Companies issuing RTO mandates “lose their best talent”: Study
Return-to-office (RTO) mandates have caused companies to lose some of their best workers, a study tracking over 3 million workers at 54 "high-tech and financial" firms at the S&P 500 index has found. These companies also have greater challenges finding new talent, the report concluded.
The paper, Return-to-Office Mandates and Brain Drain [PDF], comes from researchers from the University of Pittsburgh, as well as Baylor University, The Chinese University of Hong Kong, and Cheung Kong Graduate School of Business. The study, which was published in November, spotted this month by human resources publication HR Dive, and cites Ars Technica reporting, was conducted by collecting information on RTO announcements and sourcing data from LinkedIn. The researchers said they only examined companies with data available for at least two quarters before and after they issued RTO mandates. The researchers explained:
To collect employee turnover data, we follow prior literature ... and obtain the employment history information of over 3 million employees of the 54 RTO firms from Revelio Labs, a leading data provider that extracts information from employee LinkedIn profiles. We manually identify employees who left a firm during each period, then calculate the firm’s turnover rate by dividing the number of departing employees by the total employee headcount at the beginning of the period. We also obtain information about employees’ gender, seniority, and the number of skills listed on their individual LinkedIn profiles, which serves as a proxy for employees’ skill level.
There are limits to the study, however. The researchers noted that the study "cannot draw causal inferences based on our setting." Further, smaller firms and firms outside of the high-tech and financial industries may show different results. Although not mentioned in the report, relying on data from a social media platform could also yield inaccuracies, and the number of skills listed on a LinkedIn profile may not accurately depict a worker's skill level.