For this week’s blogpost, I analyzed the payroll for all Los Angeles City departments beginning in 2017. I chose this dataset as it provides an interesting account of the city’s fiscal expenditures, and therefore its priorities.
The dataset organizes its 265,000 records through 35 content types. These content types include (among many more): year, department title, job class title, pay grade, employment type, projected annual salary, hourly or event rate, overtime, lump sum pay, and average city health cost. This organizational method allows for easy statistical and comparative readings of average salaries. This data set is useful for those looking for jobs with the city or those already employed looking to appraise salary cuts/raises, or other payroll related analyses.
While the payroll is informative in a numerical sense, it does not necessarily offer a lot of information pertaining to the more qualitative aspects of these jobs. For example, we cannot see how long someone has been employed with the city, a major determinant of salary, or things as average hours worked. Because full-time jobs do not offer overtime, it is hard to assess the work-life balance these jobs offer.
To offer a different ontology, I would wish to see more information pertaining to employees’ backgrounds. What type of degrees do employees have and do levels of education correspond with pay rates? Do gender, sexuality, or ethnic/racial identity make a difference in salary? While the data set offered, provides a lot of statistical information, its omission of employees’ idiosyncrasies treats workers as numbers rather than people — and while this is all fine and good from a purely numbers standpoint, it offers very little to interpret for social analysis.
I would also be interested in knowing the employee’s backgrounds and how that correlate to the things that they do. As a student, it’s more interesting to me to see the connection between the employees’ backgrounds and the jobs that they have. It would also be interesting to track payroll of freelancers, especially for people who work in the art and design and entertainment field.
I completely agree as to how these datasets offer little qualitative information about the work environments that these people are experiencing. Those details that are omitted can serve to help us make an improvement to these industries. Coming from a humanities perspective, many of these datasets leave out the human component.