Pay-it-forward reciprocity encourages the spread of prosocial behavior. However, existing empirical evidence of pay-it-forward behavior has been largely based on laboratory experiments, which are limited in sample size and external validity. Extending this research, our study uses a natural experiment to examine pay-it-forward reciprocity in a real-life context with a large-scale dataset of 3.4 million users of an online platform. Our natural experiment is enabled by the randomness in the mechanism that WeChat, a Chinese online social networking platform, uses to split an online monetary gift (also known as a "red packet") to its recipients. Our results show that recipients on average pay forward 10.34 \% of the amount they receive.  We further find that  " Luckiest draw" recipients , or those who obtain the largest shares of their corresponding red packets, are 1.5 times more likely to pay it forward than other recipients . Our analyses indicate that in a multiple recipient setting, users' pay-it-forward behavior is enforced by a group norm that luckiest draw recipients should send the first subsequent gift and promoted by their distributional social preferences of the random amounts split by the platform. Finally, our study shows that those recipients without any in-group friends do pay it forward, even though their pay-it-forward behavior is less likely to be driven by their reputational concerns among acquaintances. Overall, our work provides insights into mechanisms and conditions that encourage pay-it-forward reciprocity, which have implications for fostering prosocial behavior .
Does receiving a gift encourage the recipient to send more gifts? Causal identification of behavioral contagion is very challenging, especially based on observational data from social networks. Given that the online monetary gift (also known as a "red packet") in WeChat groups is randomly split between group members, we are able to conduct a natural experiment to identify the causal effect of receiving online red packets on the contagion of gift exchange. Analyzing gifting behavior using a large-scale dataset of 3.4 million WeChat users, we find that recipients on average repay 18.29 \% of the amount they receive. Moreover, our analysis shows that recipients' contagion may be driven by different types of reciprocity. We further find that the " luckiest draw" recipients repay 1.5 times more than other recipients , and identify a group norm that luckiest draw recipients should send payment back to the group .
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Pay-it-forward reciprocity encourages the spread of prosocial behavior. However, existing empirical evidence of pay-it-forward behavior has been largely based on laboratory experiments, which are limited in sample size and external validity. Extending this research, our study uses a natural experiment to examine pay-it-forward reciprocity in a real-life context with a large-scale dataset of 3.4 million users of an online platform. Our natural experiment is enabled by the randomness in the mechanism that WeChat, a Chinese online social networking platform, uses to split an online
Does receiving a gift encourage the recipient to send more gifts? Causal identification of behavioral contagion is very challenging, especially based on observational data from social networks. Given that the online
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to its recipients. Our results show
in WeChat groups is randomly split between group members, we are able to conduct a natural experiment to identify the causal effect of receiving online red packets on the contagion of gift exchange. Analyzing gifting behavior using a large-scale dataset of 3.4 million WeChat users, we find
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pay forward 10.34
repay 18.29
A
Moreover, our analysis shows that recipients' contagion may be driven by different types of reciprocity.
A
the
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Luckiest
luckiest
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, or those who obtain the largest shares of their corresponding red packets, are
repay
D
likely to pay it forward
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. Our analyses indicate that in a multiple recipient setting, users' pay-it-forward behavior is enforced by
, and identify
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the first subsequent gift and promoted by their distributional social preferences of the random amounts split by the platform. Finally, our study shows that those recipients without any in-group friends do pay it forward, even though their pay-it-forward behavior is less likely to be driven by their reputational concerns among acquaintances. Overall, our work provides insights into mechanisms and conditions that encourage pay-it-forward reciprocity, which have implications for fostering prosocial behavior
payment back to the group