An Exploration of Revenue Structure Characteristics in Rural Municipalities

Abstract

This article tests for differences in revenue structure between small rural and nonrural municipalities. Colorado serves as a case study owing to its large number of small municipalities. Empirical analyses indicate that rural municipalities are less likely to adopt a local option sales tax, receive a smaller share of their total revenue from intergovernmental aid, and have less diversified tax systems compared to similarly sized nonrural municipalities. The article also shows these conclusions are sensitive to how one defines ruralness, indicating that what scholars know about public finance in rural communities is sensitive to the definition as well.

Keywords

municipal finance rural spatial local government

Most of the general purpose, subcounty governments in the United States are located outside metro areas. According to data from the U.S. Census Bureau compiled by the author, only about 3,600 of the approximately 36,000 subcounty governments are in urban centers. Despite the abundance of subcounty governments outside of metropolitan areas, however, the literature on fiscal policy and outcomes in rural jurisdictions remains underdeveloped, with more attention recently being given to public finance in large cities (Ross, Yan, and Johnson 2015; Chernick, Langley, and Reschovsky 2015). Among the studies that explore public finance matters in small towns, scholars have directed relatively little attention toward understanding the rural context.

This study is an effort, building upon Helpap (2017), to invigorate rural public finance research. The contribution to the local government public finance literature is twofold. First, previous studies in this literature draw insufficient, if any, distinction between rural towns and small towns, the concern of which is erroneously generalizing conclusions about one to the other. All rural towns are small, but not all small towns are rural. It is an empirical question if small, nonrural towns and rural towns differ in terms of their fiscal choices and outcomes, which the literature has not yet explored. Second, existing research inadequately defines ruralness. While most authors acknowledge the complexity of defining the term, there remains a tendency to define rural along a single dimension—most frequently, whether a municipality is located inside or outside a metropolitan statistical area (MSA). Unidimensional definitions fail to capture a basic intuition that ruralness has both a population and spatial component; rural towns are more sparsely populated and more isolated than nonrural towns (Morehouse, McBeath, and Leask 1984).

Whereas Helpap (2017) studied expenditures, this study considers the revenue side of the public budget and specifically analyzes four revenue structure variables: having a local option sales tax (LOST), reliance on user fees, reliance on intergovernmental aid (IGA), and tax revenue diversification. While there are many other ways one could measure revenue structures, the existing literature focuses on these four. In following the literature, the hope is that other scholars will find it easier to compare the results of this study to past efforts.

The next section discusses the motivation for the study in greater detail. After presenting a multidimensional definition of rural, the section thereafter describes the empirical strategy to explore differences in revenue structures between rural and nonrural but similarly populated towns. The article closes with a discussion of the study’s implications, a brief summary, and suggestions for future research.

Public Finance in Small and Rural Towns

All rural towns are small towns, but not all small towns are rural. Scholars have studied a variety of aspects of small town public finance: sales tax rates and differentials (Snodgrass and Otto 1990; Mikesell and Zorn 1986; Luna 2004; Rogers 2004), sales tax collection (Brorsen and Lansford 2013), property tax incidence (Fritz 1982), revenue diversification (Carroll and Johnson 2010), fiscal illusion (Maher and Johnson 2008), fiscal policy planning (Mattson 1994; Dougherty, Klase, and Song 1999; Morton, Chen, and Morse 2008), fiscal health (Hite and Ulbrich 1986; Honadle and Lloyd-Jones 1998), and general revenue and expenditure patterns (Mattson 2016; Brown 2000; Helpap 2017).

