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
Last updated
October 29, 2018 5:14 PM EDT
Location(s)

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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
Baylis, Patrick 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
Not available
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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