Ecological Economics 122 (2016) 71–78
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Ecological Economics journal homepage: www.elsevier.com/locate/ecolecon
Methodological and Ideological Options
Inferences from sparse data: An integrated, meta-utility approach to conservation research Hayley H. Chouinard a, Philip R. Wandschneider a,⁎, Tobias Paterson b a b
School of Economic Sciences, Washington State University, PO Box 646210, Pullman, WA 99164-6210, USA Forecasting Division, Office of Financial Management, Olympia, WA, USA
a r t i c l e
i n f o
Article history: Received 19 May 2014 Received in revised form 24 September 2015 Accepted 15 November 2015 Available online xxxx Keywords: Conservation Motives Multi-utility Stewardship
a b s t r a c t Current behavioral research in conservation adoption has been unable to clearly identify the key characteristics of successful adoption. Most conservation studies employ a theory which focuses on one feature (e.g., profits, attitudes, information, norms, or technology). We propose an integrated, three-component framework to model conservation comprising: 1) motives (including stewardship) and meta-utility, 2) firm practices and technology choice, and 3) impacts. We justify this model and compare its use with others in an empirical setting. We build two empirical conservation measures and apply them to a sparse primary data set. Our results show links between the measures and underlying motives—financial and non-financial. We conclude that research and data interpretation using a multiple-motive, integrated framework can improve future research efforts and conservation policy. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Despite decades of research and billions of dollars of policy efforts, conservation policies have met with mixed success. Losses of soil due to wind and water erosion have declined modestly in recent years but still exceed 1.7 billion tons per year in the US (USDA, 2013). Over 30% of river miles, lake acres and estuary square miles cannot fully support their Clean Water Act designated uses (Heimlich, 2003). While agricultural practices are a major contributor to soil erosion and poor water quality, the environmental impacts of agricultural production practices can differ significantly. Farm operators select practices, and their motives determine the effectiveness of policy and, ultimately, the environmental impacts. As Reimer et al. (2014) observe current behavioral research in conservation adoption has been unable to clearly identify the key characteristics of successful adoption. Thus, in their meta-analysis, Knowler and Bradshaw (2007) classify a long list of conservation related factors (170) into four major categories and find inconsistent and weak results. They infer that conservation adoption is an idiosyncratic process. Similarly, in their extensive review, Pannell et al. (2006) conclude that adoption “depends on a range of personal, social, cultural and economic factors, as well as on characteristics of the innovation itself.” In a meta-analysis of recent empirical work, Baumgart-Getz et al. (2012) examine the effects of 31 social factors and find many potential variables
⁎ Corresponding author. E-mail addresses:
[email protected] (H.H. Chouinard),
[email protected] (P.R. Wandschneider).
http://dx.doi.org/10.1016/j.ecolecon.2015.11.019 0921-8009/© 2015 Elsevier B.V. All rights reserved.
to be insignificant, and where significant, small. They conclude that understanding how effects “fit together” is essential since few stand out. In response, several recent papers have called for increased effort to create new research directions (e.g., Reimer et al., 2014). Indeed, some scholars have done so. For instance, an emerging literature focuses on the heretofore neglected spatial dimensions of conservation adoption; but spatial models tend to ignore established theories and results and consign non-spatial factors to ad hoc control variables (e.g., Broch et al., 2013). Kabii and Horwitz (2006) offer another approach; they attempt to incorporate the diverse findings of many studies into a single model. However, they introduce a new systems model. In the first case (Broch et al) new components are added, but the old knowledge is set aside; in the second case (Kabii and Horwitz) an integrative model is proposed, but it comes with a completely new paradigm. We believe that success in sorting through the complex and multidimensional conservation practice decision problem depends on increasing interdisciplinary conversations through construction of an integrative framework. In this paper we hope to help remedy two problems: the lack of a framework that facilitates cross discipline communication and research integration, and the constraints on what the data reveals because the use of ad hoc “control” variables reduces the scope for inference. To be transparent we will rely heavily on economic traditions, but we freely incorporate elements of sociological and diffusionist models. There are many precedents for our proposal, and we start with existing models and theories. One economic foundation is standard production economics which very nicely models production technologies. We link the physical and production dimensions to behavior by using a multiplemotive, meta-utility approach. The meta-utility approach facilitates
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the introduction of concepts that do not fit in a traditional economic utility function. Using this framework, we examine a specific empirical case with limited data. We construct two somewhat innovative empirical conservation measures using primary data. We use these indicators to examine the motives behind adoption of conservation practices and compare the resulting inferences using four different socio-economic paradigms. We argue that researchers employing three standard disciplinary frameworks would find little in this limited data set. However, using the proposed approach we can extract a story, which helps illustrate the power of the proposed integrating framework. In the remainder of this paper we begin with a short review of the literature on conservation adoption motives. We develop the proposed integrated model of the conservation process. Then, we present two empirical measures of conservation and apply each of the idealized models to interpretation of the data.
2. Related Literature—Motives, Interests and Behavior We begin with a brief overview of theories of motives for conservation practice adoption including profit, utility, attitudes, and multiple motive or meta-utility models. The profit motive has appeared prominently in economic adoption work since Griliches' (1957, 1958) seminal and influential studies of hybrid corn adoption. Many economic studies suggest conservation practices are adopted when they increase (or do not reduce) profits (e.g., Cary and Wilkinson, 1997; Honlonkou, 2004; and Lichtenberg 2004). In most profit-based empirical studies nonfinancial factors including demographic characteristics of farmers are included as ad hoc “controls.” One weakness of a purely profit-based theory of technology choice is the implication that producer preferences are homogeneous (Nowak, 1987). Hence, if all producers manage under the same profit motive, we should observe identical producer actions across technically equivalent farms. In fact, we observe that conservation and other farm practices vary substantially over time and across people. Variation may occur because operations differ on technical factors such as weather, soil properties, crop rotations, and agronomic and machinery choice sets. However, even when econometric models incorporate technical factors, results leave considerable unexplained variation in producer practices.1 Some researchers explicitly incorporate more heterogeneous agents and non-financial goals. Klonsky et al. (2004) show that farmers can maximize profit and still be land “stewards.” Many others incorporate a significant role for stewardship and social motives (e.g., Neill and Lee, 2001, van Kooten et al., 1990). Agricultural producers may be motivated to follow a “way of life,” rather than to operate entirely as a profitmaximizing business (Wallace and Clearfield, 1997). Some part of these factors can be formally incorporated into economic models by using a utility framework (e.g., Upadhyay et al., 2003), but they are often ad hoc additions. Heterogeneity in the rate of technology adoption has been widely recognized in many disciplines, especially since Rogers' seminal book was first published in 1960 (Rogers 2003). In the diffusionist literature people are heterogeneous across information and personality: some individuals adopt new technology early and enthusiastically, while others adopt new technologies reluctantly, after a period of observation and reflection. Adoption of new technology will be spread out over time—as described by the classic innovation adoption S-curve. It has been incorporated in many sociological studies as well as some economic studies of conservation adoption (Bishop et al., 2010; Sheeder and Lynne, 2011). 1 A conventional, but problematic, economic explanation for farmer heterogeneity involves differences in risk attitudes (Marra, Pannell, Ghadim, 2003). See also discussion of farmer heterogeneity in Sheeder and Lynne (2011)
Historically, economists have shared investigation of agricultural conservation practices with sociologists (Reimer et al., 2014; Pannell et al., 2006). We highlight some social-psychological models of stewardship motives. These studies often employ attitude models (e.g., Maybery et al., 2005; Greiner and Gregg, 2011). The family of theories (Theory of Planned Behavior; Theory of Reasoned Action) founded by Fishbein and Ajzen (1975, 2010) and Ajzen (1988, 1991) underlie most models. Here, intentions are formed when agents have positive attitudes toward an action and believe that others approve or expect them to act in particular ways. While economic models treat the brain as a “black box,” attitude theories conceive of mental features as real brain processes. However, attitude theories leave the leap from mental state to action largely unexplained: the theories predict tendencies rather than distinct choices. Recently, economists have begun to explore the “black box;” behavioral, experimental and neuro-economics increasingly reveal the neural and behavioral roots of choice (Glimcher et al., 2008). Over the last few decades, a number of economists and social psychologists have attempted to explicitly incorporate multiple motives, thereby connecting the profit and utility world of economists with the mental world of other social sciences. Some early examples of use of multiple motives in studies of conservation adoption include the work of Van Kooten et al. (1990), Maybery et al. (2005); Dobbs and Pretty (2004), Upadhyay et al. (2003), and Sinden and King (1990). In this genre, producer motives can be multidimensional and include nonselfish and/or non-consequentialist motives. For example, stewardship may be grounded in a belief in the “rightness” of conservation actions based on social norms. Such a norm-based motive is deontological and non-selfish; hence, not formally compatible with the standard utility model on both counts (Sen, 1977). Several recent economic studies have developed a multidimensional motive approach and applied it to the conservation adoption decision. One emerging strand of research employs some version of a dual utility, meta-utility or multiple-interests model. A sample of studies includes: Lynne, 1995, 1999, and Lynne, 2002; Lynne et al. 1995; Lynne and Casey 1998; Hayes and Lynne, 2004; Kalinowski et al. 2006; Chouinard et al. 2008; Bishop et al. 2010; and Sheeder and Lynne, 2011. While these models are recent, multi-dimensional utility models can be traced to the foundations of economics. For discussion, see, inter alia, Brennan, 1989, Etzioni, 1986, Lutz, 1993, Sen, 1977.
3. An Integrated Conservation Framework In this section we describe our integrated model of the conservation process. It has three components: motives (meta-utility), behavior (farm practices) and impacts (farm profits and environmental impacts). The discussion presented here is similar to the model developed in Hayes and Lynne (2004, 2013) and Sheeder and Lynne (2011). However, the Hayes-Lynne-Sheeder model is intended to be a broader reconstruction of economics and contains elements beyond our scope. Our model follows more closely that found in Chouinard et al. (2008). We describe a simple algebraic model for clarity. We start with behavior—the choice of technology or farm practices, FP, described by a production function. The production technology comprises three vectors of inputs (v, w, z) and two of outputs (Y, E). 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). Farm practices generate agricultural outputs, which we summarize as crop yields which generate profits (Y) and environmental effects (E). Some practices may generate negative environmental effects (Eneg) (dust, soil erosion, water contamination), while other practices may generate zero or positive environmental amenities (EA)—such as increased soil quality or open space. Eq. 1
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shows the total production technology set encompassing all production possibilities. ðY; EÞ ¼ FP ðv; w; zÞ
ð1Þ
For convenience we assume that all “conventional” technologies produce net negative environmental effects, and all conservation practices produce net positive environmental effects. (This is obviously false, since most agriculture creates open space amenities.) The total set of technologies embraces all possible combinations of outputs. We specify the motives of the agents based on the literature discussed above. We include profits as suggested by the theory of the firm and many empirical studies. The utility model suggests some additional self-interest, but non-financial motives, such as the attractions of rural life. Self-interest utility might also include some more complex social network values such as enjoyment of small communities or the prestige of being recognized as a “good farmer.” Following the wider social science literature (Ajzen, 1988, 1991) and recent multiple interests models we expand motives beyond self-interest as in the literature cited above. Multiple utility models are most common, often reduced to dual utility models including a self-interest component and a non-selfinterest (other, social) component. Using a dual or multiple utility expression implies it is a functional with some rule for aggregating the separate utilities into a single value. However, we follow Etzioni (1986) and Sen (1977), who argue that some motives are rule and norm based (deontological) rather than utilitarian (consequentialist), and that, utility and obligation motives are incommensurable. Hence we prefer the term meta-utility, implying the possibility of both multiple dimensions and incommensurability. Following Lynne (1999, 2002), Hayes and Lynne (2004), Chouinard et al. (2008) and others we consider a farmer's meta-utility to be made up of dimensions of self-interest and concern for others –the rights and well-being of community and future generations. We label the self-interest component self or ego interest (α), and the non-egoistic component omega (ω) motives. Stewardship may be generated from either or both interest domains. A “steward- producer” motivated by both self-interest and ω-motives is willing to sacrifice some profit for an increase in environmental effects, but also some environmental effects for profit, varying under different combinations of self and ω-interest. Among these stewardshipmotivated farmers, there can be great heterogeneity, even for those in technically identical circumstances. Hence one explanation for the observed heterogeneity of farmers is differences in the importance of self-interest and ω-interest: Some farm operators are passionate stewards, others much less so. Hence, a multiple motive approach ‘natively’ entails cross-sectional and inter-temporal differences in rates of conservation adoption. The farmer motivated by both self and ω motives will make a choice that “balances” the two interests. We want to be careful to say that the farmer is not “maximizing” a functional—an aggregation of the two functions. A maximizing model may mischaracterize the decision process. The neural processes of finding a “balancing” solution might better be represented as some non-analytic computation process. There are a number of possible resolution mechanisms. Neuro-scientists Livnet and Pippenger (2006) describe a computational model of a “multiagent” brain. The recent book by Kahneman (2011) summarizes a large literature on how the unconscious and “intuitive” system 1 and the conscious and deliberative system 2 “brains” interact, and where context is important in determining the weighting given each “brain.” Hayes and Lynne (2004, 2013) postulate a higher order utility which finds a mental equilibrium, a “peace of mind,” requiring some sacrifice in each utility domain. For our purposes, the way in which the metautility system is “closed” is not important; we understand that choice and action require some resolution and leave the process unspecified. The model above provides a logical and workable foundation for an integrated model of conservation comprising three major
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components—motives (self-interest and ω-motives), behavior (technology choice, farm practices) and impacts (yields as well as environmental/conservation impacts). The meta-utility model depicts motives, and a standard production function defines the behavior-impact link. The intentions and motives which drive behavior extend beyond the profit motive to include environmental (stewardship) goals. In turn, the motivations guide the choice of technology. Finally, farm practices have impacts that may generate environmental damage and degradation on the one hand, or land preservation and environmental amenities on the other. Choose FP(v,w,z) such that: Umeta (Y,E) is “most desired,” subject to ðY; EÞ ¼ FPðv; w; zÞ
