G²LM|LIC: Hiring and Gender Bias in Nigerian Online Labor Markets, 2014-2020

Longitudinal applicant-job matching data from 2014 to 2020 from Nigeria's largest online job platform, with core analyses based on approximately 1.3 million matches. The dataset combines administrative records with experimental interventions, including a 2023 randomized controlled trial with hiring managers. It was created by Belinda Archibong and harvested by IDSC for the Dataverse platform.

Use Cases

• Analyze gender-based hiring disparities based on applicant characteristics and hiring outcomes.
• Study the impact of co-ethnicity and social norms on hiring decisions based on inferred ethnicity classifications.
• Evaluate the effects of diversity-related information treatments on employer behavior based on RCT and survey-intervention components.
• Model hiring probabilities for different job types and seniority levels based on job advertisement metadata and qualification measures.

Strengths

• Approximately 1.3 million applicant-job matches provide a substantial observational basis.
• Combines longitudinal administrative data (2014-2020) with a 2023 randomized controlled trial for causal inference.
• Covers 194,190 unique applicants, 24,081 job listings, and 5,014 hiring managers between 2016 and 2018.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

IDSC Harvested Dataverse

Collection Method

Combines administrative records from a large online recruitment platform with randomized experimental interventions and survey components.

Time Range

2014 to 2020, with core analyses from 2016 to 2018 and a 2023 RCT component.

Freshness

Last updated 2026-06-08 09:10:08; freshness should be verified.

Geography

Nigeria