Weekly Blog #3 – Gender Differences by Department Dataset

For the purpose of this assignment I selected the dataset Gender Breakdown of City Workers by Department.  The dataset can be found here.

The dataset identifies employee earnings by gender across the various city department of Los Angeles.   A record in this particular dataset includes:

• The name of the department
• The total number of employees; and the breakdown between male and female in numerical and percentage form.
• The total payroll spend of the department; and the total and percentage of total payroll spend allocated to males vs. females.
• The average salary for each gender.

Per Wallack and Srinivasan, this dataset and its meta-ontology would be used to track and understand demographics across the breadth of the different departments in the City of Los Angeles, and to understand the relationships between gender and salary.  There is an aspect of self-policing inherent in the creation and administration of the dataset, with the city seeking to monitor hiring practices and possible imbalances in payroll administration.

City Supervisors, Ethics Committees and Title IX administrators might find this data useful. Regular inspection of this information makes sense as part of ongoing efforts to create gender pay equality and also in hopes of routing out institutional discrimination that opens up the city to legal and moral liability.

The dataset details total payroll spend per department and also the breakdown between men and women, including total payroll spend per gender and average salary per gender. What the dataset illustrates is that with the exception of three departments out of forty, men drew higher average salaries than women.  What isn’t detailed is the length of employment or particular details about the inner workings of each department.  Most permanent city employees receive raises on a regular schedule commensurate with the length of time with the job, so if women joined the department after gender integration, that will have an effect on their salaries.  Pursuant to understanding the inner workings of each department, there is no information of promotion strategies within the departments that might skew results.  The fire and police departments offer regular opportunities to move up the ladder, and each promotion results in a pay increase.  If women are being denied the opportunities to advance, it could explain the lower pay across the board, but the information isn’t here.  I can parse out who makes what, but from the data given, I can’t get insight in to the culture and values of the respective departments.  Women are also subject to physical limitations of pregnancy and maternity leave after delivering, causing them to be absent for longer periods of time from the workforce on maternity pay, which is usually a percentage of their regular salary, and can affect these numbers.

A different ontology for this data would include the average length of employment for each gender by department and would allow for seniority, medical and maternity/paternity leaves. It might be interesting to include information about complaints of gender and wage discrimination per department, also logged by gender, to have a better sense of which departments might or might not be tilted toward favoring a specific gender.