“You Keep Using That Word. I do not Think it Means What you Think it Means”: Interjurisdictional Equity in Local Option Tax Policy

Abstract

Fairness in tax policy is a widely shared goal among scholars, policymakers, and the public, yet equity remains a contested concept. This study examines interjurisdictional equity in the context of local option taxes (LOTs), which have become an increasingly significant revenue source for local governments. While much of the existing research on LOTs focuses on competition, rate setting, and adoption, this paper considers how different dimensions of equity intersect in shaping their implementation and effects and presents an analytical framework that can be applied to understanding interjurisdictional equity more holistically. Using a North Carolina local sales tax revenue policy designed to distribute revenues more equitably, this study analyzes county-level data from 2013 to 2019 to assess the policy's impact across multiple equity measures. The findings highlight complementary and conflicting equity considerations, underscoring the need for policymakers to clearly define their equity objectives to achieve desired policy outcomes. This research contributes to a more nuanced understanding of fairness in local public finance.

Keywords

local government equity taxation revenue raising capacity

Introduction

Scholars, policymakers, and the public want tax policy to be fair. However, there is no single agreed-upon perspective regarding fairness or equity. What is fair or just to one may not be to another, leading many to relate to Inigo Montoya's quote in The Princess Bride, “You keep using that word. I do not think it means what you think it means.” This diversity in perspectives often results in important but ultimately narrow equity considerations, such as focusing solely on economic factors, social equity, or geography. This paper does not attempt to achieve universal agreement around equity. Instead, this paper considers the different dimensions of equity and how they may complement and contrast. We take up the call to further develop and define equity concepts in public administration research (e.g., Cepiku & Mastrodascio, 2021; Frederickson, 2010; Nabatchi & Carboni, 2019; Rivera & Knox, 2023) and to start with the finances, since no services, equitable or otherwise, can be provided without revenues. We examine the identified dimensions of equity through the lens of interjurisdictional equity, focusing on local option taxes (LOTs). We define interjurisdictional equity as the fair distribution of resources, costs, and benefits across jurisdictions. Although the equity implications of LOTs have received attention for many years, policies to address their potential inequity are now emerging, making LOTs ripe for rethinking what is fair and just in tax policy.

LOTs are increasingly important to how we pay for local government. While property taxes remain the largest source of own-source revenue, tools like local sales, occupancy, and income taxes are critical to many jurisdictions. LOTs have several defining characteristics. First, a LOT usually requires approval from residents via a referendum before the local government can levy it (Beale et al., 1996). Second, LOTs are not universally levied within the state. Local governments have the choice of whether to levy them. Third, the revenues are controlled at the local level (Goldman & Wachs, 2003).¹ While LOTs are common and have distinct features from property taxes, they have received less attention from researchers, and much of the existing work is around interjurisdictional competition, rate setting, and adoption. Embedded in each of those areas are concerns around administration and equity. Unsurprisingly, there are numerous measures of equity regarding LOTs, making the examinations of LOTs a powerful example of the complexities of equity and how different measures may or may not align with each other. We use a recent local sales tax policy in North Carolina designed to specifically correct inequities to evaluate these equity lenses.

Equity is considered one of the four pillars of public administration (Gooden et al., 2023). There has been tremendous growth and emphasis put on equity, particularly social equity.² Despite the attention, equity remains one of the most challenging concepts to define and achieve widespread support. Ultimately, equity or fairness is at the heart of many concerns around how local governments are financed and their ability to provide adequate services to their community. One initial source of potential disagreement is the level at which we should consider equity. There is a great deal of policy conversation around the equity of tax instruments for the taxpayer and the equity of tax incidence. However, it is arguably just as important to consider the equity of taxes when analyzed at the level of government and interjurisdictional equity. This is why interjurisdictional equity is the primary focus of this analysis.

This research examines different dimensions of equity identified by policymakers and scholars, exploring how these dimensions relate to one another while introducing additional fiscal concerns. To do so, we construct an analytical framework that evaluates equity relative to revenue-raising capacity, cost differentials in service provision, differences arising from a jurisdiction's urbanicity, and the impact of earmarking generated revenues. While these multifaceted dimensions frequently overlap, each remains highly nuanced and represents unique administrative challenges and equity considerations. To highlight these equity dimensions and to demonstrate how they can be applied and operationalized, it analyzes a 2017 North Carolina law (GS 105–524) that pools local sales tax revenues from across the state and redistributes them to create a more equitable distribution by approximating a per capita allocation, and how successful this policy is in terms of the identified dimensions.³ It uses data from all 100 of North Carolina's counties from 2013 to 2019. It finds that many measures complement each other, but the differences are notable, and policymakers should consider multiple lenses when creating policies like North Carolina's. Ultimately, policymakers, academics, and taxpayers need to be clear about how they conceptualize equity, as outcomes and definitions of success can vary depending on the dimension used.

Equity Dimensions in Public Finance

A primary question of this research is: What are the relevant dimensions of interjurisdictional equity for LOTs? Cepiku and Mastrodascio (2021) do a systematic literature review to understand how equity is operationalized and analyzed in public administration. They describe equity as

“concerned with the characterization, measurement, and achievement of fairness in the provision of governmental policies and services…[and] the distribution of conditions, opportunities, services, and goods among individuals living in modern societies inevitably impinges on their well-being” (p. 1019).

However, some of their primary takeaways are that even within the literature, 1) equity is not clearly defined or operationalized, 2) there are many competing conceptualizations, and 3) most scholars do not even define what they mean by equity within their analyses. For many, equity and equality are often interchangeable and mean both fairness and social justice (e.g., Gooden, 2015; Miller, 2001; Okun, 1975). Thus, we now see many approaching equity in policy as the means by which we attempt to achieve equality, where short-term unequal treatments are used to reach long-term equal opportunity or outcomes regardless of circumstance. In fact, Gooden (2015) conceptualized equity as a fair distribution of services rather than people being treated the same way. In public administration, we often focus on social equity and turn to measures of fairness across social groups defined by race, ethnicity, gender, disability, religion, and/or political affiliation (e.g., Martinez Guzman et al., 2025; Yu et al., 2023). Social equity is often operationalized at the individual level but is also approached in terms of how governments and administrators can 1) treat individuals fairly and ensure access to services, 2) assure that policies consider the impact on marginalized communities, 3) address systematic inequities including bias and barriers that some groups may face, and 4) create inclusive policies and environments (Guy & McCandless, 2020).

There are additional approaches to considering equity beyond social equity, though these dimensions often overlap. For example, equity can be viewed through a lens of economic equity, where economic disparities such as unemployment, homeownership, and poverty are the focus.⁴ Another lens could be fiscal equity, where the conceptualization of equity is around issues of revenue-raising capacity or the capacity to provide a baseline of services. A measure of interest here may be the need-capacity gap, which is the cost of providing an adequate level of services and the local government's ability to raise the needed revenue to provide those services. Many also approach equity from a perspective of interjurisdictional fairness, where equity is not just contained within the unit or organization but across organizations. A common example of how equity is operationalized from the lens of interjurisdictional equity is through tax leakage, where residents of one community contribute to a neighboring community's tax base rather than their own—for example, cross-border shopping and its impact on local sales taxes.

However, it is important to keep in mind that many local governments choose to best serve their communities by actively keeping tax burdens low or choosing not to be reliant on LOTs. In fact, it is these differences in the subsequent bundle of revenues and services that lead to the Tiebout sorting of governments and residents that allows for residents to select the local government that best represents their preferences (Tiebout, 1956). It should not be presumed that lower revenues are inherently inequitable and to recognize that it may simply be a policy choice. That being said, local governments are often only able to make marginal differences to capacity and other equity considerations we address in our proposed framework, and often do not have the legal authority to levy a particular LOT, making a great deal of these outcomes independent of policy.

There are many ways in which interjurisdictional equity can be defined and approached in public finance and public administration. In this analysis, we will work through many of those approaches through the lens of how they interact with LOT policies. We offer some definitions of how it can be defined and considered to help promote work in this area and help policymakers. This is important because when equity is more clearly defined, it is operationalized better (Gooden & Portillo, 2011; Putnam-Walkerly & Russell, 2016).

Revenue Raising Capacity

One of the most prominent ways to consider interjurisdictional equity in local public finance is through the lens of how much revenue local governments can generate or their revenue-raising capacity (RRC). This is perhaps the most common way to approach the question of local fiscal disparities, as it aims to exclude factors within a government's direct control and what may reflect policy preferences. The sources of revenue that should be included in each local government's RRC vary, and ultimately, capacity reflects the tax base.

