http://www.fbi.gov/about-us/cjis/ucr/nibrs/nibrs_wcc.pdf
U.S. Department of Justice
Federal Bureau of Investigation
Criminal Justice Information Services (CJIS) Division
The Measurement of
White-Collar Crime Using Uniform Crime Reporting (UCR) Data
Cynthia Barnett
White-Collar Crime
T
he idea of white-collar crime was first introduced by Edwin H. Sutherland during his presidential address at the American Sociological Society Meeting in 1939. He raised concern over the criminological community’s preoccupation with the low status offender and “street crimes” and the relative inattention given to the offenses perpetrated by people in higher status occupations. In his book, White Collar Crime
, Sutherland explained further that white-collar crime “may be defined approximately as a crime committed by a person of respectability and high social status in the course of his occupation” (p. 9). Unfortunately, this definition seemed to spark more debate rather than further delineate the range of criminal behaviors that constitute white-collar crime. People continue to focus on the word “approximately” and use that as a basis to stretch or shrink the scope of white-collar crime to serve their purposes.
Currently, the definition of white-collar crime is still hotly contested within the community of experts. Although there is a multitude of variations, there appears to be three major orientations: those that define white-collar crime by the type of offender (e.g., high socioeconomic status and/or occupation of trust); those that define it in terms of the type of offense (e.g., economic crime); and those that study it in terms of the organizational culture rather than the offender or offense. Additionally, there are also those that confine the definition mainly to economic crime, as well as others that include other corporate crimes like environmental law violations and health and safety law violations.
The Federal Bureau of Investigation has opted to approach white-collar crime in terms of the offense. The Bureau has defined white-collar crime as “. . . those illegal acts which are characterized by deceit, concealment, or violation of trust and which are not dependent upon the application or threat of physical force or violence. Individuals and organizations commit these acts to obtain money, property, or services; to avoid the payment or loss of money or services; or to secure personal or business advantage.” (USDOJ, 1989, p. 3.) Some experts have criticized defining white-collar crime in terms of type of offense because this definition emphasizes the nature of the acts rather than the background of the offender. Within the FBI definition, there is no mention of the type of occupation or the socioeconomic position of the “white-collar” offender.
Although it is acceptable to use socioeconomic characteristics of the offender to define white-collar crime, it is impossible to measure white-collar crime with UCR data if the working definition revolves around the type of offender. There are no socioeconomic or occupational indicators of the offender in the data. Additionally, there are no measures of corporate structure in UCR data elements. Given that, research using UCR data must approach white-collar crime in terms of type of offense.
National Incident-Based Reporting System
NIBRS Publications Series
2
Data Available Under the Traditional Summary Reporting System
Under the traditional Summary Reporting System, there is a limited amount of information available on white-collar crime. The white-collar offenses that are measured are fraud, forgery/counterfeiting, embezzlement, and
all other offenses. Because white-collar crimes are not Index crimes, the only information available on these offenses is arrest information, which includes age, sex, and race of the arrestee. Additionally, the all other offenses arrest category is very limited in its ability to measure the white-collar offenses included in its counts. This is due to the inability to differentiate the white-collar offenses from the others that also fall in this category. Based upon the most recently published data from the FBI, the arrest rates for the offenses of embezzlement, fraud, and forgery/counterfeiting are much lower than the arrest rates for property crime1
or for total crimes in general.
It is important to keep in mind that the Summary Reporting System was developed at approximately the same time, the 1920s, that Sutherland was introducing the concept of white-collar crime. Many of the statutes that criminalized certain white-collar offenses would not yet have been enacted. Most white-collar crime laws were passed during three time periods: antitrust laws were passed in the Progressive Era (1920s), social welfare laws were passed during the New Deal (1930s), and consumer protection laws were passed in the 1960s. It is well documented that the major limitation of the traditional Summary Reporting System is its failure to keep up with the changing face of crime and criminal activity. The inability to grasp the extent of white-collar crime is a specific example of that larger limitation. There is promise that the ability to measure white-collar crime will improve with further implementation of the National Incident-Based Reporting System (NIBRS), the UCR Program’s major modernization effort.
South Carolina, which hosted the initial NIBRS pilot, submitted the first NIBRS data to the FBI in 1991. Since that time, there has been a somewhat slow but steady increase in NIBRS participation. Primarily,
growth in participation has been concentrated in the small to mid-size agencies. However, there are current efforts to provide both technical and financial assistance to law enforcement in order to encourage a wider range of participants. For the years included in this study (1997-1999), the NIBRS data reflect 9.05 percent of the crime reported to the FBI in total. Because one cannot assume that the agencies that currently participate in NIBRS are representative of all agencies in the Nation, caution should be used in interpreting the NIBRS data.
Data Available through NIBRS
In order to assess the utility of using NIBRS to measure white-collar crime, a substantial, but not exhaustive, list of white-collar offenses and its classification under NIBRS is provided (see Appendix A). Based upon that analysis, the following UCR offenses could be considered white-collar crime: fraud, bribery, counterfeiting/forgery, embezzlement (all of which are Group A offenses), and bad checks (a Group B offense)
2. Fraud is further broken down into five subcategories: false pretenses/swindle/confidence game, credit card/ATM fraud, impersonation, welfare fraud, and wire fraud2. Additionally, agencies submit arrest counts for many white-collar crimes through the All Other Offenses Group B category. As is the case with the All Other Offenses
category in the Summary arrest data, the count within this category will be limited because one will be unable to distinguish the white-collar offenses from other types of offenses.
In 1997 through 1999, white-collar crime accounted for approximately 3.8 percent of the incidents reported to the FBI. The majority of those offenses are frauds and counterfeiting/forgery. Additionally, the Group B offense of
bad checks
accounted for approximately 4 percent of the arrests during 1997-1999.
Arrest Rate
a
Total
5317.0
Property Crime 635.5
Forgery & Counterfeiting 40.7
Fraud 131.5
Embezzlement 6.5
Table 1 • Arrests reported (Summary)
a
Number of arrests per 100,000 inhabitants
Figure 1 • NIBRS participation (1997-1999)
3
In addition to the different NIBRS offenses, using additional data elements can further define and describe white-collar crime. Even though there is a total of 53 data elements divided into six segments in NIBRS, not all of them will apply to white-collar crimes (See Appendix B). Many data elements are applicable only to crimes against persons, while white-collar offenses are primarily crimes against property. The four Group A offenses could potentially have all six segments represented in their data elements, but there is only a limited amount of information available on the Group B offenses. Only arrestee information is collected on Group B offenses, which will include many of the corporate offenses like tax law violations, health and safety violations, environmental law violations, etc.
Four data elements of particular interest for measuring white-collar crime are
offender(s) suspected of
using . . .
, location type, property description, and type of victim. High tech crime is well represented by the data element offender is suspected of using . . .
with computer as one of the possible choices. Offenses like fraud can be further delineated by the type of victim (e.g., government agency, financial institution, individual), property description, or location type.
Computer Crime
Within NIBRS, the investigating agency can indicate whether the offender was suspected of using a computer during the commission of the offense. By capturing the computer-aided element of the offense in this manner, there is the ability to measure the extent
Known Unknown
Incidents Offenses Victims Offenders Offenders
Total
5,428,613 5,856,985 5,845,031 4,078,106 2,025,419
Fraud Offenses
False Pretenses/Swindle/Confidence Game 61,230 61,230 66,095 63,304 6,888
Credit Card/ATM Fraud 23,308 23,308 26,492 20,568 6,303
Impersonation 8,689 8,689 9,500 8,980 1,019
Welfare Fraud 1,289 1,289 1,300 1,344 27
Wire Fraud 984 984 1,074 808 281
Bribery 191 191 198 233 5
Counterfeiting/Forgery 91,697 91,697 110,545 85,797 21,201
Embezzlement 20,694 20,694 21,356 24,506 1,738
Arson + Fraud 10 20 5 23 0
Table 2 • Economic crime—Group A offenses
Arrestees
Total 3,634,233
Bad Checks 135,060
Table 3 • Economic crime—Group B offenses
Property Crime
Credit Card/ATM Fraud
Commercial Establishments
Bar/Night Club 36,096 176 639 133 190 0 2
Commercial/Office Building 227,245 3,412 5,546 1,480 253 17 71
Convenience Store 124,909 1,231 2,459 972 164 6 8
Department/Discount Store 183,706 3,820 4,890 3,568 536 4 17
Grocery/Supermarket 119,693 866 3,514 638 318 15 12
Liquor Store 6,817 52 225 46 33 0 1
Rental Storage Facility 20,123 59 192 92 9 0 2
Restaurant 72,091 2,651 4,198 492 105 1 16
Service/Gas Station 115,952 1,054 1,850 1,363 77 2 11
Speciality Store 118,357 2,275 5,874 2,076 449 2 23
Government/Public Building
Government/Public Building 35,425 203 811 185 261 1,007 8
Jail/Prison 3,221 19 72 10 155 7 2
Other public
Air/Bus/Train Terminal 30,116 67 112 93 17 1 3
Bank/Savings and Loan 31,244 537 3,822 2,324 382 4 51
Church/Synagogue/Temple 21,036 28 94 18 4 0 2
Construction Site 40,430 56 166 8 8 1 1
Drug Store/Doctor’s
Office/Hospital 33,454 338 2,523 136 295 3 12
Field/Woods 34,955 23 93 7 28 0 1
Highway/Road/Alley 381,954 133 2,054 219 2,276 11 16
Hotel/Motel/Etc. 46,389 281 1,399 528 147 1 9
Lake/Waterway 8,079 3 13 4 3 0 0
Parking Lot/Garage 472,093 145 1,402 178 290 4 5
School/College 122,741 258 498 337 137 0 38
Private
Residence/Home 1,555,772 1,192 12,591 4,955 1,748 121 591
Other
Other/Unknown 298,470 1,815 6,193 3,446 804 82 82
Table 4 • Economic crime offenses by location
False Pretenses, etc.
Embezzlement
Impersonation
Welfare Fraud
Wire Fraud
Figure 2 • Offenses involving use of a computer
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of computer-related crime without losing the substantive nature of the offense. Of the offenses committed using computer equipment, 42 percent are white-collar offenses. The largest proportion of those offenses are larceny-thefts.
Location
NIBRS allows for the specification of location of the offense with 25 possible types. This information is available on all offenses captured in the national data set. Property crimes
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most often occur in the residence or home. In terms of white-collar offenses, three (false pretenses, etc., credit card/ATM fraud, and wire fraud) of the five fraud types also take place most frequently in the home or residence. Additionally, residence is the second most frequent location for the remaining two categories of fraud (impersonation and welfare fraud). Embezzlement, on the other hand, is more likely to occur in department/discount stores.
When locations are grouped by common characteristics, most white-collar offenses happen in either commercial establishments or noncommercial public buildings. The only exception to this is wire fraud, which most often takes place in private areas. In contrast to the majority of white-collar crime, property crime as a total category most often occurs in private areas.
Property Stolen and Recovered
NIBRS will allow analysts to assess the economic impact of white-collar crime on victims and, ultimately, society. For each incident in which property was affected by the crime, the agency assesses a value for the property. An indicator on the incident signals how the property was affected in the course of the criminal incident. The categories that are collected in NIBRS are none, burned, counterfeited/forged, destroyed/damaged/vandalized, recovered, seized, stolen/etc., and unknown. In general, the value of the property is determined by assigning fair market value to depreciated items and replacement costs to new or almost-new items. However, credit cards, nonnegotiable instruments
4, and other property types all are submitted with no value associated with them. For incidents reported to the FBI for 1997, 1998, and 1999, these no-value
property types are more frequently reported for white-collar incidents than for total property crime.
The property values associated with white-collar incidents appear to be more skewed than are property crimes in total. By having a large difference between the median (the point where 50 percent of the data lie above and below that value) and the mean (average), the property values indicate that frequently white-collar incidents are associated with low property values with a few very high dollar values reported for an incident. For this reason, the median may be a better indicator of a norm for the incidents rather than the mean. Based upon reports submitted to the FBI for the years 1997 through 1999, the median values for property loss associated with white-collar incidents are higher than for property crime.
Mean Median Mode
White-Collar Incidents
Stolen, etc./Counterfeited $9,254.75 $210.00 $100.00
Recovered $2,266.81 $172.00 $100.00
All Incidents
Stolen, etc./Counterfeited $1,855.97 $160.00 $100.00
Recovered $2,229.73 $100.00 $100.00
Table 5 • Property lost and recovered
Figure 3 • Offenses by location type
Figure 4 • Property loss associated with
Economic Crime based on value
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If an agency recovers stolen property in the course of the investigation of the incident, it can report that information within the incident data sent to the national Program. In terms of the recovery of property lost or stolen during a white-collar incident, the most likely property to be recovered is merchandise. In general, however, incidents in which there was a white-collar offense appear to have less recovery of property than do incidents with any property offense.
Victims of White-Collar Crime
One of the major benefits of using NIBRS data is the ability to identify victims other than
individual
(person) victims. Other victim types accepted in an incident report are businesses, financial institutions, government, religious organizations, society/public, other, and unknown. The current NIBRS data reflect that businesses or nonperson victims in general are as common, if not more, than individual victims. Specifically, bribery is the only white-collar offense that has a higher proportion of individual victims than other white-collar offenses or property crime in general. The data show that any effort to measure the impact of white-collar crime that only
focuses on individual victims is getting only part of the
picture. The impact of these crimes on commercial,
financial, governmental, and religious organizations is an integral part of the effect on society as a whole.
White-Collar Crime Offenders
NIBRS provides for the collection of age, sex, race, ethnicity, and resident status information on the offenders associated with an incident in which some descriptive information is known. The NIBRS data for
1997 through 1999 show white-collar crime offenders
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are, on average, in their late-twenties to early-thirties,
which is only slightly older than most other offenders
captured in NIBRS. The majority of white-collar crime offenders are white males, except for those who committed embezzlement. However, in comparison to offenders
committing property crimes, there is a higher proportion of females committing these white-collar offenses.
Law Enforcement Response to White-Collar Crime
The UCR Program considers a crime to be cleared when agencies make an arrest or there is evidence to support that the investigation will never lead to an arrest because of circumstances beyond the control of law enforcement (exceptional means). NIBRS data captures information on both the arrests associated with
Figure 6 • Offenders by offense type
Total Property Fraud Bribery Counterfeiting Embezzlement
Total victims 5,886,566 4,069,324 103,993 198 110,545 21,356
Individual 3,998,310 2,621,843 47,826 143 45,270 3,006
Business 934,469 934,469 47,907 16 55,676 17,627
Financial Institution 11,378 11,378 2,989 0 5,310 182
Government 73,623 73,623 3,844 36 2,949 260
Religious Organization 10,794 10,794 70 0 104 35
Society or Other 857,992 417,217 1,357 3 1,236 246
Table 6 • Victims by offense type
Percent of Total Victims
Figure 5 • Nonperson victims by offense type
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the incident as well as five circumstances of exceptional clearances, which include the offender died, prosecution was declined, extradition was denied, the victim refused to cooperate, and the offender was a juvenile and not taken into custody. Bribery and embezzlement have a higher clearance rate than do other offenses. In each of the white-collar offenses and all offenses in total, arrest is the most frequent means of clearing an incident. Beyond that, the refusal to prosecute is the exceptional means agencies most frequently use to clear an offense. Interestingly, it appears that a high percentage of victims of fraud also refuse to cooperate with the investigation. This may be an indication that both of these codes are measuring the same process within the investigation. The lack of cooperation on a victim’s part may result in insufficient evidence to pursue prosecution.
Limitations of NIBRS Data
NIBRS was originally conceived as a tool for law enforcement. Therefore, the configuration of the NIBRS data set is a reflection of the preferences and needs for crime statistics of the law enforcement community. The preference toward street crime reflected in NIBRS is a result of the fact that local and state agencies, not federal agencies, were originally surveyed during the development phase. White-collar crime usually falls under the jurisdiction of federal agencies, and so specialized offenses (i.e., those not considered fraud, embezzlement, counterfeiting, or bribery) are not represented as well in NIBRS offense categories as are street crimes.
Additionally, much of the investigation and regulation of corporate white-collar crime is left to regulatory agencies and professional associations (American Medical Association, American Bar Association, etc.) and not to the police or other law enforcement agencies. White-collar offenses, in these cases, probably will be reported to the UCR Program only if criminal charges are filed, which is extremely rare in instances of corporate crime. Corporate crime is usually handled within the regulatory agency (sanctions, cease-and-desist orders, etc.), or corporations are made the subject of civil cases.
The more common corporate level offenses are typically classified as
All Other Offenses
in Group B offenses. There is no way currently to distinguish among all of these different types of crimes, and only the “Arrestee Segment” data elements are collected on these crimes. Legally, the idea of holding the corporation criminally liable is not a universally supported idea. There is some case law to support the concept of a “juristic person” when considering criminal behavior perpetrated by the corporation, but other white-collar crime experts are adamant that “corporations do not kill people, people kill people.” Ultimately, a person will be held responsible for the actions of the corporation. If an agent of the corporation committed an offense while in the course of his/her duties and for the benefit of the corporation, the principal can be held liable and convicted of a criminal offense, not the corporation itself.
Additional limitations to NIBRS statistics involve problems with reporting that are already well documented in the traditional Summary Reporting System. These limitations include both victims reporting crimes to law enforcement and law enforcement reporting crimes to the UCR Program. Many victims are unaware that they have been deceived or are too ashamed to report the offense. Further, corporations tend not to report white-collar crime perpetrated against themselves because it may negatively affect the reputation of the company. Also, NIBRS is a voluntary program; consequently, agencies do not have to submit statistics to the UCR Program in either summary or incident-based form and typically do not receive funding to help them do so. The voluntary nature of the UCR Program leads to an underreporting that can distort the actual picture of the problem of white-collar crime.
Conclusion
The true extent and expense of white-collar crime are unknown. Summary-based UCR statistics can provide only a limited amount of information on a limited number of offenses. With increased agency participation in NIBRS, however, the FBI will be better able to measure newer concerns in law enforcement, including white-collar crime. The data already have begun to reveal information about crime and criminality, including white-collar crime, that has been previously unknown.
Percent Death of Prosecution Extradition Refused to Juvenile/
Cleared Arrests Offender Declined Denied Cooperate No Custody
Fraud Offenses 33.12% 79.52% 0.15% 12.51% 0.08% 7.40% 0.34%
Bribery 61.78% 93.22% 0.00% 5.93% 0.85% 0.00% 0.00%
Counterfeiting/Forgery 29.83% 88.70% 0.13% 7.55% 0.11% 3.20% 0.31%
Embezzlement 38.37% 86.74% 0.08% 6.64% 0.03% 6.04% 0.48%
Total 32.13% 83.80% 0.13% 9.98% 0.09% 5.66% 0.35%
Table 7 • Incidents cleared by type
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Appendix A – NIBRS classifications of white-collar offenses
Criminal Behavior NIBRS Offense Category
Academic crime Fraud (26A-26E)
Adulterated food, drugs, or cosmetics Fraud (26A-26E)/All Other Offenses (90Z)
a
Anti-trust violations All Other Offenses (90Z)
ATM fraud Fraud (26A-26E)
Bad checks Bad Checks (90A)
Bribery Bribery (510)
Check kiting Fraud (26A-26E)/Bad Checks (90A)
a
Combinations in restraint in trade All Other Offenses (90Z)
Computer crime Substantive offense
Confidence game Fraud (26A-26E)
Contract fraud Fraud (26A-26E)
Corrupt conduct by juror Bribery (510)
a
Counterfeiting Counterfeiting/Forgery (250)
Defense contract fraud Fraud (26A-26E)
Ecology law violations All Other Offenses (90Z)
Election law violations All Other Offenses (90Z)
Embezzlement Embezzlement (270)
Employment agency and education-related scams Fraud (26A-26E)
Environmental law violations All Other Offenses (90Z)
False advertising and misrepresentation of products Fraud (26A-26E)
False and fraudulent actions on loans, debs, and credits Fraud (26A-26E)
False pretenses Fraud (26A-26E)
False report/statement Fraud (26A-26E)/All Other Offenses (90Z)
a
Forgery Counterfeiting/Forgery (250)
Fraudulent checks Bad Checks (90A)
Health and safety laws Fraud (26A-26E)/All Other Offenses (90Z)
a
Health care providers fraud Fraud (26A-26E)
Home improvement frauds Fraud (26A-26E)
Impersonation Fraud (26A-26E)
Influence peddling Bribery (510)
Insider trading Fraud (26A-26E)
Insufficient funds checks Bad Checks (90A)
Insurance Fraud Fraud (26A-26E)
Investment scams Fraud (26A-26E)
Jury tampering Bribery (510)
a
Kickback Bribery (510)
Land sale frauds Fraud (26A-26E)
Mail fraud Fraud (26A-26E)
Managerial fraud Fraud (26A-26E)
Misappropriation Embezzlement (270)
Monopoly in restraint in trade All Other Offenses (90Z)
Ponzi schemes Fraud (26A-26E)
Procurement fraud Fraud (26A-26E)
Racketeering Influenced and Corrupt Organizations (RICO) Substantive offense
Religious fraud Fraud (26A-26E)
Sports bribery Sports Tampering (39D)
Strategic bankruptcy Fraud (26A-26E)
Subornation of perjury Bribery (510)
a
Swindle Fraud (26A-26E)
Tax law violations All Other Offenses (90Z)
Telemarketing or boiler room scams Fraud (26A-26E)
Telephone fraud Fraud (26A-26E)
Travel scams Fraud (26A-26E)
Unauthorized use of a motor vehicle [lawful access but the Embezzlement (270)
entrusted vehicle is misappropriated]
Uttering Counterfeiting/Forgery (250)
Uttering bad checks Bad Checks (90A)
Welfare fraud Fraud (26A-26E)
Wire fraud Fraud (26A-26E)
a
The classification of these offenses may depend upon the circumstances or characteristics concerning the incident.
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BOLD
data elements are mandatory
ITALICIZED
data elements are conditionally mandatory (i.e., dependent upon the answer to another data element)
ADMINISTRATIVE SEGMENT
(Group A)
ORI number
Incident number (encrypted)
Incident date/hour
Cleared exceptionally
Exceptional clearance date
OFFENSE SEGMENT
(Group A)
UCR offense code
Offense attempted/completed
Offender(s) suspected of using (p. 38)*
Bias motivation
Location type (p. 39)
Type of criminal activity**
PROPERTY SEGMENT
(Group A)
Type of property loss (p. 41)
Property description (p. 41-2)
Value of property
Date recovered
VICTIM SEGMENT
(Group A)
Victim (sequence) number
Victim connected to UCR offense code
Type of victim (p. 47)
Age (of victim)
Sex (of victim)
Race (of victim)
Optional data elements:
Ethnicity (of victim)
Resident status (of victim)
OFFENDER SEGMENT
(Group A)
Offender (sequence) number
Age (of offender)
Sex (of offender)
Race (of offender)
ARRESTEE SEGMENT
(Group A and B)
Arrestee (sequence) number
Arrest (transaction) number (encrypted)
Arrest date
Type of arrest (p. 56)
Multiple clearance indicator (p. 56)
UCR arrest offense code
Arrestee was armed with
Age (of arrestee)
Sex (of arrestee)
Race (of arrestee)
Disposition of arrestee under 18
Optional data elements:
Ethnicity (of arrestee)
Resident status (of arrestee)
* Page numbers refer to data element description in the Uniform Crime Reporting Handbook: NIBRS edition
** Counterfeiting/Forgery only
Appendix B – Data Elements available for possible White-Collar Crime offenses
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BIBLIOGRAPHY
Helmkamp, J., Ball, R., & Townsend, K., eds. (1996). Definitional Dilemma: Can and Should There Be a Universal Definition of White Collar Crime? Proceedings of the Academic Workshop, June 20-22, 1996.
