Morals in multi-unit markets

Last registered on October 07, 2020


Trial Information

General Information

Morals in multi-unit markets
Initial registration date
September 15, 2019

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
September 16, 2019, 1:55 PM EDT

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

Last updated
October 07, 2020, 11:24 AM EDT

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



Primary Investigator


Other Primary Investigator(s)

PI Affiliation
University of Amsterdam
PI Affiliation
University of Amsterdam

Additional Trial Information

Start date
End date
Secondary IDs
The result that markets erode morals (Falk & Szech, 2013) has recently been challenged (for instance by Bartling et al., 2019). Recent experiments on norm erosion restrict market participants to trade at most one unit. This choice might limit the extent to which market forces facilitate selfish behavior and outcomes. In this project, we study markets with multi-unit trading. We establish whether the moral concerns of traders are reflected in market outcomes to the degree that is theoretically predicted and whether there is norm erosion in markets by comparing valuations of a charity donation in individual decision-making to the valuations in markets with single and multi-unit trading.
External Link(s)

Registration Citation

Offerman, Theo, Giorgia Romagnoli and Andreas Ziegler. 2020. "Morals in multi-unit markets." AEA RCT Registry. October 07.
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Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
There are two main outcome dimensions in this experiment:

• Individual valuations of a charity donation, directly elicited from participants in individual choices or inferred from bid and ask prices and accepted offers in markets. In these markets, trading produces an externality by reducing donations to a charity.

• Traded quantities in the markets.

Primary Outcomes (explanation)
For a detailed explanation of out outcome variables, see the analysis plan.

Secondary Outcomes

Secondary Outcomes (end points)
We compare social norms about appropriative behavior in- and outside of markets and subjects' beliefs about the median valuation of the donation between treatments.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We run four treatments: IDM, SINGLE, MULTI and FULL. SINGLE, MULTI and FULL are three types of markets, where the total number of tradeable units change (5 in SINGLE, 15 in MULTI and FULL), as well as the maximum number of tradeable units per trader (1 in SINGLE, 3 in MULTI and 15 in FULL).
Experimental Design Details
The IDM treatment consists of repeated individual decision-making, using multiple price lists. Participants in the three treatments (SINGLE, MULTI and FULL) will participate in markets with different conditions. All markets consist of 10 participants, 5 per market side (buyers/sellers). In SINGLE, 5 units of an experimental good can be traded, and each participant can trade one unit. In MULTI and FULL, 15 units can be traded in total. In MULTI, each participant can trade up to 3 units, in FULL, each participant can trade up to 15 units. Across all markets, there is a common supply/demand schedule, which means that costs/values for trading a unit only depend on the aggregate number of units traded previously. In all market treatments (SINGLE, MULTI, FULL), participants first participate in the elicitation of their valuation of charity donations, identically to participants in IDM. Afterwards, they participate in the markets; lastly, they face the identical individual elicitation as before the start of the market.

Across all treatments, the experiment will conclude with elicitations of: i) the median of the distribution of valuations of charity donations; ii) social norm elicitation about behavior in markets and in individual decision-making; iii) risk preferences.

In the experiment, the main hypotheses are concerned with establishing whether (i) market outcomes get closer to the (selfish) competitive equilibrium behavior and selfish traders as we move from SINGLE to MULTI to FULL, (ii) treatment differences are due to different degrees of erosion of moral costs vis-a-vis different degrees to which these markets allow for selection of immoral traders. We isolate norm erosion by comparing traded quantities to the competitive equilibrium benchmarks (without norm erosion, quantities should be close to the competitive equilibrium with moral costs; hence quantity increasing towards the selfish competitive equilibrium indicates moral cost erosion). On the other hand, we isolate market selection, (i) by exploring the degree to which the benchmark equilibrium with moral costs (iiia) (as well as the distance between the equilibrium in (iiia) and (iiib) ) differs across SINGLE, MULTI and FULL market setups; and (ii) by studying to what extent the traded units are concentrated in the hand of a relatively small number of traders.

We further study treatment differences in individual-level norm erosion. With the three market treatments, we disentangle several potential forces contributing to moral cost erosion. Between IDM and SINGLE, most forces studied in the literature as potential factors of norm erosion are present (e.g. Falk & Szech, 2013; Bartling, Weber & Yao, 2015). Noteworthy factors are: (i) shared responsibility (i.e. the fact that two traders jointly decide on a trade, sharing the responsibility and guilt for a trade and introducing payoff-concerns for the counter-party) and (ii) market framing. The remaining two treatments disentangle the reasons for norm erosion we are mostly interested in. SINGLE and MULTI are identical markets, where we only scale up the market size proportionally. Treatment differences in moral cost erosion will be due to the more precise social learning possible in this treatment. Lastly, MULTI and FULL allow for a replacement-logic type of reasoning, as each trader is potentially able to serve the entire market by her/himself. In both SINGLE and MULTI, each trader is pivotal in determining whether their units are going to be sold or not. In FULL, the removal of capacity constraints of traders allows for the replacement logic to be active. To study this force, we are interested in the identity and moral costs of the marginal traders: if subjects with high moral costs are most active at low surpluses in this treatment, this indicates that the replacement logic deteriorates moral costs.
Randomization Method
Computerized randomization.
Randomization Unit
Subjects are first randomly allocated to one of the market treatments or the individual-decision making (computerized). For market treatments, subjects are then randomly allocated to a group (computerized), and subsequently each group is assigned a treatment.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
10 groups with 10 subjects each per market treatment. No clustering in individual decision making.
Sample size: planned number of observations
380 subjects
Sample size (or number of clusters) by treatment arms
For the three market treatments (SINGLE, MULTI, FULL), we aim for 10 groups with 10 subjects each (3*10*10=300 subjects). For IDM, we aim for 80 subjects (for this treatment, each subject provides one independent observation). If we run out of subjects before we reach these targets, we will collect as many observations as we can.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Economics & Business Ethics Committee (University of Amsterdam)
IRB Approval Date
IRB Approval Number
EC 20190617030625
Analysis Plan

Analysis Plan Documents

analysis plan

MD5: 34b9fb966163ecf10ed1b5dd12f88574

SHA1: 98059c016ec1235cb2f53d37b55f05ff567a5d4f

Uploaded At: September 15, 2019


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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

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Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials