Pollution, Productivity and Willingness to Pay for Defensive Investments

Last registered on June 16, 2020

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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

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

February 07, 2020, 3:43 PM EST

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

Last updated

June 16, 2020, 8:48 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

Bangladesh

Region

Dhaka

Primary Investigator

Name

Maulik Jagnani

Affiliation

Yale University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Teevrat Garg

PI Affiliation

University of California, San Diego

Contact Investigator

Additional Trial Information

Status

On going

Start date

2020-01-15

End date

2021-12-31

Keywords

Environment & Energy

Additional Keywords

JEL code(s)

Q50, Q56, Q58, Q53, O13, O10

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

Sponsors & Partners

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

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

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)

Supporting Documents and Materials

IRB