Cost signals and collusion

Last registered on June 26, 2022

Pre-Trial

Trial Information

General Information

Title
Cost signals and collusion
RCT ID
AEARCTR-0009622
Initial registration date
June 21, 2022

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
June 26, 2022, 5:22 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
BI Handelshøyskolen

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2022-08-21
End date
2022-10-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Are list prices as cost signals effective collusion instruments?
We develop a model where sellers in a market signal their costs. After observing the signals, one buyer choose which seller to approach, and gives that buyer a take-it or leave it offer.
Our experiment is designed to test the model's predictions.
External Link(s)

Registration Citation

Citation
Heggedal, Tom-Reiel. 2022. "Cost signals and collusion." AEA RCT Registry. June 26. https://doi.org/10.1257/rct.9622
Experimental Details

Interventions

Intervention(s)
This is an experimental economics study. We analyze a market with two sellers and one buyer in the PC laboratory. The game we examine has the following stages:
1.With probability s the sellers establish a cartel.
2.Each seller draw independently their type, H or L, where q is the probability of high type. A high type has production cost c_{H}, the low type has production cost c_{L}. Type is private information.
3.Sellers simultaneously and independently make announcements, h or l. A cartel always announces h,h
4.The buyer observes announcements, chooses a seller, and offers either p_{H} or p_{L} for one unit
5.The transaction goes through if accepted by the seller.

We focus on parameter combinations supporting an equilibrium with the following characteristics:
1.Without any information (empty signals), the buyer offers price p_{L}
2.With s=0 (no collusion) the unique equilibrium is separating
3.With s=1, point 1 implies that the buyer offers p_{L}
4.There exists positive values of s, at which the buyer offers p_{H}, conditional on signals h,h.

The experiment is designed to test the main predictions of our model. To do so we have four treatments that vary with respect to the probability the sellers establish a cartel s. Based on model prediction 1, we predict that sellers signal their true type when allowed to choose signal, and that this behavior is invariant to s. Further, based model prediction 2, we predict that buyers' offer conditional on observing two high signals depends on whether s is above or below the separation cut-off ((1-Δ)/Δ).
Intervention Start Date
2022-08-21
Intervention End Date
2022-10-01

Primary Outcomes

Primary Outcomes (end points)
Our main treatment measures are the signals chosen by sellers (conditional on types) and the price offers from the buyers (conditional on signals).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Design

The experiment is designed to test the main predictions of the model. To do so we have four treatments that vary with respect to the probability the sellers establish a cartel s. Based on model prediction 1, we predict that sellers signal their true type when allowed to choose signal, and that this behavior is invariant to s. Further, based model prediction 2, we predict that buyers' offer conditional on observing two high signals depends on whether s is above or below the separation cut-off ((1-Δ)/Δ).

Implementation

It is straight forward to implement a parameterized version of the market game (as given by the five stages above) in the lab. Our treatment variable is s, and we use the following parameters: q=0.5 ; p_{H}=80 ; p_{L}=50 ; c_{H}=55 ; c_{L}=10 ; v=100. With these parameters we have p_{L}-c_{L}>(1/2)(p_{H}-c_{L}), and the pooling equilibrium does not exits. Further, the cut-off ((1-Δ)/Δ)=0.667, and, hence, collusion is effective when s<0.667.
Our main treatment measures are the signals chosen by sellers (conditional on types) and the price offers from the buyers (conditional on signals). In particular, let θ∈{0,1} denote the true signal of a seller, taking value 0 if the signal is not true and 1 if the signal is true. Further, let p|_{h,h} denote the price offer from a buyer receiving two high signals. We also measure sellers' profits, and whether buyers makes an offer to the seller with the lowest signal. The following table gives an overview of the four treatments and equilibrium predictions:

T₁(s=0) T₂(s=0.25) T₃(s=0.5) T₄(s=0.75)
θ = 1 1 1 1
p|_{h,h}= p_{H} p_{H} p_{H} p_{L}

We use blocks of 9 subjects. Subjects stay within blocks, and unique subjects are used in all treatments. In our analysis we regard average behavior within blocks as independent observations. A session may include several blocks. Subjects play 30 games. Prior to the first game subjects randomly draw roles so that there are 3 buyers and 6 sellers in each block. These roles are fixed for all games. Before each game, subjects in a block are randomly matched into markets consisting of 1 buyer and 2 sellers.
In the experiment, price offers and payoffs are denominated in experimental currency units (ECU). The exchange rate is set to equalize expected payoffs between treatments. At the conclusion of the session subjects are paid privately based on accumulated payoffs in ECU from all games played.
A high cost seller that accepts to sell when offered the low price incurs a loss of 5 ECU. As an insurance against negative payoffs, all subjects are allocated 150 ECU before play starts.
The experiment is implemented by zTree (Fischbacher, 2007) and subject management is handled through ORSEE (Greiner 2015).
Experimental Design Details
Not available
Randomization Method
Random assignment of subjects into treatments. Subjects are drawn from the population of students that have signed up for lab-experiments at the BI Norwegian Business School.
Randomization Unit
Subjects are randomized into treatments, and into blocks within each treatment. We use blocks of subjects within treatments as our unit of observation.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters.
Sample size: planned number of observations
We plan to have 9 subjects in each block and 5 blocks per treatment. That is, we plan to include 9x5x4=180 subjects.
Sample size (or number of clusters) by treatment arms
We use blocks of subjects within treatments as our unit of observation. That gives us 5 independent observations per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

Documents

Document Name
Pre-study plan: Cost signals and collusion
Document Type
other
Document Description
File
Pre-study plan: Cost signals and collusion

MD5: ba0fcc7e2a05e024aa95af307214417e

SHA1: 6802a1ef1a7216689c1f19a166d67db5b34ebfd1

Uploaded At: June 21, 2022

IRB

Institutional Review Boards (IRBs)

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