For this blog post, I decided to investigate the Payroll by Department dataset from the L.A. Controller’s Office website. This is payroll information for all the City Departments of Los Angeles since 2013 which is updated on a quarterly basis. The data types used for this dataset are Department title, Year, Job class, Projected Annual Salary, quarterly payment, payments over base pay, percent over base pay and total payments. A record in this dataset refers to above mentioned payroll information specific to one of the 56 departments included in the dataset.
Wallack and Srinivasan define ontology as “systems of categories and their interrelations by which groups order and manage information about the people, places, things, and events around them.” Based on this information we can see that this dataset’s ontology looks at how total payments differs across different departments and how that is broken down into specific categories such as quarters and payments over base pay. The people that will find this information the most illuminating are individuals interested in how much money in total is used to pay employees for each department in the city of Los Angeles. For example, those who are in charge of the city budget would find this categorization of data very useful. Other individuals that might be interested are those who are trying to compare the difference in pay for different departments across different cities within California or the US.
This dataset does a good job of giving total numbers for the money that goes into paying a city department. For example, when you click on the pie chart, the information you immediately get the department name and the total payments for that department. However, these records don’t let you get into the specifics or even tell you how many payments are totaled in the calculation. For example, the LAPD section shows a total payment of $1,344,118,166.75 but we have no sense of how that number breaks down into a payment for an individual officer.
While this ontology might be very useful for budget planning, it isn’t as useful for those trying to get a sense of what the average total payment per person is in each department. These would be people potentially interested in working for the city and wanting to compare average salaries from different departments. This kind of ontology would include data types such as average projected annual salary, average quarterly payments, and average payment over base pay.
I like the dataset and your interpretation of the ontology. Given that it breaks down payroll by department and can be tracked on a quarterly basis, I argue that this would be a good tool to understand the spend cycles of various departments and to push forward on a deeper economic analysis of the reasons for inflated or deflated spending at particular times of year. The category of “Over base pay” could be broken down further into a sub-directory of payment types – was it in bonuses? expense reimbursement? Just these two categories alone would and should be distributed very differently – one is a payroll expense and the other, technically is a departmental expense that the employee is reimbursed for – but would be paid out on the payroll check and claimed by the employee at the end of the year as a deduction. It would be cool to do a deeper dive into this particular area and create a sub-ontology to explain it.
You bring up interesting points at the end, about how the pie chart shows only the total budget allocated towards payroll by department, rather than individual average pay. However, if they were to do it by average the data might be skewed, unless the median and average were both presented (since entry level jobs would be making something very different than the heads of the departments). I definitely agree that this data really helps budgeters, and that it doesn’t really help people interested in public service occupations. Interesting interpretation, and I like how you defined ontologies!
Excellent description of the dataset and you do a great job of understanding it through this week’s reading!
This idea was sparked when reading your post, but I guess another way you could say the ontology of this data set is money, and how much people get paid. What if we never had a monetary system and instead exchanges were based off of goods or favors, then this dataset would be completely irrelevant to us as these numbers would mean nothing.