A field experiment on financial incentive, social preference, and peer effects to improve parking by bike-sharing users

Last registered on November 08, 2021

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Pre-Trial

Trial Information

General Information

Title

A field experiment on financial incentive, social preference, and peer effects to improve parking by bike-sharing users

RCT ID

AEARCTR-0008504

Initial registration date

November 06, 2021

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 08, 2021, 10:51 AM EST

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

Locations

Country

China

Region

Inner Mongolia, Shandong, Fujian and Guangxi

Primary Investigator

Name

Duan Su

Affiliation

Beijing Jiaotong University

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Other Primary Investigator(s)

PI Name

Yacan Wang

PI Affiliation

Beijing Jiaotong University

Contact Investigator

PI Name

Adriaan Soetevent

PI Affiliation

University of Groningen

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Additional Trial Information

Status

In development

Start date

2021-09-16

End date

2022-01-30

Keywords

Behavior

Additional Keywords

Field experiment, social preference, transport decisions, bike-sharing.

JEL code(s)

C93, D12, H23, L9, R41

Secondary IDs

National Natural Science Foundation of China and the Joint Programming Initiative Urban Europe, National Natural Science Foundation of China

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Dockless bike-sharing is popular and provides the commuting service for "the last mile of trip". Hellobike company also taps over 450 university markets, where some shared bicycles only serve students and exclusively circulate on university campuses. In this study, we design different treatments that vary in the extent to which they appeal to users’s considerations for other users, to test the role of social preferences in bike-sharing. In a field experiment, we design four treatments: social norms, individual free-ticket, group free-ticket, and feedbacks.

External Link(s)

Registration Citation

Citation

Wang, Yacan, Adriaan Soetevent and Duan Su. 2021. "A field experiment on financial incentive, social preference, and peer effects to improve parking by bike-sharing users." AEA RCT Registry. November 08. https://doi.org/10.1257/rct.8504-1.0

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Experimental Details

Interventions

Intervention(s)

Social norms: Use information to tell users what is orderly parking, why important, and how to make orderly parking.
Individual free-ticket: Users will get free tickets to ride for the increase of orderly parking rate.
Group free-ticket: Use free tickets to ride for the subject and her partner and monthly feedbacks to encourage orderly parking.
Feedbacks: Use free tickets to ride for the subject and monthly feedbacks to increase the orderly parking.

Intervention (Hidden)

T1 Control:
None

T2 Social norms:
We publicize the social norms of orderly parking to users through app.

T3 Individual free-ticket:
Users will get free tickets to ride bicycles in December of 2021 for 1, 3, 5, or 7days, if their orderly parking rate exceeds 5%, 15%, 30%, or 50% between Nov.8 and Nov.30. In December, they also face the same rules and get free tickets in the January of 2022 due to their parking performance in December of 2021.

T4 Group free-ticket:
We will pair two users randomly. The two users will both get free tickets in December of 2021 for 1, 3, 5, or 7days, if the first user’s orderly parking exceeds 5%, 15%, 30%, or 50% between Nov.8 and Nov.30.
In December, they also face similar rules. They will both get free tickets in the January of 2022 due to another user’s parking performance in December of 2021.
At the ending of each month, we will send out feedback reports to make them know their parking information.

T5 Feedbacks

Users will get free tickets in December of 2021 for 1, 3, 5, or 7days, if their orderly parking exceeds 5%, 15%, 30%, or 50% between Nov.8 and Nov.30. In December, they also face the same rules and get free tickets in the January of 2022 due to their parking in December of 2021.

We will pair two users randomly. At the ending of each month, we will send out feedback reports to make subjects know their partners’ parking information and theirs.

Intervention Start Date

2021-11-08

Intervention End Date

2021-12-31

Primary Outcomes

Primary Outcomes (end points)

Change in fraction of bicycles in each treatment group that is parked in crowded areas

Primary Outcomes (explanation)

Each user i will complete Ti trips (Ti =0,1,…) in the experimental period. Each trip t will end in a crowdeddesigned area (xit = 1) or not (xit = 0). Per individual with Ti >0 , we compute the average value (x_i ) ̅= 1/T_i ∑_(t=1)^(T_i)▒x_it The prime outcome variable is the fraction of trips that ends in crowded designed areas, averaged over all users in a treatment group.

Secondary Outcomes

Secondary Outcomes (end points)

The change of social norms and social preference before and after treatment.

Secondary Outcomes (explanation)

Subjects are exposed to the treatment of social norms and social preferences. During the treatment period, subjects will know the social norms of orderly parking and potentially experience altruism or reciprocity from others. It is conceivable that the treatment will influence subjects i’ social norms and social preferences.

Experimental Design

During the treatment period, users will be incentivized by social norms, free riding, the interaction of free tickets and social preference, or the interaction of free tickets and feedbacks.

Experimental Design Details

Users are assigned to the control or treatment group randomly. In the beginning of treatment period, for users in the social norm group, they will read the social norm information through the app. For users in the individual free ticket group, they will know that they can get rights to ride free due to their parking. For the half users in the group free ticket group, they will read that they have been matched “bikemates”, and their orderly parking will bring the rights to ride free for themselves and their bike mates. For another half, they will know they can enjoy the free ride because of their bikemates’ performance. In the second month, the roles are reversed. In the last group, users will read the same information as the individual free ticket group. Besides, we also paring a user with another partner and inform the parking performance of their partner every month. After treatment, they will receive riding tickets for free based on the previous rules.

Randomization Method

Stratified randomization with Stata

Randomization Unit

Individual user

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

5200 users. In four-campus markets of bike-sharing, we randomly select 6000 users from the Hellobike sample pool. After data cleaning, keep valid subjects 5222. These users will be assigned to the control and treatment groups T1-T5. For T4 and T5, we double the subject size.

Sample size: planned number of observations

5200 users

Sample size (or number of clusters) by treatment arms

T1 Control: about 746 users
T2 Social norms: 746 users
T3 Individual free-ticket: 746 users
T4 Group free-ticket: 1492 users
T5 Feedbacks: 1492 users

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

Unit of measurement: the fraction of trips ending in designed parking areas at the individual user level. For a type I error probability of alpha = 0.05 and a power of 1- k = 0.8, and N =743 per treatment arm, the standardized minimum detectable effect size is 0.145 standard deviations. In the experimental area, the benchmark percentage of parking in designed areas is about 2%. We expect to increase the percentage from 2% to 3% more.

Supporting Documents and Materials

IRB