Air Quality Sensor Adoption and Use

Last registered on October 31, 2022

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Pre-Trial

Trial Information

General Information

Title

Air Quality Sensor Adoption and Use

RCT ID

AEARCTR-0010173

Initial registration date

October 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

October 31, 2022, 3:42 PM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Alexander Dangel

Affiliation

Heidelberg University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Timo Goeschl

PI Affiliation

Heidelberg University

Contact Investigator

Additional Trial Information

Status

In development

Start date

2022-10-31

End date

2023-03-31

Keywords

Behavior

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

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

Abstract

In recent years, individuals began producing air quality data (AQD) by purchasing and using air quality sensors. At a low cost to adopters, this technology can inform individual decision-making (e.g. air pollution adaptations) with real-time AQD and contribute AQD to publicly available maps. Recent research shows that socioeconomic status predicts air quality sensor adoption, potentially exacerbating existing spatial inequalities in AQD and health damages from air pollution. However, willingness to pay for this technology and its usage patterns are not yet well understood, in particular among different socioeconomic groups. We conduct a field experiment in a socioeconomically heterogeneous, high pollution setting in the United States to evaluate the adoption and use of low-cost air quality sensors.

External Link(s)

Registration Citation

Citation

Dangel, Alexander and Timo Goeschl. 2022. "Air Quality Sensor Adoption and Use ." AEA RCT Registry. October 31. https://doi.org/10.1257/rct.10173-1.0

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Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2022-10-31

Intervention End Date

2023-03-31

Primary Outcomes

Primary Outcomes (end points)

We are interested in the following variables: 1) willingness to pay for sensor, 2) sensor adoption rate, and 3) sensor usage rate.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We conduct a field study that randomly assigns households to three treatments: 1) willingness to pay elicitation type, 2) sensor offer, and 3) financial sensor use incentives.

Experimental Design Details

We conduct a field study with three experimental variations: 1) willingness to pay elicitation type, 2) sensor offer level, and 3) financial sensor use incentives.

We recruit residents in California, USA to participate in our field study with a digital marketing campaign for free and discounted high-accuracy, portable particulate matter sensors. Potential sensor adopters are asked to fill-in a socioeconomic survey in exchange for the opportunity to acquire the sensor for free or at a discount. The survey asks respondents for their ex-ante willingness to pay (WTP) for the sensor (primary outcome #1), randomizing the WTP elicitation question type they encounter (experimental variation #1: hypothetical, non-incentivized multiple price list (MPL), etc.).

Upon survey completion, survey participants are randomly assigned to receive one of four sensor offers: 1) free, 2) low price, 3) medium price, 4) high price (experimental variation #2), and we observe individual sensor adoption decisions and sensor adoption rates by price level (primary outcome #2).

At each price level, approximately half of all households who adopt a sensor are randomly assigned to receive financial incentives or not (experimental variation #3). Over a period of 2 months, financial incentives are paid out if a household reaches the target sensor usage rate (primary outcome #3).

Randomization Method

Randomization by computer.

Randomization Unit

We randomize at the individual level (WTP elicitation type and offers) and at the price group level (incentives).

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Approximately 1,000 individuals

Sample size: planned number of observations

Approximately 1,000 individuals

Sample size (or number of clusters) by treatment arms

Approximately 50 individuals free sensor without incentives,
Approximately 50 individuals free sensor with incentives,
Approximately 150 individuals low price without incentives,
Approximately 150 individuals low price with incentives,
Approximately 150 individuals medium price without incentives,
Approximately 150 individuals medium price with incentives,
Approximately 150 individuals high price without incentives,
Approximately 150 individuals high price with incentives

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

Supporting Documents and Materials

IRB