This capacity concern is often translated into questions of how much revenue is actually collected and whether the inequities between one jurisdiction's RRC and another's are due to reasons that should cause concern. For example, should we consider RRC from a representative-tax-system approach or an income-with-exporting approach (Zhao & Hou, 2008)? In the representative-tax-system approach, RRC is considered from a pure capacity perspective, where the taxes are analyzed using the same tax rates and structures to compare capacity. It may consider individual tax instruments or multiple tax instruments together. In contrast, the income-with-exporting approach considers the tax burden as a share of taxpayer income and the impact of that expected capacity on the capacity for tax exportation⁵.

For a conversation around RRC and equity, it is interesting to consider the features that impact the size of the tax base. There are factors related to the demographics and the population of the jurisdiction, such as the number of people who reside in the jurisdiction, the incomes of the residents, and the composition of the industry. The components of what impacts the tax base and to what extent vary between different tax instruments. The income-with-exporting approach considers the income of the residents and a measure of exporting. Zhao and Hou (2008) take what can be considered the traditional approach of measuring tax exportation, the difference between actual and expected revenues. For example, for local sales taxes or income taxes, residents’ income will be critically important. However, it may be less relevant for local occupancy taxes and even inversely related to revenue instruments like local tobacco taxes.

These variations in RRC can be framed through the lenses of vertical and horizontal equity, concepts traditionally applied to individual taxpayers—where vertical equity holds that those with greater ability to pay should bear a larger burden (e.g., progressive taxation), and horizontal equity posits that similarly situated taxpayers should face comparable burdens (e.g., equal treatment regardless of location or circumstances). In the context of interjurisdictional equity, however, we operationalize these ideas at the jurisdictional level: more affluent jurisdictions with larger tax bases can generate equivalent revenues with lower tax effort (e.g., lower rates), while jurisdictions with smaller tax bases must impose higher efforts to achieve the same fiscal outcomes. This disparity profoundly impacts taxpayers residing in these jurisdictions, as residents in lower-capacity areas often face heavier effective tax burdens to fund similar levels of services, potentially violating horizontal equity by treating comparable individuals differently based on geography and undermining vertical equity if lower-income communities are disproportionately strained. Such inequities explain the prevalence of tax equalization efforts, like state aid formulas or revenue-sharing mechanisms, aimed at leveling the playing field across jurisdictions and mitigating these burdens on individual taxpayers.

There are many ways in which these traditional conceptualizations of RRC may be considered from an equity perspective. One is simply the straightforward consideration that because some jurisdictions can raise considerably more revenue, their residents either benefit from lower tax rates or a higher level of service. In fact, there has been a question of the constitutionality of financing education through local sales taxes because of this unequal capacity (Craft, 2002), and education is a common earmark for sales tax revenue (Afonso, 2017). Another is to consider tax exportation more carefully.

Tax exportation. Tax exportation or leakage is one of the most often cited equity concerns. It is not simply that jurisdiction A has higher-income individuals and, therefore, a higher tax base than jurisdiction B. It is the fact that there are concerns that part of why jurisdiction A has a higher tax base than jurisdiction B is that it can shift a portion of the tax burden onto non-residents. Perhaps some of those non-residents are from jurisdiction B, which further diminishes its tax base and exacerbates the inequality of the two tax bases. It is also typically assumed that dollars flow from more rural areas to more urban and well-resourced areas. In fact, NC GS 105–524, which is being used as our example in this analysis, was in response to the perceived inequity of tax exportation between rural and urban communities. This is often explored from the perspective of cross-border shopping (Matha et al., 2017; Rohlin & Thompson, 2018).

While tax exportation can be categorized as inequitable, additional important factors should be considered. For example, exported tax burdens are generated because those non-residents are within the jurisdiction and benefit from services and infrastructure that they typically do not contribute to through revenue instruments like property tax and utilities. These non-residents benefit from public safety, roads, water, and other local services—so perhaps the question of equity should also be framed around whether these non-residents contribute more to the tax base than they benefit from the services. The impact of non-residents can be tremendous in terms of both potential costs and revenue generation. In Dare County, NC, the permanent population is approximately 37,000, but the average daily population in the summer for this coastal community is between 225,000 and 300,000 (Dare County n.d.). In Palo Alto, CA, almost 90 percent of the city's workforce comprises non-residents and in-commuters (Maciag, 2014). This conception of equity overlaps with the benefit principle commonly considered in public finance. For example, a robust tourism sector will greatly increase the tax base for local occupancy, meals, and sales taxes.

Similarly, the presence of retail agglomerations and/or being a regional retail center positively impacts a jurisdiction's sales tax base (Burge & Rogers, 2011). This increase in RRC of these retail-rich jurisdictions is due in part to their own residents, but also to the tax leakage of surrounding communities. This tax exportation from cross-border shoppers is similar to the positive impact that tourists have on contributing to local tax bases, and there are other non-residents that may be important to the tax base, such as commuters. One recent study found that, on average, an in-commuter contributes $1,000 to taxable sales monthly, and a single night's hotel booking translates to approximately $525 in taxable sales, not sales tax (Afonso & Moulton, 2024). These are not inconsequential contributions to the sales tax base and, for many governments, make a meaningful impact.

Impact of policy and management choices. Lastly, it is important to reflect on the equity of recalibrating LOT revenues based on capacity when some discrepancies between jurisdictions may have been by design. For example, jurisdictions engage in planning and zoning efforts to attract different types of businesses, such as retail or other industries, that may encourage tax exportation. These choices may also represent a trade-off with other revenue instruments.⁶ These strategic choices reflect the fiscalization of land use (Lewis & Barbour, 1999; Pagano, 2013). Pagano (2013) highlights how dependence on different revenue instruments may impact planning. For example, local sales tax-dependent cities situate development at urban edges with formulaic development that is not concentrated in the urban center; this contrasts with property tax-dependent cities, which have a concentric urban structure with wealth concentrated in the center.⁷ There are equity concerns about changing the distribution of revenues after these decisions have been made, but there are also concerns about the incentive to behave in these rent-seeking behaviors. A potential policy solution to address concerns about the fiscalization of land use or retail-rich versus poor jurisdictions is a population-based or regional distribution of revenues (Artz & Stone, 2003; Lewis & Barbour, 1999). Under such a policy, equity is considered from capacity exclusively. Shifting to a per capita distribution of local sales tax is what North Carolina considered before passing GS 105–524.

Ultimately, many would view equity as creating a fiscal structure where citizens are not worse off because they reside in a local government with low resources or capacity (Yinger, 1986). However, as this section describes it, it may not, to some, be as simple as equalization based on a metric such as per capita capacity. Should policymakers who view their LOTs as inequitable base their policies on just the revenues from one instrument or from a broader constellation of revenue instruments (considering the overall capacity or capacity of primary revenue instruments)?⁸ For example, much of the previous literature has found that the property tax base and the local sales tax base are uncorrelated or even negatively correlated, suggesting that local sales taxes may close the RRC gap (e.g., Afonso, 2016; Craft, 2002; Wang & Zhao, 2011; Zhao & Hou, 2008). Should they consider the financial cost of non-residents? Do they need to consider development efforts? And does the fact that some LOTs require a referenda factor in? Some residents may have supported the new LOT because they knew or believed that some tax burden would fall on non-residents (Martin et al., 2019). Is it fair to change the laws and distribution after the fact? Lastly, how do you account for local governments that choose to keep revenues low to best reflect the preferences of their community?

Cost of Providing Basic Services

Another important framework that should be considered when assessing the interjurisdictional equity of LOTs is whether revenues are sufficient to cover the cost of providing a baseline of critical services. Many factors could go into the cost of providing services, and it is more complex than just a per capita number, which is often how revenue-raising capacity is approached. One major difference in the cost of providing services is variation in the cost of living between communities; this is especially critical when considering major budget items like employee pay. There are also economies of scale concerns. In part, the cost of providing services to a smaller population may be more per capita than a larger population; this will be especially true in jurisdictions with less dense populations. For example, public transit requires certain infrastructure, like buses, trams, and bus stops. If it is not at capacity, it will be more expensive per capita, and if it is in a less dense region, other costs like fuel and vehicle maintenance will increase per resident and trip.

Population. Population growth or decline can also have a meaningful impact on the cost of providing services. In jurisdictions with a growing population, they may have to invest in large capital projects like new schools, extended water and sewer lines, and new roads. Similarly, in communities with a declining population, fixed infrastructure costs may become more costly per person as the population is reduced. A recent study created capacity cost indices for local governments in Indiana (DeBoer, 2020). It found, contrary to expectations, that growing and urban counties have smaller cost-capacities, which translates into needing higher tax effort or rates. This is consistent with earlier work that found that more urban areas had higher expenditure needs (Ladd, 1992). How do these findings impact the approach to equity and potential policy solutions? Should we base policy interventions on measures like cost-capacity, even when it will direct revenues to growing and urban jurisdictions? As discussed previously, equity is often considered from a per capita perspective, but that will usually be insufficient to capture the complexities.

Change in economic condition. Another way of framing this equity conversation is through a lens of economic distress or growth. The cost of providing services may be somewhat U-shaped, with rural or distressed areas having high costs and urban or growing areas having higher costs with more stability, and Teibout's (1956) optimal-sized jurisdictions having the lowest costs. Just as many may consider equity as leading to an outcome where citizens are not worse off because they live in a jurisdiction with lower revenue-raising capacity, many would view equitable interventions to ensure citizens are not worse off because they live in a jurisdiction with high costs and tax burdens (Yinger, 1986). It is also critical to carefully consider how economic distress is defined. Is it a local GDP measure, is it the trend in local GDP, or is it relative to peers or neighbors? For example, if a community's economy is stagnant but stable and is embedded in a region with high growth, is this inequitable, and are they in economic distress? Another way that this could be assessed is through financial health indicators, but those do not typically reflect the role of management, the quality or scope of services, or overall levels of jurisdictional distress, just the health of the government.

Need-capacity gap. Finally, the need-capacity gap is another way of addressing and isolating the fiscal disparities and possible inequalities that require policy intervention. The need-capacity gap considers both RRC and the level of expenditures “needed” by a jurisdiction and presents a gap in the case where the need is greater than capacity (Bradbury et al., 1984; Helen F Ladd, 1992; Helen F, 1994; Wang, 2023). Assessing needed expenditures requires agreement on what constitutes adequate expenditures, which is inherently challenging to define and measure, partly due to variation in the costs of providing services between jurisdictions and difficulty in establishing normative outcomes. The concept of adequate expenditure has arguably been most developed within public education (Downes, 2004; Duncombe & Yinger, 1998, 2005), largely due to a long history of legal cases challenging the unconstitutional inadequacy of school finance within states (see Banicki and Murphy (2014) for a review of court cases). Even for education, where there is broad precedent for claims of inadequacy and standardized measurement of performance goals, unlike other public services, measuring adequacy remains contested and involves assumptions and limitations (Baker et al., 2025).

Urban Versus Rural

The urban-rural divide is one of the most pressing concerns for many states. It is not simply differences in population density but also politics, poverty, lower social services, fewer cultural institutions, and more (Jensen, 2025; Nee, 2021). Therefore, while there is considerable overlap between how interjurisdictional equity may be approached from an urban versus rural perspective and revenue-raising capacity or cost of providing services and economic distress, we would be remiss not to consider it independently. Specifically, there may be other concerns around the urban-rural gap for public finance. Rural areas often face unique challenges, such as limited economic opportunities, sparse infrastructure, and population decline. For example, rural areas have lower broadband access, which may exacerbate difficulties in attracting growth in terms of population and economic development. LOT revenues and burdens will often reflect these differences. Therefore, redistributing revenue from urban to rural areas can help address these disadvantages by providing much-needed resources for economic development, infrastructure improvement, and public services in rural communities.

As noted before, one solution may be the regional distribution of revenues (Artz & Stone, 2003; Lewis & Barbour, 1999). From an urban-rural equity standpoint, this could be seen as a perspective on regional growth or shared benefits. Urban areas often serve as economic hubs with significant commercial activity and tax revenue generation. By sharing a portion of this revenue with rural areas, it acknowledges that economic growth and prosperity are interconnected and suggests that all regions should benefit from the broader economic success. In recognition of many of these problems, state programs are frequently aimed at helping eliminate these systemic disadvantages.⁹

Impact of Earmarks

A common element of many LOT policies is earmarks. Tax earmarks are specific provisions within tax laws or budgets that allocate tax revenues for particular purposes or projects. Earmarking revenues restricts the ability of local governments to allocate resources best to meet the needs of their community. So why would state and local governments earmark revenues? First, earmarks make LOTs and other taxes more politically palatable. This is likely one of the primary reasons we see them so frequently (Afonso, 2017, 2023). However, earmarks can also be a tool used by the state government to ensure a baseline of funding is diverted to core or essential functions. While, arguably, not all earmarks are for core functions (i.e., Texas's local sales tax for building sports stadiums), many are for services like transportation and schools. A state's restriction of revenues may help ensure that local governments adequately finance these functions, which may improve equitable outcomes when looking at core services. It is important to note, though, that the literature is mixed on whether earmarked revenues increase spending on recipient programs or whether it is treated as fungible (e.g., Afonso, 2015; Bell et al., 2020; Brunner & Schwegman, 2017; Combs & Afonso, 2025a; Dye & McGuire, 1992; Kim et al., 2009).

How might earmarked LOT revenues impact interjurisdictional equity? First, as noted, the majority of earmarks are for core government functions like education and transportation. Therefore, if these dollars could supplement spending in areas that could not previously meet the need, it could improve equitable outcomes. However, it is also feasible that it could increase disparities between jurisdictions. For example, if the LOT raised considerably more revenue per capita in thriving jurisdictions than in declining ones, additional spending on education, economic development, etc., may increase the difference between the jurisdictions. Second, earmarks used to supplement existing spending may force the hand of local governments to spend more in key areas of need. This paternalistic perspective presumes that local governments are not making the appropriate budgetary choices for their communities. Third, there are examples where LOT revenues are shared or redistributed, and those revenues are earmarked. A redistribution of LOT revenues based on factors like per capita revenues or capacity and then earmarking those revenues may present an opportunity for addressing concerns around increased disparities. Once again, much of this depends on how equity is defined and considered.

Equity Dimensions in Action: North Carolina's Local Sales Tax Redistribution

To demonstrate these different equity dimensions and considerations, we will analyze the equity of local sales tax revenue and a 2017 local sales tax (LST) policy in North Carolina, GS 105–524, passed to correct for perceived tax leakage and the interjurisdictional inequities that tax leakage created. First, we propose a process to assess equity. Second, we briefly describe the relevant policy elements of GS 105–524 and the landscape in which it is situated in North Carolina. GS 105–524 is chosen for several reasons. First, LSTs are the most common LOTs in the United States and are the second largest source of own-source revenue (Afonso, 2023). Therefore, using an LST redistribution policy to advance the conversation around the equity of LOTs makes sense. Second, LSTs (and LOTs) vary tremendously from state to state and often even from one tax instrument to another. Therefore, it is important to consider the nuances of the policy and laws when discussing equity implications. Of course, each potential policy interacts with state laws, local economies, and individual situations. We do not suggest that the process we use below will be universally appropriate; instead, it provides a basic framework to guide scholars and policymakers in considering dimensions of (in)equity more holistically.

The Proposed Process

Inequity and policies to correct inequity imply the presence of unfair inequality. Therefore, the first step in examining LOT equity is to assess the severity of inequality. Measures like the Gini coefficient can quantify revenue equality across local governments. The Gini coefficient measures income inequality on a scale from 0 (perfect equality) to 1 (perfect inequality).¹⁰ Unless these measures produce extreme values, evaluating equality without a comparison can be challenging. A natural equality benchmark is property tax revenue, as it is the largest own-source revenue for local government and has long been a target of policies intended to reduce inequality. In addition to comparing equality between LOTs and property tax revenues, it is important to assess the equality of combined revenue, as LOT revenue may offset property tax inequality.¹¹

An equal revenue distribution could be inequitably distributed relative to other jurisdictional characteristics. To gain a more holistic understanding of LOT equity, the correlation of revenue with any number of measures can be evaluated, with no correlation indicating equality. To assess fairness, we propose focusing on revenue patterns that (dis)favor jurisdictions that would generally be considered socioeconomically (dis)advantaged. For example, if LOT revenues systematically disadvantage jurisdictions with lower property wealth or larger minority populations, the case for correcting this inequality is likely to find broader agreement. In a later section, we consider various jurisdictional characteristics based on previously discussed perspectives.

A primary concern about LOT equity involves tax leakage and exporting, generating potentially unfair (dis)advantages between jurisdictions. Therefore, an analysis of LOT equity is incomplete without considering revenue-raising capacity (RRC). The difference between actual LOT revenue and expected LOT RRC in the absence of exporting provides a measure of tax leakage (revenue less than expected capacity) or exporting (revenue greater than expected capacity). Furthermore, we propose comparing property tax revenue and property tax RRC to measure excess or slack capacity. This enables an evaluation of how much LOT leakage (exporting) relates to excess (slack) property tax effort. While counties can directly alter property tax revenue by changing the property tax rate, they cannot directly alter the revenue generated from the base 2 percent LST. Therefore, it is plausible that LST revenue leakage and exporting may influence the deviation between property tax revenue and property tax RRC.

Lastly, a common counterargument to the claim that LOT exporting is inequitable is that the cost of services to support the additional consumers that drive higher revenue is insufficiently considered. While data limitations prevent directly estimating that extra cost, a regression analysis can estimate variations in spending relative to providing an average level of services. Comparing RRC to the local cost of services enables a need-based approach to justify redistributive policies to correct an unfair inequality. Like RRC, a cost model uses factors arguably outside the government's direct control, thus avoiding normative arguments that a government deserves higher revenue due to better governance.

We apply the above process to the case of LSTs in North Carolina. The potential for inequitable distribution of LST revenues was (and is) of great concern in North Carolina and was addressed in 2017 by legislation found in the general statutes, hereafter referred to as GS 105–524, a redistribution policy. Before analyzing the impacts of the redistribution policy, our approach considers the 2016 baseline of LST distribution in the state from several perspectives. First, the actual distribution that was in place in 2016 involved LSTs that were distributed by a point-of-sale methodology and a per capita methodology. Therefore, we consider the impact of the per capita distribution by estimating the LST distribution if it had been point-of-sale, the most common distribution for LSTs nationally. Then, we examine the redistribution's alignment with various equity perspectives.

Lastly, it is important to reiterate that the analysis in this section is intended as an illustrative application of the interjurisdictional equity framework developed above. Our aim is to demonstrate how various equity dimensions can be operationalized and empirically examined within a specific policy context. We do not aim to provide a verdict on GS 105–524 as policy. The process we propose and apply here is intended to serve as a replicable template for scholars and policymakers examining the interjurisdictional equity of LOTs in other settings.

Overview of North Carolina's Local Sales Tax Redistribution Pool

Since FY 2017, North Carolina has withheld a portion of LST revenues from all counties, pooling those revenues at the state level and redistributing the pooled revenues back to 79 of the 100 counties based on percentages established in legislation. GS 105–524 impacts the base 2 percent LST rate that all counties in North Carolina levy. As noted, the goal of this policy is “to address sales tax leakage that results from the different revenue-raising capacity of local option sales taxes in each taxing jurisdiction” (NC GS 105–524a).

The North Carolina General Assembly's fiscal research staff determined the percentages allocated back to counties. They calculated how much LST revenues each county would receive if the distribution were based on half of the revenues being distributed on a point-of-sale basis and the other half being distributed on a per capita basis (Afonso & Moulton, 2024). This contrasts with how revenues are allocated, where three-quarters of the LST revenues are distributed as a point of sale, and the remaining quarter is allocated on a per capita basis. To reach this benchmark, each recipient county was assigned a unique allocation percentage or share of the redistribution pool. The range of the allocation percentages for recipient counties is from 0.05 percent to 4.96 percent. The allocation percentages from the pool are static and would require legislation to be modified. In the first year, the fund was $84.8 million, and the pool size in subsequent years automatically adjusts according to the growth (or decline) in the sum of LSTs statewide. Revenue from GS 105–524 is not trivial for most recipient counties. For the average net-recipient, redistributed revenue accounted for 15 percent of their local point-of-sale revenue in 2017, with counties receiving as much as 65 percent. The revenue from the pool is earmarked for K-12 education, community colleges, and/or economic development.

Data

Since GS 105–524 initiated an acute shift in LST revenue using a pool indexed to annual growth in statewide LST revenue thereafter, the analysis focuses on fiscal years 2016, the year before implementation, and 2017.¹² We use county-level data from several sources. Local sales tax data were collected from the North Carolina Department of Revenue. All amounts include the portion of revenue allocated to municipalities. We collected various economic and population measures from the U.S. Census American Community Survey five-year estimates and North Carolina's Department of Commerce. Property tax data come from county annual financial information reports published by the Department of State Treasurer. Lastly, urban-rural status comes from the North Carolina Office of State Budget and Management. All dollar amounts are deflated using the 2023 Consumer Price Index and rescaled to per capita.

Table 1 reports descriptive statistics for the measures used in this analysis. The top panel includes variables pertaining to county LST and property tax revenues. The bottom panel includes 2016 statistics for the measures we use to assess LST equity in North Carolina, which serve as potential assessment indicators of equity for any LOT, organized by the broader dimensions discussed previously. Point-of-sale (PoS) revenue is our baseline LST revenue measure from which the equity of any adjustments is assessed. Unlike most states, North Carolina distributes 0.5 percent of the base 2 percent levied by counties on a per capita basis, resulting in an amount we label Adjusted LST revenue. By using the two baseline measures we are able to consider what the potential impact of a policy like GS 105–524 would be on a more representative state and to consider the impact on North Carolina specifically. The last three rows of the top panel show statistics on LST revenue withheld, revenue received, and net revenue related to GS 105–524.

Table 1.

Descriptive Statistics.

	Mean	SD	Min	Max
Local Revenue Variables
Point-of-sale (PoS) revenue per capita	278	134	84	1,139
Property tax revenue per capita	801	253	449	2,075
Combined property and PoS revenue	1,079	345	635	3,215
Adjusted LST revenue	297	103	152	975
105–524 revenue withheld	−8	4	−37	−2
105–524 revenue received	21	18	0	66
Net 105–524 redistribution	13	22	−37	62
County Characteristics
Socioeconomic
Property tax base per capita	104,187	51,694	45,880	380,121
Homeownership rate	69	7	52	83
Median household income	41,649	7,526	30,248	65,433
Unemployment rate	6	1	4	13
Poverty rate	19	5	8	32
Economic distress ranking	50	29	1	100
Blau Index	0	0	0	1
Percent Black	20	16	0	62
Rural	1	0	0	1
Cost of Services
Percent population growth	1	2	−3	8
Percent population decline	1	1	0	3
Need-capacity gap	320	283	−769	980
Expenditures Per Capita
Education	315	138	99	930
Economic development	46	41	0	268

The construction of several variables in the bottom panel warrants explanation. In North Carolina, local government need is often tied to a system that ranks county Economic Distress in descending order from 1 to 100. We reverse this order for easier interpretation in the analysis. The Department of Commerce calculates the rankings based on average unemployment rate, median household income, percentage population growth, and adjusted property tax base per capita, which are then used for funding decisions in many programs, such as the Job Development and Investment Grant and Community Development Block Grant for Economic Development (NC Department of Commerce, 2023). While GS 105–524 is not based on these rankings, it is helpful to analyze whether it aligns with how the state measures economic distress and whether equity is improved when defined that way. The Blau Index measures racial heterogeneity, with higher values indicating a greater likelihood that two randomly chosen people would identify as different races or ethnicities. Five groups were used to construct the index: Hispanic or Latino and non-Hispanic or Latino White, Black, Asian, and all other groups.

For the measures under Cost of Services in Table 1, Population Growth and Decline include the subset of counties experiencing each direction of population change. We separate counties by growth and decline because both directions can increase per-capita costs relative to stable populations, so a single linear measure would obscure these opposing mechanisms. To construct counties’ need-capacity gaps, we first estimated per capita total expenditures using the following OLS regression model:

E x p_{i} = β_{0} + β_{1} O c c H o u s e_{i} + β_{2} U n e m p_{i} + β_{3} M I_{i} + β_{4} B i z + β_{4} P D_{i} + β_{5} P D_{i}^{2} + ϵ

(1)

where we account for the occupied housing rate (OccHouse), unemployment rate (Unemp), median household income (MI), number of wholesale, retail, hotel, and food establishments per capita (Biz), and population density (PD) and its square. This model does not estimate a normative measure of expenditure but instead local costs relative to a statewide average.¹³ We subtracted the combined property tax and LST RRC from predicted expenditure and standardized values at mean zero. Higher values indicate greater relative need. The last set of variables in Table 1 includes expenditures in the functions earmarked for the revenue from redistribution: Education (K-12 plus community college) and Economic Development.

