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INTERNET GAMING: REGULATING IN AN ONLINE 

WORLD 

HEARING 

BEFORE THE

SUBCOMMITTEE ON COMMERCE, MANUFACTURING, 

AND TRADE 

OF THE

COMMITTEE ON ENERGY AND 

COMMERCE 

HOUSE OF REPRESENTATIVES 

ONE HUNDRED TWELFTH CONGRESS 

FIRST SESSION 

NOVEMBER 18, 2011 

Serial No. 112–105 

( 

Printed for the use of the Committee on Energy and Commerce 

energycommerce.house.gov 

COMMITTEE ON ENERGY AND COMMERCE 

FRED UPTON, Michigan 

Chairman 

JOE BARTON, Texas 

Chairman Emeritus 

CLIFF STEARNS, Florida 

ED WHITFIELD, Kentucky 

JOHN SHIMKUS, Illinois 

JOSEPH R. PITTS, Pennsylvania 

MARY BONO MACK, California 

GREG WALDEN, Oregon 

LEE TERRY, Nebraska 

MIKE ROGERS, Michigan 

SUE WILKINS MYRICK, North Carolina 

Vice Chairman 

JOHN SULLIVAN, Oklahoma 

TIM MURPHY, Pennsylvania 

MICHAEL C. BURGESS, Texas 

MARSHA BLACKBURN, Tennessee 

BRIAN P. BILBRAY, California 

CHARLES F. BASS, New Hampshire 

PHIL GINGREY, Georgia 

STEVE SCALISE, Louisiana 

ROBERT E. LATTA, Ohio 

CATHY MCMORRIS RODGERS, Washington 

GREGG HARPER, Mississippi 

LEONARD LANCE, New Jersey 

BILL CASSIDY, Louisiana 

BRETT GUTHRIE, Kentucky 

PETE OLSON, Texas 

DAVID B. MCKINLEY, West Virginia 

CORY GARDNER, Colorado 

MIKE POMPEO, Kansas 

ADAM KINZINGER, Illinois 

H. MORGAN GRIFFITH, Virginia 

HENRY A. WAXMAN, California 

Ranking Member 

JOHN D. DINGELL, Michigan 

Chairman Emeritus 

EDWARD J. MARKEY, Massachusetts 

EDOLPHUS TOWNS, New York 

FRANK PALLONE, JR., New Jersey 

BOBBY L. RUSH, Illinois 

ANNA G. ESHOO, California 

ELIOT L. ENGEL, New York 

GENE GREEN, Texas 

DIANA DEGETTE, Colorado 

LOIS CAPPS, California 

MICHAEL F. DOYLE, Pennsylvania 

JANICE D. SCHAKOWSKY, Illinois 

CHARLES A. GONZALEZ, Texas 

JAY INSLEE, Washington 

TAMMY BALDWIN, Wisconsin 

MIKE ROSS, Arkansas 

JIM MATHESON, Utah 

G.K. BUTTERFIELD, North Carolina 

JOHN BARROW, Georgia 

DORIS O. MATSUI, California 

DONNA M. CHRISTENSEN, Virgin Islands 

KATHY CASTOR, Florida 

SUBCOMMITTEE ON COMMERCE, MANUFACTURING, AND TRADE

MARY BONO MACK, California 

Chairman 

MARSHA BLACKBURN, Tennessee 

Vice Chairman 

CLIFF STEARNS, Florida 

CHARLES F. BASS, New Hampshire 

GREGG HARPER, Mississippi 

LEONARD LANCE, New Jersey 

BILL CASSIDY, Louisiana 

BRETT GUTHRIE, Kentucky 

PETE OLSON, Texas 

DAVID B. MCKINLEY, West Virginia 

MIKE POMPEO, Kansas 

ADAM KINZINGER, Illinois 

JOE BARTON, Texas 

FRED UPTON, Michigan (ex officio) 

G.K. BUTTERFIELD, North Carolina 

Ranking Member 

CHARLES A. GONZALEZ, Texas 

JIM MATHESON, Utah 

JOHN D. DINGELL, Michigan 

EDOLPHUS TOWNS, New York 

BOBBY L. RUSH, Illinois 

JANICE D. SCHAKOWSKY, Illinois 

MIKE ROSS, Arkansas 

HENRY A. WAXMAN, California (ex officio) 

(II) 

(III) 

C O N T E N T S 

Page 

Hon. Mary Bono Mack, a Representative in Congress from the State of 

California, opening statement ............................................................................. 1 

Prepared statement .......................................................................................... 4 

Hon. G.K. Butterfield, a Representative in Congress from the State of North 

Carolina, opening statement ............................................................................... 11 

Hon. Joe Barton, a Representative in Congress from the State of Texas, 

opening statement ................................................................................................ 12 

Hon. Charles F. Bass, a Representative in Congress from the State of New 

Hampshire, opening statement ........................................................................... 13 

WITNESSES

Hon. Barney Frank, a Representative in Congress from the Commonwealth 

of Massachusetts .................................................................................................. 14 

Prepared statement .......................................................................................... 17 

Hon. Frank R. Wolf, a Representative in Congress from the Commonwealth 

of Virginia ............................................................................................................. 24 

Prepared statement .......................................................................................... 26 

Hon. John Campbell, a Representative in Congress from the State of California ..................................................................................................................... 35 

Prepared statement .......................................................................................... 37 

Mark Lipparelli, Chairman, Nevada Gaming Control Board .............................. 44 

Prepared statement .......................................................................................... 47 

Charles McIntyre, Executive Director, New Hampshire Lottery Commission ... 57 

Prepared statement .......................................................................................... 59 

Answers to submitted questions ...................................................................... 285 

Frank J. Fahrenkopf, Jr., President and Chief Executive Officer, American 

Gaming Association ............................................................................................. 62 

Prepared statement .......................................................................................... 64 

Rachel A. Volberg, Senior Research Scientist, NORC at the University of 

Chicago .................................................................................................................. 236 

Prepared statement .......................................................................................... 238 

Answers to submitted questions ...................................................................... 287 

SUBMITTED MATERIAL

Memo, dated November 7, 2011, from Hon. Alfonse D’Amato to Mrs. Bono 

Mack, submitted by Mr. Barton .......................................................................... 258 

Letter, dated November 18, 2011, from Mr. Butterfield to Mrs. Bono Mack, 

submitted by Mr. Butterfield .............................................................................. 275 

Testimony, dated November 18, 2011, of Robert Martin, Chairman, Morongo 

Band of Mission Indians, submitted by Mrs. Bono Mack ................................. 279 

(1) 

INTERNET GAMING: REGULATING IN AN 

ONLINE WORLD 

FRIDAY, NOVEMBER 18, 2011 

HOUSE OF REPRESENTATIVES, SUBCOMMITTEE ON COMMERCE, MANUFACTURING, AND

TRADE, COMMITTEE ON ENERGY AND COMMERCE, Washington, DC. 

The subcommittee met, pursuant to call, at 9:03 a.m., in room 

2123, Rayburn House Office Building, Hon. Mary Bono Mack 

(chairman of the subcommittee) presiding. 

Members present: Representatives Bono Mack, Bass, Harper, 

Lance, Cassidy, Guthrie, Olson, McKinley, Barton, Butterfield, and 

Towns. 

Staff present: Paige Anderson, Policy Coordinator, CMT; Charlotte Baker, Press Secretary; Brian McCullough, Senior Professional Staff Member, CMT; Gib Mullan, Chief Counsel, CMT; Katie 

Novaria, Legislative Clerk; Shannon Weinberg, Counsel, CMT; 

Felipe Mendoza, Democratic Counsel; and Will Wallace, Democratic 

Policy Analyst. 

Mrs. BONO MACK. The subcommittee will now come to order. 

Good morning, this is our second cover the waterfront hearing on 

whether Congress should allow Internet gaming to take sail. Today 

we will hear from three of our colleagues as well as from a respected panel of experts. Let me be clear about one thing from the 

beginning. I am taking a very careful approach when it comes to 

this issue, and I want to examine all of the relevant facts before 

deciding whether or not to proceed. 

I now recognize myself for an opening statement. 

OPENING STATEMENT OF HON. MARY BONO MACK, A REPRESENTATIVE IN CONGRESS FROM THE STATE OF CALIFORNIA 

When it comes to the debate over legalizing Internet gambling is 

it time for Congress to let the genie out of the bottle, or is the genie 

already out, online, with a pile of chips playing Texas Hold ’Em? 

As chairman of the subcommittee, this is an important issue which 

I have been following very closely in hopes of making certain that 

everyone involved is dealt a fair hand. 

Today we know this, the vast majority of Americans have gambled at some point in their lives, and the number of people who 

tried gambling is going up every year. Currently the only two 

States without legalized gambling are Hawaii and Utah. Fortyeight other States allow charitable gaming, 43 States and the DisVerDate Aug 31 2005 13:20 Apr 30, 2013 Jkt 037690 PO 00000 Frm 00005 Fmt 6633 Sfmt 6633 F:\112-10~1\112-10~1 WAYNE
2 

trict of Columbia have lotteries, 40 States permit parimutuel betting, 29 States have Indian casinos, while another 28 States have 

standalone casinos or racetrack casinos. 

Today as we continue to look at whether Congress should legalize Internet gambling, there are a number of questions we will be 

raising. For example, how effective is the current enforcement of 

online gaming in jurisdictions that have legalized it? How are 

States preparing to deal with the issue? What, if any, forms of 

interstate online gaming should Congress consider allowing? What 

consumer protections exist for online gaming, and what new protections are needed? How would any easing of legal restrictions on 

Internet gaming affect American consumers and other stakeholders, especially federally recognized Native American tribes. 

Gaming policy and regulation is generally handled by the States, 

although the Federal Government has been involved in shaping the 

boundaries of what is permissible under current law. 

In 1988, gambling across the United States began to proliferate 

after Congress passed the Indian Gaming Regulatory Act addressing the jurisdiction and authority of tribes to establish gaming on 

their lands. Since its passage, tribal gaming operations have seen 

tremendous growth with revenues last year exceeding $26 billion. 

Of the 565 federally recognized tribes across the U.S., nearly half 

of them operate casinos which provide a critically important source 

of funding for tribal operations and governance. 

In my own congressional district tribal gaming has been a huge 

plus with seven casinos supporting thousands of jobs during these 

very difficult economic times. The tribes have been great neighbors, 

too, contributing regularly to charities and civic events. 

So as this debate continues to unfold, it is very important to remember how tribal gaming has improved the lives of thousands of 

Native Americans and I want to make certain that they are not adversely impacted by online gambling, legal or otherwise. 

Congress has had to step in before. In 2006 to combat proliferation of illegal Internet gaming, the Unlawful Internet Gambling 

Enforcement Act was adopted. This effectively outlawed interstate 

online gaming in the U.S. by prohibiting gambling related businesses from accepting payments in the form of checks, credit card 

payments or electronic funds transfers relating to unlawful Internet gambling. The law also establishes fines and penalties for 

banks and financial companies that process such payments. 

In April of this year three of the top poker Web sites were shut 

down and 11 people indicted for bank fraud and for money laundering, raising new questions about the law. 

Proponents argue that the statute has not reduced Internet gambling, it has simply driven it underground and offshore where 

shady operators play by their own rules. 

Legalizing Internet gaming, they argue, would actually allow the 

government to provide greater protection for consumers. But those 

who want to keep the ban on Internet gambling in place argue that 

repealing the current law will expose more Americans to serious 

problems such as compulsive gambling. They are also worried 

about an increase in fraud, money laundering and organized crime. 

Still others have expressed concern that State budgets could be 

3 

harmed by the loss of lottery and gaming revenue, and they point 

to huge potential impact on existing legitimate gaming operations. 

While most States have taken no action regarding online gaming, 

seven States, Illinois, Indiana, Washington, Louisiana, Oregon, 

Montana, and South Dakota, have now enacted express prohibitions on Internet gambling. Other States have interpreted Federal 

laws permitting intrastate online gaming, and they are beginning 

to authorize different forms of remote gaming. Nevada, for example, has already provided remote intrastate sports wagering 

through BlackBerry enabled mobile phone devices, and the State is 

also forging ahead with plans to begin licensing online poker sites. 

So in many respects the genie is already out of the bottle. And 

now it is up to Congress to decide whether Internet gambling 

across State lines should be legal or illegal. 

And I look forward to hearing all of today’s testimony. 

With that, I am happy to recognize the gentleman from North 

Carolina, Mr. Butterfield, the ranking member of the Subcommittee on Commerce, Manufacturing, and Trade, for his opening statement for 5 minutes. 

[The prepared statement of Mrs. Bono Mack follows:] 

4 


75777.001

When it comes to the debate over legalizing Internet 

gambling, is it time for Congress to let the genie out of the 

bottle? Or is the genie already online with a pile of chips 

playing Texas Hold-em? 

As Chairman of this Subcommittee, this is an 

important issue which I have been following very closely in 

hopes of making certain that everyone is dealt a fair hand. 

Today, we know this: the vast majority of Americans 

have gambled at some point in their lives, and the number 

of people who try gambling is going up every year. 

Currently, the only two states without legalized gambling 

are Hawaii and Utah. 

• 48 other states allow charitable gaming. 

• 43 states and the District of Columbia have lotteries . 

• 40 states permit pari-mutuel betting. 

• 29 states have Indian casinos, while another 28 

states have stand-alone casinos or racetrack casinos. 
5 


75777.002

Today, as we continue to look at whether Congress 

should legalize Internet gambling, there are a number of 

questions which we will be raising: 

• How effective is current enforcement of online gaming 

in jurisdictions that have legalized it? And how are 

states preparing to deal with the issue? 

• What, if any, forms of interstate online gaming should 

Congress consider allowing? 

• What consumer protections exist for online gaming 

and what new protections are needed? 

• How would any easing of legal restrictions on Internet 

gaming affect American consumers and other 

stakeholders, especially federally-recognized Native 

American tribes? 
6 


75777.003

Gaming policy and regulation is generally handled by 

the states, although the federal government has been 

involved in shaping the boundaries of what's permissible 

under current law. 

In 1988, gambling across the United States began to 

proliferate after Congress passed the Indian Gaming 

Regulatory Act, addressing the jurisdiction and authority of 

tribes to establish gaming on their lands. Since its 

passage, tribal gaming operations have seen tremendous 

growth with revenues last year exceeding $26 billion. Of 

the 565 federally-recognized tribes across the United 

States, nearly half of them operate casinos which provide 

a critically important source of funding for tribal operations 

and governance. 

In my own Congressional District, tribal gaming has 

been a huge plus, with seven casinos supporting 

thousands of jobs during these difficult economic times. 

The Tribes have been great neighbors, too, contributing 

regularly to charities and civic events. So as this debate 
7 


75777.004

continues to unfold, it's very important to remember how 

tribal gaming has improved the lives of thousands of 

Native Americans, and I want to make certain that they are 

not adversely impacted by online gambling - legal or 

otherwise. 

Congress has had to step in before. In 2006, to 

combat the proliferation of illegal Internet gambling, the 

Unlawful Internet Gambling Enforcement Act was adopted. 

This effectively outlawed interstate online gaming in 

the United States by prohibiting gambling-related 

businesses from accepting payments in the form of 

checks, credit card payments, or electronic funds transfers 

relating to unlawful Internet gambling. The law also 

establishes fines and penalties for banks and financial 

companies that process such payments. 

In April of this year, three of the top poker websites 

were shut down and 11 people indicted for bank fraud and 

money laundering, raising new questions about the law. 
8 


75777.005

Proponents argue that the statute has not reduced 

Internet gambling - it's simply driven it underground and 

offshore, where shady operators play by their own rules. 

Legalizing Internet gambling, they argue, would 

actually allow the government to provide greater protection 

for consumers. 

But those who want to keep the ban on Internet 

gambling in place argue that repealing the current law will 

expose more Americans to serious problems such as 

compulsive gambling. 

They are also worried about an increase in fraud, 

money laundering and organized crime. 

Still others have expressed concern that state 

budgets could be harmed by the loss of lottery and gaming 
9 


75777.006

revenue, and they point to a huge potential impact on 

existing, legitimate gaming operations. 

While most states have taken no action regarding 

online gaming, seven states ... Illinois, Indiana, 

Washington, Louisiana, Oregon, Montana, and South 

Dakota ... have now enacted express prohibitions on 

Internet gambling. 

Other states have interpreted federal law as 

permitting intrastate online gaming, and they are 

beginning to authorize different forms of remote gaming. 

Nevada, for example, is already providing remote 

intrastate sports wagering through Blackberry-enabled 

mobile phone devices, and the state is also forging ahead 

with plans to begin licensing online poker sites. 

So, in many respects, the genie is already out of the 

bottle, and now it's up to Congress to decide whether 
10 


75777.007

Internet gambling across state lines is legal or illegal. 

look forward to hearing today's testimony. 
11 

OPENING STATEMENT OF HON. G.K. BUTTERFIELD, A REPRESENTATIVE IN CONGRESS FROM THE STATE OF NORTH 

CAROLINA 

Mr. BUTTERFIELD. Thank you. I thank the chairman for holding 

this hearing and what I hope will be a series of hearings on this 

very important subject of Internet gambling. This is a very important issue. I think we can all agree on that, and it deserves careful 

consideration, as you referenced in your opening statement. 

Let me thank the three witnesses, my colleagues, for coming 

today and we will try to make this as painless as possible. 

According to one estimate, any action we take regarding the legal 

status of Internet gambling could impact an estimated 10 to 15 

million people who already participate in Internet gambling on a 

regular basis. Serious revenues estimated to be as high at $49 billion over 10 

years could be realized at both the State and Federal levels. But 

we must remember the policy decisions we make here and in the 

full committee could also impact people who have never been exposed to Internet gambling, potentially opening the door for dependence and addiction. That is why it is so important, so important to address potential consumer consequences in any legislation 

that we consider. 

With estimated revenues in the billions we must set aside a portion of that to reduce the social cost of problem gambling. 

In our previous hearing on this topic I was particularly struck by 

Mr. Keith White’s testimony. Mr. White indicated that 6 to 8 million adults and 1/2 million teens meet the criteria for gambling addiction, with ethnic minorities more likely to become addicted. He 

also estimated that the annual social cost of gambling related addiction at $7 billion, resulting from increases in crime, divorce and 

bankruptcy and other things. An ounce of prevention is worth a 

pound of cure, and any legislation must include, must include sufficient funds to carry out education, treatment and research services 

related to problem gambling. 

We must also include common sense safeguards for consumers 

like a self exclusion list, gambling time limits, monetary deposit 

limits and privacy, and data security requirements, just to name a 

few. 

Two of our colleagues, Mr. Campbell and Mr. Frank, are here 

today to testify about their bill. The Internet Gambling Regulation 

Consumer Protection Enforcement Act, H.R. 1174. That bill would 

give the Department of the Treasury the responsibility of implementing a national licensing regime for Internet gambling sites. 

This bill provides for fair and balanced entry into the Internet 

gambling marketplace and does not restrict permitted gaming to 

just poker. It would also would encourage State lotteries, Indian 

tribes and others to innovate their current businesses so they can 

take part in the new industry and further raise revenue. 

H.R. 1174 is just one of the bills currently on the table, but regardless of which proposal we are looking at, any legislation that 

moves through this subcommittee and that could ultimately become 

law will involve tasking one or more Federal entities with implementation and oversight. It is critically important that in addition 

to the experts we have here today we also hear from those Federal 

12 

entities. These could include Treasury and Commerce, the Federal 

Trade Commission and the Consumer Financial Protection Bureau. 

We need to learn about any concerns that these agencies might 

have and potential consequences for them if they are tasked with 

implementing the new framework. 

With an estimated 1,700 international Web sites allowing play 

and accepting wages from individuals in the U.S., it is critical that 

we act to protect American consumers by legalizing Internet gambling here. The tremendous revenue that would be realized through 

legalized Internet gambling at the local, State and Federal levels 

would be a tremendous boost to our budgets. And most importantly, American workers are poised to take advantage of this new 

industry through well paying jobs that could be created, software 

engineers and financial experts and consumer service representatives, web developers, scientists and electrical engineers who all 

would need to be supported by the industry. 

Considering the fragile and struggling state of our economy, I 

strongly believe that all potential revenues should be considered to 

spur more robust economic growth. But if we are going to do this, 

Madam Chairman, if we are going to recognize gambling as legal, 

we must do our very best to get it right. 

Thank you for listening. I yield back the balance of my time. 

Mrs. BONO MACK. I thank the gentleman, and the chair now recognizes the chairman emeritus of the full committee, Mr. Barton, 

for 3 minutes. 

OPENING STATEMENT OF HON. JOE BARTON, A 

REPRESENTATIVE IN CONGRESS FROM THE STATE OF TEXAS 

Mr. BARTON. Thank you, Madam Chairwoman. I am delighted to 

welcome our three colleagues here, Mr. Frank, Mr. Wolf, and Mr. 

Campbell. The bill that I have introduced builds on the work that 

Mr. Campbell and Mr. Frank have already done. And with regards 

to Mr. Wolf, I have worked with him on many issues over the years 

and am glad that I am not in the White House Office of Science, 

Space and Technology right now or he would be cutting my budget, 

too, and I am glad he is not. 

Congressman Kenny Hulshof, former Congressman, is in audience and I think former Congressman John Porter is in the audience. We welcome those two former colleagues. 

I want to make one comment on Mr. Butterfield’s opening statement. I support everything he said. I want to point out that the bill 

that we are hoping to mark up in this subcommittee deals only 

with Internet poker, it does not deal with generic gaming or gambling, it is just Internet poker. And as everyone knows, poker is a 

game of skill. Over time the best poker player will win the most 

money. I am living proof of that, having been much poorer by trying to play against players better than myself and having them 

laugh as they take my money. 

We have an interesting situation here in this country in that it 

is legal to play poker online, it is impossible, though, to handle the 

financial transactions winning or losing that result from it because 

of a law called UIGEA. UIGEA in my opinion is unenforceable, 

needs to be reformed. The bill that I have introduced will do that, 

H.R. 2366. I have had a number of meetings with all the stakeVerDate Aug 31 2005 13:20 Apr 30, 2013 Jkt 037690 PO 00000 Frm 00016 Fmt 6633 Sfmt 6633 F:\112-10~1\112-10~1 WAYNE
13 

holders and thanks to the skill of our chairwoman and ranking 

member we are going have a good cross-section of those on the second panel today. 

I think there is general agreement there are still some things to 

be ironed out. I would point out the bill I have introduced is a 

States’ right bill and it allows the States to make the decision 

whether citizens in their State can play poker online. If the State 

doesn’t want to do that or an Indian tribe doesn’t want to do that, 

they simply inform the Department of Commerce they don’t want 

to participate. We are not trying to telling the States how to run 

their businesses, but for those States that do we are trying to have 

a comprehensive plan to make it fair and ethical for everyone. 

So I look forward to the hearing, Madam Chairwoman. I want to 

thank the three Congressmen for taking their time to testify. I 

know how many things they could be doing and to have them give 

time, especially 9 o’clock on a Friday morning, is important. 

I also want to welcome Mr. Frank Fahrenkopf, who will testify 

on the second panel. I have dealt with him on some political issues 

for many, many years. When I was a young Congressman he 

helped me quite a bit in some of my political travails earlier in my 

career, so I appreciate that. 

With that, Madam Chairwoman, I yield back. 

Mrs. BONO MACK. I thank the gentleman, and the chair is 

pleased to recognized Mr. Bass for 2 minutes. 

OPENING STATEMENT OF HON. CHARLES F. BASS, A REPRESENTATIVE IN CONGRESS FROM THE STATE OF NEW 

HAMPSHIRE 

Mr. BASS. I thank the distinguished chairwoman for holding this 

hearing, important hearing. I also want to welcome my three colleagues to the main hearing room of the most powerful and finest 

committee in the Congress, welcome here. This is an important 

hearing. 

On our second panel, we will be hearing from Charlie McIntyre, 

who is sitting in the front row here, Executive Director of the New 

Hampshire Lottery. As highlighted in my comments in a previous 

hearing, the New Hampshire lottery just delivered funding, almost 

a billion and a half dollars, for our State’s education. It is the primary form of funding from the State level for education since its 

inception in 1964. So as our committee continues to examine online 

gambling, I believe that we should consider fully the experiences 

of existing forms of legal gaming. I am pleased to have Mr. McIntyre and our other witnesses available to us today to speak to the 

competencies of our gambling regulators, as well as the potential 

benefits and challenges posed by an online gambling world. 

So I am looking forward to both panels and with that, Madam 

Chairman, I yield back. 

Mrs. BONO MACK. I thank the gentleman. And we turn our attention to our panels. We have two panels today joining us. Each of 

our witnesses prepared an opening statement and it will be placed 

into the record. Each of you will have 5 minutes to summarize that 

statement in your remarks. 

On our first panel we are very pleased and we welcome the Honorable Barney Frank of Massachusetts, the Honorable Frank Wolf 

14 

of Virginia, and the Honorable John Campbell of California. We 

welcome you all to the Subcommittee on Commerce, Manufacturing 

and Trade. We are very pleased you are here. 

At this point we are happy to recognize Congressman Frank for 

5 minutes for his opening statement. 

STATEMENT OF THE HONORABLE BARNEY FRANK, A REPRESENTATIVE IN CONGRESS FROM THE COMMONWEALTH 

OF MASSACHUSETTS 

Mr. FRANK. Thank you. Thank you for having this hearing. 

Mrs. BONO MACK. Can you make sure the mic is on? 

Mr. FRANK. Sorry, it was not. 

I thank you for this hearing and I appreciate the work of my colleague from Texas, Mr. Barton, in this. And yes, I will say to my 

neighbor from New Hampshire it is nice to be in the Energy and 

Commerce room and to be seated in front of a portrait of John Dingell, which I think is probably older than the average Member of 

Congress—the portrait, not Mr. Dingell. 

I want to begin with the basic principle arguments. Obviously 

once we decide to do this, there are specific legislative details, and 

I think in a bipartisan way we can work them out. I will say Mr. 

Barton, Mr. Campbell and I have already had some meetings, and 

we think it is possible to come to an agreement on a lot of these 

specifics. But I say once we decide to do this, let’s define what this 

is. This is allowing adult Americans to spend their own money as 

they wish in a form of recreation that they enjoy. I cannot understand how anyone would think that it is the role of the Federal 

Government to prohibit them from doing that. Regulating the way 

in which it is done, dealing with abuses, those are inevitable aspects of the economy we live in. But let’s get to the threshold question, is it the business of the government to tell adults no, we don’t 

think you should gamble with your own money? That is a principle 

which frankly I would think there should be bipartisan support on. 

And I hear people talk about the nanny state. I hear people saying we should not be telling 8-year-olds what to eat for lunch. 

Leaving that aside, if we don’t want to tell an 8-year-old what to 

eat for lunch, why are we telling the 8-year-old’s parent, a 28-yearold, no, you can’t gamble, we don’t think that is appropriate? 

Some of it I must say is moral disapproval, I know that creeps 

in sometimes. Apparently there are people who are better biblical 

scholars than I who have found somewhere in the Bible a prohibition on gambling. I haven’t found it myself nor have I found a footnote to it which exempts bingo. But the fundamental principle remains the same. 

The second problem we have here is that it violates a principle, 

I thought held very strongly, frankly even more so by my friends 

on the Republican side; namely, hands off the Internet. As somebody said, we are putting special restrictions on things done on the 

Internet. I thought that was the reverse of what people were talking about, because the legislation that unfortunately passed the 

Congress came out of the Committee on Financial Services enforces 

the prohibition on gambling, telling adults how dare you gamble 

with your own money, by restricting the use of the Internet. And 

again I am shocked by that. 

15 

Finally, the current regime is one of the most intrusive regulations on the banking industry that you can find. If you talk to the 

bankers, they very angry at this because the way in which we now 

make it illegal is to impose on financial institutions an impossible 

obligation; namely, to figure out what the payment was for. So 

again from the principles of my Republican friends, don’t have a 

nanny state, don’t interfere with the Internet, don’t unduly burden 

private sector, I am very surprised that this is maintained. 

Now, one of the arguments against making it illegal, some adults 

will abuse it and some children will do it. Let me start with the 

latter one. As I understand it, we have alcohol made available on 

the Internet, we have cigarettes made available on the Internet. 

The notion there are sex oriented materials that are for adults 

only, the notion we should ban in a society for everybody things 

that are OK for adults because children might get to them is the 

end of freedom. If we aren’t able to maintain some distinction between adult permitted activity and things we want to prohibit to 

children, then a lot of us will have a lot of time on our hands because we will have banned a lot of things that adults ought to be 

able to do and it is in fact inconsistent. That is why I go back to 

say that inevitably it seems to me that an element of this notion 

that we should make gambling illegal altogether is a moral disapproval, which I think is inappropriate. 

I have had people say, well, are you for the government being 

pro-gambling? No, let’s be clear what the role of the government is 

in a free society. There are some things that are damaging to others and the government should prohibit. There are some things 

which are especially beneficial and the government should want to 

encourage. But the great mass of human activity is none of the government’s business. We should neither encourage it nor discourage 

it. And not making something illegal is not an encouragement of 

it or an endorsement of it. 

And I note the gentleman from New Hampshire mentioned a lot 

of reasons. I appreciate that, and we want to make sure that we 

draw on their experience. But some suggested that we should not 

allow this because it would detract from the revenues that lotteries 

get. The gentleman did not suggest that, I don’t even put that word 

in his mouth, but again from the standpoint of my Republican colleagues any suggestion that we should ban private sector entities 

from engaging in activity because it might detract from the revenue that public sector entities get; in other words, we should give 

a monopoly to the public sector, would seem to be quite far from 

what I would hope would be the prevailing philosophy here. 

I think we can deal with the addiction issue here. I will say that 

the addiction issue here does not seem to me different from the addiction issue with alcohol and other things. 

And finally, I would say with regard to college students, there 

was an argument when we did it in our committee that somebody 

cited a study how terrible this was for college students and leading 

to suicides. The author of that study said, no, that is not what I 

said. And if we were going to ban things because students might 

get addicted to them, my guess is we would probably not start with 

Internet gambling, we would start with video games. There is a 

great problem of addiction of video games and the way a rational, 

16 

free society deals with addiction is to allow the great majority of 

people to do it and to try to treat and help the people who are addicted. 

So I hope this committee will go ahead with the basic principle, 

and I look forward to our working out the specifics. 

[The prepared statement of Mr. Frank follows:] 

17 


75777.008

Testimony of Congressman Barney Frank 

before the Subcommittee on Commerce, Manufacturing, and Trade 

"Internet Gaming: Regulating in an Online World." 

November 18, 2011 

I am here today in support oflicensing and regulating online gambling, 

primarily because I believe adults should be allowed to spend their own 

money as they see fit, free of governmental intrusion. And I also 

believe that licensing and regulation of internet gambling will provide 

real consumer protection in an area that is currently vulnerable. 

In 2006, the Unlawful Internet Gambling Enforcement Act (UIGEA) 

was enacted, which restricted the use of the payments system for 

Americans who sought to gamble online. I believe that it is an 

inappropriate interference on the personal freedom of Americans, and 

should be undone. And the ban did not make consumers safe. On 

April 15 "Black Friday", the owners of Full Tilt poker were arrested and 
18 


75777.009

subsequently indicted by a federal grand jury for fraud and money 

laundering. 

I voted against the ban in 2006 and for the past three Congresses, I have 

sought to reverse that ban. I introduced legislation - the first version in 

2007 and the latest iteration, which is identical to what Financial 

Services reported out last year on a bipartisan vote is led this year by 

my colleague and friend John Campbell. This legislation is designed to 

protect consumers without restricting their freedom. I have always 

believed that it is a mistake to tell adults what to do with their own 

money. Some adults will spend their money foolishly, but it is not the 

purpose of the Federal Government to prevent them legally from doing 

it. We should ensure that they have appropriate consumer protections 

and information, but otherwise allow people to pursue activities that they 

enjoy which do not hal111 others. As John Stuart Mill said in his essay, 

On Liberty in 1869: 

2 
19 


75777.010

"The only freedom which deserves the name is that of 

pursuing our own good in our own way, so long as we do not 

attempt to deprive others of theirs, or impede their efforts to 

obtain it. Each is the proper guardian of his own health, 

whether bodily, or mental or spiritual. Mankind are greater 

gainers by suffering each other to live as seems good to 

themselves, than by compelling each to live as seems good to 

the rest." 

I have also been very pleased to have strong support for this legislation 

from the Chairman of the Homeland Security Committee, Peter King, 

whose concern for public policies that protect us against terrorism is 

well known to many of us. His support for this effort is very important. 

We believe that the legislation should be open to all operators those 

who want to get into the business and can pass a rigorous background 

3 
20 


75777.011

check and comply with all of the regulations should be able to do so. 

Under our bill, any suitable person seeking to operate an Intemet 

gambling may submit to a background investigation and apply for a 

license. I believe that having an open market fosters competition, 

promotes faimess, creates American jobs and is in the best interest of 

consumers. 

I also strongly support, and am a cosponsor of Mr. Barton's internet 

poker bill, HR 2366, which is quite similar to our legislation in many 

respects, though it is limited to poker only. I look forward to continuing 

to work with him and the rest of the Committee as you consider these 

issues. 

American consumers who wish to gamble online are currently without 

rigorous and consistent safeguards against fraud, identity theft, underage 

and problem gambling and money laundering. Some operators adhere 

to rigorous regulatory regimes in foreign jurisdictions, but U.S. 

customers have no local recourse if they have a problem. We need to 

4 
21 


75777.012

ensure that we protect American consumers by requiring that user funds 

are not commingled with operating funds, and obey strict regulatory 

protocols against fraud and cheating, with constantly updated standards, 

and audit requirements to ensure both the fairness of games and the 

soundness of financial operations. 

One argument against online gambling is that there are some people, 

including compulsive gamblers, who should not gamble. I do not agree 

that just because some people should not engage in a behavior that it 

should be prevented for all. But I do believe that we should ensure that 

self exclusion protocols, gambling limits, and other anti-compulsive 

measures are required - which can be done even more effectively online 

than in the brick and mortar world. Moreover, r am a cosponsor ofHR 

2334, Congressman Wolf and Congressman Moran's bill to establish 

and implement programs for the prevention, treatment, and research of 

pathological and other problem gambling. r have been a cosponsor of 

this legislation for several Congresses including its predecessor bill, 

5 
22 


75777.013

proposed by my former Massachusetts colleague, Marty Meehan. I also 

believe that this legislation should be made a part of any eventual 

package, and that its work should be funded out of a part of the revenue 

stream that is garnered from the tax provisions. 

The tax provisions, which are contained in separate legislation authored 

by Congressman McDermott, have been jointly scored (along with the 

implementing legislation), and they should garner more than 42 billion 

dollars over ten years. Billions of dollars in taxes - both under existing 

law and those that would be established under Mr. McDelmott's bill -

cunently remain uncollected in this area. And the revenue and jobs 

from this sector have been created overseas, due to the ban, rather than 

benefiting Americans. 

Enacting legislation to license, regulate, and tax online gambling as well 

as implement problem gambling programs, would bring this industry out 

of the shadows, benefit consumers, create American jobs, capture 

6 
23 


75777.014

revenue and allow adults to enjoy freedom from unnecessary 

government interference. 

I thank the committee for their consideration. 

7 
24 

Mrs. BONO MACK. Thank you, Congressman Frank. And at this 

point, Mr. Wolf, thank you very much for coming today and you are 

recognized for 5 minutes. 

STATEMENT OF THE HONORABLE FRANK R. WOLF, A REPRESENTATIVE IN CONGRESS FROM THE COMMONWEALTH 

OF VIRGINIA 

Mr. WOLF. Thank you, Madam Chair. I know that some members 

of the committee are aware of my strong concerns about the spread 

of gambling in our society. I can spend all day cataloguing story 

after story of ruined families, bankruptcies, suicides and official 

corruption. Gambling is harmful activity, and study after study has 

shown that many in our society there is no question it is strongly 

addictive. Following the enactment of the hard fought ban on Internet gambling in 2006, I never thought I would see a day that a Republican House would even consider weakening this law. For a 

party that champions families and traditional values, I assure you 

that Internet gambling is contrary to those values. 

The legalization of Internet poker will enable the spread of gambling to every computer, every iPad, every iPhone, every BlackBerry, every Android and Windows phone in the country. It will 

send a signal to Americans that gambling is to be encouraged. It 

will be a windfall to the most powerful gambling interests in the 

country at the expense of American families and taxpayers. 

There is no question that social and economic effects fall disproportionately on three groups, the poor, the elderly and the 

young. Notably these are the same groups of Americans that have 

been hardest hit by the recession. 

Although some have championed this legislation as a potential 

budget windfall, I assure you that what tax revenues it will generate will overwhelmingly come out of the pockets of the vulnerable 

population. Gambling is no budget panacea. What little tax revenue it will generate taxpayers will pay out far more in the criminal justice systems, gambling treatment program and social services. 

The New York Times July 2010 article said the social cost of 

gambling outweighs the revenue by 3 to 1. Reports done in both 

the 1990s and 2000s have shown the increase in legalized gambling 

have led to a significant increase in suicide rates. A 2008 report by 

a sociologist at Temple University found that, ‘‘The odds of suicide 

among Las Vegas residents was at least 50 percent greater than 

among residents elsewhere in each of the 3 decades we observed.’’ 

Gambling, according to the July 20, 2011, Daily Finance article:When it comes to severity, Americans’ gambling addiction is not 

too far behind the Nation’s drug problem. And it is growing. It says 

in 2007 Americans lost more than $92 billion gambling, about 9 

times what they lost in 1982, and almost 10 times more than what 

movie goers in the U.S. Spent on tickets that year. 

Bell University Professor Earl Grinols estimated that ‘‘addicted gambling cost the U.S. Between $32 billion and $58 billion a year. 

I have long been concerned about the predatory nature of it and 

I strongly support the recommendations of the National Gambling 

Commission, including the 2006 Internet gambling ban. 

25 

This law was important because it dramatically limited convenience, I use the word ‘‘convenience’’, gambling in the U.S. The important distinction between destination gambling and convenience 

gambling is that by its very nature destination gambling is entertainment and is generally limited to vacations for most Americans. 

This limits the opportunity for addiction to develop and reduces the 

risk of regular gambling. However, online gambling is the ultimate, 

it is the ultimate in convenience gambling. Internet gambling is the 

crack cocaine of gambling, according to the CEO of Promises Treatment Center, the crack cocaine of gambling. It is like having a casino at your fingertips 24 hours a day, 365 days a year. People can 

gamble in their bathrobes, in their family rooms, at work or in college dorms. 

And with the explosive growth of smart phones, tablets, mobile 

broadband, the potential availability of Internet poker has grown 

exponentially in the last 5 years since it was outlawed. People will 

be able to gamble whenever and wherever they want. In addition, 

pathological gamblers will become easily addicted to online gambling because of the Internet’s easy access and instant results. It 

will result, it will result in an epidemic. We will read stories about 

this if this bill passes. It will be a constant theme. 

According to CitizenLink, should the current law be overturned, 

‘‘The estimated cost of Internet problem and pathological gambling 

addictions among adults each year in the U.S. will be 18 billion. 

It would be a total cost $7 billion for those under age 16. 

Gambling leads to increases also in public corruption. Has this 

Congress forgotten the Abramoff scandal? Gambling was involved 

in the Abramoff scandal. Has the Congress learned anything from 

it or is it just like the Simon & Garfunkel song, man hears what 

he wants to hear and disregards the rest? 

I have more, Madam Chairman, but in respect to the committee 

I see my stop sign has come in. I think the passage of this will increase addiction gambling and I think it will increase suicide and 

I think the Congress will rue the day if it ever passes. 

I yield back. 

[The prepared statement of Mr. Wolf follows:] 

26 


75777.015

The Hon. Frank R. Wolf 

Consequences of Undermining the Internet Gambling Ban 

Subcommittee on Commerce, Manufacturing, and Trade 

Energy and Commerce Committee 

November 18,2011 

1 would like to thank the chair, Mrs. Bono Mack, for the 

opportunity to testifY this morning. 1 appreciate her willingness 

to let me discuss the dangers oflegalizing Internet gambling, 

including poker, and the difference between destination 

gambling and convenience gambling. 

1 know that many mem bers of this committee are aware of 

my strong concerns about the spread of gambling in our society. 

I could spend all day cataloging story after story of ruined 

families, bankruptcies, suicides and official corruption. 
27 


75777.016

Gambling is a dangerous activity and study after study has 

shown that for many in our society, there is no question that it is 

strongly addictive. 

2 

Following the enactment of the hard-fought ban on Internet 

gambling in 2006, I never thought I would see that day that a 

Republican House would even consider weakening this law. For 

a party that champions families and traditional values, I assure 

you that Internet gambling is contrary to family values. 

The legalization of Internet poker will enable the spread of 

gambling to every computer, iPad, iPhone, Blackberry, Android 

and Windows phone in the country. 
28 


75777.017

[t will send a signal to Americans that gambling is 

permissible and encouraged. And it will be a windfall to the 

most powerful gambling interests in this country at the expense 

of American families and taxpayers. 

3 

There is no question that the social and economic effects of 

gambling fall disproportionately on three groups; the poor, the 

elderly and the young. Notably, these are the same groups of 

Americans that have been hit hardest by the recent recession. 

Although some have disingenuously championed this 

legislation as a potential budget windfall, I assure you that what 

tax revenue it generates will overwhelmingly come out of the 

pockets of the most vulnerable populations. 
29 


75777.018

Gambling is no budget panacea - what little tax revenue it 

would generate, taxpayers would payout far more in the 

criminal justice system, gambling treatment programs, and 

social services. In fact, according to a July 2010 New York 

Times article, the social costs of gambling outweigh the revenue 

by a factor of3 to 1. 

Reports done in both the I 990s and the 2000s have shown 

that increases in legalized gambling have led to a significant 

increase in suicide rates. 

4 

A 2008 repmi by a sociologist at Temple University found 

that, "the odds of suicide among Las Vegas residents was at 

least 50% greater than among residents elsewhere in each of the 

three decades we observed." There's no question that this is due 

to the regular access to gambling. 
30 


75777.019

According to a July 2011 Daily Finance article, "When it 

comes to severity, America's gambling addiction isn't too far 

behind the nation's drug problem, and it's growing. In 2007, 

Americans lost more than $92 billion gambling, about nine 

times what they lost in 1982, and almost 10 times more than 

what moviegoers in the U.S. spent on tickets that same year." 

Baylor University professor Earl Grinols estimates that 

"addicted gambling cost the U.S. between $32.4 billion and 

$53.8 billion a year." 

I have long been concerned about the predatory nature of 

gambling and the corruption that is often associated with it and 

was the author of the legislation that created the National 

Gambling Impact Commission. I strongly supported its 

recommendations, including the 2006 Internet gambling ban. 
31 


75777.020

6 

This law was important because it dramatically limited 

convenience gambling in the U.S. The important distinction 

between destination gambling and convenience gambling is that, 

by its very nature, destination gambling is entertainment and is 

generally limited to vacations for most Americans. This limits 

the opportunity for addiction to develop and reduces the risk of 

regular gambling. 

However, online gambling is the ultimate in convenience 

gambling. Internet gambling is "the crack cocaine of 

gambling," according to the CEO of Promises Treatment 

Centers. 

With the explosive growth of smartphones, tablets and 

mobile broadband, the potential availability oflnternet poker 

has grown exponentially just in the 5 years since it was outlawed 

in 2006. 
32 


75777.021

It would be like having a casino at your fingertips 24 hours 

a day, 365 days a year. People would be able to gamble in their 

bathrobes, in their family rooms, at work or in college dorm 

rooms. People will be able to gamble whenever and wherever 

they want. 

In addition, pathological gamblers will become easily 

addicted to online gambling because of the Internet's easy 

access, anonymity and instant results. This legislation will only 

fuel the epidemic of gambling addiction. 

According to CitizenLink, should the current law be 

overturned, "the estimated costs ofIntemet problem and 

pathological gambling addictions among adults each year in the 

U.S. would be $18 billion. It would total $7 billion for those 

under age 18." 
33 


75777.022

Finally, Internet gambling can provide a nearly 

undetectable harbor for criminal enterprises. The anonymity 

makes online gambling more susceptible to crime. 

Gambling also leads to increases in public corruption. 

Remember, the Abramoff scandal all started with gambling. 

The current law is working - and saving lives. According 

to the Annenberg Public Policy Center, within one year of the 

Internet gambling ban's enactment, "Card playing for money 

among college-age youth (18 to 22) has declined ... Weekly use 

of the Internet for gambling also declined among this age group. 

Both declines are statistically significant." 

8 
34 


75777.023

J urge this committee to oppose any legislation that would 

weaken current law and expand online gambling, including 

poker. Should such a bill be reported out of this committee or, 

worse yet, be passed by the House, it would be a sad 

commentary. 

Thank you. 

9 
35 

Mrs. BONO MACK. Thank the gentleman. The chair is pleased to 

recognized my colleague from California, Mr. Campbell, for 5 minutes. Welcome. 

STATEMENT OF THE HONORABLE JOHN CAMPBELL, A REPRESENTATIVE IN CONGRESS FROM THE STATE OF CALIFORNIA 

Mr. CAMPBELL. Thank you, Madam Chairwoman, Mr. 

Butterfield, members. I appreciate the invitation to be here. It is 

kind of funny that I am here because I actually don’t gamble. Not 

because I think there is any evil about it, I just don’t find any fascination with it. I don’t even know the rules of poker, what is better than what, which means Mr. Barton wants to play with me all 

the time and thus far I have resisted those invitations. 

But you know, we are not here to talk about what we personally 

want to do, we are always here to do what Americans want to do. 

The fact is that millions and millions, hundreds of millions of 

Americans enjoy gambling and they want to do it and they are 

doing it. And in 2006, as was mentioned, we passed UIGEA and 

basically banned or tried to ban Internet gambling. Since that bill 

was passed, and Mr. Wolf mentioned this in his comments, he said 

the availability of Internet poker and other things have exploded. 

More than the availability has exploded, the actual amount of 

Internet gambling has exploded. I think Mr. Butterfield mentioned 

15 million people. There are millions and millions, tens of millions 

of Americans gambling online now after we passed a bill supposedly banning it. And what they are doing is they are using illegal offshore sites. When they do that, there is a lot of talk about 

revenue, not only do we obviously not get any revenue, but these 

people are unprotected. There is no regulation, there is no oversight. They don’t know if they are going to get the money they are 

betting, they don’t know if the game is fair, they don’t know all 

kinds of things. But they are doing it and they will continue to do 

it because they want—it is an activity they want to do and they 

can access it online regardless of what we do or don’t do here. And 

so we actually by banning it have expanded what is going on. 

Now Mr. Wolf talks about problem gambling and I get that. In 

fact I am a cosponsor of his bill relative to problem gambling. 

There is problem gambling and there will be problem gambling 

whether we make Internet gambling legal or illegal. But we can 

deal with it much better if it is legal, if it is regulated, if it is understood, and if we know whose doing it, then in the current situation where people are going off on these totally unregulated sites— 

I mean there is been a lot of talk about this Full Tilt Poker and 

the recent Department of Justice invasion there. To me that is the 

reason we ought to be legalizing this, because millions of Americans were on that site and they were not being treated fairly. And 

we can make sure that they are going to be treated fairly. 

Now when Mr. Wolf comments that we shouldn’t allow this because there is problem gambling, you can look at drinking and 

many Americans don’t drink, most drink responsibly, some have a 

problem. We tried making that illegal. We tried prohibiting it. It 

didn’t work. We forced a lot of honest Americans, because they 

were going to do it anyway, into a dishonest and illegal practice. 

36 

And so prohibition was ended. We essentially have that kind of 

prohibition now. And we will have a better handle on the situation 

if we legalize it and regulate it than if we leave the current situation as it is. 

And I understand the protections we can have online. You can 

have better protections than you do with brick and mortar gambling. If someone is a problem gambler you can put them on a list 

and you can have the Web site, the name, the credit card, various 

things on a list so that your regulated gambling sites have to not 

allow those people on their sites. We can verify age. There is technology now where we can verify the location, so those States, as the 

chairwoman mentioned, who wish to not have gambling in their 

States can not have gambling from their residents because the regulated sites can stop that from happening. 

And they can be audited. In the bill that Mr. Frank and I have 

proposed the servers are required to be in the United States specifically so they can be audited, so that we know that the games 

are fair, that the amounts that gamblers are promised are being 

paid out, et cetera. 

In conclusion, I want to give you four reasons I think whether 

it is the bill Mr. Frank and I have, the bill Mr. Barton has or something we have in between, that this committee should pass. First 

of all freedom. This is really about allowing adult Americans to do 

a practice that they have done for eons and want to do and will 

do. 

The second is protection. They are going to do it, they ought to 

be able to do it in a protected and fair manner where they know 

what they are getting into and they understand the risks and rewards. 

The other thing is jobs. We are talking about jobs around here. 

Right now all these things are off site. If we do this where the servers and everything has to be in the United States, which we can 

do in our bill and under WTO regulations, then we are actually 

going to create jobs in the United States. 

And then the final reason is the one everyone mentions is revenue. I don’t think it is the top reason, but it is one. There is no 

question that if you do this you can’t tax it. Gambling in every jurisdiction it is, is taxed and that will bring in revenue to the Federal Government. 

And with that, I appreciate being here and thank you, Madam 

Chairwoman. 

[The prepared statement of Mr. Campbell follows:] 

37 


75777.024

Testimony of The Honorable John B. T. Campbell, 111 

Before the House Energy and Commerce Committee 

Subcommittee on Commerce, Manufacturing, and Trade 

Topic: "Internet Gaming: Regulating in an Online World" 

November 18, 2011 

Madam Chairman and Members of the Subcommittee. my name is John Campbell and I am 

privileged to represent the people of California's 48th District here in Congress and before this 

committee today. I would also like to thank you for tbe opportunity to testify today on an issue 

that has long deserved consideration by Congress. 

In 2006, we were challenged as a society to figure out how we should proceed with the 

confluence ofa budding intcrnet gambling industry and advancing internet technology. Millions 

of Americans bad already begun using the Internet as a medium f(lr play and dozens of finns, 

both domestic and foreign, were operating witbin our borders. We thought that by punishing 

those who took play, through the Unlawful Internet Gambling Enfi.lrcement Act (U1GEA). we 

would put an end to bad practices and the victimization of Americans who were wagering online. 

In the end, enforcement was difficult and inconsistent and number of Americans wagering online 

grew exponentially. As an example, Full Tilt Poker, who until just months ago was taking play 

from Americans, was indicted by a federal grand jury and charged with fraud and money 

laundering. among other things. In the wake of this and other incidents like it, what we need 

now is not an outright ban, but rather a safe, effective, and workable framework. 
38 


75777.025

Let me be abundantly clear - I am not a gambler. I do not play poker, slot machines. cards. 

lotteries, or any other similar games. But millions of Americans do gamble, either in casinos, 

with state lotteries, or in the privacy of their own homes among family and friends. They do so 

for a variety of reasons, recreation and camaraderie among them. It is because I believe in a 

society that allows these Americans free choice to gamble online, and one that holds in high 

regard choice and personal responsibility, that I am advocating for us to revisit this issue today. 

I, along with my friend and colleague, Mr. 8arney Frank, have introduced H.R. 1174, the 

Internet Gambling Regulation, Consumer Protection, and Enforcement Act, which would bettcr 

protect consumers without restricting their ft·eedom. Our legislation would create a framework 

where Internet gaming operators can apply for licenses from the Treasury Department and 

federally approved state commissions, while agreeing to be strongly regulated by agencies in the 

United States. Under our bill, any suitable person seeking to operate an Internet gambling 

facility may submit to a background investigation and apply for a license. It is my belief that 

having an open market fosters com[letition, promotes fairness. and is in best interest of players. 

Opponents of internet gambling legalization will claim that the activity isn't safe and will o[len 

the floodgates to allow Internet gaming operators to prey on young people and problem 

gamblers. Ironically, what they are describing is the "Wild West" of Internet gambling that is 

taking place today. Americans are placing wagers with companies located offshore and with 

facilities and systems that are unaudited and untested. They cannot be assured that the games are 

fair, that their funds are not being embezzled, and that the people operating the facilities are of 

2 
39 


75777.026

sound moral character. If anything, Internet gambling is Jess safe today because of the UIGEA 

ban, not in spite of it. 

Bringing transparency, regulation, and order to this system is entirely possible. Advancements in 

technology have enabled other jurisdictions to verify that gamblers are of appropriate age, "geolocate" users to ensure they are within the jurisdiction, audit the fairness of games, catch cheaters 

and scam artists, and ensure that uscr funds are segregated out from operating funds. This 

technology is live and there are firms capable of inspecting and auditing these institutions 

operating in the market today. In fact, the ability of the Department of Justice to crack down on 

internet gambling activities on "Black Friday" speaks to the availability, accuracy, and 

usefulness of these tools. 

On the issue of problem gambling, I recognize that there are Americans who are not suited for 

any kind of gambling, including that which is conducted over the Internet. UnfOIiunately, there 

are Americans who are addicted to a whole host of products, including alcohol and tobacco. 

Ho'.vever, we, as policymakers, have chosen to trust people to consume these products 

responsibly and at their own risk, while providing mechanisms to mitigate the social costs 

incurred. To that end, I am also a cosponsor ofH.R. 2334, the Comprehensive Problem 

Gambling Aet of 20 11. The bill, authored by my colleague, Mr. Frank Wolf, would allow 

agencies of the government to research, prevent, and address problem gambling. Furthermore, it 

is my expectation that part of the government's revenue streams from any resulting taxation of 

internet gambling be used for these purposes. 

3 
40 


75777.027

Passing legislation like ours would foster the development and growth ofa new American 

industry, which would bring along with it thousands of jobs and billions of dollars of economic 

activity, the revenues of which could be taxed to assist our ailing federal budget. I would urge 

my colleagues in Congress to thoughtfully consider proposals to legalize, regulate, and tax 

internet gambling, and I look forward to working with each of you as this process moves 

forward. 

Thank you for your time and I look forward to answering any questions you may have. 

4 
41 

Mrs. BONO MACK. I thank all of our colleagues for your testimony this morning, and I will turn to the panel if any members 

have any questions for our panel. Mr. Barton, you are recognized 

for 5 minutes. 

Mr. BARTON. Thank you. I will try to be brief. 

Mr. Wolf, in your comments you repeatedly said gambling, you 

never once said poker. I mean you do recognize that poker is a 

game of skill I presume. 

Mr. WOLF. If the gentleman would, I also want to say that in the 

testimony which I didn’t finish, it said according to the Annenberg 

Public Policy Center within 1 year of the Internet gaming ban enactment card playing for money among college age youth 18 to 22 

has declined; weekly use of the Internet for gambling also has declined among this age group. Both declines are statistically significant. 

And it will not stop with this. If this bill passes, in 5 to 10 years 

you will now have come in fourth as many States—— 

Mr. BARTON. My question is do you understand—— 

Mr. WOLF. I think it will lead to other broader aspects and I 

think there will be problems with regard to corruption. And I think 

there will be a lot of problems. 

Mr. BARTON. But that is not the answer to my question. You and 

I play poker. I don’t know how good a poker player you are, but 

over time whichever one of us is the best—— 

Mr. WOLF. Actually I don’t, but I don’t think—let me just take 

the question. I am not here to tell you that poker is wrong. That 

is not my ability. What I am here to say is if you put this on Internet gambling in college dorms and people will literally in a few 

short minutes will be bankrupt and broken. And I believe, and I 

remember the case of the young kid from Lehigh University up in 

Allentown that committed suicide. It is not my role to say poker 

is not right or wrong. And God bless you, Joe, I think you are a 

fine Congressman. And so I am not in the position to be the judge. 

I think that Internet gambling will bring about suicide and problems, and so I hope you win the next time you play poker and I 

hope you can bring this guy in to play with you. 

Mr. BARTON. Mr. Frank. 

Mr. FRANK. Let me tell you first, I am not going to praise you 

because I agree with you. Around here you only praise people when 

you disagree with them. So there are no negative inferences. I just 

don’t have to say how wonderful you are before I say you are 

wrong. But I would just make the point—— 

Mr. BARTON. You and I agree that each of us disagree about 98 

percent of the time. 

Mr. FRANK. On this issue, though, and obviously poker is different than other forms of gambling and it is much more skill and 

much less luck. But I would say I just want to emphasize what Mr. 

Wolf is saying. I had thought there was a consensus in this Congress, particularly strongly held by my Republican colleagues: 

Hands off the Internet, don’t interfere with the Internet. The 

premise of this, as Mr. Wolf makes clear, is that there are activities 

that may be OK elsewhere in this society, but we should particularly ban them from the Internet, and he talks about convenience 

gambling. Well, I am not around here to make life inconvenient for 

42 

the people I represent, but that I want to make clear is the nub 

of this. Do we single out the Internet for specific prohibitions and 

restrictions? As I said, I thought that went contrary. By the way 

if you are going to do that for gambling, my guess is there are 

other things that people would say, well, it may be OK in general 

but don’t we don’t want them to be too convenient. And that is the 

major precedent that is set here. You set the precedent of putting 

specific and harsh restrictions on the Internet more than anywhere 

else in the society. 

Mr. BARTON. Well, I am going to yield back, Madam Chairwoman, because I know we want to get to the second panel. But 

to the extent we have studies on problem gamblers and addictive 

gambling, it is somewhere between 1/2 of a percent and maybe as 

high as 2 percent. So it is an issue. Congressman Wolf is totally 

right to make it an issue, but it is not an overwhelming issue that 

cannot be dealt with, in my opinion. And this at least my bill is 

simply on Internet poker, it is not slot machines and roulette and 

scratch lotteries and all that. It is just Internet poker. 

I yield back. 

Mrs. BONO MACK. I thank the gentleman. The chair now recognizes Mr. Butterfield for 5 minutes. 

Mr. BUTTERFIELD. Thank you very much, Madam Chairman. And 

again let me thank the three witnesses for their testimonies. I am 

going to start on my left. Mr. Frank is always on my left and so 

I am going to start with you and maybe end with you. But Mr. 

Frank. The bill that is proposed by our friend from Texas, Mr. Barton, would legalize only one form of Internet gambling and that is 

poker, and he painfully explains that every time that he speaks. 

His bill speaks to one, one form and that is poker. 

The bill introduced by you and Congressman Campbell allows 

Internet sites that are licensed to accept bets and wagers without 

limiting it to poker. Only bets or wagers on sports events would be 

prohibited under your bill. The scope of gambling activities authorized under your bill is obviously much broader. Can you please discuss with us the broader scope of your bill and why you think allowing bets and wagers on activities other than poker is the preferred approach? 

Mr. FRANK. Thank you. That is a very important question. First, 

let me say I agree with Mr. Barton and others. And I don’t gamble 

myself and I don’t play poker myself, but I am for letting other people do a lot of things I don’t do. The fact is that I don’t think we 

should ban either poker or anything else that is voluntary, doesn’t 

hurt anybody else, including gambling. 

Now, it does have a prohibition on sports betting. Frankly that 

was a practical fact. I was pushing this bill in the committee I 

chaired. We couldn’t get it through over the objections of mostly the 

National Football League. I will report to you that the National 

Football League believes that if we were to allow Internet gambling 

people might start betting on football games. You might find that 

a shocking possibility, but I will tell you that that is the position 

of NFL. Let us not get into the position where people might start 

betting on football games. At any rate, I accepted that reality. 

But beyond that, yes, our bill, Mr. Campbell’s and mine, does 

say, as Mr. Campbell said, adults should be allowed to do on the 

43 

Internet what they prefer to do and I don’t see any reason for banning gambling. There is a narrower issue on poker. I will say I am 

for as much freedom as I can get for people as long as they are not 

hurting others. If all we could get is poker, I would be for it. I am 

for the broader issue. I don’t think we should be restricting people’s 

freedom to do other things. 

Mr. BUTTERFIELD. Thank you. Also, Mr. Frank, let’s talk about 

oversight. The bill proposed by you and Mr. Campbell puts the responsibility for regulation on the Department of the Treasury. 

Under Mr. Barton’s proposal it is the Department of Commerce 

that is tasked with this responsibility. Will you speak to that, 

please? Which is the preferred agency for oversight? 

Mr. FRANK. Let me be very honest. The chairman of the Committee on Financial Services, which has jurisdiction over the Treasury Department, hates this bill and won’t let it come up. The Department of Commerce is under the jurisdiction of this committee. 

And frankly I don’t think it makes a great deal of moral or practical difference which agency does it. The reason, by the way, we 

originally talked about Treasury, is the concern here was that 

Internet gambling, like other Internet activities, could be a front 

for money laundering, for terrorism. I should note, by the way, as 

to that fear that it could be a front for international illegal activities, terrorism, et cetera. The chairman of the Homeland Security 

Committee, the gentleman from New York, Mr. King, is a strong 

supporter of our bill. So he believes that we have in fact solved 

that. 

But the reason for doing Commerce rather than Treasury frankly 

is committee jurisdiction. I think it can be done as well in one place 

as another. I did it with Treasury because we did have this situation where there was a concern about money laundering, and that 

is under Treasury. Later on when it goes to the floor, committee 

jurisdiction is not binding, an amendment could be made in order 

if people thought it made more sense with Treasury. But it is in 

Commerce to get it before this very distinguished panel. Mr. Paul 

says this is the best committee that ever existed and I just was 

glad to have a chance to come here. 

Mr. BUTTERFIELD. Thank you, Mr. Frank. This is my third question. A key concern for many online gamblers is that they be treated fairly and that operators minimize the risk that they will be defrauded by other players. After reviewing the testimony of another 

gentleman, it appears that the American Gaming Association is 

confident that the technology exists to prevent automated programs 

or poker bots from being used against unsuspected human players. 

It also seems judging from last month’s testimony that site operators are similarly confident that their software can determine when 

collusion or fraud is taking place. 

The Campbell-Frank bill indicates several requirements to ensure the integrity and fairness of the Internet gambling. 

You have 20 seconds to respond. 

Mr. FRANK. Well, first, I would say that we were so careful to 

put this kind of regulatory oversight in that I lost Mr. Paul’s vote 

in committee because he as a libertarian thought we were getting 

too pushy with regulation, or he voted present. But secondly, yes, 

we do think, as Mr. Campbell said, a lot of this is going to go on, 

44 

we do know when you try to prohibit adults from doing what they 

want to do you to some extent just push it into illegal channels, 

et cetera. Prohibition of an entirely voluntarily activity rarely 

works in a free society, certainly with something as expansive and 

accessible as the Internet. We believe you will get much better consumer protection, you never get perfect, if it is lawful and therefore 

regulated than if it is totally unlawful. 

I will say a lot fewer people die from bad booze today than died 

from bad booze in the twenties when we had prohibition. That 

doesn’t mean there was never a problem, it does mean that legalization is the prerequisite for effective consumer protection regulation. 

Mr. BUTTERFIELD. Thank you. 

Mrs. BONO MACK. Are there members seeking time? With that, 

we thank our panel very much for being here today, and the subcommittee will take a very brief recess while we seat the second 

panel, and thank you to our colleagues for their testimony. 

[Recess.] 

So with that, we will resume the hearing. It was very, very brief. 

We welcome our second panel. Each of our witnesses has prepared 

their opening statement and it will be placed into the record. Each 

will have 5 minutes to summarize that statement in your remarks 

and we do try to stay as close to the 5-minute mark as humanly 

possible. 

Joining us on our second panel are Mark Lipparelli, Chairman 

of Nevada Gaming Control Board; Charles McIntyre, Executive Director, New Hampshire Lottery Commission; Frank Fahrenkopf, 

Jr., President and CEO of the American Gaming Association; and 

Dr. Rachel Volberg, Senior Research Scientist at the University of 

Chicago. 

Good morning to each of you, and thank you again for coming. 

You will be recognized for the 5 minutes. I think you can see the 

timers there and when it hits yellow that means to start getting 

close to wrapping it up. And please remember to turn your microphone on and bring it close to your mouth so that the TV audience, 

and C–SPAN or whoever might be viewing it eventually can actually hear you at home. 

So with that, Mr. Lipparelli, we are pleased to recognize you for 

5 minutes. 

STATEMENTS OF MARK LIPPARELLI, CHAIRMAN, NEVADA 

GAMING CONTROL BOARD; CHARLES MCINTYRE, EXECUTIVE DIRECTOR, NEW HAMPSHIRE LOTTERY COMMISSION; 

FRANK J. FAHRENKOPF, JR, PRESIDENT AND CHIEF EXECUTIVE OFFICER, AMERICAN GAMING ASSOCIATION; AND DR. 

RACHEL A. VOLBERG, SENIOR RESEARCH SCIENTIST, NORC 

AT THE UNIVERSITY OF CHICAGO 

STATEMENT OF MARK LIPPARELLI 

Mr. LIPPARELLI. Thank you, Madam Chair, members of the committee. My name is Mark Lipparelli. I am Chairman of the State 

Gaming Control Board in Nevada, and I appreciate the opportunity 

to come and comment on what has become a very important topic 

in our State as well as the gaming industry in general. 

45 

My perspective is one from a regulator. I have been on the Gaming Control Board for 3 years now, appointed 3 years ago by our 

governor, and no sooner did I get appointed that the topic of Internet gaming became of high importance, given the interest in our 

industry and the potential for partnerships of our licensees in foreign markets. 

It has been my experience in the last 3 years, traveling to many 

of the relevant jurisdictions that are known for Internet gaming, 

that there is a substantial amount of gaming going on in the 

United States today. As much as we would like to think that the 

laws that are passed today prevent such activity from occurring, 

the U.S. is seen as a robust marketplace for these Internet operators. And ironically from our experience as regulators, when we put 

our licensees through rigorous standards, rigorous audits, many of 

the operators in these foreign markets don’t have the same kinds 

of obligations, nor do they have the same kinds of voluntary compliance that is at the bedrock of our activities as regulators. 

In the State of Nevada in my comments I pointed out that almost 

$140 billion a year is put at risk in Nevada casinos that results in 

$10 billion in revenue. And over a span of time, over 30, 40, 50 

years, we have enjoyed great success and increasing sophistication 

in the way we approach gaming regulation. I have found in my 

travels overseas that many of the fundamental kinds of regulations 

that we have in place don’t exist in these foreign markets. Robust 

audits, suitability investigations of primary owners, and the protection of people from problem gaming, compulsive gaming, don’t exist 

in the robust forms we have them today. 

The challenge for our operators as they try to consider how to 

enter these markets is to compare how they will approach these 

marketplaces with that as part of their corporate culture, that as 

part of what they have developed with their patronage and bring 

those kinds of talents to the marketplace. 

I just returned from a meeting with the International Olympic 

Committee just 2 days ago where the subject of Internet gaming 

was part of our conversations. The concern of the International 

Olympic Committee and many other sports leagues around the 

country, or around the world relates to how can they get a better 

handle on these Internet sites that are illegal. They are impressed 

by the controls that we have in place in regulated markets. And 

they were asking us as regulators how to impose those same kinds 

of standards on the sports leagues. So it is ironic that that meeting 

just occurred 2 days ago. 

I think there was a comment made about Nevada’s efforts to legalize Internet gaming within the State of Nevada, and it is true 

we have begun that process. We began through legislation 10 years 

ago legalizing Intranet gaming but there had never been an impetus to drive regulatory efforts to actually bring that about. Approximately a year ago, 2 years ago, our legislature actually passed a 

mandate that we adopt regulations. I am confident that the work 

we have put in to establishing regulatory reform, internal controls 

and technical standards that we have as part of our regular businesses in Nevada will be the basis for sound regulatory control of 

Internet gaming in the future. 

46 

A lot of questions have been raised about whether the areas of 

concern around Internet gaming can be effectively controlled. In my 

travels one of the things become noticeably interesting to me is 

that Internet gaming provides detailed information about gambling 

activity at the transaction level. When you get down to that level 

the ability for regulators to impose standards and requirements on 

operators is very robust. You can identify customers, you can identify play patterns, you can identify the people who are accessing 

the Internet from what device they are accessing the Internet. In 

the brick and mortar businesses that is very difficult. Gaming is 

a voluntary activity, it is often an anonymous activity, and we have 

substantial controls in place to look in large measure at what goes 

on in a casino environment. 

In an Internet world it is down at the transaction level. Accounts 

are established, internal controls have been developed among Internet operators today to identify where someone is playing, from 

what machine or device they are playing. They can establish the 

play patterns of an individual from time of day to amount wagered. 

There is an ability to set up individually self-regulated controls 

over how much money can be wagered in a week, how much money 

can be lost in a day, how much money can be lost over a period 

of time. Those kinds of tools do not exist generally in the brick and 

mortar businesses. 

My observations are with the combination of regulatory control, 

compliance programs, and sound regulation that these kinds of 

things can be done very successfully. 

[The prepared statement of Mr. Lipparelli follows:] 

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WRITIEN REMARKS OF MARK LlPPARELLI 

CHAIRMAN, STATE GAMING CONTROL BOARD, STATE OF NEVADA 

BEFORE THE HOUSE SUBCOMMITEE ON COMMERCE, MANUFACTURING, AND TRADE 

FRIDAY, NOVEMBER 18, 2011 

Good morning and thank you for the invitation to provide testimony to the committee. 

PERSONAL BACKGROUND 

My name is Mark Lipparelli. I am the Chairman of the State Gaming Control Board in Nevada. 

As you may know, Nevada is the international home of gaming entertainment in the world. 

I began a professional career in 1993 managing companies whose sole focus was the design, 

manufacture, and sale of technology based products for the casino gaming industry around the 

globe. The technology includes entertainment based products, such as slot machines (which 

many people associate with the traditional gaming experience), but also table games and a host 

of systems based products geared towards making casino operations more effective, more 

efficient, and more secure. 

In the over 18 years in the industry and the last 3 years as a member of our Board, I have 

personally observed a material progression in the depth and breadth of gaming technology as 

well as the talent and expertise that has been drawn to the industry. Early in my career, many 

analysts pondered cautiously how far gaming might grow beyond just Nevada and Atlantic City. 

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Few would have been bold enough to predict gaming's uncommon expansion now found in 

various forms in 48 of the 50 states with a recent opening in Queens and casino openings in 

Ohio next year. Gaming today is a mainstream entertainment industry across the United 

States, from California to Maine, and from Washington State to the Gulf Coast. Indeed, gaming 

is now a mainstream form of entertainment around the entire globe, and increasingly in 

cyberspace. 

Three years ago I was asked by then Governor Gibbons to serve on the three member Gaming 

Control Board and I was appointed Chairman of our agency in January of this year by Governor 

Sandoval. The Gaming Control Board has over 50 years of proud history and its over 400 

employees are responsible for the regulatory oversight of all casino gaming activities in Nevada. 

Areas of focus range from law enforcement activities, suitability investigations, intensive audits, 

the collection of taxes, employee backgrounds and registration, and, among others, the review 

and certification of technology exposed to the millions of customers who consume gaming in 

our state. My industry experience has been of great value to me in my current role but is also 

balanced by our statutory public policy which expresses that gaming is critical to the well-being 

of our state. Not only do we focus on criminal elements and ownership, but we also are 

increasingly concerned about the interplay of technology, innovation, and the public welfare. 

GAMING REGULATORY SUCCESS 

I believe it is important to provide you some perspective about the success of gaming 

regulation in Nevada as well as several other domestic and international jurisdictions. As you 

likely know, our agency was borne out of a desire by state leaders over fifty years ago to 

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address the infiltration of undesirables into the ownership and management of casinos in 

Nevada. This colorful history is well documented and intriguing, however, it does not lend 

sufficient credit to the many professionals who have followed from those initial focused goals. 

Over a long span of time, past staff and leadership in Nevada and our regulatory colleagues 

around the country have recognized the dynamic positive internal changes in the industry while 

lending support to it by substantially enhancing many areas of gaming regulation. These 

enhancements include robust controls over casino accounting and auditing, surveillance of 

gaming activities, methodologies of patron dispute resolution, world class investigative 

techniques, and close scrutiny and certification of technologies ranging from slot machines, 

electronic table games, card shuffling devices, and a wide range of integrated computer 

monitoring systems. 

The success of regulators can and should be measured by the fact that the regulated gaming 

industry has been relatively free of controversy despite the industry's substantial growth and 

expansion. The same cannot be said of gaming markets who have not been subjected to 

traditional gaming regulatory rigors. As further illustration, consider in Nevada alone the 

amount of money wagered or "put at risk" over an average year. In our fiscal 2011, this 

number exceeded $140 billion which translated to gaming revenues of approximately $10 

billion. Think about the number of hands played, the number of slot handles pulled, the 

number of dice rolled to equal such a number. Compare those consequential sums measured 

in patron activity against what are a relatively few number of patron disputes or, worse, 

scandal. The combination of well-developed regulation along with industry participants who 

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endorse strong controls has been a very successful model and given patrons the sense they are 

getting a fair game. 

I would not represent that this success means we, as regulators, achieve perfection nor do our 

licensees. Our regulation, at its core, relies heavily on strong voluntary compliance and 

reporting by operators and imposes a range of discipline options when such compliance falls 

short. As a privileged license holder, our licensees realize that a reputation of careless 

compliance will draw undesired attention by our agents, caution letters from our leadership, 

the potential for monetary fines and, in the worst case, revocation of their license. This 

framework is established in regulatory bodies throughout the US and is important, in context. 

Some of the questions you wrestle with around today's topics and hard to solve only in law and 

will, and should, in my view, be effectively managed through the process of regulation, 

technical standards, compliance plans, and ultimately in disciplinary actions. 

INTERNET GAMING IS HAPPENING 

As the Committee considers the topic of how to approach internet poker it is critical to 

acknowledge that even if no progress is made towards adoption of national legislation, 

unregulated gaming on the internet is, and will continue to take place in many forms (many not 

up for Congressional consideration). The reality of Internet gambling and the public policy 

issues it raises appear to be something both the supporters and opponents of regulated 

Internet gambling agree upon. In fact, I just returned to the US from Europe yesterday 

following meetings with a working group of the International Olympic Committee where a 

broad group of experts was queried about ways to combat illegal sports wagering on the 

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internet and the threat such unlicensed entities pose for sports leagues around the globe. The 

same group cited, in its discussion, great cooperation and enhanced law enforcement 

techniques among those markets where internet gaming is well-regulated. Quite 

unfortunately, the existing dynamic in the US around internet poker continues to reward bad 

actors who covet US patronage while exposing risk to the very same patrons who would benefit 

from the protections provided by sound gaming regulation. The recent well-publicized 

examples of unregulated internet operators failing to protect hundreds of millions of US 

customer deposits can largely be attributed to an absence of the kind of regulations we 

regularly enforce. As our foreign regulatory counterparts become more effective in their roles 

as regulators (and they are) through enhanced regulation, enforcement actions and blocking of 

access to our citizens, US patrons who engage in online poker are more and more likely to find 

fewer and fewer reputable operators in our markets, further exacerbating the problem. These 

rogue operators have a strong profit motivation and very little, if any, motivation to create 

sound internal controls or enlightened policies around underage or problem gaming. 

Complicating matters is the relative difficult nature of law enforcement actions, under current 

law, associated with entities operating unlawful internet gaming sites. Traditional methods of 

law enforcement such as breaking up a hidden brick and mortar back room casino are not as 

simple when it comes to computer networks. Often we may be able to establish the existence 

of an illegal gaming site but finding the wrongdoer or sponsors can be problematic. Unlike ou r 

licensees, there is no motivation to be voluntarily compliant. In many cases, these sites do 

violate federal law and many, unknowingly, violate specific state laws of exposing a gambling 

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game without a license, However, the lack of clarity, in many minds, in the federal law leads to 

speculation that operating an internet gaming site can be legal. 

Furthermore, following years of investment in their regulatory compliance systems, licensed 

gaming operators and manufacturers, who have remained relatively conservative in their 

exposure to fast growing international internet markets find themselves at a distinct 

unbalanced disadvantage, One highly reputable licensee lamented to me when considering 

entering a market in Europe, in paraphrase, "I have played by the rules, incurred the burdens of 

compliance, and supported my patrons only to observe the actions of unlicensed and untaxed 

competitors erode my strategic position in a growing important segment of our business." 

NEVADA'S EFFORT TO CRAFT REGULATION 

The Committee is likely aware Nevada has recently undertaken formal steps to adopt specific 

regulations, technical standards, and minimum internal control systems in response to state 

legislation passed over ten years ago and amended in our most recent legislative session, This 

important work is being lead by long tenured experts in our agency and has been enhanced by 

legal, accounting and technical professionals who have developed decades of knowledge 

practicing before our agency, It has further been bolstered by the generous assistance of my 

regulatory colleagues in foreign markets who have, for nearly ten years, regulated internet 

gaming. For over two years, I have been traveling to markets outside the United States to learn 

and see firsthand how internet gaming is conducted, as well as the regulatory frameworks that 

govern these operations, 

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The regulations and technical standards we have created closely follow existing successful 

mandates of our land based operators while giving special attention to the areas particular to 

online poker. I am confident that our framework will be one of the more robust if not the most 

robust regulatory frameworks for internet gaming in existence. 

It is important to note that even if we adopt our regulations and begin considering applications 

for formal licensure, the State of Nevada will still need to overcome legal questions associated 

with online poker in the absence of Congressional action. This is a requirement in our 

regulations and will ultimately have to be ruled on by our final licensing authority, the Nevada 

Gaming Commission. 

I would like to strongly emphasize that Nevada's progress on these matters, in no way, 

minimizes or diminishes our position of support for the work you are doing today and the goal 

of a well constructed national piece of legislation. I, as Chairman, Governor Sandoval and many 

other state leaders strongly believe that Congress should act to establish a framework for state 

regulatory bodies to investigate and find suitable qualified applicants to conduct internet poker, 

establish clear regulations and standards, as well as test and certify the technology supporting 

internet poker. It is our preference and, we believe, the best outcome. The complexity 

associated with a model of legalization driven only at the state level will be, I believe, a missed 

opportunity for Congress and will not achieve the base uniformity across markets which, again, 

ironically will likely benefit illegal operators and handicap licensed operators. As a prime 

example, European markets are struggling with this topic in real time. Several EU countries 

who formerly allowed patrons to play cross market have established ring fenced regulated 

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markets of their own. Very soon after such legalization, the same countries have realized the 

underlying long term success of their respective markets will undoubtedly bring them squarely 

back to the question of cross market play and how to do that effectively while giving respect to 

individual country objectives. Without action by Congress, we likely will find ourselves in the 

same quandary. 

CAN PRIMARY CONCERNS BE EFFECTIVELY MANAGED 

Several questions have been raised about the ability of industry and regulators to effectively 

deal with the policy questions posed by internet poker including underage gaming, problem 

gaming, money laundering and collusion. My answer to that question is an unconditional yes. 

It is abundantly clear to me that internet gaming operations have matured meaningfully in the 

past ten years. It is also very clear to me the underlying systems associated with internet 

gaming is, in nearly every case, more advanced than the types of systems (as much as they have 

improved) we find in our land based casinos. While land based gaming entertainment around 

the country can be a relatively anonymous activity, each and every patron of an internet poker 

site must play via from a registered account and their actions down to key strokes and mouse 

clicks are logged and retained. From a money launderer's perspective, this is not an attractive 

fact. Moreover, as licensed internet operators mature (along with their fledgling regulator 

counterparts), enhanced analytical tools to identify and combat issues associated with 

underage gaming as well as problem gaming have been developed and more will come in the 

very near future. In the area of problem gaming, online systems allow for various elements of 

self restriction (such as loss limits, access time, and self exclusion) and analytical tools are in use 

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to identify material changes in play levels which can be elevated to customer service 

representatives for analysis. 

Interestingly, the gaming industry is having real impact on underlying technology trends and 

movement online is adding to the momentum. Years ago this would not have been so likely as 

moving as many people online at relatively low cost were driving internet participation. With 

that success in the rear view mirror, the challenges of associated with identification of account 

users is becoming more relevant to other industries who are in as much need to ensure they 

know their customer and can provide adequate levels of security. These higher demands, while 

not historically as relevant to wider industry, have been commonplace in gaming for years. 

Interestingly, while a great deal offocus on these challenges is warranted by all of us in this 

analysis phase, the actual operations I have observed would indicate to me they are quite 

comfortable and welcome the emphasis. 

Additionally, while not true across the board, many enlightened internet licensees, who 

operate in highly competitive markets, have developed well-constructed policy statements and 

training programs similar to land based operators who realize their corporate reputations are 

on the line with their customers. Further, many licensees who I have met have developed 

robust risk management functions out of their own corporate self interest. 

CONCLUSION 

In conclusion, I commend the work you have started and taking on this debate. It is important. 

Additional analysis is needed to determine the best way forward in this growing element of the 

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gaming industry, but I would stress that the current backdrop provides cover to those who seek 

undue gain at the expense of those who invest heavily in adherence of our regulatory 

mandates, the communities where the operate, and the patrons who are exposed. 

I offer my continued assistance to you as you progress beyond today's hearing and happy to 

answer any questions. 

M. Lipparelli Page 10 of 10 
57 

Mrs. BONO MACK. Thank you very much. Mr. McIntyre, you are 

recognized for 5 minutes. 

STATEMENT OF CHARLES MCINTYRE 

Mr. MCINTYRE. Good morning, Madam Chair. Good morning, 

Ranking Member Butterfield. If it please the subcommittee, I am 

Charlie McIntyre and I have the honor and privilege of being the 

Executive Director of the New Hampshire Lottery. 

Let me boast for a second about the State I call home. New 

Hampshire is a state of firsts, the first presidential primary, and 

they hold that first in the Nation status religiously, fervently. To 

suggest that they are proud of it is an understatement. 

In my area it is the first modern lottery in the U.S. the State 

representative from Keene, New Hampshire, after 5 tries and 10 

years of efforts, passed a law in 1963 signed by Governor King to 

have the first lottery in the U.S., first modern lottery in the U.S. 

Governor John King bought the first ticket which I brought with 

me, show and tell. This is the first lottery ticket in the U.S. that 

was purchased. Governor King bought it. Unfortunately he did not 

win. The director that sold it to him was named Ed Powers, a retired FBI agent. And he started what is a long tradition in the lottery world to have law enforcement, current or retired, serve as lottery directors, which I myself am a proud member of law enforcement. I served as senior state prosecutor of the State of Massachusetts with organized crime as my focus under District Attorneys 

William Delahunt and Willaim Keating, both having been, one, a 

former Member of this body and the second being a current Member of this august body. 

Since 1964, when the lottery became enacted, New Hampshire 

has realized $1.5 billion in education funding, as Congressman 

Bass correctly out. And 100 percent of our profits go to education, 

100 percent of our profits go to education. 

When we needed to add liquidity to games, we joined with Maine 

and Vermont and created the first multi-state game in 1985, 26 

years ago, and that game still exists today and we still run it 

today. New Hampshire now finds itself engaged in a casino debate 

in the right place, and it is now the twelfth year of the sixth legislative session in which that debate is being engaged. And whether 

it passes or it fails, it is being waged in the correct place, in the 

statehouse of New Hampshire; similarly, the question whether to 

expand gambling offerings on the Internet and via mobile devices 

should be decided by each individual State. 

This belief and ideal has long roots within the confines of codified 

law regarding gambling. But its roots are even more basic than 

that. A State should maintain its right to determine its level of tolerance for the expansion of gambling within its own borders, being 

the moving party for that expansion. 

As the history of New Hampshire points out, it took 10 years to 

become a lottery. Maybe the answer is no for a while until it is yes. 

But it should be posed to those whose lives it most directly affects, 

citizens of that State and those elected directly, which New Hampshire is quite large, being 40 members in the House of Representatives in New Hampshire. 

58 

Internet access is global. New Hampshire has the second highest 

penetration in the U.S. So each State, given that, should decide its 

own time and pace, its tolerance for that expansion. And as for the 

question of whether it is yes or no for the State of New Hampshire, 

then the operational questions can be asked related to payment, 

PCI compliance, age verification, compulsive gambling and 

geolocation. 

The New Hampshire lottery for almost 50 years has been in control of lottery gambling within its own boards and all manner of 

operation and consistent with wishes of the State without significant controversy or issues. The lottery has adopted and integrated 

changes in technology over that time, and if the State of New 

Hampshire elected officials allows we will consider the Internet the 

next step on that path. 

An important point not to be overlooked. Please. As director of 

the State lottery in New Hampshire, I am required to transfer $70 

million of net profit by this fiscal year, $72 million next fiscal year. 

Any impact, any encroachment upon that gambling space in New 

Hampshire without execution and planning materially places those 

revenues at risk. 

Mr. MCINTYRE. Finally, this committee is an excellent example 

of the tolerance, the spectrum upon which gambling exists—Utah, 

having no lawful gambling within her borders, and New Jersey 

having robust gambling presence. But both exist within each 

State’s determination as to what is best for the citizens. 

I certainly thank the committee for its time here today and certainly welcome any questions or comments you may have. Thank 

you, Madam Chair and Ranking Member. 

Mrs. BONO MACK. Thank you, Mr. McIntyre. 

[The prepared statement of Mr. McIntyre follows:] 

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Good morning members of the Commerce, Manufacturing and Trade subcommittee. I am 

Charlie McIntyre and I have the honor and privilege bf serving as the executive director of the 

New Hampshire Lottery. 

Please indulge me for a moment, in boasting ofthe p,ace I call home and the lottery I am I 

honored to work for. New Hampshire is a state of firsts, most importantly, the first presidential I 

primary in the United States. As a transplant I was upaware of how fervently and how deeply 

the people of New Hampshire hold their role in the p)-ocess of selecting a president - but in New 

Hampshire - the time surrounding the first primary i~ sacred. To say that New Hampshire 

citizens are proud of their first in the nation status vi~tually defines understatement. 

Following in the tradition of firsts, New Hampshire tas home to the first modem lottery in the 

United States. After five attempts and ten years oftl')jing, Larry Pickett, an intrepid legislator 

from Keene NH, oversaw the passage of legislation i~ 1963 which permitted the first modem 

lottery in the US to begin selling tickets a year later; ~espite fears of corruption, social issues and 

the end of civilization as the opponents then knew, t~e bill became law. Gov. John King bought 

the first New Hampshire ticket from its first director Edward Powers, on March 12, 1964 which I 

have here today. Mr. Powers was a retired FBI agen~ and many US Jottery directors have I 

followed that Jead and come from long and distingui~hed careers in Jaw enforcement. I, myself, 

was a senior state prosecutor in Massachusetts specializing in organized criminal conduct for a 

number of years, serving under District Attorneys Wi:lliam Delahunt and William Keating, the 

former having served and the latter being a current mbmber of this august body. 

