Community Assessment of the Effects of Casinos on Quality of Life

MARK NICHOLS, B. GRANT STITT and DAVID GIACOPASSI

COMMUNITY ASSESSMENT OF THE EFFECTS OFCASINOS ON QUALITY OF LIFE

(Accepted 6 December 2001)

ABSTRACT. Recent attention has focused upon the social and economic impactof legalized gambling, particularly casino gambling. Though considerable atten-tion has been paid to the effects on individuals in such areas as problem gambling,less attention has been given to the effect casino gambling has on citizens’ day-to-day life. In particular, how does the introduction of casino gambling affect theirquality of life? This paper explores this issue utilizing multiple indicators gatheredas part of an in depth study of the effects of casino gambling on crime and qualityof life in eight new casino jurisdictions.

INTRODUCTION

Casino gambling, an activity once thought best placed in remotelocations accessible primarily to tourists, has recently becomemore widespread throughout the world. This spread has causedheated debate over the benefits and costs of gambling and spawnedthe creation of at least three government-sponsored studies, theNational Gambling Impact Study Commission in the United States,the Inquiry into Australia’s Gambling Industries conducted byAustralia’s Productivity Commission, and the Gambling Review,conducted by Great Britain’s Home Office.

The National Gambling Impact Study Commission (NGISC),Australia’s Productivity Commission (APC), and Great Britain’sHome Office (HO) all recognize that the task of evaluating theimpact of casinos on communities needs to be broadly construedto encompass the effect of casinos on people and places.1 Clearly,casinos can bring dramatic changes to a community. New roads, newbuildings, infrastructure improvements, and the economic stimulusprovided by new jobs and additional tourism may be evident tothose even casually acquainted with a community which initiatescasino gambling. Less evident and perhaps more difficult to assess

Social Indicators Research 57: 229–262, 2002.© 2002 Kluwer Academic Publishers. Printed in the Netherlands.

230 MARK NICHOLS ET AL.

is how these changes affect the lives of the people in the communityand whether these changes are evaluated positively, neutrally, ornegatively.

This paper seeks to examine the impact that casinos have onquality of life as perceived by residents in eight US communitiesthat recently adopted casino gambling. Residents were askedseveral questions pertaining to factors that influence quality of life.Responses to these questions and how individual and communitycharacteristics cause them to vary are analyzed using multinomiallogit analysis. The next section reviews some of the literature thathas examined the impacts of casino gambling on such factors ascrime, economic development, and community satisfaction withcasinos. This is followed by a brief discussion of the measurementof the quality of life. Section III presents the empirical methodology,including the selection of the eight jurisdictions and the multinomiallogit model. This is followed by a discussion of the results. Finally,Section V offers some concluding remarks.

REVIEW OF THE LITERATURE

Crime and Economic Impact Studies of Gambling

Studies that analyze the impact of casinos on crime in communitiestend to have mixed findings. A number of studies have found thatcasinos do not increase crime (Giacopassi and Stitt, 1993; Chang,1996). Other studies conclude that the number of crimes committeddoes increase (Hakim and Buck, 1989; Thompson et al., 1996).Still other studies find that the number of crimes may increase, butbelieve that the crime rate itself may actually decrease when thepopulation growth and tourism due to casinos are factored into crimerate calculations (Albanese, 1985; Curran and Scarpitti, 1991). Thelatter group of studies suggests there is nothing unique to casinosthat contributes to a more substantial increase in crime than wouldbe found in non-casino communities that attract a similar number oftourists.

The economic picture is perhaps less clouded. Casinos bringemployment and increased tax revenue to communities. A studythat empirically tested the economic effects of legalizing casinosutilizing 248 observations of quarterly, cross-sectional economic

COMMUNITY ASSESSMENT OF THE EFFECTS OF CASINOS ON QUALITY OF LIFE 231

data from ten states found that the introduction of casinos didspur economic growth (Walker and Jackson, 1998). Critics contend,however, that casinos may be owned by large corporations andmuch of the money spent in the community may be channeledto these distant corporations. At the same time, local businesses(e.g., hotels, restaurants) may suffer economic hardship as the resultof competition from new casino-related businesses (NGISC, FinalReport, 1999, pp. 2–8).

The NGISC reviewed numerous studies of the social andeconomic impact of casinos and concluded that “there are signi-ficant benefits and significant costs to the places, namely, thosecommunities which embrace gambling and that many of theimpacts, both positive and negative, of gambling spill over intosurrounding communities” (NGISC, Final Report, pp. 7–71).However, the NGISC also noted that “there is a paucity of researchin this field [and that] what does exist is flawed because of insuffi-cient data, poor or undeveloped methodology, or researchers’ bias”(NGISC, Final Report, pp. 7–71).

Few studies have attempted to analyze casinos from the moregeneral perspective of how residents perceive casinos affectingtheir quality of life. Long (1996) studied the impact of casinogambling on four small, rural communities in Colorado and SouthDakota and compared the results to a control community. Ques-tionnaires were distributed to each household in the communitieswhere the head of the household was asked to respond to anumber of questions concerning the changes that had occurred inthe three years since limited stakes casino gambling became partof their communities. Respondents were generally in agreementthat casinos provided an economic stimulus to their communitiesby providing jobs and increased personal income to residents.However, respondents in all the casino communities identified noise,traffic congestion, and crime as having increased in their respectivecommunities as a result of casino gambling. Another negativechange associated with the introduction of casinos was that residentsbelieved that they had relinquished some control over the decisionmaking processes in their communities to the casinos. Survey resultsdiffered from community to community, with the largest communityand the one that had casino gambling the longest time expressing


greater satisfaction with the decision of residents to allow casinogambling. However, in two of the communities, a majority of resi-dents disagreed with the statement that Athis community is anideal place to live.” In the other two casino communities, 39%disagreed that the community was ideal, as compared to 15% inthe control community. Of course, these communities resorted tocasino gambling because of the depressed economic conditions thatwere present, combined with little prospect of significant improve-ment. Based on Long’s findings, it appears that the casinos didprovide an economic stimulus, but at a price that left many residentsdissatisfied.

The National Opinion Research Center (NORC) conducted casestudies of ten communities to determine the effect that increasedaccess to casino gambling had on community residents (NORC,1999, pp. 64–75). The ten communities were randomly selectedbut had to have a population of at least 10 000 residents and hadto lie within a 50-mile radius of at least one casino. In each ofthese communities, seven or eight people in various key positions(such as community planners, law enforcement personnel, socialservice workers) were interviewed. All were asked a series of open-ended questions including their perceptions of the casinos’ effecton crime, the economic impact of casinos, and their perception ofpublic opinion regarding the casinos in or near their communities.The results varied by city. In five of the communities, the economicbenefit from the casinos was cited as a great advantage. Respon-dents from two communities reported a decrease in crime, whilerespondents in three cities perceived an increase in crime. Domesticviolence and bankruptcy were other negatives mentioned promi-nently by respondents in a majority of the communities surveyed.When the interviewees were asked about public opinion regardingthe casinos, one community was perceived as strongly in favor, sixwere slightly favorable, two were mixed, and one community wasperceived by respondents to be strongly negative toward casinos(NORC, 1999).

As was discussed by the NGISC, APC and HO, assessmentsof casino gambling’s impact on communities must go well beyondstudies of crime and economic factors and include a complete rangeof measurements and their effects on people and places. While none


of the studies reviewed above claim to assess quality of life specifi-cally, they do attempt to analyze the impact or perceived impact thatcasinos have on multiple dimensions of community life.

The Measurement of Quality of Life

What exactly is “quality of life?” The term “quality of life” is anoften invoked concept in both behavioral and medical sciences. Itis frequently defined as subjective well-being and in many sectorsconsidered synonymous with health (Evans, 1994). Chibnall andTait (1990) point out that quality of life is an elusive concept, whileMugenda et al. (1990) indicate that since there is little consensuson the definition of quality of life, there is little agreement on itsmeasurement. Evans (1994, p. 51) states, “What is evident from areview of the extensive literature of quality of life in the populationat large is that to date no standard definition of quality of life hasbeen adopted.”