Upon closer examination of these studies, however, it is clear that scholars often fail to empirically appreciate the difference between rural and nonrural small towns, which raises two concerns. First, scholars’ understanding about how rural and nonrural small towns look in terms of their fiscal choices, and second, any policy prescriptions lawmakers intend to help rural small towns may be off target, instead helping nonrural small towns. Ruralness has a spatial component, and two similarly populated towns may choose quite different public finance policies because of the difference in proximity to urban centers (Chervin, Edmiston, and Murray 2000; Afonso 2016; Rogers 2004; Snowgrass and Otto 1990). The closer a small town is to an urban center, the easier it will find raising revenue through consumption taxes, for instance. However, while greater distances from urban centers make raising revenue more challenging, rural governments will generally have lower per capita costs of services. Thus, rural municipalities may find the cost of the challenge of raising revenue when further from urban centers is counterbalanced, it more difficult to raise revenue than more urban small towns, but they are likely to enjoy lower total costs of providing services.

Whether or not rural and nonrural small towns differ in terms of fiscal outcomes is an empirical matter and an issue the existing research does not adequately consider. While authors tend to acknowledge the challenge of defining rural (Dougherty, Klase, and Song 1999), simplified unidimensional definitions persist. Perhaps the most common approach is to define ruralness with respect to a jurisdiction’s location inside or outside of an MSA (Rubenstein and Freeman 2003; Snodgrass and Otto 1990; Zhao and Hou 2008; Kim and Warner 2018), but since MSAs are defined by county limits, two sparsely populated towns can be similarly distant from an urban center, while one is counted rural and the other urban . The town of Ramah, Colorado, for instance, had a population of 127 and a population density per square mile of 517 in 2014 and lies within the Colorado Springs MSA. Approximately the same road mile distance away from Ramah to downtown Colorado Springs but in the opposite direction is the town of Hugo in Lincoln County, which is outside of any MSA. Hugo had an estimated population of 731 and a density of 778 per square mile. In other words, Ramah looked more like Hugo than Colorado Springs (population: 445,830; population density: 2,288 per square mile), and yet the MSA approach catalogs Ramah as urban and Hugo as rural.

A more careful appreciation of the differences between rural and small towns in terms of fiscal policy is important for both public administrators and academics. For public administrators, particularly those at higher levels of government, states are increasingly creating local government fiscal monitoring and alert systems (Levine, Scorsone, and Justice 2013). As the country’s population continues to shift toward urban areas, state and local lawmakers express increasing concern that rural towns are falling behind in terms of economic growth, the consequence of which is increasing likelihood of rural local governments facing more frequent and more prolonged periods of fiscal distress (McFarland 2018). To preserve rural governments in the face of declining self-reliance, state governments would have to increase their aid (Johnson et al. 1995), a potentially costly step for state taxpayers.

However, if rural and small, nonrural towns do not differ in significant ways in terms of fiscal outcomes, then focusing on supporting rural towns may come at the expense of small towns; they both may share the same fate. If such towns are different, though, it is crucial to know how they are different in order to tailor solutions to the particular circumstances of each. Warner and Hefetz (2003), for instance, argue that rural communities should avoid privatizing public goods delivery in favor of intergovernmental cooperative agreements, and if rural and small town governments are more similar than not, their advice would generalize. For academics, greater attention to differences between small, nonrural, and rural towns could help formalize better policy alternatives and further suggest misspecification in previous studies arising from unobserved differences between types of small towns.

This study builds upon previous research by developing a multidimensional definition of rural and then tests for differences in revenue structures between rural and similarly populated nonrural municipalities in Colorado. Colorado is an ideal case study because of the large number of small municipalities in the state and the variation in ruralness among them. Of the 122 small town municipalities, 34, or 28 percent, are in a MSA. But based upon this study’s multidimensional spatial definition, half of the state’s small towns are rural.