Since that first day in 1964, the New Hampshire lottery has provided almost $1.5 billion in 

education funding to the public schools of New Hampshire. Currently, we provide 

approximately 7% of the state's education funding and 100% • of our profits go to educate NH 

school children 
60 


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New Hampshire, along with sister states Maine and :v ennont, was also the first to create a multistate lottery game. Three governors, three separate \egislatures and three lotteries banded 

together by compact to fonn a game in 1985 which ~e still conduct to this day. This innovation 

served as the precursor to the lottery games known as Mega Millions and Powerball, that effort i 

in '85 constituted the first multi-state collaboration of its kind in the US. 

New Hampshire now finds itself engaged in a debat", over whether or not to legalize full casino 

gambling. This is the twelfth year that the debate haS been conducted, the sixth legislative 

session to consider expanded gambling. Each time, during each session, the question of 

expansion is debated and thoroughly studied by the elected officials, the press and the citizens. 

Whether or not expanded gambling passes, the impo~ant point is that it has been put to a full and 

public hearing before those whose lives may be impJcted. 

Similarly, the question of whether to expand the ganlbling offerings into the internet and via 

mobile devices should also be decided by each individual state. This belief - this ideal - has long 

roots within the confines of codified law regarding g~mbling, but its roots are even more basic 

than that. Your state should maintain its right to det~rmine its level of tolerance for the 

expansion of gambling, within its own borders, by being the moving party for that expansion. As 

the history of New Hampshire points so vividly, a qUFstion may be put to the state a number of 

times before the answer is yes; and sometimes the an~wer may be no. Nevertheless, the 

question should be posed only to those most directly ~lected by the citizens, which is admittedly 

in New Hampshire a far greater number than most pl~ces - New Hampshire boasts the second 

largest legislative body in the United States at 400 m~mbers in her house of representatives. 

The internet would allow access to virtually every home for gambling. Each state must decide, 

on its own time and pace, what the tolerance it has fot that expansion. Only after that question is 

answered in the affinnative can the state detennine aH of pertinent operational questions 
61 


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including: payment, PCI compliance, age verification, compulsive gambling issues and geolocation. The New Hampshire lottery, for almost 5~ years, has maintained control of lottery 

gambling within the borders and has handled all mru;keting, regulation, promotion and expansion 

in a manner consistent with the wishes of the state l~aders; and without significant controversy or 

issues. The lottery had adopted and integrated chan$es in technology over that time; and if the 

state of New Hampshire's elected officials allowed, Fe would consider the internet another step 

along that path, comfortable that it could be successiully managed and regulated in state. 

An important point not to be overlooked, as the direqtor of the state lottery, J am responsible for 

transferring to the New Hampshire education trust fund $70 million dollars this fiscal year in net 

I 

proceeds and $72 million next fiscal year. Any imP<Ft, any encroachment upon the gambling 

space in New Hampshire without deliberate execution and careful planning will materially place 

those revenues at risk. 

Finally, the question of gambling and expansion has ;always been reflected in each individual 

state's approach to governing. The tolerance of a state for gambling is in direct correlation to 

that states position on expansion and this committee is a wonderful example of the spectrum 

upon which gambling rests across the United States. i At one end of the spectrum is Utah, which 

has no lawful gambling within her borders that I am ?ware of; on the other end is the state of 

New Jersey, which has an active casino presence, a ,*ature and robust lottery, horse racing, 

charitable gaming and is ctUTcntly attempting to start! both sports betting and internet wagering 

on its own. Both exist based upon each state's detenbination as to what is best for their citizens. 

But regardless of where each state rests on that line, i~ was determined after a deliberate process 

that occurred within the state. 

I certainly thank the committee for your time and efterts in this matter, and I welcome any 

questions or comments you may have. 
62 

Mrs. BONO MACK. Welcome, Mr. Fahrenkopf, you are recognized 

for your 5 minutes. 

STATEMENT OF FRANK J. FAHRENKOPF, JR. 

Mr. FAHRENKOPF. Thank you, Chairman Bono Mack and Ranking Member Butterfield for the opportunity to provide testimony 

today on behalf of the American Gaming Association which represents the commercial casino entertainment industry here in 

Washington. 

Our industry operates in 22 States, directly and indirectly is responsible for the employment of 875,000 men and women, and accounts for about $114 billion in spending last year, which equaled 

nearly 1 percent of the entire 14.5 trillion U.S. GDP. We support 

Federal legislation that will allow States and other appropriate authorities to license and regulate online poker, while also ensuring 

that each State, such as New Hampshire, has the right to determine whether such activity should be permissible by residents of 

their State. 

We believe the best approach to making that happen is to modernize and strengthen the Wire Act of 1961 with conforming 

amendments to the Unlawful Internet Gaming Enforcement Act to 

unambiguously outlaw and hopefully eliminate illegal Internet 

gambling. The AGA asks that any gambling legislative proposal establish Federal guidelines so there are consistent regulations for 

online poker in all jurisdictions that choose to have them. 

In addition, the AGA asks that the legislation pass three tests. 

Number one—and I think this is very important—it must not create competitive advantages or disadvantages between and among 

legal commercial casinos, Native American casinos, State lotteries, 

and parimutuel wagering; no form of gaming that is currently legal 

should be made illegal; and the legislation must respect fundamental States rights in an appropriate manner. 

Now, we for many years were opposed to all forms of Internet 

gambling because we did not believe the technology existed to properly regulate it with appropriate law enforcement oversight. That 

has changed in the last few years. There are now new technologies 

and processes that have proven effective for regulating and overseeing Internet gambling in First World Nations such as Great 

Britain, France, Italy; within the next month, Spain and Denmark, 

and in provinces of Canada today. 

The registration processes and advanced technologies used are 

very similar to those used by Major League Baseball and CBS, for 

example, to determine game blackout areas as well as Apple, Amazon, and the online banking industry to facilitate secure 

eCommerce. These new registration processes and advanced technologies allow the online poker company to determine where the 

player is located via advanced geolocation technology and determine whether the person playing is who they say they are, using 

advanced biometrics or other tools to prevent underage gaming. 

There is urgency to this issue, as you have heard from a number 

of the witnesses already, because 10 to 15 million U.S. consumers 

annually bet online and are at risk and have been exploited, as we 

know from the full-tilt activity against them by the Justice DepartVerDate Aug 31 2005 13:20 Apr 30, 2013 Jkt 037690 PO 00000 Frm 00066 Fmt 6633 Sfmt 6633 F:\112-10~1\112-10~1 WAYNE
63 

ment. By these unregulated offshore companies, licensing and regulation would ensure U.S. residents areprotected. 

Now our member companies who are interested in providing 

these services have agreed to abide by a code of conduct which incorporates the key elements of the successful regulations which are 

followed now by U.S. land-based casinos. That code requires companies to do the following: submit to extensive background investigations of the company and key personnel; ensure proper identification of every U.S. online poker player; submit to regular testing 

and auditing of online poker software; implement effective player 

exclusion processes; incorporate the effective, responsible gaming 

protections; and implement effective anti-money-laundering procedures. 

I would like, before I conclude my testimony, to take the opportunity to address this question of problem gambling that came up 

with the earlier panel and which was before you in the last hearing. 

It is settled science—and I say that again, it is settled science— 

that at any given time about 1 percent of the U.S. adult population 

are pathological gamblers, and that is a figure that has not 

changed despite the dramatic expansion of gaming opportunities 

during the last 35 years. Researchers also have found no evidence 

that online gamblers are more likely to be pathological gamblers 

when appropriate allowances are made for participation in other 

gambling activities. 

In fact, a major British study found no increase in the rate of 

pathological gambling between 1999 and 2007, even though Internet gambling became widely available during that period. Similar 

studies emerged in a study of Swedish gamblers, but the most definitive and recent research on this topic has been conducted by the 

Division of Addictions at the Cambridge Health Alliance, an affiliate of Harvard Medical School. 

Their study of the actual transactions and behaviors of 40,000 

online gamblers directly contradicts the belief that Internet gambling breeds excessive and problematic gaming behavior. This comprehensive research, the largest study of its kind, found that the 

mast majority of online gamblers play responsibly and can moderate their behaviors. 

Researchers have also found that online gaming participation decreases over time, saying that they did not find evidence to support 

claims that Internet gambling will cause escalated or even sustained rates. I have got more, but my time is up and perhaps during the questions and answers we can get to them. 

Mrs. BONO MACK. Thank you, Mr. Fahrenkopf. 

[The prepared statement of Mr. Fahrenkopf follows:] 

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AMERICAN G AMINC, ASSOCIATION 

Written Testimony of 

Frank J. Fahrenkopf Jr., President and CEO 

American Gaming Association 

Submitted to the U. S. House Committee on Energy and Commerce 

Subcommittee on Commerce, Manufacturing, and Trade 

Hearing entitled: "Internet Gaming: Regulating in an Online World." 

November 18, 2011 

Thank you Chair Bono Mack, Ranking Member Butterfield and the subcommittee members 

for the opportunity to provide testimony on behalf of the American Gaming Association 

(AGA). Allow me a brief introduction of our organization. The AGA represents the 

commercial casino-entertainment industry by addressing federal legislative and regulatory 

issues affecting its members, their employees and customers. 

The commercial casino industry operates in 22 states, directly employs nearly 400,000 men 

and women and is responsible for an additional 475,000 jobs through the additional 

economic activity we generate across the country. In total, our industry accounted for 

about $114 billion in consumer spending last year - nearly one percent of the entire 

$14.5 trillion U.S. Gross Domestic Product. 

Clearly, our industry is squarely in the mainstream of the U.S. economy. 

Today, of course, we're here to talk about online poker. I know this is the second of two 

hearings this subcommittee has held on the topic. At the last hearing, you asked 
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AMERICAN GAMING ASSOCIATION 

whether licensing and regulation of online poker is a safe bet. Our industry believes it 

is. 

The risky bet would be to leave unchanged current law that leaves consumers, minors 

and those with gambling problems vulnerable to unregulated offshore companies. 

As you may know, the AGA has not always taken this position. For much of the time 

since online gaming was first introduced, AGA members were not convinced that online 

poker could be regulated to protect Americans against fraud, money laundering and 

other illegal activities, or to prevent minors from gambling online and protect problem 

gamblers. 

New technology and new processes have changed that. We live in a digital world where 

people can purchase everything from groceries to automobiles online. These ecommerce companies have developed new technology and processes to help them 

facilitate sales, protect customers and, in some cases, prevent minors from purchasing 

their products. The same types of technological and process advancements are being 

used in countries such as Great Britain, France, and Italy and in provinces of Canada to 

effectively regulate and oversee Internet gambling. 

Because of those changes, the AGA now supports federal legislation that will allow 

states to license and regulate online poker. We believe the best approach to making 

2 
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AMERICAN GAMINU ASSOCIATION 

that happen is to modernize and strengthen the Wire Act of 1961 with conforming 

amendments to the Unlawful Internet Gambling Enforcement Act (UIEGA) that would 

unambiguously eliminate illegal internet gambling. 

We support this for online poker only because poker is a game that vast numbers of 

Americans have historically played and that millions of Americans still play. It is also a 

game of skill, unlike other forms of Internet gambling, and it is played between or 

among individuals, whereas in other forms of Internet gambling the customer is playing 

against the "house." 

The AGA asks only that any legislative proposal establish federal guidelines so there will 

be consistent regulations for online poker in all states. Without a federal overlay, there 

will be a patchwork quilt of rules and regulations that will prove confusing for customers 

and difficult for law enforcement to manage. 

Additionally, the AGA has a long-standing policy of putting any gaming legislative 

proposal through three tests: 1) The legislation must not create competitive advantages 

or disadvantages between and among legal commercial casinos, Native American 

casinos, state lotteries and pari-mutuel wagering operations; 2) No form of gaming that 

currently is legal shall be made illegal; and 3) The legislation must respect fundamental 

states' rights in an appropriate manner. Any online poker legislation must pass these 

three tests to gain AGA support. 
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AMERICAN GAMING ASSOCIATION 

The fact is practically every adult in the country has played poker at one time or another, 

and today the preferred venue for millions of poker players is the Internet. 

There is urgency to this issue, because each day millions of U.S. consumers are playing 

online at risk. Last year, in the United States, an estimated 10 million to 1S million 

people bet billions of dollars online, even though it is illegal for companies to offer realmoney Internet gambling in the U.S. Americans will continue to bet online as long as 

there are sites they can access, and we can expect that there will always be sites they can 

access as long as there are billions of dollars to be made. 

Even the indictments of executives from several online poker companies last April did 

not stop Internet gambling. Offshore operators will continue to develop new techniques 

to circumvent the barriers we put in place. The volume may fluctuate with each closed 

website and set of indictments, but demand will prevail in the end. 

And it's likely that online gaming operators who fill this void will be even less regulated 

and less trustworthy than their predecessors, which will only hurt American consumers. 

Put simply, the current environment puts American online players at risk. It is practically 

impossible to ensure that children are not gambling online and that the online gaming 

companies are acting responsibly towards those who cannot gamble responsibly. These 

companies, by illegally operating in the U.S., are flouting our laws; they are doing it 

4 
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AMERICAN CJAM!NC ASSOCIATION 

where law enforcement cannot reach them and where, in many cases, there is little to no 

regulatory oversight. 

Consumers could be saved from this risk if Congress enacts federal legislation to 

modernize and strengthen the Wire Act of 1961 with conforming amendments to the 

Unlawful Internet Gambling Enforcement Act to unambiguously eliminate illegal internet 

gambling. Such legislation should allow states that wanted to could license and 

regulate online poker to do so, following federal guidelines. And it should create a level 

playing field so that all segments of the gaming industry have an opportunity to 

participate. 

We know U.S.-licensed gaming companies, following time-tested gaming regulations, 

would provide safe, honest, responsible sites for the use of the men and women who 

want to play online poker. A strengthened UIGEA also would protect Americans from 

unscrupulous operators and would have the added advantage of bringing the jobs and 

revenues associated with this billion-dollar industry back to the United States. 

The creation of the infrastructure to support a licensed and regulated online poker 

industry would create an estimated lO,OOO high-tech jobs and generate $2 billion in tax 

revenue, primarily at the state level, every year. 

5 
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AMERICAN G AMINe ASSOCIATION 

To realize these benefits will require action by Congress, beginning with this committee. 

The AGA does not support any specific legislation, but there are certain provisions that 

any change should include: 

Each state should have the right to determine whether online poker should be 

legalized within their jurisdictions and who should be allowed to operate the 

sites. 

- Due to the interstate nature of Internet transactions, federal guidelines should 

be established that states must follow to ensure a consistent regulatory and 

legal framework. 

U.S. law enforcement should be provided with the ability to go after illegal 

operators and successfully prosecute them. 

In addition, online poker companies licensed in the U.S. should adhere to the same 

stringent level of regulation that governs brick-and-mortar casinos in this country. Our 

companies have a strong history of regulatory compliance. The regulations we follow 

are time-proven and if online poker companies are required to comply with them, it 

would ensure American consumers are playing in a fair and secure environment 

provided by a responsible operator. 

G 
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AMERICAN GAMINe, ASSOCIATION 

The AGA introduced a Code of Conduct for US. Licensed Online Poker Companies that 

incorporates the key elements of the successful regulations followed by U.S. casinos. To 

be licensed, companies should agree to: 

- Submit to extensive background investigations of the company and key 

personnel 

- Ensure proper lD of every U.S. online poker player 

Submit to regular testing and auditing of online poker software 

- Implement effective player exclusion processes 

- Incorporate effective responsible gaming protections 

- Implement effective anti-money-Iaundering procedures 

Legislation that incorporates the provisions above and the elements of the Code of 

Conduct would effectively protect U.S. consumers and state licensing and regulating 

would eliminate illegal web sites operated by offshore companies. Fortunately, new 

technology and processes can address those concerns. This can be accomplished 

through: 

- A rigorous registration process; 

- Technology-assisted fraud and collusion monitoring; 

- Anti-money-Iaundering technology and processes; and, 

- Promotion of responsible gaming by providing players the ability to manage 

their game play in real time. 

7 
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AMERICAN GAMING ASSOCIATION 

Let's take a look at these safeguards. 

Registration processes and advanced technology very similar to those used by 

companies such as Major League Baseball, CBS and Apple would allow the online poker 

company to: 

• determine where the player is located, 

• determine whether the Social Security number used is valid and is actually the 

player's own, which would prevent underage gambling, 

• and find out if the player has any sanctions by state, federal or international 

governments. 

Geo-Iocation will be a key to ensuring an online poker player is abiding by the laws of 

the state in which he or she resides and is not playing online in a state where it is not 

permitted. The first step in this process is verifying the customer's location during the 

initial player sign-up or registration. In those cases where there are discrepancies in 

information or it is determined the player resides in a location where online poker is 

illegal, the player's registration would be rejected, and they would be unable to open an 

account. 

The second step in the geo-Iocation process takes place every time a customer logs on 

to an existing account. Each time he or she attempts to sign in, geo-Iocation technology 
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AMERICAN G AMINe ASSOCIATION 

would be applied to determine the IP address of their internet connection, thus 

determining exactly where the computer is located. Additional tools can ensure an 

Internet connection is not attempting to mask its location, and in some cases, real-time 

verification techniques can be employed when there is any ambiguity about the 

location. 

The same database service providers that assist with geo-Iocation processes also allow 

operators to verify the age of online players. This can be accomplished by confirming 

personal information, such as previous addresses or cars registered, through a series of 

challenge questions the player must answer correctly in order to log on. Additional age 

verification steps can also include a confirmation letter with a personal identification 

number sent to the address listed on government-issued identification. The PIN would 

then have to be entered on the operator's site to enable the account 

Preventing cheating, whether by humans or software programs, is made easy through 

the use of fraud and collusion monitoring technology, coupled with reporting of 

suspicious play by other players. 

For example, operators have tremendous technological tools with which to effectively 

address the use of bots, or computer programs that automatically play poker hands 

based on a certain algorithm that the cheater believes provides them with an advantage. 

9 
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AMERICAN GAMING ASSOCIATION 

Bots typically cannot match the unique traits of human players, and monitoring their 

mouse and cursor movements on the screen is an operator's first line of defense. Once 

identified as a potential bot, the player is subjected to a CAPTCHA challenge during 

which they must re-type a series of distorted letters and numbers on the screen in order 

to verify they are a human player. Further, operators can also apply subtle pixel changes 

to the player's screen that will cause a bot program to freeze up and thereby allow the 

operator to identify the cheater and seize the account. 

Technology coupled with strong regulation also can prevent money laundering online. 

Efforts to launder money are detected through a number of reports and checks used 

exclusively by the gaming industry, as well as other processes that are common in 

financial institutions. Player verification, operator monitoring, the recording of all 

transactions and other activities combined with strict compliance with federal antimoney laundering laws make a well-regulated online poker site highly unattractive to 

launderers. 

Technology also allows players to manage their gambling in real time by doing things 

such as designating a set amount of money or time they can spend on the site, asking 

for a cooling off period and, if they feel they have lost control of their gambling, 

choosing to self-exclude. 

10 
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AMERICAN GAMING ASSOCIATION 

With these processes and the technology that supports them in place, patrons could 

play poker online in a safe, honest place. Law enforcement would be helped by 

operators' ability to quickly identify possible fraud and other criminal activities. And, the 

public could be confident that operators are taking bets only from jurisdictions where it 

is legal, keeping minors from gambling and providing assistance to problem gamblers. 

Eighty-five countries have legalized online gaming, and the technology and processes 

described above are being used in many of them, including Western Europe and 

Canada, where years of experience are proof positive that the risks formerly thought to 

be a companion to online poker can be effectively managed. 

Before concluding this testimony, I would like to take the opportunity to address in 

more detail the question of the impact of online poker on problem gambling. 

It is settled science that at any given time that about 1 percent of the U.S. adult 

population are pathological gamblers, a figure that has not changed despite the 

dramatic expansion of gambling opportunities during the last 3S years. In fact, the most 

recent (2008) national prevalence study found a lifetime rate of pathological gambling 

of 0.6 percent. 

Researchers also have found no evidence that online gamblers are more likely to be 

pathological gamblers. In fact, a major British study found no increase in the rate of 

pathological gambling between 1999 and 2007, even though Internet gambling became 

11 
75 


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AMERICAN G AMINCJ ASSOCIATION 

widely available during that period. Similar results emerged in a study of Swedish 

gamblers. 

More recently, in a 2010 article in Addiction Research and Theory, Dr. Howard J. Shaffer, 

director of the Division on Addictions and associate professor of psychiatry at Harvard 

Medical School, and his colleagues offered a comprehensive look at the research 

conducted to date, including summaries of their own investigations of the gambling 

patterns of customers of bwin.party, one of Europe's largest Internet gambling 

companies. 

Professor Shaffer and his colleagues have pioneered new methods for studying Internet 

gambling by virtue of their access to the actual wagering transactions of 40,000 online 

gamblers, including every keystroke of every person who subscribes to 

the bwin.party website. These data, which reflect actual gambling patterns, provide 

"objective detailed information about betting behavior and the conditions under which 

gamblers place wagers." 

The analysis of the bwin.party data has produced more than 10 peer-reviewed 

publications that contradict the notion that Internet gambling breeds excessive and 

problematic gambling. (A summary of the findings along with full copies of the studies 

mentioned above have been included with this submission.) 

12 
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AMERICAN GAMING ASSOCIATION 

Although the prevalence of pathological gambling is low, and even though there is no 

evidence that Internet gambling would change that pattern, it is still important that 

online poker companies should implement responsible gaming programs just as brickand-mortar casinos do. 

By requiring licensed websites to include social responsibility protections, legalization of 

online poker would actually improve efforts to assist pathological gamblers. Today, 

without any U.S. regulation, there are no uniform requirements for player protection 

tools at gambling websites. Indeed, many foreign jurisdictions require no such tools, so 

gambling operators located in those jurisdictions often do not provide them. In 

addition, states can designate a portion of Internet gambling tax revenues and license 

fees to be directed to research about pathological gambling, as well as to treatment and 

to public education on the subject. For these reasons, the report by Shaffer et al. 

concluded that "regulators should be able to design sufficient protections to prevent 

any significant growth in problem gambling that results from legalization." 

In conclusion, states should be allowed to license and regulate online poker following 

federal guidelines. Such action would protect u.s. consumers, keep children from 

gambling on the Internet, and provide the tools law enforcement needs to shut down 

illegal online operators. It would also create new high-tech jobs and tax revenue at a 

time when both are sorely needed. 

13 
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AMERICAN GAMING ASSOCIATION 

Thank you for your time and consideration. 

J4 
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AMERICAN GAtvlIN(; ASSOCIATION 

EXECUTIVE SUMMARY - RESEARCH ON INTERNET GAMBLING BEHAVIORS 

The body of research on Internet gambling has grown over the past few years, due in part to Harvard professor 

Howard J. Shaffer, Ph.D., and colleagues (Shaffer, Peller, LaPlante, Nelson, & LaBrie, 2010). Their work pioneered 

new methods for studying online gambling by virtue of their access to the actual wagering transactions of 

40,000 online gamblers, including every keystroke of every person that subscribes to the bwin Interacative 

Entertainment (bwin) website, one of the largest online gaming companies in the world. Using this data 

reflecting actual gambling patterns, rather than relying on self-report, provides "objective detailed information 

about betting behavior, and the conditions under which gamblers place wagers" (Shaffer, Peller, LaPlante, 

Nelson, & LaBrie, 2010, p. 277). 

The Harvard research has produced more than 10 peer-reviewed publications that outline several key findings 

about Internet gambling behaviors listed below. These findings highlight an overarching result: they contradict 

the beliefthat Internet gambling breeds excessive and problematic gambling behavior. 

For a baseline comparison, the most commonly accepted prevalence rate for pathological gambling is 1 percent 

of the adult population, which was first established by Dr. Howard J. Shaffer and his colleagues (1997, 1999). and 

confirmed by the National Research Council of the National Academy of Sciences (1999). These rates have 

remained consistent despite the expansion of gaming, as shown in the Harvard Medical School Department of 

Health Care Policy's analysis of the National Comorbidity Survey Replication (NCS-R) in 2008 (Kessler et.al, 2008), 

which found a lifetime pathological gambling prevalence rate of 0.6 percent. 

KEY FINDINGS INCLUDE: 

A Majority of Online Gamblers Exercise Responsible Gaming Practices by Moderating Their Gambling Behavior 

In a 2009 study conducted by Dr. Debi LaPlante and colleagues from the Division on Addictions, Cambridge 

Health Alliance, a division of Harvard Medical School, researchers studied the behavior 3,445 Internet poker 

players for a period of six months. Their results showed: 
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AMERICAN G AMINU ASSOCIATION 

Online poker players automatically separated themselves into two types of gambling behavior patterns, 

similarly reflecting the science-based prevalence rates of responsible gamblers and problem gamblers 

for other types of gambling activities: 

o 95 percent of players gambled an average of 12 Euros worth of chips at each of two poker 

sessions per week. 

o 5 percent of players were more involved (or "over-involved"), gambling an average of 89 Euros 

at each of 10 sessions per week. 

Both groups of poker players (the majority group and those who were "over- involved"), also showed 

the ability to moderate their gambling behavior based upon their wins and losses - as the players lost 

more, they spent less time in their online poker session and wagered less money. 

Those Who Gamble Online Have Shown that They Can limit Their Behavior 

Overall, the Harvard researchers found only small subgroups of gamblers who appeared to be over-involved in 

gambling. In 2009, researchers Xuan and Shaffer studied 226 bwin sports bettors who reported that they 

experienced gambling-related problems and voluntarily closed their accounts. 

Even though they were nlore likely to make (and lose) a higher wager, those who closed their account 

were less likely to exhibit a clear sign of pathological gambling: chasing their losses. Instead, they were 

more likely to make wagers that were more conservative than other players. 

Dr. Sarah Nelson and her colleagues (2008) also studied 567 sports bettors on the bwin website that placed 

limits on the amount that they could deposit to the online gambling website. Researchers discovered that, after 

self-imposing deposit limits, online players reduced their gambling activity and the time spent gambling online. 

2 
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AMERICAN GAMINe ASSOCIATION 

Internet Gambling Participation Decreases OVer Time 

Similar to research showing that gambling participation will decline after the initial spike in participation when a 

new game or casino is introduced to a population (laPlante & Shaffer, 2007), Dr. laPlante and her colleagues 

discovered the same trends among online gamblers when they studied online sports betting behaviors daily lor 

18 months (2008). 

Online gamblers rapidly subscribed and placed online bets within the first few days olthe study period. 

Gambling activity peaked by the eighth day of the study, rapidly declined thereafter for the first 90 days 

and continued to fall for the remainder of the 18-month period. 

Researchers summarized that they "did not find evidence to support concerns that Internet gambling 

will overwhelm populations of gamblers, causing escalating rates of participation, or even sustained 

rates of participation" (p. 2410). 

Internet Gamblers Respond to Industry's Efforts to Encourage Responsible Play 

Internet sports betting operator bwin also partnered with the Division on Addictions to study how harmreduction techniques can work when operators impose limits on players' gambling behavior. 

Researcher Anja Broda and colleagues (2008) discovered that, when bwin imposed a limit of how much 

money an online sports better can put in their playing account, only 0.3 percent of 47,000 online players 

exceeded the deposit limits once. 

The researchers believed that one reason the deposit limits were rarely exceeded might be that "sports 

bettors are highly responsible gamblers who bet for fun and spent relatively low amounts on betting." 

College-Aged Adults Have Reported low Participation Rates of Internet Gambling 

Two national surveys of gambling behaviors among college-aged students and young adults in the United States 

have looked at Internet gambling in this subpopulation and found very low rates of participation. 

3 
81 


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AMERICAN GAMING ASSOCIATION 

In a survey of more than 10,000 college students, researchers found that almost 2.5 percent had ever 

gambled on the Internet, and only 0.6 percent did so monthly or more frequently (LaBrie, Shaffer, 

LaPlante, & Wechsler, 2003). 

In a telephone survey and interviews of 1,000 participants aged 18 to 21, approximately 1 percent of 

college-aged students reported that they gambled on the Internet (Barnes, Welte, Hoffman, & Tidwell, 

2010). 

REFERENCES 

Barnes, G. M., Welte, J. W., Hoffman, J. H., & Tidwell, M.-e. D. (2010). Comparisons of gambling and alcohol use 

among college students and noncollege young people in the United States. Journal of American Col/ege 

Health: 1 of ACH, 58(5), 443-452. doi:10.1080j07448480903540499 

LaBrie, R. A., Kaplan, S. A., Laplante, D. A., Nelson, S. E., & Shaffer, H. J. (2008).lnside the virtual casino: A 

prospective longitudinal study of actual Internet casino gambling. European Journal of Public Health, 

18(4), 410-416. 

Labrie, R. A., Laplante, D. A., Nelson, S. E., Schumann, A., & Shaffer, H. J. (2007). Assessing the playing field: A 

prospective longitudinal study of Internet sports gambling behavior. Journal of Gambling Studies, 23(3), 

347-362. 

LaPlante, D. A., Kleschinsky, 1. H., LaBrie, R. A., Nelson, S. E., & Shaffer, H. J. (2009). Sitting at the virtual poker 

table: A prospective epidemiological study of actual Internet poker gambling behavior. Computers in 

Human Behavior, 25(3), 711-717. 

LaPlante, D. A., Schumann, A., LaBrie, R. A., & Shaffer, H. J. (2008). Population trends in Internet sports gambling. 

Computers in Human Behavior, 24, 2399 - 2414. 

LaPlante, D. A., & Shaffer, H. J. (2007). Understanding the influence of gambling opportunities: Expanding 

exposure models to include adaptation. American Journal of Orthopsychiatry, 77, 616-623. 

LaBrie, R. A., Shaffer, H. J., LaPlante, D. A., & Wechsler, H. (2003). Correlates of college student gambling in the 

United States. lournal of American College Health, 52(2), 53-62. 

National Research Council (1999). Pathological Gambling: A Critical Review. Washington, D.c.: National Academy 

Press, p. 89. 

Shaffer, H. J., Hall, M. N., & Vander Bilt, J. (1997). Estimating the prevalence of disordered gambling behavior in 

the United States and Canada: A meta-analysis. Boston: Presidents and Fellows of Harvard College. 

Shaffer, H. J., Hall, M. N., & Vander Bilt, 1. (1999). Estimating the prevalence of disordered gambling behavior in 

the United States and Canada: A research synthesis. American Journal of Public Health, 89, 1369 -1376. 

Shaffer, H. J., Peller, A. J., LaPlante, D. A., Nelson, S. E., & LaBrie, R. A. (2010). Toward a paradigm shift in Internet 

gambling research: From opinion and self-report to actual behavior. Addiction Research & Theory, 18(3), 

270-283. 

Welte, J. W., Barnes, G. M., Tidwell, M.e. 0., & Hoffman, J. H. (2009). The association ofform of gambling with 

problem gambling among American youth. Psychology of Addictive Behaviors, 23(l}, 105-112. 

doi:1O.1037ja0013536 

4 
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AMERICAN G AMINCJ ASSOCIATION 

Xuan, Z., & Shaffer, H. J. (2009). How do gamblers end gambling: Longitudinal analysis of Internet gambling 

behaviors prior to account closure due to gambling related problems. Journal of Gambling Studies, 

25(2), 239-252. 

5 
83 


75777.060

P~y(hnl");;(al "'''''.~,m~l\l 

~ml. Vul. ~» No.1, 234·2404 

t.I 2(l! 1 A"""n~aA P.\.}dw\oj:'t,'al ,I\.'Ncb~~, 

IlWO •. WJ()/\II'l.J1 flO DOl IO.!o~?/a(X1H .. n4 

A Taxometric Analysis of Actual Internet Sports Gambling Behavior 

Julia Braverman, Richard A, LaBrie, and Howard J. Shaffer 

Harv,lrd Medicul School and The C<lll1bridge Health Alliance, Cambridge, Massad1U!>cHs 

1l)i.~ al'licle rre~cnts. fWIll the first j,n.Hmelric );\uuy tlf aclwl! ~lHmtJlillg behuvior to lktermtne 

whether we C-illl rCrr~'H'nj charOlClcri.<i\ic~ of C.l;tr\~mc gambling m; ql1alitil1ivdy disfinct (Le., llivnlk) 

or as rl point a!ong. a lIimcllsioll. We .ullIlyzcd lhe hl?l.<i tn'ldc durinS a 24-monlh Mll(ly period hy the 4,595 

most invojwcl gamhlers among:t cohort (1148,1 14 people using i'lIllnlcrnct service to gamble on !;[lI)fling 

events. We applied two ta;mmctric procedures (i,e., MAMOAC anti MAXCOV) to lhrcc indicators of 

bl.'lting behavior: tot,1\ oWlley Ins!, INn! nmnber of hels, und tot:l! n10lwy w~gen!d> The fI~<;ul{s fail fo 

rrovid~ SUPPOl! for the view thaI In.: mm.1 involved Illtert\cl ~p()fi~ gamblers include a UL~llncl category 

of gamb!er~. More lesean:h is ncce,<;sllry to clarify lhe similar feilture,s of recreational and extreme 

gamhkrs nnd the dimensions (hilt .,cienlisg can me to measmc these bc!lll\,JOlS, Finfilly, we dj,<;cms the 

imp!ication.~ of these nndil1g~ for c!inicnl, resemch, ,lTIU pt1blic policy activities. 

Kt'YII'ordx: l~lent stnH;HlH':. laxomc{ric analysis, Internet gambling, Mi<1ictio1\, pathological gamhling: 

Among the most long-standing debates in the psychopathology 

litermure is whether researchers and clinicians (,<lll describe mental 

disorders (e,g., pathologic"l gnmbling) as extleme expres')ions of 

continuously distributed traits or ali qunlitatively distinct pattern~ 

(Beauchaine, 20(7), Partidp.lnts in this deb1.1te describe the COIl~ 

tinuously dislribuled view as dimensional nnd the qnalil<lli-vely 

distinct view as categorical, or t;UWIlIc. 

To illustnlte; Gambling is It (.'ommOll form of enle!tainment that 

the vast nlll.iority of people cl~ioy witbout !lny adverse conse~ 

qlle.nces. However, approximately 2('/0-5% of those who have 

pm1iciputed in gambling activities experience mild to serious 

gambling-related prob!em~ at some point during their lifetime 

(Kessler et aL, 2008; Pelry, Stinson, & Grant, 2005; Shaffer & 

Hall, 2001; Shafftr, Hall. & Vander Silt, 1999; SIl<tffer & Korn, 

2002; Shaffl'r, LaBrie, LHPlante, Nelson, & Stanton, 2004; Welte, 

Eames, Tidwell, & Hoffman, 2008; Wdte, narne'S, Wiefmrek, 

Tidwell. & Parker, 200 I). Con~eptually, a dimen!>iomll view of 

gambling would loc(lH: gambling disorder~ at the end of u contin~ 

uum, nnd despite the quantit.al!YC distincrion, this extreme behavior 

Julia BravcnnfUl, Richnrd A. l.aBrie, amI H\1ward J. Sh"ffer, Division 

011 Addictions, Cmnbridg.:' Health AI!i:mcc, Camhtidgc, Ma~~<IdlUSClt~, 

and Harvard Medical Schon!. 

bwil1 iotctnctiw Eo!('rtainment AG providl"d primilry .support for (hi~ 

study. 'f11C Division on Atklictions (\1' the Cllmhridge Helllth AlIi,mc\' <1lso 

receive,\ ~uppor1 frum the National Institute on Alcohol and Alcohol 

Anllw" the Na!iom.ll !m,litut(' of Mental Health, the MassHchnsdts Council 

on Compulsive Gumbling. SI. Francis House, Hnd the Univer<;l!y of Nevada 

We thllllk Debt LuPlanlt! (wd Sarah Nebon for lhdr support and 

thoughtful comment:. on dntfts of thi:'llrudc. NOlle of the supporter:; or \lny 

of the authors has p.:-rsonal inlerc,~t:; in hwin Intcfilcllvc Enlnlaimnen! :\G 

or ifs 11.\.Suclatcd C()Hlp<1Clic~ thaI would ~uggc~t a \:onJ1ict of interest. 

Correspondence cDw:.:cmin!! this article ~h()uJd \): i(,k1rc,%cd!(l Julia Bu)\'Cfman, DiVIsion on Addiction.~, Olmhriuge Health Alliance, 101 Suuioll L:lJuj~ 

Lng, Second F!t'mr, Medford, MA 02!55. E·mai!: jbravt']"I11{ln@ 

chn.harvanJ.cdu 

234 

would be qu.utitatively similar to beh.aviors loctlled lit other points 

along the continuum. Alternatively, a categorical view of gambling 

would !ocate disorders within a qualitatively dblinct and extreme 

interval sllch Ihat the behaviors would bt': sufficiently different 

(i.c., a unique taxon) from behnviors outside the interval. 

Al!hough this area of research is not without debate, there i~ 

evidence {hilt some disorders (e,g .. unipolar clinical depression) 

are c.£Hegorknl (Solomon, Rusdo, Seeley, & Lewinsotlll, 2006). 

Evidence argues tlt<lt other di~orders (e.g., personality dbOl'ders) 

'lre exten<;iom of nonnal behavior nnd, therefore, are best de~ 

scribed l\S dimensional (c.g<, Livesley, Schroeder, Jackson, & Jang, 

1994; M,lIhm, Krueger, & Wat~()n, 2005; Widiger & MullinsSWC.1!tt, 2005,. Diagnostic d(:finitjon~ {¢.g" as the American Psyt:hiatric A~sociatjon offers in its Diagnostic lind Statistical Marlual 

()/Menfal Disorders), <llthough not inherently categorical and often 

b~sed on dime.nsional criteJia, lend to ref1ect a categorical approach. This approach enhnnces the reliability of p.<;),chometric 

assessment scores and clinic:ll evaluations. Dhlgnostic manual.. 

often encoumge clinicians (0 diagnose ClIses by identifying and 

then quantifying advcrse beh,wior~. However, this sysrem of classifie,ltJOn, h,l"{,:d 011 endorsing atypical behaviors, docs little to 

ndYMCC our IUldersl,mding about a tal'tIe! djsorder'.~ construct 

vulidi[y, ConseqLlent!y, !h~fe is n paucity of evidence informing 

clinicians, researchers and po)!e)' m,akers :tbOUf whether the fundam(',nlal precept underlying a deviant behavior reflectii a unique 

Intent nrchitecture (Bm-fOll. 1998; Carson. 11)91; Grove & Meehl, 

1996; V);lillant & Sclinurr, 1983; Widigcl' & Sanhs, 20G0). To 

date, !md perhaps because pathological gambling is a relatively 

new diagnostic cla<;s, most clinicians find researchers have defined 

and treated pmhologicn! gambling as n categorical illness, prefel'~ 

ring nosological schemes that con~jder this exces<;ive behavior 

pattern <1,<; a distinct disorder (Beauchaine, 2007), 

The goal of this study is (0 examine the betting charactc! istic$ of 

heavily involved Inwrnet sports gmnblers for the presence of u 

distinct category or taxon of belling characteristics, Evidence of a 

taxon would imply thut :mme heavy gamblers me qUalitatively 

different from more involved recreationallllternel gamhlers. 
84 


75777.061

TAXOMETRIC ANALYSIS or GAMOLING DEHAVIOR 235 

Oilly three studies have, focuscd on gambling typology nod the 

lalent structure of gambling behavior, These studies appe-arcd 

during the pust 2 years, indiCilting a growing recent interest in the 

,011ceptual architecture that rcprt~s.cnts disordere.d gamblil1g. The 

studies include the following: (0) re<;earch focusing on older ndults 

and gamblillg (Hong, Sacco, & Cunoingh;un~Wil!ianl'), 2009); (b) 

an investigafion of cOlHmunity~recruited gamblers (Cunnin,gham~ 

Williams & Hong, 20(7); nnd (c) II longitudinal study abOll! rhe 

gumbling activities of ('ollege students (Goudria:m, Slutske, Krull, 

& Sher, 2009). All of these studies used IDleot class anu!ysis 

procedures fhat identitled two to eight various gambler types 

depending on criterhl from the Diaguo.Hic and St(ltistical Matlllol 

(~f M('lItai Visorden' (41h ed., Text Revision fDSM-IV]; American 

P~ychlatric Association. 2000; Cunningham-Williams & Hong. 

2007: Hong ct at, 2009), type of giunes played (Goudriaan et (11., 

2009), source of money, or International Classification of 

Diseases~·10 (lCD~lO; World He'llth Organization, 1993) criteria 

(Cunningham-Williams & Hong, 200n These studies us:ed stll.tis~ 

ticnl techniques with imporwnt limitations for distinguishing be~ 

Iween the presence and "bs(;l1ce of u single cHtegoricHl bOllnd<u), 

(intere,,,ted readers should ;;ee Rusdo & Ruscio, 2004a, for a brief 

review and a list of limitatiom). Therefore, no study 11<Is directly 

addre~sed the question of whether excessive gambling represent'> H 

unique cfltegOlY or {axon. or, the alternative, II point along a 

continuum con:,;istent with a dimensional view of excessive gmnH 

bling, 

Taxometrks and Nosology 

Meehl and his colleagues rleve10ped the ti.!xometric method to 

iden!ify the presence or lIuscm:e oftaxonic (i.e., categorical) latent 

stmcture among psychiafric disorders (Meehl, 1999; Meehl & 

Yonce., J996). Taxometdc method includes specific MatistiC'al 

tools designed to detennine whether deviant behavior belongs to a 

Ilnique L:lxon or simply represents points along one or more dimensions. A categorical view suggests IhM distinct psychological 

features characterize devinnt hehavior and that these di~tjnctive 

features Hrc not shared with "!lormar' eases. The climen!Sional view 

suggests tbat norm,li behavior ... hares psychological features with 

deviant behavior but that tbe dcvi<Hlt group has more and perhllps 

more intense fel'ltures. Tuxornetric stutistic ... measure the illtl~nlC~ 

fion among seventl indicator variables. For example, consider the 

rollowing; Researcher." are inte.rested in determining whetber 

gende·r-rdi.l!ed traits represent .\ unique or l.\ dimen~lon. 

The correlation belweel1 voice pitch and length is negligible 

within a s.ample that includes only male or only female p<1rtid~ 

pillltS. However, within il mixed·gcnder ~ilmplc, W~ .cnn expect to 

find a substantial correlation-those who tend to have longer hair 

,1!SO are Ilkely to have highcr vokes. This strategy, testing whether 

different H\l.sociations among vari<lbJes exist for different grotlp$ of 

obscrvntions defined by the vnluts of index vuriables, underlies 

tuxometrk statistics. A review of f<1xometric sWli~!ics is beyond 

the scope of this artie It,}, However, leiHlers intenc:s.ted in leilrnillg 

more <lbout llixometric.~ !!hould rcvie\v the following rcsourcel\ as 

an enll)' to this lileruture,(Mcehl, 1995; Ruscio, Haslam, & Ruscio, 

2006; Schmidt, Kotov, & Joiner, 2004; Waller & M('~hl, 1998). 

Tnxometrics is "an increasingly popular approach for determining whether a dimensional or a c\l.tcgoricnl model of classification 

is more valid" (Widiger & S;mlUel. 2005, p. 48). More than 150 

studies applied ruxomelrlc procedures to evaluate the latent strucnne of various psychopathological and behavioral constructs (Has· 

lam, in pres~; Haslam & Kim, 2(02). However, no studies have 

ilpplied t.axometric procedures to the study of excessive gambling 

behavior. 

Until recently, sciclltists did not have thc opportunity to study 

achwl gambling bewvior. Consequently, the CUfrent nosological 

system, liS evidenced and operationaiized by DSM-IV and ICD-IO 

criteriH, rests mostly on self-report. A lX'l'tfolin of new research 

focu~jng on actual gambling behavior is now aV<lilabfe (~.g., 

131'<lYl:lllnan & Shaffer, 2010; LaPlante, Schumann, LaBrie. & 

ShatTer, 2008; Xuan & Shaffer, 2009). A series of studies con~ 

dueled with behavioral varinbJes (e.g., toral l\mount wagered, bet 

S"lze, tott'l amount lost, frequency, elc,) defined and described the 

behavior of subgroups of most involved gamblers (LaBrie, Kaplan. 

LaPlante, Nelson, & Sh,lffer, 2008; ulBrie, LaPlante, Nels()!). 

Schumann, & Shaffer, 2(07). The present study ex.tends the re-

~;ean;h experience with actual beUing behavior to answer tbe 

question of whether pl'oblematic gambling Is best considered a 

quantitative (dimensional) or I]ua!itutivc (cntegoricai) cl"ssjficl.\~ 

lion, 

The Present Study 

Our research coUaboration with an fmernet gambling service 

provider, bwin Interactive Entertainment AG (hereinafter referred 

to a~ bwin), provides access to vnluahle information. Our longitl1-

dina! datahase was uniquely well suited to answering fhe re,~emcll 

(jucstion. The dat~hase consists of ut,.'tlwl bets made during It 2·year 

period by H COhUll of 48.114 £Hmb!er1> who enrolled at bwin 

Interactive Enrertainmeni AG dllring February :1005. The use of 

:tcttlul betting records avoid..; the parenti,,1 inaccuracies introduced 

by self~report. The lurge size of the cohort and the 2·year accumulation of .]ata ,are sufficiently !1lHssive to permit confident 

investigation of a low~p!'eva'ence disorder. We have published a 

portfolio of:-;tutiics tlmt ex,ullinec1lhis longitudinal cohort's aggregated gambling behavior. This work Stlfn111arizcs the parameters of 

beftlng such as size, frequency, llnd so forth (Rraverman & Shaf~ 

fer, 2010; Broda et ai" 2008; LaBrie et al., 2008; LaBrie, LaPlunr.e, 

ef ut, 2007; LaPlante, Kles.chinsky, LaBrie, Nelson. & Shaffer, 

2009; LaPllln!e et ai., 2008; Nel~an e! at., 2008; XUfln & Shnffer, 

20(9). This longitudinal cohort enrolled at a time when hwin'.s 

principal fOCll.s was on sports gnmbllng. Only.1. small fmction (i.e., 

les,> than J%) of the cohort did not engage in betting Of) sport". Our 

rcscmch is the tina to investigate the l<:ttent structure of actual 

Internet gambling behavior. We used {he charac1eristks of sports 

gfllllhling to address whether the nosol0gk~aJ structure of intemperate gambling behJviof is categorical Of dimensionaL We npplied taxometric t\.~dl!ljqlles successfully used by olllers to determine whether n taxon of Internet sports gamblerii could he 

identified in OUf annlysis. If our analyses Were to identify group of 

Ilcavily involved gmnb!ers who i.li~p!ay .<.;imi!ar behaviors thut arc 

unique and distinct from the larger group of recreational g:,uHb!ers, 

we will have identified [he ch;lractt.'ristic", of a gambling !<lXOO. 

Evidence. that either supports or does not support a taxon for 

disordererllnternel spor!s gambling will help cljnicj~ns and policy 

makers more cfft'.<:tively identify, prevent, regulate, <\lId treat individuals with gnmbHng-related problems. 
85 


75777.062

236 BRAVERf..,tAN, LAORIE, AND SHAFFER 

Method 

Participants 

The participl.lnts for this study derive from {\. WhOl1 of 48,1 14 

people who opened Un account during FeblUllfY 2005 with the 

Internet gambling service provider, bwin Interne-live Entertainmt'.nt 

AG. Their information indudts records of betting behavior from 

enrollment through February 2007. This longitudinal cohort supplied inforrnHtinn for several other !)Hldies of m:tual gambling 

behavior (Broda et <:11., 2008; LaBrie et aL, 2008; LaBrie, LaPlante, 

el aL, 2007~ LaPlante et a1., 2009; LaPlnllte et aL, 200R; Nelson et 

aL. 200S; Xuan & Shafrer, 20(9), The average age of individuals 

in this cohort was 31 years (SD =' I{),O) ilnd most (91.6%) were 

mak. The pluyers in this col1011 were from 85 countries, The 

40,406 roh0!1 members who engaged in sports betting for more 

than 3 duys comprised the analytic sample that we used to identify 

and l'ielect the betting hehavior indicntof~ for the t;;\xometric l1nal~ 

TaXOtllctrk analysis is paJticulurty powerful wllen the potential 

proportion of faxon members comprise Olt least 10% of the nnalytic 

sample (Schmidt et aI., 20(4), 1111C vr('~~lenl,'C estimates of current 

disordered gAmbling among [he generul population afe cOllsis~ 

tently low, 0,6% to 2% (Kes,<;ler ct at, 2005; Shaffer. HUll, & 

Vander Bitt, 1997), Previous analysis of the longitudinal cohort of 

sports gamhlers (LaBrie, LaPhmte, cl ilL. 2007) reveuled 1I Jargl~ 

number of ocrasiofl:ll bettors who are unlikeJy to he membe.r~ of a 

disordered gambling taxon, Con:-;cquently, 11 research sHmple With 

the suggested propOition of di~ordered gamblers would become 

avuilable only after ~'clecting participants with cCft.iltn chllHlctelis~ 

tics from the totallongitudin<l! cohort, Ali un illustration, g<lmbling 

prohlems arc often a<;sociated with excessive betting involvement 

Therefore. we jn('re,l~ed the likelihood of including the wggested 

proportion of p.nhological gamblers in the ('ohol1 by including 

only exces!;lve gamblers in Ihe nnalytic We defined l.\ll 

exces<;!ve gambler as one whose behavior wuS the upper j!)k on 

anyone or more of the selected indkmor v[lfiahle'\, A description 

of these mensurcs follow.';. 

Measures 

Previous ana!y<;es of individu:.tllnternet ~ports bets yielded eight 

uggregntc!> dcscrihit1g partkipnnt~level gamh!ing involvement 

(LaBrie, Nelson, et al.. 2007), These measures were the following: 

(a) total amount w;'lgered, (h) total number of bets, (c) average het 

:>izt' (Le., [oral nmount w!lgeled divided by tOlal number of bets), 

(d) duratlun of befting (Li'" the difference in days between a 

pal1icipmn's. tirst and last bening day), (e) frt'.quenc)' of betting 

(i.e" the ntlmber of betting days divided by the dUraliof}}; {f) 

lI11mber of bets per day (i.e., the total llumber ofhcfS divided by !be 

numher of betting day:-;); (g) total }lmm,lIlt lost (i,e., losses minus. 

winning'i); and (h) percent IOSf (Le., total amollnt lost divided by 

total UlTIount wl.1gered). Having 100 IHllny indicators complict1tes 

the inrerprettltioll of results, pres.ents ~ubstaniii11 c~\lcul<ltion loads 

(StraCk, 20(6), :md decreases the powel' of the antllYl)is if the 

indicators happen to be reL!undtlnt (Ruscio et ill" 2006), The 

tuxomClrlt: method require:> H minimum of three. indicators that 

should be (X)sitively und nontriYil1lly correhlted with e,Kh other. 

However. many instances of psychopathology are mu!tidimen~ 

~;jonaJ concepts; that is, ment<ll di:-,:onkrs often contain Ch;.u..:tcier~ 

istks that belong to multiple diagnostic "domains" that ,lre negatively related or independcnt (e.g" positive and negative symptoms 

of schizophrenia; Cues!(I, Ugul'te. Goicoa, Eraso, & Pe.rait(j. 2007). 

For this reason, some taxomelri<:: resemchers have used item.~ from 

a single domain to select a set of appropriately related indicators 

for usc within the faxornetric Hnalysis (Ohmmji, Wi1!iams. Haslam, 

Abramowitz. & Tolin, 1008). In this study, to selecf the (\pprnpd~ 

,He indicators, we identified the underlying independent domains 

(i.e., htClors) among the c.xis!ing vnriables, We performed a 

principal-comjlQ1\ents ,lOalysi~ followed hy an orthogonal rotation 

using the complete longiHldinal cohort of 40,406 fnternet sports 

bettors. Next, we selectt.'d items from within a sin~le factor to 

ensure thld we h-ave positively correlated indicll!OrS f()l' the taxu· 

metric analysis. 

Taxomctric Procedures 

To identify the latent structure of llcfUal sports gambling hehav~ 

ior, we used two distinct taxometric procedures; MAXCOY and 

MAMBAC. We used Ruscio':; tll.xometric R program {http:// 

www.tcnj.t..du/~ruscio/taxometIics.html) to produce taxometric 

plots and perform aU calculations (RU<;c10 et ut, 2(06). 

We npplied the MAXCOV(rnaxJJlHl!n covad<Ince) procedure to 

the tbree .sclel;;{c-d vt1rinhle.s. Euch varinble. in turn, acts os an index 

vmiable, The index variable i:) ordered, <md the ob~erva!ions me 

divided into groups, termed window$, according to their index 

vnri"ble value, For each of these groups, the procetJure t:omptJles the 

covmiance between the other two variabJes. For the MAXCOV, \ve 

used Ihe maximum possible nllmher of interval!' with the recom· 

mended minimum of 2S cases. per interv<ll. The larger the number 

of intervals, the 1l101'e likely the procedure will reveal 1I taxon!c 

latent structure (H,usdo el HI., 2006; Rust'io et aL, 20! 0). By 

dividing our sample (N ",. 4,595) infO eg\llll intefval~, we obtained 

184 intervab: 183 tnlerv:ds of25 cm;es e<lch and one interva! with 

2D cases. 

We calculated Hnd plotted the covul'hmcc between Ihe two other 

iutlkators for eHeh illtt',JY'al as defined hy the input variable, We 

performed three iterations using a different input variahle for each 

itCHltioli. To ensure reliahility of the results and 10 minimize the 

sampling error (Rusdo ct at. 20(6). we comlucted the MAXCOV 

u<;ing 50 intemal replications. Inteflllli replications ure pl'1rtieuiarly 

useful when II fixed number of C,ise!> define the illterv.tls bel'(lu:;e 

cases with the Sll111e SCOre might be distributed into several djffef~ 

ent adjacent intervals, To ensure that thi$ mbitmry assignment did 

not ,1ffcct the reliability of om resuhll, we rcplic(lted the ntndom 

assignment of like ohservations 50 times} (Ruscio et nl., 2006). To 

improve the clarity lind interprcWbility of the results, we npplicd i1 

smoothing technique lIsing the locally weighted le;:lst squares 

(Cleveland, ! 979) method to all curves, 

MAMBAC (mean above minus below a ClIt) is an cxtemal 

cons.istency te~t of the MAXCOV procedure, f\ MAMBAC ana!-

1 Reccn1 $wdies dcmomlTuteti lhc pos:;ibilily 10 di!'cover I<\xtl wilh much 

lower ha~\' ralfS given favorabk~ uatn CO!luiliOilS (R\1Sc:i~) & Marl:llS, 2007; 

Ru:.eio & Rnsdo, 2004b; Rusdn, W[lHcrs, Marcus, & K,)CZC10W, 2010). 

) Originally. Mechl did {lot :;pccify the c4wJ!·,\/ condition '1<; ~ pnn of \h~ 

MAXCOV Jlro('(~duf\~. 
86 


75777.063

TAXOMETRJC ANALYSIS OF GAMBLING BEHAVIOR 237 

ysi<" re(]uires only lwO v,lriables, One variable acts (-I:; the mdex 

variable to MAXCOV and divides the range of its vulues into 

intt'fvals. with the ~ame number of observation~, The othcrvuriublc 

is the output variable and provides fhe t;:\xonic me<1suremenf. In 

MAXCOV, the measure, was the coytlriance between ~H1d among 

variables, In MAMOAC, the me,ln hi the taxonic measure, <Inti 

differences in SilC are ~ldded to tht~ search for taxons. The ()bser~ 

Vaftons in each interval constimtc a group, and adjacellt intervab 

c;m be combined 10 forlll a larger group, MAMBAC ;;tnrt::; with the 

first interval and defines observations in that group tI'\. below tht~ 

cut and "n other obServ<liiolls as above the CUL The dirference 

between the avefl:1ges of the omput varinbJe above and bell)w cut 

is calculated and plotted against {be value of the index variable, 

The proce(lilrl.~- i~ repe.a!ed by including s-lJb:.equent intervuls in the 

group below the cut. We applied MAMI3AC using the -s<:II11e 184 

intervals used for the MAXCOV analy,~es and for !he sh: combinations of two of the three measures alternating the index and 

output variubles in each paiL 

To,onic Plots 

Each tilxometric procedmc yields sevcnll piot$, MAXCOV t'e~ 

. ~ults in three plors, one. for each of the three study v~\riables as the 

index vilriablc. MAMflAC produces :six plots to accommodate till 

pairs of the three variables <Hld 1.1ltermHing the index variable tlsed 

in each pair. We constructed II single uggregated plot fa!" each 

procedure [0 summurize the outcomes of the Itlxonk "nalysis. 

To ~tccommodatc the effect of data chanlcteristks on the shapes 

of taxonic plots, we llsed the bootstmp procedure developed by 

RUSclO and his C'olteugucs (Ru:.;cio & Ku,;zerow, 2009; Rllse/a. 

Rusdo. & Memn, 2(07). The buntstrap method generates plots for 

idctlIiLC-d Illxonie and dimensional outcomes derived from the 

Hetun! analytic dat>l. These idealized daw sets shMe imp0!1ant 

features with the aetnal data set such as indicator correlutions, dara 

.'ikew, ~lJ1d kurto:"is. 

Curve Fitting 

The comparison curve fit index (CCft) me~:'lllres the similarities 

hetween the plms of tllxonic test results and both the idealized 

!<"lxonic <lnd idealized dimenSional distributions, TIle several taxOllie results pl'OdilCed by tlifferell! cOl1lbimltimlS of vanable" are 

aggregnted into a single plot for each procedure, CCFI values 

range from 0.0, indltilting agreement with the ide.d dimensional 

curve. to LO, indicating ugreemcnt with the idea! taxonic curve, 

Ruscio ct HI. (2007) suggested thnt CCfi v~!ues m Ih~ range of 0.4 

to O,{} be interpreted with C<tUiiOll, 

Results 

Index V.rinble Selection"' 

Factor analysis of a Spearman cOJ'relmion matrix comprising the 

totlll :;e( or eight gambling beh,wior mettsures (i.e" tot:ll >lmount 

wagered, total number of bets, bet .. ize, duration. frequency, num~ 

ber of bets per d<1Y, tDlal amount lost, and pen .. 'enrage lost) fol~ 

lowed by an onhogol1ul rotation to I.l simple ;;Iructufc re.ve~kd foul' 

factors. The fOUl1h factor included only H single me:lsure, percelH~ 

age lnst. This measure Wil'l confounded by the large difference in 

the dc:gigncd hou.se advantage between tlxed odds bets (about 

10%) and live action bets (aboLit 3%) and by the, need to be 

immediately inv-olved during the course of a game to plOice jive 

action bets. Becau:-.e of its uniqueness and conceptual rernotenc;.;s 

from -other mensures of gambling activity, we excluded percent<lge 

10;;£ from the input vuri.ables amI repeated the fuetor analysis and 

rotation with the remaining seven measures of gmHbling behavior 

Entering these :;evcn variables, this Hn<lJy;;is revealed three factors 

me<lsuring the dimensions that we describe as Activity (uumber of 

bets, total amotlnt wage,l'cd, tow! amount lo~t, rmd bets per day). 

Amount Risked (bet si7,e), and Tin\e Spent (frequency and ClLtnl M 

lion) .. This sO!Olion explained 82% of the varimlce. The only fuc!or 

thaf contaIned marc than three variubles wtls Activity, which wa,> 

the largest of the three factors. ex.plaining 43% of the variance. We 

~elected the four variables that loaded .. 5 01' more on this f;lctor to 

sclf'Ct the reseHr<:h sample. 

Rese-nrch Sample 

The rese<1rch sample of 4,595 sports gmnblers comprised 1 ! % of 

the lowl longillldinHl coholt. These bettors were among the 5% of 

the lolal ~ample with the largest values on tTllC or more of the 

llIl<!!ytic vt.lrlahles (i.e., totul amount wagered, total amount lost . 

luta! number of hets, :md bets pel' day) This sample was not 

different from the totn! sample by ;tge (iH 32, SD 10.2) or 

gender (91% male;;) and represented 51 different countries, As 

Tabte 1 show~, this group displ<lyed behaviors that nre an extreme 

departure frorn the entire longitudinal sample, 

Using th(~ final am\!ytic cohort, the Hfi3lysis of Spearman4 <-'or~ 

relntion coefficient ... revealed nontrivini positive correlation.'\. 

among three indicators: [OWl amount wagered, tottll amount lo~t, 

m}(l number of bets. These ;;tatistically significant (I' < .0 I; N 

4,595) correlation coefficients ranged from ,34 (between total 

amount lost <lod nLtlubcr -of bets) to .35 (between number of bets 

;md torul amount wagered) to .56 {between total nmount lost and 

total amount wagered). 'llle fourth indicafor~bt:ts per dny-was 

negntivdy correlated wilt! totu] amount lost and total amO\lI1t 

wagered. Taxometric llnntysis reqUIres positive correlation between the indkHtors; therefore, we excluded bets per day from the 

following wxomc{ric procedures .. 

As Table 2 shows, Ihe distributions of nil indicators were r()S~ 

irively skewed and evidenced substantial kul1usis, 

Taxomctric Analysis 

MAXCOV. '111e MAXCOY procedure yiekled three plots; 

these are flresented in Appendix A. each using one ()f the three 

variahles as fill index vurinblc, A vi'llml inspection of the individual 

phHS diu nOt indk,lte the outcome;; tD be ",har.lCterbtic of an 

underlying tllxonic 'ltructurc .. As figure I illustrates, the ilggrc" 

gnted plot \Va" more similnr 10 the dimensional comparison data. 

However, the CCrl (Ruscio ct aI., 2007) WHS J1nbiguous (.·19), 

.\ We ex!e!)d 1pccJnl [hanks to 10hn Rusdo HIltI Wil!inm nf(J\'(.~ for 

prt)viding Vll!llilble suggci>tion~ regnrding indicator selection mId gcncJ"al 

intcrprctutlon of the rC;'ll!t~. 

L We- lIsed Sp(.';mnan cnrrdation (0 llI..ljll:S! fllr abnmma! diMr1tmti~ll1 l)f 

the data and to !Hillel! t!le previously pub!i~hcd ~nil!Y~is (Shaffer &, Hall. 

20tH)< Hmy""Cvcr, Pearson corrc!alion :millysjs pwduced ::lrnilllT rc .. ~nlt~, 
87 


75777.064

238 BRAVERMAN, l,,\BkIE AND SHAFFER 

Table, 1 

Means (and S((//1.([ard Deviations) for Research Sample and To/al Coh()rt (~r 

Internet BI'/{ors 

No/e. f Bum, Approxima!e monelary c4lti .... abHs -arc providcu in U.S. dollar;., 

failing to provide evidence to SUpp0l1 either <1 wxonic or a dimcnsioon] view of Ink',rnet SPOIts g'lmtlJing. 

MAMBAC. The MAMBAC procedure produced six plots as 

shown in Appendix B. As with the MAXCOV procedures, a visual 

inspection of the MAMBAC plut~ fuBed to yield evidence of a 

charJctcristic underlying 1i.1XOnic structure, In addition, as with the 

MAXCOV <lnalysis, the CCFl (RLlScio et al.. '2007) was ambiguous 

(.56) and consistent with the c{)mpmis.on aggre-g<lte curve- presented in Figure 2, which does not clearly indicate the research 

data to be silllil<lr to either prototype. Taken together, these results 

fail If! provide evidence supporting either a wxonic OJ' a djmcn* 

siOlml view of Internet sport~ gambling. 

JJiscllssion 

This study is the first taxometric IJnalysis of m.'tual Internet 

sports gmnbling behavior. The results of two taxomctric proccdUl'es I~tiled to provide support for the presence of a t~xonic 

stnlcturc underlying Internet sports gambling. TIle essence of 

taxometric 11n<llysjs lies in the consistency among nnd within 

differenf procedures. Neither taxometric procedure in this study 

demons.trated dear evidence of laxonicily. 

Researchers sometimes interpret the. hJck of consistent evidenceof taxonidty as evidence for a dimcnsionallatent structure (Cuesta 

et aL, 2007: fml.ier, Youngstrom, & N<-ItlgJc, 2007; Ginestet, 

Mitchel!, & Wdlnwn, 2008; Silove e1 aI., 2(07). Given that this is 

a new area of inquiry, we prefer u more conservative interpretation. More reseDrch is ncces.<i<lfY to clnrify whether other !)1e<l.<iH)'e.~ 

of gillnbling l!{;livity and/o)' other aggregation methods (e.!!., max* 

imums find periods of peak ,lctivity) andlor other types of gam* 

bUng might i'cve,u! severnl tnX<l 01' al\ underlying tllxon of e.l(l'cssive, 

gamhling. 

Idenlifying f<lX<l is a fomplcx process that cun yieJd mixed 

results, For eX<lfnplc, the !<lxomelric res~aITh focusing on nicotine 

,md ali;:ohol dependence provides conflicting results regarding the 

latent structure of rhest': disorders. Oinesret et aL (2008) demonstrated dimensional :;tructure of nj~otjne dependence; however, 

other resemchcrs reported that smoking variables mighf retlc{,:t 

T.bl" 2 

Indicator Di:ariiJutio/l Skew aud Kurtosis 

Yadllblc- Skew K\Jfto~l!> 

Tot<.ll 1II11mlllt wIIgen.,d SJI} 102.0:1-

Tol:d fllllO\lnl jnJ;\ 5.9J 6~Jl4 

TOICl\ number (If bell' 7.57 91.50 

both an underlying categoriClll structure find an underlying dimensioml! one (Goedekcr & Tiffany, 200R). Similarly, Slade et (II. 

identified ,1 dimensional strucnll~ underlying tllcohol dependence 

(Slllde, Grove, & Tees1)oll, 20(9), but otht:r reseHH:hers repo11 that 

a taxonic strm;:ture better represents this disorder (Waiters, Hennig, 

NegoJa, & FrIcke, 2009}, Disordered gambling can be added to 

nicotine llnd alcohol dependence us- disorders needing further 

I'cscilrch \0 clarify their underlying nature. 

The gDal of this -study was to examIne wht!fhcl' there is (I distinct 

categoty or taxo-n Hs);ociated with c;I(treme Internet gHmblcrs as 

derined by their betting chl:ll"ilcteristics. Evidence of !.uch a taxon 

would imply tfillt recre.ational lnternet gum biers are qualitatively 

different from those who gamble e.xccs~ively, lInder:;tanding the 

distinct categories and characteristics between recreational and 

disorJercd gamblers would help to guide resc.trchers and clinicians 

alike to the impOli<"ll1t inf1ucntlnl associ<ltiotls be-tween excessive 

gambling nnd player atlrlbUfes. Foj' exmnple, a taxon is likely to 

reflect distinct pattern:; of comorbidity, neural substrates, \lnd 

neuropsychological and genetic (orreiates associated with rcerentional gRmblers compl-lred with disordered gamblers. Identifying 

the characteristics of a taxon for di!lordered gambling would huve 

imp0l1,Hlt implications for clinicians, policy makers, regulato!"$, 

the health cafe industry, and the g<IJlling industry. POl' example, 

currently few g;:unblers receive inSlll'l.\nCe reimbursement for {he 

Irealment of pathologit::u! gamhling despite its inclu..;ion in thc 

lJSM-IV. 1denrifylng a uniquely, and qualitativeJy different group 

uf gambh:T1) comp<lllxf with recreHtional gmnblers suggests that 

disordered gumbllng has un underlying architecture $illlilar to 

otber taxonic psychoplltholngics. For cxmnple. schizotypy (Golden 

& Me-ehl, 1979; Korfinc & LCllzcnweger. 1995; but seeRmvlings, 

Williams, HaslmTI, & Claridge, 2008, for dimensional results) <lnd 

autism~rchlled cognifive dysfunctlotl (Munson et ~t1" 20(8) have a 

.~trong evidence b,lse supporting a clltegOl'k .. l! pe!":.:p~tlve. As wilh 

thes(' oth(',r disorders., if n discrete Hlxcinic structure reflects the 

featmes of di.~ordercd gamhlers, this finding hold:;; important illlfor (lccuntfc- diilgno~i:.:) effective treatment, cmly idcntiof risk, Jnd improved understanding of etiology (Beouchuint!. 20(7). Armed with :iUdl .u finding, rC1)carl;hers and 

clinician:, should be able to stratify disordered gamblers and their 

uniqlle characteristics to better inform treatment, gambling-related 

public policy, neurogenetic research, ~Uld treatment (lulcome !l1ea~ 

sures. 

Alterniitivdy. a dimensional v!cw of Intern~t SpOl1S g(lmbling 

would thm there ih no qualitative diIitincti-on between the 

chc""cre,;''';e, of recreational rmd disordered gumb!crs, This conc1u<;iotl would Iwve imporrant impticlnio-l1S for puhlic policy, the 
88 


75777.065

TAXOMETRIC ANALYSIS OF GAMBLING BEHAVIOR 239 

Research dnta Taxonic comparison dala Dimensional comparison data 

.1 0 1 2 3 "'" 5 e .101:23456 ·1 0 1 2 3 4 5 6 

224lntor\'e.!s 

Figure J. MAXCOY plots: Purnrnctcr matrheu rc~crlidl, s!nw!Med ![tJwnic, and simulal..:'d dimtmsional data, 

gaming inoustry. researl'h. and clinical practice-, For example, 

finding a dear dimen;<;ion<11 structure of gmnbling behavior cncour~ 

ages the developlnent of public polky that targets responsible 

gaolbling programs, which should encourage new technolugy that 

can limit cxces~ive patterns of play instead of flying to identitY 

pJayers with distinctive personal ris.k characteristics. These responsible gambling prognmls <llso might e.mrha'li7C limits to gambling 

opportuniries. Similarly, researchers need to develop and imple, 

ment continuous measures of gumbling to rephlce the more common e:dsting categorical diagnostic tools that -ure traditionally 

included in most psy<:hiatlic cla~sillt:aiion system". As;m illustra~ 

tion, the DSA1-/V typically u:;es a cDtegorit'uJ diagnostic approach 

for most psychiHtric disorderf', inclmJing patbological gambling; 

that is, lIll individual ei!h~r has the disorder or does noL However, 

a dimensional view of gambling sugge~ts {hut nosologists nced to 

develop tools thllt can identify (he qUllntitative differences <lmonj.; 

gllmble~ with mild, subdiugnoslic signs and ~ymptoms compared 

with gamblers who have more moderme or severe sign.s rind 

sytllptorn~, Advancing an improved understanding of Ihese dimen~ 

siona! diffe.renees will permit clinicians. to refine treatment planw 

ning di:»tinctions so that they CUll allocate dinic,aJ re.~omccs to 

patients with different levels of need 

A dinwnsioll<11 mode! of Infernet spons gambling <l1so would 

have impoltant implications for the !re~lImeJlt of gambling and 

co-occurring problems (Widiger & Mullins-Swellll, 2005), Re-

:-.e<\f~h shows tll.'lt, in some case!;, dimensional models provide 

morc valid explanations of comorbidity than do catcgoriC<ll models 

(Widig-er & S<lnluel. 2005). For many disorders, including alcohol 

dependence. substance dependence, and pafhoJogic[tj gambling. 

l;OlllOlbidity is !Hore common than not (CunnillghHm~Williums, 

Cotl1<.'r, Compfon, Spitznagel, & Ben-Abdalluh. 2000). A dimen~ 

sianal model of disordered gambling implies that co-occurring 

gambling, alcohol, and drug use disorders might reflect a single 

<lddiclion syndrome (e.g., the syndrome model of lIddiction (Shaffer, LaPlanfe. el aL, 2004) insteati of co-occurring dislincl and 

:<;eparafe psychopathologies. Dimensionul fi!ldings would encourage clinicinns to assess pathological gambling, like mo~t anxiety 

and mood dis{lrders, using behaviorJJI features thut Hre shared by 

the !!encl'l1J population. 

In the ab~em::e of definitive results, it is import:lnt to emph:\size 

that there is vallie to both a categorical and H dimensional view of 

gambling (Peralta & Cuest.a, 20(7), Ciinici<1ns can integrate the 

clltegorical ano dimensional approilches; they shmlld apply each 

for spedFic purposes (Kwcmer, Nodfl. & O'Hara, 20(4). For 

example, the dimen~ional perspective can guide clinicians and 

l'escan::hcrs to track symptom intensity <md severity during treittmen! or when eValU;l[ing the efficacy of prevelltion efforts. A 

dimensioll;:!l I:lpproilch C<lll help c1inici,ms prep<l1'C patients: for 

long-tenn treatment outcomes: gambling and risk laking <Ire inw 

hcft'.Ht in fI1i.UlY aspects of life, so researchers should evaluate 

treafment outl'omes <Juel pn~vention efficacy against a continuous 

landscape of risk taking instead of <I gambling-no gambling di· 

chotomy Alternatively, H categoricrt! npproach is IIseful to solve 

the aumlnh;trativc need<; uSSOCiU1Cd with pOJtient gro\IP' 

ing, billing, or publiC' health progrHlH rCsomce ~UOC<llion. 

The c~l!egoricaJ interpretation of dimensional diltil may haye 

important public health vRlue. For e:quuple. there Me commonly 

Reseafch dafa Taxonlc comparison data Dimensional comparison dala 

o 1000 2000 300J 4DOO () 1000 2000 :1000 <lDOO o 1000 2000 J(I{)O 4000 

Input (cases) 
89 


75777.066

240 BRAVERMAN, LABRIE, AND SHAFFER 

upplird cutoffs for -cholesterol and hlood ptcssurc used to identif), 

patients who need tre'ltmenl. In our t~scafch, we me lH'Iking 

simi!"," efforts 10 identify cutoffs. to distinguish gamblers who need 

early interventions. 

Limitations find Strengths 

As with ull res~nrch, fhi:-; study is nm without its limitHtiom. 

Similar 10 the research focusing on other potentia! cxpn~ssiolls of 

addiction (e.g., nicotine and alcohol use), our J1lldings were a!l1~ 

bigno\ls, fHiling to rrovide. evidence for IJ disordered gambling 

taxoll. This uncertainty might be the result of methodological 

considerations, such rlS indkator selectiol}, data distribution abnor~ 

malities, and/or the multidimensionality of the phenomenon under 

investigation. The results: indicating wh1::ther disordered gambling 

behavior is continuous. or categorical depend on the selection of 

indicators. Om indicator selection was infonned by previous stud~ 

ies (LaBrie el al, 2008; LaPlante et aL, 2008) <Iud the data 

Icduction analyses specific to this study. Therefore, it is possible: 

that otller indicacms (e.g., dunltion, freql1ency) might be more 

sensitive indices of an underlying categorical !'itrllCflHt;.', We have 

no externlll criteriu such ,IS di1lgnosti(' criterlH of DSM"-IV-hased 

gambling~related problt',ms) dwt could be eXlllnincd for concurrent 

v,llidity of the indicators. Our data have strong positive skews for 

al! vllriabks, <U1d gambling behavior jf; !labk to suhstantial kurtosis. However, f>t-veral simulation studies dernon~tfHted the, rob~lstness of MAMBAC llnd MAXCOV proccdurel\ llsing skewed indicator!; (C!cJ<1nd & Hashull, 1996; Haslam & Clei,md, 1996; 

RU.9.:io & KW:::lctOW, 20(9) 

Taxometrk unaly~es. of low base-nlte- behavior~ can be problematic. Schmidt e! ~1. (2Q().t) fe-commended that mell1hers of the 

assumed taxon compose lit leust 10% of the analytic sample. 

General population surveys, however, reveal that the prevale.nce of 

people who currently satisfy clinical crireria to qualify as disor~ 

dered (e.g., pathological) gamhlers is considerahly le!.s dum 10% 

(Kessler et al.. 2005; Shaffer e! al., 1997). However, these survey'!'; 

did not indicate the relative prevalence of disorder umQllg Internet 

sports gamblers. Absent such a prevalence estimate, we considered 

it ne.cess<lry \0 limit the analytic sample fO incn,·.Hsc tilt:!. proportion 

of p<.)tentinl taxon members: tllose lntemet gambler!) most likely 10 

qu~lify as pathological gamblers. Further, because excessiw fi· 

n<lllcia! CO:-;(S often llccompnny ch{ifacteristics of dls{)rdercd gmnbling, we limited tbe amdytk ~ample to the most heavily involved 

bettors bec;:lw<e this group lost's the most mon~y. Nevertheless, 

these efforts do not gWlrulltee that the HJlal)'tic silmpk~ has (Ul 

mlequ<lte mix of tllxonic and nont(lxonic g.amhlers, If the sample 

wa" cOlllpos~d almost exclusively of one gl'Oup or the other, we 

could not identify il taxonic outcome. 

In thi!; study, we measured gamhling involyement by aggregat~ 

ing beh1wiors over time. Among behavioral problems, measure:; of 

total involvement or consumption might 110t identify exc~ssive 

episodic behavior (e.g., binge Jrinking.) Aggregations also might 

not fe:cognize people who were once heavily involve.d but who 

adnp! and continue to g<lmble modemtely or people with a long 

hl..-tory of moclenltc gambling who lire just beginning 10 enter ,,\ 

period of excessive play. Although these conditions might allow 

some {ti<;;ordered gnlllbters to escape detection, excessive finanrial 

burdens generally e~haust resources nnd force gamblers to disCl:)!l~ 

tinue play. bwin uoe~ not extend credit or arwnge for other than 

cash lind cash cquivnk!)l bets. 

Prt'violls taxometnc !;tudies frequently used ,mother taxomefric 

procedure---MAXmG~in addition to MAXCOV and MAMDAC. 

However, recent resemch demonstrated almost complete redundancy between MAXElG and MAXCOV procedures (Ruscio et 

<lL, 2010; Walters & Ruscio, 2010). Consequently, we decided to 

repOlt the results of only t\vo procedures. 

Despite these limi!atiOlv" this study has many important 

strengths. One of the substantial adv<.UlIuges of OUI' antilysiS is the 

use of a larg.e s<'\lllplc. Increasing the S<Hl1pk size is the be.~t 

so!ution for (lvoiding many h-(l1.ards associated with taxol11ctrk 

analys.es, including tbe diffirulty of gmph interpretntion (Schmidt 

et at., 2004). However, even with l\ ll:lrge sampJe sileo it is impor~ 

taut to recognize the iHlporiti.liCe of indicator validity and indicator 

corrchnions within putative groups for the anlllysis. Another illlpOJ1ilnt advanwge of this study is Ihut we used continuously 

distributed indicators. Taxomctric annlyses often fnil because of 

the ab"ence of contil1uou;). measures For ~~xnmple. psychintric 

research often uses Likert-type interval scales as openHionu! meusure" of one or more variables (Schmidt et <II., 2004). Interval 

scales typically divide indicator variables into few intervals: thi1> 

decrease" the power of the taxometric una1),sl5 and limits the 

likelihood of identifying a tflXOO. To provide II robust taxometric 

test of excessive g<HHbling, W~ llsed conlilluolls meHsures of gambling bchavior(Le., total Hmnber ofbcts, total money wagered, und 

total money 10SI). With a substantial smnpie 1iize and continuou~ 

measures, 'our study had appropriate design p,mllneten. for it» 

investig<ltive ohj(~ctives. 

C{)nclusion 

Despite optimizing the Clpp0rlunity to id(';nfify the taxonic latent 

~tru(."ture underlying exccs&ive gambling, our resull.') fail~d to .support a c<uegoricai understanding of excessive Internet sports gam· 

bling behavim. Given the limitatiOn<; of the analY$e$ and the fac-t 

tlwt thi~ urticJe represents the first attempt lQ ,Iddress the question 

of Intent structure of aCtllll1 Internet ~port'i gambling data, it is 

important to use cnution while interpn~ting these resultti. It might 

be too early to declare that excessive gambling behavior is not 

qualitatively di.ffi.~rel1t from recre~ltionat sports J~HmbHllg. Current 

evidence suggests thm excessive gamblers 'ihare the behavioml 

features (If gHmhling wirh their recreational gambling coun!erp'lrlS 

and that {lisordel'cd gamblers reside <It the extreme of the dimen-

:.ions th,lf underlie the distrtburiOlls thilt d)aracteri~e these behaviors, 

Refercnc:~s 

American f':.ychiatric Association, (20(0). Diagnostic lind SfllflSficoll1l(1l1" 

(T .... xt feY .. 41h ed.). Wil.~hjngton. DC: Am~ricall 

mi(lll (illi! fn'(lfml'llt oIpsychotoRico/ disorder.\'. W:'lshinglof1. DC: Americon P.\ychological AIi~ncialkm. doI:lO.1037110307-000 

nc<\uchainc. T. P. (2007). A brief l\ixomctf'ics primer. ioufl/a} of Chni<'(il 

Child (1m! Adole.remt P.~ye!1()logy • .1.6. 654 - 676, doi: I O. 1080! 

153744107016628,10 

Braverman. J .• & Shaffer, H. 1, 0(10). How do gamblers sIan 8<lmhling 

hJenlifYlllg hch<lvioural m<lJ l:c'f.'> for higlHisk Int~~mel gambling. Fum 
90 


75777.067

TAXOMETRIC ANALY5lS OF GAMBLING BEHAVIOR 241 

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TAXOMETRIC ANALYSIS OF GAMBLING BEHAVIOR 243 

Appendix A 

MAXCOV Curv~s Plots 

E ] 

'iY 

.9 

"0 " lij ~ 

l!l 

.8 q 

'0 

.8 

E '" '" Z " <:> 

.,; 

u 

2 

.9 

11 2/ . . "0 '"

." .. . 

2! 

.. 

'" ro r" J 

2 

,.!e 

~ '" c c 

.~ 

ro 

8 

m ·05 00 O.S 10 1 5 20 25 30 'C: 

'" .; Talal wagered (J 

o 

Number of bets 

'" :8 

0 

.8 :G E 

" 0 c 

NOfe, lnd Indicator. 

(Appendices cominne) 
93 


75777.070

244 

j 

13 

1: § E 0 :! 

~ 0 ~ 

~ 0 

0 

c 

m 

:> 

URl\VERMAN, L,\BRIE, AND SHAFFER 

Appendix Il 

MAMBAC Curves Plots 

1000 xoo 3000 4000 

Tolal wagered 

1000 2000 3000 400 0 

Total wil!Jered 

1000 2000 3000 4000 

Number ofDets 

1000 2000 3COJ 4OC(J 

Number of bets 

. 

...--_Jr 1000 2= 

ToIa!!o.st 

Tolall05t 

300) .000 

Rect!lved FebJUary 6, 20!0 

Revision received August 26, 2010 

Ai:cepted August 26, 2010 • 
94 


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informa 

he~llh(are 

RESEARCH ARTICLE 

Identifying behavioral markers of disordered Internet 

sports gambling 

Richard LaBrie & Howard J, Shaffer 

[)il'isiOlJ 011 Addictions. HWl'oni kfcdica/ School, Medfiml, MA 02 J 55, USA 

(N.eci!iw-'d 28 AUgU$i 2009; rCFised 2 July 2010: accepfI!d 26 July 20lOJ 

Objectirc To identify patterns of sports gambling 

that discriminate sports bettors with self. reported 

gambling~rclatcd problems from sports bettors 

without such difficulties. 

Methods: Secondary data analysis of the actual 

betting behavior observed during the first 2 years of 

<l longitudinal study of 47,134 subscribers to an 

Internet sports gambling site. This sample included 

the gambling behavior of 679 bettors who 

self-reported the reason for closing their accounts 

during that period. We contrasted the behavior of 

those who closed their accounts because of 

gambling-related problems (n = 215,32%) to the 

behavior of other account closers (ACs) who werc 

either not satisfied with the service (11 = 113, 17%) or 

no longer interested in betting (n = 351, 52%). 

Results.' Exploratory multivariate discriminant 

function analyses identified a sub-group of 

approximately half the ACs with gamhling.reiated 

problems who exhibited a homogeneous and distinct 

pattern of sports-betting behavior. Compared to 

other ACs, this suh·group made more and larger 

bets. bet more frequently, and \vere more likely to 

exhibit intense betting soon after enrollment. The 

group estimation formula derived from this proto~ 

type applied to an independent sample of ACs 

conl1rrned the prevalence of this distinct gambling 

pattern, 

COT/ell/slim: Because Internet gambling provides a 

unique opportunity to study .actual gambling 

behavior, H is possible to identify betting putl~rns 

that can lead to (he development of 

gambling· related problems. This. pattern recognition 

can inform the development of interventions to help 

disordered gamhlers recognize their risky behavior 

and avoid further 

Gmnh/inK, Illffmcl, lull'l1let gllmMillf!. 

[1/'1!h!e!/l.}, rd/r!\,ir/liotl. dl,wrd-:I'erl gamMing. 

INTRODUCTION 

lnternet gamhling ls one of the fastest growing 

gambling·related iUUllS!rie:;; (Christiansen Capital 

Advisors, 2006), Research now shows that in Inlllly 

in Internet gambling has 

past 10 years. Por eX<lmple, a survey 

1999 <lne! 2000 (Welle, Barnes., 

2004) reported !hat 0,4(10 of 

on the Internet during the lHst 

through 2007, the same research 

11 survey of US adolescents flnd young 

they ohserved that 211'0 of the sample reported 

Inlernrt gambling (Welte, Barnes, Tidwl'-II, 

& Hoffman, 2009). the 2007 British survey data, 

Griffith!;, WanJle, Sproslon, and Eren<; (2009) 

reported th;!! 6[1/c Df th()s<.~ "Hfveyed used the Internet to 

g<lmhlc in the l-ast year they reported gambling 

online, betting online, gambling using fI belting 

exchange). 

The rapid expansion of Interne! gambling access 

<;timuitlted pllblk healt!! concerns among policymakers 

Rich!eJ, 2004} and advocates (e.g., No More 

2004--2005). Researchers have echoed sim~ 

lint concerns (e.g., Smeatoll & Griffiths, 2004) and 

mised aclditiollul worry that some Internet gambling 

feallifes, snell as case of access, privacy of use, and 

gnps in the regulatioll of onllne betting services, pose 

.spednl risks for the development of gambling-related 

problems (Griffiths, 2(03). 

