Tourist tax elasticity in Florida: Spatial effects of county-level room tax rate variation

Tourism Management Perspectives 31 (2019) 174–183

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Tourism Management Perspectives journal homepage: www.elsevier.com/locate/tmp

Research paper

Tourist tax elasticity in Florida: Spatial effects of county-level room tax rate variation

T

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Brian M. Millsa, , Mark S. Rosentraubb, Gidon Jakarb a b

Department of Tourism, Recreation, & Sport Management, University of Florida, PO Box 118208, Gainesville, FL 32611, United States Sport Management, University of Michigan, 1402 Washington Heights, Ann Arbor, MI 48109, United States

A R T I C LE I N FO

A B S T R A C T

Keywords: Tourist taxes Spatial econometrics Sport tourism Tourism economics

We examine the spatial, county-level incidence of changes to tourist tax rates in Florida from 2003 to 2014. Florida allows county-level visitor tax increments to fund various tourism-related initiatives. As a result, there is considerable variation in visitor tax rates across counties and across time. Using a dynamic spatial autoregressive panel model, we show that direct and indirect effects of room tax rate changes on taxable hotel room expenditures are relatively small and largely exported to visitors. Because these taxes have recently been allowed for funding Major League Baseball Spring Training facilities, we also test whether these facilities impact hotel tax collections. We find no measurable effects of a small number of facility relocations on hotel spending in the new or previous home-counties. We also discuss the importance of considering contiguous spillovers and the use of cluster-robust standard errors in panel estimation of tax incidence for tourism.

1. Introduction

to fund tourism-related investments. Although the state is not specific about all the possible tourism-related uses of these tax dollars, one explicitly mentioned driver of differences in and changes to the TDT is the funding of spring training facilities for Major League Baseball (MLB) teams. The state allows up to a two percentage point increase in this tax to offset debt assumed by the county for construction of these facilities. A new facility is often touted as an amenity attracting new tourists and additional business for the hospitality sector. Advocates claim that the new spending from spring training baseball will more than offset possible losses from the possible higher prices resulting from the tax increase. This claim is challenged by an extensive literature that has pointed to an absence of large economic development effects from sport venues (Coates, 2007; Coates & Humphreys, 2008; Humphreys, 2019). MLB teams typically play only 15 spring training games in a host community, suggesting that any impact of the team's presence is likely to be small. Despite the academic community's concern with the magnitude of the effects of Spring Training baseball, Florida and its counties view the tax as a tool to export the full cost of the public's share of stadium financing to tourists visiting for spring training games. In practice, however, the bed tax is collected from all visitors throughout the year, meaning the higher costs for tourists are not limited to those attending games in March. Evaluating these collections and any distortionary tax effects and competition in a general or partial equilibrium framework is necessary to understand the net benefit to the

Tourism-reliant geographic locations often levy taxes on accommodations as a convenient tool to increase government funding or offset externalities associated with high tourist traffic (Fish, 1982; Palmer-Tous, Riera-Font, & Rossello-Nadal, 2007; Schubert, 2010; Spengler & Uysal, 1989; Weston, 1983). This strategy has allowed states like Florida in the U.S. to compensate for the lack of a state-level income tax. However, tourism industry leaders often view changes to tax rates as a threat to their business operations, as the tax may ultimately change prices to the end consumer. It is feared that higher taxes will result in reduced tourist consumption or fewer visitors (Aguiló, Riera, & Rosselló, 2005; Forsyth, Dwyer, Spurr, & Pham, 2014; Jensen & Wanhill, 2002). The incidence of tourist taxes – whether on rental cars, hotel accommodations, or other resorts and entertainment – is therefore of central interest to policymakers seeking to fund public projects or to make investments in amenities that attract visitors. If the implementation of a tax reduces economic activity, the net welfare to the destination municipality and its residents and business owners could be negative. However, if tourists are largely insensitive to small changes in prices, then much of the burden for new tourism infrastructure can be exported, possibly leaving the hospitality and tourism sector better off. In Florida, the state allows counties to implement increments to a county-level bed tax (the Tourist Development Tax, or TDT) specifically

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Corresponding author. E-mail addresses: [email protected]fl.edu (B.M. Mills), [email protected] (M.S. Rosentraub), [email protected] (G. Jakar).

https://doi.org/10.1016/j.tmp.2019.05.003 Received 9 October 2018; Received in revised form 1 May 2019; Accepted 6 May 2019 2211-9736/ © 2019 Elsevier Ltd. All rights reserved.

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estimating tourist tax elasticity and demand spillovers.

county. In this paper, we evaluate the incidence of the TDT for counties in Florida, where tourists flock each year to enjoy the state's extensive coastline and tourist attractions. We use variations in tax rates across counties and across time to estimate the direct and indirect effects of these rates on county-level bed tax collections and distributions in a dynamic spatial autoregressive panel framework. We find only weak evidence of tax avoidance by tourists, with relatively small changes to long-run hotel room consumption levels due to higher tax rates. As a result, we conclude that a large portion of the tax is exported. We concurrently estimate changes to tourist consumption upon the arrival (or departure) of an MLB team for spring training to (from) a county. From the smaller sample of affected counties in our data, we find little statistical evidence of measurable changes to hotel room consumption from these facilities. However, we note that caveats exist with estimating what are likely to be small effects across heterogeneous countylevel economy sizes. Our work contributes to the literature on tourist taxes in various ways. First, heterogeneity in geographic characteristics – or homogeneity that may result in nearby substitutes – calls for further investigation of tourist tax levies, as they could determine avoidance behaviors among tourists. Indeed, studies on changes to tourism levels based on new tourist taxes have often used a single geographic location to estimate tax revenue elasticity. Large panel models that include nearby substitutes have not been broadly employed, and to our knowledge only one paper has used a spatial econometric approach to evaluate bed tax effects with panel data (Lee, 2014). As shown by Alvarez-Albelo, Hernandez-Martin, and Padron-Fumero (2017), substitutability may be central in considering the net welfare effects of taxes focused on tourists, especially when these substitutes can be reached for the same costs. Even fewer papers have estimated revenue elasticity for taxes directly related to public sector expenditures on sport venues – one exception being Mills, Rosentraub, Winfree, and Cantor (2014) – and the issue of deadweight loss has been omitted in work discussing the breadth of costs associated with public financing of sport venues (Crompton & Howard, 2013). County-level differences in tax rates may also result in some tourists staying just outside the boundaries of where a tax is levied while enjoying the tourist attractions such as spring training that are located within the taxed county. Second, given the large MLB spring training investments made by Florida and its counties, appropriate empirical estimation of consumption changes due to either tax increases or the team's presence should be part of the information used by community leaders when determining if an investment in a venue is appropriate. Heterogeneity in both the geographic characteristics of municipalities and the scale and time-frame of the sport(s) they host calls for further investigation into the costs to secure the limited number of events. Although the effects of spring training are not our primary objective, we provide some limited results related to taxable hotel room spending with the arrival or departure of a team. Lastly, we use spatial autoregressive panel models to estimate the direct effects of changes to taxes and the possibility of spillover effects for tourism in contiguous counties. This underutilized econometric approach provides advantages to understand the state-wide effects of localized tax increases and the net burden borne by the state's accommodations sector. This could be particularly salient in the context of homogeneity in tourist attractions (beaches, etc.) across nearby counties in places like Florida, and provide evidence of tax competition between counties in the same state. The following section describes the literature on tourist taxes and their effects on tourism consumption in various geographical locations, and briefly discusses the role of the public sector in financing sport facilities. Section 3 reviews our spatial panel estimation approach, and we present and discuss the results of these models in Section 4. Section 5 presents limitations of our data and approach, while Section 6 provides conclusions and suggested future directions for research

