Beyond Revenue and Welfare: Counterfactual Analysis of Spectrum Auctions with Application to Canada's 3800MHz Allocation

TL;DR

This paper develops a parsimonious, data-driven model of spectrum auctions where bidders are assumed to act myopically and choose the bundle that maximizes immediate utility. Using round-by-round bidding data from Canada’s 3800 MHz auction, valuations are recovered via linear programming under monotonicity and restricted bundles, enabling a faithful reconstruction of auction dynamics and outcomes. The authors validate the model against the actual auction and perform a counterfactual analysis by embedding deployment obligations directly into the auction design, finding substantial gains in rural and remote coverage with modest revenue reductions. The approach offers a practical framework for ex ante evaluation of auction designs that aim to promote policy objectives like equitable deployment, bridging empirical valuation recovery with mechanism design. Together, the methodological contribution and policy-relevant findings provide a scalable tool for regulators to assess how alternative auction rules could affect efficiency and coverage in large-scale spectrum allocation.

Abstract

Spectrum auctions are the primary mechanism through which governments allocate scarce radio frequencies, with outcomes that shape competition, coverage, and innovation in telecommunications markets. While traditional models of spectrum auctions often rely on strong equilibrium assumptions, we take a more parsimonious approach by modeling bidders as myopic and straightforward: in each round, firms simply demand the bundle that maximizes their utility given current prices. Despite its simplicity, this model proves effective in predicting the outcomes of Canada's 2023 auction of 3800 MHz spectrum licenses. Using detailed round-by-round bidding data, we estimate bidders' valuations through a linear programming framework and validate that our model reproduces key features of the observed allocation and price evolution. We then use these estimated valuations to simulate a counterfactual auction under an alternative mechanism that incentivizes deployment in rural and remote regions, aligning with one of the key objectives set out in the Canadian Telecommunications Act. The results show that the proposed mechanism substantially improves population coverage in underserved areas. These findings demonstrate that a behavioral model with minimal assumptions is sufficient to generate reliable counterfactual predictions, making it a practical tool for policymakers to evaluate how alternative auction designs may influence future outcomes. In particular, our study demonstrates a method for counterfactual mechanism design, providing a framework to evaluate how alternative auction rules could advance policy goals such as equitable deployment across Canada.

Figures (4)

• Figure 1: Bidding patterns visualized as heatmaps. Left: Auction $A$ (actual); Right: Auction $B$ (simulated).
• Figure 2: Final prices in Auction $B$ (simulated) relative to Auction $A$ (actual).
• Figure 3: Bidding patterns visualized as heatmaps for the 3500 MHz auction. Left: Auction $C$ (actual); Right: Auction $D$ (simulated).
• Figure 4: Final prices in Auction $D$ (simulated) relative to Auction $C$ (actual).