Title: Replication Data for: The Value of Regulatory Discretion: Estimates from Environmental Inspections in India

Type Dataset Duflo, Esther, Greenstone, Michael, Pande, Rohini, Ryan, Nicholas (2018): Replication Data for: The Value of Regulatory Discretion: Estimates from Environmental Inspections in India. Harvard Dataverse. Dataset. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TPYSRO

Authors: Duflo, Esther (Massachusetts Institute of Technology) ; Greenstone, Michael (University of Chicago) ; Pande, Rohini (Harvard University) ; Ryan, Nicholas (Yale University) ; Duflo, Esther (Massachusetts Institute of Technology) ; Greenstone, Michael (University of Chicago) ; Pande, Rohini (Harvard University) ; Ryan, Nicholas (Yale University) ;

Links

• Item record in Abdul Latif Jameel Poverty Action Lab Dataverse
• Digital object URL

Summary

Many developing countries have high pollution despite strict environmental standards, suggesting gaps in regulatory enforcement. In collaboration with the Gujarat Pollution Control Board (GPCB), in India, we increased the rate of inspection for a random group of polluting industrial plants and required the added inspections be assigned randomly. Plants in the treatment group were twice as likely to be inspected and to be cited for violations. Yet, treatment plants were no more likely to be penalized and only slightly increased environmental compliance. We show that the weak treatment effects are not due to a lack of sanctions: penalties are often applied for extreme violators. The regulator also follows-up on control and treatment inspections in the same way. We hypothesize that the results are due to the randomized inspections being less well targeted than inspections in the status quo. To investigate, we set out a structural model of environmental regulation where the regulator targets inspections, based on a signal of pollution, to maximize plant abatement. Using the experimental variation in inspections to identify key parameters, we find that the regulator aggressively targets its discretionary inspections at plants it believes are most polluting. As a result the average regulator-chosen inspection induces three times more abatement than an inspection added at random. Counterfactual simulations show that monitoring technology that improved regulatory information about emissions would greatly increase abatement.

More information

• DOI: 10.7910/DVN/TPYSRO

Subjects

• Social Sciences

Dates

• Publication date: 2018
• Submitted: June 01, 2018
• Updated: May 03, 2019

Rights

• info:eu-repo/semantics/openAccess
• https://creativecommons.org/publicdomain/zero/1.0/ CC0 Waiver

Format

electronic resource