I chose to explore the City Payroll Data of Los Angeles, which provides the payroll information for all the city departments since 2013. The information is organized under various data types and is put into a spreadsheet, which is updated after payments for each quarter are made.
The data types include 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. Details about the data types can be found by hovering over the information button under the column headings. Each row of the spreadsheet presents a record of each city department employee in this dataset.
According to Wallack and Srinivasan, communities and states present information about people, places, things, and events around them, organized in the form of ontologies, which are essentially “systems of categories and their interrelations”. The dataset is a meta ontology, as it is a community-based ontology and a large-scale dataset with numerous quantitative indicators, looking at payroll for the Los Angeles departments.
This data would be most useful and illuminating to city department employees, enabling them to explore and compare the payroll of other employees in various job titles and departments. It would be easier for them to go through the information, as they are able to understand the financial data types and terms used in the dataset. I think it’s also important for residents to explore this dataset and grasp a better understanding of the payroll of city employees and the imbalance of taxpayers’ contributions amongst the departments.
If I had to reorganize the dataset, I would focus on the annual salary and the hourly rate, organizing them from the highest to the lowest. It would also be interesting to organize the data according to job titles. The reorganization of the dataset could make it more focused on identifying which departments are being more favored in terms of payroll. As for data collection, I would like to include gender and race indicators to gain insight on how these data types influence payroll for city department jobs and how sexism and racism still exist in the workforce.
I like how clear and easy to read your blog post is. I thought that your comment about viewing race and gender was really interesting and I think it should be done. I also thought it was interesting that you mentioned the imbalance of paying jobs. Amazing post!