Former employees are suing Oracle for pay discrimination

Dive Brief:

Former female workers at Oracle are suing the tech company for pay discrimination, TechCrunch reports. Rong Jewett, Sophy Wang and Xian Murray allege that Oracle paid male employees more than female employees who performed the same or similar work under the same working conditions.
In their complaint, Rong Jewett, et al, v. Oracle America, Inc., filed with California's Superior Court, County of San Mateo, the plaintiffs charge Oracle with: 1) violating California's Equal Pay Act; 2) failure to pay all wages due to discharged or quitting employees; 3) unfair and unlawful business practices; and 4) for penalties under the Labor Code Private Attorneys General Act.
The complaint also addresses Oracle's failure to correct the pay disparities between female and male employees, which the Department of Labor (DOL) found in January based on a compliance review. The department discovered what it called “systemic discrimination against women” and “gross disparities in pay,” which the plaintiffs said Oracle knew of but refused to address.

Dive Insight:

Oracle's ex-employees are among other workers who have sued their employers following a DOL inquiry. Three of Google's ex-employees sued their former employer for pay discrimination this month. Earlier in the year, DOL claimed it found pay disparities based on gender and race and sued Google for not releasing enough records. Ex-workers maintained an internal spreadsheet showing alleged pay inequities, which has since gone public in The New York Times.

Of interest: Both Oracle and Google are getting sued following separate DOL inquiries into their pay practices. An inquiry or investigation by a large government agency could be empowering more workers to come forward with complaints. But whether these latest suits are setting a pattern or are just coincidental, employers can avoid biased pay practices and lower their risks for liability by reviewing their pay policies and conducting audits to uncover systematic discrimination. Data analysis should be a key part of that process