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Firm Response to Analysis of Bidding Data
Last registered on January 28, 2019

Pre-Trial

Trial Information
General Information
Title
Firm Response to Analysis of Bidding Data
RCT ID
AEARCTR-0003814
Initial registration date
January 23, 2019
Last updated
January 28, 2019 12:42 AM EST
Location(s)
Region
Primary Investigator
Affiliation
Other Primary Investigator(s)
PI Affiliation
Kindai University
Additional Trial Information
Status
In development
Start date
2019-02-15
End date
2019-06-14
Secondary IDs
Abstract
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
Citation
Kawai, Kei and Jun Nakabayashi. 2019. "Firm Response to Analysis of Bidding Data." AEA RCT Registry. January 28. https://doi.org/10.1257/rct.3814-2.0.
Former Citation
Kawai, Kei, Kei Kawai and Jun Nakabayashi. 2019. "Firm Response to Analysis of Bidding Data." AEA RCT Registry. January 28. http://www.socialscienceregistry.org/trials/3814/history/40707.
Experimental Details
Interventions
Intervention(s)
Treat a subset of firms with an analysis of their bidding behavior in public procurement auctions.
Intervention Start Date
2019-02-15
Intervention End Date
2019-02-19
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?
Yes
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)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS