The University of Southern Mississippi The Aquila Digital Community
Dissertations
Fall 12-2007 DIFFUSION OF AN ECONOMIC DEVELOPMENT POLICY INNOVATION: EXPLAINING THE INTERNATIONAL SPREAD OF CASINO GAMBLING Brian Walter Richard University of Southern Mississippi
Follow this and additional works at: https://aquila.usm.edu/dissertations Part of the Gaming and Casino Operations Management Commons, Growth and Development Commons, International and Area Studies Commons, and the International Economics Commons
Recommended Citation Richard, Brian Walter, "DIFFUSION OF AN ECONOMIC DEVELOPMENT POLICY INNOVATION: EXPLAINING THE INTERNATIONAL SPREAD OF CASINO GAMBLING" (2007). Dissertations. 1335. https://aquila.usm.edu/dissertations/1335
This Dissertation is brought to you for free and open access by The Aquila Digital Community. It has been accepted for inclusion in Dissertations by an authorized administrator of The Aquila Digital Community. For more information, please contact [email protected]. The University of Southern Mississippi
DIFFUSION OF AN ECONOMIC DEVELOPMENT POLICY INNOVATION:
EXPLAINING THE INTERNATIONAL SPREAD OF CASINO GAMBLING
by
Brian Walter Richard
A Dissertation Submitted to the Graduate Studies Office of The University of Southern Mississippi in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
Approved:
December 2007
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. COPYRIGHT BY
BRIAN WALTER RICHARD
2007
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The University of Southern Mississippi
DIFFUSION OF AN ECONOMIC DEVELOPMENT POLICY INNOVATION:
EXPLAINING THE INTERNATIONAL SPREAD OF CASINO GAMBLING
by
Brian Walter Richard
Abstract of a Dissertation Submitted to the Graduate Studies Office of The University of Southern Mississippi in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
December 2007
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ABSTRACT
DIFFUSION OF AN ECONOMIC DEVELOPMENT POLICY INNOVATION:
EXPLAINING THE INTERNATIONAL SPREAD OF CASINO GAMBLING
by Brian Walter Richard
December 2007
Over the past 50 years, the international spread of casino gambling has been
remarkable. Legalization of casino gambling is often framed by policymakers as an
answer to some economic or development problem. What has been lacking has been
research into the factors that affect this spread. The diffusion of various forms of
gambling as a policy innovation across U.S. states has been explored by several
researchers. These studies have produced mixed results, often at odds with case studies,
policymaker statements, and media reports. This study uses an event history analysis to
examine the factors that lead to the adoption of casino gambling among nations around
the world. Specifically, measures of fiscal stress, economic development, tourism,
religiosity, and income levels are tested for their relationship to national decisions to
legalize casino gambling.
This study found that economic development needs, as measured by general
unemployment rates, were associated with the casino legalization decisions of national
governments. Higher unemployment rates were more likely in the years that nations
legalized casino gambling. Religiosity, measured by frequency of church attendance, was
also found to be significant in the legalization decisions. Higher rates of church
attendance were less likely in the years that nations legalized casinos.
ii
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Measures of fiscal stress, tourism, and income levels were not found to have
significant relationships with the legalization decisions. This is interesting because these
factors are often cited in case studies, media reports, and the statements of politicians
during legalization processes. In fact, case studies completed as part of this research
found significant mention of fiscal and tourism benefits in the legalization decisions in
both Denmark and Greece.
This study points to the need for further research in several areas. Statistical
models have been unable to explain significant portions of the variation in casino
legalization decisions in both domestic U.S. and international studies. Further exploration
of potential explanatory variables and more appropriate measures of currently theorized
factors is warranted. Another area for further research is the seeming contradictory
findings of multiple statistical analyses and multiple anecdotal findings of the impacts of
fiscal stress on the casino legalization decision.
iii
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ACKNOWLEDGEMENTS
I would first like to acknowledge the guidance and contributions of my committee
members. Dr. Denise von Herrmann, on whose research this dissertation is largely built
upon, provided frequent guidance, advice, and encouragement throughout the process.
This research idea was born in a class taught by Dr. J.J. St. Marie and he has provided
helpful suggestions ever since that beginning. Dr. Shahdad Naghshpour spent many hours
refining the methodology. And Dr. Bill Eadington’s knowledge of the gambling industry
provided greater depth to the study in many areas. This work was made better by each of
these scholars.
Finally, I would like to thank my partner, friend, and colleague Dr. Jennifer Foil. I
have accomplished so much during our time together, including the research, writing, and
completion of this work. That is not a coincidence. I could not have completed this
without her advice, encouragement, and support.
iv
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. TABLE OF CONTENTS
ABSTRACT...... ii
ACKNOWLEDGMENTS...... iv
LIST OF TABLES...... vii
LIST OF ILLUSTRATIONS...... viii
CHAPTER
I. INTRODUCTION...... 1
Purpose and Objectives History and Background
II. THEORY...... 12
Policy Diffusion The Spread of Casinos Problem Gambling Concerns Research Questions and Hypotheses
III. METHODOLOGY...... 36
Event History Analysis Discrete Time Models
IV. DATA...... 45
Data Constraints Missing Data Combining the Data Sets: Usable Nations
V. RESULTS OF ANALYSIS...... 63
VI. CASE STUDIES...... 73
Denmark Greece Israel China
v
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Case Study Summary
VII. SUMMARY AND CONCLUSIONS...... 96
Summary of Significant Findings Limitations Recommendations for Future Research
APPENDIX...... 105
REFERENCES...... 109
VI
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF TABLES
Table
1. Casino Legalizations, 1985 through 2000 ...... 47
2. Lagged Per Capita GDP Descriptive Statistics ...... 49
3. Government Fiscal Position Descriptive Statistics ...... 51
4. Unemployed per 100 Persons Descriptive Statistics ...... 52
5. Religious Attendance Descriptive Statistics...... 55
6. Travel Service Exports Descriptive Statistics ...... 57
7. Legalization Years ...... 59
8. Descriptive Statistics for Pooled Data Set ...... 59
9. Correlation Coefficients for Explanatory Variables ...... 62
10. Logit Estimates for Event History Analysis of Casino Legalization ...... 65
11. Amount Religiosity Varies With Unemployment ...... 71
12. Amount Religiosity Varies With Fiscal Condition...... 72
13. China’s Government Revenue and Expenditure ...... 88
14. Urban Unemployment Rate in China ...... 90
vii
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF ILLUSTRATIONS
Figure
1. Histogram of National Casino Legalization Events ...... 8
2. Government Finance Data with Estimated Values ...... 50
3. Unemployment Rate Data with Estimated Values ...... 52
4. Value Replacements for World Values Survey Data ...... 54
5. Histogram of Travel Service Exports Data ...... 60
6. Histogram of Transformed Travel Service Exports Data ...... 61
7. Danish Budget Condition, 1970 to 1990 ...... 74
8. Danish Unemployment Rates, 1970 to 1990 ...... 75
9. Greek Budget Deficits as a Percentage of Spending, 1970 to 1990 ...... 78
10. Greek Per Capita Gross Domestic Product, 1970 to 1994 ...... 79
11. Greek Unemployment Rates, 1976 to 1993 ...... 80
12. Israeli Budget Deficits as a Percentage of Spending, 1970 to 1990 ...... 81
13. Israeli Per Capita Gross Domestic Product, 1960 to 2005 ...... 82
14. Israeli Unemployment Rates ...... 83
15. China per capita gross domestic product ...... 89
Map
1. Nations in the risk set ...... 58
viii
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER I
INTRODUCTION
Purpose and Objectives
Over the past 50 years, the spread of casino gambling has been remarkable.
Casino gambling is now a short trip away from the vast majority of the world’s
population. Casino ownership has gone from wealthy individuals with questionable
character to corporations with stock traded on international exchanges.
What do casinos bring to their host jurisdictions? In many areas, casinos are the
largest local employer. They typically pay tax rates far above what surrounding
businesses pay. Thousands of tourists flock to casinos that wouldn’t otherwise visit the
area where it is located. Government officials, industry observers, and interested
researchers often cite these reasons and others for the legalization of casino gambling
(Berry & Berry, 1990; Collins, 2003; Eadington, 1991, 1995; von Herrmann, 2002).
Policy analysts refer to the introduction of new government programs as policy
innovations (Berry & Berry, 1999, p. 169). They are frequently interested in the diffusion
of those policy innovations across states or nations. This research will analyze the
diffusion of the legalization of casino gambling as a policy innovation. Specifically, the
purpose of this research is to identify conditions in countries that lead to the legalization
of casino gambling. In other words, do national conditions indicate that the stated or
theorized reasons for legalizing casino gambling are valid? Much has been written about
the process of legalization, especially in the United States. Yet little empirical analysis
exists on the subject. This research attempts to fill that void.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2
History and Background
“No one can say exactly who invented prayer, music, farming, medicine, or
money. The same must be said for gambling: It is simply older than history” (Schwartz,
2006, p. 5). Evidence of gambling or games of chance can be found throughout history.
Archeologists have discovered dice and other types of games in ruins of the earliest
civilizations in Mesopotamia and Persia, dating as early as 3000 B.C. (Schwartz, 2006, p.
8). There is evidence of dice games in the Roman Empire and professional gamblers in
Renaissance Italy (Sasuly, 1982, p. 4). Ancient Greeks placed bets on chariot races. Each
had their goddess of fortune, Fortuna for the Romans and Tyche for the Greeks
(Schwartz, 2006, p. 73).
A scientific understanding of gambling began in the 16th century. It was then that
Girolamo Cardano began exploring probability theory. “This Renaissance man’s
theoretical contributions were driven by a keen personal interest in gambling that, at
times, bordered on obsession” (Schwartz, 2006, p. 76). Galileo refined Cardano’s work in
the 17th century by computing the probabilities of each possible outcome when three dice
are thrown.
The full implication of Galileo’s math lesson - that there was a science to
gambling - would transform the activity into a full-fledged business.
Previously, professional gamblers consistently profited either through
extraordinary luck - never a good bet - or through cheating others, which
could be risky. The elaboration of probability allowed for another path:
using a discrepancy between the true odds and actual payouts to carve out
a statistically guaranteed profit. This was the most significant change in all
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. of gambling history and directly led to lotteries, bookmaking, and casinos
(Schwartz, 2006, p. 79-80).
Even before the mathematics of gambling was fully understood, lotteries were in
existence. The first recorded evidence of using a lottery as a means to generate funding
was in 1444, when the city of L’Ecluse in northern France conducted a lottery to help
repair city walls (Schwartz, 2006, p. 84). Lotteries gained in popularity in Europe
throughout the 16th and 17th centuries as governments discovered their ability to raise
funds.
The expansion of lotteries in North America was similar to that in Europe. The
English used lotteries in the colonies to raise money for various projects and even to
finance projects back in England (Cornell Law School, 1977, p. 25). The colonies
themselves also used lotteries. “Rhode Island held the largest numbers of lotteries during
this period, following its tremendous success with the Wybosset Bridge Lottery in 1744.
Churches, libraries, poor houses, and markets were built and repaired throughout the state
with funds from lotteries” (Cornell Law School, 1977, p. 32).
The Continental Congress turned to a lottery to help finance the Revolutionary
War (Schwartz, 2006, p. 146). They staged a series of four lotteries hoping to raise $1.5
million for the war effort. After winning independence from England, the use of lotteries
was common in the newly formed states. “By 1832, eight states in the East were raising a
total of $66.4 million per year by lottery. The entire federal government spent only a
quarter of that amount. New York licensed more than 30 public lotteries between 1776
and 1883. Most of the income was allocated to westward expansion, transportation, and
internal improvements” (Cornell Law School, 1977, p. 669).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. By the middle of the 19th century, the popularity of lotteries was fading. Religious
groups began more vigorously opposing the lotteries. By 1862, most states and the
federal government had enacted lottery bans (Cornell Law School, 1977, p. 672). This
ended what is known as the “First Wave” of legalized gambling in the United States
(McGowan, 1999; Rose, 1986).
The “Second Wave” occurred after the Civil War as southern states searched for
funds to rebuild. The most popular of these lotteries was operated in Louisiana and
attracted ticket buyers from all over the United States (McGowan, 1999, p. 14). The
southern lotteries were essentially starved when the U.S. government banned the use of
the postal system for lottery sales in 1890.
The “Third Wave” began in the 1930’s when Nevada legalized casino gambling
and horse race tracks opened in various locations around the country (Rose, 1991, p. 75).
The third wave grew as states began directly offering gambling services when New
Hampshire approved a lottery in 1964. As with lotteries in earlier waves, it was designed
to raise government revenue. “Proceeds from the lottery were to fund education, thereby
averting the enactment of either a sales or income tax for New Hampshire. The lottery
was an instant success, with 90 percent of the lottery tickets being bought by out-of-state
residents” (McGowan, 1999, p. 15). Over the next fifteen years, lotteries had been
approved in all northeastern states.
The third wave continued into the 1980s as states adopted lotteries and other
forms of lottery related gambling. “By 1993, only two states (Utah and Hawaii) did not
have some form of legalized gaming. Lotteries and associated forms of gaming had
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. gained a social acceptance that had not occurred in previous waves” (McGowan, 1999, p.
16).
The rise in popularity of casino gambling in the United States constitutes the
Fourth Wave. The spread of riverboat casino gambling and Native American casinos
during the late 1980s and 90s brought casino gambling to new heights of popularity. By
1993, “for the first time in U.S. gaming history, revenues from casino gaming were
greater than those from lotteries” (McGowan, 1999, p. 17).
Origins o f Casinos
Although the first state sanctioned gambling house opened in Venice in 1638
(Schwartz, 2006, p. 95), the French are credited with inventing many of the games found
in today’s casinos. Gambling houses flourished in France in the 18th and early 19th
centuries. Blackjack is first known to have been played in France in the mid -1700s.
Following the French Revolution in the 1790s, characteristics of early Italian and English
games were combined to form what is now known as roulette. The French government
banned gambling in 1837, “an act whose greatest consequence was the eventual
development of Monte Carlo as a gambling haven. Even when banning gambling, the
French could not help but encourage it” (Schwartz, 2006, p. 105).
Modem bookmaking based on horse racing took form during the later part of the
18th century in England. Methods for settling bets were developed and bookmakers
emerged, offering different odds on the various horses in a race (Sasuly, 1982).
Serious government interest in the regulation of gambling, beyond its use as a
revenue source, began in England in the 1840s (Sasuly, 1982). “The ancients had lightly
regulated gaming, or ignored it. In medieval England and colonial America the laws that
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. affected gambling sought merely to keep people at their appointed tasks. And in the first
stages of the Industrial Revolution courts of law, to the extent that gaming matters came
before them, heard complaints resulting in one way or another from default by losing
bettors” (Sasuly, 1982, p. 44). As betting on horse racing became more widespread, the
last point - default - became more of a problem. This led the Houses of Parliament to
conduct hearings into the industry in 1844. These hearings resulted in no new regulation
of the industry. In fact, a 1710 law against excessive betting was abolished following the
hearings.
The gambling industry was not regulated because it was determined that the
courts could not solve the problem. Defaulters with no money could not be forced to pay
their gambling debts. The problem was solved by the private sector. “From the 1840s on,
the bookmaker assumed his modem role, providing a service for all who wished to back a
horse with a wager. He could accept, if he wished, only bets offered in cash. He could, at
his own discretion, extend credit” (Sasuly, 1982, p. 51).
The Spread o f Casino Gambling
Over the past twenty years, casino gambling has spread across the United States
and the world. Until the late 1980’s, casino gambling existed only in Nevada and New
Jersey in the United States. Between 1989 and 1993, six states along the Mississippi and
Ohio Rivers legalized commercial casinos. State regulated casino gambling now exists in
11 states in the U.S. Indian casino gambling was also introduced to 28 states during this
time period.
The spread internationally has been similar to the U.S. experience. In 1996, Cabot
wrote “Forty years ago... [cjasino gambling was legal in only a handful of European
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. countries - most notably, Monaco, France, Italy, Germany, and Austria; some South
American and Caribbean countries, such as Argentina, Puerto Rico, and Cuba, and the
State of Nevada” (1996, p. 2). By the mid-1990’s, the story was quite different. “Casinos
are open in every country in Europe except Norway and Sweden, in every state in
Australia, and in many Central and South American Countries. Asians can visit casinos in
Korea, Macaw, Malaysia, or the Philippines” (Cabot, 1996, p. 1). Since the publication of
his book, Sweden legalized casinos.
An examination of 20th century casino legalization dates supports Rose’s
description of the third and fourth waves of gambling popularity (Figure 1). A surge of
legalizations came in the 1960’s through the mid-1970’s, coinciding with the third wave.
There were very few nations adopting casino gambling through the late 1970’s and most
of the 1980’s. The second surge of international legalizations coincided with the wave of
U.S. states that legalized casino gambling in the late 1980’s and 1990’s, Rose’s fourth
wave.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 8
5
4
3
70
0 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Figure 1. Histogram of National Casino Legalization Events.
The first surge coincided with the transformation of Las Vegas from a city
financed by organized crime to one dominated by publicly traded corporations. “In 1960,
the conventional wisdom viewed Las Vegas as a city controlled by the mob, a place filled
with transients, low lifers, and opportunists” (Eadington, 1991, p. 9). Through the 1960’s
and 1970’s, casino ownership in Las Vegas went from small groups of wealthy partners
to corporations, including Hilton, Hyatt, and Metro-Goldwyn-Mayer (Barker & Britz,
2000, p. 43).
Barker and Britz (2000) give much of the credit for this transformation to two
individuals: Howard Hughes and William F. Harrah. Between 1966 and 1970, Hughes
acquired seven casinos and became Nevada’s largest private employer. “Nevada
Governor Paul Laxalt saw in the phobia-ridden tycoon a way to repair Nevada’s image,
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 9
recently tarnished by national crime commission and mob skimming scandals” (2000, p.
42). Hughes’ investments made it clear that to compete, casinos were going to need to
attract corporate investments. Prior to the Corporate Gaming Act of 1969, it was
impossible for large corporations to have ownership stakes in casinos, as every
shareholder was required to appear before the Gaming Control Board to be licensed.
William Harrah was responsible for much of the modernization of the industry.
“Harrah’s aim was to erase the stigma of gambling and make it morally and legally
acceptable. His businesslike approach to gambling included market research, use of
consultants, and advertising” (Barker & Britz, 2000, p. 44). He was the first casino owner
to offer his company’s stock to the public.
After Harrah’s public stock offering, casino companies now had oversight by the
Securities and Exchange Commission, just like General Motors, IBM, or Coca-Cola.
Nevada regulators tightened their policies and enforcement. This period legitimized the
casino gambling industry in the eyes of many. The Nevada regulatory system became the
model for many other jurisdictions as they legalized casinos. This was especially true in
Mississippi, where lawmakers “so closely ... followed Nevada law ... that ‘Nevada’
instead o f‘Mississippi’ inadvertently appeared in the bill [authorizing casino gambling in
Mississippi] in some places” (Nelson & Mason, 2006, p. 32).
International casino gambling legalizations during this period reflected the
general distrust of the industry that was seen in Las Vegas. Bulgaria legalized casinos in
1965. Bulgarian citizens were not allowed in the casinos, only patrons from
noncommunist bloc countries were allowed to gamble. Additionally, gambling activities
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 10
were only conducted using western currencies (Tottenham, 1999, p. 342). This continued
until 1990, when the reforms in Eastern Europe took place.
The situation was similar in South Korea when it legalized casinos in 1967. Only
foreign visitors were allowed in Korean casinos. Though the casino industry was focused
on attracting tourists, government restrictions on currency movements outside of the
country made it difficult for large betters in Korean casinos (Whyte, 1999, p. 521).
The reforms that were taking place in Nevada during the 1960’s were reflected in
the regulations of the Dominican Republic as it legalized casino gambling in 1968. The
enabling law set up a Gaming Commission that was made up of four cabinet secretaries:
Finance, Interior, Tourism, and Internal Revenue (Thompson, 1999a, p. 229). Today,
potential licensees are screened though Interpol and the U.S. Federal Bureau of
Investigation. Each casino must have at least two Internal Revenue Department
inspectors on premises at all times.
There was then a slowdown in the spread of casino gambling. Between 1969 and
1988, there were only six legalizations (Jamaica and Holland in 1975; Guadeloupe,
Luxembourg and Spain in 1977, and Slovakia in 1984). In the United States, casinos
were legalized in New Jersey in 1977.
The more recent wave of casino legalizations coincided with a similar wave
among U.S. States. Prior to 1988, casino-style gambling in the United States existed only
in Nevada and Atlantic City. By 1999, state regulated casinos were open in nine other
states. Further, 28 states saw the introduction of tribal casinos (American Gaming
Association, 2006). Many other states legalized some form o f‘convenience gambling’
such as video poker in bars and truck stops. The international legalization events during
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. this period occurred primarily in Europe. There were also legalization events in such
diverse places as St. Lucia, Peru, and Vietnam during the 1990’s.
The next section provides a review of the literature concerning the expansion of
casino gambling, primarily in the United States. The literature review will begin by
briefly surveying the diffusion of policy innovation. The focus of the review is on the
motives both for and opposing the legalization of casino gambling. Following the theory
section, a discussion of relevant methodologies is presented. A discussion of the
statistical technique and explanation of the results is next. Then, several national cases
are examined to see in detail what factors lead to legalization. The final section concludes
by discussing the implications and provides suggestions for further research.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER II
THEORY
Policy Diffusion
While most policymaking activities tend to be incremental in nature, all
government policies were originated with in an initial policy innovation (Berry & Berry,
1999, p. 169). A policy innovation is a non-incremental adoption of a new government
program. The change in an existing tax rate (i.e. raising the sales tax rate) is an
incremental policy change. The legalization of a previously prohibited product such as
gambling is a policy innovation.
Policy analysts are often interested in the diffusion of government innovation.
“Diffusion is the process by which an innovation is communicated through certain
channels over time among members of a social system” (Rogers, 1995, p. 5). Berry and
Berry (1999) describe a number of policy analyses that focus on the diffusion of
government innovation, both internationally and between U.S. states. They focus on two
types of models, diffusion and internal determinants.
Diffusion models are focused on external factors related to policy innovation.
There are three main reasons that policy diffusion takes place between states or nations
(Berry & Berry, 1999, p. 171). First, states learn from each other, borrowing successful
innovations. Second, states compete with each other. For example, they might try to
develop tourist attractions to keep their citizens from traveling to, and spending money
in, other states. Finally, they respond to public pressure for new policies. Citizens see
successful policies in other states and pressure their public officials to adopt those
policies.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. In most cases, policymakers are influenced more heavily by nearby states.
Regional diffusion models take these geographic factors into account (Berry & Berry,
1999, p. 175). Neighbor models assume states are influenced by bordering states. Fixed
regions models assume states tend to imitate policies of other states in their region. A
neighbor model might look at the effects of Portugal and France on Spanish policies. A
fixed region model might include other European countries such has Great Britain and
Germany when looking at policy innovation in Spain.
Models that focus on domestic political, economic, and social factors that
influence policy innovation are known as internal determinants models (Berry & Berry,
1999, p. 178). Even when policies are introduced in neighboring states, these models
assume that it is internal factors that lead to the adoption of a policy, rather than the
influence of the neighbors. These models look at the factors that affect the motivation to
innovate and the obstacles to innovate that must be overcome.
The Spread of Casinos
The focus of this research will be on the adoption of casino gambling as a policy
innovation. The spread of casinos across the U.S. has been documented by several
researchers. The international expansion of the industry has been studied to a much lesser
extent. Eadington (1999a) briefly describes the expansion of casinos in Australia and
Canada. The article also lists a few “famous historic casino centers - such as Las Vegas,
Monte Carlo, Sun City and Macao” (p. 1). The article describes several reasons for the
spread of casinos including a development of positive attitudes towards casinos, often as
a result of some other form of gambling already in existence. Other reasons cited for the
international spread of casinos are similar to those found in the U.S. and described below.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The legalization of casinos in the United States since 1988 has been controversial
politically. Gambling is opposed as a sinful activity by many religious groups. Political
justification for allowing this ‘sinful activity’ has consisted primarily of citing the
benefits provided by the industry. The desired benefits include job creation, increased
economic activity, investment stimulation and increased tax revenues (Eadington, 1999b
& NGISC, 1999). Areas with failing tourism industries, such as the Mississippi Gulf
Coast, saw casinos as a way to attract new tourists to the area. Areas along the
Mississippi River had seen many economic development policies fail throughout the 20th
century. Casino gambling finally created jobs in some of these areas. Detroit watched
thousands of Americans cross the border to gamble at the Windsor casino in Canada. The
legalization of casinos in Detroit kept some of the gambling dollars on the south side of
the border. Illinois, facing a massive budget deficit, enacted policies aimed at creating a
gambling industry that maximized tax revenues.
Collins (2003) characterizes the attitudes governments have historically taken
towards gambling policy. “These are the following:
■ Gambling is a vice. It is the business of government to promote virtue and
to eradicate vice. Therefore it is the business of government to stamp out
gambling.
■ Gambling is undesirable. However, the moral and material costs of
enforcing the prohibition of gambling are unacceptably high. Therefore
government should do what it can to contain and discourage gambling.
■ Gambling is a harmless pastime for most people. Government should
therefore treat it as a normal part of the entertainment industry except to
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the extent that special measures are needed to keep the industry crime free
and to deal with the dangers of addiction.
■ Gambling is a good way for governments to raise money for public
interest projects. Therefore an abnormally large share of gambling
revenues should accrue to government, and gambling should either not be
discouraged or should be encouraged.
■ Gambling is a good way for a jurisdiction to earn money from foreigners.
Therefore gambling should be treated as an export business - like tourism.
(p. 7)”
Historically, the first view has dominated most societies, and continues to exist in
most Islamic countries (Collins, 2003). Many countries and U.S. states take the second
view. They allow some forms of gambling, frequently lotteries, but prohibit other forms,
such as gambling machines. The third view is quite common in Western countries.
Gambling establishments and the games they offer are closely monitored by government
agencies and employees are often subject to background checks. Regulations prevent
minors from gambling and some form of policy to reduce problem gambling is often
enacted.
The fourth view is also common in Western nations and is commonly combined
with policies based on the third view (Collins, 2003). Revenue generation drives state-
owned lotteries, with effective tax rates frequently around 50 percent of each bet.
Policymakers in Illinois take this view towards casinos, with top tax rates at one time
reaching 70 percent of incremental gross revenues1.
1 As of 2007, the maximum incremental tax rate in Illinois was 50 percent of revenues.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The fifth view is taken by policymakers in Nevada and Mississippi in the United
States, Monaco in Europe and Macao in Asia. This view essentially sees gambling as an
economic development issue. Legalizing Internet gambling could also be seen as falling
under this view (Collins, 2003).
Several researchers attempt to identify factors that have lead to the spread of
gambling in the U.S. The growth of lotteries in the U.S. primarily occurred in the 1960s,
70s and 80s. After Nevada in the 1930s and Atlantic City in 1976, the spread of casinos
did not really start until the late 1980s. Early research into the spread of gambling focuses
on lotteries while later studies either include casinos with the study of lotteries or focus
on casinos exclusively. A discussion of some of the important research into the diffusion
of gambling follows, with a focus on the following independent variables used in the
research: politics, income levels, tax revenue/fiscal health, economic development and
religion.
Politics
Berry and Berry (1990) explore several factors that they expect to impact states’
decisions to adopt lotteries: fiscal health, the proximity of an election year, personal
income levels, membership in fundamentalist religious groups, the relative strength of the
dominant political party, and the existence of lotteries in neighboring states.
Lotteries are generally popular among states’ voters. Enacting policies that are
favorable to the voters gives them the greatest electoral boost in the year they are passed.
Thus, Berry and Berry theorize that the adoption of lotteries is most likely in an election
year.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. There also may be a reluctance to enact a lottery if policymakers feel that there
are not the economic resources in the state to support it. They cite previous research that
show that participation rates in lottery games is highest among middle and upper income
citizens. They include a personal income measure and theorize that higher income states
are more likely to adopt lotteries.
Berry and Berry (1990) identify a couple of variables that might explain states
that overcame obstacles to enacting a lottery. Citing previous research on more general
taxation issues, they hypothesize that having both the executive and legislative branches
of state government under the control of a single party might make it more likely for a
state to adopt a lottery. “This is because a unified government can better avoid the
‘roadblocks’ resulting from the need for compromise between two parties” (Berry &
Berry, 1990, p. 403).
The existence of lotteries in neighboring states is also used as an independent
variable in this paper. The rationale is primarily competitive. “When a state adopts a
lottery and a neighboring state does not have one, people living near the border in the
neighboring state can cross the border to purchase tickets. This places pressure on state
officials to adopt a lottery to try to keep a state’s own ‘tax base’ from being taxed by a
neighbor” (Berry & Berry, 1990, p. 404).
Furlong (1998) uses a logistic regression model to analyze states’ adoptions of
casino gambling. He identifies four ‘rationales’ for adoption identified in the literature:
revenue, political, competitive and economic development. He explores each of these in
detail.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Studies of state lottery adoptions argue that there is a positive political benefit to
lottery legalization. As opposed to broad sales, income or property taxes, lotteries provide
tax revenues on a purely voluntary basis. The increased revenues can be used to provide
popular services to voters. “The adoption of casino gaming promises to yield quite
similar benefits to vote-maximizing elected state officials. Geographically dispersing
casino venues and sharing casino tax revenues with the local governments that host
gaming offers the potential to provide an important additional mechanism for broadening
political support” (Furlong, 1998, p. 373). The tax revenue rationale will be further
explored below.
Many studies of lottery adoptions also look at the existence of lotteries in
neighboring states as a predictor of lottery adoption, the competition rationale. The
rationale is that states adopt lotteries to keep their residents from spending money on the
lottery (and providing the benefits) in the neighboring states. This is extended to casino
adoption. In fact, most of the casinos located in U.S. states, especially those that have
developed since the late 1980s, are located near the border of the state. Furlong tempers
his enthusiasm for this explanatory variable in the case of casino gambling. “In
comparison to the rapid spread of state lotteries over time - nearly 40 states in roughly 30
years - the diffusion of casino gaming has been protracted” (Furlong, 1998, p. 374). This
concept is explored further in the economic development/job creation section below.
Many authors believe that jurisdictions legalize gambling to prevent their citizens from
gambling (and thus creating additional economic activity and jobs) in neighboring
jurisdictions.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. von Herrmann (1999) looks at states’ adoption of three types of gambling:
casinos, lotteries, and pari-mutuel wagering. Her review of the literature uncovers
evidence that decisions to legalize gambling rely on evidence from many different fields
including demographics, economics, and religion. “All of this previous research into
gambling suggests that decisions about gambling policy may not be a simple cost-benefit
analysis, but rather are complex, multi-layered choices in which citizens, special
interests, and legislatures must all play a part... .it seems relevant to examine not only
what factors lead to the decisions to adopt, as the previous research has done, but also
what factors lead states to choose one form of gambling over the others” (p. 1664).
The author uses seven independent variables in her analysis. These include two
political variables (interest group strength and legislative professionalism), prior
existence of a gambling regulatory agency, two socio-economic variables (religion and
age), liberalism, and income.
Because the decision to legalize gambling is at its core a political one, political
factors are explored by von Herrmann (1999). She uses a scoring system developed by
another author as a measure of the impact of interest groups in each state. She
hypothesizes that states with stronger interest groups are more likely to legalize lotteries
while states with lower interest group influence are more likely to adopt casinos or pari
mutuels. The other political factor analyzed is legislative professionalism. Her hypothesis
is that a professional legislature would be more likely to legalize gambling, especially
lotteries, because they provide benefits to businesses across the state, while other forms
of gambling benefit smaller groups of businesses.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 20
Because of the costs of regulating gambling activities, von Herrmann (1999)
believes that the existence of a regulatory agency should make it more likely for a state to
adopt other forms of gambling. Policymakers in states where the state police regulate
gambling might be reluctant to place a further burden on the police. States with existing
gambling regulatory agencies might be able to take on another form of gambling at a
relatively low incremental cost.
As a proxy for public opinion regarding the gambling industry, von Herrmann
(1999) uses a variable related to the age of the general populations of each state. “The use
of demographic information as a rough substitute for public opinion is well documented,
and will be used in this study as well” (von Herrmann, 1999, p. 1671). The age measure
used is the percentage of the population over age 65.
To analyze the relationship between the legalization of gambling and state social
goals, she uses a measure of state liberalism. A previous study developed the measure
based on public opinion polls conducted throughout the country. She believes that the
liberalism measure should correlate negatively to the adoption of gambling.
Income Level
The final measure used by von Herrmann (1999) is income. She hypothesizes that
states with higher incomes are more likely to adopt lotteries and lower income states are
more likely to adopt casinos or lotteries. This hypothesis is market based because she
believes that a person’s income level is related to the form of gambling that is demanded.
Tax Revenue/Fiscal Health
Berry and Berry (1990) cites previous research showing states are more likely to
adopt sales and income taxes during difficult budget years. The authors extend this to the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 21
fiscal revenues provided by lotteries. “The most important economic determinant of
motivation [for lottery adoption] should be the short-term fiscal health of a state’s
government” (p. 401).
Many authors have argued that casino legalizations are enacted to create tax
revenues. Cabot puts it bluntly, “Jurisdictions may extract large sums of tax money from
casino gaming” (1996, p. 61). As in Berry and Berry, Furlong finds that “fiscal stress has
been described as an important causal factor” (1998, p. 372) in state casino adoptions.
However, Furlong contends that the organizational structure set up by states’ casino
legalization policies is not conducive to high levels of state revenue collection. Because
casinos are subject to market forces that sometimes cause them to go out of business,
state policy makers insulate themselves from these risks by allowing private companies to
build and run the casinos. “This hedging strategy, one used so far by every state adopting
casino gaming, raises a very serious challenge to the revenue maximization thesis and
should diminish the significance of fiscal stress variables. The price to be paid by the
adopting states for financial and political risk aversion is reduced gaming tax revenues”
(Furlong, 1998, p. 372).
Sauer (2001) uses a more theoretical economic analysis of the gambling industry.
He performs a political economy analysis based on interest groups. His analysis covers
the ebbs and flows of gambling regulation throughout the history of the United States.
Sauer’s analysis concludes that the recent (late 20th century) spread of gambling
has been driven by increases in the size of government in the U.S. “Higher government
spending entails higher tax rates. By increasing the dead weight cost of taxation,
increased government spending leads to a search for alternative sources of revenue”
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 22
(Sauer, 2001, p. 14). In other words, the main push behind the legalization of gambling is
the search for tax revenues. Consumers have shown a willingness to provide that
government revenue in exchange for opportunities to gamble.
Smith (2000) looks at the taxation of gambling in Australia. “Gambling taxes
have considerable appeal to governments, as gambling taxation is perceived as voluntary,
and is therefore less resisted by the general taxpayer. Gambling has also long been a
productive revenue base, and makes rapid, if minor, contribution to public revenues” (p.
128). This highlights the attractiveness of the gambling industry for policymakers
searching for revenue. When faced with a fiscal crisis, the industry is seen as a rapid,
relatively painless, solution.
Economic Development/Jobs
Furlong’s (1998) final rationale, economic development, is often seen as a way to
promote the industry in the face of religious or moral opposition. The attraction of
tourists, and the related investment and job growth stimulation, is often the goal of casino
developments. “Because elected officials are eager to provide their constituents with and
to take credit for any additional economic opportunities like new jobs, enhanced public
services, reduced taxes, and a more favorable business climate, some argue that the
gaming industry lobbyists have exploited these electoral links skillfully to their advantage
(Goodman, 1994, 1995, cited in Furlong, 1998). During an adoption campaign, casino
opponents easily can be demonized as moralistic zealots or as antidevelopment
antiquarians” (p. 375).
Most researchers that look at the relationship between casino gambling and
economic development assert that the gambling industry must export some of their
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 23
services to create economic development. That is, patrons must visit from outside the
jurisdiction and bring in new money to the area to spur job growth and increase income in
the host jurisdiction.
There are key indicators that will determine the strength of economic
impacts that can be associated with a casino or casinos in a particular
jurisdiction. First, the degree of job creation and net economic stimulation
will depend on the volume of business that casinos generate from outside
the region where they are located. If a casino is purely a tourist facility - if
all casino patrons come from outside the jurisdiction - then the facility is
effectively exporting casino services. As a result, all revenues generated
within the casino, all jobs created within the casino, can be classified as
"exports" and will stimulate, via the multiplier process, additional
economic activity in the jurisdiction (Eadington, 1995, p. 52).
Collins lists the tourism effect as one of only two “unambiguous benefits of
legalized gambling to an economy.... In practice this only happens where.. .the foreigners
who come to gamble do not have the opportunity to engage in the desired form of
gambling in their home jurisdiction” (2003, p. 43). He goes on to point out that this gives
jurisdictions whose citizens are gambling in these gambling tourism centers an incentive
to legalize themselves to keep the benefits in the local area.
Cabot stresses the point that casinos should create extra demand, rather than
capturing demand from another industry. If a casino simply attracts spending from locals
or from an existing tourism base, no new demand is created. Another important benefit is
discouraging a loss of tourism. “Italy, for example, has strategically located its casinos
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 24
near its borders to prevent the loss of lire by its citizens to casinos in other countries....
This concept, called ‘import substitution,’ has the same benefits to the community as
would the influx of visitors’ dollars” (Cabot, 1996, p. 202).
One notable exception to the assertion that tourism is required for casino related
economic development is work by Walker and Jackson. They find that simply
introducing gambling into an economy regardless of ‘exports’ causes economic growth.
Their analysis, which looks at both the casino and greyhound racing industries, examines
two questions: “Does legalized gambling spur economic growth? And, if so, (2) Does
economic growth depend on ‘exports’” (Walker & Jackson, 1998, p. 35) Their results
indicate a positive answer to the first question and a negative answer to the second.
Religion
Berry and Berry (1990) identify religious opposition as an important potential
obstacle to the adoption of lotteries. There exists an intense opposition to gambling,
including lotteries, among many religious groups. The percentage of the population that
adhered to a fundamentalist religion is used to explain this phenomenon, von Herrmann
uses the same religious measure in her later study of the adoptions of lotteries and
casinos. “Religion [has] been shown to relate to the adoption of several forms of
gambling” (1999, p. 1671).
Cabot (1996) describes factors that impact public policy related to gambling.
While he identifies several factors included in analyses above, including fiscal stress and
unemployment, he focuses primarily on religious attitudes towards gambling. “While
support or opposition to gambling may be founded on philosophic, economic, or social
thought, the religious orientation of a society is often paramount... .religious orientation
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 25
of a society is often a good predictor of whether that society will allow gambling” (p. 18-
19).
He explores various religious denominations’ attitudes towards gambling as an
explanatory variable in policy decisions. Religions often have somewhat contradictory
teachings that relate to gambling. A summary of Cabot’s findings follows.
The Catholic Church is the most liberal regarding gambling behavior. “Gambling,
though a luxury, is not considered sinful except when the indulgence in it is inconsistent
with duty” (The New Catholic Encyclopedia, cited in Cabot, 1996, p. 25). This attitude
leaves room for both support and resistance to legalized gambling within the Catholic
Church. “Church leaders ask if the particular form of gambling puts poor people at
disadvantages, if it causes people to become dysfunctional gamblers, and if the gambling
will be adequately monitored to assure that it is honest and fair. Based on the answer,
Church leaders have opposed some public referenda while supporting others” (Cabot,
1996, p. 25).
Protestant denominations have a somewhat similar view as Catholics, although
considerable variation exists between the various Protestant denominations. The
Episcopalians have had the following view: “Gaming [the wagering of discretionary or
recreational funds] does not impact or put into jeopardy a person’s individual social or
economic obligations or status. On the other hand, Gambling [risking one’s livelihood] is
considered, in classic Christian Theology, contrary to the most important of the Moral
Virtues - Justice” (Resolution On Casino Gambling, Diocesan Council of the Diocese of
Western New York, cited in Cabot, 1996, p. 27). This leaves considerable ‘wiggle room’
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 26
in official attitudes towards gambling. By contrast, the Southern Baptists’ absolute
opposition to gambling in the U.S. is well known.
Judaism also has an unclear attitude towards gambling. On one hand, there has
been a long history of gambling in the Jewish culture. Hanukkah is known as a lucky day
and some view it as “the New Years Day for Gamblers” (Cabot, 1996, p. 30). On the
other hand, according to Cabot, Jewish law still discourages gambling.
With a couple of exceptions, the Islamic religion views gambling as contrary to
the word of the Koran. The exceptions are for wagering on horse racing and competitions
involving knowledge about Islamic law. The horse racing exception is in place because it
provides “an incentive for training necessary for the holy wars” (Cabot, 1996, p. 31).
Hinduism regards gambling to be among the most serious of vices. “Hindu law
disqualifies gamblers from being witnesses. Because of their ‘depravity.’ because, like
‘thieves and assassins,’ they are people in whom ‘no truth can be found... .the wealth
obtained by gambling is tainted” (Cabot, 1996, p. 32).
Buddhism is also generally opposed to gambling. Buddha’s teachings “warns
monks that games and spectacles - including fights between elephants, horses, buffaloes,
bulls, goats, rams, and cocks; various board games; chariot races; and dice games - are
detrimental to their virtue. Buddhist students are warned that they do wrong by gambling
and that the practice can shorten their life and condemn them to a life of hell” (Cabot,
1996, p. 33).
