Desktop version

Home arrow Sociology arrow Understanding Society and Natural Resources

Analyzing Power Within The SES Framework

The preceding discussion highlights the complexity inherent in testing a hypothesis that “power matters.” It demonstrates the importance of being deliberate and explicit about the necessary and potentially value-laden choices concerning definitions, classifications, and measurement, not to mention those imposed by the choice of inferential methods. In this section, we seek to illustrate how the SES framework may be used to organize a rigorous, broad research agenda on the effects of power by proceeding through each step of the process just outlined. Our analysis is divided into three main subsections, each of which proceeds through all four steps for specified institutional conceptualizations of power. More specifically, within each subsection, we discuss (1) the distinct institutional definition(s) of power that we are seeking to test, (2) the author(s) associated with that definition, and (3) how that definition may be classified within the SES framework. Finally, we use the IFRI database to (4) operationalize the attribute(s) and test whether there is a statistically significant relationship between each measure of power and a social-ecological outcome.

The IFRI database is perhaps the single most influential and contemporary source of information with which commons scholars develop and test hypotheses concerning the interactions of people, the environment, and institutions in small-scale SESs. The database is composed of a variety of continuous, categorical, and descriptive variables—including a wide range of attributes present in the SES framework—that are collected using a consistent case-study approach (Wertime et al. 2007). The database enables multiple-methods research, although in recent years, as the number of case studies have increased to include more than 400 forests and 600 user groups, IFRI scholars have increasingly turned to large-n quantitative studies that have historically been absent in the commons literature (Andersson and Agrawal 2011; Chhatre and Agrawal 2008, 2009; Persha et al. 2011; Coleman 2009; Coleman and Fleischman 2012). The IFRI database was chosen for this analytical exercise due to its rigor and its resonance with the SES framework. A comparable database for the study of large scale systems is the International Regimes database which asks questions concerning the formation, boundaries, and processes of international regimes in response to a wide variety of social, economic and ecological problems (Breitmeier et al. 2006; Young and Zürn 2006). Although certainly useful for a quantitative study of power, the international regimes database, because of its emphasis on international-scale processes, is less suited to respond to specific critiques from political ecology that tend to emphasize the effects of power on individuals and communities.

The sample used in this study was constructed in the following way. First, we selected the user group as the unit of analysis. Next, we dropped cases in the following order: (1) repeat observations of a user group, (2) groups found in the United States, and (3) those with missing data on any of the dependent variables. The omission of the US cases is common, as they differ substantially from the other countries in terms of economic development and the ways in which forest resources are used. Finally, we randomly dropped duplicate observations of user groups that use multiple forests, as well as forests containing multiple user groups, in order to generate a sample including a maximum of one observation per forest and user group. The dependent variable measures social-ecological benefits and is constructed by summing two multifactor indexes that measure social and ecological benefits, respectively. Social or livelihood benefits are measured by performing a factor analysis similar to that of Chhatre and Agrawal (2009), based on the contributions of a forest to the fuelwood, fodder, and timber needs of a group. Ecological benefits, on the other hand, are measured by performing a factor analysis on the polychoric correlation matrix of (1) a forester's perception of vegetation density, (2) a forester's assessment of species diversity, and (3) user group perceptions of the condition of the forest. These attributes were similarly used in Andersson and Agrawal (2011), although they simply averaged these figures.

The results are compiled in Table 6.2, which records the one-way relationship between a particular measure of power and the combined social-ecological benefits (the dependent variable). In most cases we report differences in means between groups that possess and lack power. However, polyserial and pairwise correlations are used for Ostrom's and North's definitions given that they are measured using continuous and ordinal indicators, respectively. We generally predict a positive relationship between the power of a group and the dependent variable.

Table 6.2 A preliminary assessment of the effects of institutional power on social-ecological benefits derived from forests

 
Found a mistake? Please highlight the word and press Shift + Enter  
< Prev   CONTENTS   Next >

Related topics