Bridging the digital divide: Analyzing subsidy allocation efficiency in telecom sector reforms

This paper aims to introduce the 'Subsidy Allocation Efficiencies' (SAE) metric as a practical tool for policymakers to evaluate subsidy programs for universal service provisioning . Using a qualitative case study approach, the paper investigates various subsidy allocation methods adopted by the Universal Service Fund, comparing them in terms of SAE. The SAE metric is validated by applying the ‘similarity index’ to Milgrom's optimal auction design. The study finds that subsidy allocations can be as efficient as 95% and as inefficient as −16%, generally identifying them as restrictive and prone to cartelization. Bidders often exploited allocation rules, leading to poor subsidy allocation efficiency, and subsidy benchmarking was found to lack rigor. However, ease of participation was found to reduce cartelization and improve efficiency. The saved funds from accurately benchmarked and efficiently allocated subsidies can be used to cover more beneficiaries or reduce the universal service levy rate, easing the burden on consumers. Focusing on India's Universal Service Fund, this study critically assesses subsidy allocation methods to provide policymakers with insights for optimizing public-funded infrastructure projects. It addresses the lack of empirical research on Subsidy Allocation Efficiency (SAE) and challenges the assumption that auctions alone guarantee efficient subsidy allocation, emphasizing the importance of bidding rules. • Introduces a new reliable metric for measuring Subsidy Allocation Efficiency (SAE). • A reverse auction alone is no guarantees for efficient allocation. • Tech neutrality, and dynamic benchmarking are pivotal for efficiency. • Multi-round reverse auctions yield better efficiency as compared to Single-round. • Provides a roadmap for increased coverage and lower levies on existing users.