For this week, I decided to analyze Gender Breakdown of City Workers by Department, which is a dataset that contains information about payroll men and women for a list of jobs. This is to give the readers an objective data on how men and women compare in terms of salaries, and for the readers to analyze the inequalities depending on job description. The question that naturally arises from this data is, “are women really getting paid less, and why”? The record in this dataset is the information collected from each department which contains data about # of Employee, Total Payroll, #Female, #Male, Female/Male Total Salary, and Female/Male Average Salary.
Wallack and Srinivasan identifies ontology as the “system of categories and their interrelations by which groups order and manage information about the people places, things, and events around them.” In other words, ontologies’ duty is to relay information of a reality of a certain phenomenon, which may push communities for a change. This dataset in particular is a meta-ontology, which is a state sponsored data to give an objective information to the public. In this dataset, the ontology is comparing the salary of men and women in order to report the possible income inequality between sexes.
This dataset would be the most useful for equal rights activist, to get raw and objective information on how women and men’s salary differ, and to enact change of this injustice. This data is simple in that the record can be categorized into 3 subgroups, job title, # of men and women employee, and salary difference between men and women. Therefore, by looking at the table, the user can understand exactly which job employs more men or women, and what the salary difference is. The website also allows different visualization of the data, for example, into bar graphs, pies, and treemap, which allows users to digest and compare the information more effectively.
This dataset is great in that we can easily see the difference in salaries depending on gender and the job. However, the dataset is too simplistic in that we do not know exactly how many hours both men and women work. In a society where the stigma of women as housewives still exist, perhaps women work less hours because as working parents, one usually have to take kids to school or pick kids up after school. In our society, women are often assumed to take this role. Thus, perhaps the difference in payroll could be that women are working less hours due to this social stigma. On the other hand, it is possible that men and women work the same number of hours; we would never know unless we have that informations.
From a different person’s point of view, this dataset could be information containing the gender distribution for each job. As each position have varying degrees of men and women worker, the graph shows which job is popular or more geared towards men and women. Questions that could arise from this point of view is why some job has more men or vice versa, and is this through sexism, coincidence, or other reasons.
I like your analysis of the dataset as a potential tool for gender rights activism. Agreed that women may work differently from men – leaves due to pregnancy, childcare responsibilities, and other factors typically associated with familial responsibilities could affect gross income either in reduced overall pay or reduction in the number of hours an employee can work. Seniority is also an issue, with city and county positions often on a pay schedule that reflects the amount of time with the department, some departments integrated genders much later than others – the fire department vs. City Hall administrative positions for example. If women were more recently introduced into the department, they might not have had necessary time to qualify for the upper reaches of the pay schedule. Position relative to gender is important as well: are women being denied the opportunity to advance in certain departments, and thusly unable to achieve pay raises associate with promotion?
I really like your commentary on the simplistic nature of the data and the way that it could be misconstrued due to the gaps in information. In many (or even most) instances of data presentation, one can see what they want to see in the data. Two different people looking at this same dataset can walk away with completely different understandings of the gender gap and its legitimacy.
Many internet pop-psych articles attempt to convince people of attention-grabbing, outlandish statements “backed up by data and research,” as if that statement alone proves the truthfulness of a claim. Many of those articles, upon closer inspection, have little to no support for their bold headlines. All this to say that we as humans are quick to justify our worldview in whatever way that we can, and as a digital humanities student, I think it’s imperative that the narratives we choose to create and engage with only result in spreading truth.
I agree, the simplicity of this data set can mislead many viewers. I had very similar thoughts when viewing this data set. It’s not enough to simply say women are earning less than men in various government department, we need to look at the long-term trends to get a more accurate picture, as well as specific job titles within the department.
Nice job! Remember, all of the things listed at the top of the data sheet are the data types and together they make up an individual record.
This was really interesting to read! I had no idea that this dataset was available in the L.A. Controller’s Office! I am definitely intrigued by the information presented by this dataset, but I agree with your analysis- that what we know barely scratches the surface. This is a great example of how ontologies work, and how they can strongly influence the way information is received and understood.
Great analysis! Your discussion of the potential use and improvement of this dataset is really inspiring. Not necessarily an optimal tool for the equal rights activists, the dataset actually demonstrates some of the confounding variables when the topic of gender equality is brought up: lack of information, combined with unawareness of the impact of social stereotypes, often leads to less considerate interpretations. I totally agree with your critique on its flaws.
You make some good points, depending on who is looking at the data they may crete their own narrative as to what to infer from it. Institutionalized sexism is a complex systemic problem with many interwoven complexities that need to be considered rather than generalizing data.
I really like the thoroughness with which you’ve investigated this dataset, and I appreciate the way you’ve paraphrased the Wallack and Srinivasan piece.