techniques account for these omitted factors in our simplified models. State indicator variables capture community and regulatory differences across states. Year indicator variables capture common technological change, sector maturation, and economic fluctuations over time. Panel data statistical techniques capture systematic plant characteristics like age, capacity, industrial sub-category, and profitability. The key assumption underlying the statistical validity of the “no outside data” simplification is that technical change is relatively modest, regulations are fairly static, and managerial attitudes are not evolving rapidly for most facilities over the sample period. My subjective assessment is that broad conclusions are likely applicable on the scale of a decade or so for many industries, but unlikely applicable for multiple decade periods.
Key Simplification and Justification 2: Sophisticated econometric prediction techniques meant to minimize the possibility of “reverse causality” are replaced with an analysis that includes panel-data statistical techniques and lagged monitoring and enforcement variables. The statistical techniques and lagged explanatory variable specifications used here still attempt to isolate causality and minimize statistical bias. The reverse causality concern is that plants with higher emissions or frequent non-compliance are often targeted for inspections and enforcement actions, and therefore regression models may show a positive correlation between enforcement and emissions/non-compliance. If present this reverse causality erroneously suggests that inspections and sanctions may increase emissions. Panel data statistical techniques (fixed effects, random effects, conditional random effects), however, at least partially remove cross-plant differences in overall enforcement. Further, lagged monitoring and enforcement variables should only reflect factors operating in the past, so these variables should not depend (theoretically) on the current level of environmental performance.
Simplified Methods for Measuring General Deterrence
The state-of-science white paper prepared for Task 1 of this ORD/OECA deterrence research project reveals that monitoring and enforcement spills over to deter violations at facilities beyond the sanctioned entity. Environmental facilities learn from the experiences of their neighbors, and this learning impacts compliance behavior. The environmental regulation literature indicates that inspections and enforcement actions consistently produce significant spillover effects on non-sanctioned facilities. Focusing on deterrence effects at the sanctioned facility alone may seriously underestimate the efficacy of fines and other sanctions.
The Task 1 state-of-science white paper therefore recommended that OECA should consider closely replicating statistical database analyses for measuring the general deterrence effects of monitoring and enforcement. This section presents simplified, cost- effective quantitative methods for this purpose. In this context, general deterrence refers to the effects of regulatory actions aimed at one facility on the environmental performance of other similar facilities.