Abstract

The auctioning of frequency has to comply with a multitude of requirements in order to guarantee a transparent and efficient process. The German Federal Network Agency (Bundesnetzagentur) has opted for a design that provides participants with information on the highest bid after each round for every band along with information on the bidder. We evaluate the price formation efficiency in this setup to see how fast prices become informative about the final auction value. We find that prices are partially informative right from the beginning which allows us to conclude that participants were able to learn fast from their competitors’ bidding behavior and validates the choice of the agency to implement the auction in the present format.

Highlights

  • With the introduction of the latest telecommunication standard for broadband cellular networks, 5G, the speed at which data transmission is possible has reached a new peak

  • While some countries assign these frequencies to their telecommunication firms or hold administrative hearings such as China or Japan (Krempl 2019), most governments rely on auctions to achieve an efficient distribution of the spectrum among national providers. These frequency auctions are capable of generating a substantial revenue: In the United Kingdom, the UMTS (Universal Mobile Telecommunications System) auction’s revenue equaled 2.5% of the country’s gross domestic product in 2000 (Felder 2004)

  • The learning hypothesis is to be understood as the hypothesis that learning is about to come to an end as the bid in the respective round would be a good predictor of the final auction outcome

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Summary

Introduction

With the introduction of the latest telecommunication standard for broadband cellular networks, 5G, the speed at which data transmission is possible has reached a new peak. The level of informativeness increases the fastest between round 100 and round 187 where the most new bids are submitted This is in line with theory as the participants learn about the true value of individual frequencies over time with bids becoming increasingly informative as the auction goes on. In some previous spectrum auctions, the auctioneer sold either a certain amount of spectrum licenses for a frequency band or, if individual blocks were for sale, auctioned concrete blocks of spectrum Both can give rise to price wars or predatory bidding and a potentially inefficient distribution of the spectrum. The BNetzA planned to auction these blocks in a second round to the winners of the initial auction (Bundesnetzagentur 2018) This way, only efficient network operators would be able to acquire spectrum and install 5G networks. This acted as a safety net for potential entrants, making it more attractive to compete in the auction

Auction Participants
Frequency Bands and the 5G Technology
Hypotheses
The Noise Hypothesis
The Learning Hypothesis
The Data
Price Discovery
Estimation
Learning during the Auction
Robustness Checks and Discussion
Findings
Conclusions
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