2. Literature review Empirical research on tourist-related taxation, such as hotel room taxes, has a long history in the literature on the economics of tourism. The relevance of these taxes to the tourism sector, the local government, and residents of the area are well covered in work from Mak and Nishimura (1979), Combs and Elledge (1979), Hughes (1981), Fish (1982), Weston (1983), Mak (1988) Spengler and Uysal (1989), and Hiemstra and Ismail (1993). In particular, the ability of room taxes to be exported to non-residents and provide revenue to national, state, and local governments is key to ensuring their use improves the net welfare of local residents and businesses. In the room tax scenario, hoteliers can be burdened more than other businesses if consumers are price-sensitive. This could be exacerbated by the inelastic supply of hotel rooms: building a new hotel or adding (removing) hotel rooms in the short term is not a realistic option when tax rates change. Since hoteliers cannot adjust the supply of hotel rooms, if a tax reduces the number of tourists, costs remain largely unchanged if additional room nights are left unsold. This leaves the hotel operator at a disadvantage compared to other businesses that can reduce supply to respond to reduced consumer needs when taxes are raised. Although a small tax increase will likely increase total tax collections for a local public sector, the percentage increase in tax revenue could still result in a reduction in hotel consumption. This reduced consumption would be borne by hotel owners (and local workers at the hotels) that reside in the local area. It is possible that hotels can reduce labor needs with lower capacity (room cleaning and other attendants for guests), but this would mean that the burden is passed largely onto lower income workers that reside in the area. Despite the importance of taxes on the tourism sector in funding public projects, much of the empirical work investigating tourist tax exportability has been limited to single geographic locations. The results of this work were mixed, though there is a consistent suggestion that per night room taxes are more likely to be passed onto consumers than the property taxes assessed on hotels. As a result, many locations have continued to use nightly hotel taxes to pay for local infrastructure projects or new investment in the tourism sector (Mak, 2015). However, while consumers have been shown to bear a larger burden from these taxes, there are still effects on the lodging and tourism industry. For example, Ihalanayake and Divisekera (2006) suggested that in some countries, such as Australia, the tourism industry bears a disproportionately heavy tax burden, even in cases where general taxes are applied to the tourism and other industries. Further, Mak and Nishimura (1979) find that a hotel tax in Hawaii resulted in reductions in non-lodging expenditures by tourists due to a room tax. Ultimately, this tax increased state revenue, but at the cost of reducing private sector income. Fujii, Khaled, and Mak (1985) provide further estimates of elasticity of demand in the face of an ad valorem hotel tax from 1961 to 1980 in Hawaii, showing that while much of the burden was shifted to tourists, there was a moderately large negative output effect on the tourism industry. Alternatively, Bonham, Fujii, Im, and Mak (1992) found negligible effects of hotel room taxes on hotel revenues in Hawaii. Hiemstra and Ismail (1992) identify differences in elasticities related to the initial prices of rooms using cross-sectional data, and Hiemstra and Ismail (1993) find that the lodging industry tends to indirectly pay $1 for every $6 to $7 paid by guests. Similarly, Aguiló et al. (2005) find short-term price effects of a €1 tax on rooms in the Balaeric Islands. The breadth of this work suggests that local costs of room taxes are not completely ignorable. More recent work has turned to general equilibrium models as an alternative to the various partial equilibrium models presented in prior work (Gooroochurn & Sinclair, 2005; Ihalanayake, 2012; Ponjan & Thirawat, 2016; Schubert, Matias, & Costa, 2012). Although these models provide insight into economy-wide effects, the inclusion of only 175

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Fig. 1. County-Level Tourist Development Tax Rates. Tax rates as of December 2015 and include all counties in Florida.

(Forsyth & Dwyer, 2002), there is a need for closer inspection of bed tax burdens and their geographical spillovers in the face of similar hotel and rooming choices in nearby areas with lower tax rates. This is especially important where there is close competition for tourists with similar travel costs and amenities (Fish, 1982). Indeed, even when substitutability is not immediately evident, there is evidence that various geographic locations compete heavily in the rate at which they tax tourism and other products or services (Alvarez-Albelo et al., 2017; Forsyth et al., 2014; Gade & Adkins, 1990; Hoffer & Lacombe, 2017; Jensen & Wanhill, 2002). Despite this extensive body of empirical research, very little analysis has benefitted from rich panel data and competition across multiple nearby geographical locations. The need for research on this issue was originally noted in the 1980s (Fish, 1982; Weston, 1983). We seek to fill this gap using data at the county level in Florida. This setting contrasts with past work on island locations with similar beach and coastline amenities, but fewer contiguous competitors (Mak & Nishimura, 1979; Fujii et al., 1985; Bonham et al., 1992; Aguiló et al., 2005. Therefore, in our context, there is substantial nearby competition for tourists and the possibility of spillovers in visitor spending that may be affected by relative bed tax rates. To our knowledge, there has been a dearth of work using recent advances in spatial econometrics to understand tourism spillovers. Despite the empirical estimation of competition and spillovers in the spatial panel context for other types of taxes (Hoffer, Humphreys, & Ruseski, 2019), their use in investigating bed tax incidence has been mostly neglected. The limited work that has considered the use of spatial models has focused on net effects without considering direct competition from adjoining areas (Lee, 2014). We therefore take a spatial autoregressive modeling approach in our Florida county tourist tax context. The richness of our data allows for a

a single geographic area limits an understanding of tax competition from nearby areas or spillover effects resulting from tourists visiting one area's amenities while staying in hotels in another area. Nevertheless, general equilibrium analysis has provided valuable results for the tourism sector and broader economy. For example, Ponjan and Thirawat (2016) find that a tourist tax cut in Thailand would increase inbound visitor demand, helping to alleviate negative consequences from a natural disaster. Despite these impacts on tourism arrivals when taxes are levied, Gooroochurn and Sinclair (2005) show that taxing tourism is relatively more efficient and equitable than taxing other broader sectors. Therefore, the political and economy-wide tradeoffs are particularly relevant when choosing between the goods and services to be taxed. Related to the economy-wide effects, much of the work related to tourist tax impacts has suggested that using these tax dollars for tourism promotion and investment can compensate for negative output effects in the industry, negative congestion effects, and environmental externalities (Fujii et al., 1985; Mak, 1988; Palmer-Tous et al., 2007). In the Florida example, funding venues for MLB spring training is one of the options for reinvestment. However, the cost of specific reinvestments should be evaluated relative to the deadweight loss or other costs resulting from increased taxation, particularly in cases where the tourism-specific benefit is unclear. If distortionary effects outweigh the additional tourist revenues gained from any investment, then there are important questions to be asked about net benefits from a given tax policy. A limited body of work has also noted the importance of considering the substitutability of geographic locations in estimating tourism changes in the face of industry-specific taxes. Although past work has found that many locations have substantial monopoly power in tourism 176

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more comprehensive spatial weighting matrix and fixed effects estimates of direct and indirect effects that provide insight into the competitiveness of tourist locales with many nearby substitutes.

retain a sufficient number of teams on both of Florida's coasts. Yet, as we noted earlier, there are important efficiency and equity concerns related to the taxation of visitors. Such a tax has the potential to create market distortions and deadweight loss or have little to do with consumers of baseball. These distortions could result in reductions in welfare for consumers and businesses as well as local residents where that tax is levied. This may be particularly acute when nearby substitutes are readily available or cross-border travel is low cost, as is the case in Florida. More recently, additional tax avoidance and distortion through less formal (black) markets may be even easier with the growth of services like AirBnB (Gago, Labandeira, Picos, & Rodriguez, 2009). The ease of avoidance, therefore, may also play a role in the effectiveness of a tax financing strategy for a professional sport facility (Mills et al., 2014). Most importantly, the increment allowance is a state policy. From the state perspective, the competition between counties to attract MLB spring training is not beneficial to the state as a whole. If teams are unlikely to leave Florida in the first place, then this county-level competition may increase costs to Florida residents while attracting no additional tourists or MLB teams. The state-level benefit therefore depends largely on the reality of the threat from Arizona. Even in the face of market distortions, increases in tax revenue could be used productively for residents of the area, creating externalities that improve the well-being of residents through public services, redistribution, or other interventions. Yet the assumption that sport provide these externalities has been questioned (Coates & Humphreys, 2008), and there are considerable opportunity costs to using this revenue for sport venues (e.g., education, infrastructure). With better understanding of the magnitude of tax-related changes by policymakers, appropriate measurement of the subsequent benefits from the use of taxes for financing sport venues can be compared more appropriately (Baade & Matheson, 2006). This calculation may include intangible benefits in addition to tangible economic ones, such as increased nearby property values, as alluded to in recent work (Feng & Humphreys, 2018; Matheson, 2019; Rosentraub & Joo, 2009). If positive externalities exist from the construction of a new facility – economic or otherwise – they should at least outweigh the sum of both the direct costs and deadweight loss to be considered a worthwhile investment. While the importance of minimizing deadweight loss in the context of financing sport venues was directly addressed by Baade and Matheson (2006), most work using tax collections data to estimate the impact of sport venue financing has failed to include tax rates in regressions that allow for estimation of these effects on taxable sales. This highlights two key issues. First, the exclusion of changes in tax rates could bias estimations related to tax collections attributable to a levy for building a sport venue in a given municipal boundary. Secondly, in the case of using a tax explicitly to fund sport facilities, the cost (or deadweight loss) of the tax to local residents is left out of the net benefit calculation. Given these unresolved issues, we seek to fill a gap in the literature by focusing upon changes to the bed tax rates, which are directly used to fund an MLB spring training facility. Finally, economists have suggested that smaller scale events may be more likely to provide positive economic outcomes, with these outcomes dependent on the relative size of the funding area, its regional integration with nearby municipalities, and other geographic or economic characteristics less common in larger metropolitan areas (Agha, 2013; Agha & Coates, 2015; Agha & Rascher, 2016; Agha & Taks, 2015). We propose that MLB spring training qualifies as one of these smaller scale investments. In particular, geographic isolation for some facilities may result in a larger percentage of visitors coming from outof-town. The ability for smaller counties to host MLB spring training could also induce local baseball fans to spend their money within the county, rather than travel to nearby counties to consume baseball (Agha & Taks, 2018). This may be particularly important in counties that have fewer entertainment options (fewer substitutes). Further, if labor at hotels,

