Information Gathering and Contracting Outcomes in Online Procurement

Last registered on July 10, 2020

View Trial History

Pre-Trial

Trial Information

General Information

Title

Information Gathering and Contracting Outcomes in Online Procurement

RCT ID

AEARCTR-0006114

Initial registration date

July 09, 2020

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

July 10, 2020, 10:01 AM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Christopher Stanton

Affiliation

Harvard Business School

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

On going

Start date

2020-06-10

End date

2020-12-24

Keywords

Firms & Productivity, Labor

Additional Keywords

JEL code(s)

Secondary IDs

Abstract

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

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

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

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

2020-06-10

Intervention End Date

2020-07-24

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

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?

Yes

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)

Supporting Documents and Materials

IRB