Pollution control costs include: (i) the costs of resources devoted to pollution abatement, (ii) the costs of changes in the products farmers choose to produce and the production processes and inputs they use as they seek to minimize the costs of compliance, (iii) the costs to consumers, producers and resource sup- pliers of input and/or output price changes resulting from changes in market demands and suppliers, and (iv) the social costs of monitoring, enforcement and other administrative activities.
The costs of changes in agricultural production
Analogous to the measurement of benefits to firms that enjoy productivity gains from water quality improvements, the costs to farm firms that must comply with pollution control policies are appropriately measured by changes in quasi-rents (Just et al., 1982).14If the prices of output or inputs are affected, then welfare costs to consumers of suppliers of farm inputs must also be considered.
To illustrate, consider a group of farmers producing a single good under perfectly competitive conditions. Let x=f(z) be the production function of a representative farm, where xis output and zis an input vector. In the absence of environmental policy, the farm firm maximizes profit, π = px c(x,w), where pis the output price, wis the input price vector, and
c(x,w) =min{wz: f (z) ≥x}.
Let p0 and w0 denote the pre-policy values of pand w, and π0the pre-policy
112 M. Ribaudo and J.S. Shortle
baseline optimized profit. The cost to the firm of a water pollution control instrument is the reduction in the quasi-rent from the baseline level.
As described in Chapters 2 and 3, there are many possible instruments to induce farmers to undertake changes in production practices to reduce pollu- tion. The ways in which they respond, and the impacts on their welfare will vary with the instrument. Input taxes or subsidies discourage the use of pol- luting inputs (e.g. nutrients or pesticides) and/or increase the use of pollution control inputs and can be easily analysed with the model we have presented above.15To illustrate, let tdenote a vector of tax/subsidy rates applied to the representative farm’s input vector. Since the tax/subsidy is equivalent to a change in input prices, the post-policy profit is π1=px* c(x*, w+t) where x*
indicates the optimized output. The change in quasi-rent is:
∆=π1π0
This change in quasi-rent can be depicted graphically, and measured in principle, as a change in consumer surplus (with the firm being the consumer of inputs) using the farm’s input demand curves, or a change in producer sur- plus using the farm’s supply curve (Just et al., 1982). To illustrate, suppose that only one input, for example fertilizer, is taxed, and that market input and output prices are unaffected by the intervention. Let the first element of w be the fertilizer price. Then w10is the baseline fertilizer price and w11=w10+tis the farmer’s fertilizer price with the tax, where t is the tax rate. Using standard results of applied welfare theory, ∆can be expressed as (Just et al., 1982):
where z1 (p, w) is the farm’s fertilizer demand function. This result is given by the area a +b in Fig. 4.7. The line D is the input demand curve. z10is the quan- tity of fertilizer demanded before the tax and z11 is the quantity demanded with the tax. This farm level analysis can be easily extended to the input mar- ket if all demanders are taxed uniformly.
In output space, the fertilizer tax would imply an upward shift in the farm’s marginal cost and supply curve, implying a reduction in producer sur- plus. This is illustrated graphically using Fig. 4.8. MC0 is the marginal cost curve of the farm. Under the assumption of profit maximization, the producer equates price (p0) with marginal cost (assuming price exceeds the minimum average variable cost). Prior to the pollution policy, output is x0. The quasi- rent, or producer surplus, is the area a + b. Analogous to an input price increase, the tax increases production costs, shifting the marginal cost to MC1.
Assuming the market price remains at p0, output is reduced to x1. Producer surplus for this level of production is area b. The change in producer surplus is the area a.
As another example, consider a tax, applied at the rate tr, on a farm level environmental performance proxy that aggregates over inputs (see Chapter 2). The proxy might be an estimate of field losses of pesticides, nutrients or
∆= −
∫
ww10+tz p w dw1( )
0 110
,
Benefits and Costs of Control Policies 113
soil. Let r(z) give the proxy as a function of the farm’s inputs. In contrast to the analysis presented above for input taxes, the proxy tax does not simply increase the prices producers pay for inputs. Accordingly, the cost function must be modified to include the tax as a separate argument. Specifically, the farm’s cost function becomes:
cr(x,w,tr) =mi
zn{wz trr(z): ƒ(z)≥x}
The farm’s profit function with the tax becomes px* cr(x*, w, tr) where x*
denotes the optimized output. The change in quasi-rents is:
∆=π1π0
114 M. Ribaudo and J.S. Shortle
Fertilizer price
w10
a b
D
Fertilizer w10 + t
w1
z11 z10 z1
Fig. 4.7. The welfare cost of an input tax.