Empirical Strategy

The empirical strategy relies on regression analysis to explore how ruralness affects four revenue structure outcomes: the LOST rate, the share of own-source revenue derived from user fees, the share of total revenue from IGA, and a measure of local tax revenue diversification based upon the Herfindahl–Hirschman index (HHI). It is noteworthy that this study does not consider property tax reliance, or some other feature of the property tax, due to its relative unimportance in municipal public finance. While the property tax is the single largest source of revenue for local governments in the United States, in Colorado, it is of greater importance to counties than municipalities. For counties, in 2014, the property tax composed two-thirds of local tax revenue statewide, but for municipalities, the property tax only composed 18 percent. In contrast, for municipalities, the sales tax is much more important, composing 70 percent of local tax revenue. In addition, owing to millage and assessment limitations in the state constitution, municipalities have little control over the property tax and thus little control over how much to rely on it.

Defining Rural Municipalities

The definition of rural has three components. Rural municipalities have fewer than 10,000 residents and fewer than 1,000 residents per square mile. We selected these thresholds after evaluating different distributions of municipal populations, and this cutoff captures 84 percent of Colorado’s municipalities, which preside over about 25 percent of the state’s population. Any municipality meeting the population thresholds is a small town. The third dimension is spatial; any small municipality 41 miles or further from a commercial passenger airport with 2,500 or more boardings per year is considered rural. The distance threshold is the median distance for all small municipalities. Since airports are predictors of economic development (Button, Doh, and Yuan 2010; Green 2007), a trigger from moving from rural to nonrural status, proximity to them is a more sensible criterion than proximity to alternatives such as freeways.

Based upon this three-dimensional definition, 122 of the state’s 270 functioning municipal governments were small in 2014. (The state officially recognizes 271 municipalities, but one of these (Carbonate) does not have a functioning government.) Of these, 61 are rural. Across the state, the median rural municipality has 399 residents compared to 1,983 residents for all nonrural municipalities. The median rural municipality is also about 45 percent as dense and three times further from a commercial passenger airport compared to nonrural municipalities. Additional details about the state’s rural municipalities are available upon request.

Dependent Variables

LOST

The first dependent variable is whether a municipality has a LOST. Other studies that have considered the sales tax in the small town context are Snodgrass and Otto (1990), Mikesell and Zorn (1986), Luna (2004), Rogers (2004), and Brorsen and Lansford (2013). During the study’s observation period, 83 percent of the municipalities had adopted a sales tax, while 64 percent of rural municipalities had done so.

User fee reliance

The second dependent variable is the share of own-source revenue derived from user fees, which has a mean of 9.0 percent among all municipalities and 12.0 percent among rural ones. Variation in reliance on user fees could reflect differences in political philosophies that may exist between rural and nonrural but otherwise small towns (Mattson 2016).

IGA reliance

The third dependent variable is the share of total revenue from IGA. This variable captures the extent to which municipalities are self-sufficient with more fiscally autonomous jurisdictions relying less on aid from the county, state, and federal government than more fiscally dependent jurisdictions (Maher and Johnson 2008). While the state of Colorado disburses a few of its aid programs on a per capita basis, most IGA from the state (and from the federal government through the state) is through competitive grants administered by the Department of Local Government Affairs for eligible capital improvements or public services.

Revenue diversification

The final dependent variable measures local tax revenue diversification with the well-known HHI with index values closer to 1 indicating greater reliance on a single tax. As used in this study, the HHI is the sum of the squared percentage share of each revenue source divided by 100 percent, and the resulting measure takes a value between 0 and 1 with values closer to 1 indicating greater reliance on a single tax revenue source. Tax revenue diversification is a focus because of the variety of tax instruments Colorado municipal governments could adopt, and overreliance on a few taxes could predict more frequent periods of fiscal stress, particularly if tax revenue is more income elastic. Among the major tax instruments from which municipalities can derive revenue are the property tax, sales and use tax, lodging tax, business franchise tax, a head tax on workers (called an occupation tax), and a locally imposed ad valorem vehicle tax (called a special ownership tax).

