A Natural Field Experiment on (Community) Incentives in Bicycle-share Systems

Last registered on August 23, 2018

Pre-Trial

Trial Information

General Information

Title
A Natural Field Experiment on (Community) Incentives in Bicycle-share Systems
RCT ID
AEARCTR-0002216
Initial registration date
May 30, 2017

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 30, 2017, 1:47 PM EDT

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

Last updated
August 23, 2018, 4:14 AM EDT

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

Locations

Primary Investigator

Affiliation
Grantham Research Institute, LSE

Other Primary Investigator(s)

Additional Trial Information

Status
Withdrawn
Start date
2017-05-22
End date
2018-08-31
Secondary IDs
Abstract
We have partnered with a major US city that has recently developed its bicycle-sharing system to increase access to healthy and environmentally sustainable transport for its citizens. Historically, the city's transit agencies have used sign-up discounts to incentivize residents to utilize publicly provided transportation options. However, insights from behavioral science and economics have demonstrated that financial incentives may not be the most (cost-) effective means of encouraging individuals to make presumably welfare-maximizing decisions on the margin. Here, we aim to understand the importance of personal and community incentives in motivating individuals to participate in the city’s newly expanded bicycle sharing system, a service that may arguably be underutilized due to both myopia in health-related decision-making and the lack of prices to internalize social damages (e.g., pollution and greenhouse gas emissions) associated with conventional transport options. To discern whether there is a role for altruism and incentives (and assess their interaction), we will randomize the content of mailers that will be sent to approximately 30,000 homes across seven neighborhoods to introduce an expansion of the cycle scheme into those homes’ neighborhoods. Our analysis will focus primarily on differences in initial uptake (i.e. extensive margin) and the extent of participation (i.e. intensive margin). The results will have implications for cities looking to promote engagement with sustainable public transport.


External Link(s)

Registration Citation

Citation
Gosnell, Greer. 2018. "A Natural Field Experiment on (Community) Incentives in Bicycle-share Systems." AEA RCT Registry. August 23. https://doi.org/10.1257/rct.2216-4.0
Former Citation
Gosnell, Greer. 2018. "A Natural Field Experiment on (Community) Incentives in Bicycle-share Systems." AEA RCT Registry. August 23. https://www.socialscienceregistry.org/trials/2216/history/33388
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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2017-05-22
Intervention End Date
2018-04-30

Primary Outcomes

Primary Outcomes (end points)
Cycle scheme registration and first ride (extensive margin)
Intensity of bike share use (i.e. number of rides; intensive margin)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment comprises 29,878 households within a major US City in 2017. In alignment with the SUTVA assumption of non-interference, we assume that individuals who received postcards did not discuss the postcards they received with their neighbors. However, we will investigate within-household spillovers by identifying instances in which two or more individuals sign up within the same household (in cases where we observe and track more than one sign-up at a given single-unit address).

We use address data from public tax records to perform the randomization. As such, apartment unit-level randomization is only possible for units that are owner-occupied. For all apartments where apartment units are not identifiable in tax record data, we randomize at the apartment level so that all individuals within a given apartment block receive the same mailer. To ensure that we achieve balance on location and house type (e.g., single-family, duplex, multi-unit of various sizes) in the allocation of households to the various study groups, we conduct a stratified randomization, blocking first on the neighborhood in which the household resides and second on a categorical variable that captures the number of livable units at the given address (for multi-unit addresses that are not owner-occupied). A vast majority of addresses (87.9%) are identified as single-unit, and another 7.5% are identified as duplexes; these constitute the first two levels of the livable units categorical variable. The remaining categories comprise 3-4 units (1.9%), 5-8 units (1.0%), 9-20 units (1.1%), 21-50 units (0.2%), 51-100 units (0.1%), 101-200 units (0.1%), 201-300 units (0.1%), 300-500 units (0.1%).

