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Public Finance Review

DOI: 10.1177/1091142106292777 2008;

2008; 36; 308 originally published online Feb 21,Public Finance ReviewDouglas M. Walker and John D. Jackson

Do U.S. Gambling Industries Cannibalize Each Other?

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308

Do U.S. Gambling IndustriesCannibalize Each Other?Douglas M. WalkerCollege of Charleston, South Carolina

John D. JacksonAuburn University, Alabama

Many states facing recent fiscal crises have looked to legalized gambling in anattempt to ease fiscal constraints. Although there has been some research on theeconomic effects of gambling, no study has offered a comprehensive analysis ofthe interindustry relationships of lotteries, casinos, horse racing, and greyhoundracing. In this article, we use seemingly unrelated regression (SUR) estimationto analyze the relationships among gambling industries in the United States. Ourresults indicate that some industries “cannibalize” each other (e.g., casinos andlotteries, and horse and dog racing), whereas other industries help each other(e.g., casinos and horse racing, dog racing and lotteries, and horse racing andlotteries). The study also examines the effects of adjacent-state gambling anda variety of demographic variables. This analysis provides a foundation forfurther research on how to optimize tax revenues from legalized gambling.

Keywords: casino gambling; lottery; pari-mutuel gambling; tax revenue;Indian casinos

1. Introduction

In recent years, the fiscal pressure on many state governments has increasedsignificantly. This pressure has resulted in efforts to cut spending, raisetaxes, or otherwise balance state budgets. Some states have turned to legaliz-ing gambling as a way to raise “voluntary” gambling taxes. At least twenty-two states have recently been considering introducing or expanding gamblingoperations as a way to supplement state coffers (“States, Cities Ignore Odds”2003), including ten states that had casino-specific initiatives on the ballot in2004 (Anderson 2005).1 As a specific example, politicians in Georgia con-sidered converting a Ford plant that was slated to close in 2006 into a casino

Public Finance ReviewVolume 36 Number 3

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Author’s Note: We would like to thank Ben Scafidi and two anonymous referees for helpfulcomments. We are responsible for any errors that remain.


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Walker, Jackson / Do U.S. Gambling Industries Cannibalize Each Other? 309

(“Turn Vacant Ford Plant” 2006). Although voters may be more amenable tolegalizing or expanding gambling than to tax increases or spending cuts, theoverall economic effects of gambling are unclear. Gambling industry profitsare usually taxed at higher rates than other businesses, so even when gam-bling causes reduced spending in other sectors, overall tax revenue mayincrease. Tax revenue may not, however, be the only concern, because theremay be significant social or external costs from gambling.

A key to understanding the effectiveness of legalized gambling as a fis-cal policy tool is the relationship among gambling industries. If casinos andlotteries are complementary, for example, then a lottery state can benefitby introducing casinos. If horse racing and casinos cannibalize each other,then a racing state may not want to introduce casinos. The relationshipsamong the various gambling industries have not received much attention inthe literature.

In this article, we use seemingly unrelated regression (SUR) estimationto examine if and how the various U.S. gambling industries affect each other.We analyze four industries from 1985 to 2000: casinos, greyhound racing,horse racing, and lotteries. Our model uses industry volume as the dependentvariable, with volume from the other industries, volume from the adjacent-state industries, and a variety of demographic characteristics as explanatoryvariables. We find that certain industries “cannibalize” each other (e.g., casi-nos and lotteries and horse and dog racing), whereas other industries helpeach other (e.g., casinos and horse racing, dog racing and lotteries, and horseracing and lotteries).

Our analysis provides the first evidence on the general relationshipsamong the different gambling industries in the United States.2 With infor-mation on the interindustry relationships, policy makers and voters can bemore comfortable with their decisions regarding whether to expand gam-bling in their states and with the resulting economic and tax effects. Thisarticle provides a critical foundation for studying these issues.

The article continues with a literature review in section 2. The data usedto test the relationships among gambling industries are described in section3, the model and results are described in section 4, and section 5 includes adiscussion of the results and potential extensions to this analysis, especiallyrelated to state tax policy.

2. Literature Review

The U.S. gaming industries have undergone a fascinating transformationduring the past two decades.3 Most interesting and controversial has been




the expansion of casino gambling to eleven states in the 1990s. Even now,little is known about the economic growth effects of expanding gamblingindustries. Legalized gambling is, however, receiving increased attention inthe economics literature, despite data limitations.4 Numerous studies havebeen published that focus on the demand for a particular type of gambling,or the effect of one industry on another’s revenues or on the state’s tax rev-enue. In many cases, the papers have examined a single county or state, ora small group of states, and only during a short time frame.5

Several papers focus on the effects of gambling industries on state tax rev-enues. Anders, Siegel, and Yacoub (1998) examined the effect of Indian casi-nos on transactions tax revenue of one Arizona county, and they found thattax losses from the retail, restaurant, bar, hotel, and amusement sectors weresignificant when casinos were introduced. Siegel and Anders (1999) exam-ined Missouri sales tax revenues as a result of introducing riverboat casinos.Overall, they found that aggregate taxes are not affected but taxes from cer-tain amusement industries are negatively affected. Popp and Stehwien (2002)examined county tax revenue in New Mexico and found that casinos have anegative effect on tax revenues within the county. The effect of neighboringcounty casinos is, however, somewhat odd: the first casino has a negativeeffect, whereas the second one has a positive effect on county tax revenues.

Other authors have focused more specifically on the interindustry relation-ships. Davis, Filer, and Moak (1992) tested the factors that determine whetherand when a state will adopt a lottery. Among other things, they found that statelottery revenue is higher the smaller the state’s pari-mutuel industry and thesmaller the percentage of bordering states that have lotteries. Mobilia (1992)found that a lottery dummy is negative and significant for pari-mutuel atten-dance but not for per attendee handle. Thalheimer and Ali (1995) found thatlotteries reduce racetrack handle; however, a state that has both lotteries andracetracks benefits in terms of overall tax revenue. Ray (2001) found thathorse racing and casino dummies have significantly negative effects on totalstate greyhound handle. Siegel and Anders (2001) found the number of slotmachines in Arizona Indian casinos has a significantly negative effect on lot-tery sales, but horse and dog racing have no effect on the lottery. Elliott andNavin (2002) examined the probability of lottery adoption and the determi-nants of lottery sales. They found that casinos and pari-mutuels harm the lot-tery, that adjacent-state lotteries have a small negative effect on lottery sales,and that the number of Indian casinos in a state and the number of riverboatcasinos in neighboring states do not significantly affect lottery sales. Fink andRork (2003) extended this work by taking into account that states self-selectwhen legalizing casinos. Low-revenue lottery states are more likely to legalize

310 Public Finance Review





casinos, and this partly explains the negative relationship between casinos andlotteries. Kearney (2005) found that spending on lottery tickets is financedcompletely by a reduction in nongambling expenditures, which implies thatother forms of gambling are not harmed by a lottery.

Table 1 provides a summary of many of the studies, which give informa-tion on the interindustry relationships. The table indicates the years examined,the scope of the study (e.g., one track or two states), whether the relationshipswere analyzed mainly with dummy variables, and key findings of the studies.

The literature provides some information about the relationships amonggambling industries. The most common findings are that an industry eitherharms another industry or does not affect it. No study has found that dif-ferent gambling industries help each other. There are three important caveatsregarding this area of research. First, the studies examine various, oftenshort time periods and tend to be limited to individual or small groups ofstates. Hence, it is difficult to generalize from these studies to other statesor times. Second, most papers only provide a one-way test of the relation-ship among industries. For example, papers testing the effects of lotterieson pari-mutuels typically do not analyze the effect of pari-mutuels on lot-teries. As a result, the literature lacks information on how some of theindustries affect others. Third, many of the studies account for the existenceof other gambling industries only through dummy variables.

To rectify these research issues, our model covers all states and theDistrict of Columbia during the 1985-2000 period. We examine how eachof four industries (casinos, lotteries, dog racing, and horse racing) affectsthe others. Finally, when analyzing the interindustry relationships, we relyon state-level industry volume rather than using simple dummies to repre-sent the industries’ presence.

3. Data

Our main interest in this article is to determine the relationships amongthe various gambling industries in the United States. We collected a varietyof demographic and industry volume data for all states plus Washington,DC, for 1985-2000. There are a total of 816 observations for each of thevariables, classified in three groups, discussed below.

3.1 Gambling Volume Variables

The gambling volume data for each industry in each state are sum-marized in table 2.6 The beginning year for the availability of each type of




312

Tabl

e 1

Rel

evan

t L

iter

atur

e

Prim

arily

Use

sPa

per

Yea

rsSt

ates

/Cou

ntie

sD

umm

ies?

Find

ings

a

And

ers,

Sieg

el,a

nd Y

acou

b (1

998)

1990

-199

61

coun

ty (

AZ

)Y

esIn

dian

cas

inos

har

m o

ther

ent

erta

inm

ent

Elli

ot a

nd N

avin

(20

02)

1989

-199

5A

ll st

ates

No

Cas

inos

and

par

i-m

utue

ls h

arm

lotte

ries

Kea

rney

(20

05)

1982

-199

8A

ll st

ates

No

Lot

teri

es d

o no

t har

m o

ther

for

ms

of g

ambl

ing

Mob

ilia

(199

2)19

72-1

986

All

raci

ng s

tate

sY

esL

otte

ries

har

m h

orse

and

dog

rac

ing

Popp

and

Ste

hwie

n (2

002)

1990

-199

733

cou

ntie

s (N

M)

Yes

Indi

an c

asin

os h

arm

oth

er e

nter

tain

men

tR

ay (

2001

)19

91-1

998

All

dog

raci

ng s

tate

sY

esH

orse

rac

ing

and

casi

nos

harm

dog

rac

ing

Sieg

el a

nd A

nder

s (1

999)

1994

-199

61

stat

e (M

O)

No

Cas

inos

har

m o

ther

ent

erta

inm

ent

Sieg

el a

nd A

nder

s (2

001)

1993

-199

81

stat

e (A

Z)

No

Slot

s ha

rm th

e lo

ttery

; hor

se a

nd d

og r

acin

gdo

not

aff

ect t

he lo

ttery

Tha

lhei

mer

and

Ali

(199

5)19

60-1

987

3 tr

acks

(O

H,K

Y)

No

Lot

tery

har

ms

hors

e ra

cing

a. O

ther

ent

erta

inm

ent

refe

rs to

non

gam

blin

g in

dust

ries

,suc

h as

res

taur

ants

,hot

els,

and

bars

.





Table 2Availability of Gambling, 1985-2000a

State Lottery Horse Racing Dog Racing Casino Indian Casino

Alabama 1987, 1989-2000 *AlaskaArizona * * * *Arkansas * *California 1986 * *Colorado * * * 1991 1992Connecticut * * * 1992Delaware * *District of Columbia *Florida 1988 * * *Georgia 1993HawaiiIdaho 1990 * 1988 1995Illinois * * 1991Indiana 1990 1994 1995Iowa 1986 * * 1992 1992Kansas 1988 1988 1989 1996Kentucky 1989 *Louisiana 1992 * 1993 1988Maine * 1985-1989,

1991-2000Maryland * *Massachusetts * * *Michigan * * 1999 1993Minnesota 1990 1985-1992, *

1994-2000Mississippi 1992 1994Missouri 1986 1994Montana 1988 * 1993Nebraska 1994 *Nevada 1989 *New Hampshire * * *New Jersey * * *New Mexico 1996 * 1987New York * * *North CarolinaNorth Dakota 1989 1993Ohio * *Oklahoma * *Oregon * * * 1994Pennsylvania * *Rhode Island * 1991 *

(continued)





gambling in each state is indicated. Unless otherwise noted, the data runfrom the year indicated through 2000. An asterisk (*) indicates the data arefor the entire 1985-2000 period. Some forms of gambling are exempt fromthe analysis (e.g., charity and private gambling, and noncasino and race-track video-poker or slot machines).7

As indicated above, we are interested in the volume of each type of gam-bling in each of the states. The data for greyhound racing, horse racing, andlotteries are handle per capita, that is, the total dollar value of bets placeddivided by the state population.8 The data for casino volume are revenueper capita, that is, the amount the casino keeps after paying winning betsdivided by state population.9 Finally, for Indian casinos, we use Indian-owned casino square footage as a proxy for gambling volume. (BecauseIndian casinos are not required to report revenue or handle data, this isperhaps the best measure available.)10

3.2 Adjacent-State Variables

Whether the various gaming activities act as substitutes or complementsto each other, the presence of these activities in neighboring states can leadto border crossings by consumers that may cause potentially dramatic effectson the volume of a particular gaming activity in a given state. Certainly, fail-ure to account for these effects in some way can lead to a serious misstate-ment of the effects of other in-state gaming activities on the volume of theactivity in question. State border crossings by consumers have received con-siderable attention, especially in the bootlegging and tobacco tax literature;11

Table 2 (continued)

State Lottery Horse Racing Dog Racing Casino Indian Casino

South CarolinaSouth Dakota 1988 * * 1989 *TennesseeTexas 1992 1989 1990UtahVermont * 1985-1997 1985-1992Virginia 1989Washington * * *West Virginia 1986 * *Wisconsin 1989 1996 1990 *Wyoming *

a. An asterisk (*) indicates the data are for the entire 1985-2000 period.




they have also recently found their way into the literature on state lotteries(Garrett and Marsh 2002; Tosun and Skidmore 2004).

There is no obvious “best” method for accounting for adjacent-state pur-chases of gambling services. The available measures include the aggregatevolume of adjacent-state gambling, the aggregate per capita adjacent-stategambling volume, and the percentage of adjacent states that allow a particulartype of gambling. The first measure is problematic because a higher level ofadjacent-state gambling volume may be the result of a larger population, ahigher volume of tourists, or a combination of the two. The second measure,per capita adjacent-state gambling volume, is problematic because summingper capita measures across neighboring states results in a meaningless number.A higher sum may result from more gambling, more neighbors, or fewer res-idents. The interpretation of these two options is also difficult.12 The third mea-sure presents the fewest potential problems, although it is the most generalof the measures. To account for cross-border effects in this study, we followDavis et al.’s (1992) example and use the percentage of adjacent states with aparticular form of gambling during each year.13 Although this measure will notperfectly reflect the amount of cross-border gambling, its interpretation isunambiguous and less problematic than other measures. What it does well isindicate the nearby gambling options the residents in a particular state have,regardless of whether they partake. Its limitation, of course, is that it does notmeasure the intensity with which these options are offered by the surroundingstates. Table 3 lists the states adjacent to each state.

3.3 Demographic Variables

Some studies have used surveys to get a general demographic picture ofthe typical gambler (Gazel and Thompson 1996; Harrah’s Entertainment1997; American Gaming Association [AGA] 2006). Variables on the states’population and demographic characteristics, such as education, income level,age, and religious beliefs, may be helpful in explaining variations in gam-bling volume across states and industries.

There has been somewhat conflicting evidence on the level of educationand the tendency to gamble. Obviously, those with more education are morelikely to understand the negative expected return from games of chance.Harrah’s (Harrah’s Entertainment 1997) and the AGA (2006), however, foundthat casino players tend to have an above-average level of education. Clotfelterand Cook (1990) found that lottery play falls as education level rises. As aproxy for education levels, we include a variable in the models giving thepercentage of citizens who are older than twenty-five years and holdingbachelor degrees. This variable may be related to the income variable, becauseincome and education levels tend to move together.






Eadington (1976) suggested that gambling may be perceived by lower-income people as a means of achieving a higher level of income. Evidence onlotteries (Oster 2004) has suggested that lotteries amount to a regressive tax.In an effort to test this proposition, the estimated percentage of people in thestates living in poverty is included as a variable. Although it may be true thatthe poor will tend to spend a larger proportion of their income on gambling,that does not imply that more of these people will lead to higher total gamblingrevenues for a state. Clotfelter and Cook (1990) found no clear relationshipbetween income level and lottery play. The AGA (2006) reported that themedian income of casino players is slightly higher than that for the overallpopulation. State real per capita income is included in the models to determineand account for the effect of average income on the tendency to gamble.

Table 3States and Their Adjacent States

State Adjacent States State Adjacent States

AL MS, TN, GA, FL NE CO, WY, SD, IA, MO, KSAZ CA, NV, UT, CO, NM NV CA, OR, ID, UT, AZAR TX, OK, MO, TN, MS, LA NH VT, ME, MACA OR, NV, AZ NJ DE, MD, PA, NY, CTCO AZ, UT, WY, NE, KS, OK, NM NM AZ, UT, CO, OK, TXCT NY, MA, RI, NJ NY PA, VT, MA, CT, NJDE MD, PA, NJ NC SC, TN, VA, GADC VA, MD ND MT, MN, SDFL AL, GA OH IN, MI, PA, VW, KYGA AL, TN, NC, SC, FL OK NM, CO, KS, MO, AR, TXID OR, WA, MT, WY, UT, NV OR WA, ID, NV, CAIL MO, IA, WI, IN, KY PA NY, NJ, MD, WV, OH, DEIN IL, MI, OH, KY RI CT, MAIA NE, SD, MN, WI, IL, MO SC GA, NCKS CO, NE, MO, OK SD WY, MT, ND, MN, IA, NEKY MO, IL, IN, OH, WV, VA, TN TN AR, MO, KY, VA, NC, GA, AL, MSLA TX, AR, MS TX NM, OK, AR, LAME NH UT NV, ID, WY, CO, NM, AZMD VA, WV, DC, PA, NJ, DE VT NY, NH, MAMA RI, CT, NY, VT, NH VA NC, TN, KY, WV, MD, DCMI WI, IN, OH WA OR, IDMN SD, ND, WI, IA WV KY, OH, PA, MD, VAMS LA, AR, TN, AL WI MN, MI, IA, ILMO KS, IA, IL, KY, TN, AR, NE, OK WY ID, MT, SD, NE, CO, UTMT ID, ND, SD, WY

Note: Alaska and Hawaii are omitted from the table because they have no states adjacent.




Another important demographic variable may be the age of the popula-tion. Retirees may be of particular interest, because Gazel and Thompson(1996) reported that older people make up a high proportion of casino gam-blers. Harrah’s (Harrah’s Entertainment 1997) and the AGA (2006), however,found that the median age of casino players is about the average for the U.S.population. The estimated percentage of people in the states older than sixty-five years is included as a variable in the model. Although previous evidenceseems mixed, the variable may provide information as to who gambles.

Jackson, Saurman, and Shughart (1994) and Elliott and Navin (2002) useda variable for the number of Baptists in a state to help predict the probabilityof lottery adoption. Because Baptists are a large, well-organized interestgroup opposed to gambling, they may have a negative effect on gambling rev-enues. Accordingly, we include the estimated percentage of Baptists in thestates as an explanatory variable.

Finally, we use the number of hotel employees in each state as a roughmeasure of the volume of tourism in the states during each year.

State per capita income and hotel workers are reported annually.14 Forthe data on Baptists, degree holders, older people, and poverty, we use twoyears of data to derive linear annual estimates for the 1985-2000 period foreach state.15

4. Model and Results

4.1 The Model

We are attempting to explain the relationships among gambling industriesby modeling the gambling volume in each industry as a function of thevolume in the other industries, adjacent-state gambling activity, and demo-graphic factors. A panel data model has the advantage of increasing the sizeof the data set, which is especially helpful for industries like gambling, whichare rather young in some states. We choose the econometric model based onthe fact that the dependent variable (industry volume) in our model is leftcensored. Left censoring is more prevalent for the casino and horse racingindustries than it is for dog racing and lotteries.16 Still, it is a problem for allindustries. States self-select into legalizing a certain form of gambling. Thosethat do not elect to allow gambling get zeroes for the revenue variable (e.g.,censored data). We must account for this censoring in our parameter esti-mates. We do this with a probit model to explain the probability of legaliz-ing each gambling industry. Following Heckman (1979), we obtain theinverse Mills ratio (IMR, λ) from the probit and include it in the model as an






additional explanatory variable for gambling revenue. This should correct forcensoring bias. The probit models are presented in table 4.17

In our panel model, we use a time trend to account for intertemporal vari-ation within a state, and we include regional dummies to pick up unexplainedheterogeneity across regions (à la fixed effect models). These regional dum-mies turn out to be significant to varying degrees across the industries. Theydo not, however, affect the coefficient estimates or the alternative gam-bling revenue coefficients appreciably, so we report the results withoutthese dummies.18

We estimate a system of four equations, in which each equation isintended to explain the volume of one type of gambling as a function of vol-umes of other types of gambling, the presence of various types of adjacent-state gaming, and state-specific demographic factors.19 As such, the systemattempts to explain spending, in turn, on casinos, dog racing, horse racing,and lotteries. Thus, even after accounting for problems arising from left

Table 4Sample Selection Probits

Variable Casino Dog Racing Horse Racing Lottery

Constant –3.565*** –0.677* 1.343*** –6.266***(–6.572) (–1.70) (3.24) (–10.69)

Casino dummy — –0.211 0.505** 2.303***(–1.31) (2.39) (4.77)

Dog racing dummy –0.207 — 1.075*** 0.897***(–1.41) (7.04) (5.66)

Horse racing dummy 0.434** 1.094*** — 1.060***(2.08) (6.98) (6.55)

Lottery dummy 1.202*** 0.779*** 0.881*** —(5.10) (5.21) (6.04)

Indian casino dummy 0.529*** 0.114 0.897*** –0.323**(3.74) (1.04) (6.09) (–2.05)

Hotel workers 40.112*** –10.58 –0.189 –83.355***(7.19) (–1.24) (–0.05) (–6.96)

Baptists 0.030*** 0.004 –0.030*** –0.037***(4.68) (0.76) (–6.92) (–7.46)

Degree holders 0.019 0.010 0.005 0.046***(1.21) (0.81) (0.38) (3.01)

Income per capita –0.695e–5 –0.920e–4*** –0.956e–4*** 0.0004***(–0.19) (–3.10) (–2.66) (10.38)

Note: The z statistic is indicated in parentheses below each coefficient.*p = 0.10. **p = 0.05. ***p = 0.01.




censoring of our spending measures and the panel nature of our data, westill face the problem of a potential relationship among the errors of the fourequations. Such a relationship could arise from neglected macroeconomicvariables affecting the different equations via their errors in a given year orfrom differing general attitudes and preferences regarding gambling acrossstates in a given time period. Furthermore, to the extent that this system ofequations is a variant of a demand system, the theoretically implied adding-up constraints require that the sum of the disturbances across equations bezero, so that there must be some correlation among the disturbances in thedifferent equations (Phlips 1983, 198-99). These types of problems are typ-ically lumped under the heading of contemporaneous correlation of the dis-turbances across the equations, and they can be handled by SUR estimationtechniques. This empirical procedure allows us to estimate our four-equationmodel jointly as a system of equations, rather than applying ordinary leastsquares (OLS) to each equation independently, thereby assuring us of moreefficient parameter estimates and facilitating the imposition of cross-equationparameter restrictions.

Our system is similar to, but not identical to, a demand equation system,such as Stone’s linear expenditure system or Theil’s Rotterdam model.Demand systems typically estimate a system of equations in which somemeasures of quantities demanded are functions of relative prices and realincome, so that the estimated coefficients can be interpreted as (Hicksian)compensated own- and cross-price elasticities and income elasticities.20 Oursystem, in contrast, involves expenditures (prices times quantities) rather thanprices or quantities alone. Gambling, like most service industries, encountersmeasurement difficulties in attempting to separate prices from quantities or,more particularly, in attempting to measure quantities purchased. Thus,although we would like to exploit the similarity to demand systems as muchas possible, the analogy is tenuous in at least two areas.

First, in demand systems estimates, substitute commodities are typicallyindicated by positive coefficient estimates on the prices of related goods; andcomplementarity, by negative coefficient estimates. In our expenditure system,we define substitutable (or cannibalizing or competing) gaming activitiesas ones in which increases in consumer expenditures on one activity resultin decreased consumer expenditures on the related activity, that is, a negativecoefficient estimate when the latter is a function of the former. Similarly, acomplementary relationship among gaming activities arises when increasedconsumer spending on one activity results in increased expenditures on therelated activity as well, that is, a positive coefficient estimate when the latteris a function of the former. Clearly, our definition is consistent with the more





traditional one only under some fairly restrictive assumptions concerningthe magnitudes of the price elasticities involved. Our definition is, however,heuristically valid, and it is also perhaps more meaningful if our primary inter-est is in the tax-revenue-maximizing bundle of games of chance.

Second, in demand systems estimation, the compensated cross-price elas-ticity estimates are constrained to be symmetric across equations, that is, thecompensated cross-price elasticity of demand for good A with respect to theprice of good B must be identical to the compensated cross-price elasticity ofdemand for good B with respect to the price of good A. Imposing such restric-tions not only is implied by demand theory but also results in more efficientparameter estimates. Unfortunately, it is not at all clear that this type of sym-metrical restriction is appropriate when the equations are expressed in expen-diture form. It is reasonable to expect that if a rise in expenditure on, say,casino gambling results in a decreased expenditure on, say, the lottery, then arise in expenditure on the lottery should also result in a decreased expenditureon casino gambling. There is, however, no reason to suspect that the magni-tudes involved would (or would not) be the same. Thus, although there is anempirical rationale to impose symmetry constraints across equations—theestimates’ standard errors are smaller—there is not the theoretical rationalethat is present in demand theory. Therefore, to gain some insight into how sen-sitive our estimates are to the imposition of cross-equation symmetry restric-tions, we estimated our four-equation system with them imposed and againwithout them imposed. Tables 5 and 6 present the respective SUR results.

4.2 Discussion of Results

Consider the results presented in table 5, in which the corresponding cross-equation industry volume coefficients have been constrained to equality, andtable 6, which incorporates no cross-equation constraints. In comparing theindustry volume coefficient estimates across tables, the most important resultto note is that there are no sign or significance discrepancies among corre-sponding coefficient estimates across the two tables, and there is only onenotable discrepancy in terms of magnitude (the dog racing coefficient in thelottery equation is roughly ten times as large in the unconstrained estimate asin the constrained case). Thus, even though the cross-equation restrictions areof questionable theoretical validity, their imposition appears to have no appre-ciable empirical impact on the industry volume coefficient estimates; in gen-eral, the corresponding coefficient estimates across tables are very similar interms of sign, significance, and magnitude. For this reason, we confine ourinterpretation of the results to the unconstrained case in table 6.






Table 5SUR Model with Cross-Industry Constraints


Industry Volume

Casino — –0.020*** 0.277*** –0.104***(–2.89) (14.18) (–4.93)

Dog racing –0.020*** — –0.062*** 0.136***(–2.89) (–6.10) (17.26)

Horse racing 0.277*** –0.062*** — 0.844***(14.18) (–6.10) (67.43)

Lottery –0.104*** 0.136*** 0.844*** —(–4.93) (17.26) (67.43)

Indian square 151.19*** 11.289 154.05*** –86.08*footage (3.07) (0.90) (3.68) (–1.75)

Adjacent State Industries

Adjacent casino –0.035*** 0.0006 0.087*** –0.065***(–3.71) (0.24) (11.27) (–7.09)

Adjacent dog –0.040 0.060*** 0.085 –0.022racing (–0.61) (3.55) (1.53) (–0.33)

Adjacent horse 0.136*** –0.02*** –0.269*** 0.227***racing (6.603) (–3.59) (–15.87) (11.36)

Adjacent lottery 0.007 –0.009** 0.128*** –0.118***(0.45) (–2.21) (10.00) (–7.76)

Demographic

Age > 65 –0.297e7 0.176e8*** –0.541e7 0.319e7(–0.50) (11.29) (–1.08) (0.54)

Baptists 0.489e7*** 484006.51* –239954.20 –0.126e7(4.79) (1.88) (–0.28) (–1.23)

Degree holders –0.102e8* –0.240e7 0.326e7 –0.104e8*(–1.722) (–1.59) (0.65) (–1.76)

Hotel workers 0.336e11*** 0.144e10*** –0.350e10*** –0.377e10***(42.95) (4.79) (–3.87) (–3.69)

Income per 6643.48 1016.18 –16661.41** 47573.32***capita (0.83) (0.48) (–2.48) (6.04)

Other

Constant –0.366e11*** 0.305e10 0.853e11*** –0.838e11***(–2.78) (0.92) (7.79) (–6.51)

Year 0.182e8*** –0.162e7 –0.428e8*** 0.419e8***(2.75) (–0.97) (–7.74) (6.46)

Inverse Mills 0.402e9*** 0.730e8*** 0.487e8*** 0.655e8***ratio (λ) (20.15) (17.54) (4.18) (4.12)

Note: The t-statistic is indicated in parentheses below each coefficient.* = 0.10 level; ** = 0.05 level; *** = 0.01 level.





Table 6SUR Model, Unconstrained Model


Industry Volume

Casino — –0.019*** 0.234*** –0.115***(–2.64) (10.04) (–4.06)

Dog racing –0.165 — –0.956*** 1.642***(–1.38) (–9.63) (14.46)

Horse racing 0.355*** –0.062*** — 1.042***(9.08) (–6.01) (38.42)

Lottery –0.079** 0.122*** 0.726*** —(–2.33) (15.19) (37.36)

Indian square 113.04** 12.47 180.76*** –151.26***footage (2.25) (0.99) (4.30) (–3.03)

Adjacent State Industries

Adjacent –0.042*** 0.001 0.087*** –0.083***casino (–4.26) (0.46) (11.23) (–8.79)

Adjacent dog –0.046 0.067*** 0.213*** –0.244***racing (–0.68) (3.96) (3.74) (–3.63)

Adjacent horse 0.148*** –0.018*** –0.241*** 0.242***racing (6.78) (–3.20) (–14.07) (11.61)

Adjacent –0.007 –0.010** 0.098*** –0.096***lottery (–0.40) (–2.54) (7.44) (–6.07)

Demographic

Age > 65 669352.82 0.185e8*** 0.200e8*** –0.391e8***(0.10) (11.85) (3.51) (–5.83)

Baptists 0.516e7*** 444286.82* –256651.01 –0.126e7(5.04) (1.73) (–0.30) (–1.23)

Degree –0.687e7 –0.277e7* –0.515e7 318567.45holders (–1.14) (–1.83) (–1.01) (0.05)

Hotel 0.339e11*** 0.128e10*** –0.320e10*** –0.299e10**workers (42.06) (4.23) (–3.17) (–2.50)

Income per –1796.71 2844.01 9334.11 12761.11capita (–0.21) (1.34) (1.36) (1.54)

Other

Constant –0.359e11*** 0.218e10 0.693e11*** –0.793e11***(–2.70) (0.66) (6.26) (–6.11)

Year 0.179e8*** –0.120e7 –0.349e8*** 0.401e8***(2.667) (–0.72) (–6.27) (6.12)

Inverse Mills 0.395e9*** 0.667e8*** 0.481e8*** 0.833***ratio (λ) (19.55) (15.88) (4.12) (5.18)

Note: The t-statistic is indicated in parentheses below each coefficient.* = 0.10 level; ** = 0.05 level; *** = 0.01 level.




The results for the casino revenue model indicate that increases in horse-racing handle and Indian casino gambling and decreases in lottery sales inthe state in question tend to significantly increase state-licensed casinogambling revenues in that state. In addition, the presence of casino gam-bling in adjacent states significantly decreases, and the presence of horseracing in adjacent states significantly increases, casino revenues of the statein question. Only two of the demographic variables significantly affectedcasino revenues, Baptists and tourism (hotel workers), and both affected itpositively. Although the tourism result was expected, the Baptist result wasa surprise and may simply be the result of Baptists proxying a significantregional (southeast) effect. Also, there is a significant positive trend (year)in casino revenues during the period of the sample. Finally, the significanceof the inverse Mills ratio clearly indicates the importance of correcting forleft censoring of casino revenues. All other parameters in the model werestatistically insignificant.

