The ability to delay gratification—sacrificing smaller, instant benefits for the prospect of larger future rewards—is among the most important factors predicting higher income, say scientists who used artificial intelligence to rank determinants of affluence.
The researchers from Temple University in the US found that education and occupation were the best predictors.
However, ability to delay instant gratification beat factors like age, race, ethnicity and height.
Published in Frontiers in Psychology, the study suggests that interventions to improve this "delay discounting" could have literal payoffs in terms of higher income attainment.
Many factors are related to how much money a person will earn, including age, occupation, education, gender, ethnicity and even height. Behavioral variables are also implicated, such as one relating to the famous "marshmallow test."
This study of delay discounting, or how much a person discounts the value of future rewards compared to immediate ones, showed children with greater self-control were more likely to have higher salaries later in life.
Traditional ways of analysing data have been unable to indicate which of these factors are more important than others.
"All sorts of things predict income. We knew that this behavioural 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," said William Hampton, lead author of the research.
"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," said Hampton, who is now at the University of St Gallen in Switzerland.
Traditional methods used by psychologists—such as correlations and regression—haven't allowed for a simultaneous comparison of different factors relating to an individual's affluence.
This study collected a large amount of data—from more than 2,500 diverse participants—and split them into a training set and a test set.
The test set was put aside while the training set produced model results. The researchers then went back to the test set to test the accuracy of their findings.
The models indicated that occupation and education were the best predictors of high income, followed by location 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," said Hampton.
"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," he said.