Four apartment blocks that were over 500 units were excluded from the experiment for cost purposes, since large clusters provide less benefit in terms of power. We exclude three neighborhoods that had already been exposed to the bike share program during the demonstration phase of the bicycle-share system. We additionally exclude two university neighborhoods where the ability to randomize at the household level is limited and risk of spillovers is likely high. A key component of the analysis is the ability to match billing addresses with mailer recipient addresses, especially for the control group, which does not receive a promo code to allow for tracking. Since many students use their permanent hometown addresses as their billing addresses, there is strong potential for inability to match data outcomes to interventions received.
Experimental Design Details
The experiment comprises 29,878 households within Atlanta neighborhoods where Relay bike share hubs were first installed during the Relay Bike Share scheme expansion in April 2017. A demonstration phase launched in June 2016 made 100 bikes available at 22 stations. The formal program launch in April 2017 saw an expansion of facilities to 500 bikes at 65 stations. In alignment with the SUTVA assumption of non-interference, we assume that individuals who receive postcards do not discuss the interventions with their neighbors. However, we will allow for within-household spillovers by studying instances in which two or more individuals sign up within the same household (in cases where we observe and track more than one sign-up at a given address).

We use address data from public tax records to perform the randomization. As such, apartment unit-level randomization is only possible for units that are owner-occupied. For all apartments where apartment units are not identifiable in tax record data, we randomize at the apartment level so that all individuals within a given apartment block received the same mailer. To ensure that we achieve balance on location and house type (e.g., single-family, duplex, multi-unit of various sizes) in the allocation of households to the various study groups, we conduct a stratified randomization, blocking first on the neighborhood in which the household resides and second on a categorical variable that captures the number of livable units at the given address (for multi-unit addresses that are not owner-occupied). A vast majority of addresses (87.9%) are identified as single-unit, and another 7.5% are identified as duplexes; these constitute the first two levels of the livable units categorical variable. The remaining categories comprise 3-4 units (1.9%), 5-8 units (1.0%), 9-20 units (1.1%), 21-50 units (0.2%), 51-100 units (0.1%), 101-200 units (0.1%), 201-300 units (0.1%), 300-500 units (0.1%). Four apartment blocks that were over 500 units were excluded from the experiment for cost purposes, since large clusters provide less benefit in terms of power. We exclude three Atlanta neighborhoods - Midtown, Downtown, and Castlebury Hill - since these neighborhoods had already been exposed to the bike share program during the demonstration phase of the Relay Bike Share scheme launched in June of 2016, when 100 bikes and 22 stations were established in these neighborhoods. We additionally exclude two university neighborhoods where the ability to randomize at the household level was limited and risk of spillovers is likely high. A key component of the analysis is the ability to match billing addresses with mailer recipient addresses, especially for the control group, which does not receive a promo code to allow for tracking. Since many students use their permanent hometown addresses as their billing addresses, there is strong potential for inability to match data outcomes to interventions received.

Mailers were sent to all households in the study on the same day in 2017 and recipients have just over a month to redeem their promo codes before the promotion expires. In the exploratory analysis, we will investigate whether there is persistence in participation levels and any identified treatment effects on the intensive margin, or if individuals merely sign up, use the promotion, and then discontinue use.
Randomization Method
Stratified randomization done in Stata by the PI
Randomization Unit
We randomize at the address level, where a majority (~88%) of addresses are single-unit (i.e. household-level randomization) and the remaining addresses are used in the stratification to ensure balance across the size of the complex.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
14,452 clusters (12,701 single-unit addresses; 1751 multi-unit addresses)
Sample size: planned number of observations
29,878 households
Sample size (or number of clusters) by treatment arms
Control: 4510 households (2404 clusters)
Treatment 1 (Prosocial, Own Community Hub): 4735 households (2407 clusters)
Treatment 2 (Prosocial, Low-Income Community Hub): 5183 households (2412 clusters)
Treatment 3 (Incentive, $5 Discount): 5649 households (2409 clusters)
Treatment 4 (Incentive, $8 Discount): 4642 households (2407 clusters)
Treatment 5 (Prosocial + Incentive, Low-Income Community Hub + $5 Discount): 5159 households (2413 clusters)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
If we consider identifiable unit-level households alone (i.e. either single-family homes or owner-occupied condos), and given that 0.86% of residents in Midtown and Downtown signed up for Relay Bike Share during the pilot phase from June 2016 to April 2017, we are powered (alpha=0.05, beta=0.80) to detect treatment effects of 0.085% or greater (N=2117 per group, on average). If we treat households within multi-unit apartments as independent and consider the entire sample, this minimum detectable effect decreases to 0.055% (N=4980 per group, on average). Increasing the power to beta=0.9, these minimum detectable effects increase to 0.095% and 0.065%, respectively. Note that the latter is a lower bound estimate given that the analysis will account for the potential correlation across observations within apartment blocks by clustering observations at a given address.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials