An Economic Assessment of Banana Genetic Improvement and ...

SOCIAL CAPITAL AND SOIL FERTILITY MANAGEMENT IN UGANDA 93
ers’ perceptions about the soil fertility problem
are influenced by the same variables
that influence the use of mulching or manure
technology. Thus, we are dealing with
a simultaneous equations model that is a
function of exogenous variables, predetermined
variables, and an error term. Banana
management decisions can be estimated as a
function of direct measures of perception or
by substituting for perception using exogenous
factors in the perception equation. Use
of the direct measure of perception in the
management equation creates the problem of
endogeneity. The observed indicator of perception
is correlated with the error term of
the demand equation, rendering the ordinary
least squares estimates inconsistent (Greene
2000; Woodridge 2002). Consistent estimates
can be obtained by use of a two-stage
least squares estimator to correct for endogeneity
(Wooldridge 2002). If correctly specified,
this estimation procedure will yield
estimators with greater asymptotic efficiency
than attainable by the limited information
method (Greene 2000). However, the approach
requires extensive data, which in
most cases are not available. In addition, the
full information method is complex when
the null hypothesis of sample selection bias
has not been rejected. Because our main interest
in this chapter is to test the effect of
social capital while controlling for other factors,
demand for soil fertility management is
estimated as a function of all exogenous
variables cast on the right hand side of equation
(6), expressed as a reduced form.
The extent of use of mulching and manure
application was estimated using two
methods: ordinary least squares regression
and the two-step Heckman model. The latter
model accounts for the selection bias
associated with missing observations for a
given subsample caused by the truncated
nature of the dependent variable. The motivation
underlying the use of either the ordinary
least squares or Heckman regression
model is based on their statistical performance
according to whether the null hypothesis
of sample selection bias was rejected
or not. A two-stage Heckman model
was also used to test for sample selection
bias. Although the share ranges from 0 to 1,
the sample data indicate that all households
have an extent of use that is less than 1.
Thus, a model that accounts only for censoring
at 0 was applied.
The probability of using mulch or manure
was estimated with a probit regression
in the first stage of a Heckman procedure
using the whole sample. The inverse Mills
ratio, computed from the probit regression,
was included in the second stage to test for
selection bias (Greene 2000; Wooldridge
2002). Hypothesis tests do not support the
presence of sample selection bias in the extent
of use of mulching practices, but support
it in the case of manure. Test results imply
that in the mulching equation, the subsample
of household with nonzero use is representative
of the population (Wooldridge 2002).
Consequently, the extent of use of mulching
was estimated by ordinary least squares regression
on a subsample with nonzero use,
while a Heckman model was used to estimate
the extent of manure application.
Although the explanatory variables in
the first- and second-stage regressions are
identical, nonlinearity of the inverse Mills
ratio allows the identification condition to
be met (Wooldridge 2002). The problem of
multicollinearity, often induced by use of
the Heckman procedure, was tested using
the variance inflation factor (VIF) technique
in STATA © 8.0 (StataCorp, College
Station, Texas). All explanatory variables
included in the estimation had VIF less than
5.0, which suggests that multicollinearity
does not affect the results. 2
Finally, when the factors that influence
demand of one technology are also likely to
influence demand for other technologies, it
is possible that the unobserved heterogene-
2
A VIF greater than 10 indicates a collinearity problem (Kennedy 1985).