2.1. Tourist taxes, tourist sector investments, and spring training facilities in Florida We place our inquiry in the context of funding sport facilities; however, we note that TDT increments at the county level can be earmarked for numerous tourist-related investments. For example, a tax of up to 3% may be used for the capital construction of tourist-related facilities, promotions, and beach and shoreline maintenance. For high tourism traffic areas, the state has authorized the levy of an additional 1% if desired by the local government. A specific allowance also exists for MLB spring training in response to the efforts in Arizona to attract teams away from Florida. Specifically, the state allows counties to increase their bed tax (TDT) by 2% for the purpose of constructing an MLB spring training facility. The 2% increment is used to pay the debt service on bonds sold to pay for the construction of spring training venues, typically with a term between 20 and 30 years. As noted by Baade and Matheson (2006), local municipalities elsewhere in the U.S. have also trended toward increased reliance on visitor taxes to fund professional sport venues. Altogether, the county allowances result in the TDT varying between 0% and 6% across Florida on top of the state and county sales taxes (Fig. 1). These variations establish the setting for this research on the effects of different rates of taxation on consumption. There exist other tourist and convention-related taxes in addition to the TDT, but these are not included in our data. For example, there are a taxes to offset negative environmental impacts or other externalities, municipal-level resort taxes, and food and beverage taxes to promote conventions and trade shows, fund domestic violence centers, and assist the homeless. We focus upon the TDT due to its direct relationship with the funding of spring training facilities and other tourism-specific investments. However, we note that investigation into these other tourist-related taxes could be just as interesting in the context of other uses of TDT revenue. As noted earlier, spring training facilities host only 15 MLB spring training games per year, reducing their expected impact. While there are twice as many MLB spring training games as National Football League (NFL) regular season games, attendance at these games is approximately ten times lower than that for even the worst attended NFL games. The brevity of the spring training season calls for additional investigation into whether these facilities are worth the public investments made in recent years, and whether tourism-related tax funding strategies are prudent. Recent public spring training facility funding levels have ranged from $55 million to $140 million. Typically, this investment is funded through municipal bonds repaid over 20 to 30 years using the TDT and state grants of up to $20 million (Andreassi, 2018; Gardner, 2017; Wagner, 2015). Annual economic benefits would be required in the range of the debt service payments made to justify the funding as a sound fiscal investment. Florida began permitting the sport-specific tax increment in response to competition from Arizona; however, there have been instances where counties elevate a tax to entice a team to move within the state. When that occurs, there is no gain to the state's economy. Counties rely on spring training facilities in hopes of competing for more tourists during the late winter and early spring months. However, many counties also benefit from well-regarded coastlines and beaches. This means that county-level geographic monopoly power is largely absent in Florida, and competitive differentiation can take place through other attractions such as MLB baseball. Arizona's attraction lies in the location of a large number of teams in a single county (Maricopa). The proximity of several teams also reduces the cost of spring training to the teams. In Florida, it is not unusual for teams to have to commute from coast to coast for games. As a result, it was hoped that the sport facility allowance within the TDT would help 177

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Because quarterly reported GPDI could introduce unwanted error into our models, we also estimated our models without this variable. The results with and without this variable were similar. We note that there is the possibility of endogeneity introduced in our models through income and population, as increased tourism could increase income for the local population. However, we wanted to allow estimation of neighboring county income or population spillovers, and therefore left these variables in our model. Finally, we include a dummy variable for the presence of spring training for each county. This variable is equal to one if there is at least one team present for MLB spring training, and zero otherwise (STPresent). We code our variable such that in the year that MLB spring training arrives in a county, it is equal to one starting in February of that year, when pitchers and catchers report. Alternatively, when a team leaves a county, the dummy variable is equal to one through October – the end of the MLB Regular Season – of the last year they reside in the county. We choose this latter definition because teams tend to use their facility for rehabilitation assignments and other business activities related to players' conditioning during the season. Each of the spring training facilities also hosts an affiliated minor league team. During the years studied there were only 5 moves in which a county gained or lost a team. We therefore focus more closely on the tax revenue elasticity estimates, as the dummy variable coefficients should be interpreted with caution due to the small number of moves and low power of our test. A descriptive summary of all variables can be found in Table 1 for the 53 remaining county observations. The average TouristCollections at the county level, for counties that levy a tax, is about $886,000, with substantial variation from under $200 to more than $21 million. Counties levying a tax have an average rate of 3.63%, household income just under $43,000, and an average population of about 338,000 residents. Of all county-month-year observations in our data, 23.6% coincide with the presence of a spring training facility. In our estimations, the dependent variable and all continuous control variables are logged for ease of interpretation as an elasticity in the regression models.

Table 1 Descriptive Statistics. All dollar amounts in 2015 inflation-adjusted dollars. Airfare measured through price index relative to 1982 from the St. Louis Fed. Summaries exclude counties with a 0% TDT that are also excluded from regression estimations. Variable

Mean

SD

Min

Max

TouristCollections ($) TDT (%) HHInc ($) Pop Airfare GPDI ($billions) USPop (000 s) USInc ($) STPresent

886,293 3.63 42,961 338,175 271.92 2803.8 306,040 54,937 0.236

2,166,553 1.10 7641 473,797 31.02 325.0 8505 1409 0.425

198 2 25,201 13,987 220.40 2062.4 290,820 52,666 0

21,800,000 6 67,238 2,613,692 320.60 3253.7 319,912 57,423 1

restaurants, and other businesses related to spending on spring training mostly resides within a geographically or economically isolated county, leakages may be less likely to take place (Mills & Rosentraub, 2013). Yet despite the possibility that these more isolated areas could garner larger benefits than major cities, newer facilities have been built closer to large population centers such as Palm Beach County (1.4 million residents) and Tampa (2.8 million MSA population). This trend could mean that recent moves of spring training facilities, while reducing travel costs for MLB teams, may not provide the optimal conditions to promote economic benefits for the host counties. 3. Data and methods 3.1. Data Our data on tourist tax collections and distributions (TouristCollections) began in July of 2003 and ended in December of 2014. These data come from the Florida Department of Revenue (2015), and are specific to the TDT. The county-level tax rate varies from 0% to 6% and the Department reports monthly receipts for each of the state's 67 counties. The county-level data allow us to assess effects at the same level at which funding is provided for facilities. Five counties did not levy a TDT increment during these years, and therefore had $0 in collections. While tax collections can be transformed into total consumption on hotel rooms when the tax rate is greater than zero, consumption is unobserved for zero-tax counties in our data. Specifically, only data on collections and distributions to counties were available, while detailed information on consumption across all taxed sectors was not consistently or reliably reported. Counties with no local tourist taxes were removed from the study, as they are fully unobserved. Further, due to a requirement in our spatial analysis for a fully balanced panel, we also removed any county that had one or more months without any TDT increment. Therefore, any county that experienced a tax rate of zero at some point during our time period was necessarily excluded, leaving the model with 53 counties and 7314 county-month observations. We also collected data on county-level TDT rates and increments (TDT) at the monthly level for the entire sample from the Florida Office of Economic and Demographic Research (2015). We merged our tax collection and tax rate data with yearly inflation-adjusted median household income (HHInc) and population (Pop) for each county (U.S. Census Bureau). Average monthly national airfare rates (Airfare) from data produced by the Federal Reserve Bank of St. Louis (2015) were included as a covariate and adjusted for inflation to control for changes to other travel costs (prices are indexed to the consumer price index, with 100 being the price in 1982–1984). We also included national level median household income (USInc), population (USPop), and gross private domestic investment (GPDI) to control for general changes within the U.S. economy where a large portion of visitors live. USInc and USPop are recorded monthly, while GPDI is reported quarterly.

