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Will Farmers Trade Profits for Stewardship?

Heterogeneous Motivations for Farm Practice Selection

Hayley H. Chouinard* Tobias Paterson**

Philip R. Wandschneider*** Adrienne M. Ohler****

July 2006

* Assistant professor in the School of Economic Sciences, Washington State

University. ** Economist at National Agricultural Statistics Service, USDA. *** Professor in the School of Economic Sciences, Washington State University. **** Graduate student in the School of Economic Sciences, Washington State

University. Corresponding Author: Hayley H. Chouinard PO Box 646210 Pullman, WA 99164-6210 Chouinard@wsu.edu Telephone: (509) 335-8739 Fax: (509) 335-1173

Abstract: We investigate the trade-off agricultural producers may face between profits and stewardly activities when selecting farm practices. Instead of the typical profit-maximization framework, we model producer behavior in an expanded utility framework, built on production technology, and including self and social interests. The framework introduces inherent heterogeneity, and social/environmental motivations, into farmer behavior. Based on this model, we hypothesize that there are some farmers that are willing to forego profit to engage in stewardly farm practices. With a small empirical study, we provide evidence that some farmers are willing to make this sacrifice. Results are consistent with the multi-utility hypothesis.

Keywords: multi-utility, profit maximization, stewardship, willingness-to-pay

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Will Farmers Trade Profits for Stewardship?

Heterogeneous Motivations for Farm Practice Selection

Abstract: We investigate the trade-off agricultural producers may face between profits and stewardly activities when selecting farm practices. Instead of the typical profit-maximization framework, we model producer behavior in an expanded utility framework, built on production technology, and including self and social interests. The framework introduces inherent heterogeneity, and social/environmental motivations, into farmer behavior. Based on this model, we hypothesize that there are some farmers that are willing to forego profit to engage in stewardly farm practices. With a small empirical study, we provide evidence that some farmers are willing to make this sacrifice. Results are consistent with the multi-utility hypothesis.

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Introduction

The 2002 US farm bill allocates over $38 billion to conservation programs. Nearly two-

thirds of the new funds are allocated to working land programs in an attempt to

encourage conservation activity on land used in agricultural production (ERS 2002). One

such program, the Environmental Quality Incentives Program (EQIP) was appropriated

$11 billion for a ten-year period. These funds provide agricultural producers with

technical, financial or educational assistance for implementing conservation practices.

For instance, EQIP, shares up to 75% of the costs borne by producers (Claassen 2003).

Conservation incentive and regulatory programs raise a number of practical, policy, and

disciplinary issues which generate a number of often confusing and contradictory claims.

On one side one might hear that farmers are inherent stewards of the land and, that given

the information, farmers will adopt conservation technologies. On the other hand one

might hear that farmers will only adopt conservation technology if it is profitable and that

society must “bribe” (or “incentivize”) farmers to protect social values including the

environment. In this paper, we describe a theoretical framework to rationalize the

different motivations faced and choices made by farmers with these options.

Anecdotal evidence and the conservation literature suggest that agricultural

producers engage (or don’t) in conservation activities for many reasons. Of these, the

opportunity to increase profit or wealth may be the most important. Presumably, some

conservation activity is advantageous to long term profits. However, there are also

suggestions that non-financial factors play a role in conservation decisions. Producers

may gain direct personal rewards from the improved environment beyond the long-term

agricultural profit (e.g., on-farm recreational opportunities and aesthetic or spiritual

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values). Moreover, the mere act of engaging in stewardship may generate pure personal

satisfaction (warm glow) or social recognition, regardless of the effect of the activity.

Finally, producers may engage in stewardship with no regard to personal gain. They may

participate in these activities for the social good or because they believe it is the “right

thing to do” even if it requires personal sacrifice.

While the general story about motives in the above paragraph may agree with

intuition, it has neither a clear supporting theoretical framework nor definitive empirical

corroboration. The literature on the conservation behavior of farm producers is multi-

threaded, divided and often ad hoc. The most clearly articulated account of farmer

behavior centers on profit maximization and has little room for non-financial motivations.

The theoretical literature does not have a settled, unified account of egoistic-financial and

non-financial-social motives, although recent developments are suggestive. The extensive

empirical literature tends to be descriptive. It seems especially unclear about two aspects

of farm operator behavior that motivate this study. One is whether agricultural producers

are actually willing to forego profit to engage in conservation practices. That is, is there

behavioral evidence of a trade-off between profits and conservation? The second,

related, topic concerns whether all farmers are alike – at least in regards to farm practice

decisions. If all farmers are concerned only with profit maximization, then shouldn’t they

all make the same decision under similar conditions?

In this study we contribute to an emerging integration of stewardship and profit-

based behavior models. We construct a theoretical model that explicitly treats the

willingness to trade-off profits for stewardship and that explicitly accounts for farmer

heterogeneity in the stewardship dimension. To do this we adopt a utility function

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structure rather than the profit-maximizing model often employed in farm operator

studies. Using a utility framework allows us to incorporate the standard production

function-profit mechanism, and also to introduce social goals within the systematic

framework that is the hallmark of microeconomics. To provide a fuller account of

stewardship behavior we adopt a multiple-component utility model. Finally, we apply

the model to a small empirical contingent valuation study to determine producer’s median

willingness to pay to engage in stewardship and show evidence that at least some of the

farmers may sacrifice profit for social goals.

The discussion begins with a brief review of the literature concerning

conservation practice adoption and some related literature concerning the nature of utility

functions, stewardship and social preferences. Next, we describe the conceptual

framework starting from production functions, input choices and the outputs generated

and folding them into a multi-utility model, which incorporates social motives. Then, we

implement the model within a choice framework and provide an estimate quantifying the

willingness to pay for stewardship from the empirical study. Conclusions follow in the

last section.

