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

80 CHAPTER 5
when they purchase it than the prices net
sellers of food receive when they sell.
Perennial Crops
Of land management practices, only no burning
is associated with significantly higher
production of perennial crops in the OLS
model (Table 5.9). Based on the estimated
coefficient, the impact of no burning is large,
with predicted value of production 138 percent
higher on perennial plots where no
burning is used, controlling for other factors.
The coefficient of the no burning variable
is not statistically significant in the IV
model, though of similar magnitude, again
reflecting difficulties of identification in the
IV model.
All types of external inputs are associated
with statistically significant and quantitatively
large positive impacts on perennial
production in the OLS model, with these
impacts ranging from +158 percent for herbicide
to +321 percent for fertilizer. The impacts
of two of these variables—fertilizer
and other inputs—are also large and statistically
significant in the IV model. The
insignificant impact of herbicide and insecticide
in the IV model may be due to the
low predictive power of the instrumental
variables in predicting use of these practices;
that is, the relevance tests show that
the instrumental variables are not statistically
significant predictors of these practices
(p = 0.9975 for herbicide and 0.6879
for insecticide).
Use of hired labor is associated with
greater perennial production in both the OLS
and IV models. Other types of labor use are
not statistically significant determinants of
perennial production in either model.
Many other factors are also significantly
associated with productivity of perennial
crops in the OLS structural model, including
altitude (–), rainfall deficit (–), soil fertility
(+), area of land owned (–), value of
livestock owned (+), schooling (+), dependency
ratio (+), share of female adults (–),
livelihood strategy (livestock producers,
coffee producers, and basic grains/livestock/
farmworkers have lower productivity than
basic grains producers), conservation training
(+), agricultural training (–), conservation
extension (–), membership in a producers’
organization (+), participation in an NGO
program (+), travel time to an urban market
(–, 10 percent level), road density (+), plot
size (+), plot position of top of hill compared
to bottom (–), sloping plot (–), and presence
of other trees planted on the plot (+). Most
of these results are robust in the IV model.
The reduced form regression yields many
results similar to those of the structural models.
Factors influencing production of perennial
crops, whether directly or indirectly, include
summer rainfall (–, 10 percent level),
soil fertility (+), land owned (–), schooling
(+, 10 percent level), livelihood strategy (coffee
producers have lower production per
manzana than basic grains producers), agricultural
training (–), conservation extension
(–), plot size (+), and slope (–).
Many of these findings are as one would
expect; for example, the positive effect of
soil fertility, participation in some programs
and organizations and market and road access
on productivity. Others are puzzling,
however; especially the finding of lower
perennial crop productivity of coffee producers
than those classified as basic grains
producers. This finding may not be robust
due to the small number of perennial plots
operated by basic grains producers in our
sample (14 plots). However, even when
these observations are excluded from the regression,
we still find that perennial crop
productivity is significantly higher for the
basic grains/farmworker livelihood strategy
and basic grains/livestock/farmworker livelihood
strategy than for the coffee producer
livelihood strategy. 57 Perhaps this results
57
There are at least 30 perennial plots operated by households pursuing each of the other livelihood strategies
besides basic grains.