Adoption of environmental management standards by farmers - Inra

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by farmers: An empirical application to ISO 14001. Gilles Grolleau. UMR INRA - ENESAD. Agricultural Economics and Sociology Department. 26 Bd Dr Petitjean ...
Adoption of environmental management standards by farmers: An empirical application to ISO 14001

Gilles GROLLEAU, Alban THOMAS, ********

04. 14. 151

LERNA LABORATOIRE D'ECONOMIE DES RESSOURCES NATURELLES Unité Mixte de Recherche du Commissariat de l'Energie Atomique (CEA), de l'Institut National de la Recherche Agronomique (INRA) et de l'Université des Sciences Sociales de Toulouse (UT1)

LERNA CEA-INRA-UT1 21 Allée de Brienne - 31000 TOULOUSE - FRANCE Tél. : 33 (0)5 61 12 86 21 - Fax : 33 (0)5 61 12 85 20 E-Mail : [email protected]

Adoption of Environmental Management Standards by farmers: An empirical application to ISO 14001

Gilles Grolleau UMR INRA - ENESAD Agricultural Economics and Sociology Department 26 Bd Dr Petitjean BP 87999 21079 DIJON CEDEX France Tel: + 33 (0)3 80 77 24 43 - Fax: + 33 (0)3 80 77 25 71 [email protected] Alban Thomas LERNA-INRA Université des Sciences Sociales de Toulouse 21 Allée de Brienne, 31000 Toulouse, France Tel: +33 (0)5 61 12 85 13 – Fax: +33 (0)5 61 12 85 20 [email protected]

Abstract: This paper examines empirically what factors contribute to voluntary adoption of the ISO 14001 environmental management system by farmers. The adoption model incorporates the expected profitability of implementing the standard and investigates the impact of prior knowledge on the probability of adopting, by jointly considering two information measures: a “reported information” indicator and an “estimated knowledge” score. A Probit regression is applied to 2000 original survey data on French farmers. The analysis suggests that while the expected profitability of implementing the standard is a significant determinant of adoption intentions, real and perceived prior knowledge plays a strong and potentially counterintuitive role.

Key words: Environmental management Management-based approach; Probit model.

JEL Classification Numbers: O33, Q16; Q29.

system;

ISO

14001;

Factors Affecting French Farmers’ Intentions Concerning ISO 14001 Adoption

1. Introduction Policy makers have until recently relied on technology-based and performance-based instruments to achieve environmental conservation goals. These instruments have enabled to pick the ‘lowest hanging fruits’, but appear ill-equipped to cope with certain types of pollution such as non-point source pollution. Recent analyses suggest that management-based regulation can be more cost-effective than the two previous generations of policy instruments, notably when regulated entities are heterogeneous and regulatory outputs are relatively difficult to monitor. An environmental management-based approach does not impose technology to achieve a given environmental objective or performance, but requires from firms the integration of environmental considerations in their own planning and internal rule making. Such management-based instruments encompass a broad range of processes, systems, and internal management practices, which are supposed to improve the environmental performance of the firm (Coglianese and Lazer, 2002). The international standard ISO 14001 is the most widespread management-based standard aimed at improving the environmental management system of firms. After it was launched in 1996 by the International Organization for Standardization, the ISO 14001 has been adopted by more than 50,000 entities worldwide. Despite a first certificate awarded to the cotton farm of Mike Logan in 1997, the agricultural sector remains the “poor parent” in terms of adoption rate compared to other sectors and we do not know of any empirical study devoted to ISO 14001 adoption at the farm level. Nevertheless, the agricultural sector shares some common features with industry as far as environmental program adoption are concerned. In particular, familiarity and experience with compulsory regulatory schemes or other voluntary conservation agreements are likely to play a key role in the decision to adopt. Apart from profitability associated with joining a program, other determinants include social (customer) pressure for implementing or enhancing environmental management of production units, and technical and financial characteristics of the farm. Broadly speaking, adoption would be considered if it is technically feasible, if expected future output price is above marginal cost including implementation cost, and if producer’s market share can be increased by meeting final customer demands in terms of environmental considerations. For example, Grolleau (2001) argued that products from ISO 14001 certified farms can be differentiated from conventional products by bearing an ecolabel.

This paper makes several contributions to the empirical literature on producers’ adoption of Environmental Management Systems (EMS). First, we propose the first empirical application, to our knowledge, devoted to identifying adoption determinants of the ISO 14001 standard by farmers. Second, we propose a detailed investigation of the impact of prior knowledge on the probability of adopting, by jointly considering two information measures: a “reported information” indicator and an “estimated knowledge” index, computed from a multiple-choice questionnaire. The latter index can be used to assess the degree of information accuracy about the EMS scheme and in particular, the possibility that producers be more or less willing to adopt when the information content increases. Third, we model the probability of adopting an EMS as a function, not only of prior information, but also as depending upon the expected profitability of implementing the standard. For this reason, we concentrate on farmers already engaged in an environmental conservation program, to isolate the profitability factor as a function of the future expected market price of products from ISO 14001 certified farms. The model of adoption hence implicitly considers that other determinants, such as environmental concerns and willingness to abide by existing regulatory policies, are already present in the population under scrutiny through previous adoption of other voluntary environmental programs. The remainder of the paper is organized as follows. Section 2 describes the ISO 14001 standard, and show that it shares the features of a management-based instrument. Section 3 presents the theoretical background for explaining the adoption of voluntary environmental policies. In Section 4, we provide a brief review of the literature devoted to ISO 14001 adoption, regardless of the industry. Section 5 presents the data and develops an empirical model to identify factors affecting the revealed decision of farmers about ISO 14001 adoption. The model is applied to original survey data using a discrete-choice Probit specification, to identify the main determinants of certification by French farmers. Section 6 concludes by briefly suggesting policy implications and strategies to mitigate barriers to adoption.

2. Description of the ISO 14001 standard The international Organization for Standardization (ISO), located in Geneva, Switzerland, has developed standards that help organizations to take a more pro-active approach to managing environmental issues. The ISO 14000 series refers to a list of voluntary environmental standards including an environmental management system and environmental auditing, environmental performance evaluation, environmental labeling, and life-cycle assessment. The ISO 14001 standard, first published in 1996, is the world’s most widely

recognized environmental management system framework that helps organizations to better manage the impact of their activities on the environment while demonstrating sound environmental management practices. Until the end of 2002, about 50,000 ISO 14001 certificates had been awarded in 118 countries, an increase of 34.54 percent from the end of 2001. Indeed, all business processes have environmental impacts and risks. Those implications can be routinely managed as part of the business, just as production or human resources are managed. The ISO 14001 standard provides a structured approach for doing that and can be implemented in any type of organizations because of its generic principles. “The ‘management system’ refers to the organization's structure for managing its processes or activities - that transform inputs of resources into a product or service which meet the organization's objectives, such as satisfying the customer's quality requirements, complying to regulations, or meeting environmental objectives” (www.iso.ch). Note that the ISO 14001 standard excludes setting environmental performance levels, test methods for pollutants and standardization of products. The ISO 14001 standard does not replace technical requirements embodied in statutes or regulations and does not set prescribed standards of performance for organizations. The ISO 14001 standard requires that an organization put in place and implement a series of practices and procedures that, taken together, result in an environmental management system. The environmental management system is based on the quality management "PLAN-CHECK-DO-ACT" cycle of Deming1 (Grolleau, 2001; Wall et al., 2001).

The major requirements of an environmental management system under ISO 14001 include the following. (1) An organization should define its environmental policy and ensure commitment to its environmental management system. This policy includes commitments to prevention of pollution, continual improvement of the environmental management system leading to improvements in overall environmental performance, and compliance with all applicable statutory and regulatory requirements. (2) An organization should formulate a plan to fulfill its environmental policy, which includes setting performance objectives and targets for the management system.

1

According to the ISO 14001 standard environmental management system is "the part of an overall management system that includes organizational structure, planning activities, responsibilities, practices, procedures, processes, and resources for developing, implementing, achieving, reviewing, and maintaining environmental policy." Note that ISO 14001 is the only specification document against which award of registration or certification can be measured.

(3) For effective implementation, an organization should develop the capabilities and support mechanisms necessary to achieve its environmental policy, objectives and targets. This includes employee training activities, establishing work instructions and practices, and establishing the actual metrics by which the objectives and targets will be measured. (4) An organization should measure, monitor, and evaluate its environmental performance, notably by establishing a program to periodically audit the operation of the environmental management system, checking and taking corrective and preventive actions when deviations from the environmental management system occur. (5) An organization should review and continually improve its environmental management system with the objective of improving its overall environmental performance. In summary, the environmental management system prescribed by the ISO 14001 is a typical example of management-based instrument. Indeed, the ISO 14001 standard does not prescribe specific environmental performances and/or specific ways to achieve them like command and control instruments do. Unlike market-based instruments, the ISO 14001 standard is not based on modifying price signals of regulated agents (although price differentiation could result from adopting this standard, see Grolleau, 2001). The ISO 14001 standard aims at changing the behaviour of the firm by making managers more aware of and concerned about their organization’s environmental outputs (Coglianese and Lazer, 2002). The ISO 14001 standard recommends new management practices and formal and codified procedures to integrate environmental considerations in the overall management system.

3. Theoretical background for explaining the adoption of environmental voluntary approaches The motivations behind the adoption of voluntary approaches such as ISO 14001 have been the subject of recent research. Nevertheless, economic investigation of farm level determinants of adopting an environmental management system is, to our knowledge, still lacking. From a general viewpoint, it is believed that firms adopt voluntary instruments because the benefits of voluntarism outweigh the costs (Segerson and Li, 1999; Khanna, 2001). Several rationale have been identified in the economic and management theory for the recent boost in corporate environmental activity. The most frequently quoted motivations for adoption of voluntary instruments are social benefits, market-driven benefits for firms and regulatory advantages (Segerson et Li, 1999; Reinhardt, 2000; Welch et al. 2002; Lyon, 2003). These three broad motivations are briefly developed in the following paragraphs.

(1) Voluntary approaches are supposed to reduce the costs of meeting environmental objectives through increased flexibility, faster and more cooperative process and incentives for innovation. While these different costs savings are beneficial to the firm, they also generate social benefits by enabling resources to be used for other aims, implying that social goals may be achieved more rapidly. Moreover, several studies stress that firm investments in voluntary stewardship initiatives can be driven by some form of ‘kindness’, i.e., a sense of responsibility on the part of the firm for the reduction of pollution (Karp and Gaulding, 1995; Welch et al., 2002). The literature has stressed several conditions to get such social benefits. For example, potential transaction costs savings from a shorter and more cooperative process are likely to be greater when negotiations involve small numbers of parties (Segerson and Li, 1999). (2) Adoption of a voluntary instrument can yield a private benefit in the form of a direct or indirect market-based payoff. On one hand, pollution can be a symptom of production inefficiencies, making the voluntary instrument a win-win opportunity to reduce simultaneously pollution and costs, as asserted by the Porter Hypothesis (Porter and Van der Linde, 1995). Such costs reductions can come from various sources such as input savings, better access to capital (‘green investors’) and organizational improvements2. On the other hand, firms can increase their profits by responding to the new generation of “green consumers” who are willing to pay higher prices for environment-friendly products or at least to choose them preferentially relative to conventional items (Arora and Gangopadhyay, 1996). However, according to Lyon (2003), “evidence suggests the role of cost reduction and green marketing are modest (...). Most of the action in corporate environmentalism is mediated through regulatory policy.” (3) For Lyon (2003), the main motivation for corporate environmentalism is the opportunity to influence regulation. A sophisticated example of regulation manipulation was the unilateral and voluntary decision of DuPont de Nemours to eliminate the CFC in its products. Such a decision was perceived by public authorities as a signal that competitors can do the same. Therefore, public authorities have imposed severe restrictions on CFC production, putting competitors in a competitive disadvantage. Indeed, the competitors were not able to switch quickly to CFC substitutes, which were already available to Dupont (Lyon, 2003). Regulatory influence theory (Stigler, 1971; Peltzman, 1976; Lyon and Maxwell, 1999) postulates that firms are willing to invest in voluntary approaches because such approaches give the firm greater ability to influence or manipulate the regulatory system in a sense favorable to its 2

Indeed, several studies show that the firm commitment in voluntary initiatives for the environment can improve the efficiency of human resources by facilitating recruitment, increasing employees’ moral and motivations and raising workforce productivity (Börkey et al., 1999; Phanuel, 2001).

private interest. Lyon (2003) suggests that a firm may view adoption as a strategy (1) to preempt regulatory threats, which can potentially benefit the whole industry or (2) to raise rivals’ costs, which can potentially provide a sustainable or temporary competitive advantage (Salop and Scheffman, 1983; McWilliams et al., 2002). Firms perceiving strong formal and informal regulatory pressures3 are expected to be more likely to adopt voluntary environmental actions.

4. Review of the literature devoted to the determinants of ISO 14001 adoption at the firm level There are few empirical studies devoted to the determinants of ISO 14001 adoption at the firm level and no studies concerning the agricultural sector. The very recent creation of the ISO 14001 standard (1996) with a design mainly oriented towards large manufacturing firms, and the very low diffusion in the agricultural sector, can explain this lack of empirical studies4. The studies about ISO 14001 adoption at the firm level are briefly described in Table 1. [TABLE 1 HERE] These empirical studies are multisectorial and use Logit or Probit regressions to explain a discrete voluntary decision by a vector of variables corresponding to the expected determinants. The following discussion is not meant to be exhaustive: rather, we hope simply to present some main hypotheses, corresponding variables and results found in previous empirical studies. Some hypotheses and corresponding variables are systematically used in most studies: firm size, regulatory and other group pressures, quality management system and exports. Most studies assume that the bigger the firm is, the more it is likely to adopt ISO 14001. Indeed, large firms are frequently more visible, more regulated and monitored than small ones. They have more financial and human resources and benefit from economics of scope in adoption. In most studies, ISO 14001 certification firm is, as expected, significantly positively correlated with firm size. A second frequent hypothesis assumes that the presence of a quality management system is likely to increase the likelihood of being ISO 14001 certified. Such expectation is explained by decrease in information, learning and implementing costs. Indeed, both systems are based on similar processes and ideas of system improvement and certification. ISO 14001 and ISO 9000 can be integrated in a common 3

Formal regulatory pressures come from conventional political authorities. Informal regulatory pressures come from other groups, such as environmental activists or neighbours and can be directly mediated through the market place by strategies such as reputational attacks or boycotts (Grolleau et al., 2004). 4

For presentations of ISO 14001 in the context of agriculture sector, see Wall et al. (2001) and Grolleau (2001).

system leading to cost reduction. In most studies, the ISO 14001 certification firm is, as expected, significantly positively correlated with previous ISO 9000 certification. Several studies suppose that the more export oriented the organization is, the higher the benefits it may accrue from the more visible actions taken to protect the environment. Indeed, foreign customers are less likely to monitor the environmental commitment or performance of suppliers than they can for domestically oriented firms. Exports are not significant, except in Nakamura et al. (2001), maybe because Japanese firms fear to be driven out of European markets on environmental grounds. Japan is also the first country in terms of the number of certificates. Several studies also consider that pressures from regulators and special interest groups, e.g. criticisms, threats of loss of legitimacy or boycotts, are likely to increase the likelihood of adopting the ISO 14001standard. Indeed stronger pressures encourage firms to prove higher levels of environmental commitments, e.g., by adopting a formal and certified environmental management system. The effects of regulatory and pressure from special interest groups are mitigated, maybe because of differences in studied sectors and institutional environment. A couple of recent studies (Delmas, 2002; Kollman and Prakash, 2002) about global diffusion of ISO 14001 explain the heterogeneity in diffusion rates among countries by their institutional differences. Superior financial performances are supposed to increase the likelihood of ISO 14001 adoption, because adoption requires substantial financial resources. When financial performances are incorporated in models, they are significant, notably because of the substantial financial resources needed to get certification. Employees’ characteristics such as higher education levels or lower average age are expected to increase the likelihood of ISO 14001 adoption. For example, Nakamura et al. (2001) suppose that “lower average age in a company may reflect a higher learning capacity of organizational members, since younger employees are generally more trainable, adaptive, and less resistant to change. Consequently, companies with younger employees may accrue lower costs of implementing environmental policies or obtaining certification”. The effects of employees’ characteristics are significant, but the expected effects relative to firm ownership are uncertain. According to Nakamura et al. (2001), “foreign owners may, on the one hand, be less willing to contribute to the social welfare of the country and thus less inclined to invest in environmental protection above the level required by regulation. On the other hand, it is possible that foreign owners must secure goodwill from regulatory authorities in host countries to prevent discrimination and do so by intensifying their environmental protection efforts”. The effects of firm ownership are significant, but contrasted according to the considered study. The main expected determinants used in the previous studies and the results in terms of significance are summarized in Table 2.

[TABLE 2 HERE] Considering adoption of the ISO 14001 standard in the agricultural sector may be seen as a major challenge, as this standard was mostly designed for industrial firms, and the adoption rate by agricultural producers is typically very low. Furthermore, farmers in developed countries are often engaged in voluntary or compulsory environmental programs for resource conservation, input reduction and landscape management. The difficulty for would-be adopters is to disentangle purely environmental aspects of the ISO 14001 (impact on the environment) and market-based consequences of adoption. As noted above, producers may expect a benefit from adoption, through adequate product labelling, if the profit rate can be increased for goods sold on a segmented market with labelled vs. non-labelled products. The profitability-related determinant of adoption has to be separated from the possible desire for farmers to appear as “environmental-friendly” producers to environmental agencies. This second effect is not directed towards final consumers, but towards government auditing and funding authorities, that may be more likely to grant subsidies from environmental programs or to perform less frequent environmental auditing procedures if the producer is known to comply with ISO 14001 requirements. This provides a first reason for focusing the empirical analysis on farmers already engaged in an environmental program, as the expected impact of the ISO 14001 adoption in terms of profitability can be identified when environmental regulation aspects are already accounted for. Indeed, with a population of farmers without any experience of environmental programs, only the profitability determinant described above could be identified, while the possibility of a future adoption of an environmental program, after adopting the ISO 14001 standard, would remain. A second reason for concentrating on such a population of producers is the fact that adoption is in most cases costly and time-consuming. With farmers already faced with environmental requirements as in the case we consider, we expect the reported willingness to adopt to be more consistent with the information the farmer has on such costs, hence providing a lower bound for the probability to adopt. 5. Empirical application The data In December 2001, survey questionnaires were sent to 1597 French farmers. To date, our survey is the only one to focus exclusively on farmers. The farmers were selected according

to their participation in voluntary environmental initiatives applied at the farm level5 : PEEFNCUMA , FARRE, RAD, Label Vert, AgriConfiance Vert, Quali'Terre, Certi'Terre, Eaux Champs du Gouessant, ISONIS, Ecoculture. These programs assert more or less explicitly, that they can be adapted steps to help farmers to get an ISO 14001 certificate6. This selection, similar to the methodology of Harter and Homison (1999) was performed, as noted above, because of the novelty of ISO 14001 and its very low diffusion in the French agricultural sector. The questionnaire has been elaborated in close collaboration with several agricultural specialists. The questionnaire was sent with an accompanying letter specific to each program. 534 responses have been received for a 33.44 percent response rate. 0.2 percent of returned questionnaires were not useable. An interesting aspect of the survey is the ability to infer more accurately the degree of knowledge the farmer has about EMSs. More precisely, two variables can be used as a proxy for knowledge: the reported knowledge (variable KNOW_EMS) and the “estimated” knowledge, a score function indicating the number of correct answers of each farmer to a set of questions regarding the nature and purposes of an EMS. Since interviewed farmers already adhered to an environmental conservation program, they are expected to be more aware of the characteristics, requirements and also the consequences of adopting the ISO 14001 standard. On the other hand, since the latter is mostly dedicated to industrial firms and is not yet implemented in agriculture at a sufficient scale, doubt can be cast on farmers’ precise knowledge of the program. This is particularly true of technical and financial aspects behind adoption, which are likely to be different from the ones already known by these farmers. An important point to recall at this stage is that the decision concerning adoption referes to EMS in the questionnaire, not to ISO 14001 standard directly. But since the latter is the only EMS available to farmers to this date, both are actually synonyms. Table 3 presents the set of questions used to infer farmer’s knowledge about EMS. Let us again point out that such inference is made only on technical aspects of the EMS, not on financial ones. [TABLE 3 HERE]

5

The surveyed programs includes the PEE-FNCUMA (Environmental Firm Plan applied to cooperatives of agricultural equipment), FARRE (Forum for Environment-Friendly Farming), RAD (Sustainable Agriculture Network), Pilot farms of the French public Schools of Agriculture, AgriConfiance Vert (Environmental program of agricultural cooperatives), Quali'Terre and Certi'Terre (Environmental qualification and certification programs for farms by the Chambres d'Agriculture), Label Vert (Environmental qualification program for farms in Vendée), and other small-sized programs (Eaux -Champs du Gouessant, ISONIS, Ecoculture). 6

Note that a special and publicly disseminated report for the French Ministry of Agriculture (February 2000), the so-called Paillotin Report, recommends the implementation of environmental management systems at the farm level, inspired from the ISO 14001.

The number of correct answers ranges from 0 to 4, and corresponding dummy variables (DEFS_1 to DEFS_4 for 1 to 4 correct answers) are constructed and used in estimation. Table 4 presents the main variables used in estimation.

[TABLE 4 HERE]

The econometric model Factors affecting the decision to adopt can be classified in 5 broad categories: -

Technical and financial situation

-

Knowledge of EMS (reported and estimated)

-

Experience and education

-

Expected gain/loss from adoption

-

External pressures (society, customers, regulation authorities).

Consider first the relationship between adoption and expected gain or loss from adopting an EMS. Let Π0 and Π1 respectively denote profit before and after adoption, P0 the unit output before adoption, Q is output (assumed constant), δ is the expected rate of change in output price after adopting, and C0 and C1 respectively denote output cost before and after adoption. The difference between profit levels is then Π1 − Π 0 = [P0 (1 + δ ) Q − C1 ] − [P0 Q − C 0 ] = P0δ Q + (C 0 − C1 ).

Dividing by total sales (assumed constant) yields the expected profit rate differential: Π1 − Π 0 (C − C 0 ) = δ − 1 = δ −γ, P0 Q P0 Q

where γ is the expected rate of change in the cost ratio (relative to output value), when adopting. Intuitively, one should expect a farmer to consider adopting an EMS when this difference is positive, all other things being equal. Turning now to other determinants listed above, their impact on farmer decision is less clear, even as far as knowledge of ISO 14001 is concerned. A farmer may have a greater likelihood of adopting because he knows the degree of profitability with more precision, but this is also true when adopting is not profitable. For this

reason, one may need to evaluate the impact of knowledge jointly with characteristics of the farmer. An important aspect with environmental program adoption is to check whether farmer information on the program is genuinely significant or not. More precisely, depending on data available, knowledge can be either reported directly by the farmer, or “estimated” through specific knowledge-checking questions in the questionnaire. If information about EMS is akin to knowing the existence of an EMS program without further specific details, the impact of knowledge on the likelihood of adoption may be overestimated. On the other hand, controlling for the genuine degree of knowledge of the farmer allows one to disentangle the effect of “public information” from more detailed information. As is usual in discrete-choice models with a binary dependent variable, we first specify a linear stochastic model for the underlying economic variable driving adoption (a latent, unobserved variable). Consider the following latent variable y i* = β 0 + β1 (δ − γ ) i + β 2 ' X 1i + β 3 ' X 2i + u i

i = 1,2,..., N , ,

where X1i is a vector of farmer i’s characteristics, X2i contains observed components regarding information about EMS, ui is a residual term, and β0, β1, β2, β3 are structural parameters. Note that with this notation the profitability term (δ - γ) explicitly depends on the farmer. The model of adoption for the N farmers can be stated as a discrete-choice model with the dummy variable indicating adoption as the dependent variable:

 yi ∴ 1 if y i* ] 0,   y i ∴ 0 otherwise. Parameter β1 should be positive given the discussion above, whereas the vector of parameters β3 indicates the influence of farmer’s characteristics of the likelihood to adopt. Finally, parameters in vector β3 will indicate, with either sign, whether information significantly affects this relationship. As is usual in the literature on binary choice models, we specify a continuous distribution for the error term ui, and write the probability of adopting as Pr ob( y i = 1) = Pr ob( y i* > 0) = F [β 0 + β1 (δ − γ ) i + β 2 ' X 1i + β 3 ' X 2i ] ,

where F(.) is the cumulative density function of ui , and the last equality follows from the symmetry assumption of the density function associated with ui. As estimation results in terms of goodness-of-fit are often similar when the Logit or Probit specifications is considered, we adopt the Probit model:  β + β1 (δ − γ ) i + β 2 ' X 1i + β 3 ' X 2i  F [β 0 + β1 (δ − γ ) i + β 2 ' X 1i + β 3 ' X 2i ] = Φ 0 , σ  

where Φ(.) is the cumulative Normal density function and σ is the standard deviation of ui. Maximising the log-likelihood function

log L( β ) =

N

∑ i =1

log Li =

N

∑ {y

i

log[Pr ob( y i = 1)] + (1 − y i ) log[Pr ob( y i = 0)]}

i =1

with respect to parameters β=(β0, β1, β2, β3)’ , where log Li is the contribution of observation i to the full log-likelihood, yields consistent and efficient estimates if the distributional assumption on the data generating process is valid. A robust version (when, e.g., the i.i.d. assumption on observations is relaxed) of the variance-covariance matrix of Maximum Likelihood estimates is easily obtained as N ∂ log L ( β ) ∂ log L ( β ) ∂ 2 log L ( β ) i i var(βˆ ) = Vˆ −1 HVˆ −1 where Vˆ = − and H = ∑ . ∂β∂β ' ∂β ∂β ' i =1

Estimation results The expected price-cost gap (δ – γ) above is not directly reported in the questionnaire, but we use as a proxy the variable DIFF_POS, equal to 1 if the required price (to adopt) is greater than the expected cost of adopting. As described above, two types of variables are included to account for knowledge effects (in X2): KNOW_EMS for the “reported knowledge” effect, and DEFS_1 to DEFS_4 for the “estimated degree of knowledge” about EMS. Farmer characteristics in vector X1 are dummy variables for education (FORMA1 to FORMA4), farm status (STATUS1 to STATUS3), activity size (total sales, CA1 to CA3), employment and debt (respectively BIG_WORK and BIG_DEBT), and expected trend in regulation (REGUL3). These variables are used as control variables, to capture farmer observed heterogeneity in scale of activity, education and status among others.

Table 5 presents estimation results for the binary-choice model described above, using the Probit specification. Several versions of the model are estimated, to investigate the robustness of results to variable omission. Model I contains all parameters, namely price-cost differential, information variables and farmer characteristics. Model II has reported knowledge (KNOW_EMS) excluded, while Model III has reported knowledge and computed information-content variables omitted (variables X2 in the notation above). Finally, Model IV contains only price-cost and knowledge variables omitted (variables (δ – γ) and X2 in the notation above). Hence, Model IV can be seen as the “hardcore model’ with profitability and knowledge variables only; Model III excludes information variables but includes control variables from education and production conditions; Model II is useful in determining whether omission of the reported knowledge variable, KNOW_EMS, leads to significantly different estimates, while retaining control variables as in Model III. There are no sharp differences between parameter estimates across the different model specifications. The main estimation results can be summarized as followed. First, reported knowledge is a major determinant for EMS adoption, whereas information content as approximated by our variables appears less so. On the other hand, although reported knowledge has a significant and positive impact on the probability of adopting, the information accuracy check variables DEFS_1 through DEFS_4 indicate that increasing information content on EMS increases this probability at a decreasing rate. More precisely, the coefficient associated with DEFS_2 (2 correct answers) is greater than the one associated with DEFS_1 (1 correct answer), but from DEFS_2 to DEFS_4, parameter estimates decrease in magnitude, DEFS_0 being the reference. Hence, the discrepancy between results for reported knowledge vs. information content about EMS can be interpreted as a negative signal on those environmental management systems: although people claiming to be aware of the existence of EMS are more in favour of adopting, all other things being equal, they are less likely to adopt when their degree of knowledge is actually increasing. This result could of course be understood as a consequence of technological and management constraints that the EMS are expected to impose on farmers, and which are not always known beforehand to farmers. Let us again point out that the estimated information score depends only on technical aspects of adoption, not on financial ones. Furthermore, information on adoption costs for any program is assumed to be highest for all farmers, as they are already engaged in an environmental program. Hence, for a given level of expected profitability associated with adopting an EMS, a farmer with a better knowledge of those technical aspects characterizing

an EMS may be less likely to adopt if the underlying cost is high. Therefore, the interpretation of our results regarding estimated prior knowledge should be made by keeping in mind the specific aspects of the EMS included in the questionnaire. Concerning model performance, the proportion of correct predictions is reduced by about 4 percent points when information variables are excluded (Models II and III compared to Model I). On the other hand, a fifth model (whose results are not reported here to save on space) with information variables (KNOW_EMS, DEFS_1 – DEFS_4) only produced similar results in terms of fit, with 75.55 percent of correct predictions. In other words, the adoption model performs seemingly equally well, whether information variables are excluded or when these variables only are considered. The required and expected price-cost gap from adopting an EMS is significant and has the correct sign. The size of farming activity as captured by BIG_WORK seems to have a positive impact on EMS adoption, but the impact of higher sales is not clear, because total sales variables are rarely significant (only CA1 is significantly different from 0 in Models II and III at the 5 percent level). The impact of a greater debt to sales ratio is positive and significant at the 5 percent level in 2 cases out of 3. This set of results indicates that farms that are more labour-intensive tend to adopt more. Farm status is not significant in most cases, only when status is a SCEA, which may indicate bigger farms or farms closer to an industrial structure. The role of the SCEA status is difficult to interpret and needs to be investigated in future studies. The impact of an expected strengthening in environmental regulation (REGUL3) is positive and significant in all cases, as expected. Although most farmers in the survey have a value of 1 for this variable (see Table 6), omitting REGUL3 did not change results significantly. Even though the implementation of an EMS will not permit farmers to free themselves from abiding to environmental compliance requirements, there is a possibility that farmers see here an opportunity to be less subject to administrative controls when they adopt an EMS. Finally, education dummies are not all significant, but do seem to indicate that more educated farmers are less likely to adopt. This result may call for further investigation, as it may seem counter-intuitive, given the vast literature on the beneficial aspects of education on technology or program adoption (see Traoré et al., 1998 for an example). On the other hand, this can also be compared to the result described above, regarding the information content about EMS, as more educated farmers will in principle also be likely to be more informed about the features and requirements of an EMS. Except some variables such education, the effects of other variables are relatively coherent with the previous studies indicated in table 2. Therefore, we can suppose that the specific nature of farms does not affect too much the results compared to other sectors.

[TABLE 5 HERE] Based on those parameter estimates, we compute marginal effects for our variables. As the latter are all binary, marginal effects represent in this case the change in probability of adopting following a change from 0 to 1 of the dummy variable. Table 8 presents estimated marginal effects for explanatory variables in the 4 models. When control variables are not included (Model IV), reported knowledge and profitability (DIFF_POS) have the highest contributions to the probability of adopting. With all variables included (Model I), the labourintensive nature of the farm is the dominant variable (BIG_WORK), followed by FORMA1, KNOW_EMS, FORMA2, CA1 and DIFF_POS. Hence including control variables for farm activity scale and education in particular, significantly reduces the ranking of adoption profitability (from second in Model IV to third in Models II and III and fifth in Model I). Nevertheless, the magnitude of marginal effects for the main variables of interest is not widely affected by the introduction of control variables, indicating that robustness of the model specification is satisfactory. [TABLE 6 HERE] A tentative conclusion from these marginal effects could be the following. Farmers reporting knowledge of EMS are more likely to adopt the ISO 14001 standard, but the required gain from adopting has to be positive in terms of price surplus, i.e., extra price must exceed the adoption implementation costs. However, compared to farmers than are poorly aware of EMS characteristics and requirements, a greater accuracy in farmers’ knowledge does not increase the probability of adopting: the marginal probability with respect to knowledge accuracy is in fact concave, increasing and then decreasing from 2 correct answers onwards. To further examine the joint impact on the probability of adopting, of reported and estimated knowledge of EMS on the one hand and the required price-cost differential in the other, we compute predicted probabilities of adopting evaluated at the sample mean of conditioning variables, for different values of the variables of interest. Results are presented in Figure 1, where we consider 5 different cases: no reported knowledge, estimated knowledge set to 0; reported knowledge, estimated knowledge between 1 and 4 (number of correct answers). In each case, the predicted probability of adopting is a function of the continuous variable (δ γ), ranging between -100.00 and 100.00 percent. The sample proportion of adopting farmers would be obtained by replacing this variable by the empirical mean of DIFF_POS, about 20 percent. As can be seen the Figure 1, the maximum probability profile is clearly obtained with an estimated knowledge of 2 correct answers out of 4. With the maximum possible number of correct answers (4), the probability profile is still above the profile with reported

knowledge only, and no correct answers. To achieve a twofold increase in the probability of adopting an EMS (from actual 30 percent to 60 percent), farmers with reported knowledge of EMS would require a 30 percent, 40 percent, 70 percent and 90 percent price-cost differential for estimated knowledge ratios of 0.5, 0.75, 0.25 and 1.00 respectively. As for farmers reporting no knowledge of EMS, even with required price-cost differential of 100 percent, the predicted probability of adopting would remain below the 30 percent line. [FIGURE 1 HERE]

6. Policy implications and concluding remarks Given the recent character of environmental management systems and their low diffusion in farms, our study is essentially predictive. We have built an empirical model of adoption incorporating original variables relative to actual and perceived prior knowledge. We have presented empirical estimates of the impacts of various determinants of the adoption intentions of formalized environmental management system by French farmers. We have shown the significant and unexpected effect of the bi-dimensional variable ‘prior knowledge’, which stress the need to better integrate the both dimension in future studies. The overall low level of knowledge of the ISO 14001 standard among farmers may have biased the results. On the other hand, the fact that interviewed farmers are all engaged in environmental programs means that a distinction can be made between information relative to implementation costs of a program in general, and an EMS in the present case. Furthermore, the computed information score used in the application refers only to technical aspects of EMS adoption, not to financial ones. Hence, one should interpret the computed information score as a measure of knowledge associated only with EMS specific features, independent on profitability. When a broader definition of knowledge is concerned, i.e., including economic profitability as well, it is then clear that more information can play a positive role for the willingness to adopt the ISO 14001 standard. The conclusion of this discussion is that policy makers willing to increase the proportion of adopting farmers, should therefore promote information campaigns insisting not only on technical aspects of adoption, but also expected profitability to the farmer. In a context where bandwagon effects explain the diffusion of innovation, our study provides insights to identify a target population among already sensitive farmers. Public policies encouraging the adoption of an environmental management system by such farmers can generate a bandwagon effect on other farmers who will imitate these leaders instead of computing themselves the adoption opportunity. These policies can be more cost effective than alternatives, by allowing to reach a higher overall diffusion rate at a lower cost.

Many other extensions can be analyzed such as the path of adoption, from intention to effective adoption, the incorporation social effects versus economic motives or the environmental effectiveness of the ISO 14001 certificate. Extending our setting and testing it empirically constitutes a challenging topic for future research.

References Arora, S. and Gangopadhyay, S., 1995. Toward a Theoretical Model of Voluntary Overcompliance. Journal of Economic Behavior and Organization 28(3), 289-310. Börkey, P., Glachant, M. and Léveque, F., 1999. Voluntary Approaches for Environmental Policy – an Assessment. (CERNA, École des Mines de Paris) co-ordinated by Barde, JP., OECD, Paris. Coglianese, C. and Lazer, D., 2002. Management-Based Regulation: Using Private Management to Achieve Public Goals. AEI-Brookings Joint Center for regulatory Studies, Working Paper 02-11. Delmas, M., 2002. The diffusion of Environmental Management Standards in Europe and in the United States: An Institutional Perspective. Policy Sciences 35(1), 91-119. Grolleau, G., Lakhal, T. and Mzoughi, N., 2004. Does Ethical Activism Lead To Relocation. Kyklos 57 (3), 391-406. Grolleau, G., 2001. Management environnemental et exploitation agricole. Economie Rurale 262, 35-47. Harter, C.L. and Homison, R., 1999. Factors Affecting Pennsylvania Firms' Intentions Concerning ISO 14001. Pennsylvania Economic Review 7(1), 16-27. Karp, D.R. and Gaulding, C.L., 1995. Motivational Underpinnings of Command-and-Control, Market Based, and Voluntarist Environmental Policies. Human Relations 48(5), 439-465. Khanna, M., 2001. Economic Analysis of Non- mandatory Approaches to Environmental Protection. Journal of Economic Surveys 15(3), 291-324. King, A.A. and Lenox, M.J., 2001. Lean and Green ? Exploring the Spillovers from Lean Production to Environmental Performance. Production and Operations Management 10(3), 1-13. Kollman, K. and Prakash, A., 2002. EMS-Based Environmental Regimes as Club Goods: Examining Variations in Firm-level Adoption of ISO 14001 and EMAS in UK, US, and Germany. Policy Sciences 35(1), 43-67. Lyon, T.P., 2003. ‘Green' Firms Bearing Gifts. Regulation 26( 3), 36-40. Lyon, T.P. and Maxwell, J.W., 1999. Voluntary Approaches to Environmental Regulation: A Survey. In Franzini, M. ; Nicita, A. (eds), Environmental Economics : Past, Present and Future, Aldershot, Hampshire: Ashgate Publishing, Ltd. McWilliams, A. and Van Fleet, D.D., 2002. Raising rivals' costs through political strategy an extension of resource-based theory. Journal of Management Studies 39 (5), 707-723. Melnyk, S., Calantone, R.J., Handfield, R., Tummala, R.L.,Vastag, G., Hinds, T., Sroufe, R. and Montabon, F., 1999. ISO 14000: Assessing Its Impacts on Corporate Effectiveness and Efficiency. Michigan State University, Center for Advanced Purchasing Studies.

Nakamura, M., Takahashi, T. and Vertinsky, I., 2001. Why Japanese Firms Choose to Certify: A Study of Managerial Responses to Environmental Issues. Journal of Environmental Economics and Management 42, 23-52. Peltzman, S., 1976. Towards a More General Theory of Regulation. Journal of Law and Economics 19(2), 211-240. Phanuel, D., 2001. La perception du management environnemental par le personnel de l’entreprise : modèle et application. Gestion 2000, Novembre-décembre, pp. 33-48. Porter, M. and Van der Linde, C., 1995. Toward a New Conception of the EnvironmentCompetitiveness Relationship. The Journal of Economic Perspectives 9(4), 97-118. Reinhardt, F.L., 2000. Down to Earth: Applying Business Principles to Environmental Management. Boston, MA : Harvard Business School Press. Salop, S.C. and Scheffman, D.T., 1983. Raising rivals' costs. American Economic Review, Papers and Proceedings, 73(2) : 267-271. Segerson, K. and Li, N., 1999. Voluntary Approaches to Environmental Protection. In Tietenberg, T. and Folmer, H. (eds), The International Yearbook of Environmental and Resource Economics : 1999-2000, Cheltenham, UK : Edward Elgar, 1999, pp. 273-306. Stigler, G.J., 1971. The Theory of Economic Regulation. Bell Journal of Economics and Management Science 2, 3-21. Traoré, N., Réjean, L. and Amara, N., 1998. On-farm Adoption of Conservation Practices: The Role of Farm and Farmer Characteristics, Perceptions and Health Hazards. Land Economics 74(1): 114-127. Wall, E., Weersink, A. and Swanton, C., 2001. Agriculture and ISO 14001. Food Policy 26, 35-48. Welch, E.W., Mori, Y. and Aoyagi-Usui, M., 2002. Voluntary Adoption of ISO 14001 in Japan: Mechanisms, Stages and Effects. Business Strategy and the Environment 11, 43–62.

Table 1. Empirical studies of ISO 14001 adoption at the firm level Authors

Year

Harter and Homison 1995-96 (1999) Melnyk et al. (1999) 1998 Nakamura et al. (2001) King and Lenox (2001) Welch et al. (2002)

Industry Manufacturing firms from all industries, likely to be familiar with innovative environmental actions Manufacturing firms, all industries

Country, state Econometric model Pennsylvania Logit United States

1997

Manufacturing firms, all industries

Japan

Multinomial Logit Probit

1996

Manufacturing firms, all industries

United States

Probit

1999

4 industries: chemicals, electronics, electric machinery, electric power generation

Japan

Logit

Table 2. Explanatory variables of the decision to adopt ISO 14001 standard in empirical studies

Main explicative variables Firm size Regulatory pressure and risk Pressures from other groups Exports ISO 9000 or other TQM Implementation costs Financial performance Administrative guidance Private/public ownership Foreign ownership Employee characteristics R&D expenditures Advertising expenditures Firm image Social responsibility Decentralization of decisions

References of empirical studies devoted to ISO 14001 adoption Harter and Melnyk et al. Nakamura et al. King and Welch et al. Homison (1999) (1999) (2001) Lenox (2001) (2002) ns s s s s ns s ns -

-

s

-

s

s

ns s

s s

s

-

ns ns

s -

ns s -

-

ns

-

ns

-

-

-

-

s -

ns s

s -

-

-

-

s ns

s

-

ns -

-

-

ns -

s s

ns = non-significant ; s = significant

Table 3. Part of questionnaire used to compute estimated knowledge of EMS

1. Indicate the most appropriate definition(s) of an EMS: - A standard for producing healthy goods - A label certifying the environmental quality of products - A method allowing environmental management on the farm - Doesn’t know. 2. Which of the following items are compulsory in an EMS? - The continuous improvement of environmental performance - Setting up up-to-date clean technologies - Abiding to the environmental regulation - Doesn’t know 3. Implementing an EMS is … - Imposed by command-and-control regulation for the most polluting farms - Voluntary for all farms - Doesn’t know. 4. Which if these items correspond to the spirit of EMS similar to ISO 14001: - Satisfy all environmental requirements from local ecologist associations - Define an environmental policy and implementing it on the farm - Less intensive agriculture and commitment toward customers - Doesn’t know.

Table 4. Definition of variables Variable

Definition

Mean

Non-missing observations

ADOPT

Farmer willing to adopt EMS

.3036

471

KNOW_EMS

Familiar with EMS

.3235

513

CLIENT

Increasing environmental requirements from

.5348

531

usual customers FORMA1

No education

.0510

529

FORMA2

Education BAA-CAPA

.0491

529

.2268

529

.2098

529

.2684

529

Certificate of vocational agricultural ability FORMA3

Education BPA-BEPA Degree in vocational agricultural studies

FORMA4

Education BTA-Bac Agricultural technician degree or Baccalaureate

FORMA5

Education BTS Advanced technician degree

FORMA6

Higher Education

.1909

529

CA1

Total sales less than 300 KF

.0572

524

CA2

Total sales between 300 and 600 KF

.1660

524

CA3

Total sales between 600 and 1000 KF

.2309

524

CA4

Total sales > 1000 KF

.5114

524

REGUL1

Environmental regulation will reduce

.0156

511

REGUL2

Environmental regulation will stay the same

.0332

511

REGUL3

Environmental regulation will increase

.9002

511

BIG_DEBT

Debt ratio > 40 percent

.3832

514

BIG_WORK

# Full-time workers > 10

.0400

524

DIFF_POS

Required price > extra cost of EMS

.1939

531

STATUS1

Individual farmer

.3352

531

STATUS2

EARL

.2580

531

.0753

531

Farm under Limited Liability code STATUS3

SCEA Farm under Civil Company code

STATUS4

Other

.3314

531

DEFS_0

0 correct answer on EMS (out of 4)

.2034

531

DEFS_1

1 correct answer on EMS (out of 4)

.2674

531

DEFS_2

2 correct answer on EMS (out of 4)

.2580

531

DEFS_3

3 correct answer on EMS (out of 4)

.1845

531

DEFS_4

4 correct answer on EMS (out of 4)

.0866

531

Table 5. Probit estimates. Dependent variable: ADOPT

Variable

Model I

Model II

Model III

Model IV

DIFF_POS

.6006(***)

.6559(***)

.6683(***)

.6857(***)

(3.55)

(4.02)

(4.16)

(4.41)

CLIENT KNOW_EMS

DEFS_1 DEFS_2 DEFS_3 DEFS_4 FORMA1 FORMA2 FORMA3 FORMA4

.2693

(*)

REGUL3 STATUS1 STATUS2

-

(.1405)

(.1387)

-

.9482(***)

-

-

1.0452(***)

(6.41)

-

-

(7.64)

.3663

.4652(*)

-

.3768(*)

(1.42)

(1.94)

-

(1.67)

-

.5484(**)

-

(2.44)

-

.5081(**)

(**)

(**)

.5942

.6440

(2.25)

(2.59)

(*)

(*)

.5221

.4799

(1.87)

(1.80)

-

(2.14)

.1993

.3464

-

.3870

(.64)

(1.19)

-

(1.48)

.9379(***)

.9917(***)

.7790(**)

-

(2.86)

(3.13)

(2.44)

-

(*)

.5995

.6160

.4168

(1.47)

(1.78)

(1.31)

(**)

(**)

-

(*)

.3751

.3630

(1.98)

(1.99)

(1.74)

-

-.0798

.0165

-.0380

-

(.09)

1.3701

(***)

(3.71) BIG_DEBT

.3403

(**)

(.1497)

(-.40) BIG_WORK

.3232

(**)

.2937

(*)

(-.21)

1.0605

(***)

(3.14) .2823

.3120

(**)

-

1.0458

(***)

(3.02) .3001

-

(**)

-

(1.92)

(1.97)

(2.12)

-

.6323(**)

.6247(**)

.6883(***)

-

(2.33)

(2.40)

(2.70)

-

-.1054

-.1104

-.1252

-

(-.50)

(-.55)

(-.64)

-

-.1645

-.2746

-.2849

-

(-.87)

(-1.52)

(-1.61)

-

STATUS3 CA1 CA2 CA3

.4602(*)

.5537(**)

.5340(**)

-

(1.64)

(2.06)

(2.02)

-

(**)

-.9195

-1.1214

-1.1479

(-1.41)

(-2.00)

(-2.01)

-

-.2273

-.2958

-.3345

-

(-.91)

(-1.29)

(-1.47)

-

.1251

.0756

.0816

-

(.73)

(.46) (***)

Intercept 2

Pseudo-R Correct predictions (percent) Wald test for global significance Observations

(**)

(.50) (***)

-1.4193

-

(***)

-1.4717(***)

-2.1837

-1.8451

(-5.99)

(-5.18)

(-4.87)

(-7.47)

.2404 78.67

.1598 74.77

.1450 74.08

.1729 76.21

χ2(19)=111.97 (.0000) 422

χ2(18)=75.57 (.0000) 436

χ2(14)=66.45 (.0000) 436

χ2(6)=87.43 (.0000) 454

Notes. t-statistics are in parentheses. Robust (Maximum Likelihood) standard errors are computed. (*), (**) and (***) respectively denote parameter significance at the 10, 5 and 1 percent level.

Table 6. Marginal effects on the probability of adopting Variable

Model I

Model II

Model III

Model IV

KNOW_EMS

.3358

-

-

.3702

(.0519)

-

-

(.0476)

.0899

.1093

.1158

-

(.0493)

(.0468)

(.0464)

-

reference

reference

reference

reference

-

-

-

-

.1285

.1662

-

.1316

(.0932)

(.0882)

.2121

.2332

(.0973)

(.0924)

.1883

.1742

(.1053)

(.1005)

.0699

.1259

(.1124)

(.1104)

.3567

.3776

.2974

(.1230)

(.1166)

(.1236)

.2244

.2325

.1547

(.1616)

(.1368)

(.1245)

.1327

.1298

.1115

(.0695)

(.0673)

(.0659)

-.0264

.0056

-.0129

(.0645)

(.0637)

(.0604)

reference

reference

reference

reference

-

-

-

-

.5062

.4029

.3993

-

(.1130)

(.1212)

(.1247)

.1005

.0977

.1046

(.0532)

(.0503)

(.0500)

.2169

.2402

.2459

.2487

(.0634)

(.0620)

(.0610)

(.0586)

.1773

.1792

.1952

-

CLIENT DEFS_0 DEFS_1 DEFS_2 DEFS_3 DEFS_4 FORMA1 FORMA2 FORMA3 FORMA4 FORMA5-

(.0814) -

.1946 (.0825)

-

.1824 (.0890)

-

.1395 (.0995) -

FORMA6 BIG_WORK BIG_DEBT DIFF_POS REGUL3

-

STATUS1 STATUS2 STATUS3 STATUS4 CA1 CA2 CA3 CA4

(.0605)

(.0596)

(.0561)

-.0350

-.0372

-.0424

(.0689)

(.0668)

(.0659)

-.0540

-.0899

-.0938

(.0603)

(.0569)

(.0557)

.1689

.2069

.2000

(.1091)

(.1054)

(.1039)

reference

reference

reference

reference

-

-

-

-

-.2216

-.2542

-.2602

-

(.0925)

(.0644)

(.0642)

-.0727

-.0946

-.1069

(.0755)

(.0683)

(.0668)

.0428

.0260

.0283

(.0595)

(.0571)

(.0576)

reference

reference

reference

reference

-

-

-

-

Notes. Robust standard errors of marginal effects are in parentheses.

-

-

Figure 1. Predicted probabilities of adopting

0,8

0,7

Prob(ADOPT)

0,6 P00000

0,5

P10000 P11000

0,4

P10100 P10010

0,3

P10001

0,2

0,1

-1 -0 ,9 -0 ,8 -0 ,7 -0 ,6 -0 ,5 -0 ,4 -0 ,3 -0 ,2 -0 ,1 0, 1 0, 2 0, 3 0, 4 0, 5 0, 6 0, 7 0, 8 0, 9

D IF

F

0

Required price-cost differential (ratio of total sales)

Notes. Probabilities are computed at the sample mean of the conditioning variables. P00000: No reported knowledge of EMS, and estimated knowledge= 0; P10000: Reported knowledge of EMS, and # correct answers = 1; P10100: Reported knowledge of EMS, and # correct answers = 2; P10010: Reported knowledge of EMS, and # correct answers = 3; P10001: Reported knowledge of EMS, and # correct answers = 4;