-----------~--- --------------------- c"""""""1<,,,,, R. LflBne, Division on Adrlic{j()n.'o. Harvard Medical S(:hool. lOt Slnl!oll Landing. M(.o.d(oc-d, MA 02155, USA. 

Tel: 86m. Fax' (78!) .106·$629. E-mail: rlabrlc@cha!lj;lllcc~.org 

R I [] H T Q L I l't! ,<of) 
95 


75777.072

R. LABR1E,~ 1U SHAH'EX 

Risk detection and m<tfkers for Internet 

gambling problems 

Medical science makes llse of biomarkers to ,,,ignnl 

normal Of abnormal processes, or to identify the 

presence of a condition or disease. Bionl'lrkers can 

measure the progress of 11 disea:-.e or renect a re:-.ponse 

10 treatment. The logicBl to l1loJe('ulm markers 

is behavioral markers. observe behavioru! 

markers associated with disense states to niei diagne;>is 

nnd treatment; later, behavioral markers can rnea"urL: 

to interventions, The syndrome model of 

(Shaffer et aI., 2004), for example, includes 

risk factors, temporally distal or pl'(lximal biomarkers, 

llnd hehavioral markers for addiction. Some hchaviors, 

Buell m, betting patterns, clIn be (0 the 

development of gambling-re,}:'1ted Potenli;:;] 

primary sources of information to risk factors 

and disease markers include the litcnJ!urc 

(I) gamhling problems emanating from 

gamhling activities lind Internet gall1hling. Landb;:;sed gambling information 

i~bout betting from 

public venues. Internet 

records of betting activity 

between these information sources hold significant 

potential to enhance our tlllderstanding of £<lll\bling 

addiclion <lntl treatment. 

Research "bout Internet gambling 

Our recent search of the litt!rnture 

LaPlante, Nelson, & LaBrie, 2010), 

<lnd Psychl:"JFO search enginl!s with the search terms 

"Internet {AND] gambling," ide.ntified II t articles 

n~lea:;ed through Mnrcll 7, 2001':, excluding our own 

Intcrnet gambling publi<:Htlons. Ten of these artir.:ie.'> 

mel criteria for original empirical studies. 

of Internet gnmbling, original :itudies used 

convenience snmples: three snmpled Internet 

(Wood & Willinms. 2007; Wood, Williams, 

2007; Woolley, 2003), two free carc medical and denial 

pIltients (Lndd & Petry, 2002; Petry et aI., 2(06), two 

coltcge students (Petry & Weinstock, 2007; Wood. 

Griffiths. & Parke, 2007), (1n~ college athletes 

2(05), onc casino patrons (Woodruff & 

2005). and one employee.,> of a lIlliversity 

center (Petry & Mallya, 2004). We updated Ihis 

search to include: publications available through the 

end of January 2009; we also identified un additional 

Clualifying report {not lluthored by us) that is the only 

study of Internet (wm 1\ 

tative. sample the 2007 British 

Prevalence Survey, Griffiths et aL, 

{hilt the gambling behavior represe!ltrd in 

bused on self-report limits and 

to dr:\w 50und conclusions from research. 

Our literature !.earch yielded a )'ingic \(udy in (he 

peer-reviewed litera!Ure that H$ed acturil gambling 

behavior rCl:ordeLi within an Internet environment. 

Fiedler and Rock {2009} eXtlminrd data from the 

records of poker hands to help determine whether 

poker i.~ .a game of ski!! or chance - a topic not relevant 

to our interest in disordered gambling. 

Studies of actual gambling benavior 

Internet gambling yields records of unprecedented 

detail: computer systems accllralely record and store 

virluillly cvery keystroke. Recogni7ing the opportunity 

for research, the Divi<;ion on Addictions anti bwin 

Interactive Entertainment, AG formed a research 

collaboration (detailed in Shaffer e[ a!., 2(10) to 

Ilwmole computer resources 

Hllcgml to new resean:n lKIr<1Cligm 

that can revolmlonize coltection, in general, ,lOd 

gamhling pH!tern~, in particular, focusing on Hcfual 

behavior ratber than Within this 

backgammon horse racing, 

large of game types yirlds a wide range of 

ch'lracterisrics, S11Cb m: hOl!'''e~odds and time reqUired to 

Poker is more standardized but in 

on deposits (Nelson 

gumhlers who elected 

played a wider y,lriety 

more bels than prior to 

After imposing limit;;, 

frequency, hut intrca:-.ed the 

per bel. A study of behavior 

n I 0 H T G' ( I I" i .~) 
96 


75777.073

rlFIlAVIORAL M,'!"RKERS OF PJ:"ORDERED INrERNI':T SPQ/US GAMBLl.-'G 

dming the last month bef()re i>ports gamhlers closed 

their accounts bccau<;c of gambling-relnted problems 

(Xllall & Shaffer, 2009) revealed a similar of 

increasingly risk-averse behavior of 

on proposifions with ~horter odds. Time spenf gamhling 1.U1d less selective not just 

to be lmportnnt ot' 

fot' developing gambling problems. 

The evolution of our research on actual gambling 

behavior ha.\ established the fOllndation for (~xamining 

of behavior that deviate frorn general tend enand have the potential to C;luse hmm. 

THIS STUDY 

This sliJdy hegins ;j proglTl11l of research thm seeks to 

recognize disordered gambling at the earliest moment'> 

and remediate these difficllhies. In thiS study, we 

prospectively examine sports behQvior patterns consonant with excessive or 

bling hehavior sllf!1cient to cause l!Will 

self-exclude themselves from 

ioml lllarke.r of g",,,o,,ng·""'"'' 

decis.ion by a 

oppormoities Studies of self-exclusion 

land-bnsed casinus demonstrate tl1:1t th~ very 

of people who dec! to self-exclude 

criteria for n gnmbling disorder (Ladouceur, 

Jncgtle.~, Giroux. Ferlnnd, & Leblond, 2(00); in nddition, self-excluders [Irc a segment of the total population of people in need of gumb!illg-related treatment 

(LnBrie {'.t <11.. 20(7) 

The Internet equivalent of land-based casino 

sdf-exduders iii lieeOlln! closers (ACs) who identify 

Iht reason for excJmling them"clves al-. hHving problems due (0 gmnhling (problem 

closers (PGACs}, We expect 

~teps toward rcmt~di"lion: this case. do.~illg 

account. However, unhealthy behaYlor is nOI synony~ 

mOllS with excessive risks l1nd intolcr<1bJe lo~.~es. Por 

bettors \\'ho chose to limit the 

could hct (Nelson ct aI., 2(08) 

included who did not ex.hibit excessive 

hebavior prior to the decision to limit play. 

we expect lllal some PGACs will not exbihit 

extreme betting pat!ern~ and might 

winners. Neglecting other (0 

gamble and exees~lw tilll~ 

examples of non-monetary problelTliltic 

<llso that PO\ilive belling outcomes at hwil1 

were wilh large !o"scs at other 

venues, However, excessive and into!crnble 

the rHost common CHlIses of 

theIr others, and 

"ble in record,,>. We 

be PGACs 

wlthont problems. The Internet's data 

capahilities allow scientists 10 analyze the 

aCCUl'flte records of actual gambling behavior unconstrained by the problems associllled with ",elf-recall 

Baumeister, Vohs, & Fundcr, 2007; Nisbett & 

1977) and self-reporting (e.g" Shaffer et aI., 

Williams & Wood, 2004). ThIS study represents 

galnhling dynmnics 

to the emergence of Internet 

f!11111hli.w·rela,eti problems. Our gonl was to determine 

is possibk 10 identify individuals who 

prohlems 

We hypothdecided to exclude 

for gambling-related 

reasons would: (1) gamblers who exhlhit 

shared problelnatic gamhling hchnviors; (2) represent 

gambling pattern that would be 

distinct the gambling hehavior of lbeir counterH 

parts who ali;o identified gllll.bli.'g·rel,le" 

otber reason.'i. Tbe resemch Objective for 

was to generate a predictive fonDu!a that 

could mnrk patterns ot Internet gamhling 

behavior~ that !cad to problems. 

We further IbM application of this 

strategy to an validation sample would 

confirm that these markers were not unduly 

influenced by snmple-bonnd idiosyncrasies. 

METHODS 

to;l multivariate 

In this exrloration fO!' distinct 

we the three group study by 

nsing the three tlifferell! reHsons for Hcconm 

We ~lsed MOrA to identify lhe homogeneous 

within POACs by first il11nlyzing the 

between PGAC:-. and other ACs and then comparing 

the PGACs who coulu be discriminated from otber 

RIO H T 9 i ! 1'1' , <:1) 
97 


75777.074

H LAHltl!; & H J. SHA!'rER 

sports gamblers. When repeated di~crimina!ivc analy· 

ses no longcr identify for removal indiMint~t 

of individuals, the remaining 

"pure group," ready for characterization 

III this case, testing me,an.,> 

behavior of the group is distinct and that members 

nrc part of a defined group of inlcrest; hen.:, 

!'elf~idcntjfied problem gamblers. Gamblers. who 

cho~c not (0 identify themselves as having problems 

also exhibit this distinct behHvior. The vnriable.,>, 

of vl.1fiablcs, wbich completely di.~crlmln<ltc 

group" from similar others 

markers for the devc]opnH!l"It of 

related problelnfi. 

Participants 

During th.e firs! 2 ye<:l]"s of the collabor<ltivc Jongitndi~ 

nul I,tudy, bwill had it procedure in that offered 

ACs. the opportunity to choose Ihree reasons 

for dosing their account. This procedure rrovid~~d llS 

with [he oppor!l.mity to acquire .'>iUllp!CS of 

ACs who did anti did not gambling-related 

probl~ms as their motivc for c1o,~ing theil" accmmts, 

This slUdy relles on two samples: (!) a model 

(kveiopment sample :Illd ,I model valid,qion 

Figure I proc:~~dure that re.sulted 

in model sample. of Ml9 AC::; identified durlng the 2-year follow-up of the IOllgitudinal 

cohort of 48,! 14 beHors, As Figure 1 shows, we 

eriterin In icientify study 

participants; formally an account before 

the end of the stndy 

choosing to offer the renson for 

one of the tbree proffered choiccs {Le., were not 

c;atisfied with /1"will services or they hud no further 

interest in gambling, or ' problems); 

(."3) heing 11 .~pons heHor, defined 

making bef1l on 

and placing the 

eventl-o; and (4) a net loss in 

As noted eilrlier, we did not expe.ct that 

closed their accollilts beC<lll:.e of intolerahle loss~.s. 

SOHl~ PGACs. (11 """ 21, 

sports bettors who were not 

es.tablished longitudinal 

of sports bdtor.s who 

enrolled in bwin during March 2005, tlsing the salOl.'. 

crilcri<l n.\ our longitudinal cohort. This identified 65 ACs who closed their accounts 

related prohlems during !he same period nf "h"., "";",, 

as {he original 10n.eitIJdin.t'l! sample. This v;\liclation 

sample is 'Imaller due to the monthly varintion in lotal 

eTlro!1ment~ experienced during the early day:. of 

hwill':' development. 

Measures 

The dependent measures of gmnbling behavior sum, 

marizc the daily aggregates of hetting 

fir!;! 10 last dny of sport;;: 

of observation beginning 

offers two types of bets 

prop()sition:.: flxcd~odds helt-. 

on the outcomes of sporting events or gnmes and 

Jive~action bets on propositions about outcomes within 

event. This study aggregated the bets made 

anu live-action type bets. The 

records include winnings credited 

accounts on that d:l)' and ('an include outcomes 

from wager:;. made on previous days. 

We employed four composite measures of gamh!ing 

behavior that .~llmmed the daily information: the 

lolal Number of Bel.<;; (2) Total Money 

(3) Tolal Winning.';; and (4) Active Days, the 

number of with a recorded transaction. We 

computed the of sports gamhlillg involvement 

as the numher of days from the date of the first bel to 

date of the lil!>t bet. The Prequency of involvement is 

the percent of Active Days within the Duration period 

We cakn!illcd the bets p~.r day (Rets per DnyJ 

of Bets made by the 

size of bets (Average Bet) 

Wagcled by the total 

of Rets. The net of gambling (i.e., ~et 

IS the difference between Tolal MOlley W,lgered 

find Winnillgs, The dominant outcome is a loss 

and, by subtfacting Total Winnings from Tolal 

Wagered, positive vnlues of Net Lo;;s indicate the 

cost of gambling. Net Lms 10 a percellt of 

Total Money Wagered (i.e., Lo~t) provldes an 

index of losses that is independent of ttit' total «motlnt 

wngered, The lltullber of cohort members who 

Statistical analyses 

We applied ll. series of MDFAs to investigate the 

of a of PGACs whose betting 

would dbcriminate them from 

other PGACs< A stepwise function analy:-;is entered Hlcnsures from the battery of 10 gmnbling 

measures. We entered each measure in the OrUel" of its 

contribl1tion to discrimination :\~ measured hy Wilki' 

Lambda (SPSS Inc., 200S). This procedure entered 

dependent measures until the (:otltl'ibution to discriminalion of the be,,! rClTIflining measure was not statis~ 

significaat At that point, the classification 

sllbjects occording to their djscrim-

'Icores the estimation equation 

equal (/ priori probabilities of group 

RIO H T 9 l i 1\1 ';.c) 
98 


75777.075

Bl:HII VIORAL 1\1AKKEI\S OF DISORDEl{Ei> ll-;TERNET SI'()KT~ GA1'.IlJ.LlNG 

YES 

~(;~~~;~ 

~ _1: .... ::1 SPOrlSrnOr? L-....... NO -L:3{2~¥~"!.~ __ J 

YES 

(i.e., illdepentk!1! of diffcr(~ncei: in the size of Iht' 

groups). This report provides descriptive stntislics of 

Ihe gmnbJing measures ror the unique and 

of POAC,<,: ,md the ('orrelnlions hetween 

signit1t::an! discriminant ViH'iublcs for ench 

group. 

To measure potenlia! shifts in the nccliracy of 

estimation due to sanJp"'·'o,UO" 

applied the finnl model to validation 

sample of bwin pnrticipallts who were not members of 

the original longillldinal sample; we described this 

11!'ouP earlier. 

RESUl TS 

Modeling distinctive sports gambling behavior 

We conducted the first exploratory MDF;\ to identify 

l~e variables tbo! discriminated the thrre groups 

of categorized hy their 

rC-ilson. Thj~ analy:-.is two 

statistically discriminators, Duration Ilnd 

Total Winnings. The group of bettors who closed their 

accollnts because tbey were not :-'Htlsficd with thl~ 

service they received could not he dt,<,crllllinllted from 

!ht~ other groups. Bet:-lluse. of the poor contrnsl to the 

other hy this non~holllogeneOu'i 

them from <Hla!ysei:. 

who reported 

interested in blVin services 

AC~ who 
99 


75777.076

R LABl\lE & l! j SHAHH~ 

w,-:<:oml!S for gHrnbling-rdHlcd plOb!cm~ (ACs) grouJX'd by 

- -"~"--'-"---'--~'~-'~ 

--_ .. .,_. __ ... 

'\lnEuHH. 

Dhtinct ACs 

SD !vied!;))) ---"_._. __ . ., .. - . 

3.h 

2.1 

2.0 

<.1 

(Hi 

O.R 

IA 

1.1 -_ .•....... 

>10.7% 

7.6 

€6.4 

M,lay\ 

nGrollp~ ~igl1iflc(lntly dilfcr~nt ({'<O.O!) on all mca~urcs, 

bad di;';linct hehaviors Ihm discriminated them from the 

no longer interested ACs and the remllining half of !he 

PGACs. 

The !leXI MDPA comp[ll'Ni the distilH,:tivc PGACs: 

(11::;:;; 108) 10 the other PGACs (II = ! 07). In this model, 

fOllr measures contributed to estimution: 

Bets per DuY, Duration. Total Wagers. 

correctly identified 92'1L of the 

as distinctive or not The 

was similHr across 

of 1(8) for distinctive PGACs and for 

olller PGAC:;, /\s nOlcd earlier, to identify a marker of 

ri~k fOf 

we songht a 

the distinctive behavior. cnd, we eliminarcd the 

misc!assified ACs and condllcfed another MFDA. This 

rcfonnlll:ttion again identified Frequcnt:y, Bet:-. per 

Duration, and Total n" significant 

eli"".i",·i"alo,·, This MFDA modified 

di!-icriminant function weights, lind classified 

the 100% of the PGi\Cs as distinctive or not. The finai 

discriminAnt function iCollect! the nurnbcr of no long~r 

interested ACs thill exhibited the di:>tinctive PG-rcl;l!ed 

behavior from I J.5 to %. 

Describing the distinctive sporls gamiJling behavior 

Table I presents dt'.scriptive stmistics for the final 

MFDA mcnsurcs that di;.;tingui;.;b the Jistinc! and nondistinct POACs, To orient tllese statistics to the original 

metrics, the bchuvior of tile hvo PGAC 

""",·,,'cnshes to the 

metric, Table J also includes the median vatuci> 

untr..m5formcd me:\HWes, The distinct POACs exhibit 

more intense gambling adivity ct\l)densed into a 

shorter betting period, 

In addition to diffi..'renct\~ in 

differences ]n Interconeiatiolls to among 

groups. Table If sllows !hat the correhllions between 

p:llts of discriminating variables arc mW'kedly different 

Non di~tinl't ACs 

MCilil SO Median F(I,[95)'"" Efkel 

22 

L} 

I .. 

08 JO.2<}.\' 234.4 2.19 

0.7 3.3 59.6 1.11 

10 €J8 lJA Ou'i2 

0.9 30 days 25,] 0.72 --_._-------

'L1hlc H, Pearson rorfcl;Jtlon~ bc!\vccn !oG·tntn~forl'ncd dised· 

Bet~ {leI Average Active 

l\'!c,\\Hl'C Frequency n,y "" Days 

-0,{)3 0,32"" ·-0,09 

·~O.2!" " 0.12 +iJAO>!<'f. 

" O.:U·H tU,(}tJ f-O,18 

--j"O.}\()*'" -(J,O .. l -0,15 .----.-.-.. ~ ... --.--

N()1e ~jl<OOJ; *"pdU)OL 

for several measoreS. The correlMlons between 

<'"Ind Hets pt'.r Day lind (2) Active Days: 

for nOll·dislinct PGACs but 

mca'\ures. 

The sfandardized discriminant funclion coefficients 

indicate- each variahle's relative cOIl!ributioll !O 

and the association with the distinct 

of pl"y madt' the 

discTimioalion (SC ~ 1.(177). The 

tinct PGACs made more Bets (SC - O.59R) 

smaller, negative coefficient 

Tlw di~t;riminallt function identifies 

distinct gambJ.els the combination of Frcqnent 

in relntive periods of play 

nOlHlistinct cOlmtcrpart", The discrimil\'i.\llt scores 

for the 110lHlislin!;t group ranged from "~4,60 to -0.06 

<lnd [or the distinct from Q,()6 to 4.53, This 

associated with 

he-haYlor. 

RIO H T 9 L i i\' 'i.1 
100 


75777.077

BEHAVIORAL i\1I\RKERS OF DISORDERED INTERNET SPORTS GAMBUNG 

4.00 

I 0,001 

o 0 0 o 0 

00'0 

PGAGs 

)( Nat 

o Distinct 

....... Not 

.......... Ofslinc! 

Non.mstincl RZ,,064 

Disllnct R?""O.01 

l ~ f ~--'----~~'-'--"--,--r'--r~ 1.00 2.00 3,00 4,00 5.00 6.00 

Active Days (log) 

Scatter plot and rcglc~si0n line" of Frt'(jtlcn.;;y by At,tive Days I'or PG;\C" wl1h di~linrl and non·dislilK'{ disordered gmnbHng 

m:l!kcr 

Application to Ihe validation sample 

We applied the discriminant function to the valldlition 

group, i.e., PGACs in the cohort of bwin sports bettors 

who enrolled during the montb ilfter the original 

DISCUSSION 

enrolled. The discriminant scores 

were negative (-4.18 to -0.11) 

they exlJibiled non-db-;tincl 

The other 33 POACs 

smatlest ~core in 

This illustrates the of 

loral for health in gcncnti, 

logical gambling, in partil.:u!ar. By laking 

an opportunity to acquire a targtted (I.e" 

sample of Internet sports gmnblcrs who 

gambling nccounts nnd identified 

related rroblems as the reason for 

identified a pauern of behilvrnr unique to 

of t11(', ACs. A discritnimmt function based on fonr 

bct,\; 

larger bet.:;; (3) betting more frequently; and 

soon aftcr enrollment We applied 

10 an independent valid.ation 

and confirmed that \v(' could rev!kuiC'. the 

dis.tincl behavior pattern Uf>sociated 

with the onset of gnmbJing-relatcd problems. The 

of AC" with the behavioral marker in 

in the 

risked unci llJ~t more mOlley 

than flotH.listint'l PGACs. The distinct PGAC~ lost 

RIO H T S i j 1" :"i) 
101 


75777.078

~ 

i{. LABRiE ,'V. H J SHAH-htl: 

~ 3.00 

l 200 

a: " 1.00 

000· 

Q 

PGACs 

o X No! 

o Distinct 

..... No! 

......... Drstinct 

Non.Dlstinct A 2",OOO 

Distinct A2",O.16 

.-,---,--.... ---,,.. 1.00 2 00 3.00 4.00 5,00 8.00 

ActNO Days (log) 

S{-alter plot nnd ft'glession !ine of B-e!~ PC! D;!y by i\cli\'~ D<lY" for PGJ\('s wi!h dh!iuCI alld t1())1·dh!i1ld dis()nJeI't'-d gambling 

tOlal days 

Bets per 

PGA('$ non-dlstinct 

with Idaliwly 

1I1Ore totHI days of bt~lting aho to place more bets, 

an acceleration not exhibited by the othcl PGACs, 

Thc$c figures suggc'it that the distinct patterns of 

gamhling hchavior <lSSOcialed with PGACs might be 

recogni.z,ahle after relatively few betting dnys for .<.omc 

of their very frequent play. The pattern 

thdr intense gambling was 

relatively shon peliod of timc. The 

function scores that indicate how likely the 

measures match those of the distinctive 

PClt\Cs. An of these could be 

lIserl 10 

and s.uccessive llCCllll1ulation:;-

calculated, these markers permit proprietor,\; to 

alert individual.,> whose suC'ecs.<;ive scores IllQve in the 

direction of with prohlems of tbnt their 

behavior is to resemble that of a of 

bettors who ganlbting bcc<Hlse 

rdated to gambling. 

The daHl in this 

record of betting from the 

at hwill. Live-action 

with valiallic odds the proposition that a tennis 

will he won at has higher odds than a win at 

Prcviom; rcsearcll wich bwill AD: (Xuan & 

2(09) uscd livC'-action sporlS bets to examine 

"chasing" during the immediately [1reccding 

PGACs more money due to 

but contrary to conventional 

sclcl:ted more r:on:-.ervBiive betting 

~hort('r odds. 

that Wt' 

profile of belting behavior 

exhibited by ~()mc PGACs <lIHong cont[nl\in~ Sports 

hettor'S. Amol!g the somatic illn("~.se:;, there are many 

a marker fail to identify un ilInc.'is. 
102 


75777.079

BEBA YJOI<:A{, MARKERS OF DISORDER!:l) INTERNET SPORTS GAMBLING 

whom the finnncial burden if; not a elc, 

However, thi::, research wa~ 

to identify a or set of markers that 

(ould accurately identify beh<lvior pattems thut led 

bettors to seek relief from probJem& 

by c;)ses according 

10 goa! for 

accounts. 

Figures 2 <Hl(! ) show that the hchllV1On-I 

evidenced by the group of distinct might be 

of sub-groups; if so, it should be pm,sible to 

these sub-groups from em:h other, lhereby 

improvinf!, the ulility of behavioral nlarkers. FlIlUH~ 

rc-se,lrch will expand this study, invcstigate betting 

behrwiQr on other of games, and eX<lllline (he 

potcnt] al to develop 

hehavior 

lila! are llll!versa! ltCIOSS game types. 

LIMITATIONS 

The participants ill Ihis research formally to 

c!()St~ their accoullts and identified <1S 

having gambling-related Both action:; 

rarely occurred and the smnple is relatively unique. There is no additional information, such 

ns a pathOlogical gambling assay, to determine whether 

the.se: would meet dingno~!ic criteria for a 

A numher of PGACs sbared the 

cKtend 

among sports gambIt''"'> 

Consequently, we have 

ongOing fCS\..'[Irch 

There might be other, 

di.~cernnble in a 

identified in this 

additional information that will support of our quest fOI 

behaviornl m;u'kcrs. 

The of behavioral mnrkers identitied in this 

study dlslincl within the context of typic<11 

hetting behaviOl. For most sports 

casino 

difft'rCnl 

disordered 

DC"spitc one strength of this study 

is that it provides the opportunity 10 use un information 

base ofaetunl incJividlWI helting tntnsacliom. However. 

these records are limited to those hets placet! with our 

collrlbontting research partner, bll'il1 Interactive 

Fntertainment. AG. Some ACs might have 

bets at other vcnUCi> and any disordered 

associntcd with these venues might not he evident from 

the bwiil inform(llion. 

CONClUl>lNG THOUGHTS 

tern are 

50 wi!! trigger interven~ 

early in fhe sequence of events that can 

bdwYlor and rcdnC"e assoeilltcd penalties. It might 

th;l\ the described here is fin identifinble phm;0 

gambling. There 

tendency to 

hctting followed 

to behavior th,lt 1S more moderate 

.)chumann, LaBrie, 8;. Slwffer, 2(08), 

Sdemisb ;mel clinician,,, need to dc[cnnillc how to 

the information about distinct of 

hehavior deo,lonstrntcd 

immeuiutc application 

bct1aviors. 

ACKNOWlEOGMENTS 

of 

hH'in, fnler3ctlv\..' Entl!rtainmeHt, AG provided 

support for Ihi>; study. The Division on 

has 

flOm the National Cenfer for 

the National Institute on Dru~ 

In,,>tiwte of Mental Health 

Institute of Alcohol Ahu~e and 

Alcoholism (NIAAA), the M3s.~ad1Ust'lls Conncil on 

RIO H T B 1 1 .• ) 
103 


75777.080

10 R ! ABRII:.'<. II J 5HAI'I'F.R 

dala in the ~Itldy nnd h~ takes r(.!sponsibiHty for irs. 

integrity and (ile ,KCUrlH.:Y of the d:lla ana!y~e$. 

Declaration 

ul!creW; ]I) and its associated 

would Stlggc!>t a conflict of interest. 

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fl'Y\,nuc eslmli\lC~ ilnd 

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Fiedler. 1.('.. 

ThC'ocyam! 

{(!;'l'11'\\',13, 

Griffilh~, M.D 

!nlcillet gambling, hydlO/ORI' of 

76-79. 

, H~r![\llcl, P., & Leblond, J 

& Shaffer. HJ. 

gambling. 

COII/pli/en ill Humall ileilm'ior, 2';, 

Nelson. S E .. 1 ... ,Planle, D.A., Pelier. A]., Schumann. A., 

LaBde, R.1\., & Sh:lffcr, H 1. (2008), Real limlls in the 

bchnvior of Internel gnmbkrs. 

2-/, 46J~77 

NL,beH, R.E . & WH,(IIl. T.O, (1977). Telhng mote than we can 

\..nnw: Verh>ll on menIal pl'(l\:e~scs. Psyclwl()ginil 

H., 

11m..l 

Dona!o. A~ .. & SWnlon. M.V. {lOO-H. Townrd tl ~yndrolOe 

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'ioci,l] 

alld Be!wFior. 7, 

SPSS 1m:. (2()08). SPSS (Ver~ion 17 0) [Computer softwarej. 

It: srss Welte, Barnes, O.l ... C TIdwelL M.O., & Hoffman, lH, 

(2009). The H~snciiltj(l11 of torm of with 

Well(,'-, J.W., Llarne~, G,:YL, Wiccmrek, W.I', & Tidwell, M.·C 

(2004). 

Slales 

js,u<~> in t\ Cmllldi[ltl conte)!;\. 

!'uNic {'olicy, 4. 33-45 

Wood. n:r.. Griffiths, :VI.D_, & P<1r~C. J. (20m). l\xquislIion, 

dcvclopmcm. 

gHmhklS: Belling 011 f\11ure. llNLV Uralling Re.l'eorcll 

IItIIlRrvicll'jmmwl. 9(1), 1-14. 

Woolley. R. (2003). 11lIcmc! 

XlIflll. 

Longitudinal anilly\l~ of Interne! 

10 account dO<;(lre dut; !o 

lOll/ifill o(Gullih/illg SUlflitT 

RIO ~ Tal! l'.t 1:( .. 
104 


75777.081

Add£ctum Researc.h Gild Theory 

June 2010; 18(3): 270-283 informa 

he.althc<'lte 

Toward a paradigm shift in Internet gambling research: 

From opinion and self-report to actual behavior 

HOWARD J. SHAFFER1.2, ALLYSON J. PELLER2,3, 

DEB! A. LAPLANTE,,2, SARAH E. NELSON,,2, & RICHARD A. LABRIE,,2 

1 Di11i:sion on AddicriotH) Har1.'ord ,Hccit"cal School) !O1 Station Landing, Second floor, A1edjcrd 

MA 02/55, USA, 'Ditlisiall 011 Addictions, The Cambridge Health Aliiance, Medford, MA, USA, 

and 3Brondeis Universi~YI HelluSchoolfor Social Policy Managemem~ u.'iJ{tham) MA 02453 

(Received I [ December 2008; revised 20 January 2009; "ce,pted 22 January l009) 

Abstract 

Internet gambling is one of the fastest growing ga.mbling~re!at-cd industries (Christian Capital 

Advisers 2006). As the Internet gambling industry expands, many stakeholders have created) or are 

in the process of creating] g;Jmbling~rdarcd policy. Polky makers promulgating these re!,'U1J'tions 

rely on professional opinions. and/or conventional wisdoms rdnted to Internet gambling to guide 

them because of th.c lack of sufficient scientific research. There is an ongoing nced for qualiry 

empirical research to guide me devc}opment of public policies that surround Intt.::rnci gambling. 

11lis article summarizes the ctlrrcnt <itate of scientific research aoout Internc{ gambling by 

idenrifying, dcs,ribing) and critiquing tht.:: available peer~rcvkwed literature. To identify the peer .. 

reviewed literature reIned to Internet gambling published between January I, 1967 l1nd.March 7} 

200B} we used the search term "Internet {AND) gamblingH in the PubMcd and Psyc14~FO 

search cngines. Of the J It articles identified by OUT systcmllne search, only 30 included Irtternet 

gambling as a focus, erne study merhods presen{<:d in the obstr~cts of thes.e 30 anic1t.;s ~diCElte that 

none induded actual gambling behavior: 10 provided seIf~rcpons of gambling behavior using 

samples not rc:prcscntadve of the gent!ral population, and 20 of the 30 artides were comrnetlCaries. 

In r~sponsc to {he clarion ('elf to improve me State of psychological research (Baumeister ct aI. 

2007), we have condu.:;:ted research utilizing actual imernet gambling behavior, In contrast to prior 

sdf~rc[1orr and case study res.earch, our investigations using t:lcrvaI Internet g3mbJing behavior 

suggest an overall pattern of moderate Internet gambling behavior (LaBrie et al. 2007). 

Keywords: Internet gambling) gambling, safe gmuing, asseSJme11f~ bd:a.vWr; te$earc~,metMdsJ para4t'gm 

Thi~ ~)f{kk i\. not ~ul'>j('ct \\) t.: S, wpyright law 

C{)rn:~p('lndcnc(' HowQrJ J. Sh,lll'cr. Diw;kll !m .'oJ.:i1Cfinns, Harvard Medical School. Hl! Station l.llndilllJ. !kcood f1nor. 

'\\t'dtonl, ~\..>\ Oll'S"i, USA T<'J.: ,HI·30tH!()\)tl. l!·milil: hOw>lf(Lshatfer@hm'.\"uu .... ard,edu 

ISSN 1~06.O)5r,l prinU!SSN 1-\16.13'92. on!in\! {"I 20W Informa Heahhl'm ltd. 

Dot: JG.J to9l1606(jJS09()2?77f114 

A , 0 H T. \. I 1\1 K4) 
105 


75777.082

7b,vard a paradigm shift ill /lIIeTn" gambling "".arch 271 

Introduction 

Internet gambling is one of the fostest growing gambling-related industries (Christian 

Capital Advisers 2006). Although some contend its worldwide growth is less than landbased gambling (Miller 2006), the industry i. thriving and expected to continue to grow 

(Christian Capital Advisers 2006). As the Internet gambling industry expands, many 

stakeholders have created, or are in the process of cre.ting, gambling-related policy 

despite the absence of empirical evidence. Many of the policy development effortS rely on 

professional opinions and/Qr conventional wisdoms related to gambling) gene-rally, and 

Internet gambling, specifically. For example, most recently, the United States (US) 

Congress passed the Unlawful Internet Gambling Act of 2006 (UIGA), which has 

prevented legitimate Internet gambling businesses from providing gambling opportunities 

to a vast segment of potenti.l customers (i.e., US citizens). Some negative repercussions 

of this Act have started to emerge (e.g., hundreds of millions of Euros in capitalization 

lost (Ruiz 2006), fines on the US by the World Trade Organization to be paid to Aruba 

(J.mes 2007); however, both positive and negative consequences arc possible. For 

instance} the lJIGA might meet its goal of minimizing online gambling nmong US 

citizens and) in turn) minimize uWHlfe gamhling-related activity among US citizens, 

Alternatively; US citizens might gamble online as much as they would have jf it were 

kgal, but use unscrupulous vendors to do so and lose the protections afforded by 

regu1ated, legitimate cornmcrce, Because scientific research is not guiding the 

development of public policks that surround Internet gambling, there is an ongoing 

need for new empirical research aboUt Internet gambling that can inform public policy. 

The findings from this research hold the potential to <ocourag. the Use of new 

tC'chnology to conduct research that c.m advance our understanding of Internet gambling, 

clarify the need for public policy efforts, and define best business practices [or the 

Internet gambling industry. 

A shifting horizon; Advancing 'he assessme'" of actual behavior 

Relinnce on self-reports about behavior, as opposed to actual observations of behavior, 

has become commonplace for researchers. It is likely that this reliance is due in part to 

the perception by scientists that measurement instruments have become more reliable 

and improved; in addition, investigators reduce study costs by substituting participant 

recall for repeated observations over time. Re~earchcrs, however, have started to criticize 

the measurement precision costs that derive frorn problems assQciatcd with self-report 

and recall methods. Baumeister and co-workers for example, recently noted that, 

"psychology calls itself the science of behavior ... Yet some psychological subdisciplines 

have never directly studied behavior, and studies on behavior are dwindling rapidly in 

other subdisciplines" (Bnumeister et 21. 2007, p.396). The major concern related to the 

exclusive use of self~reports is that actual behavior and sclf~rcports of behavior are often 

inconsistent (Baumeister et aL 2007). As with any other patten1 of human behavior, it 

foHows that an accurate epidemiology of Internet gambling behavior requires the 

examination of aClUal Internet gambling behavior. Studies of actual Internet gambling 

behaviors might reveal patterns that are inconsistent with self-reported patterns of 

Intemet gambling; the inconsistencies can result from self~reports that underestimate or 

overestimate the actual behavior. The ahility for scientists to shift from self-report to 

actual behavior represents a methodological paradigm change for the field of gambling 

studies. 
106 


75777.083

272 H. J. Shaffer et al. 

PreJent study 

Currently, there is vcry little published empiric!)! research about fntcrnet gambling. 

Con~equentlYJ it seems that, with some- exceptions) theoretical propo~itions and opinion 

papers. represent mOST of the schotarty discussion surrounding Internet gambling, However, 

a careful a.nd systematic review of the extant literature is necessary to determine the actual 

pattern of these papers. 'There/ore, the goal of this study is to identify systematically the 

extant available scientific literature focusing on the play patterns associated with Internet 

gambling; in addition, we v.ilt itluslrate differences betv.·cen the conclusions generated by 

distinctive methods (i.e.; self-report 'tlJ, actual behavior) rJlat have assessed the nature of 

Internet gambling. By providing a systematic approach to the identification of this literature, 

we also offer a strategy that scientists can replicate in future studies to identify investigative 

trends associated with Internet gambling research. 

Methods and procedures 

Our approach to this study was simple, yet systematic. To identifY the peer-reviewed 

literature related to Internet gambling) we used the search termS "Internet (AND] 

gambling" in the PubMed and l'sychINFO search engines; we limited this search to peer 

reviewed studies published from January 1, 1967 to March 7, 2008. This search strategy 

reviewed thes.e terms in the titles, abstracts, keywords, and text of pubUshed articles from 

a literature of over 28 million references in PsychINFO and OVer 17 miHion references in 

PubMED. We excluded our own e~isting Interne! gambling publications from this sample 

for rca.sons thot wil1 be sdf-evident when we discuss the results of this strategy. 

This search strategy identified III articles that met the search criteria. We excluded eight 

book reviews from this total. Our inspection of absrructs from the remaining J 03 articles 

revealed that 56 did not address the conceptual junction of Internet and gambling; most of 

these discussed Internet addiction, addiction 10 nongambling game play or gambling 

problems not related to the Internet. This left 47 articles that addressed Internet gambling. A 

review of these 47 articles revealed that only 30 of them included Internet gambling as 

.a focus; in the other 17 papers, i( was only a t::mgenthd inrerest. We reviewed the study 

methods of these 30 article< and classified them according to the methodological approach. 

We classified studies that did not present any original quantirative empirical data about 

Internet gambling behavior as commentaries; these COmmentaries included studie.that used 

so few case reports (i.e., < 5) that [hcse pubtioeations were not representative of the population 

of cases from which these were drawn. We classified studies with original quantitative 

empirical data about Internet gambling behavior according to the study methods and 

procedures: Sel!~report surveys or studies of actuallmerne! gambling behavior. 

Results 

As Table 1 shows, we can classifY two-thirds of the 30 articles identified by our systematic 

search as commentaries and the remaining third as self-report surveys. Of the 20 

commentaries, 16 were comments or reviews without data, tWO were C35C reports that 

included less than /ive p~tients, and two were descriptions of Internet site characteristics. 

Of the 10 self-report survey.', none included representative samples from the general 

population. All of these self-repon surveys employed convenience samples: four sampled 
107 


75777.084

Toward a parad£gm shift in Internet gambling research 273 

Tabk 1. Studies identified by system;l(ic search of internet gambling te!\i!llrch (Excludes. publisbed Ilnd under 

revir:: ..... studi,,·~ about ncWa! hUf;;rnet gambling heh3\'ior from the authors). 

(Griffiths 1990) 

(Griffith' 1999) 

(Griffiths 20DJa) 

(Griffith!! et aL "2006) 

(Griffiths jlnd WQod 2000) 

(Griffiths and Parke 2002) 

(Hayer Rod Meyt·t 200]) 

(Kerher 20o,) 

(King 1999) 

(King and B;ltak j 999) 

(J...IIdd and Petry 2002) 

Currey 2005) 

(Lamer 20(6) 

a-.\csserlian !:t aL 2004) 

(Mi!l~r 2006) 

(Milk,2001) 

(Nower 2003) 

(Pw), 2006) 

(Petry and Mally. 2004) 

(Pelry lind Wl:imtock 20(7) 

(Sevigny t:! llt 2001) 

(Shaffef t 996) 

(Smea,(on and Griffith!> 20M) 

(Watson. et at 2004) 

(Wong et al. 2007) 

(\\food et aL 2.007a) 

(Wood and Wiltiam$ '2007) 

(Wond et I1L 2007b) 

(Woodrul1' llnd Gn:gory :WO~i) 

(\,(tl!ol!cy 2(01) 

Comm~ntaJ1l 

Commentary 

Commeots!)' 

Commentllry 

Commentary 

Commentary 

Commentary 

Sl;!r.n'pon 

Cotl\meoul"Y 

Commentary 

Self~reptlrt 

Commentary 

C{)Inmcotarr 

Commemary 

Commentary 

Commt':ntary 

Comment~ry 

Self~rtport 

Self-report 

Sclf-n:pllrt 

Commentary 

COfTlm~tHacy 

COIJlm~n!:uy 

Comnwmary 

Commentary 

S~lf~report 

Self.report 

Self-fe-pon 

Self~rcPlll'l 

Sclf~rcport 

Not applicable 

Nt)t I'lpplical:lJe 

Not :lpplicable 

Not applicab!!! 

Not applicabk 

Not applicable 

Nr:rt JppJic[!bk 

Conn:nil:ncc "nn1plc tlf college 3thkte$. 

at {hret! ~ite':i (No:: 636) 

Not :\pplicable 

Not rtpplkablc 

Convenience sa-mple of uninsured) 

underpriv'iteged medical and 

dental patient!> (N=:. 369) 

Not applicable 

Case study (l";~c: I) 

Nm applicableNot arrlicllble 

Not ~pp!icable. 

Nor appHc~b!e 

COnveni~nce sample or unlnsllYtd, 

undcrpovikged medical and 

deniol patients (N::::: (000) 

Cunveniencl! $Jlmpt~ of university health 

(enter ~mp'orccs (N::::: 906) 

CtlnVt'nienc-t' sRmple of college student!> (N ~ 904) 

Descrip:i,)O of Internet ca~;ino sites 

Not spplkable 

DUCriptlO[lS of UK Internet gambling Site ... 

Nut ilppllcubh: 

C!\se st\ldiel\ (N::.4) 

Convenience sample Qf student Internet poker 

players {N "" 422) 

Com'enienf.:e s:.rnple of Internet g~mhkrii; (,V"'!! t 92Q) 

Convtni~nce !\.amplc of fntemt.·( garnbl~rs' (N;::;: 1'');10) 

Olllvt:nir:nc~ sample of J)t.'troit casino pl.ilyer~ (N;;;: 200) 

C{)nvcnicncc samples Df AWil1'aljan COnS\1nlCl'i (N ~ 2948) 

Internet gamblers, two sampled free care medical and dental patients, one sampled college 

students, one sampled college athletes) onc sampled casino patrons> and one sampled 

employees of. university health center. None of the articles identified by our systematic 

search (other than our own) were studies of actual Internet gambling behavior. In the 

discussion that follows, the studies reporting .ctual Internet gambling behavior originate 

from OUf collaborative research program with bwin Interactive Entertainment, A,G. 

Commentaries about Illrt?rnet gambli11g behaviarExisting commentaries debate and spcculat~ about thr: impact and influence that Internet 

gambling has on the public health. For example, in an attempt to clarify nomencl.ture, 

A • 0 H T a \, I N ,<c) 
108 


75777.085

274 H. J. Shaffer e1 al. 

Shaffer (1996) and Shaffer et 81. (2000) argue that the Internet, like other inanimate 

objects, does not have inherent addictive properties; the Internet is virtual space between 

computers, AddictJon is the result of a rehltionship between people and objects or 

activities of intcTeSL Computers, or the information that computers deliver, can represent 

these objects, but the Internet c-annot. However, some commentaries speculate th:n 

Internet gambling is • public health hazard that, by its ability to increase access to 

consumers, has led to an increase in the prevalence of problem gamblers (Griffiths 1996, 

1999, 2003a, 2003b; King 1999; King and Barak 1999; Griffiths and ParKe 2002; Griffiths 

ot al. 2006}. In particular, commentaries speculate that Internet-based gambling's 

structural characteristics (e.g,) rapid event frequencYl high payout ratio) encouruge 

excessive betting. However, many of the cited Internet risk factors also exist among 

nonIntemet-based gambling features (e.g., slot machines, video poker machines, Keno) 

and do not necessarily represent added risk. Commentaries also speculate that the 

potential sodal isolation of fnternet gambling consumers limits the use of safeguards that 

might be able to reduce gambling-related problems; this difficulty, in turn, might lead to 

increased access by youth.! unlimited access to cash flow, and gambling in inappropriate 

places (e.g., gambling in the workplace (Griffiths \999; King \999; King and Barak 1999; 

Griffiths and Wood 2000; Mitka 200 J; Nowcr 2003; Mcsscrlian ot 31. 2004; Griffiths ot al. 

2006»), Several commentaries recognilc the need for empirical studies to substantiate 

these specutations (Griffiths and Parke 2002; Hayer and Meyer 2003). The tWO anecdotat 

case studies about Internet gambling among Parkinson's patients do not serve to increase 

the evidence base (Lamer 2006; Wong et at. 2007): these anecdotal studies confound 

variables that provide alternative explanations for the apparent association between 

gambling-reiated problems and dopamine agonist pharmacotherapy. Two other Commentaries suggest that few sites provide safeguards for Internet gambling consumers. Some 

sites. even use unscrupulous practices (e,g.) inflated payout rates during the demo perioo) 

to lure customers (Smeaton and Griffiths 2004; Sevigny ot al. 2005). Numerous problems 

that result from unregulated Internet gambling suggest it is necessary to improve 

regulatory systems for Internet gambling websites (Watson et a1. c 2004; Laffey 2005; Miller 

2006). However, empirical data is necessary to understand the existing patterns of Internet 

gambling behavior. In short, speculations and commentaries about Int~nlet gambling 

behavior can stimulate the development of hypotheses and models, however, these 

publications are of limited utility to help develop imd test parameters for safer Internet 

gambling. 

SeJf~J'eporled Imernet gambling behavior 

Self-report data provide potentially useful descriptive information about the attitudes 

of Intemet gamblers. For example, one study indicates that land-based casino patrons 

who gamble on the Internet tend to be younger) morc educated, and use the Internet 

more regularly for other non gambling purposes compared to land~based casino gamblers 

who do not lise the Internet for gambling (Woodruff and Gregory 2005). In addition, 

Internet gamblers report preferring the convenience of Internet gambling to land-based 

casinos (e.g., Wood er a!. 200?, 200?b). One study found that wagering on racing 

and sportS betting Were the most commonly reported Internet gambling activities 

(Woolley 2003). 

Self-report studies provide a very wide range of Internet gambling prevalence estimates 

across a variety of special population segments. For example, Petry and Mally.'s (2004) 

RID H T B \.. I IV K4, 
109 


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Toward a paradigm shift iii Imem" gambling research 275 

self-report survey of 9{)6 university heal!b center employees estimated a 1.2% prevalence 

rate of Internet gambling, Studies of patients seeking free or reduced cost medical or 

dental cate surveyed show self-reponed rates of ever having gambled using the Internet 

as 8.1% among 369 patients (Petry et .1. 2004) and, in another study, 6.9% of 1000 

patients (Petry 2006). Self-report studies of students provide higher prevalence rates 

of Internet gambling. For example, prevalence of Internet gambling lifetime rates 

were 23% among undergraduates in one study (Petry and Weinstock 2007), and 10% 

among college a!bletes in another study (Kerber 2005); one study found that 

among college students the prevalence of online poker playing at least twice a week was 

33% (Wood et al. 20070). It is possible that students' self reportS suffer from recall bias 

beca.use factors other than the internal processc~ associated with their actual behavior 

influence !belt perceptions of personal behavior (Nisbett and Wilson 1977; Baumeister 

Ot al. 2007). 

Authors of several self-report studies (Kerber 2005; Petry and Weinstock 2001; 

Wood et al. 2001a; Wood and Williams 2001) claim that their study findings demonstrate 

higher rates of gambling-related problems among Imcmet gamblers than nonlnternet 

gamblers. While Internet gambling might be associated with higher rates of gambling 

problems) to date, these s.tudies do not demonstrate a causal relationship between Internet 

gambling and gambling-related problems. These cross-sectional surveys are poim-intime estimates !bat cannot reliably predict cause-effect relationships. And, as stated 

previously, reliance on participant self-report hinders the ability [() aSsess Internet gambling 

bebavior accurately. 

In addition to the limitation, of self-report, self-selected participation and low response 

ratcs (e.g.,54% for Kerber (2005) 33% for Petry and Molloya (2004) limit the 

representativeness of Internet gambling surv~y findings. Our systematic review failed to 

identify fWO prevalence studies (Welte c! al. 2002; LaBrie et a1. 2003) !bat included Internet 

gambling information. Our search failed to identify these studies becaose they did not have 

the key search terms required for inclusion in this research. Nonetheless, these studies used 

systematic sampling procedures rather !ban relying on convenience samples, increasing the 

likelihood that dIe sample under study is representative of the population from which it was 

drawn, One of !bese fWO studies (LaBrie er al. 2003), utilizing information from 10,165 

students seJected from !be 119 scientifically identified schools comprising !be 200l Harvard 

School of Public Health College Alcohol Study, found !bat l. 9% of responding students 

participated in Internet gambling a few times a year and 0.3% participated in Internet 

gambling once or more a week. These prevalence estimates suggest that studies of college 

students using convenience samples (Kerber 2005; Petry and Weinstock 2007; Wood et aJ. 

2007a) have overestimated Internet gambling among thb popUlation segmem. [n !be only 

existing prevalence study of the US adult general population (Welte ct at. 2002) that 

included data about Internet gambling, a telephone survey of 2340 nationally representative 

US adults yielded a O. J % prev"lence rate of Internet gambling during the past year. 

However) though these studies were more representative of the populations in qm.'Stion1 they 

still suffer from the previously mentioned limitations that are associated with self~report 

studies, 

The wide variation among all of the prevalence estimates suggests that the current 

rate of Internet gambling might nat be reflecting !be same target behavior, or that 

!bese estimates simply are unreliable as a result of measurement Or recall bias. 

Consequently, future research will need to employ improved assessmem and sampling 

procedures. 

A t 0 H T S l , !\j 1(4) 
110 


75777.087

276 If. J Shaffer el al. 

The arsessment Ilf actual Intcmet gambHng b~havior 

To address the deanh of scientific information about Internet gambling) rC5earchers need to 

empioy investigative strategies that can improve the prevailing methods used to assess 

Internet gambling behavior. Internet gambling websites provide a unique research 

opportunity because of their ability to track site visitors as they are gambling. Research 

taking advantage of these comprehensive data resources, therefore) has the potential to 

provide an evidence-based foundation for the study of the nexus between the Internet and 

gambling. Data derived from Internet sources (1) can monitor precisely both individuallevel and population-Icvel characteristics of online gamblers and (2) bas the capability of 

installing and testing empirically derived intervention efforts. 

Recognizing the opportunity to usc Internet gambling technology for research) bwin 

Interactive Entertainment I\G (bwin) , one of Europe's largest gambling sites, and the 

Division on Addictions (DOA) entered into a seminal research collaboration rdying 

substantially on a database of bwin subscribers' gaming activity. By centering a research 

agenda on bwin data, the DOA could study the emergence oflntemet-relaled addiction and 

take advantage of the potential for new technology to alleviate or prevent addiction. Despite 

these potential advantages, using industry associated data, and industry-.cademic 

collaborations can be associated with a variety of potential problems. These issues must 

be addressed from the outset of scientific collaboration. Before discussing the value of 

Internet-based data tracking for gambling studies, we will take a brief discursion to describe 

the nature of the bwin~Division on Addictions association. 

bwin officials initiated the relationship with the Division on Addictions. Tlle founder and 

co-director of the company approached the Division with the proposal that it might be 

possible to identify high risk gamblers early in their involvement with Internet gambling 

because all of their activities can be monitored and tracked. This idea was compelling and we 

recognized that this kind of monitoring opportunity had never before been available to 

gambling researchers. This led to the development of a contract between bw;n and Harvard 

Medical School to conduct research fOl'Using on Internet gambling. As is customary with all 

Harvard Medical School industry related projects, the contract had to satisfy university 

related policy for such collaborations. Harvard is unwavering in its requirements for 

academic freedom. Therefore, bwin had to agree to yield all control over publications, 

project review,,, and the scientific conduct of the research. Effectively, mere was no 

negotiation between tbe industry and Harvard Medical School; bwin simply had to meet 

Harvard Medical School's demands. Once the research design satisfied institutional review 

board requirements to protect the confidentiality of the data and the data transfer and 

storage requirements, bwin provided the cohort of subscriber data without any Strings 

attached. PracticaHy, this meant that the Division was free to investigate as necessary and 

publish without industry review, regardless of the nature of the findings. As with all Division 

research, academic freedom is central and industry partners have no say about the 

conclusions drawn. \'(Ie beJieve these working conditions are essential for a transparent and 

productive relationship between industry and science. 

Before describing the findings of the studies from this eollaboration that assess actual 

Internet gambling behavior in detail, it IS important to underscore the unprecedented 

contribution that data tracking actual Internet gambling behavior brings to the state of 

Intemet gambling research. "win records every keystroke of every person that subscribes to 

the bwin website. At the beginning of this project, we defined and began tracking 

a longitudinal cohort of more than 40,000 subscribers from more than 80 countries. At this 

writing, we have created multiple datasets, and the original cohort has been rollowed ror 
111 


75777.088

£ 

Toward a paradigm ,hift in In,,,,,et gambling research 277 

more than 3 yea". The published findings that dcrived from our analysis of the original 

longitudinal cohort generally are based on the first 2 years of data. During that time, 47,134 

participants made 38 million bets on sporting propositions totaling €28.6 million; these hets 

did not include poker, casino-like game play ond other regularly changing bwin 

propositions, The computer resources integral to the Internet permit a new research 

paradigm that can revolutionize data collecting: Thes.e resources allow us to collect the exact 

betting behavior of ten. of thousands of subscribers from many locations around the world. 

This data includes details about the different types of sportS bets, fixed odds bets (i.e., bets 

made on the outComes of sporting events or games in which the amount paid for a winning 

bet is set by the betting service) and live-action bets (i.e., bets made on propositions about 

outcomes within a sporting event such as which side win have the next corner kick or 

whether the next tennis game in a match will be won at love by the server). 

Research using data reflecting actual Internet gambling behavior has several methOdological advantages compared to prior research about Internet gambling behavior. Research 

utilizing actual online behavior provides objective, detailed information about betting 

behavior, and the conditions under which gamblers place wagers. This strategy avoids the 

potential biases (e.g., memory-errors, sejf~presenuHlon strategies, simple miscomprehen* 

sion, and the phrasing of survey questions) that often emerge when fe-search relies on 

participant self-report of past betting behavior (bPlante et al. 2007). In addition, by 

utilizing a longitudinal study design) research from this collaborative i!) able to examine 

prospectively gambling behavior patterns that precede the development of excessive or 

maladaptive gambling behavior among bwin subscribers. This evidence-based approach 

permits us to identify effective prevention) diagnostic) and treatment strategies. Longitudinal 

sludies often have provided landmark research findings that serve to improve public health 

interventions. For example, the Framingham Heart Study, a prospective, longitudinal study 

of more than 5000 healtllY participants, helped to identify maior cardiovascular disca,'c risk 

factors and has led to vast improvements in public health strategies for cardiovnscular disease 

prevention (Dawber and Slokes 1956). Similarly, analyses of this longitudinal cohort of 

bwin subscribers will sHow researchers to gain a greater understanding of Internet gambling 

behavior .and U1C factors that might influence the developrnent and maintenance of 

gambling-related problems. The availability of this data source has provided new research 

opportunities to study the epidemiology of Internet gambling and responsible gambling 

practices with increased statistical confidence. Thus, this methodology represents 

a paradigm shift in the way scientists study Interret gambling. 

i Toward an accurate assessment of the epidemiology of Imerllet gambling behavior 

Our research utilizing data about actual Internet gambling behavior has produced seven 

peer-reviewed publications (bBrie et .1. 2007, 2008; Broda et al. 2008; LaPlante et al. 

2008, in press; Neison er .1. 2008; Peller et 31. 2008), and other articles that arc at various 

stages in the pubHcarion pipeline (e.g" Xu an and Shaffer, in pN-ss). We conducted seven 

empirical research studies about actual gambling behavior (LaBrie et al. 2007; Broda et al. 

2008; bDrie et aJ. 2008; bPlante et ,I. 2008; Nelson et al. 2008; bPl.nte et al. in press; 

Xuan and Shaffer in press) by assembling a prospective, longitudinal cohort of bwin 

subscribers' actual betting behavior in real time. The studies utilizing data aboUl actual 

Internet gambling behavior provide a clear lens to «amine Internet gambling behavior that 

is not clouded by the recall or sampling biases often evident in prior research. 

R t 0 H T I!l I. I Iv K~> 
112 


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278 1-1. J. Shaffer " al. 

Overall, findings from these studies of aClual Internet gambling behavior (Broda et aJ. 

2008; LaBrie et 31. 2007,2008; LaPlante et a1. 2008, in press; Nelson et al. 2008; Xuan and 

Shaffer in press) contradict the speculation that most online gamblers exhibit excessive 

gambling behavior. The evidence shows instead that the vaSt majority of bw;" subscribers 

engage in moderate sports betting behavior (e.g., for the median level players, 2.5 fixed odd 

sports bet, of €4 each bet, every fourth day, or approximately US 115.30). A comparison of 

betting behavior among the different types of games available on the bw,'" site shows varying 

patterns of wagering. For example, subscribers lost more money on SpOftS gambling than 

casino gambling. The typical aggregate expense (losses) for casino players is higher than for 

Sports beltors. 111is is not because casino ha3 greater losses. In fa.ct, the house odds for 

casino play arc less than half the house CUt on sports betting. The greater aggregate is 

because casino betting is more rapid cycling and people place more bets. This finding is 

consistem with previous literature showing that people involved in casino gambling exhibit 

riskier behaviors and more frequemly present for treatment than SPOrtS bettors (Sh~ffer ot 01. 

2004; LaPlante et al. 2006). Patterns of sPOrts betting varied for lixed-odds (Le., bets made 

on the outcomes of sporting events or games in which the amount paid for a winning bet is 

set by the betting service) and live-action bets (i.e., bets made on propositions about 

outcomes within a sporting event) such as which side will have the next corner kick or 

whether the next tennis gome in a match will be won at love by the server). Advocates and 

poHcymakers have specl.llated that live-action betting leads to inore excessive gambhng 

behavior (Griffiths 1999; Kong ct a!. 2008). Although empirical evidence from this analysis 

shows that subscribers placed fewer bets and lost less money when placing live-action bets 

(i.e., median of 2.8 wagers of €4 every fourth day during the median duration of 6 weeks at 

a loss of 18% ofth. amount wagered) than when placing fixed-odds bets (i.e., 2.5 bets of€4 

eveT)' fourth day during the median 4 months from first to last bet at a loss of 29% of the 

amount wagered). Although the bwin cohort of subscribers was predominantly male, we 

conducted some analyses to examine gender differences in bcning behaVIOr. On average, 

results show that women's betting behavior was very similar to men's, but that women bet on 

more days and over a shorter period of time (LaBrie et 31. 2007). It is worth noting that 

because bwin markets itself primarily as a sports betting website, the bwin subscriber 

population might be more likely to engage in sports betting. Thus, findings from these 

studies might not be generalizable to all Internet gamblers. 

LaPlante ct 31. (2008) and Xuan and Shaffer (in press) completed studies of bwin 

subscribers that also used longiTUdinal methods with actual betting behavior. LaPlante 

et al. 's (2008) study of 46,319 bwilt sports bettors illustrates an overall healthy exposure and 

adaptation pattern of betting behavior for the entire sample during a period of 18 months 

(i.e., short term increases in activity followed by quickly developing dedines in popUlation 

participation, number of bets) and size: of stakes), Separate analyses of the most involved 

benors (i.e., top 1-5% of the sample) show that trends of more excessive gambling behavior 

arc evident for a veT)' smail minority of subscribers (LaBrie et a!. 2007, 2008), The most 

involved bettors had increasing stakes and bets for live-action betting over time (LaBrie et al. 

2007). 

Xuan and Shaffer's (in press) paper examines the multiple trajectories of gambling 

behavior among the cohort of bwin live-action bettors from February I, 2005 to June 30, 

2006 who reponed closing their accounts because of gambling-related problems (N'" 226). 

These bettors who self identified as problem gamblers exhibited more signs of excessive 

gambling behavior (Le., increasing monetary involvement and increasing loss) and more risk 

averse betting behavior than bettors who did not self identify as problem gamblers. The 

authors hypothesize that this behavior represents the self-identified problem gambler's 

A I 0 H T B ~,' i\i >'4) 
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Tuward a PQl·ad,g.11 shift ill ]m,met gambJi11g research 279 

.ttempted by to regulate excessive gambling behavior. To gain a greater understanding of 

longitudinal trends for different types of Internet gambling behavior, we need more research 

examining the stnbility of these trends among different samples of bettOrs (e.g. j groups wiTh 

various risk and resilience gradients), 

TIle assessment of actual Internet gambling u~ing data from Internet wcbsites 

provides ncw opportunities to improve this evidence base. In particular, studies evaluating 

the use of limit setting techniques (i.e., Nelson ot .1. 2008; Broda ot .1. 2008) provide 

researchers the opponunity to evaluate the efficacy of various harm reduction lCchniques. 

Discussion 

Although the field of Internet gambling is still in its infancy, we now have the technological 

ability to study real time gambling and aU. of its attendant details. This new technology 

represents a paradigm shift for both (I) the conceptual frameworks that organize how we 

think about infonnation and each other (Kipnis 1(91) and (2) research methods. This new 

technology provides researchers with the opportunity to conceptualize new and different 

research from the studies that have been available. This new research can focus on actual 

human behavior in addition to self-report, offering the opportunity to implement rigorous 

bchavioral methodologies. Taken together, this technology and the opportunity to study 

actual behuvior instead of only self-reported behavior represents a fundamental methodological shift in gambling studies that was not available in laboratory or land-based gambling 

settings. This scientific r<""lution already has yielded findings aboUt Internet gambling that 

are distinct from earlier speCUlations or self-report based-research. For example, our 

findings derived from actual Internet gambling raise important questions about the utility 

and validity of self-rep on-based gambling resenrch, Furthermore, this new body of research 

advances our understanding about the constructs and nomenclature now associated with 

excessive Internet gambling behavior. To illustrate, tl,e current clinical definition of 

pathological gambling (American Psychiatric Association 1994), with respect to "persistent 

and recurrent" behavior, derives from the self-report of self-identified problem gamblers. 

The emerging body of literature using actual Internet gambling behavior suggests that these 

patterns might be different from those reported by treatment seekers, encouraging us to test 

these constructs empirically. In addition, studies of actual Internet gambling behavior assist 

with clinical case identification by providing behavioral evidence that will help clinical 

investigators minimize classification errors, Ultimately) it will be necessary to integrate 

evidence from studies of actual gambling behavior with self-repon data that reflects the 

experience of gambling (e.g., perceptions and symptoms) to allow us to develop a more 

complete picture of Internet gambling behavior. Increased ability to understand the 

complexity and trajecmry of gambling patterns als.o has important practical implications for 

developing strategies to regulate Internet gamhling. For example, research identifying 

specific mediators and moderators of e'Xcessive Im.crnCf gambling provides poliqrrnakcrs and 

public health practitioners with an improved body of literature on which to base dccisionmaking. This improved evidence base will guide the development of policies and public 

health imerventions tl,at will promote safer Internet gambling. TI,US, continued research 

about Internet gambling utiliZing the study of actual behavior has broad public healtl. 

implications. 

R I Q H T S 1_ , N KC) 
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28'0 H. J Shaffer" al. 

Limitations 

This assessment of the peer-reviewed literature that focuses on Im:erne( gambling has some 

important limitations. Although we conducted a systematic review of the literature, US!! of 

different keywords or search engines might have resulted in a different selection of articles. 

We present a critical discussion of study methodology (e,g., assessment techniques, 

sampling procedures) that we considered pertinent to the state of Internet gambling 

research; however, other researchers might have interpreted lhese studies differently, 

The studies discussed itt our systematic revie.w evtdenct.'d important limitations, 

Commentaries do not provide any empirical cviden<;e about Internet gambling. The seJfreport studies contain empirical data about Internet gamhling behavior; however, the validity 

of these self-reported behaviors is potentially biased (e.g., recall bias). Researchers used 

convenience samples for many of these self-report surveys, and this s.trategy compromises 

the reliability of prevalence estimates and Our ability to generalize the findings, Srudies that 

utilize actual gambling behavior also have limitations. For example, these data still rely on 

subscriber self-reported demographio eharacteristics. Several people might use an aeeount 

or a single user might be making bets for others. Subscribers might be engaging in Internet 

gambling on multiple sites, including bwin. Therefore, the research hased on actual 

gambling might not capture fully all of the features associated with subscriber betting 

behavior. Although bwi" subseriber bening behavior is likely representative of betting 

behavior, these studies do not describe the players' clinical ch:uactcrislics, perceptions, or 

the social consequences associated with their betting behavior. There arc several other 

potentially important limitations associated with Internet-based gambling research. First, as 

we have noted elsewhere (LaBrie et a1. 2007; Xuan and Shaffer in press), we currendy have 

no means test, that is, no data about subscriber income. Consequently, it is difficult to know 

which subscribers might be betting beyond their means. Second, due to the absence of 

a means test and other psychosocial information about the meaning and consequences of 

gambling (e.g., debt, family/social problems, legal problems, etc.), we have little information 

to b~tsc any clinical judgm~nts about the: impact of Internet gambling on the lives of 

individual subscribers. Finall)', it is reasonable to expect that Internet gamblers might also 

gamble both online and at casinos or other gambling venues. Therefore) we cannot estimate 

the potential synergistic effects of Internet and nanlnt.met gambling. Consequently, 

estimates of how much Internet subscribers gamble might not be accurate. 

Next steps: RCJean.:h tv increase lmdersumding of Internet gambling heh(IVior 

Despite advances in the methodology used to assess Internet gambling behavior (e.g" uSe of 

aerual gambling behavior1 longitudinal studies), current gaps in knowledge about Internet 

gambling behavior demand further empirical research. By introducing the Internet 

Gambling Study Act of 2007, US policymakers have acknowledged the need for empirical 

research to guide policyrnaklng decisions. There arc severa) areas related to Internet 

gambling th.H require further inquiry. For example, research examining the psychological 

characteristics of subscrihers in more detail (e.g'l functionality measures, mood) is an 

important next step Iowards understanding how to ere-ate parameters for safer Internet 

gambling. Further research also ,an improve the efficacy of product safety parameters for 

Internet gambling, by studying the factors that mediate and moderate safe play. For 

example, because research ha~ not addressed comprehensively all of the components of the 

Epidemiologic Triangle, it is important to develop more research to address the social 

R I a H T S (. I N K4) 
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75777.092

Toward a pamdl'gtn shifi in Imernal gambling research 281 

settings with which people gamble (Peller er 21. 2008), In addition, Intemet gambling 

research wHJ need to pinpoint factors that moderate exposure and adaptation effects across 

time and space to gnin a better understanding of ways to create environment parameters for 

safer gambling behavior (LaPlante et aL 2007; Peller or aL 2008), Use of standardiud 

assessment tools (e,g" Regional Impact of Gambling Exposure (Shaffer et a!' 200'1)) Can 

facilitate quantified measurement of gambling exposure effects. Furrhcrmorc, the develop .. 

ment of research that recognizes the dynamic relationship between host, agent, and 

environment holds the potential to generate new approaches for product safety. For 

example, research shnws that time spent gambling on the Internet can be as debilitating to 

subscribers' daily functioning as the amount of money they spend gambling (Nelson et at 

2008). Therefore, interventions designed to limit Internet gambling involvemem might help 

some people wirh gambling-related problems. However) fumre research will need to 

integrate observations of behaviors with self~reports of symptoms to optimalIy target 

interventions. 

Advances in research about Internet gambling behavior will require collaborative 

partnerships between researchers, operators, and policymakcrs (Peller cr aL 200S), For 

example) allowing researchers full access to data aboul subscribers' actual betting behavior 

and characteristics requires Internet gambling operators to participate in responsible 

gambling collaboratives that bring key stakeholders together, This empirical data can, in 

rum, help policymakers to enforce regulations that promote safer gambling behavior for all 

Internet gambling subscribers, 

Acknowledgements and Disclosures 

bwin Interactive Entertainment, AG provided primary support for this article. The Division 

also receives funding from the National Center for Responsible Gaming, National Institute 

of Ment.l Health (NIMH), Nation.llnstitute of Alcohol Abuse and Alcoholism (NlAAA), 

National Institute on Drug Abuse (NIDA), the Massachusetts Council on Compulsive 

Gambling, the State of Nevada Department of Public Health, the Las Vegas Sands 

Corporation, the Massachusetts Family Institute, and others, The authors of this article take 

the responsibility for its content and do not personally benefit from their work with gamingrelated companies (e,g" stocks, etc.), 

Decla.'allan of interest; The ~uthors report no canniets of interest. The authors. alone are 

responsible for the content and writing of the paper. 

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d,)i:1U.109J!'''lrpllbj(kpl77 

Disordered gambling, type of gambling and 

gambling involvement in the British Gambling 

Prevalence Survey 2007 

Debi A. LaPlante, Sarah E. Nelson, Richard A. LaBrie, Howard J. Shaffer 

Background: The purpose of this study was to examine the rel,'Jtionships between types of gambling 

and disordered gambling, with and without controUing for gambling involvement (i.e. the number 

of types of games with which respondents were involved during the pilst 12 mon.ths). Methods: 

We (ompleted a secondary data analysis of the 2007 British Gambling Prevalence Survey (BGPS), 

which coHected data in £ngl.Jnd, Scotland and Wales between September 2006 and March 2007. The 

s;:ample included 9003 residents, aged 16 or olcier, recruited irom 10 144 randomly selected addresses. 

5832 households contributed at least on.e participant. Post·facto weighting to produce a nationally 

representative sample Yielded 896B observations. The BGPS included four primary types of measures; 

partidpat/on in gambling (during the past 12 months and during the past 7 days), disordered gambling 

assessments, attitudes toward gambling and descriptive information. Results: 5t;:atistically controlling 

for gambling involvement substanti<:lUy reduced or eliminated all statistkal!y significant relationships 

between types of gambling and disordered gambling. Condusions: Gambling involvement is an import~ 

ant predictor of disordered gambling status. Our anaJysis. Indicates that greater gambling involvement 

better c;haruct~rizes disordered gambling than does any specific type of gambling, 

Key\Nords: gambling, internet, internet g<'lmbling, games, PG. 

Introduction 

P,-ltbo!Ogkal is: a public ht'alth prohlem 

f!wt is a JlIlmbcr of mental <tnd ph,'siol 

health, interpersonal and fin<\nc!<ll problems. H Por exmnp!e, 

ilmong those with co~o(curring meJ)t.l1 HlIwss, 15% of PGs 

in the USA have mental illness that preceded their PG, about 

23% have mrntal illness th.al u!ld about 2% had 

these problems eflwrge 

that PG is associated witn violence, 

suidda! troubles, criminal hehavior :lncl 

other Tht'sC public 11e<lhh iSSl!es wan,mt 

continued empirkal attention to gambling ilnd gambling" 

related problems. 

The Df PC is lHK('ftdini however, research has 

shown a to focus on iypes ;1$ a pMcntilll 

prim.lll' causc. For example, a recent of c()rrdatcs 

of British lntcrnet gambling reported higher l'<1tcs of rli~l)rdcfed 

gambling among internet gamblc~ than among non~!nternct 

gambters.s Consequently, Griffiths et al. concluded that 

lntemct gambling pmb;lb!y i~ more Ilkdy to (ontlibutc to 

prohlems than non-internet gambling activities, 

that this might be the Glse because Internet 

gambling IltSs protective 2·1/7 access tron) 

homel of vulnerable of 

Simililfly, researchers and others Oftt"H to 

m,)(hincs a~ hcil\~ particuJarly d;llIgcrOHS to individuals 

because of their potential to promote rapid g<lmbling (for :.l 

review, sec reference 6), 

Although internet gambling and fruitfslot maLhine wunbling 

contribu!(' to the overall costs as~ociated with excessive 

gambling, the scientific approach to whcthn sptcific games. 

arc the primillY <lluse of PG hllS been ll, result of 

this situa.tion, so h",s the evidence. As Welte ('I 

Divi~il'l<l on Addictions, Camhriclti(, lk .. 11th Alliance, MA, USA 

Correspondence, Debi A. lOt 

Landing Medford, ld'\ 

,1 7Rl·30(,·f!629, ('·m~lil: ''''p''''''''@'',m,,,,,,,',,",d,, 

!'esearcn that tests how well different predict gambling 

problems Dr discriminatt- 1l1dividtlllls gamhling problems 

from those without proviclcf> more reliable information 'lbollt 

the hetwcrn and p;ambling problems than 

reports the prcvilknfe of gambling 

probkll1s among who IMrtidp,l!c in, or prefer, l 

of grm.bling, The hlttcf type of research is 

it yields findings thaf n::scafchcrs Hnd 

OHUl0t generalize to tllC general population or eVl"rl to the: 

gennal of Further, the of 

rt'MlitS generated by two In,)!;S 

of {sec reference 7 for more 

information). 

!kcent research that relying exdui>ivdy on gam(\: 

types as;:In explallatory for disordered gambling might 

contributing faciols, such as the T?nge 

(involvement). Spedfi.:a!!y, using 

of US youth, Wdtt' t'l (II? 

a number of different 

of risk W'~S not 

risk (e,g. nlpid-cyding 

the most risky) 

Funhermore, 

that (l,')!lfrolling (or involvement 

minimized or eliminated the discrimlndtive 

between Iypes of ,llld meaSl,lfC$ of gambling 

that, (OntHlIl' to cOllvention;}1 wisdom, 

might not be {he most problematic 

(or US YOllth, and that overall involvement might 

predictor of gilmb!ing~rdated problems 

type. 

research is limited by its lise of ,1 US 

Mmple. who have few legal g<1rnhling op!ion~ 

The current cx!ende-d these tlndillf.:S by 

Specifically, \\'e ('xamincd 

Prcvale!Kc Survey 2007 

Ofe,W1CS to 

o 

" L 

C 

" " 
119 


75777.096

2 of 6 filW/Ji!Ilf1 jO!lrtlal of Publf( ffl'Cllih 

involvl'ment. We expected that involwn1cn! would <lttenuate 

or eliminate the assod.;tions of games with gambling-related 

disorder. 

Methods 

This research utlHzed data from the BGPS 2007, produced 

by the National CCllirc for SOdili Rt'se,uch, sponsored by 

the Gambling Commission, <lnd supplied by the UK Dahl 

Archive,1I The data <1ft' Crown copyright. The following is 

it brief overview of the nGPS methodology, <'$ dcscdbed 

more fully in multiple somas, :','I.ltl 

Participants 

The BGPS is a publicly available d,ltJ:):et of interviews 

from a of 9003 n~,'iirle!lts of England, Scotl<md -and 

Wales, For (tJrrt'J)( study, we used dala of 

H96H observations characteristic of tbe population, 

The weighted sample comprised 52% women (Hl(l 48% men. 

With respect to age, 14% of the s~lmple was 16-24,35% 

W;lS 25-44, 31% was 45-64 and 19% W<1S and over. 

Procedures 

The BGPS recwited .n hotlsd)Olds from each of 317 

geogrllphk primary sllmpling units selected with a prohability 

that WilS proportion,,] to the Ilumber of addresses within 

them. All H,'sidents of selected households aged 16 and olckr 

wrre eligible- to in the that 

their household 

\1) the sWdy and !Il,lt f('se<Hcher:-; would soon their 

Researchers visited dwcHings a mlnlnmnl of fi.,.e times to 

recruit cligibh' residents to partkipaft" DIning J successfu! 

contact visit, res~>archers completed a brit:"f hO\lsehold .survey 

Blld distributed hard copies (i.e. of the 

.study $urvq. Panicipants could survey 

immediately, at a later point at time re.searchers 

wo,!ld collect the or online, Aboul 7(M) of the sample 

completed Researchers m3(ic a minimum of 

two reminder phone CilJ!S to [('sidellts who h,ld promised to 

complete the survey, but had not done SQ, The ovcrall response 

rate for the study WilS 52%,'1 

Meawres 

The llGPS included four primary types of meaSures. First, the 

survey included the assessment of participation in gambling 

types dminr, the past }'t:'ar and the pust 7 day:s., including; 

l1atlonalloltery tkkct~, scratch cards, other IOHeries, (nothilH 

pools, bingo, rrult/slot machincs, virtual gaming m,Khines (e.g. 

virtunl roulette, keno, binv,o, etc,) at a bookmaker's location, 

casino tahle games, online gamblit1g, online betting with 

booknwker, betting exchunge, horse racc, dog race, betting 

on Hny other event or sport in a booknwker's, by phone' or 

ilt the venue, spread betting, private betting llnd 'other' types of 

betlillg. Sewnd, the su!vey coHtain-ed two asscssments or 

disordered gambling, the Dt.1~nostic dnd Statistical Manu,lI· 

IV (DSM: 11) nnd the Canadian Problem Gamblilli; Severity 

Index (PGSI: 12). ror Iht:" DSM ilssessment within the HGPS 

2007 study, report that they adapted the DSM-IV 

criteria format {e.g. 

do YOll go another duy to win money you 

Responsc options were very ol'H-n, fJiriy often, 

~nd ncver. Positive responses included ,mswering fdilly often 

or very often to criteria 1-7 (i.e, chasing losses, rumin,l1ing 

about gJ-mbling, tolerance, wilhdril"wal. to ("$cape, 

lying to othl:'rs abOllt g,lmbling <lnd to (ut back) 

<lilt! answrring oCC<1sion.tl!y, fairly often or very nftt'!\ to 

,riteria $-10 {Le. a crime to finance gambling, 

risking relationships/job:> asking others (or mone), 

to g.amb!c), Third, the survcy included a series of V<ll!Jh!es 

lepresenling gllmblJng·rd<.lftd l\Uitudes {e.g. agree or dis~gree 

that people should have the righl to gamble}. Fourth, nongambling information included a wridy of demographic 

(e.g. gender, agt:, sQcio+economics) and he(\lth~relatt'd jn(or~ 

m<liioll rio YOll have il long-standing health illness). 

ror C\Jn"ent study, we focused 011 game type, g<l!l1bling 

problems and demographic infonni1tion. For game type, we 

used the above-defined cakgories with one exception. We 

comhined on!ine gambling, online betting with a bookrn;lker 

and use of <1 bcttin~ exchange into lin 'Intcmet gHlnbling' 

dnt,l reduction replicated that employed by 

on thiS dataset. This is a conservative measurc 

because the combimlttQI1 of three .ciltegorics of activities creates 

<l variable that definition represents grcatt:"r involvcment. 

This notati{ln ilpplies to other gambling activify 

t:;ltC"gOrlt:"s tbilt can represent multiple gambling opportuHitles 

(e.g. c<1sino table g<nnes). For gambling problems, we \l$cd the 

past yC;lr DSM·IV assrsstn('nl "nei considered aspects of tilt' 

endorsement of symptoms (i,e, % endorsing any symptoms, 

%:'H numher of symptom~ 

to create a 

gamhling status 0,,-,, 

during the pas! 

dcfini..'i1 gambling 

involvement as the number types gamhling for which 

an individu,ll ft:portcd being involved during the past 12 

months. 

AnalY5i5 plan 

The Camhridgc Health Alliance !nstitutiow\l Review Board 

reviewed and approved this sc(ondary rlMa analysis. 

We used \I'cighted data for all i:H1illyscs. Specific(lUy. the 

13GPS created a weighting v;Jflable correcting for dwelling 

and household selection probabilities, age, gender and 

individual within participating hOll~ehohk I) 

variable yields findings that 

population surveyed, 

sets of ;lnalyses, Fir$t, wc 

and by grnder, ~artidpati()n 

rales for each game type, u~ed chi-square <'In,l!),ses to 

determine whether those rates v,uicd by gender. Second, for 

each type of game, we calculated for the sample of individuals 

who h<l(! pillyt'd the game {luring thc past 12 months and, 

by gender, the proportion {eporting any gambling symptoms 

during the p"s! 12 the proportion reporting 3+ 

g:-lmhling symptoms during past 12 months, thc mean 

number of gambling symptoms reported during the 

12 mO!llhs and the mean number of gambling types 

the past 12 months, Third, we conducted a 

which used partidp~tion in each 

disordered g<llnhling status among 

We condu<:ted these logistic 

controlling ror involvemtnt and 

addcd involvement as a contro!' 

Results 

Gambling participation and problem5 

Table 1 shows the participation in all types of gambling by 

gender and for the fllll .sample. The top live gambling l)'pes 

with respect to pilrtidpation were; the natiOllal lottery. scratch 

1.":ards, betting on horses, fruit/sio! Inachines (lnd 'other' lottery. 

Also popular were privdtt' betting, bingo and other S[.loftS 
120 


75777.097

Table 1 Participation in all types of gambling by gender 

(weighted N=8968) 

Percrmt played la~t year 

T~pc of gambling Female- Male OVO!f<li! 

Na!wnallottery" 55.5 590 57.2 

Su iltch (ilfd~ 202 1'.3.1 19.7 

mller lottelY 11.7 11.7 11.7 

Footbill! pools'" 1.6 5.2 3.3-

Singo'" 9.9 4,3 7.2 

FruiV~lot m"cllin(,;~'" 99 l'U 14,5 

Vlrtu.!1 gaming !T\i\(hioes'" 14 3.9 2.6 

C<!5ino table gamc~'" 1.9 6.0 3.9 

Internet gambling'" 2.9 '.9 S.g 

netting on horses'" 12.8 2!.7 17,t 

lJ.etting on dogs'" 3.1 1.0 SO 

Other sports betting ,-, 2.1 98 6.2 

Spread betting'" 0.2 13 0.7 

Privilti! betting"" 6.4 1.0 10.'1 

Olhi!f type of bettll1g' 03 0.6 0.5 

Any gambling 652 70} ----_ '" .. _.- "'Significant difference betwp.t.!n males ilrld females, 

p<O.OOl 

"Significtlnt ,difference between males and femOlles, p< 0.01 

'Significant difflUence between males Mld females, p<O,05 

CCllubllng iIlV(1}Vfl1lenl 3 of 6 

About 0,6% of the full sample reported 3+ DSM 

the past yf'3f ~nd about 0.3% 

reported 5+ gambling symptoms during the 

T"ble 2 shows gender ::'fratlficd prevaf.cnce fates 

problems reported by individuals who 

various types of gambling. Individuals who 

betting and Wi-cd virtual 

likrlihoQds of reporting any 

the past 12 months, a.<; wel! as 

the 12 

types of games were Ih~ 

highest nH.'<lIl Jl\lfnhcr~ of DSM gambling symptoms and 

mean I1umber Dr types of g,1I11b!ing dming the past },C<H 

(i.(', involvcmenl). 

The top live !wevalctlcc ratcs of any DSM gambling 

hy types of gmll(: werr; virtual g<lming machines, 

hetting, casino tilblc gmncs, other sports belling: and 

or~ dogs, The top tivc prevalence fates of 3+ DSM 

by the type of g.unc wer(': sprNd 

gaming machines, othcr typt's of betting, 

casino tahle games and bettint; on dogs. The- top fiv{~ types 

of g.ames for the me~n numher of DSh-1 gambling 

vinual gaming lil<lchines, tabJc. 

anri oetting on do&~. The top flv," 

the mean Humber oft}'pe:s of gambling 

were: spread betting, 

g,unes, internet 

Predicting gambling-related problems 

an 

12 

Among gamblers 

least one type 

innel1sed to 

shows -a seric.~ of logistic regfession..; 

well each t)'}1e of gambling contributes to th(' 

Table 2 Prevalence of any gambling symptoms, prevalence of disordered -gambling, me,1n gambling symptoms and involvement 

for gamblers who played eil(h type of gambling (weighted N= 8968) 

---~-.. ... -----.. .. _--'-' ----,",---- N(wei9 htcd) Per{eIl1~ge of PeHentage j)fwelghtl Meilllna.of Mean no, of 

weight/3ny h gamblirlg symptoms gambling types of g.ambJing. 

9<1TIlblingsymptoms (d!~Qrdered gamb!nr5) symptoms (jnvolvemMt) 

-.---- Type of gambling M Totll! F ., Tota! F M Total M Total f ., To1al 

------.----~---

National lottery 2398 2<100 4798 5.9 101 8.1 0, 16 10 O.DS 0.18 0.13 2.31 2.91 261 S<ratchcard$ 866 771 1637 7' 13-9 10.4 0.9 3.0 1.9 0.12 0.2S 0.19 3,38 4.4B 3,90 

Olheoflottc:ry 495 467 962: 7.5 10,7 9.0 14 2.8 2.1 Q,13 0,2<1 (J.19 3.34 1\,34 3,81 

Foolball pools 66 207 273 12.1 15,9 15.0 1.S '.3 3.7 0.18 0.37 0,31 4_23 5.30 50s 

Bingo 4" 17' 00' 8.3 17.<1 11.0 1.6 6.7 31 014 0.5.0 0,25- 353 5,02 4,04 

fruit/slot machine. '" 767 1194 8.2 138 11.8 1., 33 l.7 0.15 0.30 IUS 4.14 4.75 453 

Virtual g.lming machines 56 '" m 16.\ 308 26.9 54 13.11 11.3 0.'18 \.04 Q.89 6,37 7.30 7.05 

Casino lIIhle g~mcs B1 245 326 lU 190 17.1 12 8.5 5.2 0.15 0.51 0,012 5.28 6.23 6,00 

Intcmetgamb!ing 127 35' 481 110 169 15.4 2.' 5.9 50 0,22 0.50 0..42 5.20 5.79- 5.63 

a~t1ing Oil horses 565 891 145& " 12.6 10.1 0.7 25 1.8 0.10 O.2f, 0.20 3.70 4.113· 'US 

Betting an dogs 137 '" 413 3.0 19.9 161 1.5 70 52 0.12 0.57 0.<'12 4.74 5.64 5.35 

Other ~ports betting 126 405 >31 11.7 13.1 Hi.8 1.6 '.7 <.0 0.20 0.44 0,38 5.24 5,71 5,60 

Spread betting 9 " 5S 11.1 29,2 26.3 111 16.3 155 0.53 1.101- 1.05 10.54 8.'" 8.89 

?rivdte betting 27S 580 855 9.5 145 12.9 15 2.a 23 0,15 0.11 0.25 4.1B 4.97 4.72 

of betting 15 23 " 0.0 13.0 7.9 00 8.7 5..3- 000 Q,51 0.31 3.14 4,73 4,10 

172G 2801 5527 56 102 7.9 0.' 1.5 0.9 007 Q,17 0.12 2,23 2.82 2,S} 

F"" fcmitle; M "" male; 541 participants did not answer D5M questions and thus were not Included In this table 
121 


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4 of {) EUft)PCll11 jf>lIl'1wl <'/ Publi( Health 

Table 3 Logistic regression analyses predicting disordered gambling status from type of game, with and without controlling for 

involvement 

--------.,---"- ------------ Type of game Odds ratio (95% en, Odds ratio (95% ~f), (!lange In Involvement odds ratio 

no (Qntro! (o"trolll1d for involvement odds ratios {95%C!) 

Spread bettin!J ;!1.B~ <9,91, 48, to}'" 0,70(0.21. 2.28) NS -2L1~ 1.58(1,44,1.74)'" 

Virtull[ gaming machines 211.01 (H6~,4232)''' 4,26 (US, 9.84)" -19.75 1.3B(1.24,1.53)'" 

Internet 9.58 (5,50,19.71)'" 1.53(0.69, B8jNS -805 1.50(1.36,1.66)''' 

Betting on dogs 939(536,16,47)'" 1.95 (0.95, 3,97) NS -7.44 1.'19 (U6, 1.62)'" 

CCl~if\o table game~ 8.15 (.11.50, 14.74)'" 0.79 (0.]], 0.9\) NS -7.36 1.58 (1.43, US}'" 

Other 1ports betting 6.&0 (3.7~, 11,60)'" 0.77 (0311, 1.7d) NS -5.83 1.59 (1.4.11, 1.75)'" 

F(uHhlotma(hinc. 5.1S(3.27,HJ.l0)"· U9{058,2.47)NS -4.56 1.S3 (1.40, 1.68}·" 

Other betting 7.2"11 (1.90, ~7.58)" ]. 93 (0.60, 14.013) NS _4.31 1.55(1.43,1.67)'" 

Footbal! pools 4.')6(2.211,9.29)'" o 014 {O,16, 1.21} NS ··~.12 1.62 {lAS, UB}'" 

Bingo 4.92 {V8, 8.12)'," 1.16 {0.90, 3A2}NS -·3.13 152{1.40,1.6S)'" 

Ptivate betting 3.36 (1.90,5.90)'" 0.36 (0.16, O,S4)" -3.00 1,69 (I.S~, 1.86)'" 

~crat(h card} 3.91 (2.22, 6.8~W·· 1.G9{O.56,2.1J)NS ·-282 1.54{1.'12,1.6B)"· 

Bdtingonhorses 2.Jl(UO,4.80)·" 0.46 {O.22. O.97}' -2.31 1.64 {lSD, 1.80)'" 

Other lottery 3.00 (1.70, 5,:l8)"~ 0.85 (O.43, 171) NS -2.15 1.57 (1A4. 1.70)'" 

Na:tio!,allottery 1.B~ (0.65, 5.19) NS 1.04 (O.36. 3.00) NS -0.81 1.55 (1.44,1.67)'" --'---------------- ·P<O.OS; "P<O.01; u·P.::O.001; NS=not s.ignlficant 

gambling-related problems (Le. 3+ DS.t-.,j¥[V criteria), niv<\fiate 

(lnalyscs showed that lll! types of gambling, except for the 

National Lottery, contributed signific<lntly to the prediction 

of gambling-related problems and all incn'a~ed risk (or 

gambling-related prohlems. The top five odds f-atios were 

for: virtual gaming machines, spread betting, Internet 

gambling, betting on dogs, lind casino table g<lm-cs. 

Subsequent l'cgre$SiOllS that added involvement numher 

of types of games played in the P:Jst 12 showed 

lha\ involvement contributed signifk.mtly to the prediction 

of gambling ·related problems In all models, The addition of 

invo\vcllwllt gre,lt!y reducerl the conlributioll of g\\mes to 

tIlt' prediction of g,unhling-rt'lated problems in each model. 

[or <lImos! al! games, the addition of the involvement 

variable rendered the signifi~'ant posiiivc association bt'tw{x"1l 

gambling type ,md g<lmbling·related problems lion"signiticant. 

The exception Was virtual gaming ma1.:hines, which maintained 

II signifkant positive rclationsbip to di~onk(ed gambling 

status lltter lldjllsting for involvement. Two games, 

belting and beHing on horses, had a reversal of 

After controlling for involvement, individuals who eng-.'Iged 

in private betting or betting on horses were signitiomt!y 

less Hkely to have gambling-related problems IIM1\ propk 

\.'ho did not. 

Discussion 

ll) this study, we provide a comprehensive analysis of partido 

patioo with Jiff~rclH g;um's alTIon~ Brilish residents agi'd 16 

and older. We pl'aced :\ speciill emphasis on the nature and 

strength of the <1ssociations bdween types of gmnes and 

gambling-rclHted prohlems, The types of games that had 

the strongest associations with g~lmbling-rdal-cd problrms 

did not include an of the gamf'S thaI the conve.ntional 

wisdom might expect. F,)r example, frull/slot machines wcre 

Hot included among thc top fivc ganw for gamblingrdated Vinual gaming machines Ihe strongest 

with g<lmbfing~rel(lted problems, but few people 

(i.e, 2,6%) clldors'('d that they had played these dw·ing. 

!lw pnst )2 months. These findings suggest 

perceptions of risk associated with spccific types 

for the development of gambling-related pr()b!cm~ 

represent ;letua! risk. 

Regard\tss of the type of game, past 12-moolh pankipation 

was associated with disordered gambling; however, for the 

most p;lrt, snth dssoci<lfion,; disappear, or at It;ast bewme 

weakened, when statistical analyses co!llwl for the range of 

gambling involvement. OUf findings with a primarily ~dult 

British sample are consistent with Welte f.'f nl,'s (2009) 

results for US youth. Tllken together, these two sets of 

finclinp,s suggest that rcsearchers and others use caution 

when interpreting rcsu!t$ showing that people who play 

spedtk. types of games have il higher r<l.te of g.unblingrelated problems than others, In lac!, th('sc st1.1dies reveal 

thaI sOl11e games might be indicators of unhealthy involvement, rather thllll critical factors fOI" gambling-related 

problems themselves. 

One interesting, <\lid perhaps unanticipated, finding \'l<lS: 

that the fiiltl1fC of the relationships between private hetting 

Hnd bl~tfing on horses ami gambling problems changed when 

we considered the inllucllcc of involvement: cng<1ging in these 

of gambling, but not other type.~, scclnl.'"d fa protect 

ng.lins\ devdoplng gambling problems, This finding 

suggt'sts rhilt the apparent risk between gamhling activities 

and developing gamhling-related problems resides, 

primMiiy or even entirely, alnong individnal~ who have 

mIt'S of involv('ment. For others who do not have high rates 

of involvement, phl)~ng lhese types of games might reflCft 

sodal setting characteristks (e.g. norms) that eilCOtJrage 

control .and preclllde ex(c.')slvc gilmbling. 

These findings hold SOlnl.,' dispa(,ltc possibilities (()r 

theories or 3lln1hling e):"posure. On one h,md. these findings 

might imply that more opportunities to gamble create more 

opportunities (or involvement and, therefore, might yield 

more gambling-rdated problems. On the other hand, these 

findings might suggest that more opportunities to gamble 

will have little to 1\0 impu\ on the prev,licnce and illddence 

of gilmbling-rcl.1ted problems because individu,lls afC more 

or less to lnvolvenwrlt. fnereases in gambling 

win not influence individuals who are less 

prone to involvement, but those likely to bt'Comc, or 

who alrc<1dy life, involved. Thert' some cvi,ielKe to 

tIlt' hlttt'r vicw because the rate of gambling flisorders 

changed little during the past 35 ),enrs despite the extraol'dill" 

of gambling opportllnities ilnd access around the 

Strengths and weaknesses of the study 

Notable strengths of this study include the <lllal),!,is of multiple game types simuhallcoll~!Y ,md the in(orporation of n 

measllre of involvement in!t.l analyses thill ex,Hnine the <1550-

d<ltion betwC'~n type of ttl me <lnd gamhling¥rclatcd problems, 

Controlling for illvolvement allows a marc sophisticated 
122 


75777.099

under:.tanding of the ri"k uniqm' to SO[11(' types of games 

,\Ild proviDes a \cvd of <lnalytit s.ophistic<nion morc adviHlccd 

than the m(ljofity of available research,7 By controlling for 

involvemt'nt, this research shows that involvement is <I 

potent ),lrediclor of gl:lmhfing-rdated problems lhat exceeds 

the potency of types of gnmes. In fact, for il1volvC'-

ment draslicfllly reduas the ability of games to 

stl1tisticJ!ly individuals who have gilmhling-rdatt.-d problems 

from those wnn do not. Another strt'ngth of this s!ndy is 

IhM it (ldvJnc\'s this more sophislkutcci methodology and 

linc of from it US .uio!esccnt to ;\ British 

primarily sampie. This broader sample helps 

to ,wold problems rc1<1h'd to !eg<l! ;lccess to typcs of 

g,llnb!ing observed <lmong the US sample <lUll COll(Crrl5 abOll! 

different gllming intert'sts by age- (ohorts. 

Nevcnhdcss, this study is lIot without limitations, Firs!' 

the an<llyst's rely 01\ data and nnt actual gambling 

activity, Sdf"report is to wC;lkJ1{:~scs, including 

faulty memory, factual errors and sclf.prt.'scllt?tion biJses< 

Second, lye only included one measure of involvCllwnt 

(i.e, ntllnbe( of types of gamcs during the P,lst 12 

monfhs). Other me,\S\1fes of (e.g. Intensity of 

pJa)" involvement in clusters of etc.) provide 

we,Ike-1" or stronger attcnu.J.tion bet\yecn 

jype.~ of 2nd problems. Third, this 

study of b;;h,wioJ' ,md 

some types gan1t's; conseql1t'nt!y, incrcas\Cs 

sile might alter the findings for games phlyed by small 

llumtWf'S of people, 

Fufure research should includc the longitmJilwl assessment of real·timc d;lta {I])d multiple mC,UHrCS 

of involvement to detamination of whether 

involvt'll1t:nt is <l. 

Other important dirt:dions include cxaminations 

to determine of involvcConcluding thoughts 

The wIgc of 

IJredktor of 

there might 

10 thilt 

Natiollal Institute on ,\lcohol Abuse and /\kohoHsm, and 

the Nation;:!! lmtltule on Drug Abuse. 

Con/lict oj lIIumt: Dllfing the past 5 years, lhc authors of 

this have receivt:d honorJ.rl<l <lJld ftOCS for a variety 

of induding gaOlblif)g~rdl\ted symposia, spe<lking 

events, education event~, ft'search, and consulting. HJS h'ls 

served as an expert witnc-ss for grnnllling and other 

addlctioll.rehltl.'d legal cast's. The authors have no other 

persona! or competing illterest~. The 

-sponsor had no in this resr,II'(h. We conducted this 

rr~(,ilr('h independently from the SP011S0r. 

Key points 

• The actiolo!,'Y of PG is uncertain, but research h,15 

to ol."tnmine whether specilk gJn1C 

machines, internet gillnbling} arc 

increased risk for developing disordered 

th<lt past findings linking 

disonicn'd gambling flllted to 

involvement among 

and when the extent 

game tn1c Influences 

dimini.s.h, Nt'vcnheiess, this recent rest'arch is limited 

by ils fcliance a US 

to The oment by testing 

gillnes, range of 

involvement 111H! gambling am{lng a 

natioml!y represcntative Dritish "dill! sample. 

References 

1 Pd,}' NM, StirWlIl E<" Gr,)IH RF. CnlllO!hldflY of DS;"1·jV l',lth'll"!!ir~l 

Ramhling and Nhe~· f>~rdl.i,1!'" diS(>rdert- r<~Mjlts frmn (lte Nation.11 

[pHkmi()logt' ~ur"q' nll >lkohol and rd.l\nl ("ndJ\lons, J Cim I'iyrl)irlfry 

2f)iJS,(,~:%1-74. 

~ S:uffer llj, KOIn Pi\. G~!llbtiflg ,md rdal<'\! nlwl,d di,o[l!"rI: ~ publiC !w,lllh 

<! ShaHn If], bt!,ja'lle PA, l..Jl\m: 11.A. d ~L T(1w.ud .1 ~yndf!lr!1~ Illrldd <,[ 

"dd!C(I(m: I11Uhlpk r~pr"'\'W)f\,, (Hl'lmml cli111\)!1Y. Ha"" }I,." PS,d';'llrr 

2i)(H;12:J67,,!t. 

This findillY> is he(a~lsc it 6. D""'I!n~ N, -!lINn:!, T. !~k,'lI'O":<' l;:.\millg m~dlin<\<: m: tlwy Ihr 

from tIl!! tendcncy to fotu~ on gmnes, \r,l(k' .. ,,,~m<:' pf [;Jmh';"~~ /Iddio"", )!)()5;IOO;\j-'IS 

such <IS (Hlit/slot machines as central to gamhlinp:-·reLlIcd 7 Wdw J\V, D4Wl'; eM, 'lidwdl M·CO, Bnlfm,1I) Hi. The aS~'I<.;"Ii()r} 

This l'(se<Hch does not that of IM))l or !:~mb!ins with pr\,bkut g"mb~l!lr: ~mn!)[!. Amui':M; ynoth, 

!rpe~ of games is f'ff,lwi Add/a Ikhm' W()9;l3:Hb-lJ. 

there are differenccs in the 1< N,l!i[lI\-li C,'ntn: fM 5"d,lllkw",dL fll~t;<h g'\lJlhling prr,\lkna; 'mr¥cy 

similar resul\,/ suggest the 10(l7. (ukhe"m, F,sex; UK )\11.1 Ard1JI'(\ 200f! 

aS~\.Illlptions related to the influence of gamC' t)1)CS ;lOd ';l WartU" n, Sprm!fHl K, Orford J. d al. Bnli~h Il.Hllbling Fev"lmo.; ~llm~y 

direct OlOl'C :Htentioll towllrd globa! bchnvlora! charact/.Ti~tks> lOtt?: l'<~l)flnJl Cmlr<.' for S()d~! RLItW(h. 5q)lt'mht'f 1001. 

weh as the rnngc of involvt;ment. 10 Gdffitb til, WMJk ii, Orft'rJ J. e( ~L hHon(\ r;,\!1\hliul/.' ~ $~'<:()nd~r)' 

Funding 

bwin Intemct!vc, AG provided tht' primary support for thi.~ 

research. The Division on Addictions (lts0 receives fin:lnci~l 

from: The NlltiOfl(\I Center for !k~PQn$ible G<lmlug, 

Casino Rewft, LI.C, SL Fr,weis House, the 

1 J tlmWf.111 I'sr(hi~lrI\: ;\"G«.uinrl, DSI\1-1\': dldp(l'li, M"I ~1.1{i'!I{\,1 

m,\l\1.1a; of ITwnw.! diwrders. 1th fth \II ~~hml'.((ln, DC; Amcrkan 

12 relfi, I, Wynll~ [1. Th~ CJn.a<\L'11 l'whknl C,m:blillg lilJ.:~: PilUJ Rqwrt 

OIMIY,\, ON, CUlla,l,l' Clll.hli,H1 Cenlre DJ! $Ub~MJK~ Ah<,c, 2{101. 
123 


75777.100

6 of 6 Eurvpedtl JOllY/wi of Public lit:tlifh 

D N.ltlonn! C<'llt/\' fnr :,\ocial Rr'~!Ir,h. nrithh G;m,hlm!;: I'n:v~kll{C ~U;y~y 

lUO] UWf Guide, CDkhc5tef, Es,n: UK lhla Ardllvt', 200K 

H T.\h,Khnid" lIG. Fidell15. l!~ing. ll1ultivMiale ~IR!i~lk<. !"t\~ York.;-.:Y 

H,llp.:r Collim.1991> 

1S ! JP1Jnlc DA, Sh,lffn Ill. UnLllTH.lndmg jh~ in(h,c!l(' of j«unhlini\ 

OPPOfltllHtieS: bp~ndiH~ \,~!)(I$ure models jo ind<tde uJ"j>IJtlOll, 

'\'lI! Orthop,yd,jollr, ll).Q7;77:~!r,·2.\ 
124 


75777.101

J Gambl Stud (2009) 25:239-252 

001 10,1007/sI0899·009·9118·z 

ORIGINAL PAPER 

How Do Gamblers End Gambling: Longitudinal Analysis 

of Internet Gambling Behaviors Prior to Account 

Closure Due to Gambling Related Problems 

Zhning Xuan . Howard Shaffer 

Published online: 27 February 2009 

© Springer Science+Business Media, LLC 2009 

Abstract Objective: To examine behavioral patterns of actual Internet gamblers who 

experienced gambling-related problems and voluntarily closed their accounts, Desigll: A 

nested case-<:ontrol design was used to compare gamblers who closed their accounts 

because of gambling problems to those who maintained open accounts, Setting: Actual 

play patterns of in vivo Internet gamblers who subscribed to an Internet gambling site, 

Participants: 226 gamblers who closed accounts due to gambling problems were 

selected from a cohort of 47,603 Internet gamhlers who subscribed to an Internet 

gambling site during February 2005; 226 matched-case controls were selected from the 

group of gamblers who did not close their accounts. Daily aggregates of behavioral data 

were collected during an IS-month study period, Main outcome measures: Main outcomes of interest were daily aggregates of stake, odds, and net loss, which were 

standardized by the daily aggregate number of bets, We also examined the number of 

bets to measure trajectory of gambling frequency, Results: Account closers due to 

gambling problems experienced increasing monetary loss as the time to closure 

approached; they also increased their stake per bet. Yet they did not chase longer odds; 

their choices of wagers were more probabilistically conservative (Le" short odds) 

compared with the controls. The changes of monetary involvement and risk preference 

occurred concurrently during the la!;t few days prior to voluntary closing, Conclusions: 

OUf finding of an involvement-seeking yet risk-averse tendency among self-identified 

problem gamblers challenges the notion that problem gamblers seek "long odds" during 

"chasing." 

Z, Xuan (Q:&) 

Department of Society, BI1man Development and Health, Harvard School of Public Health, 

677 Huntington Avenue, Boston, MA 02115, USA 

e-mail: zxuan@hsph,harvard,edu 

H, Shaffer 

Division on Addictions. The Cambridge Health Alliance, Harvard Medical School, 

!O1 Station Landing, 2nd Floor, Medford, MA 02155, USA 

e-mail: 11Oward_shaffer@hms,harvard,edu 

<fl Springer 
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240 J Gambl Stud (2009) 25:239-252 

Keywords Pathological gambling· Chasing . Longitudinal analysis· 

Gambling addiction . Sport~ betting 

Introduction 

The stltdy of gambling consequences is a central topic that has stimulated interest 

among researchers, clinicians, and public policy makers (Griffiths 2004; National 

Research Council 1999; Petry 2005; Shaffer and Korn 2002). Although long·run 

mathematical expectations for gambling (e.g., "Gambler's ruin" (Ross 2002)) have been 

documented, considerable complexity arises for those who try to predict actual gambling 

behaviors. Theoretical attempts to predict gambling behaviors are sometimes inconsis· 

tent with what ordinary people actually do. for example, The Saint Petersburg Paradox 

wa~ published in 1738 from Daniel Bernoulli's l>reSen!ation of the problem. An indi· 

vidual is required to pay a fixed fee to enter a coin toss game. A fair coin will be tossed 

in sequence until a "tail" appears and ends the game. The payoff starts at 1 dolJar and 

is doubled after each "head" appears. The individual wins the final payoff when the 

game ends with the first "tail". The question is how much an individual should pay to 

enter the game. In theory, the expected value of the game is positive infinity; yet in 

reality, most people are willing to pay only a small amount of money to play the game. 

The Saint Petersburg Paradox highlights a classical example where in vivo people 

behave differently from what a naIve decision theorist recommends. A naiVe decision 

theorist would suggest that people pay an infinite amount because the expected value of 

gambling is positive infinity; however, ordinary people usually follow heuristics rather 

than pure mathematical reasoning. 

Theoretical constructs regarding the development and maintenance of gambling 

disorders often arc not empirically developed and lend to remain untested against actual 

gambling behavior. One example is the construct of "chasing." Researchers have 

defined chasing in several ways. Lesieur (1979) describes the "cbase" as an individual 

episode or series of gambling events where gamblers increase their losses because they 

continue gambling compUlsively to recoup previous losses. Lesieur (1984) asserts that 

the intensity of the "chase" escalates as the money loss increases. Similarly, the 

American Psychiatric Association defines pathological gambling as a persistent and 

recurrent pattern of maladaptive gambling that is characterized behaviorally as "needs 

to gamble with increasing amounts of money" and "after losing moneY ... returtls ... to 

get even" or "chasing one's losses" (American Psychiatric Association 1997). Patho· 

logical gambling also entails elevated monetary risk (Breen and Zuckerman 1999). 

Clinicians have considered "chasing losses" as a central attribute of pathological 

gamblers. Finally, Dickerson describes cha~ing losses as "to bet more ... by a sequence 

of losing bets resulting in further betting with increased stakes and/or longer odds" 

(Dickerson 1984, p. 133). 

The suggestion that a gambler's choice for more risky bets, and a desire for a bigger 

win to "get even" more quickly, is inconsistent with the extant decision-making lit· 

erature that shows "loss aversion" (Kahneman and Tvcrsky 1979) motivates people 

more than "willning." To advance the scientific literature on this topic, it is necessary 

to empirically investigate whether the behavior of in vivo gamblers corresponds to a 

conventional depiction of "chasing." In this study, we primarily examine Dickerson's 

two· part definition of chasing: increasing stakes and/or betting on propositions with 

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j Gam!>l Stud (2009) 25:239-252 241 

longer odds (0 recoup losses. Further, we study the trajectory of gambling frequency as 

measured by the number of bets when loss occurs and conlinues. Conceptually, chasing 

is a construct that represents behavioral patterns of betting that evolve over time. 

Conscquently, this study is the first longitudinal investigation of actual gambling 

behaviors that focuses on the nature of chasing. 

The extant empirical foundation for the constructs used to identify gambling disorders is limited. For ex urn pie, despite the popularity of inferring "loss of control" 

(Blaszczynski and Nower 2002) as a construct to explain "compulsive" or "pathological" gambling, this association remains uncertain and not immune to caveats; the 

reason for this uncertainty is that the empirical base for this cxplanation rests primarily 

on self-report. The notion of gambling as an irrational impulsc control disorder derives 

primarily from gamblers who have sought help for the disorder and not from gamblers 

in the community. Clinicians typically diagnosis the presence of pathological gambling 

by interviewing help-seeking patients and then applying criteria and cutoff values (e.g., 

DSM-IV) (American Psychiatric Association 1997) to their self-reporte.d gambling 

patterns. Self-report measures of gambling wins and losses can be unreliable. Both 

selection bias and recall bias compromise the reliability of clinical assessment. These 

biases also limit the validity of inferring how problem gamblers differ from other 

gamblers in their behavioral trajectories. Similarly, experiments (Potenza et-al. 2003; 

Reuter et a1. 2(05) that shed light 011 the association between pathological gambling 

status and reduced efficacy of thc meso limbic reward system often rely on self-report 

interview questions. Further, the value of physiological evidence derived from gamblers 

experiencing artificial gambling tasks is uncertain: using gambling experiments as a 

substitute for in vivo gambling raises important validity concerns about whether proxy 

gambling can stimulate responses that are sufficiently similar to actual gambling to yield 

userul results (Anderson and Brown 1984). 

In addition (0 the limitations of self-reported behavior, if we are trying to understand 

behavioral trajectories, cross-sectional research can bc misleading. Longitudinal analysis 

is instrumental to understand the maintenance or escalation of gambling behavior 

(SJutske 2007). The longitudinal analytic approach in our study is a type of randomeffects model that represents a common and unifying approach to fitting growth trajectories and repeated-measures data (Laird and Ware 1982). To the best of our 

knowledge, there arc no studies that employ longitudinal analyses to investigate the 

patterns of actual gambling behaviors that reflect chasing-related monetary involvement, 

risk preference, and frequency. 

Recently, researchers began tn learn from the study (If actual Internet gamblers 

(LaBrie et al. 2007, 2008; LaPlante 2008<1, b). The present study extends this growing 

body of in vivo research by examining the natural course of gambling patterns evidenced by a group of Internet gamblers who reported gambling-related problems and, 

consequently, voluntarily ended their gambling accounts because of these problems. We 

will use a prospective cohort recently detailed in an epidemiologic.,1 description of 

Internet gamblers (LaBrie et al. 2007). The advent of Internet gambling provides an 

ideal context to study gamblers who might experience gambling problems because this 

technology permits us to examine prospectively every bet, with its stake and odds. This 

study will focus 011 the behavioral trajectories of in vivo gambling that precede account 

closure. In addition, this study will allow us to test the prevailing wisdom that gambling 

problems increasingly are associated with higher levels of risk-seeking (i.e" increasing 

stakes and/or engaging in longer odds bets when experiencing increasing monetary 

loss). 

<g) Springer 
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242 J Gamb! Stud (2009) 25:239-252 

Methods 

Sumplc 

The research cohort consisted of 47,603 individuals who sequentially subscribed to an 

Internet sports-betting service provider during February 2005. This service implemented a 

web-based Ilccount closure system that allowed gamblers to close their liccounts voluntarily. To close an account, players were required to report one of three listed reasons for 

the account closing: (a) I am unsatisfied with the company; (b) I have no more interest in 

betting; (c) Gaming causes me financial, social Qr personal problems; therefore, I would 

like to close my account in context with the prevention of gaming addiction. 

To be included in this study as a case, players must have wagered on live action events 

for 3 or more betting days and reported closing their accounts because of gambling 

problems (i.e., reason (c) above). Live gambling allows gamblers to follow the progress of 

a particular sports event and bet on various imminent outcomes. For example, as a tennis 

match progresses, a gambler can bet all "How many set points will the winner of the first 

set need to win the set?", "Who will serve the first ace ill the second sen", "Who will be 

the 1st to win 10 points in the third set?" and so on. To examine how cases differ from 

other gamblers, we employed a nested case-<:ontrol study design (Rothman and Greenland 

1998). To approximate the risk set, we selected those who had open accounts on the day an 

individual's account was closed due to gambling problems, but who did not close their 

accounts. We randomly sampled controls without replacement from tbe risk set. We 

employed a I: 1 ratio of case-<:onlrol malching on age and sex variables. As a resull, both 

cases and controls had the same distributions of the age and sex. We do not have other 

demographic variahles such as income level or education for this cohort, The cases and 

controls were not matched for the numher of betting days because this variable is fundamental to the imbalanced data structure for OUI' longitudinal analysis. 

We only included gamblers with a history of live action gambling activities for three 

reasons. First, live action gambling requires quick evaluation and rapid decision for a 

sequence of outcomes while sporting events are taking place. This type of quick evaluation 

and rapid decision is common among various types of casino and poker games and research 

suggests that this type of rapid evaluation and decision-making can be made quickly, with 

autonomic arousal (Sharpe et ilL 1995), and without conscious contemplation (Bechara el al. 

1997). Second, using only live action gambling data prevents bias from sports fan preference about event outcomes that support their favorite teams. a common phenomenon in 

conventional tixed-odd sports gambling. that might preclude observation of chasing-like 

behavior. Third, unlike other fixed-odds gambling data where betting and event outcome 

can be separated by days, Jive action gambling data can be aggregated easily on a daily 

basis, an efficient way to construct a dataset suitable for longitudinal mwlysis. 

During the period between Feb 1st 2005 and June 30th 2006 (study period), 264 

gamblers who had a history of live action gambling closed their accounts because they 

reported gambling problems. To predict both group-average trajectories in a statistically 

meaningful way, we excluded gamblers who had less than three live action betting days. 

As a result, the IlU rnbcr of cases in our study was a sub-sample of 226 gamblers. 

Design 

We employed a nested casc-<:ontrol design in our study. The outcome variables included 

net loss, stake, odds, and number of bets. We standardized the daily aggregates of stake, 

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1 Gambl Stud (2009) 25:239-252 243 

odds, and net winning by the daily aggregates of bets because the respective aggregates of 

stake, odds and winning were influenced by the number of the bets. We uscd log transformation to normalize the right skewness of slake per bet, odds per bet and the number of 

bets, The net loss per bet is, on average, positive (Le" gamblers Jose); this inevitable 

outcome is due, ill part, \0 the "rake" collected by the gambling provider, 

To operationally define the "time" variable for the longitudinal analysis of gambling 

behaviors prior to account Glosure, we used the following two schemes: (a) active betting 

days; and (b) calendar days, We counted backward in both schemes, In the first scheme, the 

last betting day was coded as 1, and the previous betting day was coded as 2 and so on, 

regardless of the calendar days, In the second scheme, we used the last day of our 

observation period (June 30, 2006) as day I and coullted backward to Feb 1,2005, yielding 

a total of 525 calendar days. We applied both schemes of coding "time" variable to the 

cases and controls. 

Analyses 

To determine the length of the time that would be included in the following analyses, wc 

examined descriptive stMistics. Prior to account closure, the number of active gambling 

days ranged from 3 to 299 days (Table I) among the sub-sump Ie of 226 gamblers, 50% 

had at least 23 gambling days and the mean gambling days was 45 days. We computed 

regression coefficients of (he time effect using 23 days prior to account closure as the 

maximum analytic period because this time frame covered a majority of the cases. 

Therefore, day I represents the last betting day, A case might or might not have closed 

account on that day, We plotted the group average by the betting days prior to closing, We 

stratified these means by case-<ontrol status, We employed lowess smoother (slIIoother 

span = 1/3) on the plotted means to illustrate the trends, 

Since the number of active betting days varied by individuals in our sample, we applied 

a two-stage randolll-effects model (Laird and Ware 1982; Ware 1985) to handle the 

imbalanced longitudinal data structure. We used the NLME package (Pinheiro or al. 2008) 

Table J Descriptive statistics 

for the live gambling account 

c!o.<ers who identified themselve~ 

as having gambling related 

problems Total subjects 

Total observations (daily aggregates) 

Demographics 

Age (s.d.) 

% mate 

Gambling behaviors 

Range of gambling days 

Mean gambling days 

Median gambling days 

Median If of bets per gambling day 

Median stake per bet 

Median Odds per bet 

Median actual net loss per bel 

Sub·sample 

(at least 3 days) 

226 

10,143 

29.3 (8.0) 

93 

3-299 

45 

23 

5 

f:'7.67 

2.49 

€0,67 

Full sample 

264 

10,199 

30 (8.5) 

93 

1-299 

39 

16 

5 

€7,64 

2.49 

€0.68 

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244 J Gambl Stud (2009) 25:239--252 

in R 3.1 to conduct the regression analysis. We u~ed the Restricted Maximum Likelihood 

method to compute parameter estimates. For simplicity purpose, we assumed no withinsuhject serial correlations. Our basic model can be formulated as: 

Yij "" Po + fJ 1Til1lelj + [3;0 + [3nTimeij + elj where Timeij denotes the time of the j 

measurement occasion on gambler i, and fJI denotes the coefficient of the time effect, [n 

other words, the corresponding changc of Y associated with one unit changc of Time (i.e., 

day). We assumed standard Gaussian distributions for [31(), fill, and cij' We estimated the 

time effcct with and without adjusting for age and gcndcr as covariates; this provides an 

opportunity to further detcl1l1ine the potential influence of these covarintes. We used Q-Q 

plot to examine and confirm the Gaussian distribution assumptions for the random effects. 

We used the Bayesian Information Criterion (DIe) to examine model fit and confirm the 

assumption of no within-subject serial correlation. To investigate whether cases had differcnt behavioral trajectories compared to the controls, we revised our basic model by 

adding a main effect of case status and examined the interaction term between case status 

and time in the interactioll model. 

When we estimated the time effect, we restricted the range of data points to the 

cumulative gambling days prior to account closing. Specifically. for the duration of the 23· 

day analytic period, we computed a total of 21 coefficients (from lst-3rd day to Ist-23rd 

day) for the time effect. For example, since each of the 226 gamblers had at least three 

observations of daily aggregates, we computed the coefficient of the time effect for the last 

three gambling days prior to closing using a total of 678 observations of daily aggregates, 3 

days from each of these 226 gamblers. Further, we computed the coefficient for the last 

four gambling days prior to closing by restricting the data to the last four daily aggregates; 

gamblers with only three observations contribute nil 3 days to form their respective trajectories. Because we computed the trajectory for each individual cumulatively using the 

algorithm just described, we computed and interpreted the group average trajectories as 

cumulative time. effects prior to closing. To illustrate the patterns of the time effect 

coefficients ncar account closure, we plotted the coefficients of the time effect against the 

day.~ prior (0 closing, We reversed the signs of the etTects in the plot so that the coefficients 

correspond to the time effects approaching account closure. Similar to the means, we 

stratified these coefficients by casc4:ontrol status. 

Results 

Monetary Loss 

Figure 1 illustrates that both cases and controls experienced loss, but the cases evidence 

increllsing loss per bet for the last several belting days. For example, the average losses per 

bet on the last 3 betting day were £3.3, €4.7, and £7,5 (day J) per bet for the cases. The 

average losses per bet on the equivalent last 3 betting day among the controls were £3.5, 

€3.7, and £3.7 per bet. Figure 1 indicated that gamblers who closed their account due to 

gambling problems expe.rienced an increase of monetary loss--on average €2 per bet--

each day during the last 3 betting days prior to accollnt closing. When we examined the last 

6 betting days among the cases, we observed a stlltistically significant rate of change 

(f3 == 1.02, p-value = 0.03, 95% CI [0.08, 1.97]). This rate of change with respect to loss 

per bet increased from €1 per bet during the last 6 betting days to €2 per bet during the last 

3 betting days. Bascd on the stratified analysis, the magnitude of the rate of change appears 

to be greater among the cases as they near their account closing time. This observation 

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J Gambl Stud (2009) 25:239-252 245 

reveals the increasing tendency toward monetary loss among those who eventually closed 

their accounts due to gambling problems. The rate of change regarding loss per bet among 

the controls remained constant during this time in Pig. I. 

Monetary Involvement: Stake Pcr Bet 

Figure 2 shows an increasing average log-scaled stake per bet among the cases as they near 

their account closure dales. For example, the average log-scaled stake per bet for the last 3 

betting days among the cases were 1.71, 1.75, and 1.91; the average stake per bet for the 

same period were €17.4, €18,8, and €23.3. Among the controls, the average log-scaled 

stake per bet for similar period were lAO, 1.32, and 1.32; the average stake per bet were 

€15.1. EI2.S, and EJS.7. T-tests on the log-scaled stake per bet suggested significantly 

greater monetary involvement among the cases as compared to the controls (Le., t := 2.2, 

3,1, and 4,0; p-value <(),05, <0.01, and <0.001, respectively). Lowess sfnoothers in Fig. 2 

suggest that a declining monetary involvement among the controls for the 23-day analytic 

period. The decline was much less salient among the cases; on the contrary, the beginning 

of an uptrend was evident near 5th day prior to closure. Figure 2 compares the rates of 

change of stake per bet (log scale) in the days preceding the closure for the cases and 

controls. For example, for the last 5 betting days prior to closure, the coefficient of the time 

effect is around 0.05 (p-value '" 0,02) with 95% CI [0.01,0.09). That is, I betting day 

nearer to the closure is significantly associated with an increase of stake per bet by about 

23 21 19 17 16 13 11 9 7 6 3 

Betting days (backward) priorto closing 

• N 

¢ 0 

o " 

o 

~;) 00° 0 0 [] 

898 0 c ~ ~ c 8 0 e Boo 0 a 0 c I 1 ! i f J ( t ttl j 1 I [ t I 

23 21 19 17 15 13 11 9 7 5 3 

Betting days (backward) prior to dosing 

Fig. 1 (II bove) Average loss per bet counting backward prior to account dosing. (Below) Regression 

coefficients of cumulative time effect on loss per bet in live action gambling. A solid marker indicates Iha! 

the coefficient significantly ditTel's from 0 at an alpha level of (),05 

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246 j Gambl Stud (2009) 25:239-252 

¢ N 

23 21 19 17 15 13 11 7 

Betting days (backward) priorto closing 

• c:i 

.. " lS 

• c:i 

f) • 

• • • •• ¢ f) 

• •• ¢ <> <> <> 

0 o 0 o 0 " p a 0 o 0 00" COo 0 0 

o a 

23 21 19 17 15 13 11 9 7 5 3 

Betting days (backward) prior to closing 

Fig. 2 (Above) Average tog-scaled slake per bet ~ounring backward prior to account closing. (Be/ow) 

Regression coefficients of cumulative time effect on Jog-scaled stake per bet in live action gambling. A solid 

marker indicates that the coefficient significantly differs from 0 at lIn alpha level of 0.05 

5%. Similarly, the rdte of increase of stake per bet among the lust 3 belling days shows a 

10% increase per day liS the gamblers approaches their account closure dates. In contrast, 

the rates of change for the controls appear to be constant near zero. This meuns thut, among 

the controls, there is no statistically significant stake per bet changes during parallel 

analytic periods, even for the equivalent period prior to account closure. The rates of 

change for the interaction terms in the interaction models corroborated the results of the 

stratified analyses: the coefficients for Ihe interaction tenn.<; increased from 0.04 (pvalue = 0.04) with 95% CI [0.002,0.07] for the last 9 days to 0.14 (p-value 0.02) with 

95% CI [0.02, 0.26] for the last 3 days. Coding "time" according to calendar days yields 

similar trend results: the nearer cases get to their account closure dates, the percentage 

increase of their Slake per bel advances at a more rapid rate. 

Risk Preference; Odds Per Bet 

From the lowess smoother in Fig. 3, there appears to be a small downtrend of risk preference among the cases several days preceding closure. The average log-scaled odds per 

bet among the cases for the last 3 days were 1.01, 1.07, and 0.96; the average log-scaled 

odds per bel among the controls for the last 3 days were 1.10, 1.08, and 1.12. The logscaled odds per bet among the cases for the lasl 3 days was significantly lower than the 

controls (t 1. 97, p-value < 0.05). The downtrend appears to begin around the 5th day 

preceding closure. Figure :\ shows that the rate of change in log-scaled odds per bet among 

the cases is decreasing prior to closure; however, this rate of change is not statistically 

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a 

<> a 

I I i I Iii iii I I I Ii I I 

23 21 19 17 15 13 11 9 7 5 

Betting days (backward) prior to closing 

o ¢ 

o 0 8 ~ CI 0 0 

<) () C1 o 

o 

o D 

o 

<> 

I 

3 

0 a 

17 11 9 3 

Belting days (backward) priorto closing 

247 

q 

0 

I 

1 

III 

q 

0 

o 

Flg.3 (Abol'e) Average log-scaled odds per bet cQullting backw~rd prior to account clo~ing: (Below) 

Regression coefficient' of cumulative time effect on log-scaled odds per bet in live action gambling. A solid 

marker indicates that the coefficient significantly differs from 0 at an alpha level of 0.05 

significant. Based on the data from the las! 5 betting days, 1 day closer to the account 

closure is associated with a 2% decrease of odds per bet for cases' risk preference. A 

decrease of odds (or shorter odds) per bet suggests involvement with less risky bets (Le., 

bets with more certainty). Among the controls, the log-scaled odds per bet remain constant 

in Fig. 3. Using calendar day as the "time" variable, we Hlso observed a conservative risk 

prefercnce (i.c., less risky) as indicated from the downtrend of negative coefficients, where 

some coefficients near the closure time were significantly less than zero (I.e., f1 = -0.0005 

for the last five day period, fJ-value = 0.02, 95% CI [-0,001, -0.0001]: {l = -·0,00045 

for the last six day period, p-value <0.05, 95% CI [-0.001, --0.00001]). 

Risk Prefere.nce: Number of Bets 

Figure :\ illustrates downtrends of the average log-scaled number of bets for both the cases 

approaching their account closing and for the cOlltrols. Figure 4 also shows a consistefll 

downtrend of the significant rates of change with respect to number of bets per day. This 

downtrend indicates that the percentage of decrease in the number of bets per betting day 

increased for all gamblers. In other words, gamblers with problems and the controls appear 

to make fewcr and fewer bets over time. In addition, none of the 21 coefficients of the 

interaction terms in the interaction models were statistically significant, suggesting that the 

tendency to make fewer bets is common among both cases and controls. 

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248 J Gambl Stud (2009) 25:239-252 

I i 

23 

I I I I i I I I i I I I I I i I I I i 

21 19 17 15 13 11 9 7 5 3 

Betting days (back1Nard) prior to closing 

......... • • • . . . .- II II •• 

• • 

••• • • 

• • • II 

• 

• 

• 0 

• 

• • • • •• 

23 21 19 17 15 13 11 9 7 5 

Betting days (backl/vard) prior to closing 

• 

3 

[: 

~ , 

l§ 

d 

, 

C) 

Fig. 4 (Above) Average log-scaled number of bets counting backward prior to account closing; (Below) 

Regression coefficients of cumulalive time effect on log-scaled number of bets in live action gambling. A 

solid marker indicates that the coefficient significantly differs from 0 at an alpha level of 0.05 

Discussion 

Using evidence from a group of Internet gamblers who identified themselves as having 

gambling problems, we examined the patterns of actual gambling behavior that led these 

gamblers to voluntarily close their online gambling accounts. We observed that while they 

experienced increasing loss preceding account closure, these gamblers appeared to try 

recouping their losses by increasing their stake per bet on events that were probabilistically 

less risky. We also observed decreasing gambling frequency as measured by number of 

bet~ during the analytic period. 

Our findings of in vivo Internet gamblers extend the risk preference literature. Among a 

group of gamblers who identified themselves as having gmnbling problems, we unexpectedly observed evidence of a risk-averse gambling pattern. The construct of "chasing," 

episodic interview records among Gamblers Anonymous members (Lesieur 1979, 1984), 

and structured questionnaires that assess pathological gambling (American Psychiatric 

Association 1997) suggest an increasing risk-seeking tendency among those with gambling-related problems. However, the results of Ihis study reveal the downlrcnds of average 

log-scaled odds per bet and negative coefficients (Fig. 3) as well as some significant 

departure from zero using calendar day as the coding scheme for the "lime" variable. 

These findings may retlect the tendency of problem gamblers to increasingly become 

probabilistically risk-averse. Instead of making more risky bets by increasing their stake on 

probabilislicaHy longer odds, as Dickerson (8) had suggested, this group of gamblers tried 

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to recoup losses by increasing their stake on events with higher probabilities of winning 

(Le., they become more risk averse and, therefore, bet more conservatively). 

Previously. some research seemed to suggest the opposite: using a simulated gambling 

task to study rapid processing of monetary gains and loss, Gehring and Willoughby found 

that belling choices after losses were riskier (Gehring and Willoughby 2002). Inste.1d, we 

observed a risk-averse pattern of live action gambling during a period when gamblers 

experienced loss and increasing loss. The difference in our research focus might explain 

the contrast in the findings. OUT longitudinal data was constructed using daily aggregates; 

we aimed at estimating in vivo behavioral tendencies, rather than experimental event-based 

responses. Thus, "instant utility" (Kahneman 1999), a mental state resulting from 

sequential event-based evaluations, had little influence on this data. Further, the shorHerm 

mood or emotional states (hat can impact event-based gambling sequences had little impact 

on our findings. We employed a longitudinal data structure (Le., daily aggregates) that 

operationally represents and is consistent with the premisc that (a) "the long-term chase is 

the distinguishing feature of compUlsive gambling" (Lesieur 1984; O'Connor and Dickerson 2003) and (b) the DSM-IV behavioral cross-session critcrion that a disordered 

gambler "often returns another clay (0 get evcn." With actual behavioral data, Ollr findings 

advanced the knowledge base focusing on the human tendency toward "loss aversion" 

during a period when problem gamblers experienced incrcasing monetary losses, anl1spect 

not previously studied in the above-mentioned el'cnt-based experiment. 

It is intriguing to consider the concurrency between the monetary involvement and risk 

prefe.rence behavioral changes that emerge for cases prior to closing their accounts. 

Although the plots of slopes by days prior to closing suggest that the stake per bet and odds 

per bet rate changes appear to begin near the 5th day preceding account closing, it is not 

sufficient from this study to proclaim the exact location of (his dynamic change. However. 

this finding can suggest that cOlltemplating a decision to self-exclude is perhaps a relatively 

brief temporal process, during which gamblers increase their stake and reduce their 

probabilistic risk to recover loss. If loss continued to escalate, however. it did not take long 

before players made a decision to close their account. This phenomenon of gambling larger 

stakes prior to voluntary closure resonates with the clinical observation of substance 

abusers who stop using psychoactive drugs after taking a "last fling." That gamblers bet a 

larger stake. while simultaneously becoming more conservative in their risk preference 

provides support for (he notion that losing encouragcs pcople to become more "Joss 

averse" than "gain motivated" under conditiol1s of uncertainty. Future research should 

focus on identifying possible break points where gamblers' change their behaviors with 

long series of data points. 

In general, the comparisons between the cases and controls based on the stratified 

analysis and the intcruction models appear to be consistent. The gamblers who closed their 

aecounts due to gambling problems evidenced heavier Slake per bet than the controls, 

although both groups appear to experience loss and engage in fewer bets. This finding also 

was consistent when we employcd othcr I: n matching ratios (II > !) with more precise 

standard crrors (these results are available from the authors upon request). 

The estimates of the cumulative time effect were consistent when we used adjusted 

models (i.e., for age llnd sex) instead of the unadjusted models reported in Ihis paper (these 

res\llts are available from the authors upon request). We expected this correspondence 

because, by matching on age and sex, these two covariates were uncorrelated with caseness; therefore, the estimates of the cumulative time effect in the unadjusted model should 

not be biased without controlling for age and sex. Moreover, although we observed that the 

two coding schcmes for the "time" variable (e.g., whether calendar days or betting days 

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250 J Gambl Stud (2009) 25:239-252 

were used) influenced the magnitude of the coefficient, the direction of the time effects on 

the outcomes was consistent regardless of coding scheme. Finally, the random effects 

employed in our models appeared to tit the Gaussian assumption. According to BIC, the 

assumption of no within-subject serial correlation fit the data well when the cumulative 

time spans were neur the account closure, 

This study has several limitations, First, we did not have information about individuals' 

wealtb, other gambling activities (i.e" Internet or land-based venues), personality (Blaszczynski ct al. 1986), and cognitive detcrminants; some of these attributes can be linked to 

the development and maintenance of gambling disorders (Breen and Zuckerman 1999; 

Shaffer and Korn 2002). Because wealth can affect both monetary involvement and 

gambling frequency, controlling for wealth should yield a more accurate estimate of the 

cumulative time effect. In the absence of wealth information, our choice of a nested casecontrol design should limit possible confounding, but not entirely. Fulther. monetary loss 

has been presumed as the main reason why gamblers closed accounts, Despite the absence 

of wealth information, our findings about greater tendency of gambling loss among the 

aCcollnt closers near the closure time confirmed the existence and Ilature of salient 

behavioral changes. Second, seasonality can impact gambling behaviors associated with 

sporting events, which is not accounted for in our longitudinal models, However, seasonality might not be an influential factor in our analyses because the gamblers' dates of 

voluntary termination varied during the observation period. Because our analyses focused 

on live action gambling, a Iype of gambling similar to casino games and poker games that 

requires rapid decisions and quick evaluation, seasonality likely has limited impact on our 

findings, Third, the current cohort represents Internet sports gamblers. Therefore, their 

gambling behaviors might not readily generalil.e to other types of gambling. Nevertheless, 

it is worth noting that live action gambling shares some similar cognitive characteristics as 

Poker and some Casino games where rapid evaluation and decisions are eognitively 

engaged, Finally, the last daily aggregate (day I) in Fig. 4 should not be interpreted as a 

full-day aggregate because the time of the day when a gambler closes account might vary. 

Therefore, the last observation (day I) in Fig. 4 among the cases must be read with caution: 

the average log-scaled Ilumber of bets for day 1 should be greater than the reading in 

Fig. 4. Nevertheless, the gambling frequency is trending downward among both the cases 

and controls. The measurements for stake, odds, and net loss were valid as well because we 

standardized these indices by the number of bets. 

This study has several important theoretical implications. The dictum that "losses loom 

larger than gains" (Kahneman and Tvcrsky 1979) might have ptayed an important rate in 

how gamblers chase their losses. This study suggests that people, even those who selfidentify as having gambling problems, arc more "loss averse" than "gain motivated" 

under conditions of uncertainty; they choose more conservative wagers to prevent further 

loss--despite their willingness to bet with greater stake size. It is possible that when 

players employed a more conservative gambling strategy, the momentum of losing might 

have triggered a decision to voluntarily terminate their aecount. If so, this new model of 

cha~ing raises important questions about the capacity for self-control during periods of 

excessive gambling, Because most Internet gamblers also are land based gamblers, it is 

possible that similar behavioral patterns exist within land based gambling settings. However, due to the lack of in vivo data derived frolll players in those settings, our current 

understanding of "chasing" remains limited because of the recall bias associated with 

participants in land-based gambling research. Our Ilndings suggest that it is possible for 

some gamblers to self-control (e.g., self-exclude) some aspects of their gambling behavior 

even under adverse circumstances. More research will be necessary to better identify (I) 

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J Gambl Stud (2009) 25:239-252 25t 

the heuristics that guide Internet players' decisions to close their accounts, (2) the temporal 

sequencing of the associated behavioral changes, (3) the mediators of these processes (i.e., 

co-occurring disorders, cognitive processes), and (4) the unique attributes of these gamblers compared with other types of gamblers. 

Statistically, the expected value of any bet is negative because of the service provider's 

"rake," Therefore, from a behavioral economics perspective, engaging in gambling voluntarily without contemplating the' negative expected value is paradoxical and irrational 

(Wagenaar and Albert 1988). We can view the self-initiated voluntary account closing by a 

problem gambler as a measure of self-control; this control represents a transitional state 

from irrational to rational. By providing actual gambling behavior as an evidence base, this 

study advances our understanding of the choice of risk before problem gamblers decide to 

close. 

In Sum, regarding the construct of "chasing losses," we observed evidence that supports 

the construct with respect to increasing stake during increasing monetary loss. However, 

we also observed that, among this group of gamblers who experienced gambling problems, 

belting long odds is a path "less traveled by." We observed reduced gambling frequeney 

among these problem gamblers. This apparent paradox renects an intriguing example of 

how studies focusing on actual behavior can contradict a prior theoretical proclamation. 

Acknowledgements The authors extend special thanks to Richard LaBrie, James Ware, Debi LaPlante, 

Sarah Nelson, Anja Broda, and Chri"y Thurmond for their important contributions to this project. Ziming 

Xuan had full access to all of the data in the study and takes responsibility for the integrity of the data and 

the accordey of the data analysis.bwin.com, Interactive Entertainment, AD provided primary support for this 

study. The National Institute of Mental Health (NIMH), National Institute of Alcohol Abuse and Alcoholism 

(NIAAA), National Institute on D[\,& Abose (NIDA), and the National Center for Responsible Gaming 

(NCRG) provided additional "Jpporl. None of these supporters or any of the authors has pcrnonal interests in 

bwin.coln Hnd its associated companies that would ~uggest a conflict of interest. 

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Comput"rs In r!Um~n Behavior 25 (20OG) 711-711 

Contents lists available at ScienceDlrect 

Computers in Human Behavior 

IIS!.\ II I; j ou rna! homepago: www.elsev!or.c:omllocato/comphumbeh 

Sitting at the virtual poker table: A prospective epidemiological study of 

actual Internet poker gambling behavior 

Debi A. LaPlante a.h .• , John H. Kleschinsky b, Richard A. laBrie a,b, Sarah E. Nelson a,b, Howard J. Shaffer"" 

" HUr.'ard Mf'dica/Scht;lol. 

bmVlsiQtlotllldiJiCliOtl!. 101 S([lfion illUding, bid Floar, Medford, AtA 02155, USA 

ARTICLE INFO ABSTRACT 

Aliidehistory: 

Availilb!e online 30 Janu~ry 2009 

Keywords' 

Garnhling 

l!)[t'met gambling Internet poker 

Intl'met 

Poker 

Epidemiology 

Pub{iche,l!th 

Internet gambllng is a potential object of addictive behavior and consequently an important concern for 

public hcalth, Epidemiological analyses of Internet g.Hllbling are necessary to determine the extent of 

public health threat. This paper reporfS the results of the first prospective epidemioltlgicai stud)! of <1clua! 

Internet poker gambling behavior. Participants were 3445 Internet gambling servire suhscribcrs who 

enrolled during februal)l 2005. Data include two years of recorded poker outmmes (i.e" chips hougllt 

and sold) for each poker .<>essioo played. Amnng our sample, we ldentified two subgrollps of poker play~ 

frs. Approximately 9$% of fhe sample bought a median of f12 WQrth of chips ;1r each of two poker .se,,~ 

sions per week dtJring a median duration from first to last bet of six months. A smaller subgroup (tf' .. 5%) 

of most invo[v['d poker pl<1yers bought a median of €89 \NOrth of chip at each of 10 sessions per we['k 

during a medi<ln dUfiltion from first to last bet of 18 months.. In addition to level dHTerences. we report 

the differences in patterns of behavior observed bf'tween these two subgroups, The analyses presented in 

this [laper suggest that the majolity or Internet poker player.~ moderated their bellavior based on their 

wins and losses. A minority of most involved players did not show such mOdCf.ltion< These results have 

!m[1mtaor implkations for both gambling·and addi(tion-rcl.lted rf'search, 

1. introduction 

Most people who gJmble nE'-vcr experience serious gamblingrelated problems (Sharler, LaArie, LaPlante, Nelson, & Stanton, 

2004); however. some experience poor financial and health outcomes (Petry, 2006). Thi!se gambling-related problems are simil.ar 

to the sequelae associated with other expressions of Jddiction 

(Shaffer. LaPlante et a\., 2004) and consequently, gJmbling is associated with many of the same concerns <IS other objects of addiction. For example, the availahility of common objects of 

addiction (e.g., alcohol. tob,1CI~o. gambling) tends uniformly to ('fI.'-

ate concerns about {he emergence of addictive- behavior and its sequelae among the genera! population (Shaffer, taBrie, & L<1f'lante. 

2004; Weitzman. Folkman, Folkman. & Wechs!er, 2003). With respect to g<1mb!lng, new forms of gambling often rJise concerns that 

easy access to and social approval of gambling wi!! expose more 

people to the risl{s of gambling. increase the number of people in 

the genera! population with gambling-relaled problems, and exacerbate the difficulties of those already affected by gambling (OcFlIentes-Mcri!1as, KOefer, Schippers, & VJIl den Brink, 2004; 

LaPlaJl{e & Shaffer, 2007). 

• Corre~ponding author. Ai!dn's$: DIviSion on Addktiorls. CJmbrklge Ht'alth 101 Station unding. 2nd Floor, Me(lford, MA02155. USA. TrI.: +1 781 lOG 81)00: +! 7B1 3068629. 

[·mall I1ddrp.H: dl"hU<!pl~o<£>$hms.h<lIVJH11"(1u (OA !..tP!;uw;,). 

0747-5fil2/-S - ~('f' front matter © 2009 Elsevier ltd, All right~ f('sNvcd doi:1D.l01fifj.fhb.2008.12.027 

\l') 2009 E!sevier ltd. All tights rest>lVed. 

Politicians, advocates and researchers alike have expressed concerns about the potential pun!ic health impact of Internet gambling 

(Blitz & Yeager, 2006; Griffiths. 2001, 2003; LaBrie. L<lPi30te. NeI~ 

son, Schumann, & Shaffer. 2007; Lemke, 2006; Savage, 2007). 

Unfortunately, the validity of such concerns is unclear. Although 

gambling in general is now widely recognized as a potential object 

of addiction {Shaffer, LaP!.1nte ('t .11.. 200<1). researchers know little 

about the ways that different types of gambling might influence 

lhe dev('lopment of addiction, Internet gambling, for example, 

can take many forms (e.g .. casino-type games, lotteries, and poker), 

btU we do not l<now whether diffefl~nt forms pose differential risk 

to health. Internet poker is of particular interest because of the current popularity of poker ("How to (Qunter online bets", 2006; 

McMJnllS, 2006: Mfho{'cs. 2007; Wise, 2007). A first step toward 

understanding the relative influence of different types of gambling 

on addictivt~ behavior is to determine rhe epidemiology of particular gambling activities. This paper provides the first ever epidemiological analysis of the .actual gambling behavior evidenced by a 

large -cohort of Internet poker gamblers. 

1.1, Current In[onnatioT1 aiJoUllntemet gmnbling and Internet poker 

Policy makers anti some in the general public have exprF.ssed 

concerns about Internet gambling (BUSiness Week, 2007; Ford. 

2006: Murr<lY & Grimaldi. 2006: Savage, 2007}. Examples of these 
139 


75777.116

712 DA LaP/ar\t(' el a/./CompUlers ill HIJ/tllJn &havi(lf 25 (200!) 711~717 

concelllS include: Unlimited access to gambling venues at home or 

work: easier access for underage gambling; gambling to escape 

problems; playing mUltiple games at one time; anonymity allowing users to engage in gambling without fear of stigma; harmful 

marketing practices: and gambling while intoxicMed (Griffiths, 

2001. 2003). Many such concerns ahout internet g.ambling derive 

exclusively from speculation because of a dearth of empirical r('-

search related to these issues. 

The bulk of the available Hter;;Jture related to lnternet g,unbling 

focuses on rates of participation .and related problems, <lnd rates 

vary wieldy from study to study. For instance, severa! cross-sectiona! sttldies utiJi7.ing self-report surveys estimate the prevalence 

of Internet gambling in the general papUlation to range from 1.2% 

to 8, 1% (e.g., Responsible Gambling Program of British (oiumbi<1. 

2003: Griffiths, 2001; lalomlteanll &0 Acll<lf, 2002; Volberg, 2002; 

Wardle et tll., 2007). Similarly, studies involving spedal populations, such as clinica! samples (Ladd & Petry, 2002; Petry, 2006; 

Petry & Mallya, 2004), afe also variable. 

To date, only one study has examined Internet poker play 

(Wood, Griffiths, & Parke, 2007). Researchers asked 422 college 

students from the midlands region of the United Kingdom to 

self-report retrospectively their poker play. Wood et al. found that 

approximJtely 29% (n ..-123) of the study sample pJrticipaled in 

online poker play multiple days each weej(. Eighteen percent of 

these participants endorsed at least four DSM~IV criteria fof' pathological gambling. The extant studies are limited, Most used sam~ 

pIes of convenience (I.e., non-probability samples), limiting the 

generalizabiJity of the s.tudies and obtained self-reports of hehavior 

which Jre subject to inadequacies of recall and potential hias toward more socially acceptable answers. 

1.2. Longirodinal research on actual gambling behavior 

Until rt'cently, there have been a limited but growing number of 

available longitudinal gambUng studies (e.g., Ahbott, Williams. & 

Vol berg, 2004; Barnes, Welte. Hoffman, & Dinrchrff, 2005; DeFucn~ 

tes~MerHlJs et aI., 2004: Hodgins & d~Gtleba!y, 2004; Jacques, 

Ladouceur, & F("rland, 2000; Shaffer & !lall, 2002; Slutske, Cas pi, 

Moffltt, & Poulton, 2005: Sllltske, Jackson, & Sher, 2003; Vachon. 

Vitam, Wanner, & TrrmblJY, 2004; Vander Bill. Dodge, Pandav, 

Shaffer, & Ganguli, 2004; Wiehe, Single. & Falkowski-Ham. 2003; 

Winters, Stinchfield, BatZel, & Anderson, 2002; Winters, Stinchf'wld, Ilotzct, & Slutske, 200S), These studies were self-report surveys of gambling behaviors repeated over time, and none 

examined Internet gambling specifiC-lily. 

We are not aware of any research studies before our research 

progrJ.m that examined .lctua! g,lmbllng behavior. To date, we 

have published three studies about Internet gambling using a longitudinal design and actual gambling behavior, One study (taRde 

et dL, 2007) examined the actual daily Internet gJmbHng behavior 

or more than 40,000 Internet .sports gamblers during an 8 ·month 

study reriod. A second study laPlante, Schumann, LaBrie. and Shaffer (20GB) observed a gener.1l exposure and ad;)ptation experience 

consisting of an e1ghl"day ~riod of increasing gambling after initiJ! exposure to both I1xed-odds and live-action betting followed 

by a gambling decrease during the next 3 months, This adaptation 

pattern was not observed for the 1% most involved bettors (MIB), 

identified hy the number of bets made and total monies wagered. 

During the length of the study, MlB bettors often maintained or increa.sed their betting activity. The third study (ldBrit', Kaplan, laPl;l!1te, Nelson, & Shaffer, 2008), measured the gambling behaV1Qrof 

4222 Internet casino gamblers during a two-year period, LaBrie 

et al. identitied two distinct groups of gamblers similar to sports 

bettors: moderate gamblers and an empirically determined group 

of most involved betrors whose gambling behavior exceeded that 

of 95% of the sample, 

1,3. Pre!>(,flt study 

To advance and expand the extant literature related to actual 

Internet gambling behavior, the present study examined the poker 

pl,lY or participants in an epidemiological study who used an Internet ga:mbling service provider's poker site, We .1nalyzNJ the daily 

records of poker sessions into the total amount of {hips purchased, 

amollnt of chips cashed, and seSSions conducted. We pr€sent three 

types of results: (!) an epidemiological description of the characteristics of 3445 sequentially subscribed Internet poker players; 

(2) an epidemiological description of the gambling behavior of 

these tnterne-t poker players: and (3).1n epidemiological description of the gtlmbling behavior of an empirically determined subgroup of most involved poker players. 

1.4. Hyporllesf'5 

Our previous research (l.aBrie et <ll., 2007, 2008; LaPlante ct 31., 

2008) idf;'ntined primarily moderate Internet gambling behavior 

among subscribers to this Internet gambling sefVice, compared to 

the excesses suggested by some stakeholders (Ford, 2006; Griffiths, 

2001; Kyl, 2003; Wood et aL. 2007). In those studies. we also observed that a smal! (i.e .. 1% or 5%) portion of the popUlation displayed more extreme gambling behaviors than the majority of 

the population. Because both sports gambling and poker playing 

Jre chance games that attempt to incorporate a measure of sldH 

{e.g., Jnticipating spread and irnplementing strategy, respectively). 

we hypothesize the presence of a similar pattern of gambling 

behavior among poker players. More specifically, we anticipate 

observing what many might consider to be moderate gambling 

behaVior for the majority of the poker sample, but also a lack of 

continuity in our sample's betting behavior. A small fraction of 

the population wi!! display discontinuously extreme betting 

hehavior across measures. 

2. Methods 

2.1. Sample 

The full epidemiological research cohort included 48,1 t 4 people 

who opened an account with the Inlernet betting servke provider, 

bwin Interactive Entertainment, AG (bwin), during February 2005 

(i.e., 100% of new subSCribers). The Internet .'lite, bwin, known best 

for its sports gambling servke, offers several types of gambling services including pol<er, 11115 investigation inCludes a cohort of 4459 

suhscrihers who elected to play poker online. We excluded from 

the current study 951 participants who played less than four poker 

sessions during the study period and 63 poker players who did nOl 

begin poker play until the last month of the study period (Le" after 

January 31, 20(7). The resulting sample includes the 3445 people 

with more than a passing interest in playing poker and a sufficient 

ObSelV.:ltion period. 

2.2. Measures 

The aV~1i1ahle demographic characteristics of the research sam 

ph~ included age, gender, country of residence, and preferred language, At enrotlment, participant~ elected to intera:ct with the 

wag.ering system in one of 22 languages. 

The study pa!ticipants' ,;1(tual trJnsaction records with the poker 

platform provide the gambling behavior measures. A gambling ses~ 

sion consists of the recorded value of chips purchased at entry to the 

poker platform and the value of chips at eXit, if any. We aggregated 

the dally records of poker sessions into the total amount of chips 

purchJsed, .:lmOllnt of chips (:'ashed, and sessions conducted. 
140 


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D.A. LaP/arlit! et fll./ComplI!trj in Human Behavior 25 (2(09) 711~7J7 

The gambllng behavior measures Jre b.ased on daily aggregates of betting activity r,"cords, We summarized the daily aggregations into composite measures of gambling behavior. We 

measured the duration of g.Jmbling involvement (i.e., Duration) 

as the number of calendar days from the first eligible session 

to the last. We ohtained the total number of sessions (i.e .• Total 

Sessions) and tot,)l manics wagered (i.e., Tot,)1 Wagered) hy summing the daily aggregations. To obtain .a measure of intensity, we 

cillculilted the Sessions per Day by dividing Total Sessions by 

Duration. We obtained the ilverage buy-in (value of chips purchased) per sesSion (i.e., Euros per Session) by dividing Tota! Wagered by the Total Sessions, The net result of gambling (i.e., Net 

Loss) is. the difference betv.leen Total Wagered and total wjn~ 

nings. The dominant outcome 15 a net loss and, by subtracting total winnings from Total Wagered, positive values indicate net 

losses, the cost of gambling. We converted Net Los~ to a percent 

of total wagers (i.e., Percent Lost) to (reate J.O index of losses 

thilt is independent of Total Wagered. 

2.3, Procedures 

We mnducted a secondary data analysis of the subscriber data~ 

base obtained from bwin. We received approval from our Institutional Review BOJrd to conduct secondary data analyses of the 

available information. 

2A. Data analysis 

We summarized the participants' demo~raphics and gambling 

behavior using descriplive stillisti(s. Tests f.or differences betwecn group means included testing the assumption of equal 

variances cmd, if necessary, adjusting for unequal vilrianccs. We 

organized the ilnalyses into three sections: (I) cohort characteristics; (2) cohOlt gambling behavior; and (3} thc behavior of 

most involved 1>layers, ':or cohort characteristics. we report gender and country cUstributiOllS, as weI! as gambling behavior differences by gender. For cohort gambling behavior, we report 

gambling involvement by time (i.e" Duration). betting intensity 

(i.e" Total Sessions, Sessions p~r Day, Euros per Session), and 

money at risk (Le., Total Wagere<l, Net Loss. and Percent lost). 

We also examini.'d the correlations between measures, The distribution of characteristics vio!iltes the assumptions of normality 

ne-ceSSill)' for the intclpretation of product moment (Orrei,ltion 

coefficients. Consequently. to avoid the undue !nnuence of extreme observations on the re!alionship between characteristics 

we used a nOil-parametric procedure (I.e., Spearman's rank-order 

correlation) (0 measure the degrees of associati{ln. For this s.ame 

reason, we provide medians when describing cohort gambling 

behavior. 

The measures of betting intensity were all skewed towards 

extreme values. We considered that extreme amounts of Total 

Wagered WilS- the be,o;.t measure of inVOlvement because it carw 

ried with it the highest risk for financial problems AssodJted 

with disordered gambling. Similar to a scree plot, Fig. 1 presents 

the cenliles of the distribution of Total Wagered and reveals a 

discontinuous distribution beginning at the 95th ,entfle, Ctlided 

hy this evidence, we operationally defined the most involved 

players as those among the 5% of the cohort with the largest 

Total Wagered, We report gambling behavior for the entire co~ 

hart, the most involved 5%, and the remaining nujority of 

bettors. 

The large sample size greatly reduces the likelihood that observe<l differences result by chance, limiting the usefulness of tests 

ofstatislical significance, Consequently, it is important toconsidel' 

both the statistical significance ,md the siZe of any effects to dcter~ 

mine the practical significance of .Jny differences. 

J, Results 

3.1. Cohort characteristics 

The average age (If the cohort was 27,9 years (SD'"' 8.4) and 

most (94.5%) were male. The players were from 46 countries. The 

majority (24.8%) were from Denmark. There were 17.3% from Germany, 9.6:% from France, 6.9% from Austria, and 6% from Spain. Tur~ 

key, Italy, Greece, POI'lfld, and NOIway each accounted for 

approximately 5% of the sample. Switzerland. Sweden. and Porlugal accounted for 3.1%, 2.4%, and 1.3% of the sample, respectively, 

while the remaining 33 countries accounted for the remaining 

4.2% of the cohort. 

The relatively few women (5.5%) in the s<lmple differed from the 

men on only two characteristics. The women !n this study were oldcrthan their male counterparts (Mw(lnll'n" 31.7 years of age, SO "" 11 

versus Mmt'n '" 27.7 y-cars of age, SO .. 8.2. t(199.1) .. 4.97, p<.OOl 

adjusting for unequal variance. On average, women bet for a shorter 

duration of time than men did (Mwom~ '" 208 days,SD -223 versus 

Mml:o = 267 <lays,SD "" 232, t{3443}"" -3.37,p < .(01), Given the relatively smail numherofwomen in the cohort and a single difference 

in g,lmhling behavior between men and women, we chose not to 

conduct analyses sepilrately by gender. 

3.2. Cohort gambling behavior 

The first .set of columns in Tilble 1 present the total sample's 

descriptive stJtistics for the measures of poker behavior. The differences between the medians and means and the standard de-vl.ltions, as well as the J(oll11ogorov-Smimov test for normality reveal 

that most distributions are not normal and are skewed by extreme 

values. To create the most representative characterization, we utilize the median as the description of central tendency. 

The typical poker piaYN in our sample was an active poker 

player for a median Duration of six and a half months and participated in a median of one poker !'iessinn every three days (Le" Duril~ 

tion(rotal Sessions). The median Euros per Session gambJed was 

€13 and the median Net Loss across all sessions was flOG, A ca1cu~ 

lation that divides median Net Loss by me<lian Total Sessions re·· 

veals the median cost of playing poker was £1.8 per session. 

3.3, Refations/lips between gambling behaviors 

As T,lble 1 shows, the gdOlbling behaviors were not normally 

distributed. Consequently, we calculated non-parametric rank-order correlations. (rho) between behaviors. Table 2 presents the cor~ 

relations for the entire study sample. Of note, people who played 

poker for a longer period of time (Duration) tended to play fewer 

sessions during that period (Sessions pef day, rho .... G2), Risking 

more money (Total Wagered) was related to playing more often 

(Tot.'!l Sessions, rho'" ,85) and to wagering higher stakes per session (Emos per Session, rho"" 57) However, hettofs who wagered 

more (Total Wagered) also lost a smaller portion of the monies 

bet (Percent 1.os.t, rho =: ~".36). It is notable that individuals who lost 

larger portions ofthe-ir monies bet (Percent lost) were involved for 

shorter periods (Duration, rho'" -.23) and played fewer poker ses~ 

sions when they were involved (Totalscssions, rho'" ·,,41), 

lA, Most involved players 

Fig. 1 shows the disproportionate distribution of Total Wagered 

that begins at the 95th percentile. The final columns ofTJble 1 describe the brh;}vior of the 5% most involved poker players. According to median measures of behavior, these players were active for a 

duration or 18.5 months and participated in a session and a half per 
141 


75777.118

714 D..A. Laf'/(I!1f<' et ai./Campulersin Human Behavior 25 (2009) 711M7/7 

5 1015202530354045505560657075808590 9596 979B 99 

Centlles 

Fig:. L Distributioo of total monks ..... ~gered, 

T.lbfel 

G<lmbling behavior oftnternet poker p(JYN5 ;tnd mClst invol\led [Joker players. 

DuratIOn 

Tot<l!Sesslons 

Sessions/Day 0.5 0,24 <,001 0.5 -7,a 

Ellms/Session 1J 0.43 344.'i <.001 12 -3.4 172.1 <.01 -0,370 

Tota!Wagerw 807 0,46 1445 <,00\ 704 -4.4 172.0 <.000 -0.412 

Net La,s ,00 0,)5 3445 <JlOl 106 -2.6 1712 <,05 -0.279 

% lost 20 0.11 3445 <.GOI 21 16.4 297.0 <.001 -0,779 

Notr UUf-ttion. interval !n days hetwf'f'n first .and t;m poker session; Net Loss, total wagers minUS total winnings;% 1.051, Ne! tO$$ dividr.d by Total Wagered, 

Table 2 

Sessions/d.!.y 

Euro$IScs~iorl. 

TotillWagered 

Net LOH 

I'co(centl.o$t 

0.62" 

-0.38" 

O.D" 

0.56" 

0.31" 

~O.23" 

0,59·' 

-036:" 

---------------------------------------------------------- Note, N01\paramerric Sp~Mman rUrrclJtiom . 

• Corr\'lJtion slgnificd'll atp< .Q5. 

" Corr~IJtlon significant at p.; .01 

day, They staked f89 at Nell session and Icst nCilrly two thcusand 

EuI'Ds (E194l), They wagered at least 75 times more than the 

majority of the- sample, However, the most involved players had 

a srnaHer Percent Lost than the sample majority, Ultimately. their 

Cost (i.e., Net Loss(Tota! Sessions) of playing poker per ~e"sj()n, 

f2.4. was slightly higher than for the ('Iltire sample. As Table 1 

'ihows. when comparing the differences on each of lhe-se gambling 

measures between the majority of the sample and the 5% most involved poker players, Cohen's d indicates that Dur~tion and Total 

Sessions had large effects and Sessions per day and% lost with 

the same high degree of significance exhibited medium effects. 

Table 3 Dresents the correlations among behaviors obselved for 

the 5% Illost involVl"d poker players. Among these mo~t involved 

players., the variables rne<lsuring financial intensity (Total Wa 
142 


75777.119

DJ\, /.lll'lrmte er al./ComplItt'i's in Human Behavior 25 (2009) 71/-717 715 

Table] 

Correl"ttol'!~ Among gambling belldl'ior mi',)sures for In!Nnet poker play for 5% most involwd (11" 173), 

Duration 

Total Session; 

Sl'ssions/Day 

fums/Session 

Total Wagered 

Nerlo5s 

I'cn:rntlo5t 

Duration 

Noll'. Nonpuaml'trft S~arman rorrel,lrlOflS. 

, Corri!larionsignificantHp(.05. 

,. Corre!~tion significant at p..: .01. 

Tutal Sessi()l1s Sessions/Day 

gercd, Net loss, and Percent loss) djd not relate to inrenslty as 

measured by Duration, Total Sessions. and Sessions per Day. In this 

groul), the p!a.vcrs who bought in for more money (Eum/Session) 

played less as indicated by the negative correlations between 

EUfo/Session and Duration, Total Sessions, and Sessions/Day. 

4. Discussion 

This study pre~ents the first epidemiological dcs.cription fif ai> 

tual fnternet poker playing beh.avior. During recent years, concerns 

about gambling-related addiction and other morbidity have 

sparked a worldwide debate, Some countries have regulated Internet gambling, others prohibited it, and others have not yet ruled on 

its h.>g.ll statu.~, In the absence of evidrIKe, speculation ahotJt Inter· 

net gambling abounds in the popular press and in professional 

journals (Bray, 2006; General Accounting Office, 2002; Griffiths. 

1999; Griffiths & Parke, 2002; Ladd & Petry, 2002; McBride, 

2006: Mi(ka. 2001). 

The findings from our research provide a description of the epidemiology of Internet poker behavior evidenced by a farge cohort 

of gamblers observed for two years. The description includes the 

central tendencies of this cohort and it distinguishes the behavior 

of a group of intensely involved players. The discontinuous nature 

of gambling behavior within our population stimulated us to ana~ 

lyze this extreme group to better illustrate the limits possible for 

this gambling medium. The variety of findings we present here 

has important implications for gambling-related public health re~ 

search and policy. 

4.1. Cohort cllaract€n'stics 

The majority of this sample of Internet poker players is marcs of 

European descent, in their mid to late 20s. Women comprise only 

5.5% of our research cohort of poker players, Our research focusing 

on people seeking treatment for gambling problems (laPlante, Nelson, LaBrie, & Shaffer, 2006) revealed that gender was only one of 

many predictors of game choice and more research is necessary to 

determine whether this finding also applies to Internet poker play, 

It is interesting to 110te that the patterns of Internet poker play evidenced by women in our sample were very similar to th~1t of the 

men. The only significant difference was that women played for 

a shorter duration of time between their first and last poker 

sessions, 

4.2. Cohort gambling behavior 

It is important to estahHsh an epidemiological baseline for any 

area of addictiDnwrelated research, especially potentially new objects of addiction (LaBrie et at" 2008). In thiS res.earcb, we provide 

evidence that SUPD011S the findings in our previous research (t.ilB~ 

rie et a!., 2007. 200R; LaPl;mtp. et ai., 200R) that most subscribers 

who gamble on the internet do so moderately. In fact, correlation 

EurofSession Tot~! Wagered NNtoss l'ercentLost 

-0.21" 0,09 -0,01 -0.03 

-0,14" 0.07 -0.01 O,OJ 

-1].50" -0.01 0.03 0.03 

0,55"" -0.1.3 -0.18-

··OJ7' -0.28" 

0.95'-

ana.lyses indicated that as Percent lost increased, Duration. Total 

Sessions, and Tota! Wagered .111 decreased, suggesting that the 

mJjoJity of individuals mo{kr<1ted their behavior based on their 

wins and losses - exhibiting "ration.l1" betting behavior. This suggests that, at the population level, losing discourages ongoing play 

and winning encourages continuing play. 

One other important aspect of our popu!ation~level findings is 

the discontinuous nature of Its gambling oeh.avioL Most of the 

gambling behavior varlahles in this study indicated a large sl(ew. 

This suggests that the vast majority of the sample look Similar 

when examining th('ir gambling behavior, but differently from 

the most involved poker players at the high end of the distrihution. 

This is not surprising because there is evidence from the epidemiology of other pattel'O~ of behavior associated with various expres~ 

sions of addittiQn (e,g,. drinldng, drugging, shopping, etc.) that the 

vast majority of the population can engage in these activities moderately and without meaningful health risks (Grant f.'t aI., 2004; 

Kessler. Chiu, Demler, & Walters, 2005; lejoyeux, Ades, Tassait1, 

& Solomon, 1996: Regier & Robins. 1991). The next section provides an in-depth analysis of this extreme group. 

4.3. Most involved poker players 

The suhset of most involved poker players devoted much more 

time and money to this activity than did the rest of the cohort. We 

might have expected that more intense involvement would limit 

duration: Perhaps the expenditures would become J burden and! 

or the time spent would interfere with other activities, Tbis was 

not the rase; the duration of play for the more intcn~e players 

was three times longer than the majority of the sample. 

Between groups comparisons indicated differen«('s in poker 

pl.ly between the majority of the sample and the 5% most involved. 

However, it is important not only to examine leve! differellces, but 

also the relationship between the various gambling variables. We 

examined the correlations between the gambling variables and 

(ound some interesting differences, Unlike the majority of the research sample, there' was no correlation between Percent lost 

and either Duration, Total Sessions, or Sessions per Day, However, 

with respect to monies spent, the most involved, like the rest of tbe 

sample, reduced Ellros per session and reduced Total Wagered as 

Percent Loss increased. This finding suggests differential deci~ 

sion~making for temporal and financial costs: Financial cost pat~ 

terns potentially representing more rationale behavior than 

tempora! cost patterns, The study of actual Internet gambling, 

therefore, might hold meaningful potentia! to advance our under~ 

st.wding of the important risk factors that influence how addiction 

emerges .1nd is sustained, 

4.4. Clinical significance 

AlthoU1>\:h this: study reports the first ever analysis of aewJI 

Internet poker gambling activity among a large cohort followed 
143 


75777.120

716 D.!l. LaPlaMe er ul)Compuurs ill Hurnun Behavior.2$ (2009) 711~717 

during two years of observation, it is important to note that this 

study does not include information about r.ates of clinical or subclinical gambling pathology. However, spending large amounts of 

money on gambling is consistent with some DSM-!V-R pathological gambling criteria (Americ,ln Psyci1idtric Association. 20(0) 

(e.g .. needing to gamble with increasing dmounts of money to 

achieve the desired excitement; and "chasing" one's losses). Extensive involvement with g.1mbHng also can cause problems not reflected in mon<.'y spent blJt, instead. in the amount Jnd 

appropriateness of time spent gambling {e.g., choosing to gamble 

rather than meet other obligations}. Future research is neccssJly 

to understand the dinkaJ signilicance of our findings. Rese.:ll'ch 

that indicates a disproportionate influence of time-related (actors 

on disordered gambling could direct DSMN revisiolls of the definl~ 

tion of pathological gambling. 

4.5, Limitations 

This study is not without limita.tions. The observed Internt't po~ 

ker behavior of this sample might not represent a participant's total online gambling tlehavi(lr, Unlike land~based gambling venues, 

bettors c<ln access Internet sites easily, play at several venues, and 

move among them readily, Subscribers to bwin also migbt hJ.ve 

been !letting on sports at the site, It is possible that multiple individuals might have bet using the same account. We reported gam~ 

bling behavior observed during a tWQ~ye.a.r study period. It is 

possibJe that the (ourse of activities leading to problem gambling 

might require longer exposure to Internet gambling. Additional re~ 

se.arch is necessary to clarify these issues and we contlnue to coliect information about this cohort, 

4.6, Concluding thougim 

This '5tudy takes the flrst steps necessrtry toward informing the 

wide range of gambling stakeholders .about the behavioral epide~ 

mlology of Internet pokcr gJmbling, Understanding the distribution of a phenomenon is central to building a scientific 

foundation of cxpldnation. The next step is to clarify the d[stribu~ 

tJOn or Internet pOKer playing and other online gambling among; 

vulnerable popu!alion segments, Once the distribution of this 

activity is clear, it will then become possible to identify the determinants of these patterns, with emphasis on the moderators and 

mediatDrs of gambling disorders. For now, research must move 

tD replkate the$e findings using a variety of other Internet sites 

and a variety of other types of Internet g.ambling. Research mllst 

begin to identify the population segments at greater or lesser risk 

for neveloping Internet gambling-related addiction prohlems. The 

determinants for increasing or decreasing the likelihood. of developing Internet g.1lnbling problems can [hen sClve as a guide for 

the development of gambling addiction prevention and treatment 

programs. 

Acknowlcdgemenu 

The authors extend special thanks to Cbristine Reilly, Christine 

Thurmond, and liming Xuan for their support and work on this pro~ 

ject. The authors jointly accept re5[XH1sibility forthe content of this 

ilrtic!e, Dr laBrie had full access to all (lfthe datJ in the study and 

tal~es responsibility for tbe intcgrity of the data and the .1ccurJcy 

of the data analysis. None of these supporters or ilny of the authors 

has personal interests in bwin and its associated companies that 

would suggest a conllict of interest Tbe Division on Addictions at 

CIlmbridge Health Alliance receiVes funding support fram the fol~ 

lowing resources: bwin Interactive Entertainment, AG, St. Francis 

House, National lnstitutes of Health (i.e .. NIDA, NlAAA), The Na~ 

tiona! Center for Responsible Gambllng{NCRG), Venetian Casino Resalt, ltC, University of Nevada at Las Vegas, Univcrsity of Michigan, 

rort Authority of Kansan City, Robert Wood Johnson Foundation, 

Massachusetts Council on Compulsive Gambling. 

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Harm Reduction Journal () BioMed Central 

This Provisional PDF corresponds to the article as it appeared upon acceptance, Fully formatted ~'I' PDF and full text (HTML) versions will be made available soon, 

- -.--.. ----.. --.. -~ -

Virtual harm reduction efforts for Internet gambling: effects of deposit limits on 

actual Internet sports gambling behavior 

Harm Reduction Jouma12008, 5:27 doi:10,1186/1477-7517-5-27 

Anja Broda (anja,broda@kksLuni-leipzig,de) 

Deb! A LaPlante (debUaplante@hms,harvard,edu) 

Sarah E Nelson (sarah_nelson@hms,harvard,edu) 

Richard A LaBrie (rlabrle@chalilance,org) 

Leslie B Bosworth (bosworth@chalilance,org) 

Howard J Shaffer (howard_shaffer@hms,harvard,edu) 

ISSN 1477-7517 

Article type Research 

Submission date 10 December 2007 

Acceptance date 6 August 2008 

Publication date 6 August 2008 

Article URL htlp:Uwww,harmreductionjournaLcom/contenV5I1f27 

This peer-reviewed article was published immediately upon acceptance, It can be downloaded, 

printed and distributed freely for any purposes (see copyright notice below), 

Articles in HRJ are listed in PubMed and archived at PubMed CentraL 

For information about publishing your research in HRJ or any BioMed Central journal, go to 

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© 2008 Broda et 01. , I1consee BloMed Central Ltd. 

This Is an open access article distributed under tho terms of the Cfeative Commons Attribution LIcense (bttR:ll~t!l:aljyaCQmmQns Qrg/Jicensesfby!2 0), 

which pormits unrestrlcled use, distribution, and roprodudion in any medium, provided the original work is properly cited. 
146 


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Virtual harm reduction efforts for Internet gambling: effects 

of deposit limits on actual Internet sports gambling 

behavior 

Anja Brodal 

Deb! A. LaPlante2

) 

Sarah E. Nelson2

•

3 

Richard A. LaBrie2

•

3 

Leslie B. Bosworth2 

Howard J. Shaffer2

•

J 

(Coordination Center for Clinical Trials Leipzig (KKSL) 

2Division on Addictions, Cambridge Health Alliance 

3Harvard Medical School 

Corresponding Author: 

Anja Broda 

Coordination Center for Clinical Trials Leipzig (KKSL) 

Hartelstr. 16-18, D-041 07 Leipzig, Germany. 

Phone: 49-341-97 16257 f Fax: 49-341-97 161 R9 f anja.broda@kksl.unl-leipzig.de 

E-mail addresses: 

AB: anja.broda@kksl.uni-leipzig.de 

DAL: deb(Japlante@hms.harvard.edu 
147 


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SEN: sarah~nelson @hms<harvard<cdu 

RAL: r1abrie@hms<harvard<edu 

LBB: bosworth@challiance<org 

HJS: howard <shaffer@iJms<harvard<edu 
148 


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Abstract 

Background: In an attempt to reduce harm related to gambling problems, an Internet 

SpOlts betting service provider, bwill Interactive Entertainment, AG (bwin), imposes limits on 

the amount of money that users can deposit into their online gambling accounts. We examined 

the effects of these limits Oil gambling behavior. 

Methods: We compared (l) gambling behavior of those who exceeded deposit limits 

with those who did not, and (2) gambling behavior before and after exceeding deposit limits. 

We analyzed 2 years of the actual sports gambling behavior records of 47000 subscribers to 

bwill. 

Results: Only 160 (0.3%) exceeded deposit limits at least once. Gamblers who 

exceeded deposit limits evidenccd highcr average number of bets per active betting day and 

higher average size of bets than gamblers who did not exceed deposit limits. Comparing the 

gambling behavior before and after exceeding deposit limits revealed slightly more 

unfavorable gambling behavior after exceeding deposit limits. 

Conclusions: Our findings indicate that Internet gamblers who exceed deposit limits 

constitute a group of bettors willing to take high risks; yet, surprisingly, they appear to do this 

rather sllccessfully because their percentage of losses is lower than others in the sample. 

However, some of these gamblers exhibit some poor outcomes. Deposit limits might be 

necessary harm reduction measures to prevent the loss of extremely large amollnts of money 

and cases of bankruptcy. We discuss how these limits might be modified based on our 

findings, 
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Background 

The Internet is a relatively new medium available for wagering. Research indicating 

how many people participate in Internet gambling is scarce. Two empirical studies published 

prevalence estimates of Internet gambling among the US general population: these studies 

reported rates of 0.3% [11 and 4% [2]. Among 1294 adults from a representative sample in 

Ontario, 5.3% reported having gambled on the Internet during the past 12 months [3]. Using a 

representative national sample from the United States, researchers reported a lower rate of 

2.5% for college students [4]. Although some observers note that Internet gambling growth is 

slow compared to other forms of gambling, e.g. casinos and lottery [5], Internet gambling is 

prolific and growing [6]. Therefore, examining the influence of Internet gambling on public 

health is important. 

Research examining land-based ganibling suggests that adverse gambling-related 

outcomes ofLen include financial distress, emotional and physical dcterioration, and damaged 

interpersonal relationships [7]. Some research suggests that disordered gambling relates to 

poor mental health, such as personality and psychiatric disorders [8,9]. Researchers, public 

policy makers, and public health officials have argued that Internet gambling is associated 

with similar public health threats [10-13]. One study reported that Internet gambling was 

linked to pathological gambling and associated with poor physical and mental health [14]. 

Because this is the only study providing empirical data about health COlTelates of Internet 

gambling, and because this study provides results based on retrospective self-reports of a 

locally restricted sample of patients in clinic waiting areas, what we actually know abollt the 

health dangers of Internet gambling remains limited. 

Speculations about potential hazards particular to Internet gambling include the 

apparent lack of fail-safes, sllch as the inability (0 protect individuals who are underage or 
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people known to have gambling-related problems and to prevent gambling while intoxicated 

or gambling at work [ISj. However, the Internet also provides a unique opportunity for 

implementing special safeguards and harm reduction etTorts. For example, tracking software 

can record all gambling online activity, which companies could potentially use to control the 

extent of gamhling by specific users. Web-based technology could limit the time per 

gambling session or the amount of money participants can use to gamble. Recent 

recommendations for Internet gambling operators include accepting payments with credit 

cards only, providing options to self-limit gambling expenditure, and providing options that 

allow users to self-exclude from an Internet site [16J. 

In this study, we explore a harm reduction feature currently unique to Internet 

gambling. As part of their corporate social responsibility agenda, a large Internet sports 

betting service provider, bwill Interactive Entertainment, AG (bwill), imposes limits on the 

amount of money that users can deposit into their online gambling accounts within a given 

time period. When a user tries to deposit !!lore than the allowed amount, bwifl sends the user a 

notification message about the attempt to exceed deposit limits and rejects the attempted 

deposit. We expected that users who received a notification message constitute a group of 

extremely engaged gamblers, and we therefore hypothesized that exceeding deposit limits 

would be associated with unfavorable gambling behavior, sllch as excessively large betting, 

high losses or high frequency of playing (i.e., high financial and/or temporal engagement). 

Furthermore, we expected that receiving a notification message would act as a warning sign to 

users; consequently, we hypothesized that exceeding deposit limits would attenuate gambling 

behavior that followed exceeding the limit. To examine these possibilities, this study 

compares (1) the gambling behavior of those who exceeded deposit limits with those who did 

not, and (2) the gambling behavior of consumers before and after exceeding deposit limits. 
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Methods 

Sample 

The research cohort included 48114 people who registered with bwill between 

February 1 and February 28, 2005, and who deposited money in their accounts before 

Febnlary 28,2007. bwill is primarily an Intemct sports gambling service, offering two types 

of sports bets: fixed-odds bets and live-action bets. Fixed-odds bets are made on the outcomes 

of sporting events or games before the events begin. The amount paid for a winning bet is set 

(fixed) by the belting service at the time of the bet. Live-action bets are made while the event 

is in progress. In addition to bets on the outcome of the event, the betting service offers bets 

on selected outeomes within the sporting event (e.g., which side will have the next corner 

kick). Fixed-odds hets arc relativcly slow-cycling hetting propositions. The outcomes of a bet 

are generally not known for hoUl's or days later. In contrast, live-action bets provide relatively 

quick-paced betting propositions posed in real-time during the progress of a sporting event. 

Some subscribers in the cohort did not engage in fixed-odds or live-action sports 

gambling (n:: 1114, < 3%). Consequently, these subscribers were excluded from the study, 

leaving 47000 sports-betting subscribers for the current analysis. This cohort consisted of 

43222 (92.0%) men and 3778 (8.0%) women. The mean agc of subscribcrs was 30.3 years 

(SD ::; 9.9) and the cohort included people from 84 countries, with most people (n ::; 26955, 

57.4%) from Germany, followed by Turkey (n 2846,6.] %), Poland (n:: 2834, 6.0%), Spain 

(n = 2754, 5.9%), and Greece (n '" 2586, 5.5%). The majority, 31544 (67.1 %), placed both 

fixed-odds and live-action bets, 14723 (31.3%) played fixcd-odds only, and 733 (1.6%) 

played Jive-action only. 

Measures 

bwill prepared a dataset of the actual Intemet sports gambling behavior of this cohort 

for the 2+-year period, between Febmary 1,2005 and February 28, 2007. More specifically, 
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this dataset included the daily aggregates of betting activity (i.e., the aggregate number of 

bets, amollnt of money wagered, and amount of money won for fixed-odds and live-action 

sports betting per calendar day) for all participants in the cohort. 

Exceeding Deposit Limils. bwin provides several ways of limiting the amount of 

money that users can deposit in their accounts. By default, bwill does not allow users to 

deposit more than 1000 Eures per 24 hours or 5000 Euros per 30 days (or currency 

equivalents). One exception to this default is a flexible limit system, which automatically 

increases allowable deposit limits by the subscribers' amount of winnings from gambling. A 

sccond exception occurs when subscribers can evidence exceptional financial means. In sllch 

cases, users may have higher deposit limits. At the other end of the spectlUm, users can 

choose to set for themselves lower maximum deposit amounts per 30 days. Users repeatedly 

can adjust these self-limits to their needs. 

Exceeding any deposit limit leads bwill to issue a notification message. Although we 

have information about if and when a user received such notification, we do not have 

information about the type of limit (Le., self or company) that initiated the notification 

message. Thus, this study explores the combined effects of exceeding company- and selfimposed deposit limits. 

Gambling Behavior. Based on the daily aggregates of betting activity, we computed 

four measures of gambling behavior for each user: percentage of days within the active period 

from first to last betting day on which the user placed bets (i.e., percent active betting days); 

the average number of bets per active betting day; the average size of bets in Euros; and a 

categorical measure of percent lost. These measures are more adequate. than gross totals of 

number of bets or money wagered when comparing the gambling behavior of different lIsers. 

Each measure was computed for fixed-odds and live-action betting separately, aggregated 

across the total 2-year observation period. Further, within the subset of people who received a 

notification message, each measure was computed for the period oftlrne before as well as 
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after the first receipt of a notification message (note that the day of receipt of the notification 

message was defined as an 'after' day). 

We defined the percentage of active betting days as the percent of days within the 

interval from the first to the last betting day that included a bet. We obtained the average 

number of bets per active betting day by dividing the total number of bets made by the total 

number of active betting days, and the average size of bets in Euro by dividing the total 

money wagered by the total number of bets. These two gambling behavior measures were 

highly positively skewed with many cases on the left and fewer cases (but still substantial 

numbers due to the large sample size) on the right side of the distribution. Logtransformations were performed to generate normal distributions for these measures. 

We calculated the percentage of losses by subtracting the total amount of winnings 

from the total amount of wagers and dividing the difference by the total amount of wagers. 

This measure was highly negatively skewed and transformations did not help approximate a 

normal distribution. We therefore categorized this variable to capture (1) users who were 

overall winners (negative percentage of losses), (2) users with the lowest percentage of losses 

(operationally defined as losses of 0 to <20%), (3) users with an intermediate percentage of 

losses (I.e., 20 to <80%), and (4) users with the highest percentage of losses (i.e., 80 to 

100%). We chose the cut-point oflhe lowest percentage of losses to approximately agree with 

the expected losses, which according to the target returns expected by the operator are 

approximately 13% for fixed-odds betting and 6% for live-action betting. The 20% cut-point 

retlects the nearest rounded percentage. For the cHt-point of the highest percentage of losses, 

we applied the same 20% margin, and the remaining percentage of losses was categorized as 

intermediate. 

Most Involved Bettors. We defined most involved bettors (MIB) subgroups as the top 

I % of the sample regarding the total number of bets, total amount of wagers, and net loss (i.e., 

subtracting the total amount of winnings from the total amount of wagers) on fixed-odds or 
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live-action betting. We used a scree-type analysis of centile plots to empirically identify these 

I %-subgroups [17J. This strategy allowed us to classify MIll into six non-exclusive groups: 

(I) total number of bets; (2) total amollnt of wagers; (3) net loss for fixed-odds players (each 

of these groups n := 462); (4) total number of bets; (5) total amount of wagers; and (6) net loss 

for live-action players (each of these group n::= 322). A lotal of 984 users belonged to at least 

one of the fixed-odds MIB groups (2.13% of 46267 fixed-odds players), and a total of 613 

users belonged to at least one of the live-action MlB groups (1.90% of 32277 live-action 

players). 

Analyses 

In nddition to providing descriptive statistics, we conducted two primary comparative 

analyses. First, wc examined differences in gambling behavior between users who did and did 

not exceed deposit limits using independent-samples tests. These tests employed the gambling 

behavior measures that were aggregated across 2 years. We also looked to see whether the 

proportion of most involved betters was greater among individuals who exceed limits 

compared to those who did not. Second, we analyzed individuals' differences in gambling 

behavior before and ufter exceeding deposit .limits within lIsers who exceeded limits using 

paired-samples tests. These tests compared the gambling behavior measures that were created 

for the period of time before and after receipt of the notification message. 

The procedures of limiting deposits and sending notifications were only in effect 

starting late September 2005, about 8 months after the beginning of our study. Users of our 

cohort experienced no restrictions to the amount of money they could deposit during the first 

8 months after they registered with bwill. This could potentially bias our findings: On one 

side, only a subset of people who registered in February 2005 was still active in September 

2005 (e.g., of the 47000 sports, 27726 or 59% had deposited money after September 2005) 

and thus could experience the new deposit limit policies. Short-term bettors might exhibit a 

low extent of gambling behavior, potentially resulting in overestimating the differences 
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between users who did and did not exceed limits, We therefore repeated the analyses within 

the subset of llsers who had deposited money after September 2005, 

Alternatively, certain deposit activities could have been possible before September 

2005, but might have conflicted with company-imposed or self-imposed deposit limits after 

September 2005, For example, people could deposit very large amounts during the first 8 

months but not after September 2005, People might have chosen to self-limit their deposit 

amount but had no option to do so before September 2005, We had no means of identifying 

people who would have been subject to one of the limits before September 2005, and thus no 

means of excluding these people from the analyses, However, in our analyses these people are 

considered users who did not exceed limits, yielding conservative estimates for the 

comparisons of users who did and did not exceed limits, 

Analyses involving the average number of bets per active betting day and the average 

size of bets in Eum used the log-transformed variables; however, we report means, standard 

deviations, and medians for the untransformed variables for descriptive purposes. 

Results 

Descriptive Statistics 

Of the 47000 sports bettors, 160 (0.3%) had received at least one notifieation message 

abollt exceeding deposit limits, Five (3.\ %) were women and 155 (96,9%) were men, and the 

mean age was 30,8 years (SO", 9,2), Most of the bettors who exceeded limits played both 

types of games: 159 (99.4%) were fixed-odds players and 149 (93, 1%) were live-action 

players; 148 of the 149 who played live-action also played fixed-odds, Among users who 

placed both fixed-odds and live-action bets, 0,5% (n = 148) received a notification message, 

compared to 0,1% (n = II) of users who played tixed-odds only and 0,1% (n = 1) of users 

who played live-action only (X2 = 46,95, df", 2, p < ,001), 

These 160 notified users received between I and 267 notification messages, with a 
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mean of 14 messages (SD = 29, Median:= 6). Orthe 160 users, 5 (3.1 %) stopped depositing 

money in their accounts after receiving the notification message. One user had tried to deposit 

more than the allowed amount with the very first deposit. The mean number of deposits 

before receiving the notification message was 57 (SD = 89, Median = 20) with a range of 1 to 

796. The mean number of days between the date of the tirst deposit and the date of the first 

notification message was 372 (SD:= 184, Median = 380) with a range of 0 to 741. 

To describe the general distribution of deposits, we examined the maximum amount 

deposited per 24-hour and 30-day period among the 46840 sports bettors who never received 

a notification message. Table I reports the mean (SD) and centiles for this measure. The vast 

majority of users never came close to the limits of 1000 Euros124 hours or 5000 Euros/30 

days. 

Comparing Gambling Behavior between Users who Did and Did Not 

Exceed Deposit Limits 

Table 2 presents a comparison of gambling behavior aggregated across 2 years for 

tlsers who did and did not exceed their established deposit limits. Results were similar for 

fixed-odds and live-action betting. The percentage of active betting days for these groups was 

not significantly different. The average number of bets per active betting day and the average 

size of bets were higher among users who exceeded deposit limits compared to users who did 

not exceed deposit limits. The distribution of the categorized percentage of losses was more 

favorable among users who exceeded deposit limits; that is, the likelihood of the lowest 

percentages of losses was significantly higher and the likelihood of the intermediate and the 

highest percentages of losses were significantly lowe[ among users who exceeded deposit 

limits. 

Despite losing a smaller proportion of what they wagered, users who exceeded limits 

still, on average, lost signiticantly more than users who did not exceed limits. That is, the 

mean net loss on fixed-odds of users who did exceed limits was 1,135 Euro (SD = 2,766, 
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Median =;; 2(3) compared to 185 Euro (SD = 1,028, Median =: 50) for users who did not 

exceed limits (t 11.51, P < .00l) (live-action: 1,975 Euro, SD 5,569, Median =;; 135, 

compared to 187 Euro, SD '" 1,414, Median = 13, t = 14.91, P < .001). 

Exceeding deposit limits was a significant predictor of being in the MIB subgroups. 

For example, 14.5% of users who received notification messages, belonged to at least one of 

the fixed-odds MIB groups, compared to 2.1 % who did not; this association yielded an odds 

ratio of 7.95 (95'70 CI 5.08 - 12.42). Further, J 4.8% of users who received notification 

messages compared to 1.8% of those who did not, belonged to at least one of the live-action 

MID groups; this association yielded an odds ratio of9.24 (95% C15.84 - 14.64). Table 3 

presents the associations between individual MIB groups and exceeding limits. 

Within the group of people who exceeded limits, we compared users who belonged to 

at least one of the fixed-odds or live-action MIS groups to users who did not belong to a MIB 

group on the gambling behavior measures. Users belonging to a MIB group had a higher 

percentage of active betting days and a higher average number of bets per active betting day 

on fixed-odds and live-action, and a higher average size of bet on live-action. The distribution 

of the categorized percentage of losses was not significantly different between llsers who did 

and did not belong to a MIB group. 

Comparing Gambling Behavior Before and After Exceeding Deposit 

Limits 

Table 4 shows the comparison of gambling behavior before and after exceeding 

deposit limits. Of the 159 fixed-odds players, 143 had activity both before and after exceeding 

deposit limits and are included in Table 4; likewise, of the 149 live-action players, 105 had 

activity both before and after and are included in the Table. 

Again, similar patterns of results emerged for fixed-odds and live-action hetting. The 

percentage of active betting days and the distribution of the categorized percentage of losses 

did not change. The average size of bet increased and the average lJumber of bets per active 
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75777.135

betting day decreased after exceeding deposit limits. 

Analyses for Users Who Deposited Money after September 2005 

To control for potential biases that might result from the notification messages being 

introduced only after September 2005, we repeated all the above analyses with the subset of 

27726 users (59% of the total sample) who had still deposited money after September 2005. 

These analyses compare the 159 fixed·odds players who exceeded limits with 27442 users 

who did not exceed limits, and the 149livc·action players who exceeded limits with 21433 

users who did not exceed limits. Overall these analyses yielded the same pattern of results, 

although the percentage of active betting days was significantly different hctween the Iimitexceeding and non-Iimit·exceeding groups for fixed-odds betting and the distribution of the 

categorized percentage of losses was not significantly different between the two groups for 

either fixed-odds or Iive·action betting. The odds ratios for belonging to the MIB subgroups 

were slightly lower overall: between 4.06 and 6.53 for the fixed-odds MlB groups and 

between 4.37 and 9.78 for {he live-action MIB groups. The analyses of gambling behavior 

before and after exceeding deposit limits necessarily are identical to the overall results. 

Discussion 

The eompany-imposed or self-imposed deposit limits affected only a minority of bwill 

Internet sports bettors. Very few people, only 0.3% of our sample, ever tried to exceed these 

deposit limits. Furthermore, the vast majority of the sample (Le., 95%) never deposited more 

than 500 Euro per 24 hours, half the maximum allowed I DOD Euros, and never deposited more 

than 1050 Euro per 30 days, a fifth of the maximum allowed 5000 Euros. This means that 

bwill could reduce the deposit limits substantially (e.g., by half) and still most people would 

110t exceed these limits. 

One reason for the finding that the deposit limits were hardly exceeded might be that 

the sports bettors are highly responsible gamblers who bet for fun and spend relatively low 
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amounts on betting. Another reason might be that users arc well aware of bwitl 's deposit 

limits policies and purposely avoid violating them. The deposit limits are presented as patt of 

the general terms and conditions that every user needs to accept when opening an account 

with bwi/'. Our findings seem to indicate that knowing about the deposit limits prevented 

some bettors from exceeding the deposit limits and subsequently from losing money. If this is 

correct, then the mere provision of deposil limits can serve as a harm reduction device. 

We examined whether (he deposit limits seemed 10 safeguard the gambling behavior 

of the minority that exceeded the deposit limits. People who exceed deposit limits constitute a 

group of bettors who arc willing (0 place larger bets than people who do not exceed deposit 

limits; yet, they appear to do (his in a manner that keeps their percentage of losses lower than 

others in the sample. Although the percentage of losses might be more favorable among 

people who exceed limits, compared to people who do not exceed limits, their net loss still is 

significantly higher. Because these bettors place very large bets they arc at high risk for losing 

very large amounts of money. 

We identified exceeding limits as a strong predictor for being in the MIB subgroups. 

People who exceed limits are about 6 to 14 times more likely to belong to the various MIB 

groups. Thus, exceeding the limits is associated with a high likelihood of being in the group 

of bettors that bet, wagered ancl/or lost the most; these activities are possible indicators of 

disordered gambling. Consistent with this notion, we found that among people who exceed 

limits, people who belong to MIB groups show more intensive gambling behavior than people 

who do not belong to MIB groups. That is, those who belong to MIB groups bet more often, 

place more bets, and place larger bets. 

Our comparison of the gambling behavior before and after exceeding limits found that 

exceeding the limits did not have a diminishing effect on gambling behavior. The number of 

bets was the only measure of gambling behavior that evidenced a minor decrease after 

exceeding limits. This decrease was offset by a steep increase in the size of bets after 
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exceeding limits. The number of days of play and the percentage of losses did not change. 

Thus. we found no indication that receiving the limit notification message influences users to 

curtail their betting activity. Rather. the findings suggest (hat exceeding deposit limits 

encourages players to shift their strategy; they begin to make more calculated. informed risks 

with single large bets compared to before exceeding the deposit limit. 

The finding that the feedback about a violation of a policy or regulation does not have 

the intended harm-reducing effect is a finding consistent with other evidence about regulating 

behavior. For example, people who were given feedback that their blood alcohol levels 

exceeded legal drink-drive limits have becn nonetheless subsequently observed to drive [18-

201. Drivers who received speeding tickets have been shown to be at increased risk of 

receiving subsequent speeding tickets [21]. Likewise, smokers who were given biomedical 

feedback indicating negative effects of smoking did not initiate appreciable changes towards 

quitting smoking [22]. 

No differences emerged in the patterns of results for fixed-odds and live-action 

betting. Fixed-odds and live-action propositions might differ in the extent of skill required to 

place successful (i.e .• winning) bets. Whereas placing a successful bet in fixed-odds might be 

determined more by skill (or knowledge) than by chance. placing a successful bet in liveaction likely is determined more by chance than by skill. Thus, we could have expected our 

findings to mirror the differing outcomes of games of skill versus games of chance. Our 

findings instead show that. with regard to evaluating the risk of disordered gambling among 

people who exceed deposit limits. distinguishing fixed-odds from live-action betting docs not 

provide additional information. 

Limitations 

Our study examines conceptually different deposit limits. Some limits are mandatory 

and imposed by bW;II: the default limits of 1000 Euros per 24 hours 01' 5000 Bums per 30 
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days, and the increase of the default limits by the amount of winnings. The users voluntarily 

impose other limits: restrictions to a lower amount [han the default limits, or exemption from 

dcfault limits for users with exceptional financial means. When exploring the effects of 

exceeding deposit limits, we combined the different deposit limits. Although different deposit 

limits are examined in this study, an essential similarity of all deposit limits is that they 

represent specific, predetermined maximum values that certain users are not willing to or are 

not able to comply with. To this extend, the current study investigates cffects of exceeding 

pre-set deposit limits. 

We can posit that different types of limits might be associated with different effects on 

gambling behavior. Therefore, an analysis differentiating the typcs of limits would have been 

desirable. Unfortunately, no information was available about the type of limit that led to 

issuing a notification message; thus this analysis was nol an option for this paper. 

It is important to note that the procedures of limiting deposits and sending notification 

messages were not in effect during the entire two-year study period. We performed some 

statistical controls in our analyses to account for this fact, and the overall results remained 

largely unchanged. Thus, we consider our findings to reflect generalizable effects of deposit 

limits on Intemet sports gambling behavior. 

Conclusion 

The deposit limits examined in this study are part of the corporate social responsibility 

agenda of bwin. This harm reduction practice is consistent with recommendations to integrate 

safety features for the prevention of disordered gambling into gambling websites [16). 

This study indicates that current deposit limits affect only a very small minority of 

Internet sports bettors. The vast majority of Tntemet bettors seem to be able to regulate 

themselves and require little additional safeguards; however, some bettors can benefit from 

additional limits. Consequently, for Internet gambling operators reluctant to include harm 

reduction measures, an interesting message is that a company's financial loss due to imposing 
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such safeguards such as deposit limits will he rather small and halanced by the promoting 

effect of being regarded as a socially responsible company, 

In this study, we saw that the mandatory limits exceed what most people are willing to 

spend on Intemet gambling activities. However, the mandatory limits also exceed what most 

people could possibly spend without taking substantial financial risks, Thus, while the current 

mandatory limits might help prevent the loss of extremely large amounts of money and cases 

of bankruptcy, these limits still allow users to transfer substantial amounts of money each day 

and each month, which can lead to financial problems for gamblers wilhout sufficient 

financial means. For these cases, instead of the company-imposed limits, the self-imposed 

limits might have value. 

This study shows that people who try to exceed deposit limits have some poor 

outcomes: a high likelihood of placing an extremely large number of bets, wagering 

extremely large amounts, and/or loosing extremely large amounts of money. These people 

constitute a group of bettors who appear to be willing to take high risks; yet, surprisingly, they 

appear to do this rather successfully because their percentage of losses (but not their net loss) 

is lower than others in the sample, 

Without deposit limits, the behavioral and financial consequences of gambling might 

be even more adverse, These unintended consequences of Intemet gambling indicate that the 

bwill deposit limits could aid in the prevention of adverse gambling-related consequences. 

More research is necessary to determine the extent of this intlllcnce and to monitor and revise 

such notification systems so that the promise of limits can be optimized. For example, recent 

research suggests that gambling activity, or behavioral engagement in gambling. might be as 

important to consider as financial considerations [23], Such findings suggest that corporations 

need not limit harm reduction techniques to financially-related factors. Rather, techniques that 

account for temporally-related factors (e.g., amount of time spent gambling) remain open to 

consideration and examination, Online gambling companies would benefit from testing the 
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harm reduction value of waming systems for amount of time spent gambling. 

In this study, we examined deposit limits as a single harm reduction measure of a 

single Internet gambling provider. Unfortunately, users can sidestep such single safeguards 

easily; another Internet gambling provider is just a mouse-click away. To implement effective 

safeguards, concerted harm reduction efforts of companies, users, public health organs, and 

others are necessary. Ways of achieving this goal might include the development of policy 

requiring safety provisions as a prerequisite for licensing providers, or having companies 

cooperate to employ software programs and technology tools to regulate user gambling. 

The findings of this study originate from actual gambling behavior and betting 

activity, without any direct contact with individual gamhlers. A previous study analyzing 

Intemet sports betting behavior in this mallller indicated that, at the population level, 

gambling activity is moderate, as evidenced by analyses of time (e.g., people were active less 

than half the time possible, despite infinite access), activity (e.g., most placed less than 4 

bet/day during such limited active periods), and expenditures (e.g., most placed bets less than 

5 Euros) [4, 17). Future research needs to investigate how these findings compare with 

subjective assessments of perceptions and behaviors by the individual. If additional research 

supports the findings of this study, technology-based screening tools for gambling~related 

problems could incorporate the attempt to deposit more than the allowed amount of money as 

an early indicator of a person's vulnerability to disordered gambling. 
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75777.141

Declaration of competing Interests 

The authors declare that they have no competing interests. 

Authors' contributions 

AB performed the statistical analyses and drafted the manuscript. RAL and HIS 

conceptualized the study and wcre instrumental in its design and coordination. DAL and SEN 

participated in the statistical analyses and were involved in drafting the manuscript. LBB 

made substantial contributions to the analysis and interpretation of the data. All authors read 

and approved the final manuscript. 

Acknowledgments 

The Division on Addictions receives funding for its studies ofInternet sports gambling from 

bwill Interactive Entertainment, AG. The Division also receives funding from the National 

Center for Responsible Gaming, National Institute of Mental Health (NIMH), National 

Institute of Alcohol Abuse and Alcoholism (NIAAA), National Institute on Drug Abuse 

(NIDA), the Massachusetts Council on Compulsive Gambling, the State of Nevada 

Department of Public Health, rhe Massachusetts Family Institute, and others. The authors of 

this article take responsibility for its content and do not personally benefit their work with 

gaming-related companies (e.g., stocks, etc.). Thanks are extended to Ziming Xuan and 

Christine Thurmond for their contributions to this project. 
165 


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[21 American Gaming Association, 2006 State of the States: The AGA Survey of Casino 

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[3] Ialomiteanu A, Adlaf EM. hltemet gambling among Ontario adults. From 

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[4) LaBrie RA, Shaffer HJ, LaPlante DA, Wechsler H. Correlates of college student 

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[5] Miller R. The need for self regulations and alternative dispute resolution to moderate 

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[101 Griffiths M, Parke A, Wood R, Parke J. Internet gamhling: An overview of 

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[I2J Ladd GT, Petry NM. Disordered gambling among university-based medical and dental 

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Table 1: Descriptive statistics for maximum amount of euros deposited by time period 

Mean (SD) 

Percentile 

25th 

50th (Median) 

75th 

90th 

95th 

Euro in 24 hours 

III (258) 

25 

40 

100 

250 

500 

Euro in 30 days 

243 (725) 

30 

50 

150 

500 

1050 
169 


75777.146

Table 2: Gambling behavior in users who did and did not exceed deposit limits 

---"-~,~-'"-~---.--".~--"'-, 

Fixed-odds (11 46267) Live-action (n" 32277) -_._---- .-------- Gambling behavior Users who Users who did Difference Users who Users who Difference 

measure did exceed not exceed test did exceed did nol tcst 

limits limits limits exceed 

(Il" 159) (Il" 46108) (Il= 149) limits 

Percentage of active 25 (29) 26 (28) 31 (37) 

betting days 13 16 II 

1= -1.90 I=-Ul 

Average number of Mean (SO) 7 (13) 4 (7) 8 (14) 4 (5) 

bets per active Median 3 2 4 3 

belling day Log Mean (SD) 0.60 (0.40) OM (OJ2) t=6.12' 0.68 (0.42) 0.46 (0.34) t= 7.9S' 

Avcmge size of bet Me.Il(SD) 25 (55) 11(30) 27 (41) II (25) 

in Sura Median 8 4 12 4 

Log Mean (SD) 0.96 (0.60) 0.67 (0.51) 1=7.14' 1.07 (0.58) 0.65 (0.53) I" 9.59' 

Categorized 

percentage orJosscs 

Overall winners 11(%) 26 (16.4) 6755 (14.7) 25 (16.8) 6924 

(21.6) 

Lowest n(%) 59 (37.1) 12367 (28.8) 73 (49.0) 10548 

percentage of (32.8) 

losses 

Intermediate n(%) 56 (35.2) 19338 (41.9) 40 (26.8) 9533 

percentage of (29.7) 

losses 

Hi£hesl n(%) 18 (11.3) 7648 (16.6) Chi' = 11 (7.4) 5123 Chi' = 

of 10.91* (15.9) 20.58* 

*p<.05. 
170 


75777.147

Table 3: Proportions of most involved bettors (MIB)t among users who did and did not exceed 

deposit limits 

did exceed didnol (95%Cf) did exceed did not (95%CI) 

limits exceed limits limits exceed limits 

(n ~ 159) (n ~ 46108) (n ~ 149) (n 3212R) 

Total nUJ1lber of 6.3% 1.0% 6.78 6.0% 1.0% 6.53 

bets (3.55 - 12.95) (3.30 - 12.94) 

Total amount of 8.8% 1.0% 9.8<1 11.4% 0.9% 13.44 

wagers (5.64 17.17) (8.01 - 22.55) 

Net loss 9.4% 1.0% toM 12.1% 0.9% 14.38 

(6.20·- 18.26) (8.68 - 23.85) 

IMIB are defined as the top I % of the sample regarding the total number of bets, total amount of 

wagers, and net loss on fixed-odds or live-action betting. 
171 


75777.148

Table 4: Gambling behavior before and after exceeding deposit limits 

!';xed·odds (n ~ 143) 

Gambling behavior Before After Oifference Diftercnce 

measure test test 

Percentage of aCfive Mean (SD) 23 (22) 26 (26) 27 (23) 26 (25) 

bening days Median 14 17 20 19 

Log Mean (SD) -1.37 ( =0,11 

Average number of Mean (SO) 6 (12) 7 (15) 11 (18) 9 (17) 

bets per actiye Median 3 3 6 5 

betting day Log Mean (SO) 0,59 (0.38) 0,49 (OA3) (= 4,10' 0,78 (0,42) 0,71(0.44) t = 2.47' 

Average size of bet Mean (SO) 21 (48) 44 (t07) 25 (43) 32 (45) 

in Euro Median 7 9 II 17 

Log Mean (SO) 0,90 (0,57) 1.04 (0,70) t" ,3,63' 1.03 (0.57) Lt7 (0.59) t" -4.27' 

Categorized 

percentage oflosscs 

Overall winners n(%) 30 (21.0) 20 (14,0) 17 (16,2) 21 (20.0) 

Lowest n(%) 41 (28,7) 40 (28.0) 59 (56,2) 43 (410) 

percentage of 

losses 

Intermediate n(%) 56 (39.2) 47 (32,9) 24 (22.9) 31 (29.5) 

percentage of 

losses 

Highest n(%) 16(1L2) 36 (25,2) Chi' 5 (4,8) 10(9.5) Chi' = 

percentage of 15.30 9.38 

losses .. _-------, -.... -------... ----,~~~ 

* p < ,05. 
172 


75777.149

The European Journal of Public Health Advance Access published April 23, 2008 Ermlpt'<lTI IUlinl'll I{ PH/>li( li.-<!lIh, 1--7 

{', Thl! AUlhM lOll!'\. Pllh!isll<:'d by Oxford U!lkt'r~itr Pnss (lJI hehalf of Ihe [mopt"!)) f'\Jb!i~ 1I1"ahh AS$od"tim) All right:;. H'Sl'fH.",j, 

doi:! O.! 09J/eurpllh/dm021 

Inside the virtual casino: a prospective 

longitudinal study of actual Internet 

casino gambling 

Richard A. LaBrie 1,\ Sara A. Kaplan', Debi A. LaPlante 1.2, Sarah E. Nelson 1,2, 

Howard J, Shaffer 1, 

Background: Participation in Internet gambling is growing rapidly, as is concern about its. possible 

effects on the public's health. This article reports the results of the first prospective longitudinal study of 

Clctuallnternet casino gambling behaviour, Methods: Data include 2 year-s of recorded Internet betting 

activity by a (ohort of gamblers who subscribed to an Internet gambling service during February 2005. 

We examined computer records of each transaction and transformed them into measures of gambling 

involvement. The sample included 4222 gamblers who played casinQ games. Results: The median 

betting behaviour was to play casino games once every 2 weeks during a pNiod of 9 months, 

Subscribers placed 11 median of 49 bets of £4 each playing day, Subscribers lost a median of 5.5% of 

total rnonies wagered, We deterrnined a group of heavily involved bettors whose activity exceeded that 

of 95% of the ,ample; these player> bet every fifth day during 17.5 months. On each playing day, these 

most involved bettors placed a median of 188 bets of €25. Their median percent of wagers lost. 2.5%, 

was smaller than th.:lt lost by the total sample, Condusion: Our findings suggest that Internet casino 

betting behaviour resulh in modest {Qsts for most players, while somer roughly 50/1), have targer losses. 

The findings. also show the need to consider time spent as a marker of disordered gambling. These 

findings provide the €viden<e to steer public health debates away from speculation and toward the 

creation of ernpirically-based strategies to protect the public health, 

Keywords: epidemiology, gambling, Internet. Internet gambling, public health 

Introduction 

P',l.thologkJI gamhling is ,\ public health pl'Oblem ;l$SO~ 

ciated with many physiulogical, Vsychological and social 

rt.'pcrcllssiollS-,some of which MC shared with other exprcs-

~ions of addiction <"Inti some of which are unique to ex({'s:-;!VC 

gllmbling behaviour. I There arc many influences on the prcvalena of pathological gJmbling among the grnenll poplllation. 2

,:1 For e;":,1mple. rcscdn:hers have identified an associDtion 

between incre;lS{'s. in opportunities to gmnblc and (hllnges 

ill population-level g'.llnbling behaviour.45 Researchers and 

gambling advoc.ates <llike won)' that internet-related increases 

in gJmbling opportunities (iln lead to ext;:cssivc gambling 

among .segments of the popliJiltion."'~IJ To date! \'Cry little 

rt:search ha .. ('xamincd populiltion.level internet gambling 

bl'h,wiour, This article provides the first glimpse into the ,letHa! 

gambling behllVi{)\If, as opposed to sC!f-rcport<:d gambling 

behaviour, of -online rasino gamblers. 

internet gambling and public health 

RilH.'S. of Internet gambling currently ,1ft' low comp,lf{~d to 

odwr I>Vt'S of gamhling. 13- lti Howl'vcr. fI.'sclHchcrs and actvo· 

(ates have suggested th<lt online g,ullbling wlH grow and attract 

pJayers because of some uniquely appE'.<lltng asprcts, such as 

allonYll)ity, proximity and a grcJtcr scm!.' of control. I?":!) 

Moreover, the loose I'eg-ulations implemented by H1any gambling wcbsitl.'s,n: comhined with young:£'r gl.'nerations' fJlni!-

iarily wifh, interest in dnd acecs::; to computer technology, has 

! Harv~lr{1 Mcdic<11 School, DosIOIl, MA, US,\ 

2 Divl~i()H nn AJdicliom, Camhridge ! fcalth Alli,lllte, (;ambridge, 

MA, USA 

Concspondcm:c: Richard A. 

101 Station Landing. 2nd floor, 

)06 86(JO, fllX: 41 iHl 306862.9, (' !1l;lij: m'",,,,"""'.""'.''''.''''' 

created uneasiness concerning the possihility of ~m ina("\$c jn 

gilmbling,17 

have reported higher prevalence rates or fntt'rnt'l 

gambling in special popl1!Jtions, sUHgestlng diffcrentl<i1 poru~ 

lilrily ,ltld potency oflntNJl<:t gambling for theM: groups.IS.l, ~;;: 

For ex;.tmple, !.add :,md Petry2ti investigi-~tcd the gilmbting 

behaviour of people seeking treJtment at University of 

COIHH;ail."llt health dillies (,/::::389) and found th"t 8.1% of 

the cohort afl1rnwd !)<unhling on the intNnet in their lifetime: 

those who gambled On the I Iltern~t were more likely to bl;' 

younge!" nOH-CaUCilSiJH and have higher scores on the South 

Oah Gambling Screen';? in comparison to nOll-Internet gamblers, However, th('sc pr('va!crKI.' studies have gathered data vin 

self f<'port m('olSHH'S. which often prQvide inaccllr"tc or bi<lscd 

dat<l due to paflidpl.lHts' (,ll1otional response::> to the events in 

question ;:md difficulty with recall.:'1l Baumeister t'l aJ.l! simply 

staie that people's <lcc()unts of their actions do not often t.:orre· 

late with lhdr <lctual behaviours and thus These discrep.mcics 

Jead In inaccurate reports. Evaluating gamblers' actutll online 

bch;1yiour provides a valid H .. '(ord of gambling activities and 

more j(Cl1r~te knowlerlge ahout p!<lyers' behaviour patterns, 

Assessing actual online gambling behaviour 

LaBrie cl (1/,,1') provided lhe first rcpOf't of <\chml gamblin!; 

behaviours of" IOllgifudinal sample of Internet sports gamblers. This study rcvc.alcd primarily moderate gamhling 

behtldol,.H' at th(' pOrtlJation level (i.e. 2.5 bets of €4 C,\i:h 

('vcry fourth day), suggesting that internet sports gam\)Jjllg 

does not CIKolll'agc eX('(',l;sivc gambling for many players. 

However, the research also showed tlUH a 511\<111 pcrcentagt> 

of subscril>t'rs (i.e, J%) exhibited behaviours thlH deviaied 

m;:uhdly from the norm {i,e. the medi<ln ;lctivityprotilc of this 

group \-V,IS to place 4.7 bets of f44 ench ('very o!ll('r day). 

$imil;uly, analyses of temporal p,lttcrns ·of sports gambling 

bchJviom sbowed evidence of rapid populati(lll-level ad;:q)!a. 

tioJl 10 onHn(' gtHnbling.~u The observed cycle of sports 
173 


75777.150

gambling ;l(tivlty indicated short-terrn increases in Jctivity 

followed by <l swift d{'('line, <1 model consistent with pro!o~ 

typical pUhlic health adaptation cnrvcs,12 The rapid adapt<ltion 

rnight be a !'e~;ult or previollS g<Hnbling experience; the novelty 

of the internet might have generated the inilb! short-term 

incre<1scs in activity, LaPlante and colleaguC's)/') noted u sm1.111 

segment of the population (t%) which did not ,\d<1pt, 

cous.istt:'nt with the findings of LaBrie t'l ITI.:',) 

Hypotheses 

Our previolls rcscarch2'1 provided a unique description of 

actual Internet sports gambling, nnd a descriptiun of <.lctu,\! 

Internet poker play is forthcoming, Both types of betting are 

referred to tlS 'skill' g<mH:~$. fn comparison, chance- govern,,> 

casino games entire]),. As <1 result, we /;,xpecl th<1t casino ganle 

plil}' will dirfer in i.l few important \v~lyS from sports. gambling 

and poker play, Structurally, casino play is more rapid; 

therefofl" we expect that volmne measures, such ;.,s number or 

bets, will be greater for c,\sino gambling 1han for s\-lorts betting. 

Om research focusing on the gambling behaviour of n~6 

peoph: in treatment for gambling~f-dated prohlt:ms:H -)) 

suggests thai casino games <He the game of dwire for people 

seeking treilil1lcnt. Despite the ilbsencc of epidemiologic evidelKt' olher thiu) th<lt presented here, we hypothesize Ihat 

individu<lls betting iii virtual casinos will exhibit riskier behiwioms, sHeh \\s more cx(cssivc 10.5s patterns or time spent 

r,ambling, Ihan observed among Jntcrnct sports b~,ttnr.'j and 

poker plaYCfS. Howe'ver, we CXpL'Ct to find mode-rate and 

consistent gambling ,tlllong the of the population 

with <1 small minoritr {i.e, 5% OJ' exhibiting excessive 

gamblinf, bdwviollr. 

Present study 

Thi~ ;lrtide describes the achMI Internet G\sioo g;lmbling 

bdl<\viour of a large cohort of p<uticipants dming 2 )'t:"<lrs of 

<l longitudinal study. We established II rest.'nrcb cohOl't <lnd 

accu111til<1ted their subseq1lent casino gambling transactions at 

<l: gambling website. The cumulative information b<ls~' of these 

trJllSilCtions dOC\lments ('<,ell player's gmnbJing hehaviour at 

that site. Although previous investigations examined the sclfreported betting <'!ctivit)' of Internet gambling on various types 

of gan1es;1 .. this is the first study to document the aaunl 

galllbling behaviour of Illternel gamblers playing casino-type 

games of chance. \Vt.' present three types of fesults: (i) an 

epidemiological ;,jesc:rip!ion of dt'lnogruphic ch,:uacleri.sti(.!i of 

4222 scquentially subscrihed lnternct casino gamblers; (ii) an 

epideminlogil-al dcsniption of the' casino gllmhlill~ behaviou! of these Internet gamblers llnd (iii) lin cpiclemi{)lo~ical 

description of the Internet (asino gamhling behaviour of an 

cmpirically.dctennine-d Group of ht'avily involved heftors. 

Methods 

Sample 

The full rcsearch cohort included 48114 people who opened 

an accotmt with the fnternet betting service provider, bwil1 

Interactive Entcrt<\lnment, i\G (lltvill), in Fchn)ary 2005. The 

mnjority of subscribers engaged primarily in sports gambling. 

As o:pC'Cted, relatively fe-w, 8472 (18%), elected to play ~ome 

casino games, ,and half of those, 422S partidprlllts, were 

excluded for playing (ewer than 4. days during the study periud, 

The large !lumbef of bettors excluded for limited involvement 

is typical of people <.:urious enough to try the prOd\K!, but not 

sufficiently interested to continue (dsino hetting, We also 

diJninakd 10 bettors ",ho pl;,yed for 'flln' (i.e. only played 

with betting service promotional fw1l.is) > Finally, we excluded 

15 bettors bcc;;1Use thi..'Y bad limited exposure to casino pia)', 

~tarting. their cflsino play less than ) month before the end 

of the current study period (Le. between 1 ]anu<1ry and 

30 Janl1ary 2007). The longitudinal cohort eligible for the study 

consisted of 4222 participants> 

Measures 

Iht;' <lvaih.ble demogmphk chanu:teristics of the research 

sample included age. gender, country of residente and prefen'ed limguage, At enrollmenl. p1lrticipants elected to interact 

with the wagering system in OllC' of 21Iang~luges. 

The ganlbling bch,wioHr measures 'Ire based on participants' 

mOl1tt,)ry deposits to, Hnd withdrawals rrom, their wagering 

accounts. as weB as -dllily aggregates orbcttiug activity records. 

The d,lily helting aggregates include the number of be Is mad~, 

total monies w{tgercd and winnings creditcd to the bettors' 

accounts. The daily aggregafions provided ::;ummary measures 

of gamhling bdl<'lviom. We obtained Homber ofhcts and total 

wagenxl by summing the (biJy nggrcgations> We mCilsured the 

duration of gambling invo!Vt'n1Cllt <lS the number of <i<lYs fr(1)l 

the first digibJe bet to the bst (I.e. duration). We defined the 

frequency of involvcment as the per{'ent of days within 

duration that included ,I bet (Le. fr.cf]uency» We obtained the 

avcfilgc hets per d3y by dividing the tOl<1! lH.lInbcr oCbets made 

by the tot,,1 number of dft)'$ on which 11 bet was pktc.:d (i.e. bets 

~)ef day) and the average size of bets by dividing the IOla{ 

monies- wagered hy the total number of bets (Le. eur05 per 

bt,t) , The nel result of gambling (Le. net loss) is the differen("c 

hetwe(?fl total wagers and tota! winnings. The domin,lIlt Ollt~ 

come is it net luss and. by s.ubtracting total winnings from total 

w;"\gers, posith'c values indicate net losses, the cost of gambling. 

Converting net lO.'lses to a percent of IOtal wagec~ (i,e, percent 

lo&t) provides an il1lit-x ofh$:\t~$ thiit is in-dependent of the total 

"mount \vagered. 

Procedures 

We conducted a sco,mdaty d<)1,l nnalysis of the subscriber 

dntilbnsC' obt,~i!led from hwin as described -ahove, \Vc received 

approyal from our Institutional Hcvi.('w Board to conduct this 

sccondary data <HHlyses. 

Data analysis 

We summ,lrizcd the participants) demographics and gambling 

hchaviour using descriptive statistics. Tests (or differences 

hetw('~'n group means included tf'.\ling the llssmnption or equal 

.... 3riallces and, if necessary, adjusting for uncqurll varbnc~s> 

\VC organi;r.cd the anulyses into three sections: (i) cohort 

1,~11JmCfcristic$; (in cohort gambling behaviour and (iii) the 

beh,wiour of heavily involved bctlors. For cohort characteristics. Vie- rt.'ported gender and country dIstributions, as well as 

gambling behaviour differences by gcnd~r. For ('ohort gambling hdl<1viom. we reported gambling involvement by tinH' 

(i.e. dmalion and frequcncy), betting intellsity (I.e. numbcr 

of bets, bets per {.!ay. cmos per bet), and nlouetary oulromcs 

(i.e. total wflgcrcd, !ll:'t loss and percent lost). For ~amb!ing 

behilvioHr, we report medians bccause of the skewed ""tore of 

the gambling dat,\. 

Results 

Cohort characteristics 

General demographics 

The cohort average age was 30 YC<1fS (SD:::: 9.0) and most (93%) 

were Jll31c. The pl<-1Ycrs represented 16 countries. The tn<ljority 

indicated rC'sid~ncC" in Germany (19%). Austria (11%), 
174 


75777.151

Greece (1 l%) ilnd Sp'1in (10%), but subst<llltial proportions of 

participants were from France (9%), Denmark (8%), Italy (M'o), 

Turkey (8%) ilnd Poland (5%). The remaining 10% of 

parlidpants were evenly distributed among 37 other countries. 

netting bduvioul" was similar across genders with the single 

exn:plion that WOlnen pLl(cd signitiontly more hets per day 

th<'lo mcn (M,qtm~n ~; ! 41 ) SO:;" 206 versus M nl!.:11 ~~ 114, 

SD::o 191, P< O.O~). COJlSeqHt:ntly. the d<1ia did not justify 

ildditional gender-specific analyses. 

Internet casino gambling behaviour 

The wagering of this -cohort on <:~sino gnrnes illcimk"tf 

>206000 records of dail}' nggreg<1tcs that tr<lcke<1 14.8 million 

bets, risking €l 14.7 million and losing a total of €3.S million. 

Table I summ<lrizes the betting activity for this cohort 

(N;:; 01222). The typi.cal number of days. of riaying casino 

games is roughly 18, \\1{.' estimated the number by multiplying 

the mt~diall duration (261 days) by the mcdiilll percent ofdaY5 

(7%) g"mbJcd within the duriltion (frequl.'ncy). We l'titim,Hed 

the t}11ical cost per day by dividing the median Iwt loss (€ll"!) 

by the tY).1iclll Humber of oays of play. Brirfly. tIlt' central 

tendencies (medians) describe a cohort that ph1ys casinQ·type 

gnmes abOl.lt once cv£'ry 2 weeks d\lring a 9-month period and 

loses ahollt €().5 at e,lch sessiQIl, The relationships between the 

nH"iHlS llnd the nwdi;ms, and tht' silC" of the standard deviations 

in rclJtion to the means, indicate that the total distribution is 

markedly skewed (i.e, extreme betting actJvity limits the .Ibility 

of the lllt'iJllS to :l.dequlltcly describe the gcocnlj betting 'Ktivity 

of the population In<"ljority), We will consider the heavily 

involved bettors in a later section, 

Relationships between behaviours 

The di;;ttibutions of the measures viol<\t(, assumptions of 

bivariate nnrm,llity required for prudw:HmHllent coHt'hnions. 

Consequentl}', our ,I\l31ysis of the independence among 

me,1smes med non-parametric rank-order corrr!atiol1 procl,'.~ 

durcs to avoid tllt' undue intluclKt' from extreme ohse-rvations. 

Tilbk 2 presents the Spearman rank-order correlations 

betw1..'c!l pairs of measures. In large s<ltnples, rehltivdy small 

(orre!atioo!i (in this case, as 5mall as 0,05) are Sht!istica!!y 

significant. Only one correlation presented in T<lhJe 2, the 

(orreJalion Iwtwt'en duration and bets per day, W<lS no! stati,· 

tical!y signif1cant. Therefore:. it is important to consider the 

size of thes1..' (olTtlatlo1l5 as wel! as their significantC', 

In Table 2, most of tht: correl<ltions betw(,t'H l)1t\ISUrC$ 'He 

both signit1cant <11)(.1 largC', Participants who wagenxi largcl" 

;]1110\11110$ of mont)' also placcd more iOta] bets, more bets per 

day, wagNcd 1110re per bet <lnd lost more money overJII. 

Perce-nt lo~it was neg-<1tiveJr correlated with all other measures 

of bcttin~ involvement, indinlting that bettors who bet mort:' 

and more often lost a lower percent of their fotat wagers than 

others, Thotlgh dur<ltion and frequency were high!)' neg,Hively 

(orrdatcd. indic<lting that the longer subscribers mnained 

,Klive Oil the site the lower the percent of d.1),S on which they 

bet, these t\vo measures dirt no! correlate highly with the other 

rnCJ$UI\'S of gamblinp, b-clmviour, 

Gambling behaviour of heavily involved bettors 

\-Vc eX.ilminerl subject (cntile plots to identify empirically 

whether subgroups \\lithi!l Ollt" sample evidenced discontinuously high involvement with casino wagering, Similar to int"rpreting a screen plot by idelltif}~ng the 'elbow' of that plot, 

Figure 1 de!11onst!"<'Itcs for total w<lgrrrd a discontinu()us 

distribution bt.:'ginning .H the 95th cClltile. This also was the 

case [or net loss. The total wagered and net loss me<1SUfCS of 

Tab!e 1 Garnb!ing behaviour of Internet casino bettors .n " 

MeaSUTIi! 

Duration {in days} 

frequency 

Numbef of bets 

Bet~ pflr day 

EtJro$. per bet 

Total wagered 

Net loss 

Casino Bettors {Il .. 4222} 

Mean (SD) 

299 (237) 

16% (21) 

3515 (12110) 

116 (192} 

)S (lS4} 

Median2bl 

7% 

')32 

49 

4 

!51015 

Percent lost 

2717;2 (109604) 

640 (3229) 

7.7(12) 

260) 

117 

S.5 Figure 1 Total s.takc.s wagered on casino games 

Table 2 Correlations among gambling behaviour measures: for fixed-odds betting (n:;: 4222) 

--.. ------- --.~----.---------------"-"----- ------------ Duration Fr~qlJ8n(y No. of bets Bct~ per day Euro per Bet Total wagered Net loss Percent lost 

---------.. - Duration - 0_63 0,26 0.01§ 0<05- 0.21 0.23 --0_07 

Fr~que-n<y 0_63 0.22 (J.B O.M 0,27 0.16 ·0.18 

No. of B~ts 0.26 0.2l 0.87 -D.24 0.66 0.49 -0_,6 

!:lets per day 0_Ol§ 0.13 0.8/ -0.41 0.041 0,33 -0,14 

(uros pN Bet 0.05 0.09 -0.2<1 ~OAt 0.52 0.32 " 0.27 

Tota! wagered 0.27 0.27 0.66 0.41 0.52 0.70 -0.43 

Net loss 0.23 0.16 0.49 0.33 0.32 0.70 0,20 

PercC'nt lost ,0.07 -0.18 -0.26 -0.14 -0.27 -OA3 0.20 -------, Duration, interval in days between first and las.t bet; frequency, percent of days within duration when a bet was placed; net loss, 

total wagers minus total wirmings.; Percent 10Sl, net 10$5 divided by total wagered. Non-parametrlc Spearman correlations all 

P<O.001, unless indiGlted by §. 
175 


75777.152

Table 3 Gambling behaviour of e>:treme 5 and 95% subgroups of Glsino bettors 

Mea$urea 

Ag' 

Duration 

frequency 

Number of bets 

Bets per day 

[uros per Bet 

Tota! w..lgered 

Nl;'t lO~$ 

Most Involved 

casino bettors top 5% 

(n 0 212) 

Mean (5D) Median 

34 (9) 32 

416 (232) 529 

24% (17%) 20% 

24 558 (36779) 10465 

285 (304) 188 

213 (682) 25 

345579 (354890) 233195 

87<16 (}1213) 6698 

Other 95% of 

participlints 

(n=4010) 

M-ean (5D) Median 

-- ------~-.~,.-.-" 

30 (9) 2. 290(233) 24' 16% (21%) 7% 

2<103 (7819) 4GG 

107 (176) 46 

25 (97) 4 

10338 (19360) 2284 

422 (939) 107 

Percent lost 2.6 (3) 2.5 8.0 Ill) 5.9 

a: All measures slgnifkantty different between grouJls at 

P<O.OOI 

involvement are highly correlated (Spearman ,.""0,70) ,md 

two-thirds 01 tbe most invulved bettors were mmmon to both 

mcnsmes of money <It risk. As shown in Tablt:' 2, total \\',lgcl'ed 

w~s correlated morc highly with betting activit)'. bDth total 

hets anii bets per day, and was nJnsi(kred a better mei:lsure Df 

gambling involvement. The temporal measures of duration 

,Ind rr(':qut"ncy were skewed but not Hlnrkedly discontinuous, 

We fHldlysed the most top 5°,4\ ur casino g.lmhkrs identified by 

total wagered (i.e, the 5%) separately to provide a lllore 

complete description most heavily inyolw:d Internet 

casino gamblers. 

As 'L~ble .3 shows, the top 5% of ph)yers and their 1('$$ 

involved conrHCrp<lrts signifkantly differed on n ))llmb!.'r of 

vari;)bles. The single exception was gender. The proportion 

of fCfTI;Jles in the top 5% group was 10\'\'c1', 4,2%, compared to 

the other pl;lrers, 7.21)/0; howev('r, this difference \yas not 

st,\tistimliy significant (X;' "'" 2.69, p::: 0.10). The top ,'W'& players 

were signific<lntly older by 4 years (t::::: 6..1, ndf =: 233, 

f' < 0.0(1), The top 5% exhibited significantly increased gilmbling behaviour comparcd to other gamblers on "II mC';lsm('s 

of ,.1(tivity .lOd $pending, However, thl~ fOP 5% los! a signif 

icnntly smaller peret,'!)! of their total W<lgers comp,m:o to the 

rest of the cohort {to;;, 21.0, nM - 871, P <:: 0,001). 

Discussion 

Although lntern€t gambling h often the stlh,it':ct of public 

hC3lth dc-batt"' ilnd (Oncern. there is link empJrh:al cvidcnc:c 

~lVJ.i!able to inform such debate and ilddl'tSS thilt cOllcern. 

Stakt'holders, lwwcvn, fl.lYe speculated ahollt lott.'met gan1~ 

bling and related public policy in both the popular press and 

publk health cirdes.(>,Il- l

n.'::I>"'3,'1n Fortul1<tteiy, empirical data 

descrihlng t)opuhltion-levd Internet gambling behaviour is 

llllHmting. Contributing to thi-" gmwtb, this study presents the 

first ever analysis of n.:-<lI~timc belting behaviour of lnttTllct 

casino garnblers. These findings pro1;idl,' a description of 

tbe lUiCrtlC't casino g,lmbJing bc-hi1Ylour evidenced by a large 

cohort of bettors followed prospe(tive!y for 2 We also 

identilird and n.·portcd the characteristics of a group 

of heilv;ly invo!Y1:!d plnycf& ''''ho five percellt of 

the ovcrall cohort. This information allow stakeholders 

to p.nticip<lte in cvid('n('e~hH.'ied public health debate, f<lthcr 

than rely on con\'enlioll'll\ wisdom ,md profcssiona.l 

specul~tion> 

Cohort characteristics 

It is importiHlt for public health officials who Illight be de\'cI~ 

oping Interllet gamhling-rdated policy to Ilnde-rst,lIld the 

magnitude of '(I popubtionfs involvement in various types of 

lntt"rnet gambling. We hypothesiz(·>d that g;Jmes of chance 

would no! he " popular gambling (holc(' for our longitudinal 

cohort of sports bettors. During the 2~ye'-1r study period) 18% 

of the (ohort tried thdr hand at fusino g,HTICS but half of them 

did not play on more than 3 da~ls, The finding that only 9<).il 

of the cohort plJyetl (asino·fYl~t' games fo any extcnt confirms 

our expe(:tation ahout the populari!}' of this gambling 

option for ,l'.lJorts hettofs. Tilis finding suggest~ lhat, rather 

thun <\ ficlwral interest in Internet garnbling, participants arc 

likely to be selective in the types of games that they chonse. 

to pIa)'. 

The service provkk!' that generated the s1\mplc of gamblers 

for the current investigation is most wt'll known for its 5portS 

betting 5l'fvkcs; consequently, it is not entirely clear whether 

nur findings ~HggCS! popula!ion-kvd gJOlC prefcn.'nces or 

indicate a level of specificity only observed a1110ng lntenwt 

gamblers. We noted that femalcs are underrcpresented ill 

longitudinal cohort and this rnight be the resuh of gender 

differences in game prcren:m:cs. However, gender docs not 

to influence actual bC"tting behaviour; lJ('ithcr this study 

c.lsino gilmbHng nor 1 he gamb.ling stltdl~ observed 

behavioural di(ference~ to discriminate between 

genders, AlthouBh (dsino gamblers comprise- :{ small portion 

of the longitudinal sample, both the full subscriber sample 

Clnd !he sllhsample of c<lsino gamblers arc large (i.e. 4222). The 

experience of >4000 gambkrs ohserved for as long as 2 "eMS 

constitutes H signifi("ll1t empirical information base ubout 

Internet cdsino-sty1e g,IIl)CS of dHl!1(~. t\S v.,'(, hypothesized, 

the daily (oS( of f;JS1HO gambling i.s mode!'t, bllt 

J<.lrger !h<l.o the sport.~ betting costs. of this cohorL 

As Wi.' in the Result~ sl'rtion, fhe tYVica1 daily rost of 

R<lmltling nn c<l~ino was €6.S per day which is larger 

th;l!) the €J.2 daily cost of gambling on fixed~odds 

sports <md the CO.8 typicill diliJ)' cost for livcaction However, the cohort of casino bettors played 

less frequently than the bettors. Casino bettors phlyecl 

about twice a month freqt1t'1l(y;:o:-7~'o) compared 

to about seven timc5 a month for fixed-odds bettors 

23%) ilnd !lvc~actinn bettors (rncdian 

ThE.' observation tbat Ct\.$illO game bettors 

incur larger losses <1t each gnmhling session comp.ned to spons 

bettors is consistent with our hypothesis that casino-type 

g~lmes offer .In (ldd itiol)31 risk for players. 

The correlation <lnat~'scs provide important insights [lbout 

genera! of fntcmet f\~mbling behaviour. The high 

exhibit the wns.istenc), of (asino betting patterns. 

alllong these bettor.'>. The correlation hct'ween the total number 

of bcts made and the average number of bets per day 

(Spearman ,..,:; 0.87) reflects the day·to-day belt!ng consistency 

of <':,Isino pb)'crs. The correl<ttion betwcen toto.[ monies 

wagered ;;md net loss (Spe;umnn r=O.70) is necessarily high 

becaLlse the outCOJ1){' of casino gamhling is function of 

chance ,md the house odds, In our cohort, we also ohservtd 

a general tendency for rational decision making. The total 

amount of w"f!{'fed correia fed negatively with the 

that was wagering dccn:-<l<;{~d <1S iosst's illCfI:3SC(t 

meaSllrt.\'j of b~tting activit), ,Ind amo\!nt pcr hct ,Iho 

negatively witb pcn.:ent lost. These findings suggest 

that for this (ullorl, bad luck W,IS" disincentive for gamblil1f(" 

though morc rest'arch focllsed 011 the tClnllorai nature of these 

pJ.tterns is necessary to confirm thh; suspicion. 
176 


75777.153

Although many gambling outcomc-s were uniform {I.e, 

positively (orre!<lft~d), we also ohservrd a population levd split 

in type of gambling cng<1gemenL More sp~cificillly, the time 

involvement n1e;t~ures, duration and fret!ucllI.:y. wert' llt'ga1ivdr correlated, This suggests two of (jsino play in our 

s,lmp!c: playing on more days a shurter tot<ll play 

period, and pJaying less frequently but for a longer period 

of tirnr. Both play styles hild similar outcomes ;IS measuretl 

hy monies lost. Perci..'nt lost corrcJ~ted negatively v,"ith both 

frequency (Spearman r= -0,18) ilild duration (Spearman 

r;:;::; -0.07) More frequent play Wi'S i1!'>sodated with <l smaller 

percent lost th(l!1 WllS it longer atl!'<ltion of play. Although 

future f('search is uc(C'ssary to chlrify this i~::;lH\ our Andings 

suggest that winning rcinforc~s playing on adjacent days more 

than it reinforces phlyin~ over", longer pt'riod of lime. 

Heavily involved players of casino games 

5imil<lr to our e.1rlicr analysis of Internet sports g;ullbkrs,Z-l 

the patlcl'll of gambline i!lvoivt,tnt'Ht in this cohort of (,3S1110 

gamblers was dls(ontinuous, A 5% subgroup of the lonsi~ 

tudinal (ohort (n =: ~ 12) was morc involved ~·,'ith c"sino 

gambling than the rest of the cQhort It is nOhlhk that 

<\IHong sport gillnble.rs, discontinuity occurred flt 1%;2<;1 hencc, 

a grt'ater proportion of our ,sample of ca~ino gamblers parti~ 

cip,lted in more extreme gamhling hehavIour than did sports 

bettors. It is also worth noting that, on average, the l."xtrerne 

5% subgroup lost €77 per day mmpMcd with the remaining 

casino pJayers who lost €2.3 ~wr day. ,'\11 meaSllres ret1erted 

this increa;;ed gambling im'olvement ror this S<}h subgroup, 

If such groups or heavily involved players Indicate noteworthy 

rates of disorJer1 behavioural algorithm,;; (:o!l1pris~d of 

temporal, intnlsity and financial ~JJ1lhling me<'lSl1I'cs might 

be useful indircs for developing website warning s~'stem$. 

Time involved and money spent 

Bec~llse finandlll losses arc mguJbly the most obvIous (ouS{,-

quen(~ of pathologicol g,llubling, common sense suggests 

1hal public health attention would fC<ltul'c illterventions that 

concentrate on porentiJI finand<ll andlor Jnaterial pruhlems 

und treatment outcomes. However, tln equally important 

coll~equence of pathological gHmhHng might be how gamblers 

redi<;tribute their time (e,g. ~pe!ldlng less time with family or at 

work). Ddtncd by tin,lIKia! risk1 the extreme 5% subgroup 

were actlve c<lsino players dmin~ (I longer pi,'dod, they pJa>i('d 

on more days durill~ the time ihe)' \wrc acliv(" ilnd the 

measure of time $PClll at ca:;ino session) median bdS per 

day) W<1$ four times huger than the of the sample. 

Tht~ heavily involved players phlycd frequent!},! for .1 fong 

duration, and Wt'f~ n'cognizt:d by tht-'if fill,Hlci~J commitment. 

However1 the correlations based on the totill :;amplc suggest 

that SOniC gamblers might expericnce persona! problems 

unrelated to rhe amount of money riskt:d. In the full S<H11pk, 

d1.lr<Hion ~nd frequency are strougly correlaled 

(Spearman r"" -0.61)' and both have modest with 

tilt" F.urno; per bet (Spt'Mmlm 1'""'" 0,05 ilnd 0.09, rcspectively). 

The !1egtHive correlation could signal the of gamblers 

who played in1ensely but for only a fl'!w an episodic loss 

of control that (()uld be problem<'ltic, but <lssodated with on!), 

!imit~'d tinancit1! JOSSC$_ The rdatively Sln,lU correlations o( 

duratioll and fl'cClu.cncy with monies wagered could sign;:!! the 

prcscth.-C of gJmblers who spent il long time playing (<\sino 

g;ames, hut did not (or could not) bet more than vel)' small 

sums, In this (ase, the time (,ugagcd in c,lsino hetting. rather 

than the amount los!, (ol.lld bl' the ncg,;t!v{' otHCOmc of 

disordered gamhling, The time-related findings (ont1rm the 

suggeslion th<lt Illkfvention.s need to targct a range of 

behaviours Jnd that identifi.cJtlon of disordercd gambling 

behaviour needs to move ht:yond tlll,UlCiall}' rdilted 

cOllseq1lCnces, 

Limitations 

Despite the str<'ngth of this. sample ;\!ld thl' l'esenrch focus on 

<Ktll,ll gambling behaviour, this study is not without limfta~ 

tions. Th~ observcd Internet betting behaviour might not 

represent a partidpant's total online gJmbling behaviour. 

In 'Hldition to pl<lying othtr types of games on bwil1 (e.g. sports 

betting), unlike land-based gambling venues. beltnrs can access 

Intefllt't sites easily. pia}' at sevenl venues and tnove among 

them readily. The proffered payback rattS V<II')' from site to 

site and it would not be lI!1USllal for gamblers to 'shop' (or 

tht most (,wour"b1c rates. It also is possible that multiple 

individuals bet using the 5arnt~ ,}C(Ol..I1lL The casino games 

plllycr.', in this study <'Ire a minority (9%) of the longitudinal 

cohort. The service provider, bwin, is best known as a 5ports 

gaming service. Jt is. possible thiH llUIl}' gamblers whose 

prima!'}' interest is c<lsino s,imes would select sites that emphilsi7.t.' casino g,llfH.~S. The casino players in this ;<;ampte also het on 

sports and might represent bettors with more vJried g .. lmbling 

intereslS than players at sites th<lt emphasilc casino pnHrs. 

Although epidemiologkal information fmm this ,md othcr 

studies dt't'ived from our longitudinal cohort]') advance OUT 

understanding of Internet gamhling, addition'll reseill'ch is 

necessary to determine how wd! these findings gencrali7.e to 

other tn)C~ of Jntrmet gambling. Resf<'l'ch has indicated that 

game preferenc('s <1t casinos and other lalid"b'1Scd gambling 

venues (e.g. the lottery, bingo, c<lsino g,l!l1tS) depend upon the 

pbyers' demogrdphic, cconomk ilrld he<llth-rehltcd filCtors~l 

as \'Veil as cultllr.ll and socia! acceptabHily:H Researchers now 

nted to considl'J' whether the observed pilttt'rHS of g:u)1es 

pb}'t'd <It hnd-b.as~cI gatYlbHng venues ctlrry over to Internet 

gambling. 

Games people play 

Our data did not provide inrormatioJ1 about the specific 

that individuals ill our sample played. However, 

reports of provider odd~ might shed Iip,ht (In this 

The ont{'ollles of casino g.ames ",re governed by (h,l{ln:~ 

with tht! odds set hy tbe provider. The website lndicatt's Ih:;\1 

Video Poker (lnd Slots had the I~lwest returns to the players, 

overall losses of 6,~ and 5.8%, respectively (https:llcasino. 

/JlViu,com/casino.a.spx?victv::::payoutT;lblc). ClsiJlo table games 

wert:: most favollrable \vith a foss rate of 2,39'0, followed by 

GU'd games with ;l. rate of 2.9')10. It is possible that the lower 

fnKtional }O!)S-CS for the most heilvit,' involved phyers 

arc due to a preference for table and c(lrd 

lhc higher fractional loss (median::::S.9%) 

e-xpcrienr..:ed by large majority of more casual players might 

be consistent with n prcfcrcnc:c tor slots ;md video poker. This 

is <tn important dil'('ction tor future research and eventll,llly 

could 5uggrst dirt'(tiolls fw targeted public he,llth intcrV('lltion-s b,lSt'd on gaming preferences. 

Conclusion 

The purpose of our research collaborafion with (Min is to 

provide "-11 empirical foulldation to guide till' development and 

implementation of stratcgit,S that will protC'ct the public heallh, 

The rapid !,.'XPiHlSioll Df Internet aL"CCS5 and services (Iu!pa('e~ 
177 


75777.154

the acql1isition of empil'h:ul ('vidence necessary to develop 

effective regulations and policies to assure public safety l:1I)d 

health. However, an advantilge of Internet capabilities is the 

ability to collect the <lclua! behaviour of n kugc resrl1f<:h sample 

over a long period of time. This allmvs rese;urh to avoid the 

nuances of sdf-rcport and the prohibitive loghtic"l (onst(;~inrs 

of repeatedly sun'eying large samples. This study is ~1 Ilec('~~u}' 

sh:p lowtlni informing the wide f<1ng{' of g,Hnbllng st'lkchohkrs 

"boHt the behaviouri1! epidemiology of Intcrnrt gambling on 

filsino-type ganH.'s. [{{'search must next begin to identify the 

population segments ~t greater or !essrr risk for developing 

Internet g;1mbHn.g~rda!ed ad{iictiol1 problems. The determinants for increasing or dccreasing the likelihood of developing 

Intcrnt.:'l gambling problems (an then serve as il guide for the 

deve!oplnent of prevention and trcittl11CJlt pmgralns> 

Acknowledgements 

Bwin.fom, inter<lctive Entertaillment, AG provided prirnilr)' 

support for thb study. The authors extend special thanks to 

Christine Thurmond and Ziming XU<lll for their $uppnrt and 

work on this project Dr LaUrie ll<ld full ;leeeS!; to all of the data 

ill the study and takes responsibility for the integrity of the 

data and the accuracy of the datil anal pis, 

COllj1ic/s of illten~$t; None dedared, 

Key points 

• This is the firSl study to provide cvkkncc abollt the 

actu<I! gambling behaviour of ,1 huge cohort of fntl'rlwt 

gacnbkrs who played casino games <"luring (l 2-YCM 

period. 

• The stl.!dy rewalrd 1l1{ld~!'ilIC gambling beh,lviour ilt 

the population level: the' medi;m betting b('haviour of 

Internct casino gamblers was to play casino gJll1CS 

once e\'ery 2. weeks, placing a nH'di<11l of 49 bets of 

€4 each, 

• A small percentage of the cohort (j.t..', 5%) c>:hibited 

brhavlours that devi~tcd markedly from the, norm: the 

most involved bettors played every fifth da>'. p1.1cing 

a median of 188 bets of €:25 each, 

• Two p.xUCrI1S of Internc! !."t:>ino play crnergcd ~nlOng 

the cohOlt; pl<lying on more dars during a shorter 

total play period, .md playing less frequently hut for 

a longer period of time. Little !;:vidence suggested 

" difference in outCOIl1t;S across these distilK! play 

str1es. • Internet casino gamblers incurred greater (hlD}, losses 

and phlyed less frequently (,thOllt twice a month) than 

Int('rnet sports gamblers (about 7 times per J))(ll1th). 

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\'r1ilIh (-;'~inblll1l JOO(J;6;\. 

I{l Milks ,\1. Will or ]\)$(', intull<'l .pmhling ,IAk~s Me high, I Am A-kd :\s.<r. 

UI1l!;)'s5:IO()i 

11 Wall $IH'~--:I iO\\rlhlL Should (>njine g..lmhling he b;\llt1~"(1? 

Online ;.Jews JhmnJup ]006, A'-ail.lhk fl(lIn: '~ww.wJi,c(Hn 

{,I.:(cs~~d'l t\pnl .):{l{)(>L 

L;)t'latlk' PA, Sh.ln~'r Hr. lIndNSlJ.llding !h~ lll{lUclice ni'S,lmb!ing 

"PIXilhlll(lks: np:"lading. e)'pos\Jr~' models tn lnduoe aJ.lplJliott, 

AI'I I Orth:Jp~}'(blll li)()7;77:61(r.l;. 

13 wd!t JW, Bmw;; (;/'-1, Wkoolck WE t't a!. (;,lmhlil\B p.lHKI\Mtiml 

til the lJ.S.--Rt'HlItS fr{lm II n.ljinnal S1.H'I'C}\ J GIlIJIM SIJ/d 

201n~18:~13""J7. 

14 Allnier jJ, Gwadiilll CfHIIU/iIlS lkll(lvior (111(1 Atrindrs: Summrll)' N.qwf 

Cnl.~'lry" All: C:m:H!.1 We~1 humtl.llion, 2;1101"1-

15 \\Irite IW, B;UHC~ (;.\1, WiCl:mrd, \\IF. Cl at Cnmhlinlt PM1K!patJ<1n ,wd 

p.lIhoiogy !!l Ih~ United S!'1!6~" .. \ S(1dfidell\\lgraphi.: al\.ll}~is u.<;illjl 

d",.djr,l\i{l!l !r~"s, Addifl Ikhill' 2{)()'1;~<,I.'Jln-'), 

1(1 WJrdk !I, S.proS\Oll!\, OdMd 1. el aL Ilflrhh CrllllMing Pm'nlfl)(l'SIIfI','Y 

21107 I (lnt:!oo: Ot.: NJtioll,d G:oln' for $ocLI! H,,$filn:h, ~l)<l7. 

17 \\',\brtll $, PeJls~\n II. !llNtre R~, ("I ,)1, The kg,)li7<"llinn "f Ullemd 

{:.Ml'lbling.: a U>n.m!l)ff PlOlt,hon p~n!'t:':l1\'e. I Public P<J.lifr Mmk 

2004;2J.'2()9-1.i. 

ll't \\'0".1 RT, Willlams RL 1.1\':(,)1'1 PK, Wh), do Illiel'll'::! g'l.mhlers pr~fH online 

V<'r~U~ Lln.l·~><;'t1 \'l'll11e.~: S'\)l1C prdiminarf finding,;. ,llld impli.:<lliolls, 

f Gill'IM {SHU?:> 1007. Av,lij,lhk from: hHp:ifwww.(,'lllh.nt'l/eg.,\mbli!lg! 

1:>S\ldI1107woc'l! hIm \<K(es,~<'d 7 Sel'l<:mh<'I' 20m] 

19 Gdf!ilh~ ,\-\1). ll\!cr:le\ f"l!nb!ing: 1s\Ue'i, COlKtlUS, and r~(on1mend,llio!\~. 

C}'baf"rdw! 1/(11<1" 2()HJ;6·5~'·-{i8 

zn r;rif[lth~ ~ll), Puke ,\, \Vond K d ,d. !llt~'rII~{ g;;llIbli!!g: an owrvi.:w of 

P~)'dl'J>Od,.l imp,'d5. UNL \I GllllJillg Rn 11"1' j 2U06;lIJ;t!-)9. 

""1.1 g,l'.nnt';\ln RF, \'nh~ KD, Ft)!ldn DC. f>,~rd'l'!O(!:l' ll.~ If1l.' ~{it'll(" ()f»dfrrpnris Rnd lingl'f n1U\I'i'lH'!lh: \V1Mte-H'1 h,lPI't:lll'<.l 11) a\.{II.11 h(:h'\"IOI~ 

/IN.'rlYt, l'~rdJat 5d 21),'J7;2:}%-1{):l, 

Cinv{){1d RS. FI.K~ ym.u· r.ets on !lIe kcyholni: ,In' IntNllCI ~a.linn~ k~a!! 

C'lnJpi'i'lf /.!lwR;'t'li}()2;;!:I:jJ:',$() 

P(,try NM. !n!nn;;l gMll!Jling: an (mcl~ing Clllln:rn in family pf;j'tk~ 

nudkille? filln Pr<1(! 2(XJ6:1.1;<jJI-a 

24 Wnodjuff C (!rq::ory S. Profile (1f lnll.'nKt g,ullhkn: J3c1ti!l~ 0)) th.;- ii.Jlufl'. 

UNJYGIlJllillg R" /{1'1' J 700'i;'J:1-J<!. 

25 Petty N'M, INc;lls!o,:k I, !!lln/H't gJ.mh!it1J~ is commun in ((IJ!l'$C 

~!U,klltS ,\Ild .b\l,d<ltl''\ \"ilh P(lM ml'IlI.I! heahh, tIm 1 A<irbaiou5 

?0!l7:lf,:J2:5-)1) 

16 Ljdd GT, Prhy NI\L j)j~ordcletl samhjing ,jmnn~ t1l\h'cnj{/,'b,j~lxJ lIledic,!! 

Mtd JenlJl p,1tI1·rll~; ,\ ("ellS 011 !tHeme! gilll1h1ill£,~ PIJ'(~'" Addicr lMl/)1' 

~002;11i;76-9 

27 L'~l.it'ur Hit BlunK SB. Th<~ ~(1Ulh <lab g;tolb!illg saan (SOt;~J: (l Ill'W 

imlHlll1en! f(lr (h ... ,dt:nliftt,,\(iQu of p,l!hoJ(Jtl-ic.ll-g,lH1hl""r~. Am J P,)"hi.ur:v 

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178 


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19 l.1ilne HA, iaPLmh' nA, Nd~(m Sf-, l'l .)1, '\~s<.\\..\jn~ th< pl:winf field .,3 Sharft'T Ilj, I,aHrie- ll.!\, b!)!,mt~' !),\. 1'1 aL TtIL' Iowa G,1Jnblill~ TlfatnH'ut 

,\ prOSpH'I!\'O! l()n~iltlJII)'\\ study of Intern .. \ sl'orB gambling hdHH'IOl. Progfdl11: Irt\I!Illl'nl O\liC(Hnes for;l fi1H()w.ul' \<lmple.J (31l11ibl SI",J 

f GIlW[.l SIII,12007:2.ld,!7-6l. 2Q(H:2!:59-7l. 

)0 LaPhmte l)A, $dwl\l,lun A, t.<lUde Itl\, \'t at P .. 'publj,m \l'dhlS in 31 Woot! HT, (;rift1th~ MD, P~Hke J. t\c<]uisition, &.'vdl)pmcnl. ;wt1 

Intunet ~porl> gambling, COIIIPII/ Hum Ikh'll', Availilbk vnbne:z,l MMCh )fl~jnl('J),llJ(o; o! {Illiint' poka pL\}'\ng In;\ $Iudt!nt ~<Hnpk CyLWpi)'clw/ B;!1Il1' 

,WeR, 2007; !(U5+"" 1. 

31 Sb,lll~r Ill, l.aBdt RA, L1Platlft: D,\, cl ill, Thf tVWf1 Ikf'llftlllflit {If 35 (;riftilhs ~ID, P;llla' J, Th!: Mxia! impact nf lntcrnd {(,lmh!;np,. Stl( Sci 

PuMI,' i1clllth (,mllHmx Tmlll1J(I!1 s,,-r;'i(.'i,' fOIiT yOlfl 4 !:'l'irl,'I/({." Cmnrl/l R{'1"l002:U1;312-l0, 

Bosto!\: Di"isioll 01) Addi(ljon. Ilafl',nd !I.1"dk.,! Schonl, 200~, J(i Grifl1lhs MIl Cdolbling h.'chn"l<)~ie~: pnHpc~H fm pmhbn g;unhling. 

Report N".: IDltOl·lOO. J Cmllld SIll(ll')l)'):!~:26S-8). 

12 bP!,mll' DA, Nej(CHl 5L 1 aTlrlc RA, tt ,\1. /I.'I"n and women p;,lying g.lmes: 37 Pwdu{"!ivity COnlmi.>.;iml, ;11I.ltmtl<l'$ GfJmvlillX It!dl1$tr;~5; riME RI'['<m. 

~endn 1Iild th~' gamblin~ pf<'rcr~'l1(o.'s of !OW;l (;;ullblinl,: Tre-Jlment Prngr,1ll1 (',mhnr.\: A\1~!tlf(J, )1)')'), Report No.: 10. 

partkir,Hlt~,} (;fJtlIN Stud 2fJ.06;21:b.'> ·~O, 
179 


75777.156

Available online at www.sciencedlrect.com 

" ScienceDirect Computers in 

Human Behavior 

ELSEVIER Computers in Human Behavior 24 (2008)2399··2414 

www.elsevier.com/loeatelcom phumbeh 

Population trends in Internet sports galnbling 

Debi A. LaPlante *, Anja Schumann, Richard A. LaBrie, 

Howard J. Shaffcr 

Division Oil Addictions, Cambrillge flealtlt Alliance. Han,"rd Medical School, 101 Simion Landing, Medford. 

M A 02155, United Siaies 

Available online 24 March 2008 

Abstract 

The Internet is a controversial new medium for gambling. This study presents the first longitudinal analysis of online gambling participation and activity among a population of newly subscribed 

Internet bettors. Our analyses indicate that this population of gamblers adapted to the new subscription service rapidly, as evidenced by quickly developing declines in population participation, number 

of bets, and sizc of stakes. Adaptation was not uniformly evident in our population. Among subgroups of heavily involved bettors, adaptation was generally slower or not apparent. Rather than 

adapt, involved bettors often maintained the high level of betting they escalated to in the days following SUbscription. This was particularly evident for one type of game: live-action betting. These 

involved individuals and the effect of live-action play require close scrutiny and ongoing 

examination. 

© 2008 Elsevier Ltd. All rights reserved. 

Keywords: Gambling; Public health; Internet 

1. PopUlation trends in Internet sport.s gambling 

Ten years ago, the division on addictions published the first meta-analytically derived 

estimates of lifetime and past year pathological gambling for the United States and Canada (ShalTer & Hal!, 2001; ShatTer, Hall, & Vander Bilt, 1997; ShalTer, Hall, & Vander Bilt, 

• Corresponding author. Tel.: +1 781 3068608. 

E-mail address: dcbUaplallte@hms.harvard.edu (D.A. LaPlante). 

0747-56321$ - sec front matter © 2008 Elsevier Ltd, All rights reserved. 

doi: 10.IOI6Ij.chb.2008.02.015 
180 


75777.157

2400 D.A. LaPlalile e( af./Computers ill Htlman Behavior 21 (2008) 2399-2414 

1999). Those point-in-time estimates indicated that 1-2% of the adult population met criteria for lifetime pathological gambling. Subsequent updates incorporating international 

research (Stucki & Rihs-Middel, 2007) indicate little change in these rates. Some research 

indicates that rates of gambling-related problems among scgments of the population that 

gamble are higher (Gerstein et aI., 1999); however, researchers still are narrowing in 011 the 

prevalence of pathological gambling in society at-large and among gamblers and other 

vulnerable populations (Shaffer, LaBrie, LaPlante, Nelson, & Stanton, 2004). The most 

recent estimate from a nationally representative survey in the United States (Petry, Stinson, & Grant, 2005) found lower population rates (i.e., 0.4%) oflifetime pathological gambling within the general population than those generated by the original meta-analyses; 

nevertheless, even these lower rates represent a substantial number of individuals. 

The aforementioned studies provide the prevalence of respondents who, at one pointin-timc, self-reported the number of gambling-related problems they experienced during 

certain time periods. The findings illustrate the extent of gambling and gambling-related 

problems in society. However, isolated retrospective sclf-reports are limited in terms of 

what they can tell us about changes in gambling behavior that occur over time, and, in 

response to individual or environmcntal events, such as new gambling opportunities. Studies that utilize a prospective design can provide detailed information about the intricacies 

of both internally and cxternally influenced behavior change. 

Most of the available temporally-related research presents point-in-time estimates of 

self-reported gambling and gambling-related problems before and after events considered 

likely to produce changes, such as casino openings and louery expansion (Costello, Compton, Keeler, & Angold, 2003; Govoni, Frisch, Rupcicb, & Getty, 1998; Grun & McKeigue, 

2000; Jacques & Ladouceur, 2006; Jacques, Ladouceur, & Ferland, 2000; LaBrie, Nelson 

et aI., 2007; LaPlantc & ShaJ1er, 2007; Room, Turner, & Ialomiteanll, 1999; Wallisch, 

1996). The information provided by these studies is mixed; some studies indicate increases 

in gambling and gambling-related problems, others indicate no change or inconsistent patterns over time (LaPlante & Shaffer, 2007). One reason the available temporally-related 

research might be inconsistent is because it relies Oil self-reported gambling behavior. 

The validity and reliability of self-reported behavior is oftcn uncertain (National Research 

Council., 1999) and different types of self-report can conflict for the same information 

(Jacques & Ladouceur, 200G; Shaffer cl aI., 2004). 

To date, there has been no research illustrating popUlation, or population segment, 

gambling trends in real time. Consequently, although the existing studies bave provided 

important information about the effect of environmental changes on individuals' perceptions of gambling and gambling-related problems, they cannot illustrate with specificity 

real time changes in actual gambling in a population. 

1.1. Presen t research 

Some forms of gambling are more amenable to the study of real time gambling behavior than others. For example, machine games, such as video lottery terminals (VLTs), can 

be equipped with player tracking software to collect real time gambling behavior over long 

periods of time. Those VLT systems with central registries can even track player behavior 

across machines and gambling locations. Similarly, the Internet provides a unique opportunity to record and explore actual gambling behavior because many companies record all 

gambling and non-gambling online activity using web-based technology. Examining actual 
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D, ii, LaPlame e/ ill, I Compulers in Hllmall Behavior 24 (2008) 2399-2414 2401 

gambling behavior avoids well-known issues related to retrospective self-reports (e.g., 

poor recall, self-presentation strategies, etc,), and provides an unbiased record of population trends in gambling activity. 