Independent Variables

Variable of interest

The independent variable of interest is binary and equal to 1 if a town is rural and 0 if it is small but not rural. The expected coefficient for rural with respect to having a LOST is negative. The proximity of nonrural, small towns to major urban centers suggests a greater ability to export the tax base, which would not be a fiscal strategy similarly available to more rural towns. In addition, the median per capita expenditure in our data set for rural municipalities is US$751 compared to US$1,119 for nonrural but small municipalities, suggesting that nonsales tax revenues could be sufficient for financing local public goods in the former but not the latter.

With respect to the share of own-source revenue from user fees, Propheter, Levine, and Fudge (2017) found evidence at the state level that political conservatism predicts increased reliance on fees. Generalizing this reasoning to Colorado municipalities requires the assumption that rural towns are more likely to be composed of politically conservative residents than are similarly low populated but otherwise more urban small towns (Mattson and Burke 1989), in which case rural towns may be more inclined to rely on fees than they would general taxation (Mattson 2016). Thus, the excepted coefficient for rural is positive, indicating increased reliance on user fees than on general taxation.

With respect to reliance on IGA, Maher and Johnson (2008) concluded from their study of Wisconsin towns that more urban towns rely less on IGA for roads and more on fees than less urban towns. If their finding generalizes, rural small towns in Colorado will rely more on IGA than nonrural small towns, and thus, the coefficient will be positive. In the event, there are sufficient differences between the two states in terms of state-level policies and political attitudes toward local government fiscal autonomy, though, the coefficient could be negative or not statistically different from 0. It is noteworthy that other studies have suggested a bias in aid toward urban areas driven by a political choice to more heavily invest in more populated areas, particularly in infrastructure, suggesting a possibility the coefficient could be negative (Warner and Hefetz 2003).

Finally, for tax revenue diversity, Carroll and Johnson (2010) concluded that small towns were more likely to have less diversified revenue structures than large towns. Since the expected coefficient for being rural on tax revenue relevance is positive, it is reasonable to further posit that rural towns rely on fewer tax revenue streams. By extension, the expected coefficient for the HHI is positive, indicating greater tax revenue source homogeneity.

Control variables

Each of the four regression models includes the same six control variables to account for sources of variation in the dependent variables assumed not due to the spatial characteristic of ruralness: being located at high elevations, assessed value per capita, population density, distance to the nearest interstate in miles, distance to the nearest municipality with a lower sales tax rate in miles, and an indicator equal to 1 if a municipality is home rule and 0 otherwise. The anticipated coefficient directions are as follows.

The regression equation includes a variable denoting if a town is located in the mountains because there is a considerable tourism literature predicting variation in attitudes toward visitors between mountain and nonmountain communities (Smith and Krannich 1998; McGehee and Andereck 2004). This literature bears on public finance, since tourism allows local lawmakers to export a portion of the tax base. In this study, mountain towns are those at elevations greater than 7,500 feet, roughly the elevation for the first municipality outside of Denver indisputably in the mountains, Idaho Springs. In line with the tax exportation theory, mountain towns are more likely to adopt a LOST, rely less on user fees and IGA, and have less tax revenue diversification.

Assessed value per capita enters the regression as a proxy for a town’s wealth. The variable is a two-year lag to mitigate a potential endogeneity concern. Wealthier towns are more likely to be tourist spots and thus will be more likely to use taxes to finance government, resulting in a positive coefficient for having a LOST but negative for reliance on user fees and reliance on IGA. The direction of the coefficient for revenue diversification is indeterminate. On the one hand, wealthier towns could levy a greater variety of taxes, resulting in greater diversification. On the other hand, wealthier towns will have larger property tax bases, allowing for the property tax to be an increasing share of tax revenue, thereby suggesting decreasing diversification.

Population density is an additional control variable. While population density enters the regression model indirectly as a component of the definition of rural, previous research on government expenditures suggests population density could exert an independent effect. Holcombe and Williams (2008) find that for cities with fewer than 500,000 residents, density has no effect on general spending but that increasing density reduces per capita spending on capital-intensive public goods. Presumably, then, density could have an independent effect on revenue-raising; however, it is unclear from existing literature or theory just how density should impact the four outcomes in the present study.

