Scientists for the first ever time have ranked the most important determinants of future affluence using machine learning. Their study suggests that a person’s ability to delay instant gratification was also among the most important determinants of higher income, beating age, race, ethnicity and height.
Numerous elements are identified with how much money a person will gain, including age, occupation, education, gender, ethnicity and even height. Social factors are likewise implicated, for example, one identifying with the popular “marshmallow test.”
This investigation of delay discounting, or how much a man discounts the value of future rewards contrasted with quick ones, demonstrated children with more prominent self-control will probably have higher pay rates sometime down the road.
The lead author of the study, Dr William Hampton noted that using traditional ways to find out which of these factors are more important than others won’t show them appropriate indications.
He said, “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. 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.”
Scientists collected data from more than 2,500 diverse participants and categorize them into 2 sets: a training set and a test set. The test set was put aside while the training set produced model results. Scientists then move back to the test set in order to check whether the findings are accurate or not.
Undoubtedly, the models indicated that occupation and education were the best 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, is more predictive than age, race, ethnicity or height.
Dr Hampton said, “the research approach will be part of a new era in data analysis. “This was amazing because it allowed us to check our findings and replicate them, giving us much greater confidence that they were accurate. This is particularly important given the recent wave of findings across science that does 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.
Though this 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.
Finally, Dr Hampton has an interesting observation 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 favour of larger ones that they have to wait for. This is probably easier said than done, as very few people naturally enjoy waiting, but our results suggest that those who develop the ability to delay gratification are likely investing in their own earning potential.”
The study is published in the Frontiers in Psychology.