ð2Þ
The farm operator chooses farm practices (FP) by using conventional and/or conservation practice inputs (v, w, z) that will generate the agricultural profits through yields (Y) and environmental outcomes (E) most desired by the farmer considering profits, stewardship or both in the meta-utility, (Umeta). 4. Alternative Models of Conservation Adoption Here we investigate how different theoretical perspectives will frame interpretation of data about conservation farm practices with the purpose of supporting our proposed strategy. We define four investigator types corresponding to the four strands of literature reviewed earlier: profit maximization, utility maximization, attitude models, and meta-utility. These are stylized models representing no specific authors. Consider Type One investigators who approach conservation studies from a pure profit-maximizing perspective. A perfectly idealized Type One investigator would be satisfied with information about market outcomes exclusively. The seminal Griliches' (1957) paper is an example. In modern terms one might argue that efficient markets and rational actors incorporate all relevant information. Type One researchers generally utilize non-financial factors as ad hoc control variables to improve the statistical “explanatory power” of the model, but without including the variables in the model proper. The Type Two investigator approaches the problem with the richer tool of utility theory –permitting a broader view of self-interest. The Type Two researcher includes variables describing non-market impacts of farm practices. Some investigators will focus only on the farmer's choice and so examine variables that would appear only in the agent's own utility function – factors that affect a farmer's life style. Other Type Two researchers are interested in the impacts of farm operations on other affected agents. Type Twos want to identify interactions among the potential outcomes (trade-offs, complementarities, rivalries), but they should be able to do this from the data on revealed or stated behaviors. The Type Three investigator seeks data about motives and mental states. This investigator might be an environmental psychologist or sociologist employing an attitudinal or cognitive model – for example, the theory of planned behavior. These researchers would employ surveys of farmers and collect data about their attitudes, emotions, norms and evaluations as revealed in verbal responses. They would use a variety of indices that have been developed in the literature, or, if necessary, construct a new measure. Type Three investigators would see if the connections among the types and levels of mental components would correlate as hypothesized. They would hope to connect some of the attitude information to farm practice choices. Typical tools include structural equation models (SEMs). We propose that an investigator of a fourth type would employ the integrated, three-component, meta-utility framework described above. This researcher would want the information desired by the
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other three researchers. However, where some of the data are missing, the Type Four researcher might be able to use other information to infer missing information. The remainder of the paper intends to illustrate. 5. Data and Empirical Model The data are based on a study conducted on a small sample of producers in eastern Washington State. It has a rather small set of variables, nothing like the set desired to fully model the processes described in the theory section of this paper. Nonetheless it is an interesting study because it has data about adoption of conservation technologies in the “real world” setting of an important agricultural production zone—dryland wheat farming in Eastern Washington, an area with a serious wind erosion problem affecting the nearby city of Spokane. 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 sparsely inhabited, farming region contains a few relatively 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 (N95%) with a mix of Anglo and Hispanic populations. Median incomes and median housing values range 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. The sample frame included only dryland farms. In sum, this is a very homogeneous dryland wheat farm area. The survey was administered anonymously. 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 sample frame comprised 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).2 Respondents were offered a response incentive consisting of a coupon for an ice cream sandwich from the renowned Washington State 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. While not a high response rate (roughly 15%) it is not out of line with many recent surveys. For some questions, multiple responses were recorded from the same individual—expanding the number of observations but introducing non-independent responses. Our purpose here is to see what can be done with this limited data set. We have the data shown in Table 1, which includes both raw data and some variables aggregated from the raw questions—as will be described later. From this data we know something about the farms and some basic demographics about the sample farmer population. Regarding the motives, farm practices and outputs we have the following, taking them in reverse order. We have practically no specific information about direct impacts. We have some general, indirect, and imprecise statements about profits, revenues and costs. We have no information about the physical impacts—dust in the atmosphere or soil eroded into rivers—that would be of interest to the farmers or to downwind and downstream residents. We have more information about farm 2 The 2000 version was used for this survey. The most recent edition is Dillman, Smyth, and Christian. 2009 Internet, Mail, and Mixed-Mode Surveys: the Tailored Design Method Dillman et al. (2009)
