WORKING 

 PAPERS

IDENTITY THEFT:

DOES THE RISK VARY WITH DEMOGRAPHICS?

Keith B. Anderson

WORKING PAPER NO. 279

August 2005

___________________________________________________________________________

FTC Bureau of Economics working papers are preliminary materials circulated to stimulate discussion

and critical comment. The analyses and conclusions set forth are those of the authors and do not

necessarily reflect the views of other members of the Bureau of Economics, other Commission staff, or

the Commission itself. Upon request, single copies of the paper will be provided. References in

publications to FTC Bureau of Economics working papers by FTC economists (other than

acknowledgment by a writer that he has access to such unpublished materials) should be cleared with the

author to protect the tentative character of these papers.

______________________________________________________________________________

BUREAU OF ECONOMICS

FEDERAL TRADE COMMISSION

WASHINGTON, DC 20580
*

 The views expressed in this paper are those of the author and do not necessarily

represent the views of the Federal Trade Commission or any individual Commissioner. I would

like to thank Joanna Crane, Erik Durbin, Lois Greisman, Hajime Hadeishi, Dan Hosken, Pauline

Ippolito, and two anonymous referees for useful comments on earlier drafts and Stefano Sciolli

and Marie Tansioco for assistance in preparing the data and the tables. Any remaining

shortcomings are, of course, my responsibility alone.

Identity Theft: 

Does the Risk Vary With Demographics?

Keith B. Anderson*

Bureau of Economics

Federal Trade Commission

August 2005

Abstract

This paper examines whether the likelihood that an individual will experience identity

theft varies with the individual’s demographic characteristics, using data from the Federal Trade

Commission’s 2003 survey on identity theft. The likelihood that a person will be a victim of

identity theft does appear to vary with the person’s demographics. Consumers with higher levels

of income are more likely to be victims of ID theft – particularly ID theft that only involves

placing unauthorized charges on the victim’s existing credit cards. Similarly, those with more

education may be somewhat more likely to be victims. On the other hand, older people may face

a somewhat reduced risk. Here the reduction appears to be mainly in the risk of experiencing

more than just unauthorized charges on existing credit cards, including the risk that new

accounts will be opened or other frauds committed using the victim’s personal information. 

Women are more likely to be victims than men. The risk of ID theft also appears to be related to

household composition: One is more likely to be a victim if he or she is the only adult who lives

in the household. Having more children in the household is also associated with an increased

likelihood of becoming a victim of identity theft. Finally, ID theft appears to be more likely if

consumers live in some regions of the country than if they live in other regions.

1

 As quoted on www.identitytheft911.com/education/fundamentals/

idtheftstatistics.htm, visited April 12, 2005.

2

 George Milne reports that of 1,892 articles on identity theft found in an electronic

search of the Lexis-Nexis major newspaper database through 2001, 768 had been published in

2001. The number of articles published in 2000 was just over 500, so that articles published in

those two years accounted for two-thirds of all articles on the subject. The number of articles per

year first reached 100 in 1997 and ranged between 100 and 200 in 1997, 1998, and 1999. 

(George R. Milne, “How Well Do Consumers Protect Themselves from Identity Theft?,” The

Journal of Consumer Affairs, 37 (Winter 2003), pp. 388-402.)

Much of the most recent attention has been directed at breaches in company data bases

which expose consumers’ personal information to potential misuse, including ID theft. 

According to ChoicePoint, a major provider of consumer data to businesses and government, at

least 94 breaches in databases containing consumer information have been disclosed between

January 1 and August 17, 2005. (See http://www.privacyatchoicepoint.com/index.html (visited

August 23, 2005).)

3

 Identity Theft Assumption and Deterrence Act, Public Law 105-318, 112 Stat. 3007

(October 30, 1998). (See Graeme R. Newman and Megan M. McNally, “Identity Theft

Literature Review,” Paper Presented at National Institute of Justice Focus Group Meeting,

January 27-28, 2005, available at http://www.ncjrs.org/pdffiles1/nij/grants/210459.pdf (visited

August 2, 2005), p. 2.)

4

 Federal Trade Commission, “Overview of the Identity Theft Program,” October 2003.

1

“... the greatest threat to consumers today is the growing menace of identity theft. 

Identity theft is far more insidious and harmful to our national welfare than many

realize. It attacks the trust and confidence that nurture our open economy, even

as it destroys individual lives.”

John W. Snow

Secretary of the Treasury

June 30, 20031

Rarely does a week go by without some new story about identity theft and its victims. 

However, this attention to ID theft is a fairly new phenomenon. Widespread publicity on victims

of identity theft began in the late 1990s.2

 This increased publicity ultimately gave rise to a series

of Congressional hearings and, in 1998, passage of the Identity Theft Assumption and

Deterrence Act.3 Among other things, this statute directed the Federal Trade Commission to

begin collecting consumer complaints related to identity theft, to establish a central repository

for such complaints, and to refer complaints to the appropriate entities. In addition, the

Commission was directed to provide assistance to victims and conduct consumer education

efforts in the area.4

 Until 1996, no state had a separate statute that recognized ID theft as a

distinct crime. Today, only the State of Colorado has no such statute. 
5

 In particular, the Federal Trade Commission conducted a survey on identity theft in the

spring of 2003. See Federal Trade Commission – Identity Theft Survey Report (“FTC ID Theft

Survey Report”), prepared by Synovate, September 2003. (The report is available at

http://www.ftc.gov/os/2003/09/synovatereport.pdf.)

6

 Newman and McNally (2005) point to the need for additional research about identity

theft and, in particular note the need for research about the sociodemographic characteristics of

victims. The paucity of existing research about identity theft is also noted in Julia S. Cheney,

“Identity Theft: A Pernicious and Costly Fraud,” Discussion Paper, Payment Cards Center,

Federal Reserve Bank of Philadelphia, December 2003; and Tracey Sharp, Andrea ShreveNeiger, William Fremouw, John Kane, and Shawn Hutton, “Exploring the Psychological and

Somatic Impact of Identity Theft,” Journal of Forensic Science, 49 (January 2004), pp. 1-6.

While no one has previously examined the issue of vulnerability to ID theft, an approach

similar to what we do here has been used to examine the risk of consumer fraud. See, e.g.,

Jinkook Lee and Horacio Soberon-Ferrer, “Consumer Vulnerability to Fraud: Influencing

Factors,” Journal of Consumer Affairs, 31 (Summer 1997), pp. 70-89; Jinkook Lee and Loren V.

Geistfeld, “Elderly Consumers’ Receptiveness to Telemarketing Fraud,” Journal of Public

Policy & Marketing, 18 (Fall 1999), pp. 208-217; and Keith B. Anderson, Consumer Fraud in

the United States: An FTC Survey, Federal Trade Commission Staff Report, August 2004.

2

Given the short time during which attention has been paid to identity theft, not

surprisingly, there is much that is not known about this crime and its victims. Only within the

last couple of years have there even been reliable estimates of the incidence of ID theft.5 Much

remains to be learned. This paper attempts to address the lack of knowledge in one area –

whether and how the risk of becoming a victim of identity theft varies with a person’s

demographic characteristics.6

Measures of Identity Theft

In examining this issue, the overall likelihood that an individual has experienced identity

theft will be considered, along with the likelihood of experiencing different types of ID theft. 

Three different subsets of ID theft are considered. The paper begins by defining these concepts

and looking briefly at the frequency with which these three types of ID theft occur and the costs

associated with each type.

Overall Likelihood of ID Theft. With the overall measure of identity theft, anyone whose

existing accounts – either credit card accounts or non-credit card accounts, such as bank

accounts, utility accounts, or telephone accounts – have been misused is considered to be a

victim. In addition, victims include anyone whose personal information has been misused to

open new credit accounts or to commit other types of frauds. A telephone survey of more than

4,000 individuals conducted for the Federal Trade Commission in March and April of 2003

found that 4.6 percent of survey participants had been a victim of identity theft defined in this
7

 See FTC ID Theft Survey Report, p. 7. Random digit dialing was utilized to generate

the sample and, unless otherwise noted, results are weighted to make the sample conform to the

characteristics of the population as a whole. 

The estimates of the incidence of ID theft are actually lower bound estimates. Some

participants in the survey indicated that they did not know, or declined to answer questions

about, whether they had experienced identity theft. Others indicated that they had experienced

identity theft at some point in time, but did not indicate when they had discovered that their

information had been misused. (The survey was structured so that participants were first asked

whether they had ever been a victim of the various types of identity theft. Those who indicated

that they had experienced identity theft at some time were then asked when they had first

discovered that their personal information had been misused.) In arriving at the estimates in the

FTC ID Theft Survey Report, it was implicitly assumed that those who did not know whether or

not they had been a victim had, in fact, not been a victim. Similarly, it was assumed that those

who did not know when they had discovered the misuse of their information had not, in fact,

discovered the misuse during the relevant period – either the last one or five years, depending on

the time period of the estimate. 

If we make other assumptions about the actual experiences of those who did not know the

answers to various questions – or refused to answer – we arrive at somewhat higher estimates of

the percentage of Americans who were victims of ID theft – 5.3 percent in the year prior to the

FTC survey and 13.4 percent in the last five years. These estimates are based on the assumption

that the proportion of victims among people who said that they did not know whether they were

victims of ID theft was equal to the proportion among those who did know. In addition, we

assume that those who knew that they were victims but did not know when they had discovered

that their personal information was being misused were in fact victims within the last one or five

years, respectively. (We would, of course, get even larger estimates if we assumed that everyone

who said that they did not know whether they were a victim was, in fact, a victim. However,

such an assumption seems unreasonable. Experiencing identity theft is generally quite traumatic

and we therefore suspect that most people who have, in fact, discovered that they were victims of

ID theft will recall that. In most cases, it seems more likely that people who said that they did

not know if their information had been misused were really saying that, as far as they knew, their

information had not been misused. However, they could not know for sure.)

8

 In estimating the number of victims, the share of participants reporting that they had

experienced a particular type of ID theft is simply multiplied by 215.47 million – the total U.S.

population age 18 and over as of July 1, 2002. (http://eire.census.gov/popest/data/national/

tables/asro/NA-EST2002-ASRO-01.php (visited July 14, 2003)) This methodology should

provide a reliable estimate of the number of victims of identity theft during the year preceding

the survey. However, it may overstate the number of separate incidents of ID theft that occurred

during that year. The problem arises because accounts are often jointly held by two or more

(continued...)

3

way during the previous year.7

 (See Table 1.) Projecting to the population of adults in the U.S.

suggests a total of 9.9 million U.S. adults had been victims of ID theft in the previous year.8

 A
8

 (...continued)

persons – e.g., a husband and wife. In the context of the survey, the misuse of such an account

would have been reported by any of the people who use the account since they would have

considered it to be “their” account and multiplying by the total adult population implicitly counts

each such individual separately. In terms of the number of victims, this is probably appropriate

since each of the individuals on such a joint account is presumably a victim. However, each of

these victims does not represent a separate incident of ID theft. 

9

 When participants were asked when they first discovered that their information was

being misused, the structure of the response categories included “4 years to less than 5 years”

and “5 years to less than 6 years.” We cannot therefore include those who indicated that the

misuse occurred 5 years ago without also including anyone who indicated that it occurred 5

years and 3 months ago. However, it seemed that people who were describing an event that

occurred 5 or more years ago would have been unlikely to give an answer that was any more

detailed than the nearest year. Therefore, we have included those who placed the discovery of

the misuse in the “5 years to less than 6 years” category when we examine those who discovered

the misuse in the last 5 years.

The FTC survey was conducted in four waves with approximately 1,000 people being

interviewed in each wave. Because we lacked sufficient funds to conduct full interviews with all

victims in the final wave, we chose not to ask follow-up questions of those who indicated that

their existing credit cards had been misused, but that they had not been victims of the more

complex kinds of ID theft. As a result, we do not know when the misuse was discovered for

those in the final wave of interviews who experienced the simpler type of ID theft. In estimating

incidence figures, we have assumed that the proportion of such victims in the final wave who

experienced misuse in the last year, or in the last five years, is equal to the proportion of such

victims in the first three waves who had experienced the misuse in the same time period.

10 See Javelin Strategy & Research, 2005 Identity Fraud Survey Report, January 2005.

11 Other surveys have found similarly high estimates of the incidence of ID theft. An

(continued...)

4

total of 12.7 percent of participants indicated that they had been a victim of some kind of ID theft

in the last 5 years, suggesting that 27.4 million Americans had been a victim of some form of ID

theft during that period.9

 

More recently, using generally the same questions as those used by the Federal Trade

Commission, a survey sponsored by Javelin Research estimated that 4.25 percent of American

adults – 9.3 million people when projected to the entire population – had been victims of ID theft

during the one-year period ending in September / October of 2004.10 The difference between the

Javelin estimate of the incidence of ID theft and that of the FTC is not statistically significant. 

Indeed, in most respects the Javelin survey finds that the problem of identity theft had not

changed significantly between the period covered by the FTC survey and that covered by the

Javelin survey.11
11 (...continued)

online survey of 3,462 individuals conducted in May 2003 found that 33.4 million adult

Americans had been victims of identity theft at some point in their lifetimes. Of these 30 percent

– or approximately 10 million adults – said that they had been a victim during 2002 or the first

4.5 months of 2003. (This survey was conducted by Harris Interactive for Privacy & American

Business.) Another survey, also conducted during May of 2003, estimated that 7 million U.S.

adults had been victims of identity theft during the previous year. This survey also estimated

that the existing credit card accounts of an additional 8.9 million Americans had been misused

during the same time period. (See Aviah Litan, “Underreporting of Identity Theft Rewards the

Thieves,” Gartner Inc. Market Analysis, July 7, 2003. A total of more than 11 million

Americans had their existing credit card accounts misused according to the Gartner survey. 

However, of these, approximately 2.4 million were also victims of identity theft.)

12 The Identity Theft and Assumption Deterrence Act of 1998, Pub. L. No. 105-318, 112

Stat. 3007, as amended by the Identity Theft Penalty Enhancement Act, Pub. L. 108 - 275, 118

Stat. 831, (July 15, 2004) made it a crime to “knowingly transfer[], possess[], or use[], without

lawful authority, a means of identification of another person with the intent to commit, or to aid

or abet, any unlawful activity. . .” A “means of identification” is defined as “any name or

number that may be used, alone or in conjunction with any other information, to identify a

specific individual.” The Fair Credit Reporting Act, 15 U.S.C.§ 1681 et seq., as amended by The

Fair and Accurate Credit Transactions Act of 2003, Pub. L. 108-159, 117 Stat. 1952, (December

4, 2003), gave the Commission authority to expand upon the minimal definition of “identity

theft” contained in that Act. Accordingly, the Commission defined identity theft as “a fraud

committed or attempted using the identifying information of another person without authority.”

See16 C.F.R.§ 603.2(a) (2005).

13 See Cheney (2003), p. 4.

5

“Unauthorized Credit Card Charges Only” v. “More Than Unauthorized Credit Card Charges.” 

The all-inclusive measure is consistent with the legal definition of identity theft.12 However, this

broad measure includes a variety of different situations that are likely to differ both in the

amount of damage that results from the ID theft and in the amount of effort consumers must

expend in seeking to resolve their problems. In addition to knowing how the risk of ID theft

varies with a consumer’s characteristics, it will be interesting to see whether those with

particular characteristics are more likely to experience relatively simple or relatively complicated

forms of ID theft. 

In seeking to separate the relatively simple cases from the more complicated ones, the

first thing to note is that some consumers who have been the victim of ID theft have only

experienced unauthorized charges on their credit card bills – what some experts have called

payment fraud or credit card fraud.13 This could have occurred because the consumer’s card had

been lost and someone picked it up and used it to make a purchase. Or, it could have occurred

because a “shady” telemarketer charged the consumer for a purchase even though the consumer

does not believe that he or she agreed to make the purchase. 
14 The difference in the incidence of new accounts being opened depending on whether

an existing account was taken over or not is statistically significant at the 1 percent level.

15 The median amount of time the victim spent resolving problems related to identity

theft was in the range of 10 to 39 hours when an existing credit card account was misused and

the account was taken over. (These figures include victims who, in addition to having their

existing credit cards misused and taken-over also had other existing accounts misused and/or had

their information used to open new accounts or commit other frauds.) Where existing credit card

accounts were misused, but not taken-over, the median amount of time spent was in the range of

2 to 9 hours. A chi-square test for the difference in the distributions here is statistically

significant at the 1 percent level.

In terms of money lost by the victim, the percentage of victims who incurred no expense

(continued...)

6

Where consumers’ identity theft experiences only involve unauthorized charges on an

existing credit card account, it is likely to be relatively easy for them to resolve their problems. 

Furthermore, what needs to be done is fairly widely known. A call to the credit card company

should lead to an investigation of the charge and its removal if the charge was not authorized by

the card holder.

Therefore, in seeking to determine what to include in the less-complicated measure, one

could include all cases where consumers only had their existing credit cards misused. However,

this seems to go a bit too far. In some instances where an existing credit card is misused, the

thief also fraudulently assumes ownership of the card, known as “account takeover.” For

example, the person using the account may seek to have the address to which the monthly bills

are sent changed so that the account holder will not receive the bills and therefore may not

realize that unauthorized charges are being made. The person may also attempt to have

themselves added as an authorized user of the account and ask to have an additional card sent to

him or her.

Where existing credit card accounts are not just misused, but are also taken-over, the

results of the FTC survey show that the resulting problems are substantially greater than where

accounts are just misused. (While the actual question in the survey asked whether an attempt

was made to take over an existing credit card account – and therefore presumably would capture

both successful and unsuccessful attempts – these instances will be referred to as involving

account takeovers in order to simplify the exposition.) First, the likelihood that the victim’s

personal information will be used to open new accounts or commit other types of fraud is

significantly higher where an existing account is taken over. In 49 percent of incidents involving

account takeover, the victim’s personal information was also used to open new accounts or

commit other types of fraud. In contrast, where unauthorized charges were placed on an existing

credit card account, but there was no account takeover, new accounts were opened or other

frauds committed in only 21 percent of cases.14 The costs borne by the victim – both in time and

money – are also significantly greater where there is an account takeover.15
15 (...continued)

was significantly lower when there was a takeover – 75 percent of victims incurred no expense

when there was no takeover, 52 percent where there was. The difference in the percentage of

victims who incurred no financial cost is statistically significant at the 5 percent level.

16 It might be argued that, just as we put cases that only involve unauthorized charges on

a credit card in the group of less-complex cases, we should do the same with cases that involve

only unauthorized charges on existing non-credit card accounts. We have not done this for two

reasons. First, the survey data do not permit us to distinguish between instances that were

limited to unauthorized charges on non-credit card accounts and instances that were more

complex. More importantly, however, we are not persuaded that misuse of a non-credit card

account can be simple in the same sense as it is with a credit card. For one thing, the procedures

needed to resolve the problems associated with misuse are not as simple or widely known when

a non-credit card account is involved. In addition, consumers do not have the same legal

protections if their non-credit card accounts are misused as when their credit card accounts are

misused.

17 In estimating the number of victims of Only Unauthorized Charges and of More Than

Unauthorized Charges, one has to figure out what to do with people who did not know whether

an attempt had been made to take over the existing credit card accounts that had been misused. 

We have assumed that the percentage of these people who had their accounts taken over was

equal to the percentage of take-overs among those who said that they knew whether or not this

had happened.

7

ID theft victims are therefore divided between those who have experienced more serious

types of ID theft – which are called “More Than Unauthorized Credit Card Charges,” or just

“More than Unauthorized Charges” – and those whose experience was limited to “Only

Unauthorized Credit Card Charges,” or just “Only Unauthorized Charges.” The More Than

Unauthorized Charges group includes those victims who said that their existing credit card

accounts were taken-over. In addition, it includes those cases where existing accounts other than

existing credit card accounts were misused and cases where the victim’s personal information

was used to open new accounts or commit other frauds.16 

As shown in Table 1, in the year before the FTC survey was conducted, an estimated 2.4

percent of U.S. adults experienced More than Unauthorized Charges, while an estimated 2.2

percent were victims of Only Unauthorized Credit Card Charges. Considering the five year

period before the survey, 7.5 percent of survey participants were victims of More than

Unauthorized Charges, while 5.2 percent were victims of Only Unauthorized Charges.17

New Accounts & Other Frauds. The final, and narrowest, set of identity theft experiences

includes only the most-serious kinds of ID theft, those cases where the victim’s personal
18 There are two ways in which a survey participant could be classified as suffering from

New Account & Other Frauds ID theft. First, one of the initial questions on the survey asked if

the person had “ever been the victim of a different form of Identity Theft, one that involved more

than just the misuse of existing accounts or numbers? That is, has anyone used your personal

information without your permission, to obtain NEW credit cards or loans in your name, run up

debts in your name, open other accounts, or otherwise commit theft, fraud, or some other

crime?” Later in the survey, participants who indicated that they had experienced any kind of ID

theft were asked whether various types of new accounts had been opened using their information

and whether various other frauds had been perpetrated using their information. Participants who

answered “Yes” either to the initial question or to one of the more specific questions that

indicated that a new account had been opened or another fraud committed were classified as

being victims of New Account & Other Fraud ID Theft.

