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UNIVERSIDADE ESTADUAL DE CAMPINAS
INSTITUTO DE ECONOMIA
PAULO RICARDO DA SILVA OLIVEIRA
TECHNOLOGICAL GAP, DEMAND LAG AND TRADE: A CASE STUDY ON GM-SOYBEANS
HIATO TECNOLÓGICO, LAG DA DEMANDA E COMÉRCIO: UM ESTUDO DE CASO DA SOJA
TRANSGÊNICA
CAMPINAS 2016
UNIVERSIDADE ESTADUAL DE CAMPINAS INSTITUTO DE ECONOMIA
PAULO RICARDO DA SILVA OLIVEIRA
TECHNOLOGICAL GAP, DEMAND LAG AND TRADE: A CASE STUDY ON GM-SOYBEANS
HIATO TECNOLÓGICO, LAG DA DEMANDA E COMÉRCIO: UM ESTUDO DE CASO DA SOJA TRANSGÊNICA
Prof. Dr. José Maria Ferreira Jardim da Silveira – orientador Prof. Dr. David Streed Bullock – co-orientador
Tese apresentada ao Instituto de Economia da Universidade Estadual de Campinas como parte dos requisitos exigidos para a obtenção do título de Doutor em Desenvolvimento Econômico, na área de Desenvolvimento Econômico, Espaço e Meio Ambiente. ESTE EXEMPLAR CORRESPONDE À VERSÃO FINAL DA TESE DEFENDIDA PELO ALUNO PAULO RICARDO DA SILVA OLIVEIRAI E ORIENTADA PELO PROF. DR. JOSÉ MARIA FERREIRA JARDIM DA SILVEIRA.
_______________________________________________ Orientador
CAMPINAS 2016
TESE DE DOUTORADO
PAULO RICARDO DA SILVA OLIVEIRA
TECHNOLOGICAL GAP, DEMAND LAG AND TRADE: A CASE STUDY ON GM-SOYBEANS
HIATO TECNOLÓGICO, LAG DA DEMANDA E COMÉRCIO: UM ESTUDO DE CASO DA SOJA TRANSGÊNICA
Defendida em 23/03/2016
COMISSÃO JULGADORA
A Ata de Defesa, assinada pelos membros da Comissão Examinadora, consta no processo de vida acadêmica do aluno.
DEDICATION
To all those people
whom made the last four years seem too short…
ACKNOWLEDGMENT
I would like to express the deepest appreciation to my committee chair Professor
José Maria who has the attitude and the substance of a genius: he continually and
convincingly conveyed a spirit of adventure in regard to research and scholarship, and an
excitement in regard to teaching. Without his guidance this dissertation would not have been
possible.
I would like to thank you Prof. Bullock for the hospitality and priceless
contribution to improve the dissertation and guide through my fruitful visit to the University
of Illinois.
In addition, thank very much for all my teachers and professors whose dedication
can be seen as very important foundation to this work be built upon. I still believe in a better
world, with better people and education is a reasonable way top get there.
EPIGRAPH
Surely, nothing can be more plain or even more trite common sense than a proposition that innovation […] is at the
center of practically all the phenomena, difficulties, and problems of economic life in capitalist society
(Schumpeter, 1939: 62).
ABSTRACT
The last three decades was marked by several disputes and debates over the international
trade of genetically modified organisms (GMOs). As national regulatory frameworks were
built upon unilateral basis, many conflicts emerged from trading, opening a room for
questioning the adverse effects of technology innovation and adoption on trade and
wellbeing. Therefore, the central aim of this dissertation is to investigate the role of
technological gap and demand lag on trade, in the context of high levels of technology hatred.
The technology gap is the difference or technological distance of techniques employed by
late-movers when compared with technology used by leaders. Likewise, the demand lag may
be understood as the difference or technological distance of techniques employed by
producers in exporting countries and level of acceptance or compatibility in destination
markets. The GM-soybean case is interesting since it comprises all the relevant features to
answer the key questions raised in this dissertation. The concentrate international market – in
terms of both producing and consuming markets – along with distinct technological and
regulatory postures across countries enables the analysis in spite of absence of disaggregated
data on exports of conventional and genetically modified grains. By means of a gravity
equation we empirically estimated the effects of the technology-gap and the demand-lag on
bilateral trade flows of soybeans. In order to find theoretical basis for our analysis, we also
carried out a concise literature review on related trade theories. From the theoretical
perspective this dissertation points to the need of developing models to deal with different
tastes among consumers from different countries and explicitly consider adverse
technological effects in trade – i.e. effects beyond the common relation between innovation,
efficiency and gains of market shares. The results confirm that both technological gap and
demand lag had important impacts on bilateral trade of soybeans. Furthermore, results make
clear that we need better theories to consider cases in which taste differences across countries
play a role in bilateral trade.
