Increasing Private Contributions to Environmental Goods with Behavioral Insights Hilary Byerly Flint University of Vermont

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Graduate College Dissertations and Theses Dissertations and Theses

2019 Increasing Private Contributions To Environmental Goods With Behavioral Insights Hilary Byerly Flint University of Vermont

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INCREASING PRIVATE CONTRIBUTIONS TO ENVIRONMENTAL GOODS WITH BEHAVIORAL INSIGHTS

A Dissertation Presented

Hilary Byerly Flint

The Faculty of the Graduate College

The University of Vermont

In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Specializing in Natural Resources

May, 2019

Defense Date: March 20, 2019 Dissertation Examination Committee:

Brendan Fisher, Ph.D., Advisor Meredith T. Niles, Ph.D., Chairperson Anthony D’Amato, Ph.D. Taylor H. Ricketts, Ph.D. Cynthia J. Forehand, Ph.D., Dean of the Graduate College

ABSTRACT

Privately owned lands often undersupply environmental benefits and oversupply environmental costs through land use and management decisions. Insights into human behavior suggest a range of cognitive biases and nonstandard preferences that offer alternative explanations for and, perhaps, strategies to influence landowner behavior. People respond to simple changes in context and framing, make inconsistent choices over time, and respond to social influence—the opinions and behavior of peers. This dissertation applies insights from behavioral science to strategies that seek to influence individual decisions that impact the environment, especially related to land management. First, I review existing experimental research on behavioral insights to influence decisions in six domains that have large environmental externalities. Behavioral interventions, including changing the status quo and leveraging social influence, are often more effective than simply providing information, but there are few applications to land management. Chapter Two maps behavioral insights onto farmers’ plot-level conservation decisions that benefit biodiversity. Using a case study from California, USA, I find farmers who receive information from their peers are three times more likely to adopt practices that support biodiversity than those who do not. Chapter Three tests the causal effect of social influence on engaging Vermont forest owners in bird habitat conservation. Contrary to results from similar studies in other domains, information about peer participation reduced interest in the conservation program. Chapter Four presents results from another large-scale field experiment that tested the effect of message framing on contributions to water quality in a polluted urban watershed. Participants who read an emotional, personal narrative with tenuous connections to nutrient pollution were willing to pay more for nutrient runoff-reducing landscaping products than those who read a scientific description of nutrient pollution's impacts on ecosystems and surrounding communities. The findings from these four studies contribute to our understanding of environmentally relevant behavior, with implications for privately managed land and the environmental benefits it provides.

CITATIONS

Material from this dissertation has been published in the following form: Byerly, H., Balmford, A., Ferraro, P. J., Hammond Wagner, C., Palchak, E., Polasky, S., … Fisher, B.. (2018). Nudging pro-environmental behavior: evidence and opportunities. Frontiers in Ecology and the Environment, 16(3), 159–168. https://doi.org/10.1002/fee.1777

Material from this dissertation has been submitted for publication to Conservation Science and Practice on February 13, 2019 in the following form: Byerly, H., D’Amato, A. W., Hagenbuch, S., & Fisher, B.. (Under review). Do forest owners respond to peers or pewees? A field experiment on social influence and songbird habitat conservation.

Material from this dissertation has been submitted for publication to Environmental Monitoring and Assessment on February 20, 2019 in the following form: Byerly, H., Kross, S. M., Niles, M. T., & Fisher, B.. (Under review). Behavioral science and biodiversity management in agricultural landscapes.

ii DEDICATION

To my dad, Alan,

who taught me to climb mountains and

To my mom, Gay, who gave me the opportunity to reach the top.

iii ACKNOWLEDGEMENTS

First, big ups to my committee, who provided essential guidance and encouragement over the last three years. Thank you to my advisor, Brendan Fisher, who carved out time for things big and small, shared opportunities, enthusiasm and timely doses of pragmatism, and showed me that success does not require sacrificing the important things in life. I thank Taylor Ricketts for being a mentor and modeling that role, as well as teacher, scholar, and boss-man, in ways that cultivate excellence through openness and good humor. And Tony D’Amato and Meredith Niles, who were always generous with their time and insights into research and life in and out of academia.

This research could not have been completed without the work of my tremendous collaborators. I especially thank Steve Hagenbuch, for his patience and support in developing the experiment in Chapter Two, and Paul Ferraro, for inviting me to share in his research and offering smart, sensible, and remarkably useful feedback. The community at the Gund Institute, especially the fellow grads and staff at the Johnson

House, created a space where solitary and stressful work became a shared, supportive endeavor.

I am deeply grateful to the friends and family who have been my ballast. My dearest friends have shown me how to change the world and have one heck of a good time

(cheers, E.B. White). My mom provided unconditional love, helpful text edits, and inspiration as she pursues her own opus. My stepdad offered balance and a reminder to breathe. The spirit and memory of my dad, who had extraordinary self-discipline to follow big dreams yet shared a joyful appreciation for the small things, gave me strength

iv and perspective. I hope he would be proud. Most of all, I thank my husband, Sandy, for the millions of little ways he made third college more easy and more fun, and for the big, beautiful way he makes everything in life more better. And thanks to Wilma, for puppy therapy. Bark.

v TABLE OF CONTENTS CITATIONS ...... ii DEDICATION ...... iii ACKNOWLEDGEMENTS ...... iv

INTRODUCTION ...... 1 References ...... 6 CHAPTER ONE: NUDGING PRO-ENVIRONMENTAL BEHAVIOR: EVIDENCE AND OPPORTUNITIES ...... 8 Abstract ...... 8 Methods...... 11 Evidence for pro-environmental behavior change ...... 13 What we know about contextual interventions ...... 19 Contextual interventions in practice ...... 20 Future research and program design ...... 21 Looking ahead ...... 23 Acknowledgements ...... 24 References ...... 25 Tables ...... 34 Panel 1. Behavioral evidence in recycling and energy use ...... 36 Figures...... 38 CHAPTER TWO: BEHAVIORAL SCIENCE AND BIODIVERSITY MANAGEMENT IN AGRICULTURAL LANDSCAPES...... 41 Abstract ...... 41 Introduction ...... 42 Behavioral science and biodiversity management in agricultural landscapes ...... 45 Time and social preferences in farm-level biodiversity management in California ..... 53 Integrating behavioral science into conservation in agricultural landscapes ...... 60 Conclusion ...... 64 Acknowledgements ...... 65 References ...... 66 Tables ...... 73 Figures...... 76 Appendices ...... 77 CHAPTER THREE: DO FOREST OWNERS RESPOND TO PEERS OR PEWEES? A FIELD EXPERIMENT ON SOCIAL INFLUENCE AND SONGBIRD HABITAT CONSERVATION ...... 79 Abstract ...... 79 Introduction ...... 80 Methods...... 84 Results ...... 90 vi Discussion ...... 92 Conclusion ...... 97 Acknowledgements ...... 98 References ...... 99 Tables ...... 105 Figures...... 107 Appendices ...... 109 CHAPTER FOUR: REFRAMING ENVIRONMENTAL PROBLEMS INCREASES CONTRIBUTIONS TO ENVIRONMENTAL QUALITY ...... 112 Abstract ...... 112 Introduction ...... 113 Methods...... 116 Results ...... 121 Discussion ...... 121 Acknowledgements ...... 125 References ...... 126 Tables ...... 129 Figures...... 131 Supplementary Information ...... 133 CONCLUSION ...... 137 COMPREHENSIVE BIBLIOGRAPHY ...... 140

vii

INTRODUCTION

Privately owned lands often undersupply environmental benefits and oversupply environmental costs through land use and management decisions. These market failures—externalities, public goods, and the tragedy of the commons—are attributed to missing alignment between self-interest and the interests of society (Keohane and

Olmstead 2007). Strategies to intervene include regulation, incentive-based mechanisms, and voluntary programs and education, all meant to change behavior based on an economic model of behavior that responds only to costs, benefits, and well-defined preferences (Jack, Kousky, and Sims 2008; Dolan et al. 2012).

A large and growing body of research shows how traditional economic assumptions about human behavior, summarized in the ‘rational actor model,’ are incomplete. Simon

(1955) questioned the external validity of the rational actor model and recognized the limitations to human cognitive capacity, or ‘bounded rationality.’ In reality, he argued, individuals do not comprehend the full set of possible alternatives, do not have well defined, ordered preferences, and do not exercise complex probability computations and mapping of all possible outcomes and their values. Over the next half century these insights were tested, developed, extended and classified into cognitive biases and nonstandard preferences (Madrian 2014). Key implications include the ways in which people 1) are sensitive to a decision’s context, or ‘choice architecture’, 2) make errors when evaluating risk and uncertainty, 3) care about the present relative to the future, and

4) care about the behavior and opinions of others (Camerer, Loewenstein, and Rabin

2004; Thaler and Sunstein 2008). 1

These advances, loosely grouped as ‘behavioral insights,’ have three implications for interventions designed to address environmental challenges arising from private land management.

First, cognitive biases can make it difficult to identify the optimal level of conservation and the appropriate strategy to intervene. Many environmental policies rely on stated or revealed preferences to establish the value of some nonmarket environmental good. Yet, there is often a discrepancy between the amount people are willing to pay for an environmental good and the amount they are willing to accept for its degradation, a difference explained by the ‘endowment effect’ (Croson and Treich 2014; Thaler 2018).

Moreover, individuals may not always be optimizing their preferences. When people are overwhelmed by complexity, pressed for time, or biased towards the present moment, their private marginal benefit of taking some action may be more aligned with the social optimal than observed behavior would suggest (Madrian 2014). For example, some farmers intend to adopt certain management practices but simply fail to follow through, even when subsidies are offered (Duflo, Kremer, and Robinson 2011). In these cases, changing prices or providing information does not address the primary barrier to behavior-change. By correcting for a market failure without addressing the behavioral realities of those who are affected by environmental policies, interventions risk creating a

‘second-best’ solution that does not increase welfare (Benartzi et al., 2017b)(Shogren and

Taylor 2008).

Second, existing or conventional behavior-change interventions may be less effective when failing to account for behavioral insights. Money—either too much or too little—

can counteract desired behavior by crowding out intrinsic motivations or setting the stakes too high or low (Kamenica 2012). Seemingly minor features of choice architecture, such as how information is presented and the status quo, can influence whether people choose to enroll in a program (Thaler 2018). And many people have

‘other-regarding preferences’: their behavior demonstrates that they are not strictly self- interested and are motivated by cooperation, reciprocity, altruism, and fairness (Bénabou and Tirole 2006; Fehr-Duda and Fehr 2016). These features of human behavior could inhibit the effectiveness of traditional policy interventions to achieve desired environmental outcomes.

Third, insights from behavioral science offer alternative strategies to influence behavior.

Recognizing that people respond to more than information and incentives expands the suite of behavior-change options available to policymakers (Dolan et al. 2012). ‘Nudges’ are “choice-preserving, behaviorally informed approaches to regulatory problems, including disclosure requirements, default rules, simplification, and use of salience and social norms” (Sunstein 2013, 3). Such non-pecuniary approaches are particularly attractive because they tend to be low-cost and allow for individual freedom to guide the ultimate decision (Benartzi et al. 2017). Moreover, leveraging other-regarding preferences can complement regulatory approaches by employing self-enforcement

(Ostrom 2000; Shogren, Parkhurst, and Banerjee 2010).

In recent years, behavioral science has been recognized by governments and institutions in the development of social programs (e.g., BehaviouraI Insights Team 2010; World

Bank 2015; Executive Order No. 13707 2016). By drawing on a more realistic

understanding of human behavior, researchers and practitioners have refined the delivery of interventions to improve individual and social welfare (Chetty 2015). There is clear relevance to and promise for the design of programs and policies to address environmental challenges (Fehr-Duda and Fehr 2016, Cinner 2018).

This dissertation applies insights from behavioral science to strategies that seek to influence individual decisions that impact the environment, especially related to land management.

First, I review the literature to collate evidence on behavioral insights for influencing decisions that have large environmental externalities. I find that behaviorally informed interventions consistently outperform the provision of information in changing behavior, but the bulk of research has focused primarily on recycling and energy and water use.

Very few experimental studies have been conducted on land management and decisions that directly impact biodiversity and other environmental goods.

Next, I consider how four areas of behavioral science—context, risk, time, and social influence—might explain farmers’ decisions to increase on-farm biodiversity. Using data from a survey of farmers in California, USA, I find evidence that sensitivity to social influence is highly correlated with the adoption of practices that benefit wildlife.

The third and fourth studies are field experiments that test behavioral interventions on individual decisions to manage private lands in ways that contribute to environmental public goods. In Chapter Three, I find that the provision of information about the behavior of peers reduces forest owners’ interest in a bird habitat conservation program.

In Chapter Four, I find that framing the problem of nutrient pollution in a way that elicits

emotion through narrative increases the amount people are willing to pay for landscaping products that improve water quality, compared to scientific information about the problem.

These studies demonstrate the potential for behavioral science to inform more effective strategies to address environmental challenges, especially those arising from private land management.

References

BehaviouraI Insights Team. 2010. “Applying Behavioural Insight to Health.” London, UK: United Kingdom Cabinet Office. Bénabou, Roland, and Jean Tirole. 2006. “Incentives and Prosocial Behavior.” The American Economic Review 96 (5): 1652–78. Benartzi, Shlomo, John Beshears, Katherine L. Milkman, Cass R. Sunstein, Richard H. Thaler, Maya Shankar, Will Tucker-Ray, William J. Congdon, and Steven Galing. 2017. “Should Governments Invest More in Nudging?” Psychological Science, June, 0956797617702501. https://doi.org/10.1177/0956797617702501. Camerer, Colin F., George Loewenstein, and Matthew Rabin, eds. 2004. Advances in Behavioral Economics. Princeton University Press. Chetty, Raj. 2015. “Behavioral Economics and Public Policy: A Pragmatic Perspective.” American Economic Review 105 (5): 1–33. https://doi.org/10.1257/aer.p20151108. Croson, Rachel, and Nicolas Treich. 2014. “Behavioral Environmental Economics: Promises and Challenges.” Environmental and Resource Economics 58 (3): 335–51. Dolan, P., M. Hallsworth, D. Halpern, D. King, R. Metcalfe, and I. Vlaev. 2012. “Influencing Behaviour: The Mindspace Way.” Journal of Economic Psychology 33 (1): 264–77. https://doi.org/10.1016/j.joep.2011.10.009. Duflo, Esther, Michael Kremer, and Jonathan Robinson. 2011. “Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya.” American Economic Review 101 (6): 2350–90. https://doi.org/10.1257/aer.101.6.2350. “Executive Order 13707 of September 15, 2015 Using Behavioral Science Insights To Better Serve the American People.” Code of Federal Regulations, Title 3 (2016): 371- 373. Fehr-Duda, Helga, and Ernst Fehr. 2016. “Sustainability: Game Human Nature.” Nature News 530 (7591): 413. https://doi.org/10.1038/530413a. Jack, B. Kelsey, Carolyn Kousky, and Katharine R. E. Sims. 2008. “Designing Payments for Ecosystem Services: Lessons from Previous Experience with Incentive-Based Mechanisms.” Proceedings of the National Academy of Sciences 105 (28): 9465–70. https://doi.org/10.1073/pnas.0705503104. Kamenica, Emir. 2012. “Behavioral Economics and Psychology of Incentives.” Annual Review of Economics 4 (1): 427–52. https://doi.org/10.1146/annurev-economics-080511- 110909. Keohane, Nathaniel O., and Sheila M. Olmstead. 2007. Markets and the Environment. Island Press. Madrian, Brigitte C. 2014. “Applying Insights from Behavioral Economics to Policy Design.” Annual Review of Economics 6 (1): 663–88. https://doi.org/10.1146/annurev- economics-080213-041033. 6

Ostrom, Elinor. 2000. “Collective Action and the Evolution of Social Norms.” The Journal of Economic Perspectives 14 (3): 137–58. Shogren, Jason F., Gregory M. Parkhurst, and Prasenjit Banerjee. 2010. “Two Cheers and a Qualm for Behavioral Environmental Economics.” Environmental and Resource Economics 46 (2): 235–47. https://doi.org/10.1007/s10640-010-9376-3. Shogren, Jason F., and Laura O. Taylor. 2008. “On Behavioral-Environmental Economics.” Review of Environmental Economics & Policy 2 (1): 26–44. https://doi.org/10.1093/reep/rem027. Simon, Herbert A. 1955. “A Behavioral Model of Rational Choice.” The Quarterly Journal of Economics 69 (1): 99–118. https://doi.org/10.2307/1884852. Sunstein, Cass R. 2013. “Deciding by Default.” University of Pennsylvania Law Review 162 (1): 1–57. Thaler, Richard H. 2018. “From Cashews to Nudges: The Evolution of Behavioral Economics.” American Economic Review 108 (6): 1265–87. https://doi.org/10.1257/aer.108.6.1265. Thaler, Richard H., and Cass R. Sunstein. 2008. Nudge: Improving Decisions About Health, Wealth, and Happiness. Penguin. World Bank. 2015. World Development Report 2015: Mind, Society, and Behavior. The World Bank. http://elibrary.worldbank.org/doi/book/10.1596/978-1-4648-0342-0.

CHAPTER ONE: NUDGING PRO-ENVIRONMENTAL BEHAVIOR:

EVIDENCE AND OPPORTUNITIES

Hilary Byerly1,2,*, Andrew Balmford3, Paul J Ferraro4, Courtney Hammond Wagner1,2, Elizabeth Palchak1,2, Stephen Polasky5, Taylor H Ricketts1,2, Aaron J Schwartz1,2, Brendan Fisher1,2

Abstract

Human actions are responsible for many of our greatest environmental challenges.

Studies from the human behavioral sciences show that minor features of decision settings can have major effects on people’s choices. While such behavioral insights have positively influenced individual health and financial decisions, less is known about whether and how these insights can encourage choices that are better for the environment.

We review 160 experimental interventions that attempt to alter behavior in six domains where decisions have large environmental impacts: family planning, land management, meat consumption, transportation choices, waste production, and water use. Claims that social influence (norms) and simple adjustments to automatic settings (defaults) can influence pro-environmental decisions are supported by the evidence. Yet for other interventions, knowledge gaps preclude clear conclusions and policy applications. To address these gaps, we identify four opportunities for future research and encourage collaboration between scholars and practitioners to embed tests of behavioral interventions within environmental programs.

1Gund Institute for Environment, University of Vermont, Burlington, VT; 2Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT

*([email protected]); 3Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge, United Kingdom; 4Carey Business School and Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD; 5Department of Applied Economics, University of Minnesota, St. Paul, MN

Human behavior is a key determinant of the state of the environment. Individual consumption and lifestyle choices contribute significantly to climate change (Dietz et al.

2009; IPCC 2014), ecosystem conversion and biodiversity loss (Rockström et al. 2009;

Tilman and Clark 2015), and water scarcity (Wada and Bierkens 2014). These impacts are projected to grow with the size and wealth of the global population (Ferrara and

Serrat 2008). As such, changing human behavior is essential to addressing environmental challenges (Fischer et al. 2012; Cowling 2014; Nyborg et al. 2016).

Non-regulatory policies and programs designed to influence decision making have historically been shaped by the economic model of the rational actor. With unbounded cognitive power and attention only to private costs and benefits, this actor responds to information and incentives. But there is ample evidence that people have pro-social attitudes and are not strictly self-interested (Ostrom 2000). Insights from psychology, economics and neuroscience further suggest that cognitive constraints and biases play important roles in how people make decisions (Simon 1955; Tversky and Kahneman

1974).

In fact, people respond not only to incentives, information and persuasion, but also to how these interventions are framed and communicated (Kahneman et al. 1991; Kamenica

2012). Altering the context within which decisions are made can encourage socially

desirable behaviors and discourage socially undesirable ones (Figure 1). For example, people are motivated to uphold their promises and goals, so asking for commitments

(written and oral, public and private) may increase the likelihood of certain actions. Other behaviors are more likely to follow the status quo, or default setting, in a given situation.

Choices can be swayed by the identity of the person, or messenger, who suggests the behavior change. Communicating social norms, such as expectations or peer comparisons, can influence how individuals behave. People also respond to information that is made accessible in their mind (via priming) and to which their attention is drawn

(via salience) (Figure 2). Unlike financial incentives and education, which target controlled, conscious deliberation, these contextual variables often moderate behavior through automatic, unconscious cognitive processes (Dolan et al. 2012).

Applications of these insights from behavioral science have shown positive effects on individual and social welfare. Changing the context in which choices are presented can encourage people to save for retirement (Thaler and Benartzi 2004), make healthier diet and lifestyle choices (Downs et al. 2009; Volpp et al. 2011), and participate in socially beneficial programs such as organ donation (Johnson and Goldstein 2003). Yet the potential of behavioral insights to advance sustainability is unrealized in many environmental policies and programs (Clayton et al. 2013; Dietz 2014; Reddy et al.

2017).

There is evidence that interventions targeting these contextual variables can improve recycling rates and reduce energy use (see Panel 1 for an overview), but less is known about whether such approaches can influence other environmentally significant

behaviors. We review the experimental evidence on behavior-change interventions in six other domains where individual decisions have large environmental impacts (hereafter,

“domains”): family planning, land management, meat consumption, transportation choices, waste production, and water use. For each of these six domains, we evaluate the effectiveness of eight sets of behavioral interventions (hereafter, “interventions”; Figure

3). Six of these sets aim to affect the contextual variables described above: commitments, defaults, messenger, norms, priming, and salience. We contrast these contextual interventions with two sets of traditional behavioral interventions, which target the cost- benefit calculations of rational decision makers: financial incentives and education. These traditional interventions set the performance benchmark against which contextual interventions can be compared—a comparison that allows us to draw conclusions on the full suite of behavior-change options available to policymakers and those designing conservation programs.

We seek to answer three policy-relevant questions. First, what do we know about using contextual interventions to change environmentally significant behavior? Second, are there interventions that have proven effective across domains? Last, how should we prioritize further research on behavioral science to address environmental challenges?

Methods

We conducted a systematic literature review to examine the effects of contextual interventions on pro-environmental behavior in six environmentally relevant domains

(Figure 3). We confined our review to studies that employed experimental designs in order to draw conclusions about the causal impact of interventions on behavior. Our

review was guided by four criteria: (i) experiments that (ii) study pro-environmental behavior changes (iii) with respect to our six domains and (iv) report statistical inferences. By “experiment,” we mean empirical designs in which exposure to a condition/treatment is experimentally manipulated across or within subjects to permit unbiased causal inference. By “behavior changes,” we mean self-reported or observed behaviors, rather than knowledge, attitudes, or intentions. The behaviors of interest in each domain were those that mitigate negative environmental impacts, such as using contraception to reduce population growth, regardless of whether the intent of the experimenter was environmentally motivated. We identified search terms within each of these domains (see WebTable 1) and used them in combination with the words experiment, intervention, treatment, control, behavior, sustainable, and pro- environmental, and with our eight behavior-change interventions: commitments, defaults, messenger, norms, priming, salience, financial incentives, and education. Searches were conducted in Web of Science, PsycINFO, Econlit, other electronic databases, relevant journals, and the citations of included papers. Our search centered on the peer-reviewed literature, though we also included working papers from active researchers in the field.

The studies that met our criteria were coded according to domain, behavior, sampled population, sample size, setting (field or lab), measure (reported or observed), intervention target, intervention tested, and significance of each treatment. We report our results using the instance of a single intervention as the unit of analysis. By intervention, we are referring to a treatment and its measured impact on a unique behavioral outcome.

For experiments that measured multiple behavioral outcomes (eg used contraception and reduced sexual activity), each behavior counted separately. Two authors independently 12

coded each study (81% agreement) and discrepancies were reconciled through discussion. The full set of reviewed studies can be seen in WebTable 2.

We aim to give the reader a broad survey of multiple domains and interventions, and thus our review is circumscribed in several dimensions. First, we do not report study effect sizes or weight the studies by quality. The outcome measures across domains vary greatly, and a large number of studies did not report all the elements necessary to calculate standardized effect sizes. Moreover, some studies used self-reported outcomes or experienced treatment non-compliance, which can affect their internal validity. Few studies reported power analyses, and a number of the included experiments used convenience samples with unknown effects on their external validity. Second, because we count multiple outcome estimates from a single study separately, our review is prone to the “multiple comparison problem” (inflated Type 1 errors). Third, despite inclusion of six unpublished papers, selective publication of studies may have biased conclusions toward statistically significant effects. It is also possible that researchers themselves are biased in their selection of interventions to test. Lastly, not all tested interventions fit perfectly into our defined categories. Despite these limitations, we believe the scope and breadth of our analysis offer a useful perspective on the state of the evidence.

Evidence for pro-environmental behavior change

We found 72 studies that tested 160 interventions across our six domains (Table 1).

Nearly all (96%) studies were conducted in the field, as opposed to a laboratory, and almost three-quarters (73%) measured observed, rather than self-reported, behavior.

Sample sizes ranged from 23 to over 100,000, with a median size of 379 participants. The

majority of estimates addressed water use and transportation choices, while the fewest targeted land management and meat consumption (Figure 4). Norms were the most frequently affected contextual variable (48 times), followed by commitments (25), salience (11), defaults (8), priming (2), and messenger (1). The two traditional approaches—financial incentives and education—were targeted 29 and 36 times, respectively.

Family planning

The behavioral outcomes in this domain were measured by contraception use, fertility rate (actual births) and sexual activity (Table 1). Though tested only once, an intervention targeting norms showed a strong effect on family planning. Women offered contraception vouchers alone were 25% more likely to use contraception and 27% less likely to give birth than women who received the voucher in the presence of their husbands (Ashraf et al. 2014). A single study of the effect of salience, via daily reminders to use contraception, could not detect an effect on the rate of missed birth control pills compared to a control that received no reminder (Hou et al. 2010).

More than two-thirds of tested interventions in our search were education, showing overall mixed results (a similar finding to the systematic review by Mwaikambo et al.

(2011)). Financial incentives were tested in only one study, in which neither a credit for contraception nor a credit combined with family planning services showed an effect on contraception use compared to a control group that received neither (Desai and Tarozzi

2011).

Land management

Outcome measures in this domain were divided between adopting sustainable land management practices and committing resources towards conservation. A messenger intervention, which varied the gender of agricultural extension agents, increased adoption of sustainable agriculture by farmers when the gender of the agent matched that of the farmer (Kondylis et al. 2016). Switching the default cost-share from 0% to 100% in a conservation contracting auction increased the amount farmers were willing to pay by 9 percentage points (Messer et al. 2016). In the same study, priming farmers to perceive a conservation practice as socially desirable increased the likelihood of bidding, but had no effect on the amount farmers were willing to pay. Commitments to dedicate land or time towards conservation had mixed results (Cobern et al. 1995; Lokhorst et al. 2009) and no effect was detected for a test of salience, which used message framing to engage farmers in conservation tillage (Andrews et al. 2013).

Traditional interventions produced mixed findings. Payments for land conservation reduced the rate of deforestation by half compared to villages where there was no financial incentive for conservation (Jayachandran et al. 2016). But no effect was detected for providing payments in exchange for communal litter collection (Kerr et al.

2012), nor for education about the benefits of conservation farming (Lokhorst et al.

2009).

Meat consumption

Studies on meat consumption measured vegetarian meal purchases and self-reported changes in eating meat. Changing the default cafeteria menu to vegetarian-only, while

meat options remained available on a separate menu, increased the proportion of vegetarian meals ordered by 50% (Campbell-Arvai et al. 2014) and increased the odds of choosing vegetarian meals by a factor of 15 (Campbell-Arvai and Arvai 2015).

Commitments to eat less meat reduced meat consumption by 15 percentage points compared to a group that received information alone (Loy et al. 2016).

Of the three experiments that tested education interventions, one study found education resulted in a self-reported reduction of meat consumption, though the estimated effect was small (Monroe et al. 2015). Two studies could not detect a difference in the meat consumption of groups that received education and those that did not (Campbell-Arvai et al. 2014; Campbell-Arvai and Arvai 2015).

Transportation choices

Studies in this domain focused on three types of transportation behavior: driving efficiency, self-reported driving behavior, and public transportation use. Only one contextual intervention, which targeted salience, showed promise. Increasing the salience of environmental impacts increased the likelihood of improving driving efficiency compared to framing information in economic terms, although the sample size was notably small (n = 23) (Bolderdijk et al. 2013). Evidence on commitments was split: three studies found personal goals to use public transportation were effective (Bachman and Katzev 1982; Bamberg 2002; Taniguchi and Fujii 2007), and three studies did not detect effects (Jakobsson et al. 2002; Matthies et al. 2006; Eriksson et al. 2008). No effect was found for targeting social norms (Beale and Bonsall 2007; Yeomans and

Herberich 2014; Kormos et al. 2015), nor was there an effect when changing the default for purchasing bus tickets (Katzev and Bachman 1982).

Most of the experimental literature within this domain focused on financial interventions

(Figure 4). Direct monetary incentives, such as payments, charges and discounts, largely did not show an effect (Katzev and Bachman 1982; Jakobsson et al. 2002; Schall and

Mohnen 2015). However, other financial incentives, including free bus tickets, travel vouchers, and prizes, encouraged sustainable transportation behavior (Katzev and

Bachman 1982; Bachman and Katzev 1982; Matthies et al. 2006; Bamberg 2006;

Thøgersen 2009; Yeomans and Herberich 2014; Schall and Mohnen 2015).

Waste production

This domain focused on behavioral outcomes related to waste production (ie reducing consumption) rather than waste disposal (but see Panel 1 on recycling). Results here offer evidence in favor of defaults and commitments to reduce food, paper, and plastic waste.

Reducing the default plate size reduced food waste by 20% (Kallbekken and Sælen 2013) and switching default printer settings to double-sided reduced paper consumption at a university by 15% per day (Egebark and Ekström 2016). Commitments increased self- reported food waste prevention behaviors in households (Schmidt 2016) and made shoppers 29% more likely to refuse plastic bags at a grocery store (Rubens et al. 2015).

Mixed results were found for norms and salience. Communicating social norms reduced plastic bag use and buffet food waste (de Groot et al. 2013; Kallbekken and Sælen 2013), though no effect was detected on reducing paper waste (Rommel et al. 2015; Hamann et

al. 2015; Egebark and Ekström 2016). Salience showed an effect on refusing junk mail but not plastic bags (Rubens et al. 2015; Hamann et al. 2015).

Traditional interventions showed promise in this domain. Financial incentives were effective in reducing junk mail and plastic bottle waste (Rommel et al. 2015; Santos and van der Linden 2016). Three of the four tested education interventions reduced waste (de

Young et al. 1993; Rommel et al. 2015).

Water use

Commitments and norms showed promise for reducing water consumption by households, students, and hotel guests. Interventions employing commitments were effective nine of the ten times they were tested, particularly for encouraging hotel guests to reuse their towels (Baca-Motes et al. 2013; Terrier and Marfaing 2015a, b). Targeting norms by exposing participants to messages about the water-saving behavior of their peers also reduced water use, increased towel reuse, and increased participation in conservation programs (Schultz et al. 2008, 2014; Goldstein et al. 2008; Fielding et al.

2013; Ferraro and Price 2013; Brent et al. 2015; Seyranian et al. 2015; Datta et al. 2015;

Richetin et al. 2016). Increasing the salience of personal identity had mixed effects on water use (Dickerson et al. 1992; Baca-Motes et al. 2013; Seyranian et al. 2015), but simple reminders proved effective: households that attached water-use labels to showers and appliances reduced water use by 23% compared to those that received the same information in a leaflet (Kurz et al. 2005).

Education and financial incentives showed mixed results, leading to lower water use in some cases, but not others (Geller et al. 1983; Thompson and Stoutemyer 1991;

Middlestadt et al. 2001; Kurz et al. 2005; Fielding et al. 2013; Ferraro and Price 2013;

Seyranian et al. 2015; Terrier and Marfaing 2015a, b).

What we know about contextual interventions

Experimental evidence suggests that behavioral insights show promise for altering environmentally relevant behaviors (see Table 2). Interventions aimed at affecting norms or defaults produced consistent effects on behavior across multiple studies and domains.

Several large-scale field experiments showed normative messages to reduce household water consumption by 2.5-7.7% compared to control groups (Ferraro and Price 2013;

Brent et al. 2015; Datta et al. 2015). Switching default buffet plate size, printer settings, menu offerings, and cost-share amounts made it easier for individuals to act pro- environmentally.

The evidence on commitments and salience is less straightforward. Although commitments to reuse towels and to reduce waste and meat consumption were effective, no effect was found on reducing driving or adopting land conservation practices.

Reminders to change behavior had an effect on water consumption but not on taking daily contraception or declining plastic bags at the supermarket. Reminders about financial benefits did not increase pro-environmental behavior more than facts alone, and framing behavior-change in financial terms actually reduced pro-environmental behavior compared to environmental framing and a control. Priming and messenger effects were each only tested in one study.

Contextual interventions in practice

Overall, contextual interventions outperform education interventions. Six studies compare contextual interventions directly against an education intervention and a no- intervention control and find that the contextual intervention produced the largest gain in pro-environmental behavior (Kurz et al. 2005; Ferraro and Price 2013; Campbell-Arvai et al. 2014; Schultz et al. 2014; Seyranian et al. 2015; Rommel et al. 2015). Financial incentives also outperform education interventions. Less evidence, however, can be gleaned from the relative impacts of contextual interventions compared to financial incentives. The two may be substitutes or they may be complementary. Appropriately tailored contextual interventions may optimize the acceptability and impact of financial incentives.

Indeed, our findings showed some interventions work best in combination. Several family planning studies showed education interventions to be most effective when combined with health visits, vocational training, or social networking (Chong et al. 2013;

Bandiera et al. 2015; Ahmed et al. 2015). Normative messages to promote tire inflation discouraged drivers when the service was free, but increased inflation rates (perhaps via social pressure) when an employee offered assistance (Yeomans and Herberich 2014). A number of studies combined multiple interventions into a single treatment, making it difficult to discern the causal effect of any one intervention or their interactions.

The effectiveness of contextual interventions is often sensitive to conditions both internal to the decision-maker and specific to the external context. While targeting norms reduced water consumption, effects were repeatedly moderated by other factors, such as delivery

method, baseline water use and socioeconomic status. Norms also influenced family planning behavior, but gains in contraception use were offset by a negative effect on women’s subjective wellbeing. These caveats illustrate an important limitation of behavioral interventions: because their success is often conditional on prior beliefs, characteristics, and context, universally effective solutions are unlikely. Accounting for such complexity may require combinations of interventions that target both deliberative and subconscious thought to change behavior (van der Linden 2013).

Future research and program design

Our review identified four areas where more research could yield policy guidance for encouraging pro-environmental behavior change.

1. Test interventions in domains that are most impactful on the environment.

Meat consumption, unsustainable land management, and population growth put significant stress on the environment (Wynes and Nicholas 2017), yet we could find only four, seven, and nine studies that tested behavior change in these respective domains.

More experimental research on decreasing meat consumption, for example, could reduce the rate of land conversion (Foley et al. 2011), emissions of greenhouse gases (Garnett

2011), and biodiversity loss across land and seascapes (Machovina et al. 2015). Future research should also target producer behavior. While financial incentives and regulation will remain important tools to influence corporate decisions, contextual interventions may encourage low-cost, potentially high-benefit changes that benefit the environment.

2. Test interventions that have not been well tested with respect to pro-environmental behaviors.

More evidence on messenger effects could be useful to environmental programs and policymakers. Given the strength of these in influencing health behaviors and charitable donations (Durantini et al. 2006; Landry et al. 2006; Stock et al. 2007), these interventions may be important tools for conservation.

3. Test interventions using adequately sized randomized controlled designs to better measure causal effects of contextual interventions relative to alternatives.

Well-designed experiments allow us to determine a cause-and-effect relationship between interventions and desired environmental outcomes. Yet many pro-environmental behavior-change studies are poorly designed, lacking adequate controls and randomization (Frederiks et al. 2016). Fewer than 10% of the studies in our literature review explicitly discuss the statistical power of their results. Given that nearly a quarter of the studies we reviewed had a sample size of fewer than 100 participants, it is likely that many results are underpowered. Studies with proper experimental design and sufficient sample sizes will allow us to draw stronger conclusions about the causal effects and magnitude of behavior-change interventions.

4. Evaluate conditions, cost-effectiveness, and persistence of behavior-change interventions for policy implementation.

In order to translate experimental evidence into environmental policy, more research is required to understand when certain interventions work, at what cost, and for how long.

There are roadmaps for implementation (see Clayton et al. 2013; Schultz 2014; Reddy et 22

al. 2017), but little is known about the combinations and moderators of interventions that will determine their policy significance. A meta-analysis on commitments similarly highlights a lack of empirical evidence to explain why and under which conditions the intervention is effective (Lokhorst et al. 2013). While advocates of contextual interventions highlight their low cost (Sunstein 2013; Benartzi et al. 2017), only 15 of the

72 studies in our review addressed the cost-effectiveness of the tested interventions.

Twenty studies considered the duration of behavior change, but only nine measured the effect beyond six months. If the effects of promising interventions expire with the end of their implementation, there is little hope for addressing the scale of current environmental challenges (van der Linden 2015). Future experiments should prioritize evidence on the net value of the behavioral insight and persistence of behavior change.

Looking ahead

Behavioral insights show promise for sustainability, yet much work remains to make them actionable for environmental policy design and program implementation. We encourage collaboration between scholars and practitioners to embed tests of behavioral interventions within existing environmental programs. Such tests provide both generalizable scientific knowledge and specific applications that can be incorporated into scaled-up programs. A variety of scholar-practitioner collaborations are conducting such tests in poverty alleviation, public health, criminal justice, tax compliance, and education.

Similar efforts have begun to address environmental challenges. Our review suggests there is both need and opportunity to build an evidence base of behavioral insights tailored to achieving sustainability goals.

Acknowledgements

Helpful comments and advice from Sandy Flint and Dan Fredman greatly improved this manuscript. We thank the Gund Institute for Environment for their Collaboration Grant to

BF which made this work possible. Thanks to the University of Vermont’s James Marsh

Professor-at-Large and Burack Distinguished Lecture Series for supporting SP and AB respectively.

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Tables

Table 1. Summary of included studies

Domain Behavior Interventions Observed Studies Sample size

Reduce fertility rate 4 75% Family Reduce sexual activity 2 0% 9 73 – 6275 planning Use contraception 10 30%

Land Adopt conservation practices 5 40% 7 58 – 5076 management Contribute resources to conservation 9 78%

Choose climate-friendly protein 1 0% Meat Eat vegetarian 4 100% 4 55 – 491 consumption Reduce meat consumption 1 0%

Improve driving efficiency 7 100% Transportation Reduce driving 11 0% 16 23 – 700 choices Use public transport 21 52%

Reduce food waste 3 67% Waste Reduce paper waste 9 100% 10 52 – 1302 production Reduce plastic waste 9 56%

Participate in conservation programs 3 100%

Water use Reduce water use 40 98% 26 40 – 106,669

Reuse hotel towels 21 100%

Total 160 73% 72

Notes: Behavior is the outcome variable used to measure the effect of an Intervention (see Figure 3). A single Study may test multiple interventions. Observed shows the proportion of interventions that are evaluated on an observed (vs. self-reported) behavior change. Sample size shows the lower and upper bound of the sample sizes for studies in that domain.

Table 2. Balance of evidence to change environmentally significant behaviors Intervention Promising Mixed No effect

Commitments

Defaults

Messenger

Norms

Priming

Salience

Education

Financial

Notes: = family planning; = land management; = meat consumption; = transportation choices; = waste production; = water use. Domains are allocated to a particular column according to the proportion of studies in that domain that measured a statistically significant effect of that intervention, as reported by the studies’ authors. Promising = 75% or more results found an effect; Mixed = less than 75% but more than zero results; No effect = none of the studies that tested that intervention detected an effect. See Figure 4 for the relative frequency of tested interventions within each domain. Emoji artwork is provided by EmojiOne and is licensed under CC-BY 4.0.

Panel 1. Behavioral evidence in recycling and energy use

Recycling

The experimental literature on recycling dates back to the 1980s. Today, waste management behaviors—recycling and not littering, in particular—have become so embedded in some countries that many consider them normative (Kinzig et al. 2013;

Gould et al. 2016).

Changing defaults, such as adding bins for recycled goods alongside trash cans and offering curbside pickup on the same day as trash pickup, has proven to encourage recycling. Messenger interventions, via neighbors, and commitments, via goal-setting, verbal promises, and public statements, have also increased recycling. Social norms, in the form of comparative feedback and visual presence of curbside pickup, promoted recycling behavior, but the effect was often mediated by personal values. Recent reviews suggest large gaps remain about the specific moderators and mechanisms that influence recycling behavior-change, particularly over the long term.

Note: See reviews by Hornik et al. 1995; Porter et al. 1995; Schultz et al. 1995; Osbaldiston and Schott 2011; Abrahamse and Steg 2013; Kirakozian 2016; Maki et al. 2016.

Energy

Research on behavioral interventions for energy use began in the 1970s and focuses largely on reducing residential energy consumption and improving energy efficiency.

Multiple meta-analyses and review articles synthesize the experimental evidence on energy behavior research. 36

Salience (frequent, in-home reminders of current use) and commitments (goals for reducing use) have made energy use feedback more effective in changing individual energy behavior. Defaults that automatically enroll customers in efficiency or green energy programs have also increased participation compared to opt-in programs.

Comparison messages about neighbors’ energy use have been widely employed to target social norms and shown a range of treatment effects, though they can be less impactful than other contextual interventions in reducing energy use. Messenger effects warrant further research: engaging ‘block leaders’ in neighborhoods and model employees in offices show some evidence of influencing energy behavior, but the results and contexts are limited.

Note: See reviews by Abrahamse et al. 2005; Faruqui et al. 2010; Osbaldiston and Schott 2011; Abrahamse and Steg 2013; Davis et al. 2013; Delmas et al. 2013; Frederiks et al. 2015; Karlin et al. 2015; Staddon et al. 2016.

Figures

Figure 1. Examples of targeting contextual variables to increase pro-social and pro-environmental behavior. (a) Pledges elicit commitments that spur action to reduce energy use. (b) Automatically enrolling consumers in green energy programs increases participation compared to a default where people must opt- in. (c) Health information is more effective when the messenger who is suggesting the behavior change is perceived as similar. (d) The behavior of peers and neighbors indicate social norms that promote recycling. Photo credits for Figure 1 (a) Karen McKenny, South Burlington Energy Prize Competition (b) Brad Hess, Clínica de Familia La Romana

Figure 2. Interventions that target contextual variables to change behavior also include: (a) displays of healthy foods that prime shoppers to purchase more healthful products and (b) reminders and prompts make energy use and conservation salient.

Figure 3. Interventions targeting contextual and traditional variables to influence environmentally significant behavior. Variables are adapted from Dolan et al. (2012).

Figure 4. Number of tested behavior-change interventions across six domains of environmentally impactful behavior. Column order is expressed in the key at the bottom of the chart. An empty column indicates we found no tested interventions targeting that contextual variable in that domain.

CHAPTER TWO: BEHAVIORAL SCIENCE AND BIODIVERSITY

MANAGEMENT IN AGRICULTURAL LANDSCAPES

Hilary Byerly1,2,*, Sara M. Kross3, Meredith T. Niles2,4, Brendan Fisher2,5

Abstract

The plot-level decisions of land managers (i.e. farmers, ranchers, and forest owners) influence landscape-scale environmental outcomes for biodiversity and ecosystem services. The impacts of their decisions often develop in complex, non-additive ways that unfold over time and space. Behavioral science offers insights into ways decision makers manage complexity, uncertainty, choice over time, and social influence. We review such insights to understand the plot-level conservation actions of farmers that impact biodiversity. To make these connections concrete, we provide a case study of the decision to adopt biodiversity management practices in the heavily cultivated region of the Central

Valley, California, USA. We use results from a survey of 122 farmers in the region to test whether adoption is related to time horizons and social influence. We find farmers who are more sensitive to social influence are three times more likely to adopt practices that support biodiversity, including wildflowers, native grasses, cover crops, hedgerows, and wetlands. This relationship could have important implications for how plot-level decisions aggregate to landscape-scale outcomes. Finally, we offer four priorities for future research and program design to integrate behavioral science into biodiversity conservation in agricultural landscapes. By considering land managers’ plot-level

conservation decisions with the lens of behavioral science, we identify barriers and opportunities to influence those decisions.

1 Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA 05405; 2 Gund Institute for Environment, University of Vermont, Burlington, VT, USA 05405; * [email protected]; 3 Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA, 10027; 4 Department of Nutrition and Food Sciences & Food Systems Program, University of Vermont, Burlington, VT, USA 05405; 5 Environmental Program, Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA 05405

Introduction

The loss of biodiversity and the services it provides—especially pollination and pest control—have been highlighted by the European Union and the United States as among the most pressing concerns facing agricultural landscapes (European Commission 2017;

IPBES 2018). This trend is largely the result of habitat loss and fragmentation, and compounded by chemical inputs, invasive species, and climate change (Butchart et al.

2010).

Strategies to improve biodiversity and ecosystem services in intensively farmed areas include encouraging natural or improved uncultivated areas along fields and riparian zones, altering the timing and techniques of cropping and tilling, and reducing pesticide use (Bommarco et al. 2013; Kovács-Hostyánszki et al. 2017). If these actions also support populations of beneficial organisms, then a farm may experience yield gains due to augmented provision of ecosystem services, such as pest control, soil retention, and pollination (Garibaldi et al. 2014). Such benefits may extend to nearby farms and contribute to broader landscape multifunctionality (Kremen & Merenlender 2018). 42

Despite potential private and public benefits, many farmers do not adopt practices that boost biodiversity (Lovell & Sullivan 2006). Providing habitat often comes at an opportunity cost to farmers: land that would otherwise generate profits may need to be managed less intensively. As a result, both the United States Department of Agriculture and the European Union’s Common Agricultural Policy have enlisted a suite of policies and programs, spanning regulatory, incentive-based and educational approaches, to intervene and encourage farm-level biodiversity management. Desired activities include keeping or taking land out of production, improving uncultivated land by planting native species, and participating in government or nonprofit programs that provide information or financial incentives for such practices (Vaughan & Skinner 2008).

Decisions to engage in these activities, however, are rarely straightforward. Farmers must evaluate complex and uncertain tradeoffs between private and social costs and benefits, now and into the future. A farmer deciding to provide patches of semi-natural habitat must weigh potential losses to her crops against the unknown probability of increasing bird and bee populations, and the services they provide, sometime in the future. She must incur upfront costs in time and money in hope of generating benefits for herself and for others who live nearby or even thousands of miles away, whose values of biodiversity may differ from her own.

Behavioral science offers insights into ways decision makers manage complexity, risk and uncertainty, and changes over time. Rather than acting with unlimited cognitive capacity, people often rely on mental shortcuts, biases and contextual cues to guide their decision making (Kahneman 2003). People are also sensitive to the ways their behavior

impacts and is perceived by others, so-called ‘social preferences’ (Fehr & Fischbacher

2002). Importantly, these insights demonstrate how simple changes to the decision environment can influence behavior (Thaler 2018). Recognizing this, the field of behavioral science—and its more theory-based counterpart, behavioral economics—has made cost-effective contributions to improving individual and social welfare, such as increasing college enrollment and vaccination rates (Benartzi et al. 2017).

The cognitive biases and social preferences that influence human behavior may help explain farmers’ plot-level decisions to support biodiversity. Observational studies of farmer behavior indicate that social norms and time horizons are associated with pro- environmental actions (Prokopy et al. 2008; Reimer & Prokopy 2014; Niles et al. 2016).

Yet, despite the overlap, we are not aware of any research that systematically connects these factors with the insights offered by behavioral science. A number of survey papers apply behavioral science to environmental and conservation issues (e.g., Brekke &

Johansson-Stenman 2008; Shogren & Taylor 2008; Gsottbauer & Bergh 2010; Croson &

Treich 2014; Cinner 2018), though none addresses decision making around land management to improve outcomes for biodiversity and ecosystem services.

This paper adds to the existing literature by linking the field of behavioral science to the challenge of increasing biodiversity and ecosystem services in agricultural landscapes.

The next section considers this challenge in the context of key insights gleaned over the past few decades of inquiry into human decision making. We apply insights from behavioral science to examine how four factors of influence —complexity and context- dependence, uncertainty and risk, time discounting, and social preferences—may help

explain farmers’ biodiversity management decisions. Then, we present a case study on the role of time discounting and social preferences in the adoption of biodiversity management practices by farmers in central California, USA. Finally, we discuss how integrating behavioral science into future research might improve our understanding of farmer behavior and inform more effective landscape-scale conservation. While we focus on behaviors that encourage farm-level structural changes for biodiversity and the services it provides in high-income countries, we expect the discussion herein to be useful to other land management decisions and landscape-scale challenges.

Behavioral science and biodiversity management in agricultural landscapes

In the following subsections, we consider how farmers’ actions that impact biodiversity may be explained or influenced by insights from the field of behavioral science. This paper adapts the frameworks offered by Camerer et al. (2004) and Just (2014), organizing these insights into four factors of influence: 1) complexity and context-dependence; 2) uncertainty and risk; 3) time discounting; and 4) social preferences (Table 1). For each, we explain the behavioral factor and its components, make connections to farmers’ plot- level management decisions, and discuss implications for biodiversity outcomes.