Because of the way the survey questions were structured in the first wave of 1,000

interviews, it is not possible to separate out those who had experienced New Account & Other

Fraud ID Theft from those who had existing accounts other than credit card accounts misused. 

This problem was resolved in subsequent waves. In estimating the incidence of New Account &

Other Fraud ID Theft, it was assumed that, among participants in wave 1, those who were

victims of New Account & Other Frauds made up the same proportion of those in the combined

group as was found in the subsequent three waves.

19 Using alternative assumptions about the experiences of those who did not answer the

relevant questions would result in estimates of the incidence of this type of ID theft of 1.7

percent in the one-year period prior to the survey and 4.9 percent in the five-year period. 

20 Because we could not determine which people in the first wave of the survey were

victims of New Accounts & Other Frauds (see note 18, above), the figures in this and the

following paragraph are based only on the responses of people who were interviewed in the

second, third, and fourth waves of the survey. 

8

information was used by the criminal to open new accounts or to commit other types of frauds.18

Considering only this type of ID theft – which is a subset of the cases included in the More than

Unauthorized Credit Card Charges category – captures the cases which tend to result in the

highest costs for society and the individual victim and to be the most difficult to resolve. This

category of ID theft will be called “New Accounts & Other Frauds” or just “New Accounts.” As

shown on Table 1, 1.5 percent of U.S. adults were estimated to have discovered that their

personal information had been used to commit this type of identity theft in the year prior to the

FTC survey. During the five year period before the survey, 4.7 percent of adults were estimated

to have been victims.19

New credit card accounts were the most common type of new account opened. Almost

one quarter of survey participants who were victims of New Account & Other Frauds ID theft

indicated that their personal information had been used to open a new credit card account.20 New

loan accounts and new telephone service accounts were the next most commonly cited types of

new accounts with approximately 15 percent of New Account victims indicating that each of
21 Victims were asked “What is the approximate total dollar value of what the person

obtained while misusing your information? In answering this question, include the value of

credit, loans, cash, services, and anything else the person may have obtained.”

22 Because the data on the costs were only tabulated in ranges, we can only state the

median value as falling within one of the ranges used in collecting the data.

23 Where a seller can “authenticate” the identity of the purchaser, such as when a

purchase is made in a face-to-face transaction and the purchaser presents his or her credit card to

the seller, the liability for fraud is placed on the party who issued the purchaser’s credit card,

(continued...)

9

these types of accounts had been opened using their information. About 10 percent of New

Account victims said that new checking or savings accounts had been opened.

The most commonly reported type of “Other Fraud” was presenting the victim’s

identifying information to law enforcement authorities when the person who committed the

identity theft was stopped by law enforcement authorities or was charged with a crime. Just over

10 percent of survey participants who were victims of New Account ID theft reported that the

person who stole their information had engaged in this type of activity. Seven percent of these

victims reported that the person who stole their information had used the information to obtain a

driver’s license, Social Security card or other government documents. Three types of misuse –

to rent an apartment or house, to obtain medical care, and to obtain employment – were each

reported by just over 5 percent of these victims.

The Costs of Identity Theft

Table 2 provides information on the estimated costs of identity theft. First, survey

participants who had been victims of ID theft in the previous five years were asked to estimate

the gross value of what was stolen using their personal information.21 Including all types of

identity theft, the median amount per incident was in the range of $500 to $999.22 In 46 percent

of cases, the value the thief obtained was estimated to be less than $500, while it was more than

$5,000 in 18 percent of cases. Where the identity theft involved More Than Unauthorized Credit

Card Charges, victims reported that the thief got less than $500 in 39 percent of incidents, while

victims reported that the thief got more than $5,000 in 27 percent of incidents. Where Only

Unauthorized Charges were involved, 57 percent of victims reported that the thief got less than

$500, while 40 percent of victims of New Accounts & Other Frauds reported that the thief got

$5,000 or more.

The individual whose personal information is used to obtain money or goods ultimately

does not lose the total amount that the thief obtained. For example, if the thief obtains goods or

services using an existing credit card, the loss becomes a liability of the company that issued the

credit card or the merchant who sold the product, depending on the particulars of the

transaction.23 However, victims may ultimately pay some of the cost of what was stolen – if for
23 (...continued)

provided the merchant complies with the appropriate card-authorization procedures. However,

in a situation – such as most purchases made on the Internet – where it is not possible to

“authenticate” the identity of the purchaser, the liability for fraud is placed on the seller. (Robin

Sidel and Mitchell Pacelle, “Credit-Card Breach Tests Banking Industry’s Defenses,” Wall

Street Journal, June 21, 2005, p. C-1, and Cheney (2003), pp. 8-9.)

24 Because the victim’s out-of-pocket costs include such things as amounts paid to

lawyers and amounts paid for postage that are not included in the value of what the thief

obtained, one cannot subtract the out-of-pocket cost borne by the victim from the amount stolen

using the victim’s information to arrive at an estimate of the net cost borne by businesses. 

Similarly, because the victim may have paid some of the cost of what the thief obtained, the

victim cost figure cannot be added to the estimate of what the thief obtained to obtain a “total

cost” of ID theft.

25 It is somewhat surprising that even 20 percent of the victims of this type of ID theft

report that they incurred out-of-pocket expenses, since consumers are supposed to be protected

against losses that result from the misuse of their credit cards. Unfortunately, there is no precise

way to determine what happened in the cases where consumers reported losses as a result of

unauthorized credit card charges. Possible explanations for such losses may include (i) that the

misuse was caused by a family member or friend and the card holder chose not to pursue the

matter to have the charge removed, (ii) that consumers were unable to persuade the merchant

that the charge resulted from identity theft, (iii) that consumers did not realize that the charge

was fraudulent until after the bill was paid and they chose not to pursue efforts to recoup the

money, or (iv) that consumers did not notify the company within the time allowed under the Fair

Credit Billing Act. 

10

no other reason, just to resolve the matter and protect their credit records. In addition, the victim

may incur expenses in the effort to clean up the problems that result from the ID theft.24

In only a minority of incidents did the victims actually incur any out-of-pocket costs. In

67 percent of incidents, survey participants who experienced ID theft reported that they did not

incur any such expenses. On the other hand, victims reported incurring expenses of at least $500

in 12 percent of incidents. 

Not surprisingly, the likelihood of having out-of-pocket expenses is higher in those cases

that involve more complicated problems. Where the ID theft involved Only Unauthorized Credit

Card Charges, victims reported that they incurred no expenses in 80 percent of cases, while

expenses of $500 or more were reported in only 5 percent of cases.25 However, when More Than

Unauthorized Credit Card Charges were involved, victims reported incurring no expenses in

only 58 percent of incidents. In 17 percent of these cases, victims reported incurring expenses of

$500 or more. Where New Accounts & Other Frauds were involved, victims reported incurring

costs of $500 or more in 25 percent of cases. 
11

Even where victims do not incur any monetary cost, they may have to spend time dealing

with the problems created by the theft of their information. Including all ID theft incidents, the

median amount of time victims reported spending resolving their problems was in the range of 2

to 9 hours. For cases that involved Only Unauthorized Charges, the median amount of time

spent was 1 hour or less, and victims spent less than 10 hours in 84 percent of these cases. When

New Accounts & Other Frauds were involved, the median was in the range of 10 to 39 hours,

and victims spent 80 hours or more resolving their problems in 22 percent of cases.

Predicted Demographic Effects

Turning now to the consideration of demographic effects, it may seem as an initial matter

that the risk of ID theft should not vary significantly from one individual to another and therefore

that demographics should not affect the likelihood of becoming a victim. One does not do

something to become a victim – it just happens to you. However, a little deeper reflection

suggests that this is really not the case. The risks faced by consumers do differ, and these

differences may manifest themselves in differences across groups with different demographic

characteristics.

First, consumers who have more credit and engage in more transactions using this credit

may have an increased risk of becoming a victim, ceteris paribus. There are simply more

opportunities to steal the personal information of someone who uses five credit cards to make 20

purchases per week than of a person who only uses cash. Similarly, the risk of having a noncredit card account misused is likely to rise with the number of such accounts. 

Generally, it would seem that having more accounts and making more use of them would

be correlated with the consumer’s income level. Therefore, higher income levels might be

associated with a higher risk of experiencing identity theft – particularly the misuse of existing

accounts. Similarly, older consumers may face less risk than younger consumers if they make

fewer purchases and make less use of credit cards and other forms of credit.

Second, the risk of becoming a victim of identity theft may be affected by where a

consumer does business. The risk would seem to be lower when consumers deal with businesses

they know. On the other hand, consumers who make purchases from entities that are unknown

to them, either personally or by reputation, may be running an increased risk of victimization. 

There is some reason to suspect that the likelihood that consumers deal with unknown companies

may decline with age. Anderson (2004) found that the percentage of consumers who said that, in

the last year, they had purchased something in response to a telemarketing call from a company

with whom they had not previously done business declined from 6.4 percent of those between 25

and 34 years of age to 1.4 percent of those who were 65 or over. Similarly, those who had

purchased a product by telephone, Internet, or mail after seeing a television advertisement or
26 Keith B. Anderson, Consumer Fraud in the United States: An FTC Survey, Staff

Report of the Bureaus of Economics and Consumer Protection, Federal Trade Commission,

August 2004, p. 21.

27 Federal Trade Commission, “ID Theft: What’s It All About?,” (available at

http://www.ftc.gov/bcp/conline/pubs/credit/idtheftmini.pdf.)

