Supervising skill information and violation of environmental regulations

ELSEVIER

Supervising Skill Information and Violation of Environmental Regulations

INc,-MA~E GRXN

The Beijer International Institute of Ecological Economics, The Royal Swedish Academy of Sciences, Stockholm, Sweden

E-mail: [email protected]

and

VEIJO KAn'ALA

Helsinki University of Technology, Espoo, Finland E-mail: [email protected]

Condi t ions u n d e r which an en fo rcemen t agency gains f rom disseminat ing informa- t ion on its skill on de tec t ing and convicting violators are analyzed. This seems to be a very attractive en fo rcemen t strategy because it can be accompl i shed at no or a very low cost. It is assumed that the en fo rcemen t agency signals when this implies ne t gains, which inc lude losses from violation, such as negative envi ronmenta l benefi ts and charge payments , and benefits f rom violation, i.e., expec ted fine payments of de tec ted violations. The analytical results show that the impact on violation and associated ne t gains depends on (i) in format ion strategies app l ied by the skillful and less skillful enforce- m e n t agency, respectively, and (ii) the re la t ion between losses and expec ted fine payments for violations. W h e n the informat ion strategy implies a relatively high (low) probabi l i ty for a skillful en fo rcemen t agency to reveal informat ion, d isseminat ion of in format ion genera tes ne t gains when the losses f rom violation exceed (are below) the expec ted fine incomes. An appl ica t ion to regula t ion on farmers ' manure t r ea tment practices in Got land shows that s ignaling may reduce or increase violation by abou t 25% d e p e n d i n g on the s ignal ing strategies. The calcula ted ne t benefits, the difference in env i ronmenta l benefits, and expec ted fine payments, are, however, small in magn i tude u n d e r all s ignal ing strategies. © 1997 by Elsevier Science Inc.

I. Introduction

In Sweden, almost one ha l f of the firms do no t have the requ i red permiss ion for thei r opera t ion [Swedish Envi ronmenta l Protec t ion Agency (1993)]. The si tuation is even worse for firms that have a duty to r epor t their opera t ion . Slightly more than 50% of the total 66,000 firms do no t make the reports Swedish envi ronmenta l legislation requires

International Review of Law and Economics 17:395-407, 1997 © 1997 by Elsevier Science Inc. 655 Avenue of the Americas, New York, NY 10010

0144-8188/97/$17.00 PII S0144-8188 (97) 00016-1

396 Skill information and environmental regulations

them to do. Regional and local supervising agents are responsible for the supervision of more than 90% of the regulated firms. One reason for the high degree of violation may be the limited enforcement options available for the supervising agents on the local and regional levels. Because they do not have the legal authority to change the level of the penalties, improvement of compliance must be obtained in other ways. One possibility is to increase supervision. Another option is to influence the behavior of firms by disseminating information on the Agency's skill in detecting and convicting violators. A great advantage of the latter option is that it can be accomplished at a zero, or almost zero, cost. Thus, this enforcement option is likely to be adapted by an enforcement agency if the net benefits f rom changes in violations are positive. The purpose of this paper is to analyze the impacts on violation and the net benefits from the use of information as an enforcement tool. The analysis is applied to the current enforcement of changed manure practices in Gotland, a Swedish island in the Baltic Sea where the content of nitrate in g round water is high.

The literature on the economics of crime can be traced to Becker (1968) and Stigler (1970). Application to environmental economics usually implies analysis of regulated firms' reactions to changes in the detection rate and /o r the level of the fine; see e.g., Harford (1978, 1987), Storey and McCabe (1980), Lee (1984), and Beavis and Dobbs (1987). The comparison of violation profits under various environmental regulatory schemes has been carried out by Malik (1990) and Andr6asson-Gren (1992). Monitor- ing and enforcement have been recognized as a game between the environmental agency and the polluters by, among others, Russei (1990) and Guth and Pethig (1992). To our knowledge, dissemination of supervising skill information as an option for enforcing environmental regulations has not been analyzed before. There is, however, a large amount of literature in other fields of economics on the impact of information on human economic behavior [see e.g., Boyer and Kihlstrom (1984); Fudenberg and Tirole (1992) ].

