Issues of AES
AES face a number of issues in their implementation. These issues have been largely studied in the economic literature. We present below a general review of AES issues that serves two purposes. First, it describes the main conclusions of standard economic approaches and helps identifying gaps on which behavioral approaches2 may provide additional contributions, such as the issue of participation in AES. Second, this review highlights some of the main issues that affect AES effectiveness and efficiency. We present here especially issues stemming from information asymmetry and lack of coordination of farmers. This review is made for two reasons. First, one of the innovative designs that we study in this thesis addresses specifically one of the AES issues, namely the lack of coordination. Second, this inventory of issues serves as a framework to analyze the performance of the Agri-environmental Biodiversity Offset Scheme, one of the AES innovative design we study.
The participation issue
Considering that attaining high level of participation is one of the key to ensure a significant environmental impact, empirical research has analyzed the determinants of participation. The main determinants highlighted in the economic literature are: (a) farmer and farm socio-economic characteristics, (b) contract characteristics, (c) payment level and transaction costs. The underlying assumption of these studies is the standard economic approach that considers that farmers adopt an AES if their participation constraint is fulfilled, i.e. if payments are superior to the farmers’ opportunity costs (e.g Moxey et al., 2008). The determinants identified in the literature are therefore supposed to be factors of the farm cost function to implement the prescribed agricultural practices.
a) Farmer and farm socio-economic factors
Farm size, farmers’ age and education are considered, in the literature, as the main farmers and farms socio-economic factors influencing the adoption of AES by farmers. More precisely, AES are generally adopted in larger farms (Morris and Potter, 1995; Wilson, 1997; Falconer, 2000a; Allaire et al., 2009). Younger farmers are generally more likely to adopt AES (Morris and Potter, 1995; Bonnieux et al., 1998; Wynn et al., 2001; Vanslembrouck et al., 2002a; Ruto and Garrod, 2009; Ducos et al., 2009; Chabé-Ferret and Subervie, 2013) unless the scheme is focused on extensification practices (Drake et al., 1999). Finally, farmers with higher educational levels tend to be more interested in such schemes (Wilson, 1997; Allaire et al., 2009; Louis and Rousset, 2010; Chabé-Ferret and Subervie, 2013).
Attitude towards risk is also a key factor of AES participation. Risk aversion is considered to influence adoption but in two opposite ways. First positively, mainly due to income security allowed by AES payments (Fraser, 2004). Some farmers can even sell the environmental services below opportunity costs if the payment is stable and lasting to reduce income instability (Fraser, 2004; Karsenty et al., 2010). Nevertheless, Slangen (1997) and Sumpsi et al. (1998) stated that uncertainty regarding the future of AES policy and the impact of practices on the future production may hamper farmers’ participation.
b) Contract characteristics
Contract flexibility is an important criterion in farmers’ adoption of AES. Contracts that are more likely to be adopted have a shorter duration (Bougherara and Ducos, 2006; Ruto and Garrod, 2009; Louis and Rousset, 2010; Christensen et al., 2011), leave more flexibility to farmers in plot selection (Bougherara and Ducos, 2006; Ruto and Garrod, 2009) and in technical prescriptions (Bougherara and Ducos, 2006; Ruto and Garrod, 2009; Christensen et al., 2011; Kuhfuss et al., 2014). Besides, withdrawal ease of the program is also an important criteria for farmers’ participation (Christensen et al., 2011).
c) Payment level and costs
Payment level proposed in AES and how they relate to individual opportunity and compliance costs is a major factor to explain farmers’ adoption (Brotherton, 1991; Drake et al., 1999). As mentioned previously, these payments can also be perceived as a secured source of income (Wilson and Hart, 2001).
Transaction costs have also been found to have a strong impact on adoption of AES by farmers (Falconer, 2000b; Ducos and Dupraz, 2007a; Peerlings and Polman, 2009; Vatn, 2010). High private transaction costs (real or perceived) tied to AES can represent barriers for farmers’ participation in AES. Asset specificity, which is specialised investment that cannot be easily redeployed for another transaction, strongly influenced transaction costs in AES.
