Abstract
In Part I of the book we examined the theoretical ways in which casinos could generate economic growth. We also found empirical evidence that suggests casinos do, in fact, have a positive growth effect at the state level. Also in Part I, however, we examined some of the criticisms of casinos. One key argument is that casinos expand at the expense of other industries. This is the so-called “substitution effect.” In Chap. 17 we addressed this issue with respect to non-gambling industries. However, the analysis covered only one market (Detroit) and only for commercial real estate values. There have been several other studies in the literature to examine the impact of casinos on property values, as cited in Chap. 17.
The material in this chapter is based on Walker DM, JD Jackson. 2008. Do U.S. gambling industries cannibalize each other? Public Finance Review 36(3): 308–333. Used with permission from Sage.
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Notes
- 1.
Here the terms “complementary” and “substitutes” are not referring to the relationship between the price of one good and the demand for another. Rather, it refers simply to whether two industries help each other, in terms of revenues, or harm each other.
- 2.
Other published studies have focused more narrowly on estimating demand for individual gambling industries or have examined pairs of industries within single states.
- 3.
The sources for the industry data follow. Lottery ticket sales come from La Fleur’s 2001 World Lottery Almanac, 9th edition. TLF Publications, 2001. Casino revenues are from the American Gaming Association and various states’ gaming commissions. Greyhound and horse racing handle are from the 1985–2000 issues of Pari-Mutuel Racing, published by the Association of Racing Commissioners International. The 1985–1990 dog and horse racing data and the 1995–2000 horse racing data were reported as handle. For horse and greyhound racing from 1991 to 1994, handle was calculated using the total pari-mutuel takeout and effective takeout rate (handle = total pari-mutuel takeout/effective takeout rate). The same process was used to calculate greyhound racing handle from 1995 to 2000. Thus all racing data are reported with a consistent measure. All of the above volume data are adjusted for inflation using the CPI from the Bureau of Labor Statistics (1982–1984 = 100). Annual state population estimates are from the Bureau of the Census. The states’ annual Indian casino square footage was calculated using the casino listing at casinocity.com. At the time this was written, this source listed 126 Indian-owned casinos in the United States. Square footage and opening dates were collected from the casinos’ Web pages or by phone calls to the casinos.
- 4.
Slot machines and video poker at racetracks, called “racinos,” are a relatively new phenomenon appearing in some states. Due to their relative newness and the inherent difficulties in classifying these non-racing bets (as racing handle or casino revenue?), this machine gambling is omitted from this analysis. For a discussion of racinos, see Eadington (1999, 176) and Thalheimer and Ali (2003, 908).
- 5.
In the case of lotteries, this is ticket sales per capita.
- 6.
Revenue per capita is used rather than handle per capita because casino revenue cannot be reliably converted to handle. For example, suppose a person walks into a casino and buys $100 worth of chips and plays until she loses the $100. The total handle could range from $100 to any higher amount. It would be $100 if she lost a single $100 hand of black-jack. But suppose she plays and wins several thousand dollars, but later loses it all. The total handle in this case is in the thousands of dollars, even though she only lost $100 of her own money. This example illustrates why an estimate of casino handle would be unreliable. Even if it was possible to convert revenue to handle, say by using some multiple, this adjustment would not affect relative coefficient estimates in any meaningful way.
- 7.
I inquired with Caesars Entertainment, one of the largest US casino operators, who also manages numerous Indian-owned casinos. They confirmed that there is a general industry formula for the number of slot machines and table games as a function of square footage. For this reason, Indian casino square footage is a satisfactory, albeit imperfect, measure of Indian casino volume.
- 8.
For example, see Saba et al. (1995) and references therein.
- 9.
Other attempts to measure the intensity of adjacent-state gambling have similar difficulties. The primary concern here is the availability of gambling in nearby states.
- 10.
As an example, in 2000, Florida’s adjacent state lottery observation would be 0.5, since Georgia had a lottery that year and Alabama did not.
- 11.
The hotel employee information and per capita income data come from the Bureau of Economic Analysis. The per capita income data are adjusted for inflation using the Bureau of Labor Statistics CPI data.
- 12.
Annual estimates for these are not available. The years used to derive the estimates vary due to data availability: Baptists (1980 and 1990); degree holders (1990 and 2001); older people (1990 and 2001); and poverty (1992 and 2001). The data come from the Bureau of the Census, with the exception of Baptists, from the New Book of American Rankings.
- 13.
Readers who are not interested in the technical details of the model can safely skip to Sect. 18.5.
- 14.
No model is posited for Indian casino gambling since the volume measure for this industry (square footage) is rather crude.
- 15.
Although the probit models are intended only to correct for left-censoring of the data, they do give some insight into the probabilities of adopting the various forms of gambling. Obviously their usefulness in this regard is limited because the specification of the models has a different goal.
- 16.
- 17.
- 18.
There is a variety of potential social concerns that may accompany legalized gambling, but these are not the subject of this chapter.
- 19.
Mason and Stranahan (1996) look more generally at the effects of casinos on state tax revenues, but not particularly at revenues from other forms of gambling.
- 20.
For example, see Hall and Loftus (2013).
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Walker, D.M. (2013). Relationships Among Gambling Industries. In: Casinonomics. Management for Professionals. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7123-3_18
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