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Techniques for quantifying equity, which are discussed in the companion articles by the authors, "Quantifying Equity with Messrs. Markov, Lorenz and Gini: Retaining and distributing benefits in natural resource-dependent communities", and "Social network analysis, Markov Chains and input-output models: Combining tools to map and measure the circulation of currency in small economies", in this issue of JRCD, are applied by the primary author in a case study of Dunster, British Columbia, a small rural community heavily dependent on forestry revenue. The community has forestry tenure rights over a small area in the Robson Valley area of British Columbia, but as of 2011 had not yet begun logging. The application of the techniques highlight the challenges faced by small communities with limited industrial capacity in attempts to capture benefits from the extraction of natural resources. By establishing a "pre-logging" standard, the community can measure progress towards distributing the benefits of natural resource extraction equitably within the community. [ABSTRACT FROM AUTHOR]

Localization is posited as the antidote for globalization, but little exists in the way of quantifying the micro-scale of small communities. In this paper, the reader's attention is drawn towards understanding that social network analysis, Markov chains and input-output models are equivalent, and that together these tools can be used to map and measure the circulation of currency in a small community. The "map" of the economy can be created using social network analysis, in a form equivalent to Markov chains and input-output models, by representing businesses as nodes and the percentage of expenses spent by one business at another as the strength of the edges between the nodes. Markov chain mathematics is used to measure the circulation of currency in the economy by calculating the average number of transactions (average path length) from where the dollar enters the community until it leaves. This method is equivalent to the multiplier effect from input-output models but more granular. An example using a simple loop is provided showing different methods of solutions, their equivalence and the impact of loops on the average number of transactions in a small economy. [ABSTRACT FROM AUTHOR]

In this paper, a modified version of the Gini coefficient and social network-based Markov chains are combined to quantify the distribution of benefits in natural resource-dependent communities. This distribution includes both the width and the depth of the circulation of economic benefits from resource extraction. The modified Gini coefficient is used to calculate the share of benefits each party receives (width) while the social network analysis-based Markov chains are used to map the local economy and calculate the average number of times a dollar circulates in that economy (depth). These two values are combined to create an "equity" factor that considers localization and other financial benefits to the community. This combination allows resource managers to optimize resource access based on quantified values other than highest bid/lowest cost. [ABSTRACT FROM AUTHOR]