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Cleaner or Smarter?Strategic Compliance with Federal Drinking Water Regulations
Katrina Jessoe, Lori Bennear and Sheila Olmstead
Camp ResourcesAugust 7, 2008
This work is supported by the National Science Foundation, grant #SES064-8256.
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Outline Introduction and motivation
Policy background and research objectives
Theoretical model
Econometric model
Data
Preliminary results
Next steps
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Introduction and motivation
Piped, treated drinking water systems are constructed (now and historically) primarily to reduce exposure to bacterial contaminants.
In the U.S., the main federal regulation that achieves this purpose is the Total Coliform Rule (TCR), part of the Safe Drinking Water Act.
The TCR: is applicable to 54,000 U.S. community water systems serving
approximately 264 million people establishes the legal limit for the presence of coliform bacteria in
regulated drinking water systems, as well as sampling protocols
We examine the possibility that water suppliers engage in behavior that reduces the probability of a TCR violation, but may increase the risk of public exposure to bacterial contaminants.
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Policy background
The TCR defines a routine monthly sampling protocol based on system size.
State enforcement agencies negotiate with firms to establish a routine monthly sampling plan, which may require firms to take more than the federal minimum number of samples.
The threshold for a violation varies with the sampling protocol:
Systems taking at least 40 samples per month violate the TCR more 5% of samples in a month test positive for coliform bacteria.
Systems taking less than 40 samples per month violate the TCR if they draw more than one positive sample in a month.
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Research design: We develop a theoretical model of water supplier cost
minimization that predicts the conditions under which firms are likely to engage in strategic compliance with the TCR.
The model generates a variety of empirically testable hypotheses about strategically motivated behavior. The results we will discuss today focus on “sampling out”.
“Sampling out”: Firms subject to the “5% rule” may intentionally increase their sample size, so as to reduce the probability of a TCR violation: In negotiating their sampling plan with state regulators In departing from their sampling plan when taking actual samples
Counterfactual: With the TCR currently under revision, what will happen to sampling out with changes in the cost of violations and the threshold for a violation?
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Theoretical model: probability of a TCR violation
: number of present samples Weather, chlorination, source, basin Coliform present or absent
Incentive to sample out Probability of a violation non-decreasing with firms not
in violation
ps
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Theoretical model: 5 percent rule
Marginal effect of an additional sample:
Additional sample present or absent:
Probability of a violation decreasing at increasing rate if:
0
t
p
s
s
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Theoretical model: 2P rule & 5% rule Effect of additional sample if
Probability of a violation non-decreasing
Effect of additional sample if jump to
Marginal firm incentive to oversample
40ts
40ts
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Theoretical model: cost minimization
Total cost to provide drinking water
Sampling, chlorination, fixed and violation costs Violation costs are set by the regulator
Cost of sampling : number of samples
Cost of chlorination Cl: quantity of disinfectant (Chlorine)
s Cl v fTC C C C C
tss spC ts
Cl ClC p Cl
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Theoretical model: cost minimization
Subject to
Oversample if benefit of sampling out exceeds benefit of no additional samples
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Theoretical model: conditions for sampling out
Firms oversample if move out of violation n samples to move out of violation
Positive probability of a positive sample
Binomial distribution Exogenous
Optimal stopping rule
Ps
s
t
p
)1Pr(
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Theoretical model: predictions Under certain conditions, voluntary oversampling can
reduce the probability of a TCR violation. Predict oversampling will occur for certain firms Predict sampling out will reduce MCL violations
Conditional on ps, Cv, and P, predict an optimal stopping rule for strategic behavior If , little evidence for oversampling If , oversampling is increasing in n
Sampling out benefits 5% rule firms. If no violation or 2P rule, no incentive to oversample
nn nn
n
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Two strategic behavior variables Fed deviation
Equal to the difference between a firm’s sampling plan (result of negotiation between regulators and firms) and the federal minimum.
If coefficient is positive, regulators succeed in getting better information about presence of contaminants.
If coefficient is negative: Firms succeed in adding violation-reducing samples (from “less dirty” sites,
etc.); or Regulatory oversight succeeds in identifying violation potential, firms adjust
disinfection and reduce violations.
Oversample Indicator if the difference between a firm’s actual samples, its
sampling plan + required repeat samples is greater than 0 Sampling out for five percent rule firms should reduce violations if
firms are acting strategically (coefficient should be negative)
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Econometric model: sampling out Linear panel data models with supplier fixed effects:
Observation: supplier month year
Dependent variable: MCL violation - acute and monthly MCL violations related to the TCR
Independent variables: Percent rule lagged - at least 40 coliform samples in previous month CCR mail - water suppliers required to mail CCR to customers Summer - if observation occurs during summer months Any present coliform – observed present coliform sample in month Year dummies Interaction terms
Oversample percent Oversample percent present coliform
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Data DEP data
Panel of 520 MA community water suppliers, 1996-2003 TCR violations & all bacterial violations Federal minimum samples, sampling plans and actual samples At least one coliform sample tested positive in a sampling period
Regional data – subset of DEP data Panel of 216 MA community water suppliers, 1993-2003 Number of present coliform samples in a given month
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Data: summary statisticsVariable Observations Mean Std. Dev. Min Max
MCL violation 13970 .0057 .076 0 2
MCL violation DEP 55750 .013 .116 0 2
Fed deviation 13695 .570 4.98 -25 90
Percent rule lag 13542 .246 .431 0 1
Oversample 13837 .522 .499 0 1
Any present coliform 13970 .036 .187 0 1
CCR mail 13970 .358 .479 0 1
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Preliminary results: are water suppliers sampling out? Quantitative
Qualitative
2P rule suppliers
No predicted MCL violations
Predicted MCL violations
No MCL violations
46,686 0
MCL violations 25 444
5% rule suppliers
No predicted MCL violations
Predicted MCL violations
No MCL violations 7,808
553
MCL violations 11 227
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Table 1:
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Preliminary results: summary
Is sampling out occurring? Quantitative and qualitative evidence for 5% rule firms
Do firms avoid violations by sampling out? Reduces violations for firms in 5% rule Reduces violations for firms in 5% rule with present
coliform sample Jointly significant with subset of population
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Next steps
Backing out optimal stopping rule
Policy counterfactuals
Extend model to DEP data Imputed measure of strategic behavior