not to evaluate hard close designs since a significant body of literature already suggests that hard close endings lead to suboptimal prices.

# 4. Data

## A. Data preparation

For the purposes of our analysis, we obtained three years (2015-2017) of detailed transactional data from Comprasnet’s online reverse auction system provided by the Brazilian Ministry of Planning, Budget and Management. For each year, we were provided with the following data sets: (i) a bidder-level data set containing general information about each auction, representing a total of 7 million observations across the three years; (ii) an item-level data set containing detailed information about each bid received during the auction, representing a total of 112 million observations; and (iii) a data set holding the milestone dates and times of all the electronic reverse auctions processes carried out through Comprasnet (0.9 million purchased items).

The data preparation phase consisted of three main steps. The first step involved merging the raw data provided by the government into a single data set by linking data from the same auctions across the different tables. It also required summarizing the data to obtain a final data set in which each observation represents an auction conducted for a specific item.

The second step was to clean the data to ensure its consistency as well as the coherence of the final sample used for the analysis. This involved removing observations with inconsistent dates and times, duplicated identifiers or missing values. Following the advice of the Brazilian Ministry of Planning, Budget and Management, we also removed all the data from agencies that are not part of the SISG ( _Sistema de Serviços Gerais_ ). All agencies that are part of SISG must use Comprasnet and SIASG for all their procurement purchases, while non-SISG agencies can choose to use or not to use these systems. When the use of the Comprasnet system is voluntary we do not observe the full purchasing activity of a buying organization potentially biasing the sample.

The third step was to generate new aggregates used in our models. The main variables generated during the data preparation phase included measures of: (i) price; (ii) duration of the auction’s random phase; and (iii) competition characteristics. Several control variables reflecting market characteristics and trends were also included in the final data set.

Besides the typical challenges associated with the data cleaning process, the large volume of transactional data managed in Comprasnet also stretched the capacity of the server-based infrastructure<sup>2</sup> provided by the Brazilian Ministry of Planning, Budget and Management. Eventually, an upgrade of the R server from 2 GB to 112 GB of RAM significantly improved the computational capacity, allowing us to successfully complete all the tasks of the data preparation phase.

## B. Indicators used in the analysis

To test the four hypotheses mentioned above, we use the unit price (in reais) as our dependent variable, and the duration of the random phase (in seconds) as our main independent variable. To test the second, third

> 2 An R instance running on a remote server with 2 GB of RAM.

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