Journal of Risk Research Measures of risk associated to regulations ...

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Measures of risk associated to regulations compliance: a laboratory experiment on the use of common-pool resources a

a

Daniel Alfredo Revollo-Fernandez & Alonso Aguilar-Ibarra a

Instituto de Investigaciones Económicas, Universidad Nacional Autónoma de México, Ciudad de México, México. Published online: 29 Jul 2013.

To cite this article: Daniel Alfredo Revollo-Fernandez & Alonso Aguilar-Ibarra (2014) Measures of risk associated to regulations compliance: a laboratory experiment on the use of common-pool resources, Journal of Risk Research, 17:7, 903-921, DOI: 10.1080/13669877.2013.822914 To link to this article: http://dx.doi.org/10.1080/13669877.2013.822914

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Journal of Risk Research, 2014 Vol. 17, No. 7, 903–921, http://dx.doi.org/10.1080/13669877.2013.822914

Measures of risk associated to regulations compliance: a laboratory experiment on the use of common-pool resources

Downloaded by [Unam - Centro De Nano Ciencias] at 13:13 14 October 2014

Daniel Alfredo Revollo-Fernandez* and Alonso Aguilar-Ibarra Instituto de Investigaciones Económicas, Universidad Nacional Autónoma de México, Ciudad de México, México (Received 1 February 2013; final version received 13 June 2013) Analyzing the behavior of users of natural resources towards risk is of paramount importance to sustainability. This paper analyzes the degree of risk-aversion under an experimental setting, to different fishing control measures applied by an authority. In order to have a quantitative assessment of such attitudes, we applied four measures of risk-aversion, two are standard measures: the constant relative risk-aversion utility function, and the Arrow-Pratt absolute risk-aversion measure. Furthermore, we propose two new measures: risk-elasticity of extractions and a non-compliance risk index. The risk-aversion measured in this paper demonstrates that the sample studied with groups of students are, in general, risk-averse or slightly risk-loving (rather neutral) towards enforcement. The results show that, although a general tendency of lower extractions took place in comparison to a no-enforcement (i.e. open-access) treatment, a high level of enforcement (60% probability) led to higher extractions than at 20% and 40% enforcement level. There were also gender differences: women were more risk-averse than men. We conclude that there was a Motivation Crowding Effect present in players’ behavior, and that gender differences are worth studying in further research on risk attitudes and management of common-pool resources. Keywords: risk; laboratory experiment; common-pool resources; social capital; gender Subject classification codes: G0; Q2; C91; Z13; J16

1. Introduction In common-pool resources extraction, stakeholders face the dilemma of individual against collective benefits. Clear examples are fisheries managed by quota limits, where resource users decide whether or not to harvest more than the accorded quota, depending on the probability of being enforced by an authority. In fact, the major problem with quota-managed fisheries is compliance (Beddington, Agnew, and Clark 2007), because surveillance costs might be paramount in a number of fisheries where agencies have to cope with limited budgets and insufficient personnel. In general, noncompliance results from a lack of enforcement, lack of perceived fairness, and both design and technological suitability of fisheries regulations (Jensen and Aarset 2008). Thus, authorities and users play a noncooperative game *Corresponding author. Email: [email protected] Ó 2013 Taylor & Francis


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D.A. Revollo-Fernandez and A. Aguilar-Ibarra

