Beauty, Local identity, Gender and Helping

Last registered on June 15, 2026
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Pre-Trial

Trial Information

General Information

Title
Beauty, Local identity, Gender and Helping
RCT ID
AEARCTR-0018060
Initial registration date
March 09, 2026

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
March 12, 2026, 4:23 AM EDT

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

Last updated
June 15, 2026, 8:06 AM EDT

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

Locations

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Primary Investigator

Name
Yucheng Pan
Affiliation
Central South University
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Other Primary Investigator(s)

PI Name
Jianxin Wang
PI Affiliation
Business School, Central South University
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PI Name
Yulei Rao
PI Affiliation
Business School, Central South University
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PI Name
Daniel Houser
PI Affiliation
Interdisciplinary Center for Economic Science, George Mason University
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Additional Trial Information

Status
In development
Start date
2026-06-16
End date
2027-12-30
Keywords
Behavior
Additional Keywords
Beauty, Attractiveness, Identity, Gender, Helping behavior
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
An extensive literature indicates that recipients’ characteristics are salient in fostering others’ prosocial behavior. However, early researches remain uninformative about these influences in a general and eastern situation. The current work addresses this ambiguity by specifically exploring whether recipients' beauty, local identity and gender impact receiving help in daily life in China. We will conduct a randomized controlled trial to detect such bias.
We use lost-resumes and randomly deliver the personal resumes with file bags to shared-bikes in public within cities. A wide range of unpaid passersby might notice and make decisions accordingly. We intervene by modifying the resumes in one's traits: beauty, local identity and gender, and therefore we have eight treatments that are consistent in other factors. Our interest is in the frequency with which people finding our file bags accidentally are willing to contact us by calls or emails, and whether it varies based on their preferences for the three controlled factors. Therefore, we can compare the spontaneous response rate between treatments, to examine the effects of beauty, local identity and gender on Chinese helping behavior. We predict nonlocal candidate is helped more than local candidate, plain candidate is helped more than attractive candidate, female candidate is helped more than male candidate. As a consequence, we anticipate nonlocal plain woman is helped the most, local attractive man is helped the least. Our study casts new light on the role of relevant individual characteristics in fueling prosocial behavior and improving well-being of people.
External Link(s)

Registration Citation

Citation
Houser, Daniel et al. 2026. "Beauty, Local identity, Gender and Helping." AEA RCT Registry. June 15. https://doi.org/10.1257/rct.18060-3.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We randomly assign our resumes which delivered to passersby into one of 2×2×2 treatments varied in beauty, gender and local identity.
Intervention Start Date
2026-06-16
Intervention End Date
2027-12-30

Primary Outcomes

Primary Outcomes (end points)
Whether and how soon people are willing to call to contact us:
1. Call rate: the number of the first call received for each resume relative to the number of resume delivered in each treatment.
2. Distribution of calls received over time.
3. Hazard rate.
4. Whether a helper contacts and communicates with us more than once (through phone call or text message) for one resume.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Helpers’ characteristics and other aspects that impact whether and whom they want to help: helper’s gender and location, where the resume is located, time of the day.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We put the resumes in file bags and select public locations where shared bikes are parked to drop. Shared bikes, equipped with a basket, are one of popular ways of transportation and are widely distributed in urban areas of China. Therefore, any pedestrians passing through could find it accidentally and potentially become our helpers. Each call from unpaid passersby will be answered at any time and be recorded accurately.
Experimental Design Details
Not available
Randomization Method
The order in which the resumes delivered to passersby are distributed to each bike is randomized.
Randomization Unit
Individual level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
We choose the general Cohen’s d of 0.5 for both variables (to the best of our knowledge, this value is very close to the data of a relevant experiment which test the effects of beauty on helping in tasks). We calculate the minimum sample size needed at this effect size by G Power, as shown below: t tests - Means: Difference between two independent means (two groups) Analysis: A priori: Compute required sample size Input: Tail(s) = Two Effect size d = 0.5 α err prob = 0.05 Power (1-β err prob) = 0.8 Allocation ratio N2/N1 = 1 Output: Non-centrality parameter δ = 2.8284271 Critical t = 1.9789706 Df = 126 Sample size group 1 = 64 Sample size group 2 = 64 Total sample size = 128 Actual power = 0.8014596 As shown above, the minimum sample size needed is 64 resumes in each group.
Sample size (or number of clusters) by treatment arms
We plan to deliver 5200 resumes in total which will be assigned to the eight treatments as shown in the table below.
-----------------------------------Male----------Female
Beauty---------Local----------650-------------650
-----------------Nonlocal-------650-------------650
Plainness-----Local----------650--------------650
-----------------Nonlocal-------650-------------650
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Given the sample size, we calculate the minimum detectable effect size using the G Power, as shown below: t tests - Means: Difference between two independent means (two groups) Analysis: Sensitivity: compute required effect size. Input: Tail(s) = Two α err prob = 0.05 Power (1-β err prob) = 0.8 Sample size group 1 = 2600 Sample size group 2 = 2600 Output: Non-centrality parameter δ = 2.8020995 Critical t = 1.9604205 Df = 5198 Effect size d = 0.0777163 Based on the sample size of 5200 resumes, the study has 80% statistical power at a 5% significance level to detect the difference in mean values between treatments.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
A study of helping
IRB Approval Date
2026-01-13
IRB Approval Number
STUDY00000995
Analysis Plan