Random close designs, such as the one used in Comprasnet’s e-reverse auctions, are not common in modern-day online auctions. However, they have far predated the emergence of the Internet: Cassidy (1967) provides several examples of candle auctions, a type of auctions used in Great Britain in the 17th and 18th centuries, in which the random closing time is determined by a lit candle and the auction ends when the flames die.

In Comprasnet, the auction duration is randomly determined by the system based on a uniform distribution. According to Celiktemur and Szerman (2012), the fact that the random close is rarely observed outside Comprasnet could be due in part to IBM’s patent of the system. Using transactional data from Comprasnet, Celiktemur and Szerman (2012) report interesting patterns of bidding behavior in Comprasnet’s random close auctions. They find that (i) bidders tend to defer bidding to the end of the auction; (ii) a large part of the auctions is resolved early; (iii) bid increments are usually small; and (iv) large increments are more likely to occur early in the auction. They conclude that random close designs do not necessarily prevent late bidding and consider that random close mechanisms may lead to suboptimal prices when auctions are closed in the heat of bidding. Finally, they suggest that this effect could be offset or outweighed by increased participation since randomness increases the chances of weak bidders to win the auction.

Our paper aims to complement previous work by: (i) evaluating how Comprasnet’s random close design impacts the prices of goods and services purchased by the Federal Government of Brazil and (ii) estimating the savings that could be generated if the closing rule was set differently compared to the current random setting. Building upon a framework for measuring fiscal efficiency in public procurement that has been tested and refined through implementation in eight Latin American countries, including Brazil (World Bank, 2017), we propose a model that extends the specifications used in the previous studies, using additional controls and taking into account the interactions between competition and auction length. To the best of our knowledge, this paper represents the first attempt to quantify the effects of a random close design on the outcomes of electronic reverse auctions. It is also the first estimation of the potential savings that could be generated by using big data and artificial intelligence to optimize the auction closing rule.

# A. Hypotheses

To answer the research question presented in the introduction, we test several hypotheses relating to and addressing the existing literature. This enables us to evaluate several auction ending designs and to estimate the impact of each of these variants on the prices of the goods purchased through Comprasnet’s electronic reverse auctions.

## **_H1: Auctions with a longer random phase result in lower unit prices._**

According to this premise, we expect longer random phase durations to be associated with lower prices since longer times increase the opportunities for bidders to outbid their competitors and subsequently reduce the probability of ending the auction in the heat of bidding. Conversely, auctions with short random phase are at higher risk of closing before the market clearing price (in this case the lowest price acceptable to the bidders) is reached, impeding the government from reaping all the benefits from the competition.

In order to test this first hypothesis, we examine the independent effect of the random phase duration on unit prices. This estimation of potential savings enables the pricing of the impacts of a simple variant of Comprasnet’s random close with a longer duration of the random phase.

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