Kelembagaan DAS

Elinor Ostrom

HOW DO INSTITUTIONS FOR COLLECTIVE ACTION EVOLVE?

by Elinor Ostrom
Workshop in Political Theory and Policy Analysis Department of Political Science Indiana University

Center for the Study of Institutional Diversity Arizona State University
© 2008 by author

Annual Neale Wheeler Watson Lecture presented at the Nobel Museum, Stockholm, Sweden, April 12, 2008. An earlier version of this paper was presented at a conference held at Michigan State University in honor of Allan Schmid in the spring of 2006. I am appreciative of support from the National Science Foundation and the MacArthur Foundation. Thanks also to the colleagues who have given me excellent feedback on earlier drafts—Sue Crawford, Nicolas Faysse, Robert Holahan, Marco Janssen, and Brian Steed—and to Patty Lezotte for all of her great spirit and editing skills.

Workshop in Political Theory and Policy Analysis Indiana University, 513 N. Park Avenue Bloomington, IN 47408-3895 phone: (812) 855-0441 fax: (812) 855-3150 workshop@indiana.edu http://www.indiana.edu/~workshop Page 2

HOW DO INSTITUTIONS FOR COLLECTIVE ACTION EVOLVE?

A puzzle arises from the extensive research conducted over the past several decades by scholars studying common-property arrangements in diverse sectors and countries. We have repeatedly found that resource users, who have relative autonomy to design their own rules for governing and managing common-pool resources, tend to achieve better outcomes than when experts do this for them (Ostrom, 1990; 2005; Agrawal and Gupta, 2005; Gibson, McKean, and Ostrom, 2000; Blomquist, 1992; Tang, 1992; Ostrom, Gardner and Walker, 1994; Shivakoti and Ostrom, 2001; Acheson, 2003; Schlager and Ostrom, 1992; Ostrom and Nagendra, 2006). In addition to extensive fieldwork and statistical analysis, we have used game theory to illustrate how the rules that resource users have developed generate positive outcomes (Weissing and Ostrom, 1991, 1993; Gardner and Ostrom, 1991; Ostrom, 1995; Acheson and Gardner, 2004) as well as undertaking extensive experimental studies to verify these patterns under controlled conditions (Ostrom, Walker, and Gardner, 1992; Ostrom and Walker, 1991).

The puzzle, then, is how do they do this? As briefly summarized in the next section, we find that farmers, who lack education or formal training, can on average outperform highly educated engineers in the design and operation of irrigation systems. What is the process that produces these outcomes?

Farmers in old and established systems tell researchers that they do not know much about the origin of the rules they use. In some cases, rules are treated as part of a sacred religious system and are monitored and enforced by priests (Lansing, 1991). Agricultural scientists and engineers have treated these systems as based on superstition. In Bali, for example, external experts tried to teach the farmers how to manage their irrigation systems in a “modern and more efficient manner.” The experts discovered, however, that the age-old system was really relatively sophisticated in its manner of averting the spread of pests as well as careful coordination of water delivery itself. In light of disastrous pest outbreaks after some of the farmers changed their earlier practices, the experts have had to reverse their earlier efforts to make the peasants adopt a modern system (Lansing and Kremer, 1993; Janssen, 2005).

In discussions with farmers who have built and managed more recent systems, one hears about how hard it is to find the right combination of rules that work in a particular setting. They have had to try out multiple combinations of rules and keep making small adjustments to get the system working well and ensure that most farmers actually follow the rules that they decide upon. On the basis of past field research, we can assert that when those closely involved in governing and managing a resource do have relative autonomy to devise their own rules, they cannot foresee all the outcomes that a change in rules produce. They have to learn over time by tinkering with rules so as to cope with diverse biophysical systems including rainfall patterns, soil, geology, as well as with the cultural and economic systems in which they live.

The remainder of the paper is organized in the following fashion. In the first main section, I will provide an overview of our findings from studying irrigation systems in the field so that readers who are not familiar with our prior research gain at least an initial sense of these findings. In the next section, I will provide a second overview—this time of the Institutional Analysis and Development (IAD) framework that we have been developing at the Workshop since the early 1980s in an effort to provide a general method for doing institutional analysis (Kiser and Ostrom, 1982; Ostrom, Gardner, and Walker, 1994; Ostrom, 2005).1 In the third section, I introduce the possibility of looking at the change of rules as an evolutionary process.

The new method for studying the evolution of rules, which is introduced in the fourth section, will be based on the IAD framework and on our long-term study of rules related to irrigation systems. Before one can really think of developing a general theory of institutional change, it is helpful to begin to understand change in a specific type of setting. The method will focus on a technique for arraying a norm and rule inventory and recording changes in that inventory over time brought about by diverse processes for making changes. These processes include: (1) relying on norms; (2) changing rules within collective choice arenas; (3) memory loss or ignorance of rules; (4) imitation of the rules used by others; (5) nonenforcement of rules; (6) interpretation of the meaning of rules in conflict cases; (7) changes in the biophysical world, the community, or in higher level rules, and (8) imposition of standard rules by external experts for all systems in a jurisdiction. In the conclusion, I return to the question as to why it is important to authorize resource users’ relative autonomy in the development of their own rules and to learn from the resulting institutional diversity. Rule diversity can generate higher outcomes than the institutional monocropping of imposed rules by external experts (Evans, 2004).

Comparing Farmer-Managed to Agency-Managed Irrigation Systems in Nepal

Farmers have survived over the centuries in much of Asia due to their evolved knowledge of how to engineer complex irrigation systems including dams, tunnels, and water diversion structures of varying size and complexity. None of these systems work well, however, without agreed-upon rules for allocating water as well as allocating responsibilities for providing the needed labor, materials, and money to build the systems in the first place and maintain them over time. Since Nepal was governed by a collection of princes until 1848, farmers built paddy rice systems through the centuries without a central government that took major responsibility for planning, building, or maintaining these systems. Even when the Rana family consolidated power in the mid-nineteenth century, very little national attention was paid to irrigation until the overthrow of the feudal system in 1951. In the mid-1950s, a Department of Irrigation was established and a series of Five Year Plans articulated and developed. Since then, the Asian Development Bank, the World Bank, CARE, the International Labor Organization, and other donors have invested very large sums in designing and constructing large-scale, agency-managed irrigation systems (AMIS) in some regions of Nepal.

No one really knows how many farmer-managed irrigation systems (FMIS) currently exist in Nepal. The best estimate is that there were around 20,000 such systems ten years ago and that of the total irrigated land in the country, 75 percent was served only by FMIS (APROSC and JMA, 1995). The existence of multiple systems organized in diverse ways has provided an excellent opportunity to compare the performance of systems organized by the farmers themselves as contrasted to systems designed by engineers working for a donor or a national government.

