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relatively similar to those reported in the literature. Deily and Gray (2007) found that the deterrence effect of any enforcement action on the probability of compliance was approximately 32 percent. As noted above, results in Table 1B for inspections reveal deterrence effects of inspections on the probability of compliance of approximately 27 percent. Deterrence effects seem statistically and practically similar to published deterrence effects.

It would be desirable to benchmark the specific deterrence models for continuous emissions as well as discrete compliance status indicators. However, this is not possible since the Gray and co-author papers in the scientific literature that examine the deterrence effect of monitoring and enforcement on the emissions of sanctioned or inspected facilities use confidential data from non-EPA databases. However, there is no reason to suspect, a priori, that the proposed models for these investigations are not properly calibrated since the basic statistical approach is similar in both the discrete and continuous cases. For example, the explanatory variables in each case are identical. Further, as will be discussed in the next section, the general deterrence benchmarking yields similar results when applied to both discrete compliance status indicators and continuous pollution discharge measures.

Benchmarking General Deterrence Methods

Our benchmark dataset for the general deterrence measurement methods is a pulp and paper industry dataset of 251 major mills for the period 1990-2004. Like the steel industry, the pulp and paper industry is characterized by large industrial sources with relatively similar production processes and pollution treatment technologies across facilities. Sources, however, may be geographically diverse. Deterrence effects from this dataset were examined in Shimshack and Ward (2005) and Shimshack and Ward (2008). The key data source was the EPA’s Permit Compliance System, and both water non- compliance status and continuous water pollution discharges are examined.

Results from applying the simplified models for measuring general deterrence to discharges from the pulp and paper dataset are presented in Tables 2A and 2B. All of the models show a statistically significant general deterrence effect of lagged enforcement actions. The estimated impact of a fine on another plant in the state on the ratio of actual to permitted discharges (the dependent variable) is negative and strongly significant for both biochemical oxygen demand (BOD) and suspended solids (TSS). The magnitudes are extremely consistent across models and practically meaningful. For the BOD discharges examined in Table 2A, the average discharge ratio declines approximately 0.022 in the year following a fine.11 Given the overall mean discharge ratio, this

11 Here, we explore the impact of a regulatory action in the past 1-12 months. We choose this time frame to most closely replicate the analyses in the relevant published studies. Further, Shimshack and Ward [2008] found that facilities regularly updated their beliefs about regulatory stringency. At least for the studied pulp and paper sector, the regulator reputation effect underlying general deterrence begins to decay within one year after a fine for a water pollution violation. Within 2 years of a fine, general deterrence has decayed by more than 50 percent. The implication is that regulators must maintain a monitoring and enforcement presence to induce consistent environmental performance over time. However, the general deterrence decay does not render any given study for any given period obsolete. The key consideration is whether the


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