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Journal of Institutional Economics (2011), 7: 3, 317–343 C The JOIE Foundation 2010 doi:10.1017/S1744137410000305 First published online 16 August 2010 Crafting analytical tools to study institutional change ELINOR OSTROM∗ Workshop in Political Theory and Policy Analysis, Indiana University, USA Center for the Study of Institutional Diversity, Arizona State University, USA X A V I E R B A S U R T O ∗∗ Nicholas School of the Environment, Duke University, USA Abstract: Most powerful analytical
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  Journal of Institutional Economics (2011), 7: 3, 317–343  C  The JOIE Foundation 2010  doi:10.1017/S1744137410000305First published online 16 August 2010 Crafting analytical tools to studyinstitutional change ELINOR OSTROM ∗ Workshop in Political Theory and Policy Analysis, Indiana University, USACenter for the Study of Institutional Diversity, Arizona State University, USA XAVIER BASURTO ∗∗ Nicholas School of the Environment, Duke University, USA Abstract: Most powerful analytical tools used in the social sciences are well suitedfor studying static situations. Static and mechanistic analysis, however, is notadequate to understand the changing world in which we live. In order toadequately address the most pressing social and environmental challenges loomingahead, we need to develop analytical tools for analyzing dynamic situations –particularly institutional change. In this paper, we develop an analytical tool tostudy institutional change, more specifically, the evolution of rules and norms. Webelieve that in order for such an analytical tool to be useful to develop a generaltheory of institutional change, it needs to enable the analyst to concisely record theprocesses of change in multiple specific settings so that lessons from such settingscan eventually be integrated into a more general predictive theory of change. 1. Introduction Darwin’s 200th birthday reminds us of his major accomplishments, as well asthe great challenges he faced when trying to understand the complexity of thenatural world. During his voyage aboard the HMS Beagle, he dug up fossilsand collected birds and plants representative of the many different ecosystems hevisited.Afterhisreturn,hestruggledtomakesenseofthegreatdiversityoflifehehad documented. It took him more than 20 years to explain differences between ∗ Email: ∗∗ Email: xavier.basurto@duke.eduAn earlier version of this paper was presented at the workshop on ‘Do Institutions Evolve?’, RobertSchumann Center, European University Institute, Florence, Italy, 8–9 May 2009, and discussed at theEnvironmental Norms, Institutions, and Policies Workshop, Stanford University, 8 April 2010. Weappreciate the helpful comments by Todor Arpad, Melissa Brown, James Fearon, Geoffrey Hodgson,and the other participants at these workshops. The support of the National Science Foundation andIndiana University are gratefully acknowledged. We also appreciate the editing support of Patty Lezotteand David Price. Some sections of this paper draw on, and substantially revise, sections from an earlierpaper by Elinor Ostrom, ‘Developing a Method for Analyzing Institutional Change’, in Sandra Batieand Nicholas Mercuro (eds.), Alternative Institutional Structures: Evolution and Impact  (New York:Routledge, 2008), pp. 48–76. 317  318 ELINOR OSTROM AND XAVIER BASURTO related species and the mechanisms by which such life might have evolved acrosstime and space. Darwinian theory itself has evolved considerably over time asmore and more pieces of the broad puzzle were identified by researchers workingin multiple disciplines at multiple levels of living entities, which enabled themto start describing the behavior of nested, complex adaptive systems (Holling,1973; Levin, 1998).For social science scholars to integrate complex, nested systems into theirframeworks and theories, we must also recognize the extent of diversity of manyforms that surrounds us and face the challenge of unpacking its complexity toexplaintheworldswelivein.Inworkingtowardthisgoalformorethanacentury(Veblen, 1898), the social sciences have developed powerful tools to analyze theoutcomes of diverse institutional structures (King et al  ., 1994; Wasserman andFaust, 1994; Ragin, 2000; Bernard, 2006). Most of these tools are useful forthe analysis of unchanging worlds. The fact remains, however, that the world isalways changing (Nelson and Winter, 1982).Thus, an important next step for enhancing the ability of the social sciences tounpack the complexity of the world consists of developing a cluster of tools foranalyzing dynamic situations, particularly institutional change (Hodgson, 2009;Nelson, 2009; Schmid, 2004; North, 2005; E. Ostrom, 2005; Dopfer et al  .,2004). The goal of this paper is to present an overview of a new diagnostic toolfor analyzing institutional dynamics, mainly changes in rule systems. Given thecentrality that the concept of rules has for the analysis of institutions (Hodgson,2004), we hope the diagnostic tool will develop still further and can serve asa foundation for building a more useful theory of change, and therefore of institutional evolution. 1 OurtaskisperhapsevenmoreformidablethanDarwin’s.Ontheonehand,hewas able to witness changes in the biological world; on the other, he struggled toexplain the hidden processes behind such changes. In the social sciences, we notonly struggle to explain the processes behind institutional change, but also howtoidentifywhatischanging(E.Ostrom,2007b:23).Whenwestudyinstitutionalevolution, we focus on different configurations of rules that shape humaninteractions (North, 2005; E. Ostrom, 2005). While some analysts equate ruleswithwhatiswritteninlegaldocuments,thisisonlyoneformofrecordingofwhatofficials would like to think of as rules. Many rules are, however, unwritten andmanywritten‘laws’arenotfollowedasrules.Further,wefrequentlydonotknowwhich rules are accepted by individuals in their everyday interactions. Thus, therules affecting much of our behavior are relatively invisible, which challengesour ability to identify and measure them. As we elaborate in later sections inthis paper, we follow Commons’s (1924) and V. Ostrom’s (1980) definition of  1 By ‘evolution’, we follow Lustick’s (2009) definition: ‘that patterns of change observed among unitsproducesubsequentpatternsofpopulationchangeinrelationtocircumstances’.Although,bypopulationsinthispaperwerefertotheinstitutionsthatindividualsareusingratherthantotheindividualsthemselves.  