How do different types of inequalities influence individuals’ motivation and creativity?

Last registered on August 14, 2024
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Pre-Trial

Trial Information

General Information

Title
How do different types of inequalities influence individuals’ motivation and creativity?
RCT ID
AEARCTR-0014081
Initial registration date
August 07, 2024

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
August 14, 2024, 2:19 PM EDT

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

Locations

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Primary Investigator

Name
Bruno Martorano
Affiliation
Maastricht University
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Other Primary Investigator(s)

PI Name
Leticia Micheli
PI Affiliation
Leiden University
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Additional Trial Information

Status
On going
Start date
2024-03-11
End date
2025-06-30
Keywords
Behavior, Welfare
Additional Keywords
Inequality, social mobility, creativity
JEL code(s)
C91, D31, D63, D90, O31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The project aims to investigate how disparities affect innovation. Specifically, the project addresses two research questions: Does inequality influence the innovative process? How do different types of inequality shape individuals` motivations and creativity? To address the above research questions, we will implement a laboratory experiment that exposes participants to different types of inequality - e.g. inequality of outcomes and inequality of opportunities - and operationalizes innovation as creativity. To create inequality, participants will receive a bonus in the experiment. To measure innovation, we will consider a number of tasks that address different dimensions of creativity.

External Link(s)

Registration Citation

Citation
Martorano, Bruno and Leticia Micheli. 2024. "How do different types of inequalities influence individuals’ motivation and creativity?." AEA RCT Registry. August 14. https://doi.org/10.1257/rct.14081-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2024-03-11
Intervention End Date
2024-10-31

Primary Outcomes

Primary Outcomes (end points)
In the experiment, we operationalize innovation as creativity. As measures of creativity, we consider three real-effort tasks that tackle different dimensions of creativity.
Task 1: Different uses for common objects. In this task, students will see 3 objects, one at a time, and will be asked to list as many, as different, and as unusual uses for each of these objects;
Task 2: Brainstorming. In this task, students will be asked to reflect on a question and write their answer.
Task 3: Numerical Operations. In this task, students will be asked to choose a combination of numerical operations to start with a number and reach another.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participant will be randomly assigned to the control group or one of four conditions manipulating (in)equality of outcomes and (in)equality of opportunities. In particular, bonuses are used to create inequality; in each inequality condition, we have low and high earners. Therefore, we will create a group with high inequality of outcomes, a group with low inequality of outcomes, a group with high inequality of opportunities, and a group with low inequality of opportunities. After being informed about bonuses, participants do the creativity tasks. We will compare the outcomes of the different groups to understand if inequality shapes creativity and which type of inequality matters for creativity. Moreover, our research aims at investigating the underlying psychological mechanisms through which these different types of inequality may influence the ability to be innovative.
Experimental Design Details
Not available
Randomization Method
Randomization will be done at the individual participant level, using the randomizer function built into the survey software Qualtrics.
Randomization Unit
Students
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Individual level randomization (no clustering)
Sample size: planned number of observations
300 students
Sample size (or number of clusters) by treatment arms
60 by treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethical Review Committee Inner City Faculties, Maastricht University
IRB Approval Date
2023-11-24
IRB Approval Number
ERCIC_497_25_10_2023
Analysis Plan