The results for the dog racing model indicate that increases in lotterysales and decreases in horse racing handle and casino revenues in the statein question statistically significantly increase dog racing handle in that state.Apparently, horse racing and lotteries in adjacent states compete within-state dog racing because these variables have significant and negativecoefficient estimates. Interestingly, there may be agglomeration economiesin dog racing, because the presence of dog racing in adjacent states appearsto significantly increase dog racing handle in the state in question. Increasesin population older than sixty-five and tourism significantly increase dogracing handle at the 0.01 level, and increases in Baptists and decreases indegree holders increase it at the 0.10 level. Again, the significance of theinverse Mills ratio indicates the importance of correcting for left censoring.All other parameters in the model are statistically insignificant.

The results for the horse racing model indicate that increases in the casino,Indian casino, and lottery variables and decreases in dog racing handle in thestate in question statistically significantly increase horse racing handle in thatstate. In addition, the presence of casino gambling, dog racing, and lotteriesin adjacent states significantly increase horse racing handle, whereas thepresence of horse racing in adjacent states competes with it by significantlydecreasing horse racing handle in the state in question. Increases in popu-lation older than sixty-five and decreases in tourism significantly increasehorse racing handle. The tourism result is surprising; apparently, with thepossible exception of major races (e.g., the Triple Crown and the Breeder’sCup), horse racing does not attract overnight-type tourists. Finally, there is asignificant downward trend in horse racing revenues, and the inverse Mills





ratio is again significant. All other parameters in the horse racing equation arestatistically insignificant.

The results of the lottery model indicate that increases in dog and horseracing handle and decreases in Indian casinos and casino revenues in thestate in question statistically significantly increase lottery sales in that state.Also, the presence in adjacent states of casino gambling, dog racing, andlotteries significantly decreases in-state lottery sales, whereas the presencein adjacent states of horse racing significantly increases them. Furthermore,decreases in population older than sixty-five and in tourism significantlyincrease lottery revenues. In this case, the tourism result is not so odd. Atthe time of this writing, only six states do not have some form of state-runlottery. Consequently, although tourists may indeed buy lottery tickets, it isunlikely that they go to a neighboring state and stay overnight solely to doso. Finally, there is an upward trend in lottery sales during the sampleperiod, and the inverse Mills ratio is again statistically significant. All otherparameters in the lottery equation are statistically insignificant.

4.3 Effects of Cross-Equation Constraints

Before turning to a detailed discussion of the cannibalization question, weshould address a seemingly puzzling anomaly that is obviated by a carefulcomparison of the constrained and unconstrained results in tables 5 and 6.First, note that we only constrain the corresponding industry volume coeffi-cients across equations; no constraints are directly applied to the adjacent-state industries, demographic, and other variables’ coefficients. Second,recall from above that the imposition of these industry volume constraints didnot appear to appreciably alter the constrained estimates from their uncon-strained counterparts. The constrained coefficients themselves are not, how-ever, the only estimates affected by the imposition of constraints; theunconstrained coefficient estimates can also be affected.

As an example, consider the coefficient estimates for the variable age> 65 in the constrained and unconstrained casino revenue models. In the con-strained model, age > 65 has a coefficient estimate of –0.297 × 107; and inthe unconstrained casino revenue model, its estimate is 0.669 × 106. Neithercoefficient is statistically significant. Indeed, with the exception of a coupleof cases for a couple of the demographic variables, the pattern of signs andsignificance is amazingly uniform across the constrained and unconstrainedresults. Nevertheless, a point estimate discrepancy of this magnitude is worthnoting and is almost certainly attributable solely to the imposition of thecross-equation constraints on the parameter estimates in table 5.






As an illustration, consider the dropping of an important variable froma hypothetical regression. This is nothing more than imposing a constraintto equal zero on the variable’s coefficient. Econometric theory tells us thatomitting a variable such as this can lead to biased estimates of the remain-ing included variables and that the extent of the bias depends in part on thecorrelation between the excluded and included variables. In the casino rev-enue case, age > 65 was apparently highly correlated with one of the con-strained variables. This is not always the case. Consider the constrained andunconstrained estimates of the Baptists variable in the lottery equation. Theyare almost identical, and they are both insignificant. We can infer from thisresult that Baptists is uncorrelated with any of the constrained variables inthe lottery equation of table 5. These types of anomalous results in compar-ing constrained and unconstrained estimates are not unusual; indeed, theyare replete throughout the empirical literature on demand system estimation.

In summary, we content ourselves with finding no major point estimatediscrepancies between corresponding constrained and unconstrained modelsin any of the statistically significant coefficients in any of our gamblingmodels. Because, however, there is no apparent gain from imposing the cross-equation constraints, and because there is a potential for biased estimation ifthe constraints are not justified, we will confine our attention in our subse-quent discussion to the unconstrained estimates of table 6. A summary of theinterindustry effects from the unconstrained model is presented in table 7.

5. Policy Issues and Conclusion

Our main interest is in discovering whether there exist general intrastaterelationships among the various gambling industries. It is for this reason

Table 7Summary of Intrastate Industry

Relationships, Unconstrained Model


Casino – + –Dog racing (–) – +Horse racing + – +Lottery – + +Indian square footage + (+) + –

Note: ( ) indicates statistically insignificant at normal levels.




that we focus our discussion of the results on the “industry volume” vari-ables, summarized in table 7. The results suggest that horse and dog racingare substitutes for each other. Being similar types of venues, this result isreasonable. Lotteries and casinos are negatively related. This is consistentwith findings by Elliott and Navin (2002) and Fink and Rork (2003). Lot-teries do not, however, appear to cannibalize the racing industries. This issomewhat consistent with Kearney’s (2005) findings that spending on lot-teries comes at the expense of spending on nongambling goods and services,but is contrary to the evidence by Gulley and Scott (1989), Mobilia (1992),and Thalheimer and Ali (1995).

Generally, we find that the availability of a type of gambling in adjacentstates will harm that industry in the state in question.21 Although many stud-ies have not considered adjacent-state effects, our results are consistentwith those of Davis, Filer, and Moak (1992); Elliott and Navin (2002);Garrett and Marsh (2002); and Tosun and Skidmore (2004).

