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Many cities and countries invest in air pollution monitoring systems. The value of these systems depends crucially on how the information that they collect is used by the public. For example, Mexico City has a robust monitoring network of over 30 stations with hourly pollutant readings that are available online and through an app. However, the public is not regularly using this information to engage in protective "avoidance" behaviors to reduce their exposure to air pollution and mitigate its related health risks.
We develop a messaging service that provides location and pollutant specific (ozone and particulate matter) alerts through SMS message early in the day. We use an incentive compatible mechanism to elicit willingness to pay for the SMS alert service and we randomly assign households to receive or not receive the SMS alert service. Participants are also randomly assigned to two cross-cutting treatments: (1) monthly SMS reminders of air pollution trends and avoidance behaviors, and (2) provision of a free N95 certified mask. We will estimate the causal effect of the alert service and reminder information on avoidance behavior. Because the N95 mask is effective in reducing exposure to particulate matter but not ozone, receiving a free mask lowers the cost of avoidance for days with high particulate matter relative to days with high ozone. We will use this variation to investigate the causal effect of the cost of avoidance behavior on engaging in avoidance behavior.
Air quality alerts: SMS messaging service that sends location and pollutant specific air quality alerts to participants
Reminders: monthly SMS message with reminders of air quality trends and air pollution avoidance behaviors
Masks: free provision of a certified N95 mask
Intervention Start Date
2019-06-03
Intervention End Date
2020-08-31
Primary Outcomes (end points)
Willingness to pay, air pollution information, avoidance behaviors
Primary Outcomes (explanation)
Secondary Outcomes (end points)
Beliefs about air pollution trends and levels
Secondary Outcomes (explanation)
Experimental Design
The sample consists of households in randomly selected blocks in Mexico City. Households are randomized into the pure control group or one of the 7 treatment arms described below.
Pure control: no intervention
Arm 1: free provision of mask
Arm 2: air quality alerts
Arm 3: reminders
Arm 4: air quality alerts + reminders
Arm 5: air quality alerts + mask
Arm 6: reminders + mask
Arm 7: air quality alerts + reminders + mask
Within each of the pure control and treatment arms, the participants will be split between a regular compensation group and a high compensation group.
Experimental Design Details
Randomization Method
Randomization done in office by computer
Randomization Unit
Household
Was the treatment clustered?
No
Sample size: planned number of clusters
1,850 households
Sample size: planned number of observations
1,850 households
Sample size (or number of clusters) by treatment arms
Pure control and all arms: 1/8 of the sample. Within each cell of the treatment matrix, 70% of the participants will receive the regular compensation level and 30% of participants will receive the high compensation level.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)