High asset specificity leads to strong transaction costs (Ducos and Dupraz, 2007a; Rørstad et al., 2007). Trust and good relationship between contracting partners also facilitates participation in AES by reducing transaction costs both before and during the transaction (Ducos and Dupraz, 2007a; Ducos et al., 2009; Peerlings and Polman, 2009; Louis and Rousset, 2010).
An experience with similar practices resulting from a prior participation in another kind of AES has also a positive effect on farmers’ adoption (Allaire et al., 2009; Louis and Rousset, 2010; Chabé-Ferret and Subervie, 2013). This previous experience can reduce compliance and transaction costs (Kuhfuss et al., 2013).
Although many of these factors could indeed be included in a cost function, the theoretical foundation for the influence of farmers’ characteristics such as age or education is rather weak. The influence of these variables could well be mediated through behavioral variables that are generally not included in these studies.
Direct evidence of the role of behavioral factors in the adoption of AESs remains relatively scarce in economic literature. The main behavioral factor that has been studied is the role of preferences for the environment but mainly in social psychological literature. Often presented as the influence of attitude towards the environment, it has been shown to influence participation to agri-environmental programmes in several contexts (Morris and Potter, 1995; Delvaux et al., 1999; Beedell and Rehman, 2000a; Defrancesco et al., 2008; Ducos et al., 2009; Mzoughi, 2011). Farmers participating in environmental associations and having nature hobbies are also found to have more positive attitude towards pro-environmental practices and are more likely to participate in agri-environmental programmes (Beedell and Rehman, 2000a). This factor seems to be mainly important for measures that require the most efforts by farmers (Delvaux et al., 1999; Vanslembrouck et al., 2002a).
The effect of behavioral factors on the adoption of AES has therefore not been much studied.
One of the contributions of this thesis is to contribute filling this gap.
Issues stemming from information asymmetry
Theoretical research on AES and PES in economics has mainly mobilized a contract theory perspective. The contract is seen as a Principal-Agent relationship between a seller (the farmer or land-owner) and a buyer (the State for AES and other potential buyers for PES) of an environmental service. This contractual relationship is characterized by an asymmetry of information – agents (farmers) have more information than the agency (the State for AES) – and different objectives – farmers want to maximize their profit and the State wants to maximize social welfare. Farmers may exploit this asymmetry of information to extract informational rent (Ferraro, 2008).
Two issues arise from this asymmetry of information. The first issue is that farmers have more information on the opportunity and compliance costs (hidden information) to comply with technical prescriptions of the contract. They can therefore try to negotiate higher payments by claiming their costs are higher. Likewise, in the case of contracts with standard and constant payments per hectare, which is the general rule in AES, there is a high potential for adverse selection (Fraser, 2009; Chabé-Ferret and Subervie, 2013). Adverse selection leads to selecting farmers with lower opportunity costs. This may not be a problem if opportunity costs are negatively correlated with environmental benefits, but in most cases this correlation is not verified (Claassen et al., 2008). In addition, a uniform payment leads to an overcompensation of farmers with lowest costs. In the worst situation, AES may attract farmers with zero opportunity costs, i.e. who would adopt the practices without being involved in the AES and are nevertheless being paid for that. In this case, windfall effect is maximum and the additional effect of the agri-environmental programme is null or very limited (Chabé-Ferret and Subervie, 2013; Kuhfuss and Subervie, 2015). Three solutions are proposed in the literature to overcome this problem: (1) acquire information on the environmental benefits that farmers can potentially offer and select them on this basis; (2) offer to farmers a menu of screening contracts; and (3) allocate contracts through agro-environmental auctions (Ferraro, 2008).
