Heterogeneity, trust and common-pool resource management

Fijnanda van Klingeren; Nan Dirk de Graaf

doi:10.1007/s13412-020-00640-7

Heterogeneity, trust and common-pool resource management

van Klingeren, Fijnanda; de Graaf, Nan Dirk 2020-11-26 00:00:00 Increasing migration, leading to more heterogeneous societies, may challenge the successful management of common-pool resources (CPRs) directly due to the lack of shared interests, and indirectly by reducing trust amongst local commons users, speeding up depletion of vital natural and man-made resources. Since little research has been done on this topic, we analyse the relation between economic and sociocultural heterogeneity, trust and successful commons management for fisheries and irriga- tion systems. Using multiple imputations with chained equations, random forests and predictive mean matching, we adopt an innovative and technically advanced approach to employ Elinor Ostrom’s famous CPR Database. Our approach enables us to include economic and sociocultural heterogeneity, trust and control variables in one model and to investigate both direct and indirect effects of heterogeneity on CPR success, which has not been attempted before. Results show no evidence of the negative relation between heterogeneity and CPR success. However, economic heterogeneity is negatively related to trust, and trust is found to be positively related to CPR success. Evidence is found for an indirect effect of economic heterogeneity through trust on CPR success. . . . Keywords Sustainable cooperation Heterogeneity Trust Common-pool resources Introduction unlimitedly, which leads to its decay. The effect of increasing heterogeneity on the success of CPRs, and the role of trust in Societies are becoming more diverse on ethnic, cultural and this process, is still contested (Baland and Platteau 1999; economic dimensions due to growing migrant populations all Bardhan and Dayton-Johnson 2002;Ruttan 2006, 2008; over the world, especially in Northern Africa, Western Asia Varughese and Ostrom 2001). The aim of this paper is to gain and sub-Saharan Africa (International Migration Report 2019 insight into whether and how two types of heterogeneity— 2019). This increasing heterogeneity may pose a challenge to economic and sociocultural—and their interplay with trust the successful management of common-pool resources affect the success of a CPR, that is, its sustainable long-term (CPRs) in two ways: (1) directly by diversifying interests use and quality of the resource. amongst users and (2) indirectly by reducing trust amongst Economic heterogeneity expresses inequalities in wealth, users. Both ways lead to decreased cooperation in CPRs. income and access to resources, and sociocultural heterogene- CPRs are natural or man-made resources—such as grasslands, ity represents disparities in language, ethnicity, religion and communal forests, fishing grounds or irrigation systems—for other cultural expressions (Baland and Platteau 1996;Bardhan which it is costly to exclude potential users (Ostrom 1990). and Dayton-Johnson 2002; Ruttan 2006). Most research ar- Different from a public good, a common-pool resource may gues that economic and sociocultural heterogeneity may result run out, making it vulnerable to the ‘tragedy of the commons’ in increased costs of negotiation and bargaining due to a lack as described by Hardin (1968), a situation where the short- of shared ideas, values and incentives between individuals or term dominant strategy of users is to use the limited resource groups of individuals (Aksoy 2019; Bardhan and Dayton- Johnson 2002), that it may lead to unequal sharing of decision-making rights and different motivations to cooperate (Anderson and Paskeviciute 2006;Fungand Au 2014; * Fijnanda van Klingeren fijnanda.vanklingeren@nuffield.ox.ac.uk Komakech et al. 2012) and that it may decrease social cohe- sion (Flache and Mäs 2008;Jehn et al. 1999). On the other hand, some research suggests positive effects of economic Nuffield College, University of Oxford, New Road, Oxford OX1 1NF, UK heterogeneity on the provision of collective goods, stating that 38 J Environ Stud Sci (2021) 11:37–64 it can lead to an inequality of incentives, which results such as citizen initiatives producing green energy and urban in some appropriators being motivated enough to invest agriculture projects (Bravo and De Moor 2008;De Moor in collective action on their own—hereby carrying the 2013a, b, 2018). In a time where mankind is rapidly depleting costs of cooperation (Olson 1965). the earth’s resources, research on success and failure of CPRs In order to understand the indirect relation between hetero- is more important than ever. geneity and CPR success, we introduce trust as a mediator: there is evidence that trust is influenced by heterogeneity (Alesina and La Ferrara 2002; Delhey and Newton 2005; The negative influence of heterogeneity Ostrom 1990; Putnam 2007) and that it plays an important role in influencing societal outcomes (Fukuyama 1995; Regarding economic heterogeneity it is suggested that Putnam 2000; Uslaner 2002; Zak and Knack 2001). large differences in wealth may result in a loss of incen- We will investigate the relation between trust and CPR tives to cooperate for less wealthy appropriators if the ben- success, and the role of trust in the indirect relation efits of cooperation are not high enough (Baland and between heterogeneity and CPR success. Platteau 1999), if there is no wealthy appropriator willing This paper will study 32 fisheries and 50 irrigation systems, to initiate collective action, or if the wealthy appropriators using Elinor Ostrom’s well-known Common-Pool Resource turn to their exit options—alternative ways to generate Database (Ostrom et al. 1989). Considering the field of study, income—instead (Bardhan and Dayton-Johnson 2002; which typically uses in-depth case studies, this is a relatively Jones 2004;Molinas 1998). Adding to this argument, large database (Poteete and Ostrom 2004; Ruttan 2006). Shanmugaratnam (1996) argues that sustainable use of While the CPR Database was used before to investigate the CPRs is more challenging under a more unequal distribu- relation between heterogeneity and CPR outcomes, this was tion of private wealth, as it leads to a diversification of done with correlations and Mann-Whitney U tests on the interests amongst appropriators of the CPR. When actors available data, which contains a lot of missing observations have different interests, assurance mechanisms such as (Ruttan 2006, 2008). Instead, the current paper uses innova- sanctioning are harder to implement since the actors are tive and advanced imputation techniques such as multiple im- less likely to agree on them. However, these assurance putations with chained equations, random forests and predic- mechanisms are needed for successful CPR management, tive mean matching to make the data suitable for including making diversification of the interests of actors problemat- both economic and sociocultural heterogeneity, trust and con- ic. Heterogeneity in access to exit options can also have trol variables in the same model. This enables us to test the negative effects on the CPR itself: if resource appropriators effects of one type of heterogeneity while controlling for the have relatively rewarding earning opportunities outside of other, without decreasing the sample size due to the large the appropriation of the resource, they maybemorewilling amount of missing data, something that, to the extent of our to comply with effort-restricting measures that are set in knowledge, has not been attempted yet in previous research place to maintain the CPR. On the other hand, using this database. On top of that, our preparation of the data appropriators without access to exit options may not be allows to test both direct effects of heterogeneity and trust on willing to restrict their appropriation efforts as it will have CPR success, and indirect effects of heterogeneity on CPR a greater impact on their total income (Bardhan and success through trust. This enables us to uncover part of the Dayton-Johnson 2002; Gaspart and Platteau 2007). ‘black box’ of the theoretical mechanism. Evidence from observations and experiments supporting We aim for theoretical progress in two ways. First, we test these arguments are manifold (Bardhan 2000;Hackett hypotheses regarding the relation between economic and so- et al. 1994; Varughese and Ostrom 2001). ciocultural heterogeneity and CPR success for the combined With respect to sociocultural heterogeneity, the gen- sample, and two subsamples for fishing grounds and irrigation eral argument is that collective action is more likely to systems. Second, we formulate and test a hypothesis regard- be established when the individuals involved have ing the relation between trust and CPR success specifically for strong, multistranded, interpersonal relationships, share fisheries and irrigation systems. The outcomes are relevant not common interests, and have relatively stable group only for classic CPRs as fisheries and irrigation systems, but memberships (Anderson and Paskeviciute 2006;Ostrom also for the rising number of contemporary institutions for 1990;Ostromet al. 1992; Varughese and Ostrom 2001). collective action on food, infrastructure, health and energy, Furthermore, Anderson and Paskeviciute (2006)consider heterogeneity to be an impediment to cooperation, as The database can be viewed and retrieved from https://seslibrary.asu.edu/cpr people feel threatened by others who are not part of Ruttan (2006, 2008) takes two subsamples for irrigation systems and fisher- their ‘ingroup’. ies, but does not take economic and sociocultural heterogeneity, trust and/or If subgroups of appropriators within a CPR differ in own- control variables into account in one and the same model and does not test ership of assets, skills, knowledge or sociocultural indirect effects. J Environ Stud Sci (2021) 11:37–64 39 characteristics, it is likely that these subgroups also differ in their case study of Senegalese fisheries that the division be- interests and preferred use of the resource. This can make it tween native and migrant appropriators forms an insuperable hard to reach agreements on the making and enforcing of rules problem for cooperation and mutual trust. Here, agreement on due to a lack of mutual trust and the inability to understand regulatory schemes has become nigh impossible. each other. It can be reason for conflict and thus impedes Although the literature theoretically and empirically collective action (Carpenter and Cardenas 2011;Gehrig suggests predominantly a negative effect of heterogene- et al. 2019; Johnson and Libecap 1982;Keuschnigg and ity, there are arguments for a positive effect, indirectly Schikora 2014;Ostrom 1990; Varughese and Ostrom 2001). based on a well-known theory by Olson (1965), also For instance, farmers may have different interests than no- known as the ‘Olson effect’: mads when it comes to the use of the resource, and men and women may have different actual and per- In smaller groups marked by considerable degrees of ceived costs and benefits, caused by a long history of inequality – that is, in groups of members of unequal gender inequality (Agarwal 1994, 1997;Molinas 1998; “size” or extent of interest in the collective good – there Varughese and Ostrom 2001). is the greatest likelihood that a collective good will be Nettle and Dunbar (1997) focus on another aspect of so- provided; for the greater the interest in the collective ciocultural background, namely language; they state that good of any single member, the greater the likelihood speaking the same language facilitates a feeling of social alle- that that member will get such a significant proportion giance, which is deemed important for the maintenance of of the total benefit from the collective good that he will group cohesion. Evidence in favour of these arguments is gain from seeing that the good is provided, even if he found by Wiessner (1977) with respect to language differ- has to pay all of the cost himself. (p. 34) ences between tribes in Botswana. Another example of socio- cultural homogeneity having a positive effect on sustainable Although the link with economic heterogeneity cannot direct- cooperation is given by Singleton (2001) in an analysis of ly be distilled out of this quote, it is often interpreted as an contemporary Pacific Northwest salmon fishing. The study argument in favour of a positive effect of economic heteroge- describes how homogeneous Aboriginal tribes were very neity on collective action: those with higher incomes will act efficient and sustainable in the appropriation of salmon as catalysts for collective action because they can afford it, and fishing grounds, in spite of the sometimes unequal economic it is in their interest to do so (Baland and Platteau 1997, 1999; results between individual members. The study describes how Bardhan and Dayton-Johnson 2002;Jones 2004; Ruttan 2006, conflicts about appropriation only arose between Aboriginal 2008). In addition, it is likely that the Olson effect will only tribes and the state. Based on a survey study of group take place when inequality is large and there are actors that are membership in the USA, Alesina and La Ferrara (2000)con- indeed so rich that they can afford to pay to see collective cluded that residents from racially heterogeneous communi- action happen. It is unlikely that there are many of these cases ties participate less in social activities. Carpenter and Cardenas in the current database. Based on the discussed literature, we (2011), employing a laboratory experiment with Colombians therefore expect negative effects to be more probable. Hence, and Americans, discovered that mixed groups cooperate less our first hypothesis reads: (hypothesis 1a) economic and (hy- than homogeneous groups. Keuschnigg and Schikora (2014) pothesis 1b) sociocultural heterogeneity have a negative rela- found in a study using public good games [PGGs] that reli- tion with CPR success. gious heterogeneity decreases cooperation in the presence of a leader: whereas a generous contribution of leaders in homo- geneous groups is met with reciprocity from the followers, this The role of trust was not the case in heterogeneous groups. Vermillion (1999) mentions that the absence of social divisions is a requirement A variable that we consider to play a role in the indirect effect for collective action amongst farmers in devolution programs of heterogeneity on CPR success is trust. The first part of the of irrigation systems, in which rights and responsibilities are argument, illustrating the relation between heterogeneity and transferred from the government to local water user groups. trust, is that people are more likely to trust someone who is Lastly, Gaspart and Platteau (2007) concluded on basis of more similar to themselves (Alesina and La Ferrara 2002; Coleman 1994), implying more mutual trust in a more homo- geneous setting and less mutual trust in a heterogeneous one. Johnson and Libecap (1982) illustrate this in a case study concerning fish- People tend to trust members of their family and members of ermen: fishermen with more skills and knowledge on how to set traps, trawling speed and the best locations for a good catch turn out to be the more successful the same ingroup; be it racial, social, ethnic or based on some- fishermen. This heterogeneity in productivity will lead to different points of thing else (Alesina and La Ferrara 2002;Romano etal. 2017). view regarding uniform fishing quotas and other restricting policies on re- Many studies point out a negative association between source subtraction, thus to higher transaction costs and a higher probability of conflict. heterogeneity and trust. For instance, in their study using 40 J Environ Stud Sci (2021) 11:37–64 individual level data from the USA, Alesina and La Ferrara aspects, which may influence the way sociocultural and eco- (2002) observe a negative relation between social distance and nomic heterogeneity and trust impact their success. First, trust, and point out that being economically unsuccessful and whereas fishing grounds can be considered natural resources, living in a neighbourhood with a high degree of income irrigation systems are entirely man-made. This influences the inequality reduces trust. Delhey and Newton (2005)findthat way appropriators see the resource; an open-access resource generalised trust is closely related to homogeneity in religious, available to everyone versus a self-made system only avail- cultural, social and political identification as well as to able to the ones who are granted access and/or are contributing economic equality. Similarly, Gehrig et al. (2019)find in a to its maintenance (Gaspart and Platteau 2007). Second, lab-in-field CPR experiment in Zanzibar that less trusting fish- whereas there are many different techniques to appropriate a ermen overexploit the CPR more in heterogeneous groups, fishing ground, which can be cause for conflict between while they cooperate in the homogeneous group to achieve a appropriators (Gaspart and Platteau 2007), irrigation systems sustainable use of the resource. Regarding causality, Leigh work one way for all users. Third, while mutual monitoring (2006a, b) uses an instrumental variable approach in two stud- for irrigation systems is easy, this is more difficult for fishing ies, to show that increasing inequality and ethnic and linguistic grounds, where fishing boats cannot see each other during fractionalisation reduce trust. Adding to that, Romano et al. appropriation and where illegal appropriation forms a daily (2017) find in a series of trust games in 17 countries that threat to the success of the CPR (Gaspart and Platteau 2007; people are generally more trusting towards ingroup members Regmi 2007). Fourth, while the resource flow of a fishing than towards outgroup members. ground can be considered relatively stable—conditional on The second part of the argument concerns the relation of the resource not being nearly depleted, the flow of water com- trust with positive societal outcomes—in our case CPR suc- ing from the river that provides water to the irrigation system cess. Societies with high levels of trust amongst individuals is less predictable (Regmi 2007). This poses a challenge for yield a lesser need for the individuals to protect themselves devising appropriation rules. For fishing grounds, many coun- from being taken advantage of by others in mutual transac- tries impose individual fishery quotas to improve sustainabil- tions (Knack and Keefer 1997). Instead of formal institutions, ity of fishing activities (Sanchirico et al. 2006)or even mora- mutual trust amongst individuals facilitates the use of informal toriums until specific fish populations regrow (see for instance agreements, leading to a decrease in transaction costs and a Jiang et al. 2009;Khanet al. 2018; Palmer and Sinclair 2008). greater likelihood of economic efficiency and success However, these rules do not necessarily make sense for an (Alesina and La Ferrara 2002; Knack and Keefer 1997). irrigation system, since the total discharge of a river is likely Next to economic success, trust is known to promote cooper- to change over time and is less dependent on use by farmers ation and participation in social activities (Alesina and La and more dependent on external factors such as the weather. Ferrara 2000; La Porta et al. 1997;Romano et al. 2017). Instead, time allocation rules are used that can vary depending Empirical evidence supports this argument. A multitude of on the availability of water (Regmi 2007). Fifth, whereas a studies show a positive relation between high levels of inter- fishing ground does not require much, if any, maintenance, personal trust and economic growth of societies (Knack and irrigation systems do: man-made components such as check Keefer 1997; La Porta et al. 1997; Zak and Knack 2001). gates have to be checked regularly and fixed when broken. Regarding the causal direction of the effects of trust, Acedo The government will take care of the maintenance if the sys- and Gomila (2013) find in an experiment involving an iterated tem is government owned, but for systems that are not gov- prisoner’s dilemma that higher trust results in higher levels of ernment owned, this requires farmers to work together cooperation. Likewise, Gächter et al. (2004)find thatmore (Vermillion 1999). This type of collective action is not re- trusting people contribute more than less trusting people in quired for fishermen, whose profits are not dependent on each three-person one-shot public good games. other in this way. Lastly, a big difference between fishing Summarising, we hypothesise that (hypothesis 2a) eco- grounds and irrigation systems is the constant disadvantage nomic and (hypothesis 2b) sociocultural heterogeneity have of appropriators in an irrigation system that are located down- a negative relation with trust; (hypothesis 3) trust has a posi- stream as opposed to at the head of the river (Ostrom 1990; tive relation with CPR success; (hypothesis 4a) economic and Regmi 2007). The appropriators upstream are the first ones to (hypothesis 4b) sociocultural heterogeneity have a negative receive water and are the least likely to be disadvantaged when indirect relation with CPR success through trust. other appropriators overexploit the resource. The appropriators downstream on the other hand, will experience The relevance of sector type This problem may be solved to an extent with modern technologies such as vessel monitoring systems (VMSs) and automatic identification systems The cases analysed in this paper are either fisheries or irriga- (AISs), used to track fishing vessels. However, our data comes from before tion systems. These two types of CPR vary in a multitude of 1990, when neither VMS nor AIS were in use. J Environ Stud Sci (2021) 11:37–64 41 the worst consequences of overexploitation of the resource by major publication based on the CPR Database is Elinor more upstream appropriators. Whereas fisheries usually have Ostrom’s Governing the Commons (1990), contributing to the a rotation system of sorts to equalise appropriation time at Nobel Memorial Prize in Economic Sciences that she won to- better spots, this is not possible for irrigation systems gether with Oliver E. Williamson, for her analysis of economic (Ostrom 1990;Regmi 2007). A table describing differences governance in CPRs. The dataset contains 32 fisheries and 50 in nature and various characteristics between fishing grounds irrigation systems for analysis of the variables of interest for this and irrigation systems using variables from the CPR Database paper. The 3 CPRs that are neither fisheries nor irrigation sys- can be found in Appendix 1. tems are not included since they lack cases for making a com- The differences between the two sector types may have parison between sector types. The CPRs are located in 29 dif- implications for the expected effects of trust on CPR success: ferent countries from all over the world, although many are due to almost automatic mutual monitoring and the closed- situated in the Middle East and Asia. A table comprising the access and man-made nature of the resource, trust amongst cases and their sources used in this study can be found in appropriators may be less vital in order for the irrigation sys- Appendix 2. tem to be successful. For fisheries however, trust amongst appropriators may play a more important role in reaching sus- tainable appropriation, as appropriators would need to trust Measurements each other not to overexploit the resource while not being able to see each other, or each other’sactions, straight away. Based Many of the variables that are available in the database are on the above we expect (hypothesis 5) that the relation of trust recorded for both the beginning and the end of a period of with CPR success is stronger for fishing grounds than for time, during which “the actions of the appropriators are rela- irrigation systems. tively consistent” (Ostrom et al. 1989, p. 352). These periods are of variable length, and different survey forms are provided for each period. These period forms, or ‘time slices’, are the observations in the dataset. Of the 82 separate CPRs that will Data and methods be used for our analyses, seven have more than one period form filled out, so more than one time slice; this means that Data researchers conducting the case study found that during their study, several periods could be distinguished with specific We use the Common-Pool Resource Database compiled by information for each of them. Separate periods are considered Elinor Ostrom and her team (1989) to test our hypotheses. as different observations since this period-specific information This database is based on a bibliography comprising over would get lost otherwise. Though the data has a multilevel 1800 published and unpublished original CPR case studies structure—subgroups nested in time slices nested in CPR from before 1990. A small subset of this bibliography was cases—we take all variables on the time slice level: selected, and coded into the CPR Database using extensive operationalised variables are either original CPR level vari- survey forms containing over 600 questions on topics such as ables for a specific period or aggregated subgroup variables geographic and demographic features of the CPR location, for that period. We do this because not all CPRs have multiple boundaries and physical characteristics of the CPR, the situa- time slices or multiple forms coded for their separate sub- tions faced and actions performed by appropriators of the CPR groups: there are 123 forms for 82 CPRs, existing of 95 cases and the strategies of appropriators in subgroups (Ostrom 1990; due to the extra period files. Cases that are twice in the dataset Ostrom et al. 1989;Schlager 1990;Tang 1989). It was required due to multiple coding forms for different subgroups are de- that the material is written “by a researcher who has spent leted, as we only use the aggregate information, which other- considerable time in the field” (Ostrom et al. 1989,p.10) and wise would be duplicate. The three cases that are neither fish- that the material contains “key information about the structure eries nor irrigation systems are removed. In total the number of the resource, the rules used in organizing the resource, the of observations that can be used for analyses is N = 92. If strategies adopted by the appropriators, and the outcomes variables are recorded for both the beginning and the end of achieved” (Ostrom et al. 1989,p.10).Thisway,40person- the period, the variables for the end of the period will be used years of fieldwork, conducted by researchers interested in the field of CPRs, such as social scientists, historians and engineers, are captured in one database (Ostrom et al. 1989). The first The seven items used for sociocultural heterogeneity are, according to the codebook, only filled out if there are multiple subgroups present in the CPR Selection criteria are that the case study is the result of “extended fieldwork (Ostrom et al. 1989). We thus assume that for each case there are multiple and that information be provided about (1) the structure of the resource system, subgroups; since even for cases without an extra coding form, at least one of (2) the attributes and behaviours of the appropriators, (3) the rules that the the seven items are still filled out. Even if there is one subgroup, filled out appropriators were using and (4) the outcomes resulting from the behaviours items about sociocultural heterogeneity provide information on the levels of of the appropriators” (Ostrom 1990, p. xv). heterogeneity in that CPR. 42 J Environ Stud Sci (2021) 11:37–64 (cf. Ruttan 2006, 2008). See the CPR Coding Manual for a Control variables more detailed description of the data (Ostrom et al. 1989). The following control variables were added to the final unit quality and balance models because they could influence suc- Dependent variables cess of the CPR: cultural view of the resource, number of users of the resource, closed access to the resource, opportunities for Unit quality: This variable is operationalised with an item exit options, monetary, physical and social sanctions, pollution, indicating the ‘quality of the units that are withdrawn from level of financial pressure for immediate returns from the CPR, the resource’. There are five answering categories, ranging dependence on CPR for family income, the presence of consis- from ‘extremely poor quality’ (0), to ‘extremely high quality’ tently disadvantaged appropriators who are cut off from bene- (4). The quality of the appropriated units is an indicator of the fits and variation in availability of units over space. A more quality of the resource in general, and thus represents a sub- detailed description of the variables can be found in Appendix stantive part of the success of the CPR. 3. Since most variables have no significant relations with the Balance: This variable is operationalised with an item in- outcome variables, these models are not considered for the dicating the ‘balance between the quantity of [resource] units interpretation of the results but are presented in Appendix 3. withdrawn and the number of units available in the resource’. There are five answering categories, ranging from ‘extreme shortage’ (0) to ‘quite abundant’ (4). The balance between Analytical strategy and causality withdrawal and renewal of the resource indicates the health of the resource as well as the sustainability of the behaviour of To test the hypotheses, OLS regression will be used and the the appropriators, and thus represents another substantive part unstandardised coefficients will be interpreted. Even though the of the success of the CPR. variables unit quality, balance, economic heterogeneity, socio- cultural heterogeneity and trust are not continuous but ordinal, we will consider them continuous in the interest of simplicity of Independent variables interpretation. This allows us to retain statistical power— especially given the small sample size in the subsamples—by Sector type: This independent variable indicates whether the CPR is an irrigation system (1) or not (0). Since the dataset reducing degrees of freedom. In addition, it allows us to calcu- late indirect effects in a meaningful way. Following argumen- exists only of irrigation systems and fishing grounds, the var- iable can be interpreted as being an irrigation system (1) ver- tation of amongst others Pasta (2009) and Williams (2018)on treating ordinal independent variables as continuous, a likeli- sus a fishing ground (0). In the regression tables, this variable will be named ‘Irrigation’ for the main effects and will be hood ratio test was completed to establish whether the models would significantly differ between treating the variables as or- abbreviated to ‘Irr.’ when used in an interaction effect. dinal or continuous (see Williams (2018) for a more extensive Economic heterogeneity: The independent variable economic heterogeneity is operationalised as the highest level of variation explanation of the test). The test concluded that economic het- erogeneity, sociocultural heterogeneity and trust can be treated in income within any subgroup within a CPR time slice (cf. Ruttan 2008). The item on variation in income has three outcome as continuous in the models. Ordinal logistic regression was also performed, with ordinal independent variables added as variables, ranging from ‘low’ (0) to ‘moderate’ (1) to ‘high’ (2). Sociocultural heterogeneity: This independent variable con- dummies. These models can be found in Appendix 4.The main results from the OLS analyses are largely supported by the sists of the maximum value found per time slice in any of seven items: the extent to which ethnic, racial, religious, caste, clan more conservative ordinal logistic models. In addition, a robust- ness check was performed with an alternative operationalisation and gender identification and the language spoken affect com- munication between subgroups. All seven items have the same of economic and sociocultural heterogeneity, by taking the five-point response scale ranging from ‘no difference along this mean per time slice of the variation of income in CPR sub- groups for economic heterogeneity and the mean of the seven variable’ (0) to ‘large differences which significantly affect communication’ (4). sociocultural heterogeneity items instead of the maximum val- ue. The results are very similar to the OLS models and can be Trust: This variable serves as both independent and depen- dent. It is an item indicating the extent of mutual trust amongst foundinAppendix 5. Relations found in the OLS models that appropriators within the CPR on a three-point scale, with the A likelihood ratio tests between a constrained model treating the variables as categories ‘low levels of trust’ (0), ‘modest levels of trust’ (1) continuous variables and an unconstrained model treating the variables as and ‘moderate to high levels of trust’ (2). factors (Williams 2018) was performed for each iteration of the imputed dataset, and the p values were plotted. For each test, the average p value was We take this approach following Ruttan (2006) with the same rationale; we higher than ɑ = 0.05; hence, we conclude that treating economic heterogeneity, are interested in any kind of sociocultural heterogeneity that may take place, sociocultural heterogeneity and trust as factor variables does not improve not in all of them at the same time. significantly on treating them as continuous variables. J Environ Stud Sci (2021) 11:37–64 43 are not backed up by the ordinal logistic models or the robust- missing observations in key variables, multiple imputation ness check models will not be considered robust. with chained random forests (RFs) (Breiman 2001;Van Part of the analytical strategy is to analyse the entire sample, Buuren 2019) was performed, using the MissRanger package includingbothfisheries andirrigationsystems,aswellastwo in R (Mayer 2019), an adaptation of the MissForest package subsamples of fishery- and irrigation-only cases. This allows us by Stekhoven and Bühlmann (2012) using the Ranger pack- to look at the general picture as well as to look at associations age (Wright et al. 2019). RF imputation accommodates non- between variables that may be specific to the sector type. linearities and interactions and does not need a specific regres- Questions that may arise after looking at the combined sample sion model to be defined. Predictive mean matching (PMM) may be answered when looking at the separate samples. In was used to fill in the missing values with realistic imputa- addition, analysing the combined sample allows us to not miss tions, that is, avoiding the imputation of continuous values in a out on the detection of associations between variables by discrete variable, for each iteration. PMM also enables imput- retaining statistical power compared with the subsample analy- ed values to be endowed with realistic levels of local variabil- ses, given the small sample size. To test hypothesis 5 regarding ity, effectively raising the variance of the resulting RF- the differences in the effect of trust on CPR success between estimated conditional distributions to a more realistic level fishing grounds and irrigation systems, an interaction between (Mayer 2019). We created 100 simulations and ensured the trust and sector type (Irr. × Trust) is added in addition to mea- chained RFs would stop re-fitting after 30 iterations, though in suring the effect of trust in the two subsamples. every simulated imputed dataset, this procedure took at most 5 Although causal phrases are used throughout the discus- iterations, suggesting quick convergence to optimally imputed sion of the results, the observational data only allows to test values. Imputation diagnostics, including the ‘out of bag error’ associations, and causal conclusions can in principle not be (OOB) distribution per imputed variable, were inspected for drawn. However, we have some confidence in the assumed key variables and supported our confidence in the imputation causal directions. Even though one could argue that trust model. Research comparing MissForest imputation to other could bring homogeneity about instead of homogeneity induc- imputation techniques shows that MissForest performs well ing trust, it is important to know that sociocultural heteroge- and in a lot of cases better than other established imputation neity (ethnic, racial, clan, caste, religious and gender identifi- techniques, even when applied to data with up to 30% missing cation and the language spoken) is rather fixed, as is economic values (Stekhoven and Bühlmann 2012). As the current data- inequality, although less so. Hence, in this respect, we have base has 28% missing data, using the MissRanger package some confidence in the assumed causality (see also Leigh based on the MissForest package is well suited. By making (2006a, b) and Romano et al. (2017)). With respect to trust use of multiple imputation, both sociocultural and economic and CPR success, it might also be possible that a high score on heterogeneity can be included in one model without reducing CPR unit quality and balance increases trust. However, the sample size. The value of including both forms of hetero- experimental research of Acedo and Gomila (2013)and geneity in the model is that the risk of overestimating the Gächter et al. (2004) discussed earlier provides evidence for influence of one by not controlling for the other is reduced. the causal direction reflected in our hypotheses. Table 1 provides insights in the original versus the imputed dataset. The adjusted R including the 95% confidence interval is Multiple imputation: mice, random forests and provided for the models where possible. In addition, the predictive mean matching fraction of missing information [FMI] is reported for the models where it was possible to calculate them, providing All main independent variables have missing values—some information on the uncertainty about the missing data, which more than others. The missingness of the independent vari- affects the pooled standard errors (Pan and Wei 2016;Wagner ables is not correlated with relevant variables in the model. We 2010). These statistics are retrieved using the pool function of assume the missing values to be missing at random (MAR) the Mice package (Van Buuren 2019). In addition, the Akaike (Rubin 1987) and not dependent on unobserved data. This is, information criterion [AIC] will be reported for every model. however, an untestable assumption. To prevent having to per- Lastly, tables stating the FMI per variable for main models form analyses on a smaller sample size than 92 cases due to will be provided in Appendix 6. The missingness of the variable economic heterogeneity is assumed to be a The out of bag error is the mean prediction error on each training sample; for consequence of the way the data was constructed; the data is based on a survey that was filled out on the basis of information given by published case studies. a categorical variable, ‘how often is a ‘wrong’ class imputed in a variable’ and for continuous variables, it is 1 − R , that is, the unexplained variance Many case studies did not provide information on the variance of family incomes within CPRs, and the missingness is thus more likely related to (Stekhoven and Bühlmann 2012). 11 2 coincidences or external factors rather than unobserved variables that could For some of the subsample models, adjusted R and FMI could not be be of importance to the analyses and interpretation of results (see also Dong calculated, as the Fisher transformation for pooled simulations could not be and Peng 2013). performed since some of the simulations had a negative R . 44 J Environ Stud Sci (2021) 11:37–64 Table 1 Imputed data statistics: key variables above line, control variables below line Observations per simulation 1:100 Complete Incomplete Imputed Total Used N* OOB OOBSD. Mean Economic heterogeneity** –– – 123 92 Income variance 65 58 58 123 92 0.13 0.02 Sociocultural heterogeneity** –– – 123 92 Ethnic identification 101 22 22 123 92 0.00 0.01 Race identification 101 22 22 123 92 0.02 0.00 Religious identification 88 35 35 123 92 0.03 0.01 Gender identification 101 22 22 123 92 0.04 0.01 Clan identification 92 31 31 123 92 0.10 0.01 Caste identification 71 52 52 123 92 0.06 0.01 Language spoken 115 8 8 123 92 0.01 0.01 Unit quality 118 5 5 123 92 0.05 0.01 Balance 119 4 4 123 92 0.11 0.02 Trust 112 11 11 123 92 0.06 0.01 Cultural view of resource 102 21 21 123 92 0.08 0.01 Pollution 91 32 32 123 92 0.01 0.00 Pressure 37 86 86 123 92 0.05 0.01 Income dependence 97 26 26 123 92 0.07 0.01 Variation over space 105 18 18 123 92 0.02 0.01 Worst off 74 49 49 123 92 0.01 0.00 Exit options 80 43 43 123 92 0.12 0.01 Social sanctions (informal) 72 51 51 123 92 0.17 0.02 Physical sanctions (informal) 64 59 59 123 92 0.17 0.02 Formal sanctions 62 61 61 123 92 0.22 0.02 Number of users 102 21 21 123 92 0.37 0.03 *Used N is the total number of cases (123; all CPR types + duplicates due to multiple subgroup forms) minus duplicates (− 28), minus other sector types (− 3), but keeping the different ‘time slices’ as mentioned before **These variables were constructed after multiple imputation, before deleting duplicates Results has a significant negative relation with trust in all three samples. In addition, it has a negative relation with In this section, Spearman’s rank correlations will first balance in the combined and irrigation system sample. be discussed to get an initial idea of the relation be- Sociocultural heterogeneity has a negative relation to tween variables. To test the hypotheses, we will discuss trust in the combined sample and the irrigation sample, OLS regressions for the combined sample of both fish- a significant negative relation to balance in the irriga- ing grounds and irrigation systems, and the two subsam- tion sample and a marginally significant negative rela- ples separately. Both direct and indirect effects will be tion with unit quality in the irrigation sample. Trust has discussed. Lastly, the robustness of the found results is a positive relation to both CPR success outcomes in all assessed by crosschecking the OLS regressions with the three samples except unit quality in irrigation systems. ordinal logistic regressions and the OLS regressions So far, the results thus partially support hypotheses 2a with the alternative operationalisation of the heterogene- and 2b and largely support hypothesis 3. Only limited ity variables, both of which can be found in the support is found for hypotheses 1a and 1b. Hypothesis appendices. 5 does not hold for balance but could yet hold for unit quality. Correlations Combined sample results Table 2 shows the relation between key variables using Spearman’s rank correlation. The table shows the average The OLS regression models on CPR success using the imput- coefficients over 100 imputed datasets and includes the stan- ed data are presented in Table 3. Model 1 and model 2 show dard errors in parentheses. The same table for the avail- that irrigation systems have significantly lower scores on unit able case data is shown in Appendix 7,showing very quality (B = − 0.53, p < 0.001) and balance (B = − 0.52, p = similar results. It is shown that economic heterogeneity 0.005) than fishing grounds, indicating that there may be J Environ Stud Sci (2021) 11:37–64 45 fundamental differences in success variables between the sec- effect of economic heterogeneity on unit quality through trust tor types. Model 3 and model 4 include the effect of sociocul- (B = − 0.17, p =0.017). Using the trust coefficient for irriga- tural and economic heterogeneity and show that there is no tion systems, we find a significant indirect effect of economic significant relation between either sociocultural or economic heterogeneity on balance through trust (B = − 0.20, p =0.033). heterogeneity and unit quality or balance, so far thus rejecting These results partially support hypothesis 4a stating the nega- hypotheses 1a and 1b stating a negative relation of heteroge- tive indirect effect of economic heterogeneity on CPR success neity with CPR success. Model 5 and model 6 include the through trust, but as no other significant indirect effects are effect of trust; model 5 shows a significant relation between found for the combined sample, the supportive evidence for trust and unit quality (B = 0.20, p = 0.040), andmodel 6shows hypothesis 4a is very limited and hypothesis 4b is so far a significant relation between trust and balance (B =0.54, rejected. To check the robustness of the tests for the indirect p < 0.001), supporting hypothesis 3 stating that higher levels effects, moderated mediation models using the mediate func- of trust are associated with CPR success. To test hypothesis 5, tion in R were applied (Tingley et al. 2014), to test the differ- models 7 and 8 include the interaction effect between trust and ence in mediation effects of heterogeneity through trust on sector type. Model 8 shows no improvement in fit, but model CPR success between fishing grounds and irrigation sys- 2 15 7 shows an increase from 0.28 to 0.39 for the adjusted R .The tems. The results support the found indirect effects and can main effect of trust on unit quality, thus the relation between be seen in Appendix 8. trust and unit quality in fishing grounds, is significant and positive (B =0.53, p < 0.001), adding to the support for hy- Separate sample results pothesis 3. The interaction effect is significant and negative (B = − 0.57, p < 0.001) indicating that the relation between Table 4 shows the models testing the hypotheses separately trust and unit quality for irrigation systems is basically zero for the fishing ground sample (N = 40) and the irrigation sys- and thus that trust amongst appropriators in a fishing ground tem sample (N = 52). In the fishing ground sample, a positive may play a bigger role in achieving high levels of unit quality significant relation between trust and unit quality (B =0.54, than in irrigation systems. This result only partially supports p = 0.004) in model 3 and a marginally significant relation hypothesis 5; only in the case of unit quality. The main effect between trust and balance (B = 0.50, p = 0.062) in model 4 of trust on balance in model 8, thus the relation between trust are found. Both results add to the support for hypothesis 3. and balance for fishing grounds, is marginally significant and A marginally significant relation between economic heteroge- substantive (B = 0.43, p = 0.053), indicating a 0.43 unit in- neity and trust is visible (B = − 0.28, p =0.071) in model 5. crease on a five-point scale of balance per increased unit of Although not significant at the 5% level, it is a substantive economic heterogeneity. Model 9 shows that economic het- effect of a 0.28 point decrease in the three-point scale of trust erogeneity (B = − 0.32, p = 0.004) has a significant negative per increased unit of economic heterogeneity, providing mod- relation with trust, supporting hypothesis 2a. No evidence for est support for hypothesis 2a. hypothesis 2b is found. Lastly, model 10 shows that there is no With respect to the irrigation system sample, a hint of significant main effect of sector type on trust, indicating that the indirect effect of economic heterogeneity by trust can irrigation systems and fisheries do not necessarily differ in be seen from models 2, 4 and 5. Model 2 shows a mar- levels of trust, even though trust within each sector type may ginally significant negative relation of economic hetero- affect CPR success differently. geneity and balance (B = − 0.35, p = 0.10), modestly The indirect effects of economic heterogeneity on unit supporting hypothesis 1a. Model 4 shows a significant quality and balance through trust are calculated manually, relation of trust and balance (B =0.59, p = 0.007), using Sobel’s(1982) product of coefficients approach for the supporting hypothesis 3. In addition, it shows the disap- coefficient, and Monte Carlo simulations for the standard error pearance of the significance of economic heterogeneity. and two-sided p value. Taking the coefficient of trust for Lastly, model 5 shows a significant negative relation fisheries from model 7, we calculate a significant indirect The combined sample is used, but as the main effect of trust in models 7 and The main effect of trust for irrigations in model 7 is the main effect for trust 8 is interpreted as the main effect of trust for fisheries, due to the addition of an (B = 0.53) minus the interaction coefficient (B = −0.59) which adds up to an interaction effect of sector type (irrigation system = 1) and trust. The main effect of B = − 0.06. effect of trust for irrigation systems is now the main effect of trust minus the Since the distribution of the product can be considered normal, as the prod- interaction term coefficient. Hence, we can and must specify indirect effects of heterogeneity through trust for each sector type separately. uct yields the same outcome as the difference between coefficients approach by Judd and Kenny (1981) (see also MacKinnon et al. (1995)), a Monte Carlo Due to incompatibility of the moderated mediation analysis with the Mice simulation was used, with 100,000 observations using two normal distribu- paradigm and computational tools, we cannot obtain pooled standard errors for tions based on the respective coefficients and standard errors of economic the estimates of the moderated mediation. As a result, we resolve to fit the heterogeneity on trust and trust on unit quality or balance, after which a z moderated mediation to a representative dataset; this dataset is derived by score, t score and the two-sided p value of the indirect effect could be taking the mean of numeric variables, and the mode of factor variables of calculated. the 100 imputed datasets, to create an average dataset. 46 J Environ Stud Sci (2021) 11:37–64 between economic heterogeneity and trust (B = − 0.29, p = 0.050), providing some support for hypothesis 2a. Possibly, the results support hypothesis 4a for balance: the disappearance of the significant effect of economic heterogeneity from models 2 to 4 combined with the sig- nificant negative relation between economic heterogeneity on trust could be an indicator of an indirect effect of economic heterogeneity through trust on balance. Regarding sociocultural heterogeneity, model 1 shows a significant negative relation between sociocultural hetero- geneity and unit quality (B = − 0.15, p = 0.034) and model 5 shows a negative relation with trust (B = − 0.37, p = 0.026), providing partial support for respectively hypoth- eses 1b and 2b for irrigation systems. However, the sig- nificant effect of sociocultural heterogeneity on unit qual- ity remains in model 3 (B = − 0.19, p = 0.025) and trust is not significant, indicating that there is no indirect effect of sociocultural heterogeneity on unit quality through trust. The indirect effects of economic or sociocultural heteroge- neity on balance and unit quality for fishing grounds are not significant. For irrigation systems, the indirect effect of socio- cultural heterogeneity on balance is marginally significant (B = − 0.22, p = 0.079), indicating modest support for the role of trust as stated in hypothesis 4b. The indirect effect of eco- nomic heterogeneity on balance through trust is just about not significant on the marginal level, but should, given the small sample size, not be ignored (B = − 0.17, p = 0.109). To check the robustness of the tests for the indirect effects, moderated mediation models were applied. The models can be found in Appendix 8. Table 5 shows an overview of the results found per hypothesis. Counting the three samples—combined, fish- ery and irrigation—and the three methods—OLS regres- sion as shown in main tables, the ordinal logistic regres- sion [OLR] and the robustness check [RC] models with the alternative operationalisation of economic and sociocultur- al heterogeneity—there are nine tests for each hypothesis, except for hypotheses 4a and 4b which have not been cal- culated with the OLR models and thus have six tests. From this overview, we can conclude that there is convincing evidence for hypothesis 2a on the negative relation of eco- nomic heterogeneity with trust and hypothesis 3 on the positive relation of trust with CPR success, confirmed in, respectively, eight and nine tests out of nine. Hypothesis 1b is only supported for balance in irrigation systems, and hypothesis 5 is only supported regarding unit quality; it is marked as supported in all tests because all tests point out that trust is more important for fishing grounds than irriga- tion systems for unit quality, but the hypothesis as a whole—encompassing both balance and unit quality—is still only partially supported. Hypothesis 4a is partially supported with three significant indirect effects out of six tests in addition to the supported hypotheses 2a and 3. Table 2 Spearman correlation for main variables Combined sample Fishing grounds Irrigation systems Unit quality Balance Trust Unit quality Balance Trust Unit quality Balance Trust EH − 0.09 (0.07) − 0.20† (0.06) − 0.40*** (0.06) − 0.18 (0.10) − 0.04 (0.09) − 0.39* (0.07) 0.00 (0.07) − 0.36* (0.09) − 0.541* (0.01) SH − 0.04 (0.07) − 0.17 (0.06) − 0.23† (0.07) − 0.12 (0.10) − 0.15 (0.10) − 0.05 (0.11) − 0.25† (0.01) − 0.33* (0.06) − 0.42** (0.08) Trust 0.23* (0.03) 0.44*** (0.03) – 0.47** (0.07) 0.33* (0.03) – − 0.14 (0.02) 0.51*** (0.05) – N 92 92 92 40 40 40 52 52 52 Standard errors in parentheses ***p < 0.001, **p < 0.01, *p < 0.05, †p < 0.1 two-sided J Environ Stud Sci (2021) 11:37–64 47 Table 3 OLS regression on main variables and interaction effect using the imputed sample of both fisheries and irrigation systems (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Unit quality Balance Unit quality Balance Unit quality Balance Unit quality Balance Trust Trust Irrigation − 0.53*** − 0.52** − 0.54*** − 0.55** − 0.51*** − 0.47** 0.45 − 0.78 − 0.16 (0.10) (0.18) (0.10) (0.18) (0.10) (0.17) (0.28) (0.49) (0.13) Irr. × Trust − 0.58*** 0.18 (0.16) (0.28) Trust 0.20* 0.54*** 0.53*** 0.43† (0.09) (0.15) (0.13) (0.22) Sociocultural heterogeneity − 0.06 − 0.21 − 0.02 − 0.10 − 0.07 − 0.09 − 0.19 − 0.20 (0.09) (0.15) (0.09) (0.14) (0.09) (0.15) (0.14) (0.14) Economic heterogeneity − 0.05 − 0.21 0.01 − 0.04 0.00 − 0.03 − 0.32** − 0.31** *** (0.08) (0.15) (0.08) (0.15) (0.08) (0.15) (0.10) (0.10) Constant 2.57*** 2.03*** 2.70*** 2.50*** 2.25*** 1.26*** 1.74*** 1.43** 2.17*** 2.28*** (0.08) (0.14) (0.15) (0.26) (0.27) (0.45) (0.29) (0.50) (0.19) (0.20) Adj. R 0.24 0.08 0.24 0.14 0.28 0.25 0.39 0.25 0.19 0.19 95% CI Adj. R (0.98, 0.40) (0.01, 0.21) (0.10, 0.41) (0.02, 0.31) (0.13,0.45) (0.10, 0.42) (0.21, 0.56) (0.10, 0.42) (0.03, 0.40) (0.03, 0.40) FMI 0.04 0.05 0.10 0.26 0.11 0.16 0.27 0.16 0.48 0.48 AIC − 137.80 − 25.28 − 136.74 − 29.95 − 142.52 − 38.54 − 159.67 − 37.18 − 98.90 − 99.02 N 92 92 92 92 92 92 92 92 92 92 Standard errors in parentheses ***p < 0.001, **p < 0.01, *p < 0.05, †p <0.1, two-sided 48 J Environ Stud Sci (2021) 11:37–64 Table 4 OLS regression on main variables using the imputed sample for fishing grounds (left) and irrigation systems (right) Fishing grounds Irrigation systems (1) (2) (3) (4) (5) (1) (2) (3) (4) (5) Unit quality Balance Unit quality Balance Trust Unit quality Balance Unit quality Balance Trust Trust 0.54** 0.50† − 0.10 0.59** (0.18) (0.26) (0.07) (0.21) Sociocultural heterogeneity − 0.01 − 0.14 0.01 − 0.12 − 0.05 − 0.15* − 0.23 − 0.19* − 0.01 − 0.37* (0.18) (0.24) (0.16) (0.24) (0.17) (0.07) (0.20) (0.08) (0.20) (0.16) Economic heterogeneity − 0.14 − 0.01 0.01 0.13 − 0.28† 0.03 − 0.35† 0.01 − 0.18 − 0.29* (0.16) (0.23) (0.16) (0.24) (0.15) (0.07) (0.20) (0.07) (0.20) (0.14) Constant 2.71*** 2.26*** 1.60** 1.23** 2.05*** 2.18*** 2.09*** 2.40*** 0.73** 2.29*** (0.30) (0.43) (0.46) (0.70) (0.28) (0.08) (0.27) (0.20) (0.56) (0.20) Adj. R -⁑ -⁑ 0.22 -⁑ -⁑ 0.09 -⁑ 0.12 0.29 0.30 95% CI Adj. R -⁑ -⁑ (0.02, 0.51) -⁑ -⁑ (0.00, 0.31) -⁑ (0.00, 0.34) (0.09, 0.53) (0.06, 0.56) AIC − 34.51 − 7.44 − 46.99 − 8.31 − 39.12 − 137.81 − 20.47 − 138.23 − 25.71 − 60.68 N 40 40 40 40 40 52 52 52 52 52 Standard errors in parentheses ⁑ Adjusted R and FMI could not be calculated: the Fisher transformation for pooled simulations could not be performed since some of the simulations had a negative R ***p <0.001, **p <0.01, *p <0.05, †p <0.1, two-sided Discussion success, contrary to a large body of existing literature. Economic heterogeneity, however, is found to be signif- The aim of this paper is to study whether and how icantly negatively related to trust in all but one test, economic and sociocultural heterogeneity affect the suc- indicating that the role of economic heterogeneity re- cessful management of CPRs, to explore the role of garding trust in CPRs is relevant. Trust has a positive trust and to see whether these relations differ for fish- association with both unit quality and balance in all eries and irrigation systems. Using advanced imputation tests, confirming the importance of mutual trust for techniques to prepare the famous but challenging CPR Database allowed us to test the influence of two types of heterogeneity on CPR success at the same time, as Table 5 Overview of results of hypothesis tests, where ‘x’ marks that support is found well as looking at direct and indirect mechanisms. Existing literature predominantly suggests that both Combined sample Fishing ground sample Irrigation sample types of heterogeneity negatively influence collective action and therefore CPR success, that heterogeneity OLS OLR RC OLS OLR RC OLS OLR RC negatively affects mutual trust and that trust has a pos- 1a x** x** itive effect on societal outcomes. 1b x* x* x* For the multivariate analysis, we applied OLS regres- 2a x x x x x x x x sion models instead of ordinal logistic regression, 2b x x favouring a simpler interpretation of coefficients. 3 x x x x x x x** x x** However, we tested all hypotheses in an ordinal logistic 4a x – x –– x** regression as well, plus we ran a model with alternative 4b –– x** – operationalisations of heterogeneity as a robustness 5x* x* x* x* x* x* x* x* x* check. In addition, we tested the found indirect effects through a moderated mediation analysis. Results are on- Considering the small sample size and limited statistical power, a hypoth- ly considered robust if they are found for most of the esis is marked ‘x’ when supported with evidence on at least the marginal combined and separate samples over the three analysis significance level of α =0.1 types. It appeared that neither form of heterogeneity has *Only confirmed for unit quality a robust significant relation with either measure of CPR **Only confirmed for balance J Environ Stud Sci (2021) 11:37–64 49 positive CPR outcomes. A distinction between sector It has to be noted that this study has some shortcom- type proves relevant, since the significant interaction ings and that there is potential for improvement and of trust with sector type on unit quality implies that replication. First, although the database provides a rela- trust only has a positive effect on unit quality for fish- tively large sample for a field of research dominated by eries, and not for irrigation systems, something we later case studies, the sample size has limited statistical pow- confirm in the subsample analyses. Trust seems to play er. A substantial number of missing data for specific a role in upkeeping balance in irrigation systems, so the variables implied a suboptimal operationalisation of eco- role of trust cannot be disregarded for irrigation sys- nomic heterogeneity. The imputation method used is tems. Regarding our calculations for the indirect effects, however innovative and provides imputation diagnostics, we find partial support: a significant indirect effect of such as the OOB, that gives us confidence in the im- economic heterogeneity on balance through trust is putation process and its results. Next to this, we report- found in the combined sample. To invigorate the results, ed the FMI where possible, herewith disclosing the level and to explore the findings that were not considered of uncertainty we have about the imputation of missing robust in the current analysis, more data should be gath- data. Second, individual level data instead of our case ered and more research conducted. study level data could have provided more information The difference between findings in the subsamples on the role of trust; there may be individual confound- may be related to fundamental differences between sec- ing factors influencing the level of mutual trust of tor types. For fishing grounds, both the quality (for appropriators, such as general level of trust in society, instance, size of the fish) and the balance between re- how long an appropriator has resided in the community, newal and subtraction may be affected by trust between individual cultural views or the history of interactions appropriators. For irrigation systems on the other hand, between individual appropriators. In addition, the CPR the balance may be affected but the quality of the water Database only provides very broad categorisations, even in an irrigation system may be less threatened by a lack for variables of great interest, like trust; more detailed of trust. These findings illustrate the difficulty of draw- measurements would provide more detailed results and ing conclusions from results across sector types since a subsequently more detailed conclusions. Third, the cases specific measure of CPR success might mean different in the CPR Database are all from before 1989. Whereas things for different CPR types. It is especially because the argument of difficult monitoring in fishing grounds of these differences that it is theoretically interesting to maybetruefor most fisheriesbackthen, therecurrently compare different CPRs, as it helps to understand the exist modern solutions: the vessel monitoring system mechanisms behind the failure or success of different (VMS), used from the late 1990s on, and the automatic identification system (AIS), implemented in the early types of resources. The findings are relevant given the increasing number 2000’s. Both systems have significantly improved mon- of contemporary CPRs, also known as citizen collec- itoring of fishing activities worldwide (Longépé et al. tives, or institutions for collective action, such as local 2018;Nataleetal. 2015). AIS has the main purpose communities producing their own green energy and ur- of avoiding collisions, but can also be used to track ban agriculture projects with community farms (De fishing activities (Kurekin et al. 2019;Longépéetal. Moor 2013b). Like irrigation systems, green energy pro- 2018; Matsumoto et al. 2016; Natale et al. 2015;Wu duction and community farms are self-made systems, et al. 2016). This may reduce the need for high levels monitoring is relatively easy, production may be unsta- of mutual trust amongst fishermen, as real-time monitor- ble due to the dependency on the weather, and mainte- ing is now a possibility. It is unlikely, however, that nance is required—either by the government if govern- such systems are in use in the smallest CPRs in less ment-owned, or through collective actions of farmers if developed areas. Depending on the availability of these not. If indeed our findings for irrigation systems apply modern technologies, the role of trust in achieving high to such CPRs, we can expect that trust amongst unit quality and balance may thus not be discarded. appropriators will benefit the balance rather than the Lastly, as we discussed, there may be reversed causali- quality of these CPRs, and maybe that trust may in ty. As argued, we have reasons to believe that hetero- general play a smaller role in achieving collective ac- geneity is indeed influencing trust and CPR success; we tion, given that monitoring is easy which makes trust a referred to studies using instrumental variables showing less important factor. For CPRs where monitoring re- that heterogeneity negatively affects trust, and experi- quires more effort, such as fisheries and communal for- mental studies showing that trust indeed positively af- ests, trust will be more important in achieving high fects societal outcomes such as cooperation. However, quality of the resource units and a balanced resource. in future research the causality issues could be 50 J Environ Stud Sci (2021) 11:37–64 addressed by replicating our research on CPR success Appendix 1 using for instance experimental methods, since laborato- Table 6 Differences between fishing grounds and irrigation systems ry experiments are tailor-made to point out causality. The research question on cooperative behaviour in Fishing ground Irrigation system CPRs is not only fundamental to social sciences, but also (N =40) (N =52) to the current state of affairs concerning the use and de- Variation of flow of resource units over space? pletion of natural and man-made resources, such as Yes 40 32 rainforests, fish populations, oil and gas. There is current- No 0 20 ly a rise of new CPRs: an increasing amount of green Variation of flow of resource units from year to year? Yes 38 37 energy cooperatives, local community farms, collective No 2 15 gardens and care cooperatives are part of everyday life Variation of flow of resource units within a year? due to an increasing privatisation of social services (De Yes 40 49 No 0 3 Moor 2013a, 2013b, 2018). These commons too, may Predictable variation of flow of resource units over space? become subject to the risk of overexploitation. Next to 1 (Highly predictable) 0 0 that, ‘classic’ commons like fishing grounds, forests and 226 5 37 44 pastures have new meanings nowadays, and are not only 46 3 regarded as sources of products but also as conservation 5 (Highly unpredictable) 1 0 tools and leisure areas. Contemporary problems surround- Predictable variation of flow of resource units within a year? 1 (Highly predictable) 0 0 ing CPRs include amongst others landscape planning, wa- 229 6 ter management and even climate change (Bravo and De 35 44 Moor 2008). The investigation of the impact of societal 42 2 5 (Highly inpredictable) 4 0 characteristics such as heterogeneity and trust on cooper- Predictable variation flow of resource units from year to year? ation could provide new insights into the use and preser- 1 (Highly predictable) 0 0 vation of these CPRs, demonstrating the contributions that 20 0 30 1 social and environmental sciences can make to a sustain- 439 51 able society. 5 (Highly unpredictable) 1 0 Closed access** Acknowledgements We thank the Library on Governance in Social- 1 (Yes, de jure and 11 52 Ecological Systems for making the CPR Database publicly available effective) and for providing detailed information about the database upon request. 21 0 We also thank colleagues for their feedback and especially Prof. Tine de 30 0 Moor. 412 0 53 0 63 0 Funding This research was supported by the Economic and Social 7(No) 10 0 Research Council. Exit options** Less than 10% 10 39 Data availability The data is available at https://seslibrary.asu.edu/cpr 10–25% 1 1 26–50% 0 0 51–75% 1 2 Compliance with ethical standards 76–90% 3 0 91–100% 25 10 Conflict of interest The authors declare that they have no conflict of interest *See the CPR Coding Manual (Ostrom et al. 1989) for detailed descrip- tion of variables Code availability R code is available on the first author’sGitHub. **See Appendix C for description of these variables J Environ Stud Sci (2021) 11:37–64 51 Appendix 2. Table of CPR cases in data Table 7 Generated with CPR Database, https://seslibrary.asu.edu/cpr Country Resource name Sector Cases Source(s) Australia Lakes Entrance Fishery 2 Sturgess et al. (1982) Australia Port Phillip Bay Fishery 4 Sturgess et al. (1982) Bangladesh Nabagram Irrigation Irrigation 1 Coward et al. (1979) Belize Caye Caulker Lobsterfishing Fishery 1 Sutherland (1986) Belize San Pedro Spiny Lobster Fishery Fishery 1 Gordon (1981) Brazil Arembepe Fishery Fishery 1 Kottak (1966) Brazil Coqueiral Raft Fishery Fishery 1 Forman(1970) Brazil Valenca Fishery Fishery 3 Cordell (1972) Canada Baccalaos Cove Cod Fishery Fishery 1 Powers (1984) Canada Cat Harbour Cod Fishery Fishery 1 Faris (1972) Canada Chisasibi - James Bay Fishery Fishery 1 Berkes (1977, 1982, 1987) Canada Fermeuse Cod Fishery Fishery 1 Martin (1973, 1979) Canada Petty Harbour Cod Fishery Fishery 1 Shortall (1973) Canada Port Lameron - Pagesville Finfishery Fishery 2 Davis (1975), Davis (1984) Greece Messolonghi-Etolico Lagoon Fishery Fishery 1 Kotsonias (1984) India A Tailend Watercourse in Area Two Irrigation 1 Bottral (1981) India Chawk 16,000 L Dhabi Minor Irrigation Irrigation 1 Reidinger (1974, 1980), Gustafson and Reidinger (1971), Vander Velde (1971, 1980) India Jambudwip Fishery Fishery 1 Raychaudhuri (1968, 1980) India Kottapalle - Irrigation Irrigation 1 Wade (1985, 1988) India Sananeri Tank Irrigation 1 Meinzen-Dick (1984) Indonesia A Watercourse in Area Three Irrigation 1 Bottrall (1981) Indonesia Bondar Parhudagar Irrigation Irrigation 1 Lando (1979) Indonesia Saebah Communal System Irrigation 1 Hafid and Hayami (1979) Indonesia Silean Banua Irrigation Irrigation 1 Lando (1979) Indonesia Subak A Irrigation 1 Geertz (1967) Indonesia Takkapala Communal System Irrigation 1 Hafid & Hayami (1979) Iran Deh Salm Irrigation Irrigation 1 Spooner (1971, 1972, 1974) Iran Nayband Irrigation Irrigation 1 Spooner (1971, 1972, 1974) Iraq El Mujarilin Irrigation Irrigation 1 Fernea (1970) Jamaica Farquhar Beach Fishery 1 Davenport (1956) Japan Ebibara Fishing Ground Fishery 1 Brameld (1968) Korea Kagoda anchovy grounds Fishery 1 Han (1972) Laos A watercourse in Nam Tan Irrigation 1 Coward (1980) Malaysia Kampong Mee Trawl Fishery Fishery 1 Anderson and Anderson (1977) Malaysia Perupok Fishery Fishery 1 Firth (1966) Mexico A Tramo in Diaz Ordaz Irrigation 1 Downing (1974) Mexico Andres Quinta Roo Lobster Fishery 1 Miller (1982) 52 J Environ Stud Sci (2021) 11:37–64 Table 7 (continued) Country Resource name Sector Cases Source(s) Mexico Andres Quintana Roo Scalefish Fishery 1 Miller (1982) Mexico Ascension Bay Lobster Fishery Fishery 1 Miller (1988) Nepal Argali Raj Kulo Irrigation (Jethi Kulo) Irrigation 1 Martin and Yoder (1983a, b, 1986) Nepal Char Hazar Irrigation System (Charhajar) Irrigation 1 Pradhan (1988), Laitos (1986) Nepal Chhahare Khola Ko Kulo, Baruwa Village Irrigation 1 Water and Energy Commission Secretariat (1987) Panchayat Nepal Chherlung Thulo Kulo Irrigation Irrigation 1 Pradhan (1988), Martin and Yoder (1983a, b, 1986), Sharma et al. (1989) Nepal Lothar Irrigation System Irrigation 1 Nirola and Pandey (1987), Pradhan (1988), Laitos (1986) Nepal Naya Dhara Ko Kulo (Kot Village Irrigation 1 Water and Energy Commission Secretariat (1987) Panchayat) Nicaragua Miskito Turtle Fishery Fishery 1 Nietschmann (1972, 1973) Pakistan A Watercourse in Area One Irrigation 1 Bottrall (1981) Pakistan Main Watercourse in Gondalpur Irrigation 1 Merrey and Wolf (1986) Pakistan Watercourse Ten - Dakh Branch Irrigation 1 Mirza and Merrey (1979) Pakistan Watercourse in Punjab Irrigation 1 Lowdermilk et al. (1975) Peru Hanan Sayoc Irrigation Irrigation 1 Mitchell (1976, 1977) Peru Lurin Sayoc Irrigation Irrigation 2 Mitchell (1976, 1977) Philippines A Sitio in Zanjera Danum Irrigation 1 Coward (1979) Philippines Agcuyo Irrigation System Irrigation 1 De Los Reyes (1980) Philippines Cadchog Irrigation Irrigation 1 De Los Reyes (1980) Philippines Calaoaan Irrigation Irrigation 1 De Los Reyes (1980) Philippines Laoag-Vintar Irrigation Irrigation 1 Ongkingco (1973) Philippines Mauraro Irrigation Irrigation 1 De Los Reyes (1980) Philippines NIA Irrigation in San Antonio Irrigation 2 De Los Reyes et al. (1980) Philippines Nazareno-Gamutan Irrigation Irrigation 1 Ongkingco (1973) Philippines Oaig-Daya Irrigation System Irrigation 1 De Los Reyes (1980) Philippines Pinagbayanan Water Pumps Irrigation 1 Cruz (1975) Philippines Sabangan Bato Irrigation System Irrigation 1 De Los Reyes (1980) Philippines Silag-Butir Irrigation System Irrigation 1 De Los Reyes et al. (1980) Philippines Tanowong Bwasao Irrigation Irrigation 1 Bacdayan (1980) Philippines Tanowong Traditional Irrigation Irrigation 1 Bacdayan (1980) Sri Lanka Gahavalla Village Fishery 3 Alexander (1982) Switzerland Felderin Irrigation Irrigation 1 Netting (1974, 1981) Taiwan A Watercourse in Area Four Irrigation 1 Bottrall (1981) Tanzania Kheri Irrigation Irrigation 1 Grey (1963) Thailand A Chaek in Amphoe Choke Chai Irrigation 1 Gillespie (1975) Thailand A Chaek in Kaset Samakee Irrigation 1 Gillespie (1975) Thailand Chiangmai Irrigation Irrigation 1 Potter (1976) Thailand Muang Mai Irrigation Irrigation 1 Tan-Kim-Yong (1983) Thailand Na Pae Irrigation Irrigation 1 Tan-Kim-Yong (1983) Thailand Rusembilan Kembong Fishery Fishery 1 Fraser (1960, 1966) Turkey Alanya Fishery, Turkey Fishery 1 Berkes (1986) Turkey Ayvalik-Haylazli Coop Lagoon, Turkey Fishery 1 Berkes (1986) Turkey Tasucu Bay Fishery, Turkey Fishery 1 Berkes (1986) USA Lobsterfishing, Mount Desert Island, Maine Fishery 1 Grossinger (1975) Venezuela Chiguana Fishery 1 Breton (1973) J Environ Stud Sci (2021) 11:37–64 53 Appendix 3. Table including control variables A description of the control variables is provided following the table. Table 8 OLS regression analyses on main dependent variables using the combined sample, including control variables (12) (13) Unit quality Balance Irrigation 0.50 − 0.87 (0.40) (0.65) Irr. × Trust − 0.60* 0.12 (0.23) (0.37) Trust 0.48* 0.23 (0.22) (0.34) Sociocultural heterogeneity 0.02 − 0.01 (0.09) (0.17) Economic heterogeneity 0.01 0.00 (0.08) (0.16) Cultural view of the resource − 0.06 − 0.14 (0.09) (0.15) Number of users 0.00 0.00 (0.00) (0.00) Closed access − 0.03 − 0.08 (0.05) (0.08) Exit options 0.01 0.03 (0.04) (0.06) Monetary sanctions 0.00 − 0.16 (0.06) (0.11) Physical sanctions − 0.08 − 0.12 (0.06) (0.10) Social sanctions 0.07 0.14 (0.07) (0.13) Pollution − 1.20* − 0.77 (0.51) (0.92) Pressure 0.03 − 0.05 (0.17) (0.30) Income dependence − 0.09 0.27 (0.13) (0.21) Worst off − 0.04 0.07 (0.24) (0.43) Variation over space − 0.05 0.64* (0.14) (0.29) Constant 2.04** 1.36 (0.73) (1.24) Adj. R 0.43 0.34 95% CI, adj. R (0.24, 0.60) (0.16, 0.52) FMI 0.33 0.30 AIC − 156.00 − 34.86 N 92 92 Standard errors in parentheses ***p <0.001, **p <0.01, *p <0.05, †p <0.1, two-sided 54 J Environ Stud Sci (2021) 11:37–64 Table 9 Description of control variables (as cited from the CPR Codebook (Ostrom et al. 1989)) Cultural view of the How does the general cultural view of the resource system and its use affect communication between subgroups? (scale 1–5) resource Number of users What is the actual number of individuals in this group at the end of the period? (number) Closed access As of the end of this period, are the appropriators exercising or attempting to exercise closed access to this resource? Closed access is exercised on a de facto base if it is NOT specifically sanctioned by some legitimate authority/ by a de jure base if it IS sanctioned. Outsiders are persons who are not originally appropriators. (scale 1–7) Exit options What proportion of this subgroup works a substantial amount of time in activities not associated with appropriation from this resource? (scale 1–6) Monetary sanctions If someone violated rules-in-use related to the appropriation process from this resource, how likely is it that an official monitor or guard will move to impose sanctions? (scale 1–5) Physical sanctions If someone violates rules-in-use related to the appropriation process from this resource, how likely is he/she to encounter physical sanctions imposed by other appropriators (who are not official monitors? (scale 1–5) Social sanctions If someone violates rules-in-use related to the appropriation process form this resource how likely is he/she to encounter social sanctions imposed by other appropriators who are not monitors? (scale 1–5) Pollution Are there problems of pollution of this or other resources resulting from the way units are appropriated in end of period? (scale 1–4) Pressure Does the amount of capital required to set up an appropriation team, given the assets of members of this subgroup, place pressure upon the appropriators to get immediate returns from appropriation (Y/N) Income dependence For most people in this subgroup, how dependent are they on this resource as a major source of family income? (scale 1–3) Worst off Have the relatively worst off been cut out of their benefits from this resource or substantially harmed? (Y/N) Variation over space Is there considerable variation over space in the availability of these units within the resource? (Y/N) Appendix 4 Ordinal logistic models regularise these standard deviation estimates by using a Bayesian prior encoding a reasonably large degree of For some models, the maximum likelihood estimates uncertainty over the parameters. We stress however, that provide unreliably high standard errors due to the small this prior is noninformative and only serves to control sample size and the splitting of ordinal variables into the standard deviation where needed. multiple dummies in the model, as this increases the For the subsamples, sociocultural heterogeneity was treat- number of parameters to be estimated. We resolve to ed as continuous for two reasons. First, the combined sample useaBayesianapproachfor themodels wherethe stan- model was modelled once with and once without treating dard errors are too extreme, using the R function sociocultural heterogeneity as continuous (the latter presented bayespolr from the arm package (Gelman and Su here in Appendix), which did not affect the coefficients of the 2018). For instance, we are working on the logit scale, other variables. Based on this we believe that treating socio- so a reasonable value for the standard deviation of a cultural heterogeneity as either continuous or as ordinal does parameter over which we are very uncertain is around not impact the model significantly. Second, the subsamples 2.5. The maximum likelihood approach for some of are so small that adding the variable as separate dummies the models go up to over 200 points on the standard would decrease the already limited statistical power of the deviation, which is effectively meaningless, and an ar- model, making it impossible to detect any possible relations tefact of the small sample size. Hence, we resolve to between covariates. Which is the default scale parameter in the R function bayespolr. J Environ Stud Sci (2021) 11:37–64 55 Table 10 Ordinal logistic regression on main variables using the combined sample, treating unit quality, balance, trust and heterogeneity as ordinal variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Unit quality Balance Unit quality (Bayes) Balance Unit quality (Bayes) Balance Unit quality (Bayes) Balance (Bayes) Trust (Bayes) Trust (Bayes) Irrigation − 2.63*** − 1.09** − 2.60*** − 1.41** − 2.63*** − 1.40** − 0.46 − 2.16† − 0.76 (0.55) (0.40) (0.57) (0.48) (0.60) (0.48) (1.28) (1.13) (0.59) Irr. × Trust =1 0.90 1.41 (1.78) (1.43) =2 − 3.23* 0.95 1.57 (1.20) Trust =1 2.63† 2.16* 2.30 0.81 (1.42) (0.97) (2.05) (1.16) =2 2.20† 3.29*** 4.59** 2.35* (1.21) (0.89) (1.73) (1.00) Sociocultural heterogeneity = 1 0.41 − 0.66 0.24 − 0.34 0.33 0.30 0.22 0.26 (1.08) (1.87) (1.07) (1.89) (1.10) (0.96) (1.09) (1.09) =2 − 0.68 − 2.05 − 0.66 − 1.44 − 0.69 − 0.65 − 0.69 − 0.76 (1.19) (1.98) (1.21) (2.05) (1.23) (1.10) (1.22) (1.24) = 3 0.81 − 1.65 0.76 − 1.32 0.65 − 0.29 0.19 0.13 (1.64) (2.54) (1.63) (2.58) (1.61) (1.34) (1.65) (1.68) =4 − 0.56 − 2.05 − 0.12 − 0.77 − 0.41 0.09 − 1.25 − 1.27 (1.45) (2.14) (1.38) (2.23) (1.48) (1.27) (1.77) (1.70) Economic heterogeneity =1 − 0.24 − 0.55 − 0.21 − 0.22 − 0.25 − 0.19 − 1.62 − 1.59 (0.62) (0.60) (0.64) (0.61) (0.69) (0.55) (1.06) (1.05) =2 − 0.36 − 0.97 0.10 − 0.26 0.14 − 0.19 − 2.54* − 2.56* (0.73) (0.72) (0.82) (0.75) (0.90) (0.74) (1.14) (1.13) N 92 92 92 92 92 92 92 92 92 92 AIC 121.11 234.81 149.89 235.32 148.50 226.31 145.10 261.08 141.55 143.39 Standard errors in parentheses ***p < 0.001, **p < 0.01, *p < 0.05, †p <0.1,two-sided 56 J Environ Stud Sci (2021) 11:37–64 Table 11 Ordinal logistic regression on main variables using separate samples for fishing grounds (left) and irrigation systems (right) Fishing grounds Irrigation systems (1) (2) (3) (4) (5) (6) (7) (1) (2) (3) (4) (5) (6) (7) Unit Balance Unit Balance Unit quality Balance Trust Unit quality Balance Unit quality Balance Unit quality Balance Trust quality quality (Bayes) (Bayes) (Bayes) (Bayes) (Bayes) (Bayes) Trust = 1 1.87 0.43 0.71 − 0.09 4.06† 3.34* 2.70 3.70† (1.77) (1.50) (1.48) (2.86) (2.18) (1.36) (2.02) (1.98) = 2 3.42* 1.86† 2.50† 2.07† 1.10 4.73*** − 0.45 5.00* (1.37) (1.10) (1.26) (1.19) (1.55) (1.35) (1.62) (2.11) Sociocultural − 0.08 − 0.33 − 0.04 − 0.33 − 0.12 − 1.30† − 0.57 − 1.50† 0.20 − 1.31 heterogeneity (0.64) (0.39) (0.61) (0.42) (0.77) (0.73) (0.54) (0.84) (0.77) (0.85) Economic heterogeneity =1 − 1.10 − 0.22 − 0.57 − 0.13 − 1.24 0.69 − 1.06 0.24 − 0.69 − 1.28 (1.00) (0.86) (0.86) (0.89) (1.23) (1.33) (0.83) (1.39) (0.88) (1.13) =2 − 0.87 − 0.01 0.19 1.11 − 2.12† 0.24 − 1.79 0.03 − 0.95 − 1.87 (1.21) (0.98) (1.20) (2.49) (1.37) (1.50) (1.16) (1.49) (1.26) (1.29) N 40 40 40 40 40 40 40 52 52 52 52 52 52 52 AIC 61.26 100.34 72.84 104.78 83.88 105.59 59.95 41.59 117.63 48.30 128.73 49.48 122.94 83.97 Standard errors in parentheses ***p < 0.001, **p < 0.01, *p < 0.05, †p <0.1, two-sided J Environ Stud Sci (2021) 11:37–64 57 Table 12 OLS regression on main variables and interaction effect using the imputed sample of both fisheries and irrigation systems (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Unit quality Balance Unit quality Balance Unit quality Balance Unit quality Balance Trust Trust Irrigation − 0.53*** − 0.52** − 0.47*** − 0.31 − 0.47*** − 0.32 0.61† − 0.61 − 0.16 (0.10) (0.18) (0.12) (0.22) (0.12) (0.20) (0.31) (0.27) (0.13) Irr. × Trust − 0.60*** 0.16 (0.16) (0.28) Trust 0.20* 0.54*** 0.54*** 0.44* (0.09) (0.15) (0.13) (0.22) Sociocultural heterogeneity − 0.19 − 0.60 − 0.11 − 0.37 − 0.32 − 0.32 − 0.41 − 0.43 (mean) (0.24) (0.41) (0.23) (0.40) (0.23) (0.42) (0.25) (0.30) Economic heterogeneity − 0.06 − 0.25 0.01 − 0.06 0.01 − 0.05 − 0.36*** − 0.36*** (mean) (0.08) (0.15) (0.09) (0.16) (0.08) (0.17) (0.11) (0.11) Constant 2.57*** 2.03*** 2.93*** 3.25*** 2.36*** 1.67* 2.05*** 1.76** 2.92*** 2.94*** (0.08) (0.14) (0.32) (0.58) (0.42) (0.74) (0.40) (0.74) (0.39) (0.43) Adj. R 0.24 0.08 0.24 0.14 0.28 0.25 0.39 0.25 0.19 0.19 95% CI Adj. R (0.98, 0.40) (0.01, 0.21) (0.10, 0.40) (0.02, 0.30) (0.13, 0.44) (0.10, 0.42) (0.21, 0.56) (0.10, 0.42) (0.04, 0.439) (0.03, 0.39) FMI 0.04 0.05 0.07 0.22 0.09 0.15 0.26 0.14 0.39 0.39 AIC − 137.80 − 25.28 − 136.74 − 29.92 − 142.70 − 38.96 − 161.73 − 37.31 − 97.43 − 95.79 N 92 9292 9292 9292 92 92 92 Standard errors in parentheses ***p < 0.001, **p < 0.01, *p < 0.05, †p <0.1, two-sided 58 J Environ Stud Sci (2021) 11:37–64 Appendix 5. Robustness checks for operationalisation of economic and sociocultural heterogeneity; mean instead of max Table 13 OLS regression on main variables using the imputed sample for fishing grounds (left) and irrigation systems (right) Fishing grounds Irrigation systems (1) (2) (3) (4) (5) (1) (2) (3) (4) (5) Unit quality Balance Unit quality Balance Trust Unit quality Balance Unit quality Balance Trust Trust 0.53** 0.53* − 0.07 0.57** (0.17) (0.25) (0.07) (0.20) Sociocultural 0.23 − 0.92 0.07 − 1.09 − 0.30 − 0.41** − 0.34 − 0.44** − 0.01 − 0.59† heterogeneity (mean) (0.82) (1.18) (0.74) (1.15) (0.71) (0.14) (0.45) (0.15) (0.44) (0.35) Economic − 0.16 − 0.06 − 0.01 0.10 − 0.29† 0.04 − 0.43* 0.02 − 0.22 − 0.37* heterogeneity (mean) (0.19) (0.26) (0.18) (0.27) (0.16) (0.07) (0.20) (0.07) (0.21) (0.15) Constant 2.71*** 3.31*** 1.58 2.32** 1.88*** 2.18*** 2.85*** 2.82*** 1.01 3.23*** (0.30) (1.72) (1.13) (1.74) (1.03) (0.08) (0.69) (0.31) (0.94) (0.54) Adj. R -⁑ -⁑ 0.22 -⁑ -⁑ 0.12 -⁑ 0.12 0.30 0.25 95% CI Adj. R -⁑ -⁑ (0.02, 0.51) -⁑ -⁑ (0.00, 0.33) -⁑ (0.00, 0.33) (0.09, 0.53) (0.06, 0.56) AIC − 35.33 − 7.94 − 46.77 − 8.93 − 37.42 − 137.05 − 18.37 − 135.52 − 25.71 − 54.61 N 40 40 40 40 40 52 52 52 52 52 Standard errors in parentheses Adjusted R and FMI could not be calculated: the Fisher transformation for pooled simulations could not be performed since some of the simulations had anegative R ***p <0.001, **p <0.01, *p <0.05, †p <0.1, two-sided Appendix 6. Fraction of missing information per variable for main tables Table 14 FMI per variable for OLS regression on main variables using the imputed sample (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Unit quality Balance Unit quality Balance Unit quality Balance Unit quality Balance Trust Trust Irrigation 0.04 0.05 0.08 0.12 0.08 0.11 0.17 0.09 0.17 Irr. × Trust 0.20 0.12 Trust 0.21 0.17 0.24 0.10 Sociocultural heterogeneity 0.48 0.41 0.39 0.35 0.54 0.37 0.69 0.66 Economic heterogeneity 0.29 0.38 0.32 0.40 0.33 0.40 0.38 0.38 N 92 92 92 92 92 92 92 92 92 92 AIC − 137.80 − 25.28 − 136.74 − 29.95 − 142.52 − 38.54 − 159.67 − 37.18 − 98.90 − 99.02 J Environ Stud Sci (2021) 11:37–64 59 Table 15 FMI per variable for Fishing grounds OLS regression for imputed sample of fishing grounds (1) (2) (3) (4) (5) Unit quality Balance Unit quality Balance Trust Trust 0.20 0.17 Sociocultural heterogeneity 0.35 0.30 0.31 0.32 0.43 Economic heterogeneity 0.32 0.33 0.37 0.36 0.32 N 40 40 40 40 40 AIC − 34.51 − 7.44 − 46.99 − 8.31 − 39.12 Table 16 FMI per variable for Irrigation systems OLS regression for imputed sample of irrigation systems (1) (2) (3) (4) (5) Unit quality Balance Unit quality Balance Trust Trust 0.21 0.28 Sociocultural heterogeneity 0.34 0.39 0.43 0.37 0.46 Economic heterogeneity 0.38 0.46 0.36 0.48 0.40 N 52 52 52 52 52 AIC − 137.81 − 20.47 − 138.23 − 25.71 − 60.68 Appendix 7. Spearman correlation of main variables with available (unimputed) data Table 17 Spearman correlation for main variables using available data Combined sample Fishing grounds Irrigation systems Unit quality Balance Trust Unit quality Balance Trust Unit quality Balance Trust Economic − 0.19 (N =50) − 0.25† (N =49) − 0.56*** (N =45) − 0.17 (N = 21) 0.09 (N = 21) − 0.54† (N =20) − 0.28 (N =29) − 0.51** (N =28) − 0.59** (N =25) heterogeneity Sociocultural − 0.19† (N = 81) 0.04 (N =82) − 0.17 (N =77) − 0.01 (N = 35) 0.42† (N = 36) 0.06 (N = 35) − 0.27† (N =46) − 0.37† (N = 46) − 0.46** (N =42) heterogeneity Trust 0.21† (N = 79) 0.43*** (N =80) – 0.43** (N = 36) 0.30† (N =37) – − 0.15 (N = 43) 0.53*** (N = 43) – ***p <0.001, **p <0.01, *p <0.05, †p <0.1, two-sided 60 J Environ Stud Sci (2021) 11:37–64 Appendix 8. Moderated mediation models Table 18 Moderated mediation models, testing the mediated effect of heterogeneity on CPR success through trust for fishing grounds and irrigation systems Unit quality Balance Fishing ground Irrigation systems Fishing grounds Irrigation systems EH SH EH SH EH SH EH SH ACME − 0.233*** − 0.185*** 0.046 0.036 − 0.193* − 0.155* − 0.283*** − 0.230* ADE − 0.028 − 0.163† − 0.035 − 0.167† − 0.069 0.057 − 0.076 − 0.065 Total effect − 0.261*** − 0.348*** 0.012 − 0.130 − 0.262† 0.211 − 0.359** − 0.295** Prop. mediated 0.898*** 0.537*** 0.350 − 0.207 0.716† 0.596 0.792** 0.745* N 92 92 92 92 92 92 92 92 Simulations 1000 1000 1000 1000 1000 1000 1000 1000 ***p <0.001, **p <0.01, *p <0.05, †p <0.1 Results are created using the mediate function in R (Tingley et al. 2014) References Due to incompatibility of the moderated mediation analysis with the Mice paradigm and computational tools, we cannot Acedo C, Gomila A (2013) Trust and cooperation: a new experimental obtained pooled standard errors for the estimates of the mod- approach: trust and cooperation. 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Publisher: Springer Journals
Copyright: Copyright © The Author(s) 2020
ISSN: 2190-6483
eISSN: 2190-6491
DOI: 10.1007/s13412-020-00640-7
Publisher site: See Article on Publisher Site

Abstract

Increasing migration, leading to more heterogeneous societies, may challenge the successful management of common-pool resources (CPRs) directly due to the lack of shared interests, and indirectly by reducing trust amongst local commons users, speeding up depletion of vital natural and man-made resources. Since little research has been done on this topic, we analyse the relation between economic and sociocultural heterogeneity, trust and successful commons management for fisheries and irriga- tion systems. Using multiple imputations with chained equations, random forests and predictive mean matching, we adopt an innovative and technically advanced approach to employ Elinor Ostrom’s famous CPR Database. Our approach enables us to include economic and sociocultural heterogeneity, trust and control variables in one model and to investigate both direct and indirect effects of heterogeneity on CPR success, which has not been attempted before. Results show no evidence of the negative relation between heterogeneity and CPR success. However, economic heterogeneity is negatively related to trust, and trust is found to be positively related to CPR success. Evidence is found for an indirect effect of economic heterogeneity through trust on CPR success. . . . Keywords Sustainable cooperation Heterogeneity Trust Common-pool resources Introduction unlimitedly, which leads to its decay. The effect of increasing heterogeneity on the success of CPRs, and the role of trust in Societies are becoming more diverse on ethnic, cultural and this process, is still contested (Baland and Platteau 1999; economic dimensions due to growing migrant populations all Bardhan and Dayton-Johnson 2002;Ruttan 2006, 2008; over the world, especially in Northern Africa, Western Asia Varughese and Ostrom 2001). The aim of this paper is to gain and sub-Saharan Africa (International Migration Report 2019 insight into whether and how two types of heterogeneity— 2019). This increasing heterogeneity may pose a challenge to economic and sociocultural—and their interplay with trust the successful management of common-pool resources affect the success of a CPR, that is, its sustainable long-term (CPRs) in two ways: (1) directly by diversifying interests use and quality of the resource. amongst users and (2) indirectly by reducing trust amongst Economic heterogeneity expresses inequalities in wealth, users. Both ways lead to decreased cooperation in CPRs. income and access to resources, and sociocultural heterogene- CPRs are natural or man-made resources—such as grasslands, ity represents disparities in language, ethnicity, religion and communal forests, fishing grounds or irrigation systems—for other cultural expressions (Baland and Platteau 1996;Bardhan which it is costly to exclude potential users (Ostrom 1990). and Dayton-Johnson 2002; Ruttan 2006). Most research ar- Different from a public good, a common-pool resource may gues that economic and sociocultural heterogeneity may result run out, making it vulnerable to the ‘tragedy of the commons’ in increased costs of negotiation and bargaining due to a lack as described by Hardin (1968), a situation where the short- of shared ideas, values and incentives between individuals or term dominant strategy of users is to use the limited resource groups of individuals (Aksoy 2019; Bardhan and Dayton- Johnson 2002), that it may lead to unequal sharing of decision-making rights and different motivations to cooperate (Anderson and Paskeviciute 2006;Fungand Au 2014; * Fijnanda van Klingeren fijnanda.vanklingeren@nuffield.ox.ac.uk Komakech et al. 2012) and that it may decrease social cohe- sion (Flache and Mäs 2008;Jehn et al. 1999). On the other hand, some research suggests positive effects of economic Nuffield College, University of Oxford, New Road, Oxford OX1 1NF, UK heterogeneity on the provision of collective goods, stating that 38 J Environ Stud Sci (2021) 11:37–64 it can lead to an inequality of incentives, which results such as citizen initiatives producing green energy and urban in some appropriators being motivated enough to invest agriculture projects (Bravo and De Moor 2008;De Moor in collective action on their own—hereby carrying the 2013a, b, 2018). In a time where mankind is rapidly depleting costs of cooperation (Olson 1965). the earth’s resources, research on success and failure of CPRs In order to understand the indirect relation between hetero- is more important than ever. geneity and CPR success, we introduce trust as a mediator: there is evidence that trust is influenced by heterogeneity (Alesina and La Ferrara 2002; Delhey and Newton 2005; The negative influence of heterogeneity Ostrom 1990; Putnam 2007) and that it plays an important role in influencing societal outcomes (Fukuyama 1995; Regarding economic heterogeneity it is suggested that Putnam 2000; Uslaner 2002; Zak and Knack 2001). large differences in wealth may result in a loss of incen- We will investigate the relation between trust and CPR tives to cooperate for less wealthy appropriators if the ben- success, and the role of trust in the indirect relation efits of cooperation are not high enough (Baland and between heterogeneity and CPR success. Platteau 1999), if there is no wealthy appropriator willing This paper will study 32 fisheries and 50 irrigation systems, to initiate collective action, or if the wealthy appropriators using Elinor Ostrom’s well-known Common-Pool Resource turn to their exit options—alternative ways to generate Database (Ostrom et al. 1989). Considering the field of study, income—instead (Bardhan and Dayton-Johnson 2002; which typically uses in-depth case studies, this is a relatively Jones 2004;Molinas 1998). Adding to this argument, large database (Poteete and Ostrom 2004; Ruttan 2006). Shanmugaratnam (1996) argues that sustainable use of While the CPR Database was used before to investigate the CPRs is more challenging under a more unequal distribu- relation between heterogeneity and CPR outcomes, this was tion of private wealth, as it leads to a diversification of done with correlations and Mann-Whitney U tests on the interests amongst appropriators of the CPR. When actors available data, which contains a lot of missing observations have different interests, assurance mechanisms such as (Ruttan 2006, 2008). Instead, the current paper uses innova- sanctioning are harder to implement since the actors are tive and advanced imputation techniques such as multiple im- less likely to agree on them. However, these assurance putations with chained equations, random forests and predic- mechanisms are needed for successful CPR management, tive mean matching to make the data suitable for including making diversification of the interests of actors problemat- both economic and sociocultural heterogeneity, trust and con- ic. Heterogeneity in access to exit options can also have trol variables in the same model. This enables us to test the negative effects on the CPR itself: if resource appropriators effects of one type of heterogeneity while controlling for the have relatively rewarding earning opportunities outside of other, without decreasing the sample size due to the large the appropriation of the resource, they maybemorewilling amount of missing data, something that, to the extent of our to comply with effort-restricting measures that are set in knowledge, has not been attempted yet in previous research place to maintain the CPR. On the other hand, using this database. On top of that, our preparation of the data appropriators without access to exit options may not be allows to test both direct effects of heterogeneity and trust on willing to restrict their appropriation efforts as it will have CPR success, and indirect effects of heterogeneity on CPR a greater impact on their total income (Bardhan and success through trust. This enables us to uncover part of the Dayton-Johnson 2002; Gaspart and Platteau 2007). ‘black box’ of the theoretical mechanism. Evidence from observations and experiments supporting We aim for theoretical progress in two ways. First, we test these arguments are manifold (Bardhan 2000;Hackett hypotheses regarding the relation between economic and so- et al. 1994; Varughese and Ostrom 2001). ciocultural heterogeneity and CPR success for the combined With respect to sociocultural heterogeneity, the gen- sample, and two subsamples for fishing grounds and irrigation eral argument is that collective action is more likely to systems. Second, we formulate and test a hypothesis regard- be established when the individuals involved have ing the relation between trust and CPR success specifically for strong, multistranded, interpersonal relationships, share fisheries and irrigation systems. The outcomes are relevant not common interests, and have relatively stable group only for classic CPRs as fisheries and irrigation systems, but memberships (Anderson and Paskeviciute 2006;Ostrom also for the rising number of contemporary institutions for 1990;Ostromet al. 1992; Varughese and Ostrom 2001). collective action on food, infrastructure, health and energy, Furthermore, Anderson and Paskeviciute (2006)consider heterogeneity to be an impediment to cooperation, as The database can be viewed and retrieved from https://seslibrary.asu.edu/cpr people feel threatened by others who are not part of Ruttan (2006, 2008) takes two subsamples for irrigation systems and fisher- their ‘ingroup’. ies, but does not take economic and sociocultural heterogeneity, trust and/or If subgroups of appropriators within a CPR differ in own- control variables into account in one and the same model and does not test ership of assets, skills, knowledge or sociocultural indirect effects. J Environ Stud Sci (2021) 11:37–64 39 characteristics, it is likely that these subgroups also differ in their case study of Senegalese fisheries that the division be- interests and preferred use of the resource. This can make it tween native and migrant appropriators forms an insuperable hard to reach agreements on the making and enforcing of rules problem for cooperation and mutual trust. Here, agreement on due to a lack of mutual trust and the inability to understand regulatory schemes has become nigh impossible. each other. It can be reason for conflict and thus impedes Although the literature theoretically and empirically collective action (Carpenter and Cardenas 2011;Gehrig suggests predominantly a negative effect of heterogene- et al. 2019; Johnson and Libecap 1982;Keuschnigg and ity, there are arguments for a positive effect, indirectly Schikora 2014;Ostrom 1990; Varughese and Ostrom 2001). based on a well-known theory by Olson (1965), also For instance, farmers may have different interests than no- known as the ‘Olson effect’: mads when it comes to the use of the resource, and men and women may have different actual and per- In smaller groups marked by considerable degrees of ceived costs and benefits, caused by a long history of inequality – that is, in groups of members of unequal gender inequality (Agarwal 1994, 1997;Molinas 1998; “size” or extent of interest in the collective good – there Varughese and Ostrom 2001). is the greatest likelihood that a collective good will be Nettle and Dunbar (1997) focus on another aspect of so- provided; for the greater the interest in the collective ciocultural background, namely language; they state that good of any single member, the greater the likelihood speaking the same language facilitates a feeling of social alle- that that member will get such a significant proportion giance, which is deemed important for the maintenance of of the total benefit from the collective good that he will group cohesion. Evidence in favour of these arguments is gain from seeing that the good is provided, even if he found by Wiessner (1977) with respect to language differ- has to pay all of the cost himself. (p. 34) ences between tribes in Botswana. Another example of socio- cultural homogeneity having a positive effect on sustainable Although the link with economic heterogeneity cannot direct- cooperation is given by Singleton (2001) in an analysis of ly be distilled out of this quote, it is often interpreted as an contemporary Pacific Northwest salmon fishing. The study argument in favour of a positive effect of economic heteroge- describes how homogeneous Aboriginal tribes were very neity on collective action: those with higher incomes will act efficient and sustainable in the appropriation of salmon as catalysts for collective action because they can afford it, and fishing grounds, in spite of the sometimes unequal economic it is in their interest to do so (Baland and Platteau 1997, 1999; results between individual members. The study describes how Bardhan and Dayton-Johnson 2002;Jones 2004; Ruttan 2006, conflicts about appropriation only arose between Aboriginal 2008). In addition, it is likely that the Olson effect will only tribes and the state. Based on a survey study of group take place when inequality is large and there are actors that are membership in the USA, Alesina and La Ferrara (2000)con- indeed so rich that they can afford to pay to see collective cluded that residents from racially heterogeneous communi- action happen. It is unlikely that there are many of these cases ties participate less in social activities. Carpenter and Cardenas in the current database. Based on the discussed literature, we (2011), employing a laboratory experiment with Colombians therefore expect negative effects to be more probable. Hence, and Americans, discovered that mixed groups cooperate less our first hypothesis reads: (hypothesis 1a) economic and (hy- than homogeneous groups. Keuschnigg and Schikora (2014) pothesis 1b) sociocultural heterogeneity have a negative rela- found in a study using public good games [PGGs] that reli- tion with CPR success. gious heterogeneity decreases cooperation in the presence of a leader: whereas a generous contribution of leaders in homo- geneous groups is met with reciprocity from the followers, this The role of trust was not the case in heterogeneous groups. Vermillion (1999) mentions that the absence of social divisions is a requirement A variable that we consider to play a role in the indirect effect for collective action amongst farmers in devolution programs of heterogeneity on CPR success is trust. The first part of the of irrigation systems, in which rights and responsibilities are argument, illustrating the relation between heterogeneity and transferred from the government to local water user groups. trust, is that people are more likely to trust someone who is Lastly, Gaspart and Platteau (2007) concluded on basis of more similar to themselves (Alesina and La Ferrara 2002; Coleman 1994), implying more mutual trust in a more homo- geneous setting and less mutual trust in a heterogeneous one. Johnson and Libecap (1982) illustrate this in a case study concerning fish- People tend to trust members of their family and members of ermen: fishermen with more skills and knowledge on how to set traps, trawling speed and the best locations for a good catch turn out to be the more successful the same ingroup; be it racial, social, ethnic or based on some- fishermen. This heterogeneity in productivity will lead to different points of thing else (Alesina and La Ferrara 2002;Romano etal. 2017). view regarding uniform fishing quotas and other restricting policies on re- Many studies point out a negative association between source subtraction, thus to higher transaction costs and a higher probability of conflict. heterogeneity and trust. For instance, in their study using 40 J Environ Stud Sci (2021) 11:37–64 individual level data from the USA, Alesina and La Ferrara aspects, which may influence the way sociocultural and eco- (2002) observe a negative relation between social distance and nomic heterogeneity and trust impact their success. First, trust, and point out that being economically unsuccessful and whereas fishing grounds can be considered natural resources, living in a neighbourhood with a high degree of income irrigation systems are entirely man-made. This influences the inequality reduces trust. Delhey and Newton (2005)findthat way appropriators see the resource; an open-access resource generalised trust is closely related to homogeneity in religious, available to everyone versus a self-made system only avail- cultural, social and political identification as well as to able to the ones who are granted access and/or are contributing economic equality. Similarly, Gehrig et al. (2019)find in a to its maintenance (Gaspart and Platteau 2007). Second, lab-in-field CPR experiment in Zanzibar that less trusting fish- whereas there are many different techniques to appropriate a ermen overexploit the CPR more in heterogeneous groups, fishing ground, which can be cause for conflict between while they cooperate in the homogeneous group to achieve a appropriators (Gaspart and Platteau 2007), irrigation systems sustainable use of the resource. Regarding causality, Leigh work one way for all users. Third, while mutual monitoring (2006a, b) uses an instrumental variable approach in two stud- for irrigation systems is easy, this is more difficult for fishing ies, to show that increasing inequality and ethnic and linguistic grounds, where fishing boats cannot see each other during fractionalisation reduce trust. Adding to that, Romano et al. appropriation and where illegal appropriation forms a daily (2017) find in a series of trust games in 17 countries that threat to the success of the CPR (Gaspart and Platteau 2007; people are generally more trusting towards ingroup members Regmi 2007). Fourth, while the resource flow of a fishing than towards outgroup members. ground can be considered relatively stable—conditional on The second part of the argument concerns the relation of the resource not being nearly depleted, the flow of water com- trust with positive societal outcomes—in our case CPR suc- ing from the river that provides water to the irrigation system cess. Societies with high levels of trust amongst individuals is less predictable (Regmi 2007). This poses a challenge for yield a lesser need for the individuals to protect themselves devising appropriation rules. For fishing grounds, many coun- from being taken advantage of by others in mutual transac- tries impose individual fishery quotas to improve sustainabil- tions (Knack and Keefer 1997). Instead of formal institutions, ity of fishing activities (Sanchirico et al. 2006)or even mora- mutual trust amongst individuals facilitates the use of informal toriums until specific fish populations regrow (see for instance agreements, leading to a decrease in transaction costs and a Jiang et al. 2009;Khanet al. 2018; Palmer and Sinclair 2008). greater likelihood of economic efficiency and success However, these rules do not necessarily make sense for an (Alesina and La Ferrara 2002; Knack and Keefer 1997). irrigation system, since the total discharge of a river is likely Next to economic success, trust is known to promote cooper- to change over time and is less dependent on use by farmers ation and participation in social activities (Alesina and La and more dependent on external factors such as the weather. Ferrara 2000; La Porta et al. 1997;Romano et al. 2017). Instead, time allocation rules are used that can vary depending Empirical evidence supports this argument. A multitude of on the availability of water (Regmi 2007). Fifth, whereas a studies show a positive relation between high levels of inter- fishing ground does not require much, if any, maintenance, personal trust and economic growth of societies (Knack and irrigation systems do: man-made components such as check Keefer 1997; La Porta et al. 1997; Zak and Knack 2001). gates have to be checked regularly and fixed when broken. Regarding the causal direction of the effects of trust, Acedo The government will take care of the maintenance if the sys- and Gomila (2013) find in an experiment involving an iterated tem is government owned, but for systems that are not gov- prisoner’s dilemma that higher trust results in higher levels of ernment owned, this requires farmers to work together cooperation. Likewise, Gächter et al. (2004)find thatmore (Vermillion 1999). This type of collective action is not re- trusting people contribute more than less trusting people in quired for fishermen, whose profits are not dependent on each three-person one-shot public good games. other in this way. Lastly, a big difference between fishing Summarising, we hypothesise that (hypothesis 2a) eco- grounds and irrigation systems is the constant disadvantage nomic and (hypothesis 2b) sociocultural heterogeneity have of appropriators in an irrigation system that are located down- a negative relation with trust; (hypothesis 3) trust has a posi- stream as opposed to at the head of the river (Ostrom 1990; tive relation with CPR success; (hypothesis 4a) economic and Regmi 2007). The appropriators upstream are the first ones to (hypothesis 4b) sociocultural heterogeneity have a negative receive water and are the least likely to be disadvantaged when indirect relation with CPR success through trust. other appropriators overexploit the resource. The appropriators downstream on the other hand, will experience The relevance of sector type This problem may be solved to an extent with modern technologies such as vessel monitoring systems (VMSs) and automatic identification systems The cases analysed in this paper are either fisheries or irriga- (AISs), used to track fishing vessels. However, our data comes from before tion systems. These two types of CPR vary in a multitude of 1990, when neither VMS nor AIS were in use. J Environ Stud Sci (2021) 11:37–64 41 the worst consequences of overexploitation of the resource by major publication based on the CPR Database is Elinor more upstream appropriators. Whereas fisheries usually have Ostrom’s Governing the Commons (1990), contributing to the a rotation system of sorts to equalise appropriation time at Nobel Memorial Prize in Economic Sciences that she won to- better spots, this is not possible for irrigation systems gether with Oliver E. Williamson, for her analysis of economic (Ostrom 1990;Regmi 2007). A table describing differences governance in CPRs. The dataset contains 32 fisheries and 50 in nature and various characteristics between fishing grounds irrigation systems for analysis of the variables of interest for this and irrigation systems using variables from the CPR Database paper. The 3 CPRs that are neither fisheries nor irrigation sys- can be found in Appendix 1. tems are not included since they lack cases for making a com- The differences between the two sector types may have parison between sector types. The CPRs are located in 29 dif- implications for the expected effects of trust on CPR success: ferent countries from all over the world, although many are due to almost automatic mutual monitoring and the closed- situated in the Middle East and Asia. A table comprising the access and man-made nature of the resource, trust amongst cases and their sources used in this study can be found in appropriators may be less vital in order for the irrigation sys- Appendix 2. tem to be successful. For fisheries however, trust amongst appropriators may play a more important role in reaching sus- tainable appropriation, as appropriators would need to trust Measurements each other not to overexploit the resource while not being able to see each other, or each other’sactions, straight away. Based Many of the variables that are available in the database are on the above we expect (hypothesis 5) that the relation of trust recorded for both the beginning and the end of a period of with CPR success is stronger for fishing grounds than for time, during which “the actions of the appropriators are rela- irrigation systems. tively consistent” (Ostrom et al. 1989, p. 352). These periods are of variable length, and different survey forms are provided for each period. These period forms, or ‘time slices’, are the observations in the dataset. Of the 82 separate CPRs that will Data and methods be used for our analyses, seven have more than one period form filled out, so more than one time slice; this means that Data researchers conducting the case study found that during their study, several periods could be distinguished with specific We use the Common-Pool Resource Database compiled by information for each of them. Separate periods are considered Elinor Ostrom and her team (1989) to test our hypotheses. as different observations since this period-specific information This database is based on a bibliography comprising over would get lost otherwise. Though the data has a multilevel 1800 published and unpublished original CPR case studies structure—subgroups nested in time slices nested in CPR from before 1990. A small subset of this bibliography was cases—we take all variables on the time slice level: selected, and coded into the CPR Database using extensive operationalised variables are either original CPR level vari- survey forms containing over 600 questions on topics such as ables for a specific period or aggregated subgroup variables geographic and demographic features of the CPR location, for that period. We do this because not all CPRs have multiple boundaries and physical characteristics of the CPR, the situa- time slices or multiple forms coded for their separate sub- tions faced and actions performed by appropriators of the CPR groups: there are 123 forms for 82 CPRs, existing of 95 cases and the strategies of appropriators in subgroups (Ostrom 1990; due to the extra period files. Cases that are twice in the dataset Ostrom et al. 1989;Schlager 1990;Tang 1989). It was required due to multiple coding forms for different subgroups are de- that the material is written “by a researcher who has spent leted, as we only use the aggregate information, which other- considerable time in the field” (Ostrom et al. 1989,p.10) and wise would be duplicate. The three cases that are neither fish- that the material contains “key information about the structure eries nor irrigation systems are removed. In total the number of the resource, the rules used in organizing the resource, the of observations that can be used for analyses is N = 92. If strategies adopted by the appropriators, and the outcomes variables are recorded for both the beginning and the end of achieved” (Ostrom et al. 1989,p.10).Thisway,40person- the period, the variables for the end of the period will be used years of fieldwork, conducted by researchers interested in the field of CPRs, such as social scientists, historians and engineers, are captured in one database (Ostrom et al. 1989). The first The seven items used for sociocultural heterogeneity are, according to the codebook, only filled out if there are multiple subgroups present in the CPR Selection criteria are that the case study is the result of “extended fieldwork (Ostrom et al. 1989). We thus assume that for each case there are multiple and that information be provided about (1) the structure of the resource system, subgroups; since even for cases without an extra coding form, at least one of (2) the attributes and behaviours of the appropriators, (3) the rules that the the seven items are still filled out. Even if there is one subgroup, filled out appropriators were using and (4) the outcomes resulting from the behaviours items about sociocultural heterogeneity provide information on the levels of of the appropriators” (Ostrom 1990, p. xv). heterogeneity in that CPR. 42 J Environ Stud Sci (2021) 11:37–64 (cf. Ruttan 2006, 2008). See the CPR Coding Manual for a Control variables more detailed description of the data (Ostrom et al. 1989). The following control variables were added to the final unit quality and balance models because they could influence suc- Dependent variables cess of the CPR: cultural view of the resource, number of users of the resource, closed access to the resource, opportunities for Unit quality: This variable is operationalised with an item exit options, monetary, physical and social sanctions, pollution, indicating the ‘quality of the units that are withdrawn from level of financial pressure for immediate returns from the CPR, the resource’. There are five answering categories, ranging dependence on CPR for family income, the presence of consis- from ‘extremely poor quality’ (0), to ‘extremely high quality’ tently disadvantaged appropriators who are cut off from bene- (4). The quality of the appropriated units is an indicator of the fits and variation in availability of units over space. A more quality of the resource in general, and thus represents a sub- detailed description of the variables can be found in Appendix stantive part of the success of the CPR. 3. Since most variables have no significant relations with the Balance: This variable is operationalised with an item in- outcome variables, these models are not considered for the dicating the ‘balance between the quantity of [resource] units interpretation of the results but are presented in Appendix 3. withdrawn and the number of units available in the resource’. There are five answering categories, ranging from ‘extreme shortage’ (0) to ‘quite abundant’ (4). The balance between Analytical strategy and causality withdrawal and renewal of the resource indicates the health of the resource as well as the sustainability of the behaviour of To test the hypotheses, OLS regression will be used and the the appropriators, and thus represents another substantive part unstandardised coefficients will be interpreted. Even though the of the success of the CPR. variables unit quality, balance, economic heterogeneity, socio- cultural heterogeneity and trust are not continuous but ordinal, we will consider them continuous in the interest of simplicity of Independent variables interpretation. This allows us to retain statistical power— especially given the small sample size in the subsamples—by Sector type: This independent variable indicates whether the CPR is an irrigation system (1) or not (0). Since the dataset reducing degrees of freedom. In addition, it allows us to calcu- late indirect effects in a meaningful way. Following argumen- exists only of irrigation systems and fishing grounds, the var- iable can be interpreted as being an irrigation system (1) ver- tation of amongst others Pasta (2009) and Williams (2018)on treating ordinal independent variables as continuous, a likeli- sus a fishing ground (0). In the regression tables, this variable will be named ‘Irrigation’ for the main effects and will be hood ratio test was completed to establish whether the models would significantly differ between treating the variables as or- abbreviated to ‘Irr.’ when used in an interaction effect. dinal or continuous (see Williams (2018) for a more extensive Economic heterogeneity: The independent variable economic heterogeneity is operationalised as the highest level of variation explanation of the test). The test concluded that economic het- erogeneity, sociocultural heterogeneity and trust can be treated in income within any subgroup within a CPR time slice (cf. Ruttan 2008). The item on variation in income has three outcome as continuous in the models. Ordinal logistic regression was also performed, with ordinal independent variables added as variables, ranging from ‘low’ (0) to ‘moderate’ (1) to ‘high’ (2). Sociocultural heterogeneity: This independent variable con- dummies. These models can be found in Appendix 4.The main results from the OLS analyses are largely supported by the sists of the maximum value found per time slice in any of seven items: the extent to which ethnic, racial, religious, caste, clan more conservative ordinal logistic models. In addition, a robust- ness check was performed with an alternative operationalisation and gender identification and the language spoken affect com- munication between subgroups. All seven items have the same of economic and sociocultural heterogeneity, by taking the five-point response scale ranging from ‘no difference along this mean per time slice of the variation of income in CPR sub- groups for economic heterogeneity and the mean of the seven variable’ (0) to ‘large differences which significantly affect communication’ (4). sociocultural heterogeneity items instead of the maximum val- ue. The results are very similar to the OLS models and can be Trust: This variable serves as both independent and depen- dent. It is an item indicating the extent of mutual trust amongst foundinAppendix 5. Relations found in the OLS models that appropriators within the CPR on a three-point scale, with the A likelihood ratio tests between a constrained model treating the variables as categories ‘low levels of trust’ (0), ‘modest levels of trust’ (1) continuous variables and an unconstrained model treating the variables as and ‘moderate to high levels of trust’ (2). factors (Williams 2018) was performed for each iteration of the imputed dataset, and the p values were plotted. For each test, the average p value was We take this approach following Ruttan (2006) with the same rationale; we higher than ɑ = 0.05; hence, we conclude that treating economic heterogeneity, are interested in any kind of sociocultural heterogeneity that may take place, sociocultural heterogeneity and trust as factor variables does not improve not in all of them at the same time. significantly on treating them as continuous variables. J Environ Stud Sci (2021) 11:37–64 43 are not backed up by the ordinal logistic models or the robust- missing observations in key variables, multiple imputation ness check models will not be considered robust. with chained random forests (RFs) (Breiman 2001;Van Part of the analytical strategy is to analyse the entire sample, Buuren 2019) was performed, using the MissRanger package includingbothfisheries andirrigationsystems,aswellastwo in R (Mayer 2019), an adaptation of the MissForest package subsamples of fishery- and irrigation-only cases. This allows us by Stekhoven and Bühlmann (2012) using the Ranger pack- to look at the general picture as well as to look at associations age (Wright et al. 2019). RF imputation accommodates non- between variables that may be specific to the sector type. linearities and interactions and does not need a specific regres- Questions that may arise after looking at the combined sample sion model to be defined. Predictive mean matching (PMM) may be answered when looking at the separate samples. In was used to fill in the missing values with realistic imputa- addition, analysing the combined sample allows us to not miss tions, that is, avoiding the imputation of continuous values in a out on the detection of associations between variables by discrete variable, for each iteration. PMM also enables imput- retaining statistical power compared with the subsample analy- ed values to be endowed with realistic levels of local variabil- ses, given the small sample size. To test hypothesis 5 regarding ity, effectively raising the variance of the resulting RF- the differences in the effect of trust on CPR success between estimated conditional distributions to a more realistic level fishing grounds and irrigation systems, an interaction between (Mayer 2019). We created 100 simulations and ensured the trust and sector type (Irr. × Trust) is added in addition to mea- chained RFs would stop re-fitting after 30 iterations, though in suring the effect of trust in the two subsamples. every simulated imputed dataset, this procedure took at most 5 Although causal phrases are used throughout the discus- iterations, suggesting quick convergence to optimally imputed sion of the results, the observational data only allows to test values. Imputation diagnostics, including the ‘out of bag error’ associations, and causal conclusions can in principle not be (OOB) distribution per imputed variable, were inspected for drawn. However, we have some confidence in the assumed key variables and supported our confidence in the imputation causal directions. Even though one could argue that trust model. Research comparing MissForest imputation to other could bring homogeneity about instead of homogeneity induc- imputation techniques shows that MissForest performs well ing trust, it is important to know that sociocultural heteroge- and in a lot of cases better than other established imputation neity (ethnic, racial, clan, caste, religious and gender identifi- techniques, even when applied to data with up to 30% missing cation and the language spoken) is rather fixed, as is economic values (Stekhoven and Bühlmann 2012). As the current data- inequality, although less so. Hence, in this respect, we have base has 28% missing data, using the MissRanger package some confidence in the assumed causality (see also Leigh based on the MissForest package is well suited. By making (2006a, b) and Romano et al. (2017)). With respect to trust use of multiple imputation, both sociocultural and economic and CPR success, it might also be possible that a high score on heterogeneity can be included in one model without reducing CPR unit quality and balance increases trust. However, the sample size. The value of including both forms of hetero- experimental research of Acedo and Gomila (2013)and geneity in the model is that the risk of overestimating the Gächter et al. (2004) discussed earlier provides evidence for influence of one by not controlling for the other is reduced. the causal direction reflected in our hypotheses. Table 1 provides insights in the original versus the imputed dataset. The adjusted R including the 95% confidence interval is Multiple imputation: mice, random forests and provided for the models where possible. In addition, the predictive mean matching fraction of missing information [FMI] is reported for the models where it was possible to calculate them, providing All main independent variables have missing values—some information on the uncertainty about the missing data, which more than others. The missingness of the independent vari- affects the pooled standard errors (Pan and Wei 2016;Wagner ables is not correlated with relevant variables in the model. We 2010). These statistics are retrieved using the pool function of assume the missing values to be missing at random (MAR) the Mice package (Van Buuren 2019). In addition, the Akaike (Rubin 1987) and not dependent on unobserved data. This is, information criterion [AIC] will be reported for every model. however, an untestable assumption. To prevent having to per- Lastly, tables stating the FMI per variable for main models form analyses on a smaller sample size than 92 cases due to will be provided in Appendix 6. The missingness of the variable economic heterogeneity is assumed to be a The out of bag error is the mean prediction error on each training sample; for consequence of the way the data was constructed; the data is based on a survey that was filled out on the basis of information given by published case studies. a categorical variable, ‘how often is a ‘wrong’ class imputed in a variable’ and for continuous variables, it is 1 − R , that is, the unexplained variance Many case studies did not provide information on the variance of family incomes within CPRs, and the missingness is thus more likely related to (Stekhoven and Bühlmann 2012). 11 2 coincidences or external factors rather than unobserved variables that could For some of the subsample models, adjusted R and FMI could not be be of importance to the analyses and interpretation of results (see also Dong calculated, as the Fisher transformation for pooled simulations could not be and Peng 2013). performed since some of the simulations had a negative R . 44 J Environ Stud Sci (2021) 11:37–64 Table 1 Imputed data statistics: key variables above line, control variables below line Observations per simulation 1:100 Complete Incomplete Imputed Total Used N* OOB OOBSD. Mean Economic heterogeneity** –– – 123 92 Income variance 65 58 58 123 92 0.13 0.02 Sociocultural heterogeneity** –– – 123 92 Ethnic identification 101 22 22 123 92 0.00 0.01 Race identification 101 22 22 123 92 0.02 0.00 Religious identification 88 35 35 123 92 0.03 0.01 Gender identification 101 22 22 123 92 0.04 0.01 Clan identification 92 31 31 123 92 0.10 0.01 Caste identification 71 52 52 123 92 0.06 0.01 Language spoken 115 8 8 123 92 0.01 0.01 Unit quality 118 5 5 123 92 0.05 0.01 Balance 119 4 4 123 92 0.11 0.02 Trust 112 11 11 123 92 0.06 0.01 Cultural view of resource 102 21 21 123 92 0.08 0.01 Pollution 91 32 32 123 92 0.01 0.00 Pressure 37 86 86 123 92 0.05 0.01 Income dependence 97 26 26 123 92 0.07 0.01 Variation over space 105 18 18 123 92 0.02 0.01 Worst off 74 49 49 123 92 0.01 0.00 Exit options 80 43 43 123 92 0.12 0.01 Social sanctions (informal) 72 51 51 123 92 0.17 0.02 Physical sanctions (informal) 64 59 59 123 92 0.17 0.02 Formal sanctions 62 61 61 123 92 0.22 0.02 Number of users 102 21 21 123 92 0.37 0.03 *Used N is the total number of cases (123; all CPR types + duplicates due to multiple subgroup forms) minus duplicates (− 28), minus other sector types (− 3), but keeping the different ‘time slices’ as mentioned before **These variables were constructed after multiple imputation, before deleting duplicates Results has a significant negative relation with trust in all three samples. In addition, it has a negative relation with In this section, Spearman’s rank correlations will first balance in the combined and irrigation system sample. be discussed to get an initial idea of the relation be- Sociocultural heterogeneity has a negative relation to tween variables. To test the hypotheses, we will discuss trust in the combined sample and the irrigation sample, OLS regressions for the combined sample of both fish- a significant negative relation to balance in the irriga- ing grounds and irrigation systems, and the two subsam- tion sample and a marginally significant negative rela- ples separately. Both direct and indirect effects will be tion with unit quality in the irrigation sample. Trust has discussed. Lastly, the robustness of the found results is a positive relation to both CPR success outcomes in all assessed by crosschecking the OLS regressions with the three samples except unit quality in irrigation systems. ordinal logistic regressions and the OLS regressions So far, the results thus partially support hypotheses 2a with the alternative operationalisation of the heterogene- and 2b and largely support hypothesis 3. Only limited ity variables, both of which can be found in the support is found for hypotheses 1a and 1b. Hypothesis appendices. 5 does not hold for balance but could yet hold for unit quality. Correlations Combined sample results Table 2 shows the relation between key variables using Spearman’s rank correlation. The table shows the average The OLS regression models on CPR success using the imput- coefficients over 100 imputed datasets and includes the stan- ed data are presented in Table 3. Model 1 and model 2 show dard errors in parentheses. The same table for the avail- that irrigation systems have significantly lower scores on unit able case data is shown in Appendix 7,showing very quality (B = − 0.53, p < 0.001) and balance (B = − 0.52, p = similar results. It is shown that economic heterogeneity 0.005) than fishing grounds, indicating that there may be J Environ Stud Sci (2021) 11:37–64 45 fundamental differences in success variables between the sec- effect of economic heterogeneity on unit quality through trust tor types. Model 3 and model 4 include the effect of sociocul- (B = − 0.17, p =0.017). Using the trust coefficient for irriga- tural and economic heterogeneity and show that there is no tion systems, we find a significant indirect effect of economic significant relation between either sociocultural or economic heterogeneity on balance through trust (B = − 0.20, p =0.033). heterogeneity and unit quality or balance, so far thus rejecting These results partially support hypothesis 4a stating the nega- hypotheses 1a and 1b stating a negative relation of heteroge- tive indirect effect of economic heterogeneity on CPR success neity with CPR success. Model 5 and model 6 include the through trust, but as no other significant indirect effects are effect of trust; model 5 shows a significant relation between found for the combined sample, the supportive evidence for trust and unit quality (B = 0.20, p = 0.040), andmodel 6shows hypothesis 4a is very limited and hypothesis 4b is so far a significant relation between trust and balance (B =0.54, rejected. To check the robustness of the tests for the indirect p < 0.001), supporting hypothesis 3 stating that higher levels effects, moderated mediation models using the mediate func- of trust are associated with CPR success. To test hypothesis 5, tion in R were applied (Tingley et al. 2014), to test the differ- models 7 and 8 include the interaction effect between trust and ence in mediation effects of heterogeneity through trust on sector type. Model 8 shows no improvement in fit, but model CPR success between fishing grounds and irrigation sys- 2 15 7 shows an increase from 0.28 to 0.39 for the adjusted R .The tems. The results support the found indirect effects and can main effect of trust on unit quality, thus the relation between be seen in Appendix 8. trust and unit quality in fishing grounds, is significant and positive (B =0.53, p < 0.001), adding to the support for hy- Separate sample results pothesis 3. The interaction effect is significant and negative (B = − 0.57, p < 0.001) indicating that the relation between Table 4 shows the models testing the hypotheses separately trust and unit quality for irrigation systems is basically zero for the fishing ground sample (N = 40) and the irrigation sys- and thus that trust amongst appropriators in a fishing ground tem sample (N = 52). In the fishing ground sample, a positive may play a bigger role in achieving high levels of unit quality significant relation between trust and unit quality (B =0.54, than in irrigation systems. This result only partially supports p = 0.004) in model 3 and a marginally significant relation hypothesis 5; only in the case of unit quality. The main effect between trust and balance (B = 0.50, p = 0.062) in model 4 of trust on balance in model 8, thus the relation between trust are found. Both results add to the support for hypothesis 3. and balance for fishing grounds, is marginally significant and A marginally significant relation between economic heteroge- substantive (B = 0.43, p = 0.053), indicating a 0.43 unit in- neity and trust is visible (B = − 0.28, p =0.071) in model 5. crease on a five-point scale of balance per increased unit of Although not significant at the 5% level, it is a substantive economic heterogeneity. Model 9 shows that economic het- effect of a 0.28 point decrease in the three-point scale of trust erogeneity (B = − 0.32, p = 0.004) has a significant negative per increased unit of economic heterogeneity, providing mod- relation with trust, supporting hypothesis 2a. No evidence for est support for hypothesis 2a. hypothesis 2b is found. Lastly, model 10 shows that there is no With respect to the irrigation system sample, a hint of significant main effect of sector type on trust, indicating that the indirect effect of economic heterogeneity by trust can irrigation systems and fisheries do not necessarily differ in be seen from models 2, 4 and 5. Model 2 shows a mar- levels of trust, even though trust within each sector type may ginally significant negative relation of economic hetero- affect CPR success differently. geneity and balance (B = − 0.35, p = 0.10), modestly The indirect effects of economic heterogeneity on unit supporting hypothesis 1a. Model 4 shows a significant quality and balance through trust are calculated manually, relation of trust and balance (B =0.59, p = 0.007), using Sobel’s(1982) product of coefficients approach for the supporting hypothesis 3. In addition, it shows the disap- coefficient, and Monte Carlo simulations for the standard error pearance of the significance of economic heterogeneity. and two-sided p value. Taking the coefficient of trust for Lastly, model 5 shows a significant negative relation fisheries from model 7, we calculate a significant indirect The combined sample is used, but as the main effect of trust in models 7 and The main effect of trust for irrigations in model 7 is the main effect for trust 8 is interpreted as the main effect of trust for fisheries, due to the addition of an (B = 0.53) minus the interaction coefficient (B = −0.59) which adds up to an interaction effect of sector type (irrigation system = 1) and trust. The main effect of B = − 0.06. effect of trust for irrigation systems is now the main effect of trust minus the Since the distribution of the product can be considered normal, as the prod- interaction term coefficient. Hence, we can and must specify indirect effects of heterogeneity through trust for each sector type separately. uct yields the same outcome as the difference between coefficients approach by Judd and Kenny (1981) (see also MacKinnon et al. (1995)), a Monte Carlo Due to incompatibility of the moderated mediation analysis with the Mice simulation was used, with 100,000 observations using two normal distribu- paradigm and computational tools, we cannot obtain pooled standard errors for tions based on the respective coefficients and standard errors of economic the estimates of the moderated mediation. As a result, we resolve to fit the heterogeneity on trust and trust on unit quality or balance, after which a z moderated mediation to a representative dataset; this dataset is derived by score, t score and the two-sided p value of the indirect effect could be taking the mean of numeric variables, and the mode of factor variables of calculated. the 100 imputed datasets, to create an average dataset. 46 J Environ Stud Sci (2021) 11:37–64 between economic heterogeneity and trust (B = − 0.29, p = 0.050), providing some support for hypothesis 2a. Possibly, the results support hypothesis 4a for balance: the disappearance of the significant effect of economic heterogeneity from models 2 to 4 combined with the sig- nificant negative relation between economic heterogeneity on trust could be an indicator of an indirect effect of economic heterogeneity through trust on balance. Regarding sociocultural heterogeneity, model 1 shows a significant negative relation between sociocultural hetero- geneity and unit quality (B = − 0.15, p = 0.034) and model 5 shows a negative relation with trust (B = − 0.37, p = 0.026), providing partial support for respectively hypoth- eses 1b and 2b for irrigation systems. However, the sig- nificant effect of sociocultural heterogeneity on unit qual- ity remains in model 3 (B = − 0.19, p = 0.025) and trust is not significant, indicating that there is no indirect effect of sociocultural heterogeneity on unit quality through trust. The indirect effects of economic or sociocultural heteroge- neity on balance and unit quality for fishing grounds are not significant. For irrigation systems, the indirect effect of socio- cultural heterogeneity on balance is marginally significant (B = − 0.22, p = 0.079), indicating modest support for the role of trust as stated in hypothesis 4b. The indirect effect of eco- nomic heterogeneity on balance through trust is just about not significant on the marginal level, but should, given the small sample size, not be ignored (B = − 0.17, p = 0.109). To check the robustness of the tests for the indirect effects, moderated mediation models were applied. The models can be found in Appendix 8. Table 5 shows an overview of the results found per hypothesis. Counting the three samples—combined, fish- ery and irrigation—and the three methods—OLS regres- sion as shown in main tables, the ordinal logistic regres- sion [OLR] and the robustness check [RC] models with the alternative operationalisation of economic and sociocultur- al heterogeneity—there are nine tests for each hypothesis, except for hypotheses 4a and 4b which have not been cal- culated with the OLR models and thus have six tests. From this overview, we can conclude that there is convincing evidence for hypothesis 2a on the negative relation of eco- nomic heterogeneity with trust and hypothesis 3 on the positive relation of trust with CPR success, confirmed in, respectively, eight and nine tests out of nine. Hypothesis 1b is only supported for balance in irrigation systems, and hypothesis 5 is only supported regarding unit quality; it is marked as supported in all tests because all tests point out that trust is more important for fishing grounds than irriga- tion systems for unit quality, but the hypothesis as a whole—encompassing both balance and unit quality—is still only partially supported. Hypothesis 4a is partially supported with three significant indirect effects out of six tests in addition to the supported hypotheses 2a and 3. Table 2 Spearman correlation for main variables Combined sample Fishing grounds Irrigation systems Unit quality Balance Trust Unit quality Balance Trust Unit quality Balance Trust EH − 0.09 (0.07) − 0.20† (0.06) − 0.40*** (0.06) − 0.18 (0.10) − 0.04 (0.09) − 0.39* (0.07) 0.00 (0.07) − 0.36* (0.09) − 0.541* (0.01) SH − 0.04 (0.07) − 0.17 (0.06) − 0.23† (0.07) − 0.12 (0.10) − 0.15 (0.10) − 0.05 (0.11) − 0.25† (0.01) − 0.33* (0.06) − 0.42** (0.08) Trust 0.23* (0.03) 0.44*** (0.03) – 0.47** (0.07) 0.33* (0.03) – − 0.14 (0.02) 0.51*** (0.05) – N 92 92 92 40 40 40 52 52 52 Standard errors in parentheses ***p < 0.001, **p < 0.01, *p < 0.05, †p < 0.1 two-sided J Environ Stud Sci (2021) 11:37–64 47 Table 3 OLS regression on main variables and interaction effect using the imputed sample of both fisheries and irrigation systems (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Unit quality Balance Unit quality Balance Unit quality Balance Unit quality Balance Trust Trust Irrigation − 0.53*** − 0.52** − 0.54*** − 0.55** − 0.51*** − 0.47** 0.45 − 0.78 − 0.16 (0.10) (0.18) (0.10) (0.18) (0.10) (0.17) (0.28) (0.49) (0.13) Irr. × Trust − 0.58*** 0.18 (0.16) (0.28) Trust 0.20* 0.54*** 0.53*** 0.43† (0.09) (0.15) (0.13) (0.22) Sociocultural heterogeneity − 0.06 − 0.21 − 0.02 − 0.10 − 0.07 − 0.09 − 0.19 − 0.20 (0.09) (0.15) (0.09) (0.14) (0.09) (0.15) (0.14) (0.14) Economic heterogeneity − 0.05 − 0.21 0.01 − 0.04 0.00 − 0.03 − 0.32** − 0.31** *** (0.08) (0.15) (0.08) (0.15) (0.08) (0.15) (0.10) (0.10) Constant 2.57*** 2.03*** 2.70*** 2.50*** 2.25*** 1.26*** 1.74*** 1.43** 2.17*** 2.28*** (0.08) (0.14) (0.15) (0.26) (0.27) (0.45) (0.29) (0.50) (0.19) (0.20) Adj. R 0.24 0.08 0.24 0.14 0.28 0.25 0.39 0.25 0.19 0.19 95% CI Adj. R (0.98, 0.40) (0.01, 0.21) (0.10, 0.41) (0.02, 0.31) (0.13,0.45) (0.10, 0.42) (0.21, 0.56) (0.10, 0.42) (0.03, 0.40) (0.03, 0.40) FMI 0.04 0.05 0.10 0.26 0.11 0.16 0.27 0.16 0.48 0.48 AIC − 137.80 − 25.28 − 136.74 − 29.95 − 142.52 − 38.54 − 159.67 − 37.18 − 98.90 − 99.02 N 92 92 92 92 92 92 92 92 92 92 Standard errors in parentheses ***p < 0.001, **p < 0.01, *p < 0.05, †p <0.1, two-sided 48 J Environ Stud Sci (2021) 11:37–64 Table 4 OLS regression on main variables using the imputed sample for fishing grounds (left) and irrigation systems (right) Fishing grounds Irrigation systems (1) (2) (3) (4) (5) (1) (2) (3) (4) (5) Unit quality Balance Unit quality Balance Trust Unit quality Balance Unit quality Balance Trust Trust 0.54** 0.50† − 0.10 0.59** (0.18) (0.26) (0.07) (0.21) Sociocultural heterogeneity − 0.01 − 0.14 0.01 − 0.12 − 0.05 − 0.15* − 0.23 − 0.19* − 0.01 − 0.37* (0.18) (0.24) (0.16) (0.24) (0.17) (0.07) (0.20) (0.08) (0.20) (0.16) Economic heterogeneity − 0.14 − 0.01 0.01 0.13 − 0.28† 0.03 − 0.35† 0.01 − 0.18 − 0.29* (0.16) (0.23) (0.16) (0.24) (0.15) (0.07) (0.20) (0.07) (0.20) (0.14) Constant 2.71*** 2.26*** 1.60** 1.23** 2.05*** 2.18*** 2.09*** 2.40*** 0.73** 2.29*** (0.30) (0.43) (0.46) (0.70) (0.28) (0.08) (0.27) (0.20) (0.56) (0.20) Adj. R -⁑ -⁑ 0.22 -⁑ -⁑ 0.09 -⁑ 0.12 0.29 0.30 95% CI Adj. R -⁑ -⁑ (0.02, 0.51) -⁑ -⁑ (0.00, 0.31) -⁑ (0.00, 0.34) (0.09, 0.53) (0.06, 0.56) AIC − 34.51 − 7.44 − 46.99 − 8.31 − 39.12 − 137.81 − 20.47 − 138.23 − 25.71 − 60.68 N 40 40 40 40 40 52 52 52 52 52 Standard errors in parentheses ⁑ Adjusted R and FMI could not be calculated: the Fisher transformation for pooled simulations could not be performed since some of the simulations had a negative R ***p <0.001, **p <0.01, *p <0.05, †p <0.1, two-sided Discussion success, contrary to a large body of existing literature. Economic heterogeneity, however, is found to be signif- The aim of this paper is to study whether and how icantly negatively related to trust in all but one test, economic and sociocultural heterogeneity affect the suc- indicating that the role of economic heterogeneity re- cessful management of CPRs, to explore the role of garding trust in CPRs is relevant. Trust has a positive trust and to see whether these relations differ for fish- association with both unit quality and balance in all eries and irrigation systems. Using advanced imputation tests, confirming the importance of mutual trust for techniques to prepare the famous but challenging CPR Database allowed us to test the influence of two types of heterogeneity on CPR success at the same time, as Table 5 Overview of results of hypothesis tests, where ‘x’ marks that support is found well as looking at direct and indirect mechanisms. Existing literature predominantly suggests that both Combined sample Fishing ground sample Irrigation sample types of heterogeneity negatively influence collective action and therefore CPR success, that heterogeneity OLS OLR RC OLS OLR RC OLS OLR RC negatively affects mutual trust and that trust has a pos- 1a x** x** itive effect on societal outcomes. 1b x* x* x* For the multivariate analysis, we applied OLS regres- 2a x x x x x x x x sion models instead of ordinal logistic regression, 2b x x favouring a simpler interpretation of coefficients. 3 x x x x x x x** x x** However, we tested all hypotheses in an ordinal logistic 4a x – x –– x** regression as well, plus we ran a model with alternative 4b –– x** – operationalisations of heterogeneity as a robustness 5x* x* x* x* x* x* x* x* x* check. In addition, we tested the found indirect effects through a moderated mediation analysis. Results are on- Considering the small sample size and limited statistical power, a hypoth- ly considered robust if they are found for most of the esis is marked ‘x’ when supported with evidence on at least the marginal combined and separate samples over the three analysis significance level of α =0.1 types. It appeared that neither form of heterogeneity has *Only confirmed for unit quality a robust significant relation with either measure of CPR **Only confirmed for balance J Environ Stud Sci (2021) 11:37–64 49 positive CPR outcomes. A distinction between sector It has to be noted that this study has some shortcom- type proves relevant, since the significant interaction ings and that there is potential for improvement and of trust with sector type on unit quality implies that replication. First, although the database provides a rela- trust only has a positive effect on unit quality for fish- tively large sample for a field of research dominated by eries, and not for irrigation systems, something we later case studies, the sample size has limited statistical pow- confirm in the subsample analyses. Trust seems to play er. A substantial number of missing data for specific a role in upkeeping balance in irrigation systems, so the variables implied a suboptimal operationalisation of eco- role of trust cannot be disregarded for irrigation sys- nomic heterogeneity. The imputation method used is tems. Regarding our calculations for the indirect effects, however innovative and provides imputation diagnostics, we find partial support: a significant indirect effect of such as the OOB, that gives us confidence in the im- economic heterogeneity on balance through trust is putation process and its results. Next to this, we report- found in the combined sample. To invigorate the results, ed the FMI where possible, herewith disclosing the level and to explore the findings that were not considered of uncertainty we have about the imputation of missing robust in the current analysis, more data should be gath- data. Second, individual level data instead of our case ered and more research conducted. study level data could have provided more information The difference between findings in the subsamples on the role of trust; there may be individual confound- may be related to fundamental differences between sec- ing factors influencing the level of mutual trust of tor types. For fishing grounds, both the quality (for appropriators, such as general level of trust in society, instance, size of the fish) and the balance between re- how long an appropriator has resided in the community, newal and subtraction may be affected by trust between individual cultural views or the history of interactions appropriators. For irrigation systems on the other hand, between individual appropriators. In addition, the CPR the balance may be affected but the quality of the water Database only provides very broad categorisations, even in an irrigation system may be less threatened by a lack for variables of great interest, like trust; more detailed of trust. These findings illustrate the difficulty of draw- measurements would provide more detailed results and ing conclusions from results across sector types since a subsequently more detailed conclusions. Third, the cases specific measure of CPR success might mean different in the CPR Database are all from before 1989. Whereas things for different CPR types. It is especially because the argument of difficult monitoring in fishing grounds of these differences that it is theoretically interesting to maybetruefor most fisheriesbackthen, therecurrently compare different CPRs, as it helps to understand the exist modern solutions: the vessel monitoring system mechanisms behind the failure or success of different (VMS), used from the late 1990s on, and the automatic identification system (AIS), implemented in the early types of resources. The findings are relevant given the increasing number 2000’s. Both systems have significantly improved mon- of contemporary CPRs, also known as citizen collec- itoring of fishing activities worldwide (Longépé et al. tives, or institutions for collective action, such as local 2018;Nataleetal. 2015). AIS has the main purpose communities producing their own green energy and ur- of avoiding collisions, but can also be used to track ban agriculture projects with community farms (De fishing activities (Kurekin et al. 2019;Longépéetal. Moor 2013b). Like irrigation systems, green energy pro- 2018; Matsumoto et al. 2016; Natale et al. 2015;Wu duction and community farms are self-made systems, et al. 2016). This may reduce the need for high levels monitoring is relatively easy, production may be unsta- of mutual trust amongst fishermen, as real-time monitor- ble due to the dependency on the weather, and mainte- ing is now a possibility. It is unlikely, however, that nance is required—either by the government if govern- such systems are in use in the smallest CPRs in less ment-owned, or through collective actions of farmers if developed areas. Depending on the availability of these not. If indeed our findings for irrigation systems apply modern technologies, the role of trust in achieving high to such CPRs, we can expect that trust amongst unit quality and balance may thus not be discarded. appropriators will benefit the balance rather than the Lastly, as we discussed, there may be reversed causali- quality of these CPRs, and maybe that trust may in ty. As argued, we have reasons to believe that hetero- general play a smaller role in achieving collective ac- geneity is indeed influencing trust and CPR success; we tion, given that monitoring is easy which makes trust a referred to studies using instrumental variables showing less important factor. For CPRs where monitoring re- that heterogeneity negatively affects trust, and experi- quires more effort, such as fisheries and communal for- mental studies showing that trust indeed positively af- ests, trust will be more important in achieving high fects societal outcomes such as cooperation. However, quality of the resource units and a balanced resource. in future research the causality issues could be 50 J Environ Stud Sci (2021) 11:37–64 addressed by replicating our research on CPR success Appendix 1 using for instance experimental methods, since laborato- Table 6 Differences between fishing grounds and irrigation systems ry experiments are tailor-made to point out causality. The research question on cooperative behaviour in Fishing ground Irrigation system CPRs is not only fundamental to social sciences, but also (N =40) (N =52) to the current state of affairs concerning the use and de- Variation of flow of resource units over space? pletion of natural and man-made resources, such as Yes 40 32 rainforests, fish populations, oil and gas. There is current- No 0 20 ly a rise of new CPRs: an increasing amount of green Variation of flow of resource units from year to year? Yes 38 37 energy cooperatives, local community farms, collective No 2 15 gardens and care cooperatives are part of everyday life Variation of flow of resource units within a year? due to an increasing privatisation of social services (De Yes 40 49 No 0 3 Moor 2013a, 2013b, 2018). These commons too, may Predictable variation of flow of resource units over space? become subject to the risk of overexploitation. Next to 1 (Highly predictable) 0 0 that, ‘classic’ commons like fishing grounds, forests and 226 5 37 44 pastures have new meanings nowadays, and are not only 46 3 regarded as sources of products but also as conservation 5 (Highly unpredictable) 1 0 tools and leisure areas. Contemporary problems surround- Predictable variation of flow of resource units within a year? 1 (Highly predictable) 0 0 ing CPRs include amongst others landscape planning, wa- 229 6 ter management and even climate change (Bravo and De 35 44 Moor 2008). The investigation of the impact of societal 42 2 5 (Highly inpredictable) 4 0 characteristics such as heterogeneity and trust on cooper- Predictable variation flow of resource units from year to year? ation could provide new insights into the use and preser- 1 (Highly predictable) 0 0 vation of these CPRs, demonstrating the contributions that 20 0 30 1 social and environmental sciences can make to a sustain- 439 51 able society. 