The effect of competitor information on firm strategy

Last registered on September 16, 2021
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Pre-Trial

Trial Information

General Information

Title
The effect of competitor information on firm strategy
RCT ID
AEARCTR-0003013
Initial registration date
July 03, 2018

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 06, 2018, 11:02 AM EDT

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

Last updated
September 16, 2021, 11:34 PM EDT

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

Locations

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Primary Investigator

Name
Hyunjin Kim
Affiliation
Harvard University
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Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2018-05-01
End date
2025-09-30
Keywords
Firms & Productivity, Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project aims to understand how increased access to competitor information enabled by digitization affects the strategic decisions and performance of firms, by running a field experiment across nail salons in four cities (New York, Chicago, San Francisco, and Los Angeles).
External Link(s)

Registration Citation

Citation
Kim, Hyunjin. 2021. "The effect of competitor information on firm strategy ." AEA RCT Registry. September 16. https://doi.org/10.1257/rct.3013-3.0
Former Citation
Kim, Hyunjin. 2021. "The effect of competitor information on firm strategy ." AEA RCT Registry. September 16. https://www.socialscienceregistry.org/trials/3013/history/99894
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
The intervention consists of providing firms with competitor pricing information comparing their prices to 9 competitors located closest to them.
Intervention Start Date
2018-06-18
Intervention End Date
2019-01-01

Primary Outcomes

Primary Outcomes (end points)
Firms' pricing decisions
Primary Outcomes (explanation)
I will first examine (descriptively) the extent to which firms already know information on competitor pricing. Then, I will analyze whether treated firms change their service offering prices (binary indicator) and how they change these prices (absolute and real changes in prices).

Secondary Outcomes

Secondary Outcomes (end points)
I will also examine the extent to which treatments impact firms' performance, as well as some exploratory analyses on a variety of firm decisions outside of pricing.
Secondary Outcomes (explanation)
To measure performance, I will use proxies of performance as measured by Yelp, including the number of page views, directions and map views, calls to the business, number of reviews, and survival. When available, I will also examine a binary indicator of availability the next day, as well as the number of employees and customers at the time of salon visit. Finally, for a subset of salons in San Francisco, I will look at quarterly revenues using tax data from the City of San Francisco.

In terms of firm decisions, I will look at decisions of scope (in terms of its service offerings e.g. massage, hair, etc), quality (e.g. nail polish brands offered, Yelp rating and review text on quality, cleanliness and luxuriousness of interior, etc), and practices (e.g. hours open, promotions/signs, etc).

Experimental Design

Experimental Design
Firms in my sample are randomly assigned to one of two conditions, control or treatment.
Experimental Design Details
Not available
Randomization Method
Randomization done in office
Randomization Unit
I first stratify on city, business type (whether it is an advertiser at Yelp, has a claimed Yelp page, or has not claimed its Yelp page), and rounded Yelp rating, and then randomly assign firms in each strata.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
A minimum of 3,948 businesses. If the research budget stretches further (in terms of how long canvassers take to visit businesses to deliver treatments), I will expand the sample to include up to 9,587 firms. While these are the number of observations planned, I expect some of these businesses to be a fake, closed, or duplicate listing at the time of the intervention, so the actual minimum number in the sample may be lower.
Sample size: planned number of observations
A minimum of 3,948 businesses. If the research budget stretches further (in terms of how long canvassers take to visit businesses to deliver treatments), I will expand the sample to include up to 9,587 firms. While these are the number of observations planned, I expect some of these businesses to be a fake, closed, or duplicate listing at the time of the intervention, so the actual minimum number in the sample may be lower.
Sample size (or number of clusters) by treatment arms
Firms are randomly assigned within each strata. In the minimum sample, 1,976 are in control, and 1,972 in treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Harvard University Institutional Review Board
IRB Approval Date
2018-05-21
IRB Approval Number
IRB18-0882
Analysis Plan

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