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Statistical analysis of white-collar crime.

Gerald Cliff Gerald Cliff National White Collar Crime Center
and April Wall-Parker April Wall-Parker National White Collar Crime Center
https://doi.org/10.1093/acrefore/9780190264079.013.267
Published online: 26 April 2017

As far back as the 19th century, statistics on reported crime have been relied upon as a means to understand and explain the nature and prevalence of crime (Friedrichs, 2007). Measurements of crime help us understand how much of it occurs on a yearly basis, where it occurs, and the costs to our society as a whole. Studying crime statistics also helps us understand the effectiveness of efforts to control it by tracking arrests and convictions. Analysts can tell whether it is increasing or decreasing relative to other possible mitigating factors such as the economy or unemployment rates in a community. Politicians can point to crime statistics to define a problem or indicate a success. Sociologists can study the ups and downs of crime rates and any number of other variables in the society such as education, employment rates, ethnic demographics, and a long list of other factors thought to affect the rate at which crime is committed. Property value is affected by the crime rates in a given neighborhood, and insurance rates are said to fluctuate with the ups and downs of crime.

Analyzing any criminal act’s prevalence, cost to society, impact on victims, potential preventive measures, correction strategies, and even the characteristics of perpetrators and victims has provided valuable insights and a wealth of useful information in society’s efforts to combat violent/index crimes. This information has only been possible because there is little disagreement as to exactly what constitutes a criminal act when discussing violent or property crimes or what has come to be grouped under the catch-all heading of “street crime”; this is decidedly not the case with crimes included under the white-collar crime umbrella.

white-collar crime
corporate crime
crime measurement
victimization
computer crime

White-Collar Crime: The Historical Definitional Debate

The challenge of analyzing the phenomenon of white-collar crime lies in the fact that the term “white-collar crime” can mean different things to different disciplines or even different things to different camps within those disciplines. Academics often disagree with the legal profession, who may disagree with law enforcement, who in turn, may disagree with legislators and politicians as to exactly what constitutes white-collar crime. Generally, the varying definitions tend to concentrate on either or both of the following factors: characteristics of the offender, such as social status, or positions of trust within the community and characteristics of the crime itself.

Arguments among stakeholders aside, there is no such thing as the “right” white-collar crime definition—only the definition that is right for the purposes of the entity employing it. It is, however, vital to understand what the term means to the persons using it in order to understand what they are actually saying. This consideration can be especially important when dealing with abstracted statistics. The statement “white-collar crime is increasing” is meaningless without understanding what white-collar crime means to the author. The definition impacts what questions are asked, what kinds of answers are meaningful, and where researchers look for the answers to those questions. As other researchers in the field have noted, “[h]ow we define the term white collar crime influences how we perceive it as a subject matter and thus how we research” (Johnson & Leo, 1993 ). Depending on how one goes about deciding what to study in attempting to understand white-collar crime, one can either conclude that it is a form of conduct peculiar to offenders of high status enjoying positions of trust, as Sutherland seemed to feel, or one may arrive at a different conclusion if the research is confined to those convicted of federal offenses traditionally thought of as white-collar crime. In studying convictions, court records, presentence reports, and so on, of those accused of what would ordinarily be thought of as white-collar offenses, some researchers have used the relative lack of education and lower social/economic status and occupation to claim that white-collar crime is more attributable to the middle class (Weisburd, Waring, & Chayet, 1995 ). This claim tends to “trivialize” white-collar offenses and overlooks offenders who, by virtue of their social status, education, and positions of trust within their chosen professions and their communities are able to influence how their actions are defined, investigated, prosecuted, and in some cases, even the degree to which an act is defined as criminal (Pontell, 2016 ). For example, Calavita, Pontell, and Tillman ( 1997 ) examined the savings and loan crisis that resulted in colossal financial losses that are certainly attributable to “non–middle-class offenders.”

Sociologist Edwin Sutherland is credited with having first coined the term “white-collar crime” in 1939 in a speech given at the American Sociological Society (Sutherland, 1940 ). His comments in the original speech did not formally define the term, but he would eventually come to define white-collar crimes as “crimes committed by a person of respectability and high social status in the course of his occupation” (Sutherland, 1949 ). The offender-based definition seemed to serve sociologists well as a way to label and talk about offenses committed by successful, healthy people who were not suffering from the deficits of poor surroundings, lack of education, and all the other attributes that had come to be associated with perpetrators of violent (street) crime. It helped explain why well-educated people who had ample access to societal resources (members of respectable society) could resort to crime as a means of achieving the goals they should logically have been able to achieve without violating the law. Sutherland’s contribution expanded the discussion to include illegal deviance perpetrated by those who had already achieved traditional success through socially acceptable methods.

Notably, Sutherland’s definition explicitly rejected the notion that a criminal conviction was required in order to qualify (Sutherland, 1940 ). Sutherland ( 1940 ) saw four main factors at play here: (1) civil agencies often handle corporate malfeasance that could have been charged as fraud in a criminal court; (2) private citizens are often more interested in receiving civil damages than seeing criminal punishments imposed; (3) white-collar criminals are disproportionately able to escape prosecution “because of the class bias of the courts and the power of their class to influence the implementation and administration of the law”; and (4) white-collar prosecutions typically stop at one guilty party and ignore the many accessories to the crime (such as when a judge is convicted of accepting bribes and the parties paying the bribes are not prosecuted).

A related concept that again focuses on the offender is “organizational crime”—the idea that white-collar crime can consist of “illegal acts of omission or commission of an individual or a group of individuals in a legitimate formal organization in accordance with the operative goals of the organization, which have a serious physical or economic impact on employees, consumers or the general public” (Schrager & Short, 1978 ).

Although these definitions were vital for expanding the realm of sociology and criminology, they weren’t as well suited to the needs of other criminal justice stakeholders who deal with these issues in a more practical sense (including policymakers, law enforcement, and the legal community). These definitions work well when discussing why white-collar crime occurs or who commits it, but they are not as well suited to asking questions about how much white-collar crime is occurring or whether prevention methods are working.

A model of white-collar crime that lends itself somewhat more to empirical data analysis was Herbert Edelhertz’s 1970 definition: “ An illegal act or series of illegal acts committed by nonphysical means and by concealment or guile to obtain money or property, to avoid the payment or loss of money or property, or to obtain business or personal advantage .” As a crime-based definition, it ignored offender characteristics and concentrated instead on how the crime was carried out. As a result, it covered a far larger swath of criminality—including crimes (or other illegal acts—Edelhertz’s definition also reaches to acts that are prohibited by civil, administrative, or regulatory law, whether or not the perpetrators are ever called to answer for them) perpetrated outside of a business context, or by persons of relatively low social status.

Edelhertz ( 1970 ) identified four main types of white-collar offending:

personal crimes (“[c]rimes by persons operating on an individual, ad hoc basis, for personal gain in a non-business context”),

abuses of trust (“[c]rimes in the course of their occupations by those operating inside businesses, Government, or other establishments, or in a professional capacity, in violation of their duty of loyalty and fidelity to employer or client”),

business crimes (“[c]rimes incidental to and in furtherance of business operations, but not the central purpose of such business operations”), and

con games (“[w]hite-collar crime as a business, or as the central activity of the business”).

The Federal Bureau of Investigation (U.S. Department of Justice, 1989 ) when specifically addressing white-collar crimes (the FBI [U.S. Department of Justice, 2011 ] usually references “financial crimes” instead), uses a very similar definition: “ 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 .” This definition has been operationalized by the FBI’s Criminal Justice Services Division to mean the Uniform Crime Report (UCR) offenses of fraud, forgery/counterfeiting, and embezzlement, and a rather longer list of National Incident-Based Reporting System (NIBRS) offenses (Barnett, 2000 ). Thus, while this definition and Edelhertz’s are very similar, the FBI’s definition functionally excludes noncriminal illegal activity, as well as such incidents that are not reported to police and don’t fit into a relevant UCR or NIBRS category (for those jurisdictions that participate in NIBRS). At the same time, the FBI’s definition dovetails well with already-collected data, making it a practical tool for generating statistics on white-collar crime activity.

As a practical matter, many people have rather informal interpretations of the term. White-collar crime can informally mean:

Financial crimes

Nonphysical (or abstract) crimes

That is, crimes that “occur” on a form, balance book, or computer

Crime by or targeting corporations

Crimes typically committed by the rich

Criminal businesses or organizations

Including, for some, organized crime and terroristic organizations

Corporate or professional malfeasance

For some, this crime can include acts that are immoral but that are not specifically prohibited by law

Anything that’s against the law that the average beat cop won’t handle

Essentially, everything but street crime

Many people have a general sense that they know what counts as white-collar crime and what does not, but they have no specifically articulated sense of what qualities separate the class of white-collar offenses from non–white-collar offenses.

Having so many definitions in use means that it’s often difficult to compare data gathered by different white-collar crime stakeholders and that theoretical constructs in use by one group may be completely misaligned to the needs of another. One way that various groups have tried to reduce these inefficiencies is by crafting definitions that could enjoy buy-in from larger groups of stakeholders, providing them a common language (and compatible tools) for discussing white-collar crime.

In 1996 , the National White Collar Crime Center convened a group of noted academics specifically to address this definitional dilemma (NW3C, 1996 ). 1 Participants were selected from among the most noted scholars in the criminal justice field, who had devoted significant effort to the study of white-collar crime. Several aspects of white-collar crime were examined and discussed at length. Each attendee was asked to produce a paper on his or her position on how the term should be defined, laying out their arguments in support of their preferred definition. From the presentation of these position papers, extensive discussions among the assembled academics were held. Through this process, white-collar crime was examined from a variety of perspectives.

After considerable discussion and debate, those present at the workshop reached some consensus on the elements that need to be present to satisfy the concept of white-collar crime. Most agreed that the lack of direct violence against the victim was a critical element. They agreed that the criminal activity should have been the result of an opportunity to commit the crime afforded by the offender’s status in an organization or their position of respect within the community. Deception to the extent necessary to commit the criminal offense such as misrepresentation of the perpetrators abilities, financial resources, accomplishments, some false promise or claim intended to deceive the victim, or possibly a deliberate effort to conceal information from the victim—all should be considered as elements of white-collar crime. Some even contended that the term should be abandoned altogether and replaced by something more along the lines of economic crime, elite crime, or simply financial crime (Gordon, 1996 ).

In the end, those in attendance ultimately agreed that an acceptable definition would be: “ illegal or unethical acts that violate fiduciary responsibility of public trust, committed by an individual or organization, usually during the course of legitimate occupational activity, by persons of high or respectable social status for personal or organizational gain .”

This statement may address the definitional dilemma to some degree, but to further emphasize the difficulty of arriving at a universally acceptable definition, it still does not address some aspects of white-collar crime. Financial crimes committed with a computer, using the Internet, normally do not involve physical threat or violence, they almost always involve deception in some manner, and they can result in devastating damage to the victim(s), yet they have absolutely nothing to do with the social status of the perpetrator, do not require that the perpetrator occupy a position of trust within an organization or community, and may not even require a significant level of education. Perhaps the best way to conceptualize white-collar offenders is on a continuum that considers all aspects of the crime itself, the perpetrator, the relationship to the victim, and the position the perpetrator occupied that made it possible to commit the offense.

As this article does not intend to advocate for any particular interpretation of the term, we will be using the term “white-collar crime” in the widest possible sense, so as not to exclude any of the various camps from the discussion (though many will doubtless find some aspect of the article that treats the term in a broader sense than their personally held definitions would allow).

Why White-Collar Crime Matters

Violent crime is both alarming and costly. However, despite its physical and psychological impact on victims and even witnesses, street crime pales in many ways when compared with white-collar crime. A victim of a robbery is often traumatized by the experience and suffers the loss of any valuables taken by the perpetrator. They also suffer psychologically by being put in fear of injury or death, but, assuming the victim was not injured, valuables can be recovered by the police and may be covered by insurance and, as such, may not actually be a loss at all. An armed robber can certainly empty a cash drawer, take a wallet and jewelry, even steal a victim’s car, but the loss of these items is insignificant when compared to the loss of the total contents of a person’s bank account, life savings, credit rating, home, investments, and overall peace of mind. A number of anecdotal cases and studies have pointed to the unique stressors that a victim experiences after suffering loss from fraud. For example, there is evidence that financial loss due to fraud is a direct causal factor in many cases of depression and suicides (Saxby & Anil, 2012 ).

Addressing the issue of white-collar crime is extremely important because of its serious impact on victims, society, and the economy. Additionally, white-collar crimes are unique in that in many instances there is an inherent ability to victimize large numbers of individuals, often with a single act (i.e., identity theft). Estimates of monetary loss to employees and stockholders and, ultimately, society in general due to white-collar and corporate crime have reached hundreds of billions of dollars (Public Citizen, 2002 ). A 1976 estimate of the total cost of white-collar crime puts the figure in the neighborhood of $250 billion per year (Rossoff, Pontell, & Tillman, 1998 ), while a more recent study estimates financial losses from white-collar crimes to be between $300 and $600 billion per year (Stewart, 2015 ).

It is estimated that approximately 36% of businesses (PricewaterhouseCoopers, 2016 ) and approximately 25% of households (NW3C, 2010 ) have been victims of white-collar crimes in recent years, compared to an 8% and 1.1% prevalence rate of traditional property and violent crime, respectively (Truman & Langton, 2015 ). In addition, an examination of some of the most prevalent areas in which white-collar crime seems to be found will amply illustrate the gravity of the problem.

One area of white-collar crime that consistently remains in the spotlight is health care and insurance fraud. The rising costs of medical care have driven the cost of health care insurance increasingly higher. Recent estimates put total health care spending in the United States at a massive $2.7 trillion, or 17% of GDP. No one knows for sure how much of that sun is embezzled, but in 2012 Donald Berwick, a former head of the Centers for Medicare and Medicaid Services (CMS), and Andrew Hackbarth of the RAND Corporation estimated that fraud (and the extra rules and inspections required to fight it) added as much as $98 billion, or roughly 10%, to annual Medicare and Medicaid spending—and up to $272 billion across the entire health system ( The Economist , 2014 ).

Identity theft is another type of fraud that is frequently highlighted in discussions of modern white-collar crime. This fraud can range from simply using one’s credit card under false pretenses to opening entire bank accounts or mortgages using someone else’s personally identifiable information (PII). Through use of the Internet, this particular type of fraud often strikes multiple victims at once via corporate data breaches. The 2015 Identity Fraud Study, released by Javelin Strategy & Research, found that $16 billion was stolen from 12.7 million U.S. consumers in 2014 , compared with $18 billion and 13.1 million victims a year earlier. Further, there was a new identity fraud victim every two seconds in 2014 (Javelin, 2015 ). Aside from the considerable losses caused by identity theft and other characteristics that it may share with white-collar crimes (such as the lack of face-to-face contact between the victim and perpetrator and the fact that they are financial crimes and are complex to investigate), there are those who make a compelling case that identity theft should not be characterized as white-collar crime. Certainly, there is no requirement that a perpetrator enjoy some employment-related position of trust or require above average levels of education. “Many financial cases of identity fraud are the work of con artists and organized crime rings, in which offenders possess no legitimate occupational status, which is generally a major prerequisite for inclusion into the ranks of white collar criminals” (Pontell, 2009 ).

A wide variety of fraudulent practices that could be categorized as white-collar crime (including identity theft, advance fee frauds, online and telemarketing scam complaints) are tracked by the Federal Trade Commission’s Consumer Sentinel Network. In 2015 , the network collected a total of 3,083,379 consumer complaints (Federal Trade Commission, 2016 ). This is an increase of nearly 850% since the network began reporting in 2001 (Federal Trade Commission, 2016 ), with an annual percentage growth rate of 56%. Such growth far exceeds that of more traditional crime types, which have actually been declining in recent years.

In addition to the so-called more traditional forms of white-collar crime, a long and growing list of other white-collar crimes have come into prominence in recent years—especially intellectual property crime, mortgage fraud, and financial abuse of elders.

When intellectual property (IP) crimes are mentioned, many probably think of the controversies involving the downloading of copyrighted songs and movies. But IP theft is more than that. “Intellectual property crimes encompass the full range of goods commercially traded worldwide” (Dryden, 2007 ) and involve far more serious and potentially damaging practices than are usually considered. These practices can include everything from car parts (including nonfunctioning and substandard airbags and brake parts) to tainted pet food and baby formula. For example, Operation Opson V, conducted between November 2015 and February 2016 , seized more than 10,000 tons and one million liters of hazardous fake food and drink in operations across 57 countries (Interpol, 2016 ). These products are produced and sold in underground economies or in markets where they go unregulated and escape normal tax and tariff payments. They are not subject to the most basic requirements of regulatory oversight intended to assure the safety, integrity, and purity of the product. They expose consumers to health and safety risks and impose costs on society in a multitude of ways. Counterfeit products that are of particular concern are pharmaceuticals. Recent studies suggest that only 38% of prescription drugs purchased online are genuine (European Alliance for Access to Safe Medicines, 2008 ). The International Chamber of Commerce estimated that the total global economic value of counterfeit and pirated products is as much as $650 billion every year (International Chamber of Commerce, n.d. ).

Mortgage fraud is also a continuing problem, with the most recent information available indicating that “residential mortgage loan applications with fraudulent information totaled $19.8 billion in mortgage debt for the twelve months ending the second quarter of 2014 ” (Corelogic, 2014 ). As large as these numbers are, they represent only a small percentage of the actual losses incurred, owing largely to the complexity of investigating and prosecuting these types of offenses. Executives in large corporations who engage in high-level white-collar crime enjoy a degree of insulation from exposure to the criminal justice system. This insulation derives from the complexity of investigating and prosecuting their crimes; their ability to mount expensive and challenging defenses; their own position and that of their corporations in society; and the criminal justice system’s tendency to allow the accused to negotiate a settlement without admitting guilt. For example, a recent Securities and Exchange Commission press release revealed that “that a California-based mortgage company and six senior executives agreed to pay $12.7 million to settle charges that they orchestrated a scheme to defraud investors in the sale of residential mortgage-backed securities guaranteed by the Government National Mortgage Association (Ginnie Mae). In settling the charges without admitting or denying the allegations, each of the six executives agreed to be barred from serving as an officer or director of a public company for five years” (SEC press release, 2016 ). This type of settlement is not captured in the statistics as a conviction and, although the actions of the perpetrators would certainly fulfill the definition of white-collar crime, because there was no admission of guilt and no conviction (since no trial ever took place), the entire incident would never appear in any official statistical count of white-collar crime.

This is not necessarily uncommon with offenses that can be labeled as white-collar crime and points to a larger issue involved in “measuring”: many of these crimes may not appear in the ledgers of adjudication. Unfortunately for studies of statistical trends in white-collar crime, we are often left with partial views of the true scope of this crime, painted purely through adjudication measurements. Many tend to think of white-collar crime as targeting wealthy companies and individuals or the government. This belief may allow many to rationalize this type of crime, leading to the belief that the victim impact is minimal due to already inflated financial statuses. When we take a closer look at some key examples of white-collar crime, however, we see that this area of criminal activity merits considerable concern.

One of the most high-profile cases in recent history, the Bernie Madoff Ponzi scheme, is a good illustration of how one person committing white-collar crime can victimize hundreds, even thousands, of victims. News of Madoff’s crimes hit the news cycle in 2008 . Madoff’s investors provided him with approximately $20 billion to invest; Madoff made it appear as though his investors, as a group, had earned nearly $65 billion in returns (on which they ultimately paid taxes), which was simply not the case. Discussing the recovery of a sizable portion (approximately $11 billion) of the monies lost to the victims, one author observed: “It’s as though they’d put all that money under the mattress for decades, and now they can spend a little more than half of it. Making matters worse, they all paid federal capital gains taxes on that $45 billion in investment income that never existed” (Assad, 2015 ). Based on Madoff’s accounts with 4,800 clients (as of November 30, 2008 ), prosecutors estimated his fraud to have totaled $64.8 billion. Legal efforts to recover some of the monies lost through this scam have been underway since the case first broke. Ultimately, Madoff was sentenced to 150 years in prison and ordered to pay $170 billion in restitution. His victims were left with trying to rebuild their lives, a prospect that some could not face ( The Telegraph , 2009 ).

The Madoff case is just one example of how white-collar crime can touch many lives. There are a number of “more mundane” forms of white-collar crime that alter peoples’ lives on a daily basis. Consumer crime is an all-encompassing term that covers a number of white-collar crimes affecting the populace, including but not limited to, false advertising, commercial misrepresentations, price manipulation, and a host of related criminal and/or unethical behaviors. Few statistics exist that address this group of crimes as a whole, as the underlying actions are often handled through a host of distinctly different channels and much of the information exists purely in anecdotal form. That said, existing data (though incomplete) suggest that enforcement of these matters is on the rise. Take, for example, the number of federal actions each year under the False Claims Act, which more than doubled from 1987 to 2015 (U.S. Department of Justice, 2015 ), or the number of complaints to FTC’s Consumer Sentinel Network, which increased more than eightfold from 2001 to 2015 (Federal Trade Commission, 2016 ). Whether this increase can be attributed to an increase in the underlying activities, greater likelihood to report victimization, or greater law enforcement interest or ability to combat the activities is difficult to determine.

Another rising problem that can affect all facets of society is elder financial abuse. White-collar criminals take advantage of one of the most vulnerable sectors of our society, individuals who are at their most defenseless time of life, stealing from them at a time when they can least afford to be victimized. The True Link Report on Elder Financial Abuse 2015 ( 2015 ) reveals that seniors lose $36.48 billion each year to elder financial abuse—more than 12 times what was previously reported. Moreover, the highest proportion of these losses—to the tune of $16.99 billion a year—comes from deceptive but technically legal tactics designed to specifically take advantage of older Americans. The reported incidence of this particular form of white-collar crime is likely just a shadow of the real problem, as the number of unreported cases of this crime can never truly be estimated. Elder financial abuse cases often go unreported for any number of reasons. The victim is unwilling to report crimes being committed by their family members (a frequent source of elder abuse fraud); the victim may not know who to report the crimes to; or they may not even be aware that they are being victimized in the first place. As the average age of our society increases over time, these crimes will likely also increase and keep pace with the growing number of elderly potential victims in society.

The Internet and emerging technologies have helped accelerate the growth of many white-collar crimes, providing not only a new vehicle for perpetrating crimes but also entirely new categories of criminal activity that would not be possible without emerging technologies. Computer crimes (those crimes committed using a computer as the instrument of the crime ) involve the use of technology to facilitate or initiate consumer fraud and is now so commonplace that 50% of all consumer frauds reported to the FTC in 2012 were Web- or e-mail based. In order to attempt to track and categorize crimes related to the Internet, the Internet Crime Complaint Center (formally known as the Internet Fraud Complaint Center, or IC3), was formed in 2001 . This joint effort between the National White Collar Crime Center (NW3C) and the FBI was established to track crimes committed over the Internet and refer those crimes to law enforcement. In its first year of operation, IC3 received 49,711 crime complaints. Since that time, the number of complaints received by IC3 has steadily increased at an annual growth rate of 12.4%. In 2014 , the IC3 received 288,012 complaints, with losses of over $1 billion reported (Internet Crime Complaint Center, 2016 ).

The message here is that while all of us have a healthy fear of violent/street crime, white-collar crimes can be, and often are, far more damaging in terms of costs to the society and the rate at which the crimes are multiplying. One of the most difficult challenges is measuring just how much white-collar crime exists. This task is made infinitely more difficult by the fact that there is no universally accepted definition of what constitutes white-collar crime. This lack of consensus is understandable considering the many different types of crime that can fall under the umbrella of white-collar crime. Yet, without the ability to clearly define an act as a white-collar crime, it is impossible to determine with any accuracy just how much white-collar crime is taking place, what treatments intended to mitigate its prevalence are having an effect, or what level of punishment is likely to act as a deterrent.

Statistical Evidence of White-Collar Crime

Unlike the Uniform Crime Reports (UCR) for index crimes, there is no universal dataset of white collar crime statistics. When looking for hard statistical evidence of the prevalence of white-collar crime, researchers are left with a patchwork of federal data sources (i.e., Uniform Crime Report, Judicial Business of the United States Courts, United States Attorneys Annual Statistical Report, Annual Report and Sourcebook of Federal Sentencing Statistics, and many more) citing various crime types and a handful of self-report victim surveys. Federally published data (see Table 1 ) indicate that white-collar crimes in their various officially recorded forms are decreasing, much as index crimes have been steadily decreasing over recent years (Cooke, 2015 ). The weakness of using the UCR as a measure of white-collar crime, however, is that there are far more types of white-collar crime than the UCR system tracks.

Table 1 Ten-Year Arrest Trends a

a Note : The information in this table is taken directly from Table 33 of the Uniform Crime Reports. The year range is intended to illustrate the ten-year trends in the three offense categories tracked by UCR that would logically constitute white-collar crime offenses.

Source: United States Department of Justice, Federal Bureau of Investigation. (September 2012–2015). Crime in the United States, 2011–2014 .

The UCR data, however, are at odds with self-report victim data (such as the IC3 Annual Report and Federal Trade Commission Report) and anecdotal data sources, which indicate that white-collar crimes are on the rise. Therefore, the following questions arise: is this increase due to more awareness of the problem or to actual increases in crime rates? Do the data reflect a reluctance to charge and prosecute white-collar crime, or are white-collar crimes decreasing? With no longitudinal data and without a consistent way to count arrests and prosecutions associated with white-collar crime, it is nearly impossible to determine what is affecting the incidence of white-collar crime. That said, the comparison of statistical arrest data versus self-report data is not the most desired comparison; but the sheer lack of available white-collar crime datasets leaves us little choice as far as worthwhile comparisons go. This problem is further complicated by the fact that many white-collar crime victims may not even know that they have been victimized (Friedrichs, 2007 ) or do not report their victimization to the proper authorities (e.g., a victim of credit card fraud reporting to the credit card company but not to local police) (NW3C, 2006 ), which can further frustrate statistical counts.

It is generally accepted, however, that modern instances of white-collar crime touch the public much more than traditional crimes. Reputable data show that traditional street crimes have been decreasing in frequency across the board for some time. The Bureau of Justice Statistics’ victimization studies show that, from 2005 to 2014 , reported victimization by violent crime decreased by 22.8%, and reported victimization by property crimes decreased by 18.1%; the rate of violent crime declined slightly from 23.2 victimizations per 1,000 persons in 2013 to 20.1 per 1,000 in 2014 (Truman & Langton, 2015 ). The violent crime rate did not change significantly in 2014 compared to 2013 ; violent crimes include rape or sexual assault, robbery, aggravated assault, and simple assault. In comparison, the property crime rate, which includes burglary, theft, and motor vehicle theft, fell from 131.4 victimizations per 1,000 households in 2013 to 118.1 per 1,000 in 2014 (Truman & Langton, 2015 ). The overall decline was largely the result of a decline in theft (Truman & Langton, 2015 ). The FBI’s Uniform Crime Reports (which rely on police reports instead of victim data) show that when considering 5- and 10-year trends, the 2014 estimated violent crime total was 6.9% below the 2010 level and 16.2% below the 2005 level (U.S. Department of Justice, 2014 , 2015 ).

Comparatively, the most recent comprehensive white-collar crime victimization study (NW3C’s 2010 National Public Survey on White Collar Crime) found that 24.2% of American households in 2010 reported experiencing at least one form of white-collar crime, compared to 12.5% of all households being victimized by property crime in that same year (Truman & Planty, 2012 ). In this case, the term “white-collar crime” was operationalized to mean the following specific activities: credit card fraud, price fraud, repair fraud, Internet fraud, business fraud, securities fraud, and mortgage fraud (excluding identity theft, insurance fraud, embezzlement rates, or regulatory violations, for example).

Meanwhile, there are clear indications that white-collar crime should be on the increase:

The Skills Required to Commit White Collar Crimes are Becoming More Common

Many white-collar crimes require significantly higher levels of education than street crimes, or specialized technical skills. All of these skills are becoming more available in our society as we witness a widespread increase in literacy rates, computer use, and educational attainment (UNESCO, 2016 ; File & Ryan, 2014 ; Ryan & Bauman, 2016 ).

The American Populace Is Aging

Physical crimes favor the young, while fraud is generally associated with older perpetrators. Financial scams targeting seniors have become so prevalent that they’re now considered “the crime of the 21st century ” (National Conference on Aging, 2016 ). The FBI reports that these white-collar crimes, such as Internet sweepstakes schemes, specifically target seniors because of their access to liquid assets and because their deteriorating cognitive ability makes them more susceptible to Internet fraud than the general public) (Cooper & Smith, 2011 ; Association of Certified Fraud Examiners, 2016 ).

Opportunity to Commit White-Collar Crimes Is Increasing

In traditional, “on-the-job” white-collar crime, there was a time when only a very few individuals had access to the means to commit many crimes. As recently as the 1980s, far fewer American workers had realistic access to corporate information (Bureau of Labor Statistics, n.d. ). By 2012 , the number of Americans in the agricultural sector had declined by 55% and those in the industrial sector by 41.6%, while those employed in the service sector, including management, had increased by 16.3%. In other words, 47.6% of the total workforce is now in a position to sell trade secrets, embezzle funds, or commit other traditional white collar crimes (Bureau of Labor Statistics, 2013 ).

Things of Value are Increasingly Likely to be Intangible

Moving from means and opportunity to motivations, the nation is increasingly embodying its wealth in information or information products (Apte, Karmarkar, & Nath, 2008 ). The value of a pirated CD is found in the information encoded on the disc rather than in the cheap plastic medium itself. When the Business Software Alliance reported that $62.7 billion worth of software had been illegally copied (“pirated”) as of the 2013 report (BSA, 2014 ), they were reporting on the hypothetical value of lost sales of information, not on the loss of the worth of the plastic discs (which the perpetrators likely legitimately purchased in the first place). The concept of wealth itself is increasingly represented in nonphysical units. There was a time when, if thieves did not steal hard currency, they were invariably stealing something other than money. Now, money can be stolen by manipulating digital banking information stored in computer hard drives or even digital currencies that really only exist in concept.

White-Collar Techniques are Very Effective at Obtaining Intangible Things of Value

Things of value embodied in the form of information are particularly susceptible to attacks using information technology (computers). The rise of business computing means that a great deal of sensitive information that might once have been physically secured in locked cabinets or safes is now transmitted by e-mail or stored on company servers or in the cloud. Although it is difficult to quantify the extent to which the use of digital storage and retrieval systems renders the underlying information more vulnerable, it stands to reason that the information is now less secure and, hence, more likely to be exploited.

Computer-Related Crime

Linking computers together through the Internet has led to unprecedented potential for securing money through informational manipulation. The proliferation of technology in today’s society has resulted in a situation where “almost all business crime in the 21st century could be termed computer crime, as all major business transactions are carried out with computers” (Pontell, 2011 ). Compared to “traditional” scam techniques, the Internet provides an incredibly cheap, relatively anonymous means of reaching potential victims. In the offline environment, a scam that only snares one target out of a thousand is unlikely to offer a high enough return on investment to be worth pursuing. On the other hand, the online version of that same scam can be enacted several thousand times at once with the use of a mailing list (or any other means of electronic mass distribution). If the criminal sends the opening gambit of the scam to 20,000 potential victims, he or she may well get 20 useful replies in an afternoon. This is done with very little setup cost, very little time investment, and relative anonymity compared to performing the scam in person. This also allows criminals to realistically pursue distributed victimization strategies, where the dollar loss is spread out across such a wide group of victims that no one case is worth investigating.

Thus, a single white-collar criminal (or group of criminals) can easily be at the center of what seems like a worldwide crime wave. A single fraudster—like Robert Soloway, convicted in 2008 of fraud and criminal spamming—can completely flood the Internet with unsolicited and fraudulent e-mails. In Soloway’s case, it was to the self-admitted tune of trillions of e-mails, which made him thousands of dollars a day (Popkin, 2008 ) for a period spanning 1997 to 2007 (Government Sentencing Memorandum No. CR07-187MJP, U.S. v Soloway ) and for which he received a sentence of 47 months. Similarly, hacker Albert Gonzalez recently received a 20-year sentence for leading a group of 10 people who stole and then sold 40 million credit card numbers from customers of various companies that had unsecured wireless access points in the Miami area (Qualters, 2010 ).

Advanced information technologies and communication devices make white-collar crimes easier to commit, while having little impact on street crime (as they are primarily used for interacting with nonphysical constructs, which is the general province of white-collar crime). These technologies have become increasingly common across diverse social strata in recent years (Zickuhr & Smith, 2012 ). Unlike the portable communications technologies of the 1980s, the ability to possess and utilize these new technologies is not restricted to those with substantial incomes and/or higher levels of education. Their comparatively low price, combined with their ever increasing capabilities, make them the ideal method of committing crime. The widespread adoption of these technologies in the United States is a positive sign in the vast majority of respects, but a logical consequence of increasing the online population is that there are more opportunities to either commit a white-collar crime or become a victim of one.

Although these factors give researchers confidence that white-collar crime should be occurring in relatively large numbers (and should be growing at a time when other crimes are shrinking), proving it or putting a hard number on it is extremely difficult, if not impossible, due primarily to the definitional debate that has plagued the field for decades.

Similarly, efforts to reduce white-collar crimes are difficult to analyze with respect to their effectiveness, since the inability to define what constitutes white-collar crime means an inability to track its prevalence accurately. If we can’t establish a cause-and-effect relationship between a treatment and a reduction, we can never conclusively establish the effectiveness of that treatment.

The lack of a universal definition of white-collar crime poses more far-reaching consequences than simply lack of consistency; it is actually the key to the problem of analyzing white-collar crime. If something cannot be defined, then it cannot be accurately measured. Under varying definitions, white-collar crime can constitute anything from a simple check forgery to large-scale corporate malfeasance and sophisticated computer crimes, that is, he definitional debate regarding whether some types of financial fraud, identity theft, and computer/Internet crimes really constitute white-collar crime. This definitional variance makes it extremely difficult to gather information pertaining to criminal acts because, even if white-collar crime data are captured, it does not mean that the data will be comparable to other data or that anything meaningful can be garnered from its analysis.

Adding to the confusion is an apparent lack of consistency in the handling of white-collar offenses. Some highly damaging offenses may be handled administratively or civilly by a regulatory agency as opposed to criminally, while other similarly damaging offenses may be handled through the traditional criminal prosecutorial process. Administrative regulatory actions, civil court actions, and out of court settlements (where part of the settlement includes “no admission or finding of guilt” in return for a hefty financial settlement)—all combine to conceal the true presence of what would ordinarily be considered white-collar offenses but are not captured as an offense or enforcement statistic.

A lack of crime and arrest statistics goes so far as to “implicitly suggest that white-collar crime is not as serious as conventional crimes” (which law enforcement takes exhaustive measures to count accurately) (Albanese, 1995 ). As already discussed, this is most certainly not the case. Incidences of white-collar crimes not only affect many more individuals than traditional street crimes, but they also bring with them significant financial, emotional, and even physical tolls for the victims (NW3C, 2006 ).

Without question, the analysis of statistics on crimes committed, by whom, where, and when, details about perpetrators and victims, including background, personality traits, ethnicity, and age, can be considered “essential” in understanding crime trends. Knowledge of the “who, what, when and where” of any criminal act is required for developing strategies to prevent and reduce crime. Knowledge of common characteristics of offenders is necessary for understanding how to develop sentencing practices that help deter criminal activity and for developing programs to treat offenders so that they can be rehabilitated. The lack of a common definition of the term white-collar crime then, presents a major obstacle to using normal approaches to studying and dealing with it.

For white-collar crime, there is also the problem of even knowing when one has been the victim. It’s far easier for victims of a street crime to recognize that they have been victimized than it is for the persons who have fallen for a financial scam. One of the key elements of this type of scam is to keep the victims from finding out that they have been taken, for as long as possible. This calls to mind a simple formula in criminal law that is often used to determine whether a police report is even prepared by an investigating officer: “No complainant, no crime.” If the victim refuses to prosecute, no report of the offense is prepared, which means that no crime is added to the statistics; however, a criminal act has still occurred, one that fails to appear in the overall statistical profile of crime. If the victim is not even aware that he or she has been victimized, it’s unlikely that a true measurement of the prevalence of the crime will be possible.

The decision of whether one chooses to address issues through administrative or civil avenues, as opposed to criminal, will also determine whether that act is even defined as a crime. The problem for those charged with enforcement may involve consideration of whether the offense was a product of the actions of one person or of multiple people within an organization working together. The issue then becomes whether the act was committed with knowledge and intent, with disregard for the negative impacts their act would cause, or whether the group was simply committing a misguided act with an eye toward the financial bottom line.

Regardless of how the debate ultimately resolves itself, it is critical that we continue to educate the public regarding the methods of white-collar crime victimization, better enabling them to identify when they have been victimized and encouraging them to report these crimes to the police. Furthermore, regulatory agencies need to make data more accessible to those studying white-collar crime; while many corporations are understandably reticent to provide such data, the fact remains that this is a serious issue that can relate to public safety. It needs to be dealt with partly through transparency of data. Sharing information on how various enforcement and regulatory agencies handle white-collar crimes allows multiple entities to learn from one another what works best in dealing with the problem. If researchers and practitioners cannot empirically support claims regarding the incidence and prevalence of white-collar crimes, then it is impossible to justify the expenditure of funds for research and development that could potentially impact the lives of millions of citizens through the prevention and control of these ever-expanding crimes.

White-collar crime: a neglected area in forensic psychiatry?

Rose clarkson.

a Forensicare, Victorian Institute of Forensic Mental Health, Clifton Hill, VIC, Australia

b Centre for Forensic Behavioural Science, Swinburne University of Technology, Clifton Hill, VIC, Australia

Rajan Darjee

White-collar crime (WCC) causes considerable societal harm, the economic and psychosocial costs of which exceed those of conventional crime. Despite the impact, it has received scant attention from the academic literature in forensic psychiatry. This narrative literature review covers important topics in our understanding of white-collar crime, including offender characteristics such as demographics, criminal history, mental illness, personality and psychopathy, the link with violent offending and the trajectory of the white-collar offender (WCO) through the criminal justice system. White-collar crime is under-researched, particularly with regards to psychopathology, and the field of forensic psychiatry may have important contributions to make to our understanding of this important and harmful type of crime.

Introduction

The term ‘white-collar crime’ (WCC) was coined by sociologist Edwin Sutherland in 1939, who described it as ‘a crime committed by a person of respectability and high social status in the course of his occupation’ (Simpson, 2019 , p. 190; Sutherland, 1983 ). WCC had been earlier described in academia (Bonger, 1916 ), fiction (Casey & Markopolos, 2010 ) and the popular press (Frankel, 2012 ). As Ross commented in 1907: ‘The modern high-power dealer of woe wears immaculate linen, carries a silk hat and a lighted cigar, sins with a calm countenance and a serene soul, leagues or months from the evil he causes’ (Ross, 1907 , p. 10).

The definition of WCC is ambiguous and poorly defined in the literature (Holtfreter, 2005 ; Ragatz & Fremouw, 2010 ; Simpson, 2019 ). WCC encompasses ‘illegal or unethical acts that violate fiduciary responsibility or public trust’ (Senate Economics References Committee, 2017, p. 2, as cited in Simpson, 2019 ). Definitions focus on the high social status of the offender (Menard et al., 2011 ; Sutherland, 1983 ), certain types of offending (Benson, 2013 ; Friedrichs, 2009 ), breach of trust (Ling et al., 2019 ) or the occupational setting (Benson & Chio, 2019 ; Friedrichs, 2019 ). Evolving technology has led to new types of WCC (Rebovich, 2021 ). There are many alternative terms: occupational crime, corporate crime (Ragatz & Fremouw, 2010 ), economic crime (Alalehto, 2003 ) and grey-collar crime (Ling et al., 2019 ). There is a lack of consensus on where the boundaries fall; some legislation gives prosecutors complete discretion to decide whether offending falls under criminal or civil penalties, creating a ‘blurred’ line between WCC and non-criminal wrongdoing (Feeley, 2006 ; S. P. Green, 2004 ; Sachs, 2001 ). There are so many different types of WCC that studying WCC or white-collar offender (WCO) as a homogeneous group can be difficult. Definitions based on characteristics of offenders (e.g. high social status) rather than the offending behaviour make any differences found between WCO and other offenders virtually axiomatic, so most criminological studies use an offence-based definition. This review takes a broad and inclusive approach and considers literature regarding all the above definitions.

The costs of WCC outweigh those of conventional crime by several orders of magnitude, with a large undetected figure and associated physical and environmental costs (Croall, 2016 ). The costs extend beyond the financial and physical injury/death (Cohen, 2016 ); there is a growing literature on the psychological impact on white-collar victims (Button et al., 2009 ; Piquero, 2018 ). In the United States, WCC has been quantified to cost hundreds of billions, with the non-quantified costs even greater (Cohen, 2016 ). In Europe, over 42% of larger companies have been victimised (Blickle et al., 2006 ).

In Australia, the Australia Federal Police estimated that organised fraud costs the Australian economy $6.3 billion per year, which may be an underestimate, as firms prefer to address WCC internally to avoid reputational damage (Senate Economics References Committee, 2017 ). In 2008, 5% of the Australian population were victimised by consumer fraud, with personal losses of almost $1 billion (Smith & Budd, 2009 ). In PricewaterhouseCooper’s 2014 Global Economic Crime Survey, 57% of surveyed Australian organisations experienced WCC in the past two years, with more than a third losing more than $1 million (Senate Economics References Committee, 2017 ). In New Zealand, tax evasion is estimated at $1.2 billion per year, and is under-investigated and under-prosecuted, due to limited resources of government agencies (Marriott, 2018 ). Public perceptions of sentencing of WCC in Australia are that it is endemically lenient (Freiberg, 2019 ). In New Zealand, WCOs receive more lenient treatment in the justice system than other offenders (Marriott, 2020 ). Australia has been described as a ‘paradise’ for WCC by the Australian Securities and Investments Commission (ASIC) Chairman (Senate Economics References Committee, 2017 ).

There have been several high-profile cases of WCC, which appear to raise significant questions relevant to psychiatry. In 2020 Melissa Caddick went missing hours after ASIC executed a search warrant at her mansion. Three months later her decomposed foot was found on a beach after she had apparently committed suicide (Federal Court of Australia, 2021 ). She had swindled investors out of over $20 million as her apparently successful business was a front for a Ponzi scheme. The case raised questions about what type of person would swindle family and friends, and the mental health impact of being investigated and prosecuted. The life history of Charles Ponzi, after whom Ponzi schemes are named, raises questions about the personality development and pathology of people who become ‘swindlers’ and ‘con artists’ ( Ponzi v. Fessenden , 1922 ), and there has been debate over whether Bernard Madoff, who ran the largest Ponzi scheme in history (United States Department of Justice, 2020 ), was psychopathic or whether his behaviour was symptomatic of underlying systemic failures – that is, whether the explanation was primarily in the realm of psychopathology or socioeconomics. Mental health has been raised somewhat controversially in relation to fitness to stand trial in some high-profile cases, citing depression (e.g. Nirav Modi fighting extradition from the UK to India; The Government of India v Nirav Deepak Modi , 2021 ) or dementia (e.g. Robert Brockman in the USA, Bloomberg, 2021 ; and Christopher Griggs in Australia, Australian Securities and Investment Commission, 2016 ). A concern in such cases is whether people adept at committing large-scale fraud are also adept at fooling psychiatrists and the courts. Issues raised by these cases include: the role of psychopathology in such offending behaviour; the mental health of offenders after they are apprehended; and the role of psychiatric assessment in the legal processing of cases. But what does forensic psychiatry have to say about or contribute to our understanding of WCC and the management of such cases?

Despite the harmful impact and public concern, these ‘hidden crimes or quiet violence’ (Frank & Lynch, 1992 ) have received little attention in forensic psychiatry publications, particularly when contrasted to violent or sexual offences. The academic literature on WCC comes primarily from the fields of sociology, criminology and business/accounting. Although there has been an emergence of interest in the individual psychology of WCOs over recent decades, the research into this area remains scant, with non-evidence-based assumptions being commonplace. This is an area that involves the core business of forensic psychiatry – the intersection of mental health, the legal system and criminal activity – and in which forensic psychiatry may be able to offer valuable contributions, and individual practitioners should have a basic understanding of WCC and offenders. This narrative literature review covers some of the topics particularly relevant to forensic psychiatry and identifies areas that need more research.

To locate publications relevant to WCC and psychiatry, an inclusive search approach was employed, focused on the intersection of two concepts: (a) white-collar crime, and (b) mental health and psychopathology (including individual offender characteristics).

A Boolean search strategy was used across three databases: PsychNet, EbscoHost (Health business elite, Psychology and Behavioral Sciences Collections) and Pubmed, with slightly different search terms according to the requirements of each search engine. The search terms for each database are listed in the Appendix .

The first author read the title of each article and, if relevant, reviewed the abstract. Sixty-nine publications were selected as relevant based on the abstracts (36 from PsychNet, 27 from Ebscohost and 65 from Pubmed, with overlap between results).

The reference lists from these 69 articles were examined to identify additional publications that the database searches missed; the references of these articles were then reviewed to locate additional resources in an iterative fashion, until saturation point. The diverse terminology resulted in the identification of an additional 336 publications (for a total of 405); these included resources not directly linked to mental health but considered foundational texts in the broader study of WCC. Due to the lack of standard terminology/meaning of WCC in the literature, all definitions were included.

These articles were read and critically evaluated, according to key results, limitations, methodology, quality, interpretation of results and impact in the field, and those studies with the best contributions were included (Ferrari, 2015 ). In some areas, due to the lack of data, low-quality studies are also discussed. The information was then synthesised into a narrative overview (B. N. Green et al., 2006 ), focusing on major topics, findings and debates relevant to forensic psychiatry.

Psychiatry in the WCC literature

There is very little psychiatric research or commentary in the WCC literature. Only three articles directly related to forensic psychiatry and WCC were located: one research study (Poortinga et al., 2006 ) and two review articles (Brady et al., 2016 ; Price & Norris, 2009 b). Poortinga and colleagues (Poortinga et al., 2006 ) noted that WCOs represent a very small proportion of those who are referred for psychiatric assessment (0.25%). Brady et al. argued that forensic psychiatry can make significant contributions to the field (Brady et al., 2016 ). Price and Norris argued that forensic psychiatrists should be more involved in research into WCOs, and are in a key position to study the individual characteristics of offenders (Price & Norris, 2009 b).

Psychiatrists and mental health clinicians can also be WCOs themselves (Forte, 2018 ; Jesilow et al., 1993 ; Maesen, 1991 ; Ogunbanjo & van Bogaert, 2013 ; Price & Norris, 2009 b; Timofeyev & Jakovljevic, 2020 ). This can cause considerable reputational harm to the profession, and fraud in the mental health field directly reduces the resources available for patient care (Torrey et al., 2015 ).

Who are white-collar offenders?

Demographics.

There are several distinguishing characteristics of WCOs. Wheeler et al. ( 1987 ), in the now influential ‘Yale Studies’, found that WCOs tend to be white, male, older, college-graduates and employed. These results have been supported by later research (Ragatz & Fremouw, 2010 ; Ragatz et al., 2012 ). WCOs have a mean age in their 40s (Benson & Kerley, 2001 ; Holtfreter, 2005 ; Van Onna et al., 2014 ), and mean age of 35 for onset of offending (Van Onna et al., 2014 ), a counterpoint to the classical age–crime curve of conventional offending (Benson & Kerley, 2001 ; Farrington, 1986 ). Menard and colleagues (Menard et al., 2011 ) surveyed 1725 adolescents over a 27-year follow-up period and found that white-collar offending peaked in middle age. Arnulf and Gottschalk ( 2013 ), in a study of 179 WCOs, described a subset of 28 ‘heroic leaders’, older, richer, more powerful and more likely to be leaders in group offending. They suggested that these ‘previously law-abiding people with splendid careers’ commit their first crimes subsequent to attaining leadership success, possibly caused by latent narcissistic personality traits (Arnulf & Gottschalk, 2013 ). Delisi et al. ( 2018 ) likewise identified a group of ‘de novo advanced adult-onset offenders’ with high socioeconomic status.

Socioeconomic status is particularly relevant to WCC and is considered by some to be definitional (Menard et al., 2011 ). Piff et al. ( 2016 ) suggested that upper-class individuals behave unethically out of self-interest, whilst lower-class individuals tend to behave unethically to assist others. Regarding legal sanctions, Reiman and Leighton argued that the ‘criminal justice system effectively weeds out the well-to-do’ (Reiman & Leighton, 2016, p. 114), and wealthy individuals are less likely to be investigated and prosecuted, with more lenient sanctions (Galvin & Simpson, 2019 ; Marriott, 2018 ), which may lead to lower estimations of risk of offending in those with high financial resources.

Gender has been another focus of attention. Between 80% and 92% of WCOs are men (Blickle et al., 2006 ; Gottschalk & Glasø, 2013 ; Timofeyev & Jakovljevic, 2020 ; Weisburd et al., 1991 ), and women WCOs are more likely to be white and less educated, and more likely to commit low-level offences and work alone (Daly, 1989 ; Ruhland & Selzer, 2020 ). Women’s opportunities for WCC may be restricted by their positions in organisational hierarchies (Holtfreter, 2013 ), and some have argued that female WCC will increase as more women occupy higher positions (Dodge, 2019 ; Piquero et al., 2013 ; Simon, 1996 ). Others have suggested that more women in positions of power will lead to an overall reduction in this behaviour (Galvin, 2020 ; Vieraitis et al., 2012 ). Others have suggested that the detection rate for female WCOs may be lower (Gottschalk, 2012 , 2020 ; Gottschalk & Glasø, 2013 ). There may be gender-related attitudinal differences (Fenwick, 2006 ), impacted by cultural factors, type of corruption (A. R. Lee & Chávez, 2020 ) or perceived discrimination (Casten, 2013 ).

Biological factors

Kendler and colleagues ( 2015 ), using 21,603 twin pairs from the Swedish Twin Registry, compared WCC to violent and property crime. They found that WCC had a total heritability of around 53%, similar to property crime, and more than violent crime at 45%, with about a third of the genetic influence being ‘unique’ to WCC (compared to around half for violent crime, and none for property crime). They suggested that the genetic influences unique to WCC might reflect a genetic predisposition to ‘rule breaking’, as distinct from aggression. J. J. Lee et al. ( 2015 ) looked at hormonal factors (testosterone and cortisol) in a non-offender sample ( N = 82) and found that elevated levels of cortisol and testosterone encouraged cheating, associated with subsequent reductions in cortisol and negative affect. They suggested that hormonally modulated, habitual unethical behaviour may be a means of achieving relief from psychological distress.

Others have examined neurobiological factors; Raine et al. ( 2012 ) compared 21 WCOs to matched blue-collar offenders, and found that the WCOs had significantly better executive functioning and increased cortical grey matter thickness on magnetic resonance imaging (MRI) in certain brain regions (the ventromedial prefrontal cortex, inferior frontal gyrus, somatosensory cortex and temporal-parietal junction). They hypothesised that white-collar criminals have superior cognitive and attentional functioning. Ling et al. ( 2019 ) found an association between higher frontal lobe volume on MRI (localised to the superior frontal and anterior cingulate cortex) and self-reported offending in a community sample. Krokoszinski et al. ( 2018 ) compared 11 WCOs to violent offenders and non-offenders, using electroencephalography (EEG) recordings and hypothetical moral dilemmas. They found that the fraudsters had significantly higher baseline activation of the right anterior insula than violent offenders, and made a higher percentage of utilitarian decisions than both other groups.

Forensic history

Contrary to the perception of WCOs as ‘one shot offenders’ (Perri, 2011 ), in the 1970s Yale Studies sample (Weisburd et al., 2001 ), over 40% had a prior arrest, and more than a third had a prior conviction. Benson and others (Benson & Kerley, 2001 ; Benson & Moore, 1992 ) reported similar results for a study of 2643 WCOs in the 1970s; 39% had prior arrests. Walters and Geyer ( 2004 ) found that 23/57 (40%) white-collar inmates had at least one prior arrest. Van Onna et al. ( 2014 ) reviewed 644 WCOs in the Netherlands; 22% had been incarcerated by age 18. In a sample of 74 Portuguese WCOs, 59.5% had a previous criminal conviction, not statistically different from violent offenders, including in the nature of past offending (Ribeiro et al., 2019 ).

Interestingly these prior arrests and convictions are often not for WCC. Van Onna et al. ( 2014 ) found that a quarter had committed violent offences, a quarter property offences, almost a fifth drug offences, almost a third traffic offences and two fifths other types of non-WCC offences. Their 644 WCOs could be categorised based on criminal careers into two low-frequency groups making up 78% of cases (labelled ‘stereotypical white-collar offenders’, SWO, and ‘adult onset’, AO), and two high-frequency groups making up 22% of cases (labelled ‘adult persisters’, AP, and ‘stereotypical criminals’, SC). The 39% who were SWO were usually specialists in WCC with only about one in 10 committing non-WCOs. But over half of the AO and all high-frequency cases (AP and SC) had committed non-WCOs. Walters and Geyer ( 2004 ) found that WCOs with histories of committing non-WCC had higher levels of criminal thinking, criminal identification and deviance than those who only committed WCC. In this regard they were very similar to non-WCC offenders. These studies highlight the heterogeneity of WCOs with regard to criminal careers and the substantial overlap between WCOs and non-WCOs.

Mental illness

There has been very little research into the prevalence of mental illness in WCOs. There is a general assumption that WCOs do not suffer from mental illness (Alalehto, 2015 ; Heath, 2008 ) and are ‘basically normal people who do not suffer from the psychological or personal pathologies that seem so common among street offenders’ (Benson, 2013 , p. 324). However, this area is ‘woefully understudied’ (Perri et al., 2014 , p. 83).

Poortinga et al. ( 2006 ), in a retrospective review of court-ordered psychiatric evaluations of white-collar defendants over a 12-year period, found only 73 out of 29,310 referrals for white-collar charges. They compared this sample to 73 controls matched on year of offence, and found that there were no significant differences in mood disorders (their outcome of interest) between the samples once other factors such as race, education and substance abuse were controlled for, although there were lower rates of substance use in the white-collar group. None of the white-collar defendants were recommended as not guilty by reason of insanity, and only one of the control group.

Collins and Schmidt ( 2006 ) compared 365 WCOs with a non-offender sample in upper-level positions of authority, and found higher levels of anxiety on the California Psychology Inventory (CPI) in the offender group. Ragatz et al. ( 2012 ) compared 39 white-collar-only, 88 white-collar-versatile (previous non-white-collar convictions) and 86 non-WCOs. They found no significant differences between groups on depression or anxiety scales, although they did find significantly more anxiety-related disorders (e.g. phobias, obsessive-compulsive disorder, posttraumatic stress disorder) in the white-collar versatile sample, approaching significance in the white-collar-only group. The white-collar-only offenders had lower scores on drug problems. Benson and Moore ( 1992 ) reviewed 2462 convicted WCOs and found that only 6% of WCOs had previously used illegal drugs, compared to almost half of non-WCOs, with low rates of problematic alcohol use in both groups.

The association between gambling disorders and WCC has been another focus of research (Adolphe et al., 2019 ). Problem-gambling has been cited as a motivation for WCC (Banks & Waugh, 2019 ; Binde, 2016 , 2017 ; Laursen et al., 2016 ). However, the association between problem-gambling and WCC may disappear after controlling for other factors, such as gender, age, sociodemographic factors, substance use, juvenile delinquency and low self-control (Dennison et al., 2021 ; Lind et al., 2021 ).

Psychological explanations

There have been a number of proposed psychological explanations (Severson et al., 2019 ). Brody et al. ( 2020 ) suggested that negative childhood experiences, such as an emotionally invalidating environment, can lead to fraud later in life, although concluded that more research was needed. Case reports have taken a psychodynamic approach (Brottman, 2009 ; Naso, 2012 ), exploring the psychodynamics of integrity and emotional conflict around corporate success and failure. Gottfredson and Hirschi’s ( 1990 ) General Theory of Crime links WCC to low self-control. However, later studies challenged this model, reporting that indicators of low self-control are not related to WCC (Benson & Moore, 1992 ; Simpson & Piquero, 2002 ). The General Strain Theory (Agnew, 1992 ) suggests that psychological stressors can increase the likelihood of offending, including WCC (Agnew, 2001 ; Agnew et al., 2009 ; Langton & Piquero, 2007 ). There have been a number of theories emerging from Rational Choice Theory, suggesting that offenders commit WCC if they estimate the benefits to outweigh the risk (Paternoster & Simpson, 1996 ; Shover & Hochstetler, 2005 ). WCOs have also been found to perceive their offending as non-criminal and use neutralisation techniques to legitimise their behaviour (Dhami, 2007 ; Piquero et al., 2005 ; Severson et al., 2019 ), although justification, minimisation and denial are not unique to WCOs. WCOs are less likely to identify as a criminal (Walters & Geyer, 2004 ). Piquero (2004, 2012 ) found that fear of potential losses predicts the decision to engage in WCC.

Others have highlighted the importance of the leadership role – for example, ‘financial super-predators’ – who perpetuate large-scale fraud and cause significant systemic damage to the economy (Black, 2005 ). Biggerstaff et al. ( 2015 ) found that firms managed by CEOs with ‘questionable ethics’ were more likely to engage in financial-reporting fraud. Informal sanctions and perceived attitudes of colleagues may be more effective at constraining deviant behaviours than formal sanctions (Hollinger & Clark, 1982 ; Piquero et al., 2005 ).

Other theories fall under the umbrella of Social Learning Theory; individuals learn criminality from symbolic interactions, observation and modelling of co-workers (Pratt et al., 2010 ; Sutherland, 1983 ). Subcultural theories (Apel & Paternoster, 2009 ) suggest that some organisations develop subcultures with norms of misconduct, and individuals learn to commit crime via their association with this subculture. Van Onna and Denkers ( 2019 ) highlighted weak social bonds as a causal factor.

Recently Curnow ( 2021 ) proposed a psychological theory of embezzlement, breaking down the crime into four stages: pre-existing vulnerabilities, induction to first theft, ongoing theft and detection to resolution. His model emphasised the interaction between the embezzler’s developing psychological processes and environmental context, including security, culture and financial circumstances.

Personality

The role of personality factors in WCC was discounted by Sutherland and largely ‘discarded’ by researchers (Feeley, 2006 ) in the latter half of the twentieth century, or treated as ‘completely irrelevant’ (Alalehto, 2003 ), and ignored (Perri, 2011 ). Coleman stated ‘[it] is generally agreed that personal pathology plays no significant role in the genesis of white-collar crime’ (Coleman, 2005 , p. 184), which may not accord with subsequent genetic findings. However, there has been renewed interest in this topic over recent decades, and some research has begun to emerge in offender samples (Alalehto, 2003 ; Blickle et al., 2006 ; Collins & Schmidt, 2006 ; Kolz, 1999 ; Nee et al., 2019 ; Ribeiro et al., 2019 ) and non-offender samples (De Vries et al., 2017 ; Piquero et al., 2005 ; Turner, 2014 ). These studies are outlined in Tables 1 and and2 2 , and in Simpson ( 2019 ). In brief, there are conflicting results regarding conscientiousness, desire-for-control and self-control, depending on the methodology used, and a lack of other major findings. There have also been a number of typologies of WCOs proposed (Bucy et al., 2008 ; A. Kapardis & Krambia-Kapardis, 2004 ; M. K. Kapardis, 1999 ; Van Onna et al., 2014 ; Weisburd et al., 2001 ), summarised in Table 3 . These discrepancies are due in part to varying thresholds of how white-collar samples are identified and defined, but the typologies highlight the heterogeneity of WCOs.

Studies of p ersonality and WCC in offender samples.

Note: WCC = white-collar crime; WCO = white-collar offender; DSM–III = Diagnostic and Statistical Manual of Mental Disorders–Third Edition.

Studies of personality and WCC in non-offender samples.

Note: WCC = white-collar crime; BFI = the Big Five Inventory; HEXACO -PI -R = The HEXACO Personality Inventory-Revised.

Typologies of WCOs.

Note: WCC = white-collar crime; WCO = white-collar offender.

Psychopathy

The link between psychopathy and workplace malfeasance has been another area of interest (Babiak et al., 2007 ; Boddy, 2015 ; Cleckley, 1976 ), although some have argued that ‘psychopathy can be safely ignored in the attempt to predict white-collar crime’ (Blickle et al., 2006 , p. 223). Higher rates of psychopathy have been found at senior levels of organisations, between 4% and 20% (Boddy, 2015 ; Fritzon et al., 2016 ; Howe et al., 2014 ), the so-called ‘successful psychopath’ (Howe et al., 2014 ), ‘corporate psychopath’ (Fritzon et al., 2020 ) or ‘snakes in suits’ (Babiak et al., 2007 ). Associations between psychopathic traits and attitudes supportive of WCC have been found in undergraduate students (Ray & Jones, 2011 ) and online surveys (Lingnau et al., 2017 ). However, a direct link between psychopathic traits and WCC has yet to be empirically established, and remains theoretical (Perri, 2011 ). It has been suggested that ‘corporate’/‘successful’ psychopathy may be associated with Factor 1 psychopathy (Hare et al., 1990 ; including interpersonal manipulation and callous affect), but not with Factor 2 psychopathy (erratic lifestyle and anti-social tendencies; Boddy, 2011 ; Lingnau et al., 2017 ). One possibility is that corporate psychopaths engage in misconduct that does not violate criminal law, but still causes widespread harm (Boddy, 2011 ; Passas, 2005 ). Overall, this area remains under-researched (Boddy, 2015 ).

The link with violent offending

Although WCC is conceptualised as non-violent, recent research has suggested a subtype of violent WCOs, so-called ‘red collar’ criminals (Brody & Kiehl, 2010 ; Friedrichs, 2009 ). Perri and Lichetenwald ( 2007 , 2008 ) suggested that WCOs may commit instrumental homicide/attempted homicide to conceal their crime, including ‘murder-for-hire’ cases. Perri gives 28 examples of ‘red collar’ homicide cases (Perri, 2015 ) and an additional nine attempted-homicide cases. He raises the role of narcissism and psychopathy in these ‘red collar’ criminals (Perri, 2011 , 2015 ), although this has subsequently been challenged (Alalehto & Azarian, 2018 ).

The intersection between organised crime and WCC (Edwards & Gill, 2002 ; Kleemans & Van de Bunt, 2008 ) is fuzzy (Huisman, 2019 ; Naylor, 2017 ) and another area where violence occurs (Kendall, 2010 ). Organised crime groups may require the skills of WCOs, such as money laundering (Huisman, 2019 ), and the revenues of organised crime often cannot be separated from those of WCC by investigators (Ruggiero, 2017 ). Further, WCO offending can result in physical injury and death through criminal corporate negligence (Cohen, 2016 ; Croall, 2016 ). As highlighted above, studies have found that a quarter of WCOs commit violent offences that may be unrelated to WCC (Van Onna et al., 2014 ). So, overall, we should not assume that WCOs are non-violent (Perri & Brody, 2011 ).

The heterogeneity of WCOs

WCCs include a range of offences. For example, the offences included in the studies of Wheeler et al. ( 1987 ) included antitrust offences, securities fraud, mail fraud, false claims, bribery, income tax fraud, lending and credit fraud, and bank embezzlement. Considering offenders who commit these offences as one group may obscure characteristics of those who commit particular types of WCCs. For example, antitrust offenders have been found to be quite different in terms of demographics and offending histories from mail and wire fraud offenders, with the latter group similar to non-WCOs (Weisburd et al., 2001 ). So, it may not be that some WCOs of any type overlap with non-WCOs, but that certain groups of WCOs overlap with non-WCOs.

The WCO in the legal system

Only a small percentage of identified WCCs are prosecuted by the criminal justice system (Friedrichs, 2009 ; Gottschalk, 2021 ). Many investigations are done internally or privately by law firms or fraud examiners; reports are never made public and/or subject to attorney–client privilege (Gottschalk, 2017 ). Several factors deter prosecutors from pursuing white-collar cases (Benson & Cullen, 1998 ); prosecution of WCC is time and resource heavy, and more likely to take place in an administrative or civil capacity than in a criminal court (Marriott, 2018 ). Those cases that do reach the criminal justice system have a high probability of a guilty plea to avoid an expensive trial (Weidenfeld & Spire, 2017 ).

Braithwaite ( 1982 ) argued that a ‘just’ system would result in WCOs making up the majority of the prison population, noting that ‘just desserts for the powerless, and comparative lenience for the powerful, is not just desserts at all’ (p. 761). The Yale Studies found that WCOs were treated favourably during the presentence stages, as prosecutors engage in negotiations with defence attorneys (Mann, 1985 ; Wheeler & Rothman, 1982 ), although more recent research has suggested that this may be changing (Galvin & Simpson, 2019 ). Some advocate for WCOs ‘voluntarily’ repaying their victims, in favour of custodial sentences. This has led to concerns that WCOs can ‘buy their way’ out of prison, although others have argued that voluntary restitution provides the best outcome for victims (Faichney, 2014 ).

Watkins ( 1977 ) noted that juries are reluctant to convict WCOs, even when the law has been clearly violated. Jurors are influenced by underlying racial assumptions; mock jurors are more lenient on black WCOs than white ones, although black conventional offenders are punished more harshly (Gordon, 1990 ; Gordon et al., 1988 ). Although female offenders generally receive lighter sentences than males (Van Slyke & Bales, 2013 ), in some cases the reverse may be true (Etgar et al., 2019 ). Cox et al. ( 2016 ) found juries more likely to recommend harsher sentences for WCOs perceived as remorseless and lacking empathy. Filone et al. ( 2014 ) found that a personality disorder diagnosis was less influential on mock jurors’ sentencing decisions than for violent crime.

Despite recent legislative changes aimed to increase penalties for WCC, lower court judges have been found to make significant ‘downwards departures’ from sentencing guidelines (Ford, 2008 ). Wheeler et al. ( 1988 ) interviewed 51 federal judges in the USA, and found a general belief that WCOs do not reoffend, getting caught is sufficient deterrent, WCOs have ‘more to lose’, and more weight is given to the impact on dependents. These attitudes, in combination with judges’ greater empathy with offenders with similar backgrounds and lifestyles, may lead to the observed disparity in sentencing outcomes.

Considering the sanctioning of WCOs, outcomes may be affected by indirect impacts other than conviction and punishment, such as media coverage, loss of status and opportunity to work in particular areas (Button et al., 2018 ). These may contribute to subsequent mental health problems.

Experiences in prison

A commonly-held belief is that WCOs are particularly vulnerable to the negative effects of incarceration, referred to as the ‘ special sensitivity hypothesis ’ (Hunter, 2019 ; Logan et al., 2019 ; Stadler et al., 2013 ). Advocates of this position suggest that prison is particularly shocking for WCOs, and they will have greater difficulty adapting to prison life than street-level offenders (Payne, 2003 ; Pollack & Smith, 1983 ; Wheeler et al., 1988 ). Payne ( 2003 ) described the “six Ds” of white-collar incarceration: depression, danger, deviance, denial, deprivation and doldrums. Entry into prison is a common feature of autobiographical writing by WCOs (Hunter, 2019 ), which involves ‘status degradation ceremonies’ (Garfinkel, 1956 ; Watkins, 1977 ).

However, despite this presumed vulnerability, until recently there have been no empirical studies. WCOs are almost always sent to minimum security prisons (Friedrichs, 2009 ). Stadler et al. ( 2013 ) reviewed data gathered on 78 WCOs, including offender interviews, administrative records and prison-staff observations. They found that WCOs were less likely to experience general difficulties in prison than the non-WCO group, were more likely to make friends and were no more likely to have concerns for their personal safety, trouble sleeping or problems with current or former cellmates. Crank and Payne ( 2015 ) compared 116 incarcerated WCOs to 6510 other inmates, and found WCOs were no more likely to have mental health interventions and were less likely to receive psychiatric medications than violent inmates. Logan et al. ( 2019 ) used survey data to compare WCOs (using two definitions, one offence based, N = 932, and one socioeconomic status based, N = 132) to non-WCOs. They found no statistically significant differences for either white-collar group in self-reported negative affect or mental health treatment in prison, and socioeconomic status (SES) WCOs were significantly less likely to report feeling hopeless. They suggested that these findings provided support for the ‘ special resiliency hypothesis ’; WCOs have better emotional regulation, avoid confrontation and can ingratiate themselves to prison-staff and other inmates. Button et al. ( 2018 ) found some positive prison experiences, including helping others, improving health/fitness and new friendships. It is likely that WCOs cope with prison better because they are generally older, better off financially and have more stable relationships and social circumstances than other offenders.

Convicted WCOs in the community

Home detention is increasingly used for WCOs (Friedrichs, 2009 ). However, community supervision is seen by most probation officers as ‘going through the motions’ (Benson, 1985 ). Convicted WCOs tend to reject a criminal identity (Hunter, 2019 ). Mason ( 2007 ) interviewed 35 WCOs and found they viewed supervision as ‘demeaning and demoralising paperwork’. Murphy and Harris ( 2007 ) used survey data from 652 tax avoiders, and found that those who perceived their treatment as less stigmatising were less recidivist.

Convicted WCOs have better odds of regaining stable employment than street-level offenders, although multiple prior arrests and incarceration before age 24 decreases those odds (Kerley & Copes, 2004 ). Benson ( 1984 ) found that professionals and licensed occupations (such as medicine and law) and those employed in the public sector were much more likely to lose occupational status after a conviction than those in private business. Button et al. ( 2018 ) interviewed 17 convicted WCOs in the UK post release, and found that this period may prove to be more challenging than prison itself, with 41% accessing mental health treatment, and three WCOs requiring psychiatric admission.

Despite a general perception that WCOs are unlikely to reoffend, a significant proportion commit further crimes after conviction, with similar recidivism rates to those of robbery and firearm offenders (Perri, 2011 ). A total of 683 forgers, compared with burglars and car thieves over a 14-year period, had higher rates of parole violations and revocation (McCall & Grogan, 1974 ). The Yale sample had an overall recidivism rate of 29%, with no difference between those who were incarcerated and those who were not (Weisburd et al., 1995 ). Listwan and colleagues (Listwan et al., 2010 ) followed 64 convicted WCOs over 10–12 years, and found that 53% were arrested at least once, with ‘neurotic-type’ personality (using the Jesness Inventory) as a significant risk factor for reoffending. Harbinson et al. ( 2019 ), using data on 31,306 white-collar offenders under supervision, found that 7.8% had their supervision revoked (re-arrest data were not available); of the 2.2% classified as high risk on the Federal Post-Conviction Risk Assessment (a measure not specific to WCC), the reoffending rate was around half. Goulette ( 2020 ) suggested that gender may play a role in recidivism risk, as women score lower on general risk assessment tools. However, it is unclear whether general risk assessment tools are valid in the assessment of WCOs and whether psychiatric factors are risk factors for recidivism.

In summary, the reasons for white-collar recidivism are not well understood, and risk factors have not been studied separately from factors common to all crime. Convicted WCOs (who represent a small and arguably atypical proportion of WCOs) may need higher post-release support than they receive, to prevent reoffending and improve their well-being and successful re-integration into society, an area where high-quality mental health support could play a significant role. There may be risk factors beyond the common factors for criminal/violent reoffending that are relevant to WCC, such as anxiety-related disorders, cognitions related to offending including self-identity and neutralisation, a history of non-aggressive rule breaking, or financial responsibilities to dependents, although these are yet to be established.

Limitations

This study had several limitations. Due to the diverse terminology and non-medical academic focus of the literature, some publications may have been missed, along with non-published material and other potentially relevant grey literature. Given the breadth of the topic and the different aspects to WCC, there are undoubtedly many other topics relevant to forensic psychiatry that have not been included, such as wrongdoing at the level of the corporation (rather than by individuals) and legal aspects.

Implications

There are clearly many gaps in the understanding of WCC and WCOs, particularly with respect to factors of relevance to forensic psychiatry. We are of the view that forensic psychiatry can contribute to filling these research gaps in a number of ways, and therefore contribute to the multi-disciplinary understanding of WCC. Forensic psychiatrists also have a clinical role to play in the assessment and treatment of WCOs.

Research implications

A recent edited volume on forensic neuroscience (Beech et al., 2018 ) highlighted the significant contribution that neurobiology can make to understanding offending behaviours, the conditions that underpin such behaviours and interventions for these behaviours. However, WCC did not feature, and a chapter on deception (Vendemia & Nye, 2018 ) was of limited relevance to WCC, although manipulation and deception seem to play a key role in WCC. The neurobiological understanding of psychopathy is quite well developed (Glenn & Raine, 2014 ). Research on the neurobiology of deception and psychopathy may inform the understanding of the genesis of WCC, and such research could be conducted on WCOs. Neurobiological research on WCOs is very rare compared to that on violent and sexual offenders.

Research on offenders with different trajectories and criminal careers has highlighted developmental and psychopathological differences between those who persist and those who desist (McGee & Moffitt, 2018 ). Such psychopathological differences may be relevant to WCOs, and research comparing one-off WCOs, recidivist WCOs, diverse offenders who commit non-WCC as well as WCC, and non-WCOs may help in the understanding of the personality and developmental factors predisposing to these different trajectories. Research ascertaining the rates of mental illnesses, personality disorders and psychopathy in WCOs could help with understanding such offenders but also to know what their mental health needs are. Violence may be linked to WCC in different ways. One important factor in understanding this relationship, given the relationship between various mental disorders and violence (Sariaslan et al., 2020 ), could be psychopathology. Studies of the psychopathology and mental health of WCOs both before and after sanctioning and subsequently would help with understanding the development of mental health difficulties seen in WCOs and their relationship to punishment, imprisonment, loss of status and other factors. Research on the relationship between mental health conditions and reoffending, and whether mental health treatment reduces reoffending, would help in understanding the potential role forensic mental health services could play in the rehabilitation of WCOs.

Given the impact of WCC and the recidivism rates, which are higher than those for sexual offenders and similar to those for violent offenders, there is a need for methods of identifying offenders who are more likely to recidivate. There are a number of instruments that have been validated in the prediction of general and violent recidivism (Douglas & Otto, 2020 ). Research should be undertaken to ascertain whether such instruments have predictive validity for WCOs. Instruments for general recidivism emphasise antisociality and social instability and may not cover factors of relevance to WCC. There may be other factors that need to be considered as well as, or instead of, such factors. Some of these may be psychopathological in nature, for example Factor 1 psychopathy. To assess risk of recidivism it is likely that an approach considering both the uniqueness of WCC and commonalities with other offending will be required. This is analogous to what we know about risk assessment for sex offending, stalking and intimate partner violence (Douglas & Otto, 2020 ), where some factors are common to all types of interpersonal violence offending (e.g. history of violence and antisociality), while others (e.g. sexual deviance for sexual offenders) are unique to specific groups. Understanding the role of mental health as a dynamic factor in precipitating offending and in desistance could help determine the role of mental health in risk assessment and management.

Clinical implications

Forensic psychiatrists tend to focus on violent mentally disordered offenders, and most will be unaware of the aspects of WCC and WCOs summarised in this review. So forensic psychiatry as a clinical specialty has little to do with WCOs and little understanding of such cases. This lack of involvement and non-evidence-based assumptions about WCOs may perpetuate the notion that forensic psychiatry has little to offer. However, this review challenges this.

One fundamental clinical implication that arises from this review goes to the very nature of the practice of forensic psychiatry. Forensic psychiatrists focus their clinical work on individuals with mental health conditions who commit interpersonal violence rather than ‘general offenders’. Given the impact of WCC, the recidivism rates of WCOs, the link with violent crime and the similar rates of mental health conditions, it could be argued that forensic mental health services should be more involved in the treatment and management of WCOs.

Psychiatrists undertaking assessments for courts need to know that recidivism is no less common in WCOs, they are often not specialists, and psychopathology may be relevant to their offending. The countertransference of psychiatrists to WCOs may be different from that for other offenders as they are more likely to have similar demographics. This may impact judgments about the presence and role of psychopathology, perceptions of risk and approaches to intervention. The mental health of WCOs subsequent to sanctioning and/or release may be relevant to several outcomes including the well-being and social functioning of the WCO, risk of suicide and risk of recidivism.

Conclusions

Despite its fuzzy borders, and although it does not generate the same public outrage and opprobrium as violent or sexual offending, WCC falls squarely within the realms of criminal behaviour, mental health and the legal system, with a high cost to victims and society. There has been a general neglect of WCC in the field of academic forensic psychiatry. The relationship between psychopathology, personality factors, other psychological factors and WCC has been poorly studied, and needs further exploration. Even though the vast majority of WCOs may turn out to be psychiatrically ‘well’, this has yet to be established, and the post-release period may be one of particular vulnerability. Other areas that could benefit from further study include: predisposing factors for WCC (such as personality and psychopathy, a history of non-criminal unethical behaviour/boundary violations), precipitating factors (psychosocial or financial stressors), and the role of notoriety and fear of retribution as barriers to reintegration to the community. Psychiatry has a particular role to play in understanding the role of psychopathology and mental health in predisposing to, precipitating, perpetuating and desisting from WCC.

Doctors share high levels of societal trust, respectability and similar socioeconomic and educational backgrounds with WCOs (including offenders within the medical profession itself), which may lead to bias in the average psychiatrist, as has been proposed for sentencing judges (Wheeler et al., 1988 ). There may be a reluctance to pathologise people with whom we can more easily identify, and to locate the causes of their offending in external factors. It is time to start grappling with these issues.

There are several ways in which forensic psychiatry may contribute meaningfully to the field of WCC. Forensic psychiatrists can offer valuable insights into the role of psychopathology in sentencing (particularly in jurisdictions where personality disorder is accepted as a mitigating factor, such as Victoria), treatment and management of WCOs, and understanding the meaning of WCC is helpful in clinical practice and assessment. It is surprising, given the degree of victimisation, societal harm and recidivism rates, that there are no validated risk assessment tools specific to WCC, and this is an area where forensic psychiatry may be able to provide expertise and guidance. In light of the growing public discourse about WCC and issues such as tax avoidance by wealthy individuals and banking irregularities, our understanding and response to this behaviour should be based on sound theory and evidence, rather than assumptions.

Database search terms

The search terms for each database were as follows:

PsychNet search terms: ‘white collar crim*’ OR ‘financial crim*’ OR fraud OR Ponzi OR embezzlement OR bribery OR ‘wage theft’ OR racketeering OR laundering OR forgery AND Psych* OR mental OR personality AND demographics AND characteristics. This search generated 2174 results.

EbscoHost Health business elite, Psychology and Behavioral Sciences Collection search terms: ‘white collar crim*’ OR ‘financial crim*’ OR fraud OR Ponzi OR embezzlement OR bribery OR ‘wage theft’ OR racketeering OR laundering OR forgery AND Psych* OR mental OR personality OR demographics OR characteristics. This search generated 397 results.

Pubmed search terms: (psychology OR psychiatry OR mental OR personality OR demographics OR characteristics) AND (white collar crime OR white collar criminal OR financial crime OR fraud OR ponzi OR embezzlement OR bribery OR racketeering OR laundering OR forgery). This search generated 744 results. In Pubmed, the following terms were also applied as exclusion terms, to reduce the large number of irrelevant results regarding industrial cleaning (from the search term ‘laundering’) and the psychological concept of imposter syndrome (from the search term ‘fraud’): NOT (microbial OR microbes OR bacterial OR clothing OR attire OR laundry OR impostor OR washing machine).

Ethical standards

Declaration of conflicts of interest.

Rose Clarkson has declared no conflicts of interest

Rajan Darjee has declared no conflicts of interest

Ethical approval

This article does not contain any studies with human participants or animals performed by the authors.

Coleman, J. W. (2005). The criminal elite: Understanding white-collar crime . Macmillan. [ Google Scholar ]
Ray, J. V., & Jones, S. (2011). Self-reported psychopathic traits and their relation to intentions to engage in environmental offending . International Journal of Offender Therapy and Comparative Criminology , 55 ( 3 ), 370–391. 10.1177/0306624X10361582 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Adolphe, A., Khatib, L., van Golde, C., Gainsbury, S. M., & Blaszczynski, A. (2019). Crime and gambling disorders: A systematic review . Journal of Gambling Studies , 35 ( 2 ), 395–414. 10.1007/s10899-018-9794-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Agnew, R. (1992). Foundation for a general strain theory of crime and delinquency . Criminology , 30 ( 1 ), 47–88. 10.1111/j.1745-9125.1992.tb01093.x [ CrossRef ] [ Google Scholar ]
Agnew, R. (2001). An overview of general strain theory . Explaining criminals and crime (pp. 161–174). Roxbury. [ Google Scholar ]
Agnew, R., Piquero, N. L., & Cullen, F. T. (2009). General strain theory and white-collar crime. In The criminology of white-collar crime (pp. 35–60). Springer. [ Google Scholar ]
Alalehto, T. (2003). Economic crime: Does personality matter? International Journal of Offender Therapy and Comparative Criminology , 47 ( 3 ), 335–355. 10.1177/0306624X03047003007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Alalehto, T. (2015). White collar criminals: The state of knowledge . The Open Criminology Journal , 8 ( 1 ), 28–35. 10.2174/1874917801508010028 [ CrossRef ] [ Google Scholar ]
Alalehto, T., & Azarian, R. (2018). When white collar criminals turn to fatal violence: The impact of narcissism and psychopathy . Journal of Investigative Psychology and Offender Profiling , 15 ( 2 ), 215–226. 10.1002/jip.1503 [ CrossRef ] [ Google Scholar ]
Apel, R., & Paternoster, R. (2009). Understanding “criminogenic” corporate culture: What white-collar crime researchers can learn from studies of the adolescent employment–crime relationship. In The criminology of white-collar crime (pp. 15–33). Springer. [ Google Scholar ]
Arnulf, J. K., & Gottschalk, P. (2013). Heroic leaders as white‐collar criminals: An empirical study . Journal of Investigative Psychology and Offender Profiling , 10 ( 1 ), 96–113. 10.1002/jip.1370 [ CrossRef ] [ Google Scholar ]
Australian Securities and Investment Commission (2016). ASIC permanently bans Christopher John Griggs from providing financial services . 16-413MR. https://asic.gov.au/about-asic/news-centre/find-a-media-release/2016-releases/16-413mr-asic-permanently-bans-christopher-john-griggs-from-providing-financial-services/
Babiak, P., Hare, R. D., & McLaren, T. (2007). Snakes in suits: When psychopaths go to work . Harper. [ Google Scholar ]
Banks, J., & Waugh, D. (2019). A taxonomy of gambling-related crime . International Gambling Studies , 19 ( 2 ), 339–357. 10.1080/14459795.2018.1554084 [ CrossRef ] [ Google Scholar ]
Beech, A. R., Carter, A. J., Mann, R. E., & Rotshtein, P. (Eds.). (2018). The Wiley blackwell handbook of forensic neuroscience , 2 Volume Set. John Wiley & Sons. [ Google Scholar ]
Benson, M. L. (1984). The fall from grace: Loss of occupational status as a consequence of conviction for a white collar crime . Criminology , 22 ( 4 ), 573–593. 10.1111/j.1745-9125.1984.tb00316.x [ CrossRef ] [ Google Scholar ]
Benson, M. L. (1985). White collar offenders under community supervision . Justice Quarterly , 2 ( 3 ), 429–438. 10.1080/07418828500088651 [ CrossRef ] [ Google Scholar ]
Benson, M. L. (2013). Editor’s introduction—White-collar crime: Bringing the offender back . Journal of Contemporary Criminal Justice , 29 ( 3 ), 324–330. 10.1177/1043986213496380 [ CrossRef ] [ Google Scholar ]
Benson, M. L., & Chio, H. L. (2019). Who commits occupational crimes? In The handbook of white‐collar crime (pp. 95–112). Wiley. [ Google Scholar ]
Benson, M. L., & Cullen, F. T. (1998). Combating corporate crime: Local prosecutors at work . UPNE. [ Google Scholar ]
Benson, M. L., & Kerley, K. R. (2001). Life course theory and white-collar crime. In Pontell H. N. & Shichor D. (Eds.), Contemporary issues in crime and criminal justice: Essays in honor of Gilbert Geis (pp. 121–136). Prentice Hall. [ Google Scholar ]
Benson, M. L., & Moore, E. (1992). Are white-collar and common offenders the same? An empirical and theoretical critique of a recently proposed general theory of crime . Journal of Research in Crime and Delinquency , 29 ( 3 ), 251–272. 10.1177/0022427892029003001 [ CrossRef ] [ Google Scholar ]
Biggerstaff, L., Cicero, D. C., & Puckett, A. (2015). Suspect CEOs, unethical culture, and corporate misbehavior . Journal of Financial Economics , 117 ( 1 ), 98–121. 10.1016/j.jfineco.2014.12.001 [ CrossRef ] [ Google Scholar ]
Binde, P. (2016). Gambling-related embezzlement in the workplace: A qualitative study . International Gambling Studies , 16 ( 3 ), 391–407. 10.1080/14459795.2016.1214165 [ CrossRef ] [ Google Scholar ]
Binde, P. (2017). Gambling-related employee embezzlement: A study of Swedish newspaper reports . Journal of Gambling Issues , 34 ( 34 ), 12–31. 10.4309/jgi.v0i34.3955 [ CrossRef ] [ Google Scholar ]
Black, W. K. (2005). “ Control frauds” as financial super-predators: How “pathogens” make financial markets inefficient . The Journal of Socio-Economics , 34 ( 6 ), 734–755. 10.1016/j.socec.2005.07.026 [ CrossRef ] [ Google Scholar ]
Blickle, G., Schlegel, A., Fassbender, P., & Klein, U. (2006). Some personality correlates of business white-collar crime (pp. 220–233). Blackwell Publishing. 10.1111/j.1464-0597.2006.00226.x [ CrossRef ] [ Google Scholar ]
Bloomberg (2021). Billionaire deemed competent for trial in record tax-fraud case. https://www.bloomberg.com/news/articles/2021-07-06/billionaire-competent-to-stand-trial-for-tax-crimes-experts-say
Boddy, C. R. (2011). Corporate psychopaths, bullying and unfair supervision in the workplace . Journal of Business Ethics , 100 ( 3 ), 367–379. 10.1007/s10551-010-0689-5 [ CrossRef ] [ Google Scholar ]
Boddy, C. R. (2015). Organisational psychopaths: A ten year update . Management Decision , 53 ( 10 ), 2407–2432. 10.1108/MD-04-2015-0114 [ CrossRef ] [ Google Scholar ]
Bonger, W. A. (1916). Criminality and economic conditions . Little, Brown. [ Google Scholar ]
Brady, S., Rabin, E., Wu, D., Haque, O. S., & Bursztajn, H. J. (2016). Forensic psychiatric contributions to understanding financial crime. In Financial crimes: Psychological, technological, and ethical issues. International library of ethics, law, and the new medicine (pp. 107–127). Springer International Publishing. [ Google Scholar ]
Braithwaite, J. (1982). Challenging just deserts: Punishing white-collar criminals . The Journal of Criminal Law and Criminology (1973-) , 73 ( 2 ), 723–763. 10.2307/1143113 [ CrossRef ] [ Google Scholar ]
Brody, R. G., & Kiehl, K. A. (2010). From white‐collar crime to red‐collar crime . Journal of Financial Crime , 17 ( 3 ), 351–364. 10.1108/13590791011056318 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Brody, R. G., Knight, R. C., & Nunez, J. N. (2020). Born and raised to be a fraudster . Journal of Investigative Psychology and Offender Profiling , 17 ( 1 ), 46–58. 10.1002/jip.1535 [ CrossRef ] [ Google Scholar ]
Brottman, M. (2009). The company man: A case of white-collar crime . The American Journal of Psychoanalysis , 69 ( 2 ), 121–135. 10.1057/ajp.2009.3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bucy, P. H., Formby, E. P., Raspanti, M. S., & Rooney, K. E. (2008). Why do they do it: The motives, mores, and character of white collar criminals . St. John’s Law Review , 82 , 401. [ Google Scholar ]
Button, M., Lewis, C., & Tapley, J. (2009). Fraud typologies and the victims of fraud: Literature review . National Fraud Authority. [ Google Scholar ]
Button, M., Shepherd, D., & Blackbourn, D. (2018). “ The higher you fly, the further you fall”: White-collar criminals, “special sensitivity” and the impact of conviction in the United Kingdom . Victims & Offenders , 13 ( 5 ), 628–650. 10.1080/15564886.2017.1405133 [ CrossRef ] [ Google Scholar ]
Casey, F., & Markopolos, H. (2010). No one would listen: A true financial thriller . John Wiley. [ Google Scholar ]
Casten, J. A. A. (2013). Further test of strain theory: Does gender discrimination contribute to theft by employees? [Dissertation, Sociology & Criminal Justice]. Old Dominion University. [ Google Scholar ]
Cleckley, H. (1976). The mask of sanity . Mosby. [ PubMed ] [ Google Scholar ]
Cohen, M. A. (2016). The costs of white-collar crime. In Oxford handbook of white-collar crime (pp. 78–98). Oxford University Press. [ Google Scholar ]
Collins, J. M., & Schmidt, F. L. (2006). Personality, integrity, and white collar crime: A construct validity study . Personnel Psychology , 46 ( 2 ), 295–311. 10.1111/j.1744-6570.1993.tb00875.x [ CrossRef ] [ Google Scholar ]
Cottino, A. (2004). White-collar crime. In Sumner C. (Ed.), The Blackwell companion to criminology (pp. 343–358). Blackwell. [ Google Scholar ]
Cox, J., Edens, J. F., Rulseh, A., & Clark, J. W. (2016). Juror perceptions of the interpersonal-affective traits of psychopathy predict sentence severity in a white-collar criminal case . Psychology Crime and Law , 22 , 721–740. [ Google Scholar ]
Craig, J. M. (2016). The role of personality factors in predicting embezzlement, fraud, and shoplifting . ProQuest Information & Learning. [ Google Scholar ]
Craig, J. M., & Piquero, N. L. (2017). Sensational offending: An application of sensation seeking to white-collar and conventional crimes . Crime & Delinquency , 63 ( 11 ), 1363–1382. 10.1177/0011128716674707 [ CrossRef ] [ Google Scholar ]
Crank, B. R., & Payne, B. K. (2015). White-collar offenders and the jail experience: A comparative analysis . Criminal Justice Studies , 28 ( 4 ), 378–396. 10.1080/1478601X.2015.1060971 [ CrossRef ] [ Google Scholar ]
Croall, H. (2016). What is known and what should be known about white-collar crime victimization? In The Oxford handbook of white-collar crime (pp. 59–77). Oxford University Press. [ Google Scholar ]
Curnow, D. (2021). The psychology of Embezzlement: The art of control and intervention . Palgrave. [ Google Scholar ]
Daly, K. (1989). Gender and varieties of white‐collar crime . Criminology , 27 ( 4 ), 769–794. 10.1111/j.1745-9125.1989.tb01054.x [ CrossRef ] [ Google Scholar ]
De Vries, R. E., Pathak, R. D., Van Gelder, J.-L., & Singh, G. (2017). Explaining Unethical Business Decisions: The role of personality, environment, and states . Personality and Individual Differences , 117 , 188–197. 10.1016/j.paid.2017.06.007 [ CrossRef ] [ Google Scholar ]
DeLisi, M., Tahja, K. N., Drury, A. J., Elbert, M. J., Caropreso, D. E., & Heinrichs, T. (2018). De novo advanced adult-onset offending: New evidence from a population of federal correctional clients . Journal of Forensic Sciences , 63 ( 1 ), 172–177. 10.1111/1556-4029.13545 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Dennison, C. R., Finkeldey, J. G., & Rocheleau, G. C. (2021). Confounding bias in the relationship between problem gambling and crime . Journal of Gambling Studies , 37 ( 2 ), 427–418. 10.1007/s10899-020-09939-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Dhami, M. K. (2007). White-collar prisoners’ perceptions of audience reaction . Deviant Behavior , 28 ( 1 ), 57–77. 10.1080/01639620600987475 [ CrossRef ] [ Google Scholar ]
Dodge, M. (2019). Women and white-collar crime . Oxford Research Encyclopedia of Criminology and Criminal Justice. [ Google Scholar ]
Douglas, K. S., & Otto, R. K. (2020). Handbook of violence risk assessment . Routledge. [ Google Scholar ]
Edwards, A., & Gill, P. (2002). Crime as enterprise?–The case of “transnational organised crime” . Crime, Law and Social Change , 37 ( 3 ), 203–223. 10.1023/A:1015025509582 [ CrossRef ] [ Google Scholar ]
Etgar, S., Blau, I., & Eshet-Alkalai, Y. (2019). White-collar crime in academia: Trends in digital academic dishonesty over time and their effect on penalty severity . Computers & Education , 141 , 103621. 10.1016/j.compedu.2019.103621 [ CrossRef ] [ Google Scholar ]
Faichney, D. (2014). Autocorrect: A proposal to encourage voluntary restitution through the white-collar sentencing calculus . J Crim L & Criminology , 104 , 389. [ Google Scholar ]
Farrington, D. P. (1986). Age and crime . Crime and Justice , 7 , 189–250. 10.1086/449114 [ CrossRef ] [ Google Scholar ]
Federal Court of Australia (2021). ASIC v Melissa Caddick - online file . https://www.fedcourt.gov.au/services/access-to-files-and-transcripts/online-files/asic-v-caddick
Feeley, D. (2006). Personality, environment, and the causes of white-collar crime . Law & Psychology Review , 30 , 201–213. [ Google Scholar ]
Fenwick, M. (2006). The mouse race before the rat race: Corporate crime and student ethics . School of Criminology-Simon Fraser University. [ Google Scholar ]
Ferrari, R. (2015). Writing narrative style literature reviews . Medical Writing , 24 ( 4 ), 230–235. 10.1179/2047480615Z.000000000329 [ CrossRef ] [ Google Scholar ]
Filone, S., Strohmaier, H., Murphy, M., & DeMatteo, D. (2014). The impact of DSM-5's alternative model for personality disorders on criminal defendants . Behavioral Sciences & the Law , 32 ( 1 ), 135–148. 10.1002/bsl.2097 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ford, M. A. (2008). White-collar crime, social harm, and punishment: A critique and modification of the sixth circuit’s ruling in United States v. Davis . St. John’s Law Review , 82 , 383. [ Google Scholar ]
Forte, G. Jr. (2018). Investigating physicians billing for services not rendered: Fraud detection, interviewing and referral to law enforcement. Economic Crime Forensics Capstones , 31 , 1–26. [ Google Scholar ]
Frank, N. K., & Lynch, M. J. (1992). Corporate crime, corporate violence: A primer . Harrow and Heston. [ Google Scholar ]
Frankel, T. (2012). The Ponzi scheme puzzle: A history and analysis of con artists and victims . Oxford University Press. [ Google Scholar ]
Freiberg, A. (2019). Researching white‐collar crime: An Australian perspective. In The handbook of white‐collar crime (pp. 418–436). Wiley. [ Google Scholar ]
Friedrichs, D. O. (2009). Trusted criminals: White collar crime in contemporary society . Cengage Learning. [ Google Scholar ]
Friedrichs, D. O. (2019). White collar crime: Definitional debates and the case for a typological approach. In Rorie M. (Ed.), The handbook of white‐collar crime (pp. 16–31). Wiley. [ Google Scholar ]
Fritzon, K., Bailey, C., Croom, S., & Brooks, N. (2016). Problem personalities in the workplace: Development of the corporate personality inventory . Psychology and Law in Europe: Routledge , 157–184. [ Google Scholar ]
Fritzon, K., Brooks, N., & Croom, S. (2020). Corporate Psychopathy: Investigating destructive personalities in the workplace . Palgrave. [ Google Scholar ]
Galvin, M. A. (2020). Gender and white-collar crime–theoretical issues . Criminal Justice Studies , 33 ( 1 ), 61–69. 10.1080/1478601X.2020.1709954 [ CrossRef ] [ Google Scholar ]
Galvin, M. A., & Simpson, S. S. (2019). Prosecuting and sentencing white‐collar crime in US Federal courts: Revisiting the Yale findings. In The handbook of white‐collar crime (pp. 381–397). John Wiley & Sons. [ Google Scholar ]
Garfinkel, H. (1956). Conditions of successful degradation ceremonies . American Journal of Sociology , 61 ( 5 ), 420–424. 10.1086/221800 [ CrossRef ] [ Google Scholar ]
Glenn, A. L., & Raine, A. (2014). Psychopathy: An introduction to biological findings and their implications . NYU Press. [ Google Scholar ]
Gordon, R. A. (1990). Attributions for blue-collar and white-collar crime: The effects of subject and defendant race on simulated juror decisions . Journal of Applied Social Psychology , 20 ( 12 ), 971–983. 10.1111/j.1559-1816.1990.tb00385.x [ CrossRef ] [ Google Scholar ]
Gordon, R. A., Bindrim, T. A., McNicholas, M. L., & Walden, T. L. (1988). Perceptions of blue-collar and white-collar crime: The effect of defendant race on simulated juror decisions . The Journal of Social Psychology , 128 ( 2 ), 191–197. 10.1080/00224545.1988.9711362 [ CrossRef ] [ Google Scholar ]
Gottfredson M. R., & Hirschi T. (1990). A general theory of crime . Stanford University Press. [ Google Scholar ]
Gottschalk, P. (2012). Gender and white‐collar crime: Only four percent female criminals . Journal of Money Laundering Control , 15 ( 3 ), 362–373. 10.1108/13685201211238089 [ CrossRef ] [ Google Scholar ]
Gottschalk, P. (2017). Organizational opportunity and deviant behavior: Convenience in white-collar crime (pp. vii, 243–vii). Edward Elgar Publishing. [ Google Scholar ]
Gottschalk, P. (2020). Gender and crime: Convenience for pink-collar offenders . Deviant Behavior , 1–15. 10.1080/01639625.2020.1794270 [ CrossRef ] [ Google Scholar ]
Gottschalk, P. (2021). From crime convenience to punishment inconvenience: The case of detected white-collar offenders . Deviant Behavior , 42 ( 8 ), 1021–1011. 10.1080/01639625.2020.1717840 [ CrossRef ] [ Google Scholar ]
Gottschalk, P., & Glasø, L. (2013). Corporate crime does pay! The relationship between financial crime and imprisonment in white-collar crime . International Letters of Social and Humanistic Sciences , 5 , 63–78. 10.18052/www.scipress.com/ILSHS.5.63 [ CrossRef ] [ Google Scholar ]
Gottschalk, P., & Glasø, L. (2013). Gender in white-collar crime: An empirical study of pink-collar criminals . International Letters of Social and Humanistic Sciences , 4 , 22–34. [ Google Scholar ]
Gottschalk, P., & Gunnesdal, L. (2018). White-collar crime research. In White-collar crime in the shadow economy: Lack of detection, investigation and conviction compared to social security fraud (pp. 1–14). Springer International Publishing. [ Google Scholar ]
Goulette, N. (2020). What are the gender differences in risk and needs of males and females sentenced for white-collar crimes? Criminal Justice Studies , 33 ( 1 ), 31–45. 10.1080/1478601X.2020.1709951 [ CrossRef ] [ Google Scholar ]
Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for peer-reviewed journals: Secrets of the trade . Journal of Chiropractic Medicine , 5 ( 3 ), 101–117. 10.1016/S0899-3467(07)60142-6 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Green, S. P. (2004). Moral ambiguity in white collar criminal law . Notre Dame JL Ethics & Pub Pol’y , 18, 501. [ Google Scholar ]
Harbinson, E., Benson, M. L., & Latessa, E. J. (2019). Assessing risk among white-collar offenders under federal supervision in the community . Criminal Justice and Behavior , 46 ( 2 ), 261–279. 10.1177/0093854818810317 [ CrossRef ] [ Google Scholar ]
Hare, R. D., Harpur, T. J., Hakstian, A. R., Forth, A. E., Hart, S. D., & Newman, J. P. (1990). The revised psychopathy checklist: Reliability and factor structure . Psychological Assessment: A Journal of Consulting and Clinical Psychology , 2 ( 3 ), 338–341. 10.1037/1040-3590.2.3.338 [ CrossRef ] [ Google Scholar ]
Heath, J. (2008). Business ethics and moral motivation: A criminological perspective . Journal of Business Ethics , 83 ( 4 ), 595–614. 10.1007/s10551-007-9641-8 [ CrossRef ] [ Google Scholar ]
Hollinger, R. C., & Clark, J. P. (1982). Formal and informal social controls of employee deviance . The Sociological Quarterly , 23 ( 3 ), 333–343. 10.1111/j.1533-8525.1982.tb01016.x [ CrossRef ] [ Google Scholar ]
Holtfreter, K. (2005). Is occupational fraud “typical” white-collar crime? A comparison of individual and organizational characteristics . Journal of Criminal Justice , 33 ( 4 ), 353–365. 10.1016/j.jcrimjus.2005.04.005 [ CrossRef ] [ Google Scholar ]
Holtfreter, K. (2013). Gender and “other people’s money”: An analysis of white-collar offender sentencing . Women & Criminal Justice , 23 ( 4 ), 326–344. 10.1080/08974454.2013.821015 [ CrossRef ] [ Google Scholar ]
Howe, J., Falkenbach, D., & Massey, C. (2014). The relationship among psychopathy, emotional intelligence, and professional success in finance . International Journal of Forensic Mental Health , 13 ( 4 ), 337–347. 10.1080/14999013.2014.951103 [ CrossRef ] [ Google Scholar ]
Huisman, W. (2019). Blurred lines: Collusions between legitimate and illegitimate organizations . In The handbook of white‐collar crime (pp. 139–158). Wiley & Sons. [ Google Scholar ]
Hunter, B. (2019). The correctional experiences of white‐collar offenders . In The handbook of white‐collar crime (pp. 297–313). Wiley. [ Google Scholar ]
Jesilow, P., Pontell, H. N., & Geis, G. (1993). Prescription for profit: How doctors defraud Medicaid . University of California Press. [ Google Scholar ]
Kapardis, A., & Krambia-Kapardis, M. (2004). Enhancing fraud prevention and detection by profiling fraud offenders . Criminal Behaviour and Mental Health: CBMH , 14 ( 3 ), 189–201. 10.1002/cbm.586 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kapardis, M. K. (1999). Enhancing the auditor’s fraud detection ability: An interdisciplinary approach . Peter Lang. [ Google Scholar ]
Kendall, C. C. (2010). Rape as a violent crime in aid of racketeering activity . Law & Psychol Rev , 34 ( 91 ), 107. [ Google Scholar ]
Kendler, K. S., Maes, H. H., Lönn, S. L., Morris, N. A., Lichtenstein, P., Sundquist, J., & Sundquist, K. (2015). A Swedish national twin study of criminal behavior and its violent, white-collar and property subtypes . Psychological Medicine , 45 ( 11 ), 2253–2262. 10.1017/S0033291714002098 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kerley, K. R., & Copes, H. (2004). The effects of criminal justice contact on employment stability for white-collar and street-level offenders . International Journal of Offender Therapy and Comparative Criminology , 48 ( 1 ), 65–84. 10.1177/0306624X03256660 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kleemans, E. R., & Van de Bunt, H. G. (2008). Organised crime, occupations and opportunity . Global Crime , 9 ( 3 ), 185–197. 10.1080/17440570802254254 [ CrossRef ] [ Google Scholar ]
Kolz, A. R. (1999). Personality predictors of retail employee theft and counterproductive behavior . Journal of Professional Services Marketing , 19 ( 2 ), 107–114. 10.1300/J090v19n02_06 [ CrossRef ] [ Google Scholar ]
Krokoszinski, L., Westenberger, A., & Hosser, D. (2018). Emotional responsiveness in convicted fraudsters: A study on baseline activation of the anterior insula and its influence on moral decision-making . The Journal of Forensic Psychiatry & Psychology , 29 ( 4 ), 527–543. 10.1080/14789949.2018.1425470 [ CrossRef ] [ Google Scholar ]
Langton, L., & Piquero, N. L. (2007). Can general strain theory explain white-collar crime? A preliminary investigation of the relationship between strain and select white-collar offenses . Journal of Criminal Justice , 35 ( 1 ), 1–15. 10.1016/j.jcrimjus.2006.11.011 [ CrossRef ] [ Google Scholar ]
Laursen, B., Plauborg, R., Ekholm, O., Larsen, C. V. L., & Juel, K. (2016). Problem gambling associated with violent and criminal behaviour: A Danish population-based survey and register study . Journal of Gambling Studies , 32 ( 1 ), 25–34. 10.1007/s10899-015-9536-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
Lee, A. R., & Chávez, K. (2020). Are women more averse to corruption than men? The case of South Korea . Social Science Quarterly , 101 ( 2 ), 473–489. 10.1111/ssqu.12768 [ CrossRef ] [ Google Scholar ]
Lee, J. J., Gino, F., Jin, E. S., Rice, L. K., & Josephs, R. A. (2015). Hormones and ethics: Understanding the biological basis of unethical conduct . Journal of Experimental Psychology. General , 144 ( 5 ), 891–897. 10.1037/xge0000099 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Lind, K., Hellman, M., Obstbaum, Y., & Salonen, A. H. (2021). Associations between gambling severity and criminal convictions: Implications for the welfare state . Addiction Research & Theory , 1–12. 10.1080/16066359.2021.1902995 [ CrossRef ] [ Google Scholar ]
Ling, S., Raine, A., Yang, Y., Schug, R. A., Portnoy, J., & Ho, M.-H. R. (2019). Increased frontal lobe volume as a neural correlate of gray-collar offending . Journal of Research in Crime and Delinquency , 56 ( 2 ), 303–336. 10.1177/0022427818802337 [ CrossRef ] [ Google Scholar ]
Lingnau, V., Fuchs, F., & Dehne-Niemann, T. E. (2017). The influence of psychopathic traits on the acceptance of white-collar crime: Do corporate psychopaths cook the books and misuse the news? Journal of Business Economics , 87 ( 9 ), 1193–1227. 10.1007/s11573-017-0864-6 [ CrossRef ] [ Google Scholar ]
Listwan, S. J., Piquero, N. L., & Van Voorhis, P. (2010). Recidivism among a white-collar sample: Does personality matter? Australian & New Zealand Journal of Criminology , 43 ( 1 ), 156–174. 10.1375/acri.43.1.156 [ CrossRef ] [ Google Scholar ]
Logan, M. W., Morgan, M. A., Benson, M. L., & Cullen, F. T. (2019). Coping with imprisonment: Testing the special sensitivity hypothesis for white-collar offenders . Justice Quarterly , 36 ( 2 ), 225–254. 10.1080/07418825.2017.1396488 [ CrossRef ] [ Google Scholar ]
Maesen, W. A. (1991). Fraud in mental health practice: A risk management perspective . Administration and Policy in Mental Health , 18 ( 6 ), 421–432. 10.1007/BF00707315 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Mann, K. (1985). Defending white-collar crime: A portrait of attorneys at work . Yale University Press. [ Google Scholar ]
Marriott, L. (2018). Pursuit of white-collar crime in New Zealand . Journal of Australian Taxation , 20 , 35. [ Google Scholar ]
Marriott, L. (2020). White-collar crime: The privileging of serious financial fraud in New Zealand . Social & Legal Studies , 29 ( 4 ), 486–506. 10.1177/0964663919883367 [ CrossRef ] [ Google Scholar ]
Mason, K. A. (2007). Punishment and paperwork: White-collar offenders under community supervision . American Journal of Criminal Justice , 31 ( 2 ), 23–36. 10.1007/s12103-007-9001-3 [ CrossRef ] [ Google Scholar ]
McCall, C. C., & Grogan, H. J. (1974). Rehabilitating forgers (pp. 263–268). Sage Publications. 10.1177/001112877402000306 [ CrossRef ] [ Google Scholar ]
McGee, T. R., & Moffitt, T. E. (2018). The developmental taxonomy . In The Oxford handbook of developmental and life-course criminology (p. 149). Oxford University Press. [ Google Scholar ]
Menard, S., Morris, R. G., Gerber, J., & Covey, H. C. (2011). Distribution and correlates of self-reported crimes of trust . Deviant Behavior , 32 ( 10 ), 877–917. 10.1080/01639625.2010.514221 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Murphy, K., & Harris, N. (2007). Shaming, shame and recidivism: A test of reintegrative shaming theory in the white-collar crime context . British Journal of Criminology , 47 ( 6 ), 900–917. 10.1093/bjc/azm037 [ CrossRef ] [ Google Scholar ]
Naso, R. C. (2012). When money and morality collide: White-collar crime and the paradox of integrity . Psychoanalytic Psychology , 29 ( 2 ), 241–254. 10.1037/a0024499 [ CrossRef ] [ Google Scholar ]
Naylor, R. T. (2017). 2. Predators, parasites, or free-market pioneers: Reflections on the nature and analysis of profit-driven crime. In Critical reflections on transnational organized crime, money laundering, and corruption (pp. 35–54.). University of Toronto Press. [ Google Scholar ]
Nee, C., Button, M., Shepherd, D., Blackbourn, D., & Leal, S. (2019). The psychology of the corrupt: some preliminary findings . Journal of Financial Crime , 26 ( 2 ), 488–495. 10.1108/JFC-03-2018-0032 [ CrossRef ] [ Google Scholar ]
Ogunbanjo, G. A., & van Bogaert, D. K. (2013). Ethics in health care: Healthcare fraud . South African Family Practice , 55 ( 1 ), S10–S3. 10.1080/20786204.2013.10874314 [ CrossRef ] [ Google Scholar ]
Passas, N. (2005). Lawful but awful: ‘Legal corporate crimes . The Journal of Socio-Economics , 34 ( 6 ), 771–786. 10.1016/j.socec.2005.07.024 [ CrossRef ] [ Google Scholar ]
Paternoster, R., & Simpson, S. (1996). Sanction threats and appeals to morality: Testing a rational choice model of corporate crime . Law & Society Review , 30 ( 3 ), 549–583. 10.2307/3054128 [ CrossRef ] [ Google Scholar ]
Payne, B. K. (2003). Incarcerating white-collar offenders: The prison experience and beyond . Charles C Thomas Publisher. [ Google Scholar ]
Perri, F. S. (2011). White-collar criminals: The 'kinder, gentler' offender? Journal of Investigative Psychology and Offender Profiling , 8 ( 3 ), 217–241. 10.1002/jip.140 [ CrossRef ] [ Google Scholar ]
Perri, F. S. (2015). Red collar crime . International Journal of Psychological Studies , 8 ( 1 ), 61–84. 10.5539/ijps.v8n1p61 [ CrossRef ] [ Google Scholar ]
Perri, F. S., & Brody, R. G. (2011). The Sallie Rohrbach story: Lessons for auditors and fraud examiners . Journal of Financial Crime , 18 ( 1 ), 93–104. 10.1108/13590791111098825 [ CrossRef ] [ Google Scholar ]
Perri, F. S., & Lichtenwald, T. G. (2008). The arrogant chameleons: Exposing fraud-detection homicide . Forensic Examiner , 17 ( 1 ), 52–69. [ Google Scholar ]
Perri, F. S., Lichtenwald, T. G., & Mieczkowska, E. M. (2014). Sutherland, Cleckley and beyond: White-collar crime and psychopathy . International Journal of Psychological Studies , 6 ( 4 ), 71. 10.5539/ijps.v6n4p71 [ CrossRef ] [ Google Scholar ]
Perri, F., & Lichtenwald, T. (2007). Fraud detection homicide: A proposed FBI crime classification . Forensic Examiner , 16 ( 4 ), 18–29. [ Google Scholar ]
Piff, P. K., Stancato, D. M., & Horberg, E. (2016). Wealth and wrongdoing: Social class differences in ethical reasoning and behavior. In van Prooijen J.-W. & van Lange P. A. M. (Eds.), Cheating, corruption, and concealment: The roots of dishonesty (pp. 185–207). Cambridge University Press. [ Google Scholar ]
Piquero, N. L. (2012). The only thing we have to fear is fear itself: Investigating the relationship between fear of falling and white-collar crime . Crime & Delinquency , 58 ( 3 ), 362–379. 10.1177/0011128711405005 [ CrossRef ] [ Google Scholar ]
Piquero, N. L. (2018). White-collar crime is crime: Victims hurt just the same . Criminology & Public Policy , 17 ( 3 ), 595–600. 10.1111/1745-9133.12384 [ CrossRef ] [ Google Scholar ]
Piquero, N. L., Tibbetts, S. G., & Blankenship, M. B. (2005). Examining the role of differential association and techniques of neutralization in explaining corporate crime . Deviant Behavior , 26 ( 2 ), 159–188. 10.1080/01639620590881930 [ CrossRef ] [ Google Scholar ]
Piquero, N. L., Vieraitis, L. M., Piquero, A. R., Tibbetts, S. G., & Blankenship, M. (2013). The interplay of gender and ethics in corporate offending decision-making . Journal of Contemporary Criminal Justice , 29 ( 3 ), 385–398. 10.1177/1043986213496379 [ CrossRef ] [ Google Scholar ]
Pollack, H., & Smith, A. B. (1983). White-collar v. street crime sentencing disparity: How judges see the problem . Judicature , 67 , 175. [ Google Scholar ]
Ponzi v. Fessenden , 258 U.S. 254 (1922). [ Google Scholar ]
Poortinga, E., Lemmen, C., & Jibson, M. D. (2006). A case control study: White‐collar defendants compared with defendants charged with other nonviolent theft . Journal of the American Academy of Psychiatry and the Law Online , 34 ( 1 ), 82–89. [ PubMed ] [ Google Scholar ]
Pratt, T. C., Cullen, F. T., Sellers, C. S., Thomas Winfree, L., Madensen, T. D., Daigle, L. E., Fearn, N. E., & Gau, J. M. (2010). The empirical status of social learning theory: A meta‐analysis . Justice Quarterly , 27 ( 6 ), 765–802. 10.1080/07418820903379610 [ CrossRef ] [ Google Scholar ]
Price, M., & Norris, D. (2009). White-collar crime: Corporate and securities and commodities fraud . Journal of the American Academy of Psychiatry and the Law , 37 ( 4 ), 538–544. [ PubMed ] [ Google Scholar ]
Price, M., & Norris, D. M. (2009). Health care fraud: Physicians as white collar criminals? Journal of the American Academy of Psychiatry and the Law , 37 , 286–289. [ PubMed ] [ Google Scholar ]
Ragatz, L. L., Fremouw, W., & Baker, E. (2012). The psychological profile of white-collar offenders: Demographics, criminal thinking, psychopathic traits, and psychopathology . Criminal Justice and Behavior , 39 ( 7 ), 978–997. 10.1177/0093854812437846 [ CrossRef ] [ Google Scholar ]
Ragatz, L., & Fremouw, W. (2010). A critical examination of research on the psychological profiles of white-collar criminals . Journal of Forensic Psychology Practice , 10 ( 5 ), 373–402. 10.1080/15228932.2010.489846 [ CrossRef ] [ Google Scholar ]
Raine, A., Laufer, W. S., Yang, Y., Narr, K. L., Thompson, P., & Toga, A. W. (2012). Increased executive functioning, attention, and cortical thickness in white-collar criminals . Human Brain Mapping , 33 ( 12 ), 2932–2940. 10.1002/hbm.21415 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Rebovich, D. (2021). The changing face of financial crime: New technologies, new offenders, new victims, and new strategies for prevention and control . Taylor & Francis. [ Google Scholar ]
Reiman, J., & Leighton, P. (2016). The rich get richer and the poor get prison: Ideology, class, and criminal justice . Taylor & Francis. [ Google Scholar ]
Ribeiro, R., Guedes, I. S., & Cruz, J. N. (2019). White-collar offenders vs. common offenders: a comparative study on personality traits and self-control . Crime, Law and Social Change , 72 ( 5 ), 607–622. 10.1007/s10611-019-09844-7 [ CrossRef ] [ Google Scholar ]
Ross, E. A. (1907). Sin and society: An analysis of latter-day iniquity . Mifflin. [ Google Scholar ]
Ruggiero, V. (2017). Dirty money: On financial delinquency . OUP Catalogue. [ Google Scholar ]
Ruhland, E. L., & Selzer, N. (2020). Gender differences in white-collar offending and supervision . Criminal Justice Studies , 33 ( 1 ), 13–30. 10.1080/1478601X.2020.1709950 [ CrossRef ] [ Google Scholar ]
Sachs, M. V. (2001). Harmonizing Civil and Criminal Enforcement of Federal Regulatory Statutes: The Case of the Securities Exchange Act of 1934 . University of Illinois Law Review , 2001 (4), 1025. [ Google Scholar ]
Sariaslan, A., Arseneault, L., Larsson, H., Lichtenstein, P., & Fazel, S. (2020). Risk of subjection to violence and perpetration of violence in persons with psychiatric disorders in Sweden . JAMA Psychiatry , 77 ( 4 ), 359–367. 10.1001/jamapsychiatry.2019.4275 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Schoepfer, A., Piquero, N. L., & Langton, L. (2014). Low self-control versus the desire-for-control: An empirical test of white-collar crime and conventional crime . Deviant Behavior , 35 ( 3 ), 197–214. 10.1080/01639625.2013.834758 [ CrossRef ] [ Google Scholar ]
Senate Economics References Committee. (2017). ‘Lifting the fear and suppressing the greed’: Penalties for white-collar crime and corporate and financial misconduct in Australia (Report No. 1760105449). Senate Economics References Committee. [ Google Scholar ]
Severson, R. E., Kodatt, Z. H., & Burruss, G. W. (2019). Explaining white‐collar crime: Individual‐level theories. In The handbook of white‐collar crime (pp. 159–174). Wiley & Sons. [ Google Scholar ]
Shover, N., & Hochstetler, A. (2005). Choosing white-collar crime . Cambridge University Press. [ Google Scholar ]
Simon, R. J. (1996). Types of criminal acts women are likely to commit . Psychological Reports , 79 ( 2 ), 669–670. 10.2466/pr0.1996.79.2.669 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Simpson, S. S. (2019). Reimagining Sutherland 80 years after white‐collar crime . Criminology , 57 ( 2 ), 189–207. 10.1111/1745-9125.12206 [ CrossRef ] [ Google Scholar ]
Simpson, S. S., & Piquero, N. L. (2002). Low self-control, organizational theory, and corporate crime . Law & Society Review , 36 ( 3 ), 509–548. 10.2307/1512161 [ CrossRef ] [ Google Scholar ]
Smith, R. G., & Budd, C. (2009). Consumer fraud in Australia: Costs, rates and awareness of the risks in 2008 . Trends and Issues in Crime and Criminal Justice , ( 382 ), 1–6. [ Google Scholar ]
Stadler, W. A., & Benson, M. L. (2012). Revisiting the guilty mind: The neutralization of white-collar crime . Criminal Justice Review , 37 ( 4 ), 494–511. 10.1177/0734016812465618 [ CrossRef ] [ Google Scholar ]
Stadler, W. A., Benson, M. L., & Cullen, F. T. (2013). Revisiting the special sensitivity hypothesis: The prison experience of white-collar inmates . Justice Quarterly , 30 ( 6 ), 1090–1114. 10.1080/07418825.2011.649296 [ CrossRef ] [ Google Scholar ]
Sutherland, E. H. (1983). White collar crime: The uncut version . Yale University Press. [ Google Scholar ]
The Government of India v Nirav Deepak Modi (2021). https://www.judiciary.uk/wp-content/uploads/2021/02/GOI-v.-Nirav-Modi-judgment.pdf
Timofeyev, Y., & Jakovljevic, M. (2020). Fraudster’s and victims’ profiles and loss predictors’ hierarchy in the mental healthcare industry in the US . Journal of Medical Economics , 23 ( 10 ), 1111–1122. 10.1080/13696998.2020.1801454 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Torrey, E. F., Jaffe, D., Director, M., Geller, J. L., & Lamb, R. (2015). Fraud, waste and excess profits: The fate of money intended to treat people with serious mental illness . Mental Illness Policy. [ Google Scholar ]
Turner, M. J. (2014). An investigation of big five personality and propensity to commit white-collar crime. Advances in accounting behavioral research, Vol 17. Advances in accounting behavioral research (pp. 57–94). Emerald Group Publishing. [ Google Scholar ]
United States Department of Justice (2020). United States V. Bernard L. Madoff and related cases . https://www.justice.gov/usao-sdny/programs/victim-witness-services/united-states-v-bernard-l-madoff-and-related-cases
van Onna, J. H. R., & Denkers, A. J. M. (2019). Social bonds and white-collar crime: A two-study assessment of informal social controls in white-collar offenders . Deviant Behavior , 40 ( 10 ), 1206–1225. 10.1080/01639625.2018.1472936 [ CrossRef ] [ Google Scholar ]
Van Onna, J. H., Van Der Geest, V. R., Huisman, W., & Denkers, A. J. (2014). Criminal trajectories of white-collar offenders . Journal of Research in Crime and Delinquency , 51 ( 6 ), 759–784. 10.1177/0022427814531489 [ CrossRef ] [ Google Scholar ]
Van Slyke, S. R., & Bales, W. D. (2013). Gender dynamics in the sentencing of white-collar offenders . Criminal Justice Studies , 26 ( 2 ), 168–196. 10.1080/1478601X.2012.729707 [ CrossRef ] [ Google Scholar ]
Vendemia, J. M. C., & Nye, J. M. (2018). The neuroscience of deception. In Beech A. R., Carter A. J., Mann R. E., & Rotshtein P. (Eds.), The wiley blackwell handbook of forensic neuroscience ( 2 Volume Set). John Wiley & Sons. [ Google Scholar ]
Vieraitis, L. M., Piquero, N. L., Piquero, A. R., Tibbetts, S. G., & Blankenship, M. (2012). Do women and men differ in their neutralizations of corporate crime? Criminal Justice Review , 37 ( 4 ), 478–493. 10.1177/0734016812465745 [ CrossRef ] [ Google Scholar ]
Walters, G. D., & Geyer, M. D. (2004). Criminal Thinking and Identity in Male White-Collar Offenders . Criminal Justice and Behavior , 31 ( 3 ), 263–281. 10.1177/0093854803262508 [ CrossRef ] [ Google Scholar ]
Watkins, J. C. (1977). White-collar crime, legal sanctions, and social control . Crime & Delinquency , 23 ( 3 ), 290–303. 10.1177/001112877702300305 [ CrossRef ] [ Google Scholar ]
Weidenfeld, K., & Spire, A. (2017). Punishing tax offenders in France and Great Britain: two criminal policies . Journal of Financial Crime , 24 ( 4 ), 574–588. 10.1108/JFC-05-2016-0030 [ CrossRef ] [ Google Scholar ]
Weisburd, D., Waring, E., & Chayet, E. F. (2001). White-collar crime and criminal careers . Cambridge University Press. [ Google Scholar ]
Weisburd, D., Waring, E., & Chayet, E. S. (1995). Deterrence in a sample of offenders convicted of white collar crimes . Criminology , 33 ( 4 ), 587–607. [ Google Scholar ]
Weisburd, D., Wheeler, S., Waring, E., & Bode, N. (1991). Crimes of the middle classes: White-collar offenders in the federal courts . Yale University Press. [ Google Scholar ]
Wheeler, S., & Rothman, M. L. (1982). The organization as weapon in white-collar crime . Michigan Law Review , 80 ( 7 ), 1403–1426. 10.2307/1288554 [ CrossRef ] [ Google Scholar ]
Wheeler, S., Mann, K., & Sarat, A. (1988). Sitting in judgment: The sentencing of white-collar criminals . Yale University Press. [ Google Scholar ]
Wheeler, S., Weisburd, D., & Bode, N. (1982). Sentencing the white-collar offender: Rhetoric and reality . American Sociological Review , 47 ( 5 ), 641–659. 10.2307/2095164 [ CrossRef ] [ Google Scholar ]
Wheeler, S., Weisburd, D., Waring, E., & Bode, N. (1987). White collar crimes and criminals . American Criminal Law Review , 25 , 331. [ Google Scholar ]

White-Collar Research

First Online: 18 January 2020

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Petter Gottschalk 2

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This chapter presents results from surveys among business school students concerning the theory of convenience applied to white-collar offenders. Business school students are relevant for this research, as they will occupy positions in the future where they can commit financial crime, prevent crime, blow the whistle on suspicions or become victims of such crime. The survey presented in this chapter shows that 11 out of 109 claims in the research literature were not supported by the business school students. Among them, we find: social care for others is a strong motive for white-collar crime, white-collar offenders are too powerful to be blamed for misconduct and crime, signals regarding white-collar crime are always weak, and white-collar offenders are relatively more sensitive in prison.

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Adler, P. S., & Kwon, S. W. (2002). Social capital: Prospects for a new concept. Academy of Management Review, 27 (1), 17–40.

Google Scholar

Agnew, R. (2005). Pressured into crime: An overview of general strain theory . Oxford: Oxford University Press, UK.

Agnew, R. (2012). Reflection on “A revised strain theory of delinquency”. Social Forces, 91 (1), 33–38.

Article Google Scholar

Agnew, R. (2014). Social concern and crime: Moving beyond the assumption of simple self-interest. Criminology, 52 (1), 1–32.

Aguilera, R. V., Judge, W. Q., & Terjesen, S. A. (2018). Corporate governance deviance. Academy of Management Review, 43 (1), 87–109.

Aguilera, R. V., & Vadera, A. K. (2008). The dark side of authority: Antecedents, mechanisms, and outcomes of organizational corruption. Journal of Business Ethics, 77 , 431–449.

Andrade, J. A. (2015). Reconceptualising whistleblowing in a complex world. Journal of Business Ethics, 128 , 321–335.

Arjoon, S. (2008). Slippery when wet: The real risk in business. Journal of Markets & Morality, Spring, 11 (1), 77–91.

Baird, J. E., & Zelin, R. C. (2009). An examination of the impact of obedience pressure on perceptions of fraudulent acts and the likelihood of committing occupational fraud. Journal of Forensic Studies in Accounting and Business, 1 (1), 1–14.

Barry, B., & Stephens, C. U. (1998). Objections to an objectivist approach to integrity. Academy of Management Review, 23 (1), 162–169.

Barton, H. (2004). Cultural reformation: A case for intervention within the police service. International Journal of Human Resources Development and Management, 4 (2), 191–199.

Benartzi, S., Beshears, J., Milkman, K. L., Sunstein, C. R., Thaler, R. H., Shankar, M., Tucker-Ray, W., Congdon, W. J., & Galing, S. (2017). Should governments invest more in nudging? Psychological Science, 28 (8), 1041–1055.

Benson, M. L., & Simpson, S. S. (2015). Understanding white-collar crime: An opportunity perspective . New York: Routledge.

Bernburg, J. G., Krohn, M. D., & Rivera, C. J. (2006). Official labeling, criminal embeddedness, and subsequent delinquency. Journal of Research in Crime and Delinquency, 43 (1), 67–88.

Bjørkelo, B., Einarsen, S., Nielsen, M. B., & Matthiesen, S. B. (2011). Silence is golden? Characteristics and experiences of self-reported whistleblowers. European Journal of Work and Organizational Psychology, 20 (2), 206–238.

Bjørnestad, S. (2016). To varslere fra PwC dømt til betinget fengsel i Luxembourg (two whistleblowers from PwC sentenced to prison in Luxembourg) , daily Norwegian newspaper Aftenposten reporting also from the Panama papers, Thursday, June 30, page 10.

Blickle, G., Schlegel, A., Fassbender, P., & Klein, U. (2006). Some personality correlates of business white-collar crime. Applied Psychology: An International Review, 55 (2), 220–233.

Bosse, D. A., & Phillips, R. A. (2016). Agency theory and bounded self-interest. Academy of Management Review, 41 (2), 276–297.

Bradshaw, E. A. (2015). “Obviously, we’re all oil industry”: The criminogenic structure of the offshore oil industry. Theoretical Criminology, 19 (3), 376–395.

Brightman, H. J. (2009). Today’s white-collar crime. In Legal, investigative, and theoretical perspectives . New York: Routledge, Taylor & Francis Group.

Brown, A. J., & Olsen, J. (2008). Internal witness support: the unmet challenge. In A. J. Brown (Ed.), Whistleblowing in the Australian Public Sector. Enhancing the theory and practice of internal witness management in public sector organisations (pp. 203–232). Canberra: ANU Press, Australian National University.

Chapter Google Scholar

Brown, J. O., Hays, J., & Stuebs, M. T. (2016). Modeling accountant whistleblowing intentions: Applying the theory of planned behavior and the fraud triangle. Accounting and the Public Interest, 16 (1), 28–56.

Bucy, P. H., Formby, E. P., Raspanti, M. S., & Rooney, K. E. (2008). Why do they do it? The motives, mores, and character of white collar criminals. St. John’s Law Review, 82 , 401–571.

Bussmann, K. D., Niemeczek, A., & Vockrodt, M. (2018). Company culture and prevention of corruption in Germany, China and Russia. European Journal of Criminology, 15 (3), 255–277.

Campbell, J. L., & Göritz, A. S. (2014). Culture corrupts! A qualitative study of organizational culture in corrupt organizations. Journal of Business Ethics, 120 , 291–311.

Chang, J. J., Lu, H. C., & Chen, M. (2005). Organized crime or individual crime? Endogeneous size of a criminal organization and the optimal law enforcement. Economic Inquiry, 43 (3), 661–675.

Chatterjee, A., & Pollock, T. G. (2017). Master of puppets: How narcissistic CEOs construct their professional worlds. Academy of Management Review, 42 (4), 703–725.

Chattopadhyay, P., Glick, W. H., & Huber, G. P. (2001). Organizational actions in response to threats and opportunities. Academy of Management Journal, 44 (5), 937–955.

Chrisman, J. J., Chua, J. H., Kellermanns, F. W., & Chang, E. P. C. (2007). Are family managers agents or stewards? An exploratory study in privately held family firms. Journal of Business Research, 60 (10), 1030–1038.

Cleff, T., Naderer, G., & Volkert, J. (2013). Motives behind white-collar crime: Results of a quantitative study in Germany. Society and Business Review, 8 (2), 145–159.

Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44 , 588–608.

Cohen, S. (2001). States of denial: Knowing about atrocities and suffering . Cambridge: Polity Press.

Comey, J. B. (2009). Go directly to prison: White collar sentencing after the Sarbanes-Oxley act. Harvard Law Review, 122 , 1728–1749.

Craig, J. M., & Piquero, N. L. (2016). The effects of low self-control and desire-for-control on white-collar offending: A replication. Deviant Behavior, 37 (11), 1308–1324.

Craig, J. M., & Piquero, N. L. (2017). Sensational offending: An application of sensation seeking to white-collar and conventional crimes. Crime & Delinquency, 63 (11), 1363–1382.

Dion, M. (2008). Ethical leadership and crime prevention in the organizational setting. Journal of Financial Crime, 15 (3), 308–319.

Dodge, M. (2009). Women and white-collar crime . Saddle River: Prentice Hall.

Donk, D. P. V., & Molloy, E. (2008). From organizing as projects, to projects as organizations. International Journal of Project Management, 26 , 129–137.

Eberly, M. B., Holley, E. C., Johnson, M. D., & Mitchell, T. R. (2011). Beyond internal and external: A dyadic theory of relational attributions. Academy of Management Review, 36 (4), 731–753.

Eisenhardt, K. M. (1985). Control: Organizational and economic approaches. Management Science, 31 (2), 134–149.

Engdahl, O. (2015). White-collar crime and first-time adult-onset offending: Explorations in the concept of negative life events as turning points. International Journal of Law, Crime and Justice, 43 (1), 1–16.

Fanelli, A., & Misangyi, V. F. (2006). Bringing out charisma: CEO charisma and external stakeholders. Academy of Management Review, 31 (4), 1049–1061.

Fehr, R., Yam, K. C., & Dang, C. (2015). Moralized leadership: The construction and consequences of ethical leader perceptions. Academy of Management Review, 40 (2), 182–209.

Ferraro, F., Pfeffer, J., & Sutton, R. I. (2005). Economics language and assumptions: How theories can become self-fulfilling. Academy of Management Review, 30 (1), 8–24.

Friedrichs, D. O., Schoultz, I., & Jordanoska, A. (2018). Edwin H. Sutherland, Routledge key thinkers in criminology . London: Routledge.

Froggio, G., & Agnew, R. (2007). The relationship between crime and “objective” versus “subjective” strains. Journal of Criminal Justice, 35 , 81–87.

Füss, R., & Hecker, A. (2008). Profiling white-collar crime. Evidence from German-speaking countries. Corporate Ownership & Control, 5 (4), 149–161.

Galvin, B. M., Lange, D., & Ashforth, B. E. (2015). Narcissistic organizational identification: Seeing oneself as central to the organization’s identity. Academy of Management Review, 40 (2), 163–181.

Gao, P., & Zhang, G. (2019). Accounting manipulation, peer pressure, and internal control. The Accounting Review, 94 (1), 127–151.

Geest, V. R., Weisburd, D., & Blokland, A. A. J. (2017). Developmental trajectories of offenders convicted of fraud: A follow-up to age 50 in a Dutch conviction cohort. European Journal of Criminology, 14 (5), 543–565.

Glasø, L., & Einarsen, S. (2008). Emotion regulation in leader-follower relationships. European Journal of Work and Organizational Psychology, 17 (4), 482–500.

Glasø, L., Einarsen, S., Matthiesen, S. B., & Skogstad, A. (2010). The dark side of leaders: A representative study of interpersonal problems among leaders. Scandinavian Journal of Organizational Psychology, 2 (2), 3–14.

Glasø, L., Ekerholt, K., Barman, S., & Einarsen, S. (2006). The instrumentality of emotion in leader-subordinate relationships. International Journal of Work Organisation and Emotion, 1 (3), 255–276.

Goldstraw-White, J. (2012). White-collar crime: Accounts of offending behavior . London: Palgrave Macmillan.

Book Google Scholar

Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime . Stanford: Stanford University Press.

Gottschalk, P. (2017). Organizational opportunity and deviant behavior: Convenience in white-collar crime . Cheltenham: Edward Elgar Publishing.

Gottschalk, P. (2019). Convenience triangle in White-collar crime – Case studies of fraud examinations . Cheltenham: Edward Elgar Publishing.

Gottschalk, P., & Tcherni-Buzzeo, M. (2016). Reasons for gaps in crime reporting: The case of white-collar criminals investigated by private fraud examiners in Norway. Deviant Behavior, 38 (3), 267–281.

Hamilton, S., & Micklethwait, A. (2006). Greed and corporate failure: The lessons from recent disasters . Basingstoke: Palgrave Macmillan.

Hansen, L. L. (2009). Corporate financial crime: Social diagnosis and treatment. Journal of Financial Crime, 16 (1), 28–40.

Hassink, H., Vries, M., & Bollen, L. (2007). A content analysis of whistleblowing policies of leading European companies. Journal of Business Ethics, 75 , 25–44.

Hatch, M. J. (1997). Organizational theory – Modern, symbolic, and postmodern perspectives . Oxford: Oxford University Press.

Hefendehl, R. (2010). Addressing white collar crime on a domestic level. Journal of International Criminal Justice, 8 , 769–782.

Hirschi, T., & Gottfredson, M. (1987). Causes of white-collar crime. Criminology, 25 (4), 949–974.

Hoel, H., Glasø, L., Hetland, J., Cooper, C. L., & Einarsen, S. (2010). Leadership styles as predictors of self-reported and observed workplace bullying. British Journal of Management, 21 , 453–468.

Hoffmann, J. P. (2002). A contextual analysis of differential association, social control, and strain theories of delinquency. Social Forces, 81 (3), 753–785.

Hollow, M. (2014). Money, morals and motives. Journal of Financial Crime, 21 (2), 174–190.

Holt, R., & Cornelissen, J. (2014). Sensemaking revisited. Management Learning, 45 (5), 525–539.

Huang, L., & Knight, A. P. (2017). Resources and relationships in entrepreneurship: An exchange theory of the development and effects of the entrepreneur-investor relationship. Academy of Management Review, 42 (1), 80–102.

Huisman, W., & Erp, J. (2013). Opportunities for environmental crime. British Journal of Criminology, 53 , 1178–1200.

Huseman, R. C., Hatfield, J. D., & Miles, E. W. (1987). A new perspective on equity theory: The equity sensitivity construct. Academy of Management Review, 12 (2), 222–234.

Jones, S., Lyman, D. R., & Piquero, A. R. (2015). Substance use, personality, and inhibitors: Testing Hirschi’s predictions about the reconceptualization of self-control. Crime & Delinquency, 61 (4), 538–558.

Jonnergård, K., Stafsudd, A., & Elg, U. (2010). Performance evaluations as gender barriers in professional organizations: A study of auditing firms. Gender, Work and Organization, 17 (6), 721–747.

Jordanoska, A. (2018). The social ecology of white-collar crime: Applying situational action theory to white-collar offending. Deviant Behavior, 39 (11), 1427–1449.

Kamerdze, S., Loughran, T., Paternoster, R., & Sohoni, T. (2014). The role of affect in intended rule breaking: Extending the rational choice perspective. Journal of Research in Crime and Delinquency, 51 (5), 620–654.

Kang, E., & Thosuwanchot, N. (2017). An application of Durkheim’s four categories of suicide to organizational crimes. Deviant Behavior, 38 (5), 493–513.

Kaptein, M., & Helvoort, M. (2019). A model of neutralization techniques. Deviant Behavior, 40 (10), 1260–1285.

Karim, K. E., & Siegel, P. H. (1998). A signal detection theory approach to analyzing the efficiency and effectiveness of auditing to detect management fraud. Managerial Auditing Journal, 13 (6), 367–375.

Katz, J. (1979). Concerted ignorance: The social construction of cover-up. Urban Life, 8 (3), 295–316.

Keaveney, S. M. (2008). The blame game: An attribution theory approach to marketer-engineer conflict in high-technology companies. Industrial Marketing Management, 37 , 653–663.

Keil, M., Tiwana, A., Sainsbury, R., & Sneha, S. (2010). Toward a theory of whistleblowing intentions: A benefit-cost differential perspective. Decision Sciences, 41 (4), 787–812.

Kempa, M., Carrier, R., Wood, J., & Shearing, c. (2009). Reflections on the evolving concept of ‘private policing’. European Journal on Criminal Policy and Research, 7 , 197–223.

Kölbel, R., & Herold, N. (2019). Whistle-blowing from the perspective of general strain theory. Deviant Behavior, 40 (2), 139–155.

Kostova, T., Roth, K., & Dacin, M. T. (2008). Institutional theory in the study of multinational corporations: A critique and new directions. Academy of Management Review, 33 (4), 994–1006.

Kouchaki, M., & Desai, S. D. (2015). Anxious, threatened, and also unethical: How anxiety makes individuals feel threatened and commit unethical acts. Journal of Applied Psychology, 100 (2), 360–375.

Kroneberg, C., & Schulz, S. (2018). Revisiting the role of self-control in situational action theory. European Journal of Criminology, 15 (1), 56–76.

Lange, D. (2008). A multidimensional conceptualization of organizational corruption control. Academy of Management Journal, 33 (3), 710–729.

Langton, L., & Piquero, N. L. (2007). Can general strain theory explain white-collar crime? A preliminary investigation of the relationship between strain and select white-collar offenses. Journal of Criminal Justice, 35 (1), 1–15.

Lee, F., & Robinson, R. J. (2000). An attributional analysis of social accounts: Implications of playing the blame game. Journal of Applied Social Psychology, 30 (9), 1853–1879.

Leigh, A. C., Foole, D. A., Clark, W. R., & Lewis, J. L. (2010). Equity sensitivity: A triadic measure and outcome/input perspectives. Journal of Managerial Issues, 22 (3), 286–305.

Lewis, D., and Vandekerckhove, W. (2018). Trade unions and the whistleblowing process in the UK: An opportunity for strategic expansion? Journal of Business Ethics, 148 , 835–845.

Leonard, W. N., & Weber, M. G. (1970). Automakers and dealers: A study of criminogenic market forces. Law & Society Review, 4 (3), 407–424.

Logan, M. W., Morgan, M. A., Benson, M. L., & Cullen, F. T. (2019). Coping with imprisonment: Testing the special sensitivity hypothesis for white-collar offenders. Justice Quarterly, 36 (2), 225–254.

Lopez-Rodriguez, S. (2009). Environmental engagement, organizational capability and firm performance. Corporate Governance, 9 (4), 400–408.

Lyman, M. D., & Potter, G. W. (2007). Organized crime (4th ed.). Uppler Saddle River: Pearson Prentice Hall.

Martin, J., & Peterson, M. M. (1987). Two-tier wage structures: Implications for equity theory. Academy of Management Journal, 30 (2), 297–315.

Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50 (4), 370–396.

Mathieu, C. (2013). Personality and job satisfaction: The role of narcissism. Personality and Individual Differences, 55 (6), 650–654.

Mawritz, M. B., Greenbaum, R. L., Butts, M. M., & Graham, K. A. (2017). I just can’t control myself: A self-regulation perspective on the abuse of deviant employees. Academy of Management Journal, 60 (4), 1482–1503.

McElwee, G., & Smith, R. (2015). Towards a nuanced typology of illegal entrepreneurship: A theoretical and conceptual overview. In G. McElwee & R. Smith (Eds.), Exploring criminal and illegal enterprise: New perspectives on research, policy & practice: Contemporary issues in entrepreneurship research (Vol. 5). Bingley: Emerald.

Mears, D. P., & Cochran, J. C. (2018). Progressively tougher sanctioning and recidivism: Assessing the effects of different types of sanctions. Journal of Research in Crime and Delinquency, 55 (2), 194–241.

Menon, S., & Siew, T. G. (2012). Key challenges in tackling economic and cybercrimes – Creating a multilateral platform for international co-operation. Journal of Money Laundering Control, 15 (3), 243–256.

Mesmer-Magnus, J. R., & Viswesvaran, C. (2005). Whistleblowing in an organization: An examination of correlates of whistleblowing intentions, actions, and retaliation. Journal of Business Ethics, 62 (3), 266–297.

Miceli, M. P., & Near, J. P. (2013). An international comparison of the incidence of public sector whistle-blowing and the prediction of retaliation: Australia, Norway, and the US. Australian Journal of Public Administration, 72 (4), 433–446.

Miceli, M. P., Near, J. P., & Dworkin, T. M. (2009). A word to the wise: How managers and policy-makers can encourage employees to report wrongdoing. Journal of Business Ethics, 86 , 379–396.

Mpho, B. (2017). Whistleblowing: What do contemporary ethical theories say? Studies in Business and Economics, 12 (1), 19–28.

Murphy, P. R., & Dacin, M. T. (2011). Psychological pathways to fraud: Understanding and preventing fraud in organizations. Journal of Business Ethics, 101 , 601–618.

Murphy, P. R., & Free, C. (2015). Broadening the fraud triangle: Instrumental climate and fraud. Behavioral Research in Accounting, 28 (1), 41–56.

Naylor, R. T. (2003). Towards a general theory of profit-driven crimes. British Journal of Criminology, 43 , 81–101.

Ngo, F. T., & Paternoster, R. (2016). Toward an understanding of the emotional and behavioral reactions to stalking: A partial test of general strain theory. Crime & Delinquency, 62 (6), 703–727.

NOU. (2018). Varsling – verdier og vern (Whistleblowing – values and protection) , Norges offentlige utredninger (Norway’s public inquiries) no. 6, Oslo, Norway.

Obodaru, O. (2017). Forgone, but not forgotten: Toward a theory of forgone professional identities. Academy of Management Journal, 60 (2), 523–553.

Olafsen, A. H. (2017). The implications of need-satisfying work climates on state mindfulness in a longitudinal analysis of work outcomes. Motivation and Emotion, 41 (1), 22–37.

Olafsen, A. H., Niemiec, C. P., Halvari, H., Deci, E. L., & Williams, G. C. (2017). On the dark side of work: A longitudinal analysis using self-determination theory. European Journal of Work and Organizational Psychology, 26 (2), 275–285.

Onna, J. H. R., & Denkers, A. J. M. (2019). Social bonds and white-collar crime: A two-study assessment of informal social controls in white-collar offenders. Deviant Behavior, 40 (10), 1206–1225.

Oslo Economics. (2017). Verdien av varsling (the value of whistleblowing) . Oslo: Oslo Economics.

Ouimet, G. (2010). Dynamics of narcissistic leadership in organizations. Journal of Managerial Psychology, 25 (7), 713–726.

Patel, P. C., & Cooper, D. (2014). Structural power equality between family and nonfamily TMT members and the performance of family firms. Academy of Management Journal, 57 (6), 1624–1649.

Paternoster, R., Jaynes, C. M., & Wilson, T. (2018). Rational choice theory and interest in the “fortune of others”. Journal of Research in Crime and Delinquency, 54 (6), 847–868.

Petrocelli, M., Piquero, A. R., & Smith, M. R. (2003). Conflict theory and racial profiling: An empirical analysis of police traffic stop data. Journal of Criminal Justice, 31 (1), 1–11.

