Public Sector Financial Reporting Transparency and Accountability Outcomes

Abstract

This research investigates the relationship between public sector financial reporting transparency and accountability outcomes through a novel computational
framework that integrates natural language processing, network analysis, and evolutionary algorithms. Unlike traditional accounting studies that rely on manual
content analysis or survey-based metrics, we develop an automated system capable of quantifying transparency across unstructured financial documents from
142 municipal governments over a ten-year period. Our methodology introduces
a multi-dimensional transparency index derived from semantic analysis of narrative disclosures, structural analysis of financial statement linkages, and temporal
analysis of reporting consistency. We employ a bio-inspired optimization algorithm—specifically an artificial immune system model—to identify optimal transparency configurations that maximize accountability outcomes measured through
citizen engagement metrics, audit opinion improvements, and fiscal performance
indicators. The findings reveal non-linear relationships between transparency dimensions and accountability, with diminishing returns beyond certain thresholds
and unexpected interactions between narrative clarity and data granularity. We
identify three distinct transparency archetypes among municipalities and demonstrate how evolutionary pressure from public scrutiny shapes reporting practices
over time. The study contributes to both public administration and computational
social science by providing a dynamic, systems-oriented approach to understanding
financial governance that moves beyond static compliance frameworks. Our computational model offers public sector entities a diagnostic tool for evaluating and
optimizing their transparency strategies to enhance democratic accountability.