I chose the Gender Breakdown of City Workers by Department dataset from the L.A. Controller’s Office that analyzes salaries of full-time employees by gender from various Departments of the City of Los Angeles from the year 2013. This dataset is presented in a spreadsheet format, also known as a table, and it can also be visualized in a number of ways, such as the bar chart, pie chart, and the line chart. It has 41 rows with each row constituting a record in this dataset. And all the records have year, department title, total employee count, total payroll($), number and percentage of females and males in the department, total salary for females and males($), average salary for females and males($), and percentage of payroll given to females and males, making it a total of 14 variables.
Wallack and Srinivasan define ontology as a “system of categories and their interrelations by which groups order and manage information about the people, places, things, and events around them” (2009:1). As such, this dataset can be categorized under payroll and gender. The website also includes tags for this data, such as “city profile”, “demographic”, “employees”, “gender”, “women”, “equality”, and “wage gap”. Then, this dataset’s statistically motivated ontology is for policymakers and women’s rights activists who are interested in gender discrimination in the workplace as indicated by the gender-based differences in salaries. Since the dataset is primarily focused on the gaps between females and males regarding their payroll, it would make most sense for women’s rights activists to fully utilize this information to push for policy changes and reforms, which may lead to policymakers, both in private and public sectors, working towards ending gender discrimination (someday hopefully).
This dataset tells us that in 2013, except for the Departments of Recreation and Parks, Disability, and Neighborhood Empowerment, females have earned a lower average salary than their male counterparts in their respective departments. And even when females have earned a higher average salary than males, the difference is small compared to other wage gaps across all the departments. For example, in Recreation and Parks, females earned an average salary of $66,834.60, while males earned an average salary of $66,080.69. However, in the Department of General Services, females earned only an average salary of $60,854.75, whereas males earned an average salary of $73,128.41. The purpose of this dataset seems to be to expose the gender discrimination that still exists in the Departments of the City of Los Angeles. It also shows the proportions of females and males in a department, and in some departments, the difference in the proportions of females and males is staggering, which can lead to the perpetuation of biased labeling of a job function as “too manly” or “for women”.
Although the dataset does a good job of listing statistics regarding the payrolls of females and males, it can be better by perhaps adding information about different age groups as well to compare the difference in wage between females and males within separate age groups. And, perhaps, information about education level can further illuminate the characteristics of the females and males and the significance of the wage discrimination taking place.
If I were to start the data-collection all over, I would base my ontology on education and career. Since I am graduating soon, I would love to know what kind of job I want to have in the future. I would collect data on education level, such as college degree and major, industry, position, salary, job satisfaction, and years employed and divide the information based on gender, so that job seekers can have a sense of how much they can earn with their education level.