White Collar Crime: Origin, Factors And Preventions
When thinking about criminal behaviour, one would name murder, rape and assault. Those are the crimes most people are afraid of. But those are not the most prevailing ones. White Collar Crime, first coined in 1939, defined as criminal behaviour committed by a higher social class is the most common type. Money laundering and insider trading are the first ones that come to mind as those can easily be associated with the stereotype of a rich, upper-class men. White collar crime is financially motivated, non-violent but has a tremendous impact on society. Measuring the damage that white collar crime does to society is quite hard, still the FBI estimates costs between $300 to $660 billion, per year (Friedrichs. 2009). White collar crime is far more costly than conventional criminal behaviour, some argue even 50 times (Friedrichs. 2009), has negative health effects and is deadlier (Lynch, Michalowski. 2006).
What is the origin for white collar crime? Are there certain physical, personal or social factors that are prevailing? Is it dependent on background, employment or age? How can one determine risk factors, and most important, what are ways to prevent criminal behaviour? The following paper aims at answering all those questions and gives a broader picture about the most prevailing and underestimated type of crime.
One way to predict crime is using biological indicators, such as biomarkers as genes or brain scans. This helps predicting the likelihood of crime engagement and thus can be used for preventive purpose via biological interventions, or less formally, brain-active drugs. (Sing et al. 2013). Scientists usually tend to focus on biomarkers that show aggression or low self-control as those are linked with murder or other conventional crimes. Because of that, little scientific research has been conducted about white collar crime and studies as well as papers discussing this issue only developed in recent years, even though reasons to prevent this kind of criminal behaviour are significant. Focusing on predicting and preventing conventional crime has led to intensifying the already existing unequal research resource allocation between both crime types and made white collar crime seem even more unpredictable.
In order to clarify the terminology, a few comments will follow. The terms used from now on are “white collar crime” (criminal behaviour by a higher income group) and “blue collar crime” (referring to crime committed by a lower social class). Common white collar crimes except the ones mentioned above are fraud, identity theft and bribery. In this paper, white collar crimes are characterized violence free, but still illegal and harmful activities. Important to note is, that the terminology is not definite, as a blue collar crime could still be committed by an upper-class person, it rather developed from historical class associations.
For blue collar crime, the measurement seems straightforward. One method would be to use the forgone lifetime earnings in order to come up with a number. For white collar crime, this is much harder. One factor playing an important role is the broad spectrum of incidents that are (or not) related with each other. This uncertainty goes in line with the cost estimates, ranging from $250 billion (Friedrichs. 2009) to $1 trillion (Ragatz et al. 2012) annually. What is also often overlooked is the lethality of white collar crime. Taking the inflation of cancer drug prices for higher profits as an example. Not only the financial costs, but also the costs to humans and society are hard to measure. Taking asbestos as another example, this formally marketed “magic mineral” has caused more than 100,000 deaths and over a million disability adjusted life years, according to the WHO in 2015. Even though insiders were aware of the health risks in the first half of the 20th century (Smith 2005).
Not only the private sector, also the government has to face consequences of white collar crime, taking money laundering, corruption or tax evasion as an example. One recent legal case about this topic was US government v. Siemens Bangladesh in 2008, where the later admitted bribery towards Bangladeshi officials in order to be the preferred candidate for public contracts. The payments that have been in the lower million USD seem pretty small compared with the previous estimated numbers, however this money has a totally different local purchasing power and could have been used for health treatments, building houses or sending children to school. That being said, one might conclude that white collar crimes create circumstances which could foster the development of blue collar ones.
When describing criminal behaviour in literature or movies, stereotypes are mostly minorities, urban and young men and crimes like assault or robbery. Thus, scientific research and data is mostly focused on markers that act as indicators for behaviour associated with those crimes such as violence or impulsiveness (Buckholtz, Meyer-Lindenberg. 2009). One theory could be, that biomarkers that are associated with blue collar crime are the same for white collar crime, as it was argued that a central element behind all crime is low-self control (Gottfredson, Hirschi. 1990). Risk seeking and shot-term perspective thus seem common factors for both kinds of criminal behaviour. However, multiple studies showed that corporate crimes are unrelated to deficits in self control (Simpson, Piquerro. 2002), and even attributed brain superiorities to them (Raine et al. 2012). This leads to the conclusion that white collar criminals invest in careful calculation, develop complex plans and rather not engage in impulsive action. According to a recent study, white collar criminals even score higher on measures of psychopathy when compared to the blue collar group (Ragatz et al. 2012).
So why is there such a low amount of research regarding white collar crime, while the research for biomarkers that are brought into association with blue collar crime was already successful? Biomarker research for blue collar crime has been fruitful, making more researchers likely to investigate this topic further. One example would be the MAOA gene, (when combined with maltreatment in childhood) which is linked to risk-taking (Caspi et al. 2002) and can be a good predictor of (conventional) crime. This – in contrast – is not the case for white collar crime, simply because motives appear to be more diverse and individual to each situation. To say it pretty harsh, it seems as if researchers intentionally did not invest into white collar crime research just because it seems unlikely that they will be successful.
Driven by the already unequal allocated research resources between both types of crime, there is way less data on biomarkers or indicators than can predict white collar crime. So why not take an already known marker and check for indication?
One example would be poverty, as this is often found as a driver for violent offending (and thus blue collar crimes) among young people (McAra, McVie. 2016) as it fosters inequality. Homelessness is also another marker that is associated with people in prison, as the Ministry of Justice in the United Kingdom found out in 2012. It seems like people being caught for blue collar crime already start with a societal disadvantage in their lives which stands in contrast to the upper-class living standard that the stereotype of a white collar criminal pictures. But once again, it is definitely the wrong conclusion to label people as potential criminals due to the background they are coming from and have no power in changing.