Some authors have argued that quality of life is best conceptua-lized as a subjective measure (Cheng, 1988). Diener and Suh (1997)believe that both objective and subjective components are integralto quality of life measures. Cummins (2000), however, has foundthat objective and subjective indicators are often poorly correlated.Objective measures would include variables ranging from healthstatus to standard of living and are grounded in “verifiable condi-tions inherent in the given cultural unit” (Evans, 1994, p. 53).Subjective measures are the individual’s assessment of his or herstatus, most often utilizing general, nonspecific criteria to attain asummary evaluation. An example of a summary subjective measureis, “How do you feel about your life as a whole?” (Andrews andWithey, 1976).

Cummins’ (1996) analysis of quality of life measures found thatthe most commonly included domains are emotional well-being,health, social and family connections, material well-being, and workor other form of productive activity. Michalos and Zumbo (2000,p. 246) state that “virtually every list of social indicators produced inthe last 30 years purporting to provide a comprehensive set capableof capturing all important aspects of the quality of life of a popula-tion has included some measures of crime or personal safety.” Theygo on to note, however, that their crime related measures accounted


for only 9% of the variation in quality of life scores and 38% of thevariation in neighborhood satisfaction scores.

Measures of crime and quality of life illustrate the complexnature of the relationship. For example, the elderly often reporta high level of fear of crime yet have among the lowest rates ofvictimization in society (Schmalleger, 2001). Their fear may berelated not to likelihood of victimization, but rather consequencesof victimization for individuals who are often physically and finan-cially the most vulnerable to the potentially disastrous effects ofcrime.

Given this lack of consensus on conceptual and operationalissues, the present research utilizes subjective measures and focuseson what the authors believe to be significant facets of quality of lifewhich may be positively or negatively influenced by the presenceof a casino in the community. In addition to questions concerningperceptions of crime and economic conditions, respondents areasked to assess how casinos affect quality of life within the familyand at the community level. These questions are described in thenext section.

EMPIRICAL METHODOLOGY

Jurisdiction Selection and Survey Description

The present analysis is part of a larger study to determine the effectof casino gambling on crime and the quality of life in new casinojurisdictions. Since possible casino effects on crime were criticalelements to be studied, for communities to be eligible for inclusionin the study, their police departments had to agree to make availablePart I and Part II crime data for their communities dating back atleast four years prior to casinos opening in their community. Infor-mation on Part II crime, which is not available anywhere other thandirectly from the agency that collects it, was critical to determinehow casinos affect non-index offenses such as fraud, embezzle-ment, bad checks, and public disorder crimes. All the communitiesselected for the study initiated casino gambling in the 1990s andhave had casino gambling for a minimum of four years. This timeframe allows comparisons to be made before and after the casinoswere in operation.2 A number of communities with casinos could


not be included in the study due to incomplete, nonexistent, orinaccessible data.

The communities which were ultimately included in the study areSioux City, Iowa; St. Joseph, St. Louis City, and St. Louis County,Missouri; Alton, Peoria, and East Peoria, Illinois; and Biloxi,Mississippi. In the above communities, Alton has had gambling thelongest, since September 1991, whereas St. Joseph has had it theleast amount of time, since June 1994. All the cities lost popula-tion from 1980 to 1990 (Bureau of the Census, 1998). Of the sevencities, only Peoria does not have a casino at this time. Peoria had ariverboat casino in 1991, but for regulatory reasons it was moved toEast Peoria, directly across the Illinois River and easily accessiblefrom Peoria, in 1993. However, Peoria shares in the tax revenuefrom the riverboat with East Peoria, and many citizens of Peoriaare customers and/or employees of the casino. Peoria, therefore,presents a unique case study of the impact of casino gambling onneighboring jurisdictions. Each of the other cities has one riverboatcasino, except for Biloxi, which has nine casinos located on a bay oron the Gulf Coast on stationary barges. These barge casinos tend tobe larger than the riverboat casinos, and their number and concentra-tion have resulted in the casinos and the tourists they draw playinga much larger role in Biloxi than in the other communities studied.The other extreme is St. Louis, a relatively large city with a singleriverboat casino within the city limits, but with several other casinoriverboats nearby (in East St. Louis, St. Charles, Maryland Heights,and Alton). In comparison to the other communities in the study, theSt. Louis riverboat has relatively little impact on tourism and on theoverall economy of the city and county.

The data for the present study were obtained from the communitysurvey portion of the larger study. The community survey wasconducted between October 1998 and June 1999 and was accom-plished using a computer assisted telephone interviewing (CATI)survey that was completely voluntary and anonymous (for a discus-sion of the complete telephone survey methodology see AppendixA). The overall purpose of the survey was to collect opinionand perceptual data from community respondents regarding theimpacts that casinos have had on crime and quality of life intheir communities. The community survey represents a total of


2768 interviews. The number of completed interviews for eachcommunity varied: Alton (n = 405); Biloxi (n = 403); East Peoria(n = 101); Peoria (n = 314); Sioux City (n = 416); St. Joseph (n =420); St. Louis City (n = 366); and St. Louis County (n = 343). Thenumber of interviews for East Peoria and Peoria, and for St. LouisCity and County, are smaller because, for sampling purposes, theywere treated as combined jurisdictions.

The survey was designed to obtain three general types of infor-mation. First, demographic data on the survey participants wasobtained which included age, marital status, and gender. Only thoseof legal gambling age who resided in the community both beforeand after the introduction of casino gambling were interviewed.3

Information on economic variables such as income, educationalattainment, and church affiliation was also gathered. The secondgeneral area in which questions were asked dealt with the indi-viduals’ own gambling behavior including whether or not theygambled, how often, for how long, and how much they spent. Ques-tions concerning general attitudes and perceptions about gamblingwere asked covering such issues as morality of gambling, effectsof gambling on individuals and communities, and relationship ofgambling to crime. The third general area in which questionswere asked attempted to assess perceived effects that the presenceof casinos had on their community. These questions dealt withquality of life issues including effects of the casinos on economicconditions, crime, families, juveniles, and city services.

Five questions are examined in this analysis relative to howcasinos affect quality of life in new casino communities. They arethe following:

“What effect do you think the presence of casinos has had on the amount of crimein your community? Would you say casino gambling has caused an increase,decrease, or has had no effect at all?”

“Since the introduction of casinos in your community, has the fear of crime,increased, decreased or stayed about the same?”

“Since the introduction of casinos in your community, has the standard ofliving increased, decreased or stayed about the same?”

“Since the introduction of casino gambling, is your community a better placeto live, a worse place to live, or is it about the same?”

“With regard to the quality of family life, do you think casino gambling hascaused a large increase, a moderate increase, a small increase, no change at all,or a small decrease, moderate decrease, or a large decrease?”


Two questions relate to crime, one to economic conditions, andtwo to general community satisfaction. Other questions, such as“Since the introduction of casino gambling has your satisfactionwith your neighborhood as a place to live increased, decreased, orstayed the same?” were also examined but yielded similar resultsand are therefore not included in the analysis. The crime questionsare asked to distinguish between perceived amount of crime and therespondent’s fear of crime. Crime may increase but not impact aperson’s overall fear of crime if, for example, the increase occursin nonviolent crimes such as embezzlement, or occurs at a distancefrom the person’s home or work.

Empirical Model and Logistic Transformation

In order to analyze the impact that casino gambling has on qualityof life, a multinomial logit model is employed.4 The dependent vari-able in this model will be one of the five questions listed above. Theexplanatory variables can be grouped into several categories: demo-graphic (gender, age, education, and income); proximity and rela-tionship with casino gambling (how far live from casino(s), whetherthe respondent gambles in the casino(s) in their community, everworked in the casino(s)); moral attitudes toward gambling (morallyopposed to gambling); and which community the respondent livesin. The basic empirical model takes on the following form:

P (Yj ) =(1)

f (Xdemographic, Xcasino, Xmoral, Xlocation, ε)

where Yj is the response given to the above questions, j = 0, 1 or2.5 The demographic variables consist of a dummy variable equalto one if the respondent is male (MALE); their age in years (AGE);highest education level achieved (less than high school, high schoolgraduate, college graduate, and advanced degree); and income(INCOME), which is gross household income from all sources,categorized as follows: (less than $20 000; 20 000 to 35 999; 36 000–49 999; 50 000–74 999; 75 000–99 999; over $100 000). The casinovariables consist of how many miles the respondent lives from thecasino (0 = under 5 miles; 1 = 5 to 10 miles; 2 = 11 to 15 miles, etc.);a dummy variable equal to one if the respondent has ever worked in acasino; and a dummy variable equal to one if the respondent gambles


at casinos in their community. The morality variable is a dummyvariable equal to one if the respondent agrees with the statementthat gambling is immoral. The location variables are dummy vari-ables equal to one if the respondent lives in a particular community.Finally, ε is a stochastic error term.

The specific model to estimate equation (1) is the multinomiallogit model. The general form of this model is:

P (Yj ) = eβ ′

j xt∑Jj=0 e

β ′j xt

, j = 0, . . . J(2)

where j is the number of outcomes given in the above questions, βjare coefficients to be estimated and X are the various characteristicsof the individuals surveyed and described above as independentvariables. Greene (2000, p. 817) notes that the coefficients fromequation (2) are difficult to interpret since every subvector of β, thevector of coefficient estimates, enters the marginal effect. However,Greene (2000, p. 817) shows that the change in, or marginal,probability that Y = j, δj, is given by:

δj = ∂Pj

∂xi

= Pj

[βj −

J∑k=o

Pkβk

](3)

where Pj is the probability that Y = j, xi is the ith independentvariable, and βj is the coefficient estimate from the multinomiallogit model. For brevity, only results from equation (3) are providedbelow.6 That is, only the marginal effects, not the actual logitestimates, are provided.

RESULTS

Before discussing the results from the multinomial logit analysis,Table I provides basic descriptive statistics for the variables used inthis analysis. Table I reveals several interesting facts. First, nearly50% (45.35) of the sample gamble at a casino in their community,while nearly 27% indicate that they are morally opposed togambling. Only a small percentage, 5.67, have ever worked in acasino. In addition, many people live within five miles of the casino,


TABLE I

Descriptive statistics

Variable Description Mean(St. Dev.)

MALE (n = 2768) Dummy variable equal to one if survey parti-cipant is male zero otherwise.

0.4162(0.4930)

AGE (n = 2750) Age of survey participant, in years. 50.0324(15.6433)

INCOME (n = 2207)

Less than $20 000 Dummy variable equal to one if participantearned less than $20 000 per year, zero other-wise.

0.1568(0.3637)

Dummy equal to one if annual income is $20 000to $35 999.

$20 000 to $35 999 0.2389

Dummy equal to one if annual income is $36 000to $49 999.

(0.4264)

$36 000 to $49 999 Dummy equal to one if annual income is $50 000to $74 999.

0.2021(0.4016)

$50 000 to $74 999 Dummy equal to one if annual income is $75 000to $100 000.

0.2134(0.4098)

Dummy equal to one if annual income is over$100 000.

$75 000 to $100 000 0.1097(0.3125)

Over $100 000 0.0791(0.2702)

How far live from casino(n = 2711)

Discrete variable equal to zero if live 0 to 5 milesfrom casino, 1 if 5 to 10 miles from casino, etc.

0.8443(1.087)

Ever worked in a casino(n = 2767)

Dummy variable equal to one if survey parti-cipant has ever worked in a casino.

0.0567(0.2314)

GAMBLE (n = 2765) Dummy variable equal to one if surveyrespondent gambles in casinos in the community.

0.4535(0.4979)

Morally opposed tocasino gambling(N = 2592)

Dummy variable equal to one if person agreedwith the statement: “Gambling is immoral.”

0.2697(0.4439)

EDUCATION (n = 2757)

Less than high school Dummy variable equal to one if person did notgraduate high school.

0.0762(0.2676)

High school degreeor some college

Dummy variable equal to one if person has ahigh school degree or some college.

0.6199(0.4855)


TABLE I

Continued

Variable Description Mean(St. Dev.)

College degree Dummy variable equal to one if person has abachelor’s degree.

0.2057(0.4043)

Advanced degree Dummy variable equal to one if person has aMaster’s or Doctorate degree.

0.0982(0.2958)

Amount of crime(n = 2466)

Discrete variable equal to zero if survey parti-cipant thought casinos brought an increase in theamount of crime, one for a no change, and twofor a decrease.

0.7003(0.4956)

Fear of crime(n = 2629)

Discrete variable equal to zero if person indi-cated that fear of crime had increased sincecasino gambling, one for no change, and two fora decrease.

0.7945(0.5000)

Standard of living(n = 2649)

Discrete variable equal to zero if person indi-cated that standard of living had increased sincecasino gambling, one if no change, and two for adecrease.

0.8033(0.5195)

Community as a place tolive (n = 2714)

Discrete variable equal to zero if person thoughtcommunity was a better place to live since casinogambling, one if no change, and two if worse.

0.9580(0.5535)

Quality of family life(n = 2561)

Discrete variable equal to zero if person thoughtcasino improved family life, one if no change,two for a decrease.

0.7470(0.7786)

given the small value (0.84) for “how far do you live from the nearestcasino.”

From a demographic perspective, 41.62% of the population ismale and the average age is 50 (recall, respondents had to be at least25 to be eligible). Nearly 16% of the sample earn less than $20 000;23.89% earn $20 000 to $36 000; slightly over 20% earn $36 000to $49 999 and $50 000 to $74 999; 11% earn $75 000 to $100 000;and 7.91% earn over $100 000 annually. The majority of the sample,61.99%, have a high school degree or some college, while 7.62%have less than a high school degree. Finally, 20.57% have a collegedegree, and approximately 9% have advanced degrees.

Examining the descriptive statistics for our dependent variables,31.8% of the respondents perceived an increase in crime since the


advent of casinos, while 1.8% perceived a decrease, and 66.5% feltthere was no effect. Regarding fear of crime, 24.9% perceived anincrease, 4.3% perceived a decrease, and 70.8% felt fear of crimeremained about the same. For standard of living, 25.3% felt therewas an increase after the advent of casinos, 5.6% perceived therewas a decrease, and 69.2% felt the standard of living was about thesame. For the question of whether the community was a better orworse place to live, 17.5% felt it was better, 13.3% felt it was worse,and 69.2% believed it remained about the same. As for quality offamily life, 43.3% of the sample indicated no change in the qualityof family life, while 27.6% and 29.1% indicated a decrease orincrease, respectively.

Tables II through VI provide marginal effects from the multino-mial logit analysis. Here, we are able to analyze which character-istics determine whether a respondent thinks casinos contribute to ordetract from quality of life. The marginal effects for any two choices(increase and stayed the same, decreased and stayed the same,or increased and decreased) are sufficient to determine the third.However, the standard error, and consequently the t statistic, cannotbe determined from the other two standard errors. Consequently,the marginal effects for all three choices are provided in Tables IIthrough VI.

Table II provides results for the perceived impact of casinogambling on the amount of crime. Gamblers are 18% less likelyto indicate a perceived increase in crime than nongamblers, whilethose who are morally opposed to gambling are 30% more likelyto indicate a perceived increase. Income and education also matter.Citizens in the “middle” income categories, ranging from $20 000 to$75 000, have a higher probability of indicating a perceived increasein crime than those with incomes over $100 000, the omittedcategory. Those with incomes less than $20 000 and between$75 000 and $100 000 are not statistically different. Although notstatistically significant in every category, those with less educa-tion are less likely to indicate a perceived increase in the levelof crime. Relative to those with advanced degrees, the omittedcategory, those with less than a high school education are 8.29%less likely to indicate a perceived increase in crime. This proba-bility decreases as education increases, with high school graduates


TABLE II

Marginal effects: “Perceived impact on level of crime”

Increased Stayed same Decreased

MALE –0.0135 0.0091 0.0044

(0.58) (0.39) (1.10)

AGE –0.0005 0.0003 0.0001

(0.57) (0.41) (0.91)

INCOME

< $20 000 –0.0032 0.0062 0.0030

(0.06) (0.11) (0.37)

$20 000–$35 999 0.0834* –0.0721 –0.0113

(1.70) (1.47) (1.40)

$36 000–$49 999 0.0994** 0.0901* –0.0093

(2.04) (1.83) (1.19)

$50 000–$74 999 0.1012** 0.0953** –0.0059

(2.11) (1.97) (0.82)

$75 000–$100 000 0.0784 –0.0823 0.0039

(1.45) (1.52) (0.54)

How far live from casino 0.0078 –0.0084 0.0006

(0.65) (0.71) (0.30)

Ever worked in casino –0.0235 0.0054 0.0181***

(0.51) (0.12) (2.89)

GAMBLE –0.1831*** 0.1785*** 0.0046

(7.36) (7.13) (1.09)

Morally opposed to casino gambling 0.3006*** –0.2939*** –0.0069

(11.52) (11.05) (1.14)

EDUCATION

Less than HS –0.0828 0.0696 0.0133

(1.36) (1.14) (1.23)

HS graduate –0.0788* 0.0695* 0.0093

(1.95) (1.70) (1.09)

College graduate –0.0585 0.0508 0.0077

(1.34) (1.15) (0.87)

BILOXI 0.3978*** –0.4087*** 0.0110

(8.47) (8.63) (1.18)

ALTON –0.0665 0.0505 0.0161*

(1.36) (1.03) (1.77)

E. PEORIA 0.1236* –0.1293* 0.0057

(1.81) (1.87) (0.42)


TABLE II

Continued


PEORIA 0.0268 –0.0262 0.0006

(0.56) (0.54) (0.06)

St. JOSEPH –0.0310 0.0333 –0.0024

(0.67) (0.71) (0.21)

St. LOUIS COUNTY 0.0077 –0.0150 0.0073

(0.16) (0.31) (0.77)

SIOUX CITY 0.1768*** –0.1665*** –0.0103

(3.94) (3.66) (0.79)

Constant –0.2236*** 0.2820*** –0.0584***

(3.00) (3.74) (3.29)

P(yj | x) 0.277 0.712 0.011

A *, **, and *** represent significance at the 10%, 5%, and 1% level respec-tively. Absolute value of the t statistic in parenthesis.N = 1872. χ2(42) = 448.36, significant at the 1% level.

and college graduates 7.88% and 5.85% less likely, respectively, toindicate a perceived increase in crime. It should be noted, however,that only the high school graduate category is statistically significantat conventional levels.

Finally, there are significant differences by community as well.Biloxi, by far the largest casino market in our sample, has nearly40% probability of indicating a perceived increase in the amount ofcrime (St. Louis City is the omitted jurisdiction). This is followedby Sioux City (17.68%) and East Peoria (12.36%), both of whichwere more likely to indicate a perceived increase in the amount ofcrime. Alton, in contrast, is 1.6% more likely to indicate a decreasein crime. The coefficients for the remaining jurisdictions were notstatistically significant.

How accurate are the residents’ perceptions about the amount ofcrime? Although a detailed discussion is not possible here, whenexamining per capita and per population at risk (i.e., includingaverage daily tourist population) crime rates, Stitt, Giacopassi andNichols (2000) find that Biloxi had the greatest number of crimesincrease after the introduction of casino gambling (15 of 22). Simi-larly, Sioux City had a substantial number of increases (12 of 22).


Alton, in contrast, only had 2 of 16 crimes increase, whereas 8crimes significantly decreased, the most of any jurisdiction studied.The other jurisdictions had mixed results, with the exception ofPeoria where 9 of 12 crimes increased. Unfortunately, actual crimedata for East Peoria were unavailable, so a comparison betweenperceived and actual crime rates is not possible. However, resi-dents in Biloxi and Sioux City appear to have accurately perceivedan increase, whereas Alton residents appear to have perceived andwitnessed a decrease. Based on this, it appears as though residents’perceptions about crime are relatively accurate.

Further examination of Table II reveals that “no change” is thealternative most likely to be chosen, whereas only those who haveworked in a casino and the citizens of Alton had a higher proba-bility of indicating a perceived decrease in the amount of crime.Overall, when evaluated at sample means, the probability that anaverage respondent chosen at random would indicate a perceiveddecrease in the amount of crime is only 1.1% (i.e., P(decrease | x)= 0.011). In contrast, there is a 27.7% and 71.2% probability thatan average respondent chosen at random would indicate a perceivedincrease or no change, respectively. These conditional probabilitiesare provided at the bottom of Table II.

Table III provides results for changes in the fear of crime. Here,there are some similarities and differences with the “amount ofcrime” results provided in Table II. As before, those who gamblehave a lower probability of indicating an increased fear of crime(14.73%), whereas those morally opposed are 22% more likelyto indicate an increase. Differences in income parallel the resultsreported in Table II, with the lower income categories more likelyto report an increase in the fear of crime. Education, while positive,is not statistically significant, and males are 2.5% more likely toindicate a decrease in the fear of crime than females.

Perhaps most striking are the differences in the communities. InTable III, all jurisdictions are more likely than St. Louis City toindicate an increase in the fear of crime. Again, Biloxi and SiouxCity are the most pronounced at 30% and 21%, respectively. Inter-estingly, while Alton is 1.6% more likely to indicate a decrease inthe amount of crime, it is 21% more likely to indicate an increasein the fear of crime. As will be discussed further below, a plau-


TABLE III

Marginal effects: “Perceived impact on fear of crime”


MALE –0.0234 –0.0017 0.0252***

(1.19) (0.08) (3.04)

AGE –0.0007 0.0007 0.0000

(1.10) (1.06) (0.02)

INCOME

< $20 000 0.0263 –0.0039 0.0076

(0.55) (0.69) (0.46)

$20 000–$35 999 0.0713 –0.0440 –0.0273*

(1.67) (1.00) (1.73)

$36 000–$49 999 0.0808* –0.0628 –0.0180

(1.90) (1.43) (1.19)

$50 000–$74 999 0.0596 –0.0341 –0.0255*

(1.41) (0.78) (1.69)

$75 000–$100 000 0.0539 –0.0167 –0.0371**

(1.14) (0.34) (1.98)


(0.78) (0.85) (0.25)

Ever worked in casino –0.0613 0.0448 0.0164

(1.54) (1.08) (1.15)

GAMBLE –0.1473*** 0.1339*** 0.0134

(7.07) (6.13) (1.52)

Morally opposed to casino gambling 0.2203*** –0.2212*** 0.0008

(10.43) (9.58) (0.08)

EDUCATION

Less than HS 0.0276 –0.0247 –0.0029

(0.55) (0.46) (0.13)

HS graduate –0.0069 0.0004 0.0064

(0.19) (0.01) (0.42)

College graduate 0.0226 –0.0231 0.0004

(0.59) (0.57) (0.03)

BILOXI 0.3043*** –0.3082*** 0.0038

(7.32) (7.15) (0.26)

ALTON 0.1177*** –0.1120*** –0.0058

(2.83) (2.62) (0.37)

E. PEORIA 0.1002* –0.0480 –0.0522

(1.65) (0.72) (1.38)


TABLE III

Continued


PEORIA 0.0955** –0.0742* –0.0211

(2.21) (1.66) (1.20)

St. JOSEPH 0.0545 –0.0362 –0.0182

(1.31) (0.85) (1.16)

St. LOUIS COUNTY 0.0647 –0.0659 0.0011

(1.47) (1.46) (0.08)

SIOUX CITY 0.2151*** –0.1786*** –0.0364***

(5.40) (4.26) (1.98)

Constant –0.3105*** 0.4009*** –0.0904***

(4.72) (5.88) (3.45)

P(yj | x) 0.214 0.748 0.038


sible explanation for the increased fear is the dramatic change to asmall community brought by the introduction of casino gambling.All other jurisdictions are positive and statistically significant, withthe exception of St. Joseph and St. Louis County. As with theamount of crime, the most frequent response was no change in thefear of crime. Evaluated at sample means, the probability that anaverage respondent chosen at random would indicate an increase,no change, or decrease in the fear of crime is 21.4%, 74.8%, and3.8%, respectively.

Table IV provides marginal effects for the perceived impact onthe standard of living. Those that gamble have an 11% higher proba-bility of indicating an increase in the standard of living as opposedto staying the same or decreasing. Individuals who are morallyopposed to gambling are 7.72% more likely to indicate a decrease inthe standard of living relative to those who are not morally opposed.Males are 6.27% more likely to indicate an increase in the standardof living than females, and an individual who is 10 years older thanthe average of 47 is 2.4% less likely to indicate an increase in thestandard of living, but 2.2% more likely to indicate no change. Notsurprisingly, those that have ever worked at a casino are 15% less


TABLE IV

Marginal effects: “Perceived impact on standard of living”


MALE 0.0627*** –0.0684*** 0.0057

(2.74) (2.89) (0.65)

AGE –0.0024*** 0.0022** 0.0002

(2.89) (2.56) (0.53)

INCOME

< $20 000 0.0417 –0.0481 0.0064

(0.78) (0.87) (0.29)

$20 000–$35 999 0.0132 –0.0193 0.0061

(0.28) (0.39) (0.30)

$36 000–$49 999 –0.0549 0.0271 0.0278

(1.15) (0.55) (1.43)

$50 000–$74 999 –0.0128 0.0159 –0.0031

(0.28) (0.33) (0.15)

$75 000–$100 000 –0.0185 –0.0041 0.0226

(0.36) (0.08) (1.05)

How far live from casino –0.0210 0.0220* –0.0009

(1.61) (1.66) (0.21)

Ever worked in casino 0.1159** –0.1511*** 0.0352**

(2.46) (2.95) (1.99)

GAMBLE 0.1104*** –0.0868*** –0.0236**

(4.67) (4.80) (2.29)

Morally opposed to casino gambling 0.0165 –0.0938*** 0.0772***

(0.58) (3.19) (7.86)

EDUCATION

Less than HS –0.1600** 0.1851*** –0.0251

(2.50) (2.81) (1.12)

HS graduate –0.0643 0.0804* –0.0160

(1.59) (1.92) (1.06)

College graduate –0.0566 0.0737 –0.0171

(1.31) (1.64) (1.02)

BILOXI 0.6649*** –0.6795*** 0.0146

(12.26) (11.78) (0.70)

ALTON 0.2212*** –0.2194*** –0.0018

(4.74) (4.63) (0.11)

E. PEORIA 0.1783*** –0.1642** –0.0141

(2.72) (2.42) (0.53)


TABLE IV

Continued


PEORIA 0.1420*** –0.1417*** –0.0003

(2.84) (2.82) (0.02)

St. JOSEPH –0.0339 0.0229 0.0109

(0.62) (0.43) (0.68)

St. LOUIS COUNTY 0.1010* –0.0912* –0.0098

(1.93) (1.73) (0.52)

SIOUX CITY 0.1331*** –0.1382*** 0.0052

(2.76) (2.84) (0.31)

Constant –0.2515*** 0.3790*** –0.1274***

(3.29) (4.80) (4.14)

P(yj | x) 0.228 0.727 0.045


likely to indicate that there had been no change. However, whilecurrent or former employees are 11.59% more likely to indicatean increase, the coefficient is also positive and significant for adecrease. This likely reflects dissatisfaction with a former or currentemployer and is consistent with the casino industry having thehighest rate of employee turnover of any major industry (Stedhamand Mitchell, 1996). Finally, those with less education have higherprobabilities of indicating no change in the standard of living. Thosewith less than a high school degree are 18% more likely than thosewith advanced degrees to indicate no change (and 16% less likelyto indicate an increase), while those with a high school degree andthose with a college degree are 8% and 7% more likely to indicateno change, respectively.

As before, there are significant differences by community. Moststriking is the fact that residents of Biloxi had a 66.48% higherprobability of indicating an increase in the standard of living thanresidents in St. Louis City. This is indeed indicative of the remark-able transformation that has taken place in Biloxi. Following Biloxiis Alton, with a 22% higher probability of indicating an increase.


All other jurisdictions, with the exception of St. Joseph, also havehigher probabilities of indicating a higher standard of living.

Table V provides results to the question “is your communitya better place to live, a worse place to live, or about the same?”Those who gamble have a 10% higher probability of indicatingthat the community is a better place to live. Again, those whoare morally opposed to gambling view the casinos as a negative,and are nearly 15% more likely to indicate that their communityis a worse place to live than those not morally opposed. Interest-ingly, living further from the casino lowers the probability that thecommunity is a better place to live and raises the probability that itremains the same. Given that the average distance from the casinois within five miles, a person living ten miles from the casino has a2.64% higher probability of indicating no change in the community.This confirms Eadington (1986), who suggests that the perceivedimpact of the casino is stronger in the immediate-surrounding area.Males have a higher probability than females of indicating that thecommunity is a better place to live, and not surprisingly, so do thosewho have worked in a casino. Opinions are not divided amongst thevarious educational categories, and those with lower incomes areless inclined to indicate an improvement in the neighborhood as aplace to live and more inclined to indicate no change.

Nearly all of the jurisdictions indicated an improvement in thecommunity as a place to live as the result of casino gambling, withBiloxi again having the greatest probability. Interestingly, however,Biloxi also has the greatest probability of indicating a deteriorationin the community as place to live. This is not surprising given theremarkable transformation of Biloxi. While growth provides jobs,income, and additional entertainment options, it also brings negativeaspects, such as congestion, pollution, and as demonstrated above,an increased fear of crime. These results confirm that the changesin Biloxi brought on by the legalization of casino gambling havebeen both good and bad. Sioux City residents also are more likelyto indicate that their community is a worse place to live as theresult of the casino. Of all the communities studied here, Sioux Citystands out as being the least satisfied, or at least the most divided,with the decision to legalize casino gambling. The only reason theresearchers can give to account for this polarization is gleaned from


TABLE V

Marginal effects: “Perspective on community as place to live”

Better Same Worse

MALE 0.0328** –0.0336* 0.0008

(2.29) (1.74) (0.06)

AGE –0.0001 –0.0001 0.0002

(0.09) (0.33) (0.36)

INCOME

< $20 000 –0.1547*** 0.2443*** –0.0897***

(4.46) (5.28) (2.73)

$20 000–$35 999 –0.1000*** 0.1151*** –0.0151

(3.44) (2.94) (0.55)

$36 000–$49 999 –0.0953*** 0.1272*** –0.0366

(3.15) (3.25) (1.31)

$50 000–$74 999 –0.0620** 0.0706* –0.0085

(2.25) (1.87) (0.32)

$75 000–$100 000 –0.0416 0.0458 –0.0042

(1.38) (1.09) (0.14)

How far live from casino –0.0225** 0.0264** –0.0040

(2.36) (2.34) (0.53)

Ever worked in casino 0.0652*** –0.0117 –0.0534

(2.64) (0.28) (1.60)

GAMBLE 0.0998*** –0.0157 –0.0841***

(6.70) (0.76) (5.44)

Morally opposed to casino gambling –0.0749*** –0.0744*** 0.1493***

(3.77) (3.09) (10.19)

