In today’s Washington Post, reporter Jonnelle Marte summarized a new study, conducted by researchers at the University of Miami, that indicates that there is a direct correlation between grade point average in high school and future annual income.
This conclusion isn’t earth-shattering. But take a look at this crazy graph:
Can this be true? Is it possible, really, for the relationship to be so intensely linear?
The researchers, including Michael T. French, director of the health economics research group, think so. They surveyed about 10,000 people in their early-30s and consulted high school transcripts to confirm grade point averages. For every point a GPA went up, men earned 12 percent more and women earned 14 percent more.
Side note: Not surprising that the graph above has two bars, one for men and one for women. As stark as the linearity of the graph is the earnings gap between men and women.
After getting over my initial shock, my second thought is whether to use this graph with students (and if so, how).
My gut says yes. You see, students, your grades really do matter! Look at this graph!
But a few things make me think twice. One concern is that students won’t think this data applies to them. Teenagers are notorious for thinking that they’re unique. Which of course they are. Even if I explained that the graph represents 10,000 unique individuals, students wouldn’t necessary buy it.
Another worry is the reverse: that students will believe that this graph represents cause and effect. “Mr. Isero, my GPA is 1.5. Are you telling me that all I can make is $30,000 a year? I might as well drop out.” Sometimes, provocative images and graphs can lead to negative consequences, no matter how much explanation a teacher offers.
One wish I have about this graph would be to add educational attainment into the mix. For example: What percentage of students with 1.5 GPAs have college degrees? What happens to your income if you have a 1.5 GPA and a college degree? What if you don’t get a college degree?
I’d love to hear your thoughts about this graph. Would you use it? If so, how? I can’t wait to read your brilliant insights!