In discussions with farmers who have built and managed irrigation 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 produces. 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.
Comparing Farmer-Managed to Agency-Managed Irrigation Systems in Nepal
Farmers have survived over the centuries in much of Asia due to their 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. SinceNepalwas 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. 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 ofNepal.
During the 1990s, colleagues associated with the Irrigation Management Systems Study Group at the Institute of Agriculture and Animal Science, Tribhuvan University in Nepal, worked with colleagues at the Workshop in Political Theory and Policy Analysis, Indiana University (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). 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.
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 grown during a year) at both the head end of a canal and the tail end of the canal. 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 an official guard is hired, while only six out of ten FMIS 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.
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 biophysical and cultural worlds in which they structure incentives, we have had to develop a general framework that enables us to use a metatheoretical 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 (Poteete, Janssen, and Ostrom 2010).
Confusing Terms: Strategies, Norms, and Rules
In our effort to understand institutional change, we must 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 material payoffs would be greater when telling a lie (Crawford and Ostrom 2005). 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. 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 changes at a community level as well.
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 sufficiently 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. 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, Schwarz-Shea, and Simmons 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. 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 (Wilson 2007; Wilson and Gowdy 2011). 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 higher performance 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; Poteete, Janssen, and Ostrom 2010).
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 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 resources 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);
- 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 obtained;
- 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. Agent-based modeling is a likely analytical tool since it 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, 2007). 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, fishery, 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 institutional 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). 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 helpless are the ones who pay the big costs.
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.
[Note] This is a short version of “Do Institutions for Collective Action Evolve?”
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