Segerson (1988) took a very different approach to the non-point problem than Griffin and Bromley (1982) and Shortle and Dunn (1986). Rather than monitoring the input choices of farmers who are suspected of contributing to environmental degradation, she proposed the use of an ambient-based tax that shifts monitoring from the source of emissions to the receptor. Segerson devised an ambient tax/subsidy scheme that could achieve the efficient outcome for non-point sources as a Nash equilibrium. The scheme pays firm-specific subsidies when the ambient pollution concentration falls below a target and charges firm-specific taxes when the ambient concentration exceeds the target.17
For simplicity and without loss of generality, consider the tax portion of this ambient-based incentive. A farm-specific ambient tax can be defined by tia + ki, where ti is the ambient tax rate for farm i and ki denotes a farm- specific lump-sum tax or subsidy. Each farm will choose input use to maximize
Environmental Instruments for Agriculture 49
expected after-tax profit, Vi=πi(xi)E{tia}ki. The first-order necessary conditions for an interior solution are
(12) Horan et al.(1998) found that the same problems that limit the cost- effectiveness of estimated runoff-based incentives limit the cost-effectiveness of ambient-based incentives – namely, an optimal tax rate ti will exist only when m= 1 or when the covariance between marginal damages and marginal ambient pollution, cov{λW(a*), a*/riri*/xij}, equals zero for all farms and for all inputs (e.g. as when W=E{a}, which, as described above, neglects variations in ambient pollution, which could be important). The intuition is the same as above. An ambient tax only provides firms with an incentive to choose input levels to control expected ambient levels. However, reductions in E{a} do not necessarily correspond to reductions in E{W}
when W is non-linear, because the variance and other distributional moments of a may be influenced by the actions that farms take to reduce E{a} (Shortle, 1990). If var{a} increases as E{a} is reduced, for example, then E{W} may increase. When only a single input influences runoff, the linear tax scheme optimally manages such risk effects.
Horan et al.(1998) (see also Hansen, 1998) identified two cost-effective ambient taxes that apply under less restrictive conditions. The first is the following linear and state-dependent (i.e. it is determined after the realization of all random variables) form:
(13) The tax rate is the marginal damage given the first-best choices. It is conditional on the realization of all random variables. This tax is applied uniformly to all farms. Ex ante, the expected tax faced by the farm is E{ta} =E{λ* W(a*)a}.
Alternatively, a first-best ambient tax could be defined as a non-linear function of ambient levels, Ti(a), where (see also Hansen, 1998)
(14) Therefore, in contrast to the linear ambient tax in equation (13), each farm pays an amount equal to total damages. As with the linear tax, the non-linear tax is state-dependent and applied uniformly across all farms.18
Entry and exit considerations are especially important with ambient taxes because ambient pollution, and hence the incentives generated by the tax, depend on the performance of all farms that contribute to the ambient pollution level. The ambient taxes defined by equations (13) and (14) do not ensure long-run efficiency, and so lump-sum taxes or subsidies, analogous to those derived for the input taxes, are required in addition to the
T ai
( )
=λ*W a( )
ti =λ*W a
( )
* ∀i∂
∂ − ∂
∂
∂
∂
= ∀ πi
ij i
i i
x E t a ij
r r
x 0 i j,
50 R.D. Horan and J.S. Shortle
ambient tax (see Horan et al., 1998, for a discussion of entry and exit considerations).
The ambient tax schemes presented above have, at first glance, substan- tial appeal compared with input tax schemes. Firstly, except for the lump-sum charges to induce optimal entry and exit, there is no need to devise farm-specific policies. Secondly, while we have not included point sources in our analysis, if point sources were also present in the region, an ambient tax would optimally coordinate control of point and agricultural sources without the need to develop and implement separate instruments for each type of source.
