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Information Gathering and Contracting Outcomes in Online Procurement
Last registered on July 10, 2020


Trial Information
General Information
Information Gathering and Contracting Outcomes in Online Procurement
Initial registration date
July 09, 2020
Last updated
July 10, 2020 10:01 AM EDT
Primary Investigator
Harvard Business School
Other Primary Investigator(s)
Additional Trial Information
On going
Start date
End date
Secondary IDs
Parties in procurement contracting often lack information about the capabilities or actions of their potential partners. Hiring is a special case of labor procurement where bilateral asymmetric information plays an important role in the efficiency of matching talent to appropriate opportunities (Oyer and Schaefer, 2010). Several practices may reduce information asymmetry in hiring and other procurement contexts, but the use and efficacy of these practices is unclear. Two recommendations are: 1) test applicants and stage information gathering, where a buyer observes small portions of work before committing to a larger project, and 2) introduce competition among applicants. This project seeks to understand the effects of these practices for online procurement outcomes. An additional goal is to measure whether informational treatments make buyers more likely to adopt best practices and to understand determinants of buyers’ uses of different practices.
External Link(s)
Registration Citation
Stanton, Christopher . 2020. "Information Gathering and Contracting Outcomes in Online Procurement." AEA RCT Registry. July 10. https://doi.org/10.1257/rct.6114-1.0.
Experimental Details
Buyers in a large online market for services procurement will see different prompts that provide either control messages or suggested best practices for hiring. We will measure how these prompts change whether buyers adopt staged hiring, which entails formally defining milestones that are smaller than a full project. We will also measure whether treated buyers change the number of bidders they evaluate. Auxiliary data analysis will seek to understand how these treatments change buyers’ time use during the formal contracting/negotiation phase and subsequent time spent communicating after a bidder begins working.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Project fill rates, log project revenue, project success, client retention, number of project milestones, number of freelancers evaluated, number of freelancers offered milestone contracts.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Number of freelancers evaluated on future projects, number of milestones on future projects, buyer time use and extent of chat (to measure effort costs), measures of freelancer quality (to measure match composition change). The exact details of these measures are not yet known because we do not yet have access to data.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Please see details under intervention for randomization.
Experimental Design Details
The main issue with data handling and the design is specifying how to deal with buyers who have multiple interactions with recruiters during the experiment. For these buyers, we will code their treatment based on the maximum extent of information they have seen previously. For example, a buyer who sees message 1, message 3, then message 2 will have treatment coded as 1, 3, 3 because treatment 3 contains more information than treatment 2.
Randomization Method
In office by computer
Randomization Unit
Clustered by recruiter.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
There are 16 individual units where treatment will be assigned, with different treatment assignment each week.
Sample size: planned number of observations
We do not have access to data to include this information, as it depends on the inflow of buyers to the site. We cannot release the planned sample size without consent of the company in a data use agreement.
Sample size (or number of clusters) by treatment arms
The expected number of clusters per treatment (recruiter-weeks) for a 4 week run-time is 21. We cannot disclose the sample size without consent of the company in a data use agreement.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Harvard University
IRB Approval Date
IRB Approval Number
Analysis Plan

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Study Withdrawal
Is the intervention completed?
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Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)