Week 3-Dataset “Payroll by Department”

I found Payroll by Department (aka All City Departments by Payroll) for Los Angeles in the year 2015 quite interesting.  When you first click its icon from the homepage, the dataset is in a chart form mainly indicating proportion of every department’s payment in the total state government payment while if you choose “view it as a table,” “view it as a rich list” or “view it as a single row” you will see the chart is a visualization of a tabular data. A record in this dataset includes department title, year, job class title, projected annual salary, payments by quarter, payments over base pay, percentage over base pay and total payments.

Wallack and Srinivasan define ontologies as “systems of categories and their interrelations by which groups order and manage information about the people, places, things, and events around them.”[1] According to those two scholars ontologies reflect register different elements and their interrelation with each other in groups.  In this dataset, government employees’ salaries are first categorized by the different employer departments. And then those subsections are divided into four by quarter. At last, the projected salaries are compared to actual expenditures in one department.

It seems that this dataset is most useful for government sections related to finance and human resource such as “Economic and Workforce Development Department” and “Personnel” to track the payroll. They can detect any quarterly anomaly within one department in 2015 or investigate the discrepancies between salary budget and actual expenditure to make better plans for the future. Because of the bird-eye view this dataset offers, the upper-level government management agencies such as the mayor would also benefit from this dataset to understand how different departments work financially.

The dataset dedicated to express the differences of the payments among departments, reveals to me that the city invested greatly in legal enforcement, basic supplies and fire prevention due to the prominent payments to LAPD, DWP and LAFD. Those departments also pertain to more job class titles and more percentages over base pay. It could be an indication that jobs in those departments were more specialized, demanding or dangerous. Meanwhile, most departments worked most in the third quarter and lest in the last quarter, which suggests the city were very busy in the summer and more relaxed in the winter. The outstanding percentage over base pay in Employee Relations Board also caught my attention. I would like to know more about why this department had so few job class titles but the workers in the department seemingly worked extra hard.

This dataset seems less meaningful for an employee who considers joining in the government service. He or she cannot find out which position pays more in which department or how high his or her salary could be if he or she can get to the top of the department. He cannot even find the median of salaries in one department. Due to the different natures of tasks in different departments, it is hard to compare which job in which department is more rewarding.  The existing data also need more interpretation: why did the government spent so much on law enforcement? Compared to the dataset documenting the same ontologies such as the one from last year or the one from New York City of 2015, did LA spend less or more?

If I was to design a new ontology, I would add the percentage of increased payment for every department to show the yearly change. Decision makers may need the information. Also I would list the numbers of employers, the highest and lowest salary in one department to show how the expenditure was distributed in one department to inform those who consider joining in. It may not be a bad idea to merge this tabular data with “Payroll by Position” which offers a more micro-level perspective.

[1] Jessica Seddon Wallack and Ramesh Srinivasan. “Local-Global: Reconciling Mismatched Ontologies in Development Information Systems.” Proceedings of the 42nd Hawaii International Conference on System Sciences – 2009.