An economy consists of various inter-dependent sectors. An output of a sector is the sum of demands from all sectors (Abt et al. 2002). Based on interdependencies amongst sectors, input-output model can predict impacts in other sectors of the economy caused by changes in one sector (Leontief 1996).
IMPLAN (Impact Analyses for Planning) is an economic simulation model which is based on input-output model. It was originally developed by the United States Department of Agriculture (USDA) Forest Service in cooperation with Federal Emergency Management Agency and the University of Minnesota to analyze the forest plan alternatives in national forests (Abt et al. 2002). It consists of regional data that are collected from different sources, primarily federal agencies (Day et al. 2012). In IMPLAN, input-output models for a specific regional economy can be created and economic impacts can be estimated in terms of employment, labor income, value added, and output (Nielsen-Pincus and Moseley 2013). It is time and cost effective to create economic reports using IMPLAN. Therefore, it is widely used to evaluate economic impacts of the various economic events including restoration projects (Nielsen-Pincus and Moseley 2013, Edwards et al. 2013, Prato and Hey 2006).
The IMPLAN is based on some assumptions (Bernat and Johnson 1998):
1. Relationships between input and output of sectors are assumed to be linear, that is proportionate change in all inputs results in the same proportionate change in output.
2. Technology is fixed during the base period (2012 for this study).
3. The supply of inputs is never a constraint on production.
In addition, IMPLAN helps estimate multiplier effect for each sector. Multiplier measures the i...
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...re from other local and private contractors was assigned to it.
In addition, sensitive analysis tests were done with datasets having different assumed values for the missing data. The first dataset included mean percentage of local expenditures of contractors with similar characteristics. Then, other datasets were developed in which missing values were assumed at different levels. For example, a local private contractor information was missing. By averaging the percentage of local expenditures of other local private contractors, 80% of local spending was assigned to it. Then, to test the sensitivity of this value, other datasets were developed with 85%, 75%, 90%, and 70% of local spending by this particular contractor. All datasets were then tested using Wilcoxon Rank Sum test. This helped us examine the sensitivity of the assumed values to the tests of significance.
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