Monitoring hiring discrimination through online recruitment platforms
Posted on January 30, 2022
The paper proposes a method to study hiring discrimination based on tracking recruiter's search behaviors, while controlling relevant and visible characteristics of the candidates.
The motivating research question is: “is there discrimination bias on online recruiting?”; while the concrete operationalization is: “is there hiring discrimination in the online recruitment platform of the Swiss public employment service?”
The authors assume that because they provide a study that focuses on a work that hadn't been done before (i.e. "focusing on online recruitment for the offline labour market"), that it's more generalizable than previous work, thus falling a little into motivation by absence.
Also, the authors argue that "the highest level of work experience is insufficient to offset the ethnic penalty faced by many immigrant jobseekers". However, they don't mention the ratio of highly experienced people according to race. In the extreme case where all the applicants with high experience are from the reference race, it would not affect such ethnic penalty.
Even more, the authors use as one of the metrics, the time between opening a profile and contacting the candidate or leaving the page. This could be a powerful source of systematic error as a recruiter might open many profiles at a time and work their way through; or even opening one—or some—and leave the page open while multitasking.
The data used can be characterized as non-reactive, as just for measuring things like the time spent in a profile page won't affect the recruiter's behavior. Also, it could be considered incomplete as it doesn't reflect if the time spent in the profile page was active or passive time. Moreover, it's non-representative, as the authors make generalizations based solely on data from the Swiss public employment service.
The observational research strategy implemented was counting things. Simply put, the authors study how many times are White people selected for a position through an online recruitment platform, compared to other races.