Thank you. It’s all from self-reported EEO-1 data made public by each company. The raw data and links to their source are here: https://www.diversify.fyi

Yeah there are a few companies where the sum is > 100%. I presume at least one reason is double-counting some employees who are multi-racial. Maybe some companies just tally these statistics in different ways. I wanted to originally normalize each company's data to 100%, but forgot to.

All the companies shown in the figure are headquartered in one of those states (or outside the USA), so that I why I showed those ones in particular. As far as I know, Datadog is headquartered in New York, so I used that state for it's state-level reference. But I see what you're saying, since they are visually close together people may inadvertently compare them. Maybe I could have added some text to indicate which state each company's HQ is in.

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Also possible I was a bit overly ambitious in this figure by providing both USA and state-level comparisons, but I felt it was worth while since the demographics of some states looks quite difference to the USA overall.

Thanks for the feedback.

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- Good idea. I'll do that next time.

- Hm, I disagree with you here. I added the line smoothing for the purpose of readability, otherwise the bottom left two figures would be a cloud of points. Having colored lines connecting the points makes it easier to locate each data point for a specific company/race. Besides, there was no real interpolation done here, just smooth lines connecting the categories (i.e. companies).

- I agree. I made a similar figure in the past and that is what I did, but I forgot to this time. The race percentages provided by a few companies does add up to > 100%. I presumed this is due to double counting some people who are multi-racial.

- Yeah I am not 100% sure what is the best approach. Would be incredibly hard for me to get data on how many employees are in each state for each company. On the other hand, companies having people in other countries is not a concern here because EEO-1 data is strictly USA employees. There are of course other approaches I could take for comparison, like comparing to the industry trends or demographics of people applying/interviewing, etc... but each approach has their pros/cons.

Companies in the USA are required by law to report their USA employee demographics to the government. Although they are not required to make that data publicly available, some chose to do so. https://www.eeoc.gov/data/eeo-1-data-collection

The problem is that very few of these companies break asian down into categories like Indian, South Asian, East Asian, etc. Otherwise I certainly would have displayed that data.

The data is from here: https://www.diversify.fyi/
I created this figure using Python and Plotly.

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As someone who lives in the USA, I’d guess it’s because of multiple factors:

(1) USA is one of the most diverse places in the world. So when you have so many people with diverse cultural, linguistic, and religious backgrounds, this naturally leads to tension.

(2) Equality and freedom are a strong part of the American identity, so people vigorously fight and protest for whatever their version of that is… and our strong freedom of speech laws and non-tyrannical government let’s them do that.

(3) Compared to other ways people can be diverse, race is usually pretty easy to identify.

(4) Race-related topics are frequently shoved in our faces by the media and company/school diversity initiatives.

Thanks. Yeah I was a bit worried this figure was a bit busy and hard to match up each company with its respective data.

I manually gathered this data from the self-reported EEO-1 data reported by each company. The data and links to their source are here: https://docs.google.com/spreadsheets/d/1Uev9f_sqN7laMtntuf-YQJZrGcj2UFAo18TFNrSap5s/

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I collected all the data I could find in a reasonable amount of time. Most companies do not publicly disclose their EEO-1 data.

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I created this figure using Python and Plotly.

Pretty interesting and unique dataset. There seems to be a higher number of lost items in the middle of the year (summer?). Maybe due to more tourists? There are also some short-term spikes in more lost items… I wonder what those correspond to.

I wonder what this would look like if you adjusted by average miles per gallon for the cars in that year. I would expect that might indirectly make the total cost of gas cheaper with more efficient cars. Though I have no idea if that type of data is available, or if car efficiency changed significantly since the 90s.

Might be more interesting to visualize this by county instead of state if the data is available.

Really interesting! Took me a bit to figure out, though. My only critique is I feel the figure on the right could be more define/separated from the map visualization.

The figure is a bit hard to read. Looks like a spiderweb. I’d suggest try visualizing in a way that provides more continuity.