Pillay, S., & Kluvers, R. (2014). An institutional theory perspective on corruption: The case of a developing democracy. Financial Accountability & Management, 30 (1), 95–119.

Pinto, J., Leana, C. R., & Pil, F. K. (2008). Corrupt organizations or organizations of corrupt individuals? Two types of organization-level corruption. Academy of Management Review, 33 (3), 685–709.

Piquero, N. L. (2012). The only thing we have to fear is fear itself: Investigating the relationship between fear of falling and white-collar crime. Crime and Delinquency, 58 (3), 362–379.

Piquero, N. L., Schoepfer, A., & Langton, L. (2010). Completely out of control or the desire to be in complete control? How low self-control and the desire for control relate to corporate offending. Crime & Delinquency, 56 (4), 627–647.

Pontell, H. N., Black, W. K., & Geis, G. (2014). Too big to fail, too powerful to jail? On the absence of criminal prosecutions after the 2008 financial meltdown. Crime, Law and Social Change, 61 (1), 1–13.

Pratt, T. C., & Cullen, F. T. (2005). Assessing macro-level predictors and theories of crime: A meta-analysis. Crime and Justice, 32 , 373–450.

Ramoglou, S., & Tsang, E. W. K. (2016). A realist perspective of entrepreneurship: Opportunities as propensities. Academy of Management Review, 41 , 410–434.

Rehg, M. T., Miceli, M. P., Near, J. P., & Scotter, J. R. V. (2009). Antecedents and outcomes of retaliation against whistleblowers: Gender differences and power relationships. Organization Science, 19 (2), 221–240.

Reyns, B. W. (2013). Online routines and identity theft victimization: Further expanding routine activity theory beyond direct-contact offenses. Journal of Research in Crime and Delinquency, 50 , 216–238.

Rodriguez, P., Uhlenbruck, K., & Eden, L. (2005). Government corruption and the entry strategies of multinationals. Academy of Management Review, 30 (2), 383–396.

Roehling, M. V., Roehling, P. V., & Boswell, W. R. (2010). The potential role of organizational setting in creating “entitled” employees: An investigation of the antecedents of equity sensitivity. Employee Responsibilities & Rights Journal, 22 , 133–145.

Salter, C. R., Green, M., Ree, M., Carmody-Bubb, M., & Duncan, P. A. (2009). A study of follower’s personality, implicit leadership perceptions, and leadership ratings. Journal of Leadership Studies, 2 (4), 48–60.

Sampson, R. J., & Laub, J. H. (1993). Crime in the making. Pathways and turning points through life . Cambridge: Harvard University Press.

Schnatterly, K., Gangloff, K. A., & Tuschke, A. (2018). CEO wrongdoing: A review of pressure, opportunity, and rationalization. Journal of Management, 44 (6), 2405–2432.

Schoepfer, A., & Piquero, N. L. (2006). Exploring white-collar crime and the American dream: A partial test of institutional anomie theory. Journal of Criminal Justice, 34 (3), 227–235.

Schwendinger, H., & Schwendinger, J. (2014). Defenders of order or guardians of human rights? Social Justice, 40 (1/2), 87–117.

Shadnam, M., & Lawrence, T. B. (2011). Understanding widespread misconduct in organizations: An institutional theory of moral collapse. Business Ethics Quarterly, 21 (3), 379–407.

Siponen, M., & Vance, A. (2010). Neutralization: New insights into the problem of employee information security policy violations. MIS Quarterly, 34 (3), 487–502.

Skivenes, M., & Trygstad, S. C. (2016). Whistleblowing in local government: An empirical study of contact patterns and whistleblowing in 20 Norwegian municipalities. Scandinavian Political Studies, 39 (3), 264–289.

Slyke, S. V., & Bales, W. D. (2012). A contemporary study of the decision to incarcerate white-collar and street property offenders. Punishment & Society, 14 (2), 217–246.

Smith, O., & Raymen, T. (2018). Deviant leisure: A criminological perspective. Theoretical Criminology, 22 (1), 63–82.

Smith, R. (2009). Understanding entrepreneurial behavior in organized criminals. Journal of Enterprising Communities: People and Places in the Global Economy, 3 (3), 256–268.

Sonnier, B. M., Lassar, W. M., & Lassar, S. S. (2015). The influence of source credibility and attribution of blame on juror evaluation of liability of industry specialist auditors. Journal of Forensic & Investigative Accounting, 7 (1), 1–37.

Srivastava, S. B., & Goldberg, A. (2017). Language as a window into culture. California Management Review, 60 (1), 56–69.

Sutherland, E. H. (1939). White-collar criminality. American Sociological Review, 5 (1), 1–12.

Sutherland, E. H. (1983). White collar crime – The uncut version . New Haven: Yale University Press.

Sykes, G., & Matza, D. (1957). Techniques of neutralization: A theory of delinquency. American Sociological Review, 22 (6), 664–670.

Szalma, J. L., & Hancock, P. A. (2013). A signal improvement to signal detection analysis: Fuzzy SDT on the ROCs. Journal of Experimental Psychology: Human Perception and Performance, 39 (6), 1741–1762.

Thaxton, S., & Agnew, R. (2018). When criminal coping is likely: An examination of conditioning effects in general strain theory. Journal of Quantitative Criminology, 34 , 887–920.

Tonoyan, V., Strohmeyer, R., Habib, M., & Perlitz, M. (2010). Corruption and entrepreneurship: How formal and informal institutions shape small firm behavior in transition and mature market economies. Entrepreneurship: Theory & Practice, 34 (5), 803–831.

Trahan, A., Marquart, J., & Mullings, J. (2005). Fraud and the American dream: Toward an understanding of fraud victimization. Deviant Behavior, 26 (6), 601–620.

Tsahuridu, E. E., & Vandekerckhove, W. (2008). Organizational whistleblowing policies: Making employees responsible or liable? Journal of Business Ethics, 82 , 107–118.

Vandekerckhove, W. (2018). Whistleblowing and information ethics: Facilitation, entropy, and ecopoiesis. Journal of Business Ethics, 152 , 15–25.

Vandekerckhove, W., & Lewis, D. (2012). The content of whistleblowing procedures: A critical review of recent official guidelines. Journal of Business Ethics, 108 , 253–264.

Vandekerckhove, W., & Tsahuridu, E. E. (2010). Risky rescues and the duty to blow the whistle. Journal of Business Ethics, 97 , 365–380.

Victor, B., & Cullen, J. B. (1988). The organizational bases of ethical work climates. Administrative Science Quarterly, 33 , 101–125.

Weick, K. E. (1995). What theory is not, theorizing is. Administrative Science Quarterly, 40 , 385–390.

Welsh, D. T., & Ordonez, L. D. (2014). The dark side of consecutive high performance goals: Linking goal setting, depletion, and unethical behavior. Organizational Behavior and Human Decision Processes, 123 , 79–89.

Welsh, D. T., Ordonez, L. D., Snyder, D. G., & Christian, M. S. (2014). The slippery slope: How small ethical transgressions pave the way for larger future transgressions. Journal of Applied Psychology, 100 (1), 114–127.

Welter, F., Baker, T., Audretsch, D. B., & Gartner, W. B. (2017). Everyday entrepreneurship: A call for entrepreneurship research to embrace entrepreneurial diversity. Entrepreneurship: Theory and Practice, 41 (3), 323–347.

Wheelock, D., Semukhina, O., & Demidov, N. N. (2011). Perceived group threat and punitive attitudes in Russia and the United States. British Journal of Criminology, 51 , 937–959.

Wikstrom, P. O. H., Mann, R. P., & Hardie, B. (2018). Young people’s differential vulnerability to criminogenic exposure: Bridging the gap between people- and place-oriented approaches in the study of crime causation. European Journal of Criminology, 15 (1), 10–31.

Williams, J. W. (2008). The lessons of Enron: Media accounts, corporate crimes, and financial markets. Theoretical Criminology, 12 (4), 471–499.

Williams, M. L., Levi, M., Burnap, P., & Gundur, R. V. (2019). Under the corporate radar: Examining insider business cybercrime victimization through an application of routine activities theory. Deviant Behavior, 40 (9), 1119–1131.

Wood, J., & Alleyne, E. (2010). Street gang theory and research: Where are we now and where do we go from here? Aggression and Violent Behavior, 15 , 100–111.

Yam, K. C., Christian, M. S., Wei, W., Liao, Z., & Nai, J. (2018). The mixed blessing of leader sense of humor: Examining costs and benefits. Academy of Management Journal, 61 (1), 348–369.

Zhu, D. H., & Chen, G. (2015). CEO narcissism and the impact of prior board experience on corporate strategy. Administrative Science Quarterly, 60 (1), 31–65.

Zvi, L., & Elaad, E. (2018). Correlates of narcissism, self-reported lies, and self-assessed abilities to tell and detect lies, tell truths, and believe others. Journal of Investigative Psychology and Offender Profiling, 15 , 271–286.

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'White Collar' Does This Better Than the Other Crime Procedural TV Shows

'White Collar' keeps things interesting and takes the procedure out of procedural.

The Big Picture

White Collar on Netflix offers unique and intriguing mysteries inspired by history and white-collar crimes, setting it apart from typical crime shows.
The dynamic between Peter and Neal is a key strength of White Collar, adding depth and complexity to their unconventional partnership.
Based on a real-life con artist, Neal Caffrey brings charm and intelligence to the series, making White Collar a refreshing and captivating crime drama.

All six seasons of White Collar are streaming now on Netflix, and it's evident why audiences are so captivated by the cons and crime-solving in this hit crime drama series. The show follows the highly intelligent and charismatic con man, Neal Caffrey ( Matt Bomer ), working as a consultant alongside the FBI agent that caught him in the first place, Peter Burke ( Tim DeKay ). With a resurgence in popularity and talks of a reboot in the works , White Collar once again proves that it has a distinct flair that sets it apart from other popular crime procedurals. Not one to be lost among other popular police television series like Law & Order and NCIS , White Collar boasts some of the most interesting, unconventional, and surprisingly worldly mysteries and solutions in each of its episodes, making each season feel like a tasting menu of captivating stories.

White Collar leaves such a distinct impression on audiences because of its sheer variety of puzzles that not only take inspiration from sources not typically found in crime shows, but also demand equally interesting solutions. And if that wasn't enough, the rivals-turned-friends relationship between Peter and Neal that serves as the foundation of the show is one of the most compelling dynamics on television . Suited up with endearing relationships and complex mysteries, White Collar's approach to crime procedural storytelling is exciting, appealing, and just as charming as Neal Caffrey himself.

White Collar

A white-collar criminal agrees to help the FBI catch other white-collar criminals using his expertise as an art and securities thief, counterfeiter, and conman.

'White Collar's Mysteries Combine History and Heisting

Breaking from the mold of other procedurals, most of the cases Peter and Neal tackle in White Collar are starkly different from other series in the genre. The series follows agent Peter Burke as he leads the FBI's White Collar Crime Unit while working with former con artist and master forger, Neal Caffrey. White-collar crimes, as referenced by the title, deal with financially motivated crimes, often committed by people and organizations of higher-class standing, meaning that the issues Neal and Peter have to solve aren't just your typical police mysteries . By focusing on a type of crime that isn't often the focus in most procedural TV shows, White Collar's weekly problems in each episode are unique and distinct for most audiences. The sheer variety of these episodes is one of the main reasons fans are so enraptured with the series.

Each episode’s conflict isn’t just varied in subject, but also features interesting solutions , whether it be through a masterful forgery or a sly double-crossing performance. As an example of the myriad mysteries in the show, in just the first season alone, Peter and Neal infiltrate New York Fashion Week, search for a stolen Bible of miracles, and investigate a suspicious medical charity. White Collar doesn't deal with murders as often like other crime shows. Neal has a Batman-esque rule against guns and is understandably squeamish around dead bodies. Instead, the show focuses on combining mystery solving with suave social interactions, reminiscent of shows like Suits.

Another important distinguishing feature of White Collar is its sprawling connections to history and culture. With a focus on white collar crime, art history is one of the most frequent subjects that the series tackles. Neal's criminal background makes him equal parts suave con man and high-class art and history savant. Though other series typically reserve the more technical aspects of evidence analysis to side characters (think Abby from NCIS ), that responsibility falls to Neal instead. With his expertise and scrutinizing eye, he's able to identify anything from fake historical bonds to forged IDs.

The added historical element to the crimes adds another layer of complexity to each episode. When a fake escort murders multiple people, the crime's connection to stolen jade artifacts nearly makes it an international incident. When Neal has a competition with a rival forger trying to pay off his mob debt, even an auction of a revolutionary-era wine bottle is turned into a high-stakes chess match. Though history isn't always seen as exciting for most audiences, White Collar successfully combines history with crime drama to make the show mentally stimulating in its own unique way.

Neal and Peter have a Cat and Mouse Relationship

The most important foundation of the series is the unlikely duo of Peter and Neal. As an FBI agent and master con artist, the two are equipped with vastly different, but complementary, skills. They are forced to contend with their own mistrust of one another as they face their weekly case and also navigate their personal motivations. However, the relationship between the two is far more personal than just a cop working with a robber .

Since it was Peter who first caught and arrested Neal , there is an already established familiarity between the two, even if the fondness is rather lacking. The two had already been playing a cat-and-mouse game of chase for years, which led to them developing intimate knowledge of the other person, despite not actually being friends. This created a dynamic between characters that feels wholly unique , even as it bears similar strengths to other prominent show duos. Whenever the FBI is mistrustful of Neal, it's surprisingly Peter who comes to his defense, as his expertise on Caffrey's criminal activity makes him an acute judge of his actions. And, in turn, Neal is there to provide Peter with the social and emotional nudges in the right direction that the work-obsessed agent often needs.

Neal Caffrey Is Based on the Same Person as 'Catch Me If You Can'

With such a variety of subjects in each of its weekly episodes, White Collar feels like a Renaissance show, much in the same way that it's led by Renaissance man, Neal Caffrey. Charming, suave, and wickedly intelligent, Neal is a protagonist who is distinct in a genre where most characters come from more straight-laced backgrounds, but that's why he's such a joy to watch. Though it may not follow so closely to crime shows, it's actually strikingly reminiscent of the hit movie, Catch Me If You Can , a Steven Spielberg movie that follows a con man evading an FBI agent.

Those similarities are no coincidence. Neal's con man talents are actually based on Frank Abagnale Jr. (Leonardo DiCaprio), which would make Peter his very own Agent Hanratty ( Tom Hanks ). Like Abagnale, Neal was a forger who lied his way into wealth and privilege, while avoiding a federal agent on his tail. And in real life, Abagnale Jr. did go on to work as a consultant for the FBI ; yet another similarity with White Collar's very own.

Ultimately, it’s these unique perspectives juxtaposed with one another and the endearing connection between Neal and Peter that keeps the series feeling fresh and unique throughout its six seasons. Each serialized episode has a distinct flair that makes them all more memorable than your run-of-the-mill mysteries each week , adding spice and variety to your typical crime procedural programming.

White Collar is streaming now on Netflix in the U.S.

Watch on Netflix

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White-Collar Crime: A Review of Recent Developments and Promising
White-collar crime is one of the least understood and arguably most consequential of all crime types. This review highlights and assesses recent (primarily during the past decade) contributions to white-collar crime theory (with special emphasis on critical, choice, and organizational theories of offending), new evidence regarding the sentencing and punishment of white-collar offenders, and ...
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A 1976 estimate of the total cost of white-collar crime puts the figure in the neighborhood of $250 billion per year (Rossoff, Pontell, & Tillman, 1998 ), while a more recent study estimates financial losses from white-collar crimes to be between $300 and $600 billion per year (Stewart, 2015 ).
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White-collar crime (WCC) causes considerable societal harm, the economic and psychosocial costs of which exceed those of conventional crime. Despite the impact, it has received scant attention from the academic literature in forensic psychiatry. This narrative literature review covers important topics in our understanding of white-collar crime ...
Challenging Existing Regulatory Approaches for White-Collar and
Thus, a core goal of the Journal of White-Collar and Corporate Crime (JWCCC) is to support new socio-legal and political interventions through targeted policy change and critique (Alvesalo-Kuusi & Barak, 2020), and by embracing proposals to regulate and prevent white-collar and corporate crime from a multidisciplinary background (see for ...
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Juveniles' Race and Police Relations. Online citation: McGurrin, Danielle, Melissa Jarrell, Amber Jahn and Brandy Cochrane. 2013. "White Collar Crime Representation in the Criminological Literature Revisited, 2001-2010.". Western Criminology Review 14(2):3-19.
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Understanding White Collar Crime. Hazel Croall. Published 1 June 2001. Law, Sociology. This comprehensive overview of white collar crime begins by introducing the concept, looking at its definition, its identification with class and status, and its development within criminology. The problems of estimating the vast extent of white collar and ...
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This chapter first reviews the literature on comparative studies of white-collar and corporate crime published to date in English, in a review divided into two categories: (i) domestic studies on white-collar and corporate crime in countries other than the United States, and (ii) comparative studies of several countries and regions on white ...
Antecedents of white collar crime in organizations: A literature review
Referring to a crime committed by someone of high social status, the literature suggest that the major causes of prevalence of white collar crimes are peer support, corporate culture, lack of accountability and lack of reporting. This review helps to understand the importance of white collar crime in today's public sector organizations. Key….
Digitisation of Financial Markets: A Literature Review on White-Collar
It is a leading cause in today's world as the underdeveloped economies, and it is a simultaneous cause of the poverty of any country. Whereas the white-collar criminals are easily engaging institutional weakness and bad leadership and governance. The white-collar corruptions are increasing day by day in our daily life.
Investigating and prosecuting white-collar and corporate crime
Many countries have established national authorities to investigate and prosecute serious and complex white-collar and corporate crime incidents. This article reviews research literature regarding external challenges and barriers for national agencies in Norway (Økokrim), New Zealand (SFO), the United Kingdom (SFO), and the Netherlands (OSF).
White-Collar Research
The literature review identified the following claims in the research literature regarding individual economic benefits as motives for financial crime by white-collar offenders: 1. Economic greed is a strong motive for financial crime (Goldstraw-White 2012 ; Bucy et al. 2008 ; Hamilton and Micklethwait 2006 ).
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White Collar Crime
Sutherland (1983:7) defined white collar crime as "a crime committed by a. person of respectability and high social status in the course of his occupation." The definition has its problems. The concept of "respectability" defies preci-. sion of use. The requirement that a crime cannot be a white collar crime unless.
Review of Comparative Studies on White-Collar and Corporate Crime
This chapter first reviews the literature on comparative studies of white-collar and corporate crime published to date in English, in a review divided into two categories: (i) domestic studies on white-collar and corporate crime in countries other than the United States, and (ii) comparative studies of several countries and regions on white ...
Insider Threat and White-Collar Crime in Non-Government Organisations
The authors describe recent literature on insider threats and white-collar crime in non-government organisations and industries and identify management strategies used to counter them, both internationally and in the Australian context. ... Insider Threat and White-Collar Crime in Non-Government Organisations and Industries: A Literature Review ...
Digitisation of Financial Markets: A Literature Review on White-Collar
A white-collar. crime takes place due to sel ﬁshness, and for its execution, they use very well-intended approaches. But when we. compare the blue-collar crimes these are carried out because. of ...
Thinking About White Collar Crime
This two-part paper critically reviews conceptual themes in white-collar crime literature, proposes additional definitional distinctions, and suggests a research agenda. Abstract The term 'White-Collar crime' does not clearly identify the offenses or types of offenders, the social location of deviant behavior, status of the actor, modus ...
Literature Review on Fraud/White Collar Crime,...
The cases of white-collar crimes have been increasing exponentially in the 21st century due to the advent of technology because fraudsters apply technological tools in cheating, swindling, embezzling, and defrauding people or organizations. White-collar crime is a complex issue in society because its occurrence is dependent on many factors such ...
PDF Antecedents of white collar crime in organizations: A literature review
social status, the literature suggest that the major causes of prevalence of white collar crimes are peer support, corporate culture, lack of accountability and lack of reporting. This review helps to understand the importance of white collar crime in today's public sector organizations. Key words: White collar crime, public sector organizations.
India: White-Collar Crime
In summary. This article offers an overview of India's white-collar crime landscape, recent regulatory developments and enforcement measures. It examines the central legislation governing white-collar offences and explores judicial trends in interpreting and expanding financial crime laws. The article also delves into penalties for ...
'White Collar' Does This Better Than the Other Crime ...
TV-PG. Drama. Comedy. Crime. A white-collar criminal agrees to help the FBI catch other white-collar criminals using his expertise as an art and securities thief, counterfeiter, and conman ...
Literature Review on Fraud/White Collar Crime,...
White collar crime is a term created by Edwin Sutherland in 1939 that refers to crimes committed by people of higher social status, companies, and the government according to the book "White-Collar Crime in a Nutshell" by Ellen Podgor and Jerold Israel. White collar crimes are usually non-violent crimes committed in order to have a ...