The second issue is that farmers are better informed about their actions than the buyer (hidden action) which leads to moral hazard. On the one hand, the seller has an incentive to deviate from the contract terms and respect his individually rational level of compliance. On the other hand, the buyer cannot force the seller to implement the Pareto-optimal outcome due to monitoring costs (Wu and Babcock, 1996). One of the possible ways to address the issue of moral hazard is to make seller’s payment contingent on environmental outcomes instead of actions. These outcome or result-oriented schemes have the advantage to give a direct incentive to the seller to provide environmental services. This type of contract however transfers the risk of a lack of environmental outcome, which may be due to stochastic natural hazard, to the farmer. Farmers may not be willing to accept this risk and enter in the contract (Zabel and Roe, 2009; Matzdorf and Lorenz, 2010).
Issues of farmers’ coordination at a landscape scale
Commonly-used uniform payment contracts, which rely on voluntary participation, do not include mechanisms to ensure spatial coordination of efforts at a landscape scale, although it is often essential to have a significant environmental impact (Goldman et al., 2007). For example, the conservation of endangered species generally requires the creation or protection of natural habitats in the shape of corridors, patches, or mosaics that usually cut across several farms (Forman, 1995). One of the main innovations proposed to overcome this lack of coordination of farmers at the landscape scale has been the introduction of an agglomeration bonus. The performance of this mechanism has been empirically tested in laboratory experiments (Parkhurst et al., 2002; Warziniack et al., 2007; Parkhurst and Shogren, 2007; Banerjee et al., 2012) and through mathematic simulations (Drechsler et al., 2010; Bamière et al., 2013).
In addition to the issues of spatial coordination, another type of coordination problem occurs in presence of environmental threshold. This is when the environmental state does not improve significantly until the global environmental effort (in terms of improved practices) has not reached a sufficient area in the zone of interest (Dupraz et al., 2009). In this thesis, we will particularly test the performance of an innovative contract designed to address this coordination issue.
This section briefly highlighted issues identified by standard economy on AES. In this thesis, we intend to use behavioral approaches to address some of AES issues and to study innovative AES designs. The next section presents these approaches and how they have been applied to the study of pro-environmental behavior.
Behavioral approaches of pro-environmental decisions
Behavioral approaches are increasingly being used to analyze pro-environmental behavior in economics. Although this is an emerging trend in economics, social psychology has already been studying pro-environmental decisions for a long time. In this section, we present the main principles of these behavioral approaches and their application to pro-environmental behavior.
Behavioral economics applies insights from psychology in the field of economics. The ambition is to enrich economic models to better explain and predict human behavior. It “explores, catalogues, and rationalizes systematic deviations from rational choice theory” (Shogren and Taylor, 2008).
Mullainathan and Thaler (2000) have classified behavioral factors into three main categories: bounded rationality, bounded willpower, and bounded self-interest. Bounded rationality means that people do not have unlimited information processing capabilities (Simon, 1955) and rather use heuristics that lead to systematic biases in judgment (beliefs) and choice. For example, people may be subject to loss aversion, framing effects, endowment effects…
Bounded willpower means that people may take decisions that they know are not in their self-interest (eat and drink too much, cheat their wife…) because of lack of self-control. Finally, bounded self-interest means that people have social preferences such as altruism, fairness, norms and inequity aversion (Mullainathan and Thaler, 2000; Shogren et al., 2010).
How relevant is behavioral economics to study environmental matters? The assumption of rational behavior is largely tied to the existence of an active market exchange that yields an optimal allocation of resources (Arrow, 1986). Assuming this rational choice behavior may therefore be problematic in environmental matters that largely lack market-like arbitrage (Crocker et al., 1998). Environmental goods are indeed often public goods whose provision has been shown to depend on many socio-psychological factors (e.g. Shang and Croson, 2009). Besides environmental issues are often associated with strong moral feelings and are characterized by a high level of complexity that lead to bounded rationality (Croson and Treich, 2014). Shogren and Taylor (2008) make the parallel between market failure and “behavior failure » in environmental economics that may lead to a new second-best problem and call for the emergence of “Behavioral-Environmental Economics”. One of the consequences of behavioral economics has been the emergence of new policy instruments such as “Nudges” (Thaler and Sunstein, 2008). The basic idea is that behavior can be modified by subtle modification of the decision context, often referred as the choice architecture. Among the most known examples are the nudges based on social comparison to reduce energy use (Allcott, 2011) or to increase towel reuse in hotels (Goldstein et al., 2008).
