The state of affairs of one’s personal finances have helped behavioural scientists model two aspects of their professional lives: to estimate the level of risk they may be willing to take at work to complement their personal risk propensity, and to signal to potential employers or relevant authorities on their reliability to adhere to regulations in the future.
Jennifer graduated at the top of her class in Purdue the same year she married her high school boyfriend. Over the next decade her life saw its ups and downs: she joined a bulge bracket investment bank in New York, moved into a charming townhouse on Long Island; she worked her way up in the trading team for institutional markets, and went through a messy, stressful divorce- during the settlement of which she was diagnosed with rheumatoid arthritis and forced to take a sabbatical from her job. Her high levels of debt brought on by the burgeoning needs of her fast-track lifestyle, multiple mortgages, medical bills and alimony payments to her unemployed husband, meant that she was back looking for high-profile jobs before she had made a full recovery.
By then her debt had caused her consumer credit score, like millions of other New Yorkers, to plummet. As of 2015, the average revolving debt for an American household is $15,609, and the average mortgage debt is $156,706. Credit card debt is now second only to student loans as the largest source of unsecured debt in the United States.
But here’s the kicker: Jennifer’s low credit score puts her at a considerable disadvantage during job interviews. FICO awards no concessions to single divorcees paying alimony, and a large number of employers have now begun to see credit history as a substitute for an applicant’s family background, reliability and a sense of responsibility. This is truer in the financial services industry, particularly for job profiles such as trading, that require employees to make quick, sometimes risky, but extremely profitable decisions on behalf of their company. If the risk pays off, a portion of profit made on the trade is awarded to the employee. Outside of employment, credit scores have also become an important point of reference when leasing apartments or purchasing a cellphone on contract.
The behavioural trigger for such risk taking behaviour is explained by a concept in behavioural economics called loss aversion: picture a visitor to a casino who has already lost $10,000 at roulette. He is more likely to take riskier bets now as compared to when he first stepped in, hoping to quickly nullify his initial loss- and being gleefully ignorant of the prospect of losing $10,000 more in the process.
It is natural for readers to feel indignant at the unfairness meted out to Jennifer. From employers’ perspective, the motivations for such discrimination can be explained by two related concepts: agency dilemma and moral hazard. The former comprises of a principal who hires an agent to make decisions on her behalf. The agent, however, is motivated to pursue his own interests, not those of the principal. A necessary condition is that there is asymmetric information between the two parties: the principal is not aware or not capable of being aware of all of the agent’s activities. The second concept – moral hazard, refers to a particular situation of the agency dilemma, where the agent engages in riskier behaviour because he is protected from its consequences.
The subprime mortgage crisis of 2008 is an example of a continuing chain of moral hazard. The players at each step of the sequence- realtors, consumer bankers, investment bankers, insurance companies, credit rating agencies and regulators- all fuelled by ever-increasing property prices, took higher risks because each felt protected from the repercussions of their decisions as long as they passed the buck up the chain. At the tipping point of the crisis, property prices started to fall and set off a chain reaction that brought the sequence of moral hazard to its worst possible outcome – a global financial meltdown.
Financial regulators have since commandeered several important legislations to minimise moral hazard. For obvious reasons these new legislations have relied on several non-conventional fields of study, including behavioural sciences- which have progressed far beyond being merely an intuitive, behind-the-scenes predictor in assessing such risk. New algorithms by leading credit bureaus attempt to model behavioural traits that predict not just one’s creditworthiness, but also how reliable they are in their personal and professional lives. The Fair Isaac Co. (FICO), for example, uses their Medical Adherence Score to predict the likelihood of a patient following through with their prescribed medication. Other bureaus use similar models to predict asset-side information of clients, which have so far been a major blind spot for the industry: the Income Insight Score by Experian uses credit and personal history to predict an individual’s income levels. Similarly, Equifax offers two products to companies to help predict potential consumers’ discretionary spending power.
The successful use of holistic personal data showcases the predictive power of behavioural models, and thus the ability of university departments and firms that deal with behavioural economics- to widen the scope of predictive analytics. Variables from one’s personal life- number of marriages or broken relationships, frequent visits to party towns and pilgrimage towns (or both), and even the extent of social media presence- may wield a substantial advantage over traditional variables that are restricted to their isolated sphere of study. Moreover, such singular risk assessment models of ex-ante moral hazard come with limitations. In France, for example, young adults whose parents have had few or no accidents (and presumably are safe drivers), are offered lower premiums for their own insurance policies. Over the years, the probability of these youth being involved in accidents has proven by studies to be equal to those youth with parents who were deemed ‘reckless’. Yet, moral hazard may explain these figures in reverse – those youth with lower premiums purchase more comprehensive insurance policies, and are thus more likely to take higher risks on the road. It is this familiar sinusoidal pattern and its moderating effects that must make Jennifer hopeful about not just about her credit prospects, but life in general.
Image Source: The Daily Omnivore/Original Owner: The New Yorker