Groundwater management institutions in the face of rapid urbanization – Results of a framed field experiment in Bengaluru, India

Ecological Economics 166 (2019) 106432

Contents lists available at ScienceDirect

Ecological Economics journal homepage: www.elsevier.com/locate/ecolecon

Analysis

Groundwater management institutions in the face of rapid urbanization – Results of a framed field experiment in Bengaluru, India

T

Johannes Wegmann , Oliver Mußhoff ⁎

University of Goettingen, Farm Management Group, Department of Agricultural Economics and Rural Development, Faculty of Agricultural Sciences, Georg-AugustUniversität Göttingen, Platz der Göttinger Sieben 5, D-37073 Göttingen, Germany

ARTICLE INFO

ABSTRACT

Keywords: Common pool resource management Monitoring Sanctioning Urbanization

Many aquifers in semi-arid and arid regions with rapid urbanization are over-exploited or even at the point of depletion. Driven by the increased demand for food and other agricultural products, irrigated agriculture constitutes the biggest user of groundwater, and has thus contributed to this critical situation. In this paper, we compare different designs of groundwater management institutions in order to avoid aquifer over-exploitation and ensure secure water sources. We assess externally imposed reward-based and punishment rules as well as communication on their effectiveness to reduce water extraction behavior of groundwater users. Moreover, we evaluate how different user types affect the outcome of these institutional designs. To do so, we conducted a framed field experiment with 600 households along the rural-urban interface of the fast growing city of Bengaluru, India. Results indicate that all treatments can prolong the life of the resource but reward-based and punishment rules seem to be more effective than communication. Moreover, we find that user type behavior identified in the baseline trial is persistent in the treatment trial despite interventions.

1. Introduction In many arid and semi-arid areas, groundwater is the most important freshwater source. However, many of these aquifers are overexploited (Famiglietti, 2014). One reason for the over-use of aquifers is the rising demand for water in emerging cities, which are mainly located in semi-arid areas (Khan et al., 2016). In addition to the growing domestic and industrial water demands, most freshwater is still used in irrigated agriculture to satiate the increased demand for agricultural products (Ross and Martinez-Santos, 2010; Siebert et al., 2010; Shah, 2014). Moreover, proximity to cities increases marketing opportunities and makes intensive, irrigated agriculture more attractive to farmers. To avoid a fast depletion of groundwater in proximity to emerging cities, groundwater governance is needed. The governance of groundwater is challenging due to the invisible boundaries and the uncertainty of the stocks and flows of underground water (Ostrom, 1990). Moreover, groundwater is a common-pool resource (CPR) as it is subtractable but users cannot be excluded. Therefore, each users' decision could result in externalities experienced by other users. This creates a social dilemma situation in which short-term profit maximization leads to a fast depletion of the resource. In order to prolong the life of the resource, users would need to relinquish some of their immediate profits. ⁎

The literature has identified different groundwater management institutions to avoid this social dilemma situation. One large strand in the literature sees an important role for governmental or external regulation for ensuring the sustainable use of the resource (Schlager, 2007; Ross and Martinez-Santos, 2010). The authors argue that collaboration of local resource users with regional or national governance entities can account for uncertainties that resource users face. In particular, institutions which monitor and sanction resource users are considered effective at ensuring the sustainability of the resource (Cox et al., 2010). However, not all researchers share this view. Rules and regulations which are imposed from outside might even cause the opposite of the intended outcome if the intrinsic motivation to prolong the life of the resource is crowded out (Cardenas et al., 2000; Vollan, 2008). Therefore, another strand argues that strengthening cooperation and collective action should be taken into account when designing effective management institutions, in particular when governmental groundwater regulation has limited capacity (Ostrom, 1990; Poteete and Ostrom, 2004; Meinzen-Dick et al., 2016). Thus, further insight into the design of groundwater management institutions that ensure sustainable use of the resource is needed. Whether the design of a management institution is effective also depends on the user behavior. In a theoretical model approach, Madani and Dinar (2012a, 2012b) have shown that without regulation a risk-

Corresponding author. E-mail addresses: [email protected] (J. Wegmann), [email protected] (O. Mußhoff).

https://doi.org/10.1016/j.ecolecon.2019.106432 Received 3 January 2019; Received in revised form 11 July 2019; Accepted 6 August 2019 0921-8009/ © 2019 Elsevier B.V. All rights reserved.

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

averse myopic user would deplete the resource quickly leading to a tragedy of the commons situation. Still, users who take into account the long-term effects and externalities of their decision-making would manage a CPR sustainably, yet may increase extraction if an intervention is applied that lowers their long-term revenues. While different studies have already shown that resource users do not always act in an ignorant and myopic way (Salcedo, 2014), the implications for the design of CPR management institutions becomes more complex when also considering different user behavior types. An additional challenge for the design of effective management institutions stems from the process of urbanization. There is little knowledge regarding the effect urbanization has on other background processes, such as social norms and collective action. Neglecting these background processes might result in inefficient management institutions (Castillo et al., 2011). For instance, Prediger et al. (2011) show that differences in the experience of cooperation or ecological preconditions require a different institutional setup for managing a CPR sustainably. Therefore, a better understanding of how urbanization affects the performance of groundwater management institutions can help to craft a more appropriate institutional setup. Given the importance of groundwater governance in general, and in the context of urbanization in particular, it is important to craft effective groundwater management institutions. Therefore, the aim of the paper is threefold: Firstly, we evaluate three different designs of management institutions with regard to their effectiveness to prolong the use of groundwater: a reward-based and a punishment rule which are imposed externally, as well as communication. Secondly, we analyze how different user types affect the outcome of these institutional designs. Thirdly, we assess the performance of these institutional designs along the rural-urban interface. In order to achieve these objectives, a framed field experiment was conducted with 600 participants along the rural-urban interface of Bengaluru, India. All participants depend on groundwater as their only water source. We chose Bengaluru, India for our experiment because India is the largest user of groundwater globally (Wada et al., 2012) and the city of Bengaluru exemplifies cities with rapid growth in population and middle-class consumption. Moreover, a big part of the city's demand for agricultural products is satisfied by local irrigated agriculture. Over the past three decades, the groundwater level has drastically fallen and many aquifers are over-exploited (Srinivasan et al., 2017). So far, effective groundwater management institutions are not in place, which makes ex-post analysis unfeasible to evaluate institutions and calls for different methods. Framed field experiments are commonly proposed for ex-ante policy evaluations (List and Price, 2016) and are frequently used in the analysis of CPR issues (Anderies et al., 2013; Janssen et al., 2015). However, there are only a few examples thus far in the context of groundwater use (Salcedo, 2014; Meinzen-Dick et al., 2016; Meinzen-Dick et al., 2018). Lab experiments have been used more frequently in this context. However, the level of external validity of lab experiments often remains unclear because they are often conducted with convenience groups. For example, Salcedo (2014) shows that groundwater users are more pro-social and less myopic than students. Conducting experiments with actual users can also reveal systematic differences between users of the resource and non-users, making the inference more robust towards real-world applications (Buchholz et al., 2016). Few studies have considered the effects of urbanization on water demand in adjacent peri-urban and rural areas, in particular on agricultural water demand. We contribute to the literature with an analysis of the effect of urbanization on the design of groundwater management institutions. Moreover, this is the first framed field experiment in the context of urbanization. Using this analysis, we provide insights for policy makers on household decision-making in the context of rapid urbanization. These insights can be used to design adequate institutions. The remainder of the article is as follows: Firstly, the literature on

the effectiveness of groundwater management institutions is reviewed. Secondly, a theoretical model and benchmark scenarios related to the framed field experiment are described. Thirdly, the experimental design as well as the study region is introduced. Lastly, the results are discussed while the final chapter concludes. 2. Literature review on the effectiveness of groundwater management institutions There is a vast literature on how institutional arrangements can lead to a sustainable management of resources. These institutions range from governmental intervention to collective actions. Institutions are understood as informal and formal rules or regulations which encourage individuals to utilize CPR over long periods of time (Ostrom, 1990). As important design principles, (i) monitoring and (ii) sanctioning have been identified in the literature as essential to crafting robust CPR management institutions (Ostrom, 1990; Cox et al., 2010). In (i), a regulator monitors the appropriation of the user as well as the condition of the resource. In (ii), graduated sanctions are likely to be applied to those who violate a rule. Results of framed field experiments have shown that external monitoring and sanctioning can have a stabilizing effect on collective action towards the sustainable use of a resource when they are perceived to be supportive in terms of minimizing the information asymmetries about other users' behaviors (Cardenas et al., 2000; Frey and Jegen, 2001; Narloch et al., 2012). A punishment, for example, is perceived to be supportive if trust levels and the degree of self-determination within the group are low. Similar to sanctions, positive reinforcement of rules and regulation by rewards can crowd-in pro-social behavior if users act mainly selfishly. In this case, a reward makes it less attractive to free ride and provides a reciprocal signal to other group members (Vollan, 2008; Narloch et al., 2012). However, the literature has also acknowledged that not all institutional arrangements lead to the same outcome. In fact, a poorly designed institutional arrangement can have perverse outcomes and even lead to faster degradation of a CPR. One reason is that external regulators often suffer from imperfect information when imposing policies and thus may crowd-out the intrinsic motivation to prolong the life of a CPR (Cardenas et al., 2000). Framed field experiments have shown that this is the case if strong social norms already exist. The crowding-out effect is supposedly larger under punishment than under reward-based scenarios (Cardenas and Carpenter, 2008). Aside from the problem of crowding-out, external monitoring and sanctioning is often very costly. As an alternative, researchers have shown in various CPR settings that improved communication alone can enhance collective action. This enables communities to solve social dilemmas on their own by finding internal arrangements. However, the success of these internal mechanisms is sensitive to group dynamics, such as income inequalities (Ostrom, 2010). While social norms at the group level play an important role, it has been shown that institutional designs are also very sensitive to different user behavior. Three user behaviors can be distinguished: (1) myopic, (2) individual rational and (3) social optimal (Suter et al., 2012). In a CPR setting, extraction decisions are interdependent with other users' decisions and have a long-term effect on the usage of the resource. Within this setting, the (1) myopic user maximizes immediate profits but does neither take into account decisions made by others nor longterm appropriation. The (2) individual rational user maximizes profits, taking into account the long-term impacts of current extraction but ignores the consequences of their behavior on others. The (3) social optimal user maximizes profits while considering the actions of others and the long-term implications of today's actions. Studies, which use theoretical models, have shown that different institutional designs achieve different outcomes according to the users' behavior (Madani and Dinar, 2012a, 2012b, 2013). For example, a groundwater quota imposed by an external regulator is most effective in terms of prolonging the life of the resource when users are myopic, while it is 2

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

less effective when they behave in a social optimal way and extraction rate are already lower (Madani and Dinar, 2013). Users can even achieve a long-term use of the CPR by extracting heuristics and learning. This is the case when they take into account long-term consideration and externalities (Madani and Dinar, 2012b). Thus, understanding the users' behavior is crucial in designing effective groundwater management institutions. To date, little research has been done so far on the influence of urbanization on household behavior and institutional processes, therefore the effects are unclear. Nevertheless, the background setting is relevant for the success of CPR institutions. Prediger et al. (2011) show that cultural and ecological preconditions determine the willingness to cooperate in collective action problems. Though the overall effect of urbanization remains ambiguous. On the one hand, social norms in urban areas might not be as strong as in rural sites. As social norms, such as trust, need time to evolve, they might be less evolved where in-migration occurs (Ostrom, 2000). On the other hand, other household income sources in more urbanized areas decreases the dependency on agricultural production. A long-lasting resource might be of more importance for these households than maximizing profits in the short-run. Considering the effects of urbanization on the design of the groundwater management institutions, external punishment and rewards are more likely to account for less evolved or missing social norms (Rodriguez-Sickert et al., 2008). Therefore, we would expect to see a more pronounced effect of external regulation in more urbanized areas while internal arrangements have a stronger effect in areas which are less urbanized.

where r denotes the natural recharge in each period which is considered to be constant, A denotes the area of the aquifer, S the storativity and n the number of users. For the experiment, the model introduced here was operationalized such that the parameters met the local conditions. The values were taken from the Central Groundwater Board of Bengaluru (CGWB, 2015) (see Table A1).1 3.2. Benchmark scenarios Following Suter et al. (2012) as well as Feinerman and Knapp (1983), the underlying model can be used to define three benchmark scenarios for the myopic, individual rational, and social optimal user as described in Section 2. Given these definitions, the benchmark for the myopic type can be derived as follows. As the myopic user type maximizes his/her profits in every period, the first derivative of the profit function with respect to the quantity of water pumped is taken. This yields the amount of water pumped for each period t:

d t =0 dx t

In order to assess the effectiveness of different designs of groundwater management institutions, a theoretical model is needed which will be the basis for the experimental design later on. The model is described in the first part of this section. Additionally, different benchmark scenarios for the myopic, individual rational and social optimal user are derived from the model. This makes up the second part of this section.

max

2

dt x it

dt + 1 = dt +

AS

2

x it2

dt x it

s. t. d (t + 1) = dt +

n i = 1x it

r

AS

where δ denotes the discount rate. Solving Eq. (4) and tacking into account that the rational user takes the decision of all other users as given, the Lagrangian yields:

L =

(1 + )

t

xt

t=0

xt r AS

dt +

2

xt2

dt xt + (1 + )

1

t+1

dt + 1

(4a)

with resulting first order conditions for xt, dt and λt

x t = dt (1 + )

(1)

dt + 1

where πit denotes the profit of decision maker i in period t; α and γ are parameters of the benefit function, and xit is the quantity of the groundwater applied. The form of the benefit function is quadratic as an excessive use of water would reduce the yield. The third term of the equation denotes the cost function with the cost parameter ϕ and the depth to groundwater dt in period t. The depth to groundwater is determined by the underlying geohydrological dynamics of the aquifer for which the bathtub model was chosen. The bathtub model is a highly simplified model where extraction of one member influences all other group members equally in the next period (Suter et al., 2012; Liu et al., 2014). Using the bathtub model for the geohydrological dynamics, the depth to groundwater function is given by: n x i = 1 it

xit

(4)

The underlying model of the CPR experiment consists of an economic and a geohydrological model, which are interlinked. The economic model consists of a profit function with groundwater as the only input and depth to groundwater level as the only cost-influencing factor. Following Suter et al. (2012), Gardner et al. (1997), and Feinerman and Knapp (1983), the profit function for each participant has a quadratic form:

xit2

t

(1 + ) t=0

3.1. Underlying model of the CPR experiment

= x it

(3)

by definition Eq. (3) does not include any long-term or externality considerations. The individual rational user type takes into account long-term considerations but ignores the externalities, so optimization for this user type becomes a dynamic problem. Assuming an infinite planning horizon, the rational user type maximizes the sum of all discounted profits:

3. Theoretical model and benchmark scenarios

it

dt

xt =

1

t+1

dt =

t+1

(1 + ) AS t

= xt

nxt r . AS

(4b) (4c) (4d)

Assuming stationarity for the Lagrange multipliers λ = λt = λt+1, i.e. future and current marginal private cost associate with a marginal increase in pumping remains constant, and solving for the pumping rate in period t yields:

xt =

dt AS

.

(5)

The optimization problem of the social optimal user is similar to the problem of the individual rational user but it differs in that the social 1 Note that the use of the bathtub model was necessary for the ease of explanation and to keep the execution of the experiment within a reasonable time frame. Therefore, other geohydrological factors were not considered. See Brozović et al. (2006) for a discussion.

r (2) 3

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

optimal user takes into account the discounted profits for all n users:

given period and the individual choice of pumping hours. The initial value of the groundwater level was 100 ft. The water level of the each subsequent period was determined by the group members' pumping choices. The profits of each period were recorded individually on a separate sheet. These profits were later translated into remunerations. In the baseline trial, no additional rules were imposed. For the treatment trial, one out of four different management institutions was introduced to each group:

L (1 + )

=

t

n

xt

t=0

dt +

nx t r AS

dt + 1

2

xt2

dt xt + (1 + )

1

.

t+1

(6a)

The first order conditions for xt and λt are the same as in Eqs. (4b) and (4d), respectively but for dt it becomes

(1 + )

1

t+1

t

= n xt

• Reward rule : Per period, a reward of 200 tokens was paid out if 3

(6b)

where n indicates the total number of decision makers in the group. Solving for the optimal pumping rate in period t yields

xt =

dt n AS

(7)

(Feinerman and Knapp, 1983; Suter et al., 2012). 4. Experimental design To analyze the design of different management institutions, a CPR group experiment framed in the context of groundwater extraction was conducted. Aside from the CPR experiment, a Holt-and-Laury lottery (HLL) for risk preference elicitation was carried out. The HLL took place two to three weeks prior to the CPR experiment within an extensive socio-economic survey. The following section describes the two experiments briefly and introduces the study region, as well as the sampling process. 4.1. CPR experiment design The framed field experiment is a dynamic CPR experiment similar to the work of Janssen et al. (2012), and Meinzen-Dick et al. (2016). The experiment was designed as a group experiment, as groundwater extraction decisions from one aquifer are interlinked. In the experiment, each participant became an agricultural entrepreneur who privately managed a hypothetical farm for a certain period of time. To conduct the experiment, four members in a village were randomly selected (see sampling procedure in Subsection 4.3) and grouped at a central point. In each urban ward/village, two to three groups were selected depending on the size of the ward/village. In each group, two sequences of the experiment with five periods in each sequence were carried out. The first sequence is called baseline and the second sequence treatment trial. The number of periods in each trial was not announced beforehand but was the same for all treatments in both trials to make them comparable in the analysis.2 In order to ensure that the experiment was understood by everybody, a test trial of three rounds was conducted. This test trial was not remunerated. Participants were asked in each period to make a choice concerning how much water they want to apply daily to their fields. To make choices easier and more familiar to the participants, xit was translated into a discrete choice variable, which was called “daily pumping hours”. Each pumping hour represented 10 units of water in the model. Subjects could choose between zero and eight hours with half-hour steps in between, resulting in 17 ordered options. This variable is used later as the dependent variable. According to the profit table (see Table B1 in Appendix B), the profits from each period depended on the groundwater level in the

•

•

2

3

The extraction rates of the benchmark user types were calculated using a discount rate of 0.2, which is the inverse of the total number of rounds in each trial. As the participants were not informed about the total number of rounds, some deviation from the benchmark user types are expected. Nevertheless, long-term strategies can still be distinguished from short-term strategies.

participants did not exceed pumping rates of more than one hour. One hour of pumping is the amount equal to the social optimum in which the group benefits are maximized. As a consequence, the water level remains constant. The bonus was randomly rewarded to one group member. If the selected group member did not behave according to the rule, the bonus was not paid. To determine which participant would be selected, a 10-sided die was rolled. Beforehand, each participant received two numbers on the die and in two cases, no inspection was conducted. Hence, the chances getting selected were 1/5. This random selection is often used in low and medium income countries to capture the imperfect monitoring or auditing in these countries (Cardenas et al., 2000; Vollan, 2008). This design was used earlier in CPR experiments (Vollan, 2008; Travers et al., 2011; Hayo and Vollan, 2012). The reward was chosen rather low such that the expected payoffs for myopic and individual rational users are still higher with the strategies achieved rather than without any intervention. A change in behavior is therefore not expected. For example, using the benefit table in the appendix, the maximum payoff for a myopic user is 1600 tokens in the first round. However, the expected payoff under the reward design is only 740. The value is composed of the profit for extracting one hour of groundwater plus the reward times the chance of being selected. A similar result holds for the individual rational type. Thus, one would not expect a change in behavior of a myopic or an individual rational user. As noted by Rodriguez-Sickert et al. (2008), complying with the norm under such a low reward can be interpreted as a deliberate decision and excludes a decision made by incident or mistake. Punishment rule: A punishment of 100 tokens for every additional hour that the participants exceeded the optimal extraction rate of one hour was subtracted. For the inspection, the same random selection process was used as in the reward treatment. If the selected group member behaved in accordance with the rule, the punishment was not applied. In this treatment the benefit function changes: x2 dt x it pz (x it s ) where p is the chance of it = x it 2 it being monitored, z is the penalty amount and s the amount of water allowed by the external regulator. While the outcome for the social optimal user would not change, the myopic user would reduce their pumping decision in accordance with Eq. (3) from four hours to three hours in the first round. The expected extraction rate would hence be lower but still above social optimal extraction behavior. The expected behavioral change for the individual rational type is similar to the myopic type. For example, the individual rational type would reduce their extraction decision by half an hour in the first round and eventually extract as much as the social optimal user type in the last two rounds. Fig. A1 shows how extraction behavior would change. Communication: In this treatment, communication among group members was allowed. Before the start of each round, the

The instructions contain normative wording. While the instructions for the reward mentions that the participants are encouraged to pump for one hour, in the punishment treatment the participants are allowed to pump for one hour. However, it cannot be distinguished how much the wording would contribute to a change in pumping behavior. 4

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

•

participants had up to 5 min time to discuss their extraction strategy. Even though extraction decisions were discussed in this scenario, the actual decisions were made individually and privately. Control: In this scenario, the baseline scenario was repeated in order to control for learning effects. This treatment serves as the control group.

household lists from the angandwadis (kindergarten) were acquired. These household lists are updated regularly by the angandwadi-officers and included all households, even those without children. 20 households were then randomly selected from the household lists. Ultimately, 1275 households were surveyed from which 600 households were randomly selected for participation in the experiment. We invited the main decision maker of the household to the experiment, who was also the individual who completed the questionnaire. In order to avoid spillover effects within a village, each group was randomly assigned to a treatment. While the survey was carried out between December 2016 and April 2017, the experiment was conducted between February and April 2017. The city of Bengaluru was chosen because it is rapidly expanding and, therefore, transformational changes can be studied. In addition, India's agriculture depends heavily on groundwater. Though many inner parts of the city do not depend on groundwater as it is connected to the Bengaluru water and sewage system, which receives water from the Cauvery River 100 km away. However, newly developed parts of the city are not connected to the system yet. Their main water source thus remains groundwater. The high urban and agricultural water demand have led to over-exploited aquifers and a fast declining groundwater table in Bengaluru and its surrounding areas (Srinivasan et al., 2017). Urban water usage has another side effect. In contrast to the upstream regions (northern transect), the downstream areas (southern transect) are supplied with water all year long as they are connected to the sewage system of the city. This reduces the pressure on groundwater aquifers as sewage irrigation is a common practice. Nevertheless, weather patterns have shifted and droughts have occurred more often in recent years, which have led to conflicts over the water rights of the Cauvery River between several states along the river.

To incentivize decisions in the CPR experiment, each participant received remuneration at the end of the experiment. The remuneration was determined by the sum of all payoffs in each round of the CPR experiment. This amount was converted at a ratio of 1:50, i.e. for 100 tokens earned in the experiment, the participants received 2 INR.4 The range of remuneration paid out was between 120 and 250 INR. 4.2. Holt and Laury Lottery The Holt and Laury Lottery (HLL) is a measure used to determine risk attitudes (Holt and Laury, 2002). The method has been carried out successfully in different low and middle income countries (Brauw and Eozenou, 2014; Moser and Mußhoff, 2016). We visualized the HLL with a decision card to make it more easily understandable (see Appendix C for a description and the decision card). The cards contained two blocks with lottery A and lottery B. Each block contained a high and a low payoff. In lottery A, the high payoff was 100 INR and the low 80 INR while in Lottery B, payoffs were 192 INR and 5 INR for the high and low payoffs, respectively. The variation between the two payoffs is lower in lottery A, therefore it is considered the safer alternative. The two blocks contained 10 lines. With each line, the chance to win the high payoff was increased by 10%. In line one, the chance to win either a high or low payoff was 10 and 90%, respectively. As probabilities are often not understood, a 10-sided die was used to illustrate the chances. The individual risk attitude is measured on an ordinal scale according to the number of times lottery A was chosen. This is the so called HLL-value. A HLL-value of up to three indicates risk-loving behavior, whereas four indicates risk-neutrality and greater than four implies risk-aversion. Extreme choices of never or only choosing lottery B are consolidated to one or nine, respectively (Holt and Laury, 2002). For the analysis, the total number of safe choices was used (Holt and Laury, 2002). This includes observations where participants switched multiple times between the two options. As in the CPR experiment, decisions were incentivized. After choosing lottery A or B in the 10 rows, the participant rolled a 10-sided die which determined the line. According to the participant's choice, lottery A or B was considered. Rolling the 10-sided die a second time determined whether the high or the low payoff was paid out. Participants were paid a cell phone top-up, which was placed on the participants' account immediately after the completion of the survey.

5. Results and discussion 5.1. Descriptive statistics In total, the experiment was conducted with 600 households assigned to 150 groups. Table 1 displays the descriptive statistics of the participants' individual and household characteristics, farm characteristics, location as well as the experimental outcomes of the HLL. Overall, the participants in the four treatments are very similar. To test this, a Kruskal Wallis test was applied. The results show that only the pvalue for the variable “elevation” is below 0.05. The majority of the participants are male and on average mid-aged with about six years of formal education. According to the caste classification system of the Government of India, half of the participants belong to the general caste and the other half to socially and economically disadvantaged castes. The number of assets, according to the socio-economic classification (SEC) of India, is used as a measure of the wealth of the household. It indicates that the majority of the households belong to the Indian lower middle class (MRSI, Market Research Society of India, 2011). The majority of the participants are farmers and 27% own a private borewell. The number of safe choices shows that the participants are on average risk-averse.

4.3. Study region and sampling design The experiment was conducted with 600 households in two transects which cover urban, peri-urban and rural sites in the north and south of Bengaluru, India. 27 households declined to participate in the experiment and were replaced by other households from the list. The sample was drawn in three steps. First, the villages within the two transects were stratified into six groups such that each group represented the state of urbanization. For the stratification, the survey stratification index (SSI) was used. This index consists of the distance to the city center and the built-up density (Hoffmann et al., 2017). After the stratification, 61 urban wards/villages were randomly selected. Fig. 1 shows a map of the research villages by strata. Secondly,

5.2. User type analysis Before the design of the management institutions are evaluated, the behavioral type of the participants are analyzed. This can be done given the decisions obtained in the baseline trial. Following Suter et al. (2012), the individual pumping decisions of each individual in each period is regressed on the three benchmark outcomes given in Eqs. (3), (5) and (7), respectively. For the operationalization of the model, the values in Table A1 of Appendix A are taken. For instance, the user who 180 dit behaves myopically would choose x it = , where xit stands for the 2

4 The exchange rate was 72 INR/EUR. Day laborers are paid between 150 and 300 INR.

5

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

Fig. 1. Research Area.a) a) Note: Shaded areas depict the two transects. Colored points depict selected villages.

amount of water applied and dit for the depth to groundwater table. If an individual behaves myopically, then the outcome of regressing 180 dit on the individual pumping decisions should not be statistically 2 significantly different from one. For this regression, the Praise-Winston feasible generalized least squares method with an autoregressive-1 error process is used in order to account for the repeating nature of the decision-making process. The same procedure is applied to identify the individual rational and the social optimal user. If one subject was assigned to two user types categories, the test statistic with the largest pvalue was taken to get a unique user type behavior. In total 8.33% of the participants were categorized both individual rational and social optimal while 13.67% were categorized both individual rational and myopic. No participant was categorized myopic and social optimal at the same time. Table 2 shows the outcome of the regressions according to the behavioral type. The majority (55.5%) of the participants behave individually rationally. 17.33% fall into the myopic category, while 8.17% act in a social optimal way. 7.33% of the participant extract

strictly more than the respective myopic user (termed excessive user) while only 0.33 extract strictly less than the respective social optimal user (termed conservative user). 11.33% of the participants could not be assigned to any of these groups. This result is different from the lab experiment with students of Suter et al. (2012), who find a high share of myopic users within the bathtub model. However, it is comparable to results of Salcedo (2014) who also find that students in the lab behave more myopically, whereas farmers are more cautious in their extraction decisions. 5.3. Analysis of pumping decisions Fig. 2 displays the average pumping choice per period within the four different treatments in the experiment. The dashed line indicates the start of the treatment trial in which the different management institutions were introduced and values set back to the initial level. The solid lines in Fig. 2 represent the benchmark scenarios of idealized user types from Eq. (3), (5) and (7).

6

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

Table 1 Descriptive statisticsa. Treatments

Reward

Punishment

Communication

Control

Total

Kruskal Wallis Statistic

N/Number of groups 156/39 Individual and household characteristics Age 47.33 (Years) (16.04) Caste General Castes 49.02 (Percent) (−) Scheduled Castes 15.69 (Percent) (−) Scheduled Tribes 9.15 (Percent) (−) Other Backward Castes 24.84 (Percent) (−) Other 1.31 (Percent) (−) Education 5.54 (Years) (4.95) Farming 70.51 (Percent) (−) Gender 66.23 (Male in Percent) (−) Household Size 4.83 (Number of People) (2.12) Wealthb 5.60 (Number of Assets) (1.65)

160/40

164/41

120/30

600/150

p-value

42.15 (14.45)

43.06 (14.24)

46.52 (14.86)

44.61 (15.03)

0.24

52.23 (−) 13.38 (−) 6.37 (−) 26.11 (−) 1.27 (−) 6.49 (5.16) 63.52 (−) 58.75 (−) 4.55 (2.17) 5.55 (1.71)

48.17 (−) 17.68 (−) 12.20 (−) 20.73 (−) 1.22 (−) 6.16 (5.14) 77.50 (−) 65.03 (−) 4.73 (2.13) 5.76 (1.45)

45.69 (−) 28.45 (−) 6.03 (−) 18.97 (−) 0.86 (−) 5.51 (6.38) 77.43 (−) 57.62 (−) 4.88 (2.32) 5.40 (1.7)

48.98 (−) 18.14 (−) 8.64 (−) 22.88 (−) 1.36 (−) 5.96 (5.37) 71.95 (−) 62.18 (−) 4.47 (2.18) 5.59 (1.63)

0.82

1.51 (3.01) 25.64 (−)

1.40 (2.25) 23.75 (−)

1.69 (2.31) 32.92 (−)

1.33 (1.63) 26.67 (−)

1.50 (1.63) 27.33 (−)

0.22

0.65 (0.20) 0.53 (−) 770.01 (82.22)

0.63 (0.22) 0.47 (−) 762.82 (83.41)

0.64 (0.21) . 43 (−) 751.681 (92.39)

0.62 (0.21) 0.43 (−) 742.57 (93.19)

0.64 (0.21) 0.47 (−) 757.59 (88.01)

0.64

5.42 (2.64)

5.91 (2.49)

5.59 (2.38)

5.75 (2.33)

5.67 (2.47)

Farm characteristics Land Holdings (Acres) Owns a Borewell (Percent) Location SSIc Transect (North in Percent) Elevation (Meter) Preferences and behavior Safe Choicesd a b c

d

0.16 0.77 0.69 0.97 0.12 0.10 0.48 0.52 0.35

0.51

0.37 0.03

0.35

Values in means, S.D. in parentheses. The asset list comprises ceiling fans, LPG stoves, TVs, refrigerators, washing machines, PC/laptops, air conditioners, two wheelers, cars/jeeps/vans (MRSI, 2011). SSI ranges between 0 (fully urbanized) and 1 (remote rural area). A value between 0 and 3 indicates risk-seeking, a value of 4 risk-neutrality, and a value between 5 and 10 risk-aversion

In the baseline trial, all four treatment groups are very similar in behavior. The Kruskal-Wallis test supports this impression. We fail to reject the null hypothesis of no systematic difference between all four treatments during the baseline trial (p-value = 0.8481). The general behavior type lies between the myopic and the individual rational type.

The depth to groundwater remains in the space between the myopic and individual rational benchmark scenario. Introducing the treatments, both externally imposed rules (reward and punishment) push the behavior towards the social optimal type, while communication alters the pumping decision towards the rational behavior type. We see

Table 2 Share of user type by treatments and in total using Praise-Winston feasible generalized least squares regression on baseline trial data. Treatments

Reward

Punishment

Communication

Control

Total

Kruskal-Wallis

N/Number of groups Conservative Social optimal Individual rational Myopic Excessive Undefined

156/39 – 7.32 56.71 16.46 9.76 9.76

160/40 0.64 7.05 58.33 18.59 4.49 10.90

164/41 0.63 11.25 51.25 15.63 6.25 15.00

120/30 – 6.67 55.83 19.17 9.17 9.17

600/150 0.33 8.17 55.5 17.33 7.33 11.33

(p-value) 0.61 0.42 0.62 0.83 0.24 0.37

7

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

Fig. 2. Outcome of the CPR experiment by treatments in comparison with benchmark usersa) a) Note: Number of observation = 6000 (600 households × 10 rounds). All parameters were set back to initial level after round 5. A dashed line indicates the introduction of treatments. Solid lines depict the benchmark scenarios.

no difference between the baseline trial and the control group which repeats the experiment without changes to the instructions. The Wilcoxon signed rank test also fails to reject the null hypothesis of no difference between the control and baseline scenario in the treatment trial (p-value = 0.3019). Otherwise, the null is rejected at the 1% significance level for the other three treatments. As a first result, we conclude that the behavior of participants in the control group has not changed considerably and that learning effects are negligible in this context. Moreover, we can conclude that unmanaged aquifers remain over-exploited, which would also explain the drawdown in the region described by Srinivasan et al. (2017). For further analysis of the effectiveness of groundwater management institution designs, we use a panel ordered probit model. This model is appropriate as our variable of interest, “pumping hours”, is discrete and ordered. The panel structure accounts for repeated decisions of one individual. For the analysis, clustered robust standard errors at the group level are computed as choices and outcomes within one group are not independent. Table 3 shows the results of four different models stemming from the ordered probit regression. In order to cancel out learning effects, only the treatment trial is considered in the analysis. Model (1) shows

the regression results of the treatments on the pumping decisions, while model (2) adds a full-set of explanatory variables. Model (3) interacts the treatment variables with the pumping behavior indicators while model (4) controls for interaction of the treatments with our urbanization variable. 5.3.1. Treatment effects on pumping decisions The results of model (1) in Table 3 show that all coefficients of the treatments are negative and statistically significant at least at the 10% level. The coefficient for the reward rule shows the largest magnitude and is statistically significant at the 1% level, followed by the punishment rule with a slightly lower coefficient but at the same significance level. The coefficient of the communication treatment, however, has a considerably lower coefficient which is statistically significant at the 10% level. A Wald test was conducted to test whether reward and punishment rules are more effective than communication. The difference is statistically significant at the 1% level for both externally imposed rules. These results do not change qualitatively when we include a set of explanatory variables in model (2). Against the behavioral predictions resulting from the experimental design, the externally awarded reward reduces the outtake of

8

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

Table 3 Results of a panel ordered probit regressiona. Model Treatments (ref.-group: Control) Reward Punishment Communication

(1)

(2)

(3)

(4)

−0.8603*** (0.1259) −0.8506*** (0.1117) −0.1978* (0.1057)

−0.8737*** (0.1052) −0.8479*** (0.1084) −0.1872* (0.0962)

−0.9125*** (0.1203) −0.8575*** (0.1201) −0.2466*** (0.0907)

−0.4839* (0.2599) −0.8017*** (0.2384) −0.4194* (0.2443)

0.0071*** (0.0026)

0.0076*** (0.0027)

0.0068** (0.0026)

0.0874 (0.0726) 0.0917 (0.1194) 0.0807 (0.0700) 0.2302 (0.2188) 0.0221*** (0.0074) −0.0317 (0.0929) −0.0129 (0.0604) −0.0145 (0.0121) −0.0242 (0.0215)

0.0878 (0.0754) 0.0864 (0.1197) 0.1056 (0.0688) 0.2161 (0.2318) 0.0216*** (0.0073) −0.0567 (0.0919) −0.0014 (0.0595) −0.0115 (0.0121) −0.0289 (0.0212)

0.0836 (0.0716) 0.0863 (0.1195) 0.0908 (0.0706) 0.2201 (0.2226) 0.0217*** (0.0074) −0.0306 (0.0935) −0.0085 (0.0605) −0.0153 (0.0119) −0.0251 (0.0212)

−0.0159 (0.0129) −0.0183 (0.0731)

−0.0146 (0.0124) −0.0335 (0.0744)

−0.0153 (0.0127) −0.0168 (0.0732)

−0.1355 (0.2588) 0.0146 (0.1856) 0.0025** (0.0011)

−0.1077 (0.2612) 0.0262 (0.1881) 0.0024** (0.0011)

−0.0273 (0.3480) −0.0356 (0.1589) 0.0028*** (0.0010)

0.0046 (0.0110)

0.0076 (0.0107)

0.0045 (0.0111)

−0.4675*** (0.0910) −0.2290* (0.1231) 0.2011** (0.0912) 0.2803** (0.1321) 0.0045 (0.1020)

−0.4366*** (0.1432) −0.8526*** (0.3269) 0.2564* (0.1488) 0.3511** (0.1398) −0.0907 (0.1809)

−0.4689*** (0.0876) −0.2440** (0.1246) 0.2001** (0.0910) 0.2774** (0.1261) −0.0019 (0.1006)

Individual and household characteristics Age (Years) Caste (ref group: General Castes) Scheduled Castes Scheduled Tribes Other Backwards Classes Other Education (Years) Farming (No = 0/Yes = 1) Gender (Female = 0/Male = 1) Household Size (Number of Persons) Wealthb (Number of Assets) Farm characteristics Land Holdings (Acres) Owning Borewell (No = 0/Yes = 1) Location SSIc Transect (South = 0/North = 1) Elevation (Meter) Preferences and behavior Safe Choicesd User Types (ref. group: Individual Rational) Conservative Social Optimal Myopic Excessive Undefined Interactions with user types Reward × Conservative

−0.0585 (0.2335) 0.8354** (0.3932) −0.1316 (0.2435) −0.0374 (0.3756) 0.1524 (0.2584) – –

Reward × Social Optimal Reward × Myopic Reward × Excessive Reward × Undefined Punishment × Conservative

(continued on next page)

9

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

Table 3 (continued) Model

(1)

(2)

(3)

Punishment × Social Optimal

0.6471* (0.3679) −0.2160 (0.2332) −0.5481** (0.2465) 0.2859 (0.2834) – – 0.7782* (0.4020) 0.1318 (0.2190) 0.1018 (0.2773) −0.1570 (0.2360)

Punishment × Myopic Punishment × Excessive Punishment × Undefined Communication × Conservative Communication × Social Optimal Communication × Myopic Communication × Excessive Communication × Undefined Interactions with SSI

(4)

Reward × SSI

−0.6004 (0.4066) −0.0818 (0.3970) 0.3503 (0.4112)

Punishment × SSI Communication × SSI Ne Aic

3000 10,097

2815 9438

2815 9439

2815 9438

a Observations were taken from the treatment trial only; Clustered robust standard errors at group level were used; Single, double, and triple asterisks (*, **, and ***) denote p < 0.10, p < 0.05, and p < 0.01, respectively. b The asset list comprises ceiling fans, LPG stoves, TVs, refrigerators, washing machines, PC/laptops, air conditioners, two wheelers, cars/jeeps/vans (MRSI, 2011). c SSI ranges between 0 (fully urbanized) and 1 (remote rural area) d The number of safe choices range from 0 to 10 e The number of observations consists of repeated pumping decisions of the participants in the second trial of the experiment: 5 × 600 = 3,000.

groundwater drastically. While it was predicted that no behavioral change would take place, pumping hours are notably reduced. The same holds for the punishment design which also reduces the outtake of water greater than expected. This result suggests that having externally imposed management institutions in place has already an effect while specific realizations seem to matter less. One interpretation of these results is that both the reward and punishment rule have a stabilizing effect on social norms and overcome coordination problems within the communities (Narloch et al., 2012). As most of the pumped water is used for privately-operated irrigated agriculture, profits are also made privately. Thus, a divergence from the proposed norm is sanctioned (positively or negatively) and will affect immediately the household's profits. This may decrease the incentive to free-ride and conform to the social norm (Rodriguez-Sickert et al., 2008). This result underpins the importance of monitoring and sanctioning. One limitation of this result is, however, that it cannot be distinguished how much the normative wording of the experimental instructions or the incentives have contributed to the change in behavior. This is left for further research. In contrast to the groundwater game of Meinzen-Dick et al. (2016), who find no statistically significant difference in the behavior between both the baseline group and the treatment with communication, our coefficient is statistically significantly different at the 10% level. This is in line with other CPR experiments. Enabling the possibility to communicate is associated with higher cooperation and a decrease in CPR extraction rates (Ostrom, 2006). These internal arrangements, however, do not seem to have the same reciprocity-based self-enforcing effect as in the case of external regulation. Therefore, free-riding behavior cannot be banned effectively and collective action will not fully evolve. When we interact the variables of the user type analysis with the treatments in model (3), we still find similar magnitudes of the

treatment variables, yet the communication treatment becomes significant at the 1% level as well. We also find that user type behavior identified in the baseline trial is persistent in the treatment trial. In models (2) to (4), the coefficients for the user type behavior (with the exception of the undefined user) are statistically significant, at least at the 10% level. The conservative and social optimal users are more likely to extract less water while excessive and myopic users are more likely to extract more water, even after the introduction of the treatments.5 Moreover, all interaction terms of the social optimal user with the treatments are positive and statistically significant, at least at the 10% level. This result underpins the finding of Madani and Dinar (2012b) that user exists that would manage a CPR sustainably without any intervention. Another interpretation is that crowding-out effects occur in any intervention. With the different designs of groundwater management institution in place, the social optimal user extracts more water than the optimal user in the control group. One could interpret this effect as a crowding-out effect of intrinsic motivation to safeguard the sustainability of the resource. However, the results also show that the interaction of the excessive user and the punishment treatment is negative and statistically significant at the 5% level. This indicates that an external enforcement of a treatment has also a crowding-in effect.

5 In order to analyze determinants of the different user type behavior, we conducted several logit regressions with age, caste, education, gender, household size, wealth, number of safe choices in the HLL as explanatory variables. However, only one coefficient was statistically significant in most regressions. For instance the coefficient for gender was negative and statistically significant at the 5% level for the social optimal behavior type. This indicates that women are more likely to behave socially optimal. Otherwise all other variables for this regression were not statistically significant.

10

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

5.3.2. Groundwater management and urbanization In order to study the influence of urbanization on the effectiveness of management institutions, interaction terms of the SSI index with the treatment variables were introduced in model (4). In contrast to the other models, the coefficient of the communication treatment is not statistically significantly different from zero. The significance level of the reward rule lowers to the 5% level and also the magnitude of the effect is reduced. The coefficient of the punishment rule remains almost unchanged. The same holds for the other explanatory variables. We have hypothesized that externally enforced designs of groundwater management institutions would have a stronger effect in more urban areas compensating for lower social norms which need time to evolve. Following the same argument, we have expected that communication would be more effective in more rural areas. Neither the coefficients of the interactions terms nor of SSI is statistically significant. Therefore, we cannot support the hypotheses. One explanation might be the overall strong effect of externally imposed institutions. Another interpretation is that there is no difference in attitudes towards groundwater use in rural and urban areas. This interpretation goes along with the observation that there is no statistically significant difference in groundwater extraction in the experiment along the ruralurban interface. This observation also withdraws the basis of our hypothesis that people in urban areas are less dependent on agricultural production and would hence be more interested in long-lasting resources.

Table 4 Social efficiency scores and welfare gains over control group by treatmentsa.

2

xo2

do x o

Communication

Control

N/Number of groups Baseline trial Treatment trial Welfare gains against control groupb

156/39 90 95 6***

160/40 88 97 8***

164/41 87 90 1

120/30 86 89 –

Values in percent. Only treatment trial considered; Single, double, and triple asterisks (*, **, and ***) denote p < 0.10, p < 0.05, and p < 0.01, respectively.

Moreover, welfare gains of the treatments over the control group were treatment j control × 100 of each treatment j using obtained by calculating control the observations in the treatment trial. Table 4 shows the result of the outcome. In the baseline trial the reward treatment has a slightly higher efficiency score than the other three treatments. Using a Kruskal Wallis test shows that there is no statistically significant difference between the treatments' social efficiency scores in the baseline trial. Social efficiency increases during the treatment trials. Comparing the treatments to the control group in the treatment trial, welfare gains are highest for punishment treatment. A ttest also shows that the effect is statistically significant at the 1% level. 6. Conclusions The provision of groundwater is a challenging issue in semi-arid and arid areas with rapid urbanization and an emerging urban middle-class. The excessive groundwater extraction to satisfy demand for food and other agricultural products has led to the over-exploitation of aquifers in these areas. Those who economically depend on the resource are in a social dilemma whether to maximize profits or to prolong the usage of a resource. So far, the focus has been on the provision of water for the urban population but less on the consequences of urban development. With this paper, we add to the discussion on how management institutions need to be designed in order to prolong the use of a CPR, and particularly groundwater in the context of urbanization. We discuss three different designs of management institutions: a reward rule, a punishment rule, and communication. We do this by conducting a framed field experiment with 600 households along the rural-urban gradient in Bengaluru, India. Our results suggest that unmanaged aquifers will lead to a rapid decline in groundwater level. Both external imposed institutions with a monitoring and sanctioning design are very effective in prolonging the life of the CPR. These results hold for rural as well as for urban areas. This result indicates that monitoring and sanctioning can effectively overcome free riding behavior by making it less attractive to deviate from a norm. Communication is less effective but is still statistically significantly different to the scenario without any regulation. After interacting different behavioral types with the treatment variables, all interaction coefficients with the social optimal type show a positive and statistically significant effect. This result indicates that all treatments have a crowing-out effect on users who were categorized as social optimal. Moreover, we find that the user type behavior identified in the baseline trial is persistent in the treatment trial despite interventions. Given the rate of aquifer over-exploitation, the massive drawdown in water tables, and the potential water shortage in many semi-arid and arid areas, there is a need for improved groundwater governance. Monitoring and positive reinforcement of rules and regulation should be an essential part of groundwater management institutions. Such measures could help to avoid myopic behavior and reinforce communal or collective action. This paper is an initial step and provides ex-ante analysis on the design of management institutions in areas with high population

In order to analyze welfare implications of the treatments, social efficiency scores for each treatment were calculated. Social efficiency is the ratio of the actual group benefits and group benefits under optimal user behavior. Actual group benefits are the sum of all individual benefits of one group across all five rounds in a trial. Group benefits under social optimal behavior are calculated using the equation xo

Punishment

b

5.4. Analysis of welfare implications

=n

Reward

a

5.3.3. Other explanatory variables Including other explanatory variables provides important information on pumping choices. Considering other individual and household variables, education and age increase the likelihood to extract more water. The coefficients are both positive and statistically significant at least at the 5% level. This is in contrast to the results of Meinzen-Dick et al. (2018) but not exceptional in the CPR literature (Prediger et al., 2011). Other factors like caste or wealth are not significant. Controlling for land-surface elevation at household level, the coefficients in models (2) to (4) all have a positive sign, at least at the 5% level. This means that households which are located in more elevated areas are more likely to extract more water in the experiment. Landsurface elevation is used here as a proxy for the availability of the resource. This result is in accordance with experimental findings from Castillo et al. (2011) and Prediger et al. (2011), which show that participants prolong the life of a CPR when they come from a resource abundant region. This result indicates that in resource scarce regions competitive behavior increases, which fosters a tragedy of the commons situation.6 In the context of irrigation, risk attitudes are also crucial. Several studies have shown that risk-averse decision makers are more likely to extract more water (Groom et al., 2008; Buchholz et al., 2016). In our case, risk attitudes do not statistically significantly affect the extraction decisions even though the sign is positive as expected.

o

Treatments

where the subscript “o” stands social optimal user.

6 As a robustness check, we also tested whether water availability is perceived differently by participants involved in farming. However, interaction terms for the variables borewell and surface elevation as well as farming and elevation were not statistically significant.

11

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

growth and scarce resources. Future work could analyze how to reinforce communal cooperation. For example, the effects of costly punishment within a user group on cooperation and collective action. Moreover, complex aquifer models could help to refine the analysis of user behavior. Declaration of competing interest None.

Acknowledgements The data were compiled within the Research Unit 2432 “Ecological and Social Systems at the Indian Rural-Urban Interface: Functions, Scales and Dynamics of Transition” funded by the German Research Foundation (DFG).

Appendix A. Variables and parameters of the operationalized model Table A1

Variables and parameters of the operationalized model Symbol

Description

Value

Unit

x π d α γ ϕ r A S δ

Quantity of groundwater pumped Profit Depth to groundwater Parameter of the benefit function Parameter of the benefit function Cost parameter Natural recharge Area Storativity Discount rate

– – – 180 2 1 40 10,000 918,274∙10−4 0.2

100,000 ft3 tokens ft tokens tokens tokens 100,000 ft3 ac – –

Fig. A1. Comparison of benchmark users in baseline and treatment trial under punishmenta). a) Note: Dashed line indicates the introduction of punishment treatment.

Appendix B. Description of the CPR experiment Welcome everybody! We are very happy that you have accepted our invitation to participate again in our research. Last time you answered many questions. Thank you again for the patience to answer all the questions. This time we want to do an activity with you. It will take between 45 min and 1 h. It is necessary that you stay the whole time. If one of you has to leave before, we need to reschedule the whole activity with all four members. We kindly ask you to switch off your mobile phone, so nobody will get distracted. For this activity, imagine that you are all farmers and every one of you owns a private borewell. With the water of the borewell you can irrigate a field and increase your agricultural production. As you might know, crops that need water usually generate more money on the market. However, the groundwater comes from a pool beneath the ground that you share with the other three here in the room. So, if one of you takes out water, the groundwater level falls and it falls for everybody to the same level. If the groundwater level drops, you will have to deepen the borewell to reach the water. Deepening the borewell is costly and with the falling groundwater level your benefits will also decrease.

12

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

Remember that you can gain some money in this activity. We will explain how. Water Depth and Profit Table and Decision Sheet We will now hand out the water depth and profit table which you will need for your decision (Table B1). Everybody gets the same table. In the upper row, you can find the depth to groundwater. This activity will be played for several rounds. Each round represents one production period. So the groundwater level drops according to how much every one of you takes out of the ground. Keep in mind that you can earn points according to your choices. These points will later be converted into real money. For every 100 points you will get 2 rupees. We will start at 100 ft of depth. You see that you will get the most points if you pump four hours. Please consider that you play this activity as a group and the more the group pumps out of the ground, the faster the groundwater declines which increases the costs. For the decision making we will hand out decision sheets (Figure B2). In each round you will get a new decision sheet. Please write down your name and the number of the round on the upper part of the sheet. Then you can tick a box with the hours you want to pump. After you have completed your choice, you hand me or one of my assistants back your decision card. I will then calculate the new ground water level and announce it. You can see how deep the water level is on the scale we put up on the wall. Please do not tell your neighbors about your choices. Please also write down how much you have earned in each round so we can sum up your total earnings at the end of the game. Any questions? Otherwise, we will now play a trial round so we can see if any questions come up. Treatment A (Reward): Now we will introduce a new rule. You are encouraged to pump for one hour. The supervision of this program will be ensured by an external regulator. After you have made your decision, one of you will be selected randomly and his/her decision will be controlled. If you chose one hour or less, you will receive the bonus of 200 tokens. If you chose more than one hour, you will not receive a bonus. For the selection process, you can now draw sheets. On each sheet there are two numbers between 1 and 10. If the die shows one of your numbers, you will be controlled. Note that in two cases, of the rolled die, nobody will be controlled. Treatment B: (Punishment) Now we will introduce a new rule. You are allowed to pump for one hour. This will be controlled by an external regulator. After you have made your decision, one of you will be selected randomly and his decision will be controlled. If you chose one hour or less, nothing will happen. If you chose more than one hour, 100 tokens for every additional hour will be subtracted from your profits. For the selection process, you can now draw sheets. On each sheet there are two numbers between 1 and 10. If the die shows one of your numbers, you will be controlled. Note that in two cases of the rolled die, nobody will be controlled. Treatment C (Communication): Now we would repeat the whole game again. This time you are allowed to discuss with your neighbors before each pumping decision. You can discuss now but don't tell your neighbor your actual decision later when you write your decision on the sheet. Treatment D (Control) Now we will start from the beginning. We set back the groundwater table to the initial level. Table B1

Profit table (extract). Depth to Groundwater/ Pumping hours

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

100 Profit 0 375 700 975 1200 1375 1500 1575 1600 1575 1500 1375 1200 975 700 375 0

Water pumped 0 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 60.00 65.00 70.00 75.00 80.00

Depth to Groundwater/ Pumping hours

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

101.25 Profit 0 369 688 956 1175 1344 1463 1531 1550 1519 1438 1306 1125 894 613 281 −100

Water pumped 0 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 60.00 65.00 70.00 75.00 80.00

13

Depth to Groundwater/ Pumping hours

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

102.5 Profit 0 363 675 938 1150 1313 1425 1488 1500 1463 1375 1238 1050 813 525 188 −200

Water pumped 0 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 60.00 65.00 70.00 75.00 80.00

Depth to Groundwater/ Pumping hours 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

103.75 Profit 0 356 663 919 1125 1281 1388 1444 1450 1406 1313 1169 975 731 438 94 −300

Water pumped 0 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 60.00 65.00 70.00 75.00 80.00

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

Date:________________________ Village___________________________Group________________ NAME: ______________________ HH ID ____________________________ TM_________________ PERIOD________

DECISION

Benefit

Pumping Hours 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 Fig. B2. Decision sheet.

Appendix C. Holt and Laury Lottery In the next problem you can earn additional credit for your phone. Your earnings will depend partly on your decisions and partly on chance. In this subsection, there are 10 questions (Table C1). In each question, we will offer you two plans: Plan A and Plan B. We would like you to choose either Plan A or Plan B for each question. After you completed all questions, the die will be rolled twice. The first one determines the row which your payment is chosen from. The next role determines your payment according to your plan you have chosen.

14

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff

Table C1

Holt and Laury lottery decision table (excerpt).

09.002. Hoffmann, E., Jose, M., Nölke, N., Möckel, T., 2017. Construction and use of a simple index of urbanisation in the rural–urban interface of Bangalore, India. Sustainability 9 (12), 2146. https://doi.org/10.3390/su9112146. Holt, C.A., Laury, S.K., 2002. Risk aversion and incentive effects. Am. Econ. Rev. 92 (5), 1644–1655. Janssen, M.A., Bousquet, F., Cardenas, J.-C., Castillo, D., Worrapimphong, K., 2012. Field experiments on irrigation dilemmas. Agric. Syst. 109, 65–75. https://doi.org/10. 1016/j.agsy.2012.03.004. Janssen, M.A., Lindahl, T., Murphy, J.J., 2015. Advancing the understanding of behavior in social-ecological systems: results from lab and field experiments. Ecol. Soc. 20 (4). https://doi.org/10.5751/ES-08097-200434. Khan, M.R., Koneshloo, M., Knappett, P.S.K., Ahmed, K.M., Bostick, B.C., Mailloux, B.J., Mozumder, R.H., Zahid, A., Harvey, C.F., van Geen, A., Michael, H.A., 2016. Megacity pumping and preferential flow threaten groundwater quality. Nat. Commun. 7, 12833. https://doi.org/10.1038/ncomms12833. List, J.A., Price, M.K., 2016. The use of field experiments in environmental and resource economics. Rev. Environ. Econ. Policy 10 (2), 206–225. https://doi.org/10.1093/ reep/rew008. Liu, Z., Suter, J.F., Messer, K.D., Duke, J.M., Michael, H.A., 2014. Strategic entry and externalities in groundwater resources: evidence from the lab. Resour. Energy Econ. 38, 181–197. https://doi.org/10.1016/j.reseneeco.2014.07.002. Madani, K., Dinar, A., 2012a. Cooperative institutions for sustainable common pool resource management: application to groundwater. Water Resour. Res. 48 (9). https:// doi.org/10.1029/2011WR010849. n/a-n/a. Madani, K., Dinar, A., 2012b. Non-cooperative institutions for sustainable common pool resource management: application to groundwater. Ecol. Econ. 74, 34–45. https:// doi.org/10.1016/j.ecolecon.2011.12.006. Madani, K., Dinar, A., 2013. Exogenous regulatory institutions for sustainable common pool resource management: application to groundwater. Water Resour. Econ. 2–3, 57–76. https://doi.org/10.1016/j.wre.2013.08.001. Meinzen-Dick, R., Chaturvedi, R., Domenech, L., Ghate, R., Janssen, M.A., Rollins, N.D., Sandeep, K., 2016. Games for groundwater governance: field experiments in Andhra Pradesh, India. Ecol. Soc. 21 (3). https://doi.org/10.5751/ES-08416-210338. Meinzen-Dick, R., Janssen, M.A., Kandikuppa, S., Chaturvedi, R., Rao, K., Theis, S., 2018. Playing games to save water: collective action games for groundwater management in Andhra Pradesh, India. World Dev. 107, 40–53. https://doi.org/10.1016/j.worlddev. 2018.02.006. Moser, S., Mußhoff, O., 2016. Ex-ante evaluation of policy measures: effects of reward and punishment for fertiliser reduction in palm oil production. J. Agric. Econ. 67 (1),

References Anderies, J.M., Folke, C., Walker, B., Ostrom, E., 2013. Aligning key concepts for global change policy: robustness, resilience, and sustainability. Ecol. Soc. 18 (2). https:// doi.org/10.5751/ES-05178-180208. Brauw, A. de, Eozenou, P., 2014. Measuring risk attitudes among Mozambican farmers. J. Dev. Econ. 111, 61–74. https://doi.org/10.1016/j.jdeveco.2014.08.002. Brozović, N., Sunding, D., Zilberman, D., 2006. Optimal management of groundwater over space and time. In: Frontiers in Water Resource Economics, pp. 109–135. Buchholz, M., Holst, G., Musshoff, O., 2016. Irrigation water policy analysis using a business simulation game. Water Resour. Res. 52 (10), 7980–7998. https://doi.org/ 10.1002/2016WR018857. Cardenas, J.C., Carpenter, J., 2008. Behavioural development economics: lessons from field labs in the developing world. J. Dev. Stud. 44 (3), 311–338. https://doi.org/10. 1080/00220380701848327. Cardenas, J.C., Stranlund, J., Willis, C., 2000. Local environmental control and institutional crowding-out. World Dev. 28 (10), 1719–1733. https://doi.org/10.1016/ S0305-750X(00)00055-3. Castillo, D., Bousquet, F., Janssen, M.A., Worrapimphong, K., Cardenas, J.C., 2011. Context matters to explain field experiments: results from Colombian and Thai fishing villages. Ecol. Econ. 70 (9), 1609–1620. https://doi.org/10.1016/j.ecolecon.2011. 05.011. CGWB, 2015. Groundwater Year Book (2014–15). Government of India, Bengaluru. Cox, M., Arnold, G., Villamayor Tomás, S., 2010. A review of design principles for community-based natural resource management. Ecol. Soc. 15 (4). https://doi.org/ 10.5751/ES-03704-150438. Famiglietti, J.S., 2014. The global groundwater crisis. Nat. Clim. Chang. 4, 945 EP -. https://doi.org/10.1038/nclimate2425. Feinerman, E., Knapp, K.C., 1983. Benefits from groundwater management: magnitude, sensitivity, and distribution. Am. J. Agric. Econ. 65 (4), 703–710. Frey, B.S., Jegen, R., 2001. Motivation crowding theory. J. Econ. Surv. 15 (5), 589–611. https://doi.org/10.1111/1467-6419.00150. Gardner, R., Moore, M.R., Walker, J.M., 1997. Governing a groundwater commons: a strategic and laboratory analysis of western water law. Econ. Inq. 35 (2), 218–234. Groom, B., Koundouri, P., Nauges, C., Thomas, A., 2008. The story of the moment: risk averse cypriot farmers respond to drought management. Appl. Econ. 40 (3), 315–326. https://doi.org/10.1080/00036840600592916. Hayo, B., Vollan, B., 2012. Group interaction, heterogeneity, rules, and co-operative behaviour: evidence from a common-pool resource experiment in South Africa and Namibia. J. Econ. Behav. Organ. 81 (1), 9–28. https://doi.org/10.1016/j.jebo.2011.

15

Ecological Economics 166 (2019) 106432

J. Wegmann and O. Mußhoff 84–104. https://doi.org/10.1111/1477-9552.12114. MRSI, Market Research Society of India, 2011. Socio-Economic Classification 2011. The SEC System. Narloch, U., Pascual, U., Drucker, A.G., 2012. Collective action dynamics under external rewards: experimental insights from Andean farming communities. World Dev. 40 (10), 2096–2107. https://doi.org/10.1016/j.worlddev.2012.03.014. Ostrom, E., 1990. Governing the Commons: The Evolution of Institutions for Collective Action (the Political Economy of Institutions and Decisions). Cambridge University Press. Ostrom, E., 2000. Collective action and the evolution of social norms. J. Econ. Perspect. 14 (3), 137–158. https://doi.org/10.1257/jep.14.3.137. Ostrom, E., 2006. The value-added of laboratory experiments for the study of institutions and common-pool resources. J. Econ. Behav. Organ. 61 (2), 149–163. https://doi. org/10.1016/j.jebo.2005.02.008. Ostrom, E., 2010. Beyond markets and states: polycentric governance of complex economic systems. Am. Econ. Rev. 100 (3), 641–672. https://doi.org/10.1257/aer.100. 3.641. Poteete, A.R., Ostrom, E., 2004. Heterogeneity, group size and collective action: the role of institutions in forest management. Development & Change 35 (3), 435–461. https://doi.org/10.1111/j.1467-7660.2004.00360.x. Prediger, S., Vollan, B., Frölich, M., 2011. The impact of culture and ecology on cooperation in a common-pool resource experiment. Ecol. Econ. 70 (9), 1599–1608. https://doi.org/10.1016/j.ecolecon.2010.08.017. Rodriguez-Sickert, C., Guzmán, R.A., Cárdenas, J.C., 2008. Institutions influence preferences: evidence from a common pool resource experiment. J. Econ. Behav. Organ. 67 (1), 215–227. https://doi.org/10.1016/j.jebo.2007.06.004. Ross, A., Martinez-Santos, P., 2010. The challenge of groundwater governance: case studies from Spain and Australia. Reg. Environ. Chang. 10 (4), 299–310. https://doi.

org/10.1007/s10113-009-0086-8. Salcedo, R., 2014. Dynamic Decision Making in Common-pool Resource Economic Experiments: Behavioral Heterogeneity in the Field and the Lab. Schlager, E., 2007. Community management of groundwater. In: Giordano, M., Villholth, K.G. (Eds.), The Agricultural Groundwater Revolution. Opportunities and Threats to Development. 3. CABI, Wallingford, pp. 131–152. Shah, T., 2014. Groundwater governance and irrigated agriculture. TEC Background Papers 19, 69. Siebert, S., Burke, J., Faures, J.M., Frenken, K., Hoogeveen, J., Döll, P., Portmann, F.T., 2010. Groundwater use for irrigation – a global inventory. Hydrol. Earth Syst. Sci. 14 (10), 1863–1880. https://doi.org/10.5194/hess-14-1863-2010. Srinivasan, V., Penny, G., Lele, S., Thomas, B.K., Thompson, S., 2017. Proximate and underlying drivers of socio-hydrologic change in the upper Arkavathy watershed, India. Hydrol. Earth Syst. Sci. Discuss. 1–28. https://doi.org/10.5194/hess-2017543. Suter, J.F., Duke, J.M., Messer, K.D., Michael, H.A., 2012. Behavior in a spatially explicit groundwater resource: evidence from the lab. Am. J. Agric. Econ. 94 (5), 1094–1112. https://doi.org/10.1093/ajae/aas058. Travers, H., Clements, T., Keane, A., Milner-Gulland, E.J., 2011. Incentives for cooperation: the effects of institutional controls on common pool resource extraction in Cambodia. Ecol. Econ. 71, 151–161. https://doi.org/10.1016/j.ecolecon.2011.08. 020. Vollan, B., 2008. Socio-ecological explanations for crowding-out effects from economic field experiments in southern Africa. Ecol. Econ. 67 (4), 560–573. https://doi.org/10. 1016/j.ecolecon.2008.01.015. Wada, Y., van Beek, L.P.H., Bierkens, M.F.P., 2012. Nonsustainable groundwater sustaining irrigation: a global assessment. Water Resour. Res. 48 (6), 219. https://doi. org/10.1029/2011WR010562.

16