Lexis diagrams were invented about 150 years ago and have been used mostly by demographers. Even if you're not a demographer, they're worth learning about because they can reveal subtle patterns if you have the right kind of data.

This example, created using R and ggplot and Current Population Survey data, shows the percent of men and women aged 25-80 who obtained a bachelor's degree or higher between 1995-2025. The diagonal white lines show the birth year for each age group. The diagonal bands therefore represent age cohorts. Dark colors indicate low rates of degree attainment, while bright colors indicate higher levels. For example, in the second graph, you can see that early generations of women had lower attainment than men, but have reversed that trend and now have higher rates.

There's a lot to learn about Lexis diagrams. Check out my recent Substack article.

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dmalitz

21 comments

  2. I love these! 3 variables on the same plot, all continuous, is always a challenge.

    I would suggest for this one using birth year, not year degree was attained. IIRC a lot more students now are “nontraditional” which includes being older, as well as married / had kids.

    EDIT: wait, u/The-Jolly-Llama pointed out this is cross sectional and thus every year is mostly degrees from before, sometimes decades before. *That* needs fixed for sure: it’s going to hide a lot of real trends and also be confounded by longevity.

  3. What is with the dark ban of men and women born around 1960?

    Why did so few of them finish college compared to those before and after?

  4. I am confused about what exactly this represents. 

    Each point is an (x,y,z) triple where the z coordinate is represented by the color. So each point is a triple of (Year, Age, %).

    But then aren’t the points (60, 2010, z) and (60, 2015, z) representing the same people? So how is the second point *dimmer*? Oh, is that caused by deaths in that cohort? 

    Okay wait, I think I do understand. Following the age cohort lines represents the same cohort passing through time, which is why it mostly gets brighter as you move up and right. 

  5. What happened to people born in 1960? Was coming of age during the late 1970’s that bad for their outcomes?

  7. If you had actually added more of this text onto the diagram itself instead of into the reddit post it would have been better. I agree that it is useful to visualize this type of development where you have things spread out in time going through a process.

  8. for the life of me i cannot figure out what is going on here.

  9. If you think this particular way of presenting the data shows a pattern otherwise hidden, can you please compare it against more traditional ways of presenting that data where that pattern is not visible?

  10. This chart as presented is pretty unreadable. There is no indication on the chart itself what any of the data means. The axis and the diagonal lines are not labeled.

  12. Very good graph. Should extend down to 20 years though considering a lot of people get their bachelor’s before 25

  13. That was painful for me to read initially. Once I wrapped my head around it, very interesting!

  14. So we see more men obtaining degrees than ever before, which does kind of counter some of the cultural mythology around this subject

  16. I’d be interested to have a derivative plot to see the change in percent. It looks like the 1950 and 1970 cohort have some areas of regression and expansions, such that there are more deaths than new degrees at some points. There’s similar splotchyness around younger generations but it’s hard to tell if it’s stagnation or reversal.

  17. maybe because it’s got way too much going on there, but to me, give me the old line graph. One look i know what i’m looking at. I can see why they were not used as much. It’s just the X axis to me don’t really match up with the other line with the year? like 1980 is around the 2005 mark??? WTF? To me simple line graph says all this and easier to read.

  18. I’m just not a fan of these. There are much clearer ways to present this data.

  19. You should really check out the visuals you can create with GAMs (generalized additive models)

  21. It’s interesting you can see a band of men born in the late 40/early 50s getting college degrees. I’m guessing there was an increase in college enrollment among that cohort to avoid Vietnam? Or maybe getting GI money from their father’s WWII service?

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