3.2. Estimation procedure We estimate the effects of changes to the TDT and the presence of MLB spring training on total collections and distributions using various model specifications with and without standard errors robust to heteroscedasticity and within-county clustering. To simplify our estimation procedure, we demean all data at the monthly level and de-trend all data using a linear time trend. This avoids complication with estimation of monthly and yearly effects in the dynamic spatial model and supports a focus on the direct and indirect effects of tax rate changes on tax collections through the residual values. We note that similar models that included yearly and monthly dummy effects revealed nearly identical TDT elasticity estimates. We first estimate standard panel models with random effects for county to allow for U.S. level macroeconomic control variables. This random effects model is specified as:

ln(TouristCollectionsimt ) = β0 + β1 ln(Popimt ) + β2 ln(HHIncimt ) + β3 ln(Airfaremt ) + β4 ln(GPDIimt ) + β5 ln(USIncimt ) + β6 ln(USPopimt ) + β7 ln(TDTimt ) + β8 STPresentimt + εimt In this specification, TouristCollectionsimt refers to tourist tax collections for county i in month m of year t, while TDTimt refers to the county-level tourist tax rates as noted earlier in the respective countymonth-year. The coefficient of central interest in this study is β7. We test this model against an alternative county fixed effects estimation using a Hausman test, which suggested that fixed effect estimation is preferred (χ2 = 32.78, p < 0.01). We note, however, that because the 178

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respect to coefficient estimates in our models, and we therefore proceed in discussing only results using the queen contiguity weighting matrix.1

macroeconomic variables are reported at the monthly (quarterly) level – and do not vary by county – our fixed effects specification includes only county-level variables. This specification is as follows, where county-level fixed effects are represented by δi:

4. Results and discussion

ln(TouristCollectionsimt ) = β0 + β1 ln(Popimt ) + β2 ln(HHIncimt ) The results of our models are presented in Table 2, with long- and short-run direct and indirect elasticities of the TDT presented in Table 3. For simplicity, we only present main effect estimates for control variables in the regressions, rather than the direct and indirect effects, as indirect effects for these control variables were not statistically significant. However, we present direct and indirect effects of the room tax rate for SAR models in both the short-run and long-run, where applicable. As our estimations are in the log-log format, we discuss changes in the context of percentage changes in the tax rate, rather than percentage point changes in the tax rate. For example, if a tax rate begins at 1% and changes by 1 percentage point to 2%, in our context this is a change in the rate of 100%. We find only weak evidence for spatial autocorrelation in our tax collections data. However, as expected, there is an increase in TouristCollections as the TDT rate increases, with strong agreement in coefficient magnitude across all models. The direct effect elasticity estimate is below 1.0, indicating that a 1% increase in the tax rate increases collections by about 0.89%.2 These estimates are essentially identical for both the short- and the long-run. As you can see from Table 2, R2 values are improved for fixed effects models due to the ability to control for otherwise unobserved heterogeneity, despite not including macro-level control variables for the U.S. as a whole. Given these results in combination with the Hausman test, we focus on the fixed effects coefficient estimates from this point on. We note that while the TDT coefficients are statistically different from zero, only the model without robust standard errors indicates statistical difference from the 1.0, or the null expected value if no distortion took place from the change in tax rate. Table 4 presents 95% confidence intervals for each of our space-time lagged SAR estimates depending on the choice of standard errors. The effects are only statistically significant when the within-cluster correlation is ignored. This is the case with or without including spatial adjustments in the model, and we note that spatial autocorrelation is at most a minimal worry across all of our models. This provides important insight for future inquiry into the effects of bed tax rates on consumption and collections, as evidence for major distortions within the tourism industry is weakened when this correction is applied. Moving onto the indirect effects, the models consistently estimate a long-run coefficient of about 0.03 to 0.04, with short-run point estimates slightly smaller. These estimates imply that a 1% increase in the TDT for one county results in spillovers in collections for contiguous counties of about 0.03% to 0.04%, presumably through capturing lost hotel expenditure in the higher taxed counties. In the context of a change from 1% to 2%, this would imply a spillover to neighboring counties of 0.3% to 0.4% in additional collections. However, it is important to note that, due to our data limitations, these estimates may be biased downward. As noted earlier, counties with a 0% TDT rate could not be included, and we therefore do not observe spillover expenditures on hotels in these estimates. Thus, we propose this is a lower bound on expected spillovers from these tax rate changes. As with the direct effects, these estimates are not statistically significant with standard errors robust to clustering at the county level. These results as a whole contrast with past work using random effects spatial panel models that exhibited clear negative effects of a small bed tax to the hotel sector

+ β3 ln(TDTimt ) + β4 STPresentimt + δi + εimt Due to the geographic proximity and similarity of Florida counties, we also perform panel estimation with a correction for spatial autocorrelation using a Cliff-Ord type model through quasi-maximum likelihood estimation (Kopczewska, Kudla, & Walczyk, 2017; Kopczewska, Kudla, Walczyk, Kruszewski, & Kocia, 2016; Lee & Yu, 2010; LeSage & Pace, 2009; Ord, 1975; Yu, de Jong, & Lee, 2008). This model corrects non-independence of observations due to geographic proximity by creating a spatial autocorrelation matrix using latitude and longitude coordinates for each county. As with our previous models, we performed our estimations both with and without standard errors robust to heteroscedasticity and county-level clustering, and with both random and fixed effects for county. The general structure of the spatial panel model is as follows:

yit = τyi, t − 1 + ψ Wyi, t − 1 + ρ Wyit + βXit + θDZit + δi + vit In this specification, the error term vit = λΣvit + uit is spatially adjusted such that uit is a normally distributed error term, and Σ is the spatial matrix for the idiosyncratic error component (Drukker, Peng, Prucha, & Raciborski, 2013). W is the spatial matrix for the autoregressive component. The explanatory variables and their coefficients are denoted by the vectors X and β, respectively. Fixed effects for county are specified as in the previous panel model with δi. The spatial model was estimated using two different specifications for robustness, each of which is nested in the full spatial panel model above. First, we estimated a spatial autoregressive (SAR) model, allowing only for spatial autocorrelation within the dependent variable. In this model only long-run direct and indirect effects were estimated. We note that this specification implies θ = λ = ψ = τ = 0 from the general structure above, such that:

yit = ρ Wyit + βXit + δi + vit We then allow for the inclusion of a space-time lagged dependent variable (θ = τ = 0), which allows the estimation of both short-run and long-run tax rate effects. This model is specified as,

yit = ψ Wyi, t − 1 + ρ Wyit + βXit + δi + vit The dynamic model, however, can only be estimated with countylevel fixed effects, and therefore no random effects estimation was included for this model. We also estimated separate models using either the contiguity (queen) weighted spatial autocorrelation matrix or using an inversedistance weighting matrix. The inverse distance matrix, W, parameterization is such that wij = 1 D (i, j) , where D(i, j) is the distance between places i and j. We choose the min-max normalization procedure for the inverse-distance weighting matrix, but note that very little changed with respect to our coefficients of interest across various normalization choices (Drukker et al., 2013). Alternatively, the contiguity matrix identifies contiguous neighbors, where dij is a weight:

d if i and j are neighbors wij = ⎧ ij ⎨ ⎩ 0 otherwise We note that the contiguous matrix is a more conservative estimation, as only neighbors can affect one another, while the inverse-distance matrix allows all places to have effects on one another with diminishing impact based on centroid distance. We also utilize a rook scheme contiguity matrix as a robustness check, and find nearly identical results. As expected (LeSage & Pace, 2014), the choice of the contiguous or inverse-distance matrix made little difference with