However, there are three important characteristics that may severely limit what ambient taxes can accomplish in practice. Firstly, producers are not rewarded or penalized according to their own performance. Rather, rewards and penalties depend on group performance. This makes polluters’
expectations (or conjectural variations) about the behaviour of other polluters, and the regulator’s knowledge of these expectations, critical to the actual design and performance of ambient-based instruments.
A second important feature is that ambient taxes shift the burden of information from regulators to producers. A producer’s response to an ambi- ent tax will depend on its own expectations about the impact of its choices, the choices of others and natural events on ambient conditions. In other words, it will depend on the polluter’s theory of fate and transportation.
However, given that the typical producer has limited technical information and capacity in this area, it is not obvious that this shift makes good economic sense. Cabe and Herriges (1992) and Horan et al.(1999b) explore the conse- quences of asymmetric prior information about transport and fate. If polluters’ prior information about transport and fate differs from that of the regulator, then incentives designed on the assumption that they are the same will not have the desired properties. For instance, a polluter may perceive no impact of its choices on pollution. In this case, an ambient tax may have either no impact, or it will result in a decision to escape the tax entirely by ending the suspect activity. The regulator has a choice between adjusting the incentives for the mismatch, educating the polluter, or a combination of both.
The data collection and programme design issues involved in systematically measuring mismatches, developing educational programmes that would effectively close them and adjusting incentives for their effects could be enor- mous. Similar concerns would apply to expectations about the behaviour of other polluters.
Thirdly, ambient incentives cannot produce a first-best outcome by them- selves when polluters are risk-averse (Horan et al., 1999b). The reason is that the tax cannot simultaneously manage environmental risk and the risk to producers that is created by the inherent randomness of the tax. Cost- effectiveness can be achieved in this situation only if the ambient tax is used in conjunction with input taxes to manage the additional risk.
Several other considerations deserve mention. Firstly, monitoring ambient conditions can be highly costly and subject to considerable error. This is illustrated
Environmental Instruments for Agriculture 51
by the uncertainty that exists about groundwater quality in many areas.
Secondly, changes in observed conditions may have little relationship to con- temporary actions. For instance, nitrates and pesticides may take years to move from fields to wells. Accordingly, incentives based on contemporary changes in ambient conditions may bear little relationship to current behaviour. In such cases, the incentives may do little to encourage improved performance. Finally, there is a capricious aspect to ambient-based taxes that would likely limit politi- cal and ethical acceptability. In particular, individuals who take costly actions to improve their environmental performance could find themselves subject to larger rather than smaller penalties, due to environmental shirking on the part of others, natural variations in pollution contributions from natural sources, or stochastic variations in weather. Conversely, individuals who behave badly may end up being rewarded by the good actions of their neighbours or nature.
These and other considerations led Weersink et al.(1998) to suggest that ambient taxes may be best suited to managing environmental problems in small watersheds in which agriculture is the only source, farms are relatively homogeneous, water quality is readily monitored and there are short time lags between polluting activities and their water quality impacts. We would add, especially until there is significant real or experimental evidence on how individuals respond to ambient incentives, that the scope of environmental problems should be limited. The theory has been developed for a single pollu- tant, and asks for fairly complex decisions in response to expectations about the pollutant. With multiple pollutants, particularly if they interact, the decision environment would be much more complex.
Liability rules
Liability represents another form of ambient-based incentive. Individuals who are damaged monetarily or otherwise by the activities of others may have the right to sue for damages in a court of law. If the suit is successful, the court may be guided in its compensation decision by a rule of law or precedent, known as a liability rule. Two important classes of liability rules are: (i) strict liability; and (ii) negligence. Under strict liability, polluters are held liable for full payment of any damages that occur. Under a negligence rule, polluters are only liable if they failed to act with the ‘due standard of care’ (Segerson, 1995). For example, a producer may not be found negligent (and hence liable) in contaminating groundwater with pesticides if the pesticide was applied in accordance with the manufacturer’s specification and the laws regarding application procedures.