To measure LST leakage and export, we start with the revenue counties would generate if distributed solely on a PoS basis and divide by the uniform rate of 2 percent to obtain their RRC. This version of RRC includes revenue gained from exporting and excludes revenue leaked to other jurisdictions. Therefore, we follow Zhao and Hou (2008) and construct a version of RRC absent of exporting for county i according to the following equation:

\tilde{R R C_{i}} = 0.02 \cdot P I_{i} \cdot \bar{S I R a t i o}

(2)

where PI is total personal income and

\bar{S I R a t i o}

is the statewide average sales-to-income ratio. Equation (2) calculates expected revenue given a county's total personal income under a standard tax rate and propensity to spend on taxable goods and services. If RRC

>

\tilde{R R C}

, the difference represents the amount of revenue the county gains from other jurisdictions. If RRC

< \tilde{R R C}

, the difference represents the amount of revenue leaked to other jurisdictions.

Part of our proposed analysis involves whether LST exporting (leakage) relates to property tax slack (excess). To construct slack/excess property tax effort, we compare property tax revenue to property tax RRC, the latter of which is calculated using the representative tax system approach:

P T R R C_{i} = B a s e_{i} \cdot \bar{T a x r a t e}

(3)

where Base is the property tax base in county i and

\bar{T a x r a t e}

is the average property tax rate. If property tax revenue exceeds RRC, it indicates that a county levies a relatively excessive burden. If revenue is less than RRC, a county has slack capacity in its property tax. Slack and excess burden can be a reflection of policy choices or need. This requires nuanced examination and consideration. However, this measure gives us the opportunity to examine interjurisdictional effort.

We establish baseline claims regarding the equity of LOT revenue in Table 2. The first column lists our measures for assessing LOT equity. The second column lists the direction of the correlation between each measure and LOT revenue that would likely be considered inequitable. Turning the focus to North Carolina, the third and fourth columns report the observed direction of correlations between each measure and LST leakage and exporting, respectively, in 2016. The last column reports the expected equity of a redistributive policy designed to correct LST leakage based on the observed correlations. That is, the redistribution would be expected to increase revenues for jurisdictions experiencing LST leakage and decrease revenue for those gaining revenue through LST exporting, and the direction of those LST revenue changes either aligns with or opposes equity with respect to that measure. For example, property tax base values do not significantly correlate with LST leakage and correlate positively with LST exporting. The expected decline in revenue for exporters would counteract an inequitable positive correlation between LST revenue and property tax bases. Therefore, Column 4 indicates the redistribution is expected to be equitable with respect to property tax base values. By contrast, homeownership rates are positively correlated with LST leakage. The expected increase in revenue would exacerbate an inequitable positive correlation with homeownership. Therefore, the redistribution could be considered inequitable if viewed through that lens. Homeownership and population growth are the only measures for which the redistribution is expected to be inequitable. Since the correlations predict an equitable relationship with population decline, it is therefore reasonable to expect an inequitable relationship with population growth despite its null correlation with leakage and exporting. The redistribution is expected to operate equitably for half of the measures. However, expectations for the remaining measures are ambiguous due to insignificant correlations between LST leakage and exporting, or instances in which the redistribution would operate in both equitable and inequitable directions, as in the case of median household income.

Table 2.

Equity Dimensions and Expected (in)Equity of Redistribution to Correct Local Sales Tax Leakage.

		Observed Correlations
	InequitableLOT RevenueCorrelation	LST Leakage	LST Exporting	Expected (In)Equity of Redistribution
Socioeconomic
Property Tax Base	Positive	Null	Positive	Equitable
Homeownership	Positive	Positive	Null	Inequitable
Median HHI	Positive	Positive	Positive	Ambiguous
Unemployment	Negative	Null	Null	Ambiguous
Poverty Rate	Negative	Null	Negative	Equitable
Econ Distress	Negative	Null	Negative	Equitable
Blau Index	Negative	Null	Negative	Equitable
Pct. Black	Negative	Null	Null	Ambiguous
Rural	Non-zero	Null	Null	Ambiguous
Cost of Services
Pop Growth Rate	Negative	Null	Null	Inequitable
Pop Decline Rate	Negative	Positive	Null	Equitable
Need-Capacity Gap	Negative	Positive	Negative	Equitable
Earmarked Spending
Education	Positive	Null	Positive	Equitable
Economic Development	Positive	Null	Null	Ambiguous

Notes: Inequitable correlations in Column 1 are based on that which would generally be considered (dis)favorable to socioeconomically (dis)advantaged jurisdictions. Expected (in)equity in Column 4 is based on the observed correlations with leakage and exporting. “Equitable” indicates that a policy correcting LST leakage would be expected to improve equity for that measure as well. “Ambiguous” indicates conflicting or negligible correlations. Property tax (PT) base includes taxable property value. Economic distress is an index constructed by the North Carolina Department of Commerce based on counties’ ranks in PT base, median household income, unemployment rate, and percent population change. Higher values correspond to higher distress. Population growth and decline include the subset of counties experiencing that direction of change. The Blau Index measures racial heterogeneity, with higher values indicating more diversity. Five groups were used to construct the Blau Index: Hispanic or Latino and non-Hispanic or Latino White, Black, Asian, and all other groups.

Table 2 also highlights the complexity of holistically addressing LOT equity and the challenge of balancing stakeholder concerns that may differ in priority. If a policy were designed to correct LST leakage, except for homeownership rates, it would appear to avoid counteracting equity in several respects, although equitable outcomes remain ambiguous in many others. However, the expectations in Table 2 are based solely on sales tax leakage and therefore reflect the policy decision to prioritize this aspect of sales tax equity instead of, for example, combined LST and property tax revenue, as discussed above in our proposed process. If LST revenue offsets inequities in property tax revenue, then the equity of a redistribution to correct sales tax leakage may deviate from the expectations listed in Table 2. Throughout the analysis that follows, we highlight how results align or deviate from Table 2.

Baseline (in)Equity of Local Sales Taxes

To examine the baseline equity of LSTs, we begin by considering the distribution of LST revenues per capita in 2016 under a PoS basis, as shown in Figure 1. The Gini coefficient for PoS revenue equaled 0.233. For comparison, property taxes had a Gini coefficient of 0.157. Therefore, inequality was notably higher for PoS revenue than for property tax revenue. Figure 2 shows combined PoS LST and property tax revenue among counties in 2016, arranged in ascending order. There are numerous cases in which a county with relatively low (high) property tax revenue is ranked higher (lower) within the combined distribution due to the amount of LST revenue generated. The Gini coefficients reflect this offsetting relationship. While LST revenue was distributed less equally than property tax revenue, the Gini coefficient for combined revenue was 0.150. Therefore, LST revenue slightly offsets the inequality present in property tax revenue.

Figure 1.

Distribution of point-of-sale local sales tax revenue per capita in 2016. Notes: Each bar represents a county in North Carolina. The amounts include all revenue from Articles 39, 40, and 42 that all 100 counties levy, inclusive of portions shared with municipalities.

Figure 2.

Distribution of combined local sales tax and property tax revenue in 2016. Notes: Counties are ranked in ascending order of combined revenue.

Table 3, Column 1, reports the correlation coefficients between PoS LST revenue and the measures listed in Table 2. Values in bold indicate inequitable correlations, according to Table 2, statistically significant at the five percent level. Overall, counties that generate more PoS revenue tend to be socioeconomically advantaged, except for having lower homeownership rates. Also, LST revenue favors counties that spend more on education, possibly because those revenues are partially earmarked for education. The only other factor that could be considered equitable involves the cost-of-service provision, as measured by higher population growth, which is associated with higher LST revenue. However, LST revenue disadvantages counties with population decline and a larger need-capacity gap.

Table 3.

Correlations Between Baseline Revenues and County Characteristics, 2016.

	PoSRevenue	PTRevenue	PoS & PT Revenue
	(1)	(2)	(3)
Socioeconomic
PT Base	0.63*	0.79*	0.83*
Homeownership	−0.27*	0.19	0.03
Median HHI	0.38*	0.32*	0.38*
Unemployment	−0.14	0.03	−0.03
Poverty Rate	−0.38*	−0.33*	−0.39*
Econ Distress	−0.47*	−0.42*	−0.52*
Blau Index	−0.15	−0.16	−0.18
Pct. Black	−0.26*	−0.13	−0.19
Rural	−0.23*	0.03	−0.06
Cost of Services
Pop Growth	0.34*	0.38*	0.40*
Pop Decline	−0.51*	−0.07	−0.20
Need-Capacity	−0.60*	−0.54*	−0.63*
Earmarks
Education	0.55*	0.50*	0.58*
Econ Dev	0.00	0.20	0.14

p < 0.05 Notes: Bold values in Column 1 indicate a statistically significant correlation considered inequitable. Combined revenue in Column 3 is the sum of PoS and property tax (PT) revenue. Bold values in Column 3 highlight cases in which the coefficient magnitude for combined revenue is of equal or greater inequity as PT revenue. Spearman's rank correlation was used for Economic Distress. All other values are Pearson's correlation coefficient.

