Assessing the Feasibility of Downsizing Municipalities in Saudi Arabia: A Path Toward Efficiency

This study investigates the feasibility and implications of municipal downsizing in the Kingdom of Saudi Arabia, focusing on improving governance efficiency, service delivery, and institutional coordination. Against the backdrop of Saudi Vision 2030 and decentralization efforts, three questions are addressed. (1) Can downsizing municipalities enhance local governance efficiency? (2) What structural, cultural, and economic barriers impede such consolidation? and (3) How can reforms be tailored to balance cost efficiency with the preservation of local identity? A mixed-methods approach was employed, integrating quantitative survey data from 375 stakeholders, including policymakers, municipal employees, citizens, and urban experts, and using qualitative thematic analysis. The findings reveal moderate but differentiated support for downsizing, with variables such as age, education, and stakeholder role influencing perceptions. The regression and analysis of variance (ANOVA) results indicate statistically significant divergences, particularly around expected cost savings and perceived impact on service quality. A case analysis from the Eastern Province further illustrates mixed satisfaction with previous mergers, underscoring concerns about rural marginalization and administrative overreach. This study identifies digital transformation, public service enhancement, and improved municipal coordination as the preferred reform priorities. However, concerns regarding representation, identity erosion, and uneven development remain. The results emphasize the need for a phased, inclusive, and adaptive reform strategy that aligns with local contexts and is anchored in measurable performance outcomes. By bridging local insights with global experiences, this study contributes to the broader literature on public sector restructuring and offers actionable pathways for Saudi policymakers navigating the complexities of territorial reforms.