert, Jean-Robert Tyran). Comparing the two forms of tax competition indicates two distinct, but not necessarily mutually exclusive, underlying mechanisms that drive the process. One is the exit mechanism that is based on mobility of capital, firms, or households. The other is the voice mechanism that is based on the visibility and salience of taxes. If the exit mechanism is the primary driver of tax competition, then one might expect to see high competition among governments for taxes with highly mobile tax bases or bases that are greatly affected by the mobility of capital, firms, and households (Rork, 2003). In contrast, if the voice mechanism is the primary driver of tax competition, then competition should be greater among governments for the taxes that are more visible and salient to voters and other taxpayers.

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Examining the economic and political qualities of different forms of local taxation and revenues shows that shoppers and workers are much more mobile than households and firms within a designated region (Rork, 2003). In contrast, property and income taxes are much more visible and salient to residents, firms, and workers than sales taxes or other types of revenues (e.g. charges or utility taxes) (Lowery, 1985; ACIR, 1991). Thus, if mobility determines tax competition, then competition should be higher for sales tax rates (or burden) than property tax rates (or burden). Alternatively, if competition is based on visibility and salience then it should be higher for property taxes than sales taxes. Based on the fiscal illusion argument, tax competition also would be less likely for total revenues under the voice mechanism, because of the reduced visibility of individual taxes, but more likely under the mobility mechanism due to the combined impact of all mobile factors affecting multiple revenue bases. This research tests these propositions using the models of tax competition for different tax bases among neighboring municipalities in the Chicago metropolitan region. The models estimate sales tax burden, property tax burden, and total revenue burden separately using a spatial- lag component that relates each municipality’s tax or revenue burden to that of its neighbors, controlling for other factors affecting municipal tax and revenue burdens. Here, the level of “neighborliness” is measured in two ways— spatial distance between municipalities and a binary indicator of simple contiguity. A positive spatial lag component for these measures indicate that municipalities closer or next to each other have more similar tax or revenue burdens by comparison to municipalities further away or not contiguous to each other. As such, a positive coefficient for either measure also indicates the existence of tax competition. Our findings show that tax competition exists for property taxes, but not sales taxes or total revenue

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burden, which suggests that tax and revenue competition among municipalities in this region is based primarily on visibility and voice rather than the mobility and exit mechanism. The next section describes, compares, and identifies important features of the exit and voice mechanism as generally applied in economics, public choice, and political science. This section also discusses the general form of empirical models used to test tax competition, and their findings with respect to different types of taxes. Subsequent sections present the reaction function we use to examine the effects of tax competition for sales tax rate, property tax rate, and total revenue burden. Exit and Voice Competition There are many forms of strategic interaction among governments, some of which can be described as competition. According to Brueckner (2003), the empirical models and theoretical frameworks fit into two broad categories: resource flow and spillover. The resource flow framework, which is derived from Tiebout (1956), is well known and applied extensively in economics, political science, and other fields. At its core, the “Tiebout hypothesis” specifies that citizens choose the community to live in that best satisfies their preferences for public goods and services and ability to pay. In this case, the service packages offered by governments are “more or less set.” Thus, it is the existence of numerous governments within a region (offering different service and revenue packages), the mobility of residents, and their knowledge of the contents of revenue and spending packages that create a competitive public market for residents (Stein, 1987; Dowding, et al, 1994). In the long run, competition leads to the efficient allocation and production of goods and services by governments and a homogenous sorting of residential populations within that market (Kenyon, 1997).

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Tiebout’s work precipitated a great volume of empirical research and theoretical elaboration. With respect to tax competition, one fundamental development occurred in the work by Oates (1972) who expanded Tiebout’s argument from services to taxes. He established that resident or household mobility would be affected by tax rates or burdens in addition to service packages. Another important extension occurred with Oates and Schwab (1991) who applied the logic of tax competition to mobile capital rather than households. In their model, governments compete for capital by lowering taxes and providing services to firms to locate within their community, which increases residential wages. As such, the focus is still on maximizing the welfare of residents, who are voters, although the logic of competition could be easily extended to include the mobility and welfare of firms (Wilson, 1999). An important feature of this conceptualization is that competition between governments is indirect. Each government independently chooses fiscal policies that maximize the welfare of residents (and firms) within the region, rather than directly examining the fiscal policies of other governments in the region. A government’ policy choices are intended to increase demand for property and attract more capital within its boundaries, which indirectly affects the level of capital in other governments and gives rise to competition. Thus, when a government lowers its tax rate on mobile capital it gains capital at the expense of other governments (Wilson, 1999). For example, the rate differences in sale taxes among municipalities in a region will influence shoppers to migrate to municipalities with lower rates. In this case, a rise in a jurisdiction’s sales tax rate increases the amount of shopping done by residents in lower tax jurisdictions which then increases the latter’s tax base. This tax base change is called the "public consumption effect,” and is a tax externality (Dahlby et al, 1999). Similarly, an increase in property tax rates will encourage residents and businesses to move to lower tax jurisdictions,

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which increases demand for property and property values in the lower tax jurisdiction. This tax choice also adds capital to the lower tax, higher service jurisdictions through growth. Spillover models examine the impacts of jurisdictional externalities on other jurisdictions in a region. These externalities can be beneficial or harmful and can encompass revenue and spending effects. For instance, if government A spends more on police services than neighboring community B it may push crime from community A into community B, which then increases the need for police services in community B (Revelli, 2002b). Another type of spillover effect occurs when voters, taxpayers, and other consumers evaluate their government’s services and costs by comparing its performance to others in the region (Salmon, 1987). These groups then pressure their government to perform equal or better than other governments, which results in governments mimicking each other’s fiscal behavior (Ladd, 1992; Case et al, 1993). In this case, information spillover from other jurisdictions directly affects the level of public goods and services relative to taxes, and the service-tax ratio becomes the yardstick criterion used by taxpayers and consumers when voicing their approval or disprova l of government (Brueckner, 2003). The primary form of pressure or voice recognized by this research is voting because of its direct impact on the behavior of self- interested politicians who desire re-election and face competition from electoral opponents (Boyne, 1996; Hettich and Winer, 1997). However, other forms of voice and political participation may be used to influence government behavior and policy choices (Sharp, 1984; Lyons and Lowery, 1989). Unlike the mobility mechanism that specifies that residents have an indirect effect on government fiscal behavior, via the demand for and cost of public goods and services, the voice mechanism specifies that stakeholders, who are