1

All models with various weighting matrix choices are available in online Appendix A to show robustness of our result with respect to tax rate elasticity. 2 We note that estimates of both the TDT effects on consumption and spatial autocorrelation may be downward biased due to the exclusion of 0% tax rate counties. 179

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Table 2 Panel Estimation Results. ***, **, and * denote statistical significant at the 1%, 5%, and 10% level. All data is de-trended with a linear time trend and de-seasoned at the monthly level. Random effects model control variables include county median household income, county population, county-level spring training facility presence, monthly domestic U.S. airfare, monthly U.S. median income estimates, monthly U.S. population estimates, and quarterly Gross Private Domestic Investment as a general measure of economic health in the U.S. Standard errors are in parentheses.

Model Fixed effects Space-time lag Cluster-robust S.E. ρ ψ Main effects ln(TDT) ln(Population) ln(HHInc) ln(Airfare) ln(GPDI) ln(USInc) ln(USPop) STPresent N R2 Counties

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Panel N – N – – – –

Panel Y – N – – – –

SAR N N N −0.058* (0.035) – –

SAR Y N N 0.067** (0.034) – –

SAR Y Y N 0.061 (0.039) 0.029 (0.055)

Panel N – Y – – – –

Panel Y – Y – – – –

SAR N N Y −0.058 (0.102) – –

SAR Y N Y 0.067 (0.101) – –

SAR Y Y Y 0.061 (0.063) 0.029 (0.147)

0.972*** (0.032) 1.028*** (0.078) 0.262** (0.122) −0.256** (0.125) 0.411*** (0.075) 0.349 (0.409) −14.789*** (3.646) −0.008 (0.035) 7314 0.766 53

0.887*** (0.033) 0.236* (0.125) 0.461*** (0.096) – – – – – – – – −0.001 (0.035) 7314 0.792 53

0.968*** (0.032) 0.985*** (0.092) 0.246** (0.123) −0.266** (0.125) 0.430*** (0.076) 0.371 (0.408) −13.975*** (3.689) −0.007 (0.035) 7314 0.766 53

0.891*** (0.033) 0.232* (0.125) 0.448*** (0.095) – – – – – – – – −0.003 (0.036) 7314 0.791 53

0.891*** (0.033) 0.211* (0.127) 0.441*** (0.096) – – – – – – – – −0.002 (0.036) 7261 0.786 53

0.972*** (0.084) 1.028*** (0.189) 0.262 (0.225) −0.256*** (0.081) 0.411*** (0.069) 0.349 (0.333) −14.789*** (5.418) −0.008 (0.042) 7314 0.766 53

0.887*** (0.094) 0.236 (0.388) 0.461*** (0.168) – – – – – – – – −0.001 (0.039) 7314 0.792 53

0.966*** (0.083) 0.984*** (0.252) 0.252 (0.210) −0.268*** (0.077) 0.433*** (0.062) 0.361 (0.343) −13.867** (5.675) −0.09 (0.041) 7314 0.766 53

0.891*** (0.096) 0.232 (0.388) 0.448*** (0.172) – – – – – – – – −0.003 (0.039) 7314 0.791 53

0.891*** (0.098) 0.211 (0.396) 0.441** (0.173) – – – – – – – – −0.002 (0.040) 7261 0.786 53

Table 3 Short-Run and Long-Run TDT Elasticities. ***, **, and * denote statistical significant at the 1%, 5%, and 10% level. Results come from SAR models in Table 2, with column numbers matched accordingly. Standard errors are in parentheses. (3)

(4)

(5)

(8)

(9)

(10)

SAR N N

SAR Y N

SAR Y Y

SAR N N

SAR Y N

SAR Y Y

N

N

N

Y

Y

Y

Short-run effects Direct – – Indirect – – Total – –

– – – – – –

0.891*** (0.033) 0.026 (0.016) 0.917*** (0.037)

– – – – – –

– – – – – –

0.890*** (0.096) 0.026 (0.027) 0.916*** (0.109)

Long-run effects Direct 0.967*** (0.032) Indirect −0.024* (0.015) Total 0.943*** (0.033)

0.890*** (0.032) 0.029** (0.015) 0.919*** (0.036)

0.891*** (0.033) 0.038** (0.017) 0.930*** (0.037)

0.966*** (0.083) −0.022 (0.041) 0.944*** (0.099)

0.889*** (0.095) 0.032 (0.043) 0.921*** (0.117)

0.890*** (0.096) 0.039 (0.028) 0.929*** (0.111)

Model Fixed effects Space-time lag Clusterrobust S.E.

Table 4 Tax Revenue Elasticity Confidence Intervals. Confidence intervals presented from fixed effects models that include control variables and a space-time lag of the dependent variable from Table 3, with columns matched accordingly. Direct effects tested against elasticity of 1.0, while indirect effects are tested against elasticity of 0.0.

Cluster S.E. Direct Short-Run 95% C.I. Indirect Short-Run 95% C.I. Direct Long-Run 95% C.I. Indirect Long-Run 95% C.I.

