LA City Employee Payroll

I chose to look at Los Angeles City Employee Payroll Data. This dataset contains information about the pay of all City employees, with each row being an individual who is represented by a unique ID number, and the columns giving more information about their position and a breakdown of their pay and benefits. This seems to be the main function of their dataset: describing an individual’s position within the city by indicating their Department, title, and job class, and then breaking down the different ways they are paid by the City. This dataset reveals the complexity of payroll, which is broken down into categories like ‘Event or Hourly Rate’ and ‘Projected Hourly Salary’, and then less easily understood categories, like ‘Longevity Bonus Pay’ and ‘Other Pay and Adjustments’.

This Dataset has 294,229 rows, and is updated on a quarterly basis. The web-based interface allows users to apply their own filters, sort the data, search within it, and highlight cells. But even with the interactive tools, I found this dataset to be overwhelming due to my limited accounting knowledge.

This dataset makes the most sense to someone with background knowledge payroll and different positions within the city–for internal use, such as audits of City spending payroll. This dataset is valuable for purposes of comparison across departments–looking at which department spends the most on Payroll, or has the most individuals on payroll. It could also be used to find average pay and benefit rates, or the number of people within a certain pay amount. The job title is not self-explanatory, so I appreciate that each row links to a PDF that describes each job class in detail. I can imagine that this dataset might upset Angelinos who feel like certain individuals are paid too much with their tax dollars–I was surprised to find that the top 5 highest salaries all come from the Los Angeles airports, with ‘General Manager Airports’ all earning over $360K a year. The mayor appears as the 408th highest paid individual. These are interesting facts for a member of the public, but the data is definitely focused on identifying spending within departments of the City.

What this dataset does not include is more detailed demographic information–I think it would be interesting to know the gender, or race of the individuals listed.  My suggestion for an alternative ontology would highlight these characteristics for the purpose of comparing pay between different groups and identifying disparities.  A user would be able to compare pay by gender, enabling further investigation into the wage gap within the City. I can understand why the inclusion of such personally descriptive information might raise concerns about privacy; it would challenge the limitations of government transparency, and the extent to which individuals become publicly visible when they work in government.

While this dataset seems impressively comprehensive and well-maintained, Open Government Data often leaves me asking the “so what” question—what is the value of such a dataset beyond the gesture of transparency? I’m interested in learning about how these open data portals are used at a community level to enact change, and how data open data can be made more useful to the public.