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voters primarily, have a direct effect on government choices. 1 In this case, the majority of theoretical and empirical work on mimicking behavior focuses on revenues (Ladd, 1992; Heyndels and Vuchelen, 1998; Revelli, 2001; Case, 1993), rather than spending (Case et al, 1993, Revelli, 2002b). Another issue that arises with the voice mechanism is what tax features are relevant to taxpayers’ opinions about government and voters decisions. There is a wealth of empirical and theoretical literature on the ignorance and attentiveness of the public to tax levels, some of which is contradic tory. What is generally known, however, is that taxes are highly salient to the public as demonstrated by their strong opinions to taxation in surveys (Hansen, 1983; Lowery, 1985), but they are also unresponsive to tax levels as demonstrated by the weak link between voting behavior and tax changes (Bowler and Donovan, 1995). One reason for this weak link is the public’s lack of knowledge of true taxation levels, due to the fiscal illusion created by complex tax systems or the high costs (rational ignorance) of obtaining tax information (Wagner, 1976). Complex tax systems, such as exists in governments with highly diversified revenue structure, are costly for taxpayers to understand and create the illusion of lower tax burdens than what actually exists. The lumpiness of tax payments can also affect taxpayers’ level of information and perceptions of tax burden (Hansen, 1983; Bowler and Donovan, 1995). Taxes that are paid incrementally, such as sales or payroll taxes, are more lumpy, which makes their total tax burden less visible to taxpayers. Consistent with these arguments, Besley and Case (1995) test a model of yardstick competition that assumes that voters respond to tax changes, which are more visible than other types of spending and revenue information. Models of Tax Competition

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Conceivably, taxes and spending may be affected by voice and exit mechanisms equally and simultaneously.

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Empirical models of tax competition for both exit and voice specify a jurisdictional reaction function that explains a jurisdiction’s policy decision as a function of preferences (taxpayer, consumer, or voter), spending needs, resources, and the policy choices of “neighboring” municipalities (Brueckner, 2001). It is important to emphasize, however, that neighboring jurisdictions’ policy choices affect each other indirectly in the resource flow framework, but directly within the spillover framework. More specifically, the reaction function for the spillover model directly incorporates the policy choices of neighboring governments. In the resource flow model, government policy choices directly affect the resources of neighboring jurisdictions, which then affects the policy choices of those jurisdictions. Thus, competition under resource flow is implicit but explicit for the spillover framework. Theoretically, then, the reaction function for the resource flow model does not contain the policy choices of neighboring municipalities. However, in reduced form, the resource flow and spillover models are technically the same and may generate the same results empirically. This condition makes it difficult to distinguish between these two underlying mechanisms through empirical research. In most cases, the concept of neighbor is defined in term of spatial proximity—either contiguity (sharing a border) or spatial distance. However, the term may be defined more broadly to include other criteria or definitions of neighbor that encompass economic or demographic similarities. For instance, Case et al. (1993.) define neighbor according to similarity of income and racial make-up, but neighboring governments can share other similarities that are not spatial. Within the exit framework, defining neighbors according to geographic proximity makes the most the sense at the local level, and especially for local governments in a large metropolitan area (Heyndels and Vuchelen. 1998). Such cond itions offer residents, firms, workers, and shoppers numerous options for location, employment, or shopping.

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In contrast, the voice mechanism may be more pronounced at the state level or in smaller metropolitan areas where there are fewer governments to compare (Besley and Case, 1995). Although their empirical models may be indistinguishable, one way to discriminate between the mechanisms of exit and voice in tax competition behavior is to examine their implications for different types of taxes or revenues. If exit is the predominant mechanism among, for example, local governments in a metropolitan area, and if the capital generating tax revenues has various levels of mobility, then one would expect to see more evidence of tax competition for taxes generated from mobile capital than taxes generated from immobile capital. Similarly, if voice is the predominant mechanism, then one would expect to see more evidence of tax competition for visible and salient taxes than invisible or insignificant taxes. Table 1 below demonstrates this logic for different types of local revenues.

TABLE 1: OBSERVED COMPETITION AMONG LOCAL GOVERNMENTS WITHIN A LARGE METROPOLITAN AREA. Observed intermunicipal tax competition

VOICE MECHANISM

High visibility High competition Low visibility Low competition

EXIT MECHANISM High mobility High competition Payroll tax A indeterminate Sales tax, developer fees, To C total revenue burden A

Low mobility Low competition Property & B income tax Utility tax, water fee indeterminate

D

Revenue Mobility and Visibility Although residents and firms are relatively mobile by comparison to buildings and structures, the high costs of moving households and firms constrain migration in response to fiscal policy choices. The mobility of residents and firms is further constrained by the capitalization of changes in property tax rates, which alters property values (Oates, 1969; Yinger, et al, 1988). An

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increase in property tax rates is capitalized into property prices, which ultimately leads to lower property values. Decreases in property tax rates have the opposite effect. In either case, current property owners bear the full impact of the tax change in the form of a capital gain or loss (Yinger, et al, 1988). Due to a reduction in their property va lues, owners have more difficulty relocating from communities where property taxes are high by comparison to communities with low property taxes, which stimulates demand and increases property values. The combined effects of the high costs of migration and the capitalization of property taxes make local property tax a revenue source with relatively low mobility. In contrast, the capital that generates sales tax revenue is relatively mobile due to the ease and low cost of changing shopping locations, which moves sales tax revenue from one jurisdiction to another and motivates government to compete with each other. Similar to the capitalization of property taxes, higher sales tax rates increase the cost of goods, which encourage shoppers to migrate to jurisdictions with lower sales tax rates. The mobility of income tax capital depends on whether the tax is levied on firms’ payrolls or residential and business income within the jurisdiction. Because changing jobs within a large metropolitan region is relatively easy and less costly by comparison to moving residents or firms, the capital that generates payroll tax revenue (levied on workers within a jurisdiction) is more mobile than the capital that generates property or general income taxes (levied on businesses and residents within a jurisdiction). Other sources of revenue capital for local governments, such as charges, fines, and fees, have varying levels of mobility depending on the base. According to this argument, if mobility and exit are the primary mechanisms driving tax competition among such governments, then a reaction function for sales and payroll tax rate or burden would show more evidence of competition than a reaction function for property and income taxes.

