Sustainability, Prices and Emotions

Last registered on December 02, 2022

Pre-Trial

Trial Information

General Information

Title
Sustainability, Prices and Emotions
RCT ID
AEARCTR-0010389
Initial registration date
November 23, 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
December 02, 2022, 3:49 PM EST

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

Locations

Region

Primary Investigator

Affiliation
LMU Munich

Other Primary Investigator(s)

PI Affiliation
LMU Munich

Additional Trial Information

Status
In development
Start date
2022-11-28
End date
2022-12-15
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We examine whether the sustainable character of an investment impacts asset prices. We use experimental markets with the structure introduced by Smith, Suchanek, and Williams (1988) to investigate the impact of sustainable attributes. Empirical evidence suggests that investors experience positive emotions when choosing a sustainable investment and that positive emotions correlate with purchases and overpricing. We analyze investors' emotions using a face-reading software and posit that a high sustainability level leads to positive emotions and price increases. Conversely, a low sustainability level is associated with fear and, thus, price decreases. Moreover, we test how participants’ sustainability preferences influence their trading behavior.
External Link(s)

Registration Citation

Citation
Balbaa, Mennatallah and Mareike Worch. 2022. "Sustainability, Prices and Emotions." AEA RCT Registry. December 02. https://doi.org/10.1257/rct.10389-1.0
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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2022-11-28
Intervention End Date
2022-12-15

Primary Outcomes

Primary Outcomes (end points)
We use the following groups of outcome variables:
1) SSW market variables, such as magnitude of asset pricing bubbles
2) emotions, namely positive and negative emotional states
3) social preferences
4) climate awareness and carbon offset projects support
4) cognitive reflection
Primary Outcomes (explanation)
We might construct a variable that captures both social preferences and support for carbon offset projects. The weights with which each question enters the calculation are hard to determine ex-ante since it is challenging to forecast the distribution of individuals' responses to the questions regarding carbon offset projects. Depending on participants' responses, it might also be more appropriate to run analyses based on the two variables, social preferences and carbon offset support, separately. For example, we could exclude participants that score low on social preferences and high on carbon offsets support as well as participants that score high on social preferences and low on carbon offset support since they would not react to our treatments.
Moreover, we might be forced to exclude questions whose responses are biased in one direction.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We use experimental markets that follow the structure by Smith et al. (1988) to investigate the effect of the sustainability nature of an investment on asset prices. We use adaptions to the original SSW markets, as in Caginalp et al. (2001), to promote bubble formation. The asset in our experiment has a finite lifetime of 15 periods, and participants trade its shares in an open order book continuous double auction market. The latter allows each participant to take on the role of both the buyer and the seller.

We use three treatments with nine participants each. The treatments differ with regard to the sustainability information they receive. Participants are randomly assigned to one of the three treatments.
Experimental Design Details
Basic Procedure

Our experimental markets follow the structure by Smith et al. (1988). We use adaptions to the original SSW markets, as in Caginalp et al. (2001), to promote bubble formation. The asset in our experiment has a finite lifetime of 15 periods, and participants trade its shares in an open order book continuous double auction market. The latter allows each participant to take on the role of both the buyer and the seller.
The asset pays a random dividend drawn from a uniform distribution at the end of each trading round. The dividend has four potential outcomes of 0, 8, 28, and 60 ECU with equal probabilities. Thus, the expected dividend for each round is equal to 24 ECU. The asset's fundamental value at the beginning of the experiment is equal to 360 ECU. After each round, the fundamental value decreases by the amount of the expected dividends of 24 ECU. The distribution of the dividends, the realized dividend payment in each round, and the current fundamental values are public information known to all participants. At the end of the experiment, the asset terminates worthless.

There are nine participants in each market. Using computerized instructions, we familiarize participants with the trading rules and mechanisms and give them information regarding the asset to be traded. Our participants are initially endowed with cash and shares: Three are endowed with 1800 ECU and 1 share of the risky asset, three with 1440 ECU and 2 shares of the risky asset, and three with 1080 ECU and 3 shares of the risky asset. The trading screen shows subjects all current information on their shares and cash holdings, buy and sell offers, and transaction prices.

We will videotape participants before and during trading. Participants are informed that they are being videotaped but are not informed about the purpose for which the videos will be used. The videos will only be used by the researchers to analyze participants' emotions.

Variation between Treatments

We divide participants into three treatments. Our treatments aim to test whether and how varying degrees of sustainability associated with an asset affect the size of bubbles. Hence, we manipulate the market setting for each treatment group in a way such that they knowingly trade an asset with a certain degree of sustainability. The degree of sustainability is different for each treatment group. To make participants aware of the asset's degree of sustainability, we include a description of the company's sustainability level in the instructions. Specifically, we tell participants that they are trading the shares of a fictitious company X that has been assigned a specific sustainability score and provide an explanation of the score. To make scores comparable across treatments and understandable for subjects, we add a scale with the possible score ranges and the corresponding sustainability performance levels. Additionally, to make the situation close to reality, we tie dividend payments from the company to real-world investments into carbon offset projects approved by the United Nations that we commit to undertake. The following paragraph describes each treatment in more detail.

Participants are randomly assigned to one of the following three treatments:

Positive: We provide participants with a positive description of the company's sustainability performance. We tell participants that the company has an above-average sustainability score and that it supports climate action. To actualize the positive impact of the company on the environment in the experiment, with each dividend payment of the company, we plan to increase the amount we donate for UN-approved carbon offset projects after the experiment.

Neutral: We provide participants with a neutral description of the company's sustainability performance. We tell participants that the company has an average sustainability score and do not tie the dividend payments to any real-world investments in carbon offsets projects.

Negative: We provide participants with a negative description of the company's sustainability performance. We tell participants that the company has a below-average sustainability score and that it harms the environment through its production by releasing carbon dioxide. To actualize the negative impact of the company on the environment in the experiment, with each dividend payment of the company, we decrease the amount we initially planned to donate for UN-approved carbon offset projects after the experiment.

Post-Experiment Questionnaire

In a post-experiment questionnaire, we ask participants about:
- gender, age, and ethnic background (required for the analysis of emotions using Facereader Software)
- social preferences
- degree of concern about climate crisis and carbon offset support
- cognitive ability (using standardized questions)
Randomization Method
Each participant gets randomly invited to a session of one of three treatments.
Randomization Unit
Per subject
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
For budget restriction reasons, we will use the first three sessions from each treatment to estimate the effect size. We plan to obtain 0.8 power at the standard 0.05 alpha error probability.
Sample size: planned number of observations
For budget restriction reasons, we will use the first three sessions from each treatment to estimate the effect size. We plan to obtain 0.8 power at the standard 0.05 alpha error probability.
Sample size (or number of clusters) by treatment arms
For budget restriction reasons, we will use the first three sessions from each treatment to estimate the effect size. We plan to obtain 0.8 power at the standard 0.05 alpha error probability.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials