Higher income is the desire and dream of all workers. But do you know how much money you will earn in the future. While instant gains and benefits may be on most minds, forgoing it for bright future prospects can lead to higher income. And the prospect of larger future rewards may seem far-fetched but is real.
A study conducted by a group of scientists in the US states that the "ability to delay gratification... sacrificing smaller, instant benefits for the prospect of larger future rewards" is one of the key factors of predicting higher income.
Key factors that determine higher income in the future
The scientists from the Temple University in the US, who used artificial intelligence to rank determinants of affluence, found that education and occupation were the best predictors. However, the ability to delay instant gratification beat factors like age, race, ethnicity and height, states the study published in Frontiers in Psychology.
The research suggests that interventions to improve this "delay discounting" could have literal payoffs in terms of higher income attainment.
How much money a person will earn is based on several factors.
Traditional methods of income prediction
"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.
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"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
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.
"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," said Hampton.
(With PTI inputs)