Sources: North Carolina Department of Revenue https://www.ncdor.gov/local-government-distributions, North Carolina Department of State Treasurer https://logos.nctreasurer.com/Reporting/Report/External?applicationCode = AFIR, North Carolina Office of State Budget and Management https://demography.osbm.nc.gov, U.S. Census American Community Survey 5-Year Estimates.

LSTs may contribute to disparities that jurisdictions consider unfair despite a more equal distribution of combined revenue. Table 3, Column 2 reports correlations between the set of equity factors and property tax revenues for comparison to Column 3, which reports correlations for combined revenue. Bold values in Column 3 indicate statistically significant correlations for which combined revenue is as inequitable or more so than property tax revenue, based on coefficient magnitudes. Though coefficient differences are modest, LSTs maintain or increase the potential for perceived inequity regarding property base value, income, poverty rates, economic distress, need-capacity gaps, and education spending, when combined with property tax revenue.

The most salient lens in the debate around LST equity and redistribution is arguably the amount of revenue leaked or gained from other counties. Figure 3 compares LST $\tilde{R R C}$ without exporting from Equation (2) to RRC based on PoS revenue to examine exporting and leakage. The X symbols represent each county's revenue without exporting, and dashed (solid) lines show the amount of revenue lost (gained) due to exporting. The endpoint of each line represents each county's RRC based on PoS revenue. In 2016, 50 counties gained an average of $77 per capita, or 22 percent of this group's average PoS revenue, with gains ranging between $1 and $750. The other 50 counties lost an average of $70 per capita, or 35 percent of their average PoS revenue, with values ranging between $1 and $198.

Figure 3.

LST tax leakage and exporting in 2016. Notes: The figure compares observed PoS unadjusted revenue to PoS revenue without exporting (own-county revenue), as calculated by Equation 2. If observed revenue is less than revenue without exporting, the remainder is tax leakage (dashed lines). If observed revenue exceeds revenue without exporting, the remainder is export gain (solid lines). Counties are ranked in order of observed PoS unadjusted revenue. It excludes deductions to cover the cost of LST collection and property tax administration.

Part of the salience of LST leakage is the argument that it contributes to excess property tax burdens (Combs & Afonso, 2025b). Figure 4 examines whether LST exporting (leakage) is related to property tax slack (excess). A moderate association exists between the two deviations (r = 0.52). Of the 55 counties that leaked LST revenue, 34 (62 percent) generated excess PT revenue. Among the 45 counties that gained LST revenue from exporting, 23 (51percent) had slack PT capacity. Therefore, despite LSTs increasing equality when combined with property taxes, counties could argue that disparate capacity to export LST burdens enables unfair property tax burdens and warrants policy intervention.

Figure 4.

Local sales tax and property tax revenue-raising capacity deviations. Notes: LST revenue-capacity deviation on the x-axis equals the difference between a county's LST revenue with per capita adjustment and its LST RRC. Counties generating less (more) LST revenue than their capacity are leaking revenue (exporting burden) to other jurisdictions. Excess PT revenue or slack on the y-axis is the difference between a county's PT revenue and its PT RRC. Counties with less (more) capacity than actual revenue are levying excess (slack) PT burden.

LST leakage and exporting demonstrate several patterns that support the case for pursuing a more equitable distribution of revenues, as well as others that may contradict it. Of the 50 counties that leaked LST revenue, 41 are below the median of revenue per capita. These 41 sales tax-poor counties losing revenue to other counties are disproportionately rural, providing a case for rural counties to demand redistribution. Counties leaking LST revenue also tend to have lower median household incomes and higher poverty rates. However, these counties tend to have higher homeownership rates, lower unemployment, and less racially diverse populations, each of which, depending on the equity lens being applied, could be used to question the fairness of redistributing revenue to address LST leakage.

Linking these initial results back to the conceptual discussion and Table 2, several key points emerge. First, when considering the distribution of PoS revenue alone, concerns about inequality are warranted, but without relating it to another dimension, it is unclear whether that inequality is unfair. Second, if the equality of property tax revenue is an appropriate benchmark, given historical concerns regarding equity, then one may argue that LSTs are even more inequitable based on Gini coefficient comparisons. However, the distribution of LST revenue offsets a portion of the inequality of property tax revenue, and from this perspective, one could argue that LSTs are equitable. Third, instead of offsetting property tax revenue inequality, several alternative lenses could be prioritized for which the argument that LST revenue is inequitable is once again reasonable, even when combined with property tax revenue, while prioritizing other lenses would enable the opposite claim. It is no wonder that consensus around the equity of LSTs (or LOTs) can prove elusive.

Examining North Carolina's Per Capita Adjustments to Local Sales Tax Revenue

North Carolina differed from most states even before GS 105–524 due to allocating a portion of the base 2 percent LST revenue on a per capita basis. Therefore, it is important to incorporate this per capita adjustment and assess its impact on LST equity before evaluating GS 105–524.

Considering first the distribution of revenues, the per capita adjustment improved equality, reducing the Gini coefficient from 0.23 if strictly distributed on a PoS basis to 0.16. Next, Figure 5 shows the effect of the adjustment on 2016 revenue relative to LST leakage and exporting, with Dare County excluded for scale. As in Figure 3, the X symbols represent PoS revenue without exporting ( $\tilde{R R C}$ ). The triangles (squares) show PoS revenue with leakage (export gain), which were the endpoints of each line in Figure 3. The lines now represent the amount of revenue gained (solid) or lost (dashed) due to the per capita adjustment. Therefore, the combination of a triangle and solid line, or a square and dashed line, represents an equitable adjustment. The empty distance between the endpoint of each line and the X symbol represents the amount of leakage or export gain remaining after the adjustment. Per-capita adjustments exceed the amount leaked or gained in a few cases, as shown by lines extending past the X.

Figure 5.

LST per capita adjustments in 2016. Notes: X symbols represent a county's PoS revenue without exporting. Triangles and squares represent PoS revenue after leakage and export gains, respectively. Solid lines indicate positive per capita adjustments, resulting in increased county revenue. Dashedd lines indicate negative per capita adjustments, resulting in a decrease in county PoS revenue. The combination of a triangle and solid line, or a square and dashed line, represents an equitable adjustment. The distance between the endpoint of each line and the X symbol represents the amount of leakage or export gain present after the adjustment. Lines that run past the X represent an over-adjustment. Dare County is excluded for scale. Dare County received an adjustment of -$164 per capita.

Seventy-nine counties received positive adjustments averaging $31. The per capita adjustment reduced the number of counties leaking revenue from 50 to 34, and the average amount leaked per capita from $70 to $49. However, 16 counties received per capita revenue exceeding the amount leaked, and 29 counties with export gains received per capita revenue, thereby increasing their gains. The 21 counties that had revenues reduced forfeited an average of $26, lowering average export gains from $70 to $66.

Table 4, Column 1, reports correlations between per capita adjusted LST revenue and the set of equity factors, for comparison with PoS revenue in Table 3, Column 1. Bold values highlight cases in which the per capita adjustment maintains or increases the potential for perceived inequity with respect to that measure. Though the per capita adjustment achieved notable reductions in the Gini index and tax leakage, there is little difference in correlations compared to those for PoS revenue. Among the 11 statistically significant correlations, inequity is unchanged or slightly higher in eight, while the remaining three show slight improvement. Table 4, Column 2, shows the correlations for combined adjusted LST and property tax revenue. Compared to combined PoS and property tax revenue in Table 3, Column 3, the per capita adjustment maintains or increases inequity in only two of the seven significant correlations, while slightly decreasing inequity in five.

Table 4.

Correlations for Adjusted Revenue and Tax-Disadvantaged Counties, 2016.

	Adjusted LST Revenue	Adjusted LST & PT Revenue
	(1)	(2)
Socioeconomic
PT Base	0.65*	0.83*
Homeownership	−0.27*	0.06
Median HHI	0.38*	0.38*
Unemployment	−0.14	−0.02
Poverty Rate	−0.38*	−0.38*
Econ Distress	−0.49*	−0.51*
Blau Index	−0.16	−0.18
Pct. Black	−0.26*	−0.18
Rural	−0.22*	−0.05
Cost of Services
Pop Growth	0.35*	0.40*
Pop Decline	−0.49*	−0.17
Need-Capacity	−0.61*	−0.62*
Earmarks
Education	0.56*	0.57*
Econ Dev	0.00	0.15

p < 0.05 Notes: Column 1 includes actual LST revenue after per capita adjustments. Bold values indicate equal or worse inequity than PoS revenue in Table 3. Bold values in Column 2 highlight cases in which combined adjusted and PT revenue has equal or worse inequity than combined PoS and PT revenue in Table 3. Spearman's rank correlation was used for Economic Distress. All other values are Pearson's correlation coefficient.

