As part of a bigger project, I analyzed more than 20,000 teachers and staff from my county’s school district (data is public record in the state). It’s a top 10 school district in the country (in terms of student population). I discovered the “gender_guesser” module in Python and used it to guess the gender of all of the employees. The get_gender() method just looks at the first name and takes a guess as to man or woman. On smaller subsets, it seemed to do a pretty good job when I tested manually, so I’m pretty confident the percentages are at least in the ballpark. Obviously this is a pretty crude way to go about this and it cannot possibly figure in non-binary, transgender, men who were named Janet, etc. but, again, it’s in the ballpark.
It wasn’t my first choice to have the TOTAL in the middle-ish, but I am using datawrapper for this whole project with little customization allowed, so it is what it is. It’s growing on me, though, since it’s easy to compare all the positions at a glance pretty quickly. Women overwhelmingly are employed by the school district (I imagine this is not abnormal), but there isn’t a single woman mechanic!
It’s almost as if men and women *naturally gravitate towards different careers and interests.
OP it’s a nice start.
I’ve noticed this kind of data is usually presented with categories on the y/left axis to make them easier to read, and usually the data is stacked to make it easier to compare (these all obviously add up to 100, but the way it’s presented here the reader doesn’t realize that immediately)
Is this all the categories? It seems like there should be more – from the existence of “driver’s Ed” and “ROTC” I assume this is all grades through high school, but I don’t see teachers for grades 6-8 or 9-12. For that matter, I see one topic-specific teacher (PE) but not others like math, art, music, etc. (I’m not sure if “Technology” refers to technology teachers or IT professionals). And wouldn’t “Kindergarten” overlap with “K-5”?
Sorry about all the questions, this looks like a pretty interesting project!
You should make a second plot with the actual numbers. Upon first glance this chart looks like a roughly 50-50 split, but it’s clear that some categories have much more people given that the total is not 50-50.
where do I apply for the salary > $100k job?
I’d like to see this more clearly broken down by elementary vs secondary and possibly along subject areas in secondary as well.
![[OC] Teacher and School District Employee Gender Breakdown](https://www.europesays.com/wp-content/uploads/2024/08/whxiqddzz2jd1-1920x1024.png)
8 comments
As part of a bigger project, I analyzed more than 20,000 teachers and staff from my county’s school district (data is public record in the state). It’s a top 10 school district in the country (in terms of student population). I discovered the “gender_guesser” module in Python and used it to guess the gender of all of the employees. The get_gender() method just looks at the first name and takes a guess as to man or woman. On smaller subsets, it seemed to do a pretty good job when I tested manually, so I’m pretty confident the percentages are at least in the ballpark. Obviously this is a pretty crude way to go about this and it cannot possibly figure in non-binary, transgender, men who were named Janet, etc. but, again, it’s in the ballpark.
It wasn’t my first choice to have the TOTAL in the middle-ish, but I am using datawrapper for this whole project with little customization allowed, so it is what it is. It’s growing on me, though, since it’s easy to compare all the positions at a glance pretty quickly. Women overwhelmingly are employed by the school district (I imagine this is not abnormal), but there isn’t a single woman mechanic!
It’s almost as if men and women *naturally gravitate towards different careers and interests.
OP it’s a nice start.
I’ve noticed this kind of data is usually presented with categories on the y/left axis to make them easier to read, and usually the data is stacked to make it easier to compare (these all obviously add up to 100, but the way it’s presented here the reader doesn’t realize that immediately)
Here’s an example of similar data presented in a way that’s easier to read (about halfway down the page): https://carolinademography.cpc.unc.edu/2015/03/16/male-and-female-dominated-occupations-2013/
Is this all the categories? It seems like there should be more – from the existence of “driver’s Ed” and “ROTC” I assume this is all grades through high school, but I don’t see teachers for grades 6-8 or 9-12. For that matter, I see one topic-specific teacher (PE) but not others like math, art, music, etc. (I’m not sure if “Technology” refers to technology teachers or IT professionals). And wouldn’t “Kindergarten” overlap with “K-5”?
Sorry about all the questions, this looks like a pretty interesting project!
You should make a second plot with the actual numbers. Upon first glance this chart looks like a roughly 50-50 split, but it’s clear that some categories have much more people given that the total is not 50-50.
where do I apply for the salary > $100k job?
I’d like to see this more clearly broken down by elementary vs secondary and possibly along subject areas in secondary as well.
What is ROTC in this context?