The previous two blog posts explained that searching governmental financial reporting statements is difficult. We also discussed that after the stock market crash of 1929, the government made financial reporting for publicly traded companies more transparent. It has been an almost 100-year improvement process in public company reporting starting with the rules promulgated through the SEC in 1933 until today. Now the entire information a company reports is searchable through a programming language called Inline XBRL. We know XBRL is a language to report financial data, but how does it work, and how would it help governmental reporting?
Let’s first explore precisely what XBRL is and how it works.
XBRL works on a system of “tagging” data. Tagging, in XBRL terms, is a method of electronically attaching a label to data that is part of a taxonomy. So what? What do you mean by a taxonomy?
A taxonomy in XBRL and financial reporting is just like a taxonomy of insects or animals, a hierarchy of information that explains from where something comes. In other words, its basic origins. Taxonomic ranks include species, genus, family, order, class, phylum, kingdom, domain, etc. So, for example, if we talk about a wolf taxonomy, it will look like this:
Species: Canis lupus (Linnaeus, 1758) - gray wolf
There are even more subspecies we could discuss, like your own little Fido, but you get the idea. Taxonomies in accounting are the same. Each item in a financial report is tagged with a label that contains information about its origins and characteristics. The closest similarity you are familiar with daily is barcode readers found at grocery stores. When we go to the grocery store, we aren’t programming the bar codes on the items we scan. We are simply scanning and observing the information outputted on our computer screens. Just as the scanners at the grocery store read bar codes for us, XBRL allows machines to read financial information for us. XBRL taxonomies are electronic, machine-readable dictionaries with many linked files containing thousands of elements connected. The machines read the back-end data and spit it out to us clearly and understandably. Furthermore, we get only what we need because we can limit our searches to get precisely the information we want and need. XBRL.us describes it this way:
“The taxonomy contains human-readable labels such as financial statement line-item captions, definitions, and applicable authoritative references for each element. In addition, the taxonomy specifies other attributes for elements, as described in greater detail later in this section.”
Because XBRL is machine-readable, it is easy to export the information, run checks for the information you seek, and have some assurance that all data is comparable since it is all using the same taxonomy or hierarchy. You saw examples of this in the Walmart information you searched in the previous blog post. In addition, you could search any company and receive a similar-looking An example of questionable disclosure practices is found in the report.
For example, when a word like “asset” is reported in the EDGAR database, it has a hidden tag that tells us the background of the word. Going back to the grocery store example, it is like the scannable codes you find on soup cans. You scan the can, and up pops the price, the item, and maybe even the manufacturer. That is what having a financial statement coded with XBRL can do for us. XBRL gives us similar information about the data reported by public companies as a UPC does on a soup can in a store.
So now, take a short break from reading this. Go to your favorite search engine and type in these terms: Colorado 2022 ACFR. This search will turn up the fiscal year ended 2021 financial report for the state of Colorado. You will get a beautifully colored picture of a train engine and 304 pages of PDF. Now download it to your desktop and open it. You have created a searchable PDF. If you scroll to page 102, you will see an excellent disclosure on actuarial assumptions.
Now, right-click on your mouse and find “search text.” Then search for the number 3.5 – that number is in the PDF as a percentage of wage inflation in the actuarial assumptions table on page 102. Does it come up? No. The table must have come from another document and is read as an image, not as searchable data points. Try this for yourself. How many items that you search don’t come up? And every state is not created equally. Some states, such as New Mexico, have many embedded images. How do you even begin to search for data if a machine cannot read it? Truth in accounting struggles with this every day.
Now that you have shared the frustration of searching for data in images let’s imagine if all government entities would tag their information with a consistent XBRL tagging taxonomy. Not only would it be searchable, but taxpayers would get a roadmap of where that number came from. In addition, the data could tell us anything we would need to know if it were tagged correctly. If grocery stores, manufacturers, and publicly traded companies can do this, why can’t our government do it?
Imagine if our governments would be required to report information as transparently as they force publicly traded companies to report their information.
Shouldn’t taxpayers be entitled to the same transparency and consistency as Wall Street investors?
Illinois budgeting and financial reporting process, specifically regarding pension contributions. In 1994, then-Gov. Jim Edgar led an effort to pass a bipartisan bill to solve the state’s $15 billion pension deficit. Read Blog Four Here.