EDUCATION

Less than HS 0.0563 0.0498 –0.0065

(1.46) (1.00) (0.19)

HS graduate 0.0226 0.0268 –0.0494**

(0.83) (0.78) (2.18)

College graduate 0.0106 0.0174 –0.0280

(0.37) (0.48) (1.15)

BILOXI 0.3235*** –0.4606*** 0.1371***

(8.69) (10.03) (4.62)

ALTON 0.2465*** –0.2127*** –0.0337*

(7.14) (5.08) (1.13)

E. PEORIA 0.1456*** –0.1316** –0.0140

(3.20) (2.30) (0.33)


TABLE V

Continued

Better Same Worse

PEORIA 0.0838** –0.0984** 0.0147

(2.13) (2.24) (0.52)

St. JOSEPH 0.0647* –0.0346 –0.0300

(1.66) (0.79) (1.05)

St. LOUIS COUNTY –0.0065 –0.0001 0.0066

(0.14) (0.01) (0.23)

SIOUX CITY 0.0551 –0.1215*** 0.0664**

(1.40) (2.81) (2.52)

Constant –0.2569*** 0.3805*** –0.1235***

(4.92) (5.90) (2.83)

P(yj | x) 0.111 0.783 0.106


interviews done with community leaders where a number suggestedthat significant controversy, largely based on moral grounds, existedwhen the community debated legalizing casinos (Giacopassi et al.,1999). Finally, as seen at the bottom of Table V, when evaluatedat sample means, the greatest probability rests with no change inthe community as a place to live, 78.3%, with the remainder lyingequally split between an increase and decrease.

Lastly, Table VI provides results for the impact of casinogambling on the quality of family life. Both gamblers and malesare more likely to indicate that casino gambling has had no impacton the quality of family life. Those who are morally opposed togambling have the greatest probability, nearly 17%, of indicating thequality of family life has decreased. Notice, too, that those morallyopposed have a positive and significant coefficient for an improve-ment in family life. One explanation for this is that some of thetax money collected from the casino goes back into the community.In speaking with community leaders, the authors were told thatimproved municipal services, often in the form of parks and river-front redevelopment, were one of the main benefits resulting fromcasino gambling (Giacopassi et al., 1999). Such improvements may


TABLE VI

Marginal effects: “Perspective on quality of family life”

Increase No change Decrease

MALE –0.0623 0.0477* 0.0146

(2.78) (1.93) (0.68)

AGE –0.0002 0.0025*** –0.0023***

(0.24) (2.90) (3.00)

INCOME

< $20 000 0.0670 0.0498 –0.1169**

(1.20) (0.85) (2.27)

$20 000–$35 999 0.0387 –0.0170 –0.0216

(0.80) (0.33) (0.51)

$36 000–$49 999 0.0184 0.0063 –0.0247

(0.38) (0.12) (0.58)

$50 000–$74 999 0.0138 –0.0284 0.0146

(0.29) (0.56) (0.35)

$75 000–$100 000 –0.0109 –0.0611 0.0721

(0.20) (1.09) (1.60)


(0.93) (1.42) (0.73)

Ever worked in casino 0.09512*** –0.0291 –0.0659

(2.24) (0.53) (1.33)

GAMBLE 0.0007 0.1386*** –0.1393***

(0.03) (5.42) (6.17)

Morally opposed to casino gambling 0.0895*** –0.2561*** 0.1667***

(3.41) (8.22) (6.85)

EDUCATION

Less than HS 0.0880 0.0825 –0.1706***

(1.49) (1.25) (2.89)

HS graduate 0.0814* 0.0514 –0.1329***

(1.90) (1.13) (3.76)

College graduate –0.0359 0.0719 –0.0359

(0.76) (1.47) (0.95)

BILOXI 0.1717*** –0.2408*** 0.0331***

(4.03) (4.08) (0.74)

ALTON –0.0917** 0.0443 0.0474

(2.05) (0.94) (1.14)

E. PEORIA 0.0000 –0.0533 0.0533

(0.01) (0.74) (0.87)


TABLE VI

Continued


PEORIA –0.0303 0.0189 0.0114

(0.68) (0.39) (0.26)

St. JOSEPH –0.0335 0.0602 –0.0267

(0.80) (1.33) (0.64)

St. LOUIS COUNTY –0.0784* 0.0125 0.0659

(1.66) (0.26) (1.56)

SIOUX CITY 0.0508 –0.1193*** 0.1485***

(1.21) (4.18) (3.72)

Constant –0.1226* –0.0130 0.1357**

(1.67) (0.16) (2.03)

P(yj | x) 0.295 0.442 0.263

A *, **, and *** represent significance at the 10%, 5%, and 1% level respec-tively. Asolute value of the t statistic in parenthesis.N = 1941. χ2(42) = 365.31, significant at the 1% level.

improve family recreational activities and the quality of family life,even for those morally opposed to gambling.

As for the individual jurisdictions, the same pattern as beforearises. Biloxi is most likely to indicate that the quality of familylife improved, while Sioux City has the highest probability of indi-cating a decrease in the quality of family life. Overall, this questionprovides the most varied responses. Evaluated at sample means, theprobability that the casino had no change on the quality of life was44.2%, by far the lowest of all of the quality of life measures. Theprobability of an increase or decrease was 29.5% and 26.3%, respec-tively. While evenly split between an increase or decrease, thesepercentages are clearly higher than those for previous measures.This division may reflect the polar opinion that, on one hand, theamenities brought to the community from casino tax revenue haveimproved family entertainment options (e.g., parks, riverfront devel-opment) and, on the other hand, that casino gambling is an adult,as opposed to family, activity. Indeed, the finding that gamblersare more likely than non-gamblers to indicate no change in qualityof family life (while indicating an improvement in other areas) isconsistent with this interpretation.


SUMMARY AND CONCLUSIONS

This research has studied the effect that the introduction of casinogambling has on five factors relevant to quality of life. For theperception of prevalence of crime, fear of crime, standard of living,and whether the community is a better or worse place to live,the overall consensus is that casinos had little measurable effect.For quality of family life, a majority of respondents indicated thatcasinos elicited a change, but respondents were almost equallydivided as to whether that change was positive or negative. Overall,it appears as though casino gambling has largely left the quality oflife of the average citizen unchanged.

There are, however, significant numbers of respondents whoperceive differences in each of the quality of life indicators. Thosewho perceive changes tended to perceive an increase in crime andfear of crime. Indeed, when evaluated at sample means, the proba-bility that an average respondent chosen at random would indicatean increase in crime or the fear of crime is 27.7% and 21.4%, respec-tively. Those who gamble and those with less education are lesslikely to indicate an increase in either crime variable, whereas thosewho are morally opposed to gambling and those in the “middle”income categories ($20 000 to $70 000) had a higher probability ofindicating an increase.

For the other quality of life indicators, standard of living andthe kind of place the community is to live, there tended to beagreement that the community stayed about the same. Evaluated atsample means, the probability that the standard of living increased,stayed the same, or decreased for an average respondent was 22.8%,72.7%, and 4.5%, respectively. For the community as a place to live,the probabilities are 11.1%, 78.3%, and 10.6% that it was better,stayed the same, or was worse. Gamblers see an improvement inthe community as the result of casino gambling, as do males andyounger people. Those morally opposed view casinos as a degrada-tion, while individuals who have less education and live further fromthe casino are more likely to indicate that there had been no changein the standard of living. Biloxi, Alton, and East Peoria have thehighest probability of indicating an improvement in the standard ofliving and the community as a place to live.


Finally, when asked about quality of family life, gamblersand males had a higher probability of indicating no change thannongamblers and females. Those morally opposed to gambling hada greater probability of indicating that the quality of family life haddecreased. However, many of those who were morally opposed alsoindicated that quality of family life had increased. As noted above,this is most likely due to the improved city services (e.g., parks andrecreation areas) resulting from casino tax revenues. Biloxi was themost likely to indicate that the quality of family life had improvedwhile Sioux City was the most likely to indicate a decline in thequality of family life.

