Understanding Defensive Health Behavior: Evidence from Pollution Masks in Delhi

Last registered on October 29, 2018

Pre-Trial

Trial Information

General Information

Title
Understanding Defensive Health Behavior: Evidence from Pollution Masks in Delhi.
RCT ID
AEARCTR-0003428
Initial registration date
October 27, 2018

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 29, 2018, 5:14 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
University of Chicago

Other Primary Investigator(s)

PI Affiliation
University of Chicago
PI Affiliation
University of British Columbia
PI Affiliation
University of Chicago

Additional Trial Information

Status
In development
Start date
2018-10-27
End date
2020-02-28
Secondary IDs
Abstract
We study the effects of distributing free pollution masks on short-term health outcomes in slums in Delhi. We also investigate what drives the take-up of pollution masks across slum residents and rickshaw drivers, using experimental variation in prices and information and exogenous variation in local pollution levels. In addition, we explore how social costs and network effects influence demand. Finally, we use data on the demand for pollution masks to estimate the willingness-to-pay for clean air in Delhi.
External Link(s)

Registration Citation

Citation
, et al. 2018. "Understanding Defensive Health Behavior: Evidence from Pollution Masks in Delhi.." AEA RCT Registry. October 29. https://doi.org/10.1257/rct.3428
Former Citation
, et al. 2018. "Understanding Defensive Health Behavior: Evidence from Pollution Masks in Delhi.." AEA RCT Registry. October 29. https://www.socialscienceregistry.org/trials/3428/history/36414
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2018-10-27
Intervention End Date
2020-02-28

Primary Outcomes

Primary Outcomes (end points)
pollution-related health symptoms/biometrics, demand for pollution masks, usage of pollution masks, wages
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
randomly vary price, information, social costs, and effectiveness, to evaluate demand and estimate impacts of masks.
Experimental Design Details
Phase 1 in winter 2018-2019:
We draw sampling locations from across the city of Delhi. Each sampling point assigned to one of the treatment or control groups at random, and all individuals within that sampling point receive the same treatment. We ensure that the treatments are balanced within areas in Delhi and within each of sampling weeks. Additionally, surveyors are assigned with 50% probability (balanced across treatment group and week) each day to wear or not wear the pollution mask before and after conducting the non-control surveys (cultural courtesy requires removing the mask for the survey). Finally, half of the control group is treated with an additional module focused on questions relating to air pollution.
-randomize over slums
-give freely distributed pollution masks (and placebos)
-offer masks at varying prices
-vary info treatments
-evaluate impacts on outcomes at 2,4,6, and 8 week intervals

Phase 2 in early 2019 to early 2020
-randomize over slums and subway stations
-offer masks at different price points to individuals and rickshaw drivers
-deliver information treatments
-estimate willingness to pay (demand)
-use experimental variation in pollution mask effectiveness to infer WTP for clean air
-deliver an intensity study where we give masks to increasing portion of slum communities. afterwards, survey demand for neighbors of those who received a mask.
-perform trust games by varying surveyor's wearing of mask
Randomization Method
spatial randomization on computer is done using lat-longs of slum centroids and subway stations.
Randomization Unit
individual
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
we define neighborhoods using government given list of slums. we then draw several 100m circles around each neighborhood center, and use k-means clustering to group these circles into regions. we sample from these circles, balancing on regions and weeks of our study

we treat each subway station as a circle, and sample based on subway traffic volume
Sample size: planned number of observations
3,000 individuals
Sample size (or number of clusters) by treatment arms
Phase 1 circles:
Control, no info: 48
Control, info: 24
Placebo, no info: 24
Free, no info: 24
Free, info: 48
T10, no info: 24
T10, info: 24
T30, no info: 24
T30, info: 24
T50, no info: 24
T50, info: 24

Phase 2: (pending)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
AURA IRB (University of Chicago)
IRB Approval Date
2018-10-26
IRB Approval Number
IRB18-1464
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