Frequently Asked Questions

There are five families of sources that we unify and standardize to construct a comprehensive sense of the workforce: (i) online professional profiles: full time series of an individual, including dates of employment, companies, titles, education, skills, and more; (ii) job postings: posting dates, job descriptions, and salaries for each position; (iii) employee sentiment: both review scores and actual reviews; (iv) government data: published labor statistics, domestically and globally, in addition to immigration filings, census data, social security administration data, and voter registration data; and (v) firmographic data: subsidiary-parent relationships of companies, industry classifications, and mapping to financial identifiers.
Our data covers all public and private companies, which amounts to roughly 4.5 million companies worldwide when taking into account subsidiaries and holding companies.
We deliver data on the 15th of every month, which represents the previous month’s counts. For example, by January 15th, we would deliver all of the data that occurred over the course of December.
We deliver our workforce data in three ways: (i) API access, where data is delivered by request. (ii) Data feed, where large portions of the data are delivered monthly. And (iii) our self service dashboard, where you can instantly start getting insights.
Our customers include investors, corporate strategists, HR teams, and governments.
When a company acquires or merges with another company, our standard delivery is to include the subsidiary as a part of the parent company, even retroactively, before the acquisition took place. For example, we include all of Whole Foods employees as part of Amazon, during 2008-2016, even though Amazon only acquired Whole Foods in 2017. The reason for this decision is that we want to avoid seeing an artificial spike in headcount when an acquisition or spinoff occurs. Upon request, we can also present subsidiaries as separate entities retroactively.
Company's 10-ks typically report on the W2 employees of their company, but omit contingent workers, which in some cases can make up the majority of a company's workforce. Unlike in reported counts, we work to track all members of a workforce (employees and contingent workers) to provide a more comprehensive view of company composition and trends. For that reason, our headcounts are often (depending on industry) higher than a company's reported counts.
Because we collect data from online professional profiles, we face an issue of data being drawn from a non-representative sample of the underlying population. To resolve this, we impose sampling weights to adjust for roles and locations that are underrepresented. For example, if 9/10 engineers in San Francisco have an online profile, when we see an engineer located in San Francisco, we count them as 1.1. Similarly, if 1/3 nurses in Germany have an online profile, we count them as 3. This allows us to approximate, as closely as possible, the true estimate of the underlying population.
There is a lag that exists from the time someone gets a new job to the time they report that change on their profile. Further, if someone gets laid off, they may not update their profile until they secure a new job. To account for these lags, we use a method called nowcasting where we look at periods in a company's history and see what was reported at that time and see – now that the lags have disappeared – what that reporting actually represented, and then apply that weight to the most recent period. For example, if two years ago a company reported 5 people leaving, but today we know that really 10 people had actually left at that time, we then assume in the most recent period where 10 people reported leaving, that 20 people have in fact left.

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