A field experiment on financial incentives, social preferences, and social comparisons to improve parking by users of bike-sharing services

Last registered on May 26, 2022

Pre-Trial

General Information

Title

RCT ID

AEARCTR-0009499

Initial registration date

May 26, 2022

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

May 26, 2022, 11:46 AM EDT

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

Locations

Country

China

Region

Chengdu, Zhengzhou, and Fuzhou

Primary Investigator

Name

Duan Su

Affiliation

Beijing Jiaotong University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Adriaan Soetevent

PI Affiliation

University of Groningen

Contact Investigator

PI Name

Yacan Wang

PI Affiliation

Beijing Jiaotong University

Contact Investigator

Additional Trial Information

Status

In development

Start date

2022-04-07

End date

2022-07-31

Keywords

Behavior

Additional Keywords

Field experiment, social preferences, social comparisons, transport decisions, bike-sharing.

JEL code(s)

C93, D12, H23, L9, R41

Secondary IDs

National Natural Science Foundation of China

Prior work

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

Abstract

Dockless bike-sharing is a very popular short-distance commuting service among city dwellers. The Hellobike company is a large supplier of such services. It taps over 450 university markets, where some shared bicycles only serve students and exclusively circulate on university campuses. In this study, we design and implement several treatments that vary the extent to which an appeal is made to users’ considerations for other users and to infer the role social preferences play in bike-sharing. In a field experiment, we compare four treatments: a feedback treatment, one that involves social comparisons, an individual free-ticket treatment, and a group free-ticket treatment.

External Link(s)

Registration Citation

Citation

Soetevent, Adriaan , Duan Su and Yacan Wang. 2022. "A field experiment on financial incentives, social preferences, and social comparisons to improve parking by users of bike-sharing services." AEA RCT Registry. May 26. https://doi.org/10.1257/rct.9499-1.0

Sponsors & Partners

Partner

Name

Hello Inc.

Type

private_company

URL

https://www.hello-inc.com/

Experimental Details

Interventions

Intervention(s)

1. Control: The benchmark treatment
2. Feedback group: Give users information about their historical parking behavior and the location of designated areas.
3. Comparison group: Use texts to inform users on how their orderly parking rate compares to similar users.
4. Individual incentive group: Users will receive free-riding tickets. The number of additional free days they receive is a function of the increase in their orderly parking rate.
5. Group incentive group: Users will be paired and receive free-riding tickets. The number of additional free days they receive is a function of the increase in their joint orderly parking rate.

Intervention Start Date

2022-05-27

Intervention End Date

2022-06-27

Primary Outcomes

Primary Outcomes (end points)

Change in average individual orderly parking rate (number of times a user parked in designated areas divided by a user’s total number of parking in the treatment period.

Primary Outcomes (explanation)

Each user i will complete Ti trips (Ti =0,1,…) in the experimental period. Each trip t will end in a designed 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 designated areas, averaged over all users in a treatment group.

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

During the treatment period, users in the treatments groups will receive feedback information on their individual past parking behavior, complemented with either a) social comparison information on the past parking behavior of other, similar users, b) an individual incentive in the form of free-riding cards, c) a group incentive in the form of free-riding cards.

Experimental Design Details

Users are randomly assigned to either the control or a treatment group. At the beginning of each stage of the campaign, all users will receive the reminders of designated parking areas. Users in the feedback group or social comparison group will read the intervention information through the app and SMS. For users in the individual free ticket group, they will know that they will get the 1-day or 3-day free ticket to ride due to their orderly parking. For the users in the group incentive group, every two users will be matched as “bikemates”, and their orderly parking will give them the rights to ride free for themselves and their bike mates. In the second stage, all subjects will face the similar interventions as before.

Randomization Method

Stratified randomization with STATA

Randomization Unit

Individual user

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

2 589 users. Explanation: In campus markets of bike-sharing, we randomly select 2 589 users with five more trips during one month from the Hellobike sample pool. These users will be assigned to the control (T1) and treatment groups (T2-T5).

Sample size: planned number of observations

Not sure

Sample size (or number of clusters) by treatment arms

T1 Control: about 518 users
T2 Feedback group: about 517 users
T3 Comparison group: 518 users
T4 Individual incentive group: 518 users
T5 Group incentive group: 518 users

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

Unit of measurement: the fraction of trips ending in crowded 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 =518 per treatment arm, the standardized minimum detectable effect size is 0.175 standard deviations. The standard deviation of out outcome variable is highest when half of the users always parks in a crowded area and the other half never, in which case s.d. = 0.5. In this most extreme case, the minimum detectable effect size (MDE) would be a change in parking in disorderly areas of about 8.8 percentage points (=0.5x0.175). If only half of the selected individuals takes up treatment (that is: completes at least one trip in the experimental period), the MDE would increase to 12.3 percentage points. Increasing the power to 1-k =0.9 increases the MDE to 10.1 percentage points.

Supporting Documents and Materials

IRB