Barriers to Civic Action on Air Pollution

Last registered on January 19, 2024

Pre-Trial

Trial Information

General Information

Title
Barriers to Civic Action on Air Pollution
RCT ID
AEARCTR-0012818
Initial registration date
January 15, 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
January 19, 2024, 2:07 PM EST

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

Locations

Region

Primary Investigator

Affiliation
UCSD

Other Primary Investigator(s)

PI Affiliation
UBC
PI Affiliation
JPAL

Additional Trial Information

Status
In development
Start date
2024-01-11
End date
2025-01-11
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In numerous developing urban areas, there is a prevalent issue of high levels of air pollution, leading to increasingly severe consequences. The World Health Organization (WHO) estimates that one out of every nine global deaths can be attributed to air pollution, with 90% of these fatalities occurring in low and middle-income countries (WHO 2016). This problem is particularly critical in India, which is home to 14 of the world's 20 most polluted cities. Some estimates suggest that if New Delhi were to meet the air quality standards set by the WHO, it could potentially extend the average life expectancy by up to 10 years (Greenstone and Fan, 2019).

Despite the significant health toll that air pollution exacts in these areas, there is a notable absence of public pressure on elected officials to implement policy solutions that would improve air quality. The demand for air quality improvements among the public seems to be relatively low. However, information has the potential to be a powerful tool in aligning people's beliefs and preferences with the goal of cleaner air, thereby incentivizing policymakers to respond to this demand. The central questions here are: Can information be a driving force in generating civic demand for better air quality? And in what manner does information act as a barrier? Is it more critical to emphasize the adverse effects of pollution exposure (related to individual stakes and beliefs) or to help citizens overcome the perception that this issue cannot be resolved by highlighting state capacity and action (fatalism concerning state)?

This study aims to investigate whether the limited private demand for reductions in air pollution plays a significant role in explaining the persistence of poor air quality. Such an understanding is crucial for governments to prioritize efforts to combat air pollution and reduce pollution-related mortality. To achieve this goal, we intend to conduct an incentivized field experiment to gauge and compare private demand for cleaner air with the corresponding willingness of the public to engage in various civic actions that would lead to improved air quality. Furthermore, we will evaluate how different information treatments influence these measures and whether they increase support for environmental safeguards.
External Link(s)

Registration Citation

Citation
Baylis, Patrick, Shweta Bhogale and Teevrat Garg. 2024. "Barriers to Civic Action on Air Pollution ." AEA RCT Registry. January 19. https://doi.org/10.1257/rct.12818-1.0
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Experimental Details

Interventions

Intervention(s)
Our information interventions are designed to meaningfully increase respondents’ willingness to engage in civic action by solving possible market failures that could stand in their way. Both our treatment arms will examine two types of potential information barriers including health impacts of air pollution and state capacity and action targeted towards air pollution.
Intervention (Hidden)
Treatment 1: (stakes-dependent Information Nudges): To test for the possibility that individuals the impacts of air pollution on health is not salient to an individual's choices in demanding cleaner air, the treatment group will be provided with periodic (weekly) information snippets on the different ways in which air pollution affects health. Individuals will also get regular nudges to check the app for the current AQIs and tell us how they are feeling about the air today by choosing one of the mood indicator buttons.

Treatment 2: (state capacity and policy potential): The second treatment arm will test for the possibility that the barrier to civic action is a belief that policy cannot be successful or the state capacity is missing. In this arm, app users will receive information snippets on evidence on policy effectiveness, different ways in which the state is tackling the air pollution issues, and how their engagement can contribute to the efficacy of these actions.

Treatment 3 (optional depending on recruited sample): This will be a low-touch treatment arm that will only get nudges to check the daily AQI and tell us how they are feeling about the air today by choosing one of the mood indicator buttons.

Control: Members of the control group will use the same AirPulse app as the treatment groups and will be incentivized to keep it installed throughout the lifetime of the study. They will not receive nudges or incentives to engage with the app other than survey notifications. Participants in this group are required to complete the baseline and endline surveys.
Intervention Start Date
2024-01-24
Intervention End Date
2024-03-31

Primary Outcomes

Primary Outcomes (end points)
Willingness to pay for clean air quality through incentivized offers for air purifiers and pollution masks
Stated preferences for pollution avoidance behavior and civic engagement
Revealed willingness to engage in civic actions that vary across effort/time cost as measured on our app
Preferences for specific anti-pollution policies and hypothetical political representatives with differing air quality priorities
Primary Outcomes (explanation)
Revealed willingness to engage in civic actions that vary across effort/time cost as measured on our app: we will test whether respondents are more likely to click on action buttons that allow them to file complaints or sign petitions when the option is readily available to them in our notification versus when they have to navigate back to the home screen to choose in an effort to vary time cost of taking the action.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
All our treatment arms will examine potential information barriers:

1. To test for the possibility that individuals are poorly informed about air pollution, this group will be provided with periodic reminders of the current level of air pollution as well as its health effects.
2. To test for the possibility that respondents don’t think that there are viable paths to pollution reduction by engaging with policy, this arm will describe several concrete policy changes and local state actions that could substantially reduce air pollution and benefit from their engagement.
3. To measure the effect of only providing local air quality information on mobilizing demand for air quality as a yardstick for our other treatments