The differences in our findings and those of previous studies can beexplained by differences in the industries and states considered, time peri-ods analyzed, and econometric methodology. Of course, a specific state orindustry might behave differently than the aggregates studied here. Some ofour results are not intuitive. For example, casinos and horse racing helpeach other, but casinos and dog racing harm each other. Indian casinos tendto complement casinos and horse racing, but harm lotteries. These resultsmay be due to the peculiarities in certain states that exert significant influ-ence in the overall model.

This is the first study to examine all the industries in all states in an effortto provide a comprehensive understanding of the relationships among allthe various gambling industries. The findings in this article may be used asa starting point for analyzing the expected effects of introducing or expand-ing gambling industries in a state or region. Readers should, however, keepin mind that although this study is nationwide, the relationship between twoindustries in a particular state may be different than that indicated here. Inaddition, this study does not examine the relationship between gamblingand nongambling industries. Although some authors have addressed thisissue, it has not been dealt with rigorously here or elsewhere.

Should a particular state legalize casinos, for example? Casinos are morelabor intensive and are taxed at a higher rate than many other industries.So even if casinos “cannibalize” other industries, they may provide a netincrease to employment and tax revenues. (The tax issue is discussed in detailbelow.) Of course, the political concerns surrounding gambling go far beyondthe “economic” effects. There is evidence that some problem gamblers may






impose social costs on society.22 Still other people may have moral objectionsto gambling. Although we have not addressed all of these issues, we have pro-vided some econometric evidence applicable to public policy.

The fact that we did not find, for example, a clear and consistent canni-balization effect among the different industries suggests that state legisla-tors should be careful to study their specific cases prior to acting to introduceor expand gambling in their state. Legalizing additional forms of gamblingmay have either a positive or negative impact on the state’s economy andtax revenue. These are issues that call for further empirical study.

5.1 Tax Policy

Tax policy has a lengthy history in the public economics literature.Various authors have examined “optimal taxes,” including Ramsey (1927),Mirrlees (1971), Slemrod (1990), Sobel (1997), and Holcombe (1998). Thisliterature typically deals with setting tax rates in an effort to minimize dis-tortions or maximize welfare. It would seem, however, that state govern-ments are not so much interested in efficiency as they are in revenuemaximization, at least when it comes to gambling legalization.23 (Thisseems especially relevant now, when many states and municipalities arefacing record budget deficits.) Consider the fact that the legal restrictionson gambling are extremely inefficient—they cause enormous deadweightlosses. Also, because the states rarely allow a competitive market in gam-bling when they do decide to legalize, it is unlikely that their primary con-cern is efficiency.24 A much more likely goal or motivation is maximizingtax revenues given regulated or limited gambling industries.

There are a few papers that examine revenue maximization from excisetaxes, including Lott and Miller (1973, 1974) and Caputo and Ostrom(1996). Several papers address this issue with respect to gambling. Forexample, Borg, Mason, and Shapiro (1993) found that $1.00 in net lotteryrevenue has a cost of 15-23 cents in other types of government revenue.Fink, Marco, and Rork (2004) found that overall state tax revenues declinewith increased lottery sales. These studies are more general, however, anddo not account specifically for tax revenues from other forms of gambling.25

To illustrate the problem, consider a lottery state that is contemplatinglegalizing casinos. Gross state revenue from a lottery is about 50 cents ofeach dollar bet (Garrett 2001), whereas the taxes on casino revenues aretypically a much lower percentage. Our results indicate that casinos and lot-teries cannibalize each other. The magnitude of this relationship, among




other variables, will determine the extent to which tax revenue will changeif casinos are introduced in the lottery state.26

As an example, if lotteries and casinos act as perfect substitutes (in sucha way that people who lose $X at newly opened casinos now spend $X lesson lottery tickets), then the introduction of casinos would lead to a decreasein state tax revenue from gambling. In New Jersey, for example, the revenuefrom the lottery is 50 cents per ticket. The state tax on gross casino gam-bling revenues is 8 percent. If lotteries and casinos were perfect substitutes,if New Jersey already had a lottery, and if all casino revenue were from lostlottery ticket sales, then we would expect the state’s total gambling tax rev-enues to fall after casinos were introduced. In reality, most states have morecomplicated mechanisms for casino taxes, it is unlikely that any two indus-tries are perfect substitutes, and the introduction of a new good (casinogambling) to a state’s consumption menu is likely to draw in additionalconsumers. Thus, our example is purely hypothetical.

In any case, our empirical results do not provide the necessary data to con-fidently predict the net tax effect, say, of introducing another type of gam-bling. This is because the coefficient estimates in tables 5 and 6 are notstandard elasticities.27 What our results do provide that is important is infor-mation on the sign of the various gambling volume coefficients and theirstatistical significance, which in turn provides information on whether thegambling industries tend to be substitutes or complements.

5.2 Conclusion

State governments are in a unique situation when contemplating gam-bling legalization, as they control not only the tax rates, but also the quan-tity of gambling. (For example, the states issue a limited number of casinopermits.) The revenue maximization problem depends on the size and typesof existing gambling industries within the state, the intensity of their sub-stitutability or complementarity, the prospective size of new industries, andthe tax rates applied to the various industries. These issues require furtherstudy. The rush to legalize gambling in the 1990s was surprising given howlittle was known about the economic effects of these industries and the rela-tionships among them. The empirical evidence in this article can provide afoundation for studying the relationships among the gambling industriesand their net tax effects on state governments. This is important, becausemany states are still contemplating the introduction or expansion of gam-bling opportunities in an attempt to deal with fiscal crises.






Notes

1. Levine (2003) described the gambling debate taking place in many states. In addition tointroducing new industries, some state governments have been considering raising tax rates onexisting gaming industries (Husband 2003). In an extreme example, the governor of Illinoiswas reportedly considering a state takeover of all casinos “to operate them for the state’sprofit” (Kelly 2003). Instead, in 2003 the state imposed the country’s highest marginal tax rateon casino revenues, 70 percent. In 2005, that rate was lowered to 50 percent.

2. Published studies have focused more narrowly on estimating demand for individualgambling industries or have examined pairs of industries within single states.