Colleagues associated with the Irrigation Management Systems Study Group at the Institute of Agriculture and Animal Science, Tribhuvan University in Nepal, have been working with colleagues at Indiana University since the early 1990s (Benjamin et al., 1994; Lam, Lee, and Ostrom, 1994). We have jointly developed the Nepal Irrigation Institutions and Systems (NIIS) database that now has information about 231 irrigation systems located in 29 out of the 75 districts in Nepal (Joshi et al., 2000).2 Our consistent finding, and that of other scholars doing research on irrigation in Nepal (Gautam, Agrawal, and Subedi, 1992), is that on average FMIS outperform AMIS on multiple dimensions. Let me provide a very brief overview of our findings from the NIIS database.3

Focusing on three evaluations of the physical condition of the irrigation system at the time of data collection, as shown in Table 1, a larger proportion of FMIS are able to maintain the overall physical condition of the system in excellent or moderately good condition as contrasted to AMIS, as well as achieving higher technical and economic efficiency (see Lam, 1998 for definitions of these concepts). The better physical condition of the canals enables FMIS to achieve increased levels of cropping intensity (the number of crops are grown during a year) at both the head end of a canal and the tail end of the canal as shown in Table 2. Thus, the investment of farmers in keeping their systems in good physical condition pays off in regard to significantly more agricultural productivity.

About two-thirds of both FMIS and AMIS have formal written rules that include provisions for imposing fines on farmers for not contributing resources to operate and manage the systems (Joshi et al., 2000: 75). On the other hand, in eight out of ten AMIS systems an official guard is hired, while only six out of ten FMIS system rely on an official guard (ibid.). The presence of an official guard, however, does not translate into an increased likelihood that fines will actually be imposed. On 75 percent of the FMIS, fines are actually imposed when farmers are observed to break a rule while fines are actually imposed on only 38 percent of the AMIS (ibid.: 76). Farmers follow the rules of their system to a greater extent on FMIS than on the AMIS and they also tend to achieve a higher level of mutual trust (ibid.).

The specific rules that the farmers use in governing their systems on a day-to-day basis vary substantially from one system to another. The “official” guard on many of these systems in one of the farmers themselves who “rotates” into this position on a regular basis. The rules specifying allocation rules, responsibilities for monitoring, and punishment, however, are not consistent from one system to the next. Thus, the monitoring of water allocation and contributions to maintenance is largely performed by farmers who have participated in the crafting of the specific rules of their own system and have a strong interest in seeing their system perform well and ensure that others on the system are not free riding or taking more water than their official share.

The study of irrigation systems in Nepal is only one of the empirical studies we have undertaken over the past quarter of a century focusing on institutional arrangements and their impact on incentives, behavior, and outcomes. In our effort to build a more general theoretical understanding of how institutions interact with the bio-physical and cultural worlds in which they structure incentives, we have had to develop a general framework that enables us to use a meta-theoretical language. The framework enables us to compare work conducted in formal game-theoretical analyses with research conducted in the experimental lab and with findings from field research. In our effort to begin a serious study of the dynamics of rule systems, we will again build on the framework that has been so important in all of our past research.

The Institutional Analysis and Development (IAD) Framework

Colleagues at the Workshop in Political Theory and Policy Analysis have developed a broad framework—the Institutional Analysis and Development (IAD) framework that encompasses game theory as one of the powerful theories that can be used to analyze human-interaction situations. Agent-based models are a second formal method that can be used to analyze complex systems (Janssen, 2002; Janssen and Ostrom, 2006b). The IAD framework is used to analyze both the simple arenas that are amenable to specifying a formal game or an agent-based model for analyzing more complex structure with too many nodes and links to be analyzed formally. Amy Poteete and I (2005) have described a number of research methods that can be employed in the study of these more complex settings. At the most general level, the IAD framework can be represented as shown in Figure 1.

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Focusing on Action Situations

In order to focus on the structure of any particular focal arena and likely interactions and outcomes, formal theorists have usually assumed that the underlying factors affecting a particular structure are fixed for the purpose of analysis. This makes it feasible to concentrate effort on predicting what would happen within a static game itself. All science advances by initially undertaking a careful delimiting of a field of inquiry so as to be to address some questions well. By assuming the rules of a game are fixed so one could analyze the outcomes of the game itself, game theorists have made considerable progress. To begin to understand institutions change, however, one must dig under the current structure where the rules themselves are selected.

To model a human-interaction as a game, the theorist must decide which components to use from a set of seven working parts of an interaction as well as how the individuals who are interacting will be modeled. As shown in Figure 2, one can think of human-interaction situations as composed of participants in positions choosing among actions at particular stages of a decision process in light of their control over a choice node, the information they have, the outcomes that are likely, and the benefits and costs they perceive for these outcomes. When analyzed formally, these are the working parts of a game.

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Before predicting likely actions of participants and resulting outcomes, a theorist must make assumptions about the individual participants: the information they have, their preferences, and how they make decisions (maximize own net benefits, or use heuristics, or engage in conditional cooperation). Most game-theoretical analyses rely on a highly simplified theory of human behavior that has proved itself useful in predicting behavior in competitive situations (Alchian, 1950; Satz and Ferejohn, 1994). Explanations of human behavior in social dilemma situations, however, must use a broader theory of boundedly rational, potentially norm-using, individual behavior (Ostrom, 1998; Cox, 2004; Frohlich, Oppenheimer, and Kurki, 2004).

Further, many of these models treat the arena containing a human-interaction situation and the individuals in it as an analytic whole to be dissected without digging into the exogenous variables underlying it. When one models a resource appropriation game, for example, specific aspects of it being located in a forest, a fishery, or a lake may not make a difference in the focal game to be analyzed (see Ostrom, Gardner, and Walker, 1994). In other cases, specific aspects of the biophysical world are essential components to be included (Acheson and Gardner, 2004; Ostrom, 1995). In addition, the type of disturbance facing one system may vary dramatically from those facing another similar system (Baker, 2005; Janssen, Anderies, and Ostrom, 2005). If the situation is repeated and individuals learn about the strategies chosen by others and their outcomes, adaptation, imitation, and learning may lead to significant changes in outcomes (of the fixed structure) over time (see Axelrod, 1984; Güth and Kleimt, 1998; Gintis, 2004).

Focusing on the Biophysical World, Communities, and Rules

Whenever one is interested in understanding processes of structural change of a particular situation itself, however, one has to open up and overtly include one or more of the underlying “exogenous” sets of variables. As shown in Figure 3, underlying all situations are three broad variables: (1) the biophysical world, (2) the broader community of the participants themselves, and (3) the rules-in-use. All of these variables are composed of multiple sub-parts. Further, all are nested in larger systems that may vary themselves over time.

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The Biophysical World. When analyzing problems of irrigated agriculture, for example, the rainfall patterns, underlying geologic structure, stream and lake size, soil types, and slope are all important variables affecting a focal arena. The problems of running an irrigation system in a monsoon climate pattern are entirely different than in a semi-arid zone. Irrigation systems along steep slopes face different problems than systems located along a relatively flat domain (Regmi, 2007).

The biophysical world does not, of course, need to be viewed as only an “exogenous” variable. Whenever the research question of interest to scholars relates to changes in the biophysical world, this variable moves from being exogenous to being a part of the analysis. Research on the resilience of social-ecological systems focuses precisely on fast and slow-moving biophysical variables as they affect human interactions and changes in strategies (Anderies, Janssen, and Ostrom, 2004; Berkes, Colding, and Folke, 2003; Hughes et al., 2005; Gunderson and Holling, 2002).

Attributes of Community. Many variables can be used to analyze relevant attributes of a community that are likely to affect behavior in human-interaction situations (Richerson and Boyd, 2005). Whether farmers, who are trying to manage an irrigation system, live in a small, stable community or a large, ever-changing area does make a substantial difference in regard to the presence or absence of shared norms that facilitate coping with such difficult processes. Many studies of self-organizing resource regimes have found that when a local community is relatively homogeneous and stable the likelihood of managing a locally owned resource in a sustainable manner is much higher (McCay and Acheson, 1987; NRC, 1986, 2002). Situations with stable, productive outcomes during one period of time, however, may not be robust when there is substantial out-migration of local residents due to increased market value of labor (Baker, 2005).

Rule Configurations. While philosophers, logicians, and legal scholars have focused heavily on the relationships between rules and likely behavior, there has been an unfortunate disconnect—until recently—between the self-conscious study of rules and how specific combinations of rules together with biophysical and community attributes generate efficient or inefficient, just or unjust, improving or deleterious patterns of interaction and outcomes (to name just a few of the evaluative criteria used by scholars). The effort to keep social science theories as simple as possible has deterred many from opening the black box called rules. Fortunately, considerable work both in political science (Shepsle, 1979, 1989; Miller, 1992; Levi, 1988; Tsebelis, 1990; Frohlich, Oppenheimer, and Young, 1971) and economics (North, 1990, 2005; Williamson, 1985; Eggertsson, 1990; Libecap, 1989; Hodgson, 2004) has now produced useful analyses that examine how specific rules affect the incentives and outcomes of a wide diversity of situations. So many specific rules have been examined, however, that cumulation has been slow. Further, while excellent work has been done on the impact of specific rules on outcomes, little research has focused on how resource users change rules (Faysse, 2005)

In our own effort to bring some order to the massive number of specific rules that one could analyze, we have clustered rules into seven broad types that could be present at any of three levels. The first level underlies operational situations where individuals interact and directly affect some worldly variables (e.g., goods are exchanged, water is allocated, fish are captured, humans expend energy). The second level underlies policy situations where individuals interact to choose some of the rules that are in effect at operational levels (e.g., legislatures passing laws related to market exchanges or use of natural resources, a water user committee deliberating about proposed rule changes, or a dean of a school making policies regarding the listing of new positions and hiring procedures). The third level includes constitutional rules that create the rules used by legislatures, a water user committee, or a dean in making policy choices.

At each of these levels, we have found it useful in our research to cluster rules according to the element of an action situation directly affected. Thus, boundary rules affect which participants can enter or leave (and under what conditions) a particular situation. Position rules create the positions (such as member of a committee, judge, dean, etc.) that participants hold. Choice rules assign action sets to positions filed by participants. Aggregation rules affect the level of control that individual participants exercise at a linkage within or across situations (must more than one agree to an action before it can be taken—such as the exchange of goods in a market). Information rules affect the level of information available to participants about actions and the link between actions and outcome linkages. Payoff rules affect the benefits and costs assigned to participants in light of the outcomes achieved and the actions chosen by the participant. Scope rules affect which outcomes may, must, or must not be affected within a situation. Thus, the components of each of these seven types of rules together provide a set of instructions about how to build each of the working parts of a situation and put them together in a structure.

Confusing Terms: Strategies, Norms, and Rules

In our effort to understand institutional change, we must also confront three concepts that are used almost interchangeably in social science literature: strategies, norms, and rules. We cannot move forward in our effort to understand institutional change without sorting these concepts out.

Strategies are plans of actions that individuals adopt primarily for prudential reasons to achieve preferred outcomes in light of expectations of the likely strategies of others. One of the reasons why formal game theory has been so useful is that it enables the theorist to assume that all participants will assume that all other participants use the same strategic assessment when they study a game and that all will choose a best response to what they predict will be the best strategy chosen by others.

Norms represent preferences related to prescriptions about actions or outcomes that are not focused primarily on short-term material payoffs to self. A participant who holds a truth telling norm—gains an internal reward (which can be modeled as a delta parameter) for telling the truth even when materials payoffs would be greater when telling a lie. While norms can evolve entirely internal to an individual, most norms are acquired in the context of the community in which the individual interacts frequently and change in this context. Thus, the chance that others in a relevant community may learn about a norm-breaking action, strongly reinforces the internal value assigned to the norm- conforming action (see Richerson and Boyd, 2005 for an important analysis of the role of shared norms in cultural evolution).

Rules are linquistic statements similar to norms but rules carry an additional, assigned sanction if forbidden actions are taken and observed by a monitor. For rules to exist, any particular situation must be linked to a rule-making situation and some kind of monitoring and sanctioning must exist.4 Rules may be crafted in any of a wide diversity of collective choice or constitutional choice arenas in local, regional, national, or international domains. Contemporary scholarship tends to focus on rules that are formally prescribed by a national government, but we must understand the process of rule change at a community level as well.

Many rules were crafted through the ages by communities over time. An example of a customary rule is from Waterford in medieval England related to the use of “hue and cry” mechanisms for public safety.

Furthermore if hue and cry be raised by day or by night, every neighbor who does not come at the cry, as reason demands, shall be amerced [fined] 6s8d by the law of the city. And he who raises the hue and cry shall be brought to the prison, and shall be replevied out of prison: – and this unless his life was in danger or his house broken into, or other injury was threatened whereby he was forced to raise the hue and cry, and provided the neighbors round about can bear witness for him before the bailiffs. And if he makes hue and cry where there is no need, he shall go to prison, and if he has any friend who will replevy him, he very well may be replevied and the amercement is 10s. And if he has nothing whereon the 10s can be levied, he shall stay in prison forty days. And if he wishes to go on living in the city, he shall find good security that no harm or mischief or hue or cry shall in any way arise through him or any of his people. And if he cannot do this, he shall leave the town forever and shall never come back (Bateson, 1972. Selden Society Vol. XVIII, p. 245, cited in Ørebech et al., 2005: 109).

In the days before telephones and other ways of calling on an official police force, citizens were “authorized” to raise a hue and cry and other citizens were required to respond upon hearing such a call for help. This customary rule clearly defines the punishment for citizens who do not respond and also do not have a reason for not responding. Further, irresponsible use of the hue and cry mechanism for gaining input from others in times of emergency will also be strongly punished—including an indication of exile from a town if it is used irresponsibly for a second time. This classic statement of a customary rule clearly lays out all of the elements that are required to make a statement a rule rather than a norm or a strategy. It specifies clearly the actions that must or must not be taken by each of several participants. It clearly state how these actions will be monitored. If a person is found to have broken this rule, a clear (and graduated) sanction is specified.

Representing Rules, Norms, and Strategies

In a formal game-theoretic analysis, the rules are not represented in the game as they are part of the (temporarily) exogenous factors that create the structure of the game in the first place. As Anatol Rapoport (1966) long ago stressed, once the theorists understand the rules underlying the game sufficient to model the game itself, the rules themselves disappear from further analysis. When doing fieldwork, it is always a challenge to determine what the rules structuring patterns of interaction are. Formal rules may exist in writing but not be followed or even known to the participants.5 In doing effective field research, one has to determine the “rules-in-use” by the participants if one wants to understand their norms and strategies and why the “rules-in-use” differ from the “rules-in-form.”

In a formal game, norms are frequently not represented at all. Crawford and Ostrom (2005) proposed to represent norms in the preference function of the players as positive or negative delta parameters that are invoked either by internal feelings of regret or internal satisfaction (personal norms) or by external observation of their behavior (community norms) that leads to shame or pride. Shared norms can be sustained in a game-theoretic analysis when players are able to exit a game upon discovery of a player who does not follow the norms (Orbell et al., 1984). In the field, one learns about shared norms when farmers tell you that “everyone here thinks it is shameful if . . .” some statement like: “if one of us shirks when we all have to contribute a work day to clean our canals.”

A game theorist posits the strategies the theorist argues are what a rational player would do at every choice point in a game (assuming that all other players are rational and have complete information). Strategies are prudential plans in light of the structure of the situation and the preferences of the participants. Changes in rules and norms would frequently lead to a change in strategies. In the field, strategies are observable activities unlike rules and norms.

Changing Rules as an Evolutionary Process

Given the logic of combinatorics, it is impossible for public officials or for direct beneficiaries to conduct a complete analysis of the expected performance of all of the potential rule changes that could be made by the individuals served by a self-organized resource governance system trying to improve its performance. A similar impossibility also exists for many biological systems—they evolve. Let us explore these similarities.

Self-organizing resource governance systems have two structures that are somewhat parallel in their function to the concepts of a genotype and a phenotype in biological systems. Phenotypic structures characterize an expressed organism—how bones, organs, and muscles develop, relate, and function in an organism in a particular environment. The components of an action situation (or a game) characterize an expressed situation—how the number of participants, the information available, and their opportunities and costs create incentives, and how incentives lead to types of outcomes in a particular environment. The genotypic structure characterizes the set of instructions encoded in DNA to produce an organism with a particular phenotypic structure. A rule configuration is a set of instructions of how to produce the structure of relationships among individuals in an action situation that is also affected by the biophysical world and the kind of community or culture in which an action situation is located.

Rule systems evolve like all cultural phenomena (Boyd and Richerson, 2003). The evolution of cultural phenomena—including rules—follows different mechanisms from the evolution of species (Boyd and Richerson, 1985; Campbell, 1975; Nelson and Winter, 1982; Greif and Laitin, 2004). As an evolutionary process, of course, there must be the generation of new alternatives, selection among new and old combinations of structural attributes, and retention of those combinations of attributes that are successful in a particular environment. In evolving biological systems, genotypic structures are changed through mechanisms such as crossover and mutation and the distribution of particular types of instructions depends on the survival rate of the phenotypes they produce in given environments.

Instead of blind variation, however, human agents try to use reason and persuasion in their efforts to devise better rules, but the process of choice always involves  experimentation. Self-organized resource governance systems use many types of decision rules to make collective choices ranging from deferring to the judgment of one person or elders, to using majority voting, to relying on unanimity (Ostrom, 1998; Walker et al., 2000). In all of our efforts to study the performance of common-pool resource systems in the field, however, we have not found a particular set of collective-choice rules developed by resource users to be uniformly superior to others. We and other scholars have consistently found, however, that rules developed with considerable input of the resource users themselves (if not fully their own decision), achieve a higher performance rate than systems where the rules are entirely determined by external authorities (Lam, 1998; Tang, 1992; Bardhan, 2000; Bardhan and Dayton-Johnson, 2002; Ostrom, Gardner, and Walker, 1994).

In light of all of our past field and experimental research, the next important step is developing a method for representing rule change so that one can overtly study the relationship between rules and the resulting operational-level situations in the field and the lab. Thus, I now turn to the task of developing a method for studying rule change.

A Method for Studying Rule Changes

When one purchases a new recreational game to be played at home, it comes with a list of rules that are to be used by those playing the game along with the game board, the pieces, and/or the computer disk. Learning the rules used to govern and manage local natural resources is much more challenging. Some rules may have evolved over multiple centuries, as those used in regulating the Bali irrigation systems described by Lansing (1991), the Alpine meadows described by Netting (1981), or customary law in England, Norway, and Africa (Ørebech, 2005). Others may be of more recent origin, but may not be committed to written form (such as for many farmer-constructed and managed irrigation systems in developing countries) (Tang, 1992; Lam, 1998; Shivakoti and Ostrom, 2001).

In undertaking a meta analysis of a large set of case studies written about local fisheries and irrigation systems and in our current over-time study of more than 200 forests located in twelve countries, we have identified a very large number of rules actually used in practice (Ostrom, Gardner, and Walker, 1994; Hayes and Ostrom, 2005; Gibson, Williams, and Ostrom, 2005). In regard to boundary rules, for example, we have identified 27 different boundary rules used in at least one setting to define who is authorized to appropriate (a general term for any form of withdrawing resource units from a resource system) (Ostrom, 1999). Examples include requiring residence in a local community, requiring a commercial license be purchased for a defined season, and requiring that a potential appropriator purchase the right to harvest by purchasing land, or a fishing berth, or a long-term right to appropriate a defined amount of the resource. When one considers a rule configuration consisting of seven types of rules—each of which may contain 15-30 specific rules—the number of potential combinations is extremely large.

Because the diversity of potential rules is so very large, we should not assume that the choice of institutional rules to improve the performance of common-pool resource institutions—or any other set of rules—is a process of designing optimal rules. We need to understand the rule evolution process as involving an effort to tinker with a large number of component parts (see Jacob, 1977). Those who tinker with any tools— including rules—try to find combinations that work together more effectively than other combinations. Policy changes are experiments based on more or less informed expectations about potential outcomes and the distribution of these outcomes for participants across time and space (Campbell, 1969, 1975). Whenever individuals agree  to add a rule, change a rule, or adopt someone else=s proposed rule set, they are conducting a policy experiment. Further, the complexity of the ever-changing biophysical world, combined with the complexity of rule systems, means that any proposed rule change faces a nontrivial probability of error.6

Default Conditions

Let us now focus on what is being changed when appropriators change rules. What do these underlying building blocks for creating an action situation at an operational level look like? Before we can turn to the rules themselves, however, we have to ask how to represent a “none-rule.” What should we think about the structure of a game in the absence of any rules? This question is particularly important due to the configurational nature of rules. One needs to know the basic contents of a full rule configuration, rather than just a single rule, to infer both the structure of the resulting situation and the likely outcome of any particular rule change.

If irrigators are involved in appropriating water in a “state of nature” one can think of a set of default conditions that one would use in constructing such a game. The seven default conditions listed on Table 3 are those that would be used by a participant or an observer in a general legal system that presumed freedom to act in any manner that was not specifically prohibited or mandated by an existing rule. Thus, this set is the broadest conditions that would be used in a common-law legal system.7 If one wants to analyze changing rules, the initial situation before any rules are established is one where there are no rules. Thus, the “rule configuration” for the base situation would contain only the default conditions. Hobbes’s analysis of the state of nature and Garrett Hardin’s (1968) analysis of “The Tragedy of the Commons” implicitly relied on the above set of default conditions as structuring the situations they analyzed.

The default conditions are self-consciously changed to rules in a linked collective- choice situation that makes rule changes for a particular operational situation. For an irrigation system governed by the farmers it serves, for example, the collective-choice situation is likely to be an annual meeting of all of the farmers or a water user committee elected by the farmers. For governmental systems, the rules may be prescribed by an administrative agency of the state or national government involved. In some situations, multiple collective-choice organizations compete to make the rules for an operational situation, but we will not address that problem in this paper.

In an earlier paper (1995), I examined the linkage between collective-choice decisions about rules for an operational irrigation system by formalizing the resultant games and the likely equilibria outcomes in the irrigation games structured by each rule change. The paper illustrates that the biophysical world is as important as the rules in affecting outcomes and can lead to strong heterogeneity among participants. Trying to find rules that work when the difference between upstream and downstream farmers is large is a classic example of the challenge of designing fair rules when participants bear different streams of benefits and costs. Given the examples in the earlier paper, what I want to do in the rest of this section is to develop a method for analyzing rule changes over time.

In Table 4, I have arrayed a set of three “proto” rule statements for each of the seven types of rules discussed on page 13 for an operational-level irrigation system.8 Three proto rule statements for each type of rules is a very small set given the large number of rules of each type we have recorded from case studies written about resource government institutions in the world. I am trying at this point to develop a method for recording and analyzing institutional change rather than just examining the full inventory of all rules already identified. I refer readers to prior work where a large number of each type of rule is discussed (Tang, 1992; Ostrom, Gardner, and Walker, 1994; Ostrom, 1999).

0t4

I will draw on, and slightly modify, the method that Blomquist, Schlager, Tang, and I used in coding rules for the meta-analysis reported in the third section of Ostrom, Gardner, and Walker (1994) and in Ostrom (1999). Rules that have frequently been used in governing irrigation systems are identified in the rule inventory displayed in Table 5 (see Tang, 1992 for a good description of these rules). The inventory is divided into seven broad fields with specific proto-prescriptions that might be either a norm or a rule listed in each of the seven fields of the inventory.9 If no norm or rule is used at all, the rule statement will be coded 0. If a norm has evolved that participants “should” follow a particular proto-prescription, an S will be entered for that proto-prescription. If a rule has been established, I will code that statement as either:

R = Required; P = Permitted; or F = Forbidden

(Table 5 about here)

This method will be used to examine processes of rule change and the fit of rules to biophysical and community characteristics of a particular setting. In Table 5, I have used the numbering system of Table 4 for the columns. Thus, the three columns under the heading Boundary Rules in Table 5 represent the three rules listed under that category in Table 4. The other numeric column headings on Table 5 are similarly described in Table 4.

The first row of Table 5 represents a rule configuration when there are no norms or rules in use—all entries are zeros. Thus, Row 1 represents a lawless “state of nature” that Hardin (1968) envisioned leading to a “tragedy” of the commons. If one were to model the resulting appropriation situation as a formal game (assuming that the farmers live next to a water source have a high demand for the water), the Nash equilibrium would be an inefficient outcome (Ostrom, Gardner, and Walker, 1994: chapter 3). Thus, the prediction for behavior and outcomes in an irrigation game constituted by the total absence of normative prescriptions is every farmer grabbing as much water as they can when it is available. This would mean that the farmers located at the head end of a system would obtain most of the water, and the overall crop yield for the system as a whole would be below the yield that would be feasible if water were allocated to all of the parcels adjacent to the system.

For very simple and isolated systems, the farmers located adjacent to a system might develop a simple set of norms over time that would lead to a water rotation system along the canal. If there were 14 farmers and they agreed on a simple set of norms such as: only the 14 farmers should take water from system, no watering at night, and each farmer takes a half-a-day turn before turning the water distribution over to the next farmer, it is conceivable that such a set of norms—illustrated in Row 2—might suffice for some time. They would need three norms: (1) only the 14 adjacent farmers should use the irrigation water, (2) they should rotate water distribution during the daylight hours following a specific schedule, and (3) everyone should maintain the canal in front of their own farm, and should pitch in and help in times of emergency repair.

Such a simple norm-based system might survive for a long time if land was relatively flat so headenders did not have a strong advantage given to them by nature, if the land was always inherited by one child (rather than being divided each generation—a general inheritance rule for a larger community10), if no one sold their land to outsiders, and if the system was relatively isolated from changes in the value of land, labor, or commodities. These are four large “ifs.” Robert Netting (1974) described such a system that he observed in his fieldwork in Switzerland. I do not know of any other irrigation system where the farmers rely on norms alone. Given the high value of irrigation water for many families (since their survival depends on their getting enough water), conflicts can easily arise over who takes water under what conditions. Conflicts undermine shared norms if they are not resolved.

Changing Rules within Collective-Choice Arenas

Conflict could arise and stimulate changes to the use of rules in this simple system in many different ways. As an example, if one of the 14 farming households sold their land, a new resident might argue that they bought the land in order to grow a crop that requires more water than the other farmers in the system. If they began to take water at night or try to take a longer turn than the norm, conflict would certainly be generated. This would likely lead to a meeting of the farmers. The farmers might then decide to organize a Water Users Association and in a collective choice situation within the new association11 make four new rules:

  1. formalize the rotation system that had evolved only sustained by norms (a change from S to R for allocation rule C3);
  2. create a new position of official monitor and that each household rotates into that position on a day when they do not take water following a pre-determined schedule (a change from 0 to R for position rule P1);
  3. create a new rule that both farmers must be present at the time when the water turn changes from one farmer to the other (a change from 0 to R in aggregation rule A1); and
  4. impose a penalty on any farmer who does not follow the first three rules (a change from 0 to R in payoff rule Y1).

Row 3 represents this new set of rules that the Water Users Association might devise in trying to establish some initial rules to keep their water allocation system operating as it had using only norms. If, however, the new farmer was very wealthy and had considerable political power, they might instead fear challenging his demands and give him one day a week to take as much water as he wanted. They might decide to allocate water on a fixed percentage basis—giving the powerful farmer the percentage of water he demanded, and all of the other 13 farmers an equal percentage of the remaining water. This would represent a change in the allocation rule from C3 being required to C2 now being required and a formula devised to keep the powerful farmer happy while allocating the rest of the water to the other 13 players (see Row 4).

Over time farmers in the Water Users Association might find themselves in a changing economic situation in which more and more settlers move into the region. New settlers are unlikely to know the norms of who can use how much water from which water source. Members of the Water Association may then find some strangers taking water from their system. That may lead them to decide to change from a norm, regarding who can use the water, to a rule that requires a farmer to own land within a specified region to take water from this source (C1 would change from S to R if that rule were adopted as shown in Row 5). The official monitor that they had already created could then be charged with evicting anyone not among the authorized landowners if found using water.

Once a collective choice arena has been established, rules may sometimes be changed as a result of proposals submitted in that arena and efforts made at conscious design using whatever aggregation rule they have settled upon for making operational rules. That rule, will of course, affect the type of rules selected – if a small clique can make rules at a collective choice level, one can expect to see them try to change rules that advantage the clique (Ostrom, 1999; Ensminger and Knight, 1997). Given the large number of potential rules that could be used at an operational level, however, resource users never fully search the potential rule inventory and pick the one set that is optimal for the system (or for any subset of farmers). If the farmers are relatively homogeneous in regard to resources and in regard to the likely results of a rule change, they may just tinker with their rules until they find a good combination for their setting, but this may take multiple years. If external factors do not change during a period of rule trial-and-error, they may eventually find a relatively well-operating set of rules for their conditions.

If, however, the farmers are heterogeneous in regard to their location, assets and other variables—as discussed in Ostrom (1995)—then, they may find themselves faced with proposals made by some farmers that advantage the proposing farmers and disadvantage others. In a system where the maintenance costs are relatively low, headenders on a canal may demand rules that they have prior rights to water and take as much as they can grab and not worry about water getting to the tail end of the system or about getting the other farmers to help with maintenance. If the costs of maintaining a system are very high, however, headenders cannot afford this kind of asymmetric rule. They need the tailenders and would be willing to share water equitably so long as the tailenders were also willing to share maintenance equitably (see Ostrom, 1995: figures 5 and 6).

Memory Loss and Ignorance of Rules

Once the farmers have established a Water Users Association, they may be able to sustain their system for some time without challenge particularly if the powerful new farmer were able to bring real benefits to the other farmers, such as getting a road built to their village or a school located nearby. On the other hand, some of the initial farmers may grow dissatisfied with the new rules. From time to time, they may skip their monitoring obligation and stay at home rather than monitoring what is happening on the canal. Since the farmers have not set up a punishment for breaking this rule, nothing can really officially happen to a farmer who does not keep his obligation to monitor.

Over time, more and more of the farmers might “forget” to monitor on their day. Without a monitor on the canal, other farmers may see an opportunity to take a longer turn and risk being caught only by their neighbor and not be a neighbor and a monitor. On days without a monitor on the canal, fights may break out among the farmers over the water rotation timing as they want more water than they have been allocated. As water allocation rules get broken more frequently due to monitors not showing up, the rules-in- use may deteriorate to just rules-in-form. Other than the boundary rule, the other rules may revert to zero as shown in Row 6.

Imitation of Rules Used by Others

In an environment where many farmers manage irrigation systems and there are arenas—such as weekly markets—where most farmers go and talk with one another about problems and possibilities, it is likely that new systems or those early in their existence can imitate the rules that they think are operating in the more successful systems. Imitation is one of the important processes identified as underlying cultural evolution (Richerson and Boyd, 2005). Crucial elements of whether imitation leads to improved performance is whether the indicators used for evaluating the success of others are reliable and whether the systems are relatively similar in regard to their size and biophysical attributes. Imitation can lead to decreased effectiveness when local circumstances are not similar to the system that is being imitated, the rules in that system are misunderstood, or there are existing rules in the imitating system that are not compatible.

Robert Yoder (1991a, 1991b) and Prachandra Pradhan (1989) used an effort to enhance self-conscious learning and imitation in a project they designed in 1990 for a national governmental agency in Nepal funded by the Ford Foundation (WECS/IIMI, 1990). In addition to having the farmers have a strong role in the design of modest engineering improvements, they also built in “farmer to farmer” training. They took the farmers from a district in Nepal where the farmers had not yet designed very effective rules to a district where the farmers were quite successful in building and maintaining systems with high crop yields. They then had the farmers from the unsuccessful systems spend a day attending the annual meeting of several of the successful systems and listening to the other farmers (who were obviously getting better crop yields) telling how they managed their systems and coped with different kind of problems. Yoder and Pradhan specifically encouraged the visiting farmers not to copy the rules of the successful systems in a rote fashion but to think about why they worked and what aspects they could use in designing their own systems. Lam and Shivakoti (2002) compared performance before and after this intervention in regard to crop yields and distribution of water and evaluated it positively.

Imitation may also lead to biased transmission from system to another. Biased transmission occurs because some rules are particularly easy to understand, remember and seem to work well. In Nepali, panch means hand. Many collective-choice units have five members (like the five fingers on a hand) and are called panchayats. In many settings it does not matter whether governing board is five or seven or nine. However, when the Asian Development Bank set up a system to “help” some 80 irrigation systems in the Rapti valley they insisted that all of the system adopt the same set of rules before they were eligible for funding (Shukla et al., 1993). The rules called for the uniform creation of irrigation “panchayats.” That worked well for small systems, but when a system had 9 or 15 sub-canals, having a council of only five members rather than one from each sub-canal was not a good design. I will return to discuss this effort to impose an institutional monoculture in the last section.

If there are not substantial differences in the biophysical characteristics of the land around a meeting place (or major socioeconomic difference among the farmers using these systems), then imitation of systems that have enables farmers to obtain high agricultural yields may enable a new system to “design” relatively effective rules by copying many aspects of the rules of a neighboring system that is successful. There are, of course, always details about each system that need to be taken into account including the reliability of the water source, the soil conditions as they affect how easy a headworks or canal can break apart and need repair, the actual number of farmers using the system, etc. If an irrigation system is relatively steep and headenders can have a physical advantage, adopting the rules of a successful system that is relatively flat may lead to worse performance than slowly developing specific rules that relate to the particulars of the biophysical world.12

Rules may also change simply by nonenforcement and the withering away of what had been agreed upon earlier. Rules as constraints are not the same as physical constraints (such as the headworks and diversion weirs of an irrigation system). Rules are composed of mere words and, as Vincent Ostrom (1997) has frequently pointed out, words are not always understood by all participants in the same way. In systems where there is a regularized system for hearing conflicts and reach solutions that are viewed by legitimate by the participants, rules may be added, taken away, or modified as a result of conflict over their meaning.

In common-law settings, losers may challenge an interpretation they find strongly against them (Stake, 2004). In competitive settings, one may find relatively good rule mix when they are open to innovation and relatively low cost and fair conflict resolutions. While I would be hesitant to assert that common-law processes lead to optimal rules, I would assert that the opportunity to challenge rules can lead to more efficient rules than when rules are simply proclaimed by central authorities.

Changes in the Biophysical World, the Community, or in Higher-Level Rules

As mentioned above in regard to new settlers moving into a region, changes in any of the “exogenous” variables can lead to a mismatch between whatever rule configuration has been evolved over time and the overall changed situation. One of the major factors that can affect how a resource system operates over time is climate variation and storms. Major storms can destroy the irrigation works that farmers (or a government agency) have constructed and rules may not exist about how to deal with emergency repairs. If the farmers do not rely on norms of reciprocity, they may find themselves faced with a physical disaster that is not repair unless they do create and enforce new rules for how to cope with environmental challenges.

Similarly, economic changes may challenge any existing system either by luring labor away from the region (thereby reducing the input for maintenance) or by luring labor to the region (thereby increasing the threat of water theft). Baker (2005) provides an in-depth analysis of the robustness of some, but not all, irrigation systems in a region of India that were threatened not only by floods and landslides but also by changes in the economy that lured potential agricultural labor into the cities. He shows that some of the irrigation systems were unable to cope with these threats and stopped operating or were taken over by a governmental agency. He documents that those systems where farmers did confront the changes in their own collective choice arenas and passed rules that better coped with the changing situation were able to survive and achieve good economic returns.

Obviously, another external source of change is the policies adopted by higher governments that affect the authority of a local resource governance unit. During the 1970s and 1980s many national governments took over the ownership of natural resources in their countries and refused to recognize the authority of self-organizing resource systems (see NRC, 1986). Once a larger-scale government takes over from local farmers, it is then difficult to turn the system back to the farmers at a later juncture.


The Need to Study Rule-Changing Processes

In this paper, I have only been able to provide a brief overview of the processes that I speculate do occur leading to changes in rule configurations and to posit a method for recording such changes over time. Most of our past work has focused—as most social science research has—on static processes. Using the IAD framework has enabled us to gain a more solid foundation for the study of rules, their diversity, and their change over time. Colleagues associated with various past projects have undertaken enough in- depth field research and meta analysis of existing written case studies that I think I have captured an initial overview of some of the major processes involved in the evolution of rules over time in relationship to small-scale user-governed resources—particularly irrigation systems.

It is a challenge, however, to begin to move to a self-conscious effort to study dynamic processes. We have at least initiated some of this effort growing out of our analysis of the impact of diverse irrigation rules on system performance (Tang, 1992; Lam, 1998; Ostrom, 1992). In 1993, we were asked by Marilyn Hoskins of FAO to undertake a study of various forms of forest governance on diverse measures of forest conditions in addition to the well-being of forest users. When we agreed to undertake this study, we made the commitment that we would design a set of research protocols that would be used by a network of research centers to collect comparable data over time (Gibson, McKean, and Ostrom, 2000). We have now collected a common set of data about forest governance in over 200 forests in 12 countries with the initial support of FAO and the longer-term support of NSF, the Ford Foundation and the MacArthur Foundation. We are just now beginning to return to some of the earliest sites for a third time and thus can begin to study which rules have been changed, why, and the processes that have led to these changes. This is an agonizingly slow process but we hope to be able to begin do further research on these changes based on the approach outlined above as well as returning to the historical case study literature and hoping enough scholars have written about institutional change in the sites they have studied that we can begin to mine that important source of data as we did earlier for our static meta analysis (Ostrom, Gardner, and Walker, 2004).

From our own field work and reading about changes in rules over time, we will begin this process with at least some speculations about the conditions that are likely to enhance learning about the impact of rule changes and how productive rules evolved over time. Let me turn to these speculations before the final section of this paper.

Conditions Likely to Enhance Learning and Productive Rule Evolution

Analytically, one can begin to identify the conditions and processes likely to enhance the learning process of farmers and others making institutional decisions regarding irrigation systems (or other local resources) and the likelihood of an institutional evolutionary process to lead to better, as contrasted to poorer outcomes. In general, one would expect the rules structuring operational interactions within similar types of situations—such as smaller irrigation systems in a region—to evolve toward more productive outcomes when:

  • most participants affected have some voice in proposing rule changes and making decisions about rule changes;
  • most participants within systems have sufficiently large payoffs at stake that they are willing to invest in the transaction costs of searching, debating, and learning about better options;
  • participants with the largest stakes have an interest broadly congruent with increased productivity for the system. (This will tend to occur in an irrigation system when the richest farmers are located toward the tail end, are dependent on the others to contribute resource toward the maintenance of the system, or when big differences in the wealth and power of the farmers are not present);
  • internal processes within systems have generated substantial variety in the rules used to structure interactions within different systems leading to a range of performance in regard to agricultural productivity, maintenance of the physical capital, and distribution of income to participants;
  • participants are in a social and economic environment where they can learn from successes and failures of others (such as, regular meeting places where farmers gossip about the problems they are facing, existence of officials who are charged with helping farmers learn how to get better productivity from their systems (e.g., extension agents or NGOs); federations of local water associations who meet annually).
  • the participants have developed regular procedures for reviewing their experience over time, revising rules and procedures when they evaluate that they could be improved, and recording their changes so that they gain a good history of what they have tried and what results they obtained13;
  • the systems are in a political environment that encourages local autonomy but also provides oversight regarding corruption and accountability as well as conflict resolution; and
  • biophysical disturbances happen frequently enough so that participants learn how to cope with them rather than occurring only occasionally leaving farmers unprepared.

For rule configurations to evolve, there must be processes that (1) generate variety, (2) select rules based on relatively accurate information about comparative performance in a particular environment, and (3) retain rules that perform better in regard to criteria such as efficiency, equity, accountability, and sustainability.

It would be naive to assume that any evolutionary process will always lead to better outcomes. In biological systems, competition among populations of diverse species did lead to the weeding out of many individuals over time that were out-competed for mates and food in a given environment. Evolutionary processes can also lead to equilibria imposing higher costs on some species and eliminating others. The huge investment made by peacocks in their tails is one example. Thus, one should not expect that all locally governed systems will eventually find effective rule configurations. Some will experiment with rule configurations that are far from optimal. And, if the leaders of these systems are somehow advantaged by these rules, they may resist any effort to change.

In our future research, we hope to use the approach outlined above to study how rules evolved in multiple cases and then to use agent-based modeling to explore diverse initial conditions and change over time. Several colleagues and I are in the initial stages of a study of the “dynamics of rules” (Anderies, Janssen, and Ostrom, 2004; Janssen and Ostrom, 2006a; Janssen, 2007).14 We will use agent-based modeling as one of our tools since that does enable one to examine the pattern of likely outcomes over time when agents who have limited information are making choices over time (Janssen, 2002). We also intend to study institutional choice overtly, both in the experimental laboratory as well as in the field with companion modeling by participants who have experience in working with irrigation, fisheries, and forest resources (Cardenas and Ostrom, 2004; Cardenas, 2000; Cardenas, Stranlund, and Willis, 2000; Bousquet et al., 2002). We know there are both better and worse processes of institution change and hope to build on and test the above speculations so as to develop a more solid basis for encouraging processes more likely to lead to improved performance than has been the dominant way of thinking about institution change and development. We are also in the process of developing a more extensive framework building on the IAD framework discussed herein (see Ostrom, 2007).

Conclusion: The Danger of Institutional Monocultures

The conditions posited above as likely to enhance the quality of institutional evolution have not characterized irrigation investments in most of the developing world during the last several decades. The monetary investment in irrigation has been huge, however. The World Bank alone contributed around $10.6 billion in loans for irrigation projects between 1983 and 1999 (Pitman, 2002: 12; see also Yudelman, 1985). International donors were contributing about $2 billion per year during the 1990s (Winpenny, 1994). These investments have not generated high returns. Hugh Turral (1995: 1) captured the judgment of many analysts by concluding that “irrigation schemes have often under-performed in economic terms, and field research has highlighted substantial shortcomings in management (operation and maintenance), equity, cost- recovery and agricultural productivity.” Some critics like William Easterly (2001) assert that most of the funding spent by international aid agencies since the 1960s has tragically not achieved promised results (see also Gibson et al., 2005).

One of the causes of this waste of investment (and worse, the tragic cost in terms of human well-being in the developing world), is the hubris of experts relying on simple models of the best engineering plans and idealized sets of rules (if they pay any attention to institutions at all). An illustration of this hubris that is well documented by Rita Hilton (1990, 2001) is the investment made by the USAID-funded Rapti Integrated Rural Development Project in the Chiregad system in the Rapti valley of Nepal. The new system was built in a location that had been irrigated by five farmer built and managed irrigation systems that had evolved diverse rules fitting to their own systems over time. The existence of these systems was not recognized by the engineers constructing the new system except to leave parts of them standing to serve as distribution canals. Nor did the engineers take seriously the warnings by farmers about the loose gravel regions of the area that wash out easily—which did cause substantial damage to the physical system after construction.

The Nepal Department of Irrigation then started what they called a “participatory process” to involve the farmers in managing the system (after they had been ignored previously). They formed a Water User Committee and appointed the chair of the local village panchayat to head this committee even though he did not own any land in the service area and thus received no water from it. He then appointed the other members and overlooked entirely the five water managers of the earlier systems that were trying to distribute water once it reached (if it did) their established distribution canals. When Hilton interviewed these official members of the Water User Committee they indicated that they rarely met, and they could provide her with no information about the characteristics of the system or how it operated. Hilton (2001) found that the area receiving irrigation water after the Chiregad system was fully operational was smaller than the area earlier served by the five independent systems, that maintenance levels were lower, and that other farmer-managed systems in the Rapti valley had achieved high productivity than the new system supposedly built to improve the economic performance of the area.

If this were just one story about a single project in Nepal, we could write it off as simply a bad experience by some untrained engineers and irrigation officials. Unfortunately, stories of this kind have occurred throughout the developing world and are also occurring more and more frequently in western countries where government officials are exerting more authority over the government and management of local resources) (Wilson, 2005). As Peter Evans (2004: 31-32) articulates: “Currently, the dominant method of trying to build institutions that will promote development is to impose uniform institutional blueprints on the countries of the global South—a process which I call ‘institutional monocropping’.” Even worse than the initial problems of having the wrong institutions imposed almost everywhere is the “lock in” that can occur when powerful individuals gain advantage from such institutions leading to major problems of path dependence (Arthur, 1989). The power and helpless are the ones who pay the big costs.

Lant Pritchett and Michael Woolcock (2004) develop a complementary analysis to that of Peter Evans. They puzzle over the problem of new solutions when the dominant Weberian paradigm was the solutions used by development agencies, and now it is the problem confronting anyone concerned about development. They graphically describe the systematic failure of development agencies to improve any services to rural areas including those related to irrigation. Donor activity often amounts to sending “experts” who operation institutions in “Denmark” to design institutions in “Djibouti.” At best this would be like sending a cab driver to design a car” (ibid.: 199).

So how can we get out of the kind of institutional monocropping that currently dominates much of social science thinking as well as that of development agencies? There is obviously not one way to solve this problem! As academics, we can help by being willing to develop more complex theories for explaining the behavior of humans in widely divergent settings. We do not need to be complex, just to be complex. But we need to get over our simplicity hang-ups. Readers may be interested in looking at the special feature of the Proceedings of the National Academy of Sciences for September 2007 where scholars who are very familiar with the problems discussed in this paper have discussed how to move beyond the frequent use of panaceas in recommending policies related to natural resources around the world (see Ostrom, Janssen, and Anderies, 2007).

Notes

  1. Readers who would like to gain an overview of the type of research undertaken at the Workshop over the years may wish to look at the 2005 special issue of the Journal of Economic Behavior & Organization edited by Peter J. Boettke on “Polycentric Political Economy” (vol. 57, no. 2).
  2. Currently, there is considerable rebel activity in Nepal that is disrupting many activities especially in the countryside and creating many tragedies for Nepali farmers. The findings discussed in this paper are based on data most of which was collected in earlier peaceful times.
  3. Readers who wish to dig deeper are encouraged to read Lam (1998), Joshi et al. (2000), and Shivakoti and Ostrom (2001) and the extensive references cited therein.
  4. Sue Crawford and I have worked hard over more than a decade to clarify the close link between these three concepts. We have developed a grammar that can be used to “parse” each of them. For example, each rule can be parsed into five components that specify: (1) the attributes of a participant (such as age, education, gender) affected by a rule; (2) the deontic modal verb of the rule which include “may” (permitted), “must” (obliged), and “must not” (forbidden); (3) where the rule aims—at the set of potential actions or the outcomes of the situation; (4) the conditions specifying when and where an action or outcome is permitted, obligatory, or forbidden, and (5) the consequences specified for not following a rule (the “or else”) (see Crawford and Ostrom, 2005).
  5. Ganesh Shivakoti reported on visiting an irrigation system officially under “joint
    management” in Nepal only to find that the farmers did not know how had been selected to be on their council, whether they had ever met, and what kind of policies had been adopted.
  6. When only a single governing authority makes decision about rules for an entire region, policymakers have to experiment simultaneously with all of the common-pool  resources within a jurisdiction with each policy change. And, once a change has been made and implemented, further changes will not be made rapidly. The process of experimentation will usually be slow, and information about results may be contradictory and difficult to interpret. Thus, an experiment that is based on erroneous data about one key structural variable or one false assumption about how actors will react, can lead to a very large disaster. In any design process where there is substantial probability of error, having redundant teams of designers has repeatedly been shown to have considerable advantage (see Landau 1969, 1973; Bendor 1985).
  7. In a Roman law country, the default conditions would be entirely different since Roman law systems presume that most things are forbidden unless specifically permitted.
  8. The proto rule statements for boundary, choice, and payoff rules are the rules that Tang (1992) identified as the most frequently observed rules in his meta-analysis of irrigation cases located in many different countries. The proto rule statements for the other rules are derived from extensive field research regarding irrigation systems in many countries—particularly Nepal. They are the rules that I have frequently encountered (see Shivakoti and Ostrom, 2001; Joshi et al., 2000).
  9. We have long relied on the symbols used in deontic logic for modal operators. For background, see Hilpinen (1981) and von Wright, 1951; 1963)
  10. The rules set for any one interaction situation are always affected by rules determined by larger regime—such as inheritance rules in force.
  11. To keep this paper from growing exponentially, I will not discuss here their effort to constitute a new association and to establish a mechanism that can make collective-choice decisions regarding the rules that they will use in the future. One could be keeping an inventory of rules at a constitutional, collective choice, and operational level to keep track of the processes and changes involved.
  12. Heterogeneity among resource users can lead to very substantial transaction costs in trying to find rules that most participants find fair and are willing to follow. Varughese and Ostrom (2001) found that when farmers with strong locational heterogeneity were able to devise unique rules for their use of a local forest, the heterogeneity did not lead to lowered performance. Of course, they would have paid somewhat higher transaction costs in working out rules that allowed some farmers to obtain wood based on labor inputs and other farmers to obtain wood based on monetary payments.
  13. Several scholars have been studying customary law and multiple countries. Fred Bosselman (2005) has been particular concerned with what characteristics of the process of customary law lead to relatively resilient systems. Bosselman asks this question in chapter 6 of a book with Peter Ørebech. He reviews a large number of cases and identifies five characteristics of those systems that he thinks have demonstrated a capacity to adapt in sustainable ways. These are:
    1. Does the system have a good historical record, oral or written, of the way the system has worked in the past under different environmental conditions?
    2. Is an effective procedural mechanism for making rule changes built into the system?
    3. Does the system feed back the right information on current operations into the rule modification process?
    4. Are the rules sufficiently finely detailed that they can be “tweaked” without wholesale revision?
    5. Do the rules facilitate negotiation of modifications by providing for a balance of rights and responsibilities relating to a wide range of ecosystem functions? (Bosselman, 2005: 176)
      In our effort to code rule change processes, we will add some of these questions to our coding forms as well as the seven characteristics that I have identified in the text.
  14. Marty Anderies, Robert Goldstone, Filippo Mercer, Juan-Camilo Cardenas, and Francois Bousquet are all senior researchers working together on this project. Page 43

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