For my blog this week I chose to look at the data set titled “Payroll by Job Class.” The general purpose of this data set is to show the amount paid to different job types. This naturally lends itself towards a comparison between and analysis of what job categories get paid the most and why that may be.
There are 34 data types including: year, department title, payroll department, projected annual salary, payments over base pay, base pay, overtime pay, average health cost, pay grade, benefits plan, and several more. A record within this data set would therefore be a new row which includes an entry under each these 34 data types.
In their paper, Wallack and Srinivasan explain how a lack of compatibility between ontologies used by the government and those used by communities can result in serious consequences. They also provide several strategies that can be used to combat these “mismatched ontologies.” After consulting the definitions which they provide, I believe that this ontology was created from the state’s point of view and very much mimics government records and the ontology used to create them. This ontology makes the most sense of the government’s point of view. It documents mostly quantitative measures on the amount of pay for different employees. The fact that it takes into account the cost for the city to provide insurance and other benefits for the employee is an indicator that this data set would be most useful from the state’s point of view as it addresses information that the state would be interested in.
This data set attempts to explain several different phenomena. It looks at what kind of roles get paid the most and which departments have the higher paid employees. Information about which roles tend to cost the state the most in insurance and health benefits can also be gleaned from this data set. Furthermore, a person viewing this data set can clearly see which roles tend to receive more bonuses and extra pay.
However, while this ontology caters to the information needs of the government, it fails to provide some points of information that the community citizens would find to be useful. Especially interesting data points would be the demographics of the employee including gender and ethnicity. Also interesting would be the level of education of the employee because then the viewer of the data set could analyze the relationship between level of education and pay. If I were to construct this data set using a completely different ontology I would construct it from the viewpoint of the citizens and I would address all the data types that were left out. Particularly, I would emphasize the age and education levels of employees. Also, I would want to have a clearer idea on the proportion of taxes being spent on the paying of government employees.
I agree! While the dataset appears to be very comprehensive with 34 different data types, the contents are highly geared towards government entities invested in budgeting and administrative efficiency. It is interesting to note that even such an elaborate data collection process fails to provide any real analysis related to the social, political and economic attributes in relation. The great disparity between the goals and intentions of the governing bodies (state) and the governed (community) is apparent even in the aspects least expected, such as payroll expense information.