Does Prospect Theory help explain why women and sellers have worse outcomes in privately negotiated markets?

Last registered on June 28, 2023


Trial Information

General Information

Does Prospect Theory help explain why women and sellers have worse outcomes in privately negotiated markets?
Initial registration date
June 22, 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
June 28, 2023, 4:20 PM EDT

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


Primary Investigator

university of wyoming

Other Primary Investigator(s)

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Agriculture is a risky proposition. One of the risks faced by agricultural producers is the risk that sale negotiations do not result in a trade (negotiation failure risk). The current shift in agricultural markets away from auction settings to more privately negotiated contracting exacerbates this risk. Yet, relatively little is known about how this risk may impact market outcomes. Moreover, Expected Utility Theory fails to explain why buyers have been found to have higher earnings than sellers, and why men have higher earnings than women, in privately negotiated experimental market studies. This study augments a Cumulative Prospect Theory (CPT) lottery survey to include negotiation failure risk in order to explore whether the broader CPT framework may help explain these prior experimental findings, and how potential loss in the context of negotiation failure may impact outcomes. Study participants were presented with a series of lotteries framed in a private negotiation context to measure factors that likely influence their decisions in this bargaining environment, including levels of risk and loss aversion. Half of the participants also took part in a market experiment prior to the lottery survey in order to determine whether prior market experience affects the values of these CPT characteristics. Findings indicate that CPT does indeed help explain differences in negotiating behavior and outcomes during private negotiations between buyers and sellers, and between men and women. Specifically, sellers and women have higher levels of loss aversion than buyers and men, respectively. Further, individuals with high levels of loss aversion have lower market earnings, potentially explaining several anomalies found in previous studies. Moreover, we find that individual decisions under risk are context-dependent, suggesting opportunities for agricultural producers (who generally self-identify as sellers), and especially women, to improve bargaining outcomes.
External Link(s)