The interest for the role of behavioral factors in environmental economics initially started in the field of environmental valuation with the worry that psychological factors may affect stated preferences for environmental goods: hypothetical bias, starting point bias, framing bias and more (Cummings et al., 1986). In the last decades, the development of experimental economics has provided a tool to test the predictions and assumptions of standard economic theory and has largely led to question many of these. One example that has strong implications in environmental economics is the extensive literature on public good and common pool resources games (e.g. Ledyard, 1995; Ostrom, 2006).
“Green nudges”, nudges that aim at promoting environmentally responsible behavior, have increasingly been tested in the last years either experimentally or in pilot interventions. Schubert (2016) proposes to classify these nudges in three categories: green nudge that capitalize on consumers’ desire to have a good self-image by making green characteristics of a product more salient (eco-labels, Prius car), green nudges that exploit people’s inclination to follow the herd and green nudge that exploit bounded willpower by purposefully setting default choices (among different goods or services) as the green one.
Another less developed field of behavioral environmental economics research is to determine how the performance of traditional incentive mechanisms recommended by environmental economics (taxes, subsidies, tradable permits…) is affected by behavioral factors, and how optimal incentives should be adjusted accordingly. The impact of bounded self-interest is a focus of interest in this literature. Ariely et al (2009) defines that people may have different motivations to behave pro-socially that can be divided in three categories: extrinsic, intrinsic and image motivations. Extrinsic motivation is any material reward associated with the pro-social behavior. This motivation is the one traditionally studied in research that only considers self-interest. Monetary incentives generally included in environmental policies target people’s extrinsic motivations. Intrinsic motivation is the private preference for others’ well-being often referred as “other-regarding preferences”. Examples of such motivations are pure altruism, or the preference for the environment. Finally, image motivation is associated with the tendency to be influenced by others’ opinion or social approval, such as the influence of social norms. These three motivations may interact with each other and influence the effectiveness of monetary incentives used to promote pro-social behavior. The most famous example is the introduction of monetary incentives for blood donation that actually had a negative impact on this prosocial behavior (Titmuss, 1970; Mellström and Johannesson, 2008). When monetary incentives have negative interactions with intrinsic motivation, they are said to crowd out, while if they have positive interactions they are said to crowd in intrinsic motivations. For example, Benabou and Tirole (2006) set up a model that determines how incentives to carry out a prosocial activity can be determined, considering that individuals are altruist and have reputational concerns, and that monetary incentives may partially crowd them out. Another example is Nyborg (2010) who analyzes cases in which environmental taxes may crowd in or crowd out moral motivations.
Table of contents :
1 AES history and main issues
1.1 AES brief history
1.2 Issues of AES
1.2.1 The participation issue
1.2.2 Issues stemming from information asymmetry
1.2.3 Issues of farmers’ coordination at a landscape scale
2 Behavioral approaches of pro-environmental decisions
2.1 Behavioral economics
2.2 Pro-environmental behavior in social psychology
2.2.1 Moral motivation theories
2.2.2 The Theory of Planned Behavior (TPB)
2.3 The growing role of behavioral approaches in environmental policies
3 Research questions, hypotheses and methods
3.1 What is the role of behavioral factors in the adoption of AES?
3.1.1 Research question 1: Do behavioral factors intervene in the adoption of AES?
3.1.2 Research question 2: Do norms influence the adoption of AES?
3.2 What is the performance of innovative AES design and how it is affected by behavioral factors?
3.2.1 Research question 3: Does the introduction of a conditionality on collective participation improve the effectiveness and efficiency of AES contracts in presence of an environmental threshold?
3.2.2 Research question 4: Are AES performant to achieve the objectives of biodiversity offsets?
3.2.3 Research question 5: Does contract framing have an impact on the participation to AES?
PART I: WHAT IS THE ROLE OF BEHAVIORAL FACTORS IN THE ADOPTION OF AES?