Price
MC1
MC0
x0 Quantity a
x1 b
p0
Fig. 4.8. Costs of an environmental tax to a single firm.
By the envelope theorem, the cost to the firm of a marginal increase in the tax on the proxy is r(z(p,w,tr)), where z(p,w,tr) is the vector of profit maximizing inputs. Analogous to our treatment of the cost of an input tax, the cost of the tax to the farm firms can be analysed graphically and measured by the change in the producer surpluses.
If the number of producers subject to an environmental policy is suffi- ciently large to affect the market supplies of agricultural commodities or the market demands for resources that are not perfectly elastic in their supply to agriculture, then the welfare impacts will include price-induced changes in the welfare of producers, consumers and input suppliers. The same tech- niques we used to illustrate the impacts of an increase in environmental qual- ity on a perfectly competitive industry could be used to analyse the agricultural commodity market impacts of an increase in production costs due to an environmental policy for the simplest case of a sector producing a single commodity. In contrast to the prior analysis, the industry supply curve would shift to the left as a result of the policy-induced cost increases. The market price would rise, reducing consumers’ surplus. As in the case of the productivity change, the impact on producers’ surplus would be ambiguous since the welfare losses due to the production cost increase will be offset to some degree by the gains resulting from the price increase.
As with the measurement of the benefits of production cost reductions induced by water quality improvements, the measurement of the costs of changes in farm resource allocation for water quality protection becomes more complicated as the complexity of the structure of production, markets and information is increased. Topics include welfare measurement when pro- ducers are uncertain about prices, technology, weather and policy, produce multiple products or products that may vary in quality characteristics, experi- ence capital adjustment costs, and participate in input or output markets that are imperfect or distorted by government policies (Just et al., 1982).
Producer–consumer costs versus social costs
The preceding discussion focuses on the costs of water pollution controls to producers and consumers. It is important to note that the costs to society may differ from the costs to producers and consumers. To illustrate, consider again the tax imposed on a farmer’s use of fertilizer (Fig. 4.7). The tax increases the unit cost and reduces fertilizer use. Since the farmer was maximizing profits before the tax, the reduction in use implies a reduction in pre-tax profits. The total cost to the farmer is this pre-tax profit reduction plus the profit loss due to the tax payment (area a +b). The social cost is simply the reduction in pre- tax profits. The loss to the farmer from the payment of the tax (area a) is exactly offset by social gain from the use of the tax revenues.
Alternatively, suppose the farmer is paid a subsidy to reduce fertilizer use.
Again, if the farmer was maximizing profits before the tax, then the reduction
Benefits and Costs of Control Policies 115
in use implies a reduction in profits before the receipt of the subsidy payment.
The cost to the farmer is this profit reduction less the subsidy payment.
Depending on how the subsidy is offered, the farmer could well be better off.
The social cost is simply the reduction in pre-subsidy profits. The gain to the farmer from the payment of the subsidy is exactly offset by social loss from the use of the public funds.
Finally, consider a policy regulating fertilizer use through the use of per- mits that limit the quantity that may be used. If the permits are tradeable, the opportunity cost of fertilizer use is the market price of fertilizer plus the mar- ket price of fertilizer permits. The increased cost will lead to reduced fertilizer use, and a profit loss before transfers related to permit trades. However, farms that are net permit sellers will receive payments that offset their profit losses, analogous to the payment of subsidies, while farms that are net purchasers will make payments that increase their losses, analogous to taxes. On balance, the monetary transfers between private agents are a social wash.
Social cost measurement with agricultural and fiscal policy distortions
Some important implicit assumptions in our analysis to this point are that farm commodity prices reflect the marginal social value of the goods to con- sumers, farm input prices represent the marginal social opportunity cost of the resources, and the social opportunity cost of a dollar of tax or subsidy is equal to a dollar. Adjustments in the measurement of social costs of pollution controls may be required if these conditions are not satisfied.16There are sev- eral important cases to consider.
