An Economic Assessment of Banana Genetic Improvement and ...

CONCEPTUAL FRAMEWORK AND SAMPLE SURVEY DESIGN 15
framework to identify the determinants of
hybrid use and test the impact of adoption
on production vulnerability.
Establishing a
Counterfactual
In the literature about assessing the effects
of agricultural research, the “factual” describes
the state or situation in the presence
of technological change from the adoption
and diffusion of new crop cultivars or crop
management techniques; the “counterfactual”
refers to the situation in the absence of
the technology. Methodological and conceptual
challenges have long plagued attempts
to define the two states and separate
the effects of the technology per se from
countervailing social, political, and economic
changes that occurred simultaneously
and may have been driven by similar underlying
factors (Kerr and Kolavalli 1999;
Meinzen-Dick et al. 2007).
The controlled conditions achievable
during the implementation of physical experiments
are not an observable but a heuristic
state. Even in a controlled biological
experiment, two types of problems are involved
in measuring the effects (typically a
continuous variable) of a treatment (often a
dichotomous variable). First, the effect of
the treatment is heterogeneous, varying
across individuals. Heckman developed
both a general model (Heckman 1990) and
a simple two-step method to address selection
bias (Heckman 1976) that have been
widely applied in the adoption literature.
The proliferation of applications of this influential
approach has since led to some
concern for their quality (Johnston and Di
Nardo 1997). For example, the parameters
of the model appear be very sensitive to
heteroskedasticity, and the approach is relatively
inefficient compared to maximum
likelihood estimation. Instead, simpler estimation
approaches involving instrumental
variables are often employed, though finding
variables that are correlated with treatment
but not outcome can be difficult. Recently,
Duflo and Kremer (2003) have
argued that there is considerable scope for
greater use of randomized evaluation methods
in addressing selection biases in impact
evaluation. Though such methods eliminate
selection bias by construction, they generate
highly location-specific analyses of project
interventions that may ignore the numerous
factors operating at a larger geographical
scale of analysis—such as agroecological
differences, variation in market infrastructure,
prices, and other institutional and policy
factors. These are some of the most important
factors that influence the capacity of
individual households to respond to changes
in the agricultural economy.
In addition to this generic dilemma
faced by any social scientist assessing the
impacts of improved crop cultivars, we have
faced several particular challenges in this
research. First, our “factual” is itself a prediction,
because no transgenic cultivars of
banana have yet been released. Second, the
“counterfactual” is difficult to define because
of the range of banana types currently
grown by farmers. For example, farmers
grow numerous clones of endemic highland
bananas. They also grow “elite” or “superior”
farmers’ cultivars from the region that
have been multiplied through tissue culture
for dissemination by the national research
program. Farmers also grow “exotic” cultivars,
which are farmers’ cultivars that have
been introduced from other regions of the
world. More recently, hybrids have been released
and introduced into the region. That
is, several of these counterfactuals themselves
represent the ex post analysis of agricultural
research, conducted here for the
first time.
To retain the cooking quality that is preferred
in Uganda, the National Agricultural
Research Organization (NARO) has targeted
cooking bananas for genetic transformation.
FHIA hybrids are dessert bananas
that have been used by farmers for multiple
purposes. Consequently, some of the factors
affecting adoption of FHIA hybrids will not
be the same as those that will affect the