in which fishermen risk-aversion presumably plays a major role in conducing them to either comply or not with recommended extractions levels. Hence, the behavior of fishermen is presumably influenced by their risk-aversion towards enforcement, which depends mainly on the expected payoff from legal activity vs. illegal activity (violating the law) (Becker 1968; Sutinen and Andersen 1985; Honneland 1999; Jentoft 2000; Nielsen and Mathiesen 2003). For example, Hatcher et al. (2000) concluded that the probability of transgressing quota restrictions in the UK was inversely related to the perceived risk of detection and the level of expected fine. A similar result was found by Jensen and Aarset (2008) who demonstrated in their analysis of coastal Norwegian fishermen that low risk of being controlled is an economic incentive for noncompliance with fisheries regulations. Therefore, analyzing the behavior of stakeholders under low enforcement levels is of paramount importance to common-pool resources sustainability. Few studies in common-pool resources extraction (e.g. fisheries) have been carried out on risk-aversion towards enforcement; most analyses rather deal with climate, health, and working conditions risks (Smith and Wilen 2005; Eggert and Lokina 2007; Nguyen and Leung 2009). Under limited budgets for enforcement activities, a key question for environmental agencies is to seek for the optimal level of enforcement level, as well as the attitudes that stakeholders have before government regulations. Therefore, the objective of this paper is to evaluate the level of risk-aversion that benefits-maximizing subjects present when faced with an openaccess regime and different levels of enforcement, under an experimental setting. 2. Methods 2.1. Experiments We carried out laboratory experiments with undergraduate students (sample = 135 players, where men = 67 and woman = 68, average 22 years) from several careers at University of Mexico (UNAM) in order to simulate scenarios where subjects are asked to decide on harvesting levels of a fishery with well-defined extraction territories but under common access. They were selected through a public call; the experiments were performed in one-hour sessions and in no case were that participants have some experience in fisheries or natural resources management. Using students as experimental subjects is an accepted way of simulating decision-making in several fields of experimental economics (Ostrom 2006; Nayga et al. 2009; Druckman and Kam 2011). In our case, laboratory experiments with students allowed us to assess the behavior both under open-access and under enforcement, without the interaction of other components of risk preferences commonly present in fishermen’s activities, such as climate, working conditions, gears manipulation, and health, among others. The experiment was based on Cardenas (2004) and Moreno-Sanchez and Maldonado (2009). It comprised two stages, each made up of five rounds. Groups of five students are formed and, during the first stage, subjects are told to each decide on an individual level of extraction in order to maximize their profits. They choose to harvest from one to eight fishes, they then calculate their benefits according to the overall level of catches of the group made during each round. Their benefits depend on both individual and group extractions, which is a typical problem in managing common-pool resources (see Formula 1) (McGinty and Milam 2013). The collective benefits are assumed to be the asset value of the natural


Journal of Risk Research

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resource (i.e. the value of a fish left alive in the sea). Students knew before starting the game that there would be only one winner: the one who gets the maximum level of total points is granted with US$40 in cash. We decided to use this format because we argue that open-access and high valued fisheries frequently present a typical run-for-fish behavior where the first to arrive takes most of the resource rent in a short span. Besides, we reckoned US$40 to be a powerful economic incentive for a public university student in Mexico, as such amount corresponds to about ninefold the daily minimum wage rate for Mexico City (the UNAM allowance for undergraduate students is about US$130 per month). In this way, students presumably took the game seriously and focused on taking the right decisions. For the second stage, a change of rules is made. They are informed that, in order to achieve a sustainable use of fishing stocks (i.e. the Pareto or social optimum), authorities have calculated that only one fish per fisher can be harvested at each round. Besides, the authority will inspect at each round the individual extractions for a percent of randomly selected players. Treatments for this experiment consisted in 20% (subsample = 35), 40% (subsample = 40), and 60% (subsample = 30) of players being randomly monitored, and a control or baseline group (subsample = 30) who remained in open-access (i.e. no enforcement was applied). If a player chose a level higher than one fish and was monitored, a zero benefit was assigned to his/her in that round. In such case, monitors communicated in private the player about the penalty. If he/she was not enforced, he/she kept his/her benefits for that round. Cheap talk was allowed during the experiment but data from groups, where collusion (agreed on a target) took place (subsample = 20), were set aside from the analyses (Appendix 1 contain the record sheets and other related material). At the end of each experiment, participants signed a written consent and filled in a questionnaire containing information on gender, age, and household income. We handed out the prize and asked the students about their decisions and points of view. Their comments were annotated as qualitative observations. Payoffs were calculated according to Cardenas (2004) and Moreno-Sanchez and Maldonado (2009); hence, each subject (i) was confronted with a profits function in each round (t): ! n n X X ðbXit2 Þ pit ¼ f ðXit ; St Þ þ g Xit ¼ ðaXit Þ þc ðe Xit Þ ð1Þ 2St i¼1 i¼1 fX 0; fXX 0; fS 0; fSS 0; gX 0; gXX 0; a > 0; b 0; S > 0; c 0 where Xit is the harvested amount, St is the resource stock, α is price, β is a technical parameter related to harvesting costs, g is a parameter related to resource conservation, and e is the highest allowed catch of the group (when the difference is less between e and the harvest amount, the greater the collective benefit). The fish stock presents the following dynamics function in time (t = round): Stþ1 ¼ St

n X i¼1

Xit þ FðSt Þ

n X i¼1

St Xit þ hSt 1 k

ð2Þ

906


where h is the intrinsic rate of growth, k is the carrying capacity, and F(St) is the resource growth function. Hence, subjects maximize their profits function subject to the stock dynamics function (2):

maxXit

T X


t¼0

d pit ¼ t

T X t¼0

( d

t

bXit2 ðaXit Þ þ ðcneÞ 2St

c

n X

!) Xit

ð3Þ

i¼1

Subject to: Equation (2). Our hypothesis was that subjects would show a risk-averse attitude towards enforcement applied by the authority. The higher the probability of being enforced, the higher the risk-aversion. 2.2. Measures of risk-aversion In order to have a quantitative assessment of attitudes towards risk, we applied four measures of risk-aversion; two are standard measures: the constant relative risk-aversion (CRRA) utility function and the Arrow-Pratt absolute risk-aversion measure. And, two new that we propose: risk-elasticity of extractions and a noncompliance risk index (NCRI). Applying all four measures allowed us to assess their performance. 2.2.1. The CRRA utility function parameter In our experiments, students decided the amount of their catch, taking into account each round’s benefits (both individual and collective), and the probability of being caught fishing over the social optimum. Therefore, a subject faces an uncertain situation which is defined as follows: Payoffs: X1, X2, …, Xn Probability of being enforced: λ1, λ2, …, λn The expected value is defined as: EU ðX Þ ¼ U ðX1 Þk1 þ U ðX2 Þk2 þ þ U ðXn Þkn ¼

n X

U ðXi Þki

ð4Þ

i¼1

The expected utility is defined as: EðX Þ ¼ X1 k1 þ X2 k2 þ þ Xn kn ¼

n X

ð5Þ

Xi ki

i¼1

The utility of the expected value is defined as: U ½EðX Þ ¼ U ðX1 k1 þ X2 k2 þ þ Xn kn Þ ¼ U

n X i¼1

! Xi ki

ð6Þ


907

Hence, risk-aversion is defined as the utility of the expected value with certainty being greater than the expected utility: ! n n X X U Xi ki [ U ðXi Þki ð7Þ i¼1

i¼1


Following Martinez et al. (2010) and, assuming that subjects have a Neumann–Morgenstern utility function with CRRA, with positive and decreasing marginal utility, we have: U ðCt Þ ¼ @U ðCt Þ ¼ Ctr > 0; @Ct

Ct1r ; 1r

r > 0

ð8Þ

@ 2 U ðCt Þ ¼ rCtð1þrÞ \ 0 @Ct2

where σ is the relative risk-aversion coefficient and σ > 0. Thus, risk-aversion is present when: ðX1 k1 þ X2 k2 Þ1r U ðEðX ÞÞ ¼ > 1r

X11r X 1r k1 þ 2 k2 1r 1r

¼ EU ðX Þ

ð9Þ

For example, if each subject decides to extract eight units, each player has two possible outcomes, depending on whether or not he/she is enforced. On the one hand, he/she has a probability of 20% (i.e. he/she is enforced) of obtaining solely the collective benefits (e.g. 160 points in that round). On the other hand, he/she has a probability of 80% (i.e. not enforced) of obtaining both individual and collective points for that round (e.g. 640 points). The same logic applies to all extractions combinations and enforcement treatments (20, 40, and 60%). Hence, the relative risk-aversion coefficient determines whether the expected value with certainty is higher or equal to the expected utility of each round extraction. 2.2.2. The Arrow-Pratt absolute risk-aversion measure The measure of aversion of Arrow-Pratt (A-P) estimates the curvature of the utility function, which is given by the following formula: s¼

U 00 ðX Þ U 0 ðX Þ

ð10Þ

The first derivative of the utility function is always considered positive, as increases in consumption levels (i.e. extractions) generate gains in welfare. In this sense, the sign of the A-P measure is determined by the second derivative and its magnitude depends on the degree of concavity or convexity of the utility function (increasing or decreasing marginal returns). If τ is equal to zero, the subject will be risk-neutral, less than zero will be risk-lover, and greater than zero will be risk-averse.

