Dataset

As a labor organizer, I am excited about using data visualization to make visible patterns and information that can inform organizing strategies. For this lab report, I worked with the Department of Labor Wage and Hour Division Enforcement dataset which compiles concluded compliance actions going back to 2005, including the number of violations found and amount of back wages owed. It is a massive dataset with 110 fields with numerous types of violation, and also documenting employer name, address, and NAICS industry codes. 

First iteration

I created my first Tableau dashboard in our class lab. In this round, I used filters to explore violations for a specific date range to look at trends over time across all industries, and to filter by the amount of violation to obtain the industry sectors with the highest amount of back wages owed. I also began experimenting with filtering data by NAICs code specific to the food industry. 

In this stage, my main research questions were: What trends does this data show regarding how violations have shifted over time and what patterns does it reveal about how the food industry compares to other industries.

From this preliminary exploration, some overall trends over time were revealed, and it was clear that restaurants have significantly higher amounts of back wages assessed than any other sector.

I presented this visualization in class and noted the challenge of presenting data on multiple food industry sectors legibly, especially with the very long legend. Some concrete feedback I received was to show the trends of all industries compared to an overall grouping of food industry data, and to consider a filter option where users could highlight one sector in particular. There was also a suggestion to consider a storytelling approach, and the use of icons. 

Further research

To build on my first draft, I wanted to understand how researchers have used this dataset, and get more clarity on the different fields within it.  Good Jobs First uses this data to show the decline of penalties and argues this is due to cuts in enforcement resources and not declining violations in workplaces overall.  

The Food Chain Workers Alliance used this data to illustrate patterns of violations in the food industry compared to other industries (working with an average per industry). 

The NYC Office of the Comptroller’s Employer Violations Dashboard uses this dataset (and an equivalent one for New York State) to list top employers with violations in NYC.

Second Iteration

I began by updating my charts in Tableau Public (I had previously used Tableau Desktop and had not been creating the proper union of the multiple files). These edits widened the years available and more clearly showed the downward trend others had documented. With a better understanding of the fields, I wanted to show back wages owing for overtime and minimum wage in one graph.

I also sorted the top violations by sector for both overtime and minimum wage. Once again full-service restaurants were the sector with the most violations when sorted by NAICS code. 

I wanted to incorporate feedback received from the lab presentation to compare total violations in relation to the food industry (or a specific sub-sector) in one visualization. This was challenging to accomplish at first. While I had learned how to use the dual axis function to display two measures within one graph, I could not figure out how to isolate a filter to only one of the measures, especially when my filter related to a NAICS code. 

After learning about creating parameters to apply a filter, I was able to create a specific parameter for the NAICS code I wanted, and then apply it to one graph only, before using the dual axis function to combine the two graphs. The graph below shows overtime back wages owed for restaurants compared to overtime owed across all industries. With more time, I would use this same process to create a parameter for all food industry NAICS codes. Even though full-service restaurants are the highest violator, this chart doesn’t make their share seem notable. 

For this dashboard, I chose to use a color scheme that had a consistent color for overtime and minimum wage data, while highlighting data specific to restaurants. Unfortunately, I could not get my color edits to consistently apply for 2 of the graphs and this needs further troubleshooting.

After researching how others have used this data, I added an additional research question:  How can this data reveal organizing targets in a specific region and in a specific sector. 

I was inspired by how the NYC dashboard lists specific employers within the geographic region with the most violations. Notably, restaurants were again high on the list. I wanted to zero in on employers with violations for specific industries within NYC (which is not possible in the NYC dashboard).  To create this visualization, I filtered by city to include New York City and all boroughs, and filtered the field by the relevant NAICS code. I also filtered the date of violation to be a more recent range of 2021-2025. I chose a circle chart to be able to visually compare the size of employers with the highest amount of violations owing.

Next steps

I would like to get feedback from organizers who might use this data to test its usefulness. I would ask about its legibility and what other information could be helpful. Mapping the data for NYC on a map could further reveal geographic patterns (ie concentration in a specific borough or cluster). Furthermore, I’d love to layer on more than one NAICS code to reveal a bigger picture of the food industry in NYC. 

[Explore final Tableau dashboards here and here]

References:

Food Chain Workers Alliance. (2025, February 12). Food chain workers in 2025: Labor and exploitation in the food system. https://foodchainworkers.org/wp-content/uploads/2025/02/Food-Worker-Data-Update-2025-FCWA-3.pdf

Office of the New York City Comptroller. (2025, September 3). Employer Violations Dashboard Wage theft. https://comptroller.nyc.gov/services/for-the-public/employer-violations-dashboard/violations/wage-theft/

Standaert, S. (2025, December). Worker protections in freefall: The collapse of federal labor enforcement under the second Trump administration. Good Jobs First. https://goodjobsfirst.org/worker-protections-in-freefall-the-collapse-of-federal-labor-enforcement-under-the-second-trump-administration/