Los Angeles’ 2013 City Payroll by Job Class

I observed the Payroll by Job Class data set, which is based on the city payroll data for all Los Angeles City Departments since 2013. It is organizes into a very detailed spread sheet with a lot of useful information and is updated on a quarterly basis (when in its current year) to ensure accurate and up-to-date information.

The spreadsheet has dozens of data types. In the order from which they are read left to right, the data types are: year, department title, payroll department, record number, job class titles, employment type, hourly or event rate, projected annual salary, Q1-Q4 payments, payments over base pay, percentage over base pay, total payments, base pay, permanent bonus pay, longevity bonus pay, temporary bonus pay, lump sum pay, overtime pay, other payroll and adjustments, MOU titles, department class, pay grade, average health cost, average dental cost, average basic life insurance, average benefit cost, benefits plan, and finally, a job class link. A record in this database is whenever a row is added, in which each of these data types, or columns, are filled out for every individual and their position added.

Using Wallack’s and Srinivasan’s definition of ontology, I would classify this dataset as a meta ontologies. It is created by the state in an attempt to inform its citizens about pay roll in certain cities, yet the data might overwhelm or allude citizens who do not understand how to read data or have technological barriers to access it. Because of this, I believe that government officials, whether it is the mayor or a local city police officer, will find this information most useful and illuminating. This is not to say, however that a local citizen might wonder where their tax dollars are going to or how much the pay is for a certain position. Yet, I believe it is most useful to government attempting to figure out the fiscal year.

The phenomenon in this data sets describes the way pay increases as you move higher in the city ranks, as well as responsibility. It attempts to show how often and how much officials get paid in the position they are in, as well as how they spend that money when using government resources. However, even though this dataset is very detailed, I find it missing some things. It does not have the names of government officials, which might confuse people when they see the same titles. It also does not mention how long the person have been working for the government, which might result in confusion for differing salaries within the same position.

If I was starting over with this data collection and was someone else with a different point of view (and didn’t know it was made by the city), I would think this was a critique of how much government officials are being paid using tax payer money.