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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