Sutherland, Edwin Hardin (1949). White Collar Crime. New York: Dryden Press.
U.S. Department of Justice, Federal Bureau of Investigation (1996). National Incident-Based Reporting System: Data Collection Guidelines. Washington, D.C.: Government Printing Office.
U.S. Department of Justice, Federal Bureau of Investigation (1990). National Incident-Based Reporting System: Supplemental Guidelines for Federal Participation. Washington, D.C.: Government
Printing Office.
U.S. Department of Justice, Federal Bureau of Investigation (1992). UCR Handbook: NIBRS Edition. Washington, D.C.: Government Printing Office.
U.S. Department of Justice, Federal Bureau of Investigation (1989). White Collar Crime: A Report to the Public. Washington, D.C.: Government
Printing Office.
ENDNOTES
1
The category of property crime arrests in the Summary Reporting System includes burglary, larceny-theft, motor vehicle theft, and arson.
2
See glossary for definition of offenses.
3
In NIBRS, the crimes against property are arson, bribery, burglary, counterfeiting/forgery, destruction/damage/vandalism of property, embezzlement, extortion/blackmail, fraud offenses, larceny-theft offenses, motor vehicle theft, robbery, and stolen property offenses.
4
Nonnegotiable instruments are “any document requiring further action to become negotiable.” They include traveler’s checks, unendorsed checks, unendorsed money orders, food stamps, and stocks and bonds.
5
Offenders submitted with an age of less than 12 years old or 99 years old or older were excluded from the analysis.
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GLOSSARY
Bribery
The offering, giving, receiving, or soliciting of any thing of value (i.e., a bribe, gratuity, or kickback) to sway the judgment or action of a person in a position of trust or influence.
Counterfeiting/Forgery
The altering, copying, or imitation of something, without authority or right, with the intent to deceive or defraud by passing the copy of thing altered or imitated as that which is original or genuine; or the selling, buying, or possession of an altered, copied, or imitated thing with the intent to deceive or defraud.
Embezzlement
The unlawful misappropriation by an offender to his/her own use or purpose of money, property, or some other thing of value entrusted to his/her care, custody, or control.
Fraud Offenses
The intentional perversion of the truth for the purpose of inducing another person or other entity in reliance upon it to part with some thing of value or to surrender a legal right.
False Pretenses/Swindle/Confidence Game
The intentional misrepresentation of existing fact or condition, or the use of some other deceptive scheme or device, to obtain money, goods, or other things of value.
Credit Card/ATM Fraud
The unlawful use of a credit (or debit) card or automatic teller machine for fraudulent purposes.
Impersonation
Falsely representing one’s identity or position, and acting in the character or position thus unlawfully assumed, to deceive others and thereby gain a profit or advantage, enjoy some right or privilege, or subject another person or entity to an expense, charge, or liability which would not have otherwise been incurred.
Welfare Fraud
The use of deceitful statements, practices, or devices to unlawfully obtain welfare benefits.
Wire Fraud
The use of an electric or electronic communications facility to intentionally transmit a false and/or deceptive message in furtherance of a fraudulent
activity.
