Mapping (White-Collar) Crime

This morning we had a little fun in lecture. The standard formula for crime is (1-a)L – aJ – e, where a is the likelihood of being apprehended, L is the loot I anticipate earning, aJ is the cost if apprehended, and e is the expense of the tools of the trade. By design the model is highly subjective. If I can earn what I anticipate I should be earning through legal activities — i.e., if the risk a and costs aJ and e outweigh the L loot I anticipate from illegal activity — then I will obey the law. If not, I won’t.

How do you use crime maps?

Even if you have not used mapping — to decide where to buy a home, which neighborhoods to avoid — it is likely that you have used mapping to get from point A to point B. Between these points, you have witnessed, first-hand, places you would not want to go, not at night, not alone.

J Katz notes how “crime news provides the sociologist with a handy, detailed map to trace the institutional geography of the sacred in modern society” (1987:53); toward what do we gravitate; from what do we flee?

To this we can add an entire racialized, linguistic, aesthetic and gendered geography of shapes, sounds, and images that are familiar to us from popular movies, novels, news hours, NPR and FOX.

Our neighborhoods, as nearly as possible, are packed full of the artifacts we cherish; our lived experience — safe, green, warm, happy, healthy, fit, opulent spaces. To individuals who live in the “fear zones” — the “red” zones displayed on crime maps — our neighborhoods both attract and repel. For, it is clear, occupants of the “fear zones” also cherish safety, green lawns, and warm, happy, and fit neighbors and neighborhoods; and, yet, they also know that, when they cross the invisible boundary separating our neighborhood from theirs, they — as living artifacts of the “fear zones” — become objects of attention, objects of fear, of danger. Their speech, their dress, their race or carriage singles them out. The attention is palpable.

Our desire is to contain and limit the spread of “fear zones.” And we do so by controlling for and aJ. That is to say, we increase a, the likelihood of being apprehended, and aJ, the cost if apprehended, to a point where, we hope, crime will not pay for occupants of the “fear zones” — criminals for whom even a small L rises so far above their potential legitimate income that “crime pays” up to the margin: increased jail sentencing, increased incarceration, increased policing, increased surveillance — decreasing the likelihood and opportunity for crime.

Here is where white-collar crime comes into play. White-collar crime obeys the same formula as blue-collar crime: (1-a)L – aJ – e. In the case of white-collar criminals, e is an important factor since it includes education at Booth, or Harvard, and Haas, where white-collar criminals learn the tricks of the trade. In order for crime to pay, it must cover e. Of course, if a, the likelihood of being apprehended, is low, then this reduces pressure on covering e. Finally, if aJ, the penalty I will suffer if apprehended is low, then this means I am willing to settle for less L, the loot I anticipate winning. Yet, in every respect, the formula holds good for both white and blue-collar crime.

Here is where mapping comes in. Even though it constitutes the chief economic risk for occupants of safe neighborhoods — they will lose their homes, lose their savings, lose their pensions, and lose their jobs — white-collar crime appears nowhere on our crime maps. The thefts and assaults in the red “fear zones”? Minuscule. No one loses their job, their home, their pension, their savings. At worst, they get hit on the head and get their wallet or purse stolen. Meh. A nuisance, but only a nuisance. Moreover, since the most violent crimes are reserved for permanent occupants of these “fear zones” the impact of such crimes on occupants of safe neighborhoods is more imaginary than real; part of the “the sacred in modern society” as J Katz puts it — the demons we fear, the angels we adore.

Nevertheless it is to these “fear zones” that we send our police and attack dogs. It is in these “fear zones” that we establish surveillance, even though the high-stakes crime is occurring elsewhere, in the white “empty” zones on our maps of crime.

In a few days, Congress will repeal much of the Dodd-Frank legislation that established surveillance over these blank spaces. Dodd-Frank made it more likely that I would be apprehended, a, and so it decreased the likelihood that I would defraud my depositors. Of course, it would also take a significant bump in aJ, the cost of crime, for white-collar crime not to pay. Wells Fargo and Bank of America have dished out huge sums in penalties under Dodd-Frank; but still nowhere near the benefits, the L, won by defrauding customers. Yet, rather than establishing more surveillance over white-collar crime, rather than increasing the cost of crime, Congress is poised to eliminate the surveillance and lower the cost. Our model indicates that this will infallibly give rise to greater white-collar crime: more foreclosures, lost savings, lost pensions, lost dreams.

There in the white areas, in the “empty” spaces and “uncharted” waters; there is where crime pays the highest dividends. There is where we should be setting up surveillance, sending in more police; it is from these predators that we need protection.


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