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Additionally,

consistent with the claims that competition for residential and firm location is based on the total tax and revenue package imposed by governments within a region, one would expect to see high competition for total revenues. With respect to visibility, taxpayers may know the sales tax rates or charges of different communities, but the total sales tax or charges are less evident due to the lumpiness of these payments. In contrast, property taxes and income taxes, which are usually paid in one lump sum, are much more visib le. Also, the salience of property taxes to taxpayers is very evident from the widespread existence of tax and expenditure limitations that target property taxes (Joyce and Mullins, 1991). According to this logic, if voice (visibility and salience) is the primary mechanism driving tax competition, then a reaction function for property and income taxes would show more evidence of competition or tax mimicking than a reaction function for sales taxes or charges. One would also expect low tax competition for total tax or revenue burden under the voice mechanism due the complexity of assessing the burden of all taxes and revenues combined. What makes these arguments particularly relevant for comparing exit and voice mechanisms are the contradictory outcomes associated with each mechanism in cells B and C in Table 1. In contrast, the outcomes associated with cells A and D regarding exit and voice are indeterminate because the mechanisms predict the same outcome in each cell. Most of the empirical research that examines spatial tax competition for different types of taxes focuses on state governments, where there are lower levels of capital mobility overall compared to local governments. For instance, both workers and shoppers are less mobile between states tha n localities, which affects personal income taxes and sales taxes respectively. At the state level, Besley and Case (1995) find evidence for tax competition among states for change in personal income tax liability and change in total sales, income, and corporate taxes per

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capita combined. Rork (2003) finds that state tax rates respond positively to neighboring tax rates for cigarettes, gas, and corporate income, but negatively for sales tax and personal income. He argues that at the state level, the capital affecting tax bases for the former group of taxes (positive response) is more mobile than for the latter group of taxes (negative response) that are more broad-based. It should be noted that, according to his results, state tax rates decrease for the latter group when neighbors tax rates increase, and vice versa, which suggests a “backwards” competition rather than a lack of competition. At the local level there is much evidence for the existence of tax competition for property taxes (Revelli, 2001; Brueckner and Saavedra, 2001; Heyndels and Vucchelen, 1998; Ladd, 1992) and other taxes including personal income (Heyndels and Vucchelen, 1998) and total tax burden (Ladd, 1992). However, Ladd (1992) found no evidence of tax competition for sales taxes. Research Design and Model The general form of the spatial reaction function in the competition literature can be expressed as:

Ti = ∂ X i + β

∑ω j ≠i

ij

Tj + εi

where Xi represents the set of non-spatial independent variables, and ∂ represents their coefficients. The expression Σ(ωijTj) is the spatial dependence variable (known as a spatial lag operator) in which Tj is the tax variable for all other municipalities in the system. The term ωij is a matrix representing the set of proximities of each municipality to all other municipalities, or some subset of municipalities that are designated as neighbors with other values being zero. Thus, the spatial lag operator represents an aggregation of the tax variable for all municipalities weighted by their proximities to each other or some other neighbor criterion. 2 The coefficient β

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The concept of spatial lag is similar to a time lag, except that spatial lags are in N dimensions and time lags are in one dimension only. Theoretically, spatial lag operators can be defined for high orders spatial processes, similar to

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represents the estimated effect of the lag operator on the dependent variable, and ε i is the error term which is assumed to be normally distributed with a constant variance and independent across observations. (See Appendix A for details on how distance is calculated and other forms of spatial dependence). Most of the empirical research cited here measures the tax variable using tax rate (Rork, 2003, Brueckner and Saavedra, 2001; Revelli, 2001; Heyndels and Vuchelen, 1998). However other variables are used such as change in tax burden (per capita) (Besley and Case, 1995), tax burden (calculated with income) (Ladd, 1992), and changes in tax liability (index calculated with variables such as income, family size, and other taxes paid) (Case, 1993). In this case, however, tax burdens make more sense for jurisdictions that define tax bases in dissimilar ways. These models also include a wide variety of va riables and ways of determining the ωij matrix. As indicated previously, spatial proximity is usually measured as simple distance or contiguity, but sometimes the matrix is weighted to reflect variations in population, distance decay, or other factors. Another component of the reaction functions tested in previous studies is spending needs that are measured using a variety of variables depending on type of government such as race, education, unemployment, population age (young and old), population density, housing need, debt, school enrollment, and urbanization. Resource variables in these models include income per capita, grants or other shared revenues, employment, population growth, and measures of the size of the tax base. Research on tax and expenditure mimicking also contain measures of voter or government policy preferences using features of the voting population (e.g. % vote for

higher orders of time lags (e.g. t -2 or t-3), but the multiple dimensions of spatial lags and the variety of potential ωij matrices create unique methodological and estimation problems.

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incumbent) or the government (e.g. party in power) to control for the effects of politics on tax and revenue levels. The equations we use to test the relevance of exit or voice in tax competition estimate property tax rate, sales tax rate, and total own-source revenue burden as a function of a spatial lag variable and other control variables for 238 municipalities in the Chicago metropolitan areas. 3 More specifically, we model spatial dependence as spatial autocorrelation, which stipulates a functional relationship between tax and revenue burdens (or rates) in neighboring municipalities. Competition and mimicking exist when neighboring municipalities’ tax and revenue burdens are observed to affect each other, and this effect should increase the closer the municipalities are to each other. A detailed expression of the model shown below presents all the variables (Ti and Xi). The model shows three Ti equations estimated in two stages. The first stage regresses various indicators of spending needs, demands, and costs on municipal expenditures to obtain predicted expenditures (Y) for all municipalities. The second stage uses the predicted values as a replacement, or instrumental variable, for the actual expenditures in the property tax and total revenue effort equations. This two-stage method reduces the problems of inconsistent and biased parameters associated with expenditures being endogenous in the primary equations. 4 T = own-source revenue burden (OSR), property tax rate (PT), sales tax rate (ST) OSR = f (X1 , RD) PT = f (X1 ) ST = f (X1 ) X1 = EX, RC, RL, CP, HR, CK, PG RC: tax or revenue base (wealth or capacity) 3

Although there are 264 municipalities in the Chicago region, 26 of them have missing crime data, which removes them from the analysis. These municipalities, most of which are very small, contract with the county to provide police services and, therefore, do not collect crime statistics. 4 A more accurate method of calculating the instrumental variables would be to estimate separate expenditure equations for the property tax and revenue effort equations using all exogenous variables from the appropriate 2nd stage equation. However, this implies that there are two sets of expenditures for each municipality, which is not the case.

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RL: CP: HR: CK: PG: RD: EX:

residential land use fiscally conservative & informed population home rule Cook County population growth revenue diversification predicted expenditures = f (DS, SN, CS, IA, FD) DS: demand for services, SN: spending needs CS: cost of services IA: intergovernmental aid FD: municipality within a fire district

In addition to spending levels, the Ti equations control for a range of other conditions that may affect tax rates and own-source revenue burden including revenue capacity (RC), fiscal preferences and information of residents (CP), differences in statutory restrictions on taxation (HR), and the government’s fiscal structure (RD). The years of data for which the model is estimated are 1998-2000, inclusive. The property tax and revenue burden equations are also estimated using two forms of the spatial lag variable, one form representing contiguity and the other distance. The sales tax equation is estimated using only the distance form of the spatial lag variable because, as explained below, few municipalities with home rule sales tax privileges are likely to be contiguous. In the contiguity form, ωij is either zero or one, with one indicating that municipalities share a boundary, and zero indicating they are not ne ighbors. In the distance form, ωij represents the inverse distances (1/dij) between municipalities measured in longitude and latitude. The Data and Metropolitan Region The source of data for most financial variables is from the Illinois Office of the Comptroller that collects yearly fiscal information for all Illinois local governments from their audited comprehensive annual financial reports. These reports, which municipalities must produce by state law, contain the primary balance sheets and statements of revenues, expenditures, and

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expenses for a fiscal year. Other sources of data are from the 1990 and 2000 US Census and the Illinois Department of Revenue. To control for differences in accounting practices, especially cash versus accrual, and other differences in the timing of financial transactions, all financial data represent averages of the 1998-2000. Averaging financial data over time (three to five years) provides a broader view of a government’s financial structure and condition than just one year, helps to reduce measurement error, and negates many of the differences in when and where municipalities record their revenue and expenditure transactions. To obtain comparable data for the demographic and socioeconomic variables, Census figures for 1990 and 2000 were interpolated to the years 1998-2000. Cook County, which contains about 45 percent of all municipalities in the database, also has a different system of property classification and assessment rate than the other counties in the state. In addition to having many more classes of property, the designated rate of assessment relative to market value for the main classifications of residential, commercial, and industrial varies from the statewide assessment standard of 33.3%. Within Cook County the designated rates of assessment for the main classifications are 16% (residential), 38% (commercial), and 36% (industrial). An additional complicating factor is that the actual rates of assessment in Cook County are lower than these designated rates. Although the state’s equalization process brings the median assessment rate for all properties in Cook County to 33.33%, a comparison of equalized assessed value (EAV) across the counties is not possible without correction for the underassessments of property in Cook County. Two features of this region and state, one of which we are exploiting, limit the generalizability of our findings to other regions and states. First, the State of Illinois is fairly liberal in the types of taxes it allows municipalities to levy and the rates they may apply. In

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addition to the property tax, all municipalities in Illinois may levy a sales tax on utilities (telephone, natural gas, and electricity) 5 , automobile rental, cigarettes, and motor vehicles. However, only home-rule municipalities6 possess the constitutional authority to tax sales receipts on general merchandise 7 , real estate transfers, gasoline, and motel occupancy. Thus, only home rule municipalities can have sales tax burdens higher than what is generated by the state sales tax. Additionally, unlike non-home rule municipalities, home rule municipalities have no property tax restrictions. 8 This feature allows us to compare tax competition among local governments for different types of taxes. The second unique feature to this region and state is that Illinois has more local governments (6,835) than any other state, many of which are concentrated in the six-county Chicago region (2126) (U.S. Bureau of the Census, 1999) . These units consist of special districts, authorities, corporations, and commissions that supply important services to communities, and most have either guaranteed or non-guaranteed bonding authority. Because these units are proximate and overlapping with the municipalities, and they do contribute to the overall tax burden on residents and businesses in the municipality, tax competition theory suggests governments, residents, and firms will take these factors into account in determining their own tax levels (vertical competition). Data on these overlapping governments’ fiscal policies were not collected. Thus, additional analyses may need to incorporate these effects into the models.

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Utility taxes imposed by municipalities are limited to 5 percent of the gross receipts of businesses that supply gas and electricity to users within their boundaries, and telecommunications taxes are limited to 5 percent of the gross charges for services. 6 The Illinois constitution defines a “home-rule” municipality as any incorporated municipality with population greater than 25,000 unless a popular referendum rejects it, or any other municipality whose voters approve homerule status. 7 Non-home rule municipalities may levy a sales tax on general merchandise under certain conditions, and all municipalities receive a portion of the state sales tax based on the sales receipts within their boundaries. Thus, state sales tax revenues distributed to Illinois municipalities are similar to other own-source revenues. 8 Growth of property tax collections in non-home rule municipalities is limited to the lesser of five percent or the rate of inflation.

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The Variables Appendix B presents the operationalization of all variables in the model, and Appendix C presents descriptive statistics for those variables. Own-source revenue burden (OSR) is defined as the total own-source revenues divided by own-source revenue capacity or wealth.

Own-

source revenues include all taxes received by municipalities that are generated by the value of the resource bases within their boundaries. This includes state-designated sales taxes, which are distributed back to the municipalities based on point of sale, but not state income tax that is distributed based on population. Own-source revenues also include all non-tax revenues collected by the municipality that are not part of their enterprise funds. Thus, own-source revenues exclude charges and fees that are used to deliver services such as water, sewerage, and parking. The denominator for this variable is defined in great detail in Hendrick (2004). It is comprised of the weighted sum of three component indicators of the value of different municipal revenue bases. These indicators are EAV per square mile (property tax), sales receipts per capita (sales tax), and personal income per capita (all other taxes and revenue). The weights for each component are standardized regression slopes from a regression analysis with own-source revenues per capita regressed against the three indicators. Separate regression analyses are estimated for home rule and non-home rule municipalities to account for their differential access to the different revenue bases. Sales receipts per capita and own-source revenues per capita in the regressio n equation and the numerator of the final ratio are multiplied by the percent of the EAV in the municipality that is residential. This correction deflates the per capita measures for

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municipalities with high levels of nonresidential property and inflates it for municipalities with high level of residential properties. 9 Property tax rate (PT) is measured as total property tax revenue divided by total equalized assessed value (EAV) of all property in the municipality. This variable represents the percent of EAV that is paid by the average taxpayer to the municipal government in property taxes and, thus, represents the property tax rate. Sales tax rate (ST) is measured as total sales tax revenue divided by the total value of sales receipts. To control for different service needs and preferences across municipalities, which affect the level of taxes necessary to support spending, all models include predicted expenditures per capita (weighted by percent residential EAV) as an independent variable (EX). These predicted values are calculated from a separate regression equation that includes the following independent variables: income per capita, crime per capita (weighted), median age housing, square miles, whether the municipality is in a fire district, population density, and intergovernmental revenue per capita (weighted). Income per capita measures both the wealth of residents and their demand for services. Crime per capita and presence of a fire district are indicators of public safety needs, and population density measures relative costs per capita, which decline as density increases. Median age of housing measures the age of the infrastructure. This variable and square miles are indicators of public works needs (e.g. infrastructure repair and replacement). Intergovernmental aid, which is predominately state revenue sharing, is included in the expenditure equation to acknowledge the well-recognized flypaper effect of such aid on

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Hendrick (2004) demonstrates the extent to which all per capita measures distort the conditions being measured as the level of nonresidential properties increases in a municipality. This distortion is more problematic among suburban communities where levels of residential, commercial, and industrial properties can vary tremendously. This distortion is also the reason for using EAV per square mile rather than EAV per capita.

