The employee earnings report data from Boston’s portal enables rich analysis of compensation trends across 30+ municipal departments. Sophisticated modeling approaches like the following can derive key insights:
Regression analysis with algorithms like lasso and ridge could reveal the impact of tenure, role type and department on earnings growth trajectories over time. Detecting predictors of rising income inequality even within public sector jobs is crucial.
Unsupervised clustering via models like K-prototypes can group employees and departments exhibiting similar pay increase patterns annually. This typology using earnings trend similarities informs standardized pay scale policy decisions.
Neural embedding frameworks can encode complex departmental differences into low-dimensional vectors to serve as inputs for visualization and predictive tools. Linking earnings vectors with budget vectors can assess fiscal sustainability.
Lastly, CNN deep learning networks could treat earnings tables as images, discerning spatial patterns in how compensation metrics relate across roles and divisions. This method often catches subtle signals missed by conventional techniques.
This data capturing intricate public agency wage changes warrants such advanced modeling. The insights distilled can guide equitable, consistent and financially prudent compensation best practices across a diverse municipal workforce. I’m eager to apply these modern algorithms to unlock essential learnings for judicious and ethical governance.