Menu Effects and The Gender Wage Gap: An Experimental Study

Last registered on May 31, 2022


Trial Information

General Information

Menu Effects and The Gender Wage Gap: An Experimental Study
Initial registration date
February 15, 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
February 17, 2022, 5:19 PM EST

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

Last updated
May 31, 2022, 6:10 AM EDT

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



Primary Investigator

Drexel University

Other Primary Investigator(s)

PI Affiliation
Drexel University

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
One of the most prominent disparities in labor markets is the gender wage gap. According to the U.S. Bureau of Labor Statistics, women’s median weekly earnings were approximately 82% of men’s median weekly earnings in 2019 for full-time workers. While factors such as education, experience, occupations, union status, discrimination, and more account for a large part of the gender gap, a significant part of the gap remains unexplained. At the same time, there is ample evidence that individual choices are affected by the composition of the menu of available alternatives, i.e., that preferences are context-dependent. These so-called menu effects could influence important decisions in the labor market, such as career or contract choice, potentially exacerbating wage disparities among different demographic groups.
The goal of this project is to explore gender differences in choice over labor contracts due to menu effects and measure its potential contribution to the wage gap. To do so, we will run an experiment on a sample of the US population aimed at identifying and measuring gender differences in menu effects with real-effort tasks that vary in pay and flexibility/duration. The project will also look at whether existing theories of context-dependent preferences can explain the data and estimate the distribution of preferences.
External Link(s)

Registration Citation

Serrano-Padial, Ricardo and Yin Zhang. 2022. "Menu Effects and The Gender Wage Gap: An Experimental Study." AEA RCT Registry. May 31.
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Experimental Details


The study does not involve any treatment.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1. Differences between male and female subjects on the size of menu-effects.
2. Asymmetries in the incidence (and size) of different types of menu effects (e.g., attraction effect, compromise effect).
3. Asymmetries in valuations of different contract attributes: wages versus flexibility/duration.
4. Frequency and welfare impact of menu effects.
Primary Outcomes (explanation)
The size of menu effects will be measured by the wage markup needed to induce the subject to choose from each menu the optimal contract according to his/her revealed preferences.
Welfare impact will also be measured in terms of wage markups.

Secondary Outcomes

Secondary Outcomes (end points)
Context-dependent preferences that best explain subjects' contract choices.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment involves two stages and will be conducted on a (gender-balanced) sample of the US population. At the beginning of the experiment we collect some sociodemographic information from the respondents. In the first stage, we elicit reservation wages from participants for different flexibility/duration levels. Specifically, we elicit the lowest payment a subject is willing to accept in exchange for completing a number of tasks. In the second stage, participants choose their most preferred contracts from various menus. The data on reservation wages from the first stage will be used in the second stage to create personalized contract menus for each participant. Each contract consists of a payment and a number of counting tasks. We randomize the order of menus for each subject.
Experimental Design Details
We use an online, real-effort experiment to answer our research questions and test our hypotheses. We use the same task as the experiment run by Bushong, Rabin, and Schwartzstein (2021), which involves counting the number appearances of a specific character in a matrix of size 10x15.
We define a contract in the experiment as the combination of two attributes, the payment and a schedule to perform blocks of tasks at specific times of the day. Each block of ten tasks must be done at a specific time of the day. For example, a contract can specify a payment of $10 to do a total of 30 tasks tomorrow in three blocks of 10 tasks to be done at 10:30, 13:00 and 14:30, respectively. A higher number of tasks leads to a less flexible contract by involving a higher number of times at which the participant must perform the tasks. The design includes 7 different numbers of tasks, from 10 to 70 tasks.

We set up two stages in this experiment. The first stage of the experiment is to get participants’ reservation wages for different numbers of tasks/schedules. Before eliciting their responses, to allow participants to form a better understanding of the difficulty of the task, we offer participants a couple of trial tasks before they write down their reservation wages.
To ensure that participants give their truthful reservation wages, we implement the Becker–DeGroot–Marschak method (Becker, DeGroot, and Marshak; 1964). First, we ask participants to give their reservation wage for different numbers of matrices with their associated schedules. After that, a random number generator will generate a number of matrices M and an actual pay P. Their reservation wage R for M matrices will be compared to the actual pay. If P ≥ R , they will count M matrices at the specified schedule and receive P as their compensation. Par- ticipants must finish counting the matrices to receive any payment. If P < R, the participant will not perform any task and finish the experiment with only her participation fee.

The second stage of the experiment is to ask subjects to choose their favorite contract from various menus. We use the data on reservation wages from the first stage to construct contracts and menus. The second stage will be conducted within two weeks after the completion of the first stage experiment. We will randomly resample a smaller group of participants from the pool of participants in the first stage. Before randomly resampling, we may exclude some participants whose answers strongly suggest lack of comprehension, non-truthful responses or unwillingness to perform the tasks. Specifically, we may exclude participants that consistently report higher reservation wages for lower number of tasks or that report unusually high reservation wages relative to the distribution of reservation wages in the sample. Participants will not know about the possibility of being selected to the second stage unless they receive an invitation after the first stage is completed. The reason for doing so is to ensure that the incentive-compatible Becker–DeGroot–Marschak method works correctly and that participants report their truthful reservation wages in the first stage.
In the second stage, participants will start choosing contracts from menus after reading instructions. They will choose from around 30 to 40 menus. After choosing from all menus, one of the participant’s contract choices might be chosen at random and executed. Doing so ensures their decisions are all incentivized. Participants must finish the number of tasks dictated by the realized contract to receive any payment in the second stage.

To measure menu effects, each menu will contain 2 or 3 contracts, one of them (the optimal contract) with a wage higher than the reported reservation wage in stage one. Another contract (the suboptimal contract) will have a payment equal to its reservation wage. If there is a third contract it will either have also a payment equal to its reservation wage or lower. If a participant does not choose the optimal contract, we increase the markup on its payment above its reservation wage and continue to do so until the participant finally chooses the optimal contract. We then measure the size of the particular menu effect by the largest markup before the switch to the optimal contract happens.
The experiment involves three types of menu effects that introduce an inferior contract to an existing 2-contract menu: the attraction effect (adding a contract close to the suboptimal one); the compromise effect (adding a contract with the attributes in between the optimal and suboptimal contracts) and the similarity effect (adding a contract close to the optimal one).
We vary the application of markups so that sometimes the optimal contract involves lower wages and higher flexibility than the suboptimal ones and vice versa. This way we will identify potential asymmetries in menu effects within and across participants.
The order of presentation of menus will be random for each participant.

Becker, Gordon M, Morris H DeGroot, and Jacob Marschak (1964). “Measuring utility by a single-response sequential method”. In: Behavioral science 9.3, pp. 226–232.
Bushong, Benjamin, Matthew Rabin, and Joshua Schwartzstein (2021). “A model of relative thinking”. In: The Review of Economic Studies 88.1, pp. 162–191.
Randomization Method
Randomization done by a computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
4000 contract choices
Sample size (or number of clusters) by treatment arms
600 individuals for stage 1
300 individuals for stage 2
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
Menu Effects and The Gender Wage Gap: An Experimental Study
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Intervention Completion Date
May 13, 2022, 12:00 +00:00
Data Collection Complete
Data Collection Completion Date
May 13, 2022, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
128 males, 118 females
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials