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Pollution, Productivity and Willingness to Pay for Defensive Investments
Last registered on June 16, 2020

Pre-Trial

Trial Information
General Information
Title
Pollution, Productivity and Willingness to Pay for Defensive Investments
RCT ID
AEARCTR-0005364
Initial registration date
February 04, 2020
Last updated
June 16, 2020 8:48 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
Yale University
Other Primary Investigator(s)
PI Affiliation
University of California, San Diego
Additional Trial Information
Status
On going
Start date
2020-01-15
End date
2021-12-31
Secondary IDs
Abstract
Air quality has become a pressing concern in many developing countries, particularly in South Asia. The 2019 State of Global Air study found that in South Asia, air quality levels represented a loss of 2-3 years in average life expectancy. In Bangladesh, where we propose our study, it was estimated that 200,000 people died in 2017 due to poor air quality. While the human capital costs of air pollution have been documented extensively, there remain major gaps in our understanding of the effects of air pollution and more importantly the ability and willingness of individuals and firms to avoid these damages. We propose a field experiment with randomized allocation of air purifiers in small-scale textile firms in Bangladesh to estimate the effect of air pollution on worker productivity as well as willingness to pay for defensive investments that help reduce exposure to air pollution.
External Link(s)
Registration Citation
Citation
Garg, Teevrat and Maulik Jagnani. 2020. "Pollution, Productivity and Willingness to Pay for Defensive Investments." AEA RCT Registry. June 16. https://doi.org/10.1257/rct.5364-2.0.
Experimental Details
Interventions
Intervention(s)
Intervention #1: We installed air quality monitors across a random sample of garment factories (firms) in Dhaka.

Intervention #2: We will inform owners and workers across a random sample of garment factories (firms) in Dhaka through pollution report cards describing outdoor and indoor pollution levels, effect of pollution on productivity (from Intervention #1), and the protective effects of air pollution masks.

Intervention Start Date
2020-01-15
Intervention End Date
2021-12-15
Primary Outcomes
Primary Outcomes (end points)
productivity indicators and willingness to pay measures
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Our research design has two parts: the first part will estimate the effect of pollution on productivity (N = 40 firms); the second part will examine the willingness-to-pay for air pollution masks (N = 600 firms).

Part 1: In the first part, we will randomly sample 40 firms and install air quality monitors in each firm. We will install air purifiers in 20 randomly selected firms for a period of 6 months (treatment group). In our analysis, we will compare productivity indicators between the treatment and control group to estimate the impact of air filters on productivity.

Part 2: In the second part, we will randomly sample 600 firms.

Baseline Survey Visit: Surveyors will visit 600 firms and speak to both the factory owner and factory workers about pollution: Surveyors will conduct a baseline survey, which will include a module on pollution and beliefs about outdoor and indoor pollution levels, effect of pollution on productivity, and the protective effects of face masks. During the visit, objective indoor and outdoor pollution levels for factories in both the treatment and control group will be collected via air monitors. After the survey, during the same visit, surveyors will conduct the information intervention for the treatment group.

Information Intervention and Pollution Report Cards (Treatment Group Only): Surveyors will walk factory owners and factory workers through pollution report cards describing (objective) outdoor and indoor pollution levels, effect of pollution on productivity (from Part 1), and the protective effects of air pollution masks (engineering estimates).

Willingness-to-Pay Experiment: At the end, both factory owners and factory workers will be asked to take part in a real-stakes willingness-to-pay experiment; we will elicit willingness to pay for face masks using an incentive-compatible Becker-deGroot-Marschak (BDM) mechanism, which both induces exogenous variation in take-up and yields high resolution data on individual face-mask demand.

In our analysis, we will simply compare willingness-to-pay estimates across as well as between workers and owners in the treatment and control groups.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Unit of randomization: garment factories (firms)
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
Intervention #1: 40 firms

Intervention #2: 600 firms
Sample size: planned number of observations
Intervention #1: 400 workers Intervention #2: 600 firm owners and 600 workers
Sample size (or number of clusters) by treatment arms
Intervention #1: 20 garment factories/firms in the control group, 20 garment factories/firms in the control group

Intervention #2: 300 garment factories/firms in the control group, 300 garment factories/firms in the control group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Innovations for Poverty Action - USA
IRB Approval Date
2019-12-19
IRB Approval Number
15026