(5)

(10)

No 0.891*** [0.827, 0.955] 0.026 [−0.006, 0.058] 0.891*** [0.827, 0.955] 0.038** [0.006, 0.071]

Yes 0.890 [0.701, 1.079] 0.026 [−0.027, 0.079] 0.890 [0.702, 1.079] 0.039 [−0.016, 0.095]

research into the possibility of heterogeneous effects of sport facility investment, particularly given the heterogeneity in the size of county economies that host these facilities. It seems likely that detecting effects in smaller locales may be more tractable, given the lower levels of tourism and collections in those areas could result in underpowered tests of effects (Matheson, 2019). Nevertheless, it is likely that these effects are at most rather small. Moving to the control variables, there are robust statistically significant effects of both local HHInc and local Population on county TDT collections. Although not reported in our tables, we find no indirect (spillover) effects of these control variables to nearby counties. Given that residents are not subject to the room taxes, the positive and significant coefficient for income is somewhat curious. Because the regression models include fixed effects, this is unlikely to be due to differences in amenities across counties that are correlated with local income. Therefore, changes in income that result in changes to tax collections may imply that, over time, the locations that experience

(Lee, 2014). We find no statistically significant effect of the presence of MLB spring training within the county (STPresent) on TouristCollections. However, we note that any effects of these additions are likely to be relatively small, subsumed by general variation in the hotel consumption data for the entire county, and ignore any tourism that takes place from nearby counties that do not stay in hotel rooms. We suggest future 180

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Table 5 Estimated average monthly tax revenue and taxable consumption changes from 1% rate increase. For exhibition, we use the average collections and TDT rate across taxed counties for all months in 2014. All dollar amounts are in 2015 U.S. dollars. We calculate changes with respect to a 1 percentage point change in the tax rate using the long-run effect estimates from the first column of Table 4.

LR Direct LR Indirect

Avg. Coll.

TDT Rate (%)

Taxable expenditure

ΔRate (%)

ΔColl

ΔTaxable Exp.

ΔTotal spend

$1,121,112 –

4.00 –

$28,027,800 –

1.00 –

+ $249,447 + $10,931

- $616,620 + $273,275

- $367,173 + $284,206

earlier, there are wide confidence intervals on all coefficient estimates related to the TDT rates, and our estimates do not include untaxed counties. With the additional counties included, it seems likely that the estimates of spillover expenditure would be larger. We also caution that our limited data source does not allow us to differentiate between price and quantity changes that may lead to total expenditure increases and decreases. As a whole, our models indicate that a large majority of the TDT is exported to visitors, with only small effects on the hospitality industry. While our results are specific to the case of Florida, the strong competition across counties for tourists provides an environment in which small price changes may have been expected to have larger effects. Given this, we propose that the results here are likely to generalize to other destinations where nearby competition is less fierce. Further, the lack of effects of spring training facilities on the local hotel sector provides insight into recent work on smaller scale sport event impacts, particularly as they relate to areas where tourism is already likely to be common. With this in mind, the lessons from the work of Agha (2013) and Agha and Rascher (2016) may be particularly informative for areas where tourism is not already the central economic driver in the region. Nevertheless, further research needs to look more closely at the returns to the hospitality sector of a wide range of amenities funded by bed taxes.

larger income changes are also experiencing larger changes to hotel room stays or to prices of these rooms. We note that estimations without control variables for population and income revealed similar results with respect to tax elasticity and spring training effects. In the random effects models, there is consistent evidence that airfare increases reduce TDT collections in these counties. The magnitude of the effect implies that tax collections (and, in turn, hotel room consumption) decrease by about 0.26% for every 1% increase in airfare price. Presumably, increased airfare results in fewer visitors to the county and therefore fewer rooms occupied. GPDI is positively associated with collections, as expected; however, USInc is not associated with changes to local TDT collections. Finally, USPop is negatively associated with TDT collections among Florida counties, which could be due to increases in supply of tourism closer to areas where population has changed most, thereby reducing travel costs for some trips elsewhere. As a whole, our findings sustain other studies noting that countylevel tourism in Florida is not particularly sensitive to small tax changes. Even those studies that find some burden borne by the hotel industry have shown that the majority of hotel room tax incidence lies upon tourists. Florida has long been a worldwide tourist destination, and the salience of room taxes is often quite low. When purchasing hotel room nights online, these taxes are sometimes only included in the final price after the room has been selected based on its pre-tax price. As shown in previous work, the salience of taxes can impact the propensity for consumers to change their behavior due to the tax (Chetty, Looney, & Kroft, 2009; Goldin, 2015). Nevertheless, even small distortions from the tax may outweigh benefits gained from funding certain initiatives, such as spring training. Indeed, as our models find little effect of spring training on TDT collections, any distortion may indicate the costs associated with financing could outweigh the direct fiscal benefits to doing so. To provide a more interpretable overview of the small changes estimated in our models, we present the expected pre-tax hotel room expenditures in Table 5 using the point estimates from our models in Table 2 and assuming a 1 percentage point change to the tax. This is half the amount allowed by the state to fund MLB spring training facilities, and implies a 25% change in the 2014 average TDT rate of 4% among all taxed counties.3 We apply this change to the 2014 average county-month pre-tax hotel room expenditure and tax rate as a baseline for analysis based upon the average 2014 monthly county-level collection of $1,121,112. The point estimates from our models imply a decrease of more than $616,000 in taxable expenditure for the county that increased the TDT, but an increase of about $273,000 in contiguous counties. Along with the decrease in taxable expenditure in the direct effect estimate, there is a within-county net decrease in total spending (including the remitted tax) of approximately $367,000 and increase of approximately $284,000 in contiguous counties. Despite the reduction of within-county total spending, these changes are associated with increases in TDT collections of almost $250,000 for the county making the increase (direct effect) and an increase of nearly $11,000 for contiguous counties from this change (indirect effect). We note that our discussion of Table 5 assumes some level precision in the point estimates of tax revenue elasticity from the panel models. As noted 3

5. Limitations We do recommend some caution with generalizing our results too far beyond the scope of study here, as Florida is geographically unique with few substitutes in terms of overall tourist attractions. Our estimation also did not include locations with no room tax at all, and future work may be well served by using multiple imputation techniques paired with spatial panel models to estimate more generalizable effects of tax changes. While unavailable in our case, explicit consumption data would allow a better estimate of spillover (indirect) effects particularly in untaxed counties, as our estimates of contiguous spillovers are likely to be downward biased. The spillover estimates may be further downward biased with increasing availability of black market type room availability like that found on AirBnB that are also unobserved in this data. This change may have happened more recently in the face of growth in these services since 2015. Further, the small magnitude of changes could be due to low tax salience in the tourism and hotel sector, a characteristic shown to be an important determinant in how individuals respond to taxation (Chetty et al., 2009; Goldin, 2015). Future work estimating the effects of room tax salience would be a valuable addition to the literature. The investigation into the effect of MLB spring training itself is also limited in that only 5 moves in or out of a county were available during the years studied. In addition, tax collections data at the monthly level are likely to be noisy in estimating small localized effects. Part of this relates to our span of data ending in 2015, shortly before some additional spring training moves. Geographic and demographic differences across counties could result in heterogeneous micro-level effects not captured in our models. We therefore encourage further work into the heterogeneity in effects of smaller events like MLB spring training with a longer span of data.

