Credit: Sebastien Thibault
Numerous experiments have demonstrated that people are more generous with money they’ve earned unethically. For instance, a salesperson who earns a big commission by misleading a client may mentally cordon off that money and be more willing to spend it on others.
But research by Chicago Booth’s Alex Imas, Carnegie Mellon’s George Loewenstein, and Carey K. Morewedge of Boston University finds that there’s a way to avoid this self-imposed penalty: psychologically “launder” the money by obfuscating its source.
Researchers of behavioral finance have spent decades documenting how mental accounting—our propensity to treat money differently depending on things such as where it came from or how we intend to use it—affects decision-making. Mental accounting violates the idea that money is fungible, or perfectly exchangeable: people may treat some of their money more frivolously than the rest of it, for example, if they acquired it easily or by chance. Imas, Loewenstein, and Morewedge’s findings hint at the complicated effects this informal bookkeeping can have, as it can cause people to be generous with dirty money but also to find ways to avoid such generosity.
In a series of experiments, the researchers explored how participants treated ill-gotten gains under various conditions. In one, they set up a game in which some participants were given a monetary incentive to lie to an anonymous and randomly assigned partner. Lying, in these cases, meant maximizing the participants’ own earnings from the experiment but diminishing how much their partners would receive. The researchers then gave all participants the option to donate some of their earnings to charity. The experiment confirmed the finding established in past research that people tend to be more generous with earnings that they have produced unethically.
Imas, Loewenstein, and Morewedge then took the experiment a step further, entering a subset of participants into a lottery in which they risked their experimental earnings but had a very high probability of receiving an identical sum in return. For those whose earnings had come in part through lying, processing the money through this lottery effectively sanitized it: they donated significantly less than those who lied but didn’t play the lottery, and their donation behavior was similar to those who told the truth and then participated in the lottery.
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In further experiments, Imas, Loewenstein, and Morewedge find evidence that mental money laundering also applied to situations in which ethically and unethically earned money was pooled. When “dirty” money mixed with clean, participants tended to treat the entire pool of money as though it were ethically earned. Moreover, people recognize their tendency to treat laundered money differently than unlaundered money, and seek out opportunities to sanitize it, the research suggests.
The study’s findings have implications for both individuals and businesses. “Most people . . . that are engaged in morally questionable activities are also engaged in legal, ethical ventures,” the researchers write. New technologies for payments and budgeting, such as Venmo and Mint, “offer novel opportunities for creatively pooling resources” and “may also provide a tool aiding mental money laundering and encouraging behaviors with social costs.”
Mental money laundering may also be a boon to companies whose practices or products have negative social effects. Such companies—which, prior research has shown, often have to pay workers a wage premium to overcome their qualms about the detrimental nature of their labor—may be able to reduce that premium through “greenwashing,” or putting some portion of their revenue toward a prosocial purpose. Doing so could allow employees to mentally launder the company’s revenue, and their role in generating it, by pooling the company’s harmful impact with its positive works.
Alex Imas, George Loewenstein, and Carey K. Morewedge, “Mental Money Laundering: A Motivated Violation of Fungibility,” Journal of the European Economic Association, forthcoming.
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