Consistent with Long (1996) and NORC (1999) the resultspresented here demonstrate that casino gambling’s impact on qualityof life is not uniform, either between or within communities. Thatsaid, nearly all jurisdictions had a higher probability of indicatingan increase in the fear of crime. There are many potential reasonsfor this. For example, the fear of crime could reflect the perceptionof a general increase in crime, which may or may not be due tothe casino(s). On the other hand, since casinos can cause drasticchanges in communities, the fear of crime could reflect a sense ofdistress by residents who have not become accustomed to the trans-formation. There is reason to at least partly suspect the latter basedon how the results vary by community. As mentioned above, Biloxihas been remarkably transformed due to the introduction of ninelarge casinos. Biloxi also has the highest probability of indicatingan increase in the fear of crime. Moreover, St. Joseph does notindicate any change in the fear of crime. St. Joseph has been a touristdestination for some time, and it is probable that any influx of newpeople brought on by casino gambling went less noticed. Similarly,St. Louis County, a large metropolitan area adjacent to St. LouisCity, also has no change in the fear of crime. It seems plausible thatthe introduction of a casinos in St. Louis County would not be asdisruptive to daily life as in the smaller communities.

As a further test of the hypothesis that casinos are morelikely to disrupt daily life when they are larger or placed insmaller communities, the logit analysis was re-estimated with casinorevenue per capita replacing the community dummy variables.7

Casino revenue per capita, defined as total casino revenue in 1999


divided by the adult population in 1999, is a continuous variable thatshould lend greater insight into the impact that relative casino sizehas on quality of life. The results of this re-estimation parallel thosepresented in Tables II through IV so, for brevity, only the marginaleffects for the casino revenue per capita variable are provided inTable VII.

Table VII reaffirms that the size of the casino relative to thepopulation plays a significant role in determining the quality ofcommunity life. An increase in casino size relative to the popula-tion increases the likelihood of an increase in the perceived leveland fear of crime, while decreasing the probability of indicating nochange. Similarly, the perceived impact on the standard of living andquality of family life are also more likely to be positive as relativesize increases. For the community as a place to live, an increase inrelative size reduces the probability that a survey participant indi-cates no change, while both an improvement and deterioration arestatistically significant. Overall, the results of this analysis suggestthat the size of the casino relative to the community matters. Parti-cularly, introducing casino gambling in small communities withlittle or no tourism base, or building casinos on a large scale, hasa greater impact on the overall quality of life, both positive andnegative, than small-scale casinos or casinos in communities withan existing tourism base.

Our results also reveal that the impacts may be positive ornegative depending on one’s perspective. Not surprisingly, indi-viduals with moral opposition to gambling view casinos as anegative addition to their community. In contrast, those that gambleare more likely to view casinos in a positive light. There are manypotential reasons for this, including the fact that most gamblersgain satisfaction or utility from gambling. Beyond this, familiaritywith casinos may break down well entrenched stereotypes aboutcasinos and crime, best typified by the notion that casinos are asso-ciated with organized crime. In fact, when asked, “Do you agreeor disagree with the statement that the casino industry has connec-tions with organized crime,” 46% of the respondents agreed. Also,familiarity with casino environs is likely to acquaint clientele withthe extensive security operations associated with casinos, whichmay deter crime or greatly increase the likelihood of apprehension.


TABLE VII

Marginal effects for the variable “casino revenue per capita”


Perceived impact on level of crime 0.0345*** –0.0355*** 0.0009

(10.62) (10.76) (1.62)

Perceived impact on fear of crime 0.0188*** –0.0207*** 0.0019*

(6.85) (7.08) (1.66)

Perceived impact on standard of living 0.0572*** –0.05778*** 0.0006

(15.09) (13.81) (0.39)

Perspective on community as a place to live 0.0263*** –0.0380*** 0.0117***

(11.60) (11.73) (5.36)

Perspective on quality of family life 0.0182*** –0.0161*** 0.0021

(6.03) (4.38) (0.63)

A *, **, and *** represent significance at the 10%, 5%, and 1% level respec-tively. Absolute value of the t statistic in parenthesis.

Finally, those who gamble may be more aware of the contributionsthat the casinos make to the communities in terms of tax revenue,part of which is an admission tax.8 Also, those who gamble are morelikely to be exposed to information about the casinos’ activities inthe community, such as supporting local charities.

Quality of life is indeed a complex entity and what is seenas positive effects by one person may be ignored, obscured, non-existent, or even negative for another person. Not surprisingly, one’sfamiliarity and moral outlook on gambling have a strong influenceon whether one views casino gambling positively or negatively.Significant differences also exist between communities (e.g., Biloxivs. Sioux City, Biloxi vs. St. Joseph) with community character-istics and the scale of the casino(s) found to be important factors indetermining how residents view the impact of casinos on quality oflife.

Overall, the results suggest that casino gambling has primarilyhad a neutral effect on the quality of life for the average citizenin the communities studied here, with economic related indicatorsperceived positively and crime related indicators perceived nega-tively. When evaluating the casino effect on quality of family life,opinions diverged sharply, with a large proportion of respondentsviewing the impact positively and a nearly equal proportion eval-


uating the effects negatively. Similar results were also found inCanada by Room et al. (1999) using a pre/post design. Examiningthe impact of the Niagra casino, Room et al. (1999, p. 1459)conclude: “Comparing expectations in 1996 with these perceptionsof what actually happened, the general picture is that reality turnedout to be less dramatic, both for good and bad, than expected.”Jurisdictions contemplating the legalization of casino gambling asa panacea for their economic troubles should carefully consider thetradeoffs involved relative to quality of life issues and, in particular,the lack of agreement within the communities on the effects ofcasinos on the quality of family life.

Appendix A

Telephone Survey Methodology

SAMPLE SELECTION

The sample frame was generated as a stratified random digit dial sample oftelephone numbers provided by Survey Sampling, Inc. (SSI), a well knowncommercial sampling firm. The sample frame is randomly pre-screened for busi-nesses and non-working numbers since only households meeting specific criteriawere eligible for inclusion (discussed below).

The sample frame was generated using all prefixes associated with each ofthe stratum that represent the targeted geographic locations to be surveyed. Thesestrata are as follows: (1) Alton, IL; (2) Biloxi, MS; (3) East Peoria, IL; (4) Peoria,IL; (5) Sioux City, IA; (6) St. Joseph, MO; (7) St. Louis City, MO; and (8) St.Louis County, MO. The St. Louis County stratum includes the following cities: (1)Afton; (2) Bridgeton; (3) Chesterfield; (4) Clayton; (5) Creve Cour; (6) Eureka;(7) Fenton; (8) Ferguson; (9) Florissant; (10) Hazelwood; (11) Ladue; (12) Kirk-wood; (13) Manchester; (14) Riverview; (15) Valley Park; (16) Overland; and (17)Webster Groves.

ELIGIBILITY CRITERIA AND RESPONDENT SELECTION PROCESS

In order to be eligible for inclusion in the telephone survey, household respondentshad to meet three separate criteria for eligibility. Specific “screening” questionswere asked to establish whether each randomly selected household met all threeeligibility criteria in order to qualify for participation in the full telephone survey.


The first eligibility criterion is that each randomly selected household had tobe located in one of the eight (8) geographic strata described above, a processwhich resulted in the exclusion of many households due to the method by whichmany telephone companies assign telephone numbers. That is, two telephonenumbers with the same prefix were often located in different cities, resulting inthe exclusion of many households which were reached by telephone, yet whichfailed to pass through the geographic screening question. Households with thesame telephone prefixes – which common sense would lead one to think wouldbe located in the same geographic area – but which frequently were not, had to beeliminated as ineligible when the household was not located in one of the eighteligible geographic regions.

