Improving broadband subsidy allocation: Algorithmic and policy insights from Israel

Public broadband subsidy programs increasingly require regulators to allocate limited public funds across many geographically interdependent projects under both informational and computational constraints. When firms may submit bundled offers over clusters of areas, the winner-determination problem becomes combinatorial: the regulator must balance coverage, budget, and overlap across competing offers. We study this problem through the case of broadband deployment procurement in Israel. We compare three allocation rules: (i) a Greedy base-2 algorithm with a worst-case approximation ratio of 1 − e − 1 ≈ 0 . 632 relative to the optimal solution, with computational complexity O ( n 4 ) ; (ii) the algorithm implemented by the Israeli Ministry of Communications (MoC), which runs in O ( n 2 ) time but has no known worst-case bound; and (iii) a Hybrid algorithm that combines elements of both approaches. Using a case study of fiber-optic deployment in Israel, we show that the Ministry’s algorithm and the Hybrid variant achieve broader coverage at lower cost by reducing costly overlap and improving budget efficiency, thus more effectively meeting auction objectives. Benchmarked against the optimal solution on small instances, all three methods perform close to optimal, with a modest advantage for the Hybrid and MoC algorithms. • Broadband subsidy allocation is compared under a fixed public budget. • A hybrid algorithm combines approximation with redundancy removal. • Redundancy removal increases coverage and lower public costs. • Israeli auction data show near-optimal performance on small instances.