The paper is organized as follows. First, we present models of the behavior of the enforcement agency and of the regulated firms, respectively. In Section III, the analysis is applied to the regulation of farmers' manure practices in Gotland, a Swedish island in the Baltic Sea. The paper ends with a brief summary.

II. The Model

The impact on violation of an enforcement agency's dissemination of information is regarded as a simple dynamic game. The game is played in two stages during which each player, the enforcement agency and the regulated firm, acts once. The enforcement agency acts by disseminating information, or signaling, its skill or by not signaling. Given the action by the supervising agent, the regulated firm reacts by modifying his or her prior expectations of the skill of the agent into updated posterior probabilities. The firm then makes its decision on the level of violation based on the updated probabilities.

An important assumption underlying the analysis is that the regulated firms are assumed to be uncertain about the ability of the enforcement agency's to detect violation. This is, however, not plausible when the regulated firms have experiences with the supervising agent because then the skill of the agent is revealed by her or his actions. Such interaction usually takes place when relatively large point sources, which are subjected to frequent control, are regulated. Continuous uncertainty about the skill of the supervising agent is more likely to occur when nonpo in t sources, such as agricultural pollution are regulated. Then, because there are many small firms, only a

I.-M. GREN AND V. KAITALA 397

fraction o f all firms is subjected to control. Further, in Sweden, there is a r andom choice of firms every year, which implies that a mutual unders tanding of the firms and the enforcement agency is usually not built up. Due to these factors, we believe that the assumption of uncertainty of the agency's supervising skill is plausible for the regulation of a nonpo in t source. The following analysis should therefore be regarded as applicable to the continuous regulation of nonpo in t sources.

The type o f signaling suggested here can also be regarded as a temporary enforcement tool for regulating point sources under changing circumstances, which have an impact on the enforcement agency's supervising capacity. Examples of such changes in Sweden are policies aimed at decreasing budget deficits. The regulated firms cannot be sure about how these reductions in resources are allocated between different public activities. Changes in the current environmental law provide another example of temporary uncertainty. The legislation being considered will change the regulation o f individual firms to environmental quality standards. It is, however, currently unclear how this will influence the supervising routines. A third example is provided by orga- nizational changes. In 1991, a change was made from a previously nationally determined design of the organization of the supervision of environmental regulations to regional and local serf-determination. It is most likely that uncertainty concerning the skill of the enforcement agency prevails dur ing a period of transition. During this period, the dissemination of supervising skill information as an enforcement option, can probably be used only once in the case of point sources. Then, the firms learn about the skill o f the agency.

The enforcement agency

Assume that the enforcement agency can be either an expert (E) or a nonexper t (N), who differ only in their capacity to detect a n d / o r convict violators. The differences are assumed to be reflected in the c o m p o u n d e d probabilities for detection and conviction, q and z, respectively. These probabilities are assumed to depend on the level of violation, q = q(V) and z = z(V). However, detection and conviction of violators cannot be carried out without any costs. There is thus a cost associated with each level of q and z, respectively. We now define the difference between the expert and the nonexper t as, at a given detection and conviction cost, z(V) < q(V). The enforcement agency's decision problem is then formulated as if, at a given detection and conviction cost, she or he should disseminate information on the supervising skill.

We may think of several circumstances when an enforcement agency is interested in disseminating information on its skill. As ment ioned above, when regulating nonpo in t source pollution, there are in general no continuous contacts with all firms. The skillful agency, the expert, might then be interested in disseminating information on its supervising and convicting capacity. Another case is provided by improved moni tor ing techniques, which may enable a nonexper t agency to obtain the competence of an expert agency. The question is then whether the agency should announce its increased competence. This question is addressed in the application in Section III.

The expert or the nonexper t acts by signaling, S, or not signaling, S, where the signaling implies an expert competence of the enforcement agency. The signaling opt ion of the nonexper t may then seem questionable, because we would not expect a public body to consciously disseminate false information. The nonexper t may, however, make wrongjudglnents concerning the capacity of, say, new moni tor ing techniques. We therefore assign a positive probability to the occurrence of a nonexper t signal.


Formally, the strategy of the regulatory agent is defined as a probability distribution 0r~:( • 10), over actions S and S. The strategy probabilities are defined for each type 0 = E or N. The probability that the expert signals is denoted by s, where 0 < s ~< 1. The corresponding probability of the nonexper t is r, where 0 < r ~< 1. Thus, we have

, , ,~( s l e ) = s,

~ S l N ) = r,

, r , ~ I ~ l N ) = 1 - r, (1)

The strategies of the expert and nonexpert , respectively, depend on the net benefits f rom signaling and not signaling. We presume that the cost of disseminating information is negligible. The regulatory agent is then assumed to receive two types of incomes; incomes from complying firms and incomes from fines of detected violating firms. Incomes from complying firms may consist of budget resources related to detected violation. If a charge system is implemented, the agency may receive income from complying firms' charge payments. Other types of income are associated with the environmental improvements of reductions in pollutants. These incomes are written as a function of violation, B(V), where B,, < 0. The incomes of fine payments are determined by the given detection and conviction probability functions, q(10 or z(10, and the level of the fine, F( 10, which is assumed to increase with the level of violation. This is in accordance with the Swedish Environmental Legislation, which envisages that the fine shall be determined by the "severity" of the crime. Severity is here interpreted as the level of violation. The benefits from signaling of the expert, B(S), are then written a s

B(S) = B(V(S)) + q(V(S))(V(S)) (2)

where V(S) is the level of violation when the firms react to the signal (see the second part of Section II). The expert 's benefits from not signaling, are written as

B(~) = B(V(3) ) + q(V(-S))F(V(-S)) . (3)

Note that the nonexper t ' s benefits f rom signaling and nonsignaling are the same as equations (2) and (3) except that q is replaced by z. The enforcement agency then signals when B(S) exceeds B(S), which gives the condit ion for the expert 's signaling as

B(V(S)) + q(V(S))F(V(S)) > B(V(-S)) + q(V(-S))F(V(-S)). (4)

This condit ion holds when

v(s) < ( > ) v(?s), ~ v(s)) - B(V(S)) > ( < )q(~?S)) ~(V(S)) - q(V(S))/~V(S)). (5)

Conditions for a nonexper t signaling are obtained by replacing q(V) with z(V). Accord- ing to equation (5), the expert or nonexper t signals when the level of violation is reduced and the associated benefits f rom increased compliance are higher than the losses f rom decreases in expected fine payments. If the signaling implies an increase in the level of violation, there are net gains when the increased expected fine payments exceed the losses of benefits f rom reduced compliance.

I.-M. GREN Mr) V. KarraLa 399

The F/rm

The regulated firm's level o f violation is here assumed to be determined by associated expected violation profits, which are defined as violation gains less expected violation costs. Gains from violating a regulation scheme may consist of avoided expenses for cleaning technologies and, under a charge system, evaded charge payments. The violation cost constitutes expenses for undertaking violation activities, eventual losses associated with a detrimental reputation as an environmental scoundrel, and fine payments in case of detection and conviction. In the following, we consider only the cost of the expected fine, because the inclusion of o ther violation costs would not change the analytical results. The violation cost then includes the fine, F(V) where Fv > 0, which the firm incurs if detected and convicted.

The profits when violation is not detected, ~r 1, is written simply as the benefits, G(V), according to equation (6).

"h " l = G ( V ) . ( 6 )

The profits when detected and convicted, 7r 2, are formulated as

"IT 2 = q'r 1 -- F ( V ) . ( 7 )

The firm, which is assumed to be risk neutral, then chooses the level of violation such that expected profits in the states of a successful violation and nonsuccess are maxi- mized. The expected violation profits under the expert and nonexper t regimes, 7r ~: and 7r N, respectively, are thus

~ E = (1 _ q(V)),tr 1 + q(V),tr2

TI " N = (1 -- z(V)) ' / ] "1 + z(V)'/ 'i "2. (8 )

The corresponding first-order conditions for the choices of violation level that maxi- mize the expected violation profits are

Gv = qFv + q~F,

C v = zF v + Zv F. (9)

Subscripts denote partial derivatives. The firm maximizing expected profits violates as long as it is less costly than it is to comply, i.e., when the marginal violation gain exceeds the marginal violation cost. When qFv + qvF > zFv + zvF, the optimal violation under the expert, V E, is lower than under the nonexper t agent, V N. In the following it is assumed that this holds. It should be noted that this is an important assumption, which we will come back to at the end of this section.

