Random Variables in Forest Policy: A Systematic Sensitivity Analysis using CGE Models

Computable general equilibrium (CGE) models are extensively used to simulate economic impacts of forest policies. Parameter values used in these models often play a central role in their outcome. Since econometric studies and best guesses are the main sources of these parameters, some randomness exists about the “true” values of these parameters. Failure to incorporate this randomness into these models may limit the degree of confidence in the validity of the results. In this study, we conduct a systematic sensitivity analysis (SSA) to assess the economic impacts of: 1) a 1% increase in tax on Canadian lumber and wood products exports to the United States (US), and 2) a 1% decrease in technical change in the lumber and wood products and pulp and paper sectors of the US and Canada. We achieve this task by using an aggregated version of global trade model developed by Hertel (1997) and the automated SSA procedure developed by Arndt & Pearson (1996). The estimated means and standard deviations suggest that certain impacts are more likely than others. For example, an increase in export tax is likely to cause a decrease in Canadian income, while an increase in US income is unlikely. On the other hand, a decrease in US welfare is likely, while an increase in Canadian welfare is unlikely, in response to an increase in tax. It is likely that income and welfare both fall in Canada and the US in response to a decrease in the technical change in lumber and wood products and pulp and paper sectors.