The liability rule, while imposed ex post, serves as an ex anteincentive to deter producers from engaging in activities that may be damaging to others. If polluters feel that their production decisions may result in damages for which they may be held liable, then they must weigh the benefits from participating in pollution-related activities against any penalties they may expect to face as
52 R.D. Horan and J.S. Shortle
a result of their actions. Thus, liability rules are a form of ambient-based incentive in that they are imposed after ambient pollution and damages are realized (Shavell, 1987). However, liability rules differ from traditional ambi- ent-based incentives because they are only imposed if a suit is privately or publicly initiated, and if a court of law rules in favour of the damaged parties.19Instances may therefore arise in which damages occur but no pay- ments are made. Thus, unlike incentives, liability rules do not use markets to create or alter prices (Segerson, 1995).
In the case of strict liability, suppose that the extent of a producer’s liabil- ity depends on the damages that arise as a result of the ambient pollution level. This liability can be represented by a liability rule, Li(a).20Producers are only held liable if they are sued by a damaged third party and are found to be responsible. Therefore, in addition to the uncertainty they face about a, producers face uncertainty about whether or not they will be sued and held liable. Producers have their own beliefs regarding the probability that they will be sued and held liable, denoted by qi(a,ηi), where ηi is a vector of random variables that may influence this probability. Thus, the ith producer’s expected liability payment is Ei{qi(a,ηi)Li(a)}. It is easy to see that the liability rule is very similar to a (non-linear) ambient tax.
Turning to negligence, there has been a movement at both the US state and federal levels to hold producers liable for damages resulting from chemical use only if they failed to apply registered chemicals in accordance with the manufacturer’s instructions and any related laws (Wetzstein and Centner, 1992; Segerson, 1990, 1995). For example, the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA) restricts producers’ liability in this manner. A substantial number of states make com- pliance with acceptable agricultural best management practices a defence to nuisance actions (ELI, 1997). Negligence rules of this sort are in accordance with the philosophy that producers have a basic ‘right to farm’ and that they should not be penalized as long as they adhere to standard, accepted practices.
Under a negligence rule, producers are only held liable if they failed to use the ‘due standard of care’. The standard can be defined in terms of either damages or input use. That is, liability only applies if a particular level of damages is exceeded or if the use of polluting inputs is excessive. A formal analysis of negligence rules is beyond the scope of this chapter, but an important difference from strict liability is that the lump sum components of negligence rules cannot generally ensure optimal entry and exit andthat the aggregate liabil- ity equals total damages. Instead, negligence rules will not necessarily be effective in limiting entry, because polluters may all avoid liability by producing at sub- optimal levels (Miceli and Segerson, 1991) and production may be profitable on more than the efficient number of acres without the threat of a liability penalty.
The economic performance of strict liability and negligence rules is limited in all of the ways in which ambient-based incentives are limited. In addition, producers have uncertainty about whether or not they will be successfully sued for damages, further limiting the effectiveness of this
Environmental Instruments for Agriculture 53
approach. Several factors may influence this uncertainty. For example, the characteristics of agricultural pollution, including dispersion of harm and the inability to identify sources, could make the probability of a producer being sued and held liable very small under strict liability rules. A negligence rule may be more appropriate in these cases because it is not necessary to prove a producer’s contribution to damages. Liability based on compliance with
‘accepted management practices’ would be seen as the fairest, because producers would not be held liable unless they were not in compliance with acceptable practices.
Finally, the litigation process for liability may be expensive relative to other regulatory methods. This expense may prevent individuals from attempting to claim damages, letting polluters go unregulated (Shavell, 1987). Due to these considerations, liability rules are not likely to be first-best and are probably best suited to the control of pollution related to the use of hazardous materials, or to non-frequent occurrences such as accidental chemical spills (Menell, 1990;
Lichtenberg, 1992; Wetzstein and Centner, 1992).