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Field
Abstract
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Before
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. Our study casts new light on the role of relevant individual characteristics in fueling prosocial behavior and improving well-being of people.
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After
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.
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Field
Trial Start Date
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Before
May 26, 2026
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After
June 16, 2026
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Field
Last Published
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Before
May 23, 2026 11:32 PM
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After
June 15, 2026 08:06 AM
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Field
Intervention Start Date
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Before
May 26, 2026
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After
June 16, 2026
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Field
Intervention End Date
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Before
September 30, 2026
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After
December 30, 2027
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Field
Primary Outcomes (End Points)
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Before
Whether and how soon people are willing to call to contact us:
1. Total calls: the number of the first call received for each resume in each treatment.
2. Distribution of calls received over time.
3. Hazard rate: time duration between the time a resume is delivered and the first call received.
4. Whether a helper contacts and communicates with us more than once (through phone call or text message) for one resume.
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After
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.
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Field
Sample size (or number of clusters) by treatment arms
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Before
We plan to deliver 6000 resumes in total which will be assigned to the eight treatments as shown in the table below.
-----------------------------------Male----------Female
Beauty---------Local----------750-------------750
-----------------Nonlocal-------750-------------750
Plainness-----Local----------750--------------750
-----------------Nonlocal-------750-------------750
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After
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
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Field
Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
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 = 3000
Sample size group 2 = 3000
Output: Non-centrality parameter δ = 2.8020305
Critical t = 1.9603596
Df = 5998
Effect size d = 0.0723481
Based on the sample size of 6000 resumes, the study has 80% statistical power at a 5% significance level to detect the difference in mean values between treatments.
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After
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.
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