Survey experiment on technology use among ride-hail drivers

Last registered on June 30, 2024

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Pre-Trial

Trial Information

General Information

Title

Survey experiment on technology use among ride-hail drivers

RCT ID

AEARCTR-0013809

Initial registration date

June 13, 2024

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

June 24, 2024, 1:48 PM EDT

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

Last updated

June 30, 2024, 10:36 AM EDT

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

Locations

Country

Singapore

Region

Primary Investigator

Name

Pinchuan Ong

Affiliation

National University of Singapore Business School

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Ivan Png

PI Affiliation

National University of Singapore

Contact Investigator

Additional Trial Information

Status

In development

Start date

2024-07-01

End date

2024-07-31

Keywords

Firms & Productivity, Labor

Additional Keywords

JEL code(s)

O33, M5

Secondary IDs

Prior work

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

Abstract

We study the deskilling effect of technology on the supply of labor. In previous work, we fielded a vignette experiment embedded in an online survey conducted on a sample of ride-hail drivers in Singapore. The study showed that ride-hail drivers value the use of map apps, and that its loss would lead to a decrease in supply of labor. In this study, we use a similar methodology to delve deeper and investigate valuations of the various capabilities of map and ride-hail apps---basic static route planning, provision of real-time traffic information, identification of where ride-hail demand is high, and facilitating translations of place names into different languages. Our survey also includes an information experiment on autonomous vehicles technology.

External Link(s)

Registration Citation

Citation

Ong, Pinchuan and Ivan Png. 2024. "Survey experiment on technology use among ride-hail drivers." AEA RCT Registry. June 30. https://doi.org/10.1257/rct.13809-1.1

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2024-07-01

Intervention End Date

2024-07-31

Primary Outcomes

Primary Outcomes (end points)

Willingness-to-pay for the technology shown, and labor supply impact of taking away the technology

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We field a vignette experiment embedded in an online survey, conducted on a sample of ride-hail drivers in Singapore. We show drivers multiple pairs of vignettes in a within-subject design. In each pair, respondents will be asked whether they would work in each scenario and which of the two scenarios they prefer. In the information treatment experiment, respondents are split into control and treatment groups and shown slightly different details.

Experimental Design Details

We show drivers a total of 10 pairs of vignettes (excluding the information treatment vignettes below). In one vignette of the pair, the driver can use a technology (e.g. they can use a map app) at a randomly chosen ride-hail platform commission rate (e.g. 20 percent). In the other vignette of the pair, the driver cannot use the technology at another randomly chosen commission rate. Except for one pair that serves as a check on the attention of the drivers, the commission rate in the no-technology vignette is always strictly smaller than the commission rate in the with-technology vignette.

In the information treatment experiment on self-driving technology, we show all drivers one pair of vignettes. The vignette that allows use of self-driving technology shows their current commission rate plus 3. The vignette without self-driving technology use shows their current commission rate plus 2. Drivers are randomized into control and treatment groups in a between-subject design. The control group is told that self-driving technology is available and responsibilities that come with it (e.g. they must still manage the car in case of accidents, but they can choose to turn on the technology whenever they want). The treatment group is told the above, plus they are told that the job becomes more pleasant (and why).

Randomization Method

Randomization is implemented using Qualtrics's randomization feature.

Randomization Unit

Vignette experiments: Within-individual between-vignettes level randomization.
Information treatment experiment: Individual-level randomization.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

1000 individuals.

Sample size: planned number of observations

Vignette experiments: 1000 individuals * 10 pairs of vignettes * 2 vignettes per pair = 20,000 observations Information treatment experiment: 1000 individuals

Sample size (or number of clusters) by treatment arms

Vignette experiment: On average, 100 observations per each unique vignette scenario (i.e., each technology type by can/cannot use technology condition by commission rate).
Information treatment experiment: 500 per treatment arm

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

Supporting Documents and Materials

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