5 (Highly unpredictable) 1 0 Closed access** Acknowledgements We thank the Library on Governance in Social- 1 (Yes, de jure and 11 52 Ecological Systems for making the CPR Database publicly available effective) and for providing detailed information about the database upon request. 21 0 We also thank colleagues for their feedback and especially Prof. Tine de 30 0 Moor. 412 0 53 0 63 0 Funding This research was supported by the Economic and Social 7(No) 10 0 Research Council. Exit options** Less than 10% 10 39 Data availability The data is available at https://seslibrary.asu.edu/cpr 10–25% 1 1 26–50% 0 0 51–75% 1 2 Compliance with ethical standards 76–90% 3 0 91–100% 25 10 Conflict of interest The authors declare that they have no conflict of interest *See the CPR Coding Manual (Ostrom et al. 1989) for detailed descrip- tion of variables Code availability R code is available on the first author’sGitHub. **See Appendix C for description of these variables J Environ Stud Sci (2021) 11:37–64 51 Appendix 2. Table of CPR cases in data Table 7 Generated with CPR Database, https://seslibrary.asu.edu/cpr Country Resource name Sector Cases Source(s) Australia Lakes Entrance Fishery 2 Sturgess et al. (1982) Australia Port Phillip Bay Fishery 4 Sturgess et al. (1982) Bangladesh Nabagram Irrigation Irrigation 1 Coward et al. (1979) Belize Caye Caulker Lobsterfishing Fishery 1 Sutherland (1986) Belize San Pedro Spiny Lobster Fishery Fishery 1 Gordon (1981) Brazil Arembepe Fishery Fishery 1 Kottak (1966) Brazil Coqueiral Raft Fishery Fishery 1 Forman(1970) Brazil Valenca Fishery Fishery 3 Cordell (1972) Canada Baccalaos Cove Cod Fishery Fishery 1 Powers (1984) Canada Cat Harbour Cod Fishery Fishery 1 Faris (1972) Canada Chisasibi - James Bay Fishery Fishery 1 Berkes (1977, 1982, 1987) Canada Fermeuse Cod Fishery Fishery 1 Martin (1973, 1979) Canada Petty Harbour Cod Fishery Fishery 1 Shortall (1973) Canada Port Lameron - Pagesville Finfishery Fishery 2 Davis (1975), Davis (1984) Greece Messolonghi-Etolico Lagoon Fishery Fishery 1 Kotsonias (1984) India A Tailend Watercourse in Area Two Irrigation 1 Bottral (1981) India Chawk 16,000 L Dhabi Minor Irrigation Irrigation 1 Reidinger (1974, 1980), Gustafson and Reidinger (1971), Vander Velde (1971, 1980) India Jambudwip Fishery Fishery 1 Raychaudhuri (1968, 1980) India Kottapalle - Irrigation Irrigation 1 Wade (1985, 1988) India Sananeri Tank Irrigation 1 Meinzen-Dick (1984) Indonesia A Watercourse in Area Three Irrigation 1 Bottrall (1981) Indonesia Bondar Parhudagar Irrigation Irrigation 1 Lando (1979) Indonesia Saebah Communal System Irrigation 1 Hafid and Hayami (1979) Indonesia Silean Banua Irrigation Irrigation 1 Lando (1979) Indonesia Subak A Irrigation 1 Geertz (1967) Indonesia Takkapala Communal System Irrigation 1 Hafid & Hayami (1979) Iran Deh Salm Irrigation Irrigation 1 Spooner (1971, 1972, 1974) Iran Nayband Irrigation Irrigation 1 Spooner (1971, 1972, 1974) Iraq El Mujarilin Irrigation Irrigation 1 Fernea (1970) Jamaica Farquhar Beach Fishery 1 Davenport (1956) Japan Ebibara Fishing Ground Fishery 1 Brameld (1968) Korea Kagoda anchovy grounds Fishery 1 Han (1972) Laos A watercourse in Nam Tan Irrigation 1 Coward (1980) Malaysia Kampong Mee Trawl Fishery Fishery 1 Anderson and Anderson (1977) Malaysia Perupok Fishery Fishery 1 Firth (1966) Mexico A Tramo in Diaz Ordaz Irrigation 1 Downing (1974) Mexico Andres Quinta Roo Lobster Fishery 1 Miller (1982) 52 J Environ Stud Sci (2021) 11:37–64 Table 7 (continued) Country Resource name Sector Cases Source(s) Mexico Andres Quintana Roo Scalefish Fishery 1 Miller (1982) Mexico Ascension Bay Lobster Fishery Fishery 1 Miller (1988) Nepal Argali Raj Kulo Irrigation (Jethi Kulo) Irrigation 1 Martin and Yoder (1983a, b, 1986) Nepal Char Hazar Irrigation System (Charhajar) Irrigation 1 Pradhan (1988), Laitos (1986) Nepal Chhahare Khola Ko Kulo, Baruwa Village Irrigation 1 Water and Energy Commission Secretariat (1987) Panchayat Nepal Chherlung Thulo Kulo Irrigation Irrigation 1 Pradhan (1988), Martin and Yoder (1983a, b, 1986), Sharma et al. (1989) Nepal Lothar Irrigation System Irrigation 1 Nirola and Pandey (1987), Pradhan (1988), Laitos (1986) Nepal Naya Dhara Ko Kulo (Kot Village Irrigation 1 Water and Energy Commission Secretariat (1987) Panchayat) Nicaragua Miskito Turtle Fishery Fishery 1 Nietschmann (1972, 1973) Pakistan A Watercourse in Area One Irrigation 1 Bottrall (1981) Pakistan Main Watercourse in Gondalpur Irrigation 1 Merrey and Wolf (1986) Pakistan Watercourse Ten - Dakh Branch Irrigation 1 Mirza and Merrey (1979) Pakistan Watercourse in Punjab Irrigation 1 Lowdermilk et al. (1975) Peru Hanan Sayoc Irrigation Irrigation 1 Mitchell (1976, 1977) Peru Lurin Sayoc Irrigation Irrigation 2 Mitchell (1976, 1977) Philippines A Sitio in Zanjera Danum Irrigation 1 Coward (1979) Philippines Agcuyo Irrigation System Irrigation 1 De Los Reyes (1980) Philippines Cadchog Irrigation Irrigation 1 De Los Reyes (1980) Philippines Calaoaan Irrigation Irrigation 1 De Los Reyes (1980) Philippines Laoag-Vintar Irrigation Irrigation 1 Ongkingco (1973) Philippines Mauraro Irrigation Irrigation 1 De Los Reyes (1980) Philippines NIA Irrigation in San Antonio Irrigation 2 De Los Reyes et al. (1980) Philippines Nazareno-Gamutan Irrigation Irrigation 1 Ongkingco (1973) Philippines Oaig-Daya Irrigation System Irrigation 1 De Los Reyes (1980) Philippines Pinagbayanan Water Pumps Irrigation 1 Cruz (1975) Philippines Sabangan Bato Irrigation System Irrigation 1 De Los Reyes (1980) Philippines Silag-Butir Irrigation System Irrigation 1 De Los Reyes et al. (1980) Philippines Tanowong Bwasao Irrigation Irrigation 1 Bacdayan (1980) Philippines Tanowong Traditional Irrigation Irrigation 1 Bacdayan (1980) Sri Lanka Gahavalla Village Fishery 3 Alexander (1982) Switzerland Felderin Irrigation Irrigation 1 Netting (1974, 1981) Taiwan A Watercourse in Area Four Irrigation 1 Bottrall (1981) Tanzania Kheri Irrigation Irrigation 1 Grey (1963) Thailand A Chaek in Amphoe Choke Chai Irrigation 1 Gillespie (1975) Thailand A Chaek in Kaset Samakee Irrigation 1 Gillespie (1975) Thailand Chiangmai Irrigation Irrigation 1 Potter (1976) Thailand Muang Mai Irrigation Irrigation 1 Tan-Kim-Yong (1983) Thailand Na Pae Irrigation Irrigation 1 Tan-Kim-Yong (1983) Thailand Rusembilan Kembong Fishery Fishery 1 Fraser (1960, 1966) Turkey Alanya Fishery, Turkey Fishery 1 Berkes (1986) Turkey Ayvalik-Haylazli Coop Lagoon, Turkey Fishery 1 Berkes (1986) Turkey Tasucu Bay Fishery, Turkey Fishery 1 Berkes (1986) USA Lobsterfishing, Mount Desert Island, Maine Fishery 1 Grossinger (1975) Venezuela Chiguana Fishery 1 Breton (1973) J Environ Stud Sci (2021) 11:37–64 53 Appendix 3. Table including control variables A description of the control variables is provided following the table. Table 8 OLS regression analyses on main dependent variables using the combined sample, including control variables (12) (13) Unit quality Balance Irrigation 0.50 − 0.87 (0.40) (0.65) Irr. × Trust − 0.60* 0.12 (0.23) (0.37) Trust 0.48* 0.23 (0.22) (0.34) Sociocultural heterogeneity 0.02 − 0.01 (0.09) (0.17) Economic heterogeneity 0.01 0.00 (0.08) (0.16) Cultural view of the resource − 0.06 − 0.14 (0.09) (0.15) Number of users 0.00 0.00 (0.00) (0.00) Closed access − 0.03 − 0.08 (0.05) (0.08) Exit options 0.01 0.03 (0.04) (0.06) Monetary sanctions 0.00 − 0.16 (0.06) (0.11) Physical sanctions − 0.08 − 0.12 (0.06) (0.10) Social sanctions 0.07 0.14 (0.07) (0.13) Pollution − 1.20* − 0.77 (0.51) (0.92) Pressure 0.03 − 0.05 (0.17) (0.30) Income dependence − 0.09 0.27 (0.13) (0.21) Worst off − 0.04 0.07 (0.24) (0.43) Variation over space − 0.05 0.64* (0.14) (0.29) Constant 2.04** 1.36 (0.73) (1.24) Adj. R 0.43 0.34 95% CI, adj. R (0.24, 0.60) (0.16, 0.52) FMI 0.33 0.30 AIC − 156.00 − 34.86 N 92 92 Standard errors in parentheses ***p <0.001, **p <0.01, *p <0.05, †p <0.1, two-sided 54 J Environ Stud Sci (2021) 11:37–64 Table 9 Description of control variables (as cited from the CPR Codebook (Ostrom et al. 1989)) Cultural view of the How does the general cultural view of the resource system and its use affect communication between subgroups? (scale 1–5) resource Number of users What is the actual number of individuals in this group at the end of the period? (number) Closed access As of the end of this period, are the appropriators exercising or attempting to exercise closed access to this resource? Closed access is exercised on a de facto base if it is NOT specifically sanctioned by some legitimate authority/ by a de jure base if it IS sanctioned. Outsiders are persons who are not originally appropriators. (scale 1–7) Exit options What proportion of this subgroup works a substantial amount of time in activities not associated with appropriation from this resource? (scale 1–6) Monetary sanctions If someone violated rules-in-use related to the appropriation process from this resource, how likely is it that an official monitor or guard will move to impose sanctions? (scale 1–5) Physical sanctions If someone violates rules-in-use related to the appropriation process from this resource, how likely is he/she to encounter physical sanctions imposed by other appropriators (who are not official monitors? (scale 1–5) Social sanctions If someone violates rules-in-use related to the appropriation process form this resource how likely is he/she to encounter social sanctions imposed by other appropriators who are not monitors? (scale 1–5) Pollution Are there problems of pollution of this or other resources resulting from the way units are appropriated in end of period? (scale 1–4) Pressure Does the amount of capital required to set up an appropriation team, given the assets of members of this subgroup, place pressure upon the appropriators to get immediate returns from appropriation (Y/N) Income dependence For most people in this subgroup, how dependent are they on this resource as a major source of family income? (scale 1–3) Worst off Have the relatively worst off been cut out of their benefits from this resource or substantially harmed? (Y/N) Variation over space Is there considerable variation over space in the availability of these units within the resource? (Y/N) Appendix 4 Ordinal logistic models regularise these standard deviation estimates by using a Bayesian prior encoding a reasonably large degree of For some models, the maximum likelihood estimates uncertainty over the parameters. We stress however, that provide unreliably high standard errors due to the small this prior is noninformative and only serves to control sample size and the splitting of ordinal variables into the standard deviation where needed. multiple dummies in the model, as this increases the For the subsamples, sociocultural heterogeneity was treat- number of parameters to be estimated. We resolve to ed as continuous for two reasons. First, the combined sample useaBayesianapproachfor themodels wherethe stan- model was modelled once with and once without treating dard errors are too extreme, using the R function sociocultural heterogeneity as continuous (the latter presented bayespolr from the arm package (Gelman and Su here in Appendix), which did not affect the coefficients of the 2018). For instance, we are working on the logit scale, other variables. Based on this we believe that treating socio- so a reasonable value for the standard deviation of a cultural heterogeneity as either continuous or as ordinal does parameter over which we are very uncertain is around not impact the model significantly. Second, the subsamples 2.5. The maximum likelihood approach for some of are so small that adding the variable as separate dummies the models go up to over 200 points on the standard would decrease the already limited statistical power of the deviation, which is effectively meaningless, and an ar- model, making it impossible to detect any possible relations tefact of the small sample size. Hence, we resolve to between covariates. Which is the default scale parameter in the R function bayespolr. J Environ Stud Sci (2021) 11:37–64 55 Table 10 Ordinal logistic regression on main variables using the combined sample, treating unit quality, balance, trust and heterogeneity as ordinal variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Unit quality Balance Unit quality (Bayes) Balance Unit quality (Bayes) Balance Unit quality (Bayes) Balance (Bayes) Trust (Bayes) Trust (Bayes) Irrigation − 2.63*** − 1.09** − 2.60*** − 1.41** − 2.63*** − 1.40** − 0.46 − 2.16† − 0.76 (0.55) (0.40) (0.57) (0.48) (0.60) (0.48) (1.28) (1.13) (0.59) Irr. × Trust =1 0.90 1.41 (1.78) (1.43) =2 − 3.23* 0.95 1.57 (1.20) Trust =1 2.63† 2.16* 2.30 0.81 (1.42) (0.97) (2.05) (1.16) =2 2.20† 3.29*** 4.59** 2.35* (1.21) (0.89) (1.73) (1.00) Sociocultural heterogeneity = 1 0.41 − 0.66 0.24 − 0.34 0.33 0.30 0.22 0.26 (1.08) (1.87) (1.07) (1.89) (1.10) (0.96) (1.09) (1.09) =2 − 0.68 − 2.05 − 0.66 − 1.44 − 0.69 − 0.65 − 0.69 − 0.76 (1.19) (1.98) (1.21) (2.05) (1.23) (1.10) (1.22) (1.24) = 3 0.81 − 1.65 0.76 − 1.32 0.65 − 0.29 0.19 0.13 (1.64) (2.54) (1.63) (2.58) (1.61) (1.34) (1.65) (1.68) =4 − 0.56 − 2.05 − 0.12 − 0.77 − 0.41 0.09 − 1.25 − 1.27 (1.45) (2.14) (1.38) (2.23) (1.48) (1.27) (1.77) (1.70) Economic heterogeneity =1 − 0.24 − 0.55 − 0.21 − 0.22 − 0.25 − 0.19 − 1.62 − 1.59 (0.62) (0.60) (0.64) (0.61) (0.69) (0.55) (1.06) (1.05) =2 − 0.36 − 0.97 0.10 − 0.26 0.14 − 0.19 − 2.54* − 2.56* (0.73) (0.72) (0.82) (0.75) (0.90) (0.74) (1.14) (1.13) N 92 92 92 92 92 92 92 92 92 92 AIC 121.11 234.81 149.89 235.32 148.50 226.31 145.10 261.08 141.55 143.39 Standard errors in parentheses ***p < 0.001, **p < 0.01, *p < 0.05, †p <0.1,two-sided 56 J Environ Stud Sci (2021) 11:37–64 Table 11 Ordinal logistic regression on main variables using separate samples for fishing grounds (left) and irrigation systems (right) Fishing grounds Irrigation systems (1) (2) (3) (4) (5) (6) (7) (1) (2) (3) (4) (5) (6) (7) Unit Balance Unit Balance Unit quality Balance Trust Unit quality Balance Unit quality Balance Unit quality Balance Trust quality quality (Bayes) (Bayes) (Bayes) (Bayes) (Bayes) (Bayes) Trust = 1 1.87 0.43 0.71 − 0.09 4.06† 3.34* 2.70 3.70† (1.77) (1.50) (1.48) (2.86) (2.18) (1.36) (2.02) (1.98) = 2 3.42* 1.86† 2.50† 2.07† 1.10 4.73*** − 0.45 5.00* (1.37) (1.10) (1.26) (1.19) (1.55) (1.35) (1.62) (2.11) Sociocultural − 0.08 − 0.33 − 0.04 − 0.33 − 0.12 − 1.30† − 0.57 − 1.50† 0.20 − 1.31 heterogeneity (0.64) (0.39) (0.61) (0.42) (0.77) (0.73) (0.54) (0.84) (0.77) (0.85) Economic heterogeneity =1 − 1.10 − 0.22 − 0.57 − 0.13 − 1.24 0.69 − 1.06 0.24 − 0.69 − 1.28 (1.00) (0.86) (0.86) (0.89) (1.23) (1.33) (0.83) (1.39) (0.88) (1.13) =2 − 0.87 − 0.01 0.19 1.11 − 2.12† 0.24 − 1.79 0.03 − 0.95 − 1.87 (1.21) (0.98) (1.20) (2.49) (1.37) (1.50) (1.16) (1.49) (1.26) (1.29) N 40 40 40 40 40 40 40 52 52 52 52 52 52 52 AIC 61.26 100.34 72.84 104.78 83.88 105.59 59.95 41.59 117.63 48.30 128.73 49.48 122.94 83.97 Standard errors in parentheses ***p < 0.001, **p < 0.01, *p < 0.05, †p <0.1, two-sided J Environ Stud Sci (2021) 11:37–64 57 Table 12 OLS regression on main variables and interaction effect using the imputed sample of both fisheries and irrigation systems (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Unit quality Balance Unit quality Balance Unit quality Balance Unit quality Balance Trust Trust Irrigation − 0.53*** − 0.52** − 0.47*** − 0.31 − 0.47*** − 0.32 0.61† − 0.61 − 0.16 (0.10) (0.18) (0.12) (0.22) (0.12) (0.20) (0.31) (0.27) (0.13) Irr. × Trust − 0.60*** 0.16 (0.16) (0.28) Trust 0.20* 0.54*** 0.54*** 0.44* (0.09) (0.15) (0.13) (0.22) Sociocultural heterogeneity − 0.19 − 0.60 − 0.11 − 0.37 − 0.32 − 0.32 − 0.41 − 0.43 (mean) (0.24) (0.41) (0.23) (0.40) (0.23) (0.42) (0.25) (0.30) Economic heterogeneity − 0.06 − 0.25 0.01 − 0.06 0.01 − 0.05 − 0.36*** − 0.36*** (mean) (0.08) (0.15) (0.09) (0.16) (0.08) (0.17) (0.11) (0.11) Constant 2.57*** 2.03*** 2.93*** 3.25*** 2.36*** 1.67* 2.05*** 1.76** 2.92*** 2.94*** (0.08) (0.14) (0.32) (0.58) (0.42) (0.74) (0.40) (0.74) (0.39) (0.43) Adj. R 0.24 0.08 0.24 0.14 0.28 0.25 0.39 0.25 0.19 0.19 95% CI Adj. R (0.98, 0.40) (0.01, 0.21) (0.10, 0.40) (0.02, 0.30) (0.13, 0.44) (0.10, 0.42) (0.21, 0.56) (0.10, 0.42) (0.04, 0.439) (0.03, 0.39) FMI 0.04 0.05 0.07 0.22 0.09 0.15 0.26 0.14 0.39 0.39 AIC − 137.80 − 25.28 − 136.74 − 29.92 − 142.70 − 38.96 − 161.73 − 37.31 − 97.43 − 95.79 N 92 9292 9292 9292 92 92 92 Standard errors in parentheses ***p < 0.001, **p < 0.01, *p < 0.05, †p <0.1, two-sided 58 J Environ Stud Sci (2021) 11:37–64 Appendix 5. Robustness checks for operationalisation of economic and sociocultural heterogeneity; mean instead of max Table 13 OLS regression on main variables using the imputed sample for fishing grounds (left) and irrigation systems (right) Fishing grounds Irrigation systems (1) (2) (3) (4) (5) (1) (2) (3) (4) (5) Unit quality Balance Unit quality Balance Trust Unit quality Balance Unit quality Balance Trust Trust 0.53** 0.53* − 0.07 0.57** (0.17) (0.25) (0.07) (0.20) Sociocultural 0.23 − 0.92 0.07 − 1.09 − 0.30 − 0.41** − 0.34 − 0.44** − 0.01 − 0.59† heterogeneity (mean) (0.82) (1.18) (0.74) (1.15) (0.71) (0.14) (0.45) (0.15) (0.44) (0.35) Economic − 0.16 − 0.06 − 0.01 0.10 − 0.29† 0.04 − 0.43* 0.02 − 0.22 − 0.37* heterogeneity (mean) (0.19) (0.26) (0.18) (0.27) (0.16) (0.07) (0.20) (0.07) (0.21) (0.15) Constant 2.71*** 3.31*** 1.58 2.32** 1.88*** 2.18*** 2.85*** 2.82*** 1.01 3.23*** (0.30) (1.72) (1.13) (1.74) (1.03) (0.08) (0.69) (0.31) (0.94) (0.54) Adj. R -⁑ -⁑ 0.22 -⁑ -⁑ 0.12 -⁑ 0.12 0.30 0.25 95% CI Adj. R -⁑ -⁑ (0.02, 0.51) -⁑ -⁑ (0.00, 0.33) -⁑ (0.00, 0.33) (0.09, 0.53) (0.06, 0.56) AIC − 35.33 − 7.94 − 46.77 − 8.93 − 37.42 − 137.05 − 18.37 − 135.52 − 25.71 − 54.61 N 40 40 40 40 40 52 52 52 52 52 Standard errors in parentheses Adjusted R and FMI could not be calculated: the Fisher transformation for pooled simulations could not be performed since some of the simulations had anegative R ***p <0.001, **p <0.01, *p <0.05, †p <0.1, two-sided Appendix 6. Fraction of missing information per variable for main tables Table 14 FMI per variable for OLS regression on main variables using the imputed sample (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Unit quality Balance Unit quality Balance Unit quality Balance Unit quality Balance Trust Trust Irrigation 0.04 0.05 0.08 0.12 0.08 0.11 0.17 0.09 0.17 Irr. × Trust 0.20 0.12 Trust 0.21 0.17 0.24 0.10 Sociocultural heterogeneity 0.48 0.41 0.39 0.35 0.54 0.37 0.69 0.66 Economic heterogeneity 0.29 0.38 0.32 0.40 0.33 0.40 0.38 0.38 N 92 92 92 92 92 92 92 92 92 92 AIC − 137.80 − 25.28 − 136.74 − 29.95 − 142.52 − 38.54 − 159.67 − 37.18 − 98.90 − 99.02 J Environ Stud Sci (2021) 11:37–64 59 Table 15 FMI per variable for Fishing grounds OLS regression for imputed sample of fishing grounds (1) (2) (3) (4) (5) Unit quality Balance Unit quality Balance Trust Trust 0.20 0.17 Sociocultural heterogeneity 0.35 0.30 0.31 0.32 0.43 Economic heterogeneity 0.32 0.33 0.37 0.36 0.32 N 40 40 40 40 40 AIC − 34.51 − 7.44 − 46.99 − 8.31 − 39.12 Table 16 FMI per variable for Irrigation systems OLS regression for imputed sample of irrigation systems (1) (2) (3) (4) (5) Unit quality Balance Unit quality Balance Trust Trust 0.21 0.28 Sociocultural heterogeneity 0.34 0.39 0.43 0.37 0.46 Economic heterogeneity 0.38 0.46 0.36 0.48 0.40 N 52 52 52 52 52 AIC − 137.81 − 20.47 − 138.23 − 25.71 − 60.68 Appendix 7. Spearman correlation of main variables with available (unimputed) data Table 17 Spearman correlation for main variables using available data Combined sample Fishing grounds Irrigation systems Unit quality Balance Trust Unit quality Balance Trust Unit quality Balance Trust Economic − 0.19 (N =50) − 0.25† (N =49) − 0.56*** (N =45) − 0.17 (N = 21) 0.09 (N = 21) − 0.54† (N =20) − 0.28 (N =29) − 0.51** (N =28) − 0.59** (N =25) heterogeneity Sociocultural − 0.19† (N = 81) 0.04 (N =82) − 0.17 (N =77) − 0.01 (N = 35) 0.42† (N = 36) 0.06 (N = 35) − 0.27† (N =46) − 0.37† (N = 46) − 0.46** (N =42) heterogeneity Trust 0.21† (N = 79) 0.43*** (N =80) – 0.43** (N = 36) 0.30† (N =37) – − 0.15 (N = 43) 0.53*** (N = 43) – ***p <0.001, **p <0.01, *p <0.05, †p <0.1, two-sided 60 J Environ Stud Sci (2021) 11:37–64 Appendix 8. Moderated mediation models Table 18 Moderated mediation models, testing the mediated effect of heterogeneity on CPR success through trust for fishing grounds and irrigation systems Unit quality Balance Fishing ground Irrigation systems Fishing grounds Irrigation systems EH SH EH SH EH SH EH SH ACME − 0.233*** − 0.185*** 0.046 0.036 − 0.193* − 0.155* − 0.283*** − 0.230* ADE − 0.028 − 0.163† − 0.035 − 0.167† − 0.069 0.057 − 0.076 − 0.065 Total effect − 0.261*** − 0.348*** 0.012 − 0.130 − 0.262† 0.211 − 0.359** − 0.295** Prop. mediated 0.898*** 0.537*** 0.350 − 0.207 0.716† 0.596 0.792** 0.745* N 92 92 92 92 92 92 92 92 Simulations 1000 1000 1000 1000 1000 1000 1000 1000 ***p <0.001, **p <0.01, *p <0.05, †p <0.1 Results are created using the mediate function in R (Tingley et al. 2014) References Due to incompatibility of the moderated mediation analysis with the Mice paradigm and computational tools, we cannot Acedo C, Gomila A (2013) Trust and cooperation: a new experimental obtained pooled standard errors for the estimates of the mod- approach: trust and cooperation. 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