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governmental spending (Dollery and Worthington, 1996). This variable should have a positive effect on revenue, sales, and property tax burden. Own-source revenue capacity, EAV per square mile, and weighted sales receipts per capita are included as independent variables in the appropriate equation to control for the different levels of revenue wealth and tax capacity among municipalities (RC). All things being equal, the tax rate or burden required to support the same level of services in wealthier municipalities and those with greater sales or property tax bases will be less than in poorer municipalities or those with smaller tax bases. Thus, these variables should have a negative relationship with their respective dependent variables. Although these variables may inflate the R2 ’s of the equations because revenue capacity is a component of each dependent variable, we are less concerned with the R2 values or the estimated slopes for these variables given that their primary role is to control for varying resource capacities. The percent of professional and managerial population is used as a measure of the fiscally conservative and informed population (CP). According to Clark and Ferguson (1983), municipalities with more managers and professionals are more likely to elect governments with a new fiscal populist culture. Such governments are more fiscally conservative and tend to value greater efficiency and lower taxes. Additionally, communities with more professionals and managers are likely to be more informed about their government and exercise voice methods of influence to a greater extent. This variable should have a negative effect on revenue and tax burdens. The percent of residential EAV (RL) is included in the revenue and tax equations as an indicator of opportunities for revenue generation, especially in the case of property tax burden, and the general character of the community. Municipalities that are predominately residential

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will be under more pressure to use property taxes to fund services than those with commercial or industrial properties and, thus, will have higher property tax burdens. It is not clear, however, what effect “residentialness” will have on sales tax rates. Additionally, it is not clear what impact the percent of residential EAV will have on total revenue burden, although the variable could have a positive effect to the extent that the wealth component of the total revenue burden variable does not adequately account for the exportability of all taxes and revenues to nonresidents. Population growth (PG) is an important construct to include in the tax and revenue equations because of its positive impact on tax and revenue bases. Newly developed properties in fastgrowing municipalities enhance all revenue bases beyond what is reflected in the revenue capacity variable, and often generate increases in revenues that are greater than increases in operational spending needs which create substantial budgetary reserves. 10 Municipalities in this position often have lower revenue and tax burdens, especially for property taxes, relative to municipalities that are not growing or whose revenue bases are declining. Because the distribution of percent population change is so skewed due to the rapid growth of some very small municipalities in the region, and negative changes prevent logarithmic transformations, population change is measured as the ratio of population in 2000 relative to population in 1990. All revenue equations include a dummy variable for home rule (HR) because of the fiscal limitations imposed on non-home rule municipalities, especially for property and sales taxes. In this case, municipalities with home rule privileges are expected to have higher property and sales tax rates and higher revenue burdens. Although differences in access to revenues between home rule and non-home rule municipalities are represented in the calculation of the revenue burden variable, home rule powers go beyond revenue collection. Broad-based home rule capabilities in 10

Demands for new infrastructure are usually funded through borrowing or other means such as impact fees.

21

areas such as debt, statutory regulation, and certain contractual obligations may have an indirect impact on revenues and taxes that is not recognized fully by this dependent variable. Additionally, a dummy variable designating municipalities within Cook County (CK) is included in these equations to recognize the potential impact of the different assessment rates among the property classifications in this county, and other qualities that make Cook County unique relative to the other counties in the region. Finally, an index of revenue diversification (RD) is included in the revenue burden equation as an indicator of the cumulative effect of a government’s revenue choices on its fiscal structure. The index is based on the Hirschman-Herfindahl Index of concentration, similar to that used by Suyderhoud, but includes all four categories of own-source revenue (Suyderhoud, 1994). While governments’ revenue choices are constrained by the opportunities of their fiscal environment, including revenue capacity and statutory limitations, they make numerous strategic choices within these constraints and may even expand their revenue options through activities such as economic development. The impact of RD on revenue burden is a matter of some debate among economists and financial managers. Citing fiscal illusion effects, some expect high levels of RD to increase revenue burden, while others argue that RD allows for more efficient functioning of the government and as such might reduce revenue burden (Hendrick, 2002).

Estimation Results The revenue and tax equations are estimated using two separate procedures — maximum likelihood (ML) and robust instrumental variables 2SLS (IV). 11 Estimation is performed using Spacestat (Anselin, 1992), which also calculates the two forms of the ωij matrix and the spatial

11

See Brueckner (2003) for details of the two procedures.

22

dependence variable (Σ(ωijTj)), and provides supporting statis tics that test for the presence of spatial error and heteroskedasticity. The inappropriateness of OLS estimation of spatial dependence models has focused attention on ML estimators as having the most desirable properties (Anselin, 1988). IV approach is also commonly used in the empirical literature. However, there is much debate about the superiority of ML over IV in estimating spatial regression models. Anselin and Bera (1998) note that unlike the ML approach, the instrumental variable approach does not require the assumption of normality. However, Kelejian and Prucha (2002) demonstrate that IV estimation is inconsistent unless panel data are available. Table 2 shows the results for the expenditure equation that produced the instrumental spending variable for the revenue and tax equations. It indicates that income per capita, as a measure of both resources and demand, has the greatest impact on expenditures. As expected, spending is higher in municipalities with more crime, an older infrastructure, no fire district services, lower population density, a bigger service area, and more intergovernmental revenue. The coefficient for the last variable is particularly interesting because it shows that IGR has a dollar for dollar effect on weighted expenditures; every dollar increase in IGR per capita results in one additional dollar of municipal expenditures. Results also show that being in a fire district reduces spending by $42 per person, and municipalities spend $15 more per person for every $1000 increase in income per capita. With respect to the primary research question on which tax competition mechanism is predominant among suburbs in the Chicago metropolitan area, Tables 3, 4 and 5 show that the spatial lag variables are highly significant for all property tax equations, insignificant for the sales tax equations, and highly insignificant for the total revenue equations. Thus, it seems that municipalities’ property taxes are influenced by their neighbors’ property taxes, but neither their

23

sales taxes nor total revenues are much affected by their neighbors’ levels. This lends strong evidence that tax competition is based on visibility rather than mobility in this region. Additionally, the results show that spatial dependence is more pronounced when neighbor is defined by distance rather than contiguity. In this case, a 10% decrease in a next door neighbors’ property tax rate (contiguity) yields a 2.3% decrease in a municipality’s own property tax burden or rate, but an 8.6% decrease if you consider the neighbors’ distance from the municipality using the ML estimation method. According to the IV estimation method for distance, however, a 10% decrease in a neighbors’ property tax rate yields a greater decrease in a municipality’s own burden (13%). Although the spatial lag coefficients for sales tax burden are not statistically significant, the coefficients (distance only) are large relative to those for property tax burden. Specifically, a 10% decrease in neighbors’ sales tax rate corresponds to a 4.2% and 15% decrease in a municipality’s sales tax rate for the ML and IV estimation methods respectively. The high standard errors for these coefficients, which account for their insignificance, indicate that the coefficients are not very efficient and may be a function of the inconsistent sales tax privileges among municipalities in this region. As such, this region may not provide the best test of spatial dependence for sales taxes and, ultimately, the primary research question. But clearly, property taxes, with relatively immobile base, are spatially dependent on neighboring property taxes by comparison to sales taxes and total revenue burden. 12 It should also be mentioned that, according to the tests of spatial dependence and error specification in all tables, the errors are not spatially dependent, but there is heteroskedasticity. Thus, we can have confidence in the spatial lag coefficients themselves, but the 2SLS in IV approach, which is robust with respect to

12

The spatial lag coefficients for revenue burden are not meaningful because the revenue burden index does not translate directly into dollars relative to an interpretable base.

