Governance and Compensation Index (GCI), v2.0

International Conference on Innovations in Architecture, Design, Business, Society, Language & Literature (ICADBSL 2025)Host Institution: Vellore Institute of Technology (VIT), IndiaFormat: Peer-reviewed international conference presentationRole: Student Research PresenterResearch Paper: Disproportionate CEO Compensation in U.S. 501(c)(4) Nonprofits: Governance, Equity, and AccountabilityInstitutional Affiliation: Borough of Manhattan Community College (CUNY)Faculty Facilitator: Professor Shamira Soren Malekar (Keynote Speaker)Date: October 30, 2025Description This record documents the Governance and Compensation Index (GCI), Version 2.0, a structured evaluative instrument designed for measuring governance quality in nonprofit executive compensation systems. The GCI is an analytical measurement framework applied to organizational-level data derived from IRS Form 990 Schedule J filings. It operationalizes governance quality through five dimensions: board independence, benchmarking practices, documentation rigor, transparency, and DEI integration. The instrument is designed for use with empirical datasets of nonprofit organizations and is applied in related research analyzing executive compensation structure and governance coherence in U.S. 501(c)(4) organizations. The GCI is explicitly a measurement instrument and not a dataset. It is intended to be used in conjunction with observational data sources, including the companion Dataverse dataset on nonprofit executive compensation and governance. RELATIONSHIP TO RELATED RESEARCH OUTPUTS This instrument is: Applied to: IRS Form 990–based Governance and Executive Compensation Dataset (Dataverse) Aligned with: SSRN working paper (DOI: 10.2139/ssrn.6571798) The GCI provides a structured scoring framework used to operationalize governance quality for empirical and comparative analysis. It enables consistent measurement of governance coherence across nonprofit organizations. IMPORTANT NOTE The GCI does not contain raw organizational data. It is a derived measurement framework intended for analytical application only. It should be used in conjunction with observational datasets and is not a standalone empirical source.