The datatypes available in the gender breakdown of city workers by department consist of year, department title, number of employees, numbers and percentage of male and female employees, their total salaries and averages, divided by gender, and a breakdown of the percentage of the payroll that was paid out to each.
The dataset’s ontology is a primarily gendered one, with an economically oriented epistemology, as it looks primarily at earnings and participation in the workforce, and uses gender to draw divisions within each department. It can function in a few different ways, but the clear overarching purpose is one of examining the role of gender in the workplace. Feminists, particularly, will find useful information here.
As far as the phenomenon described by the dataset, there are a couple of interesting points. Within many departments, more of the total payroll is distributed to women, but it’s only in Recreation and Parks that women actually show a higher average earning than men, and by a margin of less than $1000. This means that, in many cases, greater numbers of women are working than men, but that they tend to earn less on an individual basis.
As far as what’s left out, the dataset does insist on a binary categorization of male/female, which makes it incapable of accounting for intersex, genderqueer, and transgender identities, and the ways that those might affect one’s workplace participation and earnings. Studying this would require an ontology capable of accounting for a spectrum of identities rather than a binary.
I found your observations regarding earning rates between genders to be really fascinating. I definitely didn’t expect Recreation and Parks to be the only sector where women have a higher average earning. I also really liked your point on the gender spectrum. I think it’d be really interesting to see how these wage rates change when you take into account all of the different identities. Lastly, I thought your post was super structured and laid out very clearly. I had a very easy time reading through it!
This was a really great post. I enjoyed the organization of your thoughts, and how you broke down the data presented in this set. It was interesting seeing the different departments. Some I had not even heard of such as the El Pueblo De Los Angeles Historical Monument Authority. I would have thought there would be more sectors than Recreations and Parks where women earned higher. I’m curious as to what factors played into that, and how they affect other sectors. You also bring up a great point when talking about the binary nature of this dataset. It would be interesting to see if these other gender identities would affect pay in a positive or negative way or any at all.
You make a very good point that this gender breakdown does not account for other gender identities, which is important in any understanding of how gender relates to earnings and participation. While this dataset is interesting to me in that it provides data for conclusions on gender and workplace treatment, its limitations take away from its overall usefulness.