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Inter-firm Information and Competitive Audit Mechanisms
Last registered on September 28, 2020

Pre-Trial

Trial Information
General Information
Title
Inter-firm Information and Competitive Audit Mechanisms
RCT ID
AEARCTR-0004996
Initial registration date
January 31, 2020
Last updated
September 28, 2020 11:09 AM EDT
Location(s)

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Primary Investigator
Affiliation
Heidelberg University
Other Primary Investigator(s)
PI Affiliation
Brock University
PI Affiliation
Heidelberg University
Additional Trial Information
Status
In development
Start date
2020-02-05
End date
2021-05-05
Secondary IDs
Abstract
This project aims to design cost-effective audit mechanisms, taking into consideration the EPA's limited audit resources. To do so, we first unify in a theoretical framework with endogenous emissions and emission reports several recent advances on competitive audit mechanisms for the enforcement of environmental taxes. We then experimentally assess the effectiveness of competitive audit mechanisms relative to random audit mechanisms in terms of emissions produced and emissions reported. While random auditing assigns the same audit probability to each regulated firm, competitive audit mechanisms assign a lower audit probability to the firms with higher self-reported emissions relative to others. These resulting strategic interdependences between firms can yield audit leverage for the environmental protection agency. Hence, the performance of competitive audits depends in part on the inter-firm information structure. When firms have no information about each other’s emissions, our model predicts that competitive auditing induces higher reported emissions and the same level of actual emissions as compared to random auditing. These two results are isomorph to the main findings by Gilpatric et al. (2011) and Cason et al. (2016) respectively. In contrast, when firms have perfect information about each other’s emissions, competitive auditing should induce the socially optimal level of emission, while this is not feasible under the random audit mechanism. This result is isomorph to the main finding by Oestreich (2017). In the experiment, we also extend the model to the case where firms have limited information about each other’s emissions.
External Link(s)
Registration Citation
Citation
Goeschl, Timo, Marcel Oestreich and Alice Soldà. 2020. "Inter-firm Information and Competitive Audit Mechanisms." AEA RCT Registry. September 28. https://doi.org/10.1257/rct.4996-3.0.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2020-02-05
Intervention End Date
2021-01-05
Primary Outcomes
Primary Outcomes (end points)
Our key outcome variables are:
- The level of emissions chosen by participants.
- The level of reported emissions chosen by participants.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
This experiment is a 3x2 design based on an emission game in which we manipulate the audit mechanism (random vs. competitive) and the level of information firms have about each other’s emissions (No Information vs. Imperfect Information vs. Perfect Information).

In this game, n firms produce a level of output e that they have to self-report to a third-party. Firms receive a benefit from their production and pay a tax t on their reported output. Each round, one participant is audited by the third-party. Under the random audit mechanism, the audit probability is fixed and the same for each participant (1/n). Under the competitive audit mechanism, the third-party compares firms’ reports against a reference value and assign a larger audit probability to firms that report less and a lower audit probability to firms that report more. Participants that are audited face a penalty c for each unit of output underreported.

In our experiment, participants are randomly matched in groups of 3 and play 10 rounds of the emission game under a unique audit mechanism and a unique level of information (3 practice rounds and 7 consequential rounds). Each round is composed of 4 stages: an output stage, an information stage, a report stage and a feedback stage. Participants are randomly rematched at the beginning of each round.
In the output stage, participants choose a level of output. In the information stage, they receive information about the output of their fellow group members. In the Perfect Information treatment, participants perfectly observe the actual level of output of their fellow group members. In the Imperfect Information treatment, participants observe the level of output of their fellow group members with some noise. The noise is either +10 with probability 0.25, -10 with probability 0.25 and 0 with probability 0.5. In the No Information treatment, participants do not receive any information about their fellow group members. In the report stage, participants choose how many units of output they wish to report. In the feedback stage, participants are informed about whether they have been audited and their earnings for this round. At the end of the experiment, 4 rounds are randomly selected for payment. This design allows us to test the following hypotheses:

Hypothesis 1. Given that firms have no information about each others’ emissions, then CAMs induce: (a) the same level of emission than random audits and (b) higher emission reports than random audits.

Hypothesis 2. Given that firms have perfect information about each others’ emissions, then CAMs induce: (a) lower emissions than random audits and (b) higher emission reports than random audits.

Hypothesis 3. Given that firms have perfect information about each others’ emissions, CAMs induce socially efficientemissions.

Hypothesis 4. CAMs induce: (a) lower emissions and (b) lower emission reports when firms have perfect information about each others’ emissions as compared to when firms have no information.

Conjecture 1. Given that firms have limited information about each others’ emissions, CAMs induce: (a) lower emissions than random audits and (b) higher emission reports than random audits.

The data will be collected online in an environment that mimics the laboratory ("quasi-lab experiment"). Using a combination of oTree and HeiCONF (a videoconferencing software), we will conduct 8 experimental sessions varying between 15 and 21 participants. In each session, an experimenter will explain the unfolding of the session, read the instructions and assist the participants throughout the experiment. Our subject pool will be composed mainly of students from local engineering, business, and medical schools in Lyon (France).

In the case of subjects dropping out, their actions in the group will be replaced by the actions of a backstop player. The observations for participants that are matched with the backstop player will be excluded from the analyses for the period(s) in which the encounter happened. For example, if participant 1 and participant 2 are matched with the backstop player in period 5, we will exclude the data for both participant 1 and participant 2 in that particular period from the analyse. The data from the backstop player will also be excluded from the analyses.
Experimental Design Details
Not available
Randomization Method
The randomization of the allocation of participants to treatments will be done by the online platform Hroot.
Randomization Unit
Treatments are randomized at the experimental session level.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
30 participants * 6 treatments = 180 clusters.
Sample size: planned number of observations
30 participants * 6 treatments * 7 periods = 1260 observations.
Sample size (or number of clusters) by treatment arms
30 clusters by treatment arms.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
With a sample size of 30 clusters by treatment arms, the minimum detectable between-groups comparison effect size is d=0.73 with statistical power at the recommended .80 level (Cohen, 1988). On the basis of the expected mean, between-groups comparison effect size computed for this study (0.9 < d < 2), we will have enough statistical power to detect significant differences in treatment means. Reference: Cohen, M. A. (1988). Some new evidence on the seriousness of crime. Criminology, 26(2), 343-353.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Brock University Social Science Research Ethics Board
IRB Approval Date
2020-01-16
IRB Approval Number
19-184