Problem Gambling Concerns
The spread of gambling around the world has frequently brought controversy,
often rooted in the religious beliefs described above. There are often opponents to the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 27
legalization of gambling. For some, opposition to gambling is based on religion. For
others, it is the belief that gambling causes more harm than good. Arguments based both
on religion and negative impacts of gambling are put forth as legalization or expansion of
gambling is debated.
Religious opposition to gambling is not a modem phenomenon. In the late 19th
century a book was published by The Anti-Gambling Association entitled Fools of
Fortune, “or Gambling and Gamblers, Comprehending a History of the Vice in Ancient
and Modem Times, and in Both Hemispheres; an Exposition of its Alarming Prevalence
and Destructive Effects; with an Unreserved and Exhaustive Disclosure of Such Frauds,
Tricks and Devices as are Practiced by ‘Professional’ Gamblers, ‘Confidence Men’ and
‘Bunko Steerers.’ By John Philip Quinn, who modestly, yet with sincerity, tenders to the
world what he hopes may extenuate his twenty-five years of gaming and systematic
deception of his fellow men” (Quinn, 1892, title page). Quinn states in his book, “beware
of gaming. It dishonors God, degrades man, wrecks honor, ruins business, destroys
homes, breaks wifely hearts, steals babies’ bread, brings mothers sorrowing to the grave,
and at last, with reckless bravado, launches the sinful soul into the path of God’s
descending wrath, to be overwhelmed forever” (1892, p. 613). One hundred years later,
the criticisms of gambling were much the same.
While opposition to gambling frequently has its roots in religion, most of the
argument against the industry stem from the impacts of addicted gamblers. Research into
the prevalence of problem gambling among various populations has yielded relatively
uniform results. “There is a remarkable consistency in the findings of population
prevalence studies .... In various countries and many jurisdictions within countries,
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 28
current probable pathological prevalence estimates vary from 0.5-2.8 percent” (Abbott &
Volberg, 1999, p. 107). However, research into the impacts of addicted gamblers, both in
the United States and internationally, is sometimes inconclusive and frequently
contradictory.
“Casino gambling is a social issue, because in addition to the direct benefits to
those who own and use casinos, positive and negative externalities are reaped and bome
by those who do not gamble” (Grinols & Mustard, 2001, p. 143). Grinols and Mustard
(2001) divide the negative externalities associated with casino gambling into nine groups:
crime, business and employment costs, bankruptcy, suicide, government direct regulatory
costs, illness, social service costs, family costs, and abused dollars.
“Crime costs are real resources used for the apprehension, adjudication,
incarceration, and rehabilitation of criminals, or the police costs that result from the need
for increased police presence in areas of greater gambling activity” (Grinols & Mustard,
2001, p. 150). Academic research on the link between casinos and crime returns mixed
results. Researchers looking at the connection between gambling and crime in Canada
find that a major difficulty in conducted research on the relationship between gambling
and crime is that crime statistics do not specify whether a crime is gambling related or not
(Henriksson & Lipsey, 1999). Stitt et al. (2003) compare casino counties with non-casino
counties and find no significant relationship between crime rates in the two groups of
counties. However, Grinols and Mustard (2006) find in a more recent study that the
presence of a casino increases county level crime rates by approximately 12 percent.
Business and employment costs are related to the temporary absence from a job or
the permanent loss of employment. Lesieur (1998) conducts a review of studies related to
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 29
problem gambling. Of gamblers who are in some form of treatment, between 21 and 36
percent report a job loss related to their gambling. Additionally, “between 69 and 76
percent of pathological gamblers state they have missed time from work due to
gambling” (Lesieur, 1998, p. 156).
Many gambling opponents also cite the potential for increased bankruptcies as a
result of gambling opportunities. Lesieur’s (1998) review found that between 18 and 28
percent of males and about 8 percent of females receiving treatment for problem
gambling have filed for bankruptcy. However, as with the research into the links between
casinos and crime, the more macro-level studies have returned mixed results. Nichols,
Stitt, and Giacopassi (1999), find that personal bankruptcy rates increased in seven of
eight counties that had recently introduced casino gambling. However, de la Vina and
Bernstein (2002) find no evidence that new gambling opportunities have a positive effect
on personal bankruptcy rates.
Grinols and Mustard (2001) cite anecdotal evidence of the link between gambling
and suicide, “Dozens of stories have been reported of gamblers killing themselves after
losing at the casinos, sometimes on the premises” (p. 151). And in fact, there is evidence
of a correlation in academic studies. Frank, Lester, and Wexler (1991) survey attendees
of Gamblers Anonymous meetings and find that they have a greater tendency to attempt
suicide than the general public. Newman and Thompson (2003) review more than a
dozen studies that show varying degrees of evidence of the link between gambling and
suicide. They point out, however, that most of these studies have methodological
problems. Most choose their study group from places like Gamblers Anonymous or
treatment facilities, which likely have differences from gamblers in the general
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 30
population. Further, they point out that there may be other variables that might explain
both problem gambling and higher suicide rates. Newman and Thompson use data from
surveys of the Edmonton, Alberta general population. They, too, find an association
between problem gambling and suicide. However, they fail to find a causal link between
the two. Rather, their analysis suggests “that the commonality spanning suicidal behavior
and gambling lies in the presence of “mental disorder.” According to this explanation,
mental disorder would precede both forms of problem behavior, rather than, say,
gambling leading to depression, which in turn might lead to attempted suicide” (Newman
& Thompson, 2003, p. 86).
Costs related to government regulation of the gambling industry are fairly straight
forward. “The gambling industry has been regulated because it has historically been
subject to fraud and abuse... .Regulatory costs differ by state and depend on the type of
casinos (i. e. riverboat, Indian reservation, etc.) and extent of the responsibilities of the
regulatory agencies” (Grinols & Mustard, 2001, p. 152). The main example of these costs
is the budget of a regulatory agency such as a state or national gaming commission.
The next groups of negative externalities defined by Grinols and Mustard are
somewhat more abstract. In the category of illness, they list a variety of disorders
“associated with gambling or affected by it [including] depression, stress-related illness,
chronic or severe headaches, anxiety, moodiness, irritability, intestinal disorders, asthma,
cognitive distortions, and cardio-vascular disorders” (Grinols & Mustard, 2001, p. 151).
They provide no evidence to substantiate this statement.
Social service costs include those costs associated with the treatment and support
(welfare, food stamps, etc.) of problem gamblers. Family costs are those that are borne by
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 31
the families of problem gamblers. They include the “gambling-related costs of divorce,
separation, spousal abuse, and child neglect” (Grinols & Mustard, 2001, p. 152).
Their final category is abused dollars. This represents “lost gambling money
acquired from family, friends, or employers under false pretenses. Two examples are
stealing that is never reported because the thief is a relative, and money ‘loaned’ under
duress that is never repaid” (Grinols & Mustard, 2001, p. 152).
Grinols and Mustard go on to identify various studies that have attempted to
quantify each of these costs. They use these estimates to produce their own assessment of
the overall costs to society of problem and pathological gamblers. Morse and Goss point
out the problems with doing this type of research. “First, researchers face the formidable
problem of distinguishing between causation and correlation” (2007, p. 74). As Newman
and Thompson (2003) point out, there may be other factors that contribute to both the
gambling problem and the other social problems. Shaffer and Korn sum up the state of
research into problem gambling: “To understand fully the overall repercussions of
gambling on society, a significant research effort is necessary to document the complex
interaction among these health and socioeconomic variables, as well as their short- and
long-term costs” (2002, p. 77).
Some of the more recent research attempts to offer policy recommendations
related to problem gambling, or at least advocate the need for more research into this
area. Recommendations for the study of the relationship between public policy and
problem gambling come from many different researchers. Korn (2000), in a study of the
Canadian gambling industry, advocates a balance between economic and social impacts.
“Policy-makers at all levels of government should regularly monitor and assess the public
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 32
owner-operator models now in place, to ensure that there is a responsible balance
between encouraging gambling as entertainment and protecting the public from
gambling-related harm” (p. 63).
Shaffer and Korn (2002) conduct an extensive review of gambling prevalence and
gambling related mental disorders. They promote the analysis of both the costs and the
benefits of gambling activity. One of their recommendations is the “adoption of strategic
goals for gambling to provide a focus for public health action and accountability. These
goals can include ... promoting balanced and informed attitudes, behaviors, and policies
toward gambling and gamblers by both individuals and communities; and protecting
vulnerable groups from gambling-related harm” (p. 201).
Blaszczynski, Ladouceur, and Shaffer (2004) advocate a strategic framework to
“guide key stakeholders to develop socially responsible policies that are founded on
sound empirical evidence rather than those that emerge solely in response to anecdotally-
based socio-political influences” (p. 302). “Coordinated efforts involving all key
stakeholders must establish and assure a systematic approach to gambling research,
utilizing a common set of standardised definitions and outcome measures, thus enabling
valid cross-jurisdictional comparisons and allowing data sharing. The primary benefit
will be the compilation of valid and reliable standardized datasets and the reduction of
unnecessary and costly duplications of projects” (p. 313).
Wynne and Shaffer (2003) advocate the need for independent cost-benefit
research to inform public policy. They argue that the majority of gambling related
research is either performed or supported by either proponents or critics of the industry.
“When the end justifies the means, scientific research suffers. Despite the clear need for
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 33
trustworthy policy-relevant information, governments have been slow to commission
research that examines the socioeconomic impact of gaming” (p. 113). One of the
barriers to this research is the availability of reliable data.
Collins and Lapsley (2003) discuss economic issues related to the costs and
benefits of gambling. One of their main conclusions deals with variations in gambling
services. “Gambling is not a homogeneous service and different forms of gambling
produce different types and levels of social costs.... The size and structure of the
gambling industry varies from country to country, as does the degree of regulation that is,
presumably, designed to reduce the social cost of gambling” (p. 147).
Nichols et al. (1999) look at bankruptcy rates in casino jurisdictions, an issue
many relate to problem gambling. They look at county level data in the U.S., comparing
counties with and without casino gambling, using a controlled pairing method. The same
authors conducted a variety of studies using similar methodology looking at the
relationship between casino gambling and issues such as crime, suicide, and divorce.
Their results are often inconclusive and might be enhanced by including policy-related
variables in their analysis.
Research into the effects of problem gambling in Great Britain is equally
inconclusive. In an extensive review of British gambling policies chaired by Sir Alan
Budd, the discussion of problem gambling cautions “problem gambling remains an
under-researched phenomenon, and the research that has been undertaken does not
produce much in the way of definite conclusions” (Great Britain & Budd, 2001, p, 85).
The conclusions of these studies point to some of the challenges facing
researchers studying gambling. These challenges include problems related to data
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 34
availability and inconsistent regulatory treatment of the industry across jurisdictions.
Perhaps the difficulty in overcoming these obstacles is one of the reasons there has been
little objective research conducted, especially at the international level. This research
contributes to the filling of this void.
Research Questions and Hypotheses
Academic literature focused on explaining the spread of gambling is generally
limited to the United States. The focus is on several categories of explanatory variables:
political, fiscal, economic, and religious. This analysis will expand on previous research
to test the influence of these factors on the international spread of casino gambling.
Specifically, the following hypotheses will be tested:
■ Hypothesis 1: Nations are more likely to adopt casino gambling if they are
showing signs of fiscal stress (budget deficits).
o This follows Berry & Berry (1990), Cabot (1996) and von Herrmann
(2002). Similar measures include government revenues (Furlong, 1998;
Smith, 2000) and government spending (Sauer, 2001).
* Hypothesis 2: Nations are more likely to adopt casino gambling if the national
economy is lagging (high unemployment).
o This follows Eadington (1995), Cabot (1996), Furlong (1998) and NGISC
(1999).
■ Hypothesis 3: Nations are more likely to adopt casino gambling if they can target
tourists with their gambling industry.
o This follows Eadington (1995, 1999b), Cabot, (1996), NGISC (1999), and
Collins (2003).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 35
■ Hypothesis 4: Nations are more likely to adopt casino gambling if the income
level is high.
o This follows von Herrmann (1999).
■ Hypothesis 5: A nation’s religious intensity has an impact on the likelihood of
adopting casino gambling.
o This follows Berry & Berry (1990) and von Herrmann (1999), who used
religious fundamentalism and von Herrmann (2002), religious
membership. Related measures include dominant religious denomination
Cabot (1996).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER III
METHODOLOGY
Event History Analysis
Berry and Berry argue that policy innovation analysis should include both internal
and external factors.
We have argued that nearly all explanations of government
innovation have taken the form of either diffusion or internal determinants
models. It is clear that these two forms of models are not mutually
exclusive. The existence of internal factors that influence the probability
of adoption by a state does not preclude the possibility that his probability
is also affected by the actions of other states, and vice versa. (1999, p.
183)
Berry and Berry advocate a technique called event history analysis that can
analyze both internal and external factors in policy analyses. They use event history
analysis to examine the adoption of lotteries by various U.S. states. They attempt to
answer the research question “what determines the probability that the adoption [of
lotteries] will occur during a given time period” (Berry & Berry, 1990)?
Event history analysis is a specific type of analysis of duration data. The event
examined here is the legalization of casino gambling in a nation. “By definition,
occurrence of an event assumes a preceding time interval that represents its
nonoccurrence. More specifically, a certain time period or duration of nonoccurrence
must exist in order for an occurrence to be recognized as an ‘event’” (Yamaguchi, 1991).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 37
A common example of the use of event history analysis is in the labor market. An
‘event’ (becoming unemployed) is the endpoint of a duration interval (employment). This
analysis is interested in the endpoint of a duration of gambling prohibition.
The period that represents the nonoccurrence interval is known as the risk
interval. “[EJvent history analysis can be defined as the analysis of the duration for the
nonoccurrence of an event during the risk period or as the analysis of rates of the
occurrence of the event during the risk period” (Yamaguchi, 1991). This analysis will
focus on the latter as the length of the prohibition is much less interesting than the
likelihood (rate) of occurrence of the legalization event. This likelihood is referred to as
the hazard rate or transition rate.
The group (nations, in this analysis) that is at risk for an event is known as the risk
set (Allison, 1984). For each period prior to the event (legalization), a nation is part of the
risk set. As soon as the event occurs, the nation is no longer at risk and is dropped from
the risk set.
The dataset includes a set of explanatory variables for each nation, along with a
binary dependent variable representing the event. For each year prior to legalization, the
event measure equals zero. In the year of legalization, the event measure is one. No
subsequent years’ data are used for a nation that has experienced the event because it is
no longer at risk for the event. Thus, the number of nations in the dataset grows smaller
through time as nations legalize casino gambling.
Yamaguchi (1991) describes a difficulty related to available data in event history
modeling. “ Censoring exists when incomplete information is available about the duration
of the risk period because of a limited observation period.” Because there is a limitation
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 38
in the historical data used for explanatory variables, some nations will have already
legalized casino gambling when the observation period begins. Casino gambling in
Monaco dates back to the early 19th century. Because data for the explanatory variables
are not available back that far, Monaco is not included in the analysis. The event (casino
legalization) had already occurred when the observation period began. This is known as
left censored data. Data can also be right censored. A nation that legalizes after the
observation period is included in the risk set, but does not have an occurrence of the
event. Singapore is an example, with legalization occurring in 2005. Several of the
explanatory variables are not available as recently as 2005.
“Fully or partially parametric methods estimate the effects of explanatory
variables, called covariates, on transition rates” (Yamaguchi, 1991, p. 3). As described
above, explanatory variables for this analysis include measures of fiscal stress and
religiosity. Each of these is hypothesized to have an effect on the transition rate for casino
gambling.
Explanatory variables may be either constant or variable over time. The dominant
religion in a nation is constant over time, especially over the time period studied here.
Other variables, such as unemployment rates, will vary from year to year.
Allison (1984) describes several dimensions that are important to consider in the
proper modeling of event history data. These dimensions include repeated vs. non
repeated events, single vs. multiple kinds of events, and discrete vs. continuous time.
Each of these will be discussed in terms of the casino legalization analysis.
Events can be either repeated or non-repeated events. A person can have multiple
new jobs in their life (repeated events) but only one first job (non-repeated event). The
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 39
casino legalization data shows only single events over the time period studied, making
the analysis one of non-repeated events. Although Sauer (2001) described a history of
gambling in the United States that vacillated between gambling abolition and legalized
gambling, including lotteries, horse racing, and casinos (repeated events), there have been
only single cases of legalization since 1900. A future extension of the present research
might look at a repeated event history analysis that goes back several hundred years.
Similar types of events can be treated the same or in different ways. An analysis
of job losses might simply look at each one as an identical event. A more thorough
analysis might distinguish between voluntary job separations and non-voluntary
separations. In the study of gambling legalization, the legalization of lotteries, casinos, or
other forms of gambling might all be treated alike. However, a model could be specified
that treated each of the different types of gambling as a distinct event type. This study
will examine the legalization of casinos alone. Again, a future extension of the research
might model different types of gambling legalization if they exist in the same nation.
In specialized cases, the timing of an event may be able to be measured exactly.
For example, these exact measurements can be obtained in an experiment looking at
failure rates of a piece of equipment. This is known as continuous time analysis. In social
sciences research it is rarely possible to measure events or explanatory variables on a
continuous time basis. This study will use discrete time (annual) data.
Discrete Time Models
Yamaguchi (1991) discusses several reasons that discrete-time models are
appropriate to use as an approximation for continuous time methods. “The first pertains
to the unit o f time used for the measurement of the dependent event.... We often have a
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 40
measurement based on discrete times of fairly large intervals such as years or individual
age, instead of a measurement based on year, month, and day. In these cases, it may be
more natural to assume a model that reflects a discrete-time measurement” (p. 16).
This is certainly the case for the casino related data in this study. The data simply
identifies the year that casino gambling was legalized. While it may be possible to
identify the day, month, and year that the legislation was enacted into law, in the case of
this research it would not add to the analysis. The decision process is not based on
information that clearly varies from day to day. Policymakers do not have information on
how political, social and economic conditions vary from day to day. Annual data much
more closely approximates the changes in information available to policymakers as they
make their decision to legalize or not. Thus, years are the appropriate unit of time in this
research.
Yamaguchi’s second reason is related to the number of ties in the data. “Events
are tied when two or more subjects in the sample have the event at the same time.
Although the underlying continuous-time process has a zero probability of tied events,
ties can occur in the data because events are measured at discrete time points” (1991, p.
16-17). Results of continuous time models can produce biased parameter estimates when
there are ties in the data. Discrete time models perform handle ties without producing
biased estimates.
The data for this study has several ties. Two nations, Finland and New Zealand,
legalized in 1991. Three nations, Belgium, Mexico, and Sweden all legalized casino
gambling in 1999.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 41
“The third and most important consideration pertains to the adequacy o f the
approximation obtained from discrete-time models. This concern is related to the
conditional probabilities of having the event at discrete time points. Discrete-time models
... are adequate for the approximation of continuous time models only if the conditional
probabilities are reasonably small” (Yamaguchi, 1991, p. 17).
In the dataset for this study, the average annual probability for a nation to legalize
is just 0.0435 (207 observations, 9 legalizations). “Clogg and Eliason (1987, cited in
Yamaguchi, 1991) show that rate models can be used as an approximation for logit
models if p is approximately 0.1 or smaller. Hence logit models can be used as an
approximation for rate models under a similar condition. We can reasonably expect that
the approximation will not be adequate if the aggregate frequency data include a high
proportion of situations where p becomes larger than 0.1” (Yamaguchi, 1991, footnote p.
42).
Yamaguchi provides a theoretical foundation for discrete time models:
Suppose T is a discrete random variable that indicates the time of
an even. If T = t, it means that the event occurs at time t. Suppose that the
probability of having an event at t in the population is given by f(/).
f(fi) = P(T = fi) / = 1, 2,....
Where t ,( i - 1,2, ...) indicates the ith discrete time point and satisfies fi <
t2 < .... Then thesurvivor function, S (t), which indicates the probability of
not having the event prior to time t, is given as
S(/<) = P(7’>O = Xf(0) j*
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 42
The hazard att\ is defined as the conditional probability of having the
event at t\, given that the event did not occur prior to time t\, such that
;t,=p(r = (,|r>(,)=fft)/S(/,)
Then, we also obtain
s w - n o - v
Any parametric specification of conditional probabilities for X,j, 1, 2, ...
becomes a discrete time hazard model (1991, p. 17).
In terms of the analysis of casino gambling legalization decision, f(t) is the
probability that legalization will occur in year t. S(t) indicates the probability of
legalization not occurring prior to year t. The probability that casino gambling is
legalized in a specific year i, given that it had not been legalized before that year is A ,j, the
conditional probability. The conditional probabilities are what we are interested in
modeling, exploring for factors that impact its magnitude.
The conditional probability is known as the hazard rate, introduced earlier in the
more general discussion of event history models. Allison (1984, p. 17) describes a simple
model with one constant and one time varying explanatory variable (Allison uses P(t) to
represent the hazard rate, which was denoted by X,j in Yamaguchi above):
P(t) = a + bxxx + b2x2 (t)
For t = 1, ...., T. The time constant variable, xi could be the measure of dominant religion
in each nation. The variable represented by X 2(t) varies over time, as would a national
unemployment rate.
Allison (1984) points out a problem with this form of the model.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 43
P(t), because it is a probability, cannot be greater than one or less than
zero, while the right-hand side of the equation can be any real number.
Such a model can yield impossible predictions that create difficulties in
both computation and interpretation. This problem can be avoided by
taking the logit transformation of P(t):
As P(t) varies between 0 and 1, the left-hand side of this equation varies
between minus and plus infinity (p. 17).
The coefficient estimates produced by the logit modeling of the event history
allow for the measurement of interaction effects between independent variables. The
coefficient estimates “can be used to calculate predicted probabilities that a state with
specified characteristics will adopt a policy in any given year.... Moreover, an analysis of
such predicted probabilities allows researchers to assess the nature of interactions among
the determinants of adoption probability” (Berry & Berry, 1990, p. 407).
To determine the interaction effects, an independent variable is varied while the
remaining variables are held steady. One variable is changed to see its effects on the
probability of the event. Then, another variable is changed before varying the original
variable again to see how the second variable impacts the way the first variable affects
the probability.
For example, the unemployment rate might be varied while holding all variables
constant to see the effects on the adoption of casino gambling. In this set of calculations,
the income level might be set at the lower level of the observed income levels in the risk
set. A second set of calculation will then be performed, again varying the unemployment
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. rate. However, for the second set, the income level might be held at the upper level of
observations in the risk set. If the variations in unemployment rate cause larger variations
in the probability of casino adoption while the income level is lower - in the first set of
calculation - then an interaction effect has been observed. The unemployment rate in this
example is more likely to explain casino adoptions in lower income nations than in higher
income nations.
This allows for more complicated hypotheses to be tested than just the simple
effects of one variable, such as income level, on the transition rate from a non-casino
nation to a casino nation. For example, we can test a hypothesis such as ‘Governments
are more likely to turn to casino gambling as a revenue generator in times of fiscal stress
if the income level in the nation is higher’. The interaction effects indicate whether the
data support the hypothesis.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 45
CHAPTER IV
DATA
Data Constraints
International time series data, as is required for this analysis, are difficult to
obtain. Datasets that are comparable across a significant number of countries are limited
to a handful of economic and demographic variables. Other variables that might be of
interest in this analysis, such as public opinion, are simply not available.
The risk period for this analysis is 1985 through 2000. This coincides with the
fourth wave of gambling expansion, identified by McGowan (1999). Longer risk periods
were considered, but availability of data diminishes significantly prior to the 1980s.
Missing Data
Within the combined dataset, there exist several data points that are not available
or not usable. The missing data points exist in three of the explanatory variables:
government revenue/expenditure, unemployment, and church attendance. In most of the
cases, the data was simply not reported for a specific year. For example, data on religious
service attendance was only collected during the ‘waves’ of the World Values Survey.
SPSS provides several methods for replacing missing values. Two methods were
used for this analysis. Linear interpolation simply linearly distributes new values between
the existing prior and subsequent years’ values.
The remaining missing data points were at the ends of the datasets. Since prior
and subsequent values were not available, linear interpolation could not be used. Thus, a
linear trend was used to replace the missing values. For the purposes of this analysis, the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 46
nearest 5 data points were used to estimate the missing values when the linear trend
method was employed.
Casino Gambling Legalization Dates
Data on casino legalization dates has been obtained for 57 nations. Data on casino
legalization events has come primarily from three sources: International Casino Law
(Cabot, Thompson, Tottenham, & Braunlich , 1999), The 2001 Casino Gaming Business
Market Research Handbook (Miller & Assoc., 2001), and the Study Of Gambling
Services In The Internal Market Of The European Union: Final Report (European
Commission, 2006).
Three nations had legalized casinos before 1900. Another 29 legalized between
1900 and 1985. There are 50 nations that show casinos on the website
www.casinocity.com for which a legalization year has not yet been determined. Twenty
two nations were identified as having legalized casinos during the risk period of 1985
through 2000. Specific information about legalization events will be explored for those
nations included in the dataset: Denmark, New Zealand, Finland, Peru, Greece,
Venezuela, Belgium, and Sweden.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 47
Table 1 Casino Legalizations, 1985 through 2000
Nation Year Morocco 1988 Poland 1989 Czech Republic 1990 Denmark* 1990 New Zealand* 1990 Russia 1990 Saint Lucia 1990 Croatia 1991 Finland* 1991 Hungary 1991 Canada 1992 Peru* 1992 Greece* 1994 Estonia 1995 Saint Croix 1995 Venezuela* 1996 Malta 1997 Vietnam 1998 Belgium* 1999 Sweden* 1999 Turks and Caicos Islands 1999 * Included in the risk set for this analysis.
The Queen of Denmark signed a new casino law on October 3, 1990 (Nevries,
1999, p. 352). The government originally issued six licenses. Licenses must be renewed
every 10 years.
In New Zealand, the Casino Control Act was passed in 1990, legalizing casinos
(Falvey & Nagel, 1999, p. 590). The Casino Control Act had three aims:
1. promoting tourism, employment, and economic development
generally;
2. ensuring that gaming in casinos is conducted honestly; and
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 48
3. ensuring that the management and operations of casinos remains free
from criminal influence or exploitation (Falvey and Nagel, 1999, p.
590).
Finland had a long history with lotteries, dating back to the mid-1960s. It wasn’t
until 1991 that the Council of State granted the first casino license. “The need to found a
legally-licensed casino in Finland was to prevent illegal casinos and uncontrolled
gambling, to promote tourism, and to collect money for the public good” (Romppainen,
1999, p. 354).
Peru legalized gambling in 1992. “The Peru casino law requires that full casinos
must be located in one of tern tourist zones” (Thompson, 1999b, p. 313). Some casinos in
Peru are publicly owned, although most are owned by major corporations, including ITT
Sheraton and Ladbroke’s (Thompson, 1999b, p. 313).
The Greek government passed a law in 1994 that established the framework for
casino operation and supervision (European Commission, 2006, p. 391). The industry is
regulated by the Minister of Tourism (Anagnostaras & Melvani, 1999, p. 409).
Venezuela passed a national casino law in 1996 (Thompson, 1999c, p. 320). The
law was passed to preempt local casino legalizations. “Under the law casinos were
permitted in five star hotels with 200 rooms if they were located in tourist zones”
(Thompson, 1999c, p. 320).
The Belgian Parliament declared casinos legal in 1999 (Schwartz, 2006, p. 470).
This action legalized casinos that had previously operated illegally in coastal or spa
towns. A new casino in Brussels was also legalized (European Commission, 2006, p.
1133).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 49
“In 1999, the Swedish Government authorised a maximum of six (initially four
with two to be introduced later) intemational-style casinos” (European Commission,
2006, p. 1363). The casinos were authorized in four of Sweden’s largest cities. The
casinos are operated by a subsidiary of the national lottery.
Income Level
Income levels are measured by per capita gross domestic product (constant 2000
US dollars). Data were obtained from the World Development Indicators Online (World
Bank, 2007). Per capita GDP (constant 2000 US dollars) figures are available from 1985
through 2000 for 134 nations. For the analysis, GDP values are lagged one year as in
Berry & Berry (1990). Table 2 displays summary statistics for each nation’s annual GDP
data.
Table 2 Lagged Per Capita GDP Descriptive Statistics.
Belgium Brazil Denmark Finland Greece Iceland Indonesia Mean 18914.6 3059.1 17102.4 18016.5 6772.3 22852.5 704.2 S. D. 6226.4 1096.1 4802.9 6378.1 2059.4 5416.0 231.6 Min 8123.4 1569.1 11014.6 10554.9 4134.2 11688.9 440.4 Max 27285.3 4857.1 21702.9 27466.8 9648.8 30201.6 1146.1 Count 15 16 6 7 10 16 16
Ireland Mexico New Zealand Norway Peru Sweden Venezuela Mean 14371.9 3331.35 10419.97 27032.46 1050.21 23762.34 2856.91 S. D. 6137.7 1056.12 2716.57 7077.99 278.50 6140.79 424.42 Min 5470.3 1665.16 7033.63 14640.38 593.01 11984.43 2189.15 Max 25420.9 4879.02 13259.58 36293.16 1557.46 30585.82 3390.61 Count 16 16 7 16 8 15 12
Tax Revenue/Fiscal Health
Central government revenue and expenditure data are published in Intematioi
Financial Statistics Online (International Monetary Fund, 2007). Government revenue
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 50
and expenditure data were available for 89 nations for the period 1985 through 2000. For
the analysis, the ratio of surplus/deficit to total expenditures is calculated and lagged one
year, following Berry & Berry (1990).
Four values were missing for the government finance measure. Brazil had three of
the missing values (1965-1996 and 1999). The remaining missing value was for New
Zealand (1989). Linear interpolation was used to replace missing government finance
data values for the first set of missing values for Brazil and New Zealand’s value. A
linear trend based on the previous five years was used to estimate Brazil’s 1999 value.
Figure 2 displays the government finance measure data available for the nations
with missing values. The estimated values have larger points on the graph. Table 3
displays summary statistics for each nation’s fiscal measure.
0.5
0.4
0.3
0.2
0.1
0
- 0.1
- 0.2
-0.3
-0.4 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Brazil -A —Brazil est. — New Zealand - 8 - New Zealand est.
Figure 2. Government Finance Data with Estimated Values.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 51
Table 3 Government Fiscal Position Descriptive Statistics.
Belgium Brazil Denmark Finland Greece Iceland Indonesia Mean -0.11 -0.05 0.02 0.00 -0.33 -0.06 0.00 S.D. 0.04 0.18 0.08 0.04 0.06 0.07 0.11 Min -0.18 -0.36 -0.10 -0.05 -0.43 -0.13 -0.16 Max -0.04 0.38 0.10 0.06 -0.25 0.09 0.21 Count 15 16 6 7 10 16 16
Ireland Mexico few Zealar Norway Peru Sweden Venezuela Mean -0.07 -0.16 -0.07 0.08 -0.29 -0.07 0.05 S.D. 0.10 0.18 0.04 0.09 0.12 0.12 0.16 Min -0.24 -0.46 -0.15 -0.06 -0.44 -0.27 -0.12 Max 0.08 0.10 -0.03 0.21 -0.14 0.10 0.34 Count 16 16 7 16 8 15 13
Economic Development/Jobs
The unemployment rate in each nation is published in the Yearbook of Labour
Statistics (International Labour Office, 1997, 1998-90, 2001). For the period 1985
through 2000, these data are available for 23 nations, including all of those for the nations
included in this dataset.
The unemployment data also have several missing values. Five nations have
missing values, ranging from one year missing for New Zealand, three years for Brazil
and Indonesia, four for Peru, to 7 for Mexico. The interpolation and linear trend methods
are used to estimate the missing values for unemployment rates. Figure 3 displays the
known and estimated values for these nations. Table 4 displays summary statistics.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 52
2 -----
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
♦ ■1 Brazil ■" ■ Brazil est. "Indonesia Indonesia est. H ...... Mexico
• — Mexico est. —1+■— New Zealand - ♦ ‘New Zealand est, —• — Peru Peru est.
Figure 3. Unemployment Rate Data with Estimated Values.
Table 4 Unemployed per 100 Persons Descriptive Statistics
Belgium Brazil Denmark Finland Greece Iceland Indonesia Mean 9.2 5.9 8.8 4.7 8.1 2.4 3.6 S.D. 1.4 2.5 0.8 1.3 0.9 1.6 1.4 Min 7 2.4 7.9 3.1 7 0.4 1.9 Max 11.4 10.6 9.7 6.6 9.7 5 6.4 Count 15 16 6 7 10 16 16
Ireland Mexico New Zealand Norway Peru Sweden Venezuela Mean 13.0 2.7 5.9 4.1 5.8 5.3 9.3 S.D. 4.1 0.7 2.7 1.3 1.5 3.4 1.6 Min 4.3 1.8 2.5 2 4.8 1.5 6.7 Max 17.4 4.7 10.3 6 9.4 13.1 11.8 Count 16 16 7 16 8 16 11
Religion
Several of the previous studies outlined above used a measure of religious
intensity as an explanatory variable. The need for annual data for a large number of
nations makes this problematic for an international study. No annual measure of religious
intensity is available for nations around the world.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 53
The World Values Survey (European Values Study Group and World Values
Survey Association, 2006) has been conducted in four waves since 1980. During each
wave, surveys are conducted in a variety of countries covering topics such as
environmental issues, working conditions, family life, politics, religion, and national
identity. The first wave was conducted in 1980 through 1984, the second in 1989 to 1993,
the third in 1994 to 1999, and the fourth in 1999 to 2004.
The frequency of church attendance is used as the measure of religiosity. The
question asks, “How often do you attend religious services” (European Values Study,
2006)? Possible responses include “More than once a week, once a week, once a month”
down to “never”. For the purposes of this analysis, the percentage of respondents who
chose more than once a week or once a week is used as the measure of religiosity. These
data are available for 83 nations in at least one of the survey waves.
Because of the way it is collected, missing values for the World Values Survey
data were more extensive than for some of the other variables. The survey has been
conducted in 4 waves since 1980. Each wave is a two to three year period where surveys
are conducted in nations around the world. Not every nation is covered in each wave.
For the nations in the dataset, the number of data points over the 1981 to 2001
time period vary between one and four. Three nations (Greece, Indonesia, and New
Zealand) have data in just a single year. Three more nations (Brazil, Peru, and Venzuela)
have two years of data. Sweden has data in four years. The remaining nine nations have
three years of data.
Missing data in between two years of available data are estimated using
interpolation. Data at the tails are estimated using the regression trend line established by
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 54
the two previous (or subsequent) known data points. For the nations that have only one
data point, it is assumed that religious attendance figures are constant at that value over
the study period.
Figure 4 displays the known and estimated data points for a selection of the
religious service attendance data set. To avoid clutter, some nations are not shown.
Denmark, Iceland, and Norway would fall almost directly on top of Finland and Sweden
at the bottom of the graph. Indonesia and New Zealand had just one data point, so they
look very much like Greece on the graph, although at different levels. Mexico, Peru and
Turkey exhibit a pattern much like Nigeria, with three data points and a fairly consistent
slope.
100%
90%
80%
70%
60% __® 50%
40%
30%
20% ♦ •••#•...... 4 ...... # ...... — ...... 4 — ♦ 10%
0% (Nf'i-'t'n'sor-'Ooo-vO (Nr-i'd-in^or-cooso OOOOOOOOOOOOOOOOGOO n C7\^0,n O\0\C7'>C>'O s O \ 0 0
-Belgium—♦—Belgium Brazil * Brazil —a —Finland -air-Finland -♦•-Greece Greece » Ireland Ireland Mexico -©-M exico Nigeria Nigeria —•—Turkey -•-T u rk e y — Sweden -© —Sweden
Figure 4. Value Replacements for World Values Survey Data.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 55
The only nation where the slope of the line changes sign over the time period is
Turkey. The value rises from 1990 to 1996, and then falls slightly in 2001. The change in
slope is not significantly different than other nations. For the other nations, the most
severe changes in the slopes of the lines occur in 1990 for Ireland and 1996 for Mexico.
Table 5 displays summary statistics.
Table 5 Religious Attendance Descriptive Statistics
Belgium Brazil Denmark Finland Greece Iceland Indonesia Mean 0.24 0.34 0.03 0.04 0.14 0.03 0.65 S.D. 0.03 0.02 0.00 0.00 0.00 0.00 0.00 Min 0.19 0.31 0.03 0.04 0.14 0.02 0.65 Max 0.29 0.38 0.03 0.04 0.14 0.03 0.65 Count 15 16 5 7 10 16 16
Ireland Mexico lew Zealar Norway Peru Sweden Venezuela Mean 0.75 0.46 0.17 0.05 0.34 0.04 0.31 S. D. 0.06 0.04 0.00 0.00 0.03 0.00 0.00 Min 0.64 0.41 0.17 0.05 0.30 0.04 0.31 Max 0.82 0.55 0.17 0.05 0.38 0.05 0.32 Count 16 16 7 16 8 15 12
Since a good religiosity variable was not available on an annual basis, a proxy for
religiosity is explored. A consistent relationship between marital stability and religiosity
is found by several researchers. Shrum uses data from 1972 and 1977 to test the
relationship. “Clearly, frequent religious participation was linked with a decreased
probability of marital instability (x2 = 161.7, statistically significant at the .001 level).
Those who participated less often were more likely to have experienced marital
disruption than those who participated more” (1980, p. 139).
Sweezy and Tiefenthaler (1996) use state level data. “Both the state percentage of
individuals who regularly attend religious services and the level of conservatism
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 56
measured as the percentage of the state population who classify themselves as Christian
Fundamentalists have significant effects on the likelihood of divorce. Women who live in
states with larger percentages of regular churchgoers and Christian Fundamentalists are
less likely to divorce” (p. 63). Call and Heaton (1997) similarly find “[cjhurch attendance
is positively associated with marital stability for both men and women” (p. 385).
Based on the established relationship between religiosity and marital stability,
divorce rates are explored as a proxy for religiosity. The annual number of divorces is
available for 82 nations for the period 1985 through 2000 from the United Nations
Statistics Division (2007).
Annual divorce rate or marital status data are not available. The number of
divorces for each nation is divided by the population of the nation. The per 1,000 capita
divorce rate is used as an approximation of divorce rates.
Tourism
A measure of the importance of tourism is found in data from the World Bank
(2007) World Development Indicators. They report travel services as a percentage of
commercial service exports. These data are available on an annual basis. There are 143
nations in the World Bank dataset that lack 5 or fewer years of the travel service data
over the 1985 to 2000 time period. None of the nations in the risk set for this analysis had
any missing data points. Table 6 displays summary statistics.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 57
Table 6 Travel Service Exports Descriptive Statistics
Belgium Brazil Denmark Finland Greece Iceland Indonesia Mean 15.53 19.44 26.17 25.57 45.30 26.31 90.88 S.D. 2.00 11.24 1.94 2.44 7.80 5.42 9.17 Min 13 3 24 22 36 14 65 Max 19 40 29 30 58 35 98 Count 15 16 6 7 10 16 16
Ireland Mexico New Zealand Norway Peru Sweden Venezuela Mean 38.88 69.38 40.14 14.38 29.88 21.80 48.50 S. D. 10.94 5.73 4.60 1.86 3.98 1.61 8.03 Min 14 61 31 11 21 19 36 Max 46 77 43 18 34 24 61 Count 16 16 7 16 8 15 12
Combining the Data Sets: Usable Nations
Coverage of the datasets described above vary significantly. The number of
nations range from 134 in the per capita income dataset to just 34 in the unemployment
dataset. The nations covered by each dataset also vary.
For the period 1985 to 2000, 16 nations have data for per capita GDP, government
revenue and expenditure, church attendance, travel services as a percentage of exports,
and unemployment rate. For two of those nations, Egypt and India, the year of casino
legalization is not clear. That leaves 14 nations with complete coverage for the
explanatory variables. Eight of those nations legalized casinos between 1985 and 2000.
The remaining six have no casinos. Map 1 displays those 16 nations, where countries
with casinos are displayed in red and those without casinos are displayed in yellow.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 58
Map 1. Nations in the risk set.
Eleven other nations lack just church attendance data. If the divorce data is
substituted for the church attendance data, the risk set would include 14 nations.
Unemployment data are missing for 22 nations that have the other variables available.
Descriptive Statistics
Table 7 displays casino gambling legalization years for the nations in the dataset.
All legalization events for the risk set occurred in the 1990s. Casino gambling has not
been legalized in Brazil, Iceland, Indonesia, Ireland, Nigeria, Norway, or Turkey.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 59
Table 7 Legalization Years
Belgium 1999 Brazil no Denmark 1990 Finland 1991 Greece 1994 Iceland no Indonesia no Ireland no Mexico no New Zealand 1991 Norway no Peru 1992 Sweden 1999 Venezuela 1996
Table 8 displays descriptive statistics for the pooled data. There are a total of 176
observations in the pooled data.