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heteroskedastic errors, may provide more appropriate estimates of the standard errors of the coefficients. Also of interest here are the coefficients for some of the other variables in tables 3 through 5. The beta coefficients for expenditures show that they have a much greater effect on total revenues by comparison to property and sales tax separately, which makes sense given the wide range of municipal revenue sources in Illinois. Additionally, the size or value of the base also has a much stronger effect on total revenue burden than on property or sales tax burden, but resource base is more important for sales taxes than property taxes. Percent residential EAV has no effect on total revenue burden, but significant effects on property and sales tax rates. This indicates the extent to which residential communities must rely on property taxes to fund services, but also the pressure on higher sales taxes in residential communities. In this case a 10% increase in residential EAV yields a .06 increase in property tax rate (about 5% of the mean property tax burden), but a .03 increase in sales tax rate (about 2.5% of the mean sales tax burden). Home rule has no effect on total revenue or property tax burden, but increases sales taxes by .28 (23% of mean). These tables also show that, even when controlling for EAV and other factors, property tax rates are significantly higher in Cook County than the rest of the region, but sales taxes are lower. Moreover, there is no difference in total revenue burden between municipalities in Cook County and other counties in the region. Higher percentages of managerial and professional population lower sales taxes, property taxes, and total revenue burden, with the greatest impact occurring on property taxes (about 20% reduction for 10% increase in managerial and professional population). This is consistent with the voice mechanism because they are the most informed individuals in the community, who can enable voice mechanism effectively. Finally, population growth has no effect on total revenue burden or sales

25

tax rate, but it reduces property tax rates by about 2.5% at the mean for every 10% increase in the population ratio from 1990 to 2000. Conclusion The findings from this research are consistent with other studies showing a strong spatial relationship between property taxes in neighboring jurisdictions and, therefore, corroborate the existence of property tax competition. This research also provides evidence that property tax competition exists even where governments have many more taxing options. The research further corroborates prior findings of no spatial relationship among sales taxes. In contrast to prior studies, however, this research examines observed variations in spatial relationships for different taxes or revenue sources in the context of exit versus voice mechanisms to provide evidence on which mechanism underlies the financial choices of governments. Although exit and voice pressures on government can exist simultaneously, this research suggests that voice mechanisms, in conjunction with fiscal illusion, have a predominate effect on taxes and total revenue burden of municipalities. In particular, this research shows that voice predominates over exit even among numerous municipalities in a large metropolitan region where comparison of tax rates is more difficult, relative to states or regions with fewer municipalities, and where residents and firms have many different taxing and service locations to choose from. Future research on this subject would benefit from more formal considerations of the mobility, visibility, and salience of different types of revenues and taxes than what is provided here. The debate over exit versus voice also could be advanced by examining spatial relationships of revenues among local governments in regions with less restricted sales taxation privileges and different types of local income taxes. Another interesting subject matter for research on the influence of exit and voice is the numerous revenue sources governments use to

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finance and promote growth and development, including debt, special assessments, impact fees, in-kind developer agreements, and tax expenditures. Scholars and practitioners recognize the competitive nature of these activities and policy choices in which the mobility and direct influence of numerous stakeholders (e.g. residents, firms, developers, fiscal consultants) are likely factors in the outcomes. Such studies will promote a better understanding of the nature of competition in urban metropolitan regions.

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REFERENCES Advisory Commission on Intergovernmental Relations (ACIR). 1991. Changing Public Attitudes on Government and Taxes, (Washington, DC: US Government Printing Office) Anselin, Luc. 1988. Spatial Econometrics: Methods and Models (Boston: Kluwer Academic Publishers) Anselin, Luc. 1992. SpaceStat: A Program for the Analysis of Spatial Data. (Morgantown, WV: Regional Research Institute) Anselin, Luc and Anil Bera. 1998. “Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics,” in A. Ullah and D. Giles, eds., Handbook of Applied Economic Statistics (New York: Marcel Dekker): 237-289 Besley, Timothy and Anne Case. 1995. "Incumbent Behavior: Vote Seeking, Tax Setting, and Yardstick Competition," American Economic Review, 85(1): 25- 45. Boyne, George A. 1996. "Competition and Local Government: A Public Choice Perspective," Urban Studies, 33(4-5): 703-721. Bowler, Shaun and Todd Donovan. 1995. "Popular Responsiveness to Taxation," Political Research Quarterly, 48(1): 79-99 Brueckner, Jan K. 2003. "Strategic Interaction Among Governments: An Overview of Empirical Studies," International Regional Science Review, 26(2): 175-188. Brueckner, Jan K. and Luz A. Saavedra. 2001. "Do Local Governments Engage in Strategic Property-Tax Competition?" National Tax Journal, 54(2): 203-229. Case, Anne C., Harvey S. Rosen, and James R. Hines, Jr. 1993. “Budget Spillovers and Fiscal Policy Interdependence: Evidence from the States,” Journal of Public Economics, 52(?): 285-307. Case, Anne. 1993. "Interstate Tax Competition After TRA86," Journal of Policy Analysis and Management, 12(1): 136-148. Clark, Terry N. and Lorna C. Ferguson. 1983. City Money: Political Processes, Fiscal Strain and Retrenchment (New York: Columbia University Press) Cliff, Andrew and John K. Ord, 1981. Spatial Processes: Models and Applications (London: Pion,) Dacey, Michael. 1968. “A Review of Measures of Contiguity for Two and K-Color Maps,” in B. Berry and D. Marble, eds., Spatial Analysis: A Reader in Statistical Geography (Englewood Cliffs, NJ: Prentice-Hall); Dahlby, Bev, Robert Henry, Michael Keen, and David E. Wildasin. 2000. “Recent Developments in Tax Coordination: A Panel Discussion," Canadian Tax Journal, 48(2): 389-439. DiLorenzo, Thomas J. 1982 "Utility Profits, Fiscal Illusion, and Local Public Expenditures." Public Choice, 38: 3: 48-57. Dollery, Brian E. and Andrew C. Worthington. 1996. “The Empirical Analysis of Fiscal Illusion," Journal of Economic Surveys, 10(3): 261-297. 28

