Firm Response to Analysis of Bidding Data

Last registered on January 28, 2019


Trial Information

General Information

Firm Response to Analysis of Bidding Data
Initial registration date
January 23, 2019

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
January 25, 2019, 3:56 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
January 28, 2019, 12:42 AM EST

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator


Other Primary Investigator(s)

PI Affiliation
Kindai University

Additional Trial Information

In development
Start date
End date
Secondary IDs
The study seeks to understand how providing analysis of bidding behavior to firms influence competition among firms. In many instances, bidding behavior of firms may not coincide with that implied by the competitive equilibrium of static auction models. Instead, bidding may be more consistent with strategies of non-stationary equilibrium. We first identify a set of firms whose bidding seems inconsistent with the equilibrium of standard static auction models and treat a subset of these firms with information about their bidding behavior.
External Link(s)

Registration Citation

Kawai, Kei and Jun Nakabayashi. 2019. "Firm Response to Analysis of Bidding Data." AEA RCT Registry. January 28.
Former Citation
Kawai, Kei and Jun Nakabayashi. 2019. "Firm Response to Analysis of Bidding Data." AEA RCT Registry. January 28.
Experimental Details


Treat a subset of firms with an analysis of their bidding behavior in public procurement auctions.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
winning bid (price and score), number of bidders, bidding pattern
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
First, we identify a subset of firms whose behavior cannot be rationalized by the equilibrium of static auction models. We then group these firms using a clustering algorithm based on auction participation patterns. Each cluster of firms is paired with another cluster based on criteria such as region and industry. One cluster in each pair is chosen randomly and some firms in the cluster are treated with information about their bidding behavior.
Experimental Design Details
Randomization Method
randomization done in office by a computer
Randomization Unit
There are two levels of randomization. One unit of randomization is firm clusters. The second unit of randomization is firms within each cluster.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
26 clusters, about 250 firms.
Sample size: planned number of observations
1,000 auctions, 10,000 bids.
Sample size (or number of clusters) by treatment arms
13 clusters control, 13 clusters treatment. About 2/3 firms in the treatment arm treated.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials