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Appendix 4
Methodological difficulties in measuring the
socio-economic impact of GM crops
GM has been described as the agricultural technology
with the most rapid rate of adoption in history but it is
also the most controversial technology in the history of
plant breeding. The reason for this is not only because it
is a very powerful tool for increasing the speed and scope
of crop improvement but also both the technology and its
applications have become proprietary. The first generation
of commercially exploited GM crops were brought to the
market by large multinational companies. In addition, GM
crops have become associated with large industrial-scale
agriculture and monoculture cultivation despite the fact
that these agronomic choices are entirely unrelated to GM
technology per se. All these factors have raised societal
and even ethical concerns about the use of GM crops
and particularly their impact on small-holder farmers in
developing countries.
Many studies of the socio-economic and environmental
impact of cultivation of GM crops have been published
since the technology was adopted (reviewed in Brookes
and Barfoot, 2009; Carpenter, 2010, 2011; Finger
et al., 2011; Areal et al., 2013; and see Chapters 2
and 4 of the present report). A meta-analysis (Finger
et al., 2011) has examined the effects at farm level of
growing insect-resistant GM crops using published data
from more than a decade of field trials and surveys. This
work indicated that, at a global scale, GM crops can
lead to yield increases and to reduced pesticides used,
whereas seed costs are usually substantially higher
than for conventional seed varieties. Growing GM and
non-GM crops in the same area has also been reported to
be beneficial for non-GM crops 60 .
The nature and magnitude of effects from cultivating GM
crops do, however, differ between countries and regions,
particularly because of differences in pest pressure and
pest management practices. Published accounts are
skewed towards some countries, and individual studies
rely on different assumptions and were conducted
from different purposes. In addition, short-term studies
(focusing on one or two growing seasons) may not
necessarily reflect long-term impacts of adoption,
especially because unobserved costs that may arise with
the cultivation of GM crops (such as effect on land rents,
longer-term market responses, governmental regulation
and public acceptance) are difficult to predict and
quantify. For these reasons, the selection of single studies
can be used as evidence in support of a particular view
about GM technology when the whole picture from the
composite of evidence is rather different. There has been
selective use of evidence on both sides of the debate but
this has little to do specifically with the technology of GM,
more the outcomes from specific applications in particular
circumstances (Finger et al., 2011).
The polarisation of the GM debate may also have
influenced the choice of methodologies used for analysis
(Smale, 2012). A study of peer-reviewed articles on the
socio-economic impact of cultivation of GM crops in
developing countries analysed 321 articles covering the
subject (Smale, 2012). In terms of content, about half
of the studies examined the impact on farmers (other
actors in the value chain are underrepresented), and
most studies focused on Bt cotton. The ratio of review
articles to primary analyses is high, and the number of
socio-economic impact assessment studies has declined
in recent years. The most common methodologies used
are partial budgets, followed by farm production and
input use models (Smale, 2012). The main limitation of
these studies resides in the quality of the datasets used
(Smale, 2012). Data sources are generally farm surveys,
trial data, or company data. Some studies are based on
several datasets, and early studies were typically based on
very small samples. Conceptual limitations of early studies
include the presentation of gross rather than net margins,
which fails to take account of land or labour costs. These
early studies did not address the bias associated with the
self-selection of farmers growing GM crops (in general,
farmers who are better informed or with more resources
are more likely to adopt new technologies); self-selection
bias was only taken into account in studies from 2007
onwards (Smale, 2012).
Several studies have also highlighted the importance of
local political and economic institutional arrangements
that constrain farmers’ choices and of the social nature
of decision making in the adoption of new technologies
(Witt et al., 2006; Stone, 2007). Adoption rates are
therefore not necessarily indicative of the success or
acceptance of agricultural innovations. These accounts
stress the difficulty of interpreting socio-economic impact
assessment data without careful consideration of the
ecological and political or economic context in which the
new technology was introduced.
60
The cultivation of GM papaya resistant to Papaya Ringspot Virus in Hawaii lowered the incidence of the virus and allowed
farmers again to grow non-GM fruit trees (Fuchs and Gonsalves, 2007). Similarly, the adoption of Bt cotton in China was
reported to reduce the incidence of cotton borers in other host crops in the same area (Wu et al., 2008). See Chapter 4 for
further discussion.
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