908


Assuming a constant relative risk-averse utility function (CRRA) as in Equation (8), the A-P measure is: ! @ 2 UðCt Þ ð1þrÞ rCt @Ct2 s ¼ @U ðCt Þ ¼ ð11Þ Ctr @Ct


where σ can be reckoned as a noncompliance index. 2.2.3. Risk-elasticity of extractions We propose the risk-elasticity of extractions as a measure of the proportional change of the extracted quantity chosen by subjects with respect to a proportional variation of the level of enforcement applied by authorities. Such a measure gives the degree of risk-aversion of being caught fishing above the social optimum. In fact, we named it due to the way it is computed and not making particular assumptions on the utility function. It is calculated as follows: R ¼ where Ext ¼

Enf ¼

Ext Enf

ð12Þ

DExtractions Extraction

D level of enforcement Level of enforcement

2.2.4. Non-compliance risk index We also propose the NCRI, which we define as the ratio of the number of players extracting above the social optimum and the potential number of players capable of catching more than one unit (i.e. the social optimum). It is applied only for the second stage of the game, once enforcement monitoring is in place. Hence: NCRI ¼

NCF PNCF

ð13Þ

where NCF = number of actual noncompliant fishermen, PNCF = potential number of noncompliant fishermen = P R G, P = Number of total players, R = Number of total rounds, G = Number of total groups The NCRI takes values between zero and one (1 6 NCRI 6 0). On the one side, values near the unity indicate a high risk assumed by players, given a certain level of enforcement and, therefore, a given probability of losing that round’s benefits. On the other side, a NCRI close to zero indicates that players are risk-averse towards enforcement. 2.3. Econometric model The relationship between risk attitudes, social capital variables, and regulations enforcement was analyzed by means of a logistic model of the form:


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Overfish ¼ a0 þ a1 Cooperation þ a2 Benefits þ a3 Regulation þ a4


Risk þ a5 Gender þ a6 Age þ

ð14Þ

where the dependent variable (OVERFISH) takes a value of 1 if the player harvests more than one unit of the resource, and 0 otherwise. Several studies (Ledyard 1995; Ostrom 2000b; Fehr and Leibbrandt 2011; Gächter 2007) have shown that social capital variables (e.g. cooperation, reciprocity) are critical in common-pool resources management. Thus, we included such variables in our model. Hence, the metric for cooperation was constructed as the average extractions in the previous round of the rest of players in a group. The rationale here is that the decision of either harvesting illegally or not depends on the degree of cooperation among group members. It may be regarded as either a prize or punishment to others’ actions. The variable BENEFITS include the difference between both individual and average group benefits within his/her group. The variable REGULATION measures the effect of either implementing (1) or not (0) regulations for conserving the common-pool resource. The variable RISK is a categorical variable that indicates the risk that players present according to the enforcement level from authorities (0 = no enforcement, 1 = 20% enforcement, 2 = 40% enforcement, and 3 = 60% enforcement). Finally, GENDER (1 = male, 0 = female) and AGE variables were included. 3. Results 3.1. Experiments Figure 1 presents the results in both stages of the experiment. During the first stage (left panel in Figure 1), subjects were free to choose their level of harvest. In the second stage, subjects were monitored with three levels of probability of enforcement (20, 40, and 60%), and a control treatment (open-access with no enforcement – or 0% probability), where the conditions of the first stage remained unchanged. Thus, as the degree of inspection increases, harvesting levels surpassing the quota limit are reduced. For example, with an enforcement level of 20%, catches above the quota limit are reduced in 16%, but when 40% of subjects are monitored, harvesting is reduced in 46% and finally, the figure for 60% of enforcement probability is 26% in catch reduction. In the case of the baseline scenario (i.e. no enforcement), variation in extractions between the first and the second stage is no statistically significant at the 5% confidence level (p > 0.05). There were gender differences in harvesting behavior. With a 20% of enforcement probability, both men and women reduce their catch from one stage to the other in approximately 16%. Women seemed more likely to reduce catches when enforced than men when surveillance was 60%, while men are more susceptible with 40% of enforcement. Gender differences are also evident when comparing the average extraction over the optimum social level as shown in Figure 2. As the probability of being enforced increases, the average number of catches of subjects who decide to fish above the social optimum is reduced. Men who decided to transgress the regulation of fishing one unit extracted on average more fish than women did. For example, at a 20% enforcement probability, 38% of men caught eight units while only 11% of women did the same.


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Figure 1. Average catches before (left panel) and after (right) the application of enforcement measures (i.e. a fine) Where: Points are the mean. 0% = baseline (no enforcement) (ANOVA Test: accepts the null hypothesis, there is no difference between groups, p > 0.1); 20% = 20% enforcement (ANOVA Test: reject the null hypothesis, at least one group is different, p < 0.001); 40% = 40% enforcement (ANOVA Test: reject the null hypothesis, at least one group is different, p < 0.001); and 60% = 60% enforcement (ANOVA Test: reject the null hypothesis, at least one group is different, p < 0.001).

Figure 2. Average catches under different probabilities of enforcement, according to gender.

3.2. Risk measures 3.2.1. The CRRA risk-aversion utility function parameter Table 1 presents the values that the CRRA risk-aversion utility function parameter takes, and Table 2 shows that, as this coefficient diminishes, players become more risk-averse. The average difference between the value of the expected value with certainty is higher by 1.94 (Table 2) with respect to expected utility, indicating a generalized degree of risk-aversion among subjects (as a value of zero indicates risk neutrality). However, there were differences according to gender. Indeed, women proved to be more risk-averse than men at all enforcement levels, while men were accordingly more risk-lovers. On average, the difference between the expected value


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Table 1. Different levels for the CRRA risk-aversion utility function parameter (1st column) and associated figures for expected value (2nd col.), expected utility (3rd col.), utility of expected value (4th col.), and the difference between columns 3 and 4. Σ

E(X)

EU(X)

U(E(X))

U(E(X)) > EU(X)

0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20

544 544 544 544 544 544 544 544

18.588 17.324 21.584 30.323 45.537 71.369 115.246 190.265

18.774 17.623 22.058 31.058 46.648 72.981 117.441 192.925

0.186 0.299 0.474 0.735 1.111 1.612 2.195 2.660

Table 2. Estimated CRRA risk-aversion utility function parameter according to gender and level of enforcement (% of monitored players in each round) during the second stage of the game. σ

E(X)

EU(X)

U(E(X))

U(E(X)) > EU(X)

20% 40% 60% Average

0.48 0.19 0.37 0.34

544 544 544 544

49.674 205.449 83.490 93.097

50.875 208.140 85.293 95.034

1.201 2.691 1.803 1.937

Men

20% 40% 60% Average

0.51 0.19 0.54 0.41

544 544 544 544

44.401 198.547 38.711 67.502

45.486 201.227 39.662 69.047

1.085 2.679 0.952 1.546

Women

20% 40% 60% Average

0.46 0.18 0.24 0.29

544 544 544 544

55.361 216.283 159.063 121.892

56.680 218.985 161.597 124.153

1.319 2.703 2.534 2.261

Subjects

Enforcement

All

with certainty against the expected utility is higher for women compared to men (2.26 and 1.54, respectively). 3.2.2. The Arrow-Pratt absolute risk-aversion measure According to the Arrow-Pratt absolute risk-aversion measure (Table 3), the average group is risk-averse, but women are more risk-averse than men, confirming the results shown above. 3.2.3. Risk-elasticity of extractions Risk-elasticities are shown in Table 4. It can be observed that 1% increase of enforcement probability results in 0.42% reduction in catches level. But, this attitude is gender-wise. In fact, when probability of enforcement is increased by 1%, extractions decrease in 0.15% in the case of men, whereas for women is 0.63%. Thus, women seem to be more risk-averse than men.

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Table 3. Arrow-Pratt absolute risk-aversion measure (A-P).


Subjects

Enforcement

A-P measure

All

20% 40% 60% Average

0.48 0.19 0.37 0.34

>0 >0 >0 >0

Risk-aversion Averse Averse Averse Averse

Men

20% 40% 60% Average

0.51 0.19 0.54 0.41

>0 >0 >0 >0

Averse Averse Averse Averse

Women

20% 40% 60% Average

0.46 0.18 0.24 0.29

>0 >0 >0 >0

Averse Averse Averse Averse

Table 4. Risk-elasticities of extractions. Risk-elasticities Enforcement 10 20 30 40 50 60 70 80 90 100

Both

Men

Women

4.22 8.44 12.67 16.89 21.11 25.33 29.56 33.78 38.00 42.22

1.49 2.97 4.46 5.95 7.44 8.92 10.41 11.90 13.38 14.87

6.31 12.63 18.94 25.25 31.57 37.88 44.20 50.51 56.82 63.14

3.2.4. Noncompliance risk index The NCRI shows a trend towards decreasing extractions under enforcement (Table 5). It is worth noting that extractions under 60% probability of enforcement increased with respect to extractions under 40% of enforcement for both men and women. The average extraction over the social optimum was of about 34%. An average of 41% of men decided to catch above the social optimum compared with 29% of women. In other words, 12% more men were riskier than women, extracting above the recommended level, in spite of being sanctioned by authorities. Table 5. Noncompliance risk index. Enforcement 20% 40% 60% Average

All

Men

Women

0.480 0.185 0.367 0.344

0.506 0.192 0.538 0.412

0.456 0.175 0.235 0.289


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Table 6. Econometric model.