Dowding, Keith, Peter John and Stephen Biggs. 1994. "Tiebout: A Survey of the Empirical Literature," Urban Studies, 31(4/5): 767-797. Hansen, Susan B. 1983. The Politics of Taxation : Revenue Without Representation, (New York: Praeger) Haiyashi, M. and R. Broadway. 2001. “An Empirical Analysis of Intergovernmental Tax Interaction: The Case of Business Income Taxes in Canada” Canadian Journal of Economics 34: 481-503 Hendrick, Rebecca. 2002. “Revenue Diversification: Fiscal Illusion or Flexible Financial Management,” Public Budgeting & Finance 22(4): 52-72. Hendrick, Rebecca, 2004. “Assessing and Measuring the Fiscal Health of Local Governments: Focus on Chicago Suburban Municipalities” Urban Affairs Review, 40(1): 78-114. Hettich, Walter and Stanley Winer. 1997. "The Political Economy of Taxation." In D. Mueller (ed.), Perspectives on Public Choice, (Cambridge: Cambridge University Press). Heyndels, Bruno and Jef Vuchelen. 1998. "Tax Mimicking Among Belgian Municipalities," National Tax Journal, 51(1): 89-101. Hirschman, Albert O. 1970. Exit, Voice, Loyalty: Responses to Declines in firms, Organizations, and States, (Cambridg, MA: Harvard University Press) Joyce, Phillip and Dan Mullins. 1991. “The Changing Fiscal Structure of the State and Local Public Sector: The Impact of Tax and Expenditure Limitations," Public Administration Review, 51(3): 240-253. Kelejian, Harry H. and Ingmar R. Prucha. 2002. “2SLS and OLS in Spatial Autoregression Model with Equal Spatial Weights," Regional Science and Urban Economics, 32(?): 691-707. Kenyon, Daphne. 1997. "Theories of Interjurisdictional Competition," New England Economic Review, 97(2): 13-? Ladd, Helen F. 1992. “Mimicking of Local Tax Burdens Among Neighboring Counties,” Public Finance Quarterly, 20(4): 450-467. Lowery, David 1985 "Public Opinion, Fiscal Illusion, and Tax Revolution: The Political Demise of the Property Tax," Public Budgeting and Finance, 5(3): 76-88. Lyons, William E. and David Lowery. 1989. "Citizen Responses to Dissatisfaction in Urban Communities: a Partial Test of a General Model," The Journal of Politics,51(4): 841-68 Oates, Wallace. 1972. Fiscal Federalism (New York: Harcourt Brace Jovanovich). Oates, Wallace E. 1969. “The Effects of Property Taxes and Local Public Spending on Property Values: An Empirical Study of Tax Capitalization and the Tiebout Hypothesis” Journal of Political Economy, 77(6): 957-971 Oates, Wallace and Robert M. Schwab. 1988. “Economic Competition Among Jurisdictions: Economy Enhancing or Distortion Inducing," Journal of Public Economics, 35(?): 333-54. Oates, Wallace and Robert M. Schwab. 1991. “The Allocative and Distributive Implications of Local Fiscal Competition.” In D. Kenyon and J. Kincaid, (eds.), Competition among

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States and Local Governments: Efficiency and Equity in American Federalism. (Washington, DC. The Urban Institute Press) Revelli, Frederico 2001. "Spatial Patterns in Local Taxation: Tax Mimicking or Error Mimicking?" Applied Economics, 33(9): 1101-1107. Revelli, Frederico 2002. "Testing the Taxmimicking Versus Expenditure Spill-Over Hypotheses Using English Data," Applied Economics, 34(14): 1723-1731. Rork, Jonathan C. 2003. "Coveting Thy Neighbors' Taxation," National Tax Journal, 56(4): 775-787. Salmon, Pierre. 1987. "Decentralization as an Incentive Scheme," Oxford Review of Economic Policy, 3(2): 24-43. Sausgruber, Rupert, Jean-Robert Tyran none. "Testing the Mill Hypothesis of Fiscal Illusion," University of Copenhagen, Institute of Economics, Discussion Papers, http://www.econ.ku.dk/wpa/pink/2004/0418.pdf Sharp, Elaine B. 1984. "Exit, Voice, and Loyalty in the Context of Local Government," Western Political Quartely, 37(1): 67-83. Stein, Robert M. 1987. "Tiebout's Sorting Hypothesis," Urban Affairs Quarterly, 23(1): 140-160. Suyderhoud, Jack P. 1994. “State-Local Revenue Diversification, Balance, and Fiscal Performance,” Public Finance Quarterly 22(2): 168-194. Tiebout, Charles M. 1956. "A Pure Theory of Local Expenditure," Journal of Political Economy, 64: 416-24. H? Wagner, Richard. E. 1976. "Revenue Structure, Fiscal Illusion, and Budgetary Choice," Public Choice, 25(4): 45-61. H Wilson, John Douglas. 1999. "Theories of Tax Competition," National Tax Journal, 52 (2): 269-304. H Yinger, John, Borsch-Supan, Axel, Bloom, Howard, and Ladd, Helen F. Property Taxes and House Values: The Theory and Estimation of Intrajurisdictional Property Tax Capitalization, Academic Press, 1988. U.S. Bureau of the Census. 1999. 1997 Census of Governments: Vol. 1 Government Organization (Washington, DC: Bureau of the Census).

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TABLE 2 REGRESSION ANALYSES RESULTS: EXPENDITURES

WEIGHTED EXPENDITURES PER CAPITA R2 = .60

Adj. R2 = .58

N = 233

Variable

Slope

Beta

St. Err

p

Constant Income per capita Wgt crime per capita Median age housing Land area (sq miles) Population density Fire district (0,1) Wgt intergovt. revenue per cap

8511 .015 2.18 -4.3 .2.5 -.012 -42 .921

.685 .157 -.235 .064 -.120 -.09 .133

2044 .001 .643 1.04 1.86 .005 24.6 .303

.000 .000 .001 .000 .18 .017 .09 .003

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TABLE 3 SPATIAL REGRESSION ANALYSES RESULTS: TOTAL REVENUE BURDEN TOTAL REVENUE BURDEN: OWN-SOURCE REVENUES / WEALTH OF REVENUE BASES DISTANCE WEIGHTS (1/di j)

CONTIGUITY WEIGHTS (0,1)

MAXIMUM LIKELIHOOD ESTIMATION Pseudo R2 = .54

log likelihood = -51

Variable Coeff Constant 4.1 Pred Expenditures per cap (Y) .001 Wealth (log) -.65 % of pop mngr & prof. -.004 % Residential EAV .001 Revenue diversification -.80 Home Rule .01 Cook County .03 Pop2000/pop1990 (log) .07 Spatial lag (ρ) .03 Tests of Spatial Dependence Spatial error (λ)

N = 235

Beta St. Er .52 .59 .0002 -.73 .06 -.11 .003 .05 .001 -.18 .20 .01 .04 .035 .06 .05 .08 .33