production practices including the number, types and acreage of cultivation practices reported. We know the size of farms and we have a list of the agronomic practices each farm uses and on how many acres they use those practices. From a financial, Type One, point of view we cannot calculate profits or look at the effect on costs, revenues and profits of different practices. We do have a rather vague self-reported estimate of the relative impact that different farm practices have on the farmers expected profit. Overall, this data is not of much use to the profit approach researcher. The Type Two, utility-based researchers has even less to work with. We don't have data on farmers' “quality of life” and the value they attach to their “life-style;” nor do we have data about their “good farmer” reputation and how changes in farm practices might affect their status. Different farm practices imply different labor-leisure trade-offs and different accident rates, but again we know nothing of these things. While we are specifically concerned about environmental damages we do not have data about the emissions and their on or off-farm effects. Hence, we cannot investigate the willingness to pay value of the conservation actions – from either the on-farm or the neighboring non-farm populations. For the Type 3, attitude-based approach, we again find sparse data. Little information about the attitudes, evaluations, and norms of the farm population were collected. However, we have some interesting scraps of data. We do have some socio-demographic data including years of farm experience, age, and education. The Type One or Two analyst generally use age and education to “control” for differences among agents—for aggregation into representative agents. However, some Type One and Two investigators try to infer something about retirement planning from age. In contrast, the Type 3 investigator might see education or age as a window on attitudes. In the Type 3 view, the “demographic” variables might be indicators of theoretically important, latent, motivational variables. Data on age and education is of some help because the world of mental states definitely changes over time and with education. More promising still, we have some data on attitudes, specifically on attitudes toward risk related to conservation practices. What we propose to show is that, even with this very sparse data set, we can learn something about the link between farmer motives and farm practices—and a little about impacts. The framework we developed not only incorporates most of the variables that Type One, Two and Three investigators believe to be informative, it says they are linked. If something is missing, we have some hope of making inferences based on what the links imply about the information we do have. To capture behavior we construct two indicators of conservation farm practices from the raw data. We then develop some connections between the degree to which farmers adopt farm conservation practices and their motives. We include several rough measures of self-interest and ω-motives. From these we partially explain the adoption of farm practices. The two indicators of farm conservation practices are moderately innovative, but similar to those used in other conservation studies. The conservation practices (“PRACTICES”) indicator is constructed from the number of conservation practices a producer adopts and the degree or intensity of adoption of each practice. There are 11 predefined and one “other” conservation practices—defined in consultation with producers and crop specialists. By increasing with the number of conservation practices adopted, the PRACTICES indicator assumes an association between commitment to conservation and number of conservation practices. In contrast, most conservation studies select one or two “key” practices and use a binary (adopt or not adopt) or trichotomous (zero, 1 or multiple practice) measure of adoption. There are problems with this standard approach. First, it relies on the judgment of the research team to select the “best” practice to use as the indicator for conservation. A rationale for this approach is that the “experts” know which practices have the “best” ultimate impact on the target outcome. However, 1) experts don't always know which practice is the most effective
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Table 1 Summary statistics for conservation measures and explanatory variables. Variable name
Variables description
Sample range
Sample mean
Standard Deviation
Extent Gross conservation acres Farm size Owned acres/total acres Education Age Conservation risk Profit perception Experience
Practice adoption average Gross conservation acres Total farm acres Ratio of acres owned to total Education level Years Profit variance Profit impact perception Years farming
[0,2] [201,15,500] [156,11,400] [.000149,1] [1,6] [1,10] [1,4] [1,3] [6,50]
0.845 5753.46 3151.58 0.406 4.52 5.86 3.48 1.45 28.24
0.3043 5646.97 2618.89 0.343 0.99 2.23 0.91 0.463 11.27
conservation practice, 2) the conservation practices will have different effects on different farms with different managers, and 3) the goals of conservation are not as monolithically defined as this approach implies. While a mistake in choice of key practice would scuttle the whole study, in practice, researchers tend to do a little statistical probing for a key indicator that fits well. Unfortunately, this exposes the research to greater risks of sampling errors. Finally, note that the focus on one or two practices discards information about the other farm practices actually used by farmers. In contrast, we counted all conservation practices reported by the farmers (up to 12). Another innovation in the PRACTICES indicator is that each farm conservation practice is weighted by a three level intensity or degree of use (extent) scale: zero indicates no adoption, 2 indicates full adoption and 1 is intermediate. The weighted adopted practices are summed to construct the indicator. Hence, the PRACTICE indicator has a range from zero (no adoption of any practice) to 24 (full adoption of all 12 practices). We treat it as cardinal and continuous. Another often-used conservation indicator is the number of acres over which the farm practice is applied. Generally, researchers use the acreage for only a single practice. The argument is that one cannot compare 100 acres of practice A to 50 acres of practice B; hence, using only one practice provides a consistent measure. However, using the same practice on two different farms can have results as different as using two different practices on different farms – unless one controls carefully for farm type. In short, the aggregation problem is difficult to avoid. We simply embrace the problem and count total and overlapping acres of multiple practices as our measure. Hence, a farmer with 500 acres using three techniques may have as many as 1500 acres counted in this measure. We argue this measure is at least as likely to be consistent as the single practice measure. By using all practices we measure the practices judged by farmers to be relevant to their farms. In summary, our second conservation measure is the cumulative or gross conservation acres (ACRES) variable, which equals the number of acres devoted to conservation practices during the previous year from all practices. We simply sum the reported acreage devoted to each of the twelve listed conservation practices for the previous farming year. Now we consider adoption motives. Recall that our conceptual model suggests that the reasons for adopting conservation practices will often include both economic gain and other goals including stewardship. Based on this theory we specify a model which links the conservation practices measures (dependent variable) to a set of independent variables to investigate the motives for adoption. From the limited data available, we pick a set of independent variables that reflect financial and stewardship goals, as well as some other variables. The world being messy, many of our operational variables probably reflect a blend of financial and stewardship incentives. These include variables for education, experience, age, and owned acres/total acres. Overall the empirical model of conservation practices measures (CPM) looks like this: CPM = F(Financial Indicators, Stewardship Indicators, Control Variables). We employ three variables that are most apt to reflect financial motives—profit perception, farm size and financial capital. The survey
asked producers about their perceptions of the impact on profits of adopting each of the twelve different conservation practices. The producers could give one of three answers: reduce profits, no impact or increase profits. The profit perception variable equals the average of the profit impact perception answers for each producer. The obvious hypothesis is that the more profitable a practice is perceived to be, the greater the chance of adoption. This is a very crude, self-reported aggregate Type One investigator variable. We interpret farm size as a mostly financial indicator. Greater size gives greater access to financial capital and greater opportunity to experiment. Consider a producer with large cash savings, alternate income sources, or a large farm. Having more assets allows for the possibility of fractional adoption—such as implementing technologies on only part of the land. Fractional adoption and trialing has been identified as an important factor (Pannell et al., 2006). The large-scale producer has more opportunities to employ alternative practices, and (or) continue with the prior practices. Hence, as farm size increases the possibility of adoption increases—for financial reasons. While the financial and scale effects of size imply greater opportunity to adopt conservation practices, another possibility is that more profit-minded farmers are likely to prefer larger scale. Farms associated with “green” practices are likely to be smaller and more intenselymanaged. However, this is only a tendency and any given large scale farmer may be very stewardship-minded. To further complicate things, farm size can be a simple control variable (depending on specification) to scale the model to the relative size of the farm. In sum, we believe the farm size variable will be positive, but relatively non-informative about motives because of its ambiguity. As a more direct measure of financial capital we use the ratio of owned acres to total acres (owned acres/total acres). In the literature financial capital is often positively related to conservation adoption. The greater the owned acres per total acres, the greater the benefit the producer (land owner) may gain from investing in technologies. In contrast, as owned acres per total acres decreases, the incentive to “mine” land resources increases. Furthermore, a greater proportion of owned acres implies a greater willingness to invest in the land to gain utility from stewardship practices. Of the non-financial variables available from the survey, the most relevant is a measure of willingness to bear risk when adopting conservation practices. To be clear, we did not attempt to measure general attitudes toward risk—the survey question concerns a producer's tolerance for risk specific to conservation practices. Let's call it Conservation Risk. The respondents were provided with two scenarios—practice A and practice B—for a potential new conservation practice. Practice A has a lower profit variance over the 5 years than practice B. Practice A has an average change in profit of zero and practice B a positive average change in profit. The respondent was asked the likelihood they would choose farm practice A or B. Each answer was assigned a numerical value as follows: definitely A (1), probably A (2), no preference (3), probably B (4), and definitely B (5). We treated the variable as continuous in the model, and thus, the higher the magnitude of this variable, the more likely the producer is willing to accept higher risk.
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Generically, risk tolerance applies to both financial and new practice (stewardship) motives. Moreover, in principle, a conservation practice may be risk reducing. We assume that conservation practices are never risk reducing. Other things equal, newer practices are riskier because of lack of information and lack of practical skill (Marra et al., 2003). On these grounds we could count producers who are willing to accept higher generic risk as more likely to have stewardship motives and more likely to adopt conservation practices. However, the definitive point is that this survey question specifically asks about the risk involved in adopting a conservation practice. Conservation Risk is our clearest indicator of stewardship intentions. Most literature hypothesizes that education should have a positive relationship with the adoption of conservation practices, generally because it lowers the cost of information – a key feature of diffusionist models. The survey had six education level groups, ranging from some high school to advanced college degree, which we treated as a continuous variable, a statistical sin adopted often. The education level can affect conservation adoption through both financial and non-financial factors. Participation in educational clubs and events increase the availability of information, lowering the cost of conservation knowledge, and thus increasing adoption rates. Moreover, an increased level of education may increase a farmer's awareness of environmental issues. Again this would lower the cost of information and increase the likelihood of conservation adoption according to diffusionist and decision-cost theories. Participation in educational clubs and events may lead to a greater social reward and personal gratification from adopting conservation practices. Experience measures the length of time a producer has been farming at their present location. Again, the experience level can affect conservation adoption through both financial and non-financial factors. One economic factor is the cost of transition to newer technologies. Past research suggests that novice farmers may be more willing to try different technologies, while more experienced farmer tend to “stick with what they know”—they have a higher cost of switching technologies. Thus, more experience will lead to a lower likelihood of adoption—and this probably applies to any new technology. On the other hand, the “stewards of the soil” and “farming as a way of life” concepts suggest that greater experience may reflect greater devotion to the farm and sense of obligation to the land. Hence, a longer time spent farming or greater experience might imply a greater devotion to the land for stewardship reasons. Ideally, we would like more questions to distinguish these cases, but we don't have it. In summary, we have no strong prediction for the experience variable; perhaps it is best to treat this as a “control” variable. The farmer's age enters the model as a continuous variable. Older farmers are less likely to gain from adopting new technologies because of the delayed reward in conservation. This decreases the chance that an older farmer will adopt conservation practices because the (self) benefits are less likely. However, age may also be associated with the opposite effect; older farmers may have a large desire take care of the land in order to pass it on to future generations. Again, we learn the lesson of what should be the contents of a better survey. 6. Estimation and Results To examine the measures of conservation, we employed ordinary least squares regression models on this simple cross sectional data set. The models vary only in the conservation measure used: the number and intensity (extent) of PRACTICES adopted and the cumulative ACRES on which conservation practices were implemented. Table 2 presents results from the two models. Both models show that economic and non-economic factors play a role in use of conservation practices. In the conservation PRACTICES model, three of the explanatory variables are statistically significant: profit perception, conservation risk attitude, and age. The estimated coefficient for profit perception was positive and that for age was negative.
Table 2 Estimation results for two conservation measure models. Independent variables
Measurements & models of conservation practice ‘Practices’
Intercept Profit perception Farm size Conservation risk Education Experience Age Owned acres/total acres
‘Cumulative acres’
Coefficient estimate
P-value
Coefficient estimate
P-value
0.472 0.310⁎ −0.071 0.726⁎
0.300 0.021 0.287 0.017 0.219 0.309 0.006 0.187
−31,562.39⁎ 2506.57 3759.89⁎ 15,347.19⁎
0.0007 0.182 0.0009 0.001 0.688 0.755 0.221 0.949
0.065 0.005 −0.640⁎ −0.193 R-Square 0.504 F-Statistic 3.050
303.26 24.37 3788.90 131.39 R-Square 0.714 F-Statistic 7.161
⁎ 5% significant P-values.
As expected profit from the adoption of conservation practices increases, the amount of conservation also increases. The result from the age variable supports the concept that older farmers (in this region) are reluctant to invest in the potentially longer duration payoffs of conservation measures. The positive estimated coefficient on the conservation risk preference shows that non-economic factors may also influence conservation activity. This result supports the hypothesis that farmers who have a stated willingness to risk profits in order to practice conservation also report that they have adopted more conservation technologies—linking attitudes-motive to practice. In the ACRES model, farm size and risk preference are statistically significant. The positive estimated coefficient for farm size implies that a greater number of acres allows for a greater number of acres available for alternative practices. A producer with 500 acres cannot adopt conservation on 1000 acres, whereas a producer with 1000 or more acres certainly can. Clearly this variable mainly controls for size. An alternative formulation would have been cumulative acreage per total acres, but the reported model performed better statistically. The positive estimated coefficient on conservation risk preference indicates that stewardship-inclined farmers willing to risk profits are more likely to adopt conservation technologies. These two variables show that it is important to consider both economic and non-economic motivations for conservation adoption. 7. Discussion and Conclusions In this paper we present a general framework for analyzing conservation behavior with an application to soil conservation. Presently, conservation adoption studies employ a wide variety of models, ranging from economic studies focused narrowly on firm profits to sociological studies emphasizing farmer attitudes, norms, community and social networks. A recent assessment concludes that current behavioral research has been unable to clearly identify the key characteristics of successful adoption (Reimer et al., 2014). Our argument is not that past studies have used incorrect approaches or theories, but that they are incomplete and isolated. We believe that by expanding the concept of motives and integrating it with traditional production economics a more inclusive research framework can be constructed with benefits in coordination of research and productive interpretation of data. We propose an integrated, three-component framework for understanding conservation comprising: 1) motives and meta-utility, 2) farm practices/technology, and 3) impacts. We use conventional production functions to model the technology-impact link as do most economic studies. However, we use a meta-utility motivational theory rather than either a conventional economic profit or utility theory, or a conventional social-psychological attitude model. This conceptual framework incorporates producer heterogeneity: encompassing pure
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profit maximizing farmers, farmers who gain utility from conservation and profit, and farmers who follow norms of stewardship. We argue that investigators of all disciplines can use this framework to place their contributions in a wider setting, with more linkages, and thus extract more information, and, ideally, improve the relevance and readership of their studies. We present this approach carefully, but informally. We look forward to future research in behavioral, experimental, and neural economics to provide a more rigorous model. We use this framework to examine a case in which we have a sparse data set. We explore what a study based on this integrated framework could learn from such a data set relative to investigators using a narrower disciplinary base. We develop two measures for conservation activity (PRACTICES and ACRES). These are related to measures we find in the existing literature, but we introduce indicators that use more information. We then examine factors that influence adoption of conservation practices using a simple regression model. Our two models for the two different measures generate slightly different but nevertheless consistent results. In particular, both models generate positive and statistically significant results for the conservation risk variable. The conservation risk variable measures willingness to accept profit risk when adopting a conservation practice (It is not a general risk preference question.). Both models indicate a willingness to undertake conservations practices even when there is some risk of profit loss. We interpret this variable as supporting an underlying non-financial, stewardship “attitude” or motive that helps explain conservation. Other results are ambiguous. Variables which are significant in one model are not in the other and vice versa. Interestingly, what we interpret as the stewardship variable is the only robust finding. However, each model has at least one significant financial variable, though they differ on which one. Overall, results support the meta-utility account of motives. The pattern of results also supports our contention that the wider framework we propose can help (justifiably) extract more information than narrower disciplinary models. Where a utility model might include stewardship related variables as ad hoc controls, the sociologist considers stewardship norms and attitudes fundamental. Where the sociologist might ignore financial factors—including profit and financial resources, they are basic to the economists, even the utility economist. Here, a simple regression combines these variables, and results emerge. It is true that any investigator might use the set of variables we have used. But the basic theoretical stance in each particular discipline omits some of these variables making their use ad hoc. In contrast we advocate a framework which treats all the variables in this—and many studies – as native factors. We draw several lessons for future research, especially research of policy relevance. The first implication is to pay attention to all three levels: motives, behaviors and impacts. Consider investigators who are seeking “conservation indicators” for their studies. By conservation indicator, do they mean a measure of the final environmental impact, the physical-ecosystem level? Do they mean to measure the practices and behaviors of the agents: their adoption or non-adoption of conservation technology? Do they mean to measure the motives of the agents; do they have pro-environmental, stewardship attitudes, or are they mostly profit and finance driven? According to our framework, a strong study either includes indicators for all three levels, or places itself in a context which contains all three levels as well as multiple motives. Certainly indicators should be selected for relevance to environmental impacts (e.g., soil erosion). Surely, technologies should be clearly identified and measured. However, we argue that the essential element for a social scientist is to include indicators of selfish motives, but also for social motives, norms and attitudes, such as stewardship. Acknowledgments We wish to thank three anonymous reviewers whose insightful questions and comments helped refine our thinking, hone our
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