Rural Development Policies and Sustainable Land Use in the ...

ECONOMETRIC RESULTS 61
the model with interaction terms (Table 5.4),
we also find that the dependency ratio has a
negative direct effect on income per capita,
as does household size (which is not significant
in the regressions in Table 5.3). Thus,
having more dependents, and possibly larger
families in general, appears to reduce income
per capita. Regarding the effect of
geographic determinants of comparative
advantage on household income, we find
statistically insignificant direct associations
of market access, road density, and population
density with income. However, there are
indirect effects. For example, road density is
significantly associated with higher probability
of households pursuing the coffee
and basic grains/farmworker strategies, the
latter being associated with higher incomes
in both the OLS and median regression and
the former associated with higher incomes
in the median regression. Higher population
density reduces the likelihood of households
pursuing coffee production or the
basic grains/livestock/farmworker strategy,
and the former is associated with higher income
in the median regression, as mentioned
previously, while the latter is associated
with higher income in the model with
interaction terms (Table 5.4). Thus, road access
appears to contribute to higher incomes
while population pressure leads to lower incomes,
though these effects are via the indirect
effect of these factors on households’
choice of livelihood strategies.
We find no statistical evidence of an impact
of short-term agricultural extension or
longer term training focused on conservation
on household income, but we do find a
large and statistically significant positive association
of more general agricultural training
with household income (in both the OLS
and IV models). The magnitude of this association
is quite large: households that
have received agricultural training earn
more than 3,000 Lps per capita more in income.
It is hard to believe that agricultural
training could have such a large effect on
income, and we must consider alternative
explanations. One possibility is the endogeneity
of participation in agricultural training;
that is, households participating in such
training may be those that already have
higher incomes. The fact that these results
control for many other factors that determine
household income, and are robust in
the IV model, which addresses the issue of
endogenous participation, reduces our concern
about this alternative explanation. Another
explanation may be that this result is
a statistical anomaly, resulting from outliers
and errors in estimating income. The results
in the median regression model, which is
more robust to such errors, provide support
for this explanation, given that the coefficient
of agricultural training in this model is
much smaller and statistically insignificant.
However, we do not have full confidence in
the median regression model either, because
it is not able to account for the sampling
probabilities of the households in the sample
(hence this regression is not representative
of the population of the 19 counties
sampled for this study, but only for the
sample households). Thus, there may be a
positive impact of agricultural training on
income, but we cannot be confident of this,
and doubt that the impact is as large as the
regression coefficients in the OLS and IV
regressions suggest.
We also do not find robust statistical evidence
that membership in NGO programs,
producer organizations, or rural financial
institutions have significant impacts on income.
In the model with interaction terms
we investigated whether the impacts of
machinery/equipment and agricultural training
vary across the different livelihood strategies,
to help assess whether targeting of particular
interventions to particular livelihood
strategies would be warranted (Table 5.4).
We find that the positive impact of machinery
and equipment is mainly for households
pursuing livelihood strategies involving
coffee production, basic grains/farmworker,
and basic grains/livestock/farmworker, with
the magnitude of the impact being largest
for basic grains/farmworkers, followed by
coffee producers and households that follow