One set of problems emerges from pre-existing agricultural policy distor- tions (e.g. see Lichtenberg and Zilberman, 1986). Agricultural markets are often subject to policy interventions intended to serve farm income, trade, food price, revenue or other policy goals (see Chapter 3). The interventions take a variety of forms, including output price floors and subsidies, produc- tion quotas and input price subsidies (Gardner, 1987). These interventions can affect the location, type and severity of agricultural externalities (Antle and Just, 1991; Hrubovcak et al., 1989; Weinberg and Kling, 1996).
Furthermore, they cause divergences in the prices producers pay for resources or receive for their products, and their respective social values.
To illustrate, suppose that producers receive a price subsidy. The market price will then be less than the marginal production cost, implying excess pro- duction and a dead weight efficiency loss. An analysis of the costs of agricul- tural pollution controls must include the effects on the dead weight costs. If a policy reduces (increases) the dead weight cost, then the social cost is reduced (increased) in comparison with the normal measures presented above. The same would be true of environmental policies that affect the deadweight costs of other types of commodity market or input market distortions.
116 M. Ribaudo and J.S. Shortle
Environmental policies that involve payments to farmers must consider the social opportunity cost of public funds. Analogous to our assumption to this point, most studies of the welfare consequences of agricultural policies assume that the opportunity cost of $1 of government spending is $1 (Altson and Hurd, 1990). However, the opportunity cost is generally in excess of $1 because of distortions of labour and other markets resulting from tax mechanisms (e.g. Atkinson and Stiglitz, 1980). The marginal costs vary from country to country, but can be substantial (e.g. Browning, 1976). The impli- cation is that the subsidy payments to reduce polluting inputs or increase the use of pollution control inputs are not appropriately treated as transfers with- out efficiency consequences. Instead, the social costs of the funds will exceed the value of transfer to the recipients, and thus must be considered. Similarly, the social value of a dollar of environmental tax revenues may be more or less than a dollar depending on the use that is made of the revenues and the other factors (e.g. see Oates, 1995).
Estimating the costs of changes in agricultural production
Because the costs of environmental policies largely involve the welfare impacts of changes in the allocation and prices of goods and services traded in markets, their estimation is generally considered to be less challenging than estimating the benefits. And, in contrast to the limited research on the benefits of reducing agriculture’s contribution to water pollution, a wealth of studies estimate the costs of pollution controls in agriculture. A sample of this research is listed in Table 2.2 in Chapter 2.
A simple and common way for estimating pollution control costs is the direct compliance costs approach (USEPA, 2000c). Direct compliance costs would include expenditures to install and operate pollution control equip- ment, and possibly other partial budgeting costs. To illustrate, confined animal feeding operations are increasingly subject to regulations on the handling, storage and disposal of animal wastes to reduce odours and water pollution risks. The direct compliance costs of such regulations are the costs of installing and operating facilities to collect and store manure, and the direct expenditures of disposing of manure in accordance with the regulations. In some regions, farmers are subject to taxes on fertilizers. In this case, no expenditures are required for pollution control equipment. A direct compli- ance cost accounting (or partial budgeting) would estimate the social cost as the pre-tax reduction in net returns due to the reduction in fertilizer use hold- ing the crop mix and other inputs constant. Essentially, this approach would use the marginal value product curves for fertilizer for a given crop mix rather than the fertilizer demand curve to estimate the cost of the tax.
A major limitation of the direct compliance cost method is that it does not allow for input or output substitution effects that producers may choose to reduce compliance costs. For example, in the fertilizer example, depending
Benefits and Costs of Control Policies 117
on the size of the tax and relevant price elasticities, a tax-induced increase in fertilizer costs will lead farmers to substitute other inputs for fertilizer and change their crop mix in favour of less fertilizer intensive crops. Such responses will reduce compliance costs relative to those that would be obtained by the direct compliance method. If new consumer prices are calcu- lated using the direct compliance, they too will be overestimated resulting in an exaggerated loss to consumers, especially if the elasticity of demand is assumed to be zero (i.e. if consumers like producers do not respond to changes in costs) (USEPA, 2000c). None of the studies listed in Table 2.2 use the direct compliance cost approach.