Conservation Literature

The literature concerning what motivates producers to adopt conservation practices, and

what determines the level of adoption is extensive. For convenience, we divide the

conservation-stewardship literature into five basic genres and provide a sampling from

each genre: profitability studies, social and attitudinal studies, profitability with

stewardship studies, adoption causal factors studies, and the recent multi-motive/multi

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utility approach. We provide a separate discussion of the multi-utility approach as it

represents a significant departure form the single utility framework. We discuss the

critical matters relating to utility function behavioral models to justify and motivate the

use of the multi-utility function.

In empirical studies of conservation farming, often the most important stated (or

implied) motives for conservation adoption are “selfish,” financial-economic concerns,

generally profits, but also including related financial attributes such as asset growth, risk

reduction, and financial liquidity. For instance, Cary and Wilkinson (1997) show that the

best way to increase the use of conservation practices is to ensure that the practices are

economically profitable. Honlonkou (2004) finds that natural resource conservation

management adoption depends on financial-economic factors such as financial liquidity.

According to Lichtenberg (2004) cost considerations play a significant role in

conservation technology adoption.

Others have questioned the profitability literature’s exclusive use of economic

factors to explain conservation technology adoption. One of the main criticisms of the

selfish, financial motivations approach is that it fails to explain or incorporate

heterogeneity of producer preferences (Nowak 1987). If all farmers were self-interested

profit maximizers, then all should make the same decision when faced with the same

situation -- i.e., holding risks (and risk aversion), knowledge, and agro-climatic

conditions constant. The financial-economic focus also fails to explain why many

profitable technologies are not adopted (Neill and Lee 2001). Among many others,

Lovejoy and Napier (1986) suggest that conservation technology adoption is not solely a

question of technology, but also involves social issues.

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Another category of literature focuses on social and attitudinal issues in

agricultural production, including stewardship motives. Wunderlich (1991) examines the

evolution of the concept of stewardship among agricultural producers1. Using the

definition of a steward to be someone who is responsible for another’s property, he

explains that stewardly producers view their own land as someone else’s property. When

someone is taking care of another’s property, that action is considered to be stewardship

whether that other party is viewed as future generations or as god. Wallace and

Clearfield (1997) define stewardship in a similar manner. They examine why producers

adopt stewardship practices, and find that many producers view private ownership as

stewardship and not a right to do whatever they want with the land. Furthermore, they

view farming and ranching as a way of life rather than a business to maximize profit.

Ryan, Kaplan, and Grese (2001) show that volunteers for environmental

stewardship programs are motivated by the idea of helping the environment and learning,

while social factor and project organization affected the commitment of the volunteers.

Ryan, Erickson, and De Young (2003) examine the motives for protecting bio-diversity

and water quality in the agricultural Midwest. They find that an important factor in

motivating conservation is attachment to the land, and that producers are more likely to

engage in a practice when it makes their farm appear well managed.

In fact, a third genre of literature includes empirical studies that have attempted to

incorporate a stewardship dimension into the profitability approach. Klonsky et al (2004)

show that some producers can maximize profit and still be stewardly. In other words,

conservation practices may be adopted when profit is not affected. Van Kooten,

1 For clarification, it should be noted that the terms conservation and stewardship are used in the conservation literature to describe three very different things: 1) farmers’ motivations, 2) farm practices and 3) ecosystem/farm states. We will try to clearly distinguish these usages.

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Weisensel, and Chinthammit (1990) discuss net returns and stewardship. Their major

findings are that concern for soil quality does have an impact on changing agronomic

practices. However, when soil is relatively deep, the influence of net return and

stewardship is much harder to distinguish. In other words, when the quality of land is

high it is hard to determine whether conservation practices are adopted because of

stewardship or profitability motives.

Some studies have examined consumers’ willingness to pay for environmentally

friendly products in order to determine if a moral dimension exists in their decision-

making. Boyce et al (1992) investigate the willingness to pay (WTP) and the willingness

to accept (WTA) for a small pine tree. They find a WTA significantly larger than the

WTP. Anderson, Vadnjal, and Uhlin (2000) compare consumers’ WTP and the WTA

using conventional and ecologically (more environmentally friendly) produced eggs.

They find little difference for the conventional eggs, but find that consumers have a WTA

1.5 times larger than the WTP for ecologically produced eggs. Both studies suggest the

difference may be explained by the idea that the willingness to accept allocates the moral

responsibility to the consumer. Consumers are willing to forego “profits” in order to save

the pine tree or promote environmentally friendly egg production.

A fourth theme found in the literature comprises efforts to classify producer

motives. Maybery, Crase, and Gullifer (2005) categorize producers’ values as economic,

conservation, and lifestyle. Their findings show that there is a clear distinction between

economic and conservation values, and suggests that non-economic conservation

incentives can have an impact on adoption. Dobbs and Pretty (2004) develop a tool for

examining the different forms of support for agriculture. Because of the multi-

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functionality of agriculture, support can come in three different forms that can overlap:

production, stewardship, and social benefits. Their tool supports the idea that there are

financial-economic and non-financial factors influencing the agricultural sector. Sinden

and King (1990) also examine characteristics that influence the adoption of soil

conservation by categorizing different aspects into personal, economic, institutional, and

land factors. They find that stewardship motives increase recognition and perception of

conservation problems.

While the literature in the four loosely classified genres cited above might suggest

that profit/personal and social/stewardship factors both play a role in conservation

decisions, the wide array of themes in the literature also indicates lack of a common

framework. Most studies about social factors, including stewardship, appear as studies of

attitudes in the more sociological literature or as ad hoc variables in empirical adoption

studies. Before turning to more recent, integrated literature on stewardship and profits,

we take a slight detour into utility theory.

