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EEPSEA Research Reports are the outputs of research projects supported by the Economy and Environment Program for Southeast Asia. All have been peer reviewed and edited. In some cases, longer versions may be obtained from the author(s). The key findings of most EEPSEA Research Reports are condensed into EEPSEA Policy Briefs, which are available for download at www.eepsea.org. EEPSEA also publishes the EEPSEA Practitioners Series, case books, special papers that focus on research methodology, and issue papers. ISBN: 978-621-8041-65-3 The views expressed in this publication are those of the author(s) and do not necessarily represent those of EEPSEA or its sponsors. This publication may be reproduced without the permission of, but with acknowledgement to, WorldFish-EEPSEA. Front cover photo: Grasslands and forests, Nan province, Thailand. Photo by Chrisgel Ryan Cruz under the creative commons license at https://www.flickr.com/photos/arcibaldo/15379109054/ Suggested Citation: Tambunlertchai, K. and S. Pongkijvorasin. 2017. Optimal regulatory design in the context of weak enforcement: Do regulatory stringecy and regulatory approach matter in determining common pool resoure extraction behavior? EEPSEA Research Report No. 2017-RR24. Economy and Environment Program for Southeast Asia, Laguna, Philippines

Optimal Regulatory Design in the Context of Weak Enforcement:

Do Regulatory Stringency and Regulatory Approach Matter in Determining Common Pool Resource Extraction Behavior?

Kanittha Tambunlertchai Sittidaj Pongkijvorasin

May, 2017

Comments should be sent to: Dr. Kanittha Tambunlertchai Faculty of Economics, Chulalongkorn University, 254 Phayathai Road, Wang Mai, Khet Pathum Wan, Krung Thep Maha Nakhon 10330, Thailand Tel: (+66) 22186184 ext. 2 Email: [email protected]

The Economy and Environment Program for Southeast Asia (EEPSEA) was established in May 1993 to support training and research in environmental and resource economics. Its goal is to strengthen local capacity in the economic analysis of environmental issues so that researchers can provide sound advice to policymakers.

To do this, EEPSEA builds environmental economics (EE) research capacity, encourages

regional collaboration, and promotes EE relevance in its member countries (i.e., Cambodia, China, Indonesia, Lao PDR, Malaysia, Myanmar, Papua New Guinea, the Philippines, Thailand, and Vietnam). It provides: a) research grants; b) increased access to useful knowledge and information through regionally-known resource persons and up-to-date literature; c) opportunities to attend relevant learning and knowledge events; and d) opportunities for publication.

EEPSEA was founded by the International Development Research Centre (IDRC) with

co-funding from the Swedish International Development Cooperation Agency (Sida) and the Canadian International Development Agency (CIDA). In November 2012, EEPSEA moved to WorldFish, a member of the Consultative Group on International Agricultural Research (CGIAR) Consortium.

EEPSEA’s structure consists of a Sponsors Group comprising its donors (now consisting of

IDRC and Sida) and host organization (WorldFish), an Advisory Committee, and its secretariat. EEPSEA publications are available online at http://www.eepsea.org.

ACKNOWLEDGMENT

The authors would like to thank the Economy and Environment Program for Southeast Asia (EEPSEA) for the funding that made this research possible. A big thank you also to Dr. Herminia A. Francisco for the instrumental role that she has played in bringing about this research. We are also grateful to Dr. Jack Knetsch for his insightful comments and suggestions.

We would also like to express our appreciation to Ms. Charuwan Hirunkul for her assistance

and hospitality during our trip to Nan province. Thank you to all the support staff of EEPSEA for their administrative support, especially

Ms. Mia Mercado, Dr. Noor Aini Zakaria, Ms. Rhona Coronado, and Ms. Julienne Bariaun. Finally, we would like to thank Ms. Kei Cuevas for her work in editing the manuscript of this research.

TABLE OF CONTENTS

EXECUTIVE SUMMARY 1

1.0 INTRODUCTION 2

1.1 Background and Rationale 2

1.2 Research Problem 3

1.3 Research Questions and Objectives 4

2.0 LITERATURE REVIEW 4

2.1 Governing the Commons 4

2.2 Regulatory Design 5

3.0 EXPERIMENTAL DESIGN 7

4.0 RESULTS AND DISCUSSION 11

4.1 General Trends in Game Outcomes under Both Rewards and Punishment Treatments

11

4.2 Impacts of Different Levels of Regulatory Stringency on Extraction Behavior under the Punishment Treatments

14

4.3 Impacts of Regulatory Stringency on Compliance under the Punishment Treatments

15

4.4 Impacts of Different Levels of Regulatory Stringency on Extraction Behavior under the Reward Treatments

17

4.5 Impacts of Regulatory Stringency on Compliance under the Reward Treatments

18

4.6 Observations on the Impacts of Regulatory Approach (Carrots vs Sticks)

19

4.7 The Role of Gender 21

4.8 Game Outcomes, Social Preferences, and Behavior Outside the Game 22

5.0 CONCLUSION 24

LITERATURE CITED 26

APPENDICES 29

LIST OF TABLES

Table 1. Treatments 9

Table 2. Average individual extraction under reward treatments 12

Table 3. Average individual extraction under reward treatments 12

Table 4. Average number of players who complied with the rule in the lab-in-the-lab experiment

16

Table 5. Average number of players who complied with the rule in the lab-in-the-field experiment

16

Table 6. Average number of players per group who comply with the rule 19

Table 7. Average individual extraction under punishment and reward treatments in the lab-in-the-lab experiment

20

Table 8. Average individual extraction under punishment and reward treatments in the lab-in- the-field experiment

20

Table 9. Average extraction level in punishment treatments by gender 22

Table 10. Average extraction level in punishment treatments by gender 22

Table 11. Mean difference between groups 23

LIST OF FIGURES Figure 1. Average level of individual extraction in lab-in-the-lab experiment

under punishment treatments 13

Figure 2. Average level of individual extraction in lab-in-the-field experiment under punishment treatments

13

Figure 3. Average level of individual extraction in lab-in-the-lab experiment under reward treatments

13

Figure 4. Average level of individual extraction in lab-in-the-field experiment under reward treatments

14

Figure 5. Average individual extraction level under sticks and carrots in the lab-in-the-lab

20

Figure 6. Average individual extraction level under sticks and carrots in the lab-in-the-field

21

1 Economy and Environment Program for Southeast Asia

OPTIMAL REGULATORY DESIGN IN THE CONTEXT OF WEAK ENFORCEMENT: DO REGULATORY STRINGENCY AND REGULATORY APPROACH MATTER IN DETERMINING COMMON POOL RESOURCE EXTRACTION BEHAVIOR?

Kanittha Tambunlertchai, Sittidaj Pongkijvorasin

EXECUTIVE SUMMARY

“Common pool resources” (CPR) is a term used to refer to systems that generate finite resource units, which are rival in consumption but are non-excludable. These characteristics of CPR make them susceptible to overexploitation. The increasing severity of environmental degradation and natural resource depletion worldwide provide evidence to support this grim prediction. This negative trend indicates that successful management of CPR is a challenge anywhere in the world. Nonetheless, research and case studies show that appropriate institutions could be applied to prevent overexploitation of CPR. Research also shows that the success of such institutions varies depending on the context of their applications.

This research addresses the issue of CPR overuse from the perspective of forest resource

management in Thailand. The aim is to understand the appropriate institutional design in the context of weak monitoring and enforcement. The research focuses on two main aspects. The first is to understand the impact of the stringency level of the regulation on extraction behavior. The second is to understand the effect of regulatory approach on the decision of resource users. Additionally, the research addresses the issue of gender, as well as the relationship between real and experimental outcomes.

In the first aspect, we hypothesized that the strictest level of regulation (i.e., no extraction)

would not lead to the most desirable outcome in terms of resource use. Instead, a more practicable level of stringency (i.e., allowing a low to medium level of extraction permitted) would lead to a lower extraction overall. In the second aspect, we hypothesized that providing the carrots of reward would work better than the sticks of punishment in the context of weak monitoring and enforcement. We did not form the hypothesis for the gender question, but wanted to explore if there were gender differences in the responses to regulatory design. Finally, we expected the game outcomes to reflect real decisions made by resource users.

The main research methodology is experimental economics. A modified version of the

standard CPR game was played in the lab-in-the-lab and the lab-in-the-field settings. Undergraduate students participated in the former, whereas in the latter, we selected villagers who live within the forest vicinity in Nan province in Northern Thailand as participants.

Findings confirmed our hypothesis regarding the stringency level. Low- and medium-

stringency levels are shown to work better than high stringency in bringing about cooperation in CPR setting with weak monitoring and enforcement. This result is found in both the lab-in-the-lab and the lab-in-the-field experiments. We also find that incentives matter in successful CPR management, although positive and negative incentives work differently. Rewards work better with medium level-stringency, whereas punishment works better with low-stringency level. We also find gender differences in the players’ responses to regulation. For the men, low level of stringency works better in reducing extraction. For the women, medium level of stringency results in the least extraction. In terms of collaboration between game outcomes and real choices, we find some evidence to support that game results corroborate with decisions made by the participants in the real world.

