High pollution persists in many developing countries despite strict environmental rules. We use a field experiment and a structural model to study how plant emission standards are enforced. In collaboration with an Indian environmental regulator, we experimentally doubled the rate of inspection for treatment plants and required that the extra inspections be assigned randomly. We find that treatment plants only slightly increased compliance. We hypothesize that this weak effect is due to poor targeting, since the random inspections in the treatment found fewer extreme violators than the regulator's own discretionary inspections. To unbundle the roles of extra inspections and the removal of discretion over what plants to target, we set out a model of environmental regulation where the regulator targets inspections, based on a signal of pollution, to maximize plant abatement. Using the experiment to identify key parameters of the model, we find that the regulator aggressively targets its discretionary inspections, to the degree that half of the plants receive fewer than one inspection per year, while plants expected to be the dirtiest may receive ten. Counterfactual simulations show that discretion in targeting helps enforcement: inspections that the regulator assigns cause three times more abatement than would the same number of randomly assigned inspections. Nonetheless, we find that the regulator's information on plant pollution is poor, and improvements in monitoring would reduce emissions.
Duflo, Esther, Michael Greenstone, Rohini Pande, and Nicholas Ryan. "The Value of Regulatory Discretion: Estimates from Environmental Inspections in India." Econometrica 86.6 (November 2018): 2123-2160.