Crafting analytical tools to study institutional change 319 rulesassharedunderstandingsbyactorsabout enforced  prescriptionsconcerningwhat actions (or outcomes) are required  , prohibited  , or permitted. We start this paper by illustrating the importance of understanding howdifferent rule systems affect outcomes in empirical settings. To do this, wedraw on our previous long-term studies of irrigation institutions in Nepal –a developing country with a very heterogeneous biophysical setting. Next,we provide our working definition of rules and present the rule classificationsystem that colleagues have designed to bring order to the wide diversity of rulesystems observed in empirical settings. Then, we describe some of the multiplemechanisms of change in the context of rule systems. These sections constitutethe foundation that enables us to sketch the measuring tool that we propose forstudying institutional evolution in the following section of the paper. We useinformation about rule systems observed in our studies of irrigation systems inNepal to illustrate the tool, and later briefly discuss how it fits with the work of otherscholarsalsostrugglingtounderstandchangeandrulechangeinparticular.To conclude the paper, we expand our scope of inquiry to discuss some of thebarriers for successful rule change in developing countries. This discussion linksevolutionary and institutional economics with the economics of development. 2. Why rules are important: findings from irrigation research Some of the lessons coming out of our institutional analyses in Nepal andelsewhere show that resource users who have relative autonomy to designtheir own rules for governing and managing common-pool resources frequentlyachieve better economic (as well as more equitable) outcomes than when expertsdo this for them. 2 In addition to extensive fieldwork and statistical analysis,we have used game theory to illustrate how the rules developed by resourceusers generate positive outcomes (Weissing and Ostrom, 1991, 1993; Gardnerand Ostrom, 1991; E. Ostrom, 1995; Acheson and Gardner, 2004). We havealso undertaken extensive experimental studies to verify these patterns undercontrolled conditions (E. Ostrom et al  ., 1992, 1994; Janssen et al  ., 2008, 2010)andusedagent-basedmodelstostudycomplexprocessesofrulechanges(Janssenand Ostrom, 2006a, 2006b). Using multiple methods to study core theoreticalpuzzles helps to increase our confidence in the patterns observed (Poteete et al  .,2010). The performance of farmer-managed irrigation systems in Nepal  Farmers have survived over the centuries in much of Asia due to their evolvedknowledge of how to engineer complex irrigation systems, including dams,tunnels, and water diversion structures of varying size and complexity (Shivakoti 2 E. Ostrom (1990), Agrawal and Gupta (2005), Gibson et al  . (2000), Blomquist (1992), Tang (1992),ShivakotiandOstrom(2001),Acheson(2003),SchlagerandOstrom(1992),BasurtoandOstrom(2009).  320 ELINOR OSTROM AND XAVIER BASURTO et al  ., 2005). None of these systems works well, however, without agreed-uponrules for allocating water as well as for allocating responsibilities for providingthe needed labor, materials, and money to build the systems in the first placeand maintain them over time. A substantial puzzle still exists, however, thatrelates to how resource users in the field develop rules to increase performance.As briefly summarized, we find that farmers in Nepal, who lack academic orformal training, can on average outperform highly educated engineers in thedesign and operation of irrigation systems. What is the process that producesthese outcomes?Colleagues associated with the Workshop in Political Theory and PolicyAnalysis at Indiana University (the Workshop) have jointly developed the NepalIrrigation Institutions and Systems (NIIS) database that now has informationon over 200 irrigation systems located in 29 out of the 75 districts in Nepal(Benjamin et al  ., 1994; Lam et al  ., 1994; Regmi, 2007). Our consistent finding,and that of other scholars doing research on irrigation in Nepal (Gautam et al  ., 1992), is that, on average, farmer-managed irrigation systems (FMIS)outperform agency-managed irrigation systems (AMIS) on multiple dimensions.In particular, a larger proportion of FMIS as contrasted to AMIS maintainthe overall physical condition of the system in excellent or moderately goodcondition and achieve higher technical and economic efficiencies. 3 The specific rules that the farmers use in governing their systems on aday-to-day basis vary substantially from one system to another. The ‘officialguard’ on many of these systems is one of the farmers themselves who ‘rotates’into this position on a regular basis. The rules specifying resource allocation,responsibilities for monitoring, and punishment vary substantially from onesystem to the next. Thus, the monitoring of water allocation and contributionsto maintenance are largely performed by farmers who have participated in thecrafting of the specific rules of their own system and have a strong interest inseeing their system perform well and ensure that others in the system are notfree-riding or taking more water than their official share.  How do rules srcinate on farmer irrigation systems? Farmers in old and established systems tell researchers that they do not knowmuchabouttheoriginoftherulestheyuse.InBali,forexample,rulesareencodedin a sacred religious system and are monitored and enforced by priests (Lansing,1991, 2006). Agricultural scientists, engineers, and government officials treatedthese systems as based only on superstition. After the government of Indonesiarequired higher rice production by farmers in Bali, external experts tried toteach the farmers how to manage their irrigation systems in a ‘modern and moreefficient manner’. The rice varieties of the green revolution were recommended 3 See Lam (1998) for definitions of these concepts and Lam and Ostrom (2010) for an over-timeanalysis of rules and other factors as they affect performance of FMIS.
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