White collar crime

According to the FBI’s website, white collar crimes, which are described as being “synonymous with the full range of frauds committed by business and government professionals”, are one of the bureau’s principal sections of investigation, as “a single scam can destroy a company, devastate families by wiping out their life savings, or cost investors billions of dollars (or even all three)”. The FBI classifies these types of crimes into three types: corporate fraud, money laundering, and securities and commissions fraud. Two of these classifications, corporate fraud, and securities and commissions fraud, are examined more deeply by the articles that are summarized within this blog entry, demonstrating the potential of social network analysis techniques to illuminate illicit networks typically thought to be difficult to understand by outsiders.

In Peoples and Sutton’s (2015)⁠ article, “Congressional bribery as state-corporate crime: a social network analysis”, Peoples and Sutton examine what they assert to be a form of corporate fraud, the contributions made from PACs to Congressional legislators and their impact on their voting behaviors, which they argue to be synonymous with traditional understandings of bribery – precisely stated, their research question is “Is there a statistically significant general effect of shared PAC contributors on vote similarity among pairs of lawmakers in the 109th U.S. House, controlling for other factors?” (Peoples & Sutton, 2015, p. 110)⁠. The authors rely on secondary data analysis, drawing upon financial contribution data provided by the Federal Elections Commission, as well as voting pattern data from the website www.voteview.com . Their methodology is a synthesis of social network analysis techniques and traditional statistical methods. They construct edges between each specific member of the 109th House of Representatives characterized by the similarities in their PAC contributions, and apply a quadratic assignment procedure regression analysis to each of the dyadic pairs present within their network data. Considering the public’s overwhelmingly negative perception of Congress, their results are not surprising – the authors conclude that representatives are significantly influenced by their financial contributors, and that the best theoretical explanation for this phenomenon is an institutional culture of corruption that characterizes Congress.

In Hansen’s dissertation “The ‘bad boys’ of Wall Street: A network analysis of insider trading, 1979–1986” (2004)⁠, Hansen examines a similar white collar crime: securities and commissions fraud. Hansen applies a variety of sociological methods to answer a wide range of hypotheses, but her social network analysis’s scope of study can be summarized with the following research question: how do social network structures, and, specifically, their density, size, and degree of connectedness, impact insider trading? Similarly, Hansen also relies on secondary data analysis – the quantitative measures being borrowed from Stearns & Allan’s “ Economic Behavior in Institutional Environments: The Corporate Merger Wave of the 1980s” (1996)⁠, Stewart’s Den of Thieves (1992)⁠, Auletta’s Greed and Glory on Wall Street: The Fall of the House of Lehman (1986)⁠, and Bruck’s The Predator’s Ball: The Inside Story of Drexel Burnham and the Rise of the Junk Bond Raiders (1989)⁠. Hansen utilizes a more traditional social network analysis design, in which network metrics such as centrality and density are directly relevant to the research question, and, consequently, constructs network data that interconnects Wall Street traders as the network’s nodes. Hansen constructs two networks: a legitimate network, in which traders are connected through legitimate business relationships, and an illegitimate network, in which traders are connected through illicit transfers of money or information. Perhaps primed by the title of one of her secondary sources, Den of Thieves, Hansen anticipated that the network’s size and density would increase over time as the illegitimate network grew, but found that the size remained constant, and that the density actually decreased, reflecting, perhaps, that her illegitimate actors were self-motivated and tight-lipped. However, Hansen does demonstrate that a link exists between her legitimate and illegitimate networks, and that connectedness and centrality were key measures which patterned insider trading for her sample.

White collars crime are frequently thought of as being difficult to identify, and even more difficult to prosecute, but the application of these techniques appears to demonstrate their persisting prevalence. In any given instance, it seems, the influence of money on an individual’s decision-making process can be explained away as circumstantial, but, when such an individual is implicated in a larger network characterized by individuals in similarly compromising positions, their culpability seems much more transparent. Too often the rest of the world is characterized as wildly corrupt and antithetical to American values, when, increasingly, researchers seem to indicate that the US is similarly corrupt, if not exceptionally more corrupt, than the countries which politicians single out to illustrate American exceptionalism.

 


Auletta, K. (1986). Greed and Glory on Wall Street: The Fall of the House of Lehman. New York: Warner Books.

Bruck, C. (1989). The Predator’s Ball: The Inside Story of Drexel Burnham and the Rise of the Junk Bond Raiders. New York: Penguin Books.

Hansen, L. (2004). The “bad boys” of Wall Street: A network analysis of insider trading, 1979–1986. University of California Riverside.

Peoples, C. D., & Sutton, J. E. (2015). Congressional bribery as state-corporate crime: a social network analysis. Crime Law Soc Change, 64(103). https://doi.org/https://doi.org/10.1007/s10611-015-9584-4

Stearns, L. B., & Allan, K. D. (1996). Economic Behavior in Institutional Environments: The Corporate Merger Wave of the 1980s. ASR, 61, 699–718.

Stewart, J. B. (1992). Den of Thieves. New York: Simon & Schuster.

 

Peter Jameson

 

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