The Payroll by Job Class dataset from the L.A. Controller’s Office includes 34 different data types: Year, Department Title, Payroll Department, Record Number, Job Class Title, Employment Type, Hourly or Event Rate, Projected Annual Salary, Q1 Payments, Q2 Payments, Q3 Payments, Q4 Payments, Payments Over Base Pay, % Over Base Pay, Total Payments, Base Pay, Permanent Bonus Pay, Longevity Bonus Pay, Temporary Bonus Pay, Lump Sum Pay, Overtime Pay, Other Pay & Adjustments, Other Pay (Payroll Explorer), MOU, MOU Title, FMS Department, Job Class, Pay Grade, Average Health Cost, Average Dental Cost, Average Basic Life, Average Benefit Cost, Benefits Plan, and Job Class Link. A record in this dataset consists of a row in the spreadsheet, which shows every data type for one individual employee of a Los Angeles City Department.
Wallack and Srinivasan differentiate between the ontologies of “state-created information systems,” or meta ontologies, and local communities’ ontologies. Because the Payroll by Job Class dataset does not appear to take local contexts into account in organizing and presenting the data, it seems to be a meta ontology. The data is organized alphabetically by Department Title, but the data within each department does not seem to be in any particular order. This makes it difficult to compare the data within each department in order to discover differences among those with the same or different Job Class Titles. For instance, the first record in the dataset has the job class title, General Manager of Aging Department. The twelfth record has the same job class title and appears identical to the first record in several of the data types, but differs in data types like Q2 Payments and Base Pay.
The difference between the first and twelfth records likely has a clear explanation for officials at the L.A. Controller’s Office. The dataset is probably most intelligible to city employees, as it incorporates department and job titles, as well as financial terms, which they encounter on a daily basis. Someone who works for the city government likely approaches the dataset in pursuit of very particular information that they already understand on a basic level, such as the projected salary of the General Manager of the Aging Department. Even though there is such a great quantity of data and it is organized alphabetically, a city employee knows the context well enough to find the information.
However, it seems more difficult for those outside the city government to place the data in the context of their daily lives. For instance, how are L.A. residents supposed to discern the difference between the first and twelfth records when they are so similar to the untrained eye? How can they decide if the hourly rate for each position is adequate compensation for the work, or if a certain supervisor is justified in earning twice his/her subordinate’s salary? Are they likely to look through the entire dataset, or will they accept the first set of records (for the Aging Department) as representative of the following records? Even though the dataset claims to lend insight into payroll by job class, it is surprisingly difficult to discern the cause or meaning of the salary differences. It might help community members to interpret the data in social terms if information like the race or gender of the city employees were included with their financial information.
I think many L.A. residents might find this data interesting because it provides information about which city employees earn the most and the least. This in turn provides insight into the distribution of taxpayers’ contributions to various departments and individuals within those departments, which could prove controversial. If I were organizing the data around this ontology, I would reorganize the hourly rates and projected salaries so that they appeared in descending order, from highest to lowest. During data collection, I would also attempt to trace where the funds for each salary were obtained and to determine who decided which funds would be diverted to each department. This might allow an L.A. resident to determine whether the funds are distributed fairly or whether they believe some departments or individuals are unfairly favored over others.