Table 8 Descriptive Statistics for Pooled Data Set
GDPlag UnemplRate AttendRel FinanceLag TrvlSvcExp SqrRtTSE Mean 12465.1 6.17 0.29 -0.07 0.37 0.58 Standard Deviation 10375.0 3.74 0.24 0.15 0.24 0.19 Skewness 0.5 0.88 0.66 -0.27 1.06 0.53 Range 35852.7 17.00 0.79 0.84 0.95 0.82 Minimum 440.4 0.40 0.02 -0.46 0.03 0.17 Maximum 36293.2 17.40 0.82 0.38 0.98 0.99 Count 176 176 176 176 176 176
Skewness measures the distribution symmetry. (Hair, Balck, Babin, Anderson, &
Tatham, 2006) A plot of positively skewed data has a majority of the values to the left
and tails off to the right. Conversely, negatively skewed data has a greater number of
large values to the right. A strongly skewed sample violates the statistical assumption of
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 60
normal distribution of data. “Skewness values falling outside the range of -1 to +1
indicate a substantially skewed distribution” (Hair et al., 2006, p. 40).
The skewness value for the travel service exports data is 1.06, indicating a
potential problem with skewness. The histogram for travel service exports is displayed in
Figure 5. There are clearly more values to the left of the graph, as is indicated by the
positive skewness value.
n
0.3 0.4 0.5 0.6 0.7 0.8
Figure 5. Histogram of Travel Service Exports Data.
Hair et al. (2006) recommend transforming data with positive skewness problems
transforming the data through either a logarithm or square root. Transforming the travel
service export data through a logarithm essentially reverses the skewness problem,
yielding a skewness value of -1.0 and adds a significant kurtosis problem (kurtosis =
1.68). Using a square root transformation improves these values significantly. Summary
statistics for the transformed variable are labeled SqRtTSE in table 8. The histogram of
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 61
the transformed data is displayed in Figure 6. These data will be used for the tourism
measure in the statistical analysis.
50 45 40 35 30 25 20 15 10 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Figure 6. Flistogram of Transformed Travel Service Exports Data.
Kurtosis measures the peakedness or flatness of the distribution. (Hair et al.,
2006) Peaked distributions are identified by a positive kurtosis value, flat distributions by
a negative value. Hair et al. (2006) do not give guidelines for the kurtosis statistic as they
do with skewness. They do, however provide an equation for calculating a z statistic
(2006, p. 81):
kurtosis, kurtosis
The largest kurtosis figure is for GDPlag, where kurtosis = -0.89. This kurtosis
value yields a z statistic of -2.617. This falls just outside the critical value of± 2.58,
indicating a potential problem with kurtosis. The most common data transformation for
flat distributions is taking the inverse of the data values. However, in this case taking the
inverse makes the non-normality worse, increasing kurtosis to 5.1 and skewness to 2.4.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Hair et al. point out that variations from normality (i.e. skewness and kurtosis)
have larger impacts for smaller sample sizes. “In small samples of 50 or fewer
observations ... significant departures from normality can have a substantial impact on
the results. For sample sizes of 200 or more, however, these same effects may be
negligible” (2006, p. 80). Thus, because the number of observation in the sample are
greater than 200 and the kurtosis problem is marginal, the values for GDPlag were not
transformed.
A correlation matrix for the pooled data is displayed in Table 9. The highest
correlation comes between religious service attendance and lagged GDP. None of the
other correlation coefficients are higher than 0.5.
Table 9 Correlation Coefficients for Explanatory Variables
GDPlag Unempl AttendRel FinanceLag SqrRtTrvl GDPlag 1 Unempl 0.304 1 AttendRel -0.618 -0.064 1 FinanceLag 0.382 0.172 -0.253 1 SqrRtTrvl -0.366 0.039 0.187 0.016 1
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTERV
RESULTS OF ANALYSIS
The analysis attempts to identify some of the factors that lead to nations’ adoption
of casino gambling. The event history analysis methodology essentially follows Berry &
Berry (1990) and von Herrmann (1999, 2002). These studies also provide guidance on
explanatory variables, along with Cabot (1996), Collins (2003), Eadington (1995, 1999b),
Furlong (1998), and NGISC (1999). The hypotheses presented in the theory section are
stated in terms of null and alternative hypotheses for statistical testing. The following
hypotheses will be tested:
■ Hypothesis 1:
o Null: The fiscal condition of a nation has no impact on the probability that
casino gambling will be legalized.
o Alternative: Nations are more likely to adopt casino gambling if they are
showing signs of fiscal stress (budget deficits).
■ Hypothesis 2:
o Null: The health of a nation’s economy, as measured by unemployment,
has no impact on the decision to legalize casino gambling.
o Alternative: Nations are more likely to adopt casino gambling if the
national economy is lagging (high unemployment).
■ Hypothesis 3:
o Null: The significance of a nation’s tourism industry has no impact on the
decision to legalize casino gambling.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 64
o Alternative: Nations are more likely to adopt casino gambling if their
tourism industry is significant.
■ Hypothesis 4:
o Null: Income levels have no impact on the decision to legalize casino
gambling.
o Alternative: Nations are more likely to adopt casino gambling if the
income level is high.
■ Hypothesis 5:
o Null: Religious intensity has no impact on the likelihood of adopting
casino gambling.
o Alternative: A higher religious intensity of a nation’s citizens makes it less
likely that a nation will adopt casino gambling.
These hypotheses are tested in the following event history analysis model:
ADOPT, = <*+&FISCATtA + 132UNEMPT + faTOURISty + J3AINCOM$tA + fcRELIGlOy,
where the dependent variable ADOPT,,, is the probability that nation i will legalize casino
gambling in year t. ADOPT,,, is represented by a dummy variable equaling one if nation i
legalizes casino gambling in year t, and zero otherwise.
FISCAL,,,./ represents the ratio of the budget deficit to total expenditures in nation
i in the previous year. UNEMPL,,, represents the unemployment rate in nation i in year t.
TOURISM,,, represents the square root of travel services as a percentage of total
commercial service exports in nation i in year t. The square root is taken to correct
problems associated with skewness. INCOME,,,./ represents the gross domestic product
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 65
in constant U.S. dollars in nation i in the previous year. RELIGION, , represents the
percentage of persons attending church services once or more per week in nation i in year
t.
Event History Analysis
As described in the data section, the risk set includes 16 nations. Nine of those
nations legalized casino gambling during the risk period. The results of the Logit analysis
are displayed in Table 10.
Table 10 Logit Estimates for Event History Analysis of Casino Legalization
P S.E. Wald Sig. Exp(/?) Constant -8.247 3.988 4.277 0.039 0.000 FISCAL -0.366 2.470 0.022 0.882 0.693 UNEMPL 0.492 0.192 6.586 0.010 1.636 TOURISM 4.492 4.999 0.807 0.369 89.275 INCOME 3.861E-05 6.730E-05 0.329 0.566 1.000 RELIGION -7.909 3.137 6.359 0.012 0.0004
-2 Log likelihood 51.002 Nagelkerke R Square 0.2488
Model Assessment
SPSS produces a Nagelkerke R2, one of several pseudo R2 measures available.
For this model, the Nagelkerke R2 is .2488. In other words, the explanatory variables
account for about 25 percent of the variance in casino legalization, as indicated by the
Nagelkerke R2.
Another measure of goodness of fit is the log-likelihood statistic. “The log-
likelihood statistic is analogous to the residual sum of squares in multiple regression in
the sense that it is an indicator of how much unexplained information there is after the
model has been fitted” (Field, 2005, p. 221). When assessing a model, it is appropriate to
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 66
compare the log-likelihood statistic with that of the baseline model. The baseline model
includes just an intercept, no independent variables. The difference between the log-
likelihood of the fitted model and the log-likelihood of the baseline model, multiplied by
-2, has a chi-square distribution. This allows for the calculation of significance. (Field,
2005)
For this model, the baseline -2 log-likelihood is 65.09 and the fitted model’s -2
log-likelihood is 51.00. The difference between the -2 log-likelihoods equals 14.09,
which is significant at the .05 level. Specifically, this indicates a significance of .015 (5
degrees of freedom). Zero indicates a perfect fit for this statistic.
2 2 • A final test assessing the model is the Hosmer and Lemeshow R l- “R l is the
proportional reduction in the absolute value of the log-likelihood measure and as such it
is a measure of how much of the badness-of-fit improves as a result of the inclusion of
the predictor variables” (Field, 2005, p. 223). It varies between 0 (predictors are useless)
and 1 (perfect prediction). It is calculated as follows:
r2 - 2LL(Model) L - 2 LL{OriginaT)
For this analysis, R \ = 0.784. The chi-square statistic associated with this value is
4.56. That value has a significance of .803.
Interpretation o f Results
When interpreting the results of the logit analysis, both the sign and the
magnitude of the estimated coefficients are interesting. “Just as in a standard multiple
regression, the direction of the relationship (positive or negative) reflects the changes in
the dependent variable associated with changes in the independent variable” (Hair et al.,
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 67
2006, p. 364). A positive sign indicates that an increase in the variable has a positive
impact on the probability, and vice versa.
The magnitude of the coefficients is a little more complicated to interpret in logit
analysis. In a standard multiple regression, the coefficients are simply the slope of the
line representing the relationship between the independent and dependent variables. “In
logistic regression, we know that we have a nonlinear relationship bounded between 0
and 1, so the coefficients are likely to be interpreted somewhat differently” (Hair et al.,
2006, p. 365).
Statistical programs report both the logistic coefficient and the exponentiated
logistic coefficient. These can be seen in Table 10 above, reported as P and Exp(P). The
logistic coefficient (P) is most useful in determining the direction of the relationship. The
exponentiated coefficients (Exp(P)) “give the expected change ... in the odds of having
an event occurring versus not occurring, per unit change in an explanatory variable, other
things being equal” (Liao, 1994, p. 16). Since the values have been exponentiated, none
will be negative. An exponentiated coefficient of 1.0 indicates that the independent
variable has no effect (Exp (0) = 1). The percentage change in the odds of legalizing can
be calculated by subtracting 1 from the exponentiated coefficient and multiplying by 100
(Hair et al., 2006, p. 365).
The negative coefficient for the FISCAL variable indicates that as budget deficits
increase, nations are more likely to legalize casino gambling. The exponentiated
coefficient (0.693) indicates that for every one unit decrease in the FISCAL variable, the
odds of legalization increase by 30.7 percent (0.693 - 1 = - .307).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 68
A one unit decrease in the FISCAL variable is quite significant. Recall the fiscal
measure is the budget deficit (or surplus) divided by expenditures. Thus, an example of a
one unit decrease in FISCAL is moving from a balanced budget (FISCAL = 0) to a
budget where spending is double income (FISCAL = -1). A more appropriate example
might be a 10 percentage point decrease in FISCAL, the equivalent to a budget deficit
increasing from 10 to 20 percent of expenditures. Under this scenario, the model predicts
an increase in the probability of legalization of 3.1 percent.
However, the extremely low significance prohibits the drawing of any
conclusions from these results. The high standard error relative to the coefficient estimate
indicates a low confidence that the estimate differs significantly from zero. Thus, the
results fail to reject the null hypothesis, that fiscal stress has no impact on casino
legalization.
By contrast, the results for the UNEMPL variable are significant at the .05 level.
The positive value estimated for the coefficient indicates that a higher unemployment rate
makes it more likely that casinos will be legalized. With a sign predicted by theory and a
statistically significant result, the null hypothesis is rejected.
The exponentiated coefficient indicates that for every one unit increase in
UNEMPL, the odds of legalization increase by 63.6 percent. The UNEMPL variable
measures the unemployment rate. An increase in UNEMPL by 1 units, the equivalent of
going from a 7 to 8 percent unemployment rate, increases the odds of legalization by a
significant 64 percent.
The coefficient estimated for TOURISM is positive, indicating that a more
significant tourism industry is positively associated with casino legalization. Changes in
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 69
the importance of tourism can have quite a large effect on the odds of legalization. The
exponentiated coefficient for TOURISM (89.3) indicates that for every one unit increase
in TOURISM indicates an 88.3 percent increase in the odds of legalization. A five
percentage point change in tourism exports as a percentage of total service exports is
equivalent to a change in TOURISM of .224. Thus, a 5 percentage point increase in the
importance of tourism measure results in an increase Of 19.7 percent increase in the odds
of legalization. However, the results are not statistically significant.
The results for INCOME do not give any evidence to reject the null. The
coefficient is positive, as expected. The exponentiated coefficient is 1.000036, indicating
a .000036 percent increase in odds of legalization for each dollar increase in per capita
GDP. This indicates a 3.6 percent increase in the odds of legalization for every $1,000
increase in per capita GDP. However, the significance of the coefficient is very low.
The final variable tested was RELIGION. Theory predicted that lower religiosity,
measured by church attendance, makes it more likely that a nation will legalize casino
gambling. The coefficient estimated by the model was negative, as was predicted by
theory. The exponentiated coefficient (0.00037) indicates that for every one unit increase
in church attendance, the odds of legalization decrease by 99.96 percent. Thus, if frequent
church attendance increases by 10 percent, the odds of legalization would decrease by 9.9
percent. The RELIGION variable is significant at the .05 level.
The logit analysis gives support to two of the hypothesis. A higher unemployment
rate makes it more likely that a nation will adopt casino gambling. Additionally, lower
religiosity (as measured by church attendance), makes it more likely that a nation will
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 70
legalize casinos. Support for other motivations for legalization identified in the literature,
higher incomes, budget deficits, and tourism, are not provided by the analysis.
Interaction Effects
Logit analysis also allows for the examination of the interactions between
variables. The coefficients estimated by the model can be used to calculate predicted
probabilities for different levels of the independent variables. Exponentiating the results,
as was done above with the individual coefficient estimates, allows for the calculation of
the probability of legalization occurring based on various values of the coefficients. The
formula for calculating the probabilities is as follows: (Liao, 1994, p. 12)
K
Pr (legal = 1) = -----
l + e‘"
where Pr(legal = 1) is the probability of legalization, Pk - the coefficient estimated by the
logit model, and Xk = the value of the independent variable.
Calculating probabilities based on different levels of the independent variables
allows for the examination of the impacts of changes in variables in relation to different
levels of other variables. For example the effects of changes in religiosity can be explored
based on differing levels of unemployment within a nation. Table 11 displays results
based on varying levels of frequent church attendance for two different levels of
unemployment. All other variables (FISCAL, TOURISM, and INCOME) are set at their
sample means.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 71
Table 11 Amount Religiosity Varies With Unemployment.
High Unemployment FISCAL UNEMPL TOURISM1 INCOME RELIGION Pr(Legal) -0.07 15 0.58 12465.1 0.20 0.816 -0.07 15 0.58 12465.1 0.29 0.694 -0.07 15 0.58 12465.1 0.60 0.158 Low Unemployment FISCAL UNEMPL TOURISM1 INCOME RELIGION Pr(Legal) -0.07 4 0.58 12465.1 0.20 0.019 -0.07 4 0.58 12465.1 0.29 0.010 -0.07 4 0.58 12465.1 0.60 0.001 *The value displayed in the table is tourism exports as a percentage service exports. The square root of that value is used in the calculation of the probability, as estimated in the statistical model.
As can be seen in the table, the level of unemployment has a significant impact on
the effects of religion. At a high level of unemployment, 15 percent vs. the sample mean
of 6.2 percent, the probability of legalization varies significantly with varying levels of
religiosity. With 60 percent of the population attending church once or more per week
and high levels of unemployment, the probability of legalization is 15.8 percent. When
frequent church attendance drops to 20 percent of the population, the probability of
legalization grows to 81.6 percent.
When unemployment is lower, the relative impacts of religiosity are much
weaker. At a 4 percent unemployment rate, a 60 percent frequent church attendance level
produces a probability of legalization of 0.1 percent. Keeping the low unemployment rate
and lowering the frequent church attendance percentage down to 20, the probability of
casino legalization grows to just 1.9 percent.
The effect of unemployment rates on the impacts of religiosity is quite strong.
Changing the fiscal condition has a much smaller effect on religiosity’s impact on the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 72
probability of legalization. Table 12 displays results based on varying levels of frequent
church attendance for two different levels of the government fiscal position.
In this example, UNEMPL is returned to its sample mean, along with TOURISM
and INCOME. The top part of the table displays the effects of various levels of religiosity
while the government is experiencing a 30 percent budget deficit. The lower part of the
table shows the same variation in religiosity while the government is enjoying a 10
percent budget surplus.
Table 12 Amount Religiosity Varies With Fiscal Condition
Poor Fiscal Condition FISCAL UNEMPL TOURISM1 INCOME RELIGION Pr(Legal) -0.30 6.2 0.58 12465.1 0.20 0.059 -0.30 6.2 0.58 12465.1 0.29 0.031 -0.30 6.2 0.58 12465.1 0.60 0.003 Strong Fiscal Condition FISCAL UNEMPL TOURISM1 INCOME RELIGION Pr(Legal) 0.10 6.2 0.58 12465.1 0.20 0.051 0.10 6.2 0.58 12465.1 0.29 0.027 0.10 6.2 0.58 12465.1 0.60 0.002 'The value displayed in the table is tourism exports as a percentage service exports. The square root of that value is used in the calculation of the probability, as estimated in the statistical model.
The probabilities under the two scenarios are virtually identical. The government
fiscal situation has an insignificant impact on the importance of religiosity in the
probability of legalization.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER VI
CASE STUDIES
The case studies will examine four nations. Two, Denmark and Greece, legalized
casinos during the 1990s and are included in the event history analysis. The other two,
Israel and China, have not legalized casinos. Israel and China are not included in the
event history analysis due to problems with data availability. These cases provide insight
into the process of casinos legalization beyond that supplied by the event history analysis.
The case studies will examine each nation in terms of the explanatory variables
used in the statistical analysis. For each variable, trends will be explored that might
impact the legalization decision. The section will conclude with a summary of the
national conditions. An analysis of how the case studies relate to the statistical event
history analysis results will be provided in the conclusions section of the dissertation.
Denmark
Denmark legalized casinos in 1990, when the Queen of Denmark signed a new
casino law. (Nevries, 1999) The objectives of gambling regulation in Denmark include
keeping consumption at a moderate level, protecting gamblers, limiting problem
gambling and economic crime, and restricting non-regulated gambling for private profit.
This includes the tax revenue benefits to the government. “The significant contribution to
the financing of public interest activities is closely connected with the aim of preventing
gambling from being a source of private profit” (European Commission, 2006, p. 139).
Danish casinos are taxed at a rate of 45 percent of gross gaming revenue up to DKK 48
million and 75 percent on revenues above that level. (European Commission, 2006)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The stated goal of public revenue generation, along with the budget deficit figures
indicates this was likely a primary driver of the legalization process. Denmark has one of
the highest casino revenue tax rates in Europe. Figure 7 displays the annual budget
surplus or deficit as a percentage of government expenditures. (International Monetary
Fund, 2007) Denmark had significant budget deficits during the mid-1980’s. Budget
conditions improved in the late 80’s but were deteriorating again in the years approaching
the legalization year, returning to a deficit that year. The dataset for the statistical event
history analysis is lagged one year, so the deficit in 1990 was not used for that purpose.
However, the trend was very likely clear to policymakers as they considered legalization.
15%
10%
5%
0% 1970 1972 19881986
-5%
- 10%
-15%
- 20%
-25%
Figure 7. Danish Budget Condition, 1970 to 1990.
Denmark is among the world’s wealthiest countries. In 1990, the year casinos
were legalized, Denmark had the seventh highest per capita income in the world (World
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 75
Bank, 2007). von Herrmann (1999) found that higher incomes were positively associated
with the legalization of casinos.
The unemployment rate in Denmark fluctuated between 8 and 10 percent through
the 1980’s. (International Labour Office, 1997, 1998-90, 2001) The World Development
Indicators (World Bank, 2007) lists 1990 unemployment rates for 65 nations. Twenty one
nations had higher unemployment rates than Denmark. Significant employment is not
likely a goal of the industry in Denmark. There are only about 380 employees nationwide
in the casinos (European Commission, 2006).
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990
Figure 8. Danish Unemployment Rates, 1970 to 1990.
Church attendance is a common measure of religiosity. The World Values Survey
(European Values Study Group and World Values Survey Association, 2006) collected
data on church attendance from respondents in 41 nations in 1990. In that year, just 2 Vi
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 76
percent of Denmark’s citizens attended church once or more per week. Only 3 of the
other 40 nations in the survey had church attendance lower than Denmark. About 26
percent of all international respondents to the survey attended church once or more per
week in 1990.
Danish casinos are all located in large international hotels. While this is not a
requirement, “the explanatory notes to the Gambling Casino Act says that when the
Ministry of Justice is to decide whether a license should be granted, great importance
must be attached to the fact, that the gambling casino is placed in an area which is visited
by numerous tourists” (European Commission, 2006, p. 1161).
Denmark’s legalization of casinos in 1990 is not surprising. They had high
income levels. Often the strongest opposition to the gambling industry is based on
religion. Denmark ranks low in measures of religious intensity.
They had a significant amount of unemployment at the time of casino
legalization. Unemployment proved to be a significant driver of legalization in the
statistical analysis. However, job creation doesn’t appear to be one of the goals of
Denmark’s casino policy as they have only a few hundred employees in the nation’s
casinos.
As discussed above, at the time of legalization, their government budget situation
was worsening. The fiscal motivation, not significant in the statistical analysis, appears to
be a primary driver for casino legalization in Denmark.
Greece
Gambling opportunities in Greece include casinos, lotteries, horserace betting and
sports betting. Greece legalized casino gambling in 1994. “Generally speaking, casinos in
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 77
Greece are resort-based and primarily aimed at tourists that visit the country, especially
during the summer months” (European Commission, 2006, p. 1215). While the report
from the European Union states that there is a limit of 12 casino licenses authorized by
the enabling legislation (2006, p. 395), elsewhere it states that there are seven casinos on
the mainland and about 10 others on Greek islands. It lists the names of 15 casinos as of
2003 (2006, p. 1215).
Greece is home to some of the largest casinos in Europe. A 2004 article in
International Gaming & Wagering Business (IGWB) stated that the largest casino on the
continent at that time was the Regency Casino Thessaloniki in Macedonia Greece
(Rutherford, 2004). That casino was also the only casino in Europe with even partial U.S.
Ownership at the time. Another article in IGWB later in 2004 referred to Casino Loutraki
near Athens as another of Europe’s largest, with 70 tables and 600 slot machines (Casino
Loutraki, 2004).
One of the goals for policymakers when legalizing casinos was to maximize
public revenue from gambling (European Commission, 2006, p. 1030). Greek casinos are
taxed on a progressive scale starting at 20 percent and rising to 33 percent. They also pay
a 2 percent tax to the local government where they are located (European Commission,
2006, p. 1216).
Fiscal data from Greece underscore their stated goal of maximizing public
revenue. “Greeks have a fierce antipathy to taxes, no matter what their political
affiliation.... This has led to public debt levels topped only by Belgium and Italy”
(Anagnostaras & Melvani, 1999, p. 407). As can be seen in Figure 9, the Greek
government showed a budget deficit in every year between 1970 and the year of
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 78
legalization in 1994 (International Monetary Fund, 2007). They were in the 10 to 15
percent range during the 1970s. In the 1980s, the deficits jumped up to around 30 percent
each year. In 1994, the budget deficit approached 50 percent of expenditures.
60%
50%
40%
30%
20%
10%
0% ^ ^ ^ No(?> ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ n«PN^ ^ ^
Figure 9. Greek Budget Deficits as a Percentage of Spending, 1970 to 1990.
In the years leading up to the legalization of casinos in 1994, per capita GDP had
been relatively stagnant (Figure 10). Greek Gross Domestic Product per capita was
$8,803 (constant U.S. dollars) in 1979. It varies slightly up and down over the next 15
years but reached just $8,977 in 1994. In 1994, GDP data is available for 111 nations
from the World Bank (2007). Greece had the 27th highest GDP of those nations.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 79
$10,000
$9,000
$8,000
$7,000
$ 6,000 --
$5,000 --
$4,000 -
$3,000 -
$ 2,000 -
$ 1,000
Figure 10. Greek Per Capita Gross Domestic Product, 1970 to 1994.
Unemployment rates were also relatively high in the years leading up to the
legalization of casinos. Rates averages around 7 or 8 percent during most of the 1980s. In
the early 90s, rates rose slightly and had almost reached 10 percent the year before casino
legalization.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 80
1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
Figure 11. Greek Unemployment Rates, 1976 to 1993.
Church attendance figures for Greece are not available for the World Values
Survey of 1995. They did participate in the 1999 survey. Church attendance data are
available for 72 nations. Fourteen percent of Greek respondents attend church services
once or more each week. That was a higher percentage than just 20 other nations. The
average for all international respondents was 32 percent. In attitudes towards gambling,
“[t]he social environment is mostly indifferent as long as there is no overt connection to
organized crime” (Anagnostaras & Melvani, 1999, p. 408).
One of the goals of legalization found in the literature review was to increase
tourism. “Greece is engaged in competition for tourism revenue with its Mediterranean
neighbors both to its east and to its west” (Anagnostaras & Melvani, 1999, p. 408). As
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. was discussed in the theory section, there are numerous examples of jurisdictions using
gambling as a tourist attraction. Clearly, Greece should be included in that list.
Israel
Israel is a country that might seem like an ideal candidate for casino gambling.
They have ongoing budget deficits, high localized unemployment, and a citizenry that
seems to have a high propensity to gamble. However, a formidable obstacle stands in the
way of Israeli casinos: fundamental religious groups with strong political power.
Budget deficits have been a chronic problem for the Israeli government. Figure 12
displays budget deficits for the period 1970 to 1999. While the deficits were down from
the 25 to 25 percent range experienced in the 1970s and early 80s, deficits in the 1990s
were typically between 10 and 20 percent of expenditures.
45%
40%
35%
30%
25%
20%
15% n 10% [in
Figure 12. Israeli Budget Deficits as a Percentage of Spending, 1970 to 1990.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 82
Israeli GDP has shown strong growth since 1970. Figure 13 displays Israel’s
gross domestic product per capita. Israel’s income level of $18,367 in 2005 ranked 22nd
out of 113 nations according to data available from the World Bank (2007).
20000
18000
16000
14000
12000 J
10000
8000
6000
4000
2000 -
^ J? ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^
Figure 13. Israeli Per Capita Gross Domestic Product, 1960 to 2005.
Casino gambling is often looked at as a solution to localized unemployment and
poverty (Furlong, 1998; Eadington, 1995; Collins, 2003). Unemployment in Israel is a
moderate problem. Figure 14 displays unemployment rates from the World Development
Indicators (World Bank, 2007). These rates, primarily between 7 and 10 percent from
1986 through 2000, are not particularly alarming. Rates for Europe & Central Asia as
well as the Middle East & Northern Africa were similar to slightly higher over this period
(World Bank, 2007).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 83
12%
10%
8%
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Figure 14. Israeli Unemployment Rates.
As with many nations, Israel does have some problems with localized
unemployment and poverty. This is the case in the southern desert part of the country.
Israel’s first prime minister, David Ben Gurion, established a number of
settlements there, directed tens of thousands of immigrants to them in the
1940s and 1950s, and chose a desert site for his own retirement home.
Four decades later, the continued poverty and chronic unemployment of
the desert towns is a national embarrassment. Government programs to
encourage talented teachers to work there have had limited success, and
subsidies for industries have gone to low-skill plants that struggle and
often fail in markets that are increasingly international (Sharkansky &
Friedberg, 2002, p. 149).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 84
World Values Survey data is only available for the most recent wave of surveys
for Israel. The church attendance question was not asked in Israel. However, there is a
question asked that is an indicator of religious intensity. Respondents were asked “How
important is God in your life” (European Values Study Group and World Values Survey
Association, 2006)? Answers were given on a scale of 1 (not at all) to 10 (very
important).
The percentage of those that responded with an 8 or higher in Israel was 67.3
percent. Of the 74 nations with responses to this question, 42 had a lower percentage
responding with 8 or higher. The average for all respondents across the world was 56.3
percent. Thus, by this measure of religiosity, Israel is somewhat more religious than
average.
Judaism is the dominant religion in Israel. “Gambling has been part of the Jewish
tradition and culture, often tied to religious celebrations” (Cabot, 1996, p. 29). Even so,
modem Jewish religious leaders oppose gambling.
Another factor researchers point to is the availability of other types of gambling
within the state (von Herrmann, 1999) as well as gambling opportunities in neighboring
states (Berry & Berry, 1990; von Herrmann, 2002). The Israeli government offers lottery
games. These games are aimed at raising revenue for sports and sports education as well
as for public services (Israeli & Mehrez, 2000).
Israeli citizens travel to neighboring jurisdictions to gamble. Casino gambling
opportunities are available to Israelis within a short drive (Sharkansky & Friedberg,
2002). Depending on the level of violence between Israel and the Palestinian Authority, a
casino in Jericho has been available within a 30 minute drive of Jerusalem. An Egyptian
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 85
casino in Taba sits near the border of Israel. In addition, Eilat and Haifa are ports for
gambling boats.
The issue was brought into the political spotlight in 1995 when a committee was
appointed by the national government to examine the legalization of casino gambling.
The committee recommended the legalization of casino gambling while safeguarding
against the problems associated with compulsive gambling. “The report generated a bit of
controversy, most prominently from political parties identified with the religious
community” (Sharkansky & Friedberg, 2002, p. 147).
A few years later, another proposal surfaced, this time with the backing of the
prime minister and minister of finance. Prime Minister Ehud Barak saw the casino as a
way to develop a distressed region. The minister of finance wanted to stem the outflow of
currency and raise tax revenues. When Barak lost power in 2001, “it was on account of
renewed violence between Israelis and Palestinians, and had nothing to do with legalized
gambling” (Sharkansky & Friedberg, 2002, p. 148).
Even though the religious intensity measure only shows slightly higher than
average religiosity in Israel, religion plays a strong role in Israeli politics. Israel,
“established as a Jewish state, has no division between state and religion. In the Israel's
political arena, the large parties, Labor or Likud2, cannot secure a majority in the
parliament without the support of the several small religious parties. These religious
groups oppose the establishment of a legal gaming industry and, through political
negotiations, have managed to block discussions on the subject” (Israeli & Mehrez, 2000,
p. 282).
2 The names of the political parties reflect the conditions at the time of the political debate over casino legalization.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 86
Even though there may be a demand for casino gambling from the public and a
need for tax revenues from the government, casino gambling has not been legalized.
Strongly religious groups, typically the most outspoken opponents of casinos, have a
strong position in the government. Politicians who do support the legalization of casinos
have more important issues to deal with and are unwilling to spend political capital
fighting for casinos.
China
Some of the earliest forms of gambling can be traced to early Chinese
civilizations. “By the Chou period (circa the first millennium B.C.), Chinese culture and
cities were flourishing, and gambling was entrenched as a common pastime. In addition
to shops selling jewels, clothing, and food, most Chinese cities had gambling houses on
their commercial streets” (Schwartz, 2006, p. 16). The Chinese are credited with
inventing dominos, mahjong, “and the forerunners of lotteries, bingo, and keno.
Gamblers would owe their greatest debt, though, to a comparatively late Chinese
invention: playing cards” (Schwartz, 2006, p. 17).
China currently has lotteries as the only form of legal gambling. There are two
lotteries in China, the welfare lottery and the sports lottery. The welfare lottery began in
1987 to raise revenue for social welfare purposes. “Welfare lottery has been a major
factor that prevented the country's public welfare from falling far behind the fast
economic development, said an official with the China Welfare Lottery Center. Thanks to
money collected from welfare lottery, over 81,000 welfare projects, including special
education for mentally- retarded children, have been established across the country”
(Welfare Lottery, 2000).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 87
In 1994, the sports lottery began to fund national sports and fitness programs. “By
the end of 2004, the Administration had used the funds to build, in both urban and rural
areas, 5,627 National Fitness Program projects, which are equipped with 23,319 fitness
paths, 5,920 table tennis tables, 13,790 basketball stands and 2,820 sets of physique
testing equipment” (Most Funds, 2005).
More recently, lottery funds have been used to help pension funding. China’s
current pension system relies on current employees to fund retirees. The effects of
China’s one child policy will make this increasingly difficult in the coming years. By
2030, persons 60 and older are expected to make up about one quarter of China’s
population, compared to about 16 percent worldwide (Murton, 2005, p. 199).
The National Social Security Fund (NSSF) began in 2001 “as a medium to long
term reserve to meet the pension shortfalls of provincial governments when they cannot
meet pension liabilities and when the impact of population ageing is most severe”
(Murton, 2005, p. 205). The NSSF receives funding from the national government, from
a fee imposed on Chinese companies listed on overseas stock markets, and the welfare
lottery. Through 2004, the lottery had provided about 7 ‘A percent of the total funding of
the NSSF (Murton, 2005, p. 205).
Beyond the lotteries, a large amount of illegal gambling occurs within China. “’In
2005, the total revenue of China's legal lottery reached 70 billion yuan (8.75 billion U.S.
dollars), while the illegal betting revenue was around 700 billion yuan,’ said Dr. Wang
Xuehong, head of the China Center for Lottery Studies of Beijing University” (Chinese
Economy, 2006). Forms of illegal gambling include illegal casinos and lotteries as well
as internet gambling.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 88
Some of the economic conditions in mainland China indicate that the Chinese
government may become more likely to legalize casinos in the near future. Fiscal
difficulties, rising income levels, and high unemployment all point towards legalization
of casino gambling in China. These will be discussed in more detail below.
China’s budget deficit has been significant in recent years. Those figures are
displayed in table 13 (National Bureau of Statistics of China, 2007b). These figures
indicate that the Chinese government has been feeling fiscal stress. Further evidence of
fiscal stress is indicated by the discussion of pension shortfalls above.
Table 13 China’s Government Revenue and Expenditure
Government Government Balance Balance Revenue Expenditure (percent of (100 million yuan) (100 million yuan) (100 million yuan) expenditure)
1978 1,132.26 1,122.09 10.17 0.9% 1980 1,159.93 1,228.83 (68.90) -5.6% 1985 2,004.82 2,004.25 0.57 0.0% 1989 2,664.90 2,823.78 (158.88) -5.6% 1990 2,937.10 3,083.59 (146.49) -4.8% 1991 3,149.48 3,386.62 (237.14) -7.0% 1992 3,483.37 3,742.20 (258.83) -6.9% 1993 4,348.95 4,642.30 (293.35) -6.3% 1994 5,218.10 5,792.62 (574.52) -9.9% 1995 6,242.20 6,823.72 (581.52) -8.5% 1996 7,407.99 7,937.55 (529.56) -6.7% 1997 8,651.14 9,233.56 (582.42) -6.3% 1998 9,875.95 10,798.18 (922.23) -8.5% 1999 11,444.08 13,187.67 (1,743.59) -13.2% 2000 13,395.23 15,886.50 (2,491.27) -15.7% 2001 16,386.04 18,902.58 (2,516.54) -13.3% 2002 18,903.64 22,053.15 (3,149.51) -14.3% 2003 21,715.25 24,649.95 (2,934.70) -11.9% 2004 26,396.47 28,486.89 (2,090.42) -7.3%
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 89
China has been one of the world’s fastest growing economies in recent years.
Figure 15 displays per capita income in constant US dollars for the period 1995 to 2005
(World Bank, 2007). Per capita gross domestic product more than doubled during that
period, von Herrmann (1999) found a positive relationship between income levels and
casino legalization in U.S. states. However, China remains far below many other nations,
ranking 65th in per capita income among the world’s nations in 2005 with an average
income of just 4 percent of that in the United States.
1,600
1995 1996 1997 1998 1999 200 0 2001 200 2 2003 200 4 2005
Figure 15. China per capita gross domestic product.
The published unemployment rate in urban areas is quite low in China. Table 14
displays Chinese urban unemployment rates (National Bureau of Statistics of China,
2007a). However, the key to the data presented in the table may be the words ‘registered’
and ‘urban’.
The government's number only includes those who are officially registered
as unemployed. It does not include those who have been laid off from
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 90
state-owned enterprises but who still get a basic stipend for three years
after losing their jobs. Taking them into account, and adjusting for other
distortions, many Chinese analysts put the figure at around 8-10% in urban
areas. A survey of five large cities conducted by academics at the
University of Michigan and the Chinese Academy of Social Sciences
found unemployment rose overall from 7.2% to 12.9% between 1996 and
2001 (No Right to Work, 2004).
Table 14 Urban Unemployment Rate in China
2000 2001 2002 2003 2004 Registered Unemployment Rate in Urban Areas 3.1% 3.6% 4.0% 4.3% 4.2%
Rural unemployment is higher. The CIA World Factbook states that there is
substantial unemployment in rural areas (China, 2007). “China ignores rural areas when
calculating unemployment figures in the belief that, since villagers enjoy land-use rights,
they can make a living” (No Right to Work, 2004). So, despite official statistics that the
unemployment rate is low in China, there appears to be a large number of unemployed
persons.
Beyond the economic conditions, which theoretically make China a likely
candidate to legalize casinos, China has a very low level of religiosity. Chinese church
attendance is the lowest of any nation participating in the World Values Survey
(European Values Study Group and World Values Survey Association, 2006). Just one-
half percent of Chinese attend church once or more each week.
Chinese citizens have many gambling opportunities just across their borders.
"’China used to be circled by tanks,’ says [Michael Backman, an expert on Asia and
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 91
author of several books, including Asian Eclipse: exposing the dark side of business in
Asia]. ‘Now it's circled by casinos.’ Casinos are illegal in China, yet the Chinese love
gambling” (Filou, 2006). The most prominent casino center available to Chinese
residents is in Macau, a Special Administrative Region (SAR) of China.
Macau, a Portuguese colony since the 16th Century was returned to China as an
SAR at the end of 1999 (Macau, 2007). About the time Nevada was legalizing casinos in
the 1930s, so too was Macau. “It swept away the rabble of the fan-tan houses, replacing
them with more refined casinos in upscale hotels. Thus, the Portuguese governor argued,
Macau would attract wealthy inhabitants of surrounding Chinese cities and perhaps even
the sporting gentlemen of Europe” (Schwartz, 2006, p. 451).
The casino industry grew throughout the twentieth century until, by the 1990’s,
the industry contributed as much as 80 percent of government revenues in Macau. When
Macau reverted to Chinese control at the end of the 1990s, there was fear the Chinese
government might suppress the casino industry. “But with an eye on both prosperous
Hong Kong (which returned to Chinese control in 1997) and Taiwan (which Chinese
authorities still wanted to reunite with the mainland), the People’s Republic guaranteed
no interference in Macau’s economy for the next fifty years” (Schwartz, 2006, p. 453).
Since coming back under the control of China, the casino industry in Macau has
grown even stronger. In 2001, the governor of Macau opened bidding on new licenses to
operate casinos. The result was the major players in the U.S. casino market, Las Vegas
Sands and Wynn Resorts, were awarded the right to build and operate casinos in Macau
(Schwartz, 2006, p. 455).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 92
Around the same time, the Chinese government in Beijing relaxed border
restriction around Macau. It granted more of its citizens the right to travel to Macau.
“Macau’s annual visitation nearly double to fifteen million” (Schwartz, 2006, p. 456).
The result of liberalized tourism policies with mainland China and increased
investment and competition among casino operators has resulted in an impressive casino
center being built up in Macau. The result has been, at least by some reports, that casino
revenues in Macau are now larger than those on the Las Vegas Strip. New reports put
Macau’s gambling revenue at $6.95 billion in 2006 (Barboza, 2007). The Nevada
Gaming Control Board reported FY 2006 (June 2005 through July 2006) revenues of $6.4
billion on the Las Vegas Strip (Nevada Gaming Control Board, 2006).
It appears China has many of the conditions in place that theorists say contribute
to the likelihood of casino legalization. There is a long history of gambling in China and
gambling opportunities readily available nearby. The job opportunities and tax revenues
that could potentially be generated by casinos are needed, at least in some regions of the
country. And strong religious groups, a source of opposition to legalization in many
areas, are virtually non-existent in China. So why haven’t they legalized casinos in
China?
They may be headed in that direction, says Dr. Wang XueHong, Executive
Director of the China Center for Lottery Studies, (personal communication, May 29,
2007) Because of the success of the lotteries, the Ministry of Finance is now investigating
the possibility of legalizing gambling on horse racing. They are investigating how the
industry is run in Hong Kong, Japan, and Australia. According to Dr. Wang, they expect
to legalize horse tracks within two years.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 93
Casinos may not far behind. Even though many of the theoretical conditions are in
place that would indicate a readiness to legalize, those theories are primarily built on the
study of westem-style democracies. China, of course, operates under a single party
communist state. On one hand, this means that the government is not as sensitive to some
of the political influences studied by scholars of western political systems. On the other
hand, as Dr. Wang pointed out, under the Chinese Communist Party “casinos could be
illegal when we go to sleep one night and be legal when we wake up the next morning”
(personal communication, May 29, 2007). It will be interesting to watch.
Case Study Summary
Theory suggests that nations with high budget deficits are more likely to legalize
casino gambling (Berry & Berry, 1990, Cabot, 1996, von Herrmann, 2002). In the cases
selected here, tax revenues were a stated goal for the legalization of casinos. Denmark
and Greece both explicitly state that government revenue is important. Additionally, both
nations were experiencing budget deficits in the years leading up to legalization. In the
case of Greece the deficits were quite significant, approaching 50 percent of
expenditures.
The nations that did not legalize also have seen budget deficits. In Israel it has
been a chronic problem, although their deficits moderated somewhat in the late 1990s.
China’s deficits became larger in the late 1990s and early 2000s.
Berry & Berry (1990) and von Herrmann (1999) found that higher incomes were
positively associated with the legalization of gambling. Denmark is one of the wealthiest
nations in the world. The other case of legalization explored here, Greece, had a much
more moderate level of income at the time of legalization. Israel has had a per capita
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 94
income level about twice as high as the level in Greece. China’s income level is very low
but growing at a rapid pace.
Eadington (1995), Cabot (1996), and Furlong (1998) all suggest that nations with
high unemployment rates are more likely to legalize casinos. None of the nations in the
case studies would be considered as having an extremely high level of unemployment.
Greece’s unemployment rate was growing at the time of legalization but still remained in
single digits.
Israel had a national unemployment rate in the 7 to 10 percent range through the
1980s and 1990s. China’s official urban unemployment rate has been under 5 percent
during the 2000s. However, there is evidence of significant localized unemployment in
both of these nations.
Levels of religiosity have been shown to have an impact on the legalization of
gambling (Berry & Berry, 1990; von Herrmann, 1999, 2002). The two nations in the
cases that have legalized casinos both have very low levels of religiosity, as measured by
church attendance. China also has a very low level of religiosity. The Israeli population is
somewhat more religious than average.
The final explanatory variable in the study is tourism. Eadington (1999b) and
Collins (2003) suggest that nations are more likely to legalize casinos if their tourism
industry is declining. This could be interpreted to mean that their own citizens are going
to other nations to gamble when they would stay in country if there were gambling
opportunities.
Denmark’s casinos are primarily located in large international hotels so they can
cater to tourists. Greece has been under constant competition from other nations in the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. region in the broader tourism market. Both Israeli and Chinese citizens have been
traveling to neighboring jurisdictions to gamble in significant numbers.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 96
CHAPTER VII
SUMMARY AND CONCLUSIONS
Summary of Significant Findings
Even among U.S. states, which are relatively homogenous units, it has been
difficult to find strong statistical models of the legalization motivations. It should not be
surprising that this was the case for a cross-national study. There are many factors which
make cross-national statistical analysis of policy decisions difficult.
von Herrmann (1999) looked at states’ adoption of three types of gambling:
casinos, lotteries, and pari-mutuel wagering. Her review of the literature uncovers
evidence that decisions to legalize gambling rely on evidence from many different fields
including demographics, economics, and religion. “All of this previous research into
gambling suggests that decisions about gambling policy may not be a simple cost-benefit
analysis, but rather are complex, multi-layered choices in which citizens, special
interests, and legislatures must all play a part” (p. 1664).
This analysis found statistical evidence supporting the hypothesis that
governments are motivated by high unemployment levels. An important barrier found in
the analysis is religiosity. Statistically significant results were not found for income,
fiscal, and tourism measures.
The event history analysis results from this study were not significantly different
from previous studies focused on the expansion of gambling among U.S. states. As might
be expected, the goodness of fit measures were lower for this international model
compared with similar domestic U.S. analyses. The Nagelkerke R2 of .249 was smaller
than von Herrmann’s (2000) pseudo R2 of .323 in her study of casino adoptions but right
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 97
in line with the pseudo R2 of .233 in her study of lottery adoptions. Furlong (1998)
achieved a significantly higher pseudo R2 of .728 in his study of casino adoptions. Berry
and Berry (1990) reported estimated R2s of .48 and .44 for two models analyzing lottery
adoptions in the U.S.
von Herrmann (1999, 2002) found statistically significant negative relationships
between religiosity and the adoption of both lotteries and casinos. Berry and Berry (1990)
also found a similar negative and significant relationship between religiosity and lottery
adoption. Those findings are similar to those found in this international analysis.
Furlong (1998) was the only author identified in the literature that has tested a
measure of employment as a predictor of casino legalization. His study of U.S. states
found a significant and negative relationship between job growth and casino legalization.
This means that states with higher job growth were less likely to adopt casinos. The
results of this study were similar. Higher unemployment rates were found to have a
statistically significant positive relationship with casino adoption at the international
level.
von Herrmann (2002) tested income levels as a predictor of lottery and casino
adoption. This was the only such analysis of income levels uncovered in the literature of
gambling adoption. Her study found a positive and significant relationship between
income levels and lottery and casino adoptions. This international study did not find a
statistically significant relationship between these variables.
von Herrmann (2002) and Berry and Berry (1990) both tested a fiscal variable as
a predictor for gambling adoptions. This is a factor that is talked about frequently in
political discussions and press reports about gambling adoption. However, as with this
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 98
international study, previous researchers have not found a statistically significant
relationship between fiscal stress measures and gambling adoption.
Tourism as a driver of casino legalization is mentioned frequently in the literature,
including Eadington (1999b) and Cabot (1996), along with the National Gambling Impact
Study Commission (1999) final report. However, no previous statistical analysis of this
factor was found in the literature. This international study did not find a statistically
significant relationship between tourism importance and casino legalization.
The case studies presented in this analysis shed further light on the difficulties of
modeling casino adoption decisions. The Israeli and Chinese cases illustrate the fact that
the choice to legalize casino gambling is driven by politics, not supply and demand.
Interest groups, even when their views are in the minority, can hold tremendous veto
power over these types of decisions. Majority doesn’t always rule, especially in
authoritarian states.
The desire for job creation does not appear to have had a large importance in the
cases of Denmark and Greece. This is interesting because the unemployment variable had
the highest level of significance among the explanatory variables in the statistical model.
Beyond the cases of Denmark and Greece, there are plenty of cases where the desire for
job creation was stated as a goal for policymakers in the legalization process. Based on
this research and previous studies, job creation is certainly an important factor in the
legalization decisions for casino gambling.
While the statistical support in the event history analysis was weak for fiscal
concerns as a driver for casino legalization, it was clearly an important factor in the
legalization process in both Denmark and Greece. Anecdotal evidence such as this is
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 99
common among studies of casino legalization. This may explain why a fiscal variable
continues to be included in statistical analyses, even though it rarely is found to be
significant.
The interesting question then becomes, why is the fiscal motivation so often
stated when statistical analyses rarely if ever reveal this as important? It could simply be
that the models are not doing a good job of identifying the true motivating factors. This
will be discussed further below.
Another reason may be that politicians simply use the revenue motivation for
political cover. Voters like to hear that their tax burdens will be reduced. However, the
true motivation may have more to do with factors such as interest group politics, which
are much more difficult to model statistically.
The desire for increased tourism was also a clearly stated goal of casino adoption
in both Denmark and Greece. The tourism motivation is often cited but the literature
review did not turn up any examples of statistical tests of this hypothesis. But again, the
anecdotal evidence is strong for tourism promotion as a driver of casino legalization.
Religion clearly has an impact on casino gambling legalization decisions.
However, religion can impact the process in different ways. This makes religion another
factor that is difficult to quantify statistically.
Religious intensity was one of only two statistically significant independent
variables in the statistical analysis. Religious intensity, in this analysis measured by
frequency of church attendance, is a broad measure of the impact of religion in a nation.
The Israeli case illustrates how religion can have a more targeted impact on policy
making. Religious intensity in Israel is not particularly high, relative to other nations.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 100
However, religious groups wield tremendous power within the Israeli government. The
way in which religion can impact policy decisions varies tremendously from nation to
nation.
Another illustration of the difficulty of modeling the legalization decision comes
from the existence of other types of gambling in a jurisdiction, von Herrmann (2002)
suggests that the existence of a regulatory agency for one type of gambling makes it
easier for a state to adopt another type of gambling. The rationale is that the marginal cost
of regulating a gambling activity is much lower for an additional type of gambling than
the cost of regulating the first legalized gambling activity. In other words, for the first
gambling activity, an entire regulatory agency must be built, for the second type of
gambling perhaps a new department is simply added to the existing agency.
However, work by Mukhtar Ali and Richard Thalheimer suggests that an existing
type of gambling may actually hinder the development of other types of gambling. Ali
and Thalheimer (Thalheimer & Ali; 1995, Ali & Thalheimer, 1997; Thalheimer, 1998)
looked at cross-price elasticities between casino gambling and pari-mutuel horse racing.
They found that the presence of casinos or slot machines near race tracks reduced the
handle at pari-mutuel betting facilities by about 25 to 30 percent.
This suggests that stakeholders in existing forms of gambling have an interest in
prohibiting the legalization of other forms of gambling. This would likely manifest itself
through interest group politics. This is again a factor that is difficult to measure
statistically.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 101
Limitations
There are a number of internal and external validity concerns with this research.
Internal validity refers to the ability of the research design to answer the research
question effectively (O’Sullivan and Rassel, 1999). In other words, are the independent
variables really explaining the change in the dependent variable? External validity refers
to the ability of the research to be generalized to other cases. Do the conditions identified
as important have a similar impact on nations not in the statistical analysis?
The main internal validity concern related to this research is categorized as history
by O’Sullivan and Rassel (1999). Internal validity history concerns refer to the fact that
the relationship might be caused by events other than the treatment. There may be some
other significant factors that might contribute to the legalization of casinos.
Some of these potential factors are in the literature related to gambling
legalization. For example, Berry and Berry (1990), Furlong (1998), and von Herrmann
(2002) tested the hypothesis that a previous gambling legalization event in a neighboring
state would make it more likely that a state would legalize gambling. Each of these
authors found some level of evidence that previous neighbor’s gambling adoption did
indeed positively influence the decision to adopt.
This is a difficult measure to test statistically. In the studies of U.S. states, a
dummy variable is typically used to signify whether a neighboring state had previously
adopted a lottery or casino gambling. In the case of nations, it is likely that there are
significant differences in the relationships of neighboring states. Finland shares borders
with Norway, Sweden, and Russia. Due to their similar cultural histories, Norway and
Sweden would likely have more of an influence on policy issues than would Russia.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 102
Another problem with this measure is the identification of nations that are
considered neighbors. Australia, Iceland, Japan, and New Zealand are island nations that
share no physical borders with other nations. However, they are no doubt influenced by
neighbors in the region.
Neighbor adoption is a factor identified in the literature as having an influence on
the adoption of gambling. However, it is difficult to measure statistically, especially at
the international level. Other factors identified in the literature as having an impact on the
legalization decision are similar in that they are more difficult to measure at the
international level. These include political factors such as relative power of political
parties, the professionalism of legislatures, liberalism, and interest group strength.
There are also likely factors that have not been identified in the literature that
make it more likely that nations will adopt casino gambling. Political variables such as
form of government may become important in cross-national studies. There are also no
doubt many cultural factors that impact the legalization process.
In addition to the internal validity issues related to this study, there are external
validity concerns as well. The external validity concerns are classified by O’Sullivan and
Rassel (1999) as selection and history. Selection effects develop when the subjects of the
statistical analysis are not representative of the larger population. History concerns are
related to the time frame of the analysis.
The problems with selection come from the fact that the nations used in the
statistical analysis were not chosen at random. The nations are in the dataset based on the
availability of data. If all the necessary data was available for a nation, it was included in
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 103
the statistical analysis. If there were one or more data items unavailable for a nation, it
was excluded.
This selection method may mean that the results obtained from the analysis may
not be valid when extended to policy analysis in other nations. Statistically, the most
significant independent variable in the analysis was unemployment. Higher
unemployment was found to be positively correlated with casino legalization.
However, there may be something about the risk set chosen for the analysis that
would make that relationship more important than it is in nations not in the risk set.
About one-third of the risk set consists of northern European nations. This bias might
make the results less applicable to Asian nations, for example, where only Indonesia is
included in the risk set.
The history external validity concern is related to the risk period. The risk period
in this analysis, 1985 to 2000, coincided with the ‘fourth wave’ of gambling expansion as
described by McGowan (1999). The validity concern is that the conditions that led to
legalization during this period might not be the same as those in the fifth wave, should it
come.
Recommendations for Future Research
Clearly there is room for improvement in statistical analysis of gambling adoption
models. This research explained just 25 percent of the variance in adoption decisions.
Improvements may come through the identification of factors that better measure
political processes at the international level. Possible areas to look at include measures of
left or right leaning governments, political fragmentation, or unitary or federal systems.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. There are also likely economic or social variables that have either not been
identified as being important or are difficult to measure. Data availability proved to be a
major obstacle in this research. Further refining data sources, perhaps by focusing on a
specific region, may improve the analysis.
The question of the impact of fiscal stress on gambling legalization certainly begs
further study. It is mentioned in virtually every academic and popular press article on
gambling legalization issues. Yet statistical research rarely finds any significant result on
this question. Why is this? Are we using inappropriate measures of fiscal stress? Are
politicians and industry officials simply using potential tax revenues from the industry as
political cover for other legalization motivations? These are important questions that
deserve further examination.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 105
APPENDIX A
DATA SET
Nation Year Casino GDPlag UnemplRate AttendFinanceLag Rel SqrRtTrvI Belgium 1985 0 8123 11.4 0.285 -0.182 0.424 Belgium 1986 0 8403 11.3 0.281 -0.161 0.412 Belgium 1987 0 11677 11.3 0.277 -0.167 0.424 Belgium 1988 0 14508 10.1 0.274 -0.134 0.436 Belgium 1989 0 15724 8.3 0.270 -0.121 0.387 Belgium 1990 0 15868 7.2 0.266 -0.124 0.374 Belgium 1991 0 19782 7.0 0.257 -0.106 0.361 Belgium 1992 0 20218 7.7 0.248 -0.121 0.361 Belgium 1993 0 22475 8.2 0.240 -0.134 0.361 Belgium 1994 0 21438 9.8 0.231 -0.118 0.374 Belgium 1995 0 23280 9.3 0.222 -0.090 0.412 Belgium 1996 0 27285 9.6 0.213 -0.082 0.387 Belgium 1997 0 26506 8.9 0.205 -0.057 0.387 Belgium 1998 0 23989 9.1 0.196 -0.053 0.387 Belgium 1999 1 24441 8.6 0.187 -0.040 0.412 Brazil 1985 0 1569 3.4 0.311 0.139 0.173 Brazil 1986 0 1639 2.4 0.315 0.040 0.224 Brazil 1987 0 1931 3.6 0.320 -0.199 0.224 Brazil 1988 0 2077 3.8 0.324 0.380 0.224 Brazil 1989 0 2290 3.0 0.328 -0.359 0.632 Brazil 1990 0 3149 3.7 0.332 -0.363 0.608 Brazil 1991 0 3092 5.1 0.337 -0.099 0.557 Brazil 1992 0 2682 6.5 0.341 0.059 0.500 Brazil 1993 0 2532 6.2 0.345 -0.073 0.520 Brazil 1994 0 2799 6.1 0.350 -0.186 0.447 Brazil 1995 0 3436 6.1 0.354 -0.082 0.400 Brazil 1996 0 4364 7.0 0.358 -0.055 0.400 Brazil 1997 0 4730 7.8 0.362 -0.027 0.424 Brazil 1998 0 4857 9.0 0.367 0.000 0.436 Brazil 1999 0 4666 9.6 0.371 -0.032 0.490 Brazil 2000 0 3132 10.6 0.375 0.007 0.447 Denmark 1985 0 11015 9.1 0.027 -0.103 0.500 Denmark 1986 0 11701 7.9 0.026 -0.028 0.529 Denmark 1987 0 16568 7.9 0.026 0.103 0.539 Denmark 1988 0 20598 8.7 0.026 0.092 0.500 Denmark 1989 0 21703 9.5 0.026 0.048 0.490 Denmark 1990 1 21030 9.7 0.025 0.017 0.510 Finland 1985 0 10555 5.0 0.038 -0.034 0.469 Finland 1986 0 11089 5.4 0.038 -0.023 0.490 Finland 1987 0 14419 5.0 0.038 0.006 0.500 Finland 1988 0 17998 4.5 0.039 -0.054 0.510 Finland 1989 0 21409 3.1 0.039 0.021 0.510 Finland 1990 0 23179 3.1 0.039 0.058 0.510 Finland 1991 1 27467 6.6 0.039 0.010 0.548
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 106
Greece 1985 0 4169 7.8 0.140 -0.277 0.748 Greece 1986 0 4134 7.4 0.140 -0.348 0.762 Greece 1987 0 4855 7.4 0.140 -0.273 0.728 Greece 1988 0 5634 7.7 0.140 -0.295 0.686 Greece 1989 0 6519 7.5 0.140 -0.363 0.640 Greece 1990 0 6735 7.0 0.140 -0.431 0.632 Greece 1991 0 8275 7.7 0.140 -0.399 0.600 Greece 1992 0 8814 8.7 0.140 -0.342 0.616 Greece 1993 0 9649 9.7 0.140 -0.253 0.640 Greece 1994 1 8939 9.6 0.140 -0.353 0.656 Iceland 1985 0 11689 0.9 0.023 0.038 0.374 Iceland 1986 0 11966 0.7 0.023 -0.069 0.424 Iceland 1987 015934 0.4 0.023 -0.133 0.458 Iceland 1988 0 21723 0.6 0.024 -0.097 0.520 Iceland 1989 023714 1.7 0.024 -0.128 0.539 Iceland 1990 0 21248 1.8 0.024 -0.122 0.592 Iceland 1991 0 24336 1.5 0.025 -0.084 0.566 Iceland 1992 025757 3.0 0.026 -0.113 0.557 Iceland 1993 0 26129 4.3 0.027 -0.084 0.529 Iceland 1994 0 22706 4.8 0.028 -0.098 0.529 Iceland 1995 0 23092 5.0 0.029 -0.093 0.566 Iceland 1996 0 25458 4.3 0.030 -0.076 0.520 Iceland 1997 026312 3.9 0.030 -0.036 0.490 Iceland 1998 0 26375 2.8 0.031 0.014 0.490 Iceland 1999 0 28999 1.9 0.032 0.043 0.520 Iceland 2000 030202 1.3 0.033 0.092 0.490 Indonesia 1985 0 538 1.9 0.647 0.114 0.806 Indonesia 1986 0 526 2.5 0.647 -0.020 0.877 Indonesia 1987 0 473 2.5 0.647 -0.142 0.933 Indonesia 1988 0 440 2.7 0.647 -0.049 0.970 Indonesia 1989 0 506 2.7 0.647 -0.160 0.933 Indonesia 1990 0 569 2.5 0.647 -0.106 0.933 Indonesia 1991 0 631 2.5 0.647 0.022 0.943 Indonesia 1992 0 695 2.7 0.647 0.027 0.949 Indonesia 1993 0 743 3.0 0.647 -0.030 0.970 Indonesia 1994 0 831 3.4 0.647 0.024 0.990 Indonesia 1995 0 917 3.7 0.647 0.122 0.990 Indonesia 1996 0 1033 4.0 0.647 0.205 0.980 Indonesia 1997 01146 4.7 0.647 0.158 0.990 Indonesia 1998 0 1073 5.5 0.647 0.009 0.990 Indonesia 1999 0 468 6.4 0.647 -0.096 0.990 Indonesia 2000 0 678 6.1 0.647 -0.120 0.990 Ireland 1985 0 5470 17.4 0.816 -0.229 0.671 Ireland 1986 0 5766 17.4 0.814 -0.244 0.663 Ireland 1987 0 7778 16.90.813 -0.234 0.656 Ireland 1988 0 9205 16.3 0.811 -0.195 0.656 Ireland 1989 0 10280 15.0 0.810 -0.073 0.663 Ireland 1990 0 10704 12.9 0.808 -0.056 0.663 Ireland 1991 0 13458 14.7 0.791 -0.052 0.656 Ireland 1992 0 13563 15.1 0.774 -0.024 0.656 Ireland 1993 015171 15.7 0.757 -0.067 0.671 Ireland 1994 0 14138 14.7 0.740 -0.023 0.663
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 107
Ireland 1995 0 15293 12.2 0.723 -0.027 0.678 Ireland 1996 0 18379 11.9 0.706 -0.020 0.663 Ireland 1997 0 20055 10.3 0.688 0.008 0.656 Ireland 1998 0 21790 7.8 0.671 0.020 0.447 Ireland 1999 0 23478 5.7 0.654 0.077 0.412 Ireland 2000 0 25421 4.3 0.637 0.054 0.374 Mexico 1985 0 2357 2.7 0.409 -0.311 0.819 Mexico 1986 0 2424 2.6 0.414 -0.318 0.854 Mexico 1987 0 1665 2.5 0.419 -0.459 0.849 Mexico 1988 0 1767 2.5 0.424 -0.464 0.866 Mexico 1989 0 2261 2.4 0.429 -0.373 0.866 Mexico 1990 0 2698 2.3 0.434 -0.220 0.877 Mexico 1991 0 3117 2.2 0.439 -0.137 0.872 Mexico 1992 0 3658 2.3 0.444 -0.024 0.866 Mexico 1993 0 4150 2.4 0.449 0.102 0.860 Mexico 1994 0 4516 3.6 0.454 0.015 0.794 Mexico 1995 0 4639 4.7 0.459 -0.027 0.800 Mexico 1996 0 3099 3.7 0.464 -0.023 0.800 Mexico 1997 0 3538 2.6 0.485 -0.008 0.819 Mexico 1998 0 4198 2.3 0.506 -0.093 0.806 Mexico 1999 0 4336 1.8 0.527 -0.121 0.787 Mexico 2000 0 4879 1.9 0.548 -0.101 0.781 New Zealand 1985 0 7078 2.5 0.167 -0.152 0.557 New Zealand 1986 0 7034 4.0 0.167 -0.076 0.608 New Zealand 1987 0 8909 4.1 0.167 -0.082 0.656 New Zealand 1988 0 11196 5.6 0.167 -0.041 0.640 New Zealand 1989 0 13260 7.1 0.167 -0.047 0.656 New Zealand 1990 1 12675 7.8 0.167 -0.037 0.656 New Zealand 1991 0 12789 10.3 0.167 -0.028 0.656 Norway 1985 0 14640 2.6 0.055 0.152 0.332 Norway 1986 0 15330 2.0 0.054 0.211 0.361 Norway 1987 0 18229 2.1 0.053 0.195 0.387 Norway 1988 0 21748 3.2 0.053 0.108 0.400 Norway 1989 0 23492 4.9 0.052 0.062 0.361 Norway 1990 0 23537 5.2 0.051 0.016 0.361 Norway 1991 0 27374 5.5 0.051 0.027 0.361 Norway 1992 0 27856 5.9 0.051 -0.002 0.400 Norway 1993 0 29680 6.0 0.051 -0.063 0.387 Norway 1994 0 27171 5.4 0.050 -0.062 0.424 Norway 1995 0 28561 4.9 0.050 -0.025 0.412 Norway 1996 0 33945 4.8 0.050 0.060 0.387 Norway 1997 0 36293 4.0 0.050 0.134 0.374 Norway 1998 0 35578 3.2 0.049 0.211 0.374 Norway 1999 0 33752 3.2 0.049 0.127 0.387 Norway 2000 0 35332 3.4 0.049 0.133 0.346 Peru 1985 0 1041 4.9 0.302 -0.241 0.548 Peru 1986 0 965 5.3 0.313 -0.139 0.566 Peru 1987 0 900 4.8 0.323 -0.245 0.539 Peru 1988 0 1171 5.1 0.334 -0.404 0.583 Peru 1989 0 593 5.3 0.345 -0.278 0.574 Peru 1990 0 965 5.6 0.355 -0.443 0.548 Peru 1991 0 1209 5.8 0.366 -0.393 0.548
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 108
Peru 1992 1 1557 9.4 0.376 -0.141 0.458 Sweden 1985 0 11984 2.8 0.052 -0.191 0.447 Sweden 1986 0 12523 2.2 0.050 -0.094 0.490 Sweden 1987 0 16510 2.1 0.048 -0.072 0.480 Sweden 1988 0 20022 1.7 0.046 0.040 0.480 Sweden 1989 0 22492 1.5 0.044 0.068 0.480 Sweden 1990 0 23585 1.6 0.043 0.099 0.469 Sweden 1991 0 28059 3.0 0.042 0.084 0.436 Sweden 1992 0 29411 5.2 0.042 -0.008 0.436 Sweden 1993 0 30263 8.2 0.042 -0.083 0.469 Sweden 1994 0 22705 8.0 0.041 -0.271 0.458 Sweden 1995 0 24270 7.7 0.041 -0.242 0.480 Sweden 1996 0 28115 8.0 0.041 -0.257 0.469 Sweden 1997 0 30586 8.0 0.040 -0.104 0.458 Sweden 1998 0 27917 6.5 0.039 -0.038 0.490 Sweden 1999 1 27992 5.6 0.038 -0.004 0.458 Venezuela 1985 0 3318 13.1 0.320 0.266 0.742 Venezuela 1986 0 3345 11.0 0.319 0.338 0.748 Venezuela 1987 0 3312 9.2 0.318 0.033 0.707 Venezuela 1988 0 2482 7.3 0.317 -0.115 0.600 Venezuela 1989 03114 9.9 0.316 -0.114 0.656 Venezuela 1990 0 2189 10.4 0.315 0.093 0.663 Venezuela 1991 0 2383 9.5 0.314 0.142 0.663 Venezuela 1992 0 2560 7.7 0.313 0.183 0.608 Venezuela 1993 0 2827 6.7 0.312 -0.092 0.678 Venezuela 1994 0 2747 8.7 0.311 -0.067 0.735 Venezuela 1995 0 2615 10.3 0.310 -0.055 0.748 Venezuela 1996 1 3391 11.8 0.309 -0.118 0.781
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 109
REFERENCES
Abbott, M. W., & R. A. Volberg (1999). Gambling And Problem Gambling In The
Community: An International Overview And Critique. Report No. 1 o f the New
Zealand Gaming Survey. Wellington, NZ: Department of Internal Affairs.
