In a new study, researchers at Temple University used machine learning to better understand what human qualities are most likely to predict financial success.

Although education and occupation were the best predictors, the researchers found that a person’s ability to delay instant gratification was also among the most important determinants of affluence.

The findings, published in the journal Frontiers in Psychology, suggest that interventions to improve gratification control could have literal payoffs in terms of higher income attainment.

Many factors can influence how much money a person will earn, including age, occupation, education, gender, ethnicity and even height. Behavioral variables are also implicated, such as the trait relating to the famous “marshmallow test.” This original study of “delay discounting,” or how much a person discounts the value of future rewards compared to immediate ones, revealed that children with greater self-control were more likely to have higher salaries later in life.

But the study’s lead author, Dr. William Hampton, now at the University of St. Gallen in Switzerland, said more traditional ways of analyzing data have been unable to determine which of these factors are more important than others.

“All sorts of things predict income. We knew that this behavioral variable, delay discounting, was also predictive, but we were really curious how it would stack up against more common-sense predictors like education and age.”

“Using machine learning, our study was the first to create a validated rank ordering of age, occupation, education, geographic location, gender, race, ethnicity, height, age and delay discounting in income prediction,” Hampton said.

Traditional approaches used by psychologists, such as correlations and regression, haven’t allowed for a simultaneous comparison of different factors relating to an individual’s affluence.

The new study collected a large amount of data — from more than 2,500 diverse participants — and divided them into a training set and a test set. The test set was set aside while the training set produced model results. The researchers then went back to the test set to test the accuracy of their findings.

Not surprisingly, the models indicated that occupation and education were the top predictors of high income, followed by location (as determined by zip code) and gender ,with males earning more than females. Delay discounting was the next most-important factor, being more predictive than age, race, ethnicity or height.

“This was amazing because it allowed us to check our findings and replicate them, giving us much greater confidence that they were accurate,” Hampton said.

“This is particularly important given the recent wave of findings across science that do not seem to replicate. Using this machine learning approach could lead to more research that replicates — and we hope this spurs the use of more sophisticated analytic approaches in general.”

The researchers warn that the data sample was purposely limited to the United States, and it is possible that the rank order of variables that predict salary may differ in other countries. Hampton says he is looking forward to investigating this analytical approach in a broader context.

“I would love to see a replication of this study in another culture. I also would be very interested in future studies aiming to reduce delay discounting. There is much debate about whether delay discounting is a stable trait or whether it is malleable — longitudinal studies could help settle that.”

Finally, Hampton offers some advice for parents, “if you want your child to grow up to earn a good salary, consider instilling in them the importance of passing on smaller, immediate rewards in favor of larger ones that they have to wait for.”

Source: Frontiers