The New York Times had a summary article about the levels of Executive Compensation reported so far this year. Here is a link to the article (NYT Comp Article) by David Gelles. The article mentioned that the observations were based on proxy filings made through April 24, 2021. We had already seen some larger numbers reported in 10-K and 10-K/A filings the article mentioned that their data was limited to data collected from proxy filings. Since 4/30 was the deadline for the Part III (or proxy) filing we decided to take a bit of a dive into the compensation data we have processed and share some observations.
Before I start describing some of our findings – I have made an XLSX file available – the link is at the bottom of this post. I would appreciate a citation if you use any of the data included in the file.
Our top 20 is very different from the top 20 reported in in the article because we included those whose compensation was reported in a 10-K or 10-K/A filing as well as those filings that were filed by the 4/30 deadline. The top earner reported in the NYT article . Their highest paid executive, 211 million earned by Mr. Richison of Paycom, was only 6th on our list of all executive when we include those disclosures made in 10-K filings. Here is our top 20:
The NYT article referenced above focused on TOTAL compensation. I had already seen some really large bonus numbers – bonuses and salary tend to be the most certain forms of compensation (the amount realized is generally the amount reported) so I decided to dig into our database to identify all individuals who earned a bonus amount equal or greater than $1,000,000. We identified 549 individuals who met this criteria. The largest bonus was granted to Anthony Hsieh. He was awarded a bonus of 42.5 million dollars. Mr. Hsieh is the CEO of LOANDEPOT. There was very little explanation in their 10-K for the bonus (10-K Link). ” The amounts reported in this column reflect special one-time discretionary bonuses. Our board of directors and our CEO participated in the determination of the special bonus allocations.” Two other executives at Loandepot earned bonuses that placed them in the top 10 (Patrick Flanagan and Jeff Dergurahian were each awarded a 12.6 million dollar bonus). Here is a list of the top 20 bonus awards reported so far:
As I was comparing the two lists above something struck me – there were no women listed in the top 20 of total earned compensation and only 2 made the top 20 of bonuses. So that made me curious and I decided to sort based on GENDER (we include a GENDER field in the data file below).
There are no women in the top 40 of total compensation. The first woman is Ruth Porat at number 45. Ms. Porat is the CFO of Google – she made the list because of a stock grant that was measured at more than 50 million dollars. As a matter of fact there are only two women in the top 100 (Ms. Porat and Carrie Wheeler the CFO of OpenDoor).
Of the 1,048 individuals represented in the set of those earning more than 10 million dollars – only 82 of them are women. However the total amounts earned by women in this data amounted to only 1.36 billion. Men on the other hand earned 22.1 billion. So women represented 7.8% of those earning more than 10 million but their gross earnings represented only 5.8% of the total 23.47 billion earned by this group of executives.
Only 60 women earned a bonus GTE 1 million. The average bonus earned by these women was 2.3 million. 487 men earned a bonus greater than 1 million – the average bonus earned by men was 2.8 million (2.7 million if you disregard the eye-popping 42.5 million that was awarded to Mr. Hseih.
There are some caveats. The data pulled represents all data for either the 2020 or 2021 fiscal year end. So for example a company whose fiscal year ended in February 2021 – if they have reported compensation for 2021 then it was considered to test whether or not it met the 1 or 10 million threshold. If they have not yet reported for their most recent fiscal year then we tested their previous fiscal year that ended in 2020. But it is a bit more complicated than that. Target’s year-end is 2/1, Best Buy’s is 1/30. Target tends to report earlier than Best Buy so we have data for the year ended 2/1/2021 for Target. But Target labels that as 2020 data. We also have data labeled for Best Buy for 2020 but because of the way Best Buy labels their data it is actually for the year-ended 1/30/2020. Based on their historical filing practices I think Best Buy is probably going to report sometime today or tomorrow. The actual date the data was disseminated through the SEC EDGAR platform is an a field labeled RDATE in the file.
There are some duplicate people in the file – yes you can collect two pay checks from different companies in the same year.
Here is a link to the Excel file (directEDGAR Compensation Summary 2020/2021). There are a number of fields that exist for audit purposes. Gender is included as well as the CIK (Central Index Key) of the individuals. We use the CIK of the individuals internally to track them and simplify the matching process across entities.
Finally, as I was working on this I was reminded of the push to introduce more data analytics to the accounting curriculum. We have had some internal discussions about sorting out how to make our data store accessible directly rather than through the application. The notion here is that if you are a business faculty member who needs to help students become more comfortable with using Python and similar technologies this collection of data might be a natural fit to teach students how to use those tools. I have had a preliminary discussion with one faculty member at one of our clients schools already. It would be interesting to learn if others have this interest. I had to use SQL statements and do a couple of transformations using dictionaries to find and organize the data to create the form of the data as it is in the spreadsheet. I also had to do some consistency and error checks so there is a lot to muck around with for learning purposes.
All data included in the file was extracted from SEC filings and normalized using directEDGAR’s proprietary platform. We process other types of data as well as offering an amazing search engine with more operators and more ways to filter results than any other on the market. Our search engine is augmented with unique tools that allow users to Extract and Normalize to create the inputs they need for their analytical and research projects.