G²LM|LIC - Hiring, Gender Bias, and Information Frictions in Nigerian Online Labor Markets

The project further investigates how information interventions directed at applicants and hiring managers influence application behaviour, hiring preferences, and diversity-related decision-making. The dataset additionally contains randomized controlled trial (RCT) and survey-intervention components conducted with hiring managers on the platform in 2023. These include incentivized resume rating experiments, information treatments regarding gender hiring bias and workforce diversity, and behavioural response measures related to hiring decisions and candidate rankings.

Variables may include: applicant demographic and qualification indicators, inferred ethnicity classifications derived from names and regions, hiring outcomes and ranking behavior, employer and hiring manager characteristics, job advertisement metadata, intervention assignment variables, survey responses, resume evaluation outcomes, and derived labor market indicators. Methodology / Data Collection The dataset combines administrative records from a large online recruitment platform with randomized experimental interventions and survey components.

The observational component includes applicant-job matching records, applicant characteristics, hiring outcomes, job requirements, industry classifications, and hiring manager information. Ethnicity indicators were inferred using combinations of applicant names, state-of-origin information, and ethnolinguistic classification approaches described in the associated research publications. The experimental component includes randomized information treatments administered to hiring managers through online survey instruments and incentivized resume rating exercises designed to evaluate the effects of diversity-related information on hiring behavior.