The regression also includes two spatial controls. One variable measures the distance from the municipality to the nearest interstate in miles. Some rural towns may be far from a major passenger airport but relatively close to a major thoroughfare, and proximity to freeways predicts increased economic activity. As Snodgrass and Otto (1990) found, towns with freeways had higher amounts of sales tax revenue than other similar towns without freeways. This suggests a negative coefficient for having a LOST with increasing distance predicting a decreasing probability. Moreover, if towns near freeways can tax more, then towns further away may have to rely on user fees or IGA more than they would taxes, suggesting positive coefficients; however, user fees and IGA could be substitutes, and thus, it is possible the coefficients could have opposing signs. Following this reasoning to its logical conclusion suggests that towns further from freeways will have less diversified tax revenue structures.

The second spatial control is a variable measuring the distance to the nearest municipality with a lower sales tax rate. Agrawal (2015) motivates the inclusion of this variable; he found that the further a town is from the border of the nearest high-tax state, the lower the local sales tax rate. Thus, distance as used here is a measure of tax competition, and the expectation is that distance positively predicts having a LOST. By extension, with an additional tax revenue stream, the hypothesized coefficient for reliance on fees, reliance on IGA, and for the HHI indicating greater tax revenue diversification is negative.

Finally, an indicator for a municipality being a home rule controls for variation in municipalities’ public finance autonomy with the direction of the effect being indeterminate. (Note that the form of government does not vary among municipalities in the data set; all rural towns have weak mayor systems, so government form cannot enter the regression.) The regressions include county fixed effects to control for time-invariant differences in county government structure such as the size of board of commissioners and operating under a charter. If these characteristics predict how counties interact and support municipalities in their limits, they could predict municipal revenue structures.

Empirical Results

Due to the bounded nature of three of the four variables as proportions, the estimator used is a generalized linear model with a logit link function. Because the parameters from this estimator are not directly interpretable, however, reported results are as average marginal effects taken at mean values instead. For the LOST adoption model, ordinary least squares calculate the coefficients, implying a linear probability model.

Table 1 reports the results for each of the dependent variables. (Note that means and standard deviations for the variables are also contained in the table.) The negative coefficient from the linear probability model supports the reasoning that rural towns would be less likely to have a LOST because nonsales tax revenue sources would more likely be sufficient to fully finance desired local public goods. The results in the second column further indicate that rural small towns rely less on user fees than nonrural small towns, which is inconsistent with the theory of differences in residents’ political philosophies. The estimated marginal effect of being rural, as so defined, is a 4.8 percent decrease in reliance on user fees. Although the direction of effect is negative, the coefficient is nonetheless not statistically different from 0.

Table 1.

Marginal Effects of Being a Rural Municipality.

	Has LOST	Share of Own- source Revenue from Fees	Share of Total Revenue from IGA	Tax revenue diversity (HHI)
Rural municipality (mean: 0.50, SD: 0.50)	−0.317*** (0.003)	−0.048 (0.142)	−0.098** (0.046)	0.385*** (0.063)
Mountain municipality (mean: 0.32, SD: 0.47)	0.161 (0.084)	−0.021*** (0.004)	0.044 (0.072)	−0.060 (0.064)
Log of assessed value per capita _t− ₂ (mean: 9.4, SD: 1.3)	0.159 (0.067)	−0.023*** (0.004)	−0.011*** (0.004)	0.024*** (0.002)
Population density (mean: 517.7, SD: 266.9)	4.1e-4* (4.0e-5)	1.1e-5 (3.7e-5)	−1.8e-4*** (4.3e-5)	4.6-e5 (1.9e-4)
Distance to nearest interstate freeway (mean: 41.9, SD: 43.7)	0.003 (0.001)	0.003*** (0.001)	−0.001 (0.002)	−0.001 (0.002)
Distance to nearest municipalities with lower sales tax rate (mean: 10.5, SD: 11.2)	0.021 (0.009)	1.4e-4 (8.4e-4)	−0.006*** (0.001)	−0.004** (0.002)
Home rule municipality indicator (mean: 0.23, SD: 0.42)	0.412* (0.042)	0.062*** (0.016)	−0.107* (0.060)	−0.015 (0.039)
Constant	−1.104 (0.412)