12

infomercial declined from 25.3 percent among those in the 25 to 34 year-old age group to 17.7

percent of those aged 65 or over.26

Consumers can also take a variety of steps to reduce their risk of becoming a victim of

identity theft or reduce the extent of the problem if they do become a victim. For example, the

Federal Trade Commission recommends that consumers deposit outgoing mail in secure post

office collection boxes or at the post office rather than leaving it for the postal carrier to pick up

at their homes. In addition, the Commission recommends that consumers shred or tear up charge

card receipts, copies of credit applications or offers, insurance forms, and bank or credit card

statements before disposing of them.27

In addition, consumers can take steps to limit the damage that results if they do become

victims of identity theft. Quick discovery is a key factor in limiting the amount of damage that

occurs and the complexity of the resulting problems. Where victims took less than a month to

discover that their personal information was being misused, victims incurred no out-of-pocket

expenses in 75 percent of cases. However, where six months or more elapsed before the misuse

was detected, victims avoided incurring expenses in only 42 percent of cases. Similarly, where

the misuse was discovered within one month, victims said that they spent less than ten hours

resolving their problems in 78 percent of instances. Where more than six months elapsed before

the misuse was discovered, only 23 percent of victims were able to resolve their problems in less

than ten hours. Finally, new accounts were opened or other frauds committed in only 23 percent

of cases where the misuse was uncovered within one month of when it began. Where it took six

or more months to uncover the misuse, new accounts were opened or other frauds were

committed in 73 percent of cases. Thus, consumers can limit the potential damage from identity

theft by monitoring their financial accounts so that they more quickly discover any improper

transactions.

While consumers can clearly take steps to avoid becoming a victim of identity theft and

to minimize the problems that result if they do become a victim, in order to benefit from these

steps, the consumer must be aware of the recommendations and follow them. If certain

demographic groups are more efficient in obtaining this kind of information and in adapting their

behavior to incorporate the recommendations into their behavior, they may show a reduced

likelihood of becoming a victim of identity theft – particularly of becoming a victim of the more

traumatic types of ID theft. 
28 Theodore W. Shultz, “The Value of the Ability to Deal With Disequilibria,” Journal of

Economic Literature, 13 (September 1975), pp. 827-846.

29 Pauline M. Ippolito and Alan D. Mathios, Health Claims in Advertising and Labeling: 

A Study of the Cereal Market, Bureau of Economics Staff Report, Federal Trade Commission,

August 1989, pp. 63-64.

30 This suggestion is also found in Ippolito and Mathios (1989).

13

Schultz (1975) suggests that people with more education and more experience are better

at adapting their behavior when new information suggests a change.28 Ippolito and Mathios

(1989) also suggest that consumers who have more education may be more likely to read print

media and be exposed to news sources.29 Knowledge of identity theft and the suggestions for

protecting oneself against it are likely to come from print media and news sources. Therefore,

higher levels of education may be associated with a lowered risk of becoming a victim of

identity theft.

The ability to adapt to changed circumstances may also be reflected in other demographic

variables. Following Shultz’s suggestion that additional experience also increases the ability to

adapt to changed circumstances, the likelihood of adopting risk reducing behaviors and therefore

experiencing lower rates of ID theft may increase with age. Higher levels of income may also be

associated with superior ability to adjust to new information.30 Those who are more able to

adjust to changed circumstances in a business setting are more likely to achieve the level of

success associated with higher income levels. This same ability to adapt may be reflected in

people’s reaction to new information in their role as consumers. Thus, higher levels of income

may be associated with greater adoption of steps to protect the consumer from identity theft and

therefore lower risk. However, because higher levels of income may also be associated with

more use of credit and additional transactions thereby increasing the risk of victimization, the

overall effect of income on the likelihood of becoming a victim is unclear. The risk may rise or

fall depending on whether the increased use of credit or the superior ability to adjust dominates.

The risk of experiencing identity theft may also be affected by household composition. 

First, the risk may depend on whether there is a single adult in the household or whether two or

more adults live there. Ippolito and Mathios suggest that families with two or more adults may

have a relative advantage in acquiring new information. In households inhabited by multiple

adults, it may not be necessary that each adult be exposed to the information about identity theft. 

If one person reads about the problem and how to protect oneself, the information can be shared

with other members of the family. Furthermore, to the extent that employing the risk-reducing

strategies requires consumers to spend more time paying their bills, monitoring their accounts

and disposing of mail and records, there may be economies if two or more adults can share these
31 It is, of course, quite possible that a household with multiple adults will have more

bills than one with only a single adult. Similarly, a multiple-adult household may have more

records that need to be protected and more accounts that need to be monitored. However, the

hypothesized relationship would hold provided the number of bills, accounts, etc., does not

increase proportionately with the number of adults in the household.

32 The categories used here are limited by the data collection methods used by

Synovate’s TeleNation survey. Following the approach recommended by OMB, Synovate first

asks whether the person being interviewed is of “Hispanic, Latino or Spanish origin” and then

asks a separate question “And, which of the following best describes your race?” The answers

provided for this question are “White, Black, and Asian.” This approach reflects the fact that

being of Hispanic origin is not a racial designation and, at least conceptually, allows one to

reflect this in the analysis. However, more than 50 percent of those who answered “Yes” to the

question about being of Hispanic, Latino or Spanish origin, then indicated that they were not

white, black, or Asian, when asked the race question. That is, they are classified as being of an

“other” race. This may well reflect that they consider Hispanic to be their race. (We have seen

similar response patterns in other surveys with which we are familiar where this two-question

approach to race and ethnicity has been used.) Given this result, we chose to use a single

variable reflecting race or ethnic origin. Those who indicated that they were Hispanic in the first

question are put into the Hispanic category and those who are non-Hispanic are classified

according to their responses to the racial question.

14

duties.31 Thus, households with only a single adult may face a higher risk of experiencing

identity theft than will households with two or more adults.

Second, the number of children in the household may have an effect on the risk of

experiencing ID theft. There are at least two reasons to believe that households with more

children may be more likely to become victims of identity theft. First, where there are children,

adults in the household will necessarily spend time on the children. As a result, they may have

less time to spend acquiring new information about the risks of identity theft or taking steps to

protect the household. Second, the presence of children is likely to be accompanied by

additional transactions – possibly transactions made by the children themselves using their

parents’ credit cards, etc. Again, this increases the risk of becoming a victim of identity theft. 

In addition to the variables discussed above, the possible effects of several other

demographic variables are considered, though without specific predictions as to how they should

affect the risk of ID theft. Rather, these variables are included because they are commonly

included when looking at demographic effects. Among these variables are the gender of the

person and whether they are married or not. Also included are variables denoting the person’s

race and ethnicity. The categories included are (i) non-Hispanic white, (ii) African American,

(iii) Hispanic, (iv) Asian, and (v) Other.32 Finally, the possibility of differences depending on

the region of the country in which one lives is considered
33 Of course, the estimated relationship cannot account for other factors that may affect

the risk of identity theft but are not included in our equations. If any such factors are correlated

with any of the variables that are included in the equation, it is possible that any relationship

found is the result of the relationship between the omitted variable and the risk of ID theft and

not the relationship between the included variable and the risk.

34 For a detailed treatment of probit analysis, see John H. Aldrich and Forrest D. Nelson,

Linear Probability, Logit, and Probit Models, Sage University Paper Series on Quantitative

Applications in the Social Sciences, No. 07-045, Sage Publications, 1984, or William H. Greene,

Econometric Analysis, 3rd edition, Prentice Hall, Inc., 1997.

35 As shown in Table 3, except for one variable, missing values account for, at most,

about 3 percent of total observations. The exception is the income variable where almost 20

percent of participants failed to provide the necessary information.

36 This technique is used to provide estimated values for missing values of independent

variables only. Where the dependent variable – whether the individual is a victim of the type of

ID theft being examined – is missing, the observation is still deleted. Missing values of the

dependent variables can arise because of the failure of a participant in the survey to answer all of

the questions needed to determine whether that person is a victim. In some cases, it is possible

to determine that the person is or is not a victim of some types of ID theft, but not to determine

whether that person is a victim of another type of ID theft. As a result, the number of

observations will vary slightly across equations.

15

Statistical Methodology

Multivariate probit regression estimations are used to estimate the relationship between

ID theft risk and the various demographic characteristics. As a result, the reported results show

how the predicted probability of being a victim of ID theft changes with a change in the variable

being analyzed, holding constant the values of all other variables included in the analysis.33

Probit analysis is appropriate because, for each individual observation, the dependent variable

has one of two values, indicating that this person was or was not the victim of the type of ID

theft being examined.34

As is common in regression analysis using survey results, problems arise because not

every participant answers every question asked.35 To avoid losing a large number of

observations because of these missing data, a common approach known as conditional mean

imputation has been employed. This approach uses the relationships among the various

variables included in a model to estimate the most-likely value for those instances where the

value of a variable is missing.36 For example, assume that one wanted to estimate the equation

yi = a0i + a1 X1i + a2 X2i + a3 X3i + a4 X4i (1)
37 If there was an observation k which was missing values for both X3 and X4, one would

estimate the value of these two variables from two ancillary regressions, one with X3 as the

dependent variable and the other with X4. The independent variables in these regressions would

be X1 and X2. That is, the technique uses whatever information is available for a particular

observation in estimating the conditional mean values for the variables for which there are no

values. (For a more complete discussion of this technique see Roderick J.A. Little and Donald

B. Rubin, “The Analysis of Social Science Data With Missing Values,” in Modern Methods of

Data Analysis, John Fox and J. Scott Long, editors, Sage Publications, 1990, pp. 374-409. See

also Roderick J.A. Little and Donald B. Rubin, Statistical Analysis With Missing Data, Second

Edition, Wiley-Interscience, 2002. For a recent application of this approach, see Philip DeCicca,

Donald Kenkel and Alan Mathios, “Putting Out the Fires: Will Higher Taxes Reduce the Onset

of Youth Smoking?,” Journal of Political Economy, 110 (February 2002), pp. 144-169.)

In implementing the conditional-mean-imputation procedures with our categorical

variables, when it was possible to convert a categorical variable to a continuous variable, we did

so prior to performing the imputation. For example, with the education variables, we created a

years-of-schooling variable by assigning a value of 12 to those who graduated from high school

and a value of 14 to those who said that they had attended some college but did not graduate, etc.

The auxiliary regression then provided an estimated number of years of education for those cases

where the education variable was missing. After the imputation, the categorical variable was

recreated by assigning an observation to the category implied by the imputed value. Thus, for

example, if the predicted value of education was 15 years, the person was assumed to belong to

the attended some college category. When there was no logical way to create a continuous

variable – for example for the variables describing race and ethnicity – we created separate

dummy variables for each of the values of the categorical variables and generated imputed

values for each of the dummy variables. We then use these values, which can be considered

estimates of the likelihood that the individual fell within each category, in the final regression.

38 Estimates based only on those cases where complete data are available may be biased

(continued...)

