What would it take to get essential human capital data in the hands of every investor? One of the easiest answers to that question is: transparency. With better disclosure of human capital, we will have greater access to data and the insight that comes from them.
Right now, there are clear rules and regulations around capital disclosures that help investor decisions. These are the “10-K” annual and “10-Q” quarterly reports that all public companies must release to disclose their financial information and capital structure. Right now, there are no equally strict requirements for human capital.
Every few years or so, there is a push for more transparent human capital disclosures. Sometimes, there’s some movement on the issue, but usually the policy lands in some form of voluntary disclosure. Currently, companies are required to report on various aspects of total number of FTEs, employee types, turnover rates, and similar metrics, but these have not reached the level of capital disclosures.
Unfortunately, even with expectations around transparency, little can be learned because there is no way to compare the data that companies release. Transparency is not enough. Instead, we need clear standards so that all the data can talk to each other.
Think about it this way: Even if there was mandatory reporting on job hiring, the reported data would be mostly meaningless outside the context of that one company. If Meta says that they have about 75% of their workforce employed as engineers, for example, what does that actually tell us? What kind of engineers are they? What work do they do each day? And, more importantly, how does that level of employment of the specific type of engineers compare to other companies? Is it the same? Is it different? Has it changed much over the years?
Without standards, we end up with a “Tower of Babel” situation: Each company’s data is speaking a different language. There’s no way to know whether a “sales” position in one company is fundamentally the same thing as a “sales” position in another company.
Take, for example, the position of an “economist.” I myself am an economist. But when I introduce myself as such, no one knows what that means. It’s ambiguous. Even so, my company hires many economists. So do major companies like IBM and Amazon. Amazon, in particular, is well known for hiring a lot of economists.
However, all of these positions are completely different across companies. At IBM, the economists all work on macro-level reports, such as how the geopolitical situation in China will affect mainframe computing exports. They are basically collecting and analyzing trends that would affect the business and sharing those trends with the relevant staff. This might be more similar to a reporter than anything else, or some kind of industry analyst.
At my company, we hire economists to basically fill two roles: a hybrid role of data science consultant and client success manager, in which they take the data from our products and help companies understand what this data means. In this way, they are client-facing, almost like customer service representatives. But they also take that data and write articles for our newsletter about trends – similar to the IBM economist focused on trends, but at a micro level, or maybe even a marketer.
Amazon, however, hires economists to do three different things: forecasting, testing out new business policies, and assessing the market structure. The forecasters have backgrounds in macro and finance, and time series analysis, and support analyses that consider coming trends. The business policy roles are basically applied statisticians, using data to solve micro business problems. Those that assess the market structure are using different modeling designs to create pricing schemes and other things related to the online market to support business growth. In hiring these types of roles, Amazon is essentially using a data-driven approach to the company’s business practices, helping to maximize decision making in a number of ways. Currently Amazon hires more economists than any organization other than the Federal Reserve.
All of these people leverage the position of “economist” to get work done, but comparing the data related to these positions across companies to identify trends would be a worthless endeavor. If an investor was to try to understand workforce trends or even company trends from changes in these positions, any conclusion that would be drawn would be inexplicable or counterintuitive. An investor may look at my team of economists and say we are over investing in research, without understanding that this economist team has a direct influence on our clients happiness (by helping them understand our data) and gaining new clients (by supporting our marketing efforts).
I know these nuances between the economist roles across IBM, Amazon, and my company, but that’s because I have direct, qualitative experience in how those companies operate. I have acquired that knowledge through many conversations – and in the case of working at IBM and my own company, direct experience. Very few people have that kind of understanding of how different companies’ workforces are arranged and leveraged.
Workforce taxonomies solve this problem. With appropriately designed taxonomies, we can give more accurate titles to positions, or have a deeper understanding of what a position title means. We can see if a company is hiring a macroeconomist that specializes in forecasting or a microeconomist that will help with client success. We can say with high levels of certainty that two jobs are indeed alike, across companies or even industries, and make analyses based on those comparisons. That is because the job titles are not based on arbitrary labels, but systematically determined and categorized through a robust data analysis process. They are developed through a combination of activities, which are the essential building block of work.
With these taxonomies in place, investors can make strong inferences about the behavior and health of companies, and invest accordingly. For example, if a corporation makes a big announcement about investing in one area – like AI or VR – with the goal of becoming a market leader, we can look at the workforce data to understand how committed they are to this investment. If the company data shows that the salary they are looking to pay for engineers with an AI or VR speciality is way below market rate, you know that they are not going to obtain the top talent to execute on their bold vision. An investor interested in AI or VR might not want to put too much time or money into a company that isn’t serious about their commitment.
Excerpted with permission from the publisher, Wiley, from Job Architecture: Building a Language for Workforce Intelligence by Ben Zweig. Copyright © 2026 by John Wiley & Sons, Inc. All rights reserved. This book is available wherever books and eBooks are sold.