Registration Citation

Jones Ritten, Chian. 2023. "Does Prospect Theory help explain why women and sellers have worse outcomes in privately negotiated markets?." AEA RCT Registry. June 28.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
σ, α, λ, α in series 3 and 4, market role, market earnings, quantity traded, gender
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
price in market, race, income, age, education
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study augments the lottery experiment developed by Tanaka, Camerer, and Nguyen (2010) to include the negotiation failure risk present in agricultural markets. Participants were presented with five series of lotteries, each a sequence of lottery questions, where potential payoffs from each lottery were presented in terms of “tokens” (100 tokens=$1). For each question in a series, participants were tasked to choose between two lottery options, Lottery A and Lottery B, where Lottery A is a “safer” option and Lottery B is “riskier.” Both lotteries had a known probability of a “high payout” and a known probability of a “low payout.” Within each series of lottery questions, the probabilities associated with Lottery A and Lottery B remained the same. At the beginning of the series, the expected value of Lottery A is higher than Lottery B. As the questions in the series progress, the relative expected value of Lottery A to Lottery B decreases, and by the end of the series, the expected value of Lottery A is less than that of Lottery B.
Subjects were asked to report the question in a given series in which they switch their preference from entering into Lottery A to entering into Lottery B (from switching at the first question through never switching) (see Appendix for survey instructions provided to participants). Subjects were informed that they could only choose one question in which they would switch from Lottery A to Lottery B in a given series (that is, they could not switch back to Lottery A once they chose Lottery B) in order to enforce monotonicity. Subjects that choose to switch at any of the first few questions are more risk seeking, and those that never switch, or switch only at the last few questions, are more risk averse.
The first four lottery series had 14 questions each, and the last series had 7 questions (63 questions total). Subjects were instructed to indicate their switching point for each of these five lottery series, generating five data points for each subject. Subjects were informed that they would earn tokens based on their response to only one randomly selected question. After participants made all of their choices, the experimenter drew a random chip from a jar of 63 labeled chips to determine the specific lottery question on which all subjects’ earnings would be based. Then the experimenter drew a ball from a jar, where the color of the ball indicated which outcome prevailed (“low payout” or “high payout”) for the selected question. Following Tanaka, Camerer, and Nguyen (2010), subjects earned tokens based on their choice of Lottery (either A or B) for the specific question and outcome that were randomly selected.
The first two lottery series (Series 1 and 2) are identical to those developed by Tanaka, Camerer, and Nguyen (2010). In these series, the decision context is a simple lottery. The payoffs for Lottery A remain constant throughout the series, while those for Lottery B change.
We add two new series to the original design of Tanaka, Camerer, and Nguyen (2010) to represent the fact that risky decisions are typically not presented to agricultural producers in terms of traditional lotteries, but rather in the context of trading commodities in a private negotiation setting. In Series 3 and 4 are in the context of forward delivery and Series 5 (comparable to Series 3 of Tanaka, Camerer, and Nguyen [2010]) reflects spot delivery by adding advance production risk to sellers. In Series 3 and 4, participants were told that they were either a buyer or a seller of a product, and that they would earn tokens based upon successfully negotiating a trade. To mimic negotiation failure risk in forward markets, each question in a lottery series had a given probability that a specific asking price was accepted, leading to positive profits, and a given probability that the asking price was rejected, leading to no trade and zero profits. The expected value of these questions was identical to that of Series 1 and 2, but since Series 3 and 4 include a risk of zero profit, the token values associated with the high and low outcomes are different.
To measure the influence of role (buyer or seller) on behavior, we varied assignment of buyer and seller roles across series. In Series 3, participants were told that they were buyers bidding to purchase a product from a seller. To make a profit and earn tokens in the experiment, buyers were told they would be able to resell any product purchased to the experimenter, known as the resale value. Participants were informed that tokens were earned by purchasing the product at a price below this resale value. In Series 4, participants were informed that they were now sellers contracting a product for sale to a buyer. Participants were given a production cost and told that if they successfully negotiated a sale, their profit would be the negotiated price minus the production cost. If no sale was negotiated, participants were informed that they would neither incur production costs nor earn any tokens, consistent with forward delivery.
To elicit risk preferences across a broader range of probabilities, we created two versions of the survey. In Version 1, the probability of making a positive profit in Series 3 as a buyer was relatively low (30% and 10% for Lottery A and B, respectively, representing high negotiation failure risk) but relatively high in Series 4 as a seller (90% and 70% in Lottery A and B, respectively, representing low negotiation failure risk). In Version 2, the probability of making a positive profit in Series 3 as a buyer was relatively high (90% and 70% for Lottery A and B, respectively), but relatively low in Series 4 as a seller (10% and 30% in Lottery A and B, respectively). Because the probabilities changed between the two versions, the resale value, production cost, and potential positive profits changed as well.
In Series 5, participants were assigned the role of a seller in a spot market (in both survey versions). Since production happens before a sale in spot markets, producers risk losing their cost of production from negotiation failure. Even if a trade is agreed upon in this market, the price may be below a seller’s production cost, leading to profit loss. Series 5 used the same probabilities and outcomes as designed by Tanaka, Camerer, and Nguyen (2010), yet participants were informed that they were a seller offering to sell a product that they had already produced. Thus, in both Lottery A and Lottery B there was a 50% chance of a successful trade price that generated positive profits, and a 50% chance of either no trade (represented by a buyer counteroffer of 0 tokens) or a price below the seller’s production cost.
Series 1 and 2 are used to measure σ and α (consistent with Tanaka, Camerer, and Nguyen [2010]) as defined in equations 1 through 3. Because Series 3 and 4 were designed with a zero possible payoff, they cannot be used jointly to measure individuals’ level of risk aversion (σ) and curvature of weighting function (α). However, Series 3 and 4 are used along with the level of α from Series 1 and 2 to determine σ30_10 and σ90_70. The parameter σ30_10 is an individual’s level of risk aversion based on the series with high negotiation failure risk (i.e., probability of positive outcomes of 30% and 10% for Lottery A and B, respectively), and σ90_70 is an individual’s level of risk aversion based on the series with low negotiation failure risk (i.e., probability of positive outcomes of 90% and 70% for Lottery A and B, respectively). Lastly, Series 5 was used to measure loss aversion (λ) in the context of spot delivery to capture the advance production risk environment where loss aversion may affect seller behavior.
Market Experiment
To test the influence of prior market experience on behavior observed in the CPT survey, and the influence of σ, α, and λ on market outcomes, half of the sessions had subjects participate in a laboratory market experiment prior to the survey. Market experiments were conducted immediately before the CPT survey, with trading conducted over a computer network using software that simulated a privately negotiated market with forward delivery. During each trading period, buyers and sellers were randomly matched and negotiated trades one-on-one. Buyers made bids and sellers made offers until they reached an agreed price and a trade was made. The buyer and seller could then begin negotiations to trade the next unit (up to 8 units) until the end of the 1-minute trading period. Then, buyers and sellers were again randomly re-matched to begin trading units in the next period, where each buyer and seller pair would start over on their production cost and redemption value schedules. By doing this, matched buyers and sellers were on the same unit in their respective schedule in order to reduce the potentially confounding influence of matching risk (i.e., matched partner having a different incentive to trade because they are on a different unit in their schedule) and negotiation failure risk.
Experimental Design Details
Randomization Method
participants signed us for an experimental session. Just prior to the session, whether the market experiment would be included was randomly assigned.
Randomization Unit
session of individuals
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
unknown, but based on the number of participants in each session
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
150 in market experiment sessions, and 150 without market experiment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Given the gender differences in earnings (24.78 for men and 23.71 for women; sd of 10) based on Jones Ritten et al. (2019) for private negotiation markets, the number of observations needed is 134 for a power of .8. Given that sellers earn 10-20% less than buyers in these markets (very conservative), and given a modest sd of 10, then 135 participants is much more than the n needed based on a power of .8.

Institutional Review Boards (IRBs)

IRB Name
Study has received IRB approval. Details not available.
IRB Approval Date
Details not available
IRB Approval Number
Details not available


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