Complexity & context-dependence

Rather than being able to seamlessly navigate the complexity of the world, humans have limited cognitive capacity, or ‘bounded rationality’ (Simon 1955). As a result, peoples’ decisions often vary according to the context in which they are made. For example, the reference point from which one makes a guess or a bid influences its value (Kahneman et al. 1991). As does the order in which options are presented and the ways in which they 45

are framed (Tversky & Kahneman 1981; Shu et al. 2012). People become attached to a status quo and evaluate changes relative to that baseline, rather than considering absolute gains and losses. These “supposedly irrelevant factors” have shown to influence a range of important decisions, including saving for retirement and organ donation (Thaler 2016).

Farmers make numerous management decisions amidst dynamic market, policy, social, and climatic conditions, such that the range of options and potential tradeoffs are large and complex. Farmers’ decisions to change management or enroll in programs that subsidize conservation practices can require considerable time and energy to search for information, evaluate alternatives, and estimate costs and benefits. These real and perceived transaction costs inhibit program participation in the E.U. and the U.S.

(Mettepenningen et al. 2009; McCann & Claassen 2016; Palm-Forster et al. 2016). Habits and preferences for status quos are likely to influence management, but little is known about their importance in conservation practice adoption (Reimer & Prokopy 2014;

Dayer et al. 2017). Farm-level decisions can also be sensitive to the context in which they are presented. Narrowly framing crop insurance as an investment (with a premium) that may produce a gain (the indemnity) can reduce purchasing compared to broadly framing insurance costs and payouts over all farm assets if some event occurs (Babcock 2015).

Farmers managing for biodiversity and ecosystem services must weigh multiple options whose outcomes unfold in complex ways. Efforts to increase biodiversity may interact with other factors, such as farm or regional characteristics, that ultimately determine their effectiveness (Sardiñas & Kremen 2015; Heath et al. 2017). While the farm-level ecosystem service benefits derived from biodiversity are drivers of farmer adoption of

biodiversity-friendly practices (Kross et al. 2018), few studies have quantified these services at the farm scale. Farmers also tend to be time-scarce, further reducing their capacity to systematically assess how choices will play out on the landscape (Reimer &

Prokopy 2014). As a result, farmers may avoid making changes in management that could benefit biodiversity when the process and outcomes are not straightforward, or when the status quo fosters inaction. Programs and policies designed to incentivize conservation may fall short if they do not account for the supposedly irrelevant factors that shape decisions.

Uncertainty and risk

Judgements and choices under risk and uncertainty can be subject to systematic errors.

Accurate assessments of probabilities are difficult, even among trained statisticians

(Tversky & Kahneman 1971). Instead, people overweight insights from small samples.

They use rules-of-thumb, or ‘heuristics,’ to match uncertain situations with similar or salient scenarios in the mind (the representativeness heuristic and availability heuristic, respectively). This is especially the case in “low-validity environments”, which are highly uncertain and unpredictable (Kahneman 2011). Rather than exhibiting consistent risk preferences (i.e. being risk-loving or risk-averse), people’s choices are again sensitive to a reference point (Kahneman & Tversky 1979). Potential losses below this reference point hurt more than equivalent potential gains, called ‘loss aversion.’ As a result, people are often willing to take riskier gambles to avoid losses than they would to achieve gains.

The inherent uncertainty and risk in farming motivated a large body of work studying farmers’ responses to changes in yields and prices, which contributed to early foundations of behavioral science (Carter 2016). More recent studies have focused on risk and uncertainty related to climate change, showing that high levels of uncertainty dissuade farmers from adapting to changing weather patterns (Morton et al. 2017) and that farmers often perceive climate change risk to be greater than potential climate change benefits (Niles et al. 2013). Past experiences or stories of other farmers, such as crop losses from extreme weather events, serve as influential reference points for evaluating uncertainty and risk (Marx et al. 2007; Tonsor 2018). Loss aversion in risky decisions explains the failure of many farmers to adopt technologies that generate higher average profits but may increase losses on occasion (Bougherara et al. 2017; Du et al.

2017).

For farmers deciding how much cost to incur for future or public benefits of biodiversity, they must estimate the risks of changing practices against the likelihood of achieving gains, given varying levels of uncertainty. Yet any change in biodiversity may be perceived as stochastic, since only a fraction of that outcome can be attributed to the actions of one land manager (Hanley et al. 2012). This unpredictability is compounded by scientific uncertainty; even experts do not agree on the most effective strategies for conserving biodiversity in agricultural landscapes (Fischer et al. 2008). Such uncertainty may dissuade farmers from making any changes that could benefit biodiversity. Where outcomes are more probabilistic, farmers may instead rely on recent events or familiar stories to guide their assessments. Increasing biodiversity may increase risks of certain ecosystem disservices, such as crop destruction and disease (Jacobson et al. 2003; Zhang 48

et al. 2007). If potential losses loom large, farmers may fail to adopt biodiversity management practices that have private and social benefits because they are more risk averse over gains than they ‘should’ be.

Time discounting

People tend to discount the future and view time inconsistently. Immediate gains are worth more than those expected at some future time period. In part, this is because events that are far off in time are abstract or ‘psychologically distant’ (Trope et al. 2007).

Moreover, the difference between receiving some benefit today versus tomorrow is much greater than that equivalent one-day wait a year in the future (called ‘hyperbolic discounting’). Not only do people value the future less, but that when the future arrives, they tend to exert less self-control than predicted. This is because people tend to be biased towards the present, causing them to procrastinate costly behavior that will have future benefits, such as studying, dieting, or saving for the future (Madrian 2014). Failing to accurately predict how one will feel at some future time period (projection bias) and misremembering how one arrived at a decision (hindsight bias) obscure people’s abilities to make consistent choices over time (Christensen-Szalanski & Willham 1991;

Loewenstein et al. 2003).

Certain factors are likely to influence the rate at which farmers discount the future, including immediate need, financial or tenure security, and age. Farmers who do not own their land and landowners who lease to farmers must adjust their time horizons to the terms of their contracts, potentially interfering with adoption of conservation practices

(Ranjan et al. 2019). Although older farmers are less likely to adopt best management

practices, perhaps because of a shorter planning horizon (Baumgart-Getz et al. 2012), those with a successor to maintain farm management into the future are more likely to participate in agri-environmental schemes (Lastra-Bravo et al. 2015). A review paper on farmer decision making conducted by Niles and colleagues (in preparation) found a wide range of discount rates used by researchers with often arbitrary or missing justification.

Inconsistent time preferences may interfere with farmers acting in their own self-interest.

Some farmers want or intend to adopt practices, but when the time comes to do so, the upfront time costs overwhelm the highly discounted future benefits of those practices

(Duflo et al. 2011). This could explain evidence that farmers’ intentions to adopt climate mitigation and adaptation practices differ considerably from actual adoption (Niles et al.

2016).

Like climate change mitigation, the benefits of management changes for biodiversity are often distant in time and space. The impacts of many land management practices unfold over long time scales that conflict with the upfront costs and benefits associated with ecosystem change (Wilson et al. 2016). Planting flower strips and hedgerows to attract native pollinators, for example, requires four to seven years before yield benefits offset establishment costs (Kovács-Hostyánszki et al. 2017). This temporal disconnect between biodiversity actions and impacts may be compounded when farmers do not own the land they cultivate or have a successor to continue a legacy (Lastra-Bravo et al. 2015; Ranjan et al. 2019). Farmers’ actions to manage for biodiversity are likely to take time to produce beneficial outcomes. This makes private costs particularly difficult to justify in the present time period, thus discouraging management practices that yield benefits in the long run. 50

Social preferences

Social scientists have a long history studying the roles of social norms and cooperation in influencing behavior, including in land management (Hardin 1968; Ostrom 2000).

Behavioral economists have incorporated these insights to explain deviations from expectations of self-interest and measured their effects on economic behavior. In doing so, they have identified the contributions of altruism, fairness, reciprocity, and social norms to observed behaviors (Fehr & Fischbacher 2002; Hoff & Stiglitz 2016). Further, this research has used experimental methods to estimate how much these social preferences matter in certain decisions and contexts (Abrahamse & Steg 2013). Studies show that providing information about the expectations and behavior of others, making one’s behavior observable to others, and selecting specific messengers to deliver information can change the actions people take (Cialdini 2003; Landry et al. 2006; Yoeli et al. 2013). These insights highlight the importance of social norms, image and reputation, and trust in influencing behavior.

Social norms are associated with the management practices farmers use and their willingness to adopt alternatives (Garbach & Morgan 2017; Hillis et al. 2017). The absence of widespread support for climate change policies among farmers may influence perceptions of norms and cooperation, suggesting, “If no one else is supporting this, why should I?” (Niles et al. 2016). This social influence, or sensitivity to the views and behavior of others, is also associated with the adoption and persistence of conservation activities (Prokopy et al., 2008; Dayer et al., 2017). Offering reputational benefits, such as publicizing good stewardship, can be important for conservation program participation

(Atari et al. 2009; Banerjee & Shogren 2012). Land managers are willing to coordinate on conservation action when group performance is rewarded (Parkhurst et al. 2002), although perceptions of fairness matter (Drechsler 2017). Conversely, empathy for others drives some farmers’ decisions to adopt conservation practices and share access to private land (Sheeder & Lynne 2011; Czap et al. 2015; Niles et al. 2017).

As with other land management decisions, changes in on-farm biodiversity can influence costs and benefits incurred by neighboring parcels and communities near and far. This implies that there is an inherent social aspect to these decisions, both impacted by and impacting others (Sonter et al. 2017). In some regions, prevailing social norms may conflict with biodiversity goals, such as aesthetic preferences for manicured farms over the ‘messy’ look of natural areas (Dayer et al. 2017). Because biodiversity is maintained at large spatial scales, effectively increasing biodiversity in agricultural landscapes requires the action of many individual landowners, which leads to free-riding and concerns about fairness. While altruistic land managers may be willing to supply biodiversity without incentives, others will require reciprocity or recognition for their behavior. Yet biodiversity is a public good, contributions towards which may not be easily observed or measured. Where management actions are difficult to observe or take time to produce benefits, these social rewards will be challenging to provide.

Farmers’ social preferences, as well as their time discounting, evaluation of risk and uncertainty, and reactions to complexity and context, have clear links to decisions about managing for biodiversity. Evidence from behavioral science and research on farmer decision making suggest these factors can be barriers to adopting practices that encourage

biodiversity and, ultimately, improving biodiversity outcomes on the landscape. To illustrate these ideas, we offer a case study of farm-level biodiversity management and test whether two behavioral factors—time discounting and social preferences—are associated with farmers’ adoption of practices that provide habitat and forage for pollinators and other wildlife.

The role of time and social preferences in farm-level biodiversity management in

California

The Central Valley of California, USA is an intensively farmed region that is critically important for food production and to the state and national economies. More than 400 crops are grown in California, worth more than $50 billion in 2017, and contributing 13% of all U.S. agricultural value (CDFA 2018). In this largely agricultural landscape, the biodiversity management of individual farmers can provide refuge and habitat for birds, bats, bees, and other species. Creating hedgerows along fields, for example, increases bird abundance and diversity (Heath et al. 2017). Other actions, such as retaining existing tree lines and riparian corridors, planting wildflower strips, and providing habitat for cavity-nesting species, increase landscape complexity and have positive effects on biodiversity and the services they provide (Kross et al. 2016).

We examined farmers’ biodiversity management behavior in the Central Valley and the farm-level factors associated with that outcome. Specifically, we used results from a survey of farmers to test whether proxies for time discounting and social preferences are associated with adoption of on-farm practices that benefit biodiversity. We hypothesized

that farmers who have lower temporal discount rates and farmers who are more sensitive to social influence are more likely to adopt biodiversity management practices.

Farmers’ time horizons may be sensitive to their tenure arrangements. Farmers who own the land they cultivate have incentive to invest in practices that may not show returns in the near term, thus lowering their discount rate (Soule et al. 2000). This position contrasts with that of renters and non-operating owners looking for returns over the short time frame of farmland leases (Ranjan et al. 2019). These lease terms, which are often only one year in the United States, create insecurity for both parties, thus reducing incentives to make investments in biodiversity practices that may disrupt the current year’s revenue.

Social preferences include how sensitive people are to social influence: information from and about their peers. Since managing for biodiversity is a contribution to a public good, engaging with peers and trusting them for information may facilitate cooperation and reciprocity (Fehr & Fischbacher 2002). Conversely, farmers who draw more on personal observation and experts for information may make management decisions with greater consideration of private costs and benefits.

To test the relationship between time and social preferences and biodiversity management, we used data from a survey of California farmers that assessed management practices and opinions of wildlife (see Table A1). A detailed description of the survey can be found in Kross et al. (2018), who found that farmers’ perceptions of bats and birds were correlated with the management practices they used to attract or deter wildlife. On average, women had more favorable opinions of wildlife than men, and

organic farmers viewed wildlife more positively than conventional farmers (Kross et al.

2018).

Methods

The survey was mailed to 500 farmers randomly selected from the County Agricultural

Commissioner’s Office registers in each of five counties in central California (Butte,

Sacramento, Solano, Sutter and Yolo). An identical online version was also made available and post-hoc analysis showed no significant difference in responses between the two outreach methods (Kross et al. 2018).

The survey asked farmers to report on their perceptions of the ecosystem services and disservices on the farm from perching birds, bats, and birds of prey. Farmers also reported on the use of common biodiversity management practices, as well as their source of information about such practices, their interest in having wildlife on their farm, and their participation in five major conservation programs: Environmental Quality

Incentives Program (EQIP), Conservation Reserve Program (CRP), Conservation

Stewardship Program (CSP), Wetlands Reserve Easements (WRE), and Organic

Certification. Farmers provided demographic information, including age, gender, education, percent income from farm, and farm role.

We developed two proxy variables using survey responses to evaluate the role of time discounting and social preferences in biodiversity management (Table 2). Time discounting was represented by a farmer’s role on the property: whether he or she is the owner and/or the manager. Respondents who were both the owner and manager of the farm were considered to have a lower discount rate, while respondents who were either

the owner or the manager were expected to have a higher discount rate. Social preferences were measured by the value respondents placed on receiving information from their peers about wildlife and wildlife management. Farmers who responded that information from other landowners and growers is “very useful” were considered to be sensitive to social influence, while those who did not were considered less sensitive to social influence. The outcome measure we used is the number of biodiversity management practices farmers reported adopting, which included cover crops, hedgerows, native grasses, wetlands, wildflowers, or ‘other.’

To test whether differences in time discounting and social preferences are associated with biodiversity management, we used the nonparametric Mann-Whitney U test, which accounts for the nonnormality of our outcome distribution and smaller sizes of our subgroups. We modeled the decision to adopt biodiversity management practices using ordinal logistic regression to better estimate this relationship and account for other factors. We included as covariates participation in government programs, interest in having wildlife on farm, proportion of income from farm, and farmers’ gender, age, and level of education.

Results

Our survey received 122 responses from farmers who are majority male (74%), at least

60 years old (51%), have a college education (68%), and who rely on their farm for most of their income (mean = 65%, sd = 40%). About half of the sample have participated in at least one of the five major conservation programs listed in the survey (48%) and are very

interested in having wildlife on their farm (49%). Table A1 provides a complete list of variables and their distribution in the sample.

Native grasses are the most commonly adopted biodiversity management practice, followed by cover crops, wildflowers, hedgerows, wetlands, and ‘other’ (Table 2). Those who selected ‘other’ indicated they adopted riparian buffers, ponds, conservation tillage, and rotational grazing. On average, farmers have adopted two biodiversity management practices (Figure 1), although 22% of all respondents have not adopted any practices.

For our proxy variables, 76% were categorized as having a low discount rate and 36% were considered more social (Table 2). Figure 1 shows the distributions and median values of biodiversity management practices according to time discounting and social preferences. Mann-Whitney tests indicate farmers with more social preferences adopt more biodiversity management practices (W = 1007, p < 0.01), but we find no difference in management according to farmers’ time discounting (W = 1216, p = 0.64).

An ordinal logistic regression model estimates that social preferences, participation in government programs, and interest in wildlife all predict the adoption of biodiversity management practices (Table 3). Farmers who are sensitive to social influence—highly valuing information from peers—are three times more likely to manage for biodiversity than those who are not, given that all other values in the model are held constant (OR =

3.11, p = 0.01, 95% CI of OR 1.36 - 7.22). We do not observe a relationship between assumed discount rates—or farmers’ roles (e.g. manager, owner, both)—and biodiversity management (p = 0.74, 95% CI of OR 0.37 - 2.06).

Discussion

We found evidence that social influence is correlated with biodiversity management on

California farms. Farmers who highly value information from their peers are more likely to use practices that benefit biodiversity. The direction of the relationship is supported by behavioral science research on social preferences: as farmers communicate with each other they can share information about their contributions to a public good, which may increase cooperation (Banerjee et al. 2017), and improve reputation within groups that value those contributions (Banerjee & Shogren 2012). Other research in this region has also found peers to be an important source of information for farmers (Lubell et al. 2014;

Garbach & Long 2017).

The importance of social influence in biodiversity management has implications for the

Central Valley landscape and, ultimately, the species and services that benefit from these practices. First, because encouraging on-farm biodiversity can deliver public benefits, evidence that social factors matter for decisions about managing for biodiversity suggests a sort of alignment between action and impact. If sharing occurs between farmers who are spatially proximate, this pattern could aggregate across the landscape to increase connectivity and biodiversity outcomes. Some biodiversity management practices, such as prairie strips, produce benefits that increase nonlinearly as more farmers adopt them

(Schulte et al. 2017). Moreover, the positive relationship between social influence and adoption of biodiversity management practices indicates that, for the sampled population, there are pro-biodiversity social norms. This is promising for increasing biodiversity and

ecosystem services in this heavily cultivated region, since social norms are powerful behavior-change levers (Nyborg et al. 2016).

Perhaps unsurprisingly, participation in government programs and interest in wildlife are also positively associated with biodiversity management. The programs listed in the survey require an agreement or contract in which farmers promise to deliver some environmental action (Vaughan & Skinner 2008). If these actions are the adopted biodiversity management practices, then government programs may be delivering desired behaviors. The positive relationship between interest in wildlife and biodiversity management suggests that farmers in our sample are acting in accordance with their preferences.

The failure to find a relationship between time discounting and biodiversity management could be because there is little temporal disconnect between the costs and benefits of these biodiversity management practices. In this case, those with higher discount rates still see the benefits of managing for biodiversity, implying that agricultural extension should not ignore managers and non-operating landowners. Perhaps more likely, the proxy of farm role may not sufficiently distinguish between short-run and long-run thinking or the effect may have been too small to detect given our sample size. If the renter and non-operating landowner respondents in our sample have long lease terms or legacy plans, they may have sufficient incentives to invest in biodiversity practices. Or if owners have different discount rates than managers, then the aggregation of the two could offset their effects. There are not enough managers and owners in our sample to test these

groups individually. We are also unable to discern from this survey whether farmers display consistent time preferences.

Integrating behavioral science into biodiversity conservation in agricultural landscapes

Behavioral science shows how human behavior consistently defies traditional economic expectations, upon which many behavior-change interventions are based. Recognizing the importance of these factors offers new options the expand the toolbox for changing behavior. Indeed, we are beginning to see interest from environmental policymakers and researchers. The European Commission’s report on the Common Agricultural Policy

(2017) explicitly calls out the role of behavioral factors, including cognitive biases and social influence, as relevant to addressing environmental challenges in agriculture and rural areas. Scientists are considering the role of cognitive biases in adaptive natural resource management and conservation planning (Iftekhar & Pannell 2015; Catalano et al. 2018). Drawing from the previous two sections, we suggest four avenues to focus future research on farmer behavior and agri-environmental program design regarding biodiversity management.

Simplify complexity and carefully design decision contexts

While biodiversity outcomes are inherently complex, recognizing and reducing the complexity of adopting beneficial management practices could encourage action. One strategy might be intentionally designing programs that recognize the way people evaluate options. The ‘choice architecture’ of a decision, including default settings, reference points, message framing and other features, influences the way options are 60

perceived and evaluated (Sunstein 2015). When possible, test how changes to these features change behavior, such as automatically selecting all conservation practices and asking farmers to deselect those they will not adopt. One such study found that changing the baseline cost-share contribution from 0 to 100% increased the amount farmers were willing to pay for conservation (Messer et al. 2016).

Highlight success stories and curb loss aversion

Scientific advances that generate consensus on the impacts of managing for biodiversity would reduce uncertainty that may be inhibiting adoption. Yet even known probabilities can be subsumed by vivid stories and the potential of losses (Tversky & Kahneman 1974;

Kahneman & Tversky 1984). Employing narratives to communicate science may help convey the benefits of increasing on-farm biodiversity (Martinez-Conde & Macknik

2017). For example, translating statistical information on climate risk and uncertainty into concrete experiences increased farmers’ understanding (Marx et al. 2007). Future research could explore which types of narratives are most compelling and how to best leverage them to facilitate understanding of biodiversity benefits. Further, framing these benefits as strategies for avoiding crop or profit losses, for example, by increasing resilience, may target a sensitivity to losses over gains. This may be an effective approach in cases where managing for biodiversity can be a strategy to reduce losses from climate change or invasive species (Fischer et al. 2006).

Investigate time horizons and make it easy to follow through

The temporal disconnect between the costs and benefits of land management that develop over long time scales will always work against nonmarket values of biodiversity. A better

understanding of time horizons, whether through tenure arrangements, legacy planning or other mechanisms that encourage long-run thinking, could help address this challenge.

Results from our case study suggest no difference in biodiversity management between farmers who either manage or own their land, compared to those who do both. Perhaps time scales were not mismatched in this context. Still, we were unable to determine whether some farmers intended to adopt biodiversity management practices but had failed to follow-through, thus exhibiting present bias. Future research should explore whether and how much present bias might interfere with biodiversity management. For example, sending farmers simple reminder letters increased re-enrollment in the

Conservation Reserve Program (Wallander et al. 2017). Reminders, commitments, and other efforts that make it easier to follow through with intentions may increase biodiversity management among interested farmers.

Leverage social influence through peers and public recognition

Social influence is a promising lever to influence farm management for biodiversity.

Indeed, leveraging peer information and public recognition has increased contributions to public goods in other domains (Kraft-Todd et al. 2015). Information about neighbors’ conservation behavior increased spatial coordination of land management in laboratory experiments (Banerjee et al. 2014). Farmers in our case study were more likely to adopt biodiversity management practices if they leaned on their peers for information. Future research should employ experimental methods that might identify the causal effects of such information on management. While behavioral science theory and evidence from other domains suggest public recognition motivates pro-social behavior, more evidence

on land management decisions would be useful to programs that are already offering such incentives for participation.

Looking ahead

A first step to tackling this agenda is conducting surveys and qualitative research that incorporate a behavioral lens. These should ask questions that illuminate how behavioral factors influence decisions, such as barriers to biodiversity management and farmers’ time horizons. Results can inform experimental research that tests changes to decision environments and identifies the causal effects of factors on management behaviors. If we consider changes in land management as the product of a series of decisions that ultimately produce a difference on the landscape, those decisions may provide

‘intervention points’ to better understand and influence behavior (Valatin et al. 2016).

The Center for Behavioral and Agri-Environmental Research (CBEAR)—a consortium of major land grant and research universities—is conducting and funding field experiments in partnership with the United States Department of Agriculture, and the

European Commission issued a Science and Policy Report considering the role of economic experiments in the Common Agricultural Policy (Colen et al. 2015).

Of course, there are challenges and limitations. Many land management behaviors are unobservable to researchers and outcomes unfold over long time periods. It is also possible that decisions about land are so costly and connected to deeper processes that some behavioral insights are not relevant. They are not cheap, quick, or automatic

‘System 1’ decisions that nudges often target (Kahneman 2003). Yet policymakers are looking to move beyond the ‘low-hanging fruit’ and are leveraging behavioral insights to

address more intractable challenges (Sanders et al. 2018). And while depth of experience and high stakes of farmers’ decisions may reduce susceptibility to biases, other profit- driven firms can be subject to the behavioral factors discussed (Armstrong and Huck

2010).

Conclusion

Increasingly, conservationists are looking to working lands to encourage and steward biodiversity and ecosystem services (Fischer et al. 2006; Kremen & Merenlender 2018).

Farmers, ranchers, and forest landowners make decisions about the management of their properties that aggregate to broader, often non-linear impacts on the landscape. The cognitive biases and social preferences that influence human behavior may influence plot-level decisions to manage for biodiversity and inform more effective programs that deliver landscape-scale conservation.

The factors discussed herein are inherent to the challenge of increasing biodiversity and other public goods from private lands. Complexity, uncertainty, risk, temporal lags, and social interactions may always complicate efforts to change land manager behavior. Yet we are gaining a better understanding of how people manage these factors and how they shape behavior. Bringing behavioral science into conservation research, programs and policies may help make progress towards addressing biodiversity loss and maintaining the services private lands provide to society.

Acknowledgements

Thanks to Bettina Matzdorf and Sonoko Bellingrath-Kimura at ZALF for organizing the

Landscape 2018 discussions which inspired thinking for this paper, and to Charles

Nicholson for helpful comments on a previous draft of this manuscript. This work was supported by the Gund Institute for Environment and the USDA National Institute of

Food and Agriculture, McIntire-Stennis project 1002440. The initial farmer survey was conducted in collaboration with Katherine Ingram and Rachael Long with funding from the David H. Smith Conservation Research Fellowship (to SMK), and an Environmental

Protection Agency (EPA) Science To Achieve Results (STAR) grant (to KI).

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Tables

Table 1. Aspects of behavioral science and their connection to farmers’ plot-level decisions to improve biodiversity.

Examples of strategies to Behavioral Barriers to farmers’ adoption Summary Related biases, effects and influences intervene and increase factor of biodiversity management conservation behavior

• Anchoring and adjustment People struggle to evaluate • Status quo bias Changing the baseline cost-share Complex systems and processes Complexity complex options and are • Transaction utility contribution from 0 to 100% may dissuade action, as might a and context- sensitive to the context increased the amount farmers were • Mental accounting & choice particular reference point or the dependence (reference state) in which they bracketing willing to pay for conservation way behavior change is framed. make decisions. • Endowment effect (Messer et al. 2016). • Framing effects

High uncertainty and even small People use mental shortcuts to • Law of small numbers Translating statistical information risks of losses may overwhelm judge probabilities and • Availability and representativeness on climate risk and uncertainty Uncertainty potential benefits from

73 evaluate risk relatively, heuristics into concrete experiences and risk biodiversity, especially if weighting losses more than increased farmers’ understanding

• Confirmation bias familiar stories serve as gains. (Marx et al. 2007). • Loss aversion cautionary tales.

People tend to be farsighted Up-front costs in time and Simple reminder letters increased when costs and benefits are • Present bias money may dwarf long-term Time re-enrollment in the Conservation incurred in the future but tend • Procrastination benefits of biodiversity discounting Reserve Program (Wallander et al. to overweight those incurred management, even if farmers • Projection & hindsight bias 2017). in the present. want or intend to make changes.

• Altruism & impure altruism Prevailing social norms or People care the impacts of • Fairness missing information about Information about neighbors’ Social their actions and how they are • Reciprocity & cooperation others’ contributions to conservation behavior increases preferences perceived, as well as the • Messenger effect biodiversity may reduce spatial coordination of land behavior of others. • Reputation & image incentives to adopt beneficial management (Banerjee et al. 2014) • Social norms & influence management practices.

Table 2. Description of key variables and their summary statistics. A full description of all variables used in the model can be seen in Table A1.

Total Variable Description Values Percent Respondents Native grasses 47 Cover crops 43 Wildflowers 39 Biodiversity Number of practices Hedgerows 38 118 management adopteda Wetlands 19 Other 11 None 22 Low discount rate: farmer is Time horizon of the 76 Time both owner and manager farmer based on farm 122 discounting High discount rate: farmer role 24 is either owner or manager Social influence: high value Sensitivity to social of information from other 36 Social influence based on landowners and growers 120 preferences value of information Otherwise: less value on from peers information from other 64 landowners and growers Note: aThe values of specific biodiversity management practices are shown here to provide descriptive statistics; the variable itself is simply the count of practices. Because farmers have adopted multiple practices, the percentages do not sum to 100.

Table 3. Model results from ordinal logistic regression. Dependent variable is number of biodiversity management practices adopted by farmers. Odds Ratio indicates a change in the proportional odds of adopting biodiversity management practices for a one-unit change in that variable, holding all other variables in the model constant.

Variable Odds Ratio CI low CI high p value

Low discount rate (both owner and manager) 0.87 0.37 2.06 0.74 Social influence (high value info from peers) 3.11 1.36 7.22 0.01 Program participation 2.29 1.07 4.95 0.03 Interest in wildlife 7.95 3.54 18.74 <0.01 Dependency on farm income 1.00 0.99 1.01 0.52 Age (60 or older) 1.21 0.57 2.59 0.62 Female 1.08 0.46 2.55 0.85 College education 0.70 0.23 2.14 0.53 Graduate education 1.20 0.57 2.52 0.63

Note: Odds Ratio is the exponentiated coefficient from the ordinal logistic regression model.

See Table A2 for full model results. CI, 95 percent confidence interval.

Figures

Fig 1. Distribution of biodiversity management practices in California survey sample according to time and social preferences. Biodiversity management practices include wildflowers, native grasses, cover crops, hedgerows, wetlands, and ‘other’. The solid gray lines on each graph represent the median value for the sample (two practices). The dashed and dotted lines represent the median values for the groups matching that color in the plot legend. Note: both median values for Time Discounting are equal to two.

Appendices

Table A1. Description of variables and their summary statistics. Total Variable Description Values Percent Respondents Native grasses 47 Cover crops 43 Wildflowers 39 Biodiversity Number of practices Hedgerows 38 118 management adopteda Wetlands 19 Other 11 None 22 Low discount rate: farmer is both Time horizon of the 76 Time owner and manager farmer based on farm 122 discounting High discount rate: farmer is either role 24 owner or manager More social: highly values Sensitivity to social information from other landowners 36 Social influence based on value and growers 120 preferences of information from Less social: less value on information peers 64 from other landowners and growers Currently or previously Yes 48 participated in Program government programs 122 participation (EQIP, CRP, CSP, No 52 WRE, and organic certification) Level of interest in Very interested 49 Wildlife interest having wildlife habitat 120 on land Somewhat/Not interested or Unsure 51 60 years old and older 51 Age Age of farmer 121 Under 60 years old 49 Female 26 Gender Gender of farmer 113 Male 74 High school 9 Level of education Education College 68 117 received Graduate school 23 Farm Percent of income that 65 (40)† 120 dependence comes from farm Note: aThe values of specific biodiversity management practices are shown here to provide descriptive statistics; the variable itself is simply the count of practices. Because farmers have adopted multiple practices, the percentages do not sum to 100. †Mean (standard deviation).

Table A2. Ordinal logistic regression model results. Coefficients represent the change in the proportional log odds of the adoption of management practices given a one-unit change in the independent variable, holding all other variables constant. Standard errors are in parentheses.

Dependent variable: Adoption of biodiversity management practices Low discount rate (both owner and manager) -0.14 (0.44) Social influence (high value info from peers) 1.13*** (0.42) Program participation 0.83** (0.39) Interest in wildlife 2.07*** (0.42) Dependency on farm income -0.003 (0.005) Age (60 or older) 0.19 (0.38) Female 0.08 (0.44) College education -0.35 (0.56) Graduate education 0.18 (0.38)

Observations 103 Note: ∗p < 0.1; ∗∗p < 0.05; ∗∗∗p < 0.01

CHAPTER THREE: DO FOREST OWNERS RESPOND TO PEERS OR

PEWEES? A FIELD EXPERIMENT ON SOCIAL INFLUENCE AND

SONGBIRD HABITAT CONSERVATION

Hilary Byerly1,2,*, Anthony W. D’Amato1, Steve Hagenbuch3, Brendan Fisher2,4

Abstract

Working landscapes can provide biodiversity and ecosystem services. Many voluntary conservation programs ask those who manage working lands—farmers, ranchers, and forest landowners—to steward their resources in ways that maintain or increase these benefits. While research on landowners suggests the importance of social influence in management decisions, few studies have tested whether providing information about the behavior and opinions of others changes decisions related to private land and forest management, stewardship or conservation. Using a randomized controlled trial design, we mailed three versions of a solicitation letter for a bird habitat conservation program to

967 individuals who manage forests to produce maple syrup. Maple producers who were offered recognition for participation were as likely to ask for more information about the program as those who received only a control message that described the program.

Providing information about the participation of peers reduced the number of producers requesting information by 6 percentage points compared to the control. These unexpected results highlight the importance of context in using social influence to change land manager behavior. Findings are relevant to conservation researchers and practitioners,

offering applications of behavioral science to improve biodiversity and ecosystem service outcomes on private lands.

1 Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA 05405; 2 Gund Institute for Environment, University of Vermont, Burlington, VT, USA 05405; * [email protected]; 3 National Audubon Society, New York, NY, USA 10014; 4 Environmental Program, Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA 05405

Introduction

Working landscapes—croplands, pastures, and managed forests—cover nearly half of the of the planet’s land surface (Foley et al. 2005). Though designated for production, they can deliver biodiversity conservation and ecosystem services when well managed

(Kremen & Merenlender 2018). The decisions of farmers, ranchers, and forest landowners are often key to conservation success (Hilty & Merenlender 2003; Pasquini et al. 2010).

Since many of the environmental benefits and costs of private land management extend beyond the parcel, government agencies and non-governmental organizations offer voluntary programs encouraging private land owners and managers to account for social impacts, including conservation outcomes. These programs are often designed to address the financial costs or information needs of changing management practices (Hanley et al.

2012). Research on private land managers, however, has found a range of nonmonetary factors to influence management decisions.

Surveys of farmer and forest owner management behavior indicate the importance of social and psychological variables, including social and cultural norms, empathy, autonomy, and habit, among others (Prokopy et al. 2008; Mzoughi 2011; Huff et al.

2015). While land managers who are environmentally conscious tend to be the most likely to engage with conservation programs, participation is also related to nonpecuniary external factors and program characteristics, including the participation of peers and the complexity and clarity of information (Davis & Fly 2010; Reimer & Prokopy 2014;

Dayer et al. 2016).

Behavioral science shows how leveraging social norms and other simple changes to program design can have policy-relevant effects on behavior (Madrian 2014; Kraft-Todd et al. 2015). Rather than restricting choice or changing financial incentives, researchers and program managers have altered how or when options are presented, by whom, and in what context. Often, these strategies employ social influence—leveraging people’s sensitivity to the opinions and behavior of their peers (Abrahamse & Steg 2013). These

‘behavioral interventions’ have produced gains in a range of pro-social and pro- environmental individual behaviors, yet there have been few applications to decisions about land and natural resource management (Byerly et al. 2018). Behavioral strategies are often low-cost and preserve freedom of choice, making them well suited for stretched conservation budgets and property owners possibly resistant to mandates (Ferraro et al.

2017). Applications of behavioral insights to land management decisions may offer new and essential policy options to achieve conservation goals (Reddy et al. 2017).

Understanding what influences private forest management decisions is critical for conservation outcomes. Here, we address two questions that bring behavioral science into conservation practice. First, what is the effect of a simple change in messaging on land managers’ decisions to engage in a conservation program? Second, can social influence produce more interest in conservation programs?

To answer these questions, we conducted a field experiment that tested whether information about 1) the participation of peers or 2) public recognition influenced land managers’ interest in conservation. In partnership with two practitioner organizations, we mailed different versions of a solicitation letter for a habitat conservation program to forest landowners who produce maple syrup. We measured differences in requests for more information about the program across the two treatment groups and a control.

In our study context—the Northern Forest of the United States, a highly forested region dominated by 1.7 million family forest ownerships (Butler et al. 2016)—management decisions are essential to maintaining biodiversity benefits and ecosystem services. Every year, neo-tropical migratory bird species, including those of conservation priority, move from their wintering grounds in Central and South America to breed in the Northern

Forest (Goetz et al., 2014). Habitat suitability for these species is influenced by forest composition and structure (Thompson & Capen 1988; Bakermans et al. 2012). But for the first time in over a century, habitat availability is declining (Thompson et al. 2017). The region is also facing other large-scale environmental challenges, such as climate change and invasive pests, which threaten to reduce forest complexity (Foster et al. 2017). These

changes will be compounded, as compositional and structural diversity are important for the delivery of ecosystem services, including tree biomass production and soil carbon storage (Gamfeldt et al. 2013). A range of governmental and nongovernmental programs

(e.g., Current Use, Forest Stewardship Program) seek to increase active forest management and stewardship actions that improve diversity in forest structure and species, yet drivers of and additional gains from participation are not well known (Ma et al. 2012).

Leveraging social influence could be an effective strategy for engaging forest owners in conservation programs. Forest owners report peers as important sources of information about management decisions (Kittredge et al. 2013; Sagor & Becker 2014). Information about other landowners’ behavior is associated with participation in programs for endangered and invasive species (Sorice et al. 2011; Niemiec et al. 2016), wildfire mitigation (Fischer & Charnley 2012), and sustainable land management (Chen et al.

2009; Kuhfuss et al. 2016). However, we are not aware of any studies that have measured a causal effect of social information on observed forest landowner behavior.

By testing behavioral interventions in the context of land management we contribute 1) to the understanding of these strategies (i.e. can social influence affect land management decisions?) and 2) to the applicability of new policy tools to an important social dilemma

(i.e. can behavioral insights help increase biodiversity conservation?). We also add to the scant literature on the social dimensions of maple sugaring—a $141 million industry across 13 states and growing in extent (Snyder et al. 2018; USDA 2018).

Methods

Context

We collaborated with Audubon Vermont and Vermont Maple Sugar Makers’ Association

(VMSMA) to conduct a field experiment on songbird habitat conservation in the

Northern Forest of Vermont. Vermont is the leading producer of maple syrup in the

United States, averaging nearly 7.5 million liters annually from an estimated 37,800 hectares of privately owned forest1 (USDA 2018). These production forests (called

‘sugarbushes’) are often managed within larger parcels of forested land (Farrell 2013), which provide essential habitat for bird species that breed and nest in the region.

The conservation program we used was a joint program developed by Audubon Vermont,

VMSMA, and Vermont Department of Forests, Parks and Recreation. This program, called the Bird-Friendly Maple Project, invites producers to manage their sugarbush for multiple objectives in exchange for recognition that increases visibility and reputation.

Participants agree to an inventory of bird habitat in their forest, minimize harvesting of trees during nesting season, and have a formal forest management plan that acknowledges bird habitat as a priority. Forest bird habitat for many target species of the program requires tree species diversity and complexity of forest structure. This management also has positive co-benefits on broader forest biodiversity and the delivery of ecosystem services (Gamfeldt et al. 2013; Doerfler et al. 2018).

1 Calculated from the USDA-reported 5,670,000 taps in Vermont in 2018 and 150 taps per hectare following the density measurements of Farrell (2013) 84

At the start of our study there were 27 producers in the Bird-Friendly Maple Project representing 2,412 hectares of forest land. Early efforts to recruit producers into the program included presentations at the Vermont Maple Conferences and outreach through

VMSMA newsletters and email communications.

Sample

VMSMA provided a list of their membership, including mailing addresses and the size of the maple production operation (in membership categories based on number of taps). We included only members of VMSMA that were maple producers, had valid mailing addresses, and were not already part of the Bird-Friendly Maple Project. This resulted in a sample of 967 individuals, families and businesses. This list was merged with maple producers from the United States Department of Agriculture (USDA) Organic

INTEGRITY Database to determine which were certified organic (see Supporting

Information for more on the data and matching process).

Almost half of our sample (42%) had less than 1000 taps, which approximates to 7 hectares or less of forestland in syrup production (Farrell 2013). Producers were located across the state, and 26 producers had a mailing address outside Vermont. Eleven percent of our sample was USDA certified organic, with the proportion of organic certification increasing with size of production.

Experiment

Using a randomized controlled design, we tested messaging interventions using social influence to elicit interest in the Bird-Friendly Maple Project. We incorporated our tests into three versions of a mailing to VMSMA members about the program (Table 1). 85

The mailings asked recipients if they would like to receive more information about the

Bird-Friendly Maple Project, with an option to check “YES” or “NO”. Those who checked “YES” also provided an email address or phone number to indicate how they would like to be contacted. This request for information served as our behavioral outcome, a proxy for engagement in this conservation program. Similar designs have been used to experimentally test farmer engagement in conservation practices (Kuhfuss et al. 2016; Wallander et al. 2017), including using a request for information as the dependent variable (Andrews et al. 2013).

The content of the mailings was designed in collaboration with Audubon Vermont and pre-tested on a small group prior to deployment. All mailings were sent first-class in envelopes with VMSMA logos to increase the likelihood of opening.

All producers in our sample received a 6x9” envelope containing a promotional card

(“Promotion”), a response card (“Response”), and a postage-paid envelope (Figure S1).

The Promotion card displayed photos of forest-dwelling songbirds under the name of the program. On the back, there was a message requesting the producer to complete the enclosed survey and a brief list of benefits of program participation, all related to forest health and forest birds. The second, smaller Response card listed the name of the program on one side and a five-question survey on the other (see Survey, below), including the option to request more information.

This baseline version acted as the control. Each treatment built on this version with short phrases in three locations in the mailing (Figure 1).

Treatment 1: Peer information

This treatment highlighted the participation of other producers in the Bird-Friendly

Maple Project. This provided a descriptive social norm by indicating how others are behaving. Such information signals which behaviors are common in a given situation and can lead people to follow suit (Cialdini et al. 1991). For example, hotel guests who learned of others' water conservation behavior were more likely to reuse their towels than those who learned only of the environmental benefits of towel reuse (Goldstein et al.

2008). Similar applications of descriptive norms have shown to increase curbside recycling (Schultz 1999), household energy conservation (Allcott 2011), and voter turnout (Gerber et al. 2008). Such peer information has shown consistent effects on encouraging pro-environmental behavior (Farrow et al. 2017), even among people who rate normative information as the least motivating behavior-change lever (Nolan et al.

2008).

In addition to the text in the control version, this treatment included the statements,

“Many of your fellow sugar makers are part of (the Bird-Friendly Maple Project)” and

“Join dozens of Vermont sugar makers who are part of the program.” These statements were meant to demonstrate that other producers have made the commitment to manage their sugarbush in ways that benefit birds. Informal interviews with producers prior to designing the experiment indicated that other producers are sources of information. This is supported by survey evidence of maple producers (Murphy et al. 2012; Kuehn et al.

2017). Thus, it was expected that producers receiving this messaging would be more

likely to request more information about the Bird-Friendly Maple Project than those who received only information about the program.

Treatment 2: Recognition

This treatment made salient the recognition benefits of participating in the Bird-Friendly

Maple Project. Public recognition makes one’s behavior known to or observable by others. This engages reputational concerns, as people are often motivated to maintain a positive image (Bénabou & Tirole 2006). As a behavioral intervention, it has shown to increase charitable donations (Ariely et al. 2009), work performance (Bradler et al. 2016), and residential energy conservation (Yoeli et al. 2013). People are repeatedly more willing to incur personal costs in time, money, and effort for a socially desirable cause when others are informed of their behavior (Kraft-Todd et al. 2015).

This version of the mailing augmented the control with the statements, “Recognizing the stewardship of sugar makers through (the Bird-Friendly Maple Project)” and “Earn recognition and visibility for forest stewardship.” It also included an image of the certification sticker offered to participants, which says “Produced in Bird-friendly

Habitats.” Audubon Vermont advertises these recognition benefits to attract producers to the Bird-Friendly Maple Project and other bird habitat conservation programs. We intended to test whether the explicit mention of those benefits would in fact increase interest in the program compared to information alone. Since this messaging highlighted how the program makes producers’ behavior observable by others, it was expected to elicit concerns around image and reputation. We expected that this treatment would increase interest in the Bird-Friendly Maple Project.

Assignment to treatment

Maple producers were assigned to treatment conditions through block randomization on size of operation. This technique can increase precision in estimating treatment effects if the grouping variable predicts the outcome (Imbens & Rubin 2015). We suspected that producer size (number of taps) would be negatively correlated with our outcome measure for two reasons. First, larger producers are more likely to sell their syrup in bulk (Becot et al. 2015). These producers would be less likely to value the brand reputation and eco- marketing to consumers offered by the Bird-Friendly Maple Project. Second, maple syrup sales are more likely to be the primary source of income for larger producers (Becot et al.

2015). For them, business decisions are likely to be more profit-motivated than for smaller producers who have other income streams and smaller forests to manage.

We stratified the sample into two blocks as in Snyder et al. (2018): less than 1000 taps and 1000 taps or more. The number of subjects in each treatment and the proportion that are certified organic is shown in Table 1.

Drawing on a laboratory experiment that provided information about others’ land conservation behavior (Banerjee et al. 2014), which found a standardized effect size of

0.23 on socially efficient land use decisions (Janusch et al. 2018), we expected to detect a small effect of our treatments. We hypothesized an effect size of 0.1 at α = 0.05 and n =

967, giving us a power of 0.80.

Survey

While the primary objective of this study was to estimate treatment effects of social influence on a conservation behavior, we used this opportunity to collect information 89

about Vermont maple producers. We included a brief survey to capture more specific information on size (number of taps and number of acres) and tenure (number of years the sugarbush has been in operation). We also asked subjects about the future of their sugarbush (number of years it is expected to stay in operation) and their primary reason for producing maple syrup.

Lastly, we provided an incentive of the chance to win $50 through a lottery to encourage responses to our mailing. A meta-analysis found that incentives increase response rates to mailed surveys (Edwards et al. 2005). Producers were provided with a postage-paid business reply envelope addressed to the University of Vermont. The data collection process began July 16, 2018. A reminder email was sent from VMSMA one month after the initial mailing. The final responses were received by September 31, 2018.

Results

A total of 177 producers responded to the mailing, an 18% response rate. This is within the range of similar studies of maple producers and farmers (10 - 27%) (Andrews et al.

2013; Becot et al. 2015; Kuehn et al. 2017). On average, respondents had 1300 taps across sugarbushes of 20 hectares (Table 2), matching the size distribution of the total sample. Respondents had been producing maple syrup for an average of 30 years.

Analysis of variance (ANOVA) confirmed balance across treatments for number of acres

(F = 0.59, p = 0.55) and tenure (F = 0.03, p = 0.97). The difference between number of taps was marginally significant (F = 2.51, p = 0.08), with those in the peer information treatment having more taps on average than producers in the other two conditions (995 and 2201 taps difference of means, 95% CI, 245 to 4156 taps).

Enjoyment and income were the most frequently cited reasons for producing maple syrup

(Figure 2). Regarding producers’ intentions for their operations, we found that respondents expect their forests to stay in production for an average of 38 more years (sd

= 37 years). Of survey respondents, 17% indicated they expect or hope their forest to stay in production indefinitely, 10% did not know, and 18% replied that their sugarbush would no longer be in production in the next 10 years. Since the survey was completed after producers had been treated by the social messaging, responses to these subjective questions could have been influenced by the treatments. There was a marginally significant difference between treatments among producers who rated stewardship as their primary reason for sugaring (휒2(2, 177) = 4.92, p = 0.09) (Figure 2). We found no difference between the future outlooks of respondents in different treatments (F = 0.11, p

= 0.9).

Across all groups, the majority of those who replied to the mailing also requested more information about the Bird-Friendly Maple Project (86% of all returned Response cards).

Twenty-four producers (2.5% of the full sample) completed the survey but opted not to receive more information. There is a marginally significant difference between responses to the mailing (requested information, responded but did not request information, and did not return Response card) between the treatment groups (휒2(4, 967) = 7.97, p = 0.09).

Treatment effects

Our primary outcome of interest was whether the request for information varied across treatment groups (Figure 3; Table 3). The request rate among those in the control group was 18.6%. Those who received information about the participation of their peers had a

request rate of 12.8%, a statistically significant decrease of 5.8 percentage points compared to the control (95% CI for difference of means, -11.4 to -0.2 percentage points, p = 0.04). Those who received information about the recognition benefits of the program had a request rate of 16.1%, a 2.5 percentage points decrease compared to the control

(95% CI for difference of means, -3.4 to 8.3 percentage points), but this difference was not statistically significant (p = 0.41).

To increase the precision of the estimated treatment effects on the decision to request more information, we fit a linear probability model and included as predictors organic certification and size. The outcome variable was equal to one if the producer requested more information about the program or zero, otherwise. We estimated the intent-to-treat effects since we do not know whether all participants received the treatments.

These estimates are shown in Table 3, with full model results in Table S1. The values are similar to the differences observed without the regression. Producers who were informed about the participation of their peers, were 6.1 percentage points less likely to request information about the Bird-Friendly Maple Project than those who received only the control message (95% CI, -11.8 to -0.5 percentage points, p = 0.03). Again, we do not detect an effect of the recognition treatment on interest in the program compared to the

Control (95% CI, -8.6 to 3.2 percentage points, p = 0.37).

Discussion

We tested whether simple changes to messaging that leverage social influence could change engagement in habitat conservation among forest landowners. By running a field experiment in partnership with a conservation organization, we offer evidence about 92

working lands managers’ behavior in a real-world context. Contrary to our expectations, providing information about the participation of peers reduced interest in the songbird habitat conservation program. This unexpected negative effect demonstrates the sensitivity of behavioral interventions to the context in which they are implemented.

Instead of replicating positive effects of descriptive social norms on behavior (Kraft-

Todd et al. 2015; Farrow et al. 2017), we find things to be more complicated.

We offer several explanations for this result. First, personal identity may have conflicted with the norm. Personal characteristics, such as political affiliations, can moderate the effects of social norms and other behavioral interventions (Costa & Kahn 2013; Trujillo-

Barrera et al. 2016). Many private land managers value their self-sufficiency and autonomy in management decisions (Howley 2015; Lequin et al. 2019). Maple producers’ sense of autonomy may have felt threatened by the social pressure message of the descriptive norm, thus producing a defiant ‘no’ (a response known as ‘psychological reactance’) (Steindl et al. 2015). Second, it is possible that the norm was not sufficiently common to motivate conformity. Although positive verbal quantifiers, such as ‘many,’ have shown to be effective in encouraging pro-environmental behaviors that are not done by a majority (Demarque et al. 2015), people often do not follow a minority norm

(Sieverding et al. 2010; Mortensen et al. 2017). Third, perhaps the information that others were participating in the program and providing habitat caused some producers to free- ride. Although information about the contributions of others often increases contributions to public goods (called ‘conditional cooperation’) (Frey & Meier 2004), the provision of habitat on working lands often comes at the opportunity cost of production. If producers felt that enough others were already supporting biodiversity, they may have decided to 93

avoid the costs associated with doing so themselves. Finally, the external peer pressure could have crowded out intrinsic motives (Bowles 2008). Producers may have been interested in the program but, when feeling extrinsic pressure to participate, they decided against getting involved.

This result is supported by other studies that employed descriptive norms and failed to increase desirable social outcomes. Wallander et al. (2017) included information about other farmers’ program participation in mailings to farmers about the Conservation

Reserve Program. While the letter itself increased enrollment, the effect was unchanged by the addition of social information. Efforts to increase tax payment compliance and

401(k) contributions also found providing information about the behavior of others to have a negative effect on behavior (Beshears et al. 2015; John & Blume 2018). Together, these findings highlight the importance of exploring how norms operate across contexts, as results from previous studies may not hold for different populations or behaviors.

We failed to detect an effect of providing recognition for conservation behavior on increased engagement with the program. Although there are studies showing that offering recognition or reputational benefits can increase participation in conservation (Atari et al.

2009; Banerjee & Shogren 2012), other land managers do not report recognition as a compelling reason to engage in conservation (Nebel et al. 2017). Given the potential marketing benefits for producers who are recognized by the Bird-Friendly Maple Project, we find this result surprising. It is possible that the perceived recognition benefits were not obvious or strong enough to influence behavior, or contrarily, that such benefits were

implicitly offered to the Control group. It could also be that our study was underpowered to detect a difference between the two groups.

By using the VMSMA member list, we estimate the sample average treatment effect.

This estimate is internally valid, but we expect VMSMA members to be more informed and engaged than the remaining 500 to 2000 non-member maple producers in the state

(Becot et al., 2015) and therefore potentially not externally valid. All of the studies of

U.S. maple producers that we are aware of used maple industry membership organizations as their samples (Snyder et al., 2018; Kuehn et al., 2017; Becot et al.,

2015). As a result, there is little known about the number and demographics of non- member producers.

We also acknowledge the large number of non-respondents who may not have received the treatments. The relative proportion of responses that did not want more information compared to those that did suggests that producers who were not interested may not have responded at all. While we do not know how many of the non-responses did not receive or open the mailing, random assignment should have produced similar proportions across treatments.

Lastly, although we offer evidence on an observed behavior, rather than an attitude or intention, the behavioral outcome we measure is cheap. Checking a box on a postcard is much less costly than changing forest management practices. However, 30 producers in our sample have scheduled appointments to enroll in the Bird-Friendly Maple Project since receiving this mailing. While we are unable to attribute this action to any one

treatment due to inconsistent follow-up, this experiment has produced real conservation action in the Northern Forest.

Although the particular interventions in this study did not have a positive effect on stewardship, other behavioral interventions or different iterations of social messaging warrant future research. Highlighting stewardship in the recognition treatment may have primed producers to list stewardship as a reason for sugaring, suggesting further evidence that simple changes in messaging may influence behavior. Moreover, nearly 50% of all respondents indicated they produce syrup for enjoyment, with another 15-20% selecting stewardship or heritage. These are consistent with previous studies of maple producers

(Murphy et al. 2012; Snyder et al. 2018). The high proportion of respondents who selected the non-monetary reasons supports the notion that working lands managers value more than profits, even recognizing that our respondents are not representative of all maple producers.

Additionally, nearly a fifth of respondents indicated that their sugarbush would not be in production after 10 years. Since the average age of producers in this region is 61 years old (Kuehn et al. 2017), there is a risk that land transfers will remove forested land from maple production in favor of higher value uses. Engaging this population in conservation programs could have an important and lasting legacy on the forested landscape.

In 2011, the United States maple industry had tapped only 0.4% of maple trees that are suitable for production, most of which are on private lands (Farrell and Chabot 2012). As the industry grows, expanding the extent of maple production could be good for environmental outcomes because it keeps the forest as forest, as opposed to other land

uses such as development. Despite a paucity of research linking maple production to biodiversity outcomes, there is evidence that less intensive production systems can be consistent with conservation goals (Clark & McLeman 2012). For producers who would or already are managing in ways that support biodiversity, behavioral interventions may

‘nudge’ them to do more.

The working landscape is a critical component of bird and biodiversity conservation in the Northern Forest. With 80% of the landscape in private ownership (Thompson et al.

2017), sole reliance on protected areas and reserve lands is not a viable solution.

Conservation programs that promote and support forest products industries and successfully engage forest owners are essential to maintaining vibrant ecological and human communities.

Conclusion

We conducted a field experiment that offers evidence that land managers’ engagement in conservation programs can be influenced by simple changes in messaging. Providing information that others are participating, however, had an unexpected negative effect on conservation behavior. This result highlights the importance of tailoring behavioral interventions to specific contexts and conducting future studies to build evidence on effective interventions and reasons for failure.

Although we were unable to detect an effect of providing recognition on conservation behavior, future research should try again with larger samples or more meaningful treatments. Providing recognition for land stewardship is already a strategy used by farm and wildlife conservation initiatives, including state-funded agricultural programs and 97

bird-, pollinator-, and wildlife-friendly habitat programs. It is not clear what the causal effect of providing public recognition is on engaging land managers in conservation.

Applying behavioral science to biodiversity conservation requires creative ways to test strategies and observe impacts. Unlike electricity use or spending habits, land management decisions are difficult to observe, infrequent, and require financial and time commitments. While this makes testing behavioral insights challenging, shifting these behaviors can have long term benefits on the provision of biodiversity and ecosystem services from working landscapes.

Acknowledgements

The authors are grateful to Amanda Voyer, Vermont Maple Sugar Makers’ Association, for her collaboration. We appreciate the support of Matthias Nevins in conducting the follow-up to this research. This work was supported the Gund Institute for Environment and the USDA National Institute of Food and Agriculture, McIntire-Stennis project

1002440. This study was approved by the University of Vermont Research Protections

Office (CHRBS 18-0618). Data will be made publicly available in the Harvard

Dataverse.

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Tables

Table 1. Control and treatment groups

<100 % Mailing wording N 0 taps organic Control “The Bird-Friendly Maple Project” 323 135 9.9

Peer “Many of your fellow sugar makers are part of…” informatio “Join dozens of Vermont sugar makers who are part of the 321 134 12.1 n program”

Recognitio “Recognizing the stewardship of sugar makers through…” 323 135 12.1 n “Earn recognition and visibility for forest stewardship” N, total number of producers who received that version of the mailing; <1000 taps, number of producers within the total that were designated ‘small’ in block random assignment; % organic, proportion of sample that was USDA certified organic.

Table 2. Descriptive statistics of survey respondents

Median Mean SD Min Max Observations

Size Taps 1300 3241 5360 15 38,000 173 Hectares 20 49 77 0.4 506 151 Tenure (years)† 30 46 57 1 300 177 SD, standard deviation; Observations vary due to incomplete survey responses. †Some respondents answered this question in “generations”, which we multiplied by 28 years (Fenner 2005).

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Table 3. Effects of social influence on requests for information about the conservation program

Control Peer information Recognition Request rate 18.6 12.8 16.1

95% CI 14.3 to 22.8 9.1 to 16.4 12.1 to 20.1

Difference from control - -5.8* -2.5

95% CI - -11.4 to -0.2 -8.3 to 3.4 Regression-adjusted - -6.1* -2.7 difference 95% CI - -11.8 to -0.5 -8.6 to 3.2 Note: CI, confidence interval; *difference between treatment and control is significant at p ≤ 0.05. Regression-adjusted difference represents the coefficient from the linear probability model, which includes organic certification and size as predictors, and CIs are calculated using standard errors robust to heteroskedasticity (see Supplementary Information for full model results).

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Figures

Figure 1. Promotional card sent to maple producers and variations by treatment. The reverse side included text about the benefits of the program and treatment-specific text (Table 1).

Figure 2. Survey responses of maple producers by treatment. Left: Primary reasons for producing maple syrup among survey respondents. The total share exceeds one because some producers selected more than one reason. Error bars represent 95% confidence intervals. Right: Length of time producers expect their sugarbushes to remain in maple production.

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Figure 3. Proportions of each group that requested more information. Error bars are 95% confidence intervals.

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Appendices

Appendix A. Organic data matching

In order to determine which producers in our sample were certified organic, we used the following process. We accessed the USDA Organic INTEGRITY Database

(https://organic.ams.usda.gov/Integrity) and filtered list by State = Vermont AND

Certified Products = maple, yielding 253 results. This list was imported and combined with VMSMA list of producers using the R statistical software. The Organic list had only business names, while the VMSMA list had mostly individual names, so many could not be matched automatically. The combined list was exported to Microsoft Excel and sorted by address to look for matching addresses between businesses and individuals. Identical addresses were assigned a unique ID number to indicate they represent the same operation. This produced 96 matches between Organic producers and VMSMA members.

Another 10 certified organic producers were found to be current members of the BFMP, so they were dropped from the list. Next, we conducted an internet search for the remaining 147 organic producers, as well as the 26 producers without Vermont mailing addresses. Using the Northeast Organic Farmers Association directory, business websites, and other online directories, we matched another 14 business names with individual names, resulting in a total of 110 organic producers in our sample.

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Figure S1. Promotional card (top) and Response card (bottom). These versions were sent to those in the control group. Additional text in the treatments (see Table 1) was added to the right-hand side of each card, above ‘The Bird-Friendly Maple Project,’ and in one more bullet in the list on the Promotional card.

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Table S1. Linear regression results

Dependent variable:

Requested more information

Peer information 0.061* (0.029)

Recognition 0.027 (0.030)

Organic certified 0.062 (0.045)

Taps (1000-1999) 0.089 (0.064)

Taps (2000-3999) 0.044 (0.056)

Taps (4000-8999) 0.029 (0.052)

Taps (9000-14,999) 0.005 (0.048)

Taps (15,000-29,999) 0.005 (0.044)

Taps (30,000+) 0.045 (0.038)

Constant 0.156* (0.028)

The coefficients for each treatment show the regression-adjusted differences in request rate from the control condition. * p < 0.05; robust standard errors in parentheses.

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CHAPTER FOUR: REFRAMING ENVIRONMENTAL PROBLEMS

INCREASES CONTRIBUTIONS TO ENVIRONMENTAL QUALITY

Hilary Byerly, Paul J. Ferraro, Tongzhe Li, Kent Messer, Collin Weigel

Abstract

Strong empirical evidence links human activities to global environmental change and demonstrates how human well-being is impacted by this change. This scientific evidence, however, is often not sufficient to motivate people to bear private costs for environmental protection. Instead, emotional appeals depicting personal stories of loss or risk seem to spark public interest and action for the environment. Despite this conventional wisdom, few studies provide causal evidence on whether and how much such stories induce people to take costly and observable, rather than hypothetical or self-reported, actions that yield environmental benefits. To estimate the behavioral effect of an emotional, personal narrative in comparison to scientific information, we conducted a field experiment with 1,239 adults who maintain a lawn or garden in a polluted urban watershed. Before expressing their willingness to pay for landscaping products that reduce nutrient runoff, participants read either an emotional, personal narrative with tenuous connections to nutrient pollution or a scientific description of nutrient pollution's impacts on ecosystems and surrounding communities. Although the emotional narrative had a weak scientific foundation and failed to characterize the scope of pollution damage, it increased the amount people offered to pay for pollution-reducing products by 11%, CI

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95% [4%,18%]. This result is consistent with claims of tradeoffs between ensuring that messages are scientifically rigorous and that messages maximize environmental impacts.

Introduction

Scientific evidence for the contributions of human activities to environmental change and the subsequent consequences for human well-being has been well-documented by international scientific panels and assessments, such the Millennium Ecosystem

Assessment 1, International Panel for Biodiversity and Ecosystem Services 2, and

Intergovernmental Panel on Climate Change 3. In their reports, and in most of the articles that comprise their sources, information is provided in statistical and quantitative terms, describing impacts over large spatial and temporal scales and in probabilistic language.

Scientists often seem to operate under the assumption that this deluge of scientific evidence will catalyze action to address the environmental challenges of our day.

Instead, some of the most iconic shifts in environmental stewardship have appeared to occur not in response to novel scientific findings, but rather to media stories that engender concern and compassion. The publication of Silent Spring has been directly linked to stronger controls on the use of agricultural pesticides, despite awareness of the ecological impacts of pesticides within the scientific community for decades 4. Similarly, the death of Cecil, an African lion, rallied millions to advocate for species conservation, even though the effects of poaching on African wildlife have been widely published for years 5.

Behavioral science offers insights into why narratives attract attention in ways that statistical information does not. Humans make decisions as if guided by two systems – 113

one fast, emotional and intuitive; the other slow, rational and cognitive 6. When decisions are made with frequency or under limited cognitive bandwidth, the fast-thinking system tends to dominate 7,8. In such cases, people rely on quick and simple rules, or heuristics, to guide behavior. Heuristics use selectively recalled experiences or emotional responses to ease the burden of evaluating information and making judgements 9. For example, perceptions of climate change are influenced by local weather, which is easily available in the mind, rather than data on global climate patterns, which is complex and abstract 10.

These intuitive responses can be elicited by framing information in ways that make certain aspects salient 11. Narratives tend to be rich in imagery and emotionally engaging, qualities that innately appeal to intuitive thinking 12.

Emotional, personal narratives in the context of charitable giving have been reported to affect behavior to a greater degree than scientific or statistical information. People are more willing to act on behalf of a single, identified victim than a larger number of statistical victims 13. This victim framing increases charitable donations compared to factual information about more systemic problems 14, and the addition of scientific research to emotional appeals does not change average donation amounts 15. An advertisement that evoked sadness increased donations to an environmental organization by 21-30%, compared to a non-emotional ad 16.

Less clear is whether and how much emotional narratives are effective in changing individual behaviors that contribute to global environmental challenges and how they might be used by practitioners who otherwise rely on scientific information.

Observational research suggests worry about climate change is positively associated with

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risk perceptions, environmental policy support, and a stated willingness-to-pay for gasoline 17–19. A meta-analysis of factors related to climate change adaptation found a strong association with negative emotions but a weak relationship between behavior and knowledge 20. Yet, even in the field of climate change communication, which has been a focal point for psychology and behavioral science, there is little experimental evidence that connects emotion and framing with real, costly changes in pro-environmental behaviors 21.

The reliance on observational studies and self-reported measures of attitudes, values and intentions makes it difficult to identify the nature and direction of the relationship between efforts to change behavior and the environmental outcomes that matter, especially when those measures do not reflect actual behavior 22. While there is some experimental evidence of framing effects on pro-environmental behavior, there is a paucity of studies with large samples 23. Underpowered empirical designs and publication biases against null effects has led to a proliferation of scientific publications with exaggerated claims about the magnitudes of causal relationships. Without more precise estimates of treatment effects, these results cannot justify actionable interventions in policy and practice. Given the discomfort that policymakers and scientists might have with more emotional and potentially less scientifically sound messaging, it is important to quantify how large the framing effect could be. This information is also critical for scientists across disciplines seeking causal evidence on the impact of narratives in communicating findings 24.

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In order to address these gaps, we conducted a field experiment to test the effect of an emotional, personal narrative against scientific information on people’s willingness to take costly action to improve water quality in a polluted urban watershed. Using a design with real financial incentives and sample of 1,239 adults who maintain lawns or gardens, we elicited values for landscaping products that reduce nutrient pollution. Before expressing their values for these products, participants were randomly assigned to read one of two framing treatments: either an emotional, personal narrative with tenuous connections to nutrient pollution, or a scientific description of the impacts of nutrient pollution on ecosystems and surrounding communities. We estimated the effect of the framing treatment on participants’ willingness to pay for pollution-reducing products. We provide causal evidence of its magnitude on an observed behavior with real private costs and real private and public benefits.

We also tested whether this effect was moderated by the participant's gender. Responses to framing and to science communication have shown to vary by individual characteristics, such as partisanship and worldviews 25,26. We selected this moderating variable based on evidence that women have been reported to be more sensitive to emotional content than men 27.

Methods

We tested the effect of framing on the amount adults in the Delaware River Basin who maintain lawns or gardens were willing to pay for landscaping products that reduce nutrient runoff. Urban watersheds are increasingly polluted by excess nutrients from private land management decisions 28. Household actions, such as fertilizing lawns, have

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increased nitrogen and phosphorus runoff 29. The Delaware River Basin spans 13,539 square miles from southern New York to the Delaware Bay and is home to more than 8 million people who both rely on and impact the basin’s water quality 30.

To obtain people’s values for nutrient runoff-reducing products, we chose a well-known method that gives participants a strong incentive to reveal their true values 31. A participant reveals the most she would be willing to pay for a product. Then the product price is randomly chosen (from a normal distribution around the product’s average price).

If the participant’s revealed value for the product was more than the randomly drawn price, the participant buys the product for the drawn price; otherwise the participant does not get the product. Because the value revealed by the participant does not affect what price she pays, but rather only whether she receives the product or not, a participant can never do better than simply revealing their true value (i.e., truth telling is a weakly dominant strategy). Lying about their true values for the products could make the participants worse off if the random price chosen is above their revealed value (i.e., they cannot buy the product) but below their true value (i.e., they would have benefited from buying the product). The best strategy for participants was explained to them in an animated video.

Experimental design

Recruitment for the study occurred at thirteen locations and events in Delaware between

April and July 2017. In order to participate, individuals were required to be at least 25 years old and self-report maintaining a lawn or garden. Compensation for participation,

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which took about 15 minutes, was $15, with which study participants were able to purchase one landscaping product.

Participants (n = 1239) were provided electronic tablets with SoPHIE—Software

Platform for Human Interaction Experiments—to engage with the experiment. After confirming eligibility, participants were shown a photo and a text block that framed the problem of nutrient pollution in one of two ways. These distinct message frames acted as the experimental treatment in this study (see Treatments, below).

After receiving the treatments, participants watched a 5-minute video explaining how the price would be selected for a product, and the advantage of stating their true values for the landscaping products offered. Next, participants were shown, in random order, four landscaping products that reduce nutrient runoff: slow-release fertilizer, biochar, a soaker hose, and a soil test kit. The details and environmental benefits of each product, as well as a small photo, were provided. Participants were told that only one of the four products would be selected for potential purchase, but not until the participant had stated her willingness to pay for each product. Participants were instructed to treat each product purchase decision independently because only one would "count" (i.e., they were not constructing a portfolio of products; they would only go home with a maximum of one product). Yet they should take each decision equally seriously because they would not know, a priori, which decision would count.

Participants set their value for each product at an amount between $0 and $15. After revealing their values for all four products, participants provided personal information, including age, gender, and zip code. Finally, participants were informed of the randomly

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selected product and price. If this price was lower than their revealed value for that product, participants were given the product and their compensation minus the price. If the randomly selected price was higher than their revealed value, participants received the full $15 compensation and no product.

Treatments

Before revealing their values, participants were provided information that framed the problem of nutrient pollution. They were randomly assigned to see one of two versions, which are provided in Supplementary Information.

Scientific Information: This version described the importance of the Delaware River

Basin, statistics associated with its poor water quality, and impacts on ecosystems, humans, and other species. It was accompanied by a photo of dirty water running into a storm water drain. Content for this treatment was sourced from government agencies and local news sources. This version simulated the way experts—scientists and practitioners—communicate environmental problems.

Emotional Narrative: This version told the story of an upstanding local resident who died from an illness contracted through contact with polluted bay water. It was accompanied by a photo of the deceased individual. Content for this treatment came from local news sources. It provided a specific, personal story meant to elicit emotion through vivid descriptions and imagery. Inclusion of the name and photo of a victim and eliciting sadness have both shown to increase charitable donations 16,32,33. However, the connection between the story and the problem of nutrient runoff was tenuous, which could have raised concerns about credibility.

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Participants in both treatments were informed that nutrient pollution is caused in part by water running off residential lawns and gardens. Both texts concluded with the phrase,

“By using bay friendly landscaping products, you can reduce the nutrient runoff from your property.”

The average value for all products served as our outcome measure. Assuming participants followed the dominant strategy of revealing their true value for the landscaping products offered, this value is participants’ average willingness to pay for the impure public good of pollution-reducing products. We tested the null hypothesis that there is no difference in willingness to pay between the two framing treatments. To conduct a power analysis, we ran simulations based on data collected in an earlier experiment that used similar products and a similar elicitation procedure. The analysis indicated we could detect a main effect of 0.14 of the standard deviation given a sample size of 1200 participants

(훼 = 0.05, 훽 = 0.80). We also tested whether the treatment effect was moderated by gender.

To more precisely estimate the treatment effect of the emotional narrative on willingness to pay for pollution-reducing products, we used ordinary least-squares (OLS) linear regression. Since our dependent variable is a continuous value bounded by the minimum

($0) and maximum ($15) a participant could offer to pay for a product, we also estimated a fractional generalized linear model to test the robustness of our OLS results. We specified our main and moderator analyses and pre-registered our study on Open Science

Framework prior to examining the data and conducting the analysis herein

(DOI 10.17605/OSF.IO/XQCG4).

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Another study from this experiment tested whether a default starting value of $15 influenced participants’ values compared to a starting value of $0 (the ‘anchoring effect’). This manipulation was randomized separately from the framing treatments and is controlled for in the analysis.

Results

Participants were willing to pay an average of $7.10 (SD, $4.65) for the landscaping products offered (Table 1). A total of 737 participants (59%) purchased a product, having expressed a value more than the randomly selected price.

Participants who read an emotional narrative were willing to pay more, on average, than those who read scientific information for all products (Table 1, Figure 1). Pooling values for all four products and accounting for covariates, we find that participants who read the emotional narrative were willing to pay $0.77, or 11%, more to reduce their environmental impact (95% CI [$0.27, $1.27], p < 0.01; Table 2). The effect is more than

0.16 SD. OLS estimates are consistent with those from the fractional generalized linear model (see Supplementary Information).

We did not detect an effect of gender on response to the emotional narrative (Table 2;

Figure 2).

Discussion

Results from our experiment show that an emotional narrative can increase the amount people are willing to contribute to environmental quality. In comparison to scientific information about environmental damage, this emotional narrative caused people to bear

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additional private costs to reduce their impact on the environment. This effect of a simple change in framing adds to a growing body of research supporting calls that insights from behavioral science may offer a new toolkit to help address environmental challenges 34,35.

The response to this particular emotional narrative could have been driven by a number of factors. The difference in willingness to pay may be the result of an intuitive response to the proportion of victims in the two treatments 36. For those who read the scientific information, the scale of the problem was perhaps so large—with millions affected by poor water quality—that any one effort to reduce nutrient pollution appeared negligible.

Whereas those who read the emotional narrative, which described a single victim, may have felt their actions could make a difference in preventing the death of another individual in the future. Participants may have also reacted to the easy-to-understand terminology in the emotional narrative. As an example, people reduced beef consumption following newspaper articles describing an outbreak of “Mad Cow” disease, but not after articles used the scientific labels of the same disease 37. Emotional reactions, such as fear or sadness, may also have influenced behavior 38.

We cannot—and did not endeavor to—identify the mechanisms that drove the observed effect. The specific words chosen to deliver each treatment and their combination may have prompted responses that are not generalizable. Rather than employing multiple treatment arms to explore these variations, we prioritized statistical power. Future research could investigate which aspects of this specific narrative and scientific information influenced behavior, as well as whether effects change with different products or environmental contexts. However, there is no “emotional recipe” to advance

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sustainable behavior 21. Mimicking real-world communications can complement investigation into the psychological mechanisms that explain behavioral responses.

Other important questions remain about how reframing environmental challenges might matter in practice. For one, we do not know whether the effect varied for people with heterogeneous environmental preferences, i.e. do appeals to emotion through narrative increase contributions at the extensive or intensive margin? Emotional appeals may be more effective at increasing pro-environmental behavior among people who are less concerned about the issues 16. We also do not know whether the observed effect will persist over time or if it is scalable. There is evidence that the behavior-change effects of emotional appeals can dissipate after immediate exposure 16. The attention and feelings that fuel the intuitive response to stories are difficult to sustain over the long term and for large numbers of victims 39. Future experiments might test whether reading an emotional narrative influences consumption decisions at later time periods.

Evidence that emotional narratives influence behavior in science-based contexts has implications for how people understand and respond to complex problems. Memorable stories feed into the availability heuristic, which relies on easily recalled information when evaluating probabilities 9. If that information is not representative, people may under- or over-estimate the likelihood of some event (called ‘base rate neglect’). For example, a vivid story about an atypical individual who abused the social welfare system led to negative judgements of welfare recipients, while statistical information explaining average characteristics did not change opinions 40. Leveraging emotionally charged frames could also lead to non-optimizing behavior if people react more strongly in the

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moment than they would after deliberation. Vivid and emotional framing of risk, such as protection from “terrorist attacks,” induced people to pay more for hypothetical travel insurance than for protection against “all possible causes” 41.

Appeals to emotion through narrative, particularly in science-based contexts, might also have undesired effects. People can perceive them as manipulative, simplistic, misleading, or fatiguing 42. Hitching complex problems to single stories makes them vulnerable to debunking or misuse, as has happened when attributing isolated weather events to climate change 43. Given calls from within the Academy to “decode science to a narrative that generates feeling” 44, these side effects call for a better understanding.

Lastly, we should consider the cost-benefit of employing such strategies. At face value, achieving a tenth of a standard deviation change in behavior at zero financial cost makes for an appealing policy tool. But there are nonmonetary costs to leveraging emotion and single stories. An emotional narrative may create negative utility by making people sad or unsettling environmental practitioners and scientists who doubt its credibility. Those costs should be considered against the benefits of the environmental action. There may also be costs in terms of longer-term sustainability of the intervention, once people realize how it is being used to influence their behavior.

The complexity and psychological distance of global environmental challenges are at odds with the processes of everyday decision making. Reframing these problems using emotion and narrative can encourage people to make choices that are better for the environment. Yet, the range of unknowns and potential for unintended consequences warrant caution. Future research may fill knowledge gaps, but there are also ethical

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questions. In the face of global environmental challenges that may threaten future prosperity, can harnessing people’s humanity be ethical if it aligns their individual actions with the interests of society, both current and future?

Acknowledgements

Thank you to Jesse Gourevitch for helpful comments on a previous draft of this manuscript.

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Tables

Table 3 Descriptive statistics of sample

Full Sample Scientific Information Emotional Narrative Mean SD Mean SD Mean SD Value (all products, $) 7.10 4.65 6.86 4.62 7.33 4.67 Fertilizer ($ value) 6.85 4.41 6.55 4.32 7.15 4.48 Biochar ($ value) 6.99 4.60 6.72 4.62 7.27 4.57 Soil test kit ($ value) 5.91 4.26 5.75 4.22 6.07 4.30 Soaker hose ($ value) 8.63 4.89 8.41 4.90 8.86 4.88 Age (years) 47.4 14.9 47.6 14.8 47.2 15.1 Female (proportion) 0.55 — 0.58 — 0.52 — Observations (count)† 1239 — 616 — 623 — † Value (all products, $) includes the revealed values for all four products per person, thus increasing the number of observations (Full Sample = 4956, Scientific Information = 2464, Emotional Narrative = 2492).

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Table 4 Effect of framing on willingness to pay for landscaping products that reduce nutrient runoff and whether effects vary by gender. The treatment effect of the emotional narrative is compared to scientific information.

Main effect Gender

(1) (2) (3) (4) Emotional 0.48* 0.77** 0.38 0.63 narrative [0.03,0.92] [0.27,1.27] [-0.29,1.05] [-0.07,1.34]

Female 0.48 0.44 [-0.16,1.11] [-0.17,1.04]

Narrative X 0.25 0.24 female [-0.65,1.15] [-0.62,1.09]

Constant 6.86 6.52 6.58 6.60

Participant No Yes No Yes characteristics

Session No Yes No Yes covariates Participants 1,239 1,239 1,239 1,239 Observations in 4,956 4,956 4,956 4,956 regression Ordinary least-squares (OLS) estimation. Dependent variable is the value, in U.S. dollars, of all products. Columns 2 and 4 include controls for participant-level characteristics (gender, age, state of residence, and date and location of participation) and session covariates (additional treatments not in this study, product, and order in which products were presented). Columns 3-4 show results of moderator analysis. Standard errors are clustered at the individual level. 95% confidence intervals in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001.

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Figures

Figure 1 Average values for landscaping products that reduce nutrient runoff. Error bars represent standard errors of the mean.

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Figure 2 Effect of framing by gender on willingness to pay for landscaping products that reduce nutrient pollution. Values are the regression-adjusted means for all products. Error bars represent 95% confidence intervals.

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Supplementary Information

Table S1. Robustness checks for estimating the treatment effect of an emotional narrative on willingness to pay for landscaping products that reduce nutrient runoff. (1) (2) (3) (4) (5)

Emotional 0.48* 0.74** 0.77 ** 0.05* 0.05* narrative

(0.23) (0.27) (0.26) (0.02) (0.02)

Participant No No Yes Yes Yes characteristics

Session No Yes Yes Yes Yes covariates

Observations 4956 4956 4956 4956 4956 Dependent variable is the value revealed by a participant in U.S. dollars (Columns 1-3) and proportion of the $15 maximum value (Columns 4-5), pooling all values on the four products offered. Columns 1-4 show results from ordinary least-squares (OLS) estimation. Column 5 shows results from a fractional generalized linear model. Participant characteristics include gender, age, state of residence, and date and location of participation. Session covariates include controls for additional treatments not in this study, product, and order products were presented. Standard errors in parentheses, clustered at the individual level. * p < 0.05, ** p < 0.01, *** p < 0.001

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Figure S1. Screenshots of the framing treatments participants received before expressing their values for landscaping products. Scientific information (top) and emotional narrative (bottom) frame the problem of nutrient pollution in different ways. The name and photo of the victim in the emotional narrative have been redacted.

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Figure S2. Screenshots of biochar (top) and slow-release fertilizer (bottom), two of the four products for which participants expressed their willingness to pay.

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Figure S3. Screenshots of soaker hose (top) and soil test kit (bottom), two of the four products for which participants expressed their willingness to pay.

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CONCLUSION

Evidence from these four studies demonstrates that decisions about land management can be explained and influenced by leveraging insights from behavioral science. Social influence can impact land manager behavior, but the strategy and context matter. Among

California farmers, information from peers was positively related to adoption of biodiversity practices. In Vermont’s maple sugarbushes, information about peers had a negative effect on interest in biodiversity conservation. Information framing influences the costs people are willing to incur for water quality. These studies add to a nascent literature that integrates behavioral science and private land management while also establishing a need for further inquiry.

The first chapter laid out a research agenda to apply behavioral insights to decisions that affect the environment: more research on promising strategies in important domains using large-scale field experiments. Chapter Two expanded that research agenda by suggesting strategies that seem especially relevant to the provision of public goods from private lands. Chapters Three and Four contributed evidence by testing those strategies on decisions related to land management. Future research should continue to test these and other strategies on private land decisions, but it should also do more. To what extent, under what conditions, and with what practical implications can behavioral science inform strategies to influence land management behavior?

First, future studies should prioritize measures of persistence, spillover, robustness, and limits to behavior change that can help parameterize the effects of behaviorally informed 137

strategies. Second, future field experiments should be adequately powered to test whether and how effects are moderated by participant characteristics. Importantly, this would enable investigation into the distributional impacts of nudges. Poverty reduces cognitive bandwidth, making people of lower socio-economic status more likely to be susceptible to interventions that capitalize on cognitive biases and, perhaps, carry a greater burden of behavior change. Third, research should extrapolate observed effects to measure cost- effectiveness, environmental impact, and—especially for land management—spatial interactions among landowners. Wildfire mitigation, invasive species control, habitat provision – these are spatially dependent public goods. One landowner’s management behavior can both impact and be impacted by those of her neighbors. Incorporating the effects of social influence or reference-dependent risk aversion into models of landowner behavior could help identify priority areas or owners to target interventions.

This agenda should be pursued through applied research, through partnerships with organizations and agencies who are implementing behavior-change programs.

Experimental studies should be paired with qualitative and survey research to design appropriate interventions and interpret results in context.

Strategies from behavioral science are not alternatives to providing information, changing incentives, or enacting regulation. These conventional policy tools can make large and lasting gains in behavior change. They are necessary to address the scale and urgency of current global environmental challenges, such as climate change. But even a carbon tax or plastics ban will be established with particular default settings, framed using certain messaging, and adopted in a social context where concerns about cooperation and

138

fairness are inevitable. Small tweaks to how those kinds of interventions are developed and delivered, informed by behavioral science, could have real, nontrivial effects on their impact.

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