In this paper, we present the first real time longitudinal analysis of Internet gambling 

behavior among a popUlation of new subscribers to an online gambling service, which specializes in sports betting. This population is of interest for several reasons: (a) the worldwide ramifications of recent eongressionallegislation defining Internet sports gambling as 

illegal in the United States (i.e., the Unlawful Internet Gambling Enforcement Act); (b) the 

paucity of empirical research related to Internet gambling (e.g., Division Oil Addictions., 

2007; GrifTIths, 2001; falomiteanu, Adlaf, & M., 2002; LaBrie, LaPlante, Nelson, Schumann, & Shaffer, 2007; LaBrie, Shaffer, LaPlante, & Wechsler, 2003; Ladd & Petry, 

2002; Petry & Ma1Jya, 2004; Woodruff & Gregory, 2005); and (c) there are no longitudinal 

studies of actual Internet gambling activity. This study examines daily records of actual 

Internet gambling activities prospectively observed over a period of 18 months. 

1.2. Hypotheses 

Popular opinions hold th.at Internet gambling is a particularly virulent form of gambling, which will have profound adverse effects on the popUlation. People have argued that 

certain characteristics specific to Internet gambling present additional risks for developing 

gambling problems, such as the lack of control over participants, the immediacy of access, 

and the ready availability of a wide range of types of gambling opportunities (e.g., 13ulkeIcy, 1995; Federal Trade Commissioll, 2003; General Accounting Office., 2002; Griffiths, 

1999, 200}; Griffiths, Parke, Wood, & Parke, 2006; Mitka, 2001), Ifit is correct that thesc 

characteristics are especially dangerous, we would expect to observe enduring or escalating 

gambling activity over time among new Internet gambling service subscribers. 

Enduring or escalating betting trends should be especially evident for gambling propositions that might exaggerate the potentially dangerous characteristics of Internet gambling (i.e., games that promise relatively q1.1icker action at any time). For Internet sports 

gambling, live-action betting (i.e., bets made on real time propositions about outcomes 

within a sporting event), as opposed to fixed-odds betting (Le., bets made on the outcomes 

of sporting events or games), might have more chance of inducing this effect. Online gambling that does not involve sports betting (Le., casino-type games) might exceed even Iiveaction in this respect; however, the investigation of Internet casino games is beyond the 

scope of this paper and wc refer the reader to LaBrie, Kaplan, LaPlante, Nelson, and 

Shaffer (in press). 

2. Methods 

2.1. Sample 

This research cohort included all persons (N 47,603) who registered with the Internet 

belting service provider, blVin Interactive Entertainment AG (bwin), during February 2005. 

In this cohort, some players received promotional funds, but everyone in the sample 

deposited anel played with their own monies. We monitored bettors for the'next 18 calendar Illonths, until August 31, 2006. We excluded persons who did not start to gamble until 

one month before the end of the study period (i.e., started gambling after July 31, 2006) 
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2402 D.A. LaP/allle ef al.! Compulers ill Humall Behavior 24 (2008) 2399-2414 

and persons who did not wager on sports during the study period. We further excluded 

persons who evidenced having exceptional financial means, because bwin exempted this 

subgroup from company-imposed deposit limits, which could allow for non-representative 

betting behaviors (N = 6). 

The final sample for the analysis included 46,339 sports bettors, 42,590 (9\.9%) men 

and 3749 (8.1%) women who rcported ages ranging from 14 to 105 years (Mean 30.36, 

SD = 9.86) from 84 countries, with the majority (57.5%) from Germany. Most bettors 

played both fixed-odds and live-action propositions at least once (n =0 30,642; 66.1%); 

14,961 (32.3%) played fixed-odds only, and 736 (/.6%) played live-action only. 

2.2. Measures 

Gambling behavior measures included aggregates of betting activity (Le., number of 

bets and monies wagered per calendar day for both fixed-odds and live-action betting) 

for each bettor during the 18-month observation period. 

2.3. Procedures 

We conducted a secondary data analysis of an IS-month subscriber database obtained 

from bwin. We received approval from our institutional review board to conduct secondary data analyses of the available information. 

2.3.1. Analyses 

To generate population-level data, we ill'st computed the number of bets and the sum of 

monies wagered for each bettor. We did this for each day and for each consecutive 30-day 

period (Le., a prototypical "month"), starting with the individual's registration day. All 

bettors had at least IS complete 3~-day periods, regardless of when they registered during 

February. Because not everyone had a complete 19th "month" of observation, we dropped 

the final month from all analyses, leaving 18 periods of 30 consecutive days, which we refer 

to as "months" in this report. 

Next, we summed the number of bets and monies wagered over the entire sample for 

each month and each day. We further obtained the number of active persons (i.e., persons 

who had at least one bet on fixed-odds or live-action in a given month or on a given day). 

All analyses usc these sample-level sums and thus refer to betting activity during the first, 

second, third etc. month or day, as defined by registration day, rather than calendar dates. 

That is, month I is comprised of a range of 3D-day period calendar dates that depend on 

popUlation members' registration date. 

We present popUlation trends of betting activity over time for fixed-odds and liveaction in the total sample and in the subgroup of the most involved bettors (MIB). As 

empirically justified by a scree-type analysis of centile plots (LaBrie, LaPlante et aI., 

2007), we defined the subgroup of MIBs as the top 1% of the sample regarding the variables of interest (Le., number of bets and sum ofmol1ies staked). This strategy yielded four 

non-exclusive MIB groups: fixed-odds number of bets (FO-B; n = 456), ilxed-odds amount 

of stakes (FO-S; 11 = 454), live-action number of bets (LA-B; n = 310), and live-action 

amount of stakes (LA-S; n = 313). We examined correlation matrices of the gambling 

behavior measures for months and days, fIxed-odds and live-action betting, and gambling 

behavior measures and time. 
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D.A. LaPlanle el al.1 Computers ill Human Beliavior:U (2008) 2399-2414 2403 

3. Results 

3.1. Belting activity: full sample 

We examined the monthly and daily patterns for the lotal monies wagered (Le., Stake), 

total number of bets (i.e., Bets), and total number of active persons (i.e., N Valid) using the 

full sample. The various measures of belting activity within game (Le., FO and LA) all correlated at greater than .91 (Table I), indicating that patterns of activity on all three measures were very similar. Fixed-odds and live-action betting in the full sample also 

correlated with each other for the various measures of belling activity (Table 2), indicating 

that patterns of fixed-odds and live-action activity were similar over time. For the full sample, correlations indicated decreasing gambling activity and participation over time, for 

both months and days (Table 3). Graphical illustration of betting activities over time 

shows that the greatest betting activities of the sample occurred during the very first 

Table I 

Correlation matrices for fixed-odds anti live-action behavior in the full 

Stake Bets Nvalid 

Fixed-odds 

Stake 1.00 0.96 0.97 

!lets 0.99 1.00 0.99 

Nvalid 0.99 0.98 1.00 

Live~ac[iol1 

Stake 1.00 0.93 0.91 

Bets 0.94 1.00 0.99 

N valid 0.91 0.98 1.00 

Values above the diagonals are days and values below the diagonals are months. For all correlations, p < .001. 

Table 2 

Correlations between fixed-odds and live-action 

Months 

For all correiations. p < .001. 

Table 3 

Stake 

0.93 

0.78 

Correlations of fixed-odds ancl live-action 

Fixed-odds 

Months 

Days 

Live-action 

Months 

Stake 

--0.65" 

-0.94 

-o.n" 

-0.73 

behavior with time 

Unless otherwise indicated. for all correlations, p < .00 I. 

• P < .05. 

H p< .01. 

0.97 

0.88 

-0.65" 

-0.96 

-0.59' 

-0.S3 

Nvalid 

0.99 

0.97 

-0.58' 

-0.95 

-0.53' 

-0.89 
184 


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Fig" 1 l'Oumhcr (]f bets and Sum of Makes on fiXl'tl-odds atld livc·l,lction by month and ,J<ty for the rull sample 
185 


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D.A. LaPiaille el aI, J Compulers ill Humall Behavior 24 (2008) 2399-2414 2405 

month, followed by a sharp decrease in betting activities during the first 6 months (Fig. I), 

Thereafter, the activity fluctuated around a slightly increased level. 

We examined the daily patterns of gambling behavior during the first 90 days to determine if the monthly analyses obscured any short-term trends. In general, the daily patterns 

confinned that the highest betting activity of the sample occurred almost immediately, followed by a short increase for Bets (i.e., about one week), and then broader decreases in 

betting activity. All gambling behavior measures for fixed-odds and live-action betting 

showed the highest activity no later than on the eighth day. The daily patterns revealed 

periodic increases in betting activity about every seven days. This pattern was most evident 

for bets. The declines in betting activity were greater for fixed-odds than for live-action 

betting. 

3,2, Belli/Ig activily: most involved bellors (MIBs) 

We analyzed the patterns of betting activity (i.e., Stake and Bets) and participation (i.e., 

N Valid) separately for the four groups of MIlls: fixed-odds number of bets (FO-B); fixedodds amount of stakes (FO-S); live-action number of bets (LA-B); and live-action amount 

of stakes (LA-S). As with the full sample, the vast majority of measures of betting activity 

within game (i.e., FO or LA) correlated with each other; however, unlike the full sample, 

the range of correlations for live-action betting was greater among the MIB groups (Table 

4). In contrast with the full sample analyses, some correlations in the MIB analyses were 

quite small. The smallest correlation (I' .09) was belween the number of active participants and the daily sums of live-action stakes over 18 months for the FO-S NUBs. Hence, 

for the complete sample the number of aClive bettors was nearly synonymous with the 

other measures. Interestingly, when we look at the MIB groups, we do not find this dominance of N Valid. 

Compared to the full sample, betting activity on the two games over time was less consistent for the MIB groups (Table 5). Nevertheless, for most groups and variables FO N 

Valid rates and LA N Valid rates correlated over time. Hence, although MIB groups 

tended to be active on FO and LA on the same days/months, betting activity on these 

types of games was not necessarily synchronous (Le., 10 of 16 correlations indicated no 

significant relationship between betting activities). 

We observed declines in 170 betting across the 18 months study period for aU MIB 

groups (Table 6). However, the trends in LA betting across the 18 months were less consistent, and often increasing. For example, the FO-B group exhibited increases in LA 

Stakes and Bets over the course of the study period. Similarly, the FO-S and LA-B groups 

showed increases in LA Stakes during the same period. One exception was a decrease in 

LA Bets for the LA-S group. 

During the first 90 days, FO betting activity also was inconsistent among MIRs; and, as 

in the analyses across months, we observed substantial increases in betting activity for a 

number of variables. Most notable are the increases in Bets and Stakes for the LA-B 

and LA-S groups, respectively. Finally, although the llumber of active players over time 

decreased for monthly and daily analyses in the full sample, the number of active MIB 

players did not change over time in a consistent way. Monthly and daily patterns of 

live-action and fixed-odds betting often conflicted for the MIB. For example, the FO-D 

group experienced signiflcant decreases in FO participation across the study period, but 

there was no temporal relationship with LA participation during that time. Conversely, 
186 


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2406 D.A. LaPlante el al. / Compulers ill Human Behavior 24 (2008) 2399·-2414 

this group evidenced no relationship between FO participation and time during the first 90 

days, but showed a significant increase in LA participation during this period. 

The patterns in the subgroups of the MIBs were markedly different from the full sample 

and each other (Figs. 2 and 3). For the two MIB Bets groups (i.e., FO-B & LA-B), compared to the generally decreasing monthly trends of fixed-odds activity, which included 

broad fluctuations, the monthly patterns of live-action betting were relatively flat or 

increasing. A closer examination revealed increasing trends in FO Bets for both groups 

Table 4 

Correlatioll matrices for fixed·odds and live· action behavior in the most involved bettor 

Stake Bets N valid 

FIXED-ODDS 

1'0-8 

Stake 1.00 0.76 0,66 

Bets 0.87 1.00 0.75 

Nvalid 0.g8 0,89 1.00 

FO-S 

Stake 1.00 0.79 0.78 

Bets 0.82 1.00 0.69 

Nvalid 0.85 0.92 1.00 

LA-B 

Stake 1.00 0.62 0.59 

Bets 0.82 l.OO 0.55 

N valid 0.80 0,9) 1.00 

LA-S 

Stake 1.00 0.40 0.47 

Bets 0.88 1.00 0.50 

N valid 0,83 0.94 1.00 

L1VE-ACTrON 

FO·B 

Stake 1.00 0.33'* 0.29" 

Bets 0.78 1.00 0.48 

N valid 0.42n8 0.64" 1.00 

FO-S 

Stake 1.00 0.19 0.46 

Bets OAS' 1.00 0.50 

N valid 0,09115 0.70 1.00 

LA-B 

Stake 1.00 0.82 0.80 

BETS 0.80 1.00 0.88 

N valid 0.74 0,87 1.00 

LA-S 

Stake 1.00 0,62 0,76 

Bets 0.61" 1.00 0.62 

Nv.lid 0.55' 0.91 1.00 -----.--------- Values above the diagonals are days and values below the diagonals are months. Unless otherwise indicated, ror 

all correlations. p < .001. llS = !lot significant . 

• p< .05 . 

.. ,,< .01. 
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D.A. LaPlante et al.I Computers ill HUII1{JIJ Belwuior 24 (2008) 2399-2414 2407 

Table .5 

Correlations between fixed·oclds and live-action time in the most involved bettor 

Stake Bets 

FO-B 

Months -0.02n' 0.31115 0.82 

Days 0.32" 0.03n5 0.50 

FO-S 

Months -0.41 0.56' 0.87 

Days 0,48 0.0805 0.25' 

LA-B 

Months -0.34115 0.35n5 0.35115 

Days O.lOns 0.23' 0.38 

LA-S 

Months -0.09n5 0.65" 0.75 

0.02ns -·0.12115 -·O.07ns 

Unless otherwise indicated, for all correlations, p < ,001. ns = not significant. 

• p < .05 . 

• , p < .01. 

Table 6 

Correlations of fixed-odds and live-action beh"vior with time 

Stake Bets Nvalid ------- FIXED-ODDS 

FO-S 

Months -0.51' -0.50' 

Days 0.1805 0,47 

FO-S 

Months -0.12" -0.65" 

Days ·-0.15n5 0.04ns 

LA-S 

Months ···()6Y" 0.68" 

Days -O.Olns 0.22' 

LA-S 

Months -0.76 ·-0.85 

Days -0.22' --0.29" 

LlVE·ACTION 

FO-B 

Months 0.66" 0.49' 

Days 0.37 -O.Olns 

FO-S 

Months 0.53' O.ISos 

Days 0.36 -O.03I1S 

LA·B 

Months 0,48' 0.20n, 

Days 0.86 0.84 

LA-S 

Months 0.04ns -0.55' 

0.76 0.53 

Unless otherwise indicated, for all correlations, <.001. ns:::;: not significant. 

• l' < .05. 

" p< .01. 

-0.54' 

0.09ns 

-0.77 

-0041 

-0.6S" 

-O.Olns 

-0.92 

-0,46 

-O.Olns 

0.38 

-0.43n, 

0.29" 

0.38ns 

0.86 

-0.54' 
188 


75777.165

1 

l ... r------------------------··-·---·--···-·-· I 

Fig,l Number o(bct~ on fixclh')(\ds and hvC-,l[:tian by month and d .... y for Ihc most involVl.'d oclton on Bets 
189 


75777.166

1\ 

I \ 

\ 

\"'~" , .•..• /y " .. ' ..... .' \ \./' \-.-.~ 

-r- '-.:.' ~ 

\ .- .. ,. " " " " ,~ .. " " 

i;' 

1 

, 
190 


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2410 D,A, LaP/anle el al. J Comp"Iers ill Human Behavior 24 (2008) 2399-2414 

during approximately the first 30 days, followed by leveling and indications of decreasing 

activity. In contrast, whereas LA Bets were unchanged over time for the FO-B group, LA 

Bets increased steadily during the first 90 days for the LA-B group. 

For the two MIB Stakes groups (i.e., FO-S & LA-S), the monthly trends for Stakes 

were essentially the same as for Bets among the Bets groups. However, trends during 

the first 90 days were occasionally dissimilar. For example, among the FO-S group, there 

was a much more dramatic decrease in 1'0 Stakes following an initial increase during 

approximately the first 30 days yielding an inverse V-shaped trend. In addition, rather 

than staying relatively flat, during this time LA Stakes increased for the FO·S group. 

Trends for the LA-S groups and LA-B groups on Stakes and Bets, respectively, were 

similar. 

4. Diseussiou 

This study presents the first longitudinal analysis of real time Internet sports gambling behavior for a large sample of newly subscribed gamblers. Patterns of gambling 

behavior in our population were consistent for activity (Le., number of bets and size 

of stakes) and for games (i.e., FO and LA). There were, however, notable magnitude 

differences over time for games: the number of bets people made for FO always 

cxceeded those for LA, and by the third month of the study period, the amollnt of 

stakes placed on LA bets always exceeded those for FO. In other words, the population 

cumulatively made fewer, but larger bets Oil LA compared to FO, Any changes in population-level observations over time might be attributable to subscriber attrition in our 

population. 

In the introduction, we speculated that LA betting might be riskier than FO betting, 

These results lend some support to this speculation because the population made fewer, 

but larger LA wagers. Alternatively, the results might only reflect the better odds and 

smaller cost of gambling on LA games (nearly 50/50), People might be wagering more 

on LA games because they have a greater statistical likelihood of winning. It is worth noting that earlier research indicated that fewer subscribers participated in LA than FO betting (LaBrie, LaPlante et aI., 2007); so, the small number of people involved in LA betting 

was likely to be responsible for these higher rates. 

We observed decreasing trends of gambling behavior over time. This was true for 

monthly and daily analyses. Actual participation (Le" N Valid) also declined llniformly 

and most obviollsly during the first 90 days. Consequently, we did not find evidence to 

support concerns that Internet gambling will overwhelm populations of gamblers, callSing escalating rates of participation, or even sustained rates of participation. Rather, our 

daily analyses of gambling activity indicated rapid adaptatiol1 to the new service, as 

illustrated by a short-term increase in activity, peaking by the eighth day of activity 

and rapidly declining thereafter. This pattern is consistent with prototypical adaptation 

curves for populations (LaPlante & ShafTer, 2()07), The intra-curve peaks in activity 

occurred every seventh day. This pattern might reflect fan-based betting (i.e" weekly 

games); similar to the overall pattern for gambling, this weekly cycle also degrades over 

time aftcr an initial increase in interest. Both LA and FO gambling seemed to reach a 

fairly stable lower level, armmd which popUlation activity fl\lctuated. Longer term analyses will be necessary to provide important information about the stability of these initial findings. 
191 


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D.A. LaP/anle et al.1 Computers in Human Behavior 24 (200B) 2399-2414 2411 

It is important to note that the rapid adaptation observed among this population of 

gamblers might only generalize to people who already gamble. It is likely that the people 

who are new subscribers to an Internet service already have some gambling experience in 

their lifetime; though, there likely will be some exceptions to this rule. On the other 

hand, the novelty of Internet gambling is more likely the rule, and even those people 

who gambled previously might not have gambled exactly this way and this conveniently. 

Additional research on different populations is required to determine whether the 

observed patterns are specific to gamblers, or might generalize to people who are newly 

exposed to gambling and/or Internet gambling. Research on gambling exposure effects 

(LaPlante & Shaffer, 2007; ShafTer, Vander Bilt, & Hall, 1999; Volberg, 2002) suggest 

that newly exposed people might differ, by taking a longer time to adapt, from people 

who already gamble. 

4.1. Gambling Involvemelll and gambling behavior over lime 

We identified and examined four groups of involved bettors: groups of individuals who 

were in the top 1% of behavioral distributions for FO Bets, LA Bets, FO Stake, and LA 

Stake. Previous analyses indicated that the cumulative gambling behavior of such illvolved 

bettors is distinct from the population as a Whole (LaBrie, LaPlante et ai., 2007). Our 10llgitudinal analyses were consistent with these analyses. For example, whereas measures of 

betting activity correlated with each other within the full sample, relatiollships among 

these measures were weaker and less consistent for betting activity in the MID groups. This 

was particularly evident for LA betting, indicating more independent betting activity. This 

observation is consistent with a greater focus and attention to the type of game of choice 

and betting activity among MlB groups. 

Compared to the full population, we also observed greater variation in individuals' 

activities across game types. Whereas stakes and bets were very similar for FO and LA 

activity in the full sample, in the MID groups, betting activity on one type of game was 

relatively independent of activity on the other game. This was apparent even though 

MIDs, regardless of group, tended to be active or inactive on FO and LA betting at the 

same time. These findings suggest that individuals who were members of MIB subgroups 

were not necessarily extreme in all of their betting activity on both games. Unlike in the 

full population, for MIB groups, elevated activity on one game did not spill over to 

another game. 

The MID groups also were distinct from the full popUlation in terms of adaptation. 

That is, we did not always observe evidence of rapid adaptation in the MIB groups. 

Rather, three of the MID groups showed escalating patterns of LA gambling behavior 

during the first 90 days of activity. LA betting was relatively flat during this period for 

the FO-B group. This contrasts sharply with population analyses, which reveal relatively 

sharp declines for both FO and LA activity stal·ting by the eighth day of activity, at the 

latest. Hence, for very involved bettors, participation in LA gambling became increasingly 

attractive after their initial foray monthly analyses indicated that these MIBs maintained 

their interest for the study duration. In contrast, FO behavior did not confirm an escalating trend, but more closely approximated patterns of adaptation, with longer term 

increases in activity evident, followed by flattening and/or decline. Adaptation, however, 

was not rapid as in the full sample, and this trend only seemed to emerge after 45 days or 

so. 
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2412 D.A. LaPlan!e ot al.I Compliters in Humall Behavior 24 (2008) 2399··2414 

4.2. Limitations 

Although the findings presented here provide a unique and seminal description of 

actual Internet sports gambling behavior, some limitations are worth discussing. For 

example, we cannot determine whether the individuals who subscribed to the betting 

service subscribed and/or participated in multiple online gambling activities. It is possible that subscribers offset the decreases in gambling activities on this service by going 

to another service. If this is the case, this study would underestimate our participants' 

overall Internet gambling activity. We also cannot determine whether multiple individuals used the same gambling service suhscription account. If so, thc total number of 

people contributing to sums of bets and stakes would increase. Other information in 

our database is difficult to verify. For example, the age range for our study included 

agcs 14-105. Although it is certainly possible for individuals of thcse ages to subscribe 

and bet, it is also possible that some of the age outliers indicate inaccurate self-reporting by subscribers. 

Individuals who comprised our MIB groups could belong to mUltiple MIB groups. 

Future research should ine1ude in depth analyses of overlap among MIB groups to isolate 

individuals who fall in the extreme ends of distributions and determine whether their patterns of gambling are distinct from those presented here. Our findings do not specifically 

address issues related to gambling-related problems. Consequently, the clinical relevance 

of our findings is not yet known. However, from a public health perspective, the analyses 

presented here provide researchers, policy-makers, and public health interests with important information ahout how Internet gambling can influence gambling behavior. Additional studies are necessary to uncover both the implications for psychopathology and 

other tangible consequences of gambling-related problems, such as ruined finances and 

social relationships. 

4.3. Concluding thoughts 

Although there has been much speculation about the naturc of Internet gambling and 

its potential efTect on gamblers, to date, research on this topic has been rcstricted to gamblers' self-reported gambling. At the population-level, this research challenges common 

assumptions that Internet gambling will stimulate excessive patterns of gambling. This 

study revealed that new Intcrnet gamhling subscribers tended to adapt fairly quickly to 

betting using the service. However, for individuals who are very involved in Internet gambling (i.e., MIBs), there might still be cause for concern. Adaptation was not uniformly 

apparent in the population, particularly for one type of game - LA belting. Heavily 

involved individuals and LA betting warrant closer attention. 

Acknowledgement 

The company bwin Interactive Entertainment AG provided primary support for this 

study. The Division on Addictions also receivcs support from the National Center for 

Responsible Gaming, National Institute on Alcohol and Alcohol Abuse, National Institute of Mental Health, National Institute on Drug Abuse, the Massachusetts Council 

on Compulsive Gambling, the Nevada Department of Public .Health, the Massachusetts 

Family Institute, and others. The authors extend special thanks to Christine Reilly, 
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DA LaPlante et al. I Computers inHuman Behavior 14 (2008) 1399-·2414 2413 

Chrissy Thurmond, Sarah Nelson, and Allyson Peller for their support and work all this 

project. The authors share responsibility for the content of this paper. 

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DOl 10,1007/sI0899·008·9106·8 

ORIGINAL PAPER 

Real Limits in the Virtual World: Self· Limiting Behavior 

of Internet Gamblers 

Sarah E. Nelson' Debl A. LaPlante' Allyson .T. Pellcr • Anja Schumann' 

Richard A. LaBrie' Howard .T. Shaffer 

© Springer Science+Busine." Media, LtC 2008 

Abstract The recent expansion of Internet gambling has stimulated debate" policy, and 

research on this relatively new phenomenon llnd its potential consequences, The cun'enl 

study focllses on bettors cxperiencing problems by sampling Internet gamblers who 

imposed limits on the amount they were allowed to deposit to a betting site. We analyzed 

the betting transactions over 18 months of all gamblers who subscribed to all online betting 

site in February, 2005 (N"" 47.134), 567 of whom utilized the site's self-limit feature. 

Self-limiting gamblers played a wider variety of games and placed more bets than others 

prior to imposing limits. After imposing limits, self-limiters reduced their activity, but did 

not reduce the amount they wagered per bet. Time spent gambling, not just money spent, 

appears to be an impOIt<lnt indicator of gambling problems, Sel f-limit programs appear to 

be promising options for Internet gamblers at-risk for gambling problems, 

Keywords Gambling' Inleme! gambling· Gambling problems· Self limits· 

Harm reduction 

Introduction 

The advent and expansion of Inlemet gambling during the past decade has caused COllsiderable controversy among policymakcrs (e.g., Richte! 2(04), advocatcs (c.g., No More 

Gambling 2005), and researchers alike (e.g., Smeaton and Grifllths 2(04), Uscr anonymity, 

increased access to gambling, and lack of regulation of online betting services raise 

S. E, Nelson 0, A. LaPlante· R. A. LaBrie· H, L Shaffer 

Harvard Medical Schnol, Boston. MA, USA 

S. E. Nelson (l8l) . D. A. LaPlante· A. J. Peller' R. A. LaBrie· H. j, Shaffer 

Division 011 Addictions. Cambridge Health Alliance. !O! Station Landing. 2nd Floor, Medford. 

MA 02155, USA 

e-maiI: snelson@hms.harvard,edu 

A, Schumann 

Bremen Institute for Prevem;on Research and Social Medicine, Bremen, Gemlany 

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suspicions that Internet gambling might facilitate the development of gambling-related 

problems (Griffiths 2003). Given the number of poor mental health o~ltcomes associated 

with disordered gambling, the potential that Internet gambling can lead to gambling 

problems is a significant public health concern (Petry 2(06), 

Research on Internet Gambling 

Specu lation about the risks of Internet gambling is abundant, but there is little consenSllS 

about the prevalence of Internet gambling (estimates range from 0.2% in the UK to 36.5% 

among Detroit casino-goers; American Gaming Association 2006; Griffiths 200 I; Ialomiteallu and Adlaf 2002; LaBrie et al. 2003; Ladd and Petry 2002; Meerkerk et aL 2006; 

Petry 2006; Petry and Mallya 2004; Weltc et 81. 2002; Woodruff and Gregory 2(05). 

Further, there is very little research about the prevalencc of disordered Internet gambling. 

Three published studies (Ladd and Petry 2002; Petry 2006; Petry and Mallya 2004), all 

using convenience samples. have investigated the relationship between Internet gambling 

and gambling problems. Onc of these studies found that among a sample of people seeking 

free or reduced-cost treatment at a health care center, participants who reported Intemet 

gambling endorsed more gambling problems according to the South Oaks Gambling 

Screen (SOGS: Lesieur and Blume 1987) than other gamblers (Ladd and Petry 2002); 

another found that, among a similar sample of people seeking health care, disordered 

gamblers (i.e., those endorsing 5+ criteria on the SOGS) were morc likely to report 

Internet gambling than other gamblers (Petry 2006); the last found no relationship between 

!rIternet gambling and SOGS scores among health centcr employees (Petry and Mal!ya 

20(4). All of these studies relied on self-reported gambling behaviof, 

The first empirical study of actual Internet gambling behavior (LaBrie et al. 2007a) 

examincd the betting behavior during eight months of more than 40,000 online gamblers 

who subscribed consecutivl~ly to an Internet betting service, The study found that [nternet 

sports gamblers typically made a few small bets every four or five days, and that those who 

bet the most were not necessarily the bettors who lost the most. Only a few bettors "(i.e., 

approximately I % for each variable) deviated from this basic pattern, 1he authors concluded that detecting problem gamblers might require knowing more than their typical 

gambling behavior; unusual patterns of play and changes in behavior could contribute to 

improving identification. A consequent study confifmed this suspicion; among the same 

sample of Internet betting service subscribers, most subscribers adapted their behavior by 

reducing their participation, hets, and bet size, but heavily involved bettors failed to adapt, 

instead maintaining a bigh level of involvement (LaPlante et al. 2(08), 

One way to identify people for whom gambling has become problematic, hoth on land 

and online, is to study people who seek treatment or employ self-hclp strategics fOf their 

gambling behavior (LaBrie et al. 2007b). Self-exclusion and self-limit programs employed 

by casinos are two examples of self-help programs whose enrollees likely have problems 

with gambling. 

Self-Exclusion and Self-Limit Programs: Land-Based Casinos 

Self-e)(c1usion and self-limit programs have become popular tools for casinos attempting to 

provide responsible gaming services to their patrons. Self-exclusion programs allow 

patrons to ban themselves from casinos (see Napolitano 2003; NowatzlJ and Williams 

2(02), requesting that these casinos do not allow them on the premises or accept their 

money and, in some cases, that their trespass result in criminal prosecution {e.g., the 

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Missouri Voluntary Exclusion Program: LaBrie et al. 2007b; Nower and Blaszczynski 

2006), Sclf-limit programs, cnforced by casinos, allow patrons to impose limits on certain 

gambling-related activities (e,g., the ability to cash checks or obtain credit at a given 

casino: American Gaming Association 20m). Though self~exclusion and self-limit programs are not equivalent and likely attract different clientele, both serve gamblers who are 

seeking help to regulate their gambling behavior. 

Research about self-exclusion programs has demonstrated that, not surprisingly, the 

majority of people who utilize the service meet criteria for having clinically significant 

problems with gambling (Ladouceur et aL 2000), and that self-exclusions, to some extent, 

increase with closer gambling proximity and greater availability (LaBrie et al. 200711), 

These findings support the concept that self-exclusion is a good indicator that gambling 

problems arc present among those seeking exclusion, 

Self-limit programs, which might be considered a harm reduction technique, likely 

attract people with gambling problems who wish to rcgulate beuer, but not necessarily 

stop, their gambling. However, prior to this report, there has been no published empirical 

research about self-limit programs or their enrollees, 

In addition to serving as potentially useful markers of samples witb gambling problems, 

self-exclusion and self-limit programs can be evaluated to assess their effectiveness in 

reducing or eliminating gambling problems, As stated above, to date, no empirical research 

has been conducted on self-limit programs. The one available longirut/IMI study of casino 

self-exclusion found that participants, interviewed 6-21\ months after self-excluding, were 

generally satisfied with tbe program and reported a reduction in gambling problems after 

joining the program (Ladouceur et a1. 2007), 

Self-Exclusion and Sell~Limit Programs: ApplicHtions to Internet Gambling 

Until now, no research has examined how seH~e)(clusion and self-limit tools might extend 

to online gambling. As part of a research collaboration with the Division on Addictions, the 

online betting company, bWz'1l Interactive Entertainment, AG (bwin) implemented a selflimit program. At the lime of this study, bwill had a default limit on deposits €5,000 in a 

30 day period and € I ,000 in a 24-h period, ThrOllgh the bwil! self-limit program, subscribers can impose lower limits on the amount they are allowed to deposit in a given 

month; the company computer system then enforces these limits, Our interest in this 

potentially at-risk population segment (i,e" subscribers who impose self-limits) rests on the 

assumption that self-imposition of limits, similar to enrollment in self-exclusion and selflimit programs in land-based casinos, could be an indicator of potential disordered gambling. Subscribers who impose limits on their online gambling accounts likely recognize 

that they are, or perhaps have been in the past, (a) capable of gambling more tban they 

intend, (b) not able to control their gambling involvement without help, andlor (c) at-risk 

for excessive gambling. Givcn the possibility that this population segment has experienced 

these or other gambling concerns or problems, examining their gambling behavior prior to 

initiating self-limits might provide information about how disordered gambling manifests 

alnong online gamblers. In turn, examining how that behavior cbanges after adopting lower 

limits will mcasure the effectiveness of a self-limit strategy, 

Current Study 

The current study investigates the prospective longitudinal gambling behavior of bwill 

subseribers who elected to self-limit their gambling expenditures, The database for tbis 

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J Gambl Scud 

study consists of the daily aggregate of individuals' betting transactions and avoids the 

inaccuracies incumbent in self-report. Based on the assumptions listed above, we 

hypothesized that subscribers who imposed self-limits would be more heavily involved 

than other subscribers in Internet gambling prior to self-limiting their gambling behaviors, 

and that their gambling behaviors would improve (i.e .. decreased stakes, bets, and frequency of betting) after the imposition of limits. We also explored how self-limitation 

related to type of bets placed (Le., fixed-odds or live-action sport betting, poker, or other 

games). 

Method 

Participants 

Participants included 47,603 Internet gamblers who subscribed to bwin during February 

2005 and placed bets on thaI site between February 2005 and September 2006: We 

excluded participants who had not placed a bet by August 1st, 2006 10 ensure at least a 

month of exposure after active betting behavior began. This reduced the sample to 47,478 

subscribers. Five hundred and ninety-three of those subscribers imposed self-limits on their 

accounts between the beginning of November 2005-when bwin implemented their selflimit policy-and the end of March 2006. This sampling time period allowed us to measure 

these participants' gambling behavior for at least six months after they had elected selflimit~. We excluded participants who placed limits 011 their accounts between April 1st 

2006 and August 31st 2006, as well as participants who placed self-limits on their accounts 

that were the same or higher than bwill-imposed limits. resulting in a final sample of 47, 134 

[567 self-limiters (1.2%) and 46,567 other subscribers (98.8%)J. 

Though the vast majority of bwin subscribers engage primarily in sports betting, subscribers also can engage in other activities, such as casino games or poker. Within thc 

sample of 47,134, there were 12,121 subscribers (25.7%) who played games on the site in 

addition to or instead of placing fixed-odds and/or live-action wagers on sport events. 

Procedures 

We obtained from bwill dc-identified datasets of all transactions made on their site over the 

18 month study period by individuals who subscribed to the betting service during February, 2005. We obtained approval from the Cambridge Health Alliance· Institutional 

Review Board to conduct secondary data analyses on these datasets. 

Measures 

The daily aggregate betting database provided by bwill includes information necessary to 

create variables measuring belting behavior. These include days from first to last bet within 

the 18 month time period of the study (i.e., duration), percents of days on which a bet was 

placed within that duration (I.e., frequency), number of bets placed per day (i,e., bets/day), 

average bet size (i.e., stakes/bet), stakes wagered (Le., total wagered), stakes wagered 

minus winnings (i.e" net loss), und net loss divided by amount wagered (i.e., % loss). The 

database also provides information about demographics and types of games played. In this 

paper. we focus on (WO forms of sports gambling·--fixed-odds, and live-action--as well as 

betting on poker and other games (e.g., casino, lottery), Fixed-odds betting refers to the 

~ Sprillger 
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J Gambl Stud 

more familiar type of sports wager in which players bet on the outcome of future athletic 

events, Live-action bets can be placed in real time on propositions posed by bwill while the 

sporting event is in progress (e,g" who will score the next goal). Fixed-odds bets are 

relatively slow-cycling betting propositions. The bets can be made well in advance of the 

event and the outcome of bets made just before the event begins may not be known for 

hours. Live-action sports betting provides many relatively quick-paced betting propositions, For both types of bets the players accept the payoff offered at the time of the bet. The 

other games available at bwill include casino games, SlIpertoto, soft games, lottery, Hash 

casino, and poker, 

Analysis Plan 

We first aggregated daily betting behavior and derived measures for self-limiting subscribers before and after they first imposed limits, Because the time periods of pre-limit 

and post-limit betting varied for each self-limiting subscriber and were not directly 

comparable to the betting time periods of subscribers who did not utilize self-limits, we 

focused our analysis on betting behaviors that were averages (e.g" bets/day) or proportions 

(e.g., % loss) rather than sums (e.g., total number of bets), We also created variables that 

controlled for duration, as we describe in more detail later. 

We conducted descriptive analyses of the characteristics of self-limiting subscribers 

compared to the rest of the sample, as well as the limits they imposed, Next, we compared 

the pre-limit behavior of self-limiting subscribers to the rest of the sample, We then 

examined the change in the betting behavior of self-limiters after they imposed limits. We 

conducted all of these analyses separately for participants' live-action and fixed-odds 

betting, as well as their betting 011 poker and other games (i.e., casino, supenoto, softgames, lottery, and flash games combined), We also conducted analyses examining the 

change in betting behavior of self-limiting subscribers by their preferred game, defined as 

the gallle on which they wagered the most money. Subscribers who did not have pre-limit 

data for a given game were excluded from comparisons to the rest of the sample, Subscribers who did not have both pre-limit and post-limit data for a given game were 

excluded from pre-post analyses. To adjust for the number of comparisons (i.e" approximately 60 primary comparisons), we utilized a Bonferroni correction reSUlting in an alpha 

level for each test of p := .0009, which we rounded to p < ,001, to obtain a study-wide 

alpha level of p < ,05. 

Results 

Five hundred and sixty·seven subscribers to bwin (1,2% of the fiilal sample) chose to 

impose self·limits between November 1st, 2005 and March 31st, 2006. Self-limits, which 

subscribers imposed on the amount they were allowed to deposit within a 30 day period, 

ranged from €9.27 to €4, 177,55, Approximately seven percent (7,1 %) of self-limiters (SLs) 

placed limits on their accounts prior to engaging in any betting, and 10,6% ceased all 

betting after imposing self·limits. 

Four hundred and ninety-eight (87,8%) of SLs made no further changes to their selfimposed limits during the course of the study (i,e" by the beginning of September, 2006). 

Fifty-two SLs (9.2%) changed their self-limit once, 6 (1.1%) changed their limits twice, 

and II (l ,9%) changed their limits three or more times. SLs who changed their limits only 

once tended to decrease the amount they were allowed to deposit (41 of 52,78.8%); those 

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200 


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J Gambl Stud 

who changed their limit more than once tended to fluctuate (16 of 17 fluctuated; the other 

increased steadily). 

Self-Limiter Demographics 

SLs came from 20 different countries; lhe five most prevalent countdes of residence were 

Germany (61.2%), Turkey (7.2%), Poland (6.5'70), France (6.3%), and Spain (3.4%). These 

were also the live most prevalent countries of residence in the rest of the sample (Le., 

non-SLs), Compared to non-SLs, SLs we,re slightly younger eM = 29.3, compared to 

M = 30.4, 1[47132] 2,53, p < .05), and slightly more likely to be male, (95,9% compared to 91.7%, l[ll:= 13.30, p < .01), though these findings did not reach significance 

alp < .001. 

Self-Limiter Game Choices 

All but five SLs placed fixed-odds bets (99.1%); this proportion was only slightly higher 

than that of non-SLS (96,0%, /[1] = 14.1, p < .001). However, 81.7% of SLs placed 

live-action bets, compared to only 65,8% of other subscribers, l(1) = 63.0, p < .00l. 

More than 30% (31.4%) of SLs played other games online at bwill, compared to 25,6% of 

other subscribers, /(1):::: 9.7,p < .01. Figure I illustrates the pattern of games played hy 

SLs and non-SLs. On average, SLs played 2,5 types of games (including fixed-odds and 

live-action betting), compared to an average of 2.1 games for non·S1.$, (47132) :::: 9.57, 

p < ,001. 

Self-Limiter Pre-Limit Gambling Behnvior 

Five hundred and twenty-seven SLs (92.9%) placed bets prior to imposing limits on their 

play, The average duration from first bet to self-imposition of limits for these 527 was 

213 days (median = 247 days), For comparison, the study duration was 549-577 days, 

100% 

90% 

80% 

70% 

'" c: 

'>, 60% 

~ 

0- 50% C 

.. 

~ 40(1/0 .. 

0-

30% .. -----.-.-----'-'-~---.-.-,---------I 

20% 

10%-

Fixed Odds Llv. Action Cnslno Supertolo So/19amo. Lottery FI.sh Peker 

Fig. I Game choice of SLs and non·SLs, SL '" self-limiter; nO!l-SL non-self-limiter (i.e_, res! of the 

sample) 

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J Gambl Stud 

depending on date of enrollment, and the average duration from first to last bet in the entire 

sample was 299 days (median = 350 days), 

We first compared SLs' behavior to that of non·SLs for all sports gambling, and then 

compared their behavior to that of non-S[~~ for fixed-odds and live-action betting separately. We also compared SL's and nOll-SL's behavior for poker and for other games (i.e., 

casino, softgames, supertoto, flash, and lottery). 

For combined betting on both fixed-odds and live-action propositions, prior to imposing 

self-limits, SLs bet on more days within their interval of betting and placed more bets per 

d,IY than non-SLs. However, they wagered less money per bet. Their total wagered and net 

loss were not significantly different from non-SLs (M =: 3224,98 vs, M == 2724.81, 

r[45960) "" 0,94, P > ,05, and M == 362,98 vs. M = 253.78, 1[45960J = 2,15, p> .01, 

respectively), even though they did not have the full range of possible betting days 

afforded the rest of the sample (i.e" their duration could only entail the time from registration to limit-setting, whereas non-SLs' duration entailed the time from registration nnti! 

either account close or the end of the study), To correct for this, we created two variables, 

net loss divided by duration and amollnt wagered divided by duration, On these variable.s, 

SLs and non-SLs did not differ signillcantly. Finally, SLs' % loss was similar to that of 

non-SLs. Table I summarizes these findings, 

Sep;\rate comparisons of fixed-odds and live-action betting revealed similar patterns. 

Almost everyone placed fixed-odds bets (SLs = 98,7%; llon-SLs :::: 96,0%) and fixed-odds 

betting mirrored the pattern of significant differences observed for total sports wagering; 

SLs bet more frequently and made more but smaller bcts per day than non-SLs, Prior to 

placing their limits, live-action betting was more popular among SLs (75.3%) tban nonSLs (65.8%), X2

(1) == 21.2, p < ,001. For live-action betting, SLs also placed more bets 

per day but wagered less money per bet than non-SLs. However, the live-action frequency 

did not differ signitlcantly between SLs and non-SLs (see Table l). 

SLs who played poker prior to placing limits (5% of SLs) did not differ significantly on 

any of the available pokcr variables from non-SLs who played poker (8% of non-SLs). The 

22% of SLs (prior to placing limits) and 22% of 1l0n-SLs who played other games (i.e., 

casino, supertoto, Hash, lottery, andlor softgames) did not differ significantly on any 

variables related to those otber games except the amount wagered per bet. Non-SLs 

wagered more per bet on other games than SLs (see Table 1 ).' 

Though the comparisons between SLs and lIon-SLs detected .ltatistically reliable 

differences, the effect sizes, presented in Table I, indicate that all of these differences were 

smalL 

Se.lf-Limiter Post-Limit Gambling Behavior 

Five hundred and seven SLs (89.4%) continued to bet after imposing limits on their play. 

We first compared SLs' post-limit behavior to their pre-limit behavior for combined sports 

gambling, and then compared their bebavior for fixed-odds and live-action betting, as well 

I Previous analysis of this sample (LaGrie er al. 20(7) empirically establbhed that the top I % of the sample 

on certain variahles exhibited behavior that wa' extreme compared to the rest of the sample, Based on that 

Rnding, we repeated the comparisons between SLs and non-SLs presented in Table I excluding non-SL. 

whose bets rer day, stakes per bet, total wagered, or net loss placed them in the 101' 1% of the sample. 

(Frequency and % loss did not exhibit the same discontinuous distribution.) This resulted in !,4 10 nou-SLs 

being excluded. These comparisons revealed a pattern of difference., identical to the pattern presented in the 

Table with the following exception: for live-Rction belting and hetting on other games, CurDS per bet were no 

longer significantly different between SLs and non-SLs. 

~ Springer 

75777.179

10 

"<> '" ::1, 

" 0"< 

Table 1 Gambling behavior of SLs before imposing limjtS, compared to the rest of the sample 

Variable All sports betting M(SO) Fixed-odds betting M($D) Live-action bening M(SO) Poker' M(SO) Otber games M(SD) 

SL Nnn-SL SL NQn-SL '1' SL Non-SL SL Non-SL SL Non-SL 

'-limit (II = 45,439) pre-limit (n = 44,7(5) pre-limit (n = 30,626) pre-limit (n = 3,703) pre-limit (n = 10,657) 

= 522) (n = 520) (n = 397) (n = 30) (n _ 121) 

Frequency 33.42' 18.05' 0.0004 32.62' 26.60' 0.0004 34.12 33.04 <O.OO()() - 43,26 42.55 <0.0

129.72) (29.89) (29.32) (34.38) (37.57) (41.20) (41.71) 

Bets/day 6.72' 4.76' 0.0010 4.69- 3.70-= 0.0004 5.45' 3.97' 0.0009 - J9.93 20.04 <0.0

(6.74) (5.00) (5,21) (4.0ll (5.44) (4.10) 09.(6) 1.11.08) 

Stakes./bet 6.57' IHJO* 0.0025 6.15' 11.46' 0.0027 7,73' I LOS' 0.0006 - 14.01' 31.44>10 O,O

(Euro,) (lU2) (30.08) 00.51) (30.85) (15,28) (24,82) (4l.67) (171.14) 

Wagered! 16.90 16.06 <0.0000 7.00 8.30 0.0002 17.40 16.55 <0.0000 23.50 41.50 0.0025 159.29 l7Ul <0,0

duration (55.81) (73.1l) (14,86) f35.61) (59.29) (S1.0S) (26.58) (20:.61) (l093.40) 0184.23) 

I\ctlos-si 3.00 3.75 0.0001 2.09 3.07 0.0001 1.71 2.33 0.0001 7.82 7.56 <0.0000 7.02 11.69 0.0

duration (9.93) (24.71) (8.39) (23.19) (7.00) (20.24) 115.77) (S8,90) (36.84) (62.79) 

% 0.25 0.29 0.0001 0.28 0.31 <0.0000 0,19 0.23 0.0001 0.30 0.26 0.0002 0,20 0.20 <O,I)

(0.37) (0.49) 10.49) (0.55) (0.43) (0.59) (0.30) {D.6Ol (0.28) (0.45) 

Note: SL self-limiter; nOIl-SL = rest of the sample. Frequency = percent of days within interval from first to last bet (or first bet to limit imposition for pre-limit behav

of SLs) on which a beE was placed; Wagered = toral amQunt Net loss = sum of wagers minus sum of winnings; % Loss:::::: net loss/amount wagered; Du

tion interval from first to last bel Bonferror:i correction resulted in an alpha criterion of .001 for significance 

• Significant difference between SLs and non-SLs, p < .001 

• Information for poker limited to aggre,gate wager and winning amounts 
203 


75777.180

J Gambl Stud 

as poker and other games separately. Finally, we considered SLs' pre- and post-limit 

betting behavior on their preferred game, defined as the game on which they wagered the 

most money prior to imposing limits. 

Generally, SLs' behavior after imposing self-limits moved in the direction of fewer bets 

and less money het. As Table 2 shows, overall, SLs significantly reduced their number of 

sports bcts per day aftcr imposing self-limits. Amollnl wagered on sports bets, controlling 

for duration, also deereased. Frequency of betting days, amollnt wagered per bet, net loss, 

and % loss did not change for overall SP0l1S betting. 

Table 2 also summarizes separate comparisons of fixed-odds and live-action betting, as 

well as poker and other games. Fixed-odds betting demonstrated a similar pattern of 

change to that found for overall sports betting. For fi xed-odds betting, SLs reduced the 

frequency of days on which they bet, placed fewer bets per day, and reduced their total 

amount wagered, contrOlling for duration, after imposing limits. For live-action belting, 

poker, and other games, SLs who continued to play did not significantly change their 

behavior after imposing limits; this might be due, particularly for poker and other games, to 

the limited number of cases in these subsamples. 

As measured by the amount they wagered 011 each game, the majority of SLs preferred 

fixed-odds (64.1 %) or live-action (22.4%) betting prior to imposing limits. Less than 2% 

(1.9%) preferred poker, and 9.9% preferred other games. Analysis of post-limit changes in 

betting behavior by preferred game did not reveal any unique trends. SLs significantly 

reduced the number of bets they placed per day on their preferred game after imposing 

limits. Their frequency of betting, and the overall amount they wagered also decreased, but 

these decreases did not reach significance (.00 I < p < .0 I). SLs did not alter the size of 

their bets and their net loss and % loss did not change (see Table 3). 

Self-Limiter Post-Limit Strategics: Abstinence vs. Harm Reduction 

Some differences between fixed-odds, live-action, and other forms of betting behavior 

before and after self-limits possibly reflect different player strategies to stop or limit play 

on these different types of betting. The previous analyses examined behavior only from 

individuals who continued to engage in each type of betting after imposing limits. To 

address this issue, we compared the proportion of SLs who initiated and ceased different 

types of betting before and after imposing limits (see Fig. 2), and also investigated whether 

SLs' preferred game changed after imposing limits (see TabJe 4). 

SLs' likelihood to stop belling on a given game after imposing limits differed significantly by game (/0) = 22.2, P < .00]). More SLs stopped placing bets on live-action 

after imposing limits (20.9%) than stopped placing bets on fixed-odds (13.8%). SLs who 

played poker or other games were even more likely to stop play on those games after 

imposing limits-23.3% and 31.4%, respectively. Figure 2 displays pre- and post-limit 

play for fixed-odds, live-action, poker, and other games. The percentages in the figure 

differ from percentages presented above because the percentages presented above consider 

all SLs who played a given game prior to imposing limits, whereas the percentages in the 

tigure reflect all SLs who played a given game either before or after imposing limits. 

Analyses by preferred game revealed that the majority of players continued to prefer the 

same game after imposing limits, but that the proportion who stopped betting or switched 

their preferred game differed by type of game (X\16) '" 542.6, p < .001). Three quarters 

of SLs who initially prefelTed fixed-odds betting continued to prefer fixed-odds after 

imposing limits, and 11% stopped belting. However, only 64% of preferred live-action 

'fl Springer 

75777.181

<n 

5' 

"" " .., Table 2 Gambling behavior of SL, before and after imposing limits 

Variable All sports betting M(SD) Fixed-odds betting M(SD) Live-action betting M(SD) Poker' M(SD) [Il 23J Other games M(SD) [n 8

In = 461] [n = 448] [n = 314J 

SL SL SL SL SL SL post- SL SL SL SL 

pre-limit post-limit pre-limit post-limit pre-limit limit pre-limit POSI- pre-limit post-limit 

limit 

Frequency 31.45 28.88 0.0100 31.85* 25,19* 0.0340 33.79 32,26 0.0010 - 39.12 29.32 0.0

(29.20) (25.99) (29.80) (23.85) (33.66) (33.41) (39,11) (34.23) 

Bet<lday 6.88' 5.8S' 0.0350 4.82' 3,91' 0.0500 5.81 4.98 0.0300 - 19.33 13.73 0,0

(6,84) (6.87) (5.39) (4.57) (5.64) (6.24) (17.50) (11.63) 

Stake<lbet 6.54 7.41 0,0030 5.98 6.34 0,0020 8.18 8.91 0,0030 - 13.53 12.38 0.0

(Euros) (11.35) 03.29) (10.59) (10.36) (16.5) 06.45) (3057) (31.06) 

Wagered! 17,77' 11.63' 0.0100 7,10' 4.31' 0.0340 20.39 1539 0.0060 21.12 21.50 <0,0000 76.02 64.10 0.0

duration (59.03) (33.71) (15.44) (8.91) (66.18) (43,23) (25.75) (30.78) (l81.27) (227.00) 

Netloss! 3.02 2.56 0.0010 2.04 US 0.0070 2.17 3.65 0.0050 4.02 2.37 0.0880 3.66 3.86 <0.0

duration (10.24) (8.98) (8.80) (5.13) (7.38) (20,29) (5.54) (3.60) (8.62) (12.84) 

% Loss 0.23 0,25 <0,0000 0.26 0.26 <0.0000 0.19 0.23 0.0050 0,26 0.18 0.1080 0.14 0.22- 0.0

(0.36) (0.49) (0.47) (0.57) (0.40) (0045) (0.28) (0.21) (0.23) (0.27) 

Note: SL = self-limiter; non-SL = rest of the sample. Frequency = percent of days within interval from first to last bet (or firsl he! to limit imposition for pre-limit beha

of SLs) on which a bet was placed; Wagered == tOla! amount wagered; Net loss == sum of wagers minus sum of winnings; % Loss == net loss/amount wagered; D

tion = interval from first to last bel Bonferroni correction Tewlted in an alpha criterion of .001 for significance 

• Significant difference between pre-limit and post-limit. p < .001 

• Information for poker limited to aggregate wager and winning amounts 
205 


75777.182

J a.mbl Stud 

Table J Gambling behavior of SLs on prcfmed game before ami after imposing limits. (M(SD)] 

Variable 

Frequency (N = 441) 

Bets/day (N '" 441) 

Slakcsibel (Ruros) (N = 441) 

Wagered/duration (N = 452) 

Net loss/duration (N 452) 

% Loss (N = 452) 

SL pre-limit 

32.52 (30.08) 

7.79 (9.42)' 

9.20 (19.01) 

29.34 (97.79) 

3.31 (IOA7) 

0.22 (0.35) 

SL posi-limil 

27.80 (26.30) 0.0160 

6.28 (7.90* 0.0460 

9.48 (20.16) <0.0000 

18.64 (61.89) 0.0170 

3.23 (17.25) <0.0000 

0.21 (0.52) <0.0000 

Note: SL "" self-limiter; non-SL = rest of the sample. Frequency percent of days within interval from 

firsl 10 lasl uet (or first bello limit imposilion for pre-limit behavior of SL.I) on which a bel was placed; 

Wagered lotal amount wagered: Net loss ~ sum of wagers minus sum of winnings; % Loss"" net loss/ 

amount wagered; Duration = interval from first to lasl bet. N's differ for each analysis because three 

variables were nol available for SLs who preferred poker, Bonferron; correction resulted in an alpha 

cliterion of .001 for significance 

• p < .001 

90% 

80'%, 

70tl/Q 

60% 

;: 50% 8 

~ 40O/Q. Il. 

Play prior to but not afler 

limit 

No play prior to but play 

aflerllmll 

Play both baiera and aller 

limit 

Fig. 2 Play patterns of self-limiters before and after imposing limits. The y-axis refers to percent of SLs 

playing the given gallle who fell inlo the given category on the x-axis 

bettors continued to prefer live-action oftcr imposing limits-21 % switched to fixed-odds, 

and 13% stopped betting. See Table 4. 

Discussion 

Previous research with this sample of bwin subscribers has shown that, as a whole, the 

sample demonstrates relatively moderate betting behavior, as demonstrated by frequency 

of betting (i.e., less than half of available days and fewer than 4 bets per betting day) and 

amount bet (Le., <5 Euros per bet) (laBrie cl al. 2007a). The curren! study focused on a 

sub-sample who likely experienced or were at-risk for gombling problems: subscribers who 

placed limits on the amount they could deposit into their bwin betting account. Analyses of 

these subscribers' belting behavior before and after they placed self-limits cunfirmed our 

<fl Springer 
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Table 4 Preferred game.' of SLs before and after imposing self·limits 

--------.------------ Preferred game prior Preferred game after self-Jimit 

to self·1 imit None (no bets placed) Fixed-odds Live-action Poker Other games 

None (no hets placed) ()(O.O%) 24 (60.0%) 13 (32.5%) 0(0.0%) 3 (7.5%) 

Fixed·odds 37 (10.9%) 152 (74.6%) 37 (10.9%) 2 (0.6%) 10 (3.0%) 

Live~action 16 (12.6%) 27 (2l.3%) 8J (63.8%) 2 (1.6%) 1 (0.8%) 

Poker o «(}.O%) 1(10.0%) 0(0.0%) 8 (80.0%) 1 (10.0%) 

Other games 6 {I 1.5%) 8 (15.4%) 6 (11.5%) 6 (115%) 26 (50.0%) 

NOle: SL self-limiter. Preferred game = gume on which most money wagered. Percentages use number 

of SLs who played 8 given game prior to imposing self-limits as the denominator. Bold numbers indicate 

SLs who maintained stable preferences from pre- to post·limit 

hypotheses that their betting behavior was meaningfully different from that of other subscribers who did not place limits on their account deposits; these results also confirmed thaI 

SLs altered that behavior after imposing limits. In addition, these analyses allowed us to 

determine what type.~ of behaviors might be markers of risk for gambling problems and 

whether SLs' game preferences differed from the rest of the sample. 

Activity V$. Expenditure 

The overall pattern that emerged across anaJyses was that SLs were more active bettors 

than the rest of the sample across a variety of measures. They placed less money at stake 

per bet and did not lose a greater percent of their wagers than the rest of the sample, but 

they were more likely to bet on live-action in addition to fixed-odds propositions and more 

likely to play othec games at bwill. In addition, they placed more bets, and they bet on more 

of the days during which their account was active, though these differences wece small. 

These findings indicate that involvement, as measured by the time spent engaging in 

gambling behaviors might be as important a potential indicator of gambling problems as 

money wagered or lost. Indeed, previous research has shown that gambling expenditure 

and frequency are strong illdependem predictors of gambling problems (Currie et at 2006). 

Inclusion of time spent engaging in gambling as another criterion for diagnosis of disordered gambling could help clinicians and public health practitioners to identify a wider 

range of disordered gamblers in need of treatment services. People who exhibit disordered 

gambling because of time spent gambling and not due to money lost or wagered might 

represent a subtype of disordered gamblers with unique treatment needs. As with other 

expressions of addiction (e.g., substance use disorders), disordered gambling supplants 

other previously valued and important activities and relationships (e.g., time with family, 

work, hobbies, etc.) with gambling activity. Thus, over-involvement in gambling activities 

might have as much potential to destroy these relationships as money lost from gambling. 

Game Type 

As noted above, SLs were l110re likely to engage in live-action betting than the rest of the 

sample. Live-action belling is rapid-cycling and provides nearly immediate results. This 

type of betting might be riskier for some subscribers because of these characteristics, which 

allow for continued play without much reflection. III contrast to fixed-odds betting, SLs 

who continucd to play live-action after imposing limits did not significantly alter their 

f:! Springer 
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75777.184

J Gambl Stud 

belling behavior. However. it is notable that SLs who played both games were more likely 

to cease live-action play after imposing their limits than 10 cease fixed-odds play, suggesting that players considered live-action play more perilous. 

SL~ and non-SLs who played other games. a small proportion of the total sample. did 

not differ in their betting behavior 011 those games, and SLs did not significantly alter their 

betting on those games after imposing limits. However, SLs were more likely than non-SL~ 

to play these other games, again indicating level of involvement (i.e., number of type., of 

games played) as a possible risk marker for gambling problems. 

SLs' preferred game type tended to remain consistent before and after they placed selflimits. However, SLs who initially preferred live-action or betting on other games were 

more likely to either stop gambling or switch their preference to fixed-odds than other SLs. 

This might indicate that these SLs recognized that these games were more risky fOLthem 

thnn fixed-odds betting. 

Self-Limits as a Self-Help Tool? 

Like land-based casino self-limit and self-exclusion programs, bwin's self-limit option 

allows subscribers to seek help in controlling their gambling behavior by establishing 

external controls. Research about the effectiveness of self-exclusion programs is limited 

but promising (see Ladouceur et ul. 2007), and research about self-limit programs, online 

or otherwise, is non-existent. 

A limited number of bwill subscribers (approximately 1.2%) participated in the selflimit program. To date, we canllot determine whether this is because of the nature of the 

program provided by bwin, general hesitancy to self-limit online gambling behavior, the 

absence of need, or other reasons. Future research ought to in vesligate the accessibility of 

self-Jimit programs, as well as gamblers' impressions of these programs. 

The current study fouod that subscribers who imposed self-limits did reduce some of 

their gambling beha viors after imposing those limits, and did so in a way that shifted their 

behavior towaru that of the rest of the sample. Primarily, SLs reduced their frequency of 

play, both the number of days on which they placed bets and the number of bets they 

placed per betting day. The amount Ihey wagered per bel did not change significantly, 

though they did reduce the total amount they wagered. These behavioral changes again 

highlight the importance of activity level, not just money bet or lost. as a risk for gambling 

problems and as a target for change. 

More than 10% of the sample ceased all betting on bwin after imposing limits. It is 

possible that for this group, the very act of using bwin's self-limit feature influenced them 

to reconsider their gambling bella viol'. 

Limitations 

A strength of the current study is the access it provides to the real-time betting transactions 

of a large cohort of online gamblers. However, hecause the study includes only behavioral 

measures and 110 self-report measures, we do not know how satisfied subscribers were with 

the self-limit program, which behaviors they believed were problematic, and how their 

expenditure related to their income. 

Another caveat is that, though self-limiting subscribers are likely to have experienced 

gambling problems, they might not be representative of all subscribers with problems. 

Only a small minority of people with a gambling problem will actively seek help for that 

problem (Slulske 2006). Thus, we me limited in the conclusions we can draw about the 

~ Springer 
208 


75777.185

J Gambl Stud 

online gambling behavior of people with gambling problems who do not seek help. 

Similarly, some self-limiting subscribers might not be experiencing problems but instead 

be using the self-limit option to avoid potential problems. This is especially likely for the 

7% of self-limiters we found who placed limits prior to engaging in any betting activity. 

These analyses included only subscribers' bwil! betting activity, It is possible that SLs 

began or increased betting on other sites after imposing limits on their bwill betting. In 

particular, the 10.6% who ccased all 1m!/! betting might have switched their activity to 

another site. However, bwil! self-limits can be changed, so it is unlikely that subscribers 

participated in betting on other sites just to avoid their limits. 

Finally, bwill is primarily a sports betting site; cOllsequently, these analyses mainly 

foclIsed on sports bettors. The gambling behaviors and effects of a self-limit program on 

those behaviors might be very different for Internet gamblers who focus on other gnmes, 

such as casino games or poker. Future research will be necessary to clarify this issue. 

fmplications 

If the history of Internet commerce and casinos arc indicators, Internet gambling willgrow 

exponentially during the next decades. Responsihle gambling programs, similar to those 

now implemented by almost all casinos, likely will accompany that growth. bwin's selflimit program is one of the first of its kind and, as the findings from this study show, might 

be a promising option for subscribers experiencing or at-risk for gambling problems. This 

type of program appears to help subscribers mduce their betting activity (I.e., frequency of 

betting, bets per day, and total wagered) and ill some cases possibly cease their gambling 

behavior. More studies of this kind are necessary both to examine the effect of responsible 

gaming efforts on Internet gambling and to continue 10 assess the effect of casino selfexclusion and self-limit programs on patrons' behavior. 

In addition, the analyses from this study reveal that individuals who believe that they are 

having problems with gambling (Le., those who imposed self-limits) exhibit higher activity 

levels, but not necessarily higher expenditures than other bettors. This implies that, in 

cOllsidering risk, researchers and clinicians might need to pay at least as much attention to 

time spent gambling in relation to other activities as to money spent or lost. 

Acknowledgments bwill, Interactive Entertainment, AG provided primary support for this study. The 

allthors extend special thanks to Christine Reilly. Christine Thurmond. and Ziming Xuan for their support 

and work on this project. Dr. Nelson had full access to all of the data in the .'Indy and takes responsibility for 

its inlegrity and the accuracy of the data. analysis. None of these supporters or any of the authors has 

personal interests in hwin and its associated companies that would suggest R conflict of interest. 

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id Springer 
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75777.187

JOURNAL OF AMERICAN COLLEGE HEALTH, VOL 58, NO,S 

Comparisons of Gambling and Alcohol Use 

Among College Students and Noncollege Young 

People in the United States 

Grace M. Barnes, PhD; John W. Welte, PhD; Joseph H. Hoffman, MA; 

Marie-Cecile O. Tidwell, PhD 

Abstract. Objc{'tive; G(lmbling and <lkoho! use were compared 

for college and noncollege young adults in lhe US population, 

Partidpnnl.s: Participants were 1,000 respondents aged 18 to 2l. 

Methods: Dala were analyzed from a representative household sam~ 

pIe of US young people nged 14 to 21 years old. Telephone inter+ 

views were conducted between August 2005 nnd lanuary 2007. 

Results: After !i.lking into account gender, age, race!ethnidly, and 

socioeconomic s.tatus, college student status did not predict gambling, frequent gambling, or problem gambling. In coOlrast, being a 

college student was associated with higher levels of alcohol use and 

problem drinking, Being male was the strongest predictor of bOlh 

problem gambling and problem drinking. Blacks were less likeJy 

than whites to drink heav!ly; yet they were more likely than whites 

to gamble heavily. Conclusion: Young males should be targeted for 

prevention amJ inlelventlon efforts for hath problem gambling nnd 

problem drinking regardless of college student sta.tus. 

Keywords: alcohol, college slude.nrs, gambling, US population, 

young adults 

r

' he serious public health concern about binge drlnk~ 

ing among college students has received widespread 

attention. spotlighted by federal task force initiatives 

to address the culture of drinking on college campuses. l In 

addition, alcohol misuse is correlated with other addictive 

behaviors. in particulnr problem gambling; that is, these behaviors co-occur in the same individuais,2 However, much 

less research has been addressed to the issue of gambling 

among college students than alcohol use, Furthermore, it is 

not clear from college surveys jf there are factors unique to 

the college culture contributing to heavy drinking and gambling or whether the pattems of alcohol misuse and problem 

Dr Dames. Dr Welle, Mr llofftrlllll, and Dr Tidwell art: with 

the Research Institute all Addictions at the. University tit Buffalo in 

Buffalo, NfW York. 

Copyright © 2010 Taylor & Francis Group, LLC 

443 

gambling are characteristic of young adulthood more generally regardless of coJ1ege status. There have been no large 

representative surveys of gambling among college students 

as compared with their same-aged counterparts in the general 

population. In addition, there have been no US surveys comparing the patterns of alcohol use and misuse with the pattems 

of gambling and prohlem gambling for US college students 

and the same-aged noncoJlege young adults, The present in~ 

vcstigation will address this lack of comparative information 

on the prevalence of gambling and alcohol use among college 

students and similarly aged peers in the US population. 

Alcohol Use Among College Students 

In a representative sample of four~yeaI colleges in the 

United States, Wechsler and Nelson) reported that 2 in 5 

collegesilldents (44%) were binge drinkers (also called heavy 

episodic drinkers), defined as consuming at least .'5 drinks. in 

a row for men or 4 drinks in a row for women during the 

2 weeks prior to the survey. The National Survey on Drug 

Usc and Health (NSDUH), a Jarge annual household survey 

of persons 12 and older in the United States, showed that the 

rate of binge drinking (defined as consuming5 or more drinks 

on the same occasion on at Jca~t I day in the past 30 days) 

peaked at the age of 21 with a rate of 49%. then decreased 

with advancing age.4 Decaui)e this household sample also 

included college dormitories~ a comparison of same-aged 

college and noncollege young people was possible. Young 

adults aged 18 to 22 enrolled full~time in college were mOTe 

likely than their same-aged peers not enrolled full~time to 

binge drink (45,4% versus ::HL4%).4 Similarly, another large 

representative household survey of alcohol consumption in 

the United States (the National Epidemiologic Survey on 

Alcohol and Related Condit;ons [NESARCI) showed that 

episodic heavy drinking, ie, consuming 5 or more drinks foJ' 
211 


75777.188

Barnes cf al 

men and 4 or more drinks for women in a single day in 

the past year, was. higher among ftill-time college students 

(44.3%) than among part-time coUege students (30.7%) or 

noneollege young adults (37.4%).' 

Gambling Among College Students 

In a US survey of gambling among college students, 

LaBrie et al6 asked over 10,000 college students, attending the 119 colleges in the 2001 College Alcohol Study, 

questions about their gamhling behavior, Forty-two percent 

(42%) ofthe college students gambled in the last school year, 

but only 2,6% gambled weekly or morc often. Other investigators have curried out gambling surveys within selected 

colleges. An early college survey was carried out in 1995 by 

Winters and colleagues? in 2 Minnesota universities, Among 

the sample of 1,361, gamhling was common with 87% of 

the students having gambled once or more often in the past 

year and 12% reported gambling at least weekly; 4.4% re~ 

ported a South Oak, Gambling Screen (SOGS)8 score. in tho 

potential pathological range of 3 or 4 problem indicators. and 

an additional 3% of the participants scored in the probable 

pathological range (5+ on the SaGS). On 4 campuses of 

the Connecticut State University, Engwall et all) reported that 

67% of the students hJd gambled in their lifetime and 11 % 

(18% of men and 4% of women) were classified as problem 

or pathological gamblers (ie, 3+ negative consequences) us~ 

ing a shortened version of the SOGS.s 

Within the coJlege environment, inves.tigators have identified specific groups of students at high risk for problem 

gambling, Rockey and associates 10 surveyed 954 Greek- and 

non-Greck~affiliated college students who attended 9 large 

state universities in the southeastern United States, In the 

total sample, there werc no statistically significant differences in the prevalence of gambling and problem gambling 

for Greek-affiliated and non-Greek-affiliilted students, However, among males the differences were significant: Greck~ 

affiliated male students had a rate of 14,8% problem gambling as compared with a 5,4% rate among males who were 

not aftlHated with Greek organizations, suggesting the inHuence of peer pressure and an enahling environment for 

prohlem behaviors. 

Likewise, there has been concem about gambling among 

athletes on college campuses. A large national study of gambling among US college athlete, (II = 20,739) was sponsored 

by the National Collegiate Athletic A8sociation (NCAA) in 

2003 due to concerns about the integrity of intercOllegiate 

sports.!! This survey showed that 62% of the male athletes 

and 43% of the female athletes had gambled in the previous year; 4,3% of men and 0.4% of women were classified 

<1.'1 problem or pathological gamblers (ie. 3 or more criteria using the American Psychiatric Association's DSM~IV 

clllssification!2), Although this was a national college survey, nonathletes in coJleges we,re not surveyed and therefore there is no comparison group. In a study of gambling 

among student-athletes (n = 736) and a comparison cohort 

of students (n = 1,071) at 4 universities, Weinstock and col444 

leagues]} showed that student-athletes reported similar rates 

of gambling frequency and disordered gambling as .other 

students, These investigators concluded that problems associated with gambling arc a university-wide issue warranting 

improved prevention and intervention efforts On campuses;, 

Because there have been no large representative US surveys of gambling among college students as cornpared with 

thejr same~aged counterparts in the general population, it is 

not known if patterns of gambling and problem gambling 

are different for college students and the sameMaged l1oncojM 

lege young adults in the general population. Furthermore, it 

is {lot known if the predictors of gambling and [I!cohol use 

are similar or different in importari't population subgroups, 

ie, gender and racial/ethnic groups. The present invcstiga~ 

tion is the first study to our knowledge to address this lack 

of comparative information on the prevalence of gambling 

and nlcoho! among college and similarly aged peers in the 

US population, This study also will take into aCCollnt im~ 

portant socioeconomic factors--age, gender, raceJethnicity, 

socioeconomic status, and living arrangements-in comparing alcohol and gambling behaviors among young people in 

college and not in college. 

METHODS 

Data 

The present study is an analysis of 18~ to 21 ¥year-olds 

from the National Survey of Youth nnd Gambling (NSYG). 

The NSYG I::; a nationally representative household sample 

of 2,274 US young people aged 14 to 21 years, living in 

the United States. The study was designed to determine the 

prevalence of gambling behaviors (tnd gambling prohlems as 

well as alcohol and other substance use among youth in the 

United States. The study was reviewed and approved by the 

Social and Behavioral Sciences Institutional Review Board at 

the University at Buffalo. Participants. were interviewed be~ 

tween August 2005 and January 20G7 by trained interviewers 

using computerwassisted telephone interviewing. The sam· 

pie was selected using mndom~digit-djal telephone sampling 

procedures, Interviews were conducted in all 50 states and 

the Di8trict of Columbia. Sub.iects were mailed a check for 

$25 for their time participating in the study. The response 

rate based on completed interviews divided by completed 

interviews plus refusals was 71 %. Weighting ~djustments 

were used to align the sample with the age and race distri~ 

hutions from the US census for 2005. (See Welteet al!4 and 

Barnes et af2 for a detailed description of the sampling and 

interviewing procedures.) 

The present study consists of 1,000 J8~ to 2i-year.olds. 

This represents all of the 18~ to 21-year~0Ids inthe sample except for 103 cases who were excluded because they reported 

still being in high SChooL Thus this analysis is comprised 

of college students, defined as those young people 18 to 21 

years old who were currently enrolled in two-year or fouryear colleges and universities; and nOllcollege students, who 

were 18 to 21 year olds not enrolled in two~year or Four-year 

colleges. The weighted proportion of college stUdents among 

JOURNAL OF AMERICAN COllEGE HEAlTH 
212 


75777.189

Gambling and Alcohol U.5S Among Collegs Students and NoncoJlege Youllg People 

the 18- to 21-year-old. in this sample is 58% (578 -;- 1,000). 

The US Census for 2005 shows that the proportion of 18-

to 21-year-olds in degree-granting institutions is 46%,15,16 

Thus college students are well-represented in this household 

sample, 

Dependent Measures 

Gambling 

Participants were l.lsked whether or not they had ever gambled for money on each of 15 types of gamhllng, for exuUlpIe: participated in office pools1 fUflles, or charitable small 

stakes gambling; played the lottery; gambled for money on 

the Internet; played cards for money: bowled or played basketball, pool. golf, buckgammon, darts, or ::;ome other game 

of skill--{)ther than canJs~for money; played Bingo for 

money; and bet on sports events, For each type of gamhling 

ever done, the participant was thcu asked whether slhe had 

gambled on that type in the past 12 months, and if so, the 

frequcncy...---cveryday, at lea<;t once a week (if so, how nl~ny 

days per week), at least once a month (if so, how many 

days per month), at least once in the past 12 months (If so, 

how many days during the past 12 1l10nths). Thes.e items 

were previously developed by the present authors for a US 

national studyof gambling among adults.,!7 From these gam~ 

bHug behavior items, 2 meaSllres were derived for the present 

analyse,s. 

Gambling in the Past Year 

This was a dichotomous variable defined as gambling at 

least once til the past year based on the 15 types of gambljng. 

Frequent gambling was a dichotomous measure defined as 

gambling 52 or more times in the past year. This level of 

gambling is roughly equivaJent to gamhling once a week or 

more often and permits comparisons with other sumples that 

have used comparable definitions for frequent/heavy gam~ 

bling. !S,I? 

Gambling Problems 

Participants who indicated that they had gambled more 

than 5 times in their life were asked a series of que.stions 

about gamhling problems using the South Oaks Gambling 

Screen, Revised for Adolescents (SOGS-RA).'" This instrument is a modified version of the SOGS (South Oaks Gambling Screcn),8 The SOGS-RA consists of 12 items that are 

relate.d to the DSM criteria for pathological gambling. Exmnples of the items me going back another day to win back 

money you lost (chasing); te1!1ng other$ you were winning 

money when you really weren't winning; having problems 

such as arguments with family or friends, or having problems 

at school or work caused by gambling; gambling with more 

money than you had planned to; and borrowing or stealing 

money in order to bet or cover gambling debts in the past 12 

months. In the total sample ofthe 2,274, 14- to 21-year-olds 

in the National Survey of Youth and Gambling, the Cronbach 

alpha was,74, demonstrating good internal consistency reli-

-ability, 14 A dichotomous variable was used to he consistent 

VOL 58, MARCH! APRil 20 I 0 

with other studies 14 that represents aH'iskJproblem gambling 

and is defined as having 2 or more symptoms on the SOGS~ 

RA in the past 12 months< 

Alcohol Consumption 

A drink of alcohol was defined as a drink of beer, ale, 

malr liquor, wine, fortified wine, wine coolers, liquor, and 

flavored malt beverages or any other beverage containing 

alcohol. Drinkers were defined as those respondents whG 

indicated that they had a drink of any beverage containing 

alcohol in the past 12 months. A dichotomous measure of 

heavy drinking was based on whether or not respondents 

indicated that they drank 5 or more drinks in I day on 12 or 

more days in the past year, 

Problem Drinking 

Re.spondents who answered that t"hey had had a drink of 

any alcoholic beverage more than 5 times in their life were 

asked a series of 57 questions taken from the Adolescent Diagnostic Interview (AD!), Light" based on the DSM-IV cd-

{eria for alcohol abuse anddependence.11 Of the 57 questions~ 

19 questions asked about alcohol abuse symptoms in the past 

12 months, including missing schoGj or work more than once 

or twlce~ driving a car or motorcycle while drunk on :lIcohol; 

having sex when drunk on alcohol; having legal problems 

because of alcohol; having prohlems with friends or family; 

getting into physical fights; and having frequent arguments 

with your parents or other adults about your alcohol use, The 

remaining 38 items asked abollt alcohol dependence symp~ 

toms, induding tolerance (needing larger amounts of alcohol 

than previously to get drunk), withdrawal (having shakes or 

tremors of the hands tlfter stopping or clltting down on drinking), having to use alcohol to relieve or reduce hangover or 

withdrawal symptom~, and trying to reduce or control your 

alcohol usc. Cronbach alpha for the 57~jtem scale wa~.92 in 

the total NSYG sample, The pmblem drinking dichotomous 

measure was defined as having 3 or more alcohol symptoms 

in the past year. 

Predictor Measure, 

Predictor measures were coUege student status and so~ 

ciodemographic characteristics. Co17ege student status was 

coded 1 for those young people 18 and older who were 

not in high school Or college and 2 for those young peo~ 

ple 18 and older who were enrolled in two-year or fouryear colleges or universities. Gender was coded a for females, I for males, Age was measured in years (I8 to 

21), Participants were asked whether they considered themselves Spanish, Hispanic, Latino, or Chicano. They were also 

asked, "What is your race?" Response choices were White 

(or While Hispanic); Black or African American (or Black 

Hispanic): Asian; American Indian or Alaska Native; .and 

Mixecl!Unknowu. Sociaeconomic status (SES) was based OB 

the mean of 4 equally weighted factors: rnother'~ years of education, father's years of education, mother's occupational 

prestige, and father's occupationill prestige, Occupational 

445 
213 


75777.190

Barnes et oj 

prestige was coded from census occupation categories us~ 

ing the method described by Hauser and Warren,n Because 

we knew from previous studies that !l portion of youth are 

unable to supply informarion on their parents' education and 

occupntiotl, we asked a serie.s of questions (home ownership, 

number of books in the home, receipt of food stHmps, etc,) 

that were then used as predictor vnrinbles to impute edu~ 

cation or occupational prestige when these variables were 

missing, Imputation was performed by the SPSS Missing 

Value program. Socioeconomic status was. categorized into 

thirds for the present analyses. A dichotomous living arrangement variable was derived based on the question. "Are you 

currently living on your own or in the home of your parent 

or guardian?" The variable, live Independently, was coded 

o for those living with parents or guardians and I for those 

living on their own. 

RESULTS 

Table I gives the demographic profile of college s.tudents 

and noncollege students in thi,I{ US sample. There were no 

significant differences in the gender distributions for college 

and noncollege students. The remaining demogrJphic factors 

TABLE 1. Demographic Profile of College 

18 to 21 Years Old: US Youth and Gambling 

were significantly different between college and noncollege 

students (see X 2 statistics in Table 1). As compared with 

their non college counterparts. college students were some~ 

what younger; they were more likely to be white as compared 

with being classified in a minority group except Asian; college s.tudents were much more llkely to be in the highest 

SES group, College students were somewhat more likely to 

live with parents than their noncollege student counterparts. 

These demographic factors were controlled in later multivariate analyses. 

A comp,uison of gambling among college students and 

noncollege young adults revealed no significant differences 

in the overall prevalence rates or in the age- and race/ethnicspecific prevalence rates, For instance, three quarters (75%) 

of college students and 70% or the noncollege young adults 

gambled in the past year (X' = 2.8; df = 1; p '" .10) Cfable 

2}, However, with regard to frequent gambling (52+ times 

in the past year), noncollege young people had significantly 

higher rates (25%) than college students (18%) (X' = 8.7; 

df '" I; P = .003). These differences were further observed 

for females where noncollege females had twice the rate of 

frequent gambling (12%) as college females (6%)(X' '" 6.3; 

Noncoliege Young Adults, Aged 

= 1,000) 

College students eN 578) 

NoncoHcgc young adults (N = 

422) 

Gender 

Fcrunk 54% 49% 

Mate 47% 51% 

Not significam 

(x'= 1.7:d/= I) 

Age 

IB-19 years 50% 42% 

20-21 yeufs 50% 59% 

p <.01 

(X' = 7.7; <1/ = J) 

Racelctlmici{y 

White, not Hispanic 66% 56% 

Black, not Hispanic It% 17% 

Hispanic 15% 19% 

Asian 7% 2% 

American lndi«n 1% J% 

Mixed, Unknown 1% J% 

P < .001 

(X' ~ 33.4(,1{ = 5) 

Socioeconomic status 

Low third SES 20% 48% 

Middle third SES 36% 33% 

High third SES 45% 19% 

P < ,001 

(X' = 112.5;<1/ = 2) 

Living arrangements 

Live with parents 77% 71% 

Live independently 21% 29% 

P <.05 

(X' 5.0;df" 1) 

446 JOURNAL OF AMERICAN COllEGE HEALTH 

75777.191

v. 

,w 

n 

I 

};: 

;;: 

;; 

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o 

'" '" " 

TABLE 2. Gambling, Alcohol Use, Frequent/Heavy Use, and Related Problems (Past Year) for Coliege Students (N = 578) and Noncollege Young Adult

(N = 422) Aged 18 to 21 Years Old in the United States 

At-risk/problem 

gambling (2+ 

Drank 5+ drinks on 12 symptoms on Problem driJlking 

Gambled Drank alcohol Gambled 52+ times or more days SOGS·RA) symptoms) 

College Noncollege NoncoHege NOllc

Sociodemogr'dphic stu- young CoHegt:- Noncollege College- Noncollege College young College young College you

group (n) dents adults students young adults students young adults students adult.,> students adults srudentS adu

Total sample 75% 70%NS 76% 18% 30% 27%NS 6% 9%NS 27% 19%

Fem(lle 67% 62%NS 72% 6% 12%'" 20% 17%NS 2% 5%" 21% 12%

Male 83% 79%N5 80% 70%'" 31% 38%NS 42% 36%NS 11% 14%N5 35% 

18-19 years 76% 71%NS 70% 18% 24%NS 27% 23%NS 7% 10%,\'5 27% 19

20-21 years 73% 69%N5 81% 65%"-H 18% 26%' 33% 29%NS 5% 8%NS 27% 20

White, not Hi.spank 79% 73%NS 82% 72%U 17% 23%" 37°1" 33%NS 6% lO%N5 31% 23Black, nOt Hispanic 67% 67%NS 53% 43%NS 26% 32%NS 11% 13%N5 7% 11%NS 21% 16%Hispanic 71% 72%NS 73% 55%' 18% 22%NS 23% 25%NS 2% 8%NS 26% 17%

.05; "p 5. .OJ; .001. 
215 


75777.192

Barnes al oJ 

df = 1; p = .012). There were no significant differences 

in rates of frequent gambling for conege and none allege 

males (X' = 2,5; df = I; p = .113); indeed both groups of 

males had high rates, 31 % and 38%, respectively, Older (20-

to 21~year~0Id) noncollege young people had significantly 

more frequent gambling than their college counterparts (26% 

versus 18%; X' 5,7; 'If = 1; p = ,017), White noncollege 

young people bad significantly more frequent gambling (52+ 

times in tbe past year) than their white colJege counterparts 

(23% versus 17%; X'=4.1;df= l;p= ,04),Comparisons 

of problem gambling for college and noncoHege young adults 

revealed only one significnnt difference-col1cge females. 

had n 2% rate of problem gambling compared with a 5% rate 

for females not attending college (X' = 4.5; df = J; p = ,03), 

The comparisons of alcohol use for college students and 

noncollege young adults are different than the comparisons 

for gambling. NOllcollege young people had lower overall 

rates of drinking alcohol (61%) than their colJege student 

counterparts (76%) (X 2 = 22.7; df I; p = ,000), NoncolJege young people were also less likely to be problem 

drinkers (19%) than college students (27%) (X' = 8,6; 

df = I;p = ,003), These differences held for both males and 

females, However, the rates of heavy drinking (5+ drinks 

on 12 more days during the past year) were not significantly 

different for the 2 groups (X' = 1.3: 'If = I; p = ,25), 

Because of popular notions that particular forms of 

gambling, such as sports betting and Internet gambling. 

may be more prevalent for college students as compared 

with noncollege young people, Trlble 3 shows the rank 

order for various forms of gambling for college students~ 

noncollege young adults, and for each gender. In the overall 

ranked comparisons, the top 5 types of gambling arc the 

same for college <;Ind noncolJege young people. According 

to frequency of occurrence, these forms arc lottery, card 

games, office pools/raffteslcharitahle small stakes gambling, 

spons betting, and games of skill. Intel11et gambling has 

the lowest frequency of participation of any of the forms 

listed (3% for college students and I % for noncollege young 

people). There are striking gender differences in frequency 

of participation in various forms of gambling regardless of 

college student status. Although males play most forms of 

gambling mOre often than females, some forms of gambling, 

such as sports betting and games of skills (eg, bowling, 

basketball, pool) arc 3 or more times more common among 

males than females. Bingo is the fourth most prevalent form 

of gambling for both college and nOllcollcge females. 

To uddre..<;s the question as (0 whether or not coUege 

student sHttus predicts gambling and alcohol behaviors, we 

carried out a series of logistic regressions-first with college 

student status entered alone and then with college student 

status and demographic controls entered together (Table 4). 

College ~tudent status did not predict overall gambling or 

problem gambling in the analysis with college student status 

entered alone or when college student status was entered 

with all of the demographic controls. When college student 

status was entered alone, college students had a Significantly 

lower odds of frequent gambling, ie, 52+ limes in the past 

448 

year; however, once the demographic controls were entered, 

college status was no longer significant in predicting frequent 

gambling, Thus, being a college student or not did not have 

an efteet on gambling behavior, Male gender is the variable 

with the largest effect on gambling regardless of college 

student status--with males having approximately 5 times 

the odds of heing a frequent gambler or problem gambler as 

females. 

The effects of college student status on alcohol use and 

problem drinking showed a different pattern from thut of 

gambling. College students had 1.9~tjmes the odds of being 

a drinker as compared with young people not in college and 

college students had a significantly increased odds (1.6) of 

being a problem drinker than their noncollege counterparts. 

This finding was observed when college student status was 

entered alone or when it was considered with all ofthe control 

variables. Male gender greatly increased the probability of 

drinking, htavy drinking, and problem drinking-consistent 

with the significant effect of male gender on the increased 

probJ:lbility of gambling, frequent gambling, and problem 

gambJing. Other demographic factors showed different 

relationships to alcohol and gambling variables. Increased 

age in this sample of J8 to 21 ycar olds significantly 

predicted increased drinking and heavy drinking but did 

not have any effect on gambling behaviors. Being black 

decreased the odds of drinking and heavy drinking by 70%, 

respectively, yet being black increased the odds of frequent 

gambling by 60%. Being Hispanic was not related to either 

alcohol or gambling bchaviurs. Higher socioeconomic status 

predicted a somewhat increased probability of drinking 

and heavy drinking, whereas higher socioeconomic status 

lowered the odds of prOblem gambling, Living independently was not related to any gambling vari4lbles; on the 

other hand, young people who lived independently from 

their parents or guardians had 1.7 {hues the odds of being 

a problem drinker as those who lived with their parents or 

guardians. 

COMMENT 

There has been widespread public concern over high~risk 

.addictive behaviors among coUege students, especially binge 

drinking and, to a lesser extent, gambling. Since the 1990s, 

there have been federal task forces and numerous college 

initiatives to address the concern of college binge drinking; 

yet the question remains if rates of problem behaviors sueh as 

excessive drinking and gambling among college students arc 

high due to specific aspects of tbe college expcrience-~-such 

as increased freedom from parental supervision and increased 

peer influences-or if these problems arc. associated with the 

age group of young adulthood more generally conceived, re~ 

gardless of current college status. If these high-risk behaviors 

are age related regardless of college 1)tatus, then prevention 

and intervention effons must necessarily be targeted beyond 

the college environment. 

This ill [ile first US national study to Ollr knowledge to 

compare both gambling and alcohol use behaviors among 

JOURNAL OF AMERICAN COLLEGE HEALTH 

75777.193

Q 

u. 

P" 

r. :I: 

"-

;; 

o '" 

o 

"" " <> 

TABLE 3. Percent Participating in Various Types of Gambling (Past Year) (Listed by Frequency of Occurrence Among College Students): College 

Students and Noncollege Young Adults Aged 18 to 21 Years Old in the United States (N = 1,000) 

CoHege students Noncol1ege young adults 

Female Male Overall Female Male Overall 

Type of gambling (n 309) (n = 269) (n = 578) (n 208) (n=214) (n 412) 

Lottery 41% 45% 43% 45% 49% 47% 

Card games 21% 59% 38% 18% 46% 32% 

Office pools, raffles. charitable small stakes gambling 31% 45% 38% 16% 38% 27% 

Spons betting 13% 35% 23% 11% 36% 23% 

Games of skill---eg, bowling, basketball, pool, golf. backgammon. 11% 31% 20% 7% 36% 22% 

darts 

Casino gambling 10% 2J% 15% 8% 17% 13% 

Bingo 16% 10% 13% 14% 16% 15% 

Slot machines, pohr machines and other gambling machines 10% U% 10% 6% 13% 10% 

Dice games 3% 13% 8% 4% 19% 11% Pull tabs 6% 10% 8% 6% 10% 8% 

Bet on horses, dogs or olber animals 3% 12% 7% 3% 10% 7% 

Video keno----eg. Quick Draw or Quick Ca<;h 3% 5% 4% 4% 10% 7% 

Trading cards-such as sports cards for the resale of the insen cards 0% 6% 3% 1% 3% 2% 

Internet gambling 0% 7% 3% 1% 2% 1% 
217 


75777.194

Barnes.et af 

TABLE 4. Logistic Regressions Predicting Gambling, Alcohol Use, FrequentfHeavy Use and Related Problems 

(Past Year) for College Students and Noncollege Young Adults 18 to 21 Year. Old In the United States (n = 1,000) 

_._-_ Dichotomous dependent variable ...... _-_ ... __ ...... _-_ ...... _-------- Problem 

At-risk or problem drinking 

gambling (2+ (1+ 

Drank Gambled 521- Drank 5· fo. drinks symptoms on symp" 

Gambled alcohol timeSo/past ye~lr on 12 o. more days SOGS·RA) toms) 

Predictor OR" OR" OR" 

College student entered 1.3"-.1 [.g .... 0.6" 

alone 

College Student entered with demographic controls 

College student ! .3M 1.6>- 0.7" Male 2.4~n 1.8 .... 5.5'" 

Age (years) I.CY" 1.4'" 1.1'" 

mack 0.711s OJ'" 1.6' 

Hispanic O.91ls O.8 M 0.9'11 

SiloS I.()'" 1.2'" 

Live independently 1.3l1t 1.1'1.1 

"OR,=:: odds ratio. 

'1' <.05. 

up <.01. 

~"p <.001. 

0.9 11 •• 

0.1'" 

college students as well as other similarly aged young ;:tdults 

in the general population. The scope of this study per~ 

mits us to determine if college students, as compared with 

other young adults, are at high risk for problem gambling 

as they have been shown to be regarding heavy aJcohol 

usc. 

Consistent with findings from the few other national surveys of alcohol use that includeJ both college and non college 

young adults.4.5. this study shows that the rates of alcohol use 

and problem drinking are significantly higher for college 

students than for similarly aged nOllcoHege young adults. 

However, the rates of heavy drinking, onen referred to as 

binge drinking, were not significnntly different for college 

and non college young people in this study. The lack of dif~ 

fercnces between the 2 groups should not detract from the 

extent of the problem of alcohol misuse for both college and 

noncollege adults. In particular, over a third of the maJe.."! in 

both groups were classified as heavy drinkers. that is, they 

drank 5 or more drinks a day on 12 or more days in the past 

year. 

Monitoring the Future survey investigaton;2] followed re* 

spondents from their US national secondary school survey 

I to 4 years past high school and found that college stu~ 

dents had a significantly higher prevalence of heavy drinking 

(ie, 5 or !liore drinks in a row in the past 2 weeks) than 

their same-age peers (40% versus 35%). Interestingly, in 

high schoo!, the college-bound studcnts drank less than their 

non--collcge-bound peers, yet the alcohol consumption of 

450 

OR" OR" OR" 

1.2~$ 0.6'" 1.6" 

1.1'" 0.8" l.8'" 

3.1'" 4.6'" 2,Y" 

U'" 0.9~ 1.1"' 

0.3'" 1.0'!'! 0.7" 

0.8'" 0,)"' 0.&111 

!.I' 0.8' 0.9'" t,4l1$ 1.3"'\ 1.1" 

coIlege-bound students was greater than that of their noncollege peers Juring the college years. Thus, from past research 

and the present study. there does appear to be evidence of 

a college context factor that contributes to more drinking in 

college than would be explained by the young adult age alone. 

As in other national surveys.4.S heavy drinking in the pres.ent 

study is much more prevalent among white young people 

than among black young people regardless of college student 

status. 

Using lhis sample'· and other general popUlation sam· 

pJes,I&.19 we have s.hown that alcohol misuse and gambling 

problems co~occur within a problem behavior syndrome. 

Furthermore. young males have a higher co-occurrence -of 

alcohol problems and gambling problems (r = .42) than do 

females (r .21)2 

The patterns of gambling for -college nnd noncollege 

young people show some differences from the paHems of 

alcohol use. Whereas rates of overall alcohol usc and prob~ 

!em drinking were higher for college students than for noncollege adults, the rates of frequent gambling were highcr for 

noncoJlcge young adults than for college students, pMticu~ 

larly for females. There were no significant differences in the 

prevalence of overnll gambling or problem gambling for the 

2 groups. However, college student status was no longer significant in predicting frequent gambling once demographic 

factors were entered. Being male and being black were the 

important fuctors in increasing the risk for frequent gambling. Being male and having lower SES increased the risk 

JOURNAL OF AMERICAN COLLEGE HEALTH 
218 


75777.195

Gambling and Alcohol Use Among Coffege SIvdent5 and Noncotlege Young People 

for problem gambling (that is, having 2 or more symptoms 

on the SOGS-RA). These findings that males, blacks, and 

tower SES persons are at higher risk than others for problern 

gambling Ilre consistent with findings from our national US 

.surveys of adults.24 Males may have more problems associated with gambling due, to the fact that they engage in more 

forms of gambling more frequently than females. ft bas been 

speculated that blacks and lower SES persons may be more 

likely than others to sec gambling as a form of investment 

and u possible escape from poverty.24 

Thus, in this study, being a conege student docs not put 

a young person at added risk for gambling, frequent gambling, or problem gambling, In addition, the top 5 most popular fonns of gambling are the same for college males and 

noncollege males; these fonns are lottery. card games. pool 

and raffles, sports betting, and games of skill. Regllrdless of 

college status, the most popular forms -of gambling for fe~ 

males are lottery, card games, pools and raffles, and bingo, 

It is also interesting that Internet gambling is a low preva~ 

lenee form of gambling for college and noncollege young 

people. Findings from this study support the position that 

gambling and problem gambling are in nuenced by broad 

sociodemographic factors, especially gender and race, and 

not by coJlege factors per se. Therefore, prevention efforts 

must be targeted broadly across young adulthood regardless 

of college status. Because there are no other compamble na~ 

tiona! studies of gambling inclUding college and noncoHegc 

young people. these findings remain to be replicated by future 

studies. 

limitation, 

This sample was one of household telephone numbers, and 

therefore cel1 phone numbers were not intentionally included 

in the sample. Nonetheless, some cell phone numbers became 

a part of the sample because phone numbers from land~line 

excbanges may be pot1ed,to ceil phones, find some telephone 

exchanges (often in less populated areas) contain both !and~ 

line and cell numhers. Estimates from the National Health 

Interview Survey for 2005-200625 show that between 7.6% 

and 8.6% of US households with children had only wireless 

telephone service or no telephone service. Weighting can 

reduce. potential bias created by not including celJ-phonc~ 

only users in a household sample; this was done in the present 

study as described above. 

Conclusions 

College student status significantly increases the odds that 

a young person will drink alcohol and experience problems 

associated with alcohol use. However, college student status 

does not appear to put young people at added risk of gam~ 

bling or gambling problems. The most important and consistent risk factor for both alcohol and gambling behaviors 

is being a young male whether in college or noncoHcge settings. Age~ and gender~t.argcted prevention and intervention 

strategies are warranted to reduce the seriolls consequences 

VOL 58, MARCH/APRil 2010 

of alcohol usc and gambling alnong young adult males in the 

US popUlation. 

NOTE 

For comments and further infonnation, address corrcspon~ 

dence to Grace M. Bames, PhD, Research Institute on Addictions, University at Duffalo, lO21 Main Street, Buffalo, 

NY 14203, USA (.-mail: barnes@ria,huffalo,edu), 

REFERENCES 

!. Nati{)nal Institute on Alcohol Abuse nnd Alcoholism, US 

Deparlment of Health and Human Services, National fnstitutes of 

Health. What Colleges Need 10 KnolV Now. Bethesda. MD: National 

Institute on Alcohol Abuse rind Alcoholism; November 2007. NIH 

Publication No. 07·5010. 

2. Barnes GM, Welte JW, Hoffman JH, Tidwell M-C. Gambling, ,,!cohol and other substance use among youth in the U.S, J 

Stud Alcohol Drugs. 2009; 70:134-t42, 

3. Wechsler H. Nelson TF. What we have Ic-amed from (he 

Harvard School of Public Health College Alcohol Siudy: focusing 

attention on college student alcohol consumption and the envimn" 

mental conditions that promote it. J Stud Alcohol Drugs. 2008; 

69:48t-49O, 

4. Substance Abuse and Mental Health Services Adminislfil 

lion. Results from {lIe 2006 National Swvey 011 Drug Use !HId 

Health: National Findings. Rockville. MD:Substance Abuse and 

Mental Health Services Administration, 2007. Office of Applied 

Studies, NSDUH Series H·J2. DHHS Publication No. SMA 07-

4293, 

5. Chen eM, Dufour Me, Yi H. Alcohol consumption among 

young adults ages 18-24 in the United States: results from 

the 200!-2002 NESARC Survey. Alcohol Rese(lrch & Health. 

200412005; 4:269-280, 

6. LaBrie RA, Shaffer Hr, LaPlante DA, Wechsler H. Correlates of college student gambling the United Statts_ J Am Coli 

flealtlz. 2003; 52:53-62, 

7. Winters KC, Bengston r, Dorr D, Stinchfield R. Prevalence 

and risk [actors or problem gambling among college students. Psy· 

choIAddicrBf!hav.1998; 12:127-135. 

8. Lesieur HR, Dlume 5B. The South Onks Gambling Screen 

(SOOS): a new Instrument for the identification of pathological 

gamblers. AmJ Psychiatry. 1987; 144: 1184""1188. 

9. Engwall D, Hunter R, Steinberg M. Gambling and other 

risk: behaviors on university campuses. J Am Cofl Health. 2004; 

52:245-255, 

10. Rockey DL. Beason KR. HOWington HB, Rockey eM, 

GilbertJD. Ga.mbling by Greek-aHiliated college students: an association between affiliation and g.amhling. J Coli Student Dev. 2005; 

46:75 .. 87. 

II, HuangJH, Jacobs DF', Derevensky JL, Gupta R, Paskus TS, 

A national study on gambling among US college student-athletes. 

.I Am Call flealth, 2007; 56:93-99, 

! 2. American Psychiatric Association. DSM·lv'· Diagnostic and 

Suuistical Manual of Mental Disorders, Fourth Edition. Washington, DC:Arnerican Psychiatric Association; 1994. 

1 J. Weinstock J, Whelan IP, Meyers A W, Watson JM. Gambling 

behavior of studcnt·athletes and a student cohort: what are the odds? 

J Comb{ Stud, 2007; 23:13-24. 

14. Welte JW, Barnes GM, Tidwe!J M-C, Hoffman nI. The 

prevalence of problem gambling among U.S. adolescents and 

young adults: results from a nMional survey. J GfllIlbl Stud. 2008; 

24:tt9~D3, 

15. US Census Bureau. Currenl Population Survey, 2005 Annual 

Social and Economic Supplement. Internet release date: October 

26, 2006, Available at: http://www.ccnsus.gov/population/www/ 

socdemo/education/cps2005.htm1. Accessed Pebruary 17, 2010. 

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219 


75777.196

Barnes et 01 

16. US Department of Education, National Center for Edll~ 

cation Statistics. 2005 Integrated Posfsu'ondm), Edm;:atir:m Dora 

System. Wllshington, DC:lJS Department of Education; Spring 

2006, 

17. WelteJW, Rarnes OM, Wieczorek, WF, Tidwell M-C, Hoff. 

man JH. Type of gambling and availability as risk factors for problem gambling: a lobit regression analysis by age and gender. Inl 

Gambf Stud. 2007; 7:183-198, 

18. Welte fW. Barnes GM, Hoffman JH. Gambling. substance 

usc and other problem behaviors among youth: a test of genera! 

deviance models,.J Crlm Jlwice. 2004; 32:297-306. 

J9. Dames OM, We!te IW, Hoffman JH, DintcheffBA. Effects 

of alcohol misuse on gambling patterns in youth, J Stud Alcohol; 

2002; 63;767-775, 

20. Winters KC. Stinchfield RD. Fulkerson ]. Toward the 

development of an adolescent g'.J.rnbling problem severity s!;ulc. ] 

Gmllbi Sflld, 1993; 9;63-84, 

21. Winters KC. Henley GA, Adolescenl Diagnostic Interview 

Schedule (/nd Manual. Los Angeles:Wcstem Psychological Ser 

vices; 199), 

22, Hauser R, Warren JR, Socioeconomic indexes for occu 

pat ions: a review, update. and critique. Sodal Methodol. 1997; 

27: 177-298. 

23. Johnston LD, O'Milney PM, Bachman JG, Schulenberg 

JE, Monitoring the Future National Survey ResuJrs on Dmg 

Use, 1975"-2006: Volume j, Secondary ScholJl Students, Bethesda, 

MD:NationaJ Institute on Drug Abuse; 2007, NIH Publication No. 

07·6205, 

24, Welte, J\V, Barnes, GM, Wieczorek, WF. Tidwell, M~CO, 

Pllrker, IC, Risk factors for pathological gambling, Addict Rehav. 

2004; 29:323-335, 

25, Centers for Disease Control and Prevention (CDC). 

Cigarette smoking among adults- United States, 2006, MMWR Mort 

Mm·b Wkly Rep. 2007; 56;1157-1161, 

®©®®mBCla 

American College Health Association National College Health Assessment 

The Prem ier Student Health Survey 

Having current, relevon! dolo aoou! your sludents' 

health con only help you to enhance compuswlde 

heolih promotion nnd prevention ,ervices. The 

American College Heolth Assacialion's Nalioool 

College HeolJh Assessment IACHANCHAj - 0 

nolfondly recognized research survey -- con ossisl 

you in coll8cling precise dala oboul your studenlS' 

habits, behaviors, and perceplions on Ihe wide,;! 

fange of heolth issues: 

" Alcohol, lobocco, and other drug use 

• Sexual health 

• Weight, nlltrilian, and exercise 

" Mental heollh 

• Personal sorety and violence 

• Impooimenls to academic performance 

~" American College Health Association 

',:'. 'Vj~it,~.~~R~:~~~~.~r9 ~t.,def~ii~\i~fomiatjon ~b6ut th~ survey Cllla h'Ow' ':' , ,'; , , to'patticipqte;·Or/.~<:flll ,the:~G':IA.·,NCAA. Prpgrqm ,Offl~e ~t (.410) 8~9·1500. " " ,14~"""'" , .. '',-",' 1 if.:, i,1:.;;.1~1;t·J~,\~"-,·/.ti."~.i:\';"~"t\,';~1.)~.,,.. ',t\ l~" • i,)', ,,~. d. j '" '''. ~",,,,,~l. 

452 JOURNAL Of AMERICAN COLLEGE HEALTH 
220 


75777.197

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221 


75777.198

J Gambl Stud 

DOl I 0.1007/s j0899-007-9086-0 

'ORIGINAL PAPBR 

The Prevalence of I)roblem Gambling Among U.S. 

Adolescents and Young Adults: Results from a National 

Survey 

John W. Welte' Grace M. Barnes' Marie-Cecile O. Tidwell • 

Joseph H. Hoffman 

© Springer Science+BlIsiness Media. LLC 2007 

Abstract A random telephone survey was conducted with a representative sample of 

2,274 U.S. residents aged 14-21. The prevalence of problem gambling. as measured by the 

SOGS-RA, was 2.1 %. Sixty-eight percent (68%) of the respondents had gambled in the 

past year, and I I % had gambled more often than twice per week. Males had much higher 

gambling involvement than females, and gambling involvement increased among older 

respondents. Blacks were less likely than average to have gambled in the past year, but if 

they gambled, they were more likely to do so frequently. Low SES respondents were less 

likely to have gambled in the past year, but if they gambled, they were more likely to be 

problem gamblers. Life transitions that are associated with assuming adult roles 

(employment, living independently of parents, non-student status) are also assoeiated with 

greater gambling involvement. The rales of problem and pathological gambling were lower 

than those in an adult survey conducted earlier, when measured with the same 

questionnaire. 

Keywords Youth gambling· National survey· Youth problem gamblers 

Introduction 

Youth gambling has been the subject of increasing concern. Because of the expansion of 

legalized gambling in the U.S. in recent decades, young Americans have grown up in a 

society in which gambling is both comlllon and highly visible. The popular press has 

frequently reported alarming incidents involving young gamblers, Numerous surveys of 

U.S. stales have indicated high rates of youth problem gambling (e.g" Shaffer et a1. 1997; 

Westphal et a!. 2000). Research has shown that adolescent onset of gambling is assoeiated 

with greater gambling involvement in adulthood (Burge et al. 2004). A key step in 

addreSSing the prevalence of problem gambling among youth is to conduct a national U.S. 

survey of youth gambling. We have conducted stIch a survey, and in the following article 

J. W. Welte (0) . O. M. flames· M.·C. O. Tidwell, 1. H. Hoffman 

Research Institute Oil Addiclions, University at Buffalo, 1021 Main Street, Buffalo, NY 14203. USA 

e-mail: weite@riu.buffalo.edu 

.g] Springer 
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J Gamb! Stud 

we will pursue these ohjectives: (l) to describe our findings on the prevalence of youth 

gambling nnd problem gambling, and COlllpare these findings to other studies, (2) to 

describe how gambling involvement is distrihutcd across demographic subgroups of the 

youth population, and to test the statistical signifkance of these distributions, controlling 

simultaneously for all dcmographic variables, (3) to describe how gambling involvement is 

distributed among jointly defined age and gender groups, and test the statistical significance of these joillt distributions, and (4) to compare the rates of problem and pathological 

gambling in the current national U.S. youth survey to the rates in a national U,S. adult 

survey that used the same measure of gambling problems. 

Surveys of the prevalence of problem gambling among youth in the U.S. have produced 

varied results, depending on the screening questionnaire and definition of problem gambling employed, as well as the geographic area involved. Shaffer ct 31. (1997) conducted a 

meta-analysis of 22 methodologically sound surveys of adolescent gambling in variollS 

parts of the U.s. They determined that the average rate for past-year level 3 gambling (their 

term for serious problem or pathological gambling) for adolescents was 5.77%, although 

they point out that there is great variance among the surveys. partly due to methodological 

differences. Jacobs (2004) also summarized the results from U.S. youth gambling surveys, 

including some more recent than those studied by Shaffer and colleagues. 1[1 nine selected 

surveys from 1989 to 2002, Jacobs found an average rate of problem/pathological gam· 

bling of 3.7%. (Note-this figure Was calculated from data in Table 5 of Jacobs 2004). 

Many of the surveys reviewed by Shaffer et aL and by Jacobs used the South Oaks 

Gambling Screen, a 20-item screen originally developed for adults by Lesieur and Blume 

(1987). In Table I, we have reviewed several youth gambling surveys which used the 

SOGS-RA (Winters et aJ. (993), a version of the SaGS adapted for adolescents, This is the 

same instrument that we have used for the current study (see Methods). These surveys used 

representative samples of adolescents from five U.S. states and two foreign countries. Eaeh 

used a standard of four or more items endorsed to define problem gambling. In the U,S. 

states of Louisiana, Oregon. Alabama, Mississippi and Minnesota, the rate of problem 

gambling us measured by the SOGS·RA ranged from 2.8% to 5.8%, with an average of 

4.4%. The Canadian studies ranged from 2.2% to 5,8%, with an average of 3.7%, The 

Iceland study found a 2.7% rate of problem gambling among adolescents (Table I), 

The rates of youth problem/pathological gambling rellected in the figures cited above arc 

higher than the rates generally found for adlllt~, For example, although the meta-analysis by 

Shaffer and colleagues found a rate of 5,77% for adolescents, their meta-analysis of adult 

surveys found a rate of 1.14%. There has been a controversy in the literature about whether 

Table I Adolescent gambling surveys using SQGS·RA past year problem gambling 

Reference Location Date Cotpain! Pet (%) 

Winters et al. (\995) Minnesota IQ90 4 2.8 

Westphal ot al. (2000) Loutsiann 1997 5.8 

Carlson and Moore (1998) Oregon 1998 4 4,1 

Laoghinrichsen·Rohling et ~l. (20N) Alabama, Mississippi. and Oregon 2002 4 4.8 

Poulin (2002) Canadian Atlantic Provinces 1998 4 2.2 

Wiebe et a1. (lOOO) Manitoba 1998 3.2 

Adlaf and Laiomiteanu (2000) Ontario 1999 5.8 

Olason <I of. (2006) Iceland 2004 2.7 

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J Gambl Stud 

this is a valid relationship, or an artifact of methods. As Shaffer et a1. (1997) point oul, 

somewhat different methods have been used to measure pathological gambling for adolescents and adults. Although the conceptual criteria are the same, some criteria must be 

operationalized differently for adolescents. A question containing wording such as "borrowed money from your spouse or partner" or "borrowed money from in-laws" would not 

be appropriate for adolescents. Therefore, measures of adolescent pathological gambling 

use wording appropriate for adolescents. In addition, some adolescent studies have llsed a 

smaller number of endorsed itcms than adult studies to qualify for problem or pathological 

gambling. There are additional reasons why youth problem gambling rates might be 

inflated. Jacques and Ladouceur (2003) have pointed out that scoring errors have been made 

by some researchers using common screening instruments, and Ladouceur ct a1. (2000) 

have demonstrated tbe possibility that young respondents may misinterpret some questions 

in a way that inflates problem gambling scores. These arguments are summarized in a nn 

article by Derevensky et al. (2003) which is aptly titled "Prevalencc Rates of Youth 

Gambling Problems: Are the Current Rates Inflated?". 

The uncertainty about the extent of teenage gambling pathology was underscored by the 

results obtained by the national U,S. telephone survey of 534,16 and 17-ycar·old 

respondents conducted for the National Gambling Impact Study Commission (NORC 

1999). The investigators who conducted this study along with the larger adult study,· 

devised their own measure of pathological gambling-the NORC DSM Screen for 

Gambling (NODS). They administered the NODS with the same wording in both the 

adolescent and adult studies, and they found a lifetime rate of pathological or pfoblem 

gambling of 1.5% for adolescents, and a higher lifetime rate of 2.1 % for adults. Past-year 

rates were not reported for adolescents because of the small N. In the current study, in 

addition to the SOGS-RA, we also administered the DrS for pathological gambling (see 

Methods), the same instrument that we used in a national U.S, survey of adult gambling. 

We administered the DIS in the current study so that we would have a measure of problem 

gambling that allowed a direct comparison between the current youth survey and the earlier 

adult survey. In the current article, we will compare youth and adult rates using the same 

screening instrument. 

Surveys of youlh gambling behavior show some consistent-and some inconsistentfindings with regard to demographic patterns. In a Louisiana survey of students in grades 6 

through 12, Westphal et al. (2000) found that problem gambling was more prevalent 

among males than females, more prevalent among minority group members tban whites. In 

a high school survey conducted in Alabama, Mississippi and Oregon, Langhinrichsen et 31. 

(2004) had those same findings. In a telephone survey of New York State adolescents, 

Volberg (1998) found that frequent gambling was significantly more common among 

males, older adolescents, whites, and adolescents who were employed 10 more hours per 

week. A survcy of Connecticut high school students sponsored by the Connecticut Council 

on Problem Gambling (1998) found that males and females had the same rates of ever 

having gambled (84%), but males were three times as likely to be problem gamblers. This 

study also fOllnd that minorities had higher rales of problem gambling than whites. 

Stinchfield et 31. (1997) analyzed two large surveys, conducted in 1992 and 1995, of 

Minnesota 6th, 91h and 12th grade students. They found thai boys gambled more often than 

girls, and that 9th and 12th graders gambled more frequently than 6th graders. White and 

Asian students gambled tess frequently than Black, Hispanic and American Indian students. A survey of Texas adolescents (Wallisch 1993) found that males, older adolescents, 

Hispanics, and adolescents with disposable income of $SO/wcek or more were more likely 

to be weekly gamhlers. Btacks had tower rates of frequent gambling than whites or 

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J Gambl Stud 

Hispanics. Tn a survey of Washington adolescents, Vol berg (1993) found that males and 

adolc~cents with incomes of $50fweek or more were more likely 10 he problem ganiblers. 

Race was not significantly related to problem gambling. 

Surveys of youth gambling have of course been conducted outside of the U.S. Fisher 

(1999) analyzed a large sample of 12-·15 year olds from schools in Britain and found that 

problem gambling was associated with being male, having a higher disposable income, and 

living in a single-parent family. In a Scottish survey, Moodie and Finnigan (2006) found 

males to have higher rates of problem gambling than females and, not surprisingly, 

13-16 year olds 10 have higher rates than 11--12 year olds. 

These are mixed results. The one consistent finding in the literature is that males gamble 

more, and have more problems, than females. Generally, young people who have more 

access to money have more gambling involvement. Some studies have found that youths 

who are members of minority groups are more likely to be problem gamblers, but this is 

not a universal finding. While one might expect gambling involvement to increase with age 

among teens and young adults, that is also not II universal finding. Results related to 

socioeconomic status and religion are sparse in the literature, because most researchers did 

not measure those factors, even though some results attributed to race might be confounded 

with socioeconomic status or religion. In the current large representative U.S. study, we 

will examine the relationsbip between youth gambling involvement and demographic 

variables including SES and religion, as well as life transition variables sllch as employment and living independently of parents. 

Methods 

We conducted a national randoll1-digit-dial telephone survey with a representative s.ample 

of 2,274 U.S. residents aged 14-21. The rundom-digit-dial sample wos purchased from 

Survey Sampling International of Fairtield, Connecticut. The telephone sample was 

selected randomly from a sampling frame of all working telephone block" in the U.S. The 

sample was stratified by county and by telephone block within county. This resulted in a 

sample that was spread across the U.S. according to population, and not clustered by 

geographic area. The interviews were conducted by trained interviewers at the Research 

Institute on Addictions in Buffalo, NY. Each telephone number was called at least seven 

times to determine if that number was assigned to a household containing an eligible 

respondent. Once a household was designated as eligible, the number was called until an 

interview was obtained or refusal conversion had failed. The respondents were recruited by 

selecting randomly from the persons aged 14-21 years in each household by identifying 

the one with the next birthday. The 2,274 telephone interviews were conducted from 

August 2005 through January 2007. This relatively lengthy period of data collection 

allowed the use of a smaller but highly trained and carefully supervised crew of interviewers and captured possible seasonal variations in gambling. Interviews were conducted 

in all 50 Slates plus the District of Columbia. Results were statistically weighted to 

compensate for the number of potential respondents in the house bold. Weighting adjustments were also used to align the sample with gender, age and race distributions shown in 

the U.S. census estimates. 

Respondent's age was assessed by asking for their date of birth, and then following up 

with a verification of their age. Respondents who did not want to give their date of birth 

were simply asked their age. Respondent's race/ethnicity was assessed by asking two 

questions. First, respondents were asked if they considered themselves to he Spanish, 

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J Gambl Stud 

Hispanic. Latino or Chicano, Second. they were asked for their race, and given the choices: 

White or White Hispanic, Black or Black Hispanic, Asian, and American Indian or 

Alaskan Native. We derived the mutually exelusive race/ethnicity variable be pUlling 

everyone who said that they were Hispanic in the "Hispanic" eategory, Religion was 

assessed by simply asking: "What is your religion?". Marital status was assessed by 

asking: "What is your marital status?". and giving the choices: never married, married. 

divorced/annulled/separated, and widowed, In a second question, respondents were. asked 

if they were living with someone as if married. Employment status and educational status 

were assessed by asking: "Are you currently employed,!", and "Are you currently enrolled 

in school?", Living independently was assessed by asking: "Are you currently living on 

your own or in the home of your parent or guardian?", Our measure of socioeconomic 

status was based on the mean of four equally weighted factors: father's years of education, 

mother's yc,ars of education, father's occupational prestige and mother's occupational 

prestige. Occupational prcstige was coded from census occupation categories using the 

method described by Hauser and Warren (1997). Knowing that a few respondents would be 

unable to supply information on their parent's education and occupation, we asked a series 

of questions (home ownership, number of musical instruments and books in home, receipt 

of food stamps, etc.) gleaned from other studies that attempted to meaS\lre the SES of teens 

and young ad\llts. We used these as independent variables to impute parental education or 

occ\lpational prestige when these variables were missing. Imputation was performed by the 

SPSS Missing Value program. 

Our primary measure of problem gambling was the SOGS-RA. The best known adult 

assessment of problem gambling, the South Oaks Gambling Screen (SOGS) (Lesieur and 

Blume 1987) was modified for lise with adolescents by Winters, Stinchfield and Fulkerson 

(1993). Their instrument, the SOGS-RA, was first developed for a telephone survey. It 

demonstrated internal consistency reliability, and also demonstrated its validity by having a 

high correlation with frequent gambling. Sinee its original development, the SOGS-RA has 

been used successfully in numerous studies. The early studies using the SOGS-RA were 

reviewed by Shaffer et a!. (1997), and some of the more reccnt studies were cited in our 

introduction. The SOGS-RA consists of 12 items, which are related to the DSM-lU-R 

criteria for pathological gambling. An example is the "chasing" item: "In the past 

12 months, how of len have you gone back another day to try to win back the money that 

you lost'!" Another example dcals with loss of control: "In the past 12 months, have you 

ever gambled more than you planned to?" In the current study, the SOGS-RA items had a 

Cronbach's Alpha of 0.74, demonstrating good internal consistency reliability. Following 

the common practice in the literature, we refer to endorsement of two or three items as "at 

risk" gamhling, and we refer to endorsement of four or more items as "problem gam· 

bling". The SOGS-RA was administered to every respondent who reported any gambling 

during his or her life. 

For comparing problem/pathological gambling rates among youth with problem/path· 

ological gambling rates from our national survey of adults, we used the Diagnostic 

Interview Schedule (DIS) for pathological gambling (Robins et aL 1996). The DIS for 

pathological gambling contains 13 items that map into the 10 DSM·IV criteria for pathological gambling, such as preoccupation with gamqling and needing to gamble with 

increasing amounts of money to get the same excitement. In the current study, the DIS 

pathological gambling items had a Cronbach's Alpha of 0.79, demonstrating good internal 

consistency reliability, Endorsement of five or more criteria is considered pathological 

gamhling, and for our purposes we considered endorsement of three or more criteria to be 

problem gambling. Respondents who endorsed the requisite tHunber of criteria for the past 

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J Gambl Stud 

year are considered to be current pathological or problcm gamblers. Before adopting this 

measure for the current study. we examined the items for applicability to adolescents and 

young adults. Based on face validity, 12 of the 13 items could be used unaltered, aud we 

did so. For the 13th item. which asks abnut (rouble with "spouse or partner". we changed 

the wording to "parent, other family member or close friend." We added a 14th item, 

which asks about missing a day or more of school because of gambling. This item was 

lidded to parallel an item about job trouble. These 14 questions are mapped into 10 DSM 

criteria. We will compare results from these questions to those obtained by almost identical 

questions in our adult national survey. In both the current youth survey and the earlier adult 

survey, we administered the DIS for pathological gambling to every respondent who 

reported any gambling during his or her life. In the current study, the DIS was administered 

after the SOGS-RA; in the previous adult survey the DIS was administered after the 

original adult version of the SaGs. Although having been administered after 3pother 

screen for problem gambling could have influenced the DIS problem and pathological 

gambling rates in both surveys. comparability was maintained because the order effects 

were roughly the same for both surveys. 

We based our cxamination of youth gambling on four dependent variables. each defIned 

in terms of the 12 months before the interview: any gambling, frequent gambling (twice a 

week or more on average), at risk or problem gambling (2+ endorsements on the SOGSRA) and problem gambling (4+ endorsements). Each of these is broken down by nine 

dcmographic and life transition variables, as shown in Table 3. In Table 4, these breakdowns are repeated among past-year gamblers only. For example, 18% of males were 

frequent gamblers, and 23% of males who gambled in the past year were frequent gamblers. We repeated the breakdowns with past-year gamblers only so that the decision to 

gamble and serious gambling involvement can be considered as separate steps. For an 

illustrative example, Tuble 3 shows that respondents in the lowest SES category are significantly less likely thun middling SES respondents to have gambled in the past year. 

However, Table 4 shows that if they gambled, lower SES respondents are significantly 

more likely than others to be problem gamblers. The fact that low SES is oppositely related 

to gambling at these different steps suggests that the model which predicts who will 

gamble is different from the model which predicts those gamblers who will gamble frequently or with problems. Thus, separate examinations of all respondents and gamblers 

only are justified. 

Significance tests were generated by logistic regressions, one for each column in 

Tables 3 and 4. In the tirst step, demographic variables (gender, age, race, SES and 

religion) were entered. The significance tests for each of these variables were done with the 

other four controlled. Life transitions (marital, employment, student slatus, living 

arrangcment) were entered at the second step, so these significance tests are with all fi vc 

demographics plus the other three life transition variables controlled. In the third step, 

interactions between gender and the other predictors were entered, and were tested with all 

mull! effects and other interactions controlled. The results of this third and last step appear 

in Table 5. The terms "reP' and "trend" in the first columns of Tables 3 and 4 show how 

the contrasts were arranged in the logistic regressions, with reference categories labeled 

"ref". For cxample, the reference category for race is "White", so the *** by "Asian" in 

the "any gambling" column means that Asian youth are significantly Jess likely than 

Whites to have gambled in the past year. The contrast category for SES is the three middle 

fifths, so that the highest and lowest fifths were compared with it. Age was a continuous 

indcpendent variable, and therefore was tested as a trend. 

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Results 

Table 2 shows the prevalence of SOGS-RA scores at each level up to five or more 

endorsements. The table shows that 87.3% of our respondents failed to endorse a single 

item, The most commonly published cutpoints on the SOGS-RA have been 4+ (problem 

gambling) and 2+ (at-risk or problem gambling), for which we found 2.1 % and 6.5%, 

respectively. We have included this table so that our results can be compared with any 

study that used the SOGS-RA, regardless of the cutpoint that was used in the published 

results, 

Table 3 shows the prevalence of various gambling measures. Sixty-eight percent of our 

respondents gambled in the past year, 11 % gambled twice per week or more, 6.5% were 

Table 2 U,S, youth and Number of items % CUlIlulatiye % gambling survey past year 

pmbtem gambling scores 5+ !.3 1.3 SOGS,RA 

4 0,8 2,[ 

3 1.0 3,1 

2 3.4 6,5·· 

1 6,2 12.7 

0 87,3 100.0 

Table 3 u.s. youth and gambling survey past-year gambling percentages respondents aged 14·,·21 

N Any Frequent At-risk or Problem 

gambling gambling problem gambling 

(%) (%) gambling (%) (%) 

Overall 2.274 68 II 6.5 2,1 

Male 1.148 77'*' IS**' 10,6'" 3.3**' 

Female (ret) 1.126 58 4 2.4 0,9 

14-]:S (trend) 588 60*-+"" 9" 5.0 1.7' 

16-17 583 64 10 6,2 0.9 

18-19 564 74 II 8,5 2,6 

2()..21 53& 72 13 6,6 3,3 

White (ref) 1,408 70 9 6,2 1.6 

Black 334 60' 18*-- 8.1 3.4 

Hispanic 373 71 14 6.5 2,8 

Asian 83 48'" 5 6.7 1.0 

American Indian 27 83 28" 8.7 3.0 

Mixedlunknown 50 45*>' 9 4.4 2.4 

SES lowest lI5 455 60'" 16' 8.3 3,5 

SES second 115 (reO 454 67 12 5,0 1.0 

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Table 3 continued 

N Any Frequent At-risk or Problem 

gambling gambling problem gambling 

(%) (%) gambling (%) (%) 

SES third 115 (ref) 456 75 11 8.0 3.5 

SES fourth 1/5 (ref) 456 66 9 7.3 L3 

SES highest 115 4.54 70 6' 4.1' l.l 

Baptist 334 59'" 13 8.0 2.0 

Other protestant (ref) 858 71 10 6,4 2.4 

Catholic 606 75 IJ 5.9 1.7 

Monnon 65 37'" 1 0.0 0.0 

Jewish 36 58" 2 5.8 0.0 

Other 77 42'" 12 6A 3.2 

None 298 66' to 8.1 25 

Never married (ref) 2,143 67 11 64 1.9 

Married 41 62 13 7.9 5.2 

Living together 82 73 14 8.8 33 

Employed fnll time 311 81" 16 7.7 5.5 

Employed pal1 time 578 70 9 6.0 0.6 

Not employed (ref) 1,385 64 J() 6.5 1.9 

Not student (ref) 412 71 18 9.2 4.9 

Student 1,862 67 9" 6.0 L5 

Lives with parents (ret) 2,011 66 11 64 1.7 

Lives independently 263 76" 12 7.9 4.8' 

• Significant at 0.05 level 

•• Significant at 0.01 level 

... Significant at 0.001 level 

Not.: "ref' indicates reference group; statistical significance from logistic regression 

nt-risk or problem gamblers, and 2, I % were problem gamblers. Males were very significantly higher than females on every measure of gambling involvement. Any gambling, 

frequent gambling, and problem gambling increased with age from 14 to 21. Blacks, 

Asians and "Mixed/Unknown" were less likely to have gambled than Whites, Blacks and 

American Indians were more likely to be frequent gamblers than Whites. Eighteen percent 

(18%) of Blacks and 28% of American Indians gambled twice per week or more, as 

compared to 9% of Whites. There were no delectable differences by race in at-riSk/problem 

or problem gambling, The lowest SES respondents were least likely to gamble, However, 

frequent gambling was most common among low SES respondents, and least common 

among high SES respondents. Likewise, at-risk/problem gambling was least common at 

the high end of SES. Religion was related to "any gambling", with every religiolls group 

except Catholics less likely than "other (than Baptist) Protestants" to have gambled, 

Marital statlls showed no consistent relationship to gambling involvement. 

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J Gambl Stud 

Table 4 U.S. youth and gambling survey past-year gambling percentages respondents aged 14-21 pastyear gamblers only 

N Frequent At-risk or problem Problelo 

gambling (%) gambler (%) gambler (%J 

Overall 1,535 16 9.7 },I 

Male 878 23**' 13.9'" 4.3'" 

Female (ref) 657 7 4.1 15 

14-15 (trend) 356 IS' 8.2 2,7' 

16-17 376 16 9.6 1.5 

18-19 418 IS 11.4 3.5 

2()""21 387 19 9.2 4.5 

White (ref) 986 12 8.8 2.3 

Black 201 30'" 13.5 5.6 

Hispanic 264 19 9.2 3.9 

Asian 39 to 14.1 2.2 

American Indian 22 34' 10.5 3.7 

Mixed/unknown 22 20 9.7 5.3 

SES lowest 115 275 26*· 13.7 5.8' 

SES second 1/5 (ref) 303 19 7.5 1:5 

SES third 115 (ret) 341 IS !O.7 4.7 

SES fonrth \/5 (ref) 301 14 II.! 2.0 

SES highest 115 316 8" 5.S' 1.6 

Baptist 196 22' lJ.6 35 

Other protestant (ret) 611 14 9.0 3.4 

Catholic 456 17 7.8 2.2 

MQrmon 24 2 0.0 0.0 

Jewish 21 10.0 0.0 

Other 33 29' 15,0 7.5 

None 196 15 12.3 3,& 

Never married (ref) 1,444 16 9.5 2.8 

Married 26 20 12.8 8.5 

Living together flO 19 12.1 5.2 

Employed [ull time 251 20 9.6 6.8 

Employed part time 402 IJ 8.6 n.9 Not employed (ref) 882 16 10.2 3.0 

Not studem (ref) 294 26 12,6 6.8 

Student [,241 14**' 8.9 2.2 

Lives with parents (ref) 1,335 16 9.6 2.6 

Live,< independently 200 16 lOA 6.3 

• Significant at n.os level 

•• Significant at O.()l level 

••• Significant at 0.001 level 

Note: "ref' indicates reference group; statistical significance from logistic regression 

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Table 5 U.S. youth and gambling survey past-year gambling percentages by gender and age 

Age N Any gambling Frequent gambling At risk or pmblem gambling Problem gambling 

M F M{%) P(%) M(%) F(%) M (%) F(%) M{%) P(%) 

t4-15 282 306 68 53 JJ 5 7.3 2.4 2.6 0,6 

1617 282 301 76 52 16 " 10.9 1.1 l,9 0.0 

18-19 289 276 83 65 19 3 14,3 2.9 4.3 0.9 

20-21 274 265 80 64 23 4 10.2 ),1 4,6 1.9 

Note; Halies iodieate., gender by age interaction signincant at 0,05 level 

Notc that the statistical tests pertaining to employment, student status and living 

arrangement are all reported with every other predictor, including age, held constant. 

Those who were employed full time were significantly more likely to have gambled in the 

past year than those not employed. Non-students were significantly more likely to have 

gambled frequently than were students. Respondents who lived independently were significantly more likely to have gambled in the past year and to have been problem gamblers 

than those who lived with their parents. Although only these four contrasts on the life 

transition variables reached statistical significance, it Illay be worth noting that those who 

were employed full time, were not students and lived independently of tbeir parents were 

higher than their counterparts Oil all measures of gambling involvement. 

Table 4 reports the prevalence of frequent, at-risk and problem gambling only for those 

respondents who gambled in the past year. As mentioned earlier, we reported these results 

separately because the factors which predict whether lin individual gambles or not may be 

different from those factors which predict which gamblers will become heavily involved, 

Male gender and increased age are associated with an illcreased probability of gambling in 

the past year (as Table 3 also shows) and also with increased gambling involvement among 

those who do gamble. Table 4 shows that male gamblers are higher than females on every 

measure, and that frequent gambling and problem gambling increase with age among those 

who gambled in the past year. Blacks are less likely than Whites to have gambled in the 

past year (Table 3), but if they gambled, Table 4 shows that they are much more likely 

than Whites (30% VS. 12%) to gamble frequently. Table j shows that respondents in the 

lowest fifth of SES arc the least likely to have gambled in the past year. However, Table 4 

shows that among those who gambled, the lowest SES 1s associated with the highest 

gambling involvement, and the highest SES is associated with the lowest gambling 

involvement. Among gamblers, the lowest fifth of SES had the highest rates of fr~qu"nt 

gambling and problem gambling. The highest fifth of SES had the lowest rates of frequent 

gambling and at-risklproblem gambling, The results pertaining to religion also show 

reversals. Other religions (which include Moslem, Hindu, Buddhist, Jehovah's Witnesses 

and others) had a very low rate of having gambled in the past year (42%), but if they 

gambled, they had higher rate.s of frequent gambling than any other religious group, 

Similarly, Baptists were less likely than other Protestants to have gambled in the past year, 

but if they gambled, they had higher rates of frequent gambling. 

Table 5 and Fig, I show the noteworthy result that emerged from our tests of gender 

interactions, Only one interaction between gender and the other predictors was significant-the interaction between gender and age, with frcquent gambling as the dependent 

variable. Table:; shows the gambling measures broken down jointly by gender and age. As 

Fig. I shows more dramatically than tbe table, frequent gambling increased with age 

among males, but not among females. There is a suggestion of this same pattern with 

~ Springer 
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J Gambl Stud 

25 

e Age (> 

:E 00 14·15 .. 

0 20 0 H>-JI 

"" 0 18·19 '" 

~ • 20·21 

.. 15 

. !:! 

~ 

" ~ 10 

:0 

E 

.. 

C) 

(> 

J! 

~ 

~ o 

Female Male 

Gand.r 

Fig. 1 Percentage of frequent gamblers by age and gender 

aHiskJproblem gambling and problem gambling, but these interactions were not statistically significant 

Table 6 shows a comparison between problem/pathological gambling rates in the current study and our national U.S. adult (18 and older) survey conducted in 2000. The other 

problem gambling results reported in this article were measured using the SOGS-RA. but 

these rcsults wcre measured ll~ing the DSM-IV DIS for problem/pathological gambling. 

This measure was used in both studies, with only minor modifications as described in the 

methods section. The results for problem gambling (our term for an endorsement of 3+ 

DSM criteria) show that the rates for males in the two studies are identical (4.2%), but the 

rate for females in the adult study (2.9%) is much higher thun the rate for females in the 

youth study (0.1 %). As measured by DSM·IV criteria, problem gambling is almost non· 

existent among adolescent and young adult females. The results for pathological gamhling 

(last columll of Table 6) show that adult males have a higher rate of pathological gambling 

than adolescent and young adult males, and thnt adult females have a much higher rate than 

adolescent and young adult females. In the youth survey, pathological gambling by DSM· 

IV criteria did not occur among females. These results do not support the supposition that 

problem gambling is more common among young people than adults. These results are 

consistent with the supposition that gambling problem.~ develop later in life for females 

than for males. 

Table 6 U.S. youth and adult 

gambling survey rates of problem 

and pathological gambling 

monsured by DSM·IV criteria 

Youth survey 

N = 2,274 

Adult survey 

N = 2.631 

Male 

Female 

Overall 

Male 

Female 

Overall 

Problem gambling 

3+ DSM·IV 

criteria (%) 

4.2 

0.1 

2.2 

4.2 

2.9 

3.5 

Pathological 

gambling 5+ 

DSM-IV 

critetia ('!'o) 

0.7 

0.0 

OA 

L3 

1.4 

1.4 

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J Gamb! Stud 

Discllssion 

The rates of problem gamhling found in the current study arc not as high as the rates of 

problem gambling found in other surveys which used the SOGS-RA and defined problem 

gambling as 4 or more positive answers, It is possible that differences in study design might 

account for some of this difference. Among the eight surveys listed in Table I, five were 

school surveys. The school surveys produced the three highest rates, but also the two lowest 

rates. The remaining three studies were telephone surveys. One of these sampled from listed 

numbers, and another used an "age-targeted", non-probability sample. Only one study 

(Carlson and Moore 1998) seems to have been a random-digit-dial survey. Our currently 

reported survey was sampled with a known probability from a frame that inclLlded ail phone 

numbers, and therefore is closest to a representative household sample. Having made this 

observation, it is not clear why this would make our estimate of the rate of problem 

gambling lower than the others. For example, school surveys obtain data only from those 

who arc attendi ng school, but one would not think that this constitutes a bias for more 

problem behaviors, The 95% confidence interval for our 2,1 % rate of problem gambling is 

roughly plus or minus half a percent, making it unlikely that the current study found a lower 

problem gambling rate than the eight studies in Table ! by chance. Also, the rates of other 

problem hehaviors (such as conduct disorder and daily marijuana use) in Ollr sample are 

high relative to the rates found in the literature; and therefore. we did not obtain a sample 

low in problem behaviors, either by chance or by method bias. Some of the reasons suggested by Jacques and Ladouceur (2003) for overestimation of the rate of youthful problem 

gambling do not apply to the comparison between our survey and those in Table I. All of 

these studies all used the same cutpoint-four or more endorsements. Question misinterpretation docs not explain the difference, because the same questions were used. Wrong 

scoring is not likely with the SOGS-RA, which has a very straightforward scoring procedure. The most likely explanation is that the rate of problem gambling in the U,S, as a whole 

is lowe.r than in many of the smaller jurisdictions in which surveys have been conducted. 

Our results provide an opportunity to examine the influence of demographics and life 

transitions on gambling. Males were much higher than females on every measure of 

gambling. This was not the case in our 2000 adult survey, in which the "gender gap" in 

gambling involvement was much narrower (Welte et a1. :WOI). It seems likely that 

females' gambling involvement tends to emerge in adulthood, while male involvement can 

be high in adolescence. This pain! is underscored by Fig. 1, which shows graphically how 

male frequent gambling is increasing at an eady age, whereas female frequent gambling is 

stable. While t.hree of Ollr four measures of gambling involvement increased significantly 

with age in the 14-21 range, that increase comes primarily from males. 

Asians we.re the racial group that showed the lowest gambling involvement. This is an 

interesting result in view of the commonly accepted notion (e.g .. Liu 20(6) that Asians are 

heavy gamblers. While it is possible that Asian gambling involvement develops later ill 

life, it is also possible that examination of a representative sample simply fails to uphold a 

stereotype. Bhlcks are the least likely to have gambled. but Black gamblers are among the 

highest in gambling involvement. This replicates the finding in our national adult survey 

(Welte lOt a1. 200 I l. and also is similar to the findings in general population surveys with 

respect to alcohol, which invariably find Blacks with a high percentage of abstainers, but 

may also find relatively high rates of alcohol abuse among Blacks who are drinkers (Welte 

et a1. 20(1), We have a small sample of American Indians, so it is dimeult to obtain a 

statistically significant contrast. However, their rate of frequent gambling is so much 

higher than the reference group (28% for American Indians as opposed to 9% for Whites) 

~ Springer 
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75777.210

J Gambl Stud 

Ihat it is statistically significant. They are also relatively high on measures of problem 

gambling. We might speculate that there is a connection between this result and the rapid 

spread of legal gambling in Indian communities. 

Low SES respondents are most likely to have abstained from gambling, just as many 

studies have shown them to be the most likely to have abstained from alcohol (Welte et al. 

2001). However, among gamblers, our results show a clear tendency for gambling 

involvement and neglllive consequences to increase as SES declines. We have elsewhere 

hypothesized that lower SES persons are more prone to gamble excessively because they 

see gambling as a reasonable method to improve their financial status (Welte et a1. 2004). 

Religion clearly inlluence~ the decision to gamble, with Mormons, Jews and Baptists less 

likely to have gambled than the reference group ("Other Protestants"). Catholics were the 

most likely to gamble, not surprising as they arc practitioners of a religion that is generally 

tolerant of gambling. For the other gambling involvement variables, we see little in the 

way of significant religious differences, but here again we have a small group problem. 

Nevertheless, it may ilot be a coincidence that Mormons reported I % frequent gamblers 

(average for the entire sample was II %) and also reported 0% problem or at-risk gamblers, 

as they are practitioners of a religion that discourages gambling. 

We also examined the relationship between four life transitions (marriage, employment, 

living independently and student status) and gambling involvement. Those who work full 

time are more likely to gamble, those who are not students are more likely to gamble 

frequently, and those who live independently are more likely to gamble and to be problem 

gamblers. All statistically significant results show that greater gambling involvement is 

associated with an adult status, In fact. those who work full time, arc not students and live 

independently are higher than their countcrpa11s on all four measure of gambling 

involvement. These results suggest that, in the minds of some, gambling may be associated 

with the transition to adulthood, 

We compared the pathological and problem gambling rates in our adult and youth studies, 

using the same measure, the DIS for pathological gambling. This comparison showed lower 

rates of problem and pathological gambling among adolescents/young adulL~ than among all 

adults. When also considering the relatively low rate of problem gambling that we found 

using the SOGS-RA, our results are not consistent with the common llotion that problem 

gambling is more prevalent among adolescents than among adults. OUf results do support 

those researchers, some of whom we cited earlier, who have questioned that notion. 

While there have been numerous surveys of adolescent gambling conducted in U.S. 

stales, there has been a lack of studies of this topic in the nation as a whole, In this article, 

we have presented results from the tirst national U.S, survey of gambling among adolescents and young adults. We have found that gambling is widespread among U.S, YOllths. 

We also fOllnd a problem gambling rate which projects to approximately three quarters of a 

million problem gamblers among U.S. residents aged 14·21. In a society where young 

people are increasingly exposed to gambling influences, this is a cause for concern. 

Acknowledgment This Research was funded hy grant ROt MH 063761 to John W. Welte from the 

National Institute of Mental Health. 

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~ Springer 
236 

Mrs. BONO MACK. And Dr. Volberg, it is your 5 minutes. 

STATEMENT OF RACHEL A. VOLBERG 

Ms. VOLBERG. Good morning, Madam Chairman, Ranking Member Butterfield, and members of the subcommittee. 

Mrs. BONO MACK. Can you please pull your microphone a little 

closer? 

Ms. VOLBERG. OK, is that better? 

Mrs. BONO MACK. Yes, thank you. 

Ms. VOLBERG. My name is Rachel Volberg. I am a sociologist at 

NORC at the University of Chicago, and I have been doing research on gambling and problem gambling for 26 years. 

The Internet gambling market is large and growing rapidly. 

Poker is the most popular form of online gambling, and accounts 

for 60 percent of online gambling activity but for only 23 percent 

of online gambling revenues. The majority of Internet poker players 

are young men with relatively high levels of education, and perhaps a few older men as well. 

With regard to problem and pathological gambling, there is substantial research showing that prevalence rates are three to four 

times higher among Internet gamblers compared to non-Internet 

gamblers. Now, critics argue that Internet gambling does not cause 

problem gambling, but that instead problem gamblers are attracted 

to Internet gambling and add it to a repertoire of other gambling 

activities. 

Results from a new longitudinal study in Canada show that 

while both of these things happen, the most common pathway is 

actually for Internet gamblers to develop problems subsequent to 

beginning to gamble on the Internet. 

Now, most things that go up usually come down, and this is true 

in epidemiology as well. Research shows that problem gambling 

prevalence does eventually level out and decline, even if accessibility does continue to increase. Among the likely contributors to 

such declines are greater public awareness, decreased participation 

once the novelty has worn off of a new form of gambling, increased 

government and industry efforts to provide gambling more safely, 

expanding services for problem gamblers, the increased age of the 

population, and, unfortunately an outflow of problem gambling 

cases due to severe personal or financial crisis, criminal charges or, 

in extreme cases, suicide. 

H.R. 2366 provides for Federal oversight of State and tribal 

agencies that will issue licenses for online poker, but leaves responsibility for setting consumer protection standards to the States and 

tribes. This arrangement virtually guarantees that programs to 

prevent and mitigate problem gambling will vary significantly 

across jurisdictions. And while the competition among online gambling providers will ensure a cost-efficient and appealing consumer 

product, a free market is likely to come at the cost of less player 

protection. 

Beyond requiring licensees to establish self-exclusion programs, 

I believe some additional minimum requirements are needed. 

These include a requirement for players to set limits with regard 

to time and money, a 24-hour cooling off period before changes to 

237 

limits can be made, monthly financial statements, and self-assessment tests. 

It would be best if these measures, as well as the self-exclusion 

program, were operated by a third-party independent organization. 

Such an approach offers many advantages. One significant one is 

that it would allow players who wish to self-exclude to visit a single site and exclude themselves from all of the domestic sites at 

one time, rather than having to go to each gambling site individually. 

Finally, even these measures will be insufficient without a mechanism to adequately fund prevention, treatment, and research on 

problem gambling in the United States. I agree with the National 

Council on Problem Gambling that a minimum of $50 million in 

new Internet gambling revenue must be dedicated to these programs. 

Unfortunately, the United States lags far behind other countries 

in this regard. State funding for problem gambling services per 

capita is approximately one-twentieth the level it is in countries 

such as Australia and Canada, and there has never been a Federal 

agency with primary responsibilities to address problem gambling. 

I therefore also urge you to support H.R. 2334, which designates 

SAMHSA as the lead Federal agency on problem gambling. 

Online gambling is here to stay and will continue to evolve. The 

question is what governments can and will do to create a safety net 

for their citizens, to minimize the likely increase in the number of 

problem gamblers, to provide treatment for those afflicted, and to 

ensure that research is undertaken to understand the impacts of 

Internet gambling on society. 

Thank you for the opportunity to testify, and I look forward to 

answering your questions. 

Mrs. BONO MACK. Thank you, Dr. Volberg. 

[The prepared statement of Ms. Volberg follows:] 

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Internet gambling is a fluid and dynamic market characterized by significant inter-jurisdictional 

inconsistencies. As a result, policymakers, operators and other stakeholders face substantial ambiguities 

about the best approach to legalizing and regulating Internet gambling. My testimony today addresses 

three issues: (1) whether revenue projections for legalized Internet poker will be met; (2) whether there 

will be an increase in the number of problem gamblers as a result of legalizing Internet poker; and (3) 

what can be done from a regulatory perspective to prevent or mitigate likely increases in the prevalence 

of problem gambling in the wake of the introduction of Internet poker. 
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Good morning Chairwoman Bono Mack, Ranking Member Butterfield, and Members of the 

Subcommittee. Thank you for inviting me to testify this morning. My name is Rachel Volberg. I am a 

Senior Research Scientist at NaRC at the University of Chicago, I live in Western Massachusetts, and I 

have specialized in population studies of gambling and problem gambling for 26 years. 

Online gambling has only existed since 1995 and, internationally, different countries are 

experimenting with a range of approaches to legalization. Some countries prohibit most or all forms of 

online gambling; at the other end of the spectrum are countries that permit nearly all forms of online 

gambling. In the middle are countries that have put in some legal restrictions, those that provide for a 

domestic online market with patronage restricted to their own citizens, those that also prohibit 

residents from accessing online gambling outside the country, and those that operate online gambling 

but prohibit their own residents from accessing the sites (Williams, Wood, & Parke, 2012 in press; Wood 

& Williams, 2009). 

There are many arguments to be made in support of Internet gambling legalization. Proponents 

of legalization point out that: 

it is exceedingly difficult to effectively prohibit online gambling; 

• over time, populations adapt to the presence of problernatic products and develop 

some 'inoculation' frorn further harrn; and 

legally regulated sites better ensure player protection and deter crirnes. 
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There are also compelling arguments for prohibition, some in direct counterpoint to the arguments 

for legalization. The main arguments in defense of prohibition are: 

the purpose of laws is to codify societal values in an effort to shape, rather than 

conform to, people's behavior; 

legalization will likely increase rates of problem gambling; and 

player protection tools are likely to have only modest efficacy in preventing problem 

gambling. 

While there are strong economic incentives for governments to legalize and regulate Internet 

gambling, there is, as yet, no satisfactory model proposed to regulate these activities. 

Australia was one of the earliest countries to attempt to license and regulate Internet gambling. 

In 2001, the Australian Government passed the Interactive Gambling Act (IGA) which permits Australian 

states and territories to license and regulate online operators. The eight Australian states and territories 

have each created different gambling policies and regulations but inter-state competition has given rise 

to substantial discord; for example, around the flouting of advertising standards (Gainsbury & Wood, 

2011). The lack of cohesive policies recently led the Australian Productivity Commission (2010) to 

recommend that Australia implement a national regulatory approach. 

In Canada, most forms of gambling are regulated at the provincial level and provincial 

governments are generally the owners and operators of provincial gambling enterprises. There have 

been several forays into online gambling in Canada. These were led initially by Internet-based horse 

race wagering followed by the offer of lottery products and then other forms of gambling by provincial 

lotteries. In 2010, British Columbia and Quebec made Internet gambling available to their citizens and 
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the two provinces will soon permit their residents to compete against each other in online poker games. 

Ontario will begin offering Internet gambling to its citizens in 2012: 

In 2006, Congress passed the Unlawful Internet Gambling Enforcement Act (UIGEA) which made 

it illegal for financial transaction providers to transfer funds to online gambling sites. In the wake of this 

legislation, a significant number of online gambling sites stopped taking bets from U.S. citizens. UIGEA 

exempts online intra-state sales of lottery tickets, inter-state horse race betting and some types of intrastate online gambling. Despite the law, many U.S. players circumvent UIGEA by using non-U.S. financial 

transaction intermediaries to place bets. While overall participation in Internet gambling in the United 

States is quite low (estimates range from 0.3% to 3.0%) (Rasmussen Reports, 2006; Welte, Barnes, 

Wieczorek, Tidwell, & Parker, 2002), a recent survey of international online gamblers found that 25% of 

the respondents were from the United States (Wood & Williams, 2009). 

While revenues from Internet gambling can be difficult to determine, it is estimated that 

worldwide online gambling revenues rose from $600 million in 1998 to $16.6 billion in 2008. Poker is 

the most popularform of online gambling and accounts for approximately 60% of online gambling 

activity but for only 23% of the worldwide online gambling market, compared with 38% for 

sports/racebooks and 25% for online casinos (Global Betting and Gaming Consultants, 2008, cited in 

Wood & Williams, 2009). Extrapolating from these figures, it appears that online poker generates 

approximately $4 billion in annual revenues worldwide. 

1 Along with my colleague Rob Williams from the University of lethbridge, I am just beginning a three-year project 

to study the impacts of online gambling legalization in Ontario. The study is funded by the Ontario Problem 

Gambling Research Centre. 

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As far as I have been able to determine, there are no published estimates of the revenues that 

would likely be generated by the legalization of online poker in the United States. However, the Joint 

Committee on Taxation has published an analysis of projected tax revenues if a broader Internet 

gambling regime were legalized. Estimated federal tax revenues under four different scenarios ranged 

from $10 billion to nearly $42 billion over a ten·year period (Barthold, 2009). A separate analysis by 

PricewaterhouseCoopers estimated that between $13 billion and $26 billion in tax revenues would be 

generated over ten years. If no states were permitted to opt out of the legislation, it was estimated that 

legalized Internet gambling would generate nearly $49 billion over ten years (PricewaterhouseCoopers, 

2009). 

An interesting feature of these analyses is that they apparently assume that, if U.s. companies 

are permitted to offer online gambling, then these companies will capture the entire worldwide online 

gambling market. Although Internet gambling is relatively young, this is a mature market with 

significant barriers for new online gambling companies. Legally sanctioned domestic sites will only be 

patronized to the extent that they offer a competitive advantage to the consumer. However, existing 

'offshore' jurisdictions have a strong competitive advantage by virtue of their longer established 

presence. Furthermore, regardless of whether Internet gambling is legalized in the United States, there 

will always be many non·domestic sites available to online gamblers (Williams et aI., 2012 in press). 

In France, it is estimated that only 43% of the Internet gambling market is currently captured by 

legal domestic sites (MAG, 2011). In Britain, only 25% of the estimated £2.5 billion that British 

consumers spend annually on Internet gambling goes to operators licensed by the British Gambling 

Commission (Williams et aI., 2012 in press). A more salient example comes from Sweden where the 

introduction of a legal domestic online poker site, in 2006, led to a significant increase in overall Internet 

gambling participation (Swedish National Institute of Public Health, 2010). However, in a separate 
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survey, only 28% of Swedish online poker players reported patronizing the domestic site exclusively 

while another 25% of players reported patronizing several poker sites including the domestic site 

(Jonsson, 2012 in press). If these figures are extrapolated to a legalized U.S. market, tax revenues from 

Internet gambling are more likely to be in the range of $3 billion to $12 billion over ten years. 

Finally, it is possible that legalizing online gambling and providing domestic access may actually 

increase monetary outflow rather than retaining it. This is a lesson learned in the late 1980s and 1990s 

from the introduction of domestic-market casinos in North American states and provinces, intended to 

capture gambling dollars that were being spent in Nevada and Atlantic City. Nevada experienced an 

enormous growth in gambling revenues in this period because the creation of domestic casinos led to 

increases in casino gambling participation which led, in turn, to increased visits to major international 

gambling destinations (Williams, Belanger, & Arthur, 2011). 

Prevalence (or total stock) of a disorder is determined by incidence, or the inflow of new cases, 

duration, and the outflow of current cases through recovery, migration or death (Abbott, 2006). In the 

study of clinical disorders, pathological gambling is considered a chronic disorder. Chronic disorders 

strongly tend to recur once fully developed, constituting a lifelong vulnerability. This vulnerability to 

relapse may be effectively treated and kept in check. But a period in which the individual is relatively 

free of symptoms does not mean that the person is free of the disorder. 

One reason that legalization of Internet gambling may lead to an increase in the rate of problem 

gambling is that legalization provides tacit governmental approval for these activities and leads citizens 

to assume that the products are safe. This in turn typically leads to an increase in overall participation, 

6 
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as is also seen in the legalization/decriminalization of prostitution, abortion, and cannabis (Alan 

Guttmacher Institute, 2008; Jakobsson & Kotsadam, 2011; MacCoun, 2010; Wardle, Griffiths, Orford, 

Moody, & Volberg, 2011). Increases in overall participation tend to be reliably associated with 

increases, at least temporarily, in the prevalence of problem gambling (Grun & McKeigue, 2000; Lund, 

2008; Rose, 1985; Williams, Volberg, & Stevens, 2011). 

Another reason that legalization is likely to increase rates of problem gambling is because the 

nature of online gambling makes it inherently more problematic than most other forms of gambling. 

Greater convenience, easier access, the solitary nature of play, the ability to play when intoxicated, the 

lack of realistic cash markers, and the ability to play multiple sites and/or games simultaneously are all 

features that contribute to a diminution in players' ability to control their involvement. Another 

challenge is that Internet problem gamblers have a much more difficult time avoiding gambling venues 

which are available at the click of a mouse (Schull, 2005; Wood, Williams, & Lawton, 2007). 

There is substantial research showing that the prevalence of problem gambling is three to four 

times higher among Internet gamblers compared to non-Internet gamblers (Griffiths & Barnes, 2008; 

Jonsson, 2012 in press; Ladd & Petry, 2002; Wood & Williams, 2007, 2009). In California in 2006, 

although only 2.1% of our respondents had ever gambled on the Internet, 11.3% of these individuals 

were classified as pathological gamblers and another 19.2% were classified as subclinical problem 

gamblers. In a logistic regression analysis that controlled for individual demographics and co-occurring 

behaviors and disorders, respondents who had gambled on the Internet in the past year were ten times 

more likely to be a problem or pathological gambler compared with those who had not gambled on the 

Internet (Volberg, Nysse-Carris, & Gerstein, 2006). 

Most things that go up usually come down and this is as true in epidemiology as in other realms. 

Epidemiological research strongly suggests that problem gambling prevalence does eventually level out 
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and decline, even if accessibility continues to increase (Williams, Volberg et ai., 2011). Greater public 

awareness, expanding services for problem gamblers and regulatory, industry and public health 

measures are among the likely contributors to such declines. What is not known is how quickly these 

and other factors can have a significant impact. It is also not known if these factors can prevent problem 

escalation even if introduced concurrently with increased access to gambling (Abbott, 2005; Abbott, 

Volberg, Bellringer, & Reith, 2004). 

While longitudinal studies of gambling have only recently begun to yield results, one consistent 

and recurring theme emerging from these studies is that most gambling problems tend to resolve over 

time (Abbott & Clarke, 2007; LaPlante, Nelson, LaBrie, & Shaffer, 2008; Slutske, Jackson, & Sher, 2003). 

However, in the only study to date to examine problem gambling incidence, the researchers found that 

among adults who became problem gamblers over a 12-month period, approximately one-third were 

new cases without a previous history of problem gambling while two-thirds were classified as relapsing 

cases (Victoria Department of Justice, 2011). 

While there are good theoretical grounds to believe that Internet gambling contributes to 

problem gambling, it is possible that problem gamblers simply add Internet gambling to their repertoire. 

Very recent longitudinal research in Ontario, Canada has found that both directional routes occur. 

However, Internet gambling leading to problem gambling tends to be the most common pathway 

(Wood, Williams, & Parke, 2012 in press). Although there is speculation about an 'inoculation effect,' 

such that gamblers eventually habituate and overcome difficulties related to their gambling, most ofthe 

financial, psychological, social, work/school and legal harms associated with problem gambling cannot 

be undone (Gainsbury & Wood, 2011; Williams et ai., 2012 in press). Given this scenario, it is essential 

that regulatory policies take account of likely increases in problem gambling in the wake of the 

legalization of online poker in the United States. 
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In 1999, the National Gambling Impact Study Commission (1999) pointed out that, unlike other 

consumer businesses, legal gambling has largely been shaped by government decisions, at the federal, 

state and local levels. The Commission commented that "rivalry and competition for investment and 

resources" were the driving factors in government decision-making related to legalized gambling in 

America and noted that "even the states concede that only Washington has the potential to control 

gambling on the Internet" (1999, p. 1-5). The latest round of casino legalization in the Northeast ofthe 

United States suggests that the situation with regard to inter-state competition for gambling revenues 

has not changed in the intervening years. 

H.R. 2366 provides for Federal oversight of state and tribal agencies that will issue licenses for 

online poker through a newly-established Office of Internet Poker Oversight within the Department of 

Commerce (Section 103). This office will have the responsibility to prescribe minimum standards for 

qualifying these state and tribal agencies but will have no role in settings standards or issuing licenses to 

operate online poker (Section 104). Instead, each state and tribal agency will be required to establish 

requirements for the development of a Compulsive Gaming, Responsible Gaming, and Self-Exclusion 

Program that each licensee will be required to implement as a condition of licensure (Section 106), A 

self-exclusion program represents the minimum standard required in H.R. 2366. 

The provisions in H.R. 2366 virtually guarantee that requirements for programs to prevent and 

mitigate gambling-related problems will vary Significantly across the states. There is already 

tremendous variability in existing efforts to address problem gambling in the United States, with per 

capita expenditures on problem gambling services, including prevention, treatment and research, 

ranging from $1.36 in Iowa to less than one cent in Maryland (Marotta, Moore, & Christensen, 2011). 

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With each state responsible for its own consumer protection and harm minimization requirements, and 

with states invariably competing with one another for players and revenues, some states will implement 

far less restrictive regimes than others and players, migrating to these less restrictive sites, will not 

benefit from the tools that are put in place to protect consumers and prevent gambling problems. 

While competition among online gambling providers ensures a cost-efficient and appealing consumer 

product, a free market is likely to come at the cost of less player protection (Williams, West, & Simpson, 

2008). 

Some countries have enacted legislation that requires gambling providers to effectively mitigate 

harm from the provision of gambling. For example, Germany has legislation that, among other things, 

requires all new gambling products to be reviewed by an advisory board of gambling addiction experts 

prior to their introduction (Meyer, Hayer, & Griffiths, 2009). In Sweden, the responsible gambling 

program implemented by Svenska Spel includes limits in marketing and advertising, a self-exclusion 

feature, and a mandatory requirement for all players to set limits with regard to time and money. The 

program also includes a self-assessment (GAM-TEST) where players can receive objective feedback on 

their gambling habits. A required independent evaluation of the program, called Playscan 

(http:(/www.playscan.com/l. found that reasonable time and monetary limits were set by the majority 

of players and, for those who set reasonable limits, most abide by those limits (Jonsson, 2012 in press). 

Beyond the requirement that licensees establish self-exclusion programs, additional minimum 

consumer protection and harm minimization requirements are needed in H.R. 2366. These should 

include an opt-out requirement for players to set daily, weekly and monthly limits with regard to time 

and money with changes only possible after a 24-hour cooling-off period, monthly financial statements, 

and self-assessment tests. All of these are measures supported strongly by Internet gamblers surveyed 

worldwide (Parke, Rigbye, Parke, & Williams, 2007). It would be best if these consumer protection 

10 
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measures as well as the self-exclusion program were operated by a third-party, independent 

organization rather than by the online gaming operators or the licensing state and tribal agencies. In 

particular, this would allow players who wish to self-exclude to visit a single site where they can exclude 

from all of the domestic sites at one time rather than having to exclude themselves from each site 

individually. 

While establishing and enforcing these minimum consumer protections will be helpful, these 

measures will not be adequate without a mechanism to adequately fund prevention, treatment and 

research on problem gambling in the United States. You heard last month from Keith Whyte of the 

National Council on Problem Gambling who requested your support for H.R. 2334, the Comprehensive 

Problem Gambling Act, which would set aside $50 million in gaming revenues to fund such programs. In 

my view, this is the bare minimum required. The United States lags far behind other countries in this 

regard: there has never been a Federal agency with primary responsibility to address problem gambling 

and state funding for problem gambling prevention, treatment and, most especially, research is 

approximately one-twentieth of the level in countries such as Australia and Canada (Volberg, 2009). 

While online gambling offers better possibilities, compared to land-based forms of gambling, to 

implement player protection measures, there are unmistakable challenges in providing these tools and 

ensuring that the players most in need of protection actually use them. If Internet poker is legalized in 

the United States, it will be important to ensure that these tools are available to players on all licensed 

sites. It will also be important to establish aJl independent agency through which these tools are made 

available to players in order to overcome the reluctance demonstrated to date by the online gambling 

industry to implement cross-operator player protection tools (Dragicevic, 2011). 

11 
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The pre-commitment constraints that most online sites presently allow players to impose tend 

to be voluntary and of relatively short duration. These types of constraints are of primary benefit to 

non-problem gamblers but are unlikely to have a significant impact on the out-of-control behavior of 

pathological gamblers (Griffiths, 2012 in press; Nower & Blaszczynski, 2010; Williams et aI., 2008). This 

is why additional resources are needed to provide for adequate problem gambling prevention, 

treatment and research. 

Online gambling is clearly here to stay and will continue to evolve with continual changes and 

competition among Internet gambling sites, with new demographic groups such as women and older 

adults entering the market, and with a growing number of jurisdictions legalizing and regulating these 

activities. The question is what governments can and will do to create a safety net for their citizens, to 

minimize the likely increase in the number of problem gamblers, to provide treatment for those 

afflicted, and to ensure that research is undertaken to understand the impacts of Internet gambling on 

society (Gains bury & Wood, 2011). 

Thank you again for the opportunity to testify. I look forward to answering your questions and 

to assisting the Subcommittee in its future deliberations. 

12 
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Gainsbury, S., & Wood, R. (2011). Internet gambling policy in critical comparative perspective: The 

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Griffiths, M.D., & Barnes, A. (200S). Internet gambling: An online empirical study among student 

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Mrs. BONO MACK. And I thank the entire panel, and we will now 

turn to questioning, and I will recognize myself for 5 minutes. 

And I would like to ask Mr. Fahrenkopf this question first. If 

Congress were to enable Internet gaming, do you support the right 

for States to opt-out of such a system and to limit any online gaming participation by their residents to entrust State online operations under that State’s own control? 

Mr. FAHRENKOPF. Absolutely. We are supporters, and always 

have been, that the Tenth Amendment States rights must be recognized. States should always have the right to determine what type 

of gaming they are going to allow in their State, how they are 

going to regulate it, how they are going to tax it. So we have no 

problem with States opting out if they don’t want to participate. 

Mrs. BONO MACK. And then once they’re opted out again, just to 

be clear, that you can opt out and offer intrastate solely? 

Mr. FAHRENKOPF. Well, some of the discussion, Mr. Campbell 

was saying that UIGEA was the purchase—the purpose of that was 

to outlaw Internet gambling. It really wasn’t. The purpose of 

UIGEA, or UIGEA, as we call it, was to interfere with the financial 

transactions that went on. The bill clearly did not say what was 

legal or illegal. 

But there is within UIGEA an intrastate exemptionfrom UIGEA. 

There would still be a determination, as you know. The Justice Department to this day says that Internet wagering of any sort is illegal under the 1961 Wire Act. I don’t necessarily agree with them. 

It is hard for me to believe that anyone in this august body who 

was serving here in 1961 ever thought that there would be a worldwide Web, that there would be the intent. So clearly there is is an 

intrastate exemption from UIGEA for States to do intrastate activity, which would not violate that law. I am not going to pass judgment on what the Justice Department is going to say. I happen to 

disagree with their interpretation that that would be illegal. 

Mrs. BONO MACK. Thank you. And much of what you are talking 

about, you are speaking to the ability to regulate, to have particular Web sites and those Web sites only. Yet in Congress we are 

often dealing with rogue Web sites that are selling, you know, bad 

prescriptions, bad pharmaceuticals. There are rogue Web sites that 

are selling pirated intellectual property. 

How do you propose that you make sure that the consumer can 

truly know that this is a real Web site and a safe Web site, because 

we are are dealing with this in so many other areas right now? 

Mr. FAHRENKOPF. Well, as I said in my remarks, I think that the 

way to go about this, in the wisdom of Congress, Internet poker 

should be legalized. We have got to back, reiterate the 1961 Wire 

Act, as well as adjust UIGEA to make sure that it can be used as 

the vehicle to keep track of what, you know, other Web sites there 

are. Now, I happen to believe that the free market will make a determination. 

Mrs. BONO MACK. But the free market right now is suffering 

from rogue Web sites and from all of the bad prescription drugs 

again, and from rogue Web sites that are coming from offshore 

where the consumer has no concept whether it is a legitimate Web 

site or not. 

255 

So let me move on to Dr. Volberg, though, because time is so limited. All of you have expressed a desire to keep children and problem gamblers from gambling online. If Congress chooses to legalize 

any particular or all online gaming, how would you prevent problem gamblers or minors from continuing to go wherever they can 

place the wager, including the unregulated offshore operations I 

was just talking about? Wouldn’t the dangers for these vulnerable 

segments still persist? 

Ms. VOLBERG. Well, the dangers would persist, but there are 

ways to mitigate the dangers by placing sort of roadblocks in the 

way. Certainly I am not an expert on how to do age verification. 

You heard a speaker, or someone testified last time on that front. 

The issue that I am most familiar with is the question of exclusion programs and pre-commitment programs. And the thing to do, 

or the step that we believe is most effective, is to get people before 

they begin to gamble, to set up the constraints in terms of how 

much money per day or per week or per month that they want to 

spend, how much time they want to spend on those particular Web 

sites, and to set that up ahead of time so that in the middle of sort 

of the excitement of the action, they are not sort of going to lose 

control and keep going longer than they wanted. 

Mrs. BONO MACK. Mr. Lipparelli, I would like to change the subject a little tiny bit toward travel gaming. I believe you have some 

travel operations in Nevada. 

Mr. LIPPARELLI. We do, in fact. 

Mrs. BONO MACK. OK. Can you explain briefly how they are regulated? 

Mr. LIPPARELLI. Each of the tribes, I believe there are two, have 

entered into State compacts where they have agreed to abide by 

the State regulatory structure, so they follow our State regulatory 

regime. 

Mrs. BONO MACK. Thank you. 

Mr. Fahrenkopf, back to you. Should online gaming sites be required to help pay for the services needed to treat problem gambling, or should the State use the revenue it receives to pay for the 

services, or both? 

Mr. FAHRENKOPF. Well, most of the experience that we have had 

with land-based casinos as well as lotteries and the parimutuel industry, it varied State by State. In some States there is a—for example, in the States that have river boat gaming, you have to pay 

a fee to go on the boat, and a portion of that fee is set aside for 

responsible gaming work. 

So it varies from State to State, but either way it could, you 

know, be effective if it was clearly drawn as a means to develop the 

revenue. I might say that there is some real question, however, of 

what adequate treatment is, and Rachel has been involved in this 

business, as others, for 20 or 30 years as to what the proper treatment must be. 

For example, you have a State like Iowa that has had casino 

gaming since 1989, they have raised a tremendous amount of 

money, but in some cases they don’t know how to effectively spend 

it. And that is why research is really the key where most of the 

money should go, at least in the immediate future, along with some 

of the programs that Rachel has talked about. 

256 

Mrs. BONO MACK. Thank you. And my time has actually expired 

a while ago, so I am happy to recognize Mr. Butterfield for his 5 

minutes. 

Mr. BUTTERFIELD. I thank the chairman. Both bills that we are 

talking about here today seek to prevent children from accessing 

gambling sites, and that is certainly a good thing and we can all 

agree on that. However, these two bills take different approaches 

toward achieving the goal. 

Mr. Franks’ bill and Mr. Campbell, 1174, gives the Treasury Secretary broad regulatory and enforcement authority to implement a 

licensing program for Internet gambling sites. This authority includes a requirement that the Secretary make sure that those licensees have various processes in place to prevent underage gambling. 

Mr. Barton’s bill, 2366, allows States to license gambling sites. 

The bill requires the relevant State agencies to, quote, ensure to 

a reasonable degree of certainty that the individual placing a bet 

or wager is not less than 21 years of age, end of quote. 

Let me this time go to my right. Dr. Volberg, would you have any 

concerns with a State by State or even a tribe-by-tribe approach to 

preventing children from accessing gambling Web sites? 

Ms. VOLBERG. Well, I think actually the issue of preventing children from gaining access to Internet gambling Web sites is an important one, because while the technology may exist on a State-toState level to implement those steps—and this applies to problem 

gambling as well—there is not equal political will in every State to 

implement those measures. 

And so what you will get, as I mentioned in my testimony, you 

will get variability across the States in terms of what they are willing to do, what they are able to do. And as a result, I think that 

some States will do a very good job, but other States are going to 

do a much less good job of protecting both underage gamblers and 

problem gamblers or people who are at risk. 

I think another issue that I have great concern about because I 

have done a number of adolescent surveys, is the number of youth 

that access the Internet to gamble is extraordinarily high. In the 

survey that we did in Oregon, 30 percent of our adolescents had actually gambled on the Internet. But most of those had gambled for 

entertainment and not for money. And so when you are talking 

about sort of educating young people about gambling, part of it is 

occurring on the Internet, and I would like to see some prevention 

measures and some education that goes along with, you know, preventing them from actually gambling for money. 

Mr. BUTTERFIELD. Do you think that this issue could be addressed through consistent across-the-board requirements at the 

Federal level? And if it should be at this level, would you have concerns if more than one agency provided oversight? 

Ms. VOLBERG. I think when it comes to prevention and treatment, or certainly prevention and research, that we have not seen 

the States step up to the level that they have or that governments 

have in other countries around the world. So I would say that those 

two areas in particular do require a stronger Federal voice. 

257 

Mr. BUTTERFIELD. We are going to have to have oversight if this 

goes into law, I am just trying to find out what agencies should be 

involved and could it be multiple agencies? 

Ms. VOLBERG. I absolutely think it should be multiple agencies. 

I think that—I have seen models internationally where that does 

happen very effectively, but it does require the legislation to actually say that you have to coordinate these—you know, these departments have to coordinate. 

Mr. BUTTERFIELD. Should any proposal in this area include some 

additional minimum requirements to ensure that licensees don’t 

target children? For example, should any legislation include standards for general advertising and marketing practices and online 

sites, the specific targeting of minors, the system used by the site 

to verify that the user is of legal age before they are allowed to sign 

up? Would you quickly speak to that in 30 seconds? 

Ms. VOLBERG. Yes, I believe there is a need for that. 

Mr. BUTTERFIELD. All right, thank you. 

Mrs. BONO MACK. I thank the gentleman and recognize Mr. Barton for 5 minutes. 

Mr. BARTON. Thank you, Chairwoman. I have a letter from 

Alfonse D’Amato that was written to you on November 7, 2011, and 

it is copied to all members of the subcommittee. I would like unanimous consent to put that in the record. It has been shared with the 

minority. 

Mrs. BONO MACK. Without objection. 

[The information follows:] 

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".~ I'J~:1-J. 

~ ~ ~ ~ 

fLlIX,,('" 

www.theppa.org 

TO: CIIAlRWOMAN ~[ARY BONO MACK 

FROM: M,FONSE I)'AMATO, CHAIRMAN, POKER PLAYERS ALLIANCE 

SUBJECT: 10j251IE,\RING, "INTERNET C;,\MING; IS THERE A SlWE BET?" 