More advanced methods model responses of farm firms and markets.
Firm level models are especially useful for detailed analysis of the potential economic responses of farmers to policy initiatives, and the resulting compli- ance costs and their distribution by structural factors such as farm size, and the impacts of farmers responses on the movement of pollutants from farm fields. Numerous studies can be found at these scales. Larger scale models are required to analyse costs at watershed or other regional scales. Watershed and other regional models imply spatial aggregates of farms and fields. Farms and fields may be modelled individually, but the increased data requirements and computational complexity that attend larger spatial scales typically necessitate the use of less detailed representations of economic and physical variables and relationships than those included in firm level models.
Individual economic units will typically be replaced by regional economic aggregates. Possible representations include regional production functions, commodity supply and input demand functions, or cost functions.
Representative farms are frequently defined to capture spatial and structural variations in production and pollution control technologies, farm responses to policy initiatives, and costs. For example, the USMP model used by US Department of Agriculture’s Economic Research Service is a national model that subdivides the nation into 45 subregions. Each subregion is modelled as a multi-product farm firm. Models with endogenous output or input prices will -include commodity demand and farm input supply relationships.
The positive-normative taxonomy of economic modelling approaches presented previously (Adams and Crocker, 1991) applies to models used for analysing policy responses and costs. Among normative techniques, mathe- matical programming models have found frequent use for modelling firm and sectoral policy responses and compliance costs at all scales. Most of the studies listed in Table 2.2 use this approach. Reasons for the popularity of mathematical programming models include: (i) they can be constructed when data for estimating economic relationships are absent or when available data are inapplicable, which makes them well-suited for ex ante policy analysis; (ii) the constraint structure inherent in programming models is well suited to characterizing various forms of resource, environmental and policy con- straints; (iii) the optimization nature of the procedures is consistent with eco- nomic theory of firms and market behaviour (Adams and Crocker, 1991;
118 M. Ribaudo and J.S. Shortle
Howitt, 1995). Programming models are not without limitations. A major challenge is to represent the choice technology choice set adequately to cap- ture the range of plausible compliance options.
Positive models, and in particular, econometric models have a strong appeal for environmental policy analysis because they allow results to be inferred from the actual choices of producers and consumers. Their use is, however, generally quite limited for ex ante analysis of environmental policy options. Without prior experience, there can be no data on actual choices.
This may be of little consequence for options that induce changes in the eco- nomic environment that are analogous to past economic experiences. For example, responses to variations in fertilizer and pesticide prices will provide data for the analysis of taxes on these inputs (e.g. Shumway and Chesser, 1994; Wu and Segerson, 1995). However, many of the approaches that are of interest have no analogues.
Policy cost analysis does not require integration of economic and physi- cal models of the formation, transport and fate of pollutants. For example, a study might examine the economic consequences of a nitrogen fertilizer tax on fertilizer use and farm profits without examining the impacts on the move- ment of nitrogen into water resources. Integration of economic and physical models is essential for examining the water quality impacts of policies, and for assessing the cost-effectiveness of alternative options in achieving water qual- ity goals. While clearly desirable in general, integration of economic and physical models can be challenging, especially as the spatial scale is increased.
The studies presented in Table 2.2 provide a range of examples of model inte- gration.
Lessons from policy cost estimates
There are a number of lessons that have been learned from studies of the wel- fare costs of improving the environmental performance of agriculture. We list some important lessons below.
1. Watershed-based management. A major lesson emerging from the research is the importance of watershed- or aquifer-based strategies that simultane- ously consider both point and non-point sources. For example, US water pol- lution control policies have traditionally focused on point sources. There is now substantial evidence that the economic efficiency of surface water pollu- tion controls in the USA could be significantly improved through strategies that allocate load reductions between point and non-point sources based on the relative costs of control (Elmore et al., 1985; Malik et al., 1993; Letson, 1992; Rendleman et al., 1995; Anderson et al., 1997; USEPA, 1998c; Faeth, 2000; GLTN, 2000). (See section on Point/non-point trading in Chapter 2.) 2. Targeting. Another important lesson is that targeting agricultural controls in space and time can greatly enhance the cost-effectiveness of pollution
Benefits and Costs of Control Policies 119