Social Networks, Gender, and Graduate Hiring in a Rentier State: Disentangling Referral Types and Ascriptive Signals in Omani Labour Markets

10.7910/DVN/O5XKXRAl Hatmi, KhalifaKhalifaAl HatmiUniversity of Technology and Applied Sciences Al Mussanah, OmanSocial Networks, Gender, and Graduate Hiring in a Rentier State: Disentangling Referral Types and Ascriptive Signals in Omani Labour MarketsHarvard Dataverse2026Social Sciencesfactorial survey experimentAl Hatmi, KhalifaKhalifaAl HatmiUniversity of Technology and Applied Sciences Al Mussanah, Oman2026-03-082026-03-09111531400110822953875233364text/x-stata-syntaxtype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxtext/markdown1.0CC0 1.0This dataset accompanies a factorial survey experiment examining how referral type, applicant gender, and nationality jointly affect graduate hiring decisions among private-sector employers in Oman. Data were collected from 190 Omani private-sector employers, who each evaluated 12 vignettes drawn from orthogonally randomised decks, yielding 2,280 employer–vignette observations. The experimental design manipulates six attributes: referral source (institutional/university, employee, or none), applicant gender, nationality (Omani vs. expatriate), university tier, field of study, and GPA. The dataset includes employer-level survey data, vignette-level outcome ratings, R and Stata analysis scripts, a qualitative codebook, and a README file.Data collected via structured employer survey administered in Oman's private sector from 2019 to 2020