The second eligibility criterion required that the respondent randomly selectedfrom within the randomly selected household be at least 25 years of age in orderto be eligible to participate in this survey.9

The third criterion for eligibility was based on the participant’s length ofresidency in the specific qualifying geographic stratum in which he/she currentlylived. This length of residency requirement ranged between eight (8) and ten (10)years, based on the specific geographic stratum being sampled.

Random selection from within the randomly selected household occurred onlywhen more than one resident of the household met the three baseline require-ments for eligibility. When more than one household resident met all three ofthe eligibility requirements, a KISH table was used to determine the one eligiblerespondent to be interviewed to represent the randomly selected household.Respondent selection with the KISH table is based on the enumeration (listing)of all eligible residents in the household. The list of eligible residents, includingdesignation of age and gender, is cross referenced against the last digit of the tele-phone number to determine which person must be interviewed to ensure randomselection of potential respondents within each household.

The multiple selection criteria utilized as eligibility criteria, combined withthe use of household enumeration for households with multiple eligible respon-dents resulted in a sampling protocol that was designed to locate respondentswho qualify as a “low incidence” sample, akin at times to “finding a needle in ahaystack.”

CALLING PROTOCOL

Data collection began late in October of 1998 and was completed in June 1999.Numbers were called over the course of a minimum of a five week periodat different times of the day, including morning, afternoon, and evening weekday calls, with the same being true for the weekends (Saturday and Sunday).A maximum of 23 call attempts per number were made before any telephonenumber was no longer pursued, or until the household could be determined tobe a non-eligible household number (business, disconnected, no eligible respon-dents, language problem, fax/computer modem line, group quarters, respondent


impairment, etc). In addition, multiple soft refusal conversion attempts were madeto households in which the potential participants had initially been hesitant toparticipate. A minimum of 4 days was allowed to elapse before a subsequent callattempt was made in attempting to convert soft refusals to completed interviews.

NOTES

1. See, in particular, NGISC, Final Report, Chapter 7, “Gambling’s Impactson People and Places,” APC, Inquiry Report, Terms of Reference, and HO,Terms of Reference, for a discussion of the multifaceted approach taken byall countries.

2. It should be noted that conducting surveys both before and after the introduc-tion of casino gambling would provide a more accurate picture of the impacton quality of life and would be the best design. Unfortunately, we do have anypre-casino survey data. However, for an excellent study using pre/post designsee Room et al. (1999).

3. In particular, while the legal age to gamble is 21, respondents had to be atleast 25 years of age. This age was chosen to ensure the respondent was at ornear adult age when gambling was legalized.

4. Ordered logit is an alternative powerful estimation technique.5. The last question is grouped into increased, stayed the same, or decreased for

convenience and ease of presentation.6. Logit estimates were obtained using LIMDEP, version 7.0., which uses

Newton’s maximum likelihood method.7. We wish to thank an anonymous referee for this suggestion.8. In all jurisdictions except Biloxi, there is an admission tax. This generally is

equal to $2 per admission, per two-hour cruise, and split evenly between thecounty and the city. In most cases, however, this tax is paid by the casino andnot by the patron.

9. In those instances in which more than one respondent in the household metall three eligibility criteria.

REFERENCES

Albanese, J.: 1985, ‘The effect of casino gambling on crime’, Federal Probation48, pp. 39–44.

Andrews, F. and S. Withey: 1976, Social Indicators of Well-Being: America’sPerceptions of Life Quality (Plenum Press, New York).

Australian Productivity Commission, Australia’s Gambling Industries, InquiryReport, Report No. 10, 26 November 1999.

Chang, S.: 1996, ‘The impact of casinos on crime: The case of Biloxi, Missis-sippi’, Journal of Criminal Justice 24(5), pp. 431–436.


Cheng, S.: 1988, ‘Subjective quality of life in the planning and evaluation ofprogram’, Evaluation and Program Planning 11, pp. 123–134.

Chibnall, J. T. and R. C. Tait: 1990, ‘The quality of life scale: A preliminary studywith chronic pain patients’, Psychology and Health 4, pp. 283–292.

Cummins, R.: 1996, ‘The domains of life satisfaction: An attempt to order chaos’,Social Indicators Research 38, pp. 303–328.

Cummins, R.: 2000, ‘Objective and subjective quality of life: An interactivemodel’, Social Indicators Research 52, pp. 55–72.

Curran, J. and F. Scarpitti: 1991, ‘Crime in Atlantic city: Do casinos make adifference?’, Deviant Behavior 12, pp. 431–449.

Diener, E. and E. Suh: 1997, ‘Measuring quality of life: Economic, social, andsubjective indicators’, Social Indicators Research 40, pp. 189–216.

Eadington, William R.: 1986, ‘Impact of casino gambling on the community:Comment on Pizam and Poleka’, Annals of Tourism Research 13, pp. 279–285.

Evans, D. R.: 1994, ‘Enhancing quality of life in the population at large’, SocialIndicators Research 33, pp. 47–88.

Giacopassi, D. and B. G. Stitt: 1993, ‘Assessing the impact of casino gambling oncrime in Mississippi’, American Journal of Criminal Justice 18(1), pp. 117–131.

Giacopassi, D., M. Nichols and B. G. Stitt: 1999, ‘Attitudes of community leadersin new casino jurisdictions regarding casino gambling’s effects on crime andquality of life’, Journal of Gambling Studies 15(2), pp. 123–148.

Greene, William H.: 2000, Econometric Analysis 4th ed. (Prentice Hall: SaddleRiver, NJ).

Hakim, S. and A. Buck: 1989, ‘Do casinos enhance crime?’, Journal of CriminalJustice 17, pp. 409–416.

Long, P.: 1996, ‘Early impacts of limited stakes casino gambling on ruralcommunity life’, Tourism management 17(5), pp. 341–353.

Michalos, A. and B. Zumbo: 2000, ‘Criminal victimization and the quality oflife’, Social Indicators Research 50, pp. 245–295.

Mugenda, O. M., T. K. Hira and A. M. Fanslow: 1990, ‘Assessing the causalrelationship among communication, money management practices, satisfactionwith financial status, and satisfaction with quality of life’, Lifestyles 11, pp.343–360.

National Gambling Impact Study Commission: 1999, Final Report (Available atwww.ngisc.gov).

National Opinion Research Center: 1999, Overview of National Survey andCommunity Database Research on Gambling Behavior (University of Chicago).

Room, R., N. E. Turner and A. Ialomiteanu: 1999, ‘Community effects of theopening of the Niagra casino’, Addiction 94(10), pp. 1449–1466.

Stitt, B. G, D. Giacopassi and M. Nichols: 2000, ‘The effect of casino gamblingon crime in new casino jurisdictions’, Journal of Crime and Justice 1, pp. 1–23.

Schmalleger, F.: 2001, Criminal Justice Today (Prentice Hall: Upper Saddle River,NJ).


Stedham, Yvonne and M. Mitchell: 1996, ‘Voluntary turnover among non-supervisory casino employees’, Journal of Gambling Studies 12(3), pp. 269–290.

Thompson, W., R. Gazel and D. Rickman: 1996, ‘Casinos and crime inWisconsin: What’s the connection?’, Wisconsin Policy Institute Report 9(9).

UK Home Office: Gambling Review (Available at http://www.gamblingreview.gov.uk/).

US Census Bureau: 1998, Statistical Abstract of the United States (Washington,DC: US Government Printing Office).

Walker, D. and J. Jackson: 1998: ‘New goods and economic growth: Evidencefrom legalized gambling’, Review of Regional Studies 28(2), pp. 47–69.

Mark NicholsDepartment of EconomicsUniversity of Nevada, RenoReno, Nevada 89557

B. Grant StittDepartment of Criminal JusticeUniversity of Nevada, RenoReno, Nevada 89557

David GiacopassiDepartment of Criminology and Criminal JusticeUniversity of MemphisMemphis, TN 38152

Documents

Community Assessment of the Effects of Casinos on Quality of Life