All treatment groups will receive information on different actions they can take to directly engage with civic agencies and local governments – privately or publicly, or indirectly through petitions. For this, we will examine participants’ willingness to participate in a range of civic actions targeted to improve air quality. We will vary the effort and time cost (but not monetary costs) of engaging in these actions through our app by embedding the ease with which these action buttons are accessible for a random subset of our participants to see if the users are willing to act despite the added friction introduced by the app. This is to test whether respondents are willing to send costless signals but that willingness to participate quickly declines as the cost of the action rises in terms of effort or time. Such nudges will be introduced regularly (likely, weekly).
Experimental Design Details
As part of this randomized control trial, we have developed a custom-built mobile phone application called AirPulse, which will service as a one-stop shop for us to conduct surveys, implement interventions, and pay respondents for their time. We argue that this is a valuable way to conduct our research because (i) the population we are targeting will be smart-phone savvy, (ii) it reduces the need for enumerators and complications with payouts to participants, (iii) it enables us to easily conduct follow-up surveys and randomized interventions with participants, and (iv) it allows us to measure private pollution avoidance behavior by observing users’ movements in space (with their consent).

Our sample size and treatment group size will depend on the size of recruitment through social media channels (like Google ads, Meta ads, and civic Whatsapp groups). The study will be advertized through two types of ads including a general ad to recruit participants for a study about local issues and a targeted ad that mentions recruitment for an air pollution study. We will stratify treatment by the type of ad that attracted participants to test for selection and inherent tendencies.

At the beginning of the experiment, we will elicit a baseline estimate of the respondents’ usage of protective investments like face masks and air purifiers. We will also ascertain their willingness to pay for direct (as in, not delivered through policy changes) improvements in air quality through incentivized offers for air purifiers and face masks that protect from air pollution, their hypothetical willingness to support policies that reduce air pollution but impose taxes, and preferences for hypothetical politicians with different priorities concerning air pollution. Participants will receive “credits” for answering our surveys that can be cashed out at the end of the study period or whenever they choose to quit the study.

Our information interventions are designed to meaningfully increase respondents’ willingness to engage in civic action by solving possible market failures that could stand in their way. Importantly, this allows us to assess whether technological advances aid widespread information dissemination that can change the demand for clean air, facilitating the alignment of policy priorities with citizen’s environmental preferences. All our treatment arms will examine potential information barriers:

1. To test for the possibility that individuals are poorly informed about air pollution, this group will be provided with periodic reminders of the current level of air pollution as well as its health effects.
2. To test for the possibility that respondents don’t think that there are viable paths to pollution reduction by engaging with policy, this arm will describe several concrete policy changes and local state actions that could substantially reduce air pollution and benefit from their engagement.
3. To measure the effect of only providing local air quality information on mobilizing demand for air quality as a yardstick for our other treatments

All treatment groups will receive information on different actions they can take to directly engage with civic agencies and local governments – privately or publicly, or indirectly through petitions. For this, we will examine participants’ willingness to participate in a range of civic actions targeted to improve air quality. We will give participants information about the different channels through which they can advocate for improvements in air quality. These avenues are: 1) the Delhi government’s Green Delhi app that allows citizens to share their pollution grievances privately, 2) links to publicly available social media accounts of nearby air quality advocates and regulators 3) signing online petitions to improve Delhi’s air quality, and 4) joining a Facebook group called "Help Delhi Breathe" that keep citizens informed and organized on air pollution policy. All these avenues are accessible to citizens even in the absence of our study. Our experiment will only nudge them by providing information about these platforms. We will vary the effort and time cost (but not monetary costs) of engaging in these actions through our app by embedding the ease with which these action buttons are accessible for a random subset of our participants to see if the users are willing to act despite the added friction introduced by the app. This is to test whether respondents are willing to send costless signals but that willingness to participate quickly declines as the cost of the action rises in terms of effort or time. Such nudges will be introduced regularly (likely, weekly). Additionally, every day we will ask citizens about how they are feeling about the local air quality and twice a week if they have observed any government actions towards reducing air pollution in their neighborhood recently (example: smog guns, action against diffused polluters etc.).

The endline survey will cover additional questions on shifting beliefs and preferences (like vignettes for preferences for political candidates, stated preferences on willingness to pay for private and public pollution mitigation actions) as well as revealed willingness to pay for private adaptation to air pollution by entering a lottery to win an air purifier examine the degree to which these interventions served to shift beliefs and preferences for pollution regulation policies relative to a control group that didn’t receive any treatments. The study period will last for 3 months. We are planning to recruit respondents and conduct the baseline in January 2024. The winter is always the most polluted time of the year in New Delhi – so we are planning to time our study period to overlap with that.
Randomization Method
Randomization is done using Stata Packages.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
NA
Sample size: planned number of observations
2000
Sample size (or number of clusters) by treatment arms
NA
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IFMR Human Subjects Committee
IRB Approval Date
2018-08-18
IRB Approval Number
NA
IRB Name
UCSD IRB Administration
IRB Approval Date
2023-11-01
IRB Approval Number
IRB# 808652
IRB Name
UBC
IRB Approval Date
2023-12-19
IRB Approval Number
H23-03870

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