One of the key objectives of the current analysis was to identify and
analyze the factors that are likely to influence households to participate
in a community forest management program. The probit model is
employed to identify the determinants of the participation in community
forest activities and the results are presented in Table 2. The overall
model (LR χ2) is highly significant (at the less than 1% level) with a R2
value of 0.19, hence indicating the robustness of the variables included
in themodel. The dependent variable is a dummy i.e. 1 if the household
participates in the community forest activities and 0 otherwise. A set of
independent variables are included in the model.
The land holding coefficient is positive and significant at the 5% level
of significance signifying that the higher the land holding the higher the
likelihood of households participating in a community forestry program,
meaning that richer households are more likely to participate in
a community forest program than their poorer counterparts. This sort
of phenomenon, often referred to as ‘the elite capture of power’, devolved
to local communities. This result is not confined to Bhutan
only; it is a phenomenon observed in other countries (Behera, 2009).
The operational land holding coefficient was positive and significant at
the 5% level of significance.
The internet facility was included as a development indicator and its
coefficient was positive and significant at the 1% level of significance.
Farmers having the internet were more informed and were more likely
to participate in the community forest management and vice versa. The
education coefficient is positive and highly significant at the 1% level, indicating
that householdswith an educated head aremore likely to participate
in community forest management and vice versa. The age of the
household head is negatively associated (significant at 10%) highlighting