Search this site:

3.2 Linking forest dependence with other indicators

For purposes of this Chapter, we prefer to treat "forest dependence" as a continuous variable and focus attention on job (rather than wage) dependency. This perspective allows us to examine how variation in the level of concentration of forest related employment relates to variation in quality of life indicators. This is accomplished using Pearson's correlation coefficient (for example, see Kalbfleisch 1985). In so doing, we must stress that correlation does not imply causation, but rather indicates whether an increase in some variable is associated with an increase or decrease in another variable, or if two variables are independent. Further, this method allows us to determine the strength of the relationship between two variables. The correlation coefficient is constrained to fall between 1 and -1 and the closer the coefficient is to 1 in either direction, the stronger the linear relationship. Finally, the statistical analysis allows us to determine whether or not correlations are statistically significant (that is, significantly different from 0).

In evaluating linkages between industrial concentration (forest dependence) and various quality of life indicators, relevant comparisons can only be made between areas where specific industries are located. We therefore excluded areas that do not support particular forest related industries from the correlation analysis.