An Economic Assessment of Banana Genetic Improvement and ...

CONCEPTUAL FRAMEWORK AND SAMPLE SURVEY DESIGN 13
economywide impacts of technical change
or the social distribution of benefits at the
national and regional levels. Industry or mesoscale
analysis, conducted across larger
geographic scales and using secondary or
more aggregated forms of primary data, can
be used to examine some aspects of technical
change that farmer surveys alone cannot
address. For example, investment in R&D in
Uganda could affect geographic areas not
intentionally targeted, such as the Kagera
Region of Tanzania. From a producer perspective,
the impact of widespread technology
use can be negative if it exerts downward
pressure on prices, because crop supply
grows faster than crop demand.
An appropriate tool to answer questions
concerning the expected total magnitude
and distribution of economic benefits from
the diffusion of various technologies at the
national and regional levels is an economic
surplus model, such as that documented
fully in Alston, Norton, and Pardey (1995)
and applied in Chapter 6. In general, this
type of analysis involves the simulation of
benefit outcomes based on carefully constructed
scenarios. Scenarios might include
different technologies or price or trade policies.
Scenarios are then compared according
to investment criteria. Criteria often
include the size of social and private benefits
relative to health or environmental risks
and benefit shares earned by consumers,
commercial or smallholder producers, or
regions.
However, the economic surplus approach
also has known limitations. First, the
approach is sector based, representing partial
changes in market equilibrium. Economywide
effects can be traced through the
application of a multimarket model or a
computable general equilibrium (CGE)
model. These methods are not as informative
about the incidence of benefits and
costs across economic agents and social
groups in the industry as the economic surplus
approach, however.
A drawback that is common to the economic
surplus approach and multimarket
and CGE models is that they gloss over a
number of intervening factors and conditions
that must be met for the release of the
technology to actually result in a shift of
the industry supply curve. Ex ante models
based on the economic surplus approach
depict supply-driven effects, but take adoption
parameters as given. For this reason, in
Chapter 6, we also estimate the adoption
parameter and its determinants, and draw
inferences about the social consequences of
inserting resistance into one host cultivar as
compared to another. The appropriate analytical
framework for the models developed
and estimated at the farm level is the theory
of the agricultural household, described
next.
Technology Use on Farms
The performance of a technology (crop and
trait), either on experimental stations or on
farms, is only one consideration among
many in its adoption. Once a technology has
been developed and tested, factors that have
incidence at national, regional, and local
levels influence whether smallholder farmers
will choose to use it, the geographical
extent of use, and its continuity and duration.
For example, biophysical characteristics
of an agroecological region are important
determinants of use. The economic
impact or “success” of the technology is
determined in the first instance by use.
We often employ the term “use” rather
than “adoption” in recognition of the complexity
of defining adoption. Adoption can
be represented by a discrete decision to use
or not to use (a variable defined as either
one or zero), a proportional indicator (such
as a share of land or share of plants), an
index of scale or extent of use (the number
of hectares or the number of plants), level
choices or intensity of use (per hectare, or
per plant), or frequency of use (number of
seasons or number of applications per season).
Use at any one point in time is not a
robust indicator of “adoption.” As mentioned
in Chapter 1, there are many exam-