3. For a detailed discussion, see Eadington (1999) or McGowan (2001).4. This problem is particularly evident in the case of Indian casinos, which are prevalent

in at least twenty-eight states but are not required to publicly disclose data.5. Lotteries have received more research attention than the other gambling industries. Much

of the lottery research has, however, focused on the factors affecting the decision to adopt a lot-tery, including fiscal pressures. Relevant studies include Alm, McKee, and Skidmore (1993);Caudill, Ford, Mixon, and Peng (1995); Mixon, Caudill, Ford, and Peng (1997); Erekson, Platt,Whistler, and Ziegert (1999); Glickman and Painter (2004); and Giacopassi, Nichols, and Stitt(2006). Others have more general analyses of lotteries, most notably Clotfelter and Cook (1991)and Borg, Mason, and Shapiro (1991). For the most part, these studies do not address the rela-tionships between lotteries and other gambling industries.

6. The sources for the industry data are as follows. Lottery ticket sales come fromLaFleur’s 2001 World Lottery Almanac (2001). Casino revenues are from the AGA (n.d.) andvarious states’ gaming commissions. Greyhound and horse racing handle are from the 1985-2000 issues of Pari-Mutuel Racing, published by the Association of Racing CommissionersInternational, Inc. The 1985-1990 dog and horse racing data and the 1995-2000 horse racing data were reported as handle for both industries. For horse and greyhound racing from1991 to 1994, the authors calculated handle using the total pari-mutuel takeout and effectivetakeout rate (handle = total pari-mutuel takeout/effective takeout rate). The same process wasused to calculate greyhound racing handle from 1995 to 2000. Thus, all racing data arereported with a consistent measure. All of the above volume data are adjusted for inflationusing the Consumer Price Index (CPI) from the Bureau of Labor Statistics (U.S. Departmentof Labor, Bureau of Labor Statistics n.d.; 1982-1984 = 100). Annual state population esti-mates are from the U.S. Census Bureau (n.d.). The authors calculated the states’ annual Indiancasino square footage using the casino listing at http://www.casinocity.com (Casino City n.d.).This source lists 126 Indian-owned casinos in the United States. Square footage and openingdates were collected from the casinos’ Web pages or by phone calls to the casinos.

7. Slot machines and video poker at racetracks, so called racinos, are a relatively new phe-nomenon appearing in some states. Due to their relative newness and the inherent difficultiesin classifying these nonracing bets (as racing handle or casino revenue?), this machine gam-bling is omitted from our analysis. For a discussion of racinos, see Eadington (1999, 176) andThalheimer and Ali (2003, 908).

8. In the case of lotteries, this is ticket sales per capita.9. 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, buys $100worth of chips, and plays until she loses the $100. The total handle could range from $100 toany higher amount. It would be $100 if she lost a single $100 hand of blackjack. But suppose





she plays and wins several thousand dollars, but later loses it all. The total handle from thisplayer is in the thousands of dollars, even though she only lost $100 of her “own” money. Thisexample illustrates why an estimate of casino handle would be unreliable. Even if it were pos-sible to convert revenue to handle, say by using some multiple, this adjustment would notaffect relative coefficient estimates in any meaningful way.

10. We inquired with Harrah’s Entertainment, one of the largest U.S. casino operators,which also manages numerous Indian-owned casinos. They confirmed that there is a generalindustry formula for the number of slot machines and table games as a function of squarefootage. For this reason, we believe Indian casino square footage to be a satisfactory, albeitimperfect, measure of Indian casino volume.

11. For example, see Saba, Beard, Ekelund, and Ressler (1995) and references therein.12. Other attempts to measure the intensity of adjacent-state gambling have similar diffi-

culties. Our primary concern is the availability of gambling in nearby states.13. As an example, in 2000, Florida’s adjacent-state lottery observation would be 0.5,

because Georgia had a lottery that year and Alabama did not.14. The hotel employee information and per capita income data come from the Bureau of

Economic Analysis (U.S. Department of Commerce, Bureau of Economic Analysis 2006). Theper capita income data are adjusted for inflation using the Bureau of Labor Statistics CPI data(U.S. Department of Labor, Bureau of Labor Statistics n.d.).

15. Annual estimates for these are not available. The years we used to derive the estimatesvary due to data availability: Baptists (1980 and 1990), degree holders (1990 and 2001), olderpeople (1990 and 2001), and poverty (1992 and 2001). The data come from the U.S. CensusBureau (n.d.) with the exception of Baptists, from the New Book of American Rankings(Meltzer 1998)

16. No model is posited for Indian casino gambling, because the volume measure for thisindustry (square footage) is rather crude.

17. 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 hasa different goal.

18. The results including the regional dummies are available from the authors on request.19. We use LIMDEP version 8.0 (Econometric Software 2002) to perform the economet-

ric work.20. These demand systems are often estimated by SUR. For example, see Wooldridge

(2002, 144-45) or Greene (2003, 341, 362-69).21. The exception here is dog racing.22. See Walker and Barnett (1999) for a discussion.23. See Alm et al. (1993) and Madhusudhan (1996) on using legalized gambling to ease

fiscal constraints.24. There is a variety of potential social concerns that may accompany legalized gambling,

of course, but these are not the subject of this article.25. See Anderson (2005) for a good summary of tax issues that require additional study.26. Mason and Stranahan (1996) looked more generally at the effects of casinos on state

tax revenues, but not particularly at revenues from other forms of gambling. Despite its tan-gential relevance to the subject of this article, it does provide an interesting discussion of howgambling affects overall tax revenues.

27. This was explained in section 4.1.





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wagering and attendance. Management Science 41 (1): 129-43.———. 2003. The demand for casino gaming. Applied Economics 34 (8): 907-18. TLF Publications. 2001. LaFleur’s 2001 world lottery almanac. 9th ed. Boyds, MD: TLF

Publications. http://www.lafleurs.comTosun, Mehmet S., and Mark Skidmore. 2004. Interstate competition and state lottery revenues.

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Douglas M. Walker is an associate professor of economics at the College of Charleston, inCharleston, South Carolina. His research interests are in the social costs and benefits of, economicgrowth effects of, and government policy toward gambling. His research has been published injournals such as Journal of Gambling Studies, Review of Regional Studies, Review of Urban &Regional Development Studies, Economic Development Quarterly, and Managerial and DecisionEconomics. His book, The Economics of Casino Gambling, is published by Springer.

John D. Jackson is a professor of economics at Auburn University, in Auburn, Alabama. Hisresearch interests include applied econometrics, regional economics, and macroeconomics.His research has been published in journals such as American Economic Review, SouthernEconomic Journal, Public Choice, Applied Economics, Review of Industrial Organization,Journal of Urban Economics, and Journal of Labor Economics.




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Public Finance Review - College of Charlestonwalkerd.people.cofc.edu/pubs/PFR.pdf · 2008. 4. 15. · In many cases, the papers have examined a single county or state, or a small