16

but the data did not have an observed value for X4 for some observation j. Assuming that data on

X1, X2, and X3 were available for observation j, one would run the ancillary regression

X4i = b0i + b1 X1i + b2 X2i + b3 X3i (2)

using data for all observations where one had values for all of these variables. Using the

resulting estimated coefficients and the observed values of X1, X2, and X3, for observation j, the

mean estimate for the value of X4j conditional on the observed values for the other variables

would be estimated. Using this estimated value to replace the missing value, observation j could

then be included in estimating equation (1).37 This approach improves the efficiency of the

estimates relative to the alternative approach of simply deleting any observation that has a

missing value for one or more variables. In addition it may reduce the likelihood of biased

estimates.38
38 (...continued)

if missing observations do not occur randomly. (See Little and Rubin (1990), p. 380.)

39 Our regression results are based on weighted regressions with the weights reflecting

the relative likelihood of being included in the sample. The weights have two components. The

first are the standard weights that adjust for the under- or over-representation of certain

demographic groups within the sample of people who participated in the survey. The weights to

make this adjustment were furnished by Synovate. Second, as discussed in footnotes 9 and 18

above, we were unable to identify victims of certain types of identity theft among those who

were interviewed in certain waves of the survey. Where this affects the number of victims

included in our regressions, we have increased the weight given to known victims to reflect the

fact that there is a reduced likelihood of observing this type of ID theft. The weights used here

are the number of people interviewed in those waves of the survey where a victim of the

particular type of ID theft could be identified as a proportion of all people interviewed in the

survey.

40 With conditional mean estimation, the standard errors on the coefficients may be

biased downward. There is uncertainty about the true values where means have been used as the 

estimated value for a missing value. However, this uncertainty is not reflected in the estimates

from the final equation. As a result, the measures of statistical significance may be overstated.

(See Little and Rubin (1993), p. 376.)

41 The data in Table 3 are based on unweighted observations.

17

Empirical Results

Tables 4 and 5 provide the results of the analysis of the likelihood of identity theft

victimization using each of the measures described above.39 The dependent variable in each case

indicates whether an individual was a victim of the particular type of ID during the five year

period before the FTC survey was conducted. The results in Table 4 utilize conditional mean

imputation so that observations that have missing values are not lost. Regressions using only

those observations for which complete data was available – complete case estimation – were also

run. These results, which are reported in Table 5, provide a check on the robustness of the

results and a check against the potential of conditional mean estimation results to overstate

statistical significance.40 The distribution of the values for all explanatory demographic

variables are reported in Table 3.41 

In Tables 4 and 5, the first column of the table provides estimates of how the overall

likelihood of having experienced any type of ID theft is affected by differences in the

demographic variables, while the other columns provide similar estimates using the other

measures of ID theft. In each case, two different measures of the effect of a change in a variable

are provided. The first is the “marginal effect,” which is the percentage point change in the

likelihood of experiencing ID theft resulting from a change in the value of the variable, holding
42 In estimating the marginal effects of any one variable, we first estimate the marginal

effect for each individual observation, given the observed values of all of the other independent

variables. We then average these estimated marginal effects across individuals to arrive at an

average effect. That is, the marginal effect of variable j – MargEffj

 – is

MargEffj

 = 1/n Σi [Pr(yi | xij = 1, Xi) - Pr(yi | xij = 0, Xi) ]

where Pr(yi | xij, Xi) is the probability that individual i will be a victim given that the individual

has characteristics xij and Xi,

xij is the assumed value of variable j for individual i, and

Xi

 represents the observed values of other characteristics included in the equation for

 individual i.

In several cases, our equations include more than one variable to represent different

values of the same characteristic. For example, income is represented by four variables

representing incomes between (i) $25,000 and $50,000, (ii) $50,000 and $75,000, (iii) $75,000

and $100,000, and (iv) $100,000 and above. (A variable representing incomes of less than

$25,000 is not included because this is the base case in the regressions.) In estimating the

marginal effect of different levels of income, for example, incomes between $50,000 and

$75,000 relative to that for the base case income of less than $25,000, we set all of the other

income variables equal to zero both in the equation with income of $50,000 to $75,000 set equal

to one and where it is equal to zero. By doing this, we correctly compare the case with income

less than $25,000 – where all of the income variables are equal to 0 – with the case with income

between $50,000 and $75,000 – where that particular variable takes a value of 1 and all of the

other income variables are equal to 0. 

18

the values of all other variables in the equation constant.42 Consider, for example, the first entry

in the first row and first column of Table 4, which provides estimates of the change in the

likelihood of any kind of identity theft if one has an income between $25,000 and $50,000 rather

than an income of less than $25,000. (An income of less than $25,000 is the base case for the

analysis of the relationship between income level and risk.) The “Marginal Effect” of this

change is 1.867, which shows that a change in income from less than $25,000 to $25,000 to

$50,000 results in an increase of 1.867 percentage points in the likelihood of having experienced

any kind of identity theft during the five years before the FTC survey, holding all of the other

variables in the equation constant. 

The second measure is the relative effect – that is, the change as a percentage of the risk

in the base case. Looking again at the first row and column of Table 4, the 1.867 percentage

point increase that results from increasing an individual’s income from less than $25,000 to

between $25,000 and $50,000 represents a 19.1 percent increase from the estimated likelihood of

experiencing ID theft with the base income of under $25,000.
43 The actual probit coefficients and their standard errors are reported in Appendix

Tables 1 and 2.

44 Those between 18 and 24 appear to face a reduced risk relative to those who are 25 to

34, though the differences are not statistically significant. This may reflect the fact that many

individuals in this age group are likely to be in college and/or just starting out and therefore to

have fewer credit cards and other accounts in their own names than older consumers.

45 As noted previously, the More than Unauthorized Credit Card Charges category

includes New Accounts & Other Frauds.

19

In addition to the two measures of the effect of the variable under consideration, an

indication of the statistical significance of the particular characteristic is provided. More

precisely, the asterisks in the right-most portion of the column indicate the statistical significance

of the corresponding estimated probit coefficients that underlie the reported effects.43 Measures

of statistical significance are provided both for each individual variable and jointly for all of the

variables that describe the same demographic characteristic. While the individual coefficient

tests measure the significance of the difference in risk between those who have that particular

value of the characteristic in question and those who have the value designated as the base case,

the hypothesis that the coefficients on all of the variables representing different values of a

particular characteristic are jointly equal to zero is tested in the joint significance tests.

Age. Consider first the relationship between age and the likelihood of becoming a victim of

identity theft. In general, older consumers appear less likely to be victims of any kind of identity

theft. People between the ages of 25 and 34 have the highest risk of becoming a victim of any

kind of ID theft – column (1) of Table 4 – and the estimated risk declines steadily as age

increases.44 The overall relationship between age and the likelihood of experiencing identity

theft is significant at the 10 percent level in the conditional mean imputation results in Table 4,

though not in Table 5. Both in Table 4 and in Table 5, the only individual difference that is

statistically significant is that those who are 75 or over are significantly less likely to be a victim

than those in the base group – those who are between 35 and 44. The likelihood of a person who

is 75 or over being a victim is 55 or 60 percent lower than that for someone who is between 35

and 44.

Looking at the remaining columns, the effect of age seems to be reflected primarily in the

likelihood of experiencing More than Unauthorized Credit Card Charges, including experiencing

New Accounts & Other Frauds.45 There are no statistically significant relationships between any

of the age variables and the likelihood of experiencing ID theft limited to Only Unauthorized

Charges. On the other hand, there is a negative relationship – which is jointly significant at the 5

percent level in the Table 4 results and at the 10 percent level in Table 5 – between age and the

likelihood of experiencing More Than Unauthorized Charges. The likelihood that someone who

is 75 or over will experience More than Unauthorized Credit Card Charges or New Accounts &

Other Frauds is approximately 85 percent lower than for those who are 35 to 44. These
46 Our conditional mean imputation results show that those who are 65 to 74 have a

lower risk than those in the base group that is statistically significant at the 10 percent level. 

However, the effect here is not statistically significant in the complete case results.

47 A Chi-square test for the equality of these two coefficients rejects the hypothesis that

they are equal at the 5 percent level of significance. Tests for differences in coefficients across

equations were conducted using only those observations for which complete data were available.

48 While the coefficient on the variable for incomes of $75,00 to $100,000 is actually

larger than that for incomes of $100,000 or above, the difference in the two is not statistically

significant.

49 The one exception is in the equation for More Than Unauthorized Credit Card Charges

using the complete case approach. Here the overall relationship is significant at the 10 percent

level.

20

differences are statistically significant for both measures and in both sets of results.46 Indeed, the

marginal effect of being 75 or older is significantly greater in the More Than Unauthorized

Charges equation than in the equation for Only Unauthorized Charges.47 People between 65 and

74 have a risk that is estimated to be between 35 and 50 lower than those in the base group

depending on the estimation technique used and whether one is considering only New Accounts

& Other Frauds or is including all cases that involved More than Unauthorized Credit Card

Charges. However, these differences are not generally statistically significant.

Income Level. The likelihood of experiencing any kind of identity theft increases with higher

income levels. The relationship between income level and the overall likelihood of any kind of

ID theft is jointly significant at the 1 percent level using both estimation techniques. The

relationship is particularly pronounced at income levels of $75,000 or above. Incomes in the

$75,000 to $100,000 range are associated with at least a 50 percent increase in the risk of ID

theft relative to the risk faced with an income of less than $25,000. Incomes over $100,000 are

associated with an estimated increase of roughly 70 to 74 percent. Both of these differences are

statistically significant at a 5 percent level or higher in both tables.

The separate estimations for Only Unauthorized Credit Card Charges and More Than

Unauthorized Charges only show a significant relationship between income and the likelihood of

victimization in the Only Unauthorized Charges equation, where the overall relationship is

significant, as are the individual coefficients on incomes of $75,000 - $100,000 and $100,000

and above. Those with incomes of $75,000 or more appear to be more than twice as likely to be

a victim of Only Unauthorized Charges as are those with incomes under $25,000.48 In the More

Than Unauthorized Charges and New Accounts & Other Frauds equations – columns (3) and (4)

– the overall relationship between income and risk is not generally significant.49 In both of these

equations, those with incomes in excess of $100,000 are estimated to have a risk that is about 50

percent higher than for those with incomes under $25,000. However, these differences are only
50 The differences in the income coefficients between the Only Unauthorized Charges

and the More Than Unauthorized Charges equations are not, however, statistically significant.

51 The Table 5 results here show slightly higher, though generally similar, effects.

52 Children are defined as those under the age of 18.

53 The differences in the marginal effects of the number-of-children variables between

(continued...)

