Unveiling Workforce Dynamics: Exploring Pay Disparities in Los Angeles 2022
Report
Introduction
Our motivation revolves around investigating and uncovering potential disparities within the city of Los Angeles across diverse ethnicities and genders, particularly within various departments. Focused on payroll information for all Los Angeles City Employees, our project seeks to analyze the workforce, specifically aiming to identify any instances of discrimination in Los Angeles during the year 2022. Instead of solving a problem, our initial objective is to discern the existence of any workforce-related disparities. This will be achieved through the development of an interactive web application showcasing various graphs at the intersections of ethnicity and gender within different career fields. In conclusion, our goal is to comprehensively understand and bring attention to potential disparities in the Los Angeles workforce.
Justification of approach
The project deliverables encompass a set of charts designed for interactive exploration through the Shiny app, enabling users to visualize average pay across departments, ethnicities, and genders to see if there exist any disparities. The project includes six charts, with two offering interactive features. The first chart is a stacked bar chart displaying departments by average total pay, including regular pay, overtime pay, and all other pay categories. The second chart is a box plot depicting departments by average total pay, allowing the identification of outliers in each department.
The third chart focuses on gender-based total pay, presented as a stacked bar chart illustrating different pay types. The fourth chart is a bar chart depicting ethnicity by regular pay, offering detailed data information when hovering over specific bars. The fifth chart is a pie chart that shows how the top 20% of regular pay break down by race, with details shown as the points move to specific areas. The last trading feature allows users to select a department and explore the ethnicity of average regular pay for each department using the drop-down menu in our Shiny app.
Data description
The dataset comprises 5,270 observations and encompasses 20 attributes. Its design towards facilitating a thorough examination of the equitable distribution of public funds among employees. The primary focus is on preventing and rectifying disparities related to gender, ethnicity, and departments, specifically within the context of the year 2022. Funded by the LA City Controller and under the ownership of Riad, the dataset is updated biweekly, including job status updates until the present, surpassing the scope of 2022.
The ongoing process of filtering active employees may impact our ability to assess unfair treatment among inactive employees based on ethnicities, genders, or departments. Notably, filtering by the year 2022 will not compromise our analysis, as our focus is not on identifying trends. Our data processing involved filtering exclusively for full-time, active workers within each department before cleaning. Subsequently, we consolidated individual department datasets into a centralized location, streamlining the process for further analysis.
We actucally have people in our dataset, and it’s reasonable to assume that they anticipate our use of the data to ascertain whether they receive fair treatment from the government and to understand how their salaries compare to those of other employees.
Design process
Main deliverable:
The main deliverable of our project is an interactive user interface, meticulously designed to enhance user experience and engagement. Central to its functionality is the feature that allows users to effortlessly navigate and select specific departments they wish to focus on for data visualization. This aspect is particularly vital as it customizes the user experience, making it more relevant and tailored to individual needs.
When users access the interface, they will be presented with a smooth, intuitive dropdown menu, listing all available departments. This dropdown menu is a key feature, enabling users to easily scroll through and select the department of their interest. Upon making a selection, the interface will dynamically update to display visualizations pertinent to the chosen department. These visualizations could range from graphs and charts to more complex data representations, all aimed at providing insightful and department-specific information.
Furthermore, this interface is designed with user-friendliness in mind. It ensures that even users with minimal technical expertise can navigate and interact with the data effectively. The selection process is not only seamless but also accompanied by helpful tips and guidelines, aiding users in making informed choices.
Moreover, the interactive interface is built to be responsive and adaptive, ensuring optimal functionality across various devices and screen sizes. This means that users can access and utilize the interface with equal efficiency whether they are on a desktop, a tablet, or a mobile device.
In summary, this main deliverable promises to revolutionize the way users interact with and understand data, by providing a user-centric, customizable, and intuitive platform for department-specific data visualization.
Design process for deliverables:
In line with the Pareto principle, often referred to as the 80/20 rule in Economics, our project is centered around developing a comprehensive chart that aims to visually represent the distribution of income (wealth) among different ethnic groups. The Pareto principle, which posits that roughly 80% of consequences come from 20% of the causes, provides a fascinating lens through which to examine income distribution. Our objective is to identify and illustrate which ethnicities fall into the top 20% bracket in terms of wealth accumulation.
To achieve this, we plan to meticulously gather and analyze income data, segmented by ethnicity. The chart we intend to create will not only show the proportion of wealth held by the top 20% but also provide insights into the composition of this segment in terms of ethnic diversity. This approach will enable us to present a clear and detailed picture of economic disparity and concentration of wealth within different ethnic groups.
Our visualization aims to be both informative and accessible, designed to cater to a wide audience, ranging from economic students and professionals to policy makers and the general public. By using engaging graphical elements and intuitive design, the chart will highlight key patterns and trends in wealth distribution across ethnicities, making complex economic data easily understandable.
Furthermore, this chart can serve as a valuable tool for sparking discussions and deeper analysis regarding the socioeconomic factors that contribute to such disparities in income. It could potentially lead to a better understanding of the underlying causes and inform policies or interventions aimed at addressing economic inequality.
In summary, our project aspires to leverage the principles of economics, specifically the Pareto principle, to create a compelling and enlightening visualization that sheds light on the distribution of wealth among different ethnic groups, thereby contributing to a more nuanced understanding of economic inequality.
Design challenge:
The design challenge we are currently facing involves mastering the use of the Shiny package in R, a task that presents a significant learning curve for our team. As none of our members have previously encountered or utilized Shiny, this requires us to embark on a comprehensive learning journey. Shiny is a powerful and versatile package for building interactive web applications directly from R, and understanding its intricacies and functionalities is crucial for the success of our project. To overcome this obstacle, we plan to allocate dedicated time for team training and self-study.
This will likely involve exploring online resources, such as tutorials and user forums, and perhaps even seeking guidance from experienced Shiny developers. The goal is not only to become proficient in using Shiny but also to leverage its capabilities to enhance the interactivity and user-friendliness of our data-driven applications. This skill development is essential for us to effectively visualize and present our data analysis, making our work more accessible and engaging to our target audience.
Limitations
The first limitation concerns whether the position is unionized or not. Unionized positions have different pay structures, benefits, and work rules compared to non-unionized employees. Without this information, conducting comprehensive labor cost analysis and budgeting becomes challenging.
The second limitation involves work time and overtime frequency. Both of these factors significantly impact payroll. Generally, the more hours worked, particularly overtime hours, the higher an employee’s earnings will be.
The third limitation is that 2023 is not yet complete, meaning we can only analyze data from 2022, which is not the most current. Updated data would enable us to make more accurate predictions.
With making the shiny app accessible we chose to push the app to shinyapps.io however if can only appear for 20 hours after that we would have to make a monetary investment and pay $20/month
Acknowledgments
Data source:
https://controllerdata.lacity.org/Payroll/City-Employee-Payroll-Current-/g9h8-fvhu
plotly package:
ggplot package:
How to get access to Final deliverable
Shiny App Website: https://info5001projectzapdos.shinyapps.io/project-elegant-zapdos/
Quarto Website: https://charlesyu.quarto.pub/project-elegant-zapdos/