1. 1 Cleaner or Smarter? Strategic Compliance with Federal Drinking Water Regulations Katrina Jessoe, Lori Bennear and Sheila Olmstead Camp Resources August 7, 2008 This work is supported by the National Science Foundation, grant #SES064-8256.

2. 2 Outline Introduction and motivation Policy background and research objectives Theoretical model Econometric model Data Preliminary results Next steps

3. 3 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.

4. 4 Introduction and Motivation, cont. Violations of the TCR increase risks to public health:  Coliform bacteria can cause gastroenteritis.  Coliforms thrive in environments similar to those that support other, even more harmful pathogens (bacterial and viral). The health benefits of a single avoided TCR violation have been estimated at $91,000 to $36.5 million, depending on system size (Mancini 1997). 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.

5. 5 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.

6. 6 Policy background, cont. Compliance with the TCR standards is costly :  EPA estimates the annualized costs of compliance with the 1989 TCR at $210-230 million. If water suppliers make financially-motivated compliance decisions, the potential to reduce these costs may make noncompliance a tempting alternative.

7. 7 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?

8. 8 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

9. 9 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:

10. 10 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

11. 11 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)

12. 12 Theoretical model: cost minimization Subject to Oversample if benefit of sampling out exceeds benefit of no additional samples

13. 13 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

14. 14 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 p s, C v, 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

15. 15 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)

16. 16 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

17. 17 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

18. 18 Data: summary statistics Variable 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

19. 19 Preliminary results: are water suppliers sampling out? Quantitative Qualitative 2P rule suppliers No predicted MCL violations Predicted MCL violations No MCL violations 46,6860 MCL violations 25444 5% rule suppliers No predicted MCL violations Predicted MCL violations No MCL violations 7,808 553 MCL violations 11227

20. 20 Table 1:

21. 21 Table 2:

22. 22 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

23. 23 Next steps Backing out optimal stopping rule Policy counterfactuals Extend model to DEP data  Imputed measure of strategic behavior

24. 24 Estimation strategy: optimal stopping rule Identification  Probability of a positive coliform sample For a firm: ratio of observations with more than 5% positive coliform samples to total observations Estimate probability distribution of drawing a positive sample  Cost of a violation Use predicted probability to estimate cost of a violation