21

significant in the More than Unauthorized Credit Card Charges equation and then only at the 10

percent level.50

Education. There is some indication that people with more education are more likely to

experience identity theft, with the risk being lowest among those who have only a high school

education. In the column (1) results, the risk faced by those who attended some college,

graduated from college or had post-graduate training are all more than 20 percent higher that for

those with only a high-school education. However, the overall differences are not statistically

significant and the only individual difference that is statistically significant is that between those

who attended some college and those with only a high-school education. Looking at the subcategories of ID theft the risk of Only Unauthorized Charges ID theft appears to increase fairly

steadily as education level increases beyond high school. Those with post-graduate training are

almost twice as likely to experience this type of ID theft as those who only graduated from high

school and the difference is statistically significant. However, the overall relationship is still

insignificant. Looking at More Than Unauthorized Credit Card Charges and New Accounts &

Other Frauds, there is no apparent pattern to the relationship between income and these types of

ID theft.

Number of Adults. The risk of identity theft is greater for people who live in households with

only one adult. For these people, the conditional mean imputation results suggest that the risk of

experiencing some kind of identity theft is about 35 percent higher than for people who live in

households with multiple adults. This does not vary much by type of ID theft. Columns (2), (3),

and (4) all show increased risks of between 33 and 42 percent for households with only one

adult, though the relationship is only statistically significant in the More Than Unauthorized

Charges equation.51

Number of Children. Households with more than two children appear to be at greater risk than

those with no children.52 In both tables, the overall risk equation – column (1) – shows little

difference in risk between households with no children and those with one or two children. 

However, the likelihood of being a victim is about 45 to 50 percent higher when there are three

or more children in the household.

The increased risk appears to be concentrated in the More Than Unauthorized Charges

form of ID theft.53 There is virtually no relationship between the number of children in the
53 (...continued)

the Only Unauthorized Credit Card Charges and More Than Unauthorized Charges equations are

statistically significant at the 5 percent level both jointly and individually.

54 This difference is significant at the 10 percent level.

55 The difference in risk of experiencing More Than Unauthorized Charges between

those with three or more children and those with one or two children is significant at the 10

percent level. (Using only those observations for which we have complete data, this difference

is significant at the 5 percent level.) 

56 We examined the possibility that marital status would have a different effect for

women than for men. However, we found no evidence of any such difference.

22

household and the risk of experiencing Only Unauthorized Charges. Indeed, the risk of suffering

from Only Unauthorized Charges appears to be lower when there are one or two children in the

household than when there are no children.54 

However, the likelihood of experiencing More than Unauthorized Charges is

significantly higher even when there are only one or two children in the household in the

conditional mean imputation estimates. With one or two children the risk of this type of ID theft

is 25 or 42 percent higher than if the household has no children depending on which set of results

one considers. If there are three or more children, the risk is roughly double that with no

children and is significant at the 1 percent level.55 Looking just at those who experienced New

Accounts & Other Frauds ID Theft, the effect is similar, though the differences are only

significant at the 10 percent level in the complete case estimates.

Gender. Holding everything else constant, women appear to be more likely than men to be

victims of identity theft. The risk of experiencing some form of ID theft is just over 20 percent

higher for women than for men using conditional mean imputation and is about 35 percent

higher using just the complete cases. The pattern is very much the same whether examining the

risk of Only Unauthorized Credit Card Charges or More Than Unauthorized Charges ID theft. 

Looking at the likelihood of being a victim of New Accounts & Other Frauds, women appear to

have more than a 50 percent higher risk than men and the difference is significant at the 5

percent level in the conditional mean imputation approach and at the 1 percent level including

only the complete cases.

Marital Status. Married people may face a somewhat higher risk of identity theft, particularly of

Only Unauthorized Charges. However, none of these differences are statistically significant.56

Race and Ethnicity. The risk of experiencing identity theft appears to vary with a person’s race

and ethnicity. The race and ethnicity variables are jointly significant both in the any-kind

equation – column (1) – and the More Than Unauthorized Charges equation – column (3) – in

both tables. However, the only significant difference seems to be that those in the “Other”
57 While the difference is not statistically significant, the risk of New Account & Other

Frauds ID Theft may be lower in the Northeast than in the Mountain region. On the other hand,

the risk of experiencing Only Unauthorized Credit Card Charges in the Northeast appears to be

comparable to that in the Pacific region.

58 These predictions are based on the conditional mean imputation results for any kind of

(continued...)

23

category – that is, those who do not describe themselves as African American, Asian, Hispanic,

or non-Hispanic white are much more likely to be victims. This group exhibits more than twice

the likelihood of ID theft as non-Hispanic whites in all four of the equations. Unfortunately, the

survey data do not provide any additional information about the racial or ethnic characteristics of

those who are included in the “Other” category. Thus, it is difficult to know what, if anything, to

make of these findings.

Geographic Region. Finally, it appears that the risk of experiencing identity theft differs across

regions of the country. In most cases, the risk of being a victim appears to be lowest in the

mountain states – Arizona, Colorado, Montana, New Mexico, Utah, Wyoming, Idaho (with the

exception of that part of the state that is on Pacific time), and the very small part of Oregon that

is on Mountain time. The risk generally appears to be highest in Pacific states – California,

Nevada, Washington, and those parts of Oregon and Idaho that are on Pacific time.57

Conclusion

The likelihood that a person will be a victim of identity theft does appear to be related to

demographics. Consumers with higher levels of income are more likely to be victims of ID theft

– particularly ID theft that involves Only Unauthorized Credit Card Charges. Similarly, those

with more education may be somewhat more likely to be victims. On the other hand, older

people may face a somewhat reduced risk. Here the reduction appears to be mainly in the risk of

experiencing More Than Unauthorized Charges, including the risk of experiencing New

Accounts & Other Frauds ID theft. The risk of ID theft also appears to be related to household

composition: One is more likely to be a victim if he or she is the only adult who lives in the

household. Having more children in the household is also associated with an increased

likelihood of becoming a victim of identity theft. Women are more likely to be victims than

men. Finally, ID theft appears to be more likely if consumers live in some regions of the country

than if they live in other regions.

While the risk of ID theft varies with several of the demographic characteristics

examined, it is important to realize that even those with the characteristics associated with the

lowest risks still face a substantial risk of becoming a victim of ID theft. For example, while

those aged 75 or older were less likely than younger consumers to be victims of ID theft, an

estimated 4.3 percent of these older people were victims of some form of ID theft in the last five

years.58 For younger consumers – those between 25 and 34 – 14.0 percent are estimated to have
58 (...continued)

ID theft using the actual characteristics of those who had the characteristic in question. Thus, for

example, for the income variables in predicting the incidence for those 75 and older, we used the

actual distribution of incomes observed among those 75 and over in the survey.

24

been victims in the last five years. Looking only at the more complex cases – More Than

Unauthorized Charges – the figures are 1.0 percent for those over 75 and 8.6 percent for those

between 25 and 34.

 Similarly, the risk of victimization is lowest in the Mountain region. However, an

estimated 5.8 percent of people living in this region have experienced some form of ID theft in

the last five years and 3.3 percent have experienced More Than Unauthorized Credit Card

Charges. In the Pacific region, where the estimates suggest that the risks are the greatest – an

estimated 15.4 percent of consumers have experienced some form of ID theft and 9.0 percent

have experienced More Than Unauthorized Charges – in the last five years. 

Socio-Demographic Characteristics do matter. However, no one is immune from the risk

of ID theft.
25

Table 1: Incidence of ID Thefta

Victim of:

Misuse Discovered Within

Last Year Last 5 Yearsb

Any Type of ID Theft 4.6%

(3.8% - 5.4%)

12.7%

(11.4% - 14.0%)

Only Unauthorized Credit Card Charges 2.2%

(1.6% - 2.8%)

5.2%

(4.3% - 6.1%)

More than Unauthorized Credit Card Charges 2.4%

(1.9% - 2.9%)

 7.5%

(6.5% - 8.5%)

New Accounts & Other Frauds ID Theft 1.5%

(1.2% - 1.9%)

4.7%

(3.9% - 5.4%)

Notes: 

a. Figures in parentheses are 95 percent confidence intervals.

b. The structure of the response categories for the question about when the misuse was discovered included the

categories “4 years to less than 5 years” and “5 years to less than 6 years.” It is therefore not possible to include

those who indicated that the misuse occurred 5 years ago without also including anyone who indicated that it

occurred 5 years and 3 months ago. Because it seemed that people who were describing an event that occurred 5

or more years ago would have been unlikely to give an answer that was any more detailed than the nearest year,

we have included those who placed the discovery of the misuse in the “5 years to less than 6 years” category

within those who discovered the misuse in the last 5 years.
26

Table 2: Costs of ID Theft, Misuse Discovered Last 5 Years

All Types of ID Theft Only Unauthorized Credit Card Charges More Than Unauthorized Credit Card Charges 

Gross Value Stolen Using Victim’s PersonalInformation

Median Value $500 - $999 $100 - $499 $500 - $999 Percent of cases in which thief got:

Less than $500 46% 57% 39% $5,000 or more 18% 5% 27% 

Out-of-pocket loss to Victims

Median Value $0 $0 $0 Percent of cases in which the victim’s out-ofpocket expenses were:

$0 67% 80% 58% $500 or more 12% 5% 17% 

Hours Victims Spent Resolving Their Problems

Median Value 2 to 9 hours 1 hour or less 2 to 9 hours Percent of cases in which the time the victimspent resolving problems was:

Less than 10 hours 68% 84% 56% 80 hours or more 10% 4% 15% 
27

Table 3: Distribution of Values for Demographic Variables and Predicted Relationship to Risk of ID Theft

Observations by Characteristica

Number Percent

Income Level

Less than $25,000 870 21.4%

$25,000 - $50,000 1,058 26.1%

$50,000 - $75,000 576 14.2%

$75,000 - $100,000 396 9.8%

$100,000 or above 364 9.0%

Not known 793 19.5%

Age

18 to 24 390 9.6%

25 to 34 695 17.1%

35 to 44 744 18.3%

45 to 54 792 19.5%

55 to 64 578 14.3%

65 to 74 399 9.8%

75 or over 336 8.3%

Not known 123 3.0%

Education

Did not complete high school 389 9.6%

Graduated from high school 1,348 33.2%

 Attended some college 938 23.1%

Graduated from college 880 21.7%

Post-graduate training 413 10.2%

Not known 89 2.2%
28

Table 3 (cont.)

Observations by Characteristic

Number Percent

Number of Adults in Household

Multiple adults in the household 2,915 71.8%

One adult in household 1,017 25.1%

Not known 125 3.1%

Number of Children

No children 2,490 61.4%

1 or 2 children 1,110 27.4%

3 or more children 354 8.7%

Not known 103 2.5%

Gender

Male 1,959 48.3%

Female 2,098 51.7%

Not known 0 0.0%

Marital Status

Single 1,674 41.3%

Married 2,305 56.8%

Not known 78 1.9%

Race and Ethnicity

Black or African American 351 8.6%

Hispanic 272 6.7%

Asian 84 2.1%

Non-Hispanic White 3,119 76.9%

Other 114 2.8%

Not known 117 2.9%
29

Table 3 (cont.)

Observations by Characteristic

Number Percent

Georgraphic Region

Northeast 814 20.1%

Midwest 941 23.2%

South 1,422 35.1%

Mountain 264 6.5%

Pacific 616 15.2%

Not known 0 0.0%

Note.

a. Based on unweighted observations.

b. We hypothesize that those who live in households with only one adult will be more likely to be victims than those

who live in household with more than one adult.
30

Table 4: Likelihood of Being a Victim of Different Types of ID Theft, Effect of Demographics (Conditional Mean Imputation Estim

Any Type of ID Theft Only Unauthorized Credit Card Charges More than Unauthorized Credit Card Charges N

(1) (2) (3) 

Marginal

Effect

RelativeChange Sig. Marginal Effect Relative Change Sig. Marginal Effect Relative Change Sig. Ma

E

Age (Relative to 35 to 44)

18 to 24 -2.829 -20.5% -1.515 -32.2% -2.065 -23.1% +

25 to 34 +0.277 +2.0% +0.255 +5.4% -0.417 -4.7% +45 to 54 -0.132 -1.0% +0.726 +15.4% -1.229 -13.8% 

55 to 64 -1.741 -12.6% -0.044 -0.9% -2.230 -25.0% 

65 to 74 -2.603 -18.8% +0.455 +9.7% -3.500 -39.2% * 75 or over -8.312 -60.1% *** -1.313 -27.9% -7.432 -83.3% *** 

Joint Significance * ns ** 

Income (Relative to Less Than $25,000)

$25,000 - $50,000 +1.792 +17.3% +0.911 +30.1% +0.878 +13.0% +

$50,000 - $75,000 -0.629 -6.1% +0.471 +15.6% -0.932 -13.8% +

$75,000 - $100,000 +5.109 +49.3% ** +4.422 +146.3% ** +1.048 +15.5% +

$100,000 or above +7.900 +76.2% *** +4.381 +144.9% ** +3.493 +51.8% * +

Joint Significance *** ** ns 
31

Table 4 (cont.)

Any Type of ID Theft Only Unauthorized Credit Card Charges More than Unauthorized Credit Card Charges N

(1) (2) (3) 

Marginal

Effect

RelativeChange Sig. Marginal Effect Relative Change Sig. Marginal Effect Relative Change Sig. Ma

E

Education (Relative to High School Graduate)

Did not complete highschool +0.411 +3.8% +0.328 +9.5% +0.386 +5.7% 

Attended some college +3.275 +30.3% ** +1.279 +36.8% +1.895 +28.2% +Graduated from college +2.365 +21.9% +1.915 +55.1% * +0.756 +11.2% +

Post-graduate training +3.136 +29.0% +3.236 +93.2% ** -0.028 -0.4% 

Joint Significance ns ns ns 

Number of Adults in Household (Relative to More than One)

One adult in household +4.100 +34.9% ** +1.526 +34.5% +2.265 +32.6% * +

Number of Children in Household (Relative to None)

1 or 2 children +0.838 +7.2% -1.874 -34.6% * +2.495 +42.4% ** +

3 or more children +6.014 +51.3% ** -0.629 -11.6% +6.172 +104.8% *** +

Joint Significance ** ns *** 

Gender (Relative to Male)

Female +2.457 +21.7% ** +0.870 +20.3% +1.534 +23.2% * +

Marital Status (Relative to Single)

Married +1.919 +16.8% +1.343 +34.4% +0.109 +1.5% +
32

Table 4 (cont.)

Any Type of ID Theft Only Unauthorized Credit Card Charges More than Unathorized Credit Card Charges N

(1) (2) (3) 

Marginal

Effect

RelativeChange Sig. Marginal Effect Relative Change Sig. Marginal Effect Relative Change Sig. Ma

E

Race and Ethnicity (Relative to Non-Hispanic White)

Black or African American +0.238 +2.0% +0.070 +1.5% +0.230 +3.4% +

Hispanic +3.201 +27.2% -0.530 -11.8% +3.300 +48.4% * +

Asian +3.044 +25.8% +0.817 +18.2% +1.072 +15.7% 

Other +16.007 +135.9% *** +8.351 +185.6% ** +8.842 +129.7% *** +

Joint Significance *** ns ** 

Region of the Country (Relative to Pacific)

Northeast -3.330 -21.1% +0.407 +7.4% -4.231 -42.2% ** 

Midwest -4.932 -31.3% ** -1.595 -28.8% -3.848 -38.4% ** 

South -2.459 -15.6% -0.968 -17.5% -1.706 -17.0% 

Mountain -8.886 -56.4% *** -3.115 -56.3% * -5.768 -57.5% *** 

Joint Significance ** ns ** 

Number of Observations 3,789 3,796 3,874 Percent Positive Values 12.58% 4.72% 7.40% Chi-Square for OverallSignificance 86.07 *** 64.03 *** 88.92 *** (Notes on next page.)
33

Table 4 (cont.)Notes.a Asterisks denote the statistical significance of the underlying probit coefficients. * ** significant at the 1 percent level ** significant at the 5 percent level * significant at the 10 percent level ns not statistically significant
34

Table 5: Likelihood of Being a Victim of Different Types of ID Theft, Effect of Demographics (Complete Case Estimates)a

Any Type of ID Theft Only Unauthorized Credit Card Charges More than Unathorized Credit Card Charges 

(1) (2) (3) 

Marginal

Effect

RelativeChange Sig. Marginal Effect Relative Change Sig. Marginal Effect Relative Change Sig. Ma

E

Age (Relative to 35 to 44)

18 to 24 -3.043 -21.4% -0.963 -20.5% -2.048 -22.1% +

25 to 34 -0.122 -0.9% -0.051 -1.1% -0.515 -5.6% +

45 to 54 -0.376 -2.6% +0.897 +19.1% -1.444 -15.6% 

55 to 64 -1.384 -9.7% +0.233 +5.0% -1.865 -20.2% 65 to 74 -2.684 -18.9% +0.137 +2.9% -3.188 -34.5% 75 or over -7.845 -55.1% ** -0.349 -7.4% -7.940 -85.9% *** 

Joint Significance ns ns * 

Income (Relative to Less Than $25,000)

$25,000 - $50,000 +2.448 +23.3% +0.643 +19.9% +1.835 +27.4% +

$50,000 - $75,000 -1.433 -13.6% +0.023 +0.7% -1.012 -15.1% +

$75,000 - $100,000 +6.174 +58.7% ** +4.959 +153.7% *** +1.522 +22.7% +

$100,000 or above +7.431 +70.6% *** +3.711 +115.0% ** +3.652 +54.5% * +

Joint Significance *** ** * 
35

Table 5 (cont.)

Any Type of ID Theft Only Unauthorized Credit Card Charges More than Unathorized Credit Card Charges N

(1) (2) (3) 

Marginal

Effect

RelativeChange Sig. Marginal Effect Relative Change Sig. Marginal Effect Relative Change Sig. Ma

E

Education (Relative to High School Graduate)

Did not complete highschool +0.157 +1.4% +0.940 +26.7% -0.418 -5.9% 

Attended some college +3.454 +31.2% * +0.945 +26.8% +2.271 +32.2% +Graduated from college +2.534 +22.9% +2.211 +63.1% * +0.570 +8.1% +

Post-graduate training +4.038 +36.4% +3.508 +99.6% ** +0.614 +8.7% 

Joint Significance ns ns ns 

Number of Adults in Household (Relative to More than One)

One adult in household +5.782 +48.5% *** +1.415 +31.1% +3.842 +54.2% ** +

Number of Children in Household (Relative to None)

1 or 2 children -0.193 -1.5% -2.031 -36.0% * +1.644 +25.2% +3 or more children +5.721 +45.7% ** -1.309 -23.2% +6.365 +97.7% *** +

Joint Significance * ns *** 

Gender (Relative to Male)

Female +3.795 +34.2% *** +1.538 +37.9% * +2.353 +35.7% ** +

Marital Status (Relative to Single)

Married +2.846 +25.1% +1.456 +36.9% +1.113 +15.6% +
36

Table 5 (cont.)

Any Type of ID Theft Only Unauthorized Credit Card Charges More than Unathorized Credit Card Charges N

(1) (2) (3) 

Marginal

Effect

RelativeChange Sig. Marginal Effect Relative Change Sig. Marginal Effect Relative Change Sig. Ma

E

Race and Ethnicity (Relative to Non-Hispanic White)

Black or African American +2.102 +17.9% +1.005 +22.8% +1.192 +17.1% +

Hispanic +4.880 +41.5% * -0.115 -2.6% +4.453 +63.8% ** +

Asian +3.638 +30.9% +1.672 +37.9% +0.717 +10.3% Other +19.933 +169.5% *** +10.631 +241.3% *** +10.272 +147.2% *** +

Joint Significance *** * ** 

Region of the Country (Relative to Pacific)

Northeast -3.177 -18.8% +0.788 +12.7% -4.248 -40.4% ** 

Midwest -5.955 -35.3% ** -2.834 -45.7% ** -3.711 -35.3% * South -3.528 -20.9% -1.765 -28.5% -1.971 -18.8% 

Mountain -10.581 -62.7% *** -4.275 -68.9% ** -6.292 -59.9% *** 

Joint Significance ***   

Number of Observations 3109 3116 3176 Percent Positive Values 13.01% 4.82% 7.79% Chi-Square for OverallSignificance 97.61 ** * 69.05 *** 85.14 *** (Notes on next page.)Table 5 (cont.)
37

Notes.a Asterisks denote the statistical significance of the underlying probit coefficients. * ** significant at the 1 percent level ** significant at the 5 percent level * significant at the 10 percent level ns not statistically significant
38

Appendix Table 1: Relationship Between Risk of Being Victim of ID Theft and Demographic Characteristics,

Probit Coefficients (Conditional Mean Imputation Estimates)a

Any Type of ID

Theft

Only

Unauthorized

Credit Card

Charges

More than

Unauthorized

Credit Card

Charges

New Account &

Other Frauds

(1) (2) (3) (4)

Age (Relative to 35 to 44)

18 to 24 -0.1435

(0.1395)

-0.1880

(0.2008)

-0.1459

(0.1578)

0.0918

(0.2102)

25 to 34 0.0129

(0.0965)

0.0266

(0.1422)

-0.0272

(0.1059)

0.1323

(0.1311)

45 to 54 -0.0063

(0.0954)

0.0730

(0.1370)

-0.0833

(0.1058)

-0.0669

(0.1413)

55 to 64 -0.0855

(0.1127)

-0.0047

(0.1638)

-0.1589

(0.1220)

-0.0283

(0.1633)

65 to 74 -0.1311

(0.1318)

0.0468

(0.1795)

-0.2688

(0.1498) * -0.2690(0.2105)

75 or over -0.5273

(0.1661) *** -0.1592(0.2064)

-0.8566

(0.2435) *** -0.8948(0.3566) **

Income (Relative to Less Than $25,000)

$25,000 - $50,000 0.0971

(0.0893)

0.1228

(0.1389)

0.0672

(0.0974)

0.1252

(0.1287)

$50,000 - $75,000 -0.0369

(0.1046)

0.0668

(0.1551)

-0.0792

(0.1185)

0.0108

(0.1521)

$75,000 - $100,000 0.2531

(0.1220) ** 0.4478(0.1776) ** 0.0796(0.1369)

0.1554

(0.1870)

$100,000 or above 0.3679

(0.1264) *** 0.4448(0.1802) ** 0.2380(0.1428) * 0.2271(0.1908)
39

Appendix Table 1 (cont.)

Any Type of ID

Theft

Only

Unauthorized

Credit Card

Charges

More than

Unauthorized

Credit Card

Charges

New Account &

Other Frauds

(1) (2) (3) (4)

Education (Relative to High School Graduate)

Did not complete high

school 

0.0228

(0.1202)

0.0429

(0.1804)

0.0305

(0.1319)

-0.0187

(0.1668)

Attended some college 0.1671

(0.0838) ** 0.1521(0.1248)

0.1390

(0.0939)

0.1203

(0.1245)

Graduated from college 0.1237

(0.0889)

0.2157

(0.1239) * 0.0587(0.1033)

0.0238

(0.1365)

Post-graduate training 0.1606

(0.1119)

0.3308

(0.1458) ** -0.0023(0.1360)

-0.0441

(0.1874)

Number of Adults in Household (Relative to More than One)

One adult in household 0.1962

(0.0883) ** 0.1527(0.1336)

0.1604

(0.0980)

0.1853

(0.1294)

Number of Children in Household (Relative to None)

1 or 2 children 0.0434

(0.0780)

-0.2114

(0.1118) * 0.1916(0.0867) ** 0.2529(0.1100) **

3 or more children 0.2747

(0.1120) ** -0.0633(0.1729)

0.4075

(0.1218) *** 0.4060(0.1632) **

Gender (Relative to Male)

Female 0.1248

(0.0619) ** 0.0933(0.0873)

0.1159

(0.0701) * 0.2222(0.0903) **

Marital Status (Relative to Single)

Married 0.0983

(0.0851)

0.1479

(0.1215)

0.0082

(0.0978)

0.1025

(0.1324)
40

Appendix Table 1 (cont.)

Any Type of ID

Theft

Only

Unauthorized

Credit Card

Charges

More than

Unathorized

Credit Card

Charges

New Account &

Other Frauds

(1) (2) (3) (4)

Race and Ethnicity (Relative to Non-Hispanic White)

Black or African American 0.0124

(0.1093)

0.0077

(0.1590)

0.0180

(0.1211)

0.1062

(0.1543)

Hispanic 0.1547

(0.1192)

-0.0616

(0.1919)

0.2244

(0.1296) * 0.0924(0.1683)

Asian 0.1476

(0.2075)

0.0845

(0.2791)

0.0805

(0.2443)

-0.1188

(0.3087)

Other 0.6206

(0.1618) *** 0.5894(0.2347) ** 0.5031(0.1717) *** 0.5606(0.2176) ***

Region of the Country (Relative to Pacific)

Northeast -0.1554

(0.1043)

0.0371

(0.1464)

-0.3048

(0.1192) ** -0.5162(0.1597) ***

Midwest -0.2407

(0.1033) ** -0.1706(0.1436)

-0.2711

(0.1189) ** -0.4016(0.1622) **

South -0.1122

(0.0896)

-0.0979

(0.1296)

-0.1083

(0.0987)

-0.1325

(0.1228)

Mountain -0.5003

(0.1564) *** -0.3976(0.2279) * -0.4603(0.1733) *** -0.3994(0.2232) * 

Constant -1.4030 -2.0768 -1.5275 -1.9511

Number of Observations 3,789 3,796 3,874 3,839

Percent Positive Values 12.58% 4.72% 7.40% 4.82%

Chi-Square for Overall

Significance 86.07 *** 64.03 *** 88.92 *** 67.53 ***

Note. a Figures in parentheses are standard errors
41

Appendix Table 2: Relationship Between Risk of Being Victim of ID Theft and Demographic Characteristics,

Probit Coefficients (Complete Case Estimates)

Any Type of ID

Theft

Only

Unauthorized

Credit Card

Charges

More than

Unathorized

Credit Card

Charges

New Account

& Other

Frauds

(1) (2) (3) (4)

Age (Relative to 35 to 44)

18 to 24 -0.1540

(0.1542)

-0.1149

(0.2136)

-0.1412

(0.1734)

0.1099

(0.2246)

25 to 34 -0.00570

(0.1037)

-0.0056

(0.1573)

-0.0331

(0.1125)

0.1623

(0.1399)

45 to 54 -0.0177

(0.1048)

0.0909

(0.1524)

-0.0967

(0.1165)

-0.0644

(0.1521)

55 to 64 -0.0669

(0.1244)

0.0248

(0.1835)

-0.1274

(0.1332)

-0.0166

(0.1775)

65 to 74 -0.1344

(0.1483)

0.0148

(0.2054)

-0.2337

(0.1669)

-0.3390

(0.2437)

75 or over -0.4764

(0.1863) ** -0.0392(0.2312)

-0.9378

(0.2850) *** -0.7896(0.3727) **

Income (Relative to Less Than $25,000)

$25,000 - $50,000 0.1302

(0.0939)

0.0864

(0.1458)

0.1353

(0.1022)

0.1866

(0.1364)

$50,000 - $75,000 -0.0867

(0.1131)

0.0033

(0.1648)

-0.0874

(0.1278)

0.0118

(0.1611)

$75,000 - $100,000 0.2990

(0.1252) ** 0.4769(0.1789) *** 0.1139

(0.1406)

0.1245

(0.1949)

$100,000 or above 0.3502

(0.1285) *** 0.3851(0.1802) ** 0.2492(0.1453) * 0.2069(0.1928)
42

Appendix Table 2 (cont.)

Any Type of ID

Theft

Only

Unauthorized

Credit Card

Charges

More than

Unathorized

Credit Card

Charges

New Account

& Other

Frauds

(1) (2) (3) (4)

Education (Relative to High School Graduate)

Did not complete high

school 

0.0087

(0.1285)

0.1163

(0.1928)

-0.0335

(0.1399)

-0.0270

(0.1829)

Attended some college 0.1747

(0.0910) * 0.1167(0.1350)

0.1593

(0.1012)

0.1422

(0.1324)

Graduated from college 0.1313

(0.0962)

0.2462

(0.1349) * 0.0432(0.1106)

0.0125

(0.1404)

Post-graduate training 0.2013

(0.1235)

0.3554

(0.1605) ** 0.0465(0.1496)

-0.0619

(0.2117)

Number of Adults in Household (Relative to More than One)

One adult in household 0.2681

(0.0967) *** 0.1422(0.1385)

0.2535

(0.1088) ** 0.1958(0.1425)

Number of Children in Household (Relative to None)

1 or 2 children -0.0099

(0.0857)

-0.2268

(0.1239) * 0.1238(0.0944)

0.2011

(0.1192) * 

3 or more children 0.2567

(0.1218) ** -0.1365(0.1941)

0.3982

(0.1311) *** 0.3296(0.1812) * 

Gender (Relative to Male)

Female 0.1904

(0.0672) *** 0.1652(0.0948) * 0.1718(0.0755) ** 0.2570

(0.0969) ***

Marital Status (Relative to Single)

Married 0.1446

(0.0930)

0.1601

(0.1301)

0.0817

(0.1060)

0.1159

(0.1405)
43

Appendix Table 2 (cont.)

Any Type of ID

Theft

Only

Unauthorized

Credit Card

Charges

More than

Unathorized

Credit Card

Charges

New Account

& Other

Frauds

(1) (2) (3) (4)

Race and Ethnicity (Relative to Non-Hispanic White)

Black or African American 0.1052

(0.1168)

0.1052

(.1680)

0.0876

(0.1282)

0.1480

(0.1628)

Hispanic 0.2288

(0.1244) * -0.0132(0.1982)

0.2864

(0.1323) ** 0.1568(.01729)

Asian 0.1754

(0.2238)

0.1668

(0.2902)

0.0540

(0.2716)

-0.3874

(0.3292)

Other 0.7443

(0.1740) *** 0.7120(0.2437) *** 0.5586(0.1871) *** 0.6427(0.2257) ***

Region of the Country (Relative to Pacific)

Northeast -0.1414

(0.1134)

0.0649

(0.1565)

-0.2939

(0.1291) ** -0.5709(0.1702) ***

Midwest -0.2849

(0.1131) ** -0.3077(0.1569) ** -0.2496(0.1290) * -0.2992(0.1755) * 

South -0.1583

(0.0979)

-0.1732

(0.1395)

-0.1224

(0.1079)

-0.0860

(0.1366)

Mountain -0.5996

(0.1691) *** -0.5613(0.2379) ** -0.4961(0.1909) *** -0.3731

(0.2451)

Constant -1.4620 -2.0696 -1.6333 -2.0021

Number of Observations 3109 3116 3176 3147

Percent Positive Values 13.01% 4.82% 7.79% 5.16%

Chi-Square for Overall

Significance 97.61 *** 69.05 *** 85.14 *** 68.22 ***

Note. a Figures in parentheses are standard errors