Cooperation Benefits Regulation Risk Gender Age Constant Dependent variable No. of obs. Wald chi (df) Prob > chi square Pseudo R2

Coef.a

Std. err.b

0.260 0.002 1.196 0.322 0.181 0.036 2.651

0.054 0.001 0.133 0.061 0.106 0.020 0.523

Sig.c ⁄ ⁄ ⁄ ⁄ ⁄⁄⁄ ⁄⁄ ⁄

Odds ratiod

Odds for SDe

1.297 1.002 0.322 0.725 1.198 0.965

1.400 1.142 0.552 0.709 1.095 0.883

Overfish 1215 208.110 0.000⁄ 0.125

a

Coef. = raw coefficient. Std. Err. = robust standard errors. Sig. = ⁄, ⁄⁄ and ⁄⁄⁄denotes significance at the 1, 5 and 10% level, respectively. d Odds Ratio = exp (Coef.) = factor change in odds for unit increase in X. e Odds for SD = exp (Coef. ⁄SD) = change in odds for standard deviation (SD) increase in X. b c

3.3. Econometric model As shown in Table 6, the major finding is that the implementation of any regulation (REGULATION = 0.55) as well as risk-aversion (RISK = 0.709) negatively influence overfishing of the common-pool resource. Furthermore, men (GENDER = 1.09) presented a higher probability than women to overharvest above the social optimum. 4. Discussion and conclusion Either complying or not regulations in natural resources management, represents a risk for stakeholders. On the one hand, there exists the risk of a forgone benefit when respecting the quota while others do not. On the other hand, the risk of being caught in the act represents a probable loss of benefits. Such a behavior depends on the attitudes of subjects towards risk when faced with an enforcement authority, as well as other inner-driven attitudes. Thus, in addition to important variables such as reciprocity, altruism, and inequality aversion towards common-pool resources (Ostrom 2000a), we argue that future research needs to gain insight into risk, social capital, and gender as critical variables on the behavior of economic agents, especially on regulations noncompliance. In fact, our research is a contribution to the field of experimental economics for common-pool resources management, where few empirical studies of risk preferences have been conducted (Brick, Visser, and Burns 2011). The risk-aversion measured in this paper demonstrates that the sample studied with groups of students is, in general, risk-averse or slightly risk-loving (rather neutral) towards enforcement. The results show that, although a general tendency of lower extractions took place in comparison to a no-enforcement (i.e. open-access) treatment, a high level of enforcement (60% probability) led to higher extractions than at 20% and 40% enforcement level (see Figure 1 and Table 5). Indeed, higher penalties for enforcing common-pool resources presumably lead to lower extractions, as shown by Amacher, Koskela, and Ollikainen (2007) who apply a


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theoretical approach for modeling timber concessions. However, in practice, the conventional model of preferences theory does not always behave as hypothesized (Bowles 2004). For example, in an experiment in day-care centers in Israel, a fine increased the behavior that was supposed to be curtailed (Gneezy and Rustichini 2000). Empirical evidence suggests that monetary incentives may crowd-out intrinsic motivation, a behavior known as the Motivation Crowding Effect (Frey and Jegen 2001). Reasons for this behavior are explained in detail by Bowles (2008). As Eisenhauer (2004) points out, risk-aversion is, in fact, a deterrent to activities reckoned as bad behavior, such as illegal harvesting. Furthermore, Ostrom, Gardner, and Walker (1994) have demonstrated that social capital variables have influence in preventing free-riding behavior in common-pool resources management. Our econometric model confirmed such statements as interpreted by the change in the ratio when independent variables increased with respect to standard deviations (odds for SD). Hence, the larger the amount harvested from the average catch of the rest of the group (COOPERATION = 1.4) and the bigger the difference between economic benefits (BENEFITS = 1.14), the larger the probability of illegal catches. As both variables are social capital variables, they both show the degree of punishment (e.g. reciprocity) that a player applies when the rest of the group does not cooperate on resource conservation. External motivations, such as monetary incentives, designed to prevent certain behavior might enhance in some cases noncompliance towards common goods regulations; but internal motivations such as altruism, reciprocity, and civic virtues do have an influence on people’s decisions for promoting sustainable behavior (Bowles 2008). Some students, at the end of our experiments, commented on the importance of taking care of a common-owned resource in real life, but most of them, nevertheless, played for winning the game. Taking care of common-pool resources in the real world is observed for co-management schemes. Such an approach justifies the incorporation of fishermen in management regulations, reducing noncompliance (Jensen and Aarset 2008), lowering both enforcement costs and illegal behavior (Carlsson and Berkes 2005), and facilitating governance (Cudney-Bueno et al. 2008). In fact, field experiments have shown that a co-management regime (i.e. communication among players with external non-coercive intervention) proved to be a better management strategy for marine protected areas than external regulations based on fines (Moreno-Sanchez and Maldonado 2009). Another experiment demonstrated that most players became cooperative in the absence of enforcement rules (Rodriguez-Sickert, Guzman, and Cardenas 2008). Moreover, Falk and Kosfeld (2006) found that most of their experimental subjects showed control-averse behavior, and that trust from an authority (i.e. the principal) was a major component in complying with the principal’s rules. An open question, which warrants further research, is to assess the optimal level of enforcement, taking into account both external and internal influences on real-world decisions. Gender differences proved to be an unexpected outcome from our experiments. Early work had hinted that women cooperate more than men under social dilemmas (Stockard, Kragt, and Dodge 1988). More recent studies show that women are more risk-averse and less selfish than men in both field studies (Eckel and Grossman 2008; Croson and Gneezy 2009) and experiments (Charness and Gneezy 2012). Accordingly, in our experiments, men were less risk-averse (or more risk-lovers) than women when choosing harvesting levels above the social optimum. This result



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coincides with real-life observations off-Australia (Bird 2007) and South Africa (Brick, Visser, and Burns 2011), where women are more risk-averse to variations than men. Other explanations for the gender difference in risk-taking include: emotions affecting the evaluation of both outcomes and probabilities, overconfidence in success (i.e. viewing risks as challenges), and the interpretation of the risky situation (Charness and Gneezy 2012). Furthermore, the role of women in actual natural resources extractions, although limited in many socio-political contexts, should be explored in more detail (Agarwal 2009). A final word has to be given on methodological issues, as the use of students as experimental subjects has not yet reached a consensus. For example, on the one hand, it is reckoned that laboratory experiments systematically differ from real settings (Levitt and List 2007) and are not always capable of capturing causal effects (Bowles 2008). Moreover, rather than analyzing students behavior, field experiments in local livelihood communities which depend on common-pool resources are more appropriate in order to assess real-world behavior towards sustainability (Fehr and Fischbacher 2004; Velez, Stranlund, and Murphy 2009). Nevertheless, on the other hand, no significant differences between lab experiments with students and field experiments with real stakeholders of forest resources were found in Colombia (Cardenas 2004). Furthermore, our experimental results are consistent with field studies on fishers’ communities in South Africa as (Brick, Visser, and Burns (2011) have shown. Finally, the use of students as experimental subjects in several fields of experimental economics is commonly accepted (Ostrom 2000a; Nayga et al. 2009; Druckman and Kam 2011). Anyhow, field experiments with the same approach taken in our paper are warrant as further research. Acknowledgments This study was partially funded by National Council for Science and Technology (CONACYT) (Grant No. 81653) and Project Support Program for Research and Technological Innovation-National Autonomous University of Mexico (PAPIIT-UNAM) (Grant No. IN301310-2). DARF obtained support from a National Council for Science and Technology (CONACYT) doctoral scholarship (Grant No. 297139). We thank Juan-Camilo Cardenas, Citlalin Martinez, Fiorenza Micheli, Andrea SaenzArroyo, and Americo Saldivar for very useful comments on earlier drafts. Several professors and authorities at UNAM helped to carry out the experiments in selected schools.

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Appendix 1 The dynamic part of the game was designed as follows: if the aggregated extraction of the group´s five members exceeds 20 units, the stock of the resource for the next round becomes low


A. Table profits of high stock



B. Table profits of low stock

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C. Table sheet accounts – fisher (students)

D. Monitor table


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