The Observability of Real-Effort Input and Generosity

Last registered on December 14, 2020

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Pre-Trial

Trial Information

General Information

Title

The Observability of Real-Effort Input and Generosity

RCT ID

AEARCTR-0006806

Initial registration date

December 09, 2020

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

December 10, 2020, 12:26 PM EST

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

Last updated

December 14, 2020, 3:45 PM EST

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Xiaohong Wang

Affiliation

University of Pittsburgh

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Robizon Khubulashvili

PI Affiliation

University of Pittsburgh

Contact Investigator

PI Name

Sera Linardi

PI Affiliation

University of Pittburgh

Contact Investigator

Additional Trial Information

Status

In development

Start date

2020-12-11

End date

2022-08-31

Keywords

Behavior, Firms & Productivity, Labor, Welfare

Additional Keywords

real-effort, observability, outcome bias, empathy

JEL code(s)

C92 D63 D64 D83

Secondary IDs

Abstract

With everything happening virtually, we are removed from the process through which goods and services are produced. To make matters worse, in most real-world situations, outcome is a noisy function of effort. Despite working hard, the person providing effort gets blamed because of bad luck. We believe that inducing empathy - the capacity to imagine oneself in someone else’s position - may allow the principal to see the agent’s good intentions and separate agents’ intention from the outcome. If our experiment finds this to be true, it suggests that ways to add back the first person perspective to these no-touch goods and service delivery may be integral in economic and social activities that are supported by prosocial behavior.

External Link(s)

Registration Citation

Citation

Wang, Xiaohong, Robizon Khubulashvili and Sera Linardi. 2020. "The Observability of Real-Effort Input and Generosity." AEA RCT Registry. December 14. https://doi.org/10.1257/rct.6806-1.1

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

There are three between-subject treatments in our experiment: one baseline which is an uninformed condition under which principles can only see the win/lose outcome; the first treatment is an informed condition under which principles can see both win/lose outcome and the effort of agents; and an observing condition treatment under which principles will know the outcome, agents' effort and will be asked to watch a work video of the agent.

Intervention Start Date

2020-12-11

Intervention End Date

2022-08-31

Primary Outcomes

Primary Outcomes (end points)

Player As' sharing under different conditions

Primary Outcomes (explanation)

empathy, information

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We designed a noisy gift exchange game which starts with a real effort task and follows with a dictator game.

Experimental Design Details

All subjects were randomly assigned to the role of principal or agent at the beginning of the experiment. We employed neutral language in the study: principals were referred to as “Player As”, and agents were referred to as “Player Bs”.

Player Bs need to select a specific kind of emojis among 100 emojis. There are 50 emoji 1s and 50 emoji 2s each grid. Each selected emoji 1 increases probability of winning by 0.5%. Each selected emoji 2 decreases the probability of winning by 0.5%. The winning possibility will be from 25% (selecting all emoji 1s) to 75% (selecting all emoji 2s). After this, player A will know the outcome. Then player As need to share with player Bs. Player Bs will play 5 rounds of the game, and player B will be asked to write a message regarding each round. Player As will play 3 rounds of the allocating task when the surplus is fixed to a specific amount.

Randomization Method

Computerized Randomization

Randomization Unit

Individual

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

240 individuals

Sample size: planned number of observations

720 effective rounds

Sample size (or number of clusters) by treatment arms

80 individuals for each of the three treatments

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We use a one-tailed test, alpha = 0.05, beta (power) = 0.2 to calculate our sample size. 1) For a general dictator game, under the situation that a dictator needs to allocate 100 units between herself and the recipient, previous studies with similar subjects have shown that the variability is approximately normally distributed with a standard deviation of 25, the minimal relevant difference equals 10. The required sample size for each group is 78 (Gruener, 2019). 2) For a dictator game with real effort design (Heinz et al., 2012), if the minimal relevant difference is 10 with a standard deviation of 24. The required sample size for each group is 71. Considering information from these two literatures, we will have 80 samples each treatment.

Supporting Documents and Materials

IRB