Note: n = 122. Having a LOST is estimated with ordinary least squares (OLS). The proportion dependent variables are estimated via a generalized linear model with a logit link function. Errors are clustered at the airport proximity level. County fixed effects are included but omitted for space. The means (standard deviations) for the dependent variables are, respectively, as follows: 0.71 (0.45), 0.09 (0.16), 0.24 (0.20), and 0.53 (0.20). IGA = intergovernmental aid; SD = standard deviation; LOST = local option sales tax; HHI = Herfindahl–Hirschman index.

*p < .100.

**p < .050.

***p < .001.

With respect to reliance on IGA, Maher and Johnson’s (2008) finding in Wisconsin that urban towns rely less on IGA for transportation does not generalize to Colorado and instead support Warner and Hefetz’s (2003) theory of an IGA bias toward more populated and urban areas. Rural small towns in Colorado derive less of their total revenue from IGA—about 10 percent less—than other similar nonrural small towns. While it is possible that what holds in Wisconsin does not hold in Colorado due to differences in state-level policies, there are differences in the definition of “rural” between the two studies as well. Maher and Johnson define an urban town as one that belongs to the Urban Town Association in Wisconsin, and based upon the material on the Association’s web site currently, there is no spatial component to membership.

Finally, the results also show that rural towns have less diversified tax revenue systems than otherwise similar small but nonrural towns, extending the reasoning of Carroll and Johnson (2010) to the rural context. Rural towns during the observation period have an HHI 38.5 percent greater than nonrural small towns. Thus, rural towns are more reliant on fewer tax streams, and the magnitude of the percentage difference is substantial.

Empirical Results Using the MSA Definition of Rural

A key contribution of this study to the literature is a multidimensional operationalization of ruralness compared to the more common unidimensional strategy used in much existing research. Thus, it is worthwhile to evaluate how the results using the multidimensional definition differ from results using the unidimensional MSA definition. After reestimating the regression equation parameters using the unidimensional definition, only the coefficient for LOST retained the same sign and statistical significance at the 90 percent level. Notably, though, the coefficient magnitude is three times larger (in absolute terms) when measured using the unidimensional definition (β = −.091). For the other three outcomes, the rural coefficient is quite different in terms of direction, magnitude, and significance: share of own-source revenue (β = .002; ρ = .046), share of total revenue from IGA (β = .030, ρ = .184), and tax revenue diversity (β = −.144, ρ = .072). These results provide supporting evidence that not only are our conclusions about rural municipal public finance sensitive to how rural is measured but also of possible measurement error in unidimensional definitions.

Discussion and Conclusion

This study tested for differences in revenue structure between rural and nonrural but similarly populated municipalities in Colorado using a multidimensional and spatial definition of rural. As defined for the analysis, rural towns are municipalities having fewer than 10,000 residents with a population density equal to or less than 1,000 residents per square mile and being forty-one miles from a large commercial passenger airport or further. Nonrural, small towns fall under the population and population density thresholds but enjoy the closer proximity to passenger airports and by extension urban centers. Among the findings are that rural towns are less likely to adopt a LOST, receive a smaller share of their total revenue from IGA, and have less diversified tax systems compared to small towns. Additionally, these results are sensitive to the definition of rural with coefficients having in most cases signs, magnitudes, and statistical significance that vary by definition.