DATE: NOVEMllER 7,2011 

CC: REPUllLIC1\N AND DEMOCRATIC SUBCOMMITfEE MEMBERS 

Thank you for inviting me to testify before your subcommittee on the important topic of Internet 

gaming regulation. As a representative of more than 1.2 million poker enthusiasts, it was an honor to 

present information to the subcommittee and to explain the merits of U.S. licensed and regulated 

Internet poker. First and foremost, regulation must be about consumer protection. Today's non~U.S. 

regulated market leaves our citizens vulnerable and outsources consumer safeguards to other 

countries. Further, regulation of Internet poker is about restoring freedom; the freedom of adults to 

spend their own money in their own home over their own Internet connection competing in a timehonored game of skill. Finally, by protecting· consumers and expanding freedom, the byproduct of 

this good public policy is job creation and revenue. In these economic times we should thoroughly 

examine every opportunity for fiscal stability. 

The October 25th hearing was a good first examination of these issues. However, there are still 

several unanswered questions. I encourage the committee to hold a legislative hearing on H.R. 2366, 

the Internet Gambling Pmbibition, Poker Constl!!1er Pmtection, and Stm,gtbening UIGEA Act if 2011. In this 

hearing the committee can more deeply examine the current best practices that are in use for Internet 

gaming regulation across the globe. Questions about age-verification, problem gambling, bot-play, 

and cheating have been routinely vetted by jurisdictions such as the United Kingdom, Italy and 

France that have allowed their citizens to play on Intemet poker sites for several years. \Vorld 

renowned experts in all of these areas are able to discuss best practices with your committee. In fact, 

Washington, D.C-based Aristotle, a leading provider of online identity verification systems, has 

testified before Congress on the efficacy of keeping underage persons off Internet gaming websites1. 

I also call your attention to a study conducted by Harvard University Professor Malcolm K. Sparrow 

entitled, "Can Internet Gambling Be Effectively Regulated? Managing the Risks."2 This 

comprehensive study outlines technologies and policies that can be put in place to regulate online 

gaming. Professor Sparrow has also testified before Congress on the findings of his study, and Parry 

Aftab, who testified along \\~th me before your subcommittee, mentioned this study as well, 

In the meantime, I would like to address some of the unresolved issues raised in the October 25th 

hearing. 

I Testimony of l\1ichael Colopy, Aristotle Inc., hefore the House Committee On Financial Services: 

http://archives.financialservices.house.gov /hearingll ° /Ol4S_00l.pdf 

2 Call [lIlel1lel Gambh"lIg Be EjJetlipe/y Regula/ed? Afa"agillg the Risks, Professor Malcolm K. Sparrow, John F, 

Kennedy School of Government, Harvard Universiry: http://www.theppa.org/harvardstudy 
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Poker Bots 

\Vith respect to computer programs that impersonate humans in Internet poker games, often called 

"bots", this is an issue that the worldwide Tnternet poker industry has been dealing with for years. A 

significant amount of discussion was afforded this subject during the hearing based on testimony 

provided by Professor Kurt Eggert. \Vhile I respect Mr. Eggert's concerns, as he admitted during the 

hearing, he is not an expert in the area of poker bats, Internet security, or cheating in online poker. 

Thusly, his blanket statements about the prevalence of bot play and the inability to stop it should not 

be considered a settled matter for this committee. Admittedly, I am not an expert on this subject 

either, but 1 do have important facts to present. 

Under existing U.S. law, it is not illegal (at least not expressly so) to use a bot on an Internet poker 

site (though it would violate the terms and conditions for most poker sites), nor is it illegal to develop 

and sell bots that target Internet poker, and there are companies that do so. I-l.R. 2366 would make it 

a federal crime to use a bot on an Internet poker site or to sell a bot program in interstate commerce. 

It would also require licensed operators to use "best of breed" technologies to detect bots and 

remove them from sites. I-Io\vever, even apart from regulation, Internet poker operators have a 

significant incentive to detect and remove bots from their sites in that the integrity of their sites is of 

primary importance in the competitive marketplace. 

To be clear, as a general rule, poker bots are not good enough to make money in high-stakes games. 

Players in those games have a level of skill that current bot technology cannot match. It is true that 

some of the best software developers have developed programs that are competitive with high-end 

poker players, but players at that level generally know each other. A new or anonymous player at that 

level would be subject to a much higher level of scrutiny. Instead, the way bot operators seek to 

make money on Internet poker is by running a network of bots grinding out small amounts of 

money in relatively low-stakes games. 

For that reason, the larger Internet poker companies all dedicate substantial resources to detecting 

and removing bot players. The first level of defense against bots involves monitoring the movement 

of the mouse and the cursor on the screen. There are unique traits to the way humans move the 

mouse that a bot cannot easily mimic. Once a player has been flagged as a probable bot, they can be 

subjected to a CAPTCHA challenge, (a bot detection technique used by many industries wherein a 

consumer is asked to re-type a series of distorted letters and numbers) or subjected to other 

secondary measures. Once a player is identified as a bot, most operators immediately seize that 

player's funds, ban tllem from tl,e site, and flag their IF address and payment information to prevent 

the bot player from seeking to come back under a different identity. 

Beyond monitoring mouse movements, sites can also successfully detect bots by introducing subtle 

changes to the player's screen. Each piece of bot software is typically matched to the pixel-specific 

graphics of a particular poker operator's software, so that it can recognize cards, chips, etc. Humans 

will continue playing normally when such changes are introduced, but bats will often be confused by 

the changes and will effectively freeze up. A player that freezes up in response to such changes is 

ahllost certainly a bot, and can be detected and removed. 

There is another type of bot which does not play the game directly, but rather, monitors the game on 

the player's computer and directs the actions of the players. Obviously, such a bot (sometimes called 

"click here" bots) cannot be detected by the movements of the player's mouse, as the mouse is being 

2 
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75777.255

moved by a human. However, such bots must still be able to monitor and read the screen, and the 

subtle changes described above will frustrate such a bot. 

Another line of defense for poker companies is to constantly monitor the marketplace to see what 

bot programs are being sold. For example, one can easily huy a poker bot program at www.texasholdem-bot.com. However, the best sites have professionals constantly searching for online sites that 

sell bot programs. Those sites' client (the downloaded application that resides on the player's 

computer) will monitor what other programs the player is running; any player running a recognized 

bot program will be flagged and removed from the site. (Merely changing the name of the program 

will not hide it from the client.) In addition, the best sites monitor chat rooms, etc. which are 

frequented hy hot operators and developers, where they share intelligence, etc. 

Over tens of thousands of hands, any poker player will develop recognizable patterns of play --

folding, checking, or raising a particular percentage of the time in a particular situation. The best sites 

have algorithms that monitor the "virtual fIngerprint" of particular players. As stated above, most bot 

operators seek to make money by running the same bot as multiple players at multiple tables on a 

particular site. W1len a site detects that many different players on its site have identical play patterns, 

it recognizes a high probability that those players are all the same bot, and tllose players will be 

subject to higher scrutiny. Similarly, when a particular player is discovered to be a bot, the operator 

will then quickly flag and remove all players with identical play patterns. 

In closing, many e-commerce industries are fIghting the problem of people who seek to use bots 

fraudulently; the issue is not unique to Interuet poker, though Internet poker does present some 

unique challenges. Today, the technology exists to detect and remove nearly all bot players on 

Internet poker sites. J\S artificial intelligence technology improves, the bot threat will be greater, but 

Internet poker operators have a huge incentive to invest furtller in their R&D budgets in order to 

develop ever-better technology and practices to detect bots. H.R. 2366 would require an on-shore 

Internet poker industry to use the best avaibble technology and practices to detect and remove bots, 

and would make it a crime to commercialize bot technology. Today, the best sites go to great lengths 

to detect and remove bot players, and the worst do not. In passing H.R. 2366, Congress would limit 

Americans to playing on sites that protect them against bots, and would provide greater protection 

for U.S. players. 

Identification of Skill Levels 

Mr. Eggert shared his thoughts regarding the perceived disadvantage unskilled players could have to 

highly skilled opponents with the subcommittee. Based on his testimony comparing online poker to 

slot machines, his vision for the game seems to be one with no winners. Rather, he seems to believe 

players should happily lose their money to the "rake" over time, much like people mindlessly pulling 

a lever of a slot machine. This idea is squarely at odds with the viewpoint of the poker community. 

Our membership (correctly) sees poker as a competitive game where skillful play is rewarded and 

encouraged, not sanctioned. 

I t is unfortunate that 1\1r. Eggert - an expert in consumer advocacy but not in poker - chose to 

categorize winning players as "predators." Players and the industry alike see the fact that poker can 

be beaten with skill as a defming, positive characteristic of the game. Winning players are an integral 

part of the game. They are not predators ... they are skilled competitors. 

3 
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Mr. Eggert is correct in stating that poker is a game of skill. One of the skills of the game of poker is 

in evaluating the abilities of the competition. Players can observe one another before making the 

decision to playa single hand. Additionally, as skillful players generally migrate to higher table stakes, 

all players have the option of choosing table stakes that can generally be expected to provide for an 

appropriate level of overall opponent skill. Finally, there are many instructional poker books available 

and many poker websites offering free poker training and strategy discussion. Every player will have 

a fair opportunity to acquire the skills needed to enjoy the game of poker. 

In short, in my opinion a Congressionally-mandated player rating system would be a solution looking 

for a problem. 

Internet Service Disruption 

During the hearing Chairwoman Bono Mack asked what would happen if Internet service was 

disrupted while someone was playing online poker. In short, nearly every non-U.S. regulated Internet 

poker site has dearly stated policies and procedures in place to address this issue. The response varies 

depending on the type of poker game (cash game or tournament) the player was engaged in during 

the disconnection of service. In any scenario, the affected player hand will be folded if he or she is 

unable to reconnect after an interval of extended time, as defined by the site's policies, has expired. If 

multiple players were engaged in the hand the play would continue as normal. If only two players 

were in the hand then the non-affected player would be awarded the pot if the player who has lost 

service cannot reconnect in the allowed timeframe. Best practices in this area can be found by 

reading the Frequently Asked Questions and Tournament Rules' from PokerStars.com, the world's 

largest online poker room that is licensed and regulated in multiple jurisdictions. 

\Ve would encourage that U.S. regulation require that operators make clear to tl,eir customers what 

happens during a disruption of service. 

Comparison of Regulated Internet Poker to Ltgalization of Drugs 

A comparison was made during the hearing that arguments for regulation of Internet poker are akin 

to arguments for the legalization of drugs. Let me be clear, there is no comparison whatsoever. Poker 

is a h,ili activity in nearly every state and has been played legal/v in people's homes for almost two 

centuries. This cannot he said for the use of illicit drugs. Further, state and local governments are the 

largest providers of gaming services, whetller directly through the lotteries or indirectly through 

regulation of pari-mutuel dog/horse racing, card rooms and other commercial gaming. Today, 48 of 

50 states authorize some form of gambling. It seems unconscionable that an activity that is legal 

offline would be prohibited sinlply hecause it is offered over the Internet. TillS attitude is akin to 

outlawing the sale of shoes over the Internet because the same service is offered in a hrick and 

mortar establishment. \Ve believe that Internet poker should be appropriately regulated and available 

to adult COnsumers in a safe and accountable marketplace just as it is currently availahle to them 

offline. 

Again, thank you for inviting me to testify and for the opportunity to present tllls additional 

information. Should you Of your staff have furtber questions, please do not hesitate to contact me or 

the PPA's Executive Director, John Pappas . 

. l,\. sample disconnect policy can be found at the PP.·\ website: htlp:/lrheppa.org/sampledisconneetpolicy 

4 
262 

Mr. BARTON. I want to ask my first question to Professor 

Volberg. You kind of just alluded in passing that your study of people that play poker on the Internet, they often tend to be young 

men with high educational levels; and then you looked directly at 

me and said, ‘‘and a few older men.’’ 

And I want the record to show that I am 62. 

Mrs. BONO MACK. With objection. 

Mr. BARTON. That classifies as old, I guess, but I have never 

played online for money. I play online for play money, but not for 

money. But I respect those that do play for money, and a number 

of young men in my district play professionally and make a living. 

Could you comment a little bit more on the demographics of the 

average online poker player? 

Ms. VOLBERG. Well, I will be happy to do that. I would just 

would like to say that I said older, rather than—I did not mean 

any disrespect. 

In terms of the characteristics of online gamblers, this is not actually a study that I have done, it is a study that—a number of 

studies that have looked at online gamblers and online poker players as a subclass of online gamblers. And the E. Koeger study is 

the specific one that I was referring to here, where they found 

that—they looked at the demographic differences between online 

poker players and online casino gamblers. And what they found, 

that while the online poker players tended to mostly be, or the majority of them were young men between the ages of 18 and 24 with 

relatively high levels of education and income, in contrast the online casino gamblers were primarily or a majority were older 

women with relatively lower levels of income and education. 

Mr. BARTON. But on the poker player demographic online, my experience is that they tend to be very sharp. They all know the probabilities, they all know the permutations. Most of them excel in 

math. 

If you watch the World Series of Poker, which is not online, but 

if you watch the World Series of Poker on ESPN, they all appear 

to be MIT engineering people. I mean, they are not people, though, 

that you would tend to say could be taken advantage of, that don’t 

know what they are doing, that need to be protected. I mean, they 

are folks that are fully aware and feel that they have the ability 

to have a competitive edge. Wouldn’t you agree with that? 

Ms. VOLBERG. I think that that is certainly characteristic of the 

individuals that you described as professional poker players, people 

who make a living. 

I think there are lots and lots of young men out there and some 

young women who also are very smart and also are very well educated. 

The issue is not so much the folks who, you know, know the odds 

and know what they are doing, it is people who are coming into an 

activity where they sort of think that they have gotten pretty good 

because they have been playing on a free play site, and then they 

decide to do it for money and they go in and their expectations are 

a little unrealistic because the free play sites are set up differently. 

Mr. BARTON. I have only got about a minute left. I want to ask 

the chairman of the Nevada Gaming Commission, in your opinion 

263 

would online poker, would it hurt or help mainline brick-and-mortar casinos? 

Mr. LIPPARELLI. I think it is been our experience that if there is 

an impact, it is already reflected in operations. Clearly there is a 

lot of online gaming happening now. It has exploded in the last 7 

years, so if there has been a direct impact, it is probably already 

being felt. 

I think the industry participants that we talk to frequently see 

an opportunity there, but they see it as a compliment rather than 

something that would impact their businesses directly. 

Mr. BARTON. OK. And my last question to Mr. Fahrenkopf. There 

obviously are some very delicate issues in terms of sovereignty of 

the Indian tribes and their casino operations and their regulatory 

approach versus for-profit casinos. 

Do you feel that those issues can be handled in a fair fashion so 

that the Indian tribes concerns, because of their status, can be addressed in a fair way? 

Mr. FAHRENKOPF. We totally respect the sovereignty of the 

tribes, and that is why, as I indicated in my initial remarks, I have 

been doing this for 17 years, and for 17 years I have been saying 

that no law should treat any type of legal gaming different than 

the others. So no one should get a leg up or be hurt between 

whether you are talking about our industry, whether you are talking about Native American gaming, whether you are talking about 

the parimutuel industry, or whether you are talking about the lottery business. 

Mr. BARTON. OK. Thank you, Madam Chairwoman. 

Mrs. BONO MACK. Thank you Mr. Barton. The chair recognizes 

Mr. Towns for 5 minutes. 

Mr. TOWNS. Thank you very much, Madam Chair. And of course 

I want to thank you and Mr. Butterfield for having this hearing in 

terms of bringing the very stakeholders together to have a discussion to determine whether, you know, whether we move forward or 

not, and if we do move forward to make certain that we are doing 

it in a very effective manner. 

Let me begin with you, Mr. McIntyre. I want to make certain I 

understood your testimony. You appear to be concerned about the 

loss of State revenue, you know, being diverted from the lottery 

play to online casino play. Wouldn’t ensuring that the share of tax 

revenue from online poker make up the difference? 

Mr. MCINTYRE. One of the difficulties is, Mr. Congressman, is 

there is no State tax in New Hampshire, so there is that issue. 

There is no State income tax and there is no gaming tax in New 

Hampshire. 

But one of the difficulties we have is our research suggests that 

32 percent of our players—strike that—30 percent of the New 

Hampshire citizens visit a casino once a year, 45 percent of our 

players have visited within the last year, and 53 percent of our regular players have visited one or more times a year. So I firmly believe they draw from the same wealth. 

I don’t think it would have an impact directly, immediately. I 

think it would be over the long term, given the statistics suggested, 

in terms of the demographic that it is a younger player. Lottery 

lives and breathes in the demographic of 35 to 60 years old, and 

264 

that is not where even a poker player is now. It is where the folks 

who are in Internet poker will be in 20 years. 

So it is not for me that I make this case. It is for my successor 

or my successive successors in terms of the impact on lottery revenues, Congressman. 

Mr. TOWNS. Thank you very much. Let me go to you, Mr. 

Lipparelli. 

Given the longstanding role of States authorizing and regulating 

gaming within their own borders and the fact that New York State, 

which I come from, has a very sophisticated regulatory structure 

for gaming, are the States best positioned to handle this new formal wagering? 

Mr. LIPPARELLI. I think it has been our consistent position since 

this topic came up that we actually support a national model that 

gives the States some degree of framework to regulation. Given 

that this is an activity that crosses State borders, it is important 

to have some amount harmonization among those States. I think 

there can be particular levels of additional scrutiny that any State 

might want to impose, that there ought to be some ability to try 

to harmonize what would be a national business. Today, as you 

might know, there are 48-some different regulatory structures. And 

from a private industry perspective, many of which operate in our 

State, it is become increasingly problematic and increasingly expensive to be responsible, to be answerable to 48 different regulatory regimes. So our position from the State of Nevada is that we 

clearly support some kind of national solution. 

Mr. TOWNS. To you, Mr. Fahrenkopf. Again, a little over a 

minute left. Has the ban on online gambling prevented Americans 

from gambling online? Has there been any—— 

Mr. FAHRENKOPF. No. I think, Congressman, as the testimony 

from actually all three of them, your fellow Members of the House 

who were here on the earlier panel, Internet wagering has just exploded. It actually started back early around 2003 and -4. The passage of UIGEA, which was with all good purpose trying to put a 

dent in that, we have seen has really not been as effective as it 

should be. And that is why we believe that has to be amended to 

make very clear what is legal and not legal. And regulation, taxation, is the best way to protect those people who might be vulnerable to the things that Dr. Volberg is talking about and what Congressman Wolf is concerned about. 

Mr. TOWNS. Let me announce I am not a poker player, but how 

would I know, if I am playing, that I am not playing against a machine? 

Mr. FAHRENKOPF. We submitted some additional testimony to 

the panel after the last hearing that got into that question, because 

there was some discussions of the use of bots and other types of 

mechanical, if you will, players, rather than regular players. 

And the technology that we have seen from those jurisdictions 

primarily in Europe, they have developed software which can—and 

Mr. Lipparelli probably knows more about this than I do—software 

that can monitor the way bets are being placed—actually, believe 

it or not, where the mouse is and where it is being pushed on a 

certain period of time to determine whether or not there is a problem. 

265 

But interestingly enough, most of the bots that have been discovered have been discovered because other players, human players, 

have realized that something id not right with the way the game 

is going and report it. 

But maybe Mr. Lipparelli can tell you a little more about how 

the bots can be handled. 

Mr. LIPPARELLI. Mr. Towns, I spent a better part of 2–1/2 years 

looking at the various technologies that exist, and one thing that 

is becoming abundantly clear to me is that you can probably get 

away with trying to cheat a system or trying to play as an underage gambler or trying to utilize the services of the bot, but you will 

be uncovered fairly quickly. The analytical tools that are now being 

deployed as part of these systems have become very robust, so you 

might be able to get away with it once, but you are going to leave 

big fingerprints behind. 

Mr. TOWNS. Thank you, Madam Chair. 

Mrs. BONO MACK. Thank you, Mr. Towns, I appreciate it. And 

recognize Mr. Bass for 5 minutes. 

Mr. BASS. Thank you, Madam Chairman, this is a quick followup to Mr. Towns’ question. Are bots illegal? 

Mr. LIPPARELLI. Well, I think the only way I could answer that 

is depending on what that it is. You would have to define it. There 

are many people that will use the player’s aid on another computer 

to give them basic strategy play. That would probably not be illegal 

in most people’s mind. The use of some kind of an electronic device 

to gain an advantage, which is how we define cheating a game in 

Nevada, would probably be illegal. 

Mr. BASS. But the bill wouldn’t address that, would it? Or does 

it? 

Mr. LIPPARELLI. I am not sure whether it does or doesn’t. 

Mr. BASS. I have a general question for all the witnesses. I believe that Mr. Barton’s bill limits licensees to gaming facilities, I 

don’t know what the definition is, casinos and so forth. 

Mr. McIntyre is here today from the New Hampshire Lottery. 

Lotteries, as he testified, have been around a long time. They have 

their own infrastructures and so forth that guard against cheating 

and fraud and corruption and so forth. Why shouldn’t they be able 

to run online poker if they chose to do so? I am directing that to 

any member of the panel. 

Mr. FAHRENKOPF. As I said, any piece of legislation must treat 

all the legal gaming entities the same, lotteries as well as landbased casinos, Native American tribes and the parimutuel industry. So if the State wants to have their lottery offer online poker, 

I have no objection with that. I don’t think that that would violate 

the rule. 

Mr. BASS. Others? 

Mr. MCINTYRE. Congressman Bass, certainly I thank you for 

your comments and I certainly believe that we would be able to 

handle that function similar to our neighbors to the north, the two 

Canadian lotteries that run this now, the Atlantic Lottery Corporation, representing our neighbor directly to the north, as well as the 

British Columbia Lottery, which runs that function very well. 

Mr. BASS. Mr. Lipparelli. 

266 

Mr. LIPPARELLI. Yes, I share the views of the panelists, that I as 

a regulator—we abide by what our State law says. So in our case 

we would obviously follow that. 

The only comment that I would add to the question posed by Mr. 

Towns and McIntyre, there are several foreign markets that are experiencing the impact of a failure to regulate on existing moneyraising kinds of organizations. There are several lotteries and other 

kinds of organizations that in large measure rely on the proceeds 

of gaming activity and have highlighted the fact that illegal gaming 

activities are starting to have a real negative impact on their revenue flows. So given, you know, the kinds of organizations that are 

out there that benefit from gaming, it is a huge question to them 

as to why not regulate. 

Mr. BASS. So, in conclusion, none of you have any objection to 

amending the bill to expand the scope to allow or to give the opportunity for State lotteries to participate on an equal level and not 

have the 2-year hold-harmless period before they could be involved 

in the same online poker activities as the casinos would be allowed 

to do upon enactment of the bill? 

Mr. FAHRENKOPF. Well, I am not here representing anyone supporting either of the pieces of legislation. We have not made a determination—I would say, however, that one of the—anyone who 

knows anything about online poker knows that for it to be successful there must be liquidity, which means you have to have a lot of 

players. 

And whether or not a State as small as Nevada or New Hampshire or other States could generate the liquidity with an intraState online poker operation to really make it worth their while is 

a real question that is out there. Now maybe States as California, 

Florida, some of the bigger States, you would have that liquidity. 

But again, as I say, I have no opinion on either of the pieces of legislation that are now being considered. 

Mr. BASS. Well, any other comments? 

Mr. MCINTYRE. Certainly if the legislature of New Hampshire 

authorizes us to do so, we would welcome it. And liquidity is a matter to be determined based on the players of New Hampshire. But 

about 5 percent of our players and 5 percent of the State play now 

online currently, so it is about 60,000 people. So I imagine, based 

off of our brand recognition, which is 72 percent favorable amongst 

the citizens of New Hampshire and 85 percent favorable rating 

amongst our players, I would imagine that would increase. 

Mr. BASS. Thank you, Madam Chairman. 

Mrs. BONO MACK. Thank you, Mr. Bass. Mr. Harper, you are recognized now for 5 minutes. 

Mr. HARPER. Thank you, Madam Chairman. And I appreciate the 

witnesses being here and taking their time to enlighten us. 

And if I may start with you, Mr. McIntyre, I believe you said earlier that the State of New Hampshire received, I believe, $1.5 billion towards education since the lottery began; is that correct? 

Mr. FAHRENKOPF. That is correct, sir. 

Mr. HARPER. OK. And how much money has been sold or received for the sale of the lottery tickets, the total amount spent? 

Mr. MCINTYRE. Within State? 

267 

Mr. HARPER. Out of the 1.5 billion that went to education, what 

was the total amount bought or spent on lottery tickets during that 

time? 

Mr. MCINTYRE. It is difficult to characterize because the amount 

of prizes going back to the players has varied over 50 years. But 

currently, of a dollar spent, 67 cents goes back to the players in 

terms of prizes. We keep about 25 cents on the dollar in terms of 

profit. 

Mr. HARPER. Just a curiosity. Do you have to physically go to a 

vendor to purchase the lottery ticket, or is that done online? 

Mr. MCINTYRE. It is done through a computer system that communicates through multiple methods, and you purchase it at a convenience store, supermarket, and the rest. 

Mr. BASS. Will the gentleman yield? 

Mr. HARPER. Certainly, I will yield to the gentleman from New 

Hampshire. 

Mr. BASS. Mr. McIntyre defines online as being on a telephone 

line. You are talking about the Internet. Internet sales are not—— 

Mr. HARPER. I will certainly clarify my question. Are there Internet sales, or does an individual buying a lottery ticket in New 

Hampshire have to go to a store vendor to buy it? 

Mr. MCINTYRE. 99.6 percent of our sales are through a store. A 

very, very, small portion of our sales, what we call subscription 

sales, which are done through the Internet, and it represents less 

than half of 1 percent of our overall sales. 

Mr. HARPER. Thank you. You know, one of the concerns, as we 

look at this, is, for instance, in my district we have the Mississippi 

Band Chocktaw Indians, who have land-based casino gambling. 

And it is hard for me to envision how opening this up, which would 

require more players, would require folks to do that for it to be a 

profitable venture, how that will not have a negative impact upon 

destination gambling. And I would like to hear a response on that 

from whoever would care to answer? 

Mr. FAHRENKOPF. Congressman, some years ago we at the American Gaming Association, which is most of the land-based casinos 

in the country in the commercial area, looked very closely at this. 

The question is cannibalization. If, in fact, Internet poker was allowed, would it cannibalize the business of the brick-and-mortar 

companies? 

We looked at, for a very, very long time, we came to the conclusion that it would not; or if it did, it would be very marginal because we are only talking about poker. Our position is it should 

only be poker. 

We are more in line with the Barton bill’s approach than we are 

with the Frank-Campbell bill’s approach. 

Mr. HARPER. OK. Well, let me stop you for just a moment. If we 

are looking over all at a broader bill, more than just poker, would 

that not expand or increase the probability that it is going to hurt 

destination gambling? 

Mr. FAHRENKOPF. It could, although the demographics of the people who play online, as Dr. Volberg has talked about, particularly 

young men who are between the ages of 18 and 24, higher education, they tend to not be the same people who go to land-based 

casinos. 

268 

Mr. HARPER. OK. 

Mr. FAHRENKOPF. So our position was that the end result would 

be it would be a new profit center rather be very much of a deterioration in the business of land based. 

Mr. HARPER. All right. One of the arguments that has been used 

to support Internet gambling in the U.S., legal, is that we have 

these offshore sites that can’t be controlled. 

Well, if we do this, how—are we still in the situation of not controlling what is that problem? How are we controlling that? Why 

shouldn’t we first look towards coming out with a way to perhaps 

block all payments to them before we look at expanding it here, because I don’t see how we are going to do that. And Dr. Volberg, I 

believe, has had some, maybe in your written testimony, is you are 

going to have a higher cost for the regulated version versus the offshore unregulated version, and what is to keep players from still 

going offshore? 

Mr. FAHRENKOPF. I would answer that in two ways. As I attempted to answer earlier to the chairperson’s question, the market 

will take care of some of that, not all that. In other words, people 

who are going to gamble online, whether or not they would rather 

gamble with the brands they know, U.S. companies that they 

know, rather than going offshore to some outfit that is located in 

the Caribbean island or somewhere. So the marketplace will take 

care of some of that. 

The other step will be going back and strengthening UIGEA. The 

original purpose of UIGEA was exactly what you pointed out: how 

to block these transfers offshore. We have to go back and strengthen that act by making clear what is legal and what is illegal to give 

guidance to American banks and financial institutions so they can 

effectively block those. 

Mr. HARPER. Shouldn’t we do that first before we proceed with 

anything else? 

Mr. FAHRENKOPF. Well, I am not sure which should be first. I 

think you can both do it at the same time. 

Mr. HARPER. Thank you, I yield back. 

Mrs. BONO MACK. Thank you Mr. Harper. Mr. Lance, you are 

recognized for 5 minutes. 

Mr. LANCE. Thank you very much and good morning to you all. 

This is very interesting to me, and I want to thank the director 

from New Hampshire regarding his remarks concerning New Jersey. And New Jersey certainly has learned a great deal from New 

Hampshire. 

And to Director McIntyre, it is my understanding that regulatory 

bodies here in this country and also abroad often rely on the independent testing laboratories for confirming that equipment used in 

gaming is fair for consumers. I am also told that some foreign jurisdictions that currently allow Internet gaming have similar testing 

requirements, not only for fairness issues but also for verification 

and location matters. 

My question to you, and perhaps to others on the panel as well, 

is to what extent, if at all, do you think that statutory change here 

should include such requirements? 

Mr. MCINTYRE. I mean, certainly—Congressman, thank you for 

your comments and you have a wonderful lottery. One of the conVerDate Aug 31 2005 13:20 Apr 30, 2013 Jkt 037690 PO 00000 Frm 00272 Fmt 6633 Sfmt 6633 F:\112-10~1\112-10~1 WAYNE
269 

cerns we have is integrity, and we test and retest and retest, and 

we use outside testing firms. We use outside testing firms to test 

the testing firms for that very reason, sir. 

So I think in terms of an outside firm like Gaming Laboratories 

or the rest that offer the services and the protocols to test systems, 

I think that is actually an excellent idea. 

Mr. LANCE. Yes, thank you. And would others on the panel have 

a opinion, the director in Nevada? 

Mr. LIPPARELLI. Certainly, Mr. Lance. I spent 20 years in the industry, 18 of which was submitting products to testing labs around 

the country. And I have some colleagues that operate in other jurisdictions that don’t see as much value in pretesting. I see incredibly 

high value in that, and I don’t think there would be any regime 

that we would consider that would allow someone to deploy gaming 

technology that is not subjected to high assurance and pretesting. 

Mr. LANCE. Thank you. That was my view as well, and I would 

hope it might be included in the legislation or at least in some provision that would permit that based upon rule and regulation. 

On another area, on cannibalization, I am concerned to some extent whether this would be competition, not only for various aspects of the regime but also, for example, for State lotteries. 

And to the two gentlemen who are certainly involved in this, do 

you think that there might be only a certain gaming amount, a pie, 

and would this lead to cannibalization of lotteries, obviously an 

issue of importance to those of us in New Jersey who rely on our 

State lottery? 

Mr. LIPPARELLI. Well, I guess it is more my personal views than 

my role as a State regulator, but as a State regulator we do have 

the economic health of our industry to consider. I think what is 

ironic about the circumstances that exist today, the legitimate licensed operator who cares for their patrons and establishes policies, is that this distinct disadvantage to those who are really 

under no penalty of prosecution for playing in this field today. 

So we have got this incredible imbalance, people we enjoy great 

relationships with, people who take lots of time and energy and 

money to keep their operations aboveboard, competing against 

those that have no view of that. And so I think to the extent that 

there is, again, an erosion of market share, that is occurring today 

without abatement. 

Mr. LANCE. Thank you. Mr. McIntyre, Director McIntyre. 

Mr. MCINTYRE. Yes, Congressman. I certainly use the example 

frequently related to the gambling pie and suggest that Nevada, in 

its own decisions, has no State lottery. And they have avowed repeatedly that it is because they don’t want to compete. So I certainly believe that in terms of there being a finite number of dollars, I believe that to be true. 

Mr. LANCE. Thank you. And Chairman Fahrenkopf? 

Mr. FAHRENKOPF. Congressman, if a State is concerned about 

cannibalization of the lottery, its State legislature and Governor 

can make a determination to opt out and therefore their lottery will 

not be in danger. 

Mr. LANCE. Yes, thank you. That would be my view as well. And 

regarding pies, to all of you, a happy Thanksgiving. 

270 

Thank you very much, Madam Chair. I yield back the balance of 

my time. 

Mrs. BONO MACK. I thank my sentimental colleague and recognize Mr. Guthrie for 5 minutes. 

Mr. GUTHRIE. Thank you, Madam Chairman. I was kind of interested, and I have looked through the testimony written, and Mr. 

Fahrenkopf and Dr. Volberg, both of you cite credible studies, looks 

like the people who did the studies have good curriculum vitae, but 

you come to different conclusions about expanded online gambling 

and expanded access for problematic gamblers or people who are 

addicted to gambling. 

Could you gentleman explain, you both had studies that showed 

completely two different results. Do you want to explain your 

study, and then your study, Dr. Volberg—— 

Mr. FAHRENKOPF. Well, I think the important words that I said 

with regard to online gamblers, they are not more likely to be pathological gamblers. 

If you take into consideration what I call the allowances that are 

made for participation in other gambling activities, and that is one 

of the problems with some of the studies that have been done and 

have been cited by Dr. Volberg. 

I also realize that her studies, one of her studies had 139 people 

or 135 gamblers, another had 179, and that is why I used the Harvard studies, 40,000 gamblers online in Europe who have been examined. And I just think the weight of the evidence goes that way. 

But Dr. Volberg herself admits that you have got to be careful 

with your sample to make sure that you don’t oversample with 

young men who are more likely to be those individuals included. 

And I am not sure whether in those surveys that she cited in her 

written documents—— 

Mr. GUTHRIE. The 40,000 Harvard study had the same percentage of psychological gamblers; is what you are saying they had the 

same percentage? So there was not an evidence of an appreciable 

different percentage of problem gamblers online as it is in a casino. 

Mr. FAHRENKOPF. I think the numbers were very different with 

regard to the conclusion as to whether or not just Internet gaming 

itself is going to create more problem gamblers or whether or not, 

as Dr. Volberg has quoted in a number of her studies, it is just another element that a problem gambler is going to play; in other 

words, they are going to—— 

Mr. GUTHRIE. Dr. Volberg, do you want respond? 

Ms. VOLBERG. With all due respect to Mr. Fahrenkopf, the very 

small sample that he cited there, it looks like those results are 

from the Nevada survey that we did in 2002. 

Mr. FAHRENKOPF. No, it was a California study, 135 Internet 

gamblers. 

Ms. VOLBERG. One hundred thirty-five Internet gamblers out of 

a total sample of 7,121. 

The part that, you know—it is very difficult in a forum like this 

to get down into the nitty-gritty of research studies, and I would 

invite all of you to come to the National Council’s annual conference and listen to these things be debated. 

I think that in my mind, there is very clear evidence that problem gambling rates amongst Internet gamblers are extremely high. 

271 

They are three to four times higher than they are amongst people 

who do not gamble on the Internet but do other forms of gambling. 

The issue is that most people who gamble on the Internet also 

do other types of gambling. They are casino players, they play the 

lottery, many of them are horse betters, and so on and so forth. 

And so when you do an analysis it is very important to control for 

those things. 

In the California survey that Mr. Fahrenkopf has just referenced, 

we found that only that very small number or very small proportion of about 2 percent of our sample had gambled on the Internet, 

but 11 percent of them scored as pathological gamblers and an additional 20 percent of them or 19 percent of them scored as subclinical problem gamblers. 

And when you did a statistical analysis that controlled for the 

demographics for co-morbid disorders, for other types of gambling, 

the Internet gamblers were actually 10 times more likely to have 

a gambling problem than the people who were not gambling on the 

Internet. 

Mr. GUTHRIE. Wouldn’t you say that because you can do that 

anonymously, because some people do things on the Internet they 

don’t do in public—I mean, that is what we—is it because they can 

do it anonymously? Is that why you see it at higher rates or access, 

because nobody sees you walking into the casino and gambling, see 

you are on the Internet, and other forms of behavior people have 

done on the Internet you can’t believe they have done. We have 

had a problem in Kentucky State Government where people are 

doing stuff on the computer that is just unbelievable. And is it because of the anonymity of it all that you don’t think somebody can 

find you? 

Ms. VOLBERG. I think it is the anonymity. It is also the ease of 

access. It is the fact that, you know, there are no external controls 

in terms of, you know, being socially visible to other people. I think 

that there is a number of different features of the Internet gambling that are of great concern to people who are concerned about 

the issue of problem gambling. 

Mr. GUTHRIE. Thank you. I see my time has expired. I yield back. 

Mrs. BONO MACK. Thank you. Dr. Cassidy, you are recognized for 

5 minutes. 

Mr. CASSIDY. Again, this has been a very informative panel. I 

think at least a couple of you must have read some of the questions 

I had last time. If not, you are incredibly intuitive, in which case 

I want to gamble with you on your side, not against you. 

On the other hand, Dr. Volberg, you and Mr. Fahrenkopf actually 

pose some different conclusions. I think I read in your testimony 

that worldwide there is about 4 billion played on Internet gambling, and Mr. Fahrenkopf estimates that there would be 2 billion 

in tax revenue generated. Now, those numbers seem incompatible 

unless you are imagining, Mr. Fahrenkopf, that there would be a 

dramatic escalation in the amount of online gambling, or if you disagree with Dr. Volberg’s statement that there is 4 billion only. 

Mr. FAHRENKOPF. One of the problems with anyone 

guesstimating what the tax revenue that is going to be generated 

is, is we are dealing with an unregulated industry. So what we are 

doing, any of us who are trying to estimate it, would be we are 

272 

dealing with offshore companies that are trying to give us estimates. 

For example, there was a PriceWaterhouseCoopers report that 

was actually filed in the Financial Services Committee in the last 

cycle that said there would be $49 billion in revenues. Well, there 

were a lot of assumptions you had to make. It was that every State 

would opt in. I mean that it would include sports wagering. I mean, 

you have got to look at it carefully. 

I tend to go on the low side because I just don’t feel there has 

been sufficient evidence out there of exactly what the bottom line 

would be, plus we don’t know what the tax structure would be if 

legislation would be passed. 

Mr. CASSIDY. But if it were taxed too much, it would drive people 

to the illegal offshore sites. So, that is—— 

Mr. FAHRENKOPF. That is the problem they are have right now 

right now, Congressman, for example, in Great Britain. In Great 

Britain, they put in place such a high level of taxation on Internet 

gaming companies that most of the companies have left and have 

gone to the Isle of Man, have gone to Gibraltar, have gone to other 

places to locate because—— 

Mr. CASSIDY. Now, that is a nice segue, just because I am out 

of time, and I am going to be out of time. 

Dr. Volberg, your testimony also points out that in places like 

France and in Britain, 25 to 33 percent of the people still are offshore, if you will, at illegal sites. 

Ms. VOLBERG. It is actually the opposite way around. It is only 

about 25 to 40 percent of domestic players in those markets who 

play exclusively with the domestically provided and regulated sites, 

and it is the remainder of the market, as I understand it, or the 

remainder of the players who actually continue to play either on 

out-of-jurisdiction sites or use a mix of domestic and nondomestic. 

Mr. CASSIDY. So we really shouldn’t view this as a cash cow for 

State treasuries because if we tax it too much we will just drive 

people offshore. That is a fair statement? 

Mr. FAHRENKOPF. Well, I think—that is why I say we have to 

tighten up UIGEA and give the banking facilities in this country 

the guidance that they will need to stop financial transactions with 

those offshore operations. That will stop it. 

Mr. CASSIDY. Although going back to what Mr. Frank said, if 

people wish to do so in this age, it is hard to imagine you can somehow keep them—although I enjoyed some of your testimony about 

the online protections you could create, and thank you for adding 

those. 

There is also a little discrepancy. Dr. Volberg, you mentioned, 

one of the things—I don’t have my glasses on, I am sorry, I can’t 

see. Fahrenkopf? 

Mr. FAHRENKOPF. Yes, it is. 

Mr. CASSIDY. I apologize. Mr. Fahrenkopf, you mentioned that in 

one of your references that only 2.5 percent of college students play 

online Internet. But Dr. Volberg, I thought maybe you or someone 

else I saw referenced said 30 percent of high school—of college 

males are online. Do I have my numbers totally confused? 

Ms. VOLBERG. I think there might be some confusion about the 

numbers because—— 

273 

Mr. CASSIDY. What is the prevalence of Internet gambling 

amongst college males? 

Ms. VOLBERG. I wish I could answer that because we do not have 

any recent surveys that tell us what Internet gambling participation rates are in the U.S. population. 

I would estimate, based on what we have seen in some of the recent State-level studies, like the California study that came up earlier, is that probably while about 2 percent of the general population gambles on the Internet, amongst college-age males it is 

probably going to be closer to 8 to 10, possibly up to 15 percent. 

Mr. CASSIDY. OK. It is different from the 2.5 percent of all students, even if it is 50 percent female/male. 

Mr. FAHRENKOPF. There has been a recent study of 10,000 college students. 

Mr. CASSIDY. That is the one I am quoting. 

Mr. FAHRENKOPF. Yes, they found that almost 2.5 percent had 

gambled on the Internet and only six-tenths of a percent did so 

monthly or more frequently. That is from LaBrie. 

Mr. CASSIDY. Now that prevalence is far lower. That is why it 

seemed odd. 

Ms. VOLBERG. No, that is 2003 that it was published. 

Mr. FAHRENKOPF. 2003. That is right. 

Mr. CASSIDY. I see. That is dated data, if you will. 

Mr. FAHRENKOPF. Yes. 

Mr. CASSIDY. Well, I have other questions, but I am out of time. 

I yield back. 

Mrs. BONO MACK. Thank you, Dr. Cassidy. And the chair recognizes Mr. Olson for 5 minutes. 

Mr. OLSON. I thank the chairwoman for hold this hearing and 

also thank the witnesses for your testimony, your time, and your 

expertise. It is almost over. 

I would like to limit my comments this morning as to what is 

going on in my home State of Texas in regards to legalizing gambling, issues with UIGEA, and one question, as my colleagues have 

touched on most of my questions previously. 

In March of this year the Texas House Committee on Licensing 

and Administrative Procedures held a hearing on nine bills which 

propose a variety of options for legalizing gaming in Texas. 

Currently, gambling in Texas is limited to State lottery, three 

federally recognized Native American tribes, and gambling at horse 

and Greyhound racetracks. Supporters of gambling in Texas point 

to legalizing poker, casinos, and slots as a way to help with State’s 

budget issues in the form of nontax revenue, as you allude to up 

there, the success you have had in New Hampshire. 

Many folks in Texas also talk about the potential for thousands 

of new jobs that could be created. Others argue that gambling 

preys upon the poorest Texans and creates bigger social problems. 

This is an important debate that my State is having, and I also appreciate our committee is exploring this issue of interstate online 

gaming and the current issues with UIGEA. 

Mr. OLSON. [Continuing.] I have heard from a very, very vocal 

and savvy group of Twitter and Facebook constituents in the 22nd 

District who are very much in favor of legalizing online poker. I 

would like to read one email from one of these constituents. This 

274 

is from Valerie in Houston, Texas. And she said, Dear Representative Olson, the UIGEA slaps the Internet with unnecessary government regulation and oversight that limits innovation and growth. 

The act may have been intended to stop unlawful Internet gambling, but because it never actually spelled out what that meant, 

UIGEA ushered in unintended consequences that put the government in the role of Big Brother. In particular, the act has the effect 

of turning online payment transaction companies into informants 

and enforcers for the Federal Government, raising privacy concerns 

as well as cost to consumers. Sincerely, Valerie, from Houston, 

Texas. 

Many people believe that if you are going to play Internet poker 

you should not follow the Federal Government’s model of managing 

your budget.You should do it with money you have earned and not 

money that you expect to earn or hope to earn. As you know, legislation has been introduced in the House which would prohibit the 

use of credit cards from making deposits in Internet gaming accounts. 

My question is for you, Mr. Fahrenkopf and any of the witnesses 

who wants to get involved, do you think most companies would be 

willing to limit themselves to debit cards and electronic checks? 

Mr. FAHRENKOPF. I mean we haven’t taken a position on the 

whole question of credit cards. It was a major issue in the Frank 

bill in previous legislative sessions. The original bill did provide for 

the use of credit cards. However, when the bill was being voted out 

of committee, that was removed. At this point in time, we don’t 

have a position one way or the other on that from the standpoint 

of the American Gaming Association. 

Mr. OLSON. Anyone else want to comment? Mr. Lipparelli. 

Mr. LIPPARELLI. In the State of Nevada the use of credit cards 

is illegal for gaming. 

Mr. OLSON. Mr. Fahrenkopf, any comments? 

Mr. FAHRENKOPF. I already did. 

Mr. OLSON. I apologize. Mr. McIntyre. 

Mr. MCINTYRE. Certainly I am under the direction of the New 

Hampshire legislature and the governor’s office, so whatever they 

would say to do I would do heartily. 

Mr. OLSON. Sounds like my marriage, yes, sir. And Dr. Volberg. 

Ms. VOLBERG. I think that there would be a lot of arguments in 

favor of prohibiting the use of credit cards for Internet gaming. So 

I would be in favor of that. 

Mr. OLSON. Thank you very much. That is all my questions. I 

yield back. 

Mrs. BONO MACK. I thank the gentlemen, and the chair now recognizes Mr. Butterfield for the purposes of a unanimous consent request. 

Mr. BUTTERFIELD. Thank you very much. I am going to ask 

unanimous consent to have this letter dated November 18th, 2011, 

included in the record. The letter simply reiterates the necessity to 

hear from Federal agencies who will be impacted by any bill we 

pass relating to the legalization of Internet gaming. 

Thank you. 

[The information follows:] 

275 


75777.257

FRED UPTON, MICHIGAN 

CHAIRMAN 

HENRY A. WAXMAN, CALIFORNIA 

RANKING MEMBER 

ONE HUNDRED TWELFTH CONGRESS 

(lCongre55 of tbe I1niteb ~tate5 

1!,)01t.sc of l\epre.sentntibeg 

COMMITTEE ON ENERGY AND COMMERCE 

2125 RAYBURN HOUSE OFFICE BUILDING 

WASHINGTON, DC 20515-6115 

The Honorable Mary Bono Mack 

Chairman 

Subcommittee on Commerce, 

Manufacturing, and Trade 

U,S, House of Representatives 

2125 Raybum House Office Building 

Washington, DC 20515 

Dear Chairman Bono Mack: 

M"joritv {202,22&-2927 

Mlnoriw (202) 225""3641 

November 18, 2011 

Today, the Subcommittee on Commerce, Manufacturing, and Trade convened its sccond 

hearing this session on Intemet gambling. As Ranking Member, I want (0 thank you for 

scheduling time for us to consider this complex topic and evaluate proposed legislation, At the 

tirst hearing, I spoke (0 the importance of proceeding in a cautious and deliberate fashion and the 

need to hear from federal entities charged with implementation and oversight of Intemet 

gambling, However, I am concerned that the Subcommittee has not yet received testimony from 

the very CIltities that would oversee and implement key regulations governing a potential new 

system of legalized online gambling, These entities include, among others, the Departments of 

Justice, the Treasury, and Commerce, and the Federal Trade Commission, 

At the Subcommittee hearing on October 25, 2011, you indicated that an essential goal of 

the hearing was to determine the proper role for federal regulators, You asked, "Can online 

gambling be regulated effectively? And what role should the federal government play to protect 

Amel'ican consumers from 'sharks?",1 I strongly agree that both questions are central 10 our 

oversight of this topic, But we cannot obtnin informed answers abo lit the effectiveness of 

gambling regulation or consumer protection if we fail to invite key federal government agencies 

to testify as witnesses, 

I I·louse Committee on Energy and Commerce, Hearing Oil fnremet Gaming: Is 711ere ({ 

Sqle Bel?, I 12th Congo (Oct. 25, 2011), 
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The llonorablc l'vlary Bono rvlaek 

Noycmbcr 18. 20 II 

Pag~ 2 

Legalizing Internet gambling nationwide would involve several federal entities. each with 

difterent roles to play. Currently. the Department ofJustice enforces the Wire Act.' and has used 

that statute to treat online gambling as illegal. The Dcpm:lIncnt of Justice also enforces the 

Unlawfullnternct Gambling Enforcement Act (UIGEA).' which bans gambling enterprises [i'om 

accepting checks. credit card charges. electronic tHmslers. and other payments connceteclto 

unlawful online bets or l\'agers. For its part. the Department of the Treasury was required 11l1lkr 

UIGFA to prescribe regulations requiring tlnuncial transact.ioll providers to establish policies and 

pro~cJurcs to "ili<:ntify and block Dr olhcflvise prevent or prohibit restricted transactions ... ·1 

Under II.R. 1174. introduced by Rcprcscntativ.:s John Campbell and Barney Frank. Treasury 

\loulcl implement a licensing program for Internet gambling sites. Under II.R. 2366. introduced 

by Chairman Fmcritus Joe BHrton. the Department of Commerce would oyerscc the state or 

triballiccnsurc of online poker \·cndors. Finally. although it has been left out of current 

proposals. the Federal Trade Commission enforces l~lir business and marketing practices am! 

possesses the appropriate ('-commerce expertise to protect online gamblers' personal data and 

privacy. 

It should be no mystery why strong oversight of online gambling \\ould be required. In 

addition to monitoring industry practices, prcwnting Ibud. and prodding protections for 

indil'iduals \\ ith gambling problems. regulators must ensure that children and teens arc 

l;olllpletcly ""eluded li'()ln gambling a<.:til'ilics. 

The involvement or minors in gambling is particularly problematic and already happens 

ulltoo olkn. An October :W I 0 study by the !\nncnherg Public P()lic~ Center found that 6.2% of 

boys age 14 to 17 (roughly 530.000 teenage boys) had used an Intcnwt gambling site in the 

previous month and while participaliLm rates by girls were lower. they \\cre incrcasing5 

IV!oreoycr. in the P<lst month. the Pittsblilxh hiiJllI1<,-Re1'iclI' unci U5~-1 Today both reported that 

olTshore betting sites arc profiting !i'om wagers on televised high school football games. I

' The 

J 8 USc. ~ 1084. 

31 U.S.c. ~ 5361-5367. 

31 1l.S.C. ~ 5364(a). 

i l\nl1cnberg Public Policy Center, in/ernel Gumblill!!. Groll's lImo})!!. Jfllie folllh ARes 

i8 ro 12: Gamhling .. lfs!) Increases ill fliRIi Schoo! ilge F<'II/ale fOil/II. ACc'ording fa .\'(/11011(11 

Annen!>erg SlIrI'ej' 01Tolllh (Oct. 14.2(10) (online at 

\\'Ww .anncnbcrgpu bl icpn I icycentcf.o rgiN ells Delai Is.aspx"l11yldc

- 3 9 5 ). 

o Jason Caw. fligh School ri)()lhol! /Jellillg !lankin i't:llI1sy/nmiu .. lIhfelie Officials, 

Pittsburgh Tribune-Relicl\ (Oct. 3D. 2(11) (online at 

II \\\,.pitlsburghlil·c.comtx/pittsburghtribisports/highschoolis_764594.html): Jim llalley ami 

Nicok Auerbach, Offshore Siles Selling Helling Lilies Oil lligh School Fooll>all. USA Today 

(Nov. 4. 20J 1) (online at w\1\\.usatoday.colll/sportslpreps/football/storyI2011-11-02thighschool-football-betting!51 06548211 ). 
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Th\: Honorable l'vlary Bono Mack 

November 18,2011 

Page 3 

founder of one such site was quoted as saying, "[tJhe customers who bet the games don't have a 

problem witll morality. If the customers are happy, I am happy." Commenting on the scope of 

the problem in countries where gambling is legal, he said. "Walk to any street comer in the 

United Kingdom. You can bet on under 16-year-old soccer events, boys or girls. Any match, 

just about any amount.,,7 This practice represents the type ofenforcemcnt challenges federal 

regulators will face if online gambling is made legal. 

If the Subcommittee is to proceed with (he federal legalization and regulation of Intemet 

gambling, it must do so with careibl consideration and thought I respectfully request that our 

Subcommittee seek formal testimony from federal entities that would be tasked with any 

oversight of legalized gambling. 

Thank you very much. 

Very truly yours, 

7 Cain, High School FOOlbal! Belling Rankles Pel1n1~vl\,({l1ill Ari1lelic C?flicia!s. 
278 

Mrs. BONO MACK. Without objection, and I, too, have received 

written testimony from Mr. Robert Martin, Chairman of the 

Morongo Band of Mission Indians, that has been shared with minority counsel. I understand they have no objection to including it 

in the record of today’s hearing. Therefore, I ask unanimous consent that it be included in the record of this hearing. 

[The information follows:] 

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TESTIMONY OF ROBERT MARTIN, CHAIRMAN, MORONGO BAND OF MISSION 

INDIANS 

HOUSE ENERGY & COMMERCE COMMITTEE 

SUBCOMMITTEE ON COMMERCE, MANUFACTURING & TRADE 

"Internet Gaming: Regulating in an Online World" 

NOVEMBER 18, 2011 

Summary 

Despite the far-reaching ramifications that online gaming legislation will have on tribal gaming 

enterprises, tribal governments have not been adequately consulted in the development of 

various federal online gaming legislative proposals. Such legislation should ensure that tribes are 

given fair and equal access to the online gaming market in a timely and forthright manner and 

that those activities become subject to the regulatory oversight by the tribe that authorizes the 

gaming to exist and when appropriate, the National Indian Gaming Commission. Tribes are 

important stakeholders in the current gaming market and must be afforded the same 

opportunities as all other operators. To be sure, 100 percent of the Tribes would face new 

competition with the authorization of online gaming, while only few will likely have the 

financial strength to compete with those who are granted new online gaming franchises. With 

that understanding, the passage of any legislation authorizing the use of the Internet, including a 

bill that addresses all of the concerns raised by tribes, remains troubling. 

Written Testimony 

My name is Robert Martin and I am the tribal chairman of the Morongo Band of Mission 

Indians. Our reservation is located just east of Palm Springs in Southern California. I appreciate 

the chance to provide testimony on this important issue. 

When it comes to gaming, Morongo has a proud history of leadership. I was serving on the 

Tribal Council when Morongo and the Cabazon Band of Mission Indians won the landmark 1987 

Supreme Court case that confirmed the sovereignty of all Indian tribes and allowed Indian 

Country to establish gaming operations. That decision proved to be a watershed moment for 

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Indian Country that helped transform the lives of thousands of Native Americans by allowing 

tribes to establish gaming enterprises. 

Tribal gaming has allowed Indian Country to overcome decades of poverty, intolerance and 

neglect to provide for our people and to become sdl~reliant. Gaming has empowered tribes 

across the nation including Morongo to assist other non-gaming tribes by providing support for 

tribal education and economic development projects that help put others on the road to selfdetermination. As a result, tribes have been able to provide 628,000 jobs and $11.8 billion in 

federal. state and local revenue from gaming and non-gaming enterprises in 2009 alone. This 

revenue, combined with the hill ions in economic activity related to tribal gaming. has helped fi.lel 

the economies of our counties and states. 

But, as we all know, the nature of gaming is changing. Just as it has with so many other 

industries, tbe Internet is revolutionizing the gaming industry. The Spectrum Gaming Group, a 

weil-respected international authority on gaming, reported in 2010 that online gaming is growing 

at a rate of 10.6% annually - five times more than the growth rate of brick and mortar casinos. 

The Internet is tbe new frontier in gaming. With the right legislation, Internet poker can be a 

fantastic opportunity for Indian Country to continue along the road of selt~reliance. But the 

wrong federal legislation would be disastrous if it created a playing field that did not allow all 

operators. including tribes. from competing fairly. By having access to online poker, Indian 

Country will be able to continue providing for our people by creating and cultivating new 

opportunities to adapt and meet the demands of customers as technology changes. 

So tar, we have not seen any federal online gaming bill that puts tribes on equal footing with 

other potential online poker operators. In the last 18 months. there have been seven attempts at 

federal Internet gaming legislation. These bills have promoted some form of exclusivity for 

certain types of operators, typically casino entities based of out Nevada, including HR 2366. 

When discussing the authorization of Internet gaming, I believe there are five critical issues that 

must be addressed. 

First, is the issue of access. Tribes must have the chance to participate fairly in the online gaming 

market. As gaming operators. we mllst have the same rights as all other casino operators to offer 

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online gaming in a fair and equitable manner. Restricting the rights of tribes to participate in 

online gaming such as limiting the ability of tribes of offer Internet poker players off of 

reservation lands -would severely and unfairly inhibit our ability to compete. and will jeopardize 

our financial standing. Such restrictions would fly in the face of the very purpose and capabilities 

of the Internet. which is about breaking down borders and providing a vehicle for 

communication and com merce. What good is the Internet if it can't be used to extend beyond 

the borders of our reservations" 

The second issue is timing and entry into the marketplace. It is important that tribes have tbe 

opportunity to enter tbe online gaming market at the same time as any other potential operators. 

This ensures that tribes are afforded the same chance to compete fairly for the all-important 

market share. 

The third issue revolves around eligibility of Internet poker operators and efforts to limit tribal 

participation. Any federal legislation should not impose size restrictions that place unnecessary 

burdens on smaller tribes looking to participate in online poker while favoring large Nevada 

casinos. HR 2366 would limit the pool of potential online poker operators only to large casinos 

that have 500 or more slot machines or 250 or more poker tables for five years or more. This 

deals an unfair hand to smaller tribes who do not meet this arbitrary and exclusionary size 

standard that brazenly seeks to favor Nevada casinos. And under HR 2366, those who do not 

meet these standards would he ineligible to hecome an authorized Internet poker operator for at 

least two years, giving Nevada casinos an unfair monopoly. 

Fwthermore, we helieve online poker is an appropriate place to begin when discussing online 

gaming legislation, which brings me to our fourth issue. This is the ideal game with which to 

entcr government-sanctioned operations. Poker is a well-delined game in terms of operation. 

regulation and popularity. Other /(lrmS of games are less defined and including them in any 

online gaming legislation will undoubtedly spawn a host oflegal challenges and regulatory 

hurdles that would delay the launch of online poker and the revenues it would generate. By only 

legislating on this single game-type, we are ensuring that operators can quickly launch their 

online poker sites and begin generating economic activity, jobs and govel1lment revenue. 

rage 3 of 5 
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This brings me to our fifth issue and the topic of this hearing: regulation. Any legislation 

authorizing online gaming should not compromise existing state-Tribal compacts that generate 

millions in state and local revenues. Additionally, the question arises as to who would serve as 

regulators? HR 2366 seeks to award regulatory oversight and authority for online poker with the 

Commerce Department, which is both concerning and confusing. When IGRA was approved in 

1988, it established the primary federal regulatory authority for Indian gaming as the National 

Indian Gaming Commission (NIGe) within the Department of the Interior. The experts at NIGC 

and Interior have had the primary responsibility for regulating tribal gaming for more than two 

decades with a proven track record of capable and professional oversight of tribal gaming. 

As National Indian Gaming Association Chairman Ernie Stevens Jr. has testified, the 184 

members ofNIGA unanimously approved a set of principles in October 20 I 0 that any online 

gaming legislation must satisfy to receive Indian Country's support. Designed to preserve the 

sovereign rights of tribes, those principles include: 

• Indian tribes arc sovereign governments with a right to operate, regulate, tax, and license 

Internet gaming, and those rights must not be subordinated to any nonfederal authority; 

Internet gaming authorized by Indian tribes must be available to customers in any locale 

where Internet gaming is not criminally prohibited 

• Consistent with long held federal law and policy, tribal revenues must not be subject to 

tax 

Existing tribal government rights under tribal-state compacts and IGRA must be 

respected 

The legislation must not open up the Indian Gaming Regulatory Act (IGRA) for 

amendments 

Some have questioned the NIGA principles, particularly the long held federal law and policy that 

tribal revenues must not be subject to tax. But they are not acknowledging how tribes have 

provided billions in state and federal revenues, from payroll taxes to Tribal-State compacts 

through which tribes have provided billions in state revenues. 

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The discussion of Internet poker is not new to me. I have been an outspoken advocate for 

Internet poker for several years. But despite being on the foren'ont of this issue, I am distressed 

to report that I have not been contacted by any of the federal bill authors seeking my comment. 

For that matter, the authors of the seven federal proposals over the past 18 months have not 

adequately communicated or dialogued with Indian Country in any meaningful manner during 

the drafting of the aforementioned federal legislation. Nevertheless, tribes have taken steps to be 

heard on the issue. 

To date, there hasn't been a federal legislative proposal that meets NIGA's principles or that 

protects the rights oflndian Country. Indian Country should not be left out ofthe discussion. 

We should have a voice at the table just as so many others have. 

Tribes that want to participate in online gaming markets must have the ability to do so 

equitably and fairly in an open and competitive market. 

Page 5 firS 
284 

Mrs. BONO MACK. With that, I am happy to thank my colleagues 

for their participation today. I thank the panel very much for your 

help in shedding light on what is clearly a very complicated issue. 

I look forward to our work together if this moves forward. 

As we conclude our hearing today and prepare to depart to the 

four corners of our great Nation to celebrate Thanksgiving, please 

permit me to say what a blessing and an honor it has been to work 

with all of you this year on so many issues which are so important 

to so many people. Travel safely and I hope everyone enjoys our 

unique American holiday. 

I remind members that they have 10 business days to submit 

questions for the record, and I ask the witnesses to please respond 

promptly to any questions you might receive. And with that, the 

meeting is now adjourned. Thank you. 

[Whereupon, at 11:09 a.m., the subcommittee was adjourned.] 

[Material submitted for inclusion in the record follows:] 

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NHLQttery' Over $1 billion to education 

The Honorable Edotphus Towns 

Member 

, January5,2012 

.GOVERNOR John H, LYI,lCh 

CHAIRMAN Debra M. Doug!,1,s 

Paul J. Holloway 

Doug Scamman 

EXECUTIVE DIRECTOR Charles R. Mcintyre 

Subco~mittee on Commerce, Manufacturingm;d Trade 

2125 RlIyburn House Office Building 

Washington, DC 205l5-6l15 

Dear R~I)resent~tive Towns: 

PI;ase find attach~d answers to the questions posed which relate to the lottery 

industry and reve'nucs, I certainly appreciaie your courtesy during this hearing, If lcaf) 

be of further assistance, please do riot.hcsitatc tei contact me: ' 

CRMJdc 

Lil'c Free M Dic 

New Hampshire lottery Commissjon 14 Infegl'a Drive 

TEL 603,271.3391 FAX 603,771.1160 TDD'LsOO,7352%1I 

~(lL Charles R, MClht rc 

Executive Dir c r 
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The Honorahle Edolphus Towns 

Memher 

Suhcommittee on Commerce, Manufacturing and Trade 

L Since the advent of illegal online poker, have lotteries seen a loss of 

I'evenue'? 

The advent of illegal poker is a hard date to pinpoint, hut they have certainly 

become more prevalent starting in or around 2000. Since that time, we have seen a 

dramatic increase in the number of wehsites offering these services, and an 

exponential revenue amount cstimated to he wagered through the sites. 

Similarly, it is hard to state that lottery revenues are a constant, for two significant 

reasons: 1) new lotteries have come to be since 2000, creating entirely new playerhases and retail outlets (Tennessee, North Carolina, Arkansas); and 2) many of the 

lotteries have introduced entirely new games or products to increase revenues. For 

example, New York introduced Video Lottery terminals at a numher of facilities 

throughout the state during that time, and has seen a significant increase in revenues. 

To make an accurate comparison of revenues we can use my home state, New 

Hampshire, which has not made any substantive changes to its gambling profile since 

prior to 2000, and is considered a mature lottery. Our net revenues peaked during 

FY2006 and have heen in steady decline ever since. In FY 2006 the NH lottery 

transferred approximately eighty million dollars for education; in FY 20 II, our profit 

had dropped to a little over sixty-two million. Certainly, not all (he decline is 

attributable to internet poker, but during the time when internet poker revenues were 

surging, the NH Lottery revenues were declining in corresponding fashion. 

2. Was there allY increase in lottery sales following the recent shutdown of 

several online poker sites'! 

During the first quarter of FY 2012, of forty-three reporting lotteries, thirty-five 

were positive in year over year gross revenue growth, four were flat and four were 

negative. Of the thirty-five that showed positive growth, nineteen ,vere hy 5% or 

greater (including New Hampshire). 

Through the years, when either the state or federal government has taken a stance 

against unregulated, illegal gambling lottery proceeds have typically risen. As both a 

state prosecutor handling illegal gamhling cases, and as a lottery executive in hoth 

Massachusetts and New Hampshire. I have witnessed this tirsthand. While the 

correlation has not been carefully documented, there is ample evidence to suggest that 

where authorities moved to eliminate illegal gambling, lottery revenues have 

generally risen. 
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1. Adolescents are considered by the National Council on Problem Gambling to be at a higher risk of 

a gambling disorder. More than two-thirds of states restrict in-person casino gambling to those 

age 21 and older or completely ban it. 'All others limit such activity to those 18 and older. 

But young men and teenage boys are nonetheless managing to gamble through the Internet. An 

October 2010 study by the Annenberg Adolescent Communication Institute found that 16% of 

young men age 18 to 22 (or roughly 1.7 million individuals) had used an Internet gambling site in 

the previous month. This was almost a fourfold increase from the same survey's findings in 2008. 

The 2010 study also found that 6.2% of boys age 14 to 17 (or roughly 530,000 individuals) had 

used an Internet gambling site in the previous month. 

I understand that Annenberg's findings represent a "snapshot" of Internet gambling prevalence 

and that its survey occurred at a time prior to the April 15, 2011, seizure by the U.S. Department 

of Justice ofthe three largest poker websites operating in the United States. Nevertheless, it 

concerns me greatly that this and other recent studies suggest that online gambling by high 

school- and college-age youth has become more prevalent in recent years. 

a. Please discuss the differences in maintaining age controls in brick-and-mortar casinos and in 

the Internet environment. 

Age restrictions apply to virtually all forms of legalized gambling although such restrictions vary by 

type of gambling as well as by jurisdiction. The chief rationale for age restrictions has been that 

children and adolescents are more likely to become problem gamblers if they begin gambling at a 

young age. 

The key difference in maintaining age controls in the online environment compared with brick-andmortar casinos is the absence of the ability to verify an individual's age through visual cues and body 

language. The ability to effectively prevent underage gambling has evolved over time within specific 

sectors of the gambling industry. For example, early studies of adolescents in states where casino 

gambling was legalized found that significant numbers of underage youth were able to participate in 

this highly age-restricted form of gambling. A survey of high school students carried out in the wake 

of the introduction of casino gambling in Atlantic City found that 64% of the respondents had 

gambled at the casinos (Arcuri, Lester, & Smith, 1985). In contrast, a more recent survey of 

adolescents aged 13 to 18 in Nevada, the most mature gambling market in North America, found 

that only 1% of the respondents had gambled at a casino in the past year (Volberg, 2002). In a 

similar vein, a study of adolescents in Oregon found that underage participation in lottery games 

declined from 39% in 1998 to 8% in 2007-a change at least partly due to efforts by the State of 

Oregon to educate youth, their parents and their teachers about the risks of adolescent gambling 

(Volberg, Hedberg, & Moore, 2008). 

Ten years ago, the Federal Trade Commission (2002) issued a consumer alert about children and 

online gambling. The FTC noted the ease with which underage individuals can access online 

gambling sites using credit or debit cards. The FTC found that nearly all gambling sites had 

inadequate or hard-to-find warnings about underage gambling prohibitions and few sites had any 

effective mechanisms to block minors from entering. The FTC also called attention to the numerous 

advertisements for gambling sites that appeared on popular non-gambling and game-playing sites. 

Much has been made of a 2004 British study, where a 16-year-old girl attempted to access 37 

gambling websites using her debit card, lying about her age but being otherwise truthful. Only 

seven sites (19%) prevented her from registering (Smeaton, Poole, Chevis, & Carr, 2004). A more 
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recent study completed in 2009, after Britain began regulating online gambling, showed quite 

different results. A "mystery shopping" exercise intended to test the effectiveness of barriers to 

underage gambling found that 51% of the 37 online operators licensed by the UK Gambling 

Commission and representing 95% of all active customer accounts had measures in place to prevent 

underage players from gambling and from withdrawing any winnings if they were able to gamble 

(Gambling Commission, 2009). 

In a report commissioned by FairPlay USA, Sparrow (2009) notes that a set of regulatory methods 

and technologies already exist to prevent underage access to online gambling sites. He points to a 

number of technologies routinely used in other industries, including a variety of data-matching 

techniques, electronic or other submission of documentary evidence of age, and the possible 

application of biometric identification systems. The strongest form of age control would require 

positive matching of a player at the time of registration against existing databases of known adults 

as well as identity-verification prior to initiating any session of play. Site operators could also be 

required to provide child-protective software to parents to help prevent minors from accessing 

gambling sites. Alternatively, a separate governmental or nonprofit entity could provide such 

software. Given the existence of such measures, Sparrow concludes that online gambling can 

effectively exclude minors by combining cutting-edge technology with a strong regulatory regime. 

b. I understand that your research team found in Oregon that even when adolescents do not 

gamble with real money online, many still play the free version of popular online gambling 

games. What is known about the effects of "free play" and the practices of site operators that 

offer it? Are there any recent developments regarding "free play" that concern you? 

Playing on Internet gambling sites without money is indeed common, particularly among boys and 

young men. In the survey that I and colleagues carried out among adolescents aged 12 to 17 in 

Oregon, playing gambling-type games on the Internet for free was the most popular gambling 

activity (Volberg et aI., 2008). One-third of these adolescents (32%) had played free gambling games 

on the Internet at some time with 18% having done so in the past year. Boys were significantly 

more likely than girls to have gambled for free on the Internet (24% vs. 13%). Less than 1% of the 

respondents in the survey had gambled on the Internet for money. Similarly, in a survey of 

Canadian youth, Derevensky (2009) found that 49% of the underage respondents had gambled on 

the Internet without money in the past 12 months (including 34% of the respondents classified as 

non-gamblers) but that only 8% had gambled on the Internet with money. 

Very little is known about the effects of "free play" on underage persons. However, researchers 

have noted the importance of preventing underage gamblers from playing for free on online 

websites due to their presumed vulnerability to the development of gambling-related problems 

(Valentine, 2008; Wiebe & Falkowski-Ham, 2003). Other researchers have identified a tendency 

among college age gamblers to make bigger and riskier bets in online games compared with offline 

games, particularly in the presence of other players (Cole, Barrett, & Griffiths, 2011). This is a 

particular concern in relation to underage youth, many of whom gamble primarily for social reasons 

rather than strictly for monetary gain. 

There is some evidence that Internet gambling operators offer different odds and payout ratios 

during "free play" and "demo" sessions compared to when gamblers are playing for money. In 

2004, a listserv called Gamblingls5ueslnternational (whose members are primarily health 

professionals and researchers) complained to MGA Entertainment about a slot machine application 

that the company's Japanese licensee had placed prominently on the Bratz Dolls website. The 
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listserv members pointed out that the slot machine application invariably returned a large win 

within five spins (early large wins are a known contributor to later gambling problems) and allowed 

players to "stop" the reels, thereby contributing to an illusion of control, another known contributor 

to problem gambling (Don, 2004). Sevigny and colleagues (2005) identified significant differences in 

payouts on Internet slot machines in "demo" and "for money" modalities. While companies argue 

that demo sessions are an opportunity for players to practice their skills before playing for money, 

39% ofthe 117 Internet slot machine sites examined in this study provided a payout over 100% after 

100 trials in their "demo" games and half of these sites maintained the inflated payout rate over an 

additional 400 trials. None of the websites provided payout rates over 100% in sessions where real 

money was gambled. 

There are recent developments in relation to "free play" that are a concern. For example, many 

Internet gambling sites incorporate popular videogame technologies that appeal particularly to 

youth (Derevensky, 2009). An even greater concern relates to non-monetary gambling games, 

including poker, that are now offered on social media sites such as Facebook regardless of members' 

age. While these "virtual" games are currently only offered for points that cannot be exchanged for 

cash, there are indications that Apple, Facebook and Google are interested in eventually offering the 

games for real money (Domjen, 2012; Gladdis, 2011; Winkler, 2012a, 2012b). 

King and colleagues (2010) identify several reasons for concern about the convergence of gambling 

and digital media. They suggest that new gambling technologies make gambling more accessible 

and attractive to young people, promote factually incorrect information about the garnes, provide 

an easy escape from real world problems such as depression and social isolation, and create a 

gambling environment that facilitates peer pressures to gamble. 

c. Please briefly address whether you believe there is a role government authorities can play to 

ensure children and teens have a full understanding of gambling, including its risks. 

Beyond the regulatory question of how to effectively prevent children and teens from accessing 

legal but age-restricted forms of gambling, I do believe there is a role that government authorities 

can play to ensure children and teens have a full understanding of gambling, including its risks. 

Given the strong evidence that gambling and other risky behaviors (e.g., tobacco, alcohol and drug 

use, poor school performance, truancy) are "fellow travelers" among adolescents (Jacobs, 2000; 

Romer, 2003), education and health departments at the state and federal level should be strongly 

encouraged to include materials on gambling in youth addiction prevention and education programs 

as well as in broader information and awareness campaigns for risky behaviors. 

Another concern relates to advertisements for gambling which often target audiences along age, 

gender and ethnic lines and use persuasive techniques to promote the view that gambling is solely 

an entertainment experience. Given the likely ongoing convergence of gambling and digital media, 

there is a need for effective restrictions on online gambling advertisements directed towards young 

people (McMullan & Miller, 2008). 

Finally, efforts are needed to increase recognition of youth gambling and its associated risks among 

parents, teachers, counselors and others working with youth. Work is needed to increase 

understanding of associations between youth gambling and other risk behaviors. Parents who 

gamble need to be educated about the increased risk of gambling problems for their children. Once 

youth are gambling, peer influences should be addressed in school-based curricula. Strategies are 

needed that combine programs across family, school and community, include a range of activities 

3 
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(e.g., information, improving skills, offering alternative activities, providing problem identification 

and referral), are adaptable over time as conditions change and are rigorously evaluated to identify 

the most effective approaches (Volberg, 2009a). 

2. The self-exclusion list is the most widely adopted protection mechanism for problem gamblers. If 

individuals have a gambling problem and choose to enroll in a program that bars them from 

entering any casino in a given state, then gambling operators and regulators owe it to these 

individuals to provide a system that works. Such a system should encompass as many 

establishments as possible, ensure the confidentiality of individuals on the list, and function 

reliably to help problem gamblers avoid "slips." The same ought to be the case for individuals 

who wish to exclude themselves from online gambling establishments, if they are legalized here in 

the United States. 

However, you indicated at the Subcommittee's hearing that the self-exclusion list is not the only 

mechanism available or even the minimum necessary -for effective player protection. You 

testified: "Beyond requiring licensees to establish self-exclusion programs, I believe some 

additional minimum requirements are needed. These include a requirement for players to set 

limits with regard to time and money, a 24-hour cooling off period before changes to limits can be 

made, monthly financial statements, and self-assessment tests." 

a. Can you please more fully discuss these requirements and why you believe each is important 

to helping individuals limit their gambling? 

While self-exclusion has been widely adopted as a consumer protection mechanism, surprisingly 

little research that has been done to evaluate the effectiveness of such programs or identify what 

works best (Volberg, 2009c). The most thorough study, carried out by the Responsible Gambling 

Council of Ontario (2008), found that the self-exclusion process is increasingly viewed as a means to 

provide assistance to individuals rather than as a punitive measure, that compulsory lifetime bans 

are giving way to bans of varying length, and that better links are developing between gambling 

venues and specialist treatment programs to improve the likelihood that gamblers who so desire 

will be able to access the help they need. The Council noted, however, that there was considerable 

room for improvement in self-exclusion programs worldwide. There is a need to promote these 

programs more aggressively; there is also a need for much better regulatory. oversight of these 

programs and for better coordination between operators within jurisdictions. 

Given the lack of research on exclusion programs, it should be no surprise that there is, as yet, little 

empirical evidence for the additional measures that I recommended in my testimony. I based my 

support for measures such as limit setting, cooling-off periods, monthly financial statements and 

self-assessment tests on a theoretical understanding of how gambling problems develop and on the 

limited research that is beginning to emerge about the effectiveness of some of these measures. 

The most comprehensive review of pre-commitment strategies-as these measures are termed 

internationally-was carried out recently by the Australian Productivity Commission (2010; see 

Chapter 10). The Productivity Commission noted that the ability for players to make choices and 

place limits on their gambling involvement prior to actually gambling is important because these 

activities are intentionally designed to overwhelm individual decisions about when, how long and 

how much to gamble. Players may experience faulty cognitions, find it hard to stop playing, fail to 

appreciate the risks associated with these activities, have their judgment impaired by alcohol, or 

suffer from emotional or mental health problems. All of these factors serve as obstacles to 

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genuinely informed choice and reduce the capacity for self-control (Dickerson, Haw, & Shepherd, 

2003). 

b. What have you observed as governments or other gambling authorities have begun to 

implement these requirements? 

Serious efforts at consumer protection first emerged in relation to slot machines and have evolved 

furthest in Australia and Canada where slot machines are widespread and often located in 

previously non-gambling establishments. Consumer protection measures around slot machines 

include employee problem gambling awareness training as well as automated interventions with 

"high frequency" gamblers. Other consumer protection measures relate to modifying slot machine 

parameters (e.g., game speed, number of near misses, number of play lines, removal of bill 

acceptors, limits on bet size and maximum wins, mandatory cash-outs, etc.), setting loss limits, 

restricting access to cash and credit, and restricting advertising and promotional activities (Williams, 

West, & Simpson, 2008). Several researchers have pointed out that monitoring of player behavior is 

increaSingly popular with Internet gambling operators targeting heavy spending players and have 

called for the use of behavioral analysis to identify players who are likely to be eligible for protective 

and helpful interventions (Dragicevic, Tsogas, & Kudic, 2011; Griffiths, 2008; Peller, LaPlante, & 

Shaffer, 2008). 

Implementation of consumer protection measures in the Internet gambling environment began in 

the Nordic countries and has spread to other jurisdictions internationally. When Loto-Quebec 

introduced online gambling in 2010, players were limited to losses of under $10,000 per week and 

required to set weekly spending limits with a seven-day cooling off period set before limits could be 

increased. A government-appointed committee is monitoring the venture and will report its 

findings in 2013 (Moore, 2010). When the New Zealand Lottery introduced online access to games, 

players were required to set spending limits under $300 per week before they could access an 

online account If players reach their spending limit over four consecutive months, they are 

contacted by the Lottery to inquire whether they wish to reduce their spending limits (New Zealand 

Lotteries, 2009). 

The most promising and well-developed online gambling player protection package is Plays can 

(www.playscan.com). a corporate social responsibility product developed by an independent 

subSidiary of Svenska Spel, the Swedish state-owned gambling operator. Playscan offers players a 

range of tools, including personalized budgets, self-diagnostic tests of gambling habits, and selfexclusion options. A recent survey of Svenska Spel players found that 26% had used Playscan with 

over half of these users setting spending limits, 40% taking the self-diagnostic test and 17% using 

the self-exclusion feature (Griffiths, Wood, & Parke, 2009). Playscan is presently available to 

gamblers on the Swedish, French and Finnish government gambling operators' websites with plans 

for further expansion in Europe and Asia. 

At a recent workshop that I attended in Stockholm, several Swedish colleagues presented the results 

of a study of Playscan's self-diagnostic test with more than 20,000 players from seven Nordic gaming 

companies. The test provides individualized feedback to players about how their'gambling 

compares to other players and to their own history of play and appears to be an effective early 

warning tool providing feedback and support tailored to individuals' own gambling and risk 

behaviors (Munck, Jonsson, & Nilsson, 2011). Another recent intriguing study analyzed chatroom 

correspondence with customer service employees ofthree European online gambling operators and 

found indicators in these exchanges that predicting subsequent self-exclusion (Haefeli, Uscher, & 

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Schwarz, 2011). This study is a further indication of the importance of early detection as a building 

block for the effective prevention of gambling-related problems among Internet gamblers. 

Internationally, we are seeing the emergence of a range of third-party certification efforts in support 

of consumer protection. Organizations such as eCOGRA (e-Commerce and Online Gaming 

Regulation and Assurance), the World Lottery Association and G4 (Global Gambling Guidance 

Group) have all developed accreditation programs to assure minimum standards in corporate social 

responsibility and player protection. While not all the members of these groups have sought 

accreditation, the existence of these programs points to growing acceptance of the importance of 

consumer protection in the gaming industry (Volberg, 2011). 

What we do know about consumer protection measures in general is that not many people will use 

these tools unless their use becomes normative, that there is reasonable evidence that players will 

use these tools if they are made available, that higher-risk users find these tools most useful, that if 

players want to circumvent these systems, they probably will and that system design plays a critical 

role in the effectiveness of these systems (Hare, 2010). 

While much more development and research work is needed, the fact that Internet gambling is 

conducted in a networked, data-intensive environment offers opportunities for regulatory oversight 

and consumer protection that are impossible in the brick-and-mortar environment. Since every 

detail of every gambling transaction can be recorded and potentially analyzed in the Internet 

gambling environment, players exhibiting behaviors indicative of problematic gambling can be 

flagged and their betting habits further analyzed (Sinclair & Volberg, 2000). As I noted in my written 

testimony, requirements for players to set limits with regard to time and money, monthly financial 

statements and self-assessment tests are all measures supported strongly by Internet gamblers 

surveyed worldwide (Parke, Rigbye, Parke, & Williams, 2007). While stronger empirical support is 

needed for many of these measures, I concur with Sparrow (2009) who notes that, in a wellregulated online environment, gamblers should have opportunities and technologies easily available 

to help reduce and prevent problematic gambling behaviors. 

c. At the hearing, you suggested that it would be best if player protection programs were 

"operated by a third-party independent organization." Please explain why you believe this. 

I noted in my testimony that one important advantage to establishing a single, separate agency to 

operate player protection programs would be to allow players who wished to set limits on their time 

or money or to self-exclude to visit a single site where these services could be implemented across 

the full range of gambling sites at one time. This has been a challenge for many of the self-exclusion 

programs operated by brick-and-mortar gaming operators. 

Another important reason for operating player protection programs through a third-party 

independent organization is that both gambling operators and governments are constrained by the 

profit motive in their willingness and ability to prevent problem gambling. Governments, in 

particular, face conflicting incentives in relation to gambling legalization and the extent to which 

best practices will be adopted can be influenced by pressures from politicians, senior officials, 

industry lobby groups and other advocates. Having an independent agency or organization operate 

player protection programs would ensure that these efforts are not, and are not perceived to be, 

affected by the need to generate revenues. 

A third reason to have player protection programs operated by an independent organization is that 

this approach is more likely to result in the fielding of measures that are independently evaluated, 
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publicly accountable and transparent to consumers, operators and other interested parties. In its 

most recent report, the Australian Productivity Commission identified independence as an 

important "best practice" in the regulation of all forms of legal, commercial gambling (Productivity 

Commission, 2010; see Chapter 17). 

3. At the hearing, you contended that funding in the United States for prevention, treatment, and 

research on problem gambling is currently inadequate. You testified that "[s]tate funding for 

problem gambling services per capita is approximately one-twentieth the level it is in countries 

such as Australia and Canada." 

What steps do you believe Congress should take to address this current funding shortfall, and 

what do you believe Congress should do to ensure the long-term budgetary health of such 

programs if this body considers a bill legalizing online gambling? 

In the most recent national survey of publicly funded problem gambling services, the average adult 

per-capita allocation for the 37 states with such funding was 34 cents with approximately half of 

those funds (17 cents) directed toward treatment services (Marotta, Moore, & Christensen, 2011). 

Within the United States, it is helpful to compare this level of spending with national spending on 

substance abuse treatment. In 2005, the most recent year available, spending on substance abuse 

treatment was $22 billion which equates to in $102 in adult per-capita spending (Substance Abuse 

and Mental Health Services Administration, 2010; U.S. Census Bureau, 2005). 

Internationally, some countries spend far greater amounts on publicly funded problem gambling 

services. In my testimony, I was comparing per-capita funding for problem gambling services in the 

U.S. in 2004 with per-capita funding for these services in several other countries, including Canada, 

Australia, New Zealand and South Africa (Vol berg, 2009b). Since 2004, spending on problem 

gambling services in Canada has grown from $44 million (USD) to $80 million (USD) (Canadian 

Partnership for Responsible Gambling, 2011) while the new government in the Australian state of 

Victoria has pledged to spend $31 million (USD) annually over the next five years on problem 

gambling services, up from $10 million (USD) in 2004 (Victorian Liberal Nationals Coalition, 2010). 

While spending on problem gambling services in the United States has risen since 2004, from 

approximately $25 million to $58 million, this increase in funding is primarily due to the growing 

number of states that have legalized casino gambling and are now providing publicly funded 

problem gambling services (Marotta et aI., 2011). 

Regarding immediate steps that Congress could take to address this funding shortfall, I believe that 

an important first step would be to establish clear responsibility for overseeing problem gambling 

services within a single federal agency along with a mandate to coordinate efforts with other federal 

agencies, including the Departments of Education, Health and Justice. Leadership at the federal 

level might influence states to do a better job appropriating funds for problem gambling services 

from the revenue streams that many receive from legalized gambling. Furthermore, such leadership 

could result in the identification of best practices in problem gambling prevention, treatment and 

research and prompt states to adopt such practices. 

The experiences of rapid liberalization of lotteries in the 1970s and of casinos in the 1990s in the 

United States provides salutary lessons about the willingness of state governments to provide help 

for vulnerable populations, including you ng people and problem gam biers. In general, funding for 

services for problem gamblers has become available only when a new form of gambling is legalized. 

Furthermore, such funding tends to be highly vulnerable to changes in government and in economic 
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circumstances, as recent decisions by several states to reduce or eliminate existing funding for 

problem gambling services demonstrate (Berzon, 2011). 

To ensure the long-term budgetary health of such programs, language is needed within any bill 

legalizing online gambling in the United States to assure that a reasonable level of funding for 

problem gambling services is made available and preserved over time and to also assure that 

research will be undertaken to keep the federal government informed about new developments and 

emerging best practices in preventing and treating problem gambling. 

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