# 1. Introduction

Reverse auctions were introduced to the Brazilian public procurement system in 1997 and almost instantly became the favorite method of government agencies and suppliers for its speed when compared to traditional procurement methods: you could award a contract in about two weeks. The possibility of submitting electronic bids for the first time amplified the success of reverse auctions in Brazil. Not only government agencies and suppliers were happy with them, but also the public at large and oversight agencies touted its transparency, as the entire process can be followed online by anyone with a computer and internet connection. In 2018, reverse auctions were the most used competitive procurement method by federal government agencies in Brazil by far.

But, more recently, this impressive history of reverse auctions in Brazil has been threatened by the rise of high-frequency bids, which almost always lower the price by only decimals. It is widely believed that highfrequency bids are placed by algorithms, or “bots” and there are strong concerns that they are impacting not only prices in public procurement but also unsettling the playing field. Once known for breaking monopolies and oligopolies in public procurement, reverse auctions are now on the spotlight over concerns that bidders with the capability of placing bids using bots have a leg up over bidders that use labor power to place their bids. The widespread use of bots overwhelms the IT infrastructure and makes it hard for humans to put a bid through when the system must handle a high frequency of bids placed by machines.

At the core of the problem is the approach to wrap up a reverse auction in the Federal Government of Brazil: reverse auctions are closed randomly by computers, following a period of time that can vary between one second and 30 minutes. This period, known as “random phase” in Brazil, is when fierce competition for contracts take place and bidders that do not have the capability of placing bids with bots claim that they do not see their bids through among the high frequency of bids placed by bots. In addition, government agencies and the public now question the wisdom of a random closing to reverse auctions: a great deal of processes are closed at the heat of the bidding or within very few seconds, sparking concerns that the random closing might be cutting short bidding that would otherwise lower prices for the government.

Some governments in Brazil have started taking action against high-frequency bids. For instance, the government of the State of São Paulo will automatically extend the time allowed for submission of bids if one is received within three minutes prior to closing. The state also requires new bids to lower the bidder’s previous bid by a minimum amount, usually 0.5% of the previous price – this is to counter bids that lower the price by cents, just enough to get ahead of the competition.

Given the central importance of auction length, we investigate what would happen if the random closing rule was replaced by another rule? Would government agencies get lower prices under different scenarios? This paper will present both a conceptual framework and novel empirical evidence to answer these questions. We used data on all electronic reverse auctions done by the Federal Government of Brazil during 2015-2017 which encompassed over 112 million bids for 7 million purchased items.

Our research revealed that longer random phases resulted in lower prices, but the effect tapers off at the tail end of the 30-minute period. Thus, it seems that closing the auction randomly does not produce optimal prices, especially when closing within seconds. Unsurprisingly, we found that the size of the discount had a material effect on prices; more specifically, larger discounts within the last three minutes of the random phase resulted in lower prices. In addition, the size of the discounts mattered more for the final auction price than the frequency of discounts. This appears to indicate that the high-frequency bidding attributed to the use of bots did not help to achieve the lowest prices, which came when discounts were larger rather than more frequent. Further to these results, we also found that a larger number of bidders resulted in lower 2