Good news for WeWork: there is little to no gender bias in its jobs listings
WeWork ($WEWORK) is the target of several new lawsuits pertaining to gender and age discrimination from former employees. Despite these new allegations aimed at the IPO-bound company, new data indicates it is making a concerted effort to attract a diverse set of employees.
According to language in job-listing descriptions posted at WeWork's recruiting website, language used to attract applicants is neither biased toward women or men. This is according to thousands of job descriptions we ran through a gender decoder based on academic research in the topic. In fact, WeWork job listings score a virtual null 50% in terms of language bias.
A 50% rating is regarded as the least off-putting language among an employer's job listings between men and women. A zero represents a strong lean towards language that would be off-putting to male applicants, and 100% represents a strong amount of language off-putting to female applicants, as determined a 2011 study.
Given that we look at entire job descriptions, it is possible that WeWork's equal opportunity statement could outweigh any potential offputting language towards the beginning of a job opening. A study from "TheLadders" showed that the time spent looking at a job posting only averaged 49.2 to 76.7 seconds.
With that in mind, a full analysis of all job postings in all job categories revealed about an average split in our gender decoder.
Based on data from June 19th's job postings by department, Building Design & Development had the most language offputting to female applicants according to the decoder — but only by 9.677 basis points off true neutral. Meanwhile, the Executive category has a 25% rating, meaning that job descriptions — which are for positions that range from General Managers to a Director of Operations for WeLive — have more "feminine" language and might turn off male applicants.
Of course, this analysis comes in the face of controversy within the organization. Outside of the aforementioned lawsuits — one covers alleged evidence of gender pay discrimination, and another claims possible age discrimination — New York Magazine ran a profile on its co-founder, Adam Neumann, where it describes the company as "a boys’ club."
This isn't the first controversy at the WeWork; Neumann is technically the landlord for numerous pieces of property that WeWork leases. It uses a "community adjusted EBITDA" to report earnings, which leaves out taxes, stock-based compensation, "marketing, administrative and other costs of growing the business" in earnings. Earlier in its lifespan, it fought with cleaning people and tenants who found that the company's actual public data went against its self-narrative of constant growth and success.
And, amid all of this, it is touting "CultureOS," the platform that was first used internally at the company to "inform how we could positively evolve the WeWork culture as we grew at hyper-speed." It's goal now is to ship that IP externally, and is distributing the thought of it through channels such as the Harvard Business School.
As WeWork prepares to hit the public market, one might see all of these controversies as concerns for the co-working startup. Yet data from the gender decoder gives investors who are searching for confidence in the company's hiring practices some light at the end of the tunnel.
About the data and the gender decoder
This gender decoder is based on the research paper "Evidence That Gendered Wording in Job Advertisements Exists and Sustains Gender Inequality" (Journal of Personality and Social Psychology, July 2011, Vol 101(1), p109-28), written by Danielle Gaucher, Justin Friesen, and Aaron C. Kay. In this paper, the three looked at language in job descriptions they found to be "feminine" or "masculine" in nature and whether or not men and women may be off-putted by sample job descriptions.
Thinknum tracks companies using information they post online - jobs, social and web traffic, product sales and app ratings - and creates data sets that measure factors like hiring, revenue and foot traffic. Data sets may not be fully comprehensive (they only account for what is available on the web), but they can be used to gauge performance factors like staffing and sales.