<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>Social Networks, Gender, and Graduate Hiring in a Rentier State: Disentangling Referral Types and Ascriptive Signals in Omani Labour Markets</dcterms:title><dcterms:identifier>https://doi.org/10.7910/DVN/O5XKXR</dcterms:identifier><dcterms:creator>Al Hatmi, Khalifa</dcterms:creator><dcterms:publisher>Harvard Dataverse</dcterms:publisher><dcterms:issued>2026-03-09</dcterms:issued><dcterms:modified>2026-03-09T14:05:35Z</dcterms:modified><dcterms:description>This 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.</dcterms:description><dcterms:subject>Social Sciences</dcterms:subject><dcterms:subject>factorial survey experiment</dcterms:subject><dcterms:date>2026-03-09</dcterms:date><dcterms:contributor>Al Hatmi, Khalifa</dcterms:contributor><dcterms:dateSubmitted>2026-03-08</dcterms:dateSubmitted><dcterms:license>CC0 1.0</dcterms:license></metadata>