Social Networks, Gender, and Graduate Hiring in a Rentier State: Disentangling Referral Types and Ascriptive Signals in Omani Labour Marketsdoi:10.7910/DVN/O5XKXRHarvard Dataverse2026-03-091Al Hatmi, Khalifa, 2026, "Social Networks, Gender, and Graduate Hiring in a Rentier State: Disentangling Referral Types and Ascriptive Signals in Omani Labour Markets", https://doi.org/10.7910/DVN/O5XKXR, Harvard Dataverse, V1Social Networks, Gender, and Graduate Hiring in a Rentier State: Disentangling Referral Types and Ascriptive Signals in Omani Labour Marketsdoi:10.7910/DVN/O5XKXRAl Hatmi, KhalifaHarvard DataverseAl Hatmi, KhalifaAl Hatmi, Khalifa2026-03-08Social Sciencesfactorial survey experimentThis 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<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>05_qualitative_codebook-1.R05_qualitative_codebook.Rtext/x-stata-syntax05_qualitative_codebook-2.R05_qualitative_codebook.Rtype/x-r-syntax05_qualitative_codebook-3.R05_qualitative_codebook.Rtype/x-r-syntax05_qualitative_codebook.R05_qualitative_codebook.Rtype/x-r-syntax05_qualitative_codebook.R.R05_qualitative_codebook.Rtype/x-r-syntaxREADME.mdREADME.mdtext/markdown