Utility and Behavioral Economics Literature

Today, standard economics assumes that each person is a rational, self-interested agent

with a consistent and unique ordering of preferences, a utility function/index. However,

there have always been some economists who have recognized non-profit motives in

production and non-egoistic motives in consumers. Indeed, this tradition dates at least to

the publication of Adam Smith’s Theory of Moral Sentiments (1759). Recently, some

economists’ interests in more psychologically complex models of agents’ choice process

and behavior have revived.

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The developing fields of economic psychology, experimental economics and

evolutionary biology explore behaviors relevant to our understanding of the standard

rational, egoistic model in economics. For extensive surveys of the literature see Rabin

(1998) on psychology and economics, Fehr and Fischbacher (2003) on altruism, Hoffman

(2000) on the evolution of cooperation, and Kahneman et al (1997) on utility theory and

psychology – from which we take most of the following story. Our immediate interest is

in altruism versus egoism. The development of the “selfish gene” theory (Dawkins 1976,

2006) first suggested that findings from evolutionary psychology would support the self-

interest assumption in the standard economic behavioral model. However, evolutionary

biology has since found examples and explanations for two specific forms of altruism –

kinship altruism occurs when the benefits to relatives and the costs to the altruist result in

net increases in overall gene survival, and reciprocal altruism, when the altruist is

engaged in mutually beneficial exchange over distance or time.

Within a Darwinian framework more general forms of altruism are more difficult

to explain because the logic of group selection and group dynamics are more complex.

Still, the work on the evolution of cooperation inspired by Axelrod’s (1984) seminal

piece suggests an evolutionary logic for cooperative, altruistic agent strategies. In

Axelrod’s original piece, agents that were prepared to offer initial cooperation and

respond with tit for tat (cooperation or retaliation) were likely to build a stable “society”

even in the context of the Prisoners’ dilemma situation. Altruism might be “built into”

the individual agent either through culture or through the evolution of “hard-wired”

strategies of “strong altruism.” Evidence for a fundamental trait of “strong altruism”

from experimental economics and psychology show that, in certain cases, people will

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reward the altruistic behavior and punish the “unfair” behavior of others even at a cost to

themselves (Fehr and Fischbacher 2003). In the game of Ultimatum, player A is given a

quantity of money she is to share with player B. Player A proposes a split. If player B

accepts the amount, both receive the money, if player B rejects neither do. If A and B are

rational egoists, A should offer the smallest possible amount of money and B should

accept it, because both would be better off than with none of the money. However, in

experiments B often rejects amounts less than 20-40% and A offers closer to 50%. This

pattern, A’s altruistic offer, and B’s willingness to punish selfish offers even at her own

expense, has been found in many cultures.

There is also increasing research elucidating the underlying bio-chemical

mechanisms of pro-social behavior. In the neuroscience literature, Cory (1999) found an

ongoing tension between the egoist, self-interest, and the empathic, social-interest in most

individuals. Scientists have identified “sympathy” or mirror neurons which are active

when agent A observes agent B’s activity. Research has also shown that levels of the

“trust hormone,” oxtocin, correlate with increased pro-social behavior in humans and

other animals (Kosfield et al 2005).

There is a longer history of philosophical and theoretical criticisms of the rational

egoist model of behavior. Sen (1977), Etzioni (1986) and Hirschman (1985) are three of

the more recent, widely cited instances. In his seminal contribution, Sen (1977) critiques

the concept of rationality as used in modern economic theory. Sen notes that economics

really possesses two concepts of rationality. The utility index/revealed preference

interpretation of rationality is the formal requirement of consistency in preference

rankings. While admitting some usefulness to this interpretation, he believes the purely

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formal interpretation of rationality, “definitional egoism,” to be unnecessarily,

unrealistically weak and tending toward circularity and tautology. In revealed preference

theory behavior is explained by preferences, which are revealed by behavior -- in Sen’s

words, a “remarkably mute theory.” (See also, Kahneman et al 1997)

The second common definition of rationality in economics is that individual

actions are always grounded in self-interest. Sen suggests that having an agent cast all

her motivations into a single-dimensioned preference ordering justified solely by self

interest ignores the richness of real behavior and real motivations – it creates a “social

moron” – the “rational fool” of the article’s title. To Sen, commitment (duty) is the

element of behavior that is utterly incommensurable with standard utility theory since it

may require actions directly contrary to self-interest, however defined. Hence

commitment requires some alteration to the single dimensional egoistic utility function.

Following Sen, a number of philosophers and economists have proposed viewing

the internal choice problem as one of balancing multiple decision criteria – multiple

utilities. In these theories, social norms, altruism, and commitment may play important

and independent roles in motivating decisions (Ajzen 1988, 1991). For instance,

Etzioni’s (1986) argues for two irreducible sources of utility, pleasure and morality.

Defenders of the unified utility approach point out that one might incorporate

social and altruistic interests into a more generalized utility index through interdependent

utility functions. The agent assists others because the agent is made better off either by

the gain in welfare of the other (sympathy altruism) or the act of helping itself (warm

glow altruism). Sen (1977) argues against the incorporation approach for two reasons.

First, as noted above, this theory tends to become tautological such that you “can hardly

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escape maximizing your own utility, except through inconsistency.” In his words,

whether you are a “single-minded egoist or a raging altruist or a class-conscious militant,

you will appear to be maximizing your own utility in this enchanted world of

definitions.” Secondly, whereas, sympathy might be included in an extended model of

utility, commitment cannot be so easily assimilated.

This literature on altruism and multi-utility approaches sets the stage for recent

innovations in the literature concerning conservation behavior. Lynne, Shonkwiler, and

Rola (1988) were among the first to build on the multi-utility framework as proposed by

Etzioni (1986), and apply it to the systematic analysis of conservation decision-making.

They consider a broad set of motivations for conservation adoption. They find that farm

ownership, technological scale/ability, current profitability, risk, income per effort,

attitude toward conservation and perception of problem all are important in predicting

effort of conservation adoption. Using a similar approach, Cutforth et al. (2001) and

Casey and Lynne (1999) also conclude that social norms play a role in crop diversity and

water conservation technology adoption. Employing the idea of multiple motivations for

adoption choices, several studies explicitly categorize attitude variables and determine

the impact of self-interest and social norms (See egs. Lynne 1995; Lynne, Casey, Hodges

and Rahmani 1995; Lynne and Casey 1998; Kalinowski, Lynne, and Johnson 2006).

In sum, it seems reasonable to hypothesize that egoistic-financial and social-moral

factors may influence production decisions. Much of the literature has sidestepped a

systematic integration of these two types of goals, either by assuming that only profits

matter, or by adding social and stewardship factors in an ad hoc way. As Hayes and

Lynne (2004) suggest, the definition of rationality may need to be expanded to include

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social norms. In the next section of this paper, we investigate theoretically whether

producers might “rationally” forgo profits as modeled by Lynne and others.

Conceptual Framework

We build the model of farm operator behavior starting with the production world and

moving to the world of motives. We start with a general agricultural production

technology comprising three vectors of inputs (v, w, z) and two outputs (Y, E). The two

outputs are agricultural yields (Y) and environmental effects (E). The outputs are

produced jointly and are not separable. The three inputs enter production in different

ways. Some inputs are used only in conventional practices (v), some are used only in

conservation practices (w), and some are used in both types of practices (z). For

concreteness, consider conventional tillage versus no-till farm practices. Conventional

tillage would use standard plows and disks (v), while no-till would use specialized

herbicides and no-till drills (w), and both would use seeds, fertilizer, and tractors (z).

Of course, neither farmers nor policy-makers think in terms of production

functions, but they do think of production technology and “farm practices.” For instance,

some conservation policies and programs call for the use of specific practices with labels

such as, “Best Management Practice” or “Best Available Technology.” In this spirit, we

can identify a specific combination of inputs and outputs as a farm technology or farm

practice. Some technologies (farm practices) generate agricultural output, such as yield

(Y) and, possibly, negative environmental effects (Eneg); while other practices generate

few or no negative environmental effects and some positive environmental amenities (EA)

such as increased soil quality (organic content, tilth, carbon). We term practices that

produce Eneg, (environmentally) degrading practices; and practices that produce EA,

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conservation practices. E is an aggregate of all environmental effects. More formally,

equation 1.1 shows sets of conventional farm (practices) technologies, equation 1.2

shows conservation farm practices (technologies), and equation 1.3 shows the total

production technology set encompassing all production possibilities.

( , ) ( , )neg vY E T v z= (1.1)

( , ) ( , )A wY E T w z= (1.2)

( , ) ( , , )Y E T v w z= (1.3)

Using standard economic terminology, the overall production technology, T, can

be represented in a two dimensional graph of the production possibilities frontier (PPF)

showing agricultural output (yields, Y) on the vertical axis and the environmental effects

(E) on the horizontal axis (see figure 1). Recall that a production possibility frontier

shows the most efficient combination of two (or more) outputs. Whereas a PPF is usually

represented in only the positive quadrant, the environmental dimension exists in both the

negative quadrant and positive quadrants. Thus, we can represent net negative

environmental effects to the left of the origin, such as the common non-point farm effects

such as run-off into surface water or leaching into groundwater. The net environmental

effect is shown on the production possibility frontier. Figure 1 shows two generically

shaped production possibility frontiers. In PPF(1), the highest yields are obtained with

positive net environmental impacts. In PPF(2) the highest yields occur where net

environmental impacts are negative.

Another necessary caveat is that the representation of the production possibility

frontiers in figure 1 simplifies the very complex relationships between environmental

factors and the normal inputs and outputs of agricultural production in order to represent

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these relationships on a single, two-dimensional graph. Real world complexity includes

multiple environmental effects (e.g. simultaneous impacts on soil, air, water, and habitat

quality), each with its own relationship to yields. Moreover, the graph abstracts from the

cases where environmental effects may be inputs to production or intermediate goods

rather than the joint outputs represented in our simplified model.

A more concrete discussion of the PPF may help the intuition. Note that over

some ranges agricultural outputs and environmental outcomes are complementary, but

over other ranges they compete. The production possibilities curve shows a

complementary range, to the left of the apex, where increases in net environmental

impacts are consistent with higher yields. For example, a joint output of some production

practices might be improved soil quality (organics, tilth, carbon) and decreased soil

erosion. Improved soil quality is an intermediate product that becomes an input into

greater yields – here ignoring the process of conversion from production by-product to

production input (tilth, nutrients) and the time taken for that conversion. However, to the

right of the apex there is a range over which agricultural output and environmental

amenities are competitive. In this range, increases in environmental quality come at the

cost of decreasing yields. For instance, this range might include agricultural practices

that improve environmental amenities (e.g., habitat, water quality), but decrease yields

(and/or profits).

Using figure 1, we can divide production practices into three general types:

environmentally degrading farm practices are those that generate net environmental

disamenities. Conservation farm practices are those that produce net environmental

amenities. A sub-set of conservation farm practices are stewardship practices, which

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generate net environmental amenities in excess of the profit maximizing yield level (to

the right of the apex). In our empirical application we will consider only choice among

conservation practices, some of which are stewardship practices. For example, one might

generate higher or lower levels of soil quality. Hence, from this point forward, we will

simplify the discussion by analyzing only the positive quadrant of figure 1, where all

farm practices are conservation practices in the sense of generating some degree of net

environmental amenities. One could include the net negative side of the graph with no

loss of generality.

Now, we turn to the farm operator’s decision problem. In a typical economic

treatment, farmers chose their technology based only on profits (or other financial

criteria). For the most part, profits are an increasing function of yields. One can replace

yields with profits on the graph in figure 1 and the graph will look similar – though the

exact peak of the profits curve will not match the peak of yields curve.

In figure 2 we represent a production possibility frontier for the farmer’s choice

among farm practices relevant to the revised utility function. The vertical axis represents

profits and the horizontal axis represents environmental effects. We begin with the

indifference curve, I1, which corresponds to a farmer who makes decisions based solely

on self-interest and only values profits. Since the farm operator is indifferent to

environmental effects, her indifference curve is a straight line in profit-environment

space. The indifference curve is written in equation 2. This type of farmer has only one

type of motivation and represents the type of farmer usually modeled in the adoption

literature. This farmer will maximize utility where I1 is tangent to the production

possibilities frontier, and produce the corresponding profit and environmental effects.

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1 1( )I I π= (2)

This model implies homogeneity among farm operators; all farm operators are

essentially identical. Faced with the same production choices, they should make the

same choices – whatever maximizes profits (Nowak 1987). In this conventional adoption

model, the differences in farm decisions should come from situational differences:

differences in the geo-physical and climate characteristics of the farm, differences in

access to information, differences in access to capital and similar exogenous factors.

(The introduction of uncertainty can generate heterogeneity based on risk tolerance if one

extrapolates beyond expected utility theory, but that topic exceeds the scope of this

paper.)

Now consider more complex motives. Here, we base our treatment on the multi-

utility models that Lynne (1999) and others have employed in the conservation-

stewardship literature. In his discussions of conservation behavior, Lynne (2002) refers

to two primary motivations as self-interest and other-interest. Lynne and Casey (1998)

suggest I-influence or We-influence to describe two types of motivations. Hayes and

Lynne (2004) use the terms ego and empathy. Sen (1973) points out that one can have a

pure self-interest (isolated personal interest) as well as a self-interest including sympathy,

a commitment based index, and one or more separate indices based on additional moral

principles. Sen believes that it is possible, even probable, that none of these would

correspond exactly to the revealed behavior. For simplification we identify two

“utilities,” one based on Sen’s isolated personal interest and labeled ego-utility or e-

utility, and one based on social and moral interests from doing “the right thing” and

labeled s-utility, where s is for steward or social. In this interpretation, ego-utility can

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include some non-financial, but purely personal motives, such as on-site recreational

opportunities or self-centered aesthetic benefits such as a mountain or lake view. Strictly,

steward-utility is meant to represent purely unselfish, duty-commitment motives. This is

consistent with Sen and with the definition of stewardship in, for example, Wunderlich,

1991, cited earlier. However, this dichotomy leaves the placement of several motivations

ambiguous, including: status/social approval acquired by being a good farmer (good

works altruism), sympathy for impacts on future generations or victims of off-site

impacts (sympathy altruism), and, simply, the “warm glow” that comes from doing a

“good thing” (warm glow altruism). These motivations are generally subsumed in an

expanded sympathy-based utility. In our framework, arguments might be made for

placing these motives in either the e-utility or the s-utility but they fall cleanly in neither

category. The approach most consistent with our model would be to create one or more

additional utility components. However, in the interests of elegance, we will streamline

the model to just the two components. We believe the two-component model captures

the essence of the argument.

Figure 2 illustrates a both ego-utility and steward/social-utility. The indifference

curve, I2, represents the self-interest a farmer gains from profit and providing

environmental amenities for her personal purposes, and is given in equation 3.1. The

environmental effects yield some internal gain to the individual in the form of aesthetic or

recreational (e.g., hunting, hiking) value or some other personal benefit. The indifference

curve, I3, represents the s-interest of a farmer who makes decisions based on the social

interest, found in equation 3.2.

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2 2 ( , )I I Eπ= (3.1)

3 3 ( , )I I Eπ= (3.2)

The self- and steward-interests are separated into two discrete functions. For a

farmer that is motivated by both types of interest, I2 and I3 must be considered together in

order to describe the meta-utility or choice function – and, must be somehow reconciled

for the farmer to make a choice in the real world. The farmer motivated by these two

interests will usually not elect to produce at points A or B, but will choose a satisfactory

point within the region between A and B. Note that the farmer is not maximizing either

component of utility, or a specific combination of the two components. The problem of

combining utility components into an aggregate, meta-utility, or choice function is

similar to the aggregation problem associated with combining individual preferences into

aggregate preferences as described in Arrow (1951). We assume that the choice

mechanism searches for some sort of “best” choice, but it is explicitly not required to

generate a complete and transitive preference ordering. Note also that the choice

mechanism could lead to non-maximizing behavior (in the interior of the PPF).

However, choices on the PPF will dominate interior in many choice mechanisms since

there is always a “pareto better” outcome on the PPF than any interior outcome. Still,

some intransitivity or arbitrary element of a choice mechanism could lead to an interior

solution, but we leave exploration of that to another time and place.

Returning to the model, a producer who possesses both e-utility and s-utility

operates beyond the apex of the PPF. The producer may sacrifice some profit for

increases in environmental effects depending on the strength of the personal

environmental interests and the stewardly environmental interests, the manner in which

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the two interests are reconciled, and the choices available. Note that, among these

farmers, there can be great heterogeneity, with some being only mildly “stewardly” and

others being more passionate. We consider any choice that balances these two utility

components to be “rational,” while not in the strict neoclassical theory sense, in a

formally weaker, but more realistic and robust sense of the term rational.

In summary, our model allows for several possible behaviors along a continuum

of profitability, conservation, and stewardship. Some farm operators may be pure cases

of the profitability only archetype identified by indifference curve I1. These farmers

would be conservation farmers only incidentally – since they do not care whether their

farm operations generate positive or negative net environmental effects. Some farmers

may be pure cases of e-utility. They care about environmental effects as well as profits,

but they care about environmental effects only if they are of some personal benefit.

These farmers might be categorized as conservation or even stewardly farmers in some

empirical studies because they do consider environmental effects as well as profits. A

third category of farmer places some weight on an s-utility component to utility. These

farmers would be willing to engage in some farm operations even at personal costs. Our

hypothesis is that, if the s-utility component exists, than some farmers should be willing

to sacrifice some level of profits for a gain in environmental amenities – a more

stewardly outcome. Our test of sacrificing profits is stricter than the tests in many studies

that require conservation to be one of the motives. In this test we require the explicit

trade-off of profit for environment, a test that should be more sensitive to the existence of

the s-utility component. In the remainder of the paper we report results of a small but

22

tantalizing effort to determine if a measurable number of farmers are willing to forego

profit in order to engage in stewardship.

Empirical Model and Results

For empirical work we must address actual choice – in our case, stated preferences.

Given the multi-utility system described above, choice is based on some mechanism by

which the utility components are reconciled. We can represent this mechanism as an

empirical choice functional, C(.). As stated above, we do not know how the underlying

multiple utilities are reconciled into a single choice; we do not know the properties of the

choice functional that aggregates the separate utilities; and we do not claim that the

resulting choice functional has the properties of a neoclassical utility index.

Our empirical analysis is based on a study of a sample of producers using the

contingent valuation method (CVM) to estimate willingness to pay (WTP) for

stewardship. We seek to determine whether any such WTP exists, and to measure it, if

so. The CVM is a stated preference method, which utilizes survey responses to elicit

value estimates. We use the single-bounded approach first used by Bishop and Heberlein

(1979) and currently favored by CVM practitioners because it is incentive compatible.

Producers are presented with specific dollars costs for stewardship and are asked whether

they would be willing to pay this amount. This type of response is usually modeled using

a random utility model (RUM). For expediency we employ the structure and terminology

of the RUM and the standard CVM choice model, recognizing that these are being used

to implement a choice functional that does not meet the mathematical properties required

23

to analytically go from utility (choice) function to indirect utility (choice) function to

demand function.

In the random utility model, we employ a fictional indirect utility function. For

the empirical model, we write the indirect utility function, where, for convenience, we

represent income (profit) as M and stewardship as S. We include the price of the market

goods, p. Additionally, equation 4 is a representation of the indirect utility of all the

farmers in the sample rather than just one farmer, and so we include a variable, d, that

accounts for the demographic characteristics of the different farmers when we aggregate

up from the representative farmer. Finally we add a stochastic component, ε . In the

usual interpretation of the RUM model, the producer’s preferences are known to the

producer, but are unobserved by the researcher. The indirect utility can be written as:

( , , , , ).u p S M d ε (4)

To briefly restate our fundamental hypothesis, we claim that some producers will be

willing to pay for positive environmental effects, supporting the notion that these

producers have both an ego-interest and a societal-stewardship interest component in

their choice functional and that they are operating in the region between the e-utility and

s-utility in figure 2.

Now, to determine if a producer is willing to pay for stewardship, the producer

must consider an increase in producing environmental amenities in the stewardship

dimension, from S0 to S1. The producer is told the increase in stewardship practices will

cost $c. Then the producer is asked if this increased level of stewardship (environmental

amenities) is preferred at this price. The producer will positively respond only if the

utility associated with the new stewardship level is greater than the previous utility,

24

1 0Pr( ) Pr( ( , , , , ) ( , , , , )).yes u p S M c d u p S M dε ε= − ≥ (5)

We can also consider the producers compensating variation measure, CV. This

measure is the maximum willingness to pay for the change in stewardship levels. The

producer will prefer the increased stewardship level if the price, c, is less than the

maximum willingness to pay for that level of stewardship,

0 1Pr( ) Pr( ( , , , , , ) ).yes CV p S S M d cε= ≥ (6)

To determine the willingness to pay for increased stewardship levels using the

RUM model, we need to recognize that the maximum willingness to pay is a stochastic

variable and must be estimated. We let Gc represent the cdf of the maximum willingness

to pay. The probability of preferring the increased level of stewardship becomes

Pr( ) 1 ( ).cyes G c= − (7)

Following the description of the maximum willingness to pay function by Hanemann

(1984) we use the logit model,

1

1Pr( ) .1 cyes

e α β− +=+

(8)

The equation used in the willingness to pay estimation is given by,

1Pr(0,1) .bidα β= − (9)

Here, α represents the intercept and β1 is the estimated coefficient on the bid. Using this

estimation, Hanemann and Kanninen (1999) show that the median willingness to pay, µ,

equals the ratio of the intercept and the coefficient of the bid. That is, the median

willingness to pay for stewardship can be written as,

.αµβ

= (10)

25

To investigate the impacts of the producer characteristics on the likelihood a producer is

willing to pay for stewardship, the producer characteristics can be included in the

estimation,

1Pr(0,1) .bid dα β β= − + (11)

Here β represents a vector of parameters, and d denotes a vector of producer

characteristics. Generally, WTP estimates are made from the simpler model for

computational ease. Hence, the calculations are based on somewhat biased parameter

estimates due to omitted variables, but the data uncertainties are generally of a magnitude

such that the small bias caused by using the simple model is irrelevant for most empirical

work.

The data were collected using a mail survey sent to agricultural producers in three

contiguous counties (Adams, Grant, and Lincoln) in Washington State during the late

spring of 2004. This region is a sparsely inhabited, farming region which is populated by

a relatively few large farms and ranches. According to figures from the 2002 Census of

Agriculture, the average farm in these three counties is 810 acres versus 426 acres for the

state. Only one (Grant County) of the three counties has a sizeable town (Moses Lake,

population about 15,000). The three counties are predominantly white (>95%) with a

mix of Anglo and Hispanic populations. Median incomes and median housing values are

all very similar ranging from $35,000 to $37,000 and $83,000 to $99,500 versus state

values of $48,000 and $168,000 according to the Census bureau (figures generally from

2004 estimates). The Western parts of Grant and Franklin County are in the Columbia

Basin Project and so contain irrigated farms. However, the sample frame was restricted

to dryland farms. In sum, this is very homogeneous dryland wheat farm area.