Ali, M. M., & R. Thalheimer (1997), "Transportation Costs And Product Demand:
Wagering On Pari-mutuel Horse Racing”, Applied Economics, 29, 529-542.
Allison, P. D. (1984). Event History Analysis: Regression for Longitudinal Event Data.
Newbury Park, CA: Sage Publications.
American Gaming Association (2006). State o f the States 2006: The AG A Survey o f
Casino Entertainment. Retrieved February 18, 2007 from
http://www.americangaming.org/assets/files/2006_Survey_for_Web.pdf
Anagnostaras, J. A., & H. Melvani (1999). Greece. In Cabot, A. N., W. N. Thompson, A.
Tottenham, & C. G. Braunlich (eds.) International Casino Law. Reno, NV:
Institute for the Study of Gambling and Commercial Gaming.
Barboza, D. (2007, January 23). Macao Surpasses Las Vegas as Gambling Center. The
New York Times. Retrieved August 6, 2007 from
http://www.nytimes.com/2007/01/23/business/worldbusiness/23cnd-
macao.html?ex=T 186545600&en=386e3135026bc927&ei=5070.
Barker, T., & M. Britz (2000). Jokers Wild: Legalized Gambling in the Twenty-first
Century. Westport, CT: Praeger.
Berry, F. S., & W. D. Berry. (1990). State Lottery Adoptions as Policy Innovations: An
Event History Analysis. The American Political Science Review, 84(2), 395-415.
Berry, F. S., & W. D. Berry (1999). Innovation and Diffusion Models in Policy Research.
In Paul A. Sabatier (ed) Theories o f the Policy Process. Boulder, CO: Westview
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 110
Press.
Blaszczynski, A., R. Ladouceur, & H. J. Shaffer (2004). “A Science-Based Framework
for Responsible Gambling: The Reno Model.” Journal o f Gambling Studies,
20(3), 301-317.
Cabot, A. N. (1996). Casino Gaming: Policy, Economics, and Regulation. Las Vegas,
NV: Trace Publications.
Cabot, A. N., W. N. Thompson, A. Tottenham, & C. G. Braunlich (1999). International
Casino Law. Reno, NV: Institute for the Study of Gambling and Commercial
Gaming.
Call, V. R. A., & T. B. Heaton (1997). “Religious Influence on Marital Stability.”
Journal for the Scientific Study o f Religion, 36(3), 382-392.
Casino Loutraki Named in Possible Stock Float (2004, September). International Gaming
& Wagering Business. Retrieved on July 21, 2007 from
http://www.igwb.com/article.php?ida=1094.
China (2007). CIA World Fact Book. Retrieved July 20, 2007 from
https://www.cia.gov/library/publications/the-world-factbook/geos/ch.html
Chinese Economy Missing Out on Gambling Revenue. (2006, July 19) People’s Daily
Online. Retrieved July 19, 2007 from
http://english.peopledaily.com.cn/200607/19/eng20060719_284491.html.
Collins, D., & H. Lapsley (2003). “The Social Costs and Benefits of Gambling: An
Introduction to the Economic Issues.” Journal o f Gambling Studies, 19(2), 123-
148.
Collins, P. (2003). Gambling and the Public Interest. Westport, CT: Praeger Publishers.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Cornell Law School (1977). The Development o f the Law o f Gambling: 1776-1976.
Washington D.C.: National Institute of Law Enforcement and Criminal Justice,
Law Enforcement Assistance Administration, United States Department of
Justice.
de la Vina, L., & D. Bernstein (2002). “The Impact of Gambling on Personal Bankruptcy
Rates.” Journal o f Socio-Economics, 503-509. 31,
Eadington, W. R. (1991). Public Policy Considerations and Challenges and the Spread of
Commercial Gambling. In William R. Eadington and Judy A. Cornelius (eds)
Gambling and Public Policy: International Perspectives. Reno, NV: Institute for
the Study of Gambling and Commercial Gaming.
Eadington, W. R. (1995). Economic Development and the Introduction of Casinos:
Myths and Realities. Economic Development Review, 13(4), 51-54.
Eadington, W. R. (1999a). The Spread of Casinos and Their Role In Tourism
Development. In D. Pearce and R. Butler (eds) Contemporary Issues in Tourism
Development. London: Routledge.
Eadington, W. R. (1999b). “The Economics of Casino Gambling P Journal o f Economic
Perspectives, 13(3), 173-192.
European Commission (2006). Study o f Gambling Services in the Internal Market o f the
European Union: Final Report. Lausanne, Switzerland: Swiss Institute of
Comparative Law.
European Values Study Group and World Values Survey Association (2006). European
and World Values Surveys Four Wave Integrated Data File, 1981-2004 (v.
20060423). Retrieved August 1, 2007 from http://www.worldvaluessurvey.com/.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 112
Falvey, R., & T. Nagel (1999). New Zealand. In Cabot, A. N., W. N. Thompson, A.
Tottenham, and C. G. Braunlich (eds.) International Casino Law. Reno, NV:
Institute for the Study of Gambling and Commercial Gaming.
Field, A. (2005). Discovering Statistics Using SPSS. London: Sage Publications.
Filou, E. (2006, November 20). China’s Backyard. World Business. Retrieved July 19,
2007 from http://www.worldbusinesslive.com/article/606058/chinas-backyard/.
Frank, M. L., D. Lester, & A. Wexler. (1991). Suicidal behavior among members of
Gamblers Anonymous. Journal o f Gambling Studies, 249-254.7,
Furlong, E. J. (1998). A Logistic Regression Model Explaining Recent State Casino
Gaming Adoptions. Policy Studies Journal, 26(3), 371-383.
Goodman, R. (1994). Legalized Gambling as a Strategy for Economic Development.
Northampton, MA: U.S. Gambling Study.
Goodman, R. (1995). The Luck Business. New York, NY: Free Press..
Great Britain,, & Budd, A. (2001). Gambling review report. Norwich, England:
Stationery Office.
Grinols, E. L., & D. B. Mustard (2001). Business Profitability versus Social Profitability:
Evaluating Industries with Externalities, The Case of Casinos. Managerial and
Decision Economics, 22, 143-162.
Grinols, E. L., & D. B. Mustard (2006). Casinos, Crime, and Community Costs. The
Review o f Economics and Statistics, 88 (1), 28-45.
Hair, J. F., W. C. Balck, B. J. Babin, R. E. Anderson, & R. L. Tatham (2006).
Multivariate Data Analysis. Upper Saddle River, NJ: Pearson Prentiss Hll.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 113
Henriksson, L. E., & R. G. Lipsey. (1999). Should provinces expand gambling?
Canadian Public Policy, 25(2), 259-275.
International Labour Office (1989-90). Yearbook of Labour Statistics. Geneva:
International Labour Organization.
International Labour Office (1997). Yearbook o f Labour Statistics. Geneva: International
Labour Organization.
International Labour Office (2001). Yearbook o f Labour Statistics. Geneva: International
Labour Organization.
International Monetary Fund (2007). International Financial Statistics Online. Retrieved
July 10, 2007 from http://imfstatistics.org/.
Israeli, A. A., & A. Mehrez (2000). From Illegal Gambling to Legal Gambling: Casinos
in Israel. Tourism Management, 21, 281-291.
Korn, D. A. (2000). Expansion of Gambling in Canada: Implications for Health and
Social Policy. Canadian Medical Association Journal, 163(1), 61-64.
Lesieur, H. R. (1998). Gambling: Socioeconomic Impacts and Public Policy. Annals o f
the American Academy o f Political and Social Science, 556, 153-171.
Liao, T. F. (1994). Interpreting Probability Models: Logit, Probit, and Other Generalized
Linear Models. Thousand Oaks, CA: Sage Publications.
Macau (2007). CIA World Fact Book. Retrieved August 4, 2007 from
https://www.cia.gov/library/publications/the-world-factbook/geos/mc.html
McGowan, R. (1999). Legalized Gambling: A History. In Williams, Mary E. (ed)
Legalized Gambling. San Diego, CA: Greenhaven Press.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 114
Miller, R. K., & Associates. (2001). The 2001 Casino Gaming Business Market Research
Handbook. Norcross, GA: Richard K. Miller and Associates.
Morse, E. A., & E. P. Goss (2007). Governing Fortune: Casino Gambling in America.
Ann Arbor: University of Michigan Press.
Most Funds From China’s Sports Lottery Go to Mass Sports Cause. (2005, July 14)
People’s Daily Online. Retrieved July 20, 2007 from
http://english.peopledailv.com.cn/200507/14/eng20050714 196085.html.
Murton, T. (2005). Recent Developments in the Social Security System. In McConnell
and Bridget Maidment (eds) The China Boom and its Discontents. Canberra,
Australia: Australia National University E Press.
National Bureau of of China Statistics (2007a). Employment. Retrieved on July 20, 2007
from http://www.allcountries.org/china_statistics/ 5_l_employment.html.
National Bureau of of China Statistics (2007b). Government Revenue and Expenditure
and Their Increase Rates. Retrieved on July 20, 2007 from
http://www.allcountries.org/china_statistics/
8_ 1 go vemmentrevenueandexpenditureand. html.
National Gambling Impact Study Commission (1999). Final Report. National Gambling
Impact Study Commission.
Nelson, M., & J. L. Mason (2006). The Politics of Casino Gambling. In Denise von
Herrmann (ed) Resorting to Casinos: the Mississippi Gambling Industry. Jackson,
MS: University Press of Mississippi.
Nevada Gaming Control Board (2006). June 2006 Nevada Gaming Revenues and
Collections. Press release. Retrieved on August 6, 2007 from
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 115
http://gaming.nv.gov/documents/pdf/mrrjun06.pdf.
Nevries, H. (1999). Denmark. In Cabot, A. N., W. N. Thompson, A. Tottenham, and C.
G. Braunlich (eds.) International Casino Law. Reno, NV: Institute for the Study
of Gambling and Commercial Gaming.
Newman, S. C., & A. H. Thompson (2003). A Population-Based Study of the Association
Between Pathological Gambling and Attempted Suicide. Suicide and Life-
Threatening Behavior, 33(1), 80-87.
Nicholls, M., B. G. Stitt, & D. Giacopassi (1999). Casino Gambling and Bankruptcy in
New United States Casino Jurisdictions. Journal o f Socio-Economics, 247- 29,
261.
No Right to Work. (2004, September 11). Economist. Retrieved July 20, 2007, from
Academic Search Premier database.
O’Sullivan, E., & G. R. Rassel (1999). Research Methods for Public Administrators.
New York: Longman.
Quinn, J. P. (1892). Fools o f Fortune. Chicago: The Anti-Gambling Association.
Rogers, E. M. (1995). Diffusion o f Innovations. New York, NY: The Free Press.
Romppainen, E. (1999). Finland. In Cabot, A. N., W. N. Thompson, A. Tottenham, and
C. G. Braunlich (eds.) International Casino Law. Reno, NV: Institute for the
Study of Gambling and Commercial Gaming.
Rose, I. N. (1986). Gambling and the Law. Hollywood, CA: Gambling Times, Inc.
Rose, I. N. (1991). The Rise and Fall of the Third Wave: Gambling Will Be Outlawed in
Forty Years. In William R. Eadington and Judy A. Cornelius (eds) Gambling and
Public Policy: International Perspectives. Reno, NV: Institute for the Study of
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 116
Gambling and Commercial Gaming.
Rutherford, J. (2004, July). The UK Welcomes U.S.-Style Casinos, But the Rest of
Europe Says No Thanks. International Gaming & Wagering Business. Retrieved
on July 21, 2007 from http://www.igwb.com/article.php?ida=l059.
Sasuly, R. (1982). Bookies and Bettors. New York, NY: Holt, Rinehart and Winston.
Sauer, R. D. (2001). The political economy of Gambling regulation. Managerial and
Decision Economics, 22, 5-15.
Schwartz, D. G. (2006). Roll The Bones: The History o f Gambling. New York, NY:
Penguin Group.
Shaffer, H. J., & D. A. Korn (2002). Gambling and Related Mental Disorders: A Public
Health Analysis. Annual Review o f Public Health, 23, 171-212.
Sharkansky, I., & A. Friedberg (2002). Towards a Typology of Non-Decisions: Three
Israeli Cases. International Journal o f Organizational Theory and Behavior,
5(1 &2), 145-158.
Shrum, W. (1980). Religion and Marital Instability: Change in the 1970s? Review o f
Religious Research, 21 (2), 135-147.
Smith, J. (2000). Gambling Taxation: Public Equity in the Gambling Business. The
Australian Economic Review, 33(2), 120-144.
Stitt, B. G., M. Nicholls, & D. Giacopassi (2003). Does the Presence of Casinos Increase
Crime? An Examination of Casino and Control Communities. Crime
Delinquency, 49(2), 253-284.
Sweezy, K., & J. Tiefenthaler (1996). Do State-Level Variables Affect Divorce Rates?
Review o f Social Economy, 54(1), 47-65.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 117
Thalheimer, R. (1998), Pari-mutuel Wagering And Video Gaming: A Racetrack
Portfolio. Applied Economics, 30, 531-543.
Thalheimer, R., & Ali, M. M. (1995), Exotic Betting Opportunities, Pricing Policies And
The Demand For Pari-mutuel Horse Race Wagering, Applied Economics, 27,689-
703.
Thompson, W. N. (1999a). Dominican Republic In Cabot, A. N., W. N. Thompson, A.
Tottenham, and C. G. Braunlich (eds.) International Casino Law. Reno, NV:
Institute for the Study of Gambling and Commercial Gaming.
Thompson, W. N. (1999b). Peru. In Cabot, A. N., W. N. Thompson, A. Tottenham, and
C. G. Braunlich (eds.) International Casino Law. Reno, NV: Institute for the
Study of Gambling and Commercial Gaming.
Thompson, W. N. (1999c). Venezuela. In Cabot, A. N., W. N. Thompson, A. Tottenham,
and C. G. Braunlich (eds.) International Casino Law. Reno, NV: Institute for the
Study of Gambling and Commercial Gaming.
Tottenham, A. (1999). Bulgaria. In Cabot, A. N., W. N. Thompson, A. Tottenham, and C.
G. Braunlich (eds.) International Casino Law. Reno, NV: Institute for the Study
of Gambling and Commercial Gaming.
United Nations Statistics Division (2007). Demographic Yearbook Database. Retrieved
July 10, 2007 from http://unstats.un.org/unsd/cdb/cdb_help/cdb_quick_start.asp.
von Herrmann, D. K. (1999). The Decision to Legalize Gambling: A Model of Why
States Do What They Do. International Journal o f Public Administration, 22(11
and 12), 1659-1680.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 118
von Herrmann, D. K. (2002). The Big Gamble: The Politics o f Lottery and Casino
Expansion. Westport, CT: Praeger Publishers.
Walker, D. M., & J. D. Jackson (1998). New Goods and Economic Growth: Evidence
from Legalized Gambling. The Review o f Regional Studies, 28(2), 35-46.
Welfare Lottery Benefits China. (2000, August 2) People's Daily Online. Retrieved July
19, 2007 from
http://english.peopledaily.eom.en/english/200008/01 /eng20000801 _47017.html.
Whyte, K. (1999). Korea. In Cabot, A. N., W. N. Thompson, A. Tottenham, and C. G.
Braunlich (eds.) International Casino Law. Reno, NV: Institute for the Study of
Gambling and Commercial Gaming.
World Bank (2007). World Development Indicators Online. Retrieved July 10, 2007 from
http://devdata.worldbank.org/dataonline/.
World Travel and Tourism Council (2005). Travel and Tourism: Sowing the Seeds of
Growth. Retrieved December 5, 2005 from www.wttc.org/2004tsa.
Wynne, H. J., & H. J. Shaffer (2003). The Socioeconomic Impact of Gambling: The
Whistler Symposium. Journal o f Gambling Studies, 19(2), 111-122.
Yamaguchi, K. (1991). Event History Analysis. Newbury Park, CA: Sage Publications:
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.