Chapter 1: Motivations of farmers to adopt AES: An application of the theory of planned behavior
1 The Theory of Planned Behavior
1.1 The initial TPB model
1.1.2 Perceived Behavior Control
1.1.3 Subjective norm
1.1.4 Relative importance of TPB variables
1.2 Extensions of the TPB model
1.2.1 Descriptive norm
1.2.2 Personal norm
1.2.3 Response efficacy
1.3 Theory of planned behavior and economic theory
1.4 TPB applied to agri-environmental issues
2 Methodology of the empirical study
2.1 The case study
2.2 Sampling, survey design and administration
2.5 Statistical analysis
3 Results and discussion
3.1 Analysis of directly measured TPB variables
3.2 Belief analysis
3.2.2 Perceived behavior control
3.2.3 Subjective norm
4 Conclusion and discussion
Chapter 2: Do social norms influence the adoption of AES?
1 What are social norms?
1.1 Definitions of social norms
1.2 Reasons to conform to social norms
1.3 Descriptive and injunctive norms
1.4 Importance of subjective beliefs
1.5 Social identity
1.6 Personal norms
2 Theoretical approaches of the role of social norms
2.1 Social norms in social psychology
2.2 Norms in economic models
2.3 Modelling the effect of descriptive and injunctive norms on the adoption of AES: our proposal
2.3.1 Basic framework
2.3.2 Descriptive norm
2.3.3 Injunctive norm
2.3.4 Combination of descriptive and injunctive norms
3 Empirical analysis of the effect of social norms on AES adoption
3.1 How to measure the effect of social norms on the adoption of AES?
3.1.1 Stated and revealed preference methodologies
3.1.2 Experimental methodologies
3.2 Empirical evidence of the role of social norms on pro-environmental behavior in agriculture
3.3 A stated-preference survey on the role of social norms in the adoption of proenvironmental practices
3.3.1 Survey methodology
3.3.2 Data analysis
3.3.3 Results and discussion
4 Conclusion and policy recommendations
PART II: WHAT IS THE PERFORMANCE OF INNOVATIVE AES DESIGNS AND HOW IT IS AFFECTED BY BEHAVIORAL FACTORS?
Chapter 3: Can collective conditionality improve AES? Insights from experimental economics
1 Agri-environmental contracts with collective conditionality
2 Experimental design and procedure
2.2 Discussion of the design and theoretical predictions
3 Experimental results
3.1 Effectiveness of the subsidy schemes
3.2 Efficiency of the subsidy schemes
3.3 The crucial role of the first period of the experiment
3.4 The role of aversion to risk and beliefs
Chapter 4: Challenges of achieving biodiversity offset outcomes through AES: Evidence from an empirical study in Southern France
1 Background literature and research hypotheses
1.1 Definition of ABOS
1.2 Acceptability of ABOS
1.2.3 Social norms
1.2.4 Attitude towards the environment
1.3 Performance of ABOS
1.3.3 Link between land use and environmental outcomes
1.4 Research hypotheses
2 Materials and methods
2.1 Presentation of the case study
2.1.1 The BO project
2.1.2 The ABOS programme
2.2 Data collection and data analysis
2.2.1 Farmers’ acceptability of ABOS
2.2.2 ABOS performance
3 Results and Discussion
3.1 Determinants of farmers’ acceptability of ABOS
3.2 Performance of ABOS
3.2.1 Analysis of the survey of farmers engaged in the AES programme
3.2.2 Analysis of the plot selection process
4 Conclusion and Policy implications
Chapter 5: Compensating environmental losses versus creating environmental gains. Implications for biodiversity offsets and AES
1 Literature review
1.1 Purpose difference: compensation vs conservation
1.2 Responsibility for the loss
1.3 Environmental attitude
1.4 Trust in contracting partners
2.1 Choice experiment approach and model specification
2.2 Contract attributes
2.3 Experimental design
2.4 Data collection
3.1 Preference for contract attributes
3.2 Analysis of preference heterogeneity
4 Discussion and conclusion
1 Main findings
2 Behavioral approaches in agri-environmental policies.
3 The use of stated preferences and experimental methodologies
4 Limits and extensions