26

An anonymous survey instrument was designed, starting with questions from

earlier farm practice surveys. Early versions of the survey were evaluate by informal

groups of farmers and researchers, tested on farmers participating in regional farm

practice conferences (e.g., direct seed association, particulate matter/wind erosion

research project), and critiqued by colleagues from economics and agronomic disciplines.

Care was taken to minimize ordering bias, layout bias and other potential questionnaire

flaws.

The survey contained 38 questions resulting in a total of 173 data points (survey

with gross results available from authors). Questions were closed ended with

opportunities for comments at the end and “other” responses where appropriate. All

variables used in the estimation model were taken from the survey data.

The independent variables include the bid amount the producers is responding to,

and producer characteristics. A more complete list of producer characteristics including

some used in the more fully specified model is shown in table 1. (We make only

minimal use of the full model, as it is not relevant to the purposes of this paper.)

The dependent variable is a yes or no response, 1 or 0, for each bid amount

associated with an increased level of stewardship. Producers were asked if they would

purchase a technology that, regardless of yield impact, would reduce wind erosion and

improve soil quality over a set of seven different per acre profit impacts. In terms of the

specification of our theoretical model, we are using the price of a conservation input (w)

used specifically for a conservation farm practice as our cost or bid variable. The

question asked whether farmers were willing to buy a piece of farm equipment for a

conservation practice that would also have a positive or negative effect on net profits

27

relative to their standard equipment and practices. The bid question allowed producers to

choose from four answers: “definitely buy,” “might buy,” “probably not buy” and

“definitely not buy” to each of the seven per acre profit impacts. Only positive responses

to “definitely buy” where included as a yes response. We used four response categories

and counted only the “definitely buy” to address what is called the “yea-saying” effect in

the contingent valuation literature. While single bound referendum questions are

considered to be incentive compatible, many studies have found that they may exhibit a

tendency to upward bias on yes responses, perhaps because respondents want to respond

positively to a cue evoking something they generally support.

Each survey generated a number of responses as we asked farmers their response

to a number of bids. To increase response variety, the sample was split and two forms of

the survey were administered. Half the respondents were presented the following bid

questions (per acre profit impacts in dollars): +5, 0, -1, -5, -10, 15, and –20. The other

surveys had the following bid questions: +3, 0, -1, -3, -5, 9, and -15. We treat every bid

as an individual experiment that elicits responses of willingness to pay. This procedure

means that we generate 7 observations for each respondent, though clearly the 7

responses are not independent.

The farmer names were selected from a sample frame comprising the Direct and

Counter Cyclical Program (DCP) participant lists (farm operators, owners and

sharecroppers), obtained from the Washington State Department of Agriculture Farm

Services Agency. The mail survey was implemented using the methods developed by

Dillman (2000). (Interestingly, Dillman participated in designing some of the early

surveys in the Washington State farm practice series and some of the questions in the

28

survey may trace back to him.) We offered a responsive incentive consisting of a coupon

for an ice cream sandwich from the renowned University Creamery -- ice cream from

“Ferdinand’s” has a statewide reputation and is a valued treat at football games. Thirty-

eight of the two hundred entries in the sample frame responded. Twenty-nine of the

respondents’ surveys contained complete, useable information. This is a low response

rate (roughly 15%) though not out of line with many recent surveys. In our case, some

explanation of the low response rate may be due to the nature of our sample frame.

The DCP operator lists have a broad scope, containing sharecroppers and multiple

owners of farm units. Many of the owners are “silent partners,” not active participants in

farm operations. These “farmers” tend to ignore farm management surveys. While we

tried to purge our list of multiple entries, we know we did not succeed in getting a pure

operator sample frame. For instance, several respondents sent the survey back with

notation to that effect, and some sent the survey on to the actual active manager. Hence,

while the number of useable responses is low for statistical purposes, it is probably

representative of farms in the area. Counting multiple responses from the same

individual expanded the statistical set, but strictly speaking these are not independent

responses.

The logit model found in equation 9 was estimated using maximum likelihood.

The results found in table 2 show that the median willingness to forgo profits for

increased stewardship is $4.52/acre. Somewhere in this range of $4.52 might be the

cutoff point for producers in their decision to adopt a conservation technology. The

intuition about these estimates of profit loss per acre is that the indifference point

represents the willingness to pay measure. The median producer should be willing to pay

29

for a conservation technology in the range of $4.52 per acre in terms of lost profits. This

can also be seen in the logistic graph of the survey data, figure 3, where the median

willingness to pay is $4.52. While this number is reasonable, given our small sample the

estimate should be treated as one of rough magnitude rather than an exact estimate of the

actual willingness to pay for stewardship. However, the positive WTP does provide clear

evidence that the median agricultural producer is willing to pay some positive amount to

engage in stewardship.

Thus, we find support for the stewardship model presented in figure 2, where we

showed a producer (indifference curves 2 and 3) willing to choose a production mix that

induced a negative profit change to gain some positive level of stewardship. This

supports the assertion that at least some producers do make their decisions partly based

on unobservable characteristics of the land stewardship process, and are willing to pay for

stewardship. Given a more robust sample, these estimates could be aggregated up to

represent the entire population of producers in this area (or any acreage), representing the

total willingness to pay for a conservation technology.

Conclusions

In the literature on the adoption of conservation farm practices, we found considerable

discussion of profit maximizing as the primary motivational factor. Also, we found an

extensive, but less clear, discussion of “social factors” that influence adoption of

conservation technologies. Moreover, we found two major missing elements in much of

the empirical literature. One is that there are few studies showing an explicit trade-off

between farm profits and conservation goals; the second is that most models lack an

30

explanation of heterogeneity in farmer motives and behavior. Whereas many empirical

studies implicitly accept heterogeneity with the ad hoc inclusion of stewardship variables,

the stated models of profit maximization imply an unacknowledged homogeneity in

farmer motives. We feel that the lack of a consistent, rational model of stewardship

decision-making underlies both of these weaknesses. The literature often fails to

integrate profit motives and stewardship motives into a consistent and systematic

decision model. The literature implies that “rational” farmers would profit maximize.

The implication is that conservation practices are adopted only if, on the one hand, they

are consistent with profits, or, on the other hand, if profit-maximizing behavior is

somehow trumped by other factors. From the point of view of systematic decision-

making this is very unsatisfying and from a practical point of view it is a bit insulting to

farmers.

In this paper we have resolved some of this disjointed discussion theoretically and

empirically. To clarify these matters we separate farm practices from farm motives.

Farm practices are described in production technology that may range from

environmentally harmful, to environmentally beneficial, at some point at the expense of

yields. These are non-conservation, conservation, and stewardship farm practices

respectively. We then develop a model of different types of farmers who differ in

motivation. On the basis of our model, three principle types of farmers may be

identified, though, if our theory is correct, reality will feature a continuum. A pure profit-

maximizing farmer is motivated only by income and wealth effects of farming and is

indifferent to whether the farm is generating positive or negative environmental effects.

To model farmers who value environmental effects, we specify a multi-utility framework

31

featuring a purely personal ego-utility and a purely social or stewardly s-utility. Using

this model we can identify two additional types of farmers. One type of farmer

maximizes ego-utility, where her utility derives from both farm financial returns and

from positive personal environmental impacts. This farmer values environmental effects

only to the extent that they provide direct personal benefits, such as recreational

opportunities or a good view. We suggest another type of farmer exists who has at least

two dimensions to her utility, an ego-utility dimension and a social or stewardly

dimension. By excluding the category, we have assumed that a pure s-utility maximizing

farmer does not exist. Following Sen and others, we assume that the s-utility is based on

commitment (obligation, duty) and is essentially non-commensurable with the e-utility

dimension. We recognize “in between” motives including three additional types of

altruism: sympathy based, status-based, and “warm glow” based altruism. However,

these motives might potentially be included in an expanded general self-interest model.

In contrast, commitment requires action at the explicit sacrifice of one’s own interest.

Hence, to simplify, we do not explicitly model the quasi-social motives such as

sympathy. Nothing in our framework requires that all farmers possess an s-utility

component in their overall personal motivation set -- their meta-utility function if you

will. In fact, it is very reasonable to assume that all three types of farmers exist – some

entirely motivated by profitability and wealth, some entirely motivated by their own

needs, which include environmentally friendly components, and some that include a

sense of obligation to others (future generations, God, or spirit of the land) that requires

personal sacrifice. Thus, our overall framework predicts a heterogeneous farm operator

population.

32

A crucial test for our model is whether some farmers might make personal

sacrifices with no apparent personal reward. In truth, an experimental structure to

identify such a situation is very hard to devise, given the existence of the potential

expanded utility model. We have developed some empirical evidence that supports the

existence of the s-utility based stewardship farmers. These farmers have a stated

willingness to forgo profits for conservation. While we cannot rule out the possibility

that a more general self-interest accounts for this willingness to pay, certainly a pure

monetary loss is consistent with, even suggestive of, uncompensated sacrifice.

Our empirical study was small in terms of both true sample size and in terms of

the precision of the experimental design and the statistics. Still, the results seem both

meaningful and realistic. Not all farmers stated a willingness to forgo profits, and the

amounts stated by those willing to trade profits for conservation activities were at

reasonable levels for the farm area in question – less than $5 an acre - $5000 on a 1000-

acre farm. These results are confined to a small population, but we hope that the model

described here will stimulate a more widespread use of this multi-utility model to explain

producer choice when motivational information is available.

For policy purposes, our results support the notion that at least some producers

have a direct stewardship motive to undertake some level of conservation practices, and

that they are willing to forgo some profits to adopt these practices. This means there may

be at least some small farm operator population that would not require guarantees of

profitability to be willing to engage in stewardship practices. Clearly, if one wanted to

increase producer stewardship activity even more, one could subsidize the technology.

Invoking both the profit and the steward motives in farmers would obviously make for

33

more appeal to a larger proportion of farmers (given our assumption of heterogeneity)

and stronger responses from those who have both profitability and stewardly motives. In

conclusion, while our results are too weak to serve as a basis for policy, the idea of multi-

utility based farm behavior can inform future research and support innovative policy

thinking.

34

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Figure 1. Production Possibilities Frontiers with Yields and Environmental Effects.

Figure 2. Production Possibilities Frontier and Operator Indifference Curves.

Environmental Effects

Yields

PPF(1)

Eneg EA

PPF(2)

Profit

I2

PPF I3

Environmental Effects

I1

O

A

B

41

Figure 3. Logistic Graph for Survey Data

Pr (yes)

-5 0 5 10 20

Bids

Median WTP

42

Table 1. Description of Independent Variables Variable Description Mean St. Deviation Experience Number Years Farming 28.24 11.27 Age Years by ten year age brackets

5.86 2.23

Owned Acres Number of Owned Acres 1278 1384 Rented Acres Number of Rented Acres 1973 1594 Perception of

Natural Resource

5 point scale ranging from strongly agree to strongly disagree

1.55

Education 6 point scale ranging from some high to advanced college degree

4.51 (some college)

0.99

Firm Type 1 if non-family farm corporation 2 if family farm corporation 3 if partnership with another producer(s) 4 if farm completely by yourself

2.89 (partnership with another producer)

Profit Preference 5 point scale ranging from definitely select A to definitely select B

2.34 (less variance)

43

Table 2. Logit Regression Results for the Simple Model Variable Name: Description: Parameter

Estimate P-Values

Intercept -1.4682 <.0001 Bids Profit loss per acre -0.3246 <.0001