Assuming these results generalize beyond Colorado, the study has implications for public administrators and scholars. For public administrators, policy debates on how best to preserve rural America in a period of human capital flight to urban areas are frequent (Mattson 2016). To the extent local administrators see preservation as a desirable policy goal, rural towns need to invest in public goods and services to attract and retain younger generations, and financing such investments requires a reliable revenue structure. Rural communities are unlikely to achieve this goal alone; this study shows that rural communities receive less support from IGA compared to similarly sized but more urban communities. Whether less IGA going to rural towns is a consequence of existing policy—perhaps a policy, intentional or otherwise, that awards aid disproportionately to more urban towns—or is a state of affairs that to date has otherwise been unobserved is not clear. If the former, it seems reasonable to conclude a bias toward state support of more urban small towns exists, manifesting in part through how public policy defines rural. For example, Colorado’s Rural Economic Initiative awards grants to ostensibly rural municipalities, but rural is defined strictly on population, and had the state used a more sophisticated definition, the four communities awarded grants in 2016 would not have been eligible, thus freeing revenue for others. If the latter, it would suggest officials in higher-level governments should increase their effort to better understand the fiscal dynamics of rural towns and how they might differ with their more urban counterparts.

States devoting more attention to the needs of rural towns could also help alleviate fiscal stress, which such communities are more likely to face (Hite and Ulbrich 1986; Johnson et al. 1995; Kim and Warner 2018). Importantly, though, while it is common to envision fiscal stress stemming from volatility in the local tax base and IGA (Trussel and Patrick 2009), Lobao and Kraybill (2005) find that higher-level government mandates are also a source of fiscal stress and further that mandates harm rural governments more so than nonrural ones. Thus, not all intergovernmental support needs to come in the form of cash transfers but could come instead through, for example, temporary or permanent suspension of unfunded mandates. Encouraging interlocal agreements among rural communities is another strategy states could pursue to alleviate sources of fiscal stress in rural communities (Bell and Ever 1997; Maher and Deller 2007), but the evidence is mixed on whether rural communities in general even desire to form public service collaborations, with attitudes likely being dependent on the type of public service under consideration (Caruson and MacManus 2011; LeRoux and Carr 2007; Morton, Chen, and Morse 2008).

For academics, the primary implication of this study is more straightforward, which Carroll and Johnson (2010) also stated: small does not necessarily mean rural. Academics can take from this study two things. While all rural towns are small towns, many small towns reside in the shadow of urban centers, and this proximity affords those small towns benefits (such as a more exportable tax base) that rural towns will largely not enjoy (Afonso 2016). However, such conclusions may be sensitive to how scholars define rural. This study offered and defended one multidimensional definition with distance to major passenger airport serving as the spatial criterion. The definition presented here, though, is debatable and deserves scrutiny. With evidence that definition of ruralness matters, it is incumbent upon future researchers to make a case that one definition is better or worse than another.

This study offers a variety of avenues for future research. Rural and small town governments likely vary in more ways than just revenue structure, and other dimensions of public finance—public services, capital spending, debt management, spending priorities, and so forth—remain areas for future research. Helpap (2017) and Maher and Johnson (2008) provide useful perspectives in this regard. In addition, this study evaluated a cross section of municipalities due to completeness of the data. However, longitudinal data would provide richer insight. Rural and small towns may differ in their fiscal policy choices over time, perhaps because they face different economic trajectories or are subject to different levels of fiscal stress. Economic downturns could further harm one type of town more so than the other, suggesting more targeted state aid could be desirable. Finally, the definition of rural this study proceeded on was sensible in the Colorado context, but whether or not it is likewise sensible elsewhere requires extending the definition to other contexts.

Footnotes

Author’s Note

A version of this article was presented at the Association for Budgeting and Financial Management’s 30th Annual Research Conference.

Acknowledgments

The author is indebted to the helpful feedback from participants. The author is grateful to Emily Brixey for research assistance and to anonymous reviewers and the journal editor for constructive comments. The author takes responsibility for all errors.

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Geoffrey Propheter

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