Data Analysis: Gender Breakdown by Department

Gender Breakdown of City Works by Department documents the percentage of male and female full-time employees in 2015 across the various Departments of Los Angeles, including city planning, fire, and sub-departments of public works, such as engineering and sanitation. The data set also reports the employee count and total payroll per department, the number of males and females in each department, and what percentage of the department are male and female. Additionally, the information also breaks down the male and female total salary within departments, the average salaries of males and females within departments, and the percent of the payroll given to males and given to females.

This dataset was created by the Los Angeles City Controller’s Office. I believe Wallack and Srinivasan would identify this dataset’s ontology as a comparison between employee gender and salary within and between government departments. This data set is very easy to navigate, and theres a tool guide that allows viewers to make data visualizations for even easier juxtaposition and comparison.

The line graph above, for example, shows average female salary in navy and average male salary in orange across the various departments. This data is very straightforward: on average, men make more money than women in 37 (out of the 40) departments, with women making more only in the Library, Recreation and Parks, and Public Works – Street Lighting Departments.

On the ground level, grassroots coalitions and social justice organizations, particularly feminist advocacy groups, would find this data very useful. Pulling up these statistics could have a big impact on arguments for women’s rights or affirmative action. Seeing as though Los Angeles is one of the most liberal and diverse major cities in California and in the entirety of the Unites States, one could use these numbers to argue that there are still mass inequalities in the workforce today. At a higher level, this ontology also makes sense for policy makers and those in the City Planning and City Ethics Commission Departments who: (1) (hopefully) want equal and just opportunities for women, and (2) want to appear as though they are working towards equal and just opportunities for women.

While the numbers state the “what” in this gender breakdown, there is no “why” to explain the reasons behind them. In the fire department, for example, 92.8% of the full-time employees were male whereas only 7.2% were female. I assume this disparity has less to do with discrimination and more to do with the fact that less women want to be firefighters. Nevertheless, this could certainly lead to further social science analyses to explain this kind of information that has been left out of the data set.

If I were to start over with data-collection, I would attempt to describe the ontology of higher rates of males in leadership positions than females. In the current data set, in the City Administrative Officer Department, almost 70% of the employees are female, and yet the average female salary is about $34,000.00 less than the average male salary. This is (also hopefully) because males hold most of the leadership/managerial roles than females in this department, and not because males are making more money for the same work. By including columns stating how many males/females in each department hold leadership positions, and how many males/females in each department make over/under $50,000.00, the spreadsheet could produce different narratives based on a different ontology described by the data.