Major Life Decisions: How Much Influence Does a Coin Toss Have?

Steven Levitt, a well-known economist of “Freakonomics” fame, has a new paper on a topic that we can all relate to: How do people make big, pivotal life decisions? And how can we evaluate whether we make good ones?

When I stop and think about it, the relative scarcity of a robust literature on this topic is surprising. What could be a more pressing or pertinent subject? But — among other difficulties — it is incredibly difficult to create a controlled environment with the kind of randomization that you need for rock-solid results.

Let me explain. To try and measure whether some small behavior makes people happier, researchers could simply randomly assign participants into “Group 1” and “Group 2” and impose different conditions on each. This ensures that people with preexisting differences aren’t self-selecting into different groups and polluting the direct causal link that you’re trying to measure.

This approach — create a controlled environment, randomly divide your participants into “treatment” and “control” groups, and then measure how they fare — works great for studying things like new medications. But not so much for studying major life decisions: whether to get married, what kind of person to marry, and whether to move across the country for a new job. It turns out people aren’t willing to surrender those decisions to a social scientist in the name of advancing science. Weird, I know.

That’s where this study gets creative. Levitt did the best he could to “randomize” decisions by looking at the impact of a coin toss on people’s likelihood of making certain decisions. First, he recruited more than 10,000 volunteers. Each one took a survey that asked about a big decision they were facing. Then came the interesting part: Levitt’s website presented participants with a coin flip that “told” them which choice to make. After the experiment, Levitt followed up with the recruits to see what they decided and how happy they were.

Obviously, participants weren’t bound to follow through and obey the virtual coin. So the first question the study examined was: How much does a virtual coin flip impact which choice people end up making? And as funny as it seems, it turned out that the coin flip influenced participants’ decision making a lot. Taking account of a range of other factors, Levitt finds participants who got heads were about 25 percent more likely to make the change they were considering. And these weren’t insignificant decisions. Some of the changes the participants were mulling included quitting their job or separating from their spouse.

Equally interesting, the people who went ahead and made the change they were considering usually wound up happier as a result. Among the participants who were considering “important” decisions, those who decided to make a change later reported being a full point happier (on a 1–10 scale) than those who stuck with the status quo. Maybe there’s a lesson here: If you find a potential decision sufficiently compelling that you can’t get it off your mind, you should probably just pull the trigger. (Check out my Valentine’s Day column from 2015, “Taking Risks in Love,” for one practical application of this principle.)

The potential lesson here is intriguing. The results suggest that people leave a chunk of potential happiness untapped simply by tethering themselves to the status quo. Even a randomized virtual signal from a stranger in academia was enough to give people a little momentum and push them toward improving their lives.

Chicken or Egg: Does Happiness Itself Directly Affect Mortality?

Background

Poor health can cause unhappiness and poor health increases mortality. Previous reports of reduced mortality associated with happiness could be due to the increased mortality of people who are unhappy because of their poor health. Also, unhappiness might be associated with lifestyle factors that can affect mortality. We aimed to establish whether, after allowing for the poor health and lifestyle of people who are unhappy, any robust evidence remains that happiness or related subjective measures of well-being directly reduce mortality.

Methods

The Million Women Study is a prospective study of UK women recruited between 1996 and 2001 and followed electronically for cause-specific mortality. 3 years after recruitment, the baseline questionnaire for the present report asked women to self-rate their health, happiness, stress, feelings of control, and whether they felt relaxed. The main analyses were of mortality before Jan 1, 2012, from all causes, from ischaemic heart disease, and from cancer in women who did not have heart disease, stroke, chronic obstructive lung disease, or cancer at the time they answered this baseline questionnaire. We used Cox regression, adjusted for baseline self-rated health and lifestyle factors, to calculate mortality rate ratios (RRs) comparing mortality in women who reported being unhappy (ie, happy sometimes, rarely, or never) with those who reported being happy most of the time.

Findings

Of 719,671 women in the main analyses (median age 59 years [IQR 55–63]), 39% (282 619) reported being happy most of the time, 44% (315 874) usually happy, and 17% (121 178) unhappy. During 10 years (SD 2) follow-up, 4% (31 531) of participants died. Self-rated poor health at baseline was strongly associated with unhappiness. But after adjustment for self-rated health, treatment for hypertension, diabetes, asthma, arthritis, depression, or anxiety, and several sociodemographic and lifestyle factors (including smoking, deprivation, and body-mass index), unhappiness was not associated with mortality from all causes (adjusted RR for unhappy vs happy most of the time 0·98, 95% CI 0·94–1·01), from ischaemic heart disease (0·97, 0·87–1·10), or from cancer (0·98, 0·93–1·02). Findings were similarly null for related measures such as stress or lack of control.