Educational Inequality in the Household

Last registered on November 17, 2025
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Pre-Trial

Trial Information

General Information

Title
Educational Inequality in the Household
RCT ID
AEARCTR-0017183
Initial registration date
November 09, 2025

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
November 17, 2025, 2:17 PM EST

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
Esther Gehrke
Affiliation
Wageningen University and Research
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Other Primary Investigator(s)

PI Name
Michelle González Amador
PI Affiliation
Wageningen University and Research
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Additional Trial Information

Status
In development
Start date
2025-11-10
End date
2026-07-31
Keywords
Behavior, Education
Additional Keywords
Inequality
JEL code(s)
D13; I26; I24
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We conduct a lab-in-the-field experiment to study the effect of convexities in the returns to education on parental resource allocation between children. Our sample consists of parents with two children simultaneously enrolled in high school in San Luis Potosí, Mexico. Parents are asked to play a high-stakes (educational) resource allocation game, following a cognitive test administered to their children. Across six distinct scenarios, the order of which is randomized at the individual level, we vary the convexity of the payoff function, as well as the degree of complementarity between child performance and parental inputs.
External Link(s)

Registration Citation

Citation
Gehrke, Esther and Michelle González Amador. 2025. "Educational Inequality in the Household." AEA RCT Registry. November 17. https://doi.org/10.1257/rct.17183-1.0
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Experimental Details

Interventions

Intervention(s)
We conduct a lab-in-the-field experiment with children and parents to test how parents allocate resources between their children. In an allocation task, parents are given nine tokens which they can allocate between their children. In combination with the children's performance on a Raven's test, these are to determine the payoffs received by each child. These payoffs are non-transferable, and should be highly valued by parents and children. The allocation task is repeated six times. Across these six distinct scenarios, one of which is drawn at random and implemented after the experiment, we vary the relative cost of compensating existing performance differences between the children.
Intervention Start Date
2025-11-10
Intervention End Date
2025-12-15

Primary Outcomes

Primary Outcomes (end points)
The number of tokens allocated to the relatively better performing child.
Primary Outcomes (explanation)
Parents can either reinforce or compensate the differences in child performance. The number of tokens allocated to the better performing child per scenario is a clear indication of how parents handle these trade-offs.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
3x2 factorial design that varies across six scenarios (randomly ordered within person) the i. complementarity in child performance and parental investment, and ii. the convexity in the payoff function. We will compare allocations to the high performing child for the three levels of complementarity (low complementarity, medium complementarity, high complementarity) and allocations to the high performing child for the two levels of convexity in payoffs (no convexity, high convexity).
Experimental Design Details
Not available
Randomization Method
The order of scenarios is randomized at the individual level (computer-based).
Randomization Unit
The order of scenarios is randomized within individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
240 individuals (parents)
Sample size (or number of clusters) by treatment arms
When considering only the choice in the first scenario shown to households, the effective sample size by treatment arm is 80 (three levels of complementarity in payoffs), or 120 (two levels of convexity in payoffs). When considering all choices (all scenarios per individual), the effective sample by arm is 2*240= 480 (three levels of complementarity), and 3*240=720 (two levels of convexity).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We use two-sided tests for power calculation to test directional hypotheses on token allocations. Our analysis returns a sample of 240 individuals in order to estimate the effect of convexities changing reinforcement behavior with 92.2 power, 100 power for the effect of changing the complementarity on allocation behavior, and 100 on the effect of convexities on payoff maximizing behavior. These at cohen's d = 0.2, d = 0.2, and d = 0.3 respectively.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Wageningen University and Research, Research Ethics Committee
IRB Approval Date
2025-08-18
IRB Approval Number
2023-004A
Analysis Plan

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