For this week, I decided to analyze the Top Earners dataset from the LA City Controller’s Office. The data primarily looks at different occupations vs. the amount they earn. Their salary is further broken down into categories to help us better understand the data. The data types are the occupation, salary, and the types of pay. Some of the types of pay are base pay, bonus pay, overtime pay, and others. A record can be described as the salary for each occupation. The recorded this information from payrolls from the year 2013, and are updated on a quarterly basis.
Wallack and Srinivasan define an ontology as “systems of categories and their interrelations by which groups order and manage information about the people, places, things, and events around them.” The ontology in this particular dataset is how a single record of salary is broken up into multiple parts to better understand how the salary is structured within different occupations. For example, we can see how a main chunk of the fire captain’s salary is made up due to overtime pay, whereas the port pilots is due to bonus pay. We can also compare the different base pays across occupations and whether the majority of their income is due to other varied factors.
We can draw a conclusion that most of the highest earning individuals are port pilots, apart from that particular fire captain, in the top ten. This information is useful for researchers who would like to know how the salaries of these positions are divided and which occupations earn more on overtime vs. bonuses.
I found it interesting to see how the actually salaries were almost double the base pay, and it was very interesting to learn how they were structured. I think adding more details like how long the people held the position for, and how their salaries evolved with experience might have been useful to a researcher. It might also be helpful to see the gender of the person in this role and compare the differences in the salary of the same post between genders.
I thought this analysis was very thorough and really appreciated your thoughts at the end. In particular, your point on adding gender details to the pay rate are really enlightening. Gender gap for wage rates is a pretty distinguishing feature and is pretty telling to see which occupations have a higher male or female concentration. In addition, I really liked how you added a screenshot and really broke down the blog post!
This dataset was really informative. It was really interesting to see how much extra money these top earners receive from a variety of factors, like overtime or bonus pay. In particular, I was also amazed that the Fire Captain earns about half of his salary from overtime hours. If we were given just salary information, we would probably automatically assume that what these professions earn was purely base pay. I agree with you about how seeing more information such as gender and years worked would add a significant amount of knowledge if we were trying to piece this narrative together even more.