Equation (9) defines the optimal violation under conditions when it is assumed that there is no inconsistency between the firm's prior perception o f the skill of the environmental agency and the actual skill. I f this is not the case, the information on supervising that the firm receives f rom the regulatory agent may modify her or his subjective prior expectation, PE and PN, respectively, leading to an updated posterior probability, ~. Thus, knowing the signaling strategy, ~r~, o f the regulatory agent (see the first part o f Section II), which we can assume at a perfect Bayesian equilibrium [see e.g., Fudenberg and Tirole (1991)], and observing action a ~ S, or S, the firm can use Bayes' rule for updat ing her or his prior probabilities, JOE and PN into updated posterior probabilities ~(01am0, where 0 = E or N.


In a perfect Bayesian equilibrium the firm maximizes its profits condit ional on ap~ (an~: = S or S). Thus, the condit ional violation profits of the firm are de te rmined by the solution to equat ion (10)

"tr(aRE, O) = t.z(E[alCE)~E + ~L(J~aRE)'IT N (10)

where I~ (Liana:) and tx(A~ap~-) are the posterior probabilities of the type of the regulatory agent given an action a ~ .

The violation levels under signaling and nonsignaling, V(S) and V(S), respectively, are found by compar ing the condit ional violation profits under these cases, ~r s and w s, which are written as

~ = ~(EIS)~E + ~(NS)~ N

~ = ~ ( E I ~ ) ~ ~: + ~ ( M ~ ) ~ N (11)

From equation (9) we assume that ~ : < ~N, which implies that V E < V N Simila_rly, the violation level under signaling is lower than under nonsignaling when "rr s < ~r s. From equation (11) we then have that

V(S) <( > )V(S) when I~(EIS) > ( < )I~(E[S). (12)

According to equat ion (12), the violation level under signaling is lower than under nonsignaling when the updated probability for the en fo rcement agency being an exper t under signaling is h igher than the corresponding probability under conditions of nonsignaling.

In the Appendix it is shown that

V(S) <( > )V(S) when _r < ( > )1. (13) S

Thus, according to equat ion (13), the impact on violation f rom signaling is dependen t on the relation between the signaling strategies of the exper t and nonexper t , respectively. Violation decreases when the probability of the expert ' s signal exceeds the corresponding probabili ty of the nonexper t . The en fo rcement agency then signals if the associated increased benefits f rom compliance are higher than the reduced benefits f rom expected fine payments (see equat ion (5) in the first section of Section II). When instead r > s, violation increases as a result of signaling. A signal then occurs if the increased expected fine payments exceed the reductions in benefits f rom reduced compliance. Note that signaling always implies a decrease in violation when the probability of the nonexper t to signal is equal to zero. An exper t then signals when the associated benefits f rom increased compliance exceed the decreases in expected fine payments.

It should be noted that the assumption f rom equat ion (9) that qF v + qv F > zF v + Zv F plays an impor tan t role for the result in equat ion (13). If instead the reverse holds, i.e., when the marginal expected fine payments are higher under the nonexper t than unde r the expert, similar analysis can be used to show that the result as expressed in equat ion (13) is reversed so that V(S) > ( < ) V(S) when (r/s) < ( > ) 1. If the marginal expected fine payments are the same under the exper t and the nonexper t , the violations levels are also the same in our model , and there is no impact f rom signaling on the level of violation.


III. An Example o f Manure Regulation in Gotland

In Sweden and in several o the r countr ies the depos i t ion of manure on bare arable l and is one impor t an t reason for increased concent ra t ions of ni t rate in g round water and eu t roph ica t ion o f coastal waters. High concent ra t ions of ni t rate in g r o u n d water may imply h igher risk for cancer and also cause oxygen deficits in the b lood of infants. In eu t roph ica ted coastal water, the f requency of toxic algae b looms is relatively high, and oxygen deficits occur at the sea bottoms. The la t ter may lead to total des t ruct ion of sea-bot tom biological life. Therefore , regulat ions on the t r ea tment of manure have been in t roduced in the southern parts o f Sweden. The regula t ion is in the form of a c o m m a n d and cont ro l system where the farmers are obl iged to increase the capacity to store m a n u r e dur ing Au tumn and Winter . This will result in a lower deposi t ion of manure n i t rogen on the land at this t ime of the year when leaching is high due to lack of crops assimilating the nutr ient . The reason for choosing Got land as an appl ica t ion object is the existence of hydrological models relat ing the depos i t ion of manure n i t rogen to the g r o u n d water quality. This enables us to calculate envi ronmenta l benefi ts f rom changes in the compl iance level.

The annual total use of manure n i t rogen in Got land amounts to approximate ly 2700 tons of N [Gren et al. (1995)]. Ha l f of this a m o u n t was spread on bare g round before the regulat ion, and is thus subjected to direct ions on manure storage dur ing Au tumn and Winter . The average annual cost of s tor ing manure amounts to SEK 20,000 per ton [Gren et al. (1995)], which totals to approximate ly 27 mill ions of SEK (1 U.S. dol lar = 6.60 SEK as of May 2, 1996). This cor responds to an approx imate 10% decrease in agr icul tural incomes. Thus, the incentives for violating regulatory actions are expec ted to be strong. The economic motivat ion for noncompl i ance depends , however, not only on the income losses f rom compl iance but also on expec ted costs f rom noncompl iance , which are d e t e r m i n e d by the Got land en fo rcemen t agency's supervising skill and the fine payments.

The en fo rcemen t agency's ne t gains of manure regula t ion consist of expec ted fine payments of violat ing farmers and envi ronmenta l benefits from complying farmers. The fine payments are d e t e r m i n e d by the c o m p o u n d e d probabi l i ty of de tec t ion and conviction and the fine. In Sweden, supervision of compl iance is carr ied out by visits to a sample of regula ted firms. Given that a firm violates, the probabi l i ty of be ing de tec ted is thus d e t e r m i n e d by the probabi l i ty of be ing inc luded in the sample and by the share of violat ing firms in the sample. This probabi l i ty can be calculated if we have informa- t ion on the n u m b e r of violating firms in the popu la t ion and in the sample. Because informat ion on the fo rmer is no t available, it is assumed that the share of violators in the sample is the same as in the popula t ion . The de tec t ion probabi l i ty can then be calcula ted as the relat ion between the sample size and the popu la t ion size.

In Got land, the en fo rcemen t agency visits about 0.25 of the farmers every year. It is thus assumed that the cur ren t de tec t ion rate is 0.25. On average, 1 /10 of de tec ted firms are convicted and have to make fine payments [Swedish Envi ronmenta l Protect ion Agency (1993)]. The cu r ren t c o m p o u n d e d probabi l i ty of de tec t ion and conviction is thus 0.025. Unfor tunately , we have not been able to make any inferences on the relat ion between the level o f violation and the c o m p o u n d e d probabil i ty. Therefore , we assume it is constant .

The fines app l ied in the case o f de tec t ion and conviction are regula ted by the Swedish envi ronmenta l p ro tec t ion legislation. Accord ing to this legislation, a pol lu t ing firm has to pay an envi ronmenta l p ro tec t ion fine, which is equal to the profits enjoyed from


violating the law. We in te rpre t this pr inciple such that the violation profits are equal to the cost of the avoided costs of manure tanks, i.e., SEK 20,000 per ton of manure ni t rogen. In addi t ion, the pol lu t ing firm has to pay an extra fine that is p ropor t iona l to the income and "severity" of crime. No clear way seems to exist for re la t ing the extra fine with the severity of crime. Therefore we simply assume that this par t of the violation cost is quadrat ic in the level of violation.

The value of the coefficient in the quadrat ic fine funct ion is found by applying the actual violation level to cur ren t regulat ions on the storage of manure . In the south of Sweden, about 60% of the small regula ted firms comply with cur ren t regulat ions [Swedish Envi ronment Protect ion Agency (1993)]. It is assumed that this n u m b e r is appl icable to farmers ' compl iance of regulat ions on storage of manure in Gotland, which gives a violation level of 810 tons of ni t rogen. The constant marginal cost of compl iance amounts to SEK 20,000 per ton of ni t rogen. Assuming that the marginal cost of compliance, i.e., the marginal benef i t f rom violation, equals the expec ted marginal cost of violation at a 60% violation rate, the es t imated quadrat ic fine function, F(V), is

F(V) = (20 ,000 + (482V)) V. (14)

As men t ioned in the in t roduc t ion of this section, the envi ronmenta l benefits associated with reduct ions in the depos i t ion of manure n i t rogen are improved g round water quality and r educed eu t roph ica ted coastal waters. To compare these benefits with fine payments in equat ion (14), the envi ronmenta l benefits, decreases in concent ra t ion of ni trate in g round water, and r educed eu t roph ica ted coastal waters, must be re la ted to the amoun t of manure ni t rogen. This is to a large extent a natural science issue. In the case of Got land there is a hydrological mode l relat ing the depos i t ion of n i t rogen on land to the concent ra t ion of ni t rate in g round water [Spiller (1978)]. In Gren (1995) this hydrological mode l was used to relate the value of g round water quality to n i t rogen deposi t ion on land. Results f rom a study of the willingness to pay for a ni t rate conten t of no less than 50 mg of N/1 in g round water were then used [Silvander (1991)]. Accord ing to the results, the value is SEK 2700 pe r ton. This value will be used in this simple example.

Measurements of envi ronmenta l benefi ts f rom reduced eu t rophica t ion are also ob- ta ined from results o f ano the r study. In Gren et al. (1996) willingness to pay (WTP) for a restorat ion of the Baltic Sea to the condi t ions prevail ing 1950s were est imated. In this t ime per iod, the Baltic Sea did not show any signs of dead sea bot toms and the f requency of toxic algae b looms was very low. Two types of values were est imated: recreat ional values and so-called existence values. Existence values, which can be t raced back to Krutilla (1967), refer to the value people assign an envi ronmenta l resource without using or a iming at using the resource in question. The results indicate a value of at least 31 bi l l ion SEK per year. Abou t 90% of this constitutes existence value.

To relate this value to the n i t rogen load changes in Gotland, very s imple assumptions have to be made. One is that the value pe r ton of n i t rogen load to the Baltic Sea is constant and the same regardless of locat ion of the load. This is definitely no t true because the capacities to filter nutr ients are very different between coastal lines. How- ever, because there is no informat ion on the fi l tering capacity of different coasts, we must rely on the simple assumption. The total load of n i t rogen to the Baltic Sea amounts to approximate ly 1,100,000 tons. Given that a reduc t ion by 50% will genera te


the condi t ions prevai l ing in the 1950s the average mone ta ry value amounts to SEK 59,000 pe r ton of reduc t ion to the coast•

However, to f ind the share of the manure n i t rogen reduct ion that reaches the coast we have to apply the above-ment ioned hydrological model . Accord ing to this model , 0.7 o f the n i t rogen depos i t ion does no t infil trate to g round water and is thus a potent ia l for en te r ing the coastal waters su r round ing Gotland. Not all o f this potent ia l will load into the coastal waters, inasmuch as par t of it is t ransformed into n i t rogen gas. Accord ing to the Swedish Envi ronmenta l Protec t ion Agency (1991), about 0.6 o f the n i t rogen enter- ing surface waters finally enters coastal waters in the South of Sweden. The moneta ry value of r educed eu t rophica t ion f rom a 1-ton reduc t ion of manure n i t rogen thus amounts to SEK 35,200.

Assuming that a 1-ton manure n i t rogen reduc t ion implies a 0.3-ton reduct ion to the g r o u n d water and 0.7-ton reduc t ion to the coastal waters, the value of a I- ton change in compl iance amounts to 25,450 SEK. The en fo rcemen t agency's ne t benefits from signaling and nons ignal ing can then be writ ten as

B(S) = q(20000 + 482V(S) - 24540)V(S)

B(S) = q(20000 + 482V(S) - 24540)V(S). (15)

The profi t -maximizing levels of violation, V(S) and V(S), are de t e rmine d by the upda ted probabi l i t ies and

• r e = (20000 - q(20000 + 482V))V

~ u = (20000 -- z(20000 + 482V))V. (16)

We now assume that the en fo rcemen t agency in Got land has been successful in increas- ing its ability to de tec t violators. One reason can be the deve lopmen t o f improved mon i to r ing techniques. The adap t ion of these technologies is fur ther assumed to increase the c o m p o u n d e d probabi l i ty of de tec t ion and conviction f rom the cur ren t level of z = 0.025 to q = 0•035. The quest ion is then whe ther the agency should signal its increased competence . If s ignaling implies less violation, then if no signal occurs, the en fo rcemen t agency signals if the associated envi ronmenta l benefits are h igher than the losses f rom decreased expec ted fine payments.

To calculate violat ion levels u n d e r condi t ions of s ignaling and no signaling and to est imate associated costs and benefi ts o f the en fo rcemen t agency, assumptions are also r equ i red on the levels of s ignaling strategies, s and r, respectively, and on the pr ior probabil i t ies, pE and p U Because, accord ing to equat ion (5), the relat ion between s and r is crucial for the impac t of violat ion on opt imal violation, two alternative sets of values are used. In the first case s = 0.9 and r = 0.1 and in the second s = 0.2 and r = 0.8. F rom equat ion (5) we also know that, in the first case, violation u n d e r signaling is lower then when no signal occurs, and vice versa in the second case. Assuming that pE = pU = 0.5, the calculated violation levels, expec ted fine payments, and envi ronmenta l benefi ts are as p resen ted in Table 1.

• • 1 In the first case, the violation level decreases as a result of s lgnahng by about ~. There is, however, a small dif ference in changes in envi ronmenta l benefits, 5.4 mil l ion SEK, and expec ted fine payments, - 4 . 8 mil l ion SEK. The relative impacts on envi ronmenta l benefi ts and expec ted fine payments are of similar magn i tude in the second case when the signaling implies a h igher level of violation. Al though the impacts on violation levels are relatively high in bo th cases, ne t benefi ts show a small change. In our case o f the


TABLE 1. Violation levels, enforcement agency's benefits and costs under different signaling strategies when P~: = p N = 0

Violation Environmental benefits (tons of manure from violation Expected fine payments nitrogen~year) (millions of SEK/year) (millions of SEK/year)

s = 0.9, r = 0.1 Signaling 581 - 14.7 6.1 Nonsignaling 788 - 20.1 10.9 Signaling minus nonsignaling -207 5.4 -4.8

s=0.2 , r = 0 . 7 Signaling 760 - 19.3 10.1 Nonsignaling 627 - 15.9 7.0 Signaling minus nonsignaling 133 -3.4 3.1

regulat ion of manure in Gotland, the en fo rcement agency is then relatively indetermi- nate whether or not to disseminate informat ion on its skill.

IV. Summary

The main purpose of this pape r was to analyze whether an en fo rcemen t agency uses disseminat ion of informat ion on its professional skill as an en fo rcemen t tool. For this purpose, a simple signaling game analysis was appl ied. Two types of envi ronmenta l agents were then assumed, the exper t and the nonexper t , which differ with respect to their ability to de tec t and convict violation. The game was divided into two stages: First the en fo rcemen t agency acts by signaling or no t signaling, and then regula ted firms react by choosing their opt imal violation level based on upda t e d pr io r probabi l i t ies of the agency be ing an exper t or nonexper t .

According to the analytical results, whether or not the en fo rcemen t agency signals depends on the relat ion between the exper t ' s and nonexpe r t ' s s ignaling strategies and on the benefits and costs of violation. When the signaling strategy of the exper t is h igher than that of the nonexper t , s ignaling leads to a decreased level of violation. The enforcement agency then signals if the benefits f rom increased compl iance , or costs of decreased violation, exceed the change in expected fine payments. If, instead, the signaling strategy of the nonexpe r t is highest, violation increases as a result f rom signaling. Signaling then occurs when the increase in expec ted fine payments exceeds the decrease in benefits from compliance.

The analysis was app l ied to the regula t ion of manure practices on the Swedish island Gotland. On this island, as in many o ther areas, n i t rogen loads cause a high concen- trat ion of ni t rate in g round water and eu t roph ica ted coastal waters. A scenario was assumed where the en fo rcement agency's skill is increased due to improved mon i to r ing technology. The quest ion posed was whether the en fo rcement agency should announce its increased supervising competence . Net benefits from signaling and nonsignal ing, respectively, inc luded values from improved water qualit ies and expec ted fine payments. The example showed that the level of violation increases or decreases by about 25%, d e p e n d i n g on the signaling strategies appl ied by the exper t and nonexper t , respectively. Despite this considerable impact on violation, the differences in the en fo rcement agency's ne t gains are relatively small. The agency is thus more or less inde te rmina te whether or not to signal. It should be noted, however, that, in a social


perspective, it is always beneficial to signal when this leads to a reduced level of violation, because fine payments can be regarded as transfer payments.

The application to Gotland also clearly demonstrates the need for data when making numerical calculations o f whether or not to signal. First, we must unders tand and quantify the enforcement agency's behavior with respect to detecting and convicting violators. When conviction occurs, there is also a need for quantifying the practices of the courts for determining the fine payments. Unfortunately, these types of data could not be obtained for Gotland. The second difficult issue is to quantify the regulated firms' violation behavior. In general we would not expect that only economic factors influence the violation decision, which we have assumed. The third challenge is to relate the level of violation to environmental impacts, water quality in our example. This requires an unders tanding of transports of pollutants in different environmental media, such as air and water, associated biological effects, and impacts on human welfare.

Obviously, a l though relatively simple in our theoretical model, an appropriate numerical determination of whether or not to signal requires interdisciplinary research including scientists f rom several disciplines, such as law, political science, and the natural sciences. Admittedly, such an interdisciplinary research would also influence not only the numerical calculations, but also our suggested theoretical models of the enforcement agency's and firm's behavior. A strong simplification is made by including only economic motives in their decisions.

Appendix: Posterior Probabilities and the Level of Violation

The analysis of the updated, posterior, probabilities depends on the game theory setting of the problem. As ment ioned above, it is assumed that the firm uses the Baeysian rule to update the prior beliefs into updated after the environmental agent has acted by signaling or not signaling. Assume then that p~: < 1. The Bayesian updat ing rule defines the posterior probabilities as follows

poo'(a~40) p~(Olae~:) p,~(am~lE) + p~(a~lN) (A1)

if

pL~(a,~tE) + pm(a,~4a0 > 0,

and tx(0]am) is any probability distribution if

(A2)

p~( a~]E) + pMr( amc~lN) = 0, (A3)

Assuming that p~(a~[E) + p~ (a~N ) > 0 for am~: = S, S we have the following posterior probabilities when the firm observes a signal S

sp ~: (A4) Ix(E]S) - spE + rp N'

ypN (A5) ].L( ]~ S) -- spI'2 "~ I.pN.

The corresponding updated probabilities when no signal occurs are


iz(El~ ) = (1 - s ) P ~: (A6) (1 - s)PE + (1 - r) P N'

~ ( ] ~ ) = (1 -- r )P N

(1 - s ) P E + (1 -- r) P N" (A7)

I n s e r t i n g t he u p d a t e d p r o b a b i l i t i e s i n t o e q u a t i o n (12) in t h e s e c o n d p a r t o f Sec t i on II, we have t h a t

V(S) < ( > ) V(S) w h e n s P ~: (1 - s) pE

sPE + rP N > ( < ) (1 - s)PE + (1 -- r )P N' (A8)

w h i c h gives

V(S) < ( > ) V ( S ) w h e n _r < ( > ) 1 . (A9) S

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Documents

Supervising skill information and violation of environmental regulations