Sticky Prices as Coordination Failure: An Experimental Investigation

Last registered on January 24, 2023

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Pre-Trial

Trial Information

General Information

Title

Sticky Prices as Coordination Failure: An Experimental Investigation

RCT ID

AEARCTR-0010782

Initial registration date

January 16, 2023

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 23, 2023, 6:42 AM EST

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

Last updated

January 24, 2023, 3:17 PM EST

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

Locations

Country

United States of America

Region

Primary Investigator

Name

David Munro

Affiliation

Middlebury College

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Joy Buchanan

PI Affiliation

Samford University

Contact Investigator

Additional Trial Information

Status

In development

Start date

2023-01-18

End date

2023-03-31

Keywords

Behavior

Additional Keywords

JEL code(s)

E70, E31, D40

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Nominal rigidities play a central role in generating inefficiencies macroeconomics but our understanding of the underlying factors which cause these rigidities remains incomplete. In this project we leverage laboratory price setting games to gain further insights on the factors underlying sticky prices.

External Link(s)

Registration Citation

Citation

Buchanan, Joy and David Munro. 2023. "Sticky Prices as Coordination Failure: An Experimental Investigation." AEA RCT Registry. January 24. https://doi.org/10.1257/rct.10782-1.1

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We will examine price setting behavior in laboratory markets where subjects act as firms. Each period firms receive new cost shocks and must decide how to adjust their prices.

Intervention Start Date

2023-01-18

Intervention End Date

2023-03-31

Primary Outcomes

Primary Outcomes (end points)

Prices

Primary Outcomes (explanation)

Most prominently, we are interested in share of subjects who adjust their price in response to cost shocks.

Secondary Outcomes

Secondary Outcomes (end points)

Beliefs, loss aversion.

Secondary Outcomes (explanation)

We are also interested in measuring how beliefs matter in price adjustment decisions and how they evolve over time as subjects experience the game. And we will elicit loss aversion at the end of the experiment to see if it correlates with subject decisions in the price setting games.

Experimental Design

Experiment will be run with oTree software. We have designed price setting games with subjects compete with each other in a market (i.e. they act as firms). Our treatments will manipulate some feature of the market to explore how price setting behavior changes. Subjects will be randomized into groups.

Experimental Design Details

For general description see public design. More details are as follows. Main treatments will be to examine how price setting behavior difference for different size menu costs (price adjustment fees). The theory predicts full price flexibility without price adjustment costs and the possibility of coordination failure when price adjustment costs are present.

Some other possible treatments involve changes to the degree of strategic complementarities, which should impact the size of the range of multiple equilibrium.

Randomization Method

Randomization done by computer.

Randomization Unit

Randomization will happen at the individual level (randomized into groups)

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

24 groups

Sample size: planned number of observations

120 individuals

Sample size (or number of clusters) by treatment arms

12 groups per treatment cluster

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

MDE is roughly a 25% difference in the price adjustment frequency. For a binary outcome (adjust/don't adjust), 5% significance and power of 0.9, we'd need 53 subjects to detect a 30ppt difference in the price adjustment frequency. So, with 60 subjects, we are closer to a 25% detectable difference. This should be enough to clearly distinguish between the different equilibria (100 vs. 0% adjustment frequencies).

Supporting Documents and Materials

IRB