While the results in Table 4 indicate that the adjustment changed the distribution of LST revenue to better complement its offsetting relationship with property tax, all but one of the correlations for combined revenue remain more inequitable than those for property tax alone, thus maintaining the case that LSTs compound inequity when considered through several lenses. Given these persistent inequities and the remaining LST leakage and export gains after the per capita adjustment (Figure 5), counties could make various cases for a more equitable redistribution of revenues.

Examining the Equity of North Carolina's Local Sales Tax Redistribution Policy

The GS 105–524 redistribution immediately improved revenue equality for LSTs across jurisdictions. The Gini coefficient declined from 0.16 in 2016 to 0.12 in 2017. While property taxes had been more equally distributed than LSTs with a Gini coefficient of 0.157 in 2016, post-GS 105–524 LSTs became more equally distributed than property taxes.

GS 105–524 was explicitly discussed to address LST leakage and exporting. Figure 6 shows the relationship between net redistribution dollars in 2017 and LST revenue lost or gained in 2016. GS 105–524 revenues did not perfectly target redistributions with respect to our measure of leakage and exporting, with a correlation of −0.64. Nine counties that leaked LST revenue received negative net redistributions, and 21 counties that gained LST revenue from exporting received positive net redistributions. Urban counties were disproportionately disadvantaged in both cases. This is likely because the allocations were not tied directly to leakage, but rather a per capita allocation.

Figure 6.

Net redistribution and LST revenue-capacity deviation. Notes: LST revenue-capacity deviation on the x-axis equals the difference between a county's LST revenue with per capita adjustment and its LST RRC in 2016, compared to 2017 net redistribution amounts on the y-axis.

The impact of GS 105–524 on 2017 adjusted LST revenues relative to leakage and exporting is shown in Figure 7. Again, X symbols represent PoS revenue without exporting ( $\tilde{R R C}$ ). The triangles (squares) now show per capita-adjusted revenue for counties that leaked (gained) revenue after the adjustment, which were the endpoints of each line in Figure 5. The lines now represent the amount of revenue gained (solid) or lost (dashed) due to GS 105–524. The combination of a triangle and solid line, or a square and dashed line, represents an equitable redistribution. For these equitable redistributions, the amount of leakage or gain remaining is represented by the distance from the endpoint of the line intersecting the X, while lines extending past it represent excessive redistribution. Cases with triangles and dashed lines or squares and solid lines represent inequitable redistributions.

Figure 7.

Local sales tax leakage and exporting with Net GS 105–524 Revenue in 2017. Notes: X symbols represent a county's PoS revenue without exporting. Triangles and squares represent per capita-adjusted revenue, differentiating between counties that still leak (triangle) or gain (square) revenue after the adjustment. Solid (dashed) lines indicate net positive (negative) redistributions from GS 105–524. The combination of a triangle and solid line, or a square and dashed line, represents an equitable redistribution. The distance between the endpoint of each line and the X represents the amount of leakage or export gain present after the redistribution. Lines that run past the X represent an excess redistribution. Dare County is excluded for scale. Dare County received a net redistribution of -$37 per capita, reducing per capita-adjusted revenue from $998 to $961, compared to PoS revenue without exporting of $405.

GS 105–524 increased revenue by an average of $25 per capita among 67 counties and reduced revenue by an average of $11 for 33 counties. The redistribution reduced the number of counties leaking revenue from 34 to 25 and decreased the average amount lost from $49 to $39. However, two of those counties had more revenue withheld than they gained, resulting in additional tax leakage. Among the remaining 75 counties, 48 had export gains averaging $67, similar to the average gain in 2016, and 27 counties received revenue exceeding their tax leakage, thereby gaining revenue from other counties. While GS 105–524 reduced inequality and partially corrected tax leakage, it inequitably redistributed revenue in several cases.

Lastly, we examine GS 105–524 in relation to the other equity perspectives. Table 5, Columns 1 and 2 report correlations for revenue received and net redistributions in 2017, respectively, using the same 2016 measures as in Tables 3 and 4, since the policy was finalized that year. Bold values indicate inequity based on the claims in Table 2. GS 105–524 distributed revenue inequitably across three measures: homeownership rate, rural status, and population growth. This mostly aligns with our expectations in Table 2, though the expectation for rural status was ambiguous because it showed no significant correlation with leakage or exporting. It operated equitably across nine measures, though some correlations are relatively weak, and did not significantly correlate with unemployment, the Blau index of racial diversity, and economic development.

Table 5.

Correlations Between GS 105–524 Revenues and County Characteristics.

	Revenue Received	Net Redistribution	LST Revenue	LST & PT Revenue
	(1)	(2)	(3)	(4)
Economic
PT Base	−0.38*	−0.45*	0.67*	0.85*
Homeownership	0.39*	0.38*	−0.21*	0.11
Median HHI	−0.29*	−0.32*	0.38*	0.38*
Unemployment	0.15	0.15	−0.10	0.05
Poverty Rate	0.25*	0.29*	−0.38*	−0.38*
Econ Distress	0.43*	0.45*	−0.43*	−0.44*
Blau Index	0.08	0.10	−0.17	−0.19
Pct. Black	0.23*	0.24*	−0.25*	−0.18
Rural	0.33*	0.33*	−0.17	−0.01
Cost of Services
Pop Growth	−0.35*	−0.37*	0.29*	0.31*
Pop Decline	0.42*	0.43*	−0.41*	0.06
Need-Capacity	0.46*	0.50*	−0.62*	−0.60*
Earmarks
Education	−0.48*	−0.52*	0.40*	0.41*
Econ Dev	0.17	0.14	0.65*	0.57*

Notes: Values in Columns 1 and 2 include correlation coefficients between 2016 measures to examine how the first year of implementing GS 105–524 related to county characteristics in the year the policy was finalized. Bold values indicate inequitable correlation. Columns 3 and 4 use 2017 measures. Column 3 LST revenue is net of GS 105–524. Bold values in Columns 3 and 4 indicate cases where the magnitudes in 2017 did not change in an equitable direction compared to 2016.

Column 3 of Table 3 includes correlations between 2017 LST revenue and 2017 measures to examine if equity improved after redistribution. Bold values indicate no change or greater inequity compared to adjusted revenue in 2016 (Table 4, Column 1). Equity remained stagnant or increased across four measures despite GS 105–524 working in an equitable direction, suggesting countervailing changes in those measures. Additionally, inequity in homeownership and population growth increased, both of which were addressed inequitably by GS 105–524. When combined with property tax revenue in Column 4, equity relative to most measures is no better or worse. The most notable equity improvements were in economic development and education expenditures, both of which correlated strongly with the redistribution compared to other measures. Overall, most changes are minor, but this speaks more to the relative size between redistributed revenue, total statewide LST revenue, and property tax revenue.

Relating this analysis back to the conceptual discussion, insofar as North Carolina is a representative case, the results suggest that a redistribution to correct LST leakage and exporting does not work against equity relative to most other perspectives. Nevertheless, redistributing LST revenue, which is commonly used to shift tax burdens away from homeowners, back to counties with higher homeownership rates, is a legitimate concern. So too is reducing revenue in counties experiencing population growth and the associated rise in service costs. Furthermore, this redistribution correlates weakly with unemployment rates, racial diversity, and the percentage of the population that is black, each of which could reasonably be prioritized over LST leakage and exporting.

Policy Solutions and Alternatives

Equity is not simply binary. It will mean different things to different people because, ultimately, equity “reflects ideas of ‘fairness’ and ‘justness’ which have a normative component in that they are based on moral values or considerations” (McSherry, 2013). However, we have presented a framework for approaching equity concerns from a data-driven perspective that considers more holistic dimensions of equity. It is important to think about policy solutions once policymakers have identified an inequity that they wish to address. This section briefly highlights some of the policy considerations we believe are relevant to the conversation.

State Interventions and Equalization

States can equalize revenues and target programs at communities receiving less than their fair share. There are several ways states can accomplish this. First, they can create a methodology of redistributing local revenues in a manner deemed more equitable, as North Carolina's GS 105–524 did. Second, states can engage in revenue sharing by redistributing revenues from state sources to local governments to achieve greater equity.¹⁴

An example is Connecticut's Municipal Revenue Sharing Fund, which allocates 7.9 percent of state sales and use tax dollars to offset potential property tax revenue losses due to a state cap and tax-exempt properties. In 2021, the state diverted those funds back to its general fund (Fitch, 2022; Pinho, 2021) but recommitted in 2024 (Lowrey, 2024). This policy, while not redistributing local funds, highlights key concerns. First, it shows the state can effectively make strategic distributions for more equitable local financing, addressing tax effort, burdens to taxpayers, and revenue availability—without regard to where sales tax was generated. Second, it illustrates why locals may prefer financial autonomy, as the state's shift to balance its budget at local expense is a valid concern seen elsewhere. Similarly, Wisconsin redistributes shares of state income and sales taxes to locals (who cannot collect these themselves), but allocations have stagnated since the early 2000s (Spears, 2023).

Another example is Nevada's consolidated tax distribution (C-Tax), which combines six tax types—like liquor and sales—into a single monthly payout from the state. It eases administrative burdens and uses varied schemes (e.g., population for liquor/cigarette taxes, origin for real property transfers, tiered for sales) to reduce inequalities between Las Vegas and rural areas (Aguero, 2013). However, the formula lags macro-economic changes and is sensitive to micro shifts (Rothberg, 2020). Nebraska's municipal equalization fund similarly aims to equalize city property tax revenues. Thus, state-level solutions are widespread and can be effective, but carry concerns.

Other states intervene in similar ways as GS 105–524. Just as being a regional retail center might confer an unfair revenue advantage, other factors create inequalities. For instance, Indiana shares riverboat wagering tax revenues with counties lacking riverboats and thus unable to generate them. This extends to other scenarios: beach or mountain communities have advantages in occupancy taxes due to tourism appeal.

Earmarks for Local Option Taxes

The research on earmarked revenues is split on whether they increase spending on the recipient program or whether the revenue is treated as fungible and spread out across other programs. This mixed evidence suggests that policymakers may want to include language to specify that the funds are meant to supplement, not supplant, existing financing if this is the policy intent. Of course, that may or may not be successful. For example, Georgia's baseline LST is earmarked for reducing property tax burdens. On property tax bills, local governments are required to report how much the property tax bills were reduced due to the LST. However, knowing what property taxes would have been without the LST is difficult. Is that baseline sales tax really revenue-neutral? One study uses propensity score matching and a national sample to analyze whether the sales taxes earmarked for reducing property taxes actually do so, and it finds that LSTs are used both to decrease property tax revenue and to increase own-source revenue per capita (Afonso, 2014).

Greater Autonomy to Local Governments

If total revenues are the primary concern, not capacity and not individual taxpayer burden, then another option available to states is to increase local autonomy over their revenue sources. This could mean allowing them to adopt a more diverse set of revenue sources, having more ability to set the rates on existing revenue instruments to meet their needs, or having more flexibility with the tax base. This could be a solution to concerns around ultimate revenue collections and the cost of providing services. However, it may mean that taxpayers who live in areas with either high costs of providing services per capita or smaller tax bases will have higher effective tax rates to contend with. It could also decrease the transparency of public finance for the community and increase the burden placed on individuals and businesses to comply with taxes, especially when adjusting local tax bases. In fact, one consequence of allowing local governments to adjust their LST base may be no longer having a local economic nexus apply under the Wayfair ruling.

For example, as an alternative or supplement to GS 105–524, North Carolina could have broadened the LOTs available to local governments including the discretionary authority or the restrictions on LSTs. In North Carolina adopting an occupancy tax requires a local bill passed by the General Assembly to provide the jurisdiction with the power to levy one. Despite that restriction, almost 200 municipalities and counties have adopted one. Notably, the revenue-raising capacity of such instruments is also unequal, and this could exacerbate inequity but still provide additional resources to low-capacity governments, with a respective Gini coefficient of 0.821 for the combined revenue of the occupancy and meals taxes. This prompts the question: Are we more concerned about the equality of revenues across jurisdictions or the adequacy? If it is the adequacy of resources for key functions, then expansion of revenue sources, whether they be own-source or from the state, that are earmarked for those services with a non-supplant clause, may also be an appropriate choice to ensure the baseline of funding that is needed.¹⁵

Other Considerations

One important question that scholars and policymakers must consider when examining equity is: what are the alternatives? Are there tax and expenditure limits in place that will limit the use of a property tax? Will communities shift to fees, which are also often perceived as inequitable, for individual taxpayers? What does the state allow in terms of revenue instruments? Of course, the answer could be using a LOT and taking steps to correct any identified inequities. A common example of this is that proponents of national sales taxes often acknowledge the disproportionate burden placed on low-income households and suggest that it be coupled with a tax rebate or lump sum transfer. A policy like this would address some identified inequities. Still, it would be simpler for a state or federal government to adopt such policies and much more challenging for a local government to do so independently. A second critical question to address when considering the impact on individuals is how the revenue will be spent, returning, in part, to the question of earmarks.

Finally, it is worthwhile to acknowledge that the redistribution of revenues, and spending the earmarked programs, created possible spillovers for neighboring counties and that these spillovers may lessen the equity concerns for GS 105–524. There is evidence that there are spillover effects in local spending (e.g., Ferraresi et al., 2018; Moreno Jimenez, 2026; Ogawa & Wildasin, 2009) and investment in human capital through education expenditures and economic development are likely to create positive spillovers for neighboring jurisdictions. However, the shared revenues are pooled statewide and not regionally, diminishing this connection at the local level, but potentially benefitting the state as a whole.

Conclusions

As scholars, state policymakers, advocacy groups, residents, and local governments continue to be concerned with issues like the urban-rural divide, differences between the wealth of governments, and broader notions of equity and interjurisdictional equity, it is critical to consider the equity of revenue systems. This requires some baseline agreement on what is equitable, which is a challenging undertaking—especially since lower local revenues may reflect preferences rather than inequities. Operational definitions of equity that include assessment guidelines and performance measures to assess success are critical (Cepiku & Mastrodascio, 2021; Gooden & Portillo, 2011). The challenge is not just to reconcile different ideological notions of fairness but also different ways in which equity can be assessed. In this manuscript, we present several lenses through which the interjurisdictional equity of local option taxes can be considered, such as revenue-raising capacity and the cost of providing services. Then, we analyze a North Carolina law that pooled and redistributed LST revenue to counties that were determined to be experiencing tax leakage. We consider the outcomes of this policy with various measures and from the lenses identified.

GS 105–524 was designed around the specific equity lens of revenue-raising capacity, aiming to redistribute LST revenue to reduce sales tax leakage. The policy largely achieved this goal, with redistributed revenue correlating strongest with LST leakage and exporting. However, this narrow focus on the equity of tax leakage relates weakly to many other equity lenses, such as unemployment, poverty, racial diversity, and need-capacity gaps. Furthermore, the redistribution could be perceived as inequitable from a few perspectives. Counties with a growing population, which can raise the cost of services per capita, tended to receive less revenue, and those with higher homeownership rates received more revenue. Overall, this more holistic consideration of equity raises a question that is generally unexamined in LOT equity research: Why prioritize addressing LOT equity based on revenue-raising capacity and tax leakage instead of any other basis?

Understanding the consequences of public finance choices is important to policy and practice as local governments provide key services to their residents that impact the quality of their daily lives (Afonso et al., 2026; Kioko et al., 2011). Local governments are also becoming increasingly dependent on local option taxes like local sales, occupancy, and income taxes. Therefore, a framework for scholars and policymakers on how to consider inequities and ways to address them is incredibly valuable. Thus, the different interjurisdictional equity lenses we present, the analysis of North Carolina's policy, and the policy alternatives we discuss should provide a foundation moving forward to similar work in other contexts in terms of states, tax instruments, and policies.

Ultimately, the equity of public finance demands the attention of all public administration and management scholars, not solely those in budgeting and finance, as the capacity to generate revenue fundamentally shapes service delivery, governance, and equitable outcomes across jurisdictions. We urge scholars to explore how interjurisdictional equity in financing intersects with democratic legitimacy, effective service provision, and governance challenges like trust and inclusion. While social equity has gained prominence in research and policy, we call for a broader perspective that prioritizes the jurisdictional lens alongside the taxpayer lens, recognizing that interjurisdictional equity is pivotal to achieving just and inclusive governance.

Footnotes

ORCID iD

Whitney Afonso

Ethical Approval Statement

Not Applicable.

Consent for Publication

Not Applicable.

Participant Consent Information

Not Applicable.

Funding

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

Declaration of Conflicting Interests

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

Data Availability

Data is available upon request.

Notes

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