We round the average rate up to 4% from 3.63%, as reported in Table 1. 181

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6. Conclusions

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As a whole, our analysis reveals that tourists may not be as sensitive to small changes in tax rates as feared by some researchers and the tourism sector itself. Nevertheless, even small distortions in a large tourist economy could negatively impact the value in using hotel taxes for funding initiatives such as spring training. While some areas have used sales taxes to pay down debt service for stadium construction (Mills et al., 2014), the lower level of sensitivity to small tax changes among tourists may make these increments a more acceptable financing mechanism by exporting a portion of the cost of facility construction to non-residents. There are likely to be some general lessons with respect to funding public projects with incremental visitor taxes. For example, if the loss to the local economy is minimal, tourist taxes could be a valuable financial tool for public projects, several of which may have more value than a sport venue. Further, at the state level, a portion of the lost expenditures to one county – assuming they are real changes – are captured up by neighboring counties. Therefore, the state-level changes are likely to be smaller than changes at the individual county level, a result of considerable importance to state policymakers seeking to raise revenue for new public projects through bed taxes. For state officials, the possibility that higher local tax rates shift some room nights in lower taxed counties within the state could suggest that overall economic activity for the state is largely unchanged by the tax. This provides some evidence that state support of counties is an important consideration in the financing decision, given the possibility of localized spillovers. Finally, our results provide guidance for future work related to estimating room taxes and competition between nearby tourist locations. The spatial econometrics framework is a fruitful method for much of the tourism literature, given the possibility of geographical interdependence across nearby tourist destinations. We also show the importance of using cluster-robust standard errors, particularly given that room rate changes are quite small and variability in consumption rather large. Inferences made without corrections to standard errors may result in poor guidance for policymakers. Further although we do not directly address competition in tax rates here, estimating spillovers could also be central to more closely understanding geographical interdependence in policy-making. Future work may be well-served to use these models in directly evaluating how nearby locations to compete in tax rate, as performed recently by Hoffer and Lacombe (2017) for other types of taxes. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.tmp.2019.05.003. References Agha, N. (2013). The economic impact of stadiums and teams: The case of minor league baseball. Journal of Sports Economics, 14, 227–252. Agha, N., & Coates, D. (2015). A compensating differential approach to valuing the social benefit of minor league baseball. Contemporary Economic Policy, 33, 285–299. Agha, N., & Rascher, D. (2016). An explanation of economic impact: Why positive impacts can exist for smaller sports. Sport, Business, & Management: An International Journal, 6, 182–204. Agha, N., & Taks, M. (2015). A theoretical comparison of the economic impact of large and small events. International Journal of Sport Finance, 10, 199–216. Agha, N., & Taks, M. (2018). Modeling resident spending behavior during sport events: Do residents contribute to economic impact. Journal of Sport Management, 32, 473–485. Aguiló, E., Riera, A., & Rosselló, J. (2005). The short-term price effect of a tourist tax through a dynamic demand model. the case of the Balearic Islands. Tourism Management, 26, 359–365. Alvarez-Albelo, C. D., Hernandez-Martin, R., & Padron-Fumero, N. (2017). Air passenger duties as strategic tourism taxation. Tourism Management, 60, 442–453. Andreassi, G. (2018, July). St. Lucie cutes $6 million from First Data Field renovation; completion may be delayed until 2021. TC Palm. Retrieved November 1, 2018 from https://www.tcpalm.com/story/news/local/st-lucie-county/2018/07/10/st-lucie-

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Mark S. Rosentraub is the Bruce and Joan Bickner endowed professor of sport management at the University of Michigan. His most recent books are Sport finance and management: Real estate, entertainment, and the remaking of the business (2019) and Reversing urban decline: Why and how sports, entertainment, and culture turn cities in major league winners (2014).

Research, 17, 2–6. Matheson, V. (2019). Is there a case for subsidizing sports stadiums? Journal of Policy Analysis and Management, 38, 271–277. Mills, B. M., & Rosentraub, M. S. (2013). Hosting mega-events: A guide to the evaluation of development effects in integrated metropolitan regions. Tourism Management, 34, 238–246. Mills, B. M., Rosentraub, M. S., Winfree, J. A., & Cantor, M. B. (2014). Fiscal outcomes and tax impacts from stadium financing strategies in Arlington, Texas. Public Money & Management, 34, 145–152. Ord, J. K. (1975). Estimation methods for spatial interaction. Journal of the American Statistical Association, 70, 120–126. Palmer-Tous, T., Riera-Font, A., & Rossello-Nadal, J. (2007). Taxing tourism: The case of rental cars in Mallorca. Tourism Management, 28, 271–279. Ponjan, P., & Thirawat, N. (2016). Impacts of Thailand's tourism tax cut: A CGE analysis. Annals of Tourism Research, 61, 45–62. Rosentraub, M. S., & Joo, M. (2009). Tourism and economic development: Which investments produce gains for regions. Tourism Management, 30, 759–770. Schubert, S. F. (2010). Coping with externalities in tourism: A dynamic optimal taxation approach. Tourism Economics, 16, 231–343. Schubert, S. F., Matias, A., & Costa, C. M. G. (2012). A general equilibrium analysis of casino taxation in Portugal. Tourism Economics, 18, 475–494. Spengler, J. O., & Uysal, M. (1989). Considerations in the hotel taxation process. International Journal of Hospitality Management, 8, 309–316. Wagner, J. (2015, November). Nationals, astros break ground on new spring training facility. Washington Post. Retrieved November 1, 2018 from, https://www.washingtonpost.com /news/nationals-journal/wp/2015/11/09/nationals-astros-breakground-on-new-spring-training-facility/?utm_term=.325449df8e48. Weston, R. (1983). The ubiquity of room taxes. Tourism Management, 4, 194–198. Yu, J., de Jong, R., & Lee, L. (2008). Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large. Journal of Econometrics, 146, 118–134.

Gidon Jakar is a PhD Candidate at the University of Michigan, and he holds a Masters in Geography and Urban Studies. His current research interests focus on urban development and the geography of sports, studying the economics and locational decision making of sports facilities and franchises in North America and Europe. During his current studies Gidon has published in European Planning Studies. While completing his Masters, he was involved in an international research project focusing on the coastal environment in several countries in Europe and Asia.

Brian M. Mills is faculty in the Department of Tourism, Recreation, & Sport Management at the University of Florida. He received his Ph.D. and M.A. in Sport Management, M.A. in Applied Economics, and M.A. in Statistics from the University of Michigan. His work focuses upon the economic characteristics of sports leagues and sporting events.

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