For investigating biomarkers especially for white collar crime further, one has to differentiate between crimes that either have been committed by an in individual or an organization of people. As the later one is highly complex, I will now focus on the first group of criminal behaviour. With explaining characteristics of an individual, one might also conclude for group behaviour which will allow us to kill two birds with one stone.
Even though some white collar criminals seem to master manipulation and perform strongly in callous affect (Babiak, Hare 2007), this only matches two of the four characteristics of a clinical psychopath (the missing ones are erratic lifestyle and anti-social behaviour, according to Williams et al. 2007). As those characteristics are seen as helpful for a successful career in business or politics (Babiak 2006), it is not surprising that white collar criminals are often on a good track-record in their organization. “Bad” characteristics such as egocentricity or narcissisity can be mistaken as highly self-confident and a short-term sight might seem creative and visioning (Babiak 2006). Corporate psychopaths, characterized by the two markers mentioned above make up around 3-6% of business managers (Babiak et al. 2010). Another study found out that certain markers associated with psychopathy were more common within managers than in psychopathic patients treated in a clinic. Charm and egocentricity were a few of them (Board and Fritzon. 2005). Important to note is that the two groups (clinical psychopathic patients and managers) were differentiated by the non-existence of anti-social behaviour and erratic markers within the latter group, making them less likely to be impulsive, short-sighted and aggressive in a physical way. One even suggests than corporate psychopaths have played a role in the financial crisis 2007. Boddy (2011) suggests that changes in the employment process over four decades have put corporate psychopaths the possibility to step up in senior positions from where they used personal interests to create the crisis.
Besides the corporate world, politics is another field where white collar crime occur. Especially within countries where no strong observation and control process for politicians is present, political psychopaths can create a lot of harm.
As we now know about the characteristics of corporate psychopaths, paying attention to those characteristics during hiring processes seems as a good method to prevent white collar crimes. Such tests are already existing, but unfortunately only for a low number of professions, such as nuclear power plant operators as those can cause massive harm (Lowman 1989). So why not invent testing for other positions? Unfortunately, this process is not so easy. First, those tests consume a lot of time, resources and money. As profitability and cost effectiveness are important factors for cooperations, it is just too expensive to perform tests for every new hire, as one cannot predict whether an individual will be able to rise to a senior position or not. Second, people having traits that are found in corporate psychopaths are often good actors as they are strong in interpersonal manipulation. Thus, they are highly likely to pass those tests. Third, one would need highly skilled experts that perform those tests as it is extremely hard to differentiate a high level of self-confidence and egocentricity.
Brain imaging techniques have been used to examine the brains of psychopaths and even found correlations. Weekend connections between areas in the brain that are responsible for rewards and decision making found in people that have scored high on psychopathic measures can partly explain the tendency to neglect consequences and overvalue the immediate reward (Hosking et al. 2017). Even though those markers have been identified, it is today not fully clear how clear and predictive they are to clinical psychopathy (Hardcastle 2013). As even clinical psychopathy (which clearly has experienced more research than corporate psychopathy) struggles with clear indications, it is no surprise that brain imaging for corporate psychopathy cannot be generalistically indicative.
Nevertheless, the previous identified characteristic of callous affect appears to be heritable and may be a reliable biomarker (Viding, McCrory. 2013). I have unfortunately not found a study that investigates egocentricity, but one that researched narcissism, in particular high cortisol levels which are marginally related with narcissism (Reinhard et al. 2012). Heritability as well as hormone levels thus seem as biomarkers that correlate with corporate psychopathy. With further investments in research, I am fully convinced that this assumption will be found out to be more reliable.
With more emphasis allocated to above mentioned biomarkers for an entry to senior roles in politics and corporates, I am sure that a more healthy and trustworthy system can be created. A specific allocation of bioprediction to certain roles would thus save time and resources. Of course, highly skilled experts have to perform those tests on a case by case basis, as characteristics such as being charming even in stressful situations might be indicative of corporate psychopathy, but is highly desirable for certain roles that involve spontaneous engagement (Dutton. 2013). Thus, in order not to exclude good candidates, an automated system is definitely the wrong way to go. A way to test qualities of candidates “on the job” would simply be a higher level of monitoring, which is definitely wanted and desirable for senior roles in politics and the corporate industry. One would kill two birds with one stone. It is highly important to reduce the number of “false positives”, so that the newly implemented testing system does not create more harm than it is helpful. Of course it is inevitable to completely exclude the existence of such results which makes it even more important to implement only tests with a low degree of “false positives” and not base a decision on a single indication. Taking biomarkers as a way to deprioritize instead of exclude candidates seems as the way to go.
Research on white collar crimes and corporate psychopaths is definitely behind the one for blue collar crimes and clinical psychopathy, even though white collar crimes come with bigger costs and impact to society. Because of this, it is now the time to invest more time, work-power and resources to the latter in order to revert the unequal relationship. With more papers and studies, results will be more reliable, indicative and useful for both the political and corporate world.
As with all results, correlation does not mean causation. Findings have to be handled with care and decisions should be data driven. I am fully convinced that with an increasing investment in white collar crime research we will be able to improve hiring and step-up processes, predict risks and prevent incidents as well as crisis. Lastly, until this knowledge is collected, monitoring and increased oversight is the way to go and will for sure not be harmful. The positions that can create the biggest harm and damage in both politics and corporates currently are watched way too less, so checking processes will prove itself as a useful measure for the transition period.
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