Statistic .06

p .81

56

.000

Test of Error Specification Breusch-Pagan Heterosked

p(Z) .000 .000 .000 .16 .43 .000 .80 .54 .38 .93

Ps. R2 = .54

log liked = -51

Coeff 4.1 .001 -.65 -.004 .001 -.81 .01 .03 .07 .02

Beta St. Er p(Z) .33 .000 .58 .0002 .000 -.73 .06 .000 -.11 .003 .15 .05 .001 .43 -.18 .20 .000 .01 .04 .80 .03 .05 .58 .05 .08 .39 .05 .66

Statistic .91 57

p .34 .000

ROBUST IV (2SLS) ESTIMATION R2 = .55

N = 235

Variable Slope Constant 3.70 Pred Expenditures per cap (Y) .001 Wealth (log) -.64 % of pop mngr & prof. -.003 % Residential EAV .001 Revenue diversification -.78 Home Rule -.003 Cook County .01 Pop2000/pop1990 (log) .05 Spatial lag (ρ) .33

R2 = .53 Beta St. Er .69 .58 .0002 -.73 .07 -.09 .003 .06 .002 -.18 .21 .003 .04 .015 .06 .035 .07 .45

p(Z) .000 .000 .000 .28 .40 .000 .94 .82 .41 .45

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Slope 4.05 .0015 -.64 -.004 .001 -.78 .01 .03 .07 -.008

N = 235 Beta St. Er p(Z) .40 .000 .60 .0002 .000 -.72 .07 .000 -.12 .003 .15 .04 .002 .57 -.18 .21 .000 .01 .04 .78 .04 .05 .52 .05 .07 .26 .06 .89

N = 235

TABLE 4 SPATIAL REGRESSION ANALYSES RESULTS: PROPERTY TAX RATE PROPERTY TAX RATE: PROPERTY TAX / EQUALIZED ASSESSED VALUE DISTANCE WEIGHTS (1/di j)

CONTIGUITY WEIGHTS (0,1)

MAXIMUM LIKELIHOOD ESTIMATION Pseudo R2 = .43

log likelihood = -227

Variable Slope Constant -.03 Predicted Expenditures (Y) .0007 EAV per sq mile -.0003 % of pop mngr & prof. -.02 % Residential EAV .006 Home rule .13 Cook .57 Pop2000/pop1990 (log) -.23 Spatial lag (ρ) .863 Tests of Spatial Dependence Spatial error (λ)

N = 233

Beta St. Er .24 .15 .0004 -.11 .0002 -.32 .005 .125 .003 .07 .09 .33 .10 -.08 .18 .09

Statistic .32

p .57

55

.000

Test of Error Specification Breusch-Pagan Heterosked

p(Z) .91 .05 .09 .000 .04 .17 .000 .20 .000

Ps. R2 = .42 Slope .83 .0007 -.0003 -.02 .006 .09 .64 -.29 .23

log likhd = -231 Beta St. Er

p(Z)

.23 .0004 .0002 .006 .003 .09 .12 .18 .06

.000 .09 .09 .000 .05 .32 .000 .12 .000

.13 -.10 -.35 .125 .05 .37 -.10

Statistic .03 58

p .86 .000

ROBUST IV (2SLS) ESTIMATION R2 = .52 Variable

N = 233

R2 = .41 Slope

Constant -.55 Predicted Expenditures (Y) .0008 EAV per sq mile -.0003 % of pop mngr & prof. -.02 % Residential EAV .006 Home rule .10 Cook .48 Pop2000/pop1990 (log) -.20 Spatial lag (ρ) 1.3

Beta St. Er p(Z) .35 .16 .0003 -.11 .0002 -.32 .005 .13 .002 .06 .09 .28 .12 -.07 .11 .26

.12 .02 .10 .000 .01 .26 .000 .08 .000

33

Slope .79 .0009 -.0002 -.03 .006 .03 .63 -.25 .21

N = 233 Beta St. Er

p(Z)

.19 .0003 .0002 .005 .003 .09 .11 .11 .08

.000 .01 .25 .000 .02 .77 .000 .03 .004

.18 -.08 -.40 .14 .015 .36 -.08

N = 233

TABLE 5 SPATIAL REGRESSION ANALYSES RESULTS: SALES TAX RATE SALES TAX RATE: SALES TAX / TOTAL SALES RECEIPTS DISTANCE WEIGHTS (1/di j) MAXIMUM LIKELIHOOD ESTIMATION Pseudo R2 = .35

log likelihood = -3.8

Variable Constant Predicted Expenditures (Y) Sales receipts per cap (log) % of pop mngr & prof. % Residential EAV Cook Home rule Pop2000/pop1990 (log) Spatial lag (ρ)

Slope 1.6 .0004 -.13 -.005 .003 -.06 .28 -.03 .42

Tests of Spatial Dependence Spatial error (λ)

N = 231

Beta St. Er .44 .15 .0001 -.25 .02 -.16 .002 .11 .001 -.06 .04 .30 .04 -.02 .07 .32

Statistic 2.3

p .13

63

.000

Test of Error Specification Breusch-Pagan Heterosked

p(Z) .000 .01 .000 .005 .02 .15 .000 .63 .19

ROBUST IV (2SLS) ESTIMATION R2 = .38

N = 231

Variable Constant Predicted Expenditures (Y) Sales receipts per cap (log) % of pop mngr & prof. % Residential EAV Cook Home rule Pop2000/pop1990 (log) Spatial lag (ρ)

Slope .34 .0003 -.13 -.005 .003 -.05 .28 -.06 1.5

34

Beta St. Er 1.04 .13 .0002 -.25 .03 -.15 .002 .12 .001 -.06 .04 .31 .04 -.04 .06 .89

p(Z) .74 .03 .000 .01 .02 .17 .000 .27 .10

APPENDIX A Spatial Dependence, the ωi j Matrix, and Estimation There are many forms for the ωij matrix, and the proper specification is one of the more difficult and controversial methodological issues in spatial econometrics. For instance, the matrix may incorporate the length of common borders of all units, the relative area of units, or their population with the distance factor (Dacey, 1968; Cliff and Ord, 1981). The simplest ωij matrix is binary with values of one indicating a neighboring unit, and zero designating a non-neighbor unit, although concept of what constitutes a neighbor has different interpretations. Ultimately, the contents of this matrix should be derived according to theoretical specifications of the nature of spatial dependence rather than ad hoc specifications of spatial pattern (Anselin, 1988). The weights used here are defined in the following way: ωij = (1/dij f); where d ij is the distance between municipalities i and j measured by longitude and latitude of the city center. Distance, d ij is calculated as the square root of [(latitudei – latitudej )2 + (longitudei – longitudej )2 ]. Using the inverse of distance refines the measure to reflect the ‘nearness’ of municipalities rather than their ‘farness.’ Thus, the coefficient ρ will be positive if communities close to each other affect each other’s tax effort more than communities that are farther away from each other. The parameter f is a friction parameter that can be used to inflate or deflate the distances (nearness). In addition to the ωij matrix defined above, we test variations on the neighbor/distance concept in which values of ωij = (1/dij f) for d ij