|
Article Excerpt
Commercial casino gambling has spread rapidly since 1990 when only two states, Nevada and New Jersey, allowed private commercial gambling. By 2008, 11 states allowed private commercial gambling, while Native American casinos operated in 23 states. Arguably, the primary motivation for casino gambling's legalization and spread has been tax revenue generation. While casinos certainly generate tax revenues, that generation has not been analyzed in reference to other, more traditional tax sources, such as income or taxable sales.
This paper examines how gross casino gambling revenues differ from other major tax bases in growth and variability. We estimate the long-run and short-run income elasticities using the actual tax base of state-level gross casino revenue and state, regional and national income. We make several contributions to the literature on the income elasticity of state taxes. First, we use quarterly data for multiple states. Second, we use data on the actual tax base for the first time in the literature, thus removing the potential error inherent in previous studies that used proxies. Third, we are adding a new estimate of the income elasticity of gross casino gambling revenues to the list of past elasticity estimates that included state taxes like the individual income tax, general sales tax, corporate income tax, motor fuel tax, tobacco tax and alcohol tax. Finally, we also examine the responsiveness of the tax base to changes in regional income in the vicinity of the state and changes in national income. Our empirical analysis includes 11 states that have significant private commercial casino gambling. States with Native American casinos, with the exception of Connecticut, are not analyzed due to a lack of data. To estimate income elasticities, we run separate time-series regressions for each of these states, controlling for supply-side industry effects, namely the number of slot machines and table games. Our findings show that Nevada's gross casino revenue growth is tied more to national than state income, whereas other states are more dependent on growth in state income. States with legalized casino gambling can expect gross casino revenue to grow at the same rate as state income, making gross casino revenue growth generally faster than taxable sales, but slower than taxable income. However, analyzing Nevada and New Jersey over the first and second half of their respective samples reveals that gross casino revenue growth slows as the industry matures. This is something states should keep in mind as they consider the long-run configuration of their tax base portfolios and the expansion of casino gambling. Short-run (immediate) elasticity is, on average, lower than estimates for sales and income taxes, with an equal or more rapid adjustment to long-run equilibrium. Interestingly, casino gambling revenues are quicker to recover when below potential than they are to decline when above, a fact that may be beneficial for a state seeking to reduce downside risk in its tax base.
INTRODUCTION
Commercial casino gambling is a major industry that has experienced substantial growth in recent decades. Garrett and Nichols (2008) note that the casino gambling industry had reached $44 billion in adjusted gross revenue in 2003, which accounted for about 60 percent of all gambling revenues in the U.S. (1) Landers (2007) shows that casino gambling tax collections constitute a significant portion of total state tax collections in many states, with the highest share (16.3 percent) in Nevada as of FY2006. (2) State fiscal crises have also led many states to turn to gambling as a quick solution to state fiscal problems in recent times. (3) Given this, it is important to show what those states might expect from commercial gambling in the future. A relevant question, then, is how gross gambling revenues differ from other traditional major state tax bases, such as sales and income, in growth and variability.
While past studies on income elasticity of state taxes used calculated tax bases or national proxies, the literature also discussed the problems with this approach and advised using actual tax bases (e.g., Holcombe and Sobel, 1997). In addition, most studies used annual instead of quarterly data, missing the more accurate picture of changes in economic activity during a given year. We also see a gap in the literature as we have not come across any recent studies on the income elasticity of gross gambling revenues despite major changes in the gambling industry, particularly in the last two decades. We are addressing these weaknesses in the literature by estimating the long-run and short-run income elasticities of the actual tax base of gross gambling revenues using state-level quarterly data on gross gambling revenue and state, regional and national income. (4) Our empirical analysis includes 11 states that have significant casino gambling. We group these states as follows: Nevada, New Jersey, Mississippi (Destination Resorts); (5) Illinois, Indiana, Iowa, Louisiana, Missouri (Riverboat Casinos); Colorado, South Dakota (Mining Towns); and Connecticut (Indian Casinos). To estimate income elasticities, we run separate time-series regressions for each of these states, controlling for supply-side industry effects. Our findings show that growth in Nevada's tax base is more sensitive to changes in national than state income, while such growth is more tied to state and regional income than national income in other states. Gross casino revenue growth is generally faster than taxable sales, but slower than taxable income. Short-run (immediate) elasticity is, on average, lower than estimates for sales and income taxes, with an equal or more rapid adjustment to long-run equilibrium.
The paper is structured as follows. In the next section we review the literature on the income elasticity of state taxes, including a small literature on gross gambling revenues. We provide a detailed description of our empirical model and data in the third section. In the fourth section we present our empirical results from a regression analysis. We summarize our results and provide a discussion with our concluding remarks in the final section.
PREVIOUS STUDIES
Earlier studies on the income elasticity of state taxes gave only long-run estimates of income elasticities. The seminal paper by Groves and Kahn (1952) used double-log OLS specification to estimate long-run income elasticity of various state taxes using annual tax revenue data. Cargill and Eadington (1978) and Babbel and Staking (1983) followed suit. In fact, to the best of our knowledge, these two are the only studies that examined income elasticity of gambling, and Cargill and Eadington (1978) is the only one that has examined casino gambling specifically. (6) Cargill and Eadington used seasonally adjusted data for the period 1960-1974 and found that the income elasticity of gross gambling revenue is fairly elastic with significant variation across three regions in Nevada. The highest is in the Las Vegas region (1.75), followed by the Lake Tahoe (1.25) and the Reno-Sparks (1.05) regions. Cargill and Eadington used California personal income in the regressions for income elasticity to capture the responsiveness of gambling revenue to regional income changes. This is important since the casino gambling industry in most states is driven by visitors to the state from the neighboring region.
In the next phase of the literature, studies distinguished between growth and variability of tax bases by separately estimating long-run and short-run elasticities. (7) In one of the earlier studies, Fox and Campbell (1984) used a varying elasticity model to estimate various short-run elasticities for ten different categories of sales tax bases in Tennessee. Also differently, they use quarterly data on the sales tax base, calculated from sales tax revenue data. They note the advantages of using quarterly data as having more degrees of freedom and allowing a closer link between economic activity and consumption. They found that sales tax is an unstable revenue source as the short-run elasticities move in a procyclical fashion. (8) Dye and McGuire (1991) extended this analysis by showing evidence of both growth and variability in state income and sales taxes. They did this by estimating the trend rate of growth and the deviation from trend for different components of these tax bases? They found that taxes with high long-run elasticity (e.g., income tax) can be more stable than a tax with lower long-run elasticity (e.g., sales tax). Hence, the trade-off between growth and variability in state taxes may not hold. They used national data to approximate tax bases for states. While this brought significant simplicity in the analysis of income elasticities for different states, it also led to a potential error in the use of appropriate tax bases.
Sobel and Holcombe (1996) also distinguished between long-run and short-run income elasticities, but their econometric approach is different from the previous studies. While they used a Dynamic OLS (DOLS) specification for the long-run elasticity estimation, they used an Error Correction Model to estimate short-run elasticities. They argue that the Error Correction Model gives superior results compared to Dye and McGuire's deviation from trend approach...
|