2 Optimal Regulatory Design in the Context of Weak Enforcement: Do Regulatory Stringency and Regulatory Approach Matter in Determining Common Pool Resource Extraction Behavior?

1.0 INTRODUCTION 1.1 Background and Rationale

Common pool resources (CPR) refer to systems that produce a finite amount of resource units. CPR has the following characteristics. First, the resource unit produced by such systems is rival in nature. One user’s extraction of the resource unit prevents others from using the same. In this sense, extraction from such systems reduces the amount of resource available to others from the same system. Second, the systems are often large enough so that many people can use the resource simultaneously. The largeness of the systems also makes it costly to exclude potential users. These two characteristics result in a resource that is rival but non-excludable (Ostrom et al. 1994; Ostrom 2002). Examples of CPR include forests, rivers, and oceans.

Due to their rivalry and non-excludability nature, CPR faces the risk of overexploitation in

situations where a large number of potential users of the resource exist. All over the world, incidents of overexploitation of CPR such as forests, riverine, and marine ecosystems exist. With rising incidence of environmental degradation and natural resource depletion, questions on how to manage CPR successfully are gaining more importance over time. Experiences in many countries suggest that CPR management is a challenge anywhere in the world. Models such as that of Hardin (1968) predict that decisions made by self-interested and rational individuals over CPR would eventually lead to the depletion of such resources.

Despite this grim prediction, there has been evidence that suggests that establishing the

appropriate institutions could help to prevent overexploitation of CPR (Ostrom 2008). Although the literature has shown that the prospects for CPR may not necessarily be so grim when appropriate institutions are in place, research has shown that the successful application of such institutions varies from context to context. As such, it is important to identify the design of institutions that would help to preserve CPR.

This research addresses this problem in terms of a way to determine the appropriate

institutional design in the management of CPR in the context of a developing country. More specifically, this study aims to identify the elements of regulatory design that are most conducive to the conservation of tropical forest resources in Thailand.

The research adopted the tools of experimental economics to provide the answers to the

research questions. Two aspects of regulations form the focal point of this research: regulatory stringency and regulatory approach. Regulatory stringency refers to the strictness of the rules that govern CPR extraction. The strictest regulation would be total prohibition of CPR extraction by local communities, whereas a relaxed regulation would be a free-for-all extraction policy. Regulatory stringency, in our meaning, is distinct from the severity of sanctions that follow non-compliance. Regulatory approach, on the other hand, is the way in which the regulation is applied. The standard regulatory approach in a developing country setting is the command-and-control (CAC) method, which is an approach where the rules are set by an external authority (e.g., the state) and are externally monitored and enforced. CAC enforces compliance by using “sticks” or punishments. An alternative regulatory approach that is gaining popularity worldwide is the use of voluntary approaches, which offer incentives or “carrots” to motivate the desired behavior.

This research hypothesizes that these two factors (i.e., regulatory stringency and regulatory

approach) matter in establishing the appropriate institutions that would eventually lead to the sustainable management of forest CPRs in a developing country. However, the effects of both factors on CPR extraction in a setting where external monitoring and enforcement are weak have


not yet been much studied. Most research on regulations has focused on the effects of the varying sanction levels on compliance when regulatory stringency is given.

This study further hypothesizes that in a context where there are weak institutions for

monitoring and enforcing regulations, the stringency level of the regulation itself could affect compliance behavior. Furthermore, when the state has limited capacity to monitor and enforce regulations, the approach of administering the regulation could affect outcomes. Thus, this study proposes to examine these two factors in the context of forest CPR in Thailand. 1.2 Research Problem

Due to the rapid depletion of forest areas nationwide, the government of Thailand

instituted a number of measures to control forest loss. The regulatory approach adopted is primarily command-and-control (CAC). Currently, forest areas are being regulated through logging ban (effective since 1989), demarcation of certain forest areas as protected areas, and establishing certain tracts of forest and wetland areas as World Heritage and Ramsar sites. Stringent restrictions are imposed on the use of forest resources in protected zones. Nonetheless, with limited resources to monitor and enforce such restrictions, the CAC measures imposed by the government have not led to the successful management of forest CPR. Natural forests continue to decline, especially in the northern parts of the country, where many forest areas have been illegally destroyed and where lands have been claimed and turned into agricultural areas. Not only has the CAC approach proved to be ineffective, but the top-down approach to forest management has also led to various problems between the local communities who live in the area and who need to use the resources and the state who view forests as public property that need to be protected.

Many research studies and real-world examples have shown that local communities can

sustainably manage CPR. Accordingly, there has been a gradual shift toward the enhancement of community rights that would empower the local communities to set their own regulations regarding CPR use. Nonetheless, in Thailand, the process has been beset with difficulties and conflicts between the state and the local communities. Since the 1980s, there have been ongoing debates and drafts of a bill that would recognize community forests; however, Thailand still has no legal framework for community forest management to date.1

Immediately after the Community Forest Bill of 2007 had been passed, local community

groups who have fought hard to legalize community forestry announced their opposition to the bill. One reason for their opposition was the limitation of the rights of local communities over forest areas, especially over national parks, reserves, and sanctuaries2 (RECOFTC 2014). The Community Forest Bill was then later revoked by a Constitutional Court ruling. At the end of 2016, a new Community Forest Bill has been drafted but is yet to be enacted.

Given the context of Thailand where very stringent regulations have been imposed with

very limited results and where community forest regulations are hard to implement, it is important to explore alternative options to forest management. If the government prefers a top-down approach, then it is important to determine at what level of stringency the forestry regulations should be set, and what approach should be applied to enforce such regulations.

1 In 2007, a draft Community Forest Bill was passed and enacted. However, the communities who fought for the bill

announced that they would reject the bill due to its restrictive contents. The bill was eventually revoked by the Constitutional Court.

2 The 2007 bill granted community forest rights to communities who can show that they have lived and managed a particular forest area for at least 10 years prior to the establishment of the protected area.


The local forest communities opposed the Community Forest Bill of 2007 on the grounds that the bill was too restrictive. The opposition also indicates that, in a context of weak enforcement of regulations, any CAC approach would be too restrictive if it is not practicable to the local communities.

As such, another approach is needed if the government intends to get the local

communities on board with their goal of forest conservation. Accordingly, the findings of this study could contribute to the policy discussion with regard to community forests, and help to pave the way for a better and more sustainable management of forest resources in Thailand. 1.3 Research Questions and Objectives

This research poses the following questions:

1. With limited monitoring and enforcement, what are the different levels of extraction that would correspond to the different levels of regulatory stringency? Do male and female respond differently to regulatory stringency?

2. With limited level of monitoring and enforcement, what approaches (i.e., “sticks” or “carrots”) will result in a better outcome in common-pool resource management? Do male and female respond to different approaches differently?

3. How well do experimental outcomes corroborate with real choices?

Accordingly, this study seeks to achieve the following objectives:

1. To study the impact of regulatory stringency on individual extraction decision of a common-pool resource when monitoring and enforcement is weak;

2. To determine whether the approach to regulation (i.e., “sticks” vs. “carrots”) has an impact on individual extraction decision of a common-pool resource in a setting where the government has limited capacity for monitoring and enforcement;

3. To study the effect of gender on decision making in terms of common-pool resource use under different levels of regulatory stringency and regulatory approach (i.e., under low enforcement condition); and

4. To study the relationship between experimental outcomes and real choices.

2.0 LITERATURE REVIEW 2.1 Governing the Commons

In light of increasing environmental degradation and natural resource depletion,

determining the effective methods and means for the successful management of CPR are ever on the minds of academic researchers and policy makers. However, before policies can be chosen, it is necessary to understand the mechanism that drives environmental degradation and resource overexploitation. Understanding the underlying mechanism would allow policy makers to develop the appropriate policies that could help achieve the objective of successful CPR management.

Hardin (1968) explains the mechanism of the so-called “tragedy of the commons.”

Assuming that people are self-interested and rational, Hardin (1968) explains that, in a setting where resources are commonly used and owned, individual extraction of the resource amount of


1 unit results in the individual gaining nearly 1 unit, while the cost of that 1 unit extraction is shared by all who use the commons. Without having to bear the full costs of their actions, rational and self-interested individuals will tend to extract increasingly from the resource. In a world with a large number of people, the tendency to extract increasingly from the CPR will lead to overexploitation and depletion of the natural resource shared in common. This ending is what Hardin (1968) calls the “tragedy of the commons.”

Institutional arrangements are needed to prevent the destruction of the environment and

natural resources stemming from the self-interest and rational behaviors of humans. However, it remains unclear as to which institutional arrangements would be best suited to prevent overexploitation of CPR in any particular context. On the one hand, there is the recommendation that external intervention from the government (e.g., imposing regulations and property rights) is necessary to counter the self-destructive tendencies of individual resource users in order to achieve sustainable CPR management. On the other hand, internally crafted institutions that come from the users and the community are recommended as means to govern CPR successfully.

There are various forms of external interventions for preventing the tragedy of the

commons. For one, regulations could be used to limit overexploitation of natural resources. For another, taxes could be introduced to internalize the externalities caused from extraction of resources from the commons, and thus the full cost of extraction is borne by the extractor and not just a fraction of the cost. Likewise, property rights could be recrafted, shifting away from the open access model to private property, state ownership, or state management. Alternatively, education could be used in order to instill the values of environmental protection in the potential resource users.

In addition to prescribing external intervention, internally crafted institutions have also

been shown to be successful in the governance of the commons. Many papers have raised the role of institutions to determine people’s behavior in CPR problem, and they pointed to cases where such resources have been well managed by local communities despite the absence of a central authority (e.g., Bromley 1992; Ostrom et al. 1999; Ostrom 2008; Weiland and Dedeurwaerdere 2010; Palmer 2014). This idea led to development projects that emphasize community involvement in the management of the commons within their vicinities (Saunders 2014).

Although CPR policies are now shifting toward more involvement of the local communities

to govern the commons, research also shows that there is no one-size-fits-all solution to the design of institutions that can lead to the successful management of the commons. As Agrawal (2001) has pointed out, aspects of the resource system, user group, social, physical, and institutional environments could also impact the success of the institutions designed to govern the commons (also see Tang 1992; Ostrom, Gardner, and Walker 1994; Ostrom et al. 1999). Thus, the context within which the institutions would be built should be carefully considered. This is a crucial step toward the development of the institutional arrangement for the successful management of the commons.

2.2 Regulatory Design As mentioned in the research problem section, the predominant strategy for forest

management in Thailand is the CAC approach to regulation. If externally imposed regulations are to be used, then it is important to determine the appropriate design of the regulations that would suit the local context. In this sense, the literature on the design of regulations is examined in order to provide some insights from existing academic research on the issue of designing regulations that would achieve the objective of successful resource management in the context of common pool resources.


In terms of regulatory design, studies have found that inappropriate intervention of the government could undermine the objective of the regulation. Ostmann (1998) showed that external regulation with mild degree of monitoring might weaken social norms and encourage players to be self-interested, resulting in a worse outcome. Frey and Jegen (2001) found that external regulation might create crowding-out effect, in which external regulations undermine self-governed solutions and crowd-out public motivation to engage in cooperative behavior. Falk and Kosfeld (2006) showed that in principal agent relationships, introducing regulations that restrict the participants’ choice set result in crowding-out effects. However, external regulation has been found to induce cooperative behavior in certain settings such as among fishermen in a Caribbean island (Castillo and Saysel 2005), and when sanctions and likelihood of enforcement is increased (Rodriguez-Sickert, Guzman, and Cardenas 2008). Results have also shown that reactions to externally imposed regulations may be heterogeneous across individuals (d'Adda 2011).

In terms of the impact of regulation enforcement on resource-extraction behavior,

Cardenas, Stranlund, and Willis (2000) affirmed the existence of institutional crowding-out in resource extraction decisions. They compared the level of resource utilization with and without external regulation; they found that when external regulation is at the social optimum, resource is even more exploited compared with no regulation at all. However, the paper only compared the cases with and without external regulation, but did not investigate the effects of the different levels of regulation. Furthermore, in a later paper that employed similar design and setting as that of Cardenas, Stranlund, and Willis (2000), Cardenas (2004) found that exogenous regulations with weak enforcement and communication can encourage social cooperation. Meanwhile, Velez, Murphy, and Stranlund (2010) investigated the impact of regulations and communication in a CPR case in three regions in Colombia. The experimental design allowed for low and medium levels of penalty and non-binding verbal agreements to conserve a local natural resource. They found that the effects of regulation vary depending on the community since community heterogeneity is also a factor that should be considered.

In terms of regulatory approach, in addition to CAC, the government could also adopt a

voluntary approach by offering incentives (“carrots”) for environment-friendly behavior, rather than by offering control with punishments (“sticks”). The idea of rewarding people for good behavior is the concept that lies behind policy tools such as the payment for ecosystem services (PES) scheme (i.e., the REDD+ scheme)3, which emerged from the UN international climate negotiations. In its essence, the REDD+ mechanism sets up a framework that financially rewards developing countries for carbon emission reductions and removals of greenhouse gases, which are achieved through a variety of forest management options.

In social dilemma situations such as in the CPR problem, theory predicts that negative

incentives (punishment) and positive incentives (rewards) should both induce the cooperation of CPR users as long as they serve to reduce the discrepancy between the self-interest and group-interest that leads to the tragedy of the commons problem (Bravo and Squazzoni 2013). Empirical papers studying the impact of incentives (positive or negative) in situations where group externalities exist find that incentives do induce cooperation in such settings (e.g., Fehr and Gächter 2000; Parks, Sanna, and Berel 2001; Mulder 2008; Rand et al. 2009). However, although they have both been found to induce cooperation, negative and positive incentives do not work in the same way (Andreoni, Harbaugh, and Versterlund 2003). Punishment can be effective even if it is not used. Thus, the threat of punishment can be repeatedly applied. However, rewards need to be honored each time. This makes the application of sticks more efficient than rewards (Dari-Mattiacci and De Geest 2009). Nonetheless, in a later paper, De Geest and Dari-Mattiacci (2013) pointed out that the application of carrots is increasing in modern legal systems and in

3 REDD stands for Reducing Emissions from Deforestation and forest Degradation.


many areas of our lives. They argued that this is happening because carrots are superior to sticks when the regulatory context is more complex—the regulator does not know what to expect from the regulated, and the lawmakers wish to require significantly higher efforts from some citizens.

Empirical evidence on the effectiveness of punishment and rewards have shown mixed

outcomes in terms of getting players to play cooperatively in social dilemma situations. Andreoni, Harbaugh, and Vesterlund (2003) studied the application of costly punishment, reward, and punishment-and-reward combination in two-person proposer-responder games. They found that carrots induce people to contribute high in general; however, the distribution is highly skewed, with sticks having the higher mode. They found that the joint application of both tools work best. Meanwhile, Sefton, Shupp, and Walker (2007), who also studied both instruments, found that rewards and punishment both increase cooperation; however, punishments perform better in sustaining cooperation at a high level. Nonetheless, it has been found that when given the option to vote, groups overwhelmingly choose to adopt rewards (Sutter, Haigner, and Kocher 2010). Bravo and Squazzoni (2013) found that rewards work better when voluntary participation is permitted, whereas punishment works better when it is singly applied with no voting.

A meta-analysis by Balliet, Mulder, and van Lange (2011) on the topic indicated that

punishment is much more extensively studied than rewards. Nonetheless, the paper found no statistical difference between the two tools in inducing cooperation in the context of social dilemmas. Both are effective, and there is no statistical difference between the two approaches.

In this report, we seek to add to the existing knowledge on regulatory design and

regulatory approach in the following manner. By investigating the impact of the different levels of CAC on resource-extraction behavior, our study seeks to determine whether a more practicable regulation would induce individuals to exhibit cooperative behavior. The existing literature has focused mainly on the varying levels of sanctions (e.g., Beckenkamp and Ostman 1999) or communication (e.g., Cardenas, Stranlund, and Willis 2000; Velez, Murphy, and Stranlund 2010). Our research aims to focus on the aspect of the stringency of the regulation itself. Our second contribution lies in the comparison of the impacts on individual behavior from the application of two policy approaches: (1) the “sticks” of CAC approach and (2) the “carrots” of the voluntary approach, which uses external incentives to motivate prosocial conservation behavior. The impacts of monetary and non-monetary incentives have not been much studied in the CPR literature.

3.0 EXPERIMENTAL DESIGN In order to answer the research problems, we used the experimental approach in

combination with survey questions. The experimental approach comprised of two CPR experiments, each with eight treatments. The experiments were first pretested, and were then conducted in the lab (i.e., lab-in-the-lab) and in the field (i.e., lab-in-the-field). The lab-in-the-lab experiment was conducted on the students of Chulalongkorn University, whereas the setting of the lab-in-the-field experiment was conducted in the province of Nan in the hilly northern part of Thailand.

In the lab-in-the-field, the villagers from six farming communities who live in the vicinity of

forests played the game. Encroachment of the forest for maize farming is not uncommon in the area. Many types of forest management methods exist in the area, and deforestation is illegal. However, the resource constraints of the law enforcers result in a de facto weak monitoring and enforcement. This aspect of the problem is also reflected in our experimental design.


The baseline CPR game in this research is a static game similar to that used in the study of Ostrom, Gardner, and Walker (1994); Falk, Fehr, and Fischbacker (2002); and Velez, Murphy, and Stranlund (2010). In each round of the game, each individual player i chooses a level of extraction yi from a resource that is shared in common among his/her group. The maximum level of extraction is capped at yi

max. As is usual in a commons setting, the player faces a dilemma. Extraction from the common resource results in private benefits, but imposes negative externalities on other users who share the commons. The individual’s private payoff to extraction rises as the player takes more from the resource. However, as more people take from the resource, the resource declines. This results in a situation where payoffs to each player reduce as more and more is taken from the common pool resource. Treatments are added to the baseline CPR game that corresponds to different mechanisms aimed at providing incentives (positive or negative) for cooperation in this social dilemma situation.

The main objective of this research is to study regulatory design in the context of forest management. The context of this research is one of maize farmers who live near forest areas. The farmers face a choice of extracting from the forest resource (i.e., clearing the land to create more maize farming area) or maintaining the forest resource. If the forest exists, then the individuals’ benefit from the forest would be proportional to the amount of forest remaining. If the forest resource is cleared, then the farmers would lose benefits from the forest but would receive benefits in terms of the returns from maize farming. However, the benefit from maize farming decreases as the forest is progressively cleared. The individual must then choose how much forestland will be cleared. The payoff function for each individual is given by

∏ = i PF * �Y − �yi + y−i�� + PM * yi , subject to 0 ≤ yi ≤ yi max Equation (1)

where: Πi = payoffs for individual i, PF = returns to individual i for each unit of forest that remains intact, (Y – (yi + y– i)) = remaining forestland, PM = returns to individual i for growing maize on each unit of land, yi = individual i’s harvest decision, y– i = sum of other individuals’ harvests, and yi

max = capacity constraint of the resource.

In this setting, the Nash equilibrium results in overextraction of the CPR. Under the Nash equilibrium, the optimal level of extraction occurs below the individual level of extraction and corresponds to a very restrictive level of resource use.

In our study, we wanted to test whether applying varying stringency levels and varying

approaches under which the regulation is administered will affect individual extraction level. The purpose of the experiments was to determine the regulatory design that would induce cooperation so that resource extraction choices are more in line with the optimal level of extraction. One main hypothesis is that a medium-level regulatory stringency (i.e., one that is more practicable) would lead to a lower extraction level than that predicted by the Nash equilibrium. Another hypothesis is that the voluntary approach (i.e., using positive incentives) would induce more cooperative behavior. The underlying context is that monitoring and enforcement of regulations are weak.

As mentioned earlier, two experiments were conducted, the lab-in-the-lab experiment

(comprising of student subjects), and the lab-in-the-field experiment (comprising of farmer subjects). In each experiment, there were eight treatments with different approaches and levels of regulatory stringency. These treatments are presented in Table 1.


Table 1. Treatments

Stringency

Approach

No Regulation

Low Level of Stringency

Medium Level of Stringency

High Level of Stringency

(Optimal Level) Command-and-control (Stick of punishment)

LegNo LegLo LegMe LegHi

Voluntary approach (Carrot of reward)

VolNo VolLo VolMe VolHi

In our setup, the maximum level of extraction that was possible from the resource was 80 units per person. No regulation allowed any person to have unlimited levels of extraction up to the limit of 80. In the CAC treatments, low-level stringency allowed extraction of no more than 50 units per person. Medium-level stringency limited extraction to no more than 30 units, and high-level stringency did not allow for any extraction at all. Overextraction was punishable by a hefty fine, but there was a low probability that the infarction would be observed. In the voluntary approach treatments, group targets were imposed. Low-level stringency corresponded to a group target of 250 (50 units per person on average). Medium-level stringency corresponded to a group target of 150 (30 units per person on average). High-level stringency did not allow for any extraction at all (group target of 0). Rewards would be awarded if the group extracts at or below the group target. The high-level stringency was the social optimum.

In each level of regulatory stringency, the difference between CAC and the voluntary

approach is that under the CAC, the “sticks” of punishment were used to bring about compliance. Under the voluntary approach, the “carrots” or incentives were offered to induce compliance. Another difference is that the reward was triggered when the group met the group target, the reward was awarded as a group, and lump-sum payment was made. In the “sticks” case, the rule was an individual limit; overextraction results in an individual fine per unit of overextraction.

This setup was chosen since it is more in line with the main approaches to forest

management used in developing countries. CAC is the predominant approach to forest management in Thailand and in many other developing countries. Under CAC, individual overextraction is punishable by law. A heavy fine is levied should the infarction be observed. The voluntary approach is similar to the PES schemes, which provides lump-sum payment when the collective target of forest is maintained.

In this study, under CAC, the subject faced a probability of being caught and penalized for

overextraction. However, the probability was set to be at a low level (0.01) to represent the limited ability to monitor and enforce the regulation in the real world. If the individual overextracted and was caught, then a hefty monetary fine would be imposed. The fine was set at an (experimental) THB 50 per unit of overextraction. This is a rather large sum considering that in the Nash equilibrium level of extraction, the individual stands to gain the amount of (experimental) THB 200 earnings in total. Meanwhile, under the voluntary approach, monetary incentives would be offered if the group target would be met. No punishments would be imposed, with the exception of the potential for low payoffs that could result when individuals choose to extract a lot from the CPR.

The payoff under the CAC approach was given by

∏ = i PF * �Y − �yi + y−i�� + PM * yi − Fee* �yi − yreg�, subject to 0 ≤ yi ≤ yi

max Equation (2)


where: Πi = payoffs for individual i, PF = returns to individual i for each unit of forest that remains intact, (Y – (yi + y– i)) = remaining forestland, PM = returns to individual i for growing maize on each unit of land, yi = individual i’s harvest decision, y– i = sum of other individuals’ harvests, yi

max = capacity constraint of the resource, Fee = penalty fee per unit of resource extracted over the imposed limit, and yreg = level of extraction permitted under the regulation.

However, there is a 0.01 chance that the person would be caught. As such, the expected

payoff under the regulatory approach was given by

Expected∏ = i PF * �Y − �yi + y−i�� + PM * yi − 0.01 * Fee * �yi − yreg�,

subject to 0 ≤ yi ≤ yi max.

Equation (3)

In the voluntary approach treatment, each group was also imposed with limits on

extraction. However, instead of the punishment for violation, a lump-sum payment was made for the group that complied with the limitation. As such, the payoff function was as follows:

∏ = i PF * �Y − �yi + y−i�� + PM * yi − Reward , subject to 0 ≤ yi ≤ yi max Equation (1)

where: Πi = payoffs for individual i, PF = returns to individual i for each unit of forest that remains intact, PM = returns to individual i for growing maize on each unit of land, yi = individual i’s harvest decision, y– i = sum of other individuals’ harvests, yi

max = capacity constraint of the resource, and Reward = lump-sum payment received when the group did not extract beyond the

imposed limit.

The individual payoff matrix reflecting this payoff setup and the instructions of the game appear in Appendices 1 and 2, respectively. The payoff was designed such that the Nash equilibrium (NE) outcome for rational and self-interested individuals would be the maximum amount of extraction possible (i.e., 80 units of the resource). The social optimum (SO) outcome would occur when no one extracts from the resource (yi = 0). If no extraction would be made by all the players in the group, then each player would be better off than each player extracting the maximum amount (yi = 80).

In both the lab-in-the-lab and lab-in-the-field experiments, the game was played in groups of

five people, with subjects randomly assigned into groups. In all groups (both in the lab-in-the-lab and in the lab-in-the-field), the subjects played with the same group of five people iteratively. In our setup, there were a total of eight treatments including the baseline treatment. All participants played the baseline CPR game with no interventions for eight rounds. Intervention was imposed in Round 9, and then continued on until the end of the game. Each group was randomly assigned to receive one treatment. In each round of the game, the extraction decision was private. The participants were not allowed to discuss (or communicate it in any other way) their strategy. However, feedback was given. After each round had been played, each participant got the information on his/her group’s level of extraction and the payoff he/she had received from that round. To ensure that the players would take


the game decisions seriously, the payoffs for all rounds were used in calculating the final compensation that the participant would get.

All players played for 16 rounds, although none of the players had advance knowledge of

this fact and expected the game to continue on after Round 16. Once the game ended, the subjects were asked to fill in a post-survey questionnaire on their socioeconomic characteristics, other personal information, and their social preferences. In all, 160 people participated in the lab-in-the-lab experiment. On the other hand, we sampled 145 people in the lab-in-the-field experiment.

4.0 RESULTS AND DISCUSSION

This section presents the findings of the lab-in-the lab and lab-in-the-field experiments. In majority of the cases examined, findings from the lab-in-the-lab and the lab-in-the-field indicate the same tendencies, although differences did exist in some of the cases. The section first discusses the findings from the punishment treatments, before moving on to discuss the findings from the reward treatments.

4.1 General Trends in Game Outcomes under Both Rewards and Punishment Treatments The extraction levels of lab-in-the-lab and lab-in-the-field experiments under different

treatments are shown in Tables 2 and 3, and Figures 1–4. The results of both lab-in-the-lab and the lab-in-the-field experiments indicate that extraction behavior did not follow the pure NE outcome even in the baseline rounds without any treatment imposed on individual players. In the lab-in-the-lab experiment, the average extraction level per person was 60.4, as opposed to the predicted NE of 80 in both the reward and punishment rounds. In the lab-in-the-field experiment, the average extraction level per person when no treatments were imposed was 39.6 in the groups assigned to receive the punishment treatments, and 43.3 in the reward treatments. Although the figure from the lab-in-the-field experiment was lower than that from the lab-in-the-lab experiment, both illustrate that the NE did not happen even when the groups were left to decide on their own on resource extraction with no rules imposed upon them. This suggests that the individuals exhibited some prosocial tendencies to cooperate even in social dilemma situations.

Once the rules had been imposed, there was a significant drop in the extraction level in the

very first round after the rules had been applied in all the treatment groups. This drop was consistent regardless of the setting of the experiment (i.e., lab-in-the-lab and lab-in-field). The drop was also seen in both the punishment and reward treatments. The drop was all the more remarkable due to the fact that the groups that continued to play the baseline game did not reduce their extraction level in Round 9, the first round after which the treatments had been imposed on the treatment groups.

This pattern is clearly depicted in all graphs. Figure 1 shows the average individual

extraction in the punishment treatments and the baseline in the lab-in-the-lab setting. Figure 2 shows the same information as in the lab-in-the-field experiment. Figure 3 shows the individual average extraction level under the reward treatments in the lab-in-the-lab experiment. Figure 4 shows the same information for the lab-in-the-field experiment. In most of the treatments, this initial drop is not stable, and average extraction level bounces back up in subsequent rounds.

- 12

- Op

timal

Regu

lator

y Des

ign in

the C

onte

xt of

Wea

k Enf

orce

men

t:

Do Re

gulat

ory S

tring

ency

and R

egula

tory

Appr

oach

Mat

ter in

Det

erm

ining

Com

mon

Pool

Reso

urce

Extra

ction

Beha

vior?

Tabl

e 2.

Ave

rage

indi

vidu

al e

xtra

ctio

n un

der r

ewar

d tr

eatm

ents

Roun

ds

NE

SO

Lab-

in-t

he-L

ab

Lab-

in-t

he-F

ield

1–

8 9–

12

13–1

6 1–

8 9–

12

13–1

6

80

0 60

.4

68.6

65

.1

43.3

45

.0

50.3

Lo

w s

trin

genc

y

(Gro

up to

tal o

f no

mor

e th

an 2

50,

aver

age

50 p

er p

erso

n)

80, 5

0 0

60.4

55

.1

63.3

43

.3

39.8

36

.6

Med

ium

str

inge

ncy

(Gro

up to

tal o

f no

mor

e th

an 1

50,

aver

age

30 p

er p

erso

n)

80

0 60

.4

42.1

52

.1

43.3

33

.3

22.4

Hig

h st

ringe

ncy

(G

roup

tota

l of n

o m

ore

than

0)

80

0 60

.4

46.3

64

.3

43.3

24

.7

25.3

Not

e: N

E =

Nas

h eq

uilib

rium

, SO

= s

ocia

l opt

imum

Ta

ble

3. A

vera

ge in

divi

dual

ext

ract

ion

unde

r pun

ishm

ent t

reat

men

ts

Roun

ds

NE

SO

Lab-

in-t

he-L

ab

Lab-

in-t

he-F

ield

1–

8 9–

12

13–1

6 1–

8 9–

12

13–1

6 N

o re

gula

tion

(80)

80

0

60.4

68

.6

65.1

39

.6

45.0

50

.3

Low

str

inge

ncy

(50)

60

0

60.4

53

.4

52.8

39

.6

22.8

26

.8

Med

ium

str

inge

ncy

(30)

60

0

60.4

55

.5

57.4

39

.6

24.5

30

.3

Hig

h st

ringe

ncy

(0)

60

0 60

.4

51.4

56

.5

39.6

21

.8

31.0

N

ote:

NE

= N

ash

equi

libriu

m, S

O =

soc

ial o

ptim

um


Figure 1. Average level of individual extraction under punishment treatments and baseline in the lab-in-the-lab setting

Figure 2. Average level of individual extraction in lab-in-the-field experiment under punishment treatments

Figure 3. Average level of individual extraction in lab-in-the-lab experiment under reward treatments

medium


Figure 4. Average level of individual extraction in lab-in-the-field experiment under reward treatments

4.2 Impacts of Different Levels of Regulatory Stringency

on Extraction Behavior under the Punishment Treatments The results of the game indicate that when punishment had been imposed, the average

player chose to extract neither at the NE level nor at the SO level. Although this statement is also true for the non-treatment control rounds, imposing the punishment rules reduced the average extraction rate of the participants involved. The results of the lab-in-the-lab experiment show that in the control rounds, the average extraction rate was 60.4, which is closer to the NE figure of 80 (Table 3). The extraction levels in all rounds after the treatment had been imposed still were between NE and SO, but were less than the 60.4 average from the control rounds. Furthermore, when compared with the data in Rounds 9–16 in the group that did not receive any treatment, it is clear that all the treated groups had lower average extraction rate when punishment was imposed. The same trend is true for the lab-in-the-field experiments, although the value of the average extraction prior to any treatment was lower at 39.6.

Data from both the lab-in-the-lab and the lab-in-the-field experiments under punishment

interventions show that imposing the toughest regulation may not be the best option to reduce the overall resource extraction levels in the long run. This finding follows from an analysis of both the lab-in-the-lab and the lab-in-the-field data. In addition to reporting the average resource use per round, the average resource use for multiple rounds is also reported in this study. The separation of the rounds into 9–12 and 13–16 was meant to show the outcomes in the short run (Rounds 9–12) and the long run (Rounds 13–16).

Data from the lab-in-the-lab experiment show that when the low-stringency condition was

imposed, the groups with the most lenient rule (low-stringency treatment) extracted an average of 53.4 in the first few treatment rounds, and used 52.8 in the subsequent treatment rounds. The medium-stringency groups extracted 55.5 and 57.4, on average, in Rounds 9–12 and 13–16, respectively. The groups with the toughest regulation, in which no extraction was allowed, dropped their extraction to 51.4 and 56.4 in Rounds 9–12 and 13–16, respectively. The numbers indicate that the toughest regulation brings about the highest drop in extraction level in the short run. However, extraction rebounded in the long run. In the final four rounds, the average extraction of this treatment was even higher than the average extraction under the low-stringency treatment.

medium


Information from the lab-in-the-field experiment under punishment interventions shows a similar trend to the lab-in-the-lab data. The drop in extraction level was greatest when the high-stringency treatment had been first imposed, but extraction rebounded. In the final four rounds, the average resource use rate under the high-stringency treatment (31) was higher than that in the low stringency treatments (26.8), and slightly higher than that in the medium stringency treatment (30.3). This provides further evidence to support our initial hypothesis that applying regulations with a low- to medium-level stringency could be more beneficial than imposing high-stringency regulations in terms of inducing a reduction in resource extraction in a CPR setting where monitoring and enforcement is weak but penalty is set at a high level. 4.3 Impacts of Regulatory Stringency on Compliance

under the Punishment Treatments In addition to examining the average extraction level, this study also looked at the impacts

of the different levels of regulation on compliance with rules. Since it is to be expected that compliance rate will be low when the regulatory stringency is high, this finding from our data is not of much interest. However, the trends in the compliance level over repeated rounds as a reaction to different interventions are of interest. Thus, this section focuses on the changes in the number of players who chose to stick to the limit imposed on their group over repeated rounds.

The average number of players who complied with the rule imposed in the lab-in-the-lab

experiment over the short run (Rounds 9–12) and long run (Rounds 13–16) has an interesting pattern. In the high-stringency treatment, not only was average compliance low (as expected), but the tendency of people to stick to this rule declined over time (Table 4). On the other hand, the low-stringency treatment not only showed the expected outcome of having a higher proportion of people complying with the rule, but it also exhibited an improving pattern over time. Thus, people faced with a low requirement to limit their resource extraction actually move more toward higher compliance as the game wore on. This provides further support for our hypothesis that most effective regulation need not be the strictest one, especially when one is operating in a context of weak regulatory monitoring and enforcement.

Findings from the lab-in-the-field experiment shown in Table 5 concur with that from the lab-in-the-lab experiment with regard to the pattern of lower compliance in repeated rounds of the high-stringency game. In the short run, on average, 1.31 people per group complied with the strict rule. However, in the long run, the compliance rate dropped to 0.81. This trend is also seen in the medium-stringency case. The drop in compliance level from short run to long run decreased more substantially in the lab-in-the-field setting than in the lab-in-the-lab setting. Nevertheless, they do share the same pattern. In the low-stringency case, data from the lab-in-the-field show that there was a slight decrease in the number of people per group who complied with the rule. Nonetheless, this decline was very slight (from 4.94 to 4.88), and indicates that people in the lab-in-the-field setting were willing to comply more than the people in the lab-in-the-lab setting did. Thus, these results further support the hypothesis that having the most stringent regulation do not necessarily lead to the best outcome. A regulation that is more in line with what individuals think they can achieve could actually prove better in inducing cooperative behavior in social dilemma situations.

- 16

- Op

timal

Regu

lator

y Des

ign in

the C

onte

xt of

Wea

k Enf

orce

men

t:

Do Re

gulat

ory S

tring

ency

and R

egula

tory

Appr

oach

Mat

ter in

Det

erm

ining

Com

mon

Pool

Reso

urce

Extra

ction

Beha

vior?

Tabl

e 4.

Ave

rage

num

ber o

f pla

yers

who

com

plie

d w

ith th

e ru

le in

the

lab-

in-t

he-la

b ex

perim

ent

Roun

d 9

10

11

12

13

14

15

16

9–12

13

–16

Low

str

inge

ncy

(50)

3.

50

3.50

3.

00

2.75

3.

50

3.50

3.

50

3.50

3.

19

3.50

M

ediu

m s

trin

genc

y (3

0)

1.75

1.

50

1.50

1.

25

1.75

1.

50

1.75

1.

00

1.50

1.

50

Hig

h st

ringe

ncy

(0)

1.25

1.

00

0.50

0.

75

0.75

0.

75

0.50

0.

25

0.88

0.

56

Tabl

e 5.

Ave

rage

num

ber o

f pla

yers

who

com

plie

d w

ith th

e ru

le in

the

lab-

in-t

he-fi

eld

expe

rimen

t

Roun

d 9

10

11

12

13

14

15

16

9–12

13

–16

Low

str

inge

ncy

(50)

5.

00

4.75

5.

00

5.00

5.

00

4.50

5.

00

5.00

4.

94

4.88

M

ediu

m s

trin

genc

y (3

0)

5.00

4.

00

4.00

3.

25

3.00

3.

50

3.75

3.

50

4.06

3.

44

Hig

h st

ringe

ncy

(0)

2.75

1.

00

0.75

0.

75

1.50

0.

75

0.50

0.

50

1.31

0.

81

17

Economy and Environment Program for Southeast Asia

4.4 Impacts of Different Levels of Regulatory Stringency on Extraction Behavior under the Reward Treatments In terms of the players’ average number of extraction, results of both the lab-in-the-lab and the

lab-in-the-field experiments show that the players chose to extract at a level that lies between the Nash equilibrium outcome and the social optimum outcome. This pattern holds for both the baseline rounds (Rounds 1–8) and the intervention rounds (Rounds 9–16). Without any intervention, the lab-in-the-lab experiment resulted in an average level of extraction of 60.4 units, which is close to (but lies below) the predicted NE outcome of 80 units. In the lab-in-the-field experiment, baseline extraction level was 43.3 on average.

In the context of rewards, findings from the lab-in-the-lab experiment indicate that the players

chose neither the NE outcome nor the SO outcome, but somewhere in between the two (Table 2 and Figure 3). The data also show a sharp drop in the first round after all reward interventions were imposed. This indicates that the announcement of the rule had a visible impact on the level of extraction when it had been first announced, and people were still unsure of how the players in their group would react to it. However, when the game was played repeatedly, the impact of the reward treatments seem to dampen over time. Nonetheless, different levels of the stringency requirement result in different patterns of resource use. Different patterns of resource extraction can also be seen in the data from the lab-in-the-field experiment, albeit the lab-in-the-field observations extracted less than the lab-in-the-lab setting.

Data from both the lab-in-the-lab and lab-in-the-field experiments indicate that the different

levels of regulatory stringency resulted in different effects on extraction over repeated rounds (Table 2 and Figures 3 and 4). Nonetheless, the pattern of the data from the lab-in-the-lab experiment is similar across treatments. In the lab-in-the-lab experiment, the pattern of a high drop in the first intervention round before rebounding can be clearly seen in all levels of intervention. The drop was highest in the first round for the intervention with the highest extraction requirement, followed closely by the medium-level stringency. The initial drop was lowest in the low-stringency condition. Despite these initial drops, data indicate that extraction climbed over time.

Breaking down the results of the lab-in-the-lab experiment into the short run (Rounds 9–12) and long run (Rounds 13–16) show that this pattern of initial drop and subsequent rebound (Table 2). In the short run, the average extraction in the medium-stringency case was lowest at 42.1, followed by the high-stringency case extraction level of 46.3, and the low-stringency case of 55.1. In the short run, all three levels of intervention resulted in extraction levels lower than that in the baseline. On the other hand, as the game was repeatedly played in the long run, the high-stringency level showed the sharpest increase in extraction level. The long run average for the high-stringency case was 64.3, which is higher than that in the baseline rounds (although it is lower than the no-treatment case in the long run). The low-stringency case also saw a rebound, finishing close to the high-stringency case in the long run at 63.3. The medium-stringency treatment seemed to work best in the long run. Even with the rebound, the long run extraction level was 52.1, the lowest of all three levels of regulation.

In the reward case, the findings from the lab-in-the-field are slightly different from that from

the lab-in-the-lab, although the pattern of differences in extraction levels under different levels of regulatory stringency still holds. In Figure 4, which shows lab-in-the-field experiment results, it is clear that the high-stringency treatment shows the greatest drop in extraction level in Round 9. Medium stringency and low stringency showed some drops in this round. The pattern in the lab-in-the-field data beyond this first round differs among treatments with different stringency levels. The rebound was highest in the high-stringency case, but then the data show that groups exerted efforts to meet the goal, resulting in a downward trend in extraction level. The low-stringency case shows similar, but more muted, pattern to the high-stringency case. The medium-stringency level shows the clearest


downward pattern of extraction levels, resulting in the lowest extraction in the final rounds. The medium-stringency level also shows that although groups were not able to meet the target in the first few treatment rounds, they still kept trying until finally being able to achieve the reward in the final few rounds.

When the game outcomes from the lab-in-the-field were divided into the short-term and the

long-term outcomes (Table 2), results show that the medium-stringency level had the best performance in achieving the lowest extraction level in the long-term at 22.4. This level was within the threshold to receive the reward. High stringency was second best at 25.3, although this level of extraction did not require the participants to receive the reward. In the low-stringency case, extraction was highest in the long run at 36.6 units. However, this level was high enough for the participants to receive the reward in this case. Thus, these results show that regulatory stringency did have an impact on inducing cooperation. In the lab-in-the-field setting, imposing reward interventions was effective in getting individuals to behave cooperatively in a CPR setting. However, medium-stringency level performed best in this case, getting people to extract less with less fluctuation in the extraction level between rounds.

Findings from the lab-in-the-field experiment indicate that rewards were effective at reducing

the overall level of extraction. In all stringency levels, extraction under treatment rounds was smaller than that in the baseline, and was certainly much smaller than the average extraction of the no-regulation groups in the same rounds. For example, in Rounds 9–12, the no-regulation groups averaged 45, whereas the low-stringency, medium-stringency, and high-stringency groups extracted 39.8, 33.3, and 24.7 units on average, respectively. The pattern is even more marked in the long run. In Rounds 13–16, the extraction of the no-regulation groups was 50.3, compared with the extraction of 36.6, 22.4, and 25.3 of the low-, medium- and high- stringency groups, respectively. Data from the lab-in-the-field also indicate that the groups kept trying to reach their target, even if they did not meet it in the first few treatment rounds. This can be seen from the downward trend of the high-stringency and the medium-stringency groups. The low-stringency groups also showed a small downward trend in their average extraction level despite the fact that the members were already within their targets from the first few rounds. Given that the long-run extraction level of the medium-stringency level was closest to the social optimum, this provides further evidence to support our hypothesis that setting regulatory stringency at the highest level do not necessarily lead to the best outcome in the context of weak monitoring and enforcement.

The continuous drop in extraction levels across all three types of interventions in the

lab-in-the-field setting, even when the group was not reaching the target in the first few tries, suggests that imposing rewards somehow motivated the groups to keep reaching the target. This could be because rewards provide an impetus for group members to learn that when each member reduces his/her extraction level, individual payoff rises. When players realize this, they exhibit more prosocial behavior. One possible explanation as to why the players in the lab-in-the-lab did not exhibit this downward pattern is that the average extraction level for the lab-in-the-lab groups was higher than that in the field. Thus, the players may not have been able to reduce their individual extraction enough to see that reductions in resource extraction individually would have resulted in higher payoffs for all members of the group. Since this learning effect did not kick in, the players in the lab-in-the-lab were less cooperative in the social dilemma setting. 4.5 Impacts of Regulatory Stringency on Compliance

under the Reward Treatments Similar to the punishment treatments cases, we now compare the pattern of compliance

behavior over repeated rounds of the game under the different levels of stringency. In the rewards treatments, the data from the lab and that from the field differ. When we look at the average

19


number of players in the group that chose to stick to the rule, we find that the number of group members who complied with the specified rule at the individual level falls over time. In the low-stringency case, the average number of players that complied in the short run was 2. However, in the long run, the average dropped to a mere 0.8, which means that less than one member of each group chose to stick to the rule even though the stringency was low. In the medium-stringency case, the average in the short run was 1. This dropped to 0.3 in the long run. In the high-stringency case, the short run average was 0.3. This dropped further to non-cooperation by any member (0) in the long run. The data indicates that none of the interventions led to higher compliance in the long run.

Data from the lab-in-the-field experiment show more cooperation. In fact, except for the

high-stringency case, the other two interventions all led to increased cooperation in the long run (Table 6). The medium-stringency case was the most successful at inducing cooperation among group members in the long run. The average number of members of the group that stuck to the rule increased most dramatically in the medium-stringency case, jumping from 1.5 persons to 4 persons in a group of 5 persons. In the low-stringency case, although, on average, the groups were able to meet their low target, the impact on increasing compliance rate was present, but it was not as high as in the medium-stringency case. In the short run, the average number of players who stuck individually to the target was 3.3 in the short run and 3.8 in the long run in the low-stringency case. This provides further evidence supporting the use of a less strict rule in the CPR context where there is weak monitoring and enforcement.

Table 6. Average number of players per group who comply with the rule

Level of Regulation Stringency Lab-in-the-Lab Lab-in-the-Field

9–12 13–16 9–12 13–16 Low stringency (250) 2.0 0.8 3.3 3.8 Medium stringency (150) 1.0 0.3 1.5 4.0 High stringency (0) 0.3 0.0 0.3 0.0

4.6 Observations on the Impacts of Regulatory Approach (Carrots vs Sticks)

Before making observations about the differences between the impacts under the carrots and the sticks approaches, note that we designed our interventions to reflect real-world differences in the application of sticks and carrots. In the punishment treatments, the limit to resource extraction under different stringency levels was imposed individually. Overextraction, when observed, was punishable by a hefty fine of 50 experimental units per unit of overextraction. However, this hefty fine was coupled with a low probability that any infarction would be observed (0.01 chance that any wrongdoing would be observed). In the reward treatments, the players needed to reach the group target in order to be rewarded. If the group meets its target, then the lump-sum payment would be made.

Within this setup, we find that the punishment results were more consistent across the lab

and the field settings. In both lab-in-the-lab and lab-in-the-field experiments, we find that the stick approach was able to reduce individual extraction to below the level as that in the control rounds under all levels of regulatory stringency. However, the low-stringency level was most effective in bringing about low resource extraction in the long run; the average in lab-in-the-lab was 52.8 (Table 7). Note that although the other stringency levels resulted in a rebound in extraction rates from short run to long run, only the low-stringency treatment saw a lowering of average extraction when transitioning from the short run to the long run in both the lab-in-the-lab and the lab-in-the-field results (see Table 7 and Table 8).


Table 7. Average individual extraction under punishment and reward treatments in the lab-in-the-lab experiment

Level of Regulation Stringency SO Punishment Reward

1–8 9–12 13–16 1–8 9–12 13–16 No regulation (80) 0 60.4 68.6 65.1 60.4 68.6 65.1 Low stringency (50) 0 60.4 53.4 52.8 60.4 55.1 63.3 Medium stringency (30) 0 60.4 55.5 57.4 60.4 42.1 52.1 High stringency (0) 0 60.4 51.4 56.5 60.4 46.3 64.3

Table 8. Average individual extraction under punishment and reward treatments

in the lab-in- the-field experiment

Level of Regulation Stringency SO Penalty Reward

1–8 9–12 13–16 1–8 9–12 13–16 No regulation (80) 0 39.6 45.0 50.3 43.3 45.0 50.3 Low stringency (50) 0 39.6 22.8 26.8 43.3 39.8 36.6 Medium stringency (30) 0 39.6 24.5 30.3 43.3 33.3 22.4 High stringency (0) 0 39.6 21.8 31.0 43.3 24.7 25.3

Although the results of the reward interventions contain some dissimilarity across lab-in-the-lab and lab-in-the-field settings, the results were consistent in the fact that the level of stringency that achieved the best long run result was the medium level. In the lab, the medium-stringency level achieved the lowest extraction level in the long run at 52.1 (Table 7). In the field, the medium-stringency level also induced the participants to extract at the lowest level at 22.4 (Table 8).

In addition to the information presented in Tables 7 and 8, the average individual extraction under different regulatory approaches was also plotted with the type of regulatory stringency. The graphs for the stick treatment (labeled “penalty”) and the carrot treatment (labeled “reward”) that resulted from the lab-in-the-lab experiment are plotted in Figure 5. Similar information from the lab-in-the-field is plotted in Figure 6.

Figure 5. Average individual extraction level under sticks and carrots in the lab-in-the-lab

21


Figure 6. Average individual extraction level under sticks and carrots in the lab-in-the-field

Both sets of plots show an interesting pattern. High stringency did not lead to the lowest level of resource extraction in any of the cases—lab-in-the-lab or lab-in-the-field, penalty or reward. This provides strong evidence to support our hypothesis that a lower level of stringency requirement works better at inducing people to reduce their extraction level in the context of weak monitoring and enforcement.

The graphs also show the differences between reward and penalty treatments. In the reward treatments, medium-level stringency induced the lowest level of resource extraction, thereby being most useful in inducing cooperation in the CPR social dilemma. In the punishment treatments, setting the stringency at a low level was most conducive to prosocial outcomes. Data from the lab show that, in the long run, there is comparability in the average extraction level between the punishment intervention coupled with low stringency, and the reward intervention coupled with medium-level stringency. Extraction level in the former case was 52.8, whereas in the latter case, it was 52.1 (Table 7). Data from the field show a similar pattern, with extraction level under punishment and low stringency at 26.8, and resource use under reward and medium stringency at 22.4. These results indicate that the reward treatments require a higher set of stringency to be as effective as the punishment treatment. This finding is, of course, subjected to the caveats on the setup of the reward and punishment interventions explained earlier. 4.7 The Role of Gender

We also examined the gender aspect by looking at the average extraction level in both

punishment and reward treatments. Under the stick treatments, the women appeared to be more sensitive to the level of regulation. In both the short run and the long run, the women extracted the least when the level of stringency was high. In the long run, high level of regulation resulted in the least extraction; this is followed by low-regulatory stringency, and medium-level stringency. The findings indicate that the men were not as sensitive as the women to the level of regulatory stringency. In the long run, the low-stringency level achieved the greatest cooperation in the CPR setting among men, followed by medium-level stringency. High-stringency level led the men to extract most when coupled with punishment treatments in a context of weak monitoring and enforcement (Table 9).


Table 9. Average extraction level in punishment treatments by gender

Level of Regulation Stringency

Rounds 1–8 Rounds 9–12 Rounds 13–16 Male Female Male Female Male Female

No regulation 58.3 63.6 68.9 68.4 63.9 66.1 Low stringency (250) 58.3 63.6 47.5 59.5 48.9 57.0 Medium stringency (150) 58.3 63.6 53.1 55.3 50.8 61.0 High stringency (0) 58.3 63.6 48.3 52.7 64.6 53.0

In the reward treatments, the responses to the different level of stringency differ from the

punishment case. In the women’s group, a medium-level stringency worked best in achieving the smallest extraction level on average. This is followed by extraction under high regulation and low regulation, respectively. In the men’s group, results in the long run indicate that low-stringency level was best in terms of inducing cooperative behavior, followed by the medium-stringency level, and then the high-stringency level (Table 10). These findings imply that the men and women in our experiment reacted somewhat differently to the different levels of regulatory stringency under punishment and reward interventions. Table 10. Average extraction level in punishment treatments by gender

Level of Regulation Stringency

Rounds 1–8 Rounds 9–12 Rounds 13–16 Male Female Male Female Male Female

No regulation 58.3 63.6 68.9 68.4 63.9 66.1 Low stringency (250) 58.3 63.6 46.7 58.8 58.8 65.2 Medium stringency (150) 58.3 63.6 50.6 40.3 60.0 49.5 High stringency (0) 58.3 63.6 41.1 49.0 63.2 64.8

4.8 Game Outcomes, Social Preferences, and Behavior Outside the Game

To obtain an idea whether the game outcomes would corroborate with actual choices

made, we asked the participants to fill in a questionnaire that included a section on their social preference. Given the settings and the timeframe of our research, it was not possible to obtain other information on the behavior of the participants except for what can be obtained from the questionnaire. In order to elicit individual social preference, we based our questions on the global preference survey of Falk et al. (2016), and then modified them to suit our settings. The questions from Falk et al. (2016) aimed to determine the respondents’ key economic preferences, which include social preference. The questions were experimentally validated. In our work, we asked the respondents to rank their answers on the different social preference statements on a scale of 1 to 7. We then compared this self-assessment to the game outcomes from both the lab-in-the-lab experiment section of our work and the lab in the lab-in-the-field section.

In comparing the individual social preference answers to game outcomes in our study, two

questions were especially relevant. The first asked the respondents to rate their contribution to their community. For university students, community refers to the university community. For villagers, community refers to their village. The second question asked the respondents to rate their agreement with the statement that they would be willing to help others without receiving anything in return. Both questions asked the respondents to rate themselves on a scale of 1 to 7. The answers were then converted into dummies: equal to 1 if the respondents rate themselves highly (higher than the median value of 4), and equal to zero if otherwise. This was done for both questions. Each dummy was then used to divide the game outcomes into two groups, and a t-test

23


of mean differences was applied to test whether or not the two groups’ mean extraction levels were statistically significant.

Analysis indicates that there is a difference between the mean extraction levels of the

individuals in the two groups, albeit the strength of the relationship is weaker for the villagers than for the students. For students, those who rated themselves as contributing more to the community tended to extract less than those who rated themselves as contributing less to the community. Furthermore, individuals who said that they were less willing to contribute to society without receiving anything in return (i.e., those who were less prosocial) had a higher mean extraction level (4.49) than those who said they were more willing to contribute to society. In the lab-in-the-field data from villagers, there is agreement on the direction of the difference, but the result is weaker than that in the student sample (Table 11).

In our lab-in-the-field experiment, we sampled two different types of villages. The first

three villages sampled are from areas that conserved the forest. The last three villages sampled are from areas that did not conserve the forest. In both types of villages, we also asked the respondents how much land they held on steep slopes. We used this steep-slope landholding as an indicator of individual forest encroachment. In our study area, such types of land would be off-limits to individual landholders. To determine the corroboration of choices made in the game with actual choices, we checked to see whether the average extraction from those coming from the two different types of villages would have significant statistical difference. We also checked to see whether there was any difference between the choices within the game for those who held land on steep slopes (i.e., those who encroached on forestland) and for those who did not.

Results indicate that those respondents from villages who do not extract from the forest

tended to extract less in the game. Furthermore, those who owned land on steep slopes also tended to have a higher extraction level when compared with those who were not landowners (Table 11). Table 11. Mean difference between groups

Mean difference between groups Lab-in-the-Lab Lab-in-the-Field Contribution to community vs No contribution 2.50** 1.91* Willing to contribute to society even without

receiving anything in return vs Less willing to contribute to society

4.49*** –1.42

Belonging to a village that does not conserve the forest vs Belonging to a village that does

– 1.55**

Farms on the highlands (encroachment) vs No landholding on the highlands

– 1.42*


5.0 CONCLUSION This research aimed to study regulatory design in a common pool resource setting within

the context of weak monitoring and enforcement. The main focus of this study was two-fold: (1) to study the impact of the different levels of regulatory stringency and (2) to look at the outcome when different approaches are used to deliver regulations. The paper also looked at the role of gender, and also tried to determine whether experimental outcomes would corroborate with actual outcomes. We used the experimental economics method to study the behavior of individuals in a common pool resource game with multiple treatments. Both lab-in-the-lab and lab-in-the-field experiments were implemented. The same CPR game was played both in the lab (with students) and in the field (with villagers who engage in farming and live near the forest).

One of the key findings of this work is that low-level and medium-level regulatory

stringency bring about greatest cooperation in the CPR social dilemma situation where monitoring and enforcement is weak. On the other hand, setting regulations at a high level rarely results in the desired outcome even when the regulation is set at the social optimum level. This study finds that the average extraction per person was higher under the high-stringency treatments than in the low-level and medium-level stringency treatments. This result holds for the experiment in the lab-in-the-lab with university students and in the experiments in the lab-in-the-field with actual villagers who share usage of the commons on a daily basis. This result is also robust to the approach of enforcing the regulation (i.e., punishment or reward).

One other key finding of this research is that incentives matter in inducing cooperation;

however, negative and positive incentives work in different ways. In our study, in situations where sticks and rewards were applied, the extraction level of individual players dropped to a level lower than that in the control rounds where no limit was imposed. However, under the punishment treatments, the low-stringency level worked best in getting the players to play cooperatively and in reducing their extraction level. When reward had been used to induce cooperation, the players in the medium-level regulatory stringency achieved the lowest extraction per individual. Although social optimum (no extraction) was not achieved in any of the treatments, the low and medium levels of regulatory stringency did bring about the closest extraction level to the social optimum under the stick and carrot treatments, respectively.

In terms of gender, this study finds that there are differences in the responses of men and

women to the different levels of regulatory stringency and regulatory approach. In the experiments, when the stick approach was used, the women were more likely to reduce their extraction than when there was high stringency. In the men’s group, low stringency worked best when it was coupled with the stick of punishment. When the carrot approach was applied, the medium-level stringency worked best in reducing the average individual extraction in women in the long run. In the men’s group, the low-level regulatory stringency worked best, although the variations among the outcomes with treatments were not high for men. Furthermore, there was a general tendency for women to extract more from the resource than men. In terms of game outcomes and real choices, the study finds some evidence to support that game outcomes corroborate with actual decisions. Both in the lab-in-the-lab and lab-in-the-field settings, individuals reporting prosocial behavioral traits tended to extract less of the resource. However, the results from the lab-in-the-lab setting were stronger than in the lab-in-the-field setting.

Other interesting findings from this study include the strong reaction of the players to the

imposition of rules. When the rule had been first imposed, extraction dropped in all treatment groups. The size of the drop depended on the nature of the intervention. After this drop, there were usually rebounds in the extraction rate. The size of the rebound varied depending on the treatment; in general, the treatment rounds resulted in lower extraction. The biggest drop in the

25


first round after the intervention had been imposed was due to the high stringency rule. However, these treatments also showed the biggest rebounds after the initial drop.

This paper also finds similarities and differences between the results of the lab-in-the-lab

and lab-in-the-field experiments. The trends in the results were similar across lab-in-the-field and lab-in-the-lab settings. However, the main difference between the two was the size of extraction. University students chose to extract a lot more than the villagers did even without any treatment, and were less reactive to the reward provided for cooperative behavior.

In summary, we find evidence to support the conclusion that the level of regulatory

stringency and the regulatory approach matters in inducing prosocial behavior in the common pool resource setting within the context of weak monitoring and enforcement. High level of regulatory stringency did not lead to the lowest extraction rate in any of our treatments. However, this approach is often adopted in many forest management settings in the real world when the government uses the command-and-control tool.

We find that setting regulations at the low or medium levels of stringency can obtain

better results in inducing cooperation in the CPR social dilemma situation. We also find that incentives are effective at reducing extraction regardless of the approach. However, the incentives work in different ways and interact with stringency levels. Rewards work best when initial extraction is not high, coupled with medium-level regulatory stringency. Punishments work best with low-level regulatory stringency. These findings have implications for the design of forest management settings in the real world.


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APPENDICES Appendix 1. Payoff Matrix Table A1. Individual payoff matrix

Others’ Extraction

Your Extraction 0 10 20 30 40 50 60 70 80

0 200 216 231 245 259 273 285 297 309 10 196 211 226 240 254 267 279 291 302 20 192 207 222 236 249 262 274 285 296 30 188 203 217 231 244 256 268 279 290 40 184 199 212 226 238 251 262 273 283 50 180 194 208 221 233 245 256 267 277 60 176 190 203 216 228 240 250 261 270 70 172 186 199 211 223 234 245 255 264 80 168 181 194 206 218 229 239 249 258 90 164 177 189 201 212 223 233 242 251

100 160 173 185 196 207 218 227 236 245 110 156 168 180 191 202 212 221 230 238 120 152 164 176 187 197 207 216 224 232 130 148 160 171 182 192 201 210 218 226 140 144 156 166 177 186 196 204 212 219 150 140 151 162 172 181 190 198 206 213 160 136 147 157 167 176 185 192 200 206 170 132 143 153 162 171 179 187 194 200 180 128 138 148 157 166 174 181 188 194 190 124 134 143 152 160 168 175 181 187 200 120 130 139 147 155 163 169 175 181 210 116 125 134 142 150 157 163 169 174 220 112 121 130 138 145 152 158 163 168 230 108 117 125 133 140 146 152 157 162 240 104 113 120 128 134 141 146 151 155 250 100 108 116 123 129 135 140 145 149 260 96 104 111 118 124 130 134 139 142 270 92 100 107 113 119 124 129 133 136 280 88 95 102 108 114 119 123 127 130 290 84 91 97 103 108 113 117 120 123 300 80 87 93 98 103 108 111 114 117 310 76 82 88 93 98 102 105 108 110 320 72 78 84 89 93 97 100 102 104


Appendix 2. Experimental Instructions

Introduction

This experiment tries to replicate a situation where a group of families make decisions about the amount a family should use a resource that is shared, e.g., water, fishery, land, forest, or any other similar cases. You will be divided into groups of five people, and you will play one game for many rounds. Each round represents one year or one harvest season. In each round of the game, you will decide on how much to extract from the resource. At the end of the game, you will earn some prizes. Your prize will depend on the quantity of points you accumulate after several rounds.

Payoffs To play the game, you will be given two things. The first thing you need is a payoff table

like this one.

[SHOW PAYOFF TABLE] The payoff table gives you all the information you need to play the game. The top row of

the table shows your level of extraction. The left column of the table shows the sum of the extraction level of other people in your group. The numbers in the table correspond to the points that you will get in each round.

You will have to decide on the level of extraction (from 0 to 80). Your point for each round will depend on your level of extraction and the extraction level of other people in your group (shown in the left column). I will give you one minute to familiarize yourself with the table.

The second thing you need is a table to write your decision and record your payoff.

[SHOW CARD]

In each round, you will choose how much to extract from the forest by writing your level of

extraction in Column A of the table, and then give the card back to us. Your decision is private, and you do not have to tell your group members if you do not want to. You will know your level of extraction and your group’s level of extraction, but you may not know your group members’ level of extraction.

When we get the table from you, we will calculate your point for that round for you based

on the payoff matrix. Once we write down your point on your answer sheet, we will return the sheet back to you. Your final prize will depend on your total score.

31

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Treatments Now, the rules of the game will change a bit. Some of you might be asked to limit your

extraction to a certain level. The coordinator will give you this. If you use the resource over the limit, then you will be fined. However, the fine is only imposed when there is inspection. The probability of being inspected is 1%, and we determine that by drawing lots.

[SHOW SUGUS BAG]

If inspection occurs and you broke the law, then the fine is (experimental) THB 50 per unit of extraction above the limit. This amount will be subtracted from your payoff.

If you have any questions about the game, please ask us NOW.

*** END OF INSTRUCTIONS***

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