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Clearing the Air: The Effect of Transparency on Plant Pollution Emissions
Last registered on May 12, 2017


Trial Information
General Information
Clearing the Air: The Effect of Transparency on Plant Pollution Emissions
Initial registration date
May 12, 2017
Last updated
May 12, 2017 1:53 PM EDT
Primary Investigator
J-PAL South Asia
Other Primary Investigator(s)
PI Affiliation
PI Affiliation
Yale University
PI Affiliation
Harvard University
PI Affiliation
University of Chicago
Additional Trial Information
In development
Start date
End date
Secondary IDs
India is choking on growth. Of the 20 cities in the world with the worst fine particulate air pollution, 13 are in India, including Delhi, the worst-ranked city (WHO, 2013). The average Indian loses about three years of his or her life due to the harm of this pollution (Greenstone et al., 2015). There is also growing evidence that high levels of pollution lower labor productivity and, therefore, potentially economic growth (Zivin and Neidell, 2013; Hanna and Oliva, 2015). If good information on who pollutes is available, then traditional environmental regulation can bring down emissions somewhat (Duflo et al., 2013; Duflo et al., 2015), but regulators may lack the will or resources to penalize every polluter. What more can government due to contain such widespread damages?

In this project we propose to test this Coasian principle of transparency first by measuring the effect of information disclosure on emissions in a large-scale plant-level randomized-control trial in India. Our partner in the project is the Maharashtra Pollution Control Board (MPCB), the environmental regulator in the most industrialized state in India-Maharashtra. Working jointly with the MPCB, we have developed a star-rating program that will assign plants to categories based on their recent air pollution emissions. The experimental design assigns random subsets of plants to have their ratings privately shared with the plant alone or publicly disclosed, allowing us to distinguish own-knowledge and public pressure effects on the main outcome of plant air pollution emissions. Moreover, unlike in the traditional Coasian setting, here a regulator does exist. We will therefore also measure the effects of public disclosure on the crowd-in or -out of regulatory enforcement actions.
External Link(s)
Registration Citation
Greenstone, Michael et al. 2017. "Clearing the Air: The Effect of Transparency on Plant Pollution Emissions." AEA RCT Registry. May 12. https://doi.org/10.1257/rct.2197-1.0.
Former Citation
Greenstone, Michael et al. 2017. "Clearing the Air: The Effect of Transparency on Plant Pollution Emissions." AEA RCT Registry. May 12. http://www.socialscienceregistry.org/trials/2197/history/17587.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
The key outcome variables are change in particulate matter emissions, and change in perceptions and awareness regarding environmental performance
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The program will launch in phases. In the first phase, we will use data from 198 industries to create a website where industries are assigned a star rating based on their particulate matter emissions. The emissions data is collected by the regulator, the Maharashtra Pollution Control Board.

79 industries will be assigned the treatment of having their star rating publicly displayed on a website, 40 industries will have their star rating sent to them privately and the remaining 79 will be control industries.

As MPCB continues to collect PM emissions data, we will add more industries to the project in phases.
Experimental Design Details
Randomization Method
The randomization was done on Stata by generating a random number and then assigning to each industry. Balance checks were carried out to ensure that the industry characteristics were similar across treatment and control.
Randomization Unit
The unit of randomization is the industry itself.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
There are no clusters for this study.
Sample size: planned number of observations
198 industries. Additional industries will be added as PM emissions data is collected.
Sample size (or number of clusters) by treatment arms
79 Industries in the public disclosure treatment arm
40 Industries in the private disclosure treatment arm
79 Industries in the control treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)