Keywords: Bilateral Trade, Technology Gap, Demand Lag, Gravity Equation,
Genetically Modified Organisms (GMO), Agricultural Economics.
RESUMO
As últimas três décadas foram marcadas por inúmeros debates sobre o comércio internacional
de organismos geneticamente modificados (OGM). Como os quadros regulatórios pertinentes
foram desenhados de maneira unilateral, muitos conflitos surgiram no âmbito comercial
abrindo espaço para se questionar o efeito adverso da inovação e adoção tecnológica no
comércio e no bem-estar. Dado isto, o objeto central desta tese é investigar o papel do gap
tecnológico e do lag da demanda no comércio sob o contexto de forte rejeição da demanda. O
gap tecnológico pode ser definido como a diferença ou distância entre a tecnologia utilizada
para produção em países atrasados (late-movers) quando comparada com a tecnologia
adotada pelos país líderes. De forma similar, o lag da demanda pode ser entendido como a
distância ou diferença da tecnologia adotada pelos países exportadores e o nível de aceitação
ou compatibilidade nos mercados de destino. O caso da soja contem todas as características
relevantes para o tratamento das questões levantadas neste trabalho. A concentração da oferta
e da demanda nos mercados internacionais e padrões tecnológicos e regulatórios distintos
entre os países possibilita a análise mesmo sem dados desagregados para exportação de grãos
convencionais e transgênicos. Por meio de um modelo gravitacional nós estimamos de forma
empírica os efeitos do gap tecnológico e do lag da demanda no comércio. Buscando-se bases
teóricas, uma breve revisão da literatura sobre as teorias de comércio foi realizada. Da
perspectiva teórica, a tese aponta para a necessidade de desenvolver-se modelos capazes de
tratar preferências distintas entre os países e considerar explicitamente a possibilidade de
efeitos adversos da tecnologia – isto é, considerar efeitos para além da relação usual entre
inovação, eficiência e ganhos de mercado. Os resultados confirmam que tanto o gap
tecnológico, como a o lag da demanda tiveram impactos importantes no fluxo bilateral de
comércio da soja. Além disto, os resultados apontam para a necessidade de desenvolvimentos
teóricos capazes de tratar de forma mais recorrente casos onde a diferença nas preferências
sejam importantes.
Palavras-chaves: Comércio Internacional, Hiato Tecnológico, Rejeição da Demanda,
Modelo Gravitacional, Organismos Geneticamente Modificados, Economia Agrícola.
LIST OF ILLUSTRATIONS
TABLE 1 – EXPORTS OF SOYBEAN SEEDS 2014 19 TABLE 2 -THE WORLD'S TOP 10 SEED COMPANIES 2010 20 TABLE 3 – WORLD EXPORTS OF SOY PRODUCTS BY COUNTRY (2014) 22 TABLE 4. HECTARE AREA PLANTED WITH GM-SEEDS BY COUNTRY (2013) 24 TABLE 5 – WORLD IMPORTS OF SOYA PRODUCT BY COUNTRY (2014) 29 TABLE 6 – UNITED STATES’ APPROVED GM SOYBEAN (2015) 35 TABLE 7 – ARGENTINA’S APPROVED VARIETIES OF GM SOYBEAN (2015) 39 TABLE 8 – BRAZIL’S APPROVED GM EVENTS OF SOYBEAN 41 TABLE 9- GM SOYBEAN APPROVED IN THE EU FOR FOOD AND FEED 47 TABLE 10 - CHINA’S APPROVED GM SOYBEANS 52 TABLE 11. UNEP-GEF COUNTRIES 56 TABLE 12 – TEST FOR SAMPLE SELECTION AND FIRM HETEROGENEITY BIASES 108 TABLE 13 – DESCRIPTIVE STATISTICS 115 TABLE 14 – ESTIMATES RESULTS 116
FIGURE 1 – EUROPEAN UNION APPROVAL PROCESS OF NEW GMOS FOR FOOD AND FEED 45
CHART 1 – EUROPEAN IMPORTS BY SOURCE (BILLIONS OF USD FROM 1990-2014) 63 CHART 2 – EUROPEAN COUNTRIES’ IMPORTS BY CLUSTER 67 CHART 3 – CHINA’S IMPORTS BY SOURCE (TONES 1990-2014) 69
BOX 1 – SUMMARY OF MAJOR ESTIMATES STEPS ............................................................................................................. 109 BOX 2– MODEL’S VARIABLES DESCRIPTIONS ...................................................................................................................... 111
TABLE OF CONTENTS
Dedication ......................................................................................................................................... 5
Acknowledgment ............................................................................................................................ 6
Epigraph ............................................................................................................................................. 7
Abstract .............................................................................................................................................. 8
Resumo ............................................................................................................................................... 9
List of Illustrations ....................................................................................................................... 10
Introduction .................................................................................................................................... 12
Chapter I - The Private and Public Agents and Controversies Around GMOs .......... 16 1.1 Soybean Industry Organization .............................................................................................. 16 1.2 Countries’ Regulatory Frameworks and Public Opinion Towards GMOs ................ 31
1.2.1 Producing Countries: Regulation, Adoption and Consumers’ Perception ........................ 34 1.2.2 Importing Countries: Regulation, Adoption and Consumers’ Perception ......................... 43
1.3 Remarks .............................................................................................................................................. 58
Chapter II – Technological Effects and Trade Theories ................................................... 60 2.1 Evidences of Technological Effect on International Trade of GMOs .............................. 61 2.2 Trade theories and Dual-market System ................................................................................ 79
2.2.1 The Ricardian Models of Trade and underlying role of technology .................................... 80 2.2.2 Firm Heterogeneity Models .................................................................................................................. 87 2.2.3 Technological Gap and Trade .............................................................................................................. 93
2.3 Theories of Trade and the Case of GMOs ................................................................................. 96
Chapter III - Empirical Estimation .......................................................................................... 99 3.1 Method ............................................................................................................................................. 99 3.2 Data ................................................................................................................................................. 110 3.3 Results and Discussion ............................................................................................................. 114
IV. Conclusions......................................................................................................................... 127
References .................................................................................................................................... 131
12
INTRODUCTION
The genetically modified organisms (GMOs) have been produced and exported
since 1996, when the combination of scientific developments and genetic appropriability
mechanisms enabled the first commercial production of GM-soybeans in the United States
(US). The technology became rapidly available to other producing countries via trade in
technology headed by large multinational seed companies. But, some important consuming
markets have taken contrary positions to the production and consumption of genetically
modified food, arguing mainly about high health, environmental and economic involved
risks.
Together, Brazil, United States and Argentina accounts for approximately 87% of
world exports of soybeans, and the European Union (EU) and China accounts for more than
83% of world imports. Noteworthy, it was possible for growers in United States and
Argentina adopt GM seeds already in 1996. Policymakers in Brazil, on the other hand, took
almost a decade to legalize cultivation from GM seeds. But, on the demand side, many
European countries have been contrary to use of GM seeds in agriculture, encouraging the
raising of trade barriers or even fully banning importation of food or contents deriving from
genetic modified plants. In China, however, in spite of some few limitations to free trade of
GM food, policymakers have passed no rules preventing the country of being a certain
destination for GMOs, partially because of large amounts demanded by the internal
processing and livestock industry.
In sum, the absence of multilateral bodies powerful enough to enforce a
compromise, national policymakers ended up taking unilateral positions, in terms of
approval, coexistence, labeling, and other issues related to GMOs production and trade. As
expected, technology became a new source of trade conflicts lasting until today, as pointed
by many applied studies.
Nonetheless technology has been an issue in trade models at least since the rise of
Ricardo’s model. The baseline model assumes that countries make use of different
technologies – or different production functions –, which become a source of comparative
advantage (CA) leading to different degrees of specialization. More recent works have
advanced in technology and trade mainly under the umbrella of firm heterogeneity and
technology-gap models of trade. Current interpretations of Ricardo and firm heterogeneity
model are similar to the extent they resort to the neoclassical tools. On the other hand,
technology-gap models are based on the building blocs of evolutionary economics.
13
At first, innovation and adoption could be treated as a shock in neoclassical
models, but the problem of different tastes leading to different trade patterns cannot be
addressed straightly. Preferences has been treated as identical and homothetic in the form of
Constant Elasticity of Substitution (CES) utility functions in these models.
However, we have enough evidences to believe that the case studied is not only
impacted by relative productivity changes, but also by differentiated consumers’ perceptions
of the technology across countries. In other words, consumers in different countries can have
different tastes. Also important, they can demand more than goods, i.e., they can choose
among different technologies based not only on price or efficiency criteria. Empirics show
that this can be especially true if they are from high-income countries. That is important from
both the empirical and theoretical perspective.
Many interesting questions arise from this simple case. First, how the
technological change can impact trade flows? Will the first-movers have some advantage in
the presence of technology hatred? Is it possible for a late-mover rip some benefits of late-
adoption? What are the key variables impacting trade in the case of backward effects of
technology? We believe that current theoretical frameworks cannot provide reasonable
answers to these and other related questions.
Our central hypothesis to be assessed in this dissertation is that of technological
innovation leading to a double effect in trade in the presence of asymmetrical adoption and
acceptance of a particular technology. The first effect is the technology-gap in relation to the
most advanced countries, and the second is the demand-lag1 in relation to consuming
markets. The concepts of technology gap, i.e., how advanced or efficient a technology
employed by producers in a country is when compared to most productive technologies
available, and demand lag, i.e., how accepted a technology employed by producers in a
country is by consumers in destination markets in a point of time, are very insightful to the
purposes of this study. To the best of our knowledge these concepts were discussed firstly by
Posner (1961), but have been neglected in the new developments of trade theory, perhaps
because of the lack of cases in which these two effects play such a clear and opposing role.
One of the main goals of this dissertation, therefore, is to evaluate how
technological gap and demand lag have been impacting on the bilateral trade in presence of
unequal technology adoption and significant levels of technology rejection. We also seek to
bring out the bottleneck of the inexistence of treatment for consumer preferences beyond the
1
As it will be discussed latter on this dissertation, we can treat this distance or proximity as the other
geographical costs relating to bilateral trade.
14
“corollary” of identical tastes within and across countries. Specific objectives include
estimating trade elasticities for technology gap and demand lag for the case of soybeans from
1996 and 2012. Also, we will review literature on Ricardian, firm heterogeneity and
technology-gap models discussing their overlays, divergences and contributions to explain
the case of soybean trade. It is important to say this study is primary focused on identifying
possible stylized facts, instead of developing a new theoretical approach to deal with the
flaws in demand side modeling approaches as presented in neoclassical models 2.
We consider this particular experiment very important for several reasons. First,
this seemingly unique case is very passible of recurrence with other agricultural commodities
intend to be used as food and feed components. An example of analogous case, that is high-
income countries revealing preferences for production means, is the increased demand for
certified organic crops, decent work – e.g. no child labor, slavery, etc. – and more
environment-friendly farming activities.
Likewise, technology is becoming more complex also outside the farm gates; at
the same pace consumers are becoming more and more aware of production means used to
manufacturing the goods they acquire. Complex technologies involve social, economic,
ethical, ethnical, religious, environmental and health issues, which can potentially be new
sources of trade conflicts between countries. Biotechnology itself has many others
applications in different industries such as genetic improvement of animal and humans,
development of organic materials and new pharmaceuticals.
Hence, this study can be valuable for both policymakers and private actors since
better understanding the relation across innovation, technology adoption, market rejection
and trade can improve decision-making and consequently wellbeing – adverse trade impacts
will lead to unequal gains and losses across countries and to different actors.
This study contributes to current literature by raising issues about the role of
consumer preferences, and the impacts of these preferences towards certain technologies in
trade. We also advance by estimating the effects of demand lag and technology gap in the
same model. In spite of the importance of these concepts for the study of agricultural
production and trade, the technological-gap hasn’t been explicitly considered in empirical
models. The combination of technology-gap theories, Ricardian models, and gravity
equations are innovative to studies in this area as far as we know.
This dissertation is divided into four more chapters besides this introduction. In
2 This is a very complex subject to be treated in a future research project.
15
chapter I, we bring out figures on GM-food production and outline the trade controversy
foundations. The underlying goal of Chapter I is to show how the soybean global chain
operates, how different agents see the new developments and how country authorities acted
to mitigate increased commercial risks of adoption.
Chapter II introduces the major evidences of changes in the trade patterns from
1996-2012, and how theoretical developments can contribute to shed some light in the case of
trade of GMOs. We present a discussion on technology and trade having as background
models based on Ricardian and technology-gap theorists’ ideas. The main goal of this chapter
is to provide the reader with the grounds to understand what occurred in soybeans market,
and show how theorists that considered technology impacts on trade contributes to answering
some of the questions we have raised so far. Additionally, the inadequacies or absence of
treatment of particular points of our case are indicated for future treatment.
In chapter III we introduce and discuss the empirical results of the gravity
equation highlighting how technological variables – technology-gap and demand-lag –
impacted on GM soybeans trade from 1996-2012. The adopted estimating strategy is also
described in Chapter III, including the gravity equation foundations, limitations and
approaches we have employed in this particular study.
Finally, Chapter VI concludes the dissertation underlining the major findings
from the theoretical and empirical perspectives and retaking points that can be of interest of
policymakers and others decision makers. Also, considerations about open questions to be
addressed in future research programs are outlined.
16
CHAPTER I - THE PRIVATE AND PUBLIC AGENTS AND CONTROVERSIES
AROUND GMOS
In this chapter, we show how important players in international markets of
soybeans held different views of the technology innovation in seed industry. In addition, the
complexity of biotechnology developments and the resulting lack of compromise in terms of
principles and regulation among countries are discussed.
This chapter is split into three more sections. Section 1.1 brings some figures on
main global players and major characteristics of the commercial relations in soybeans
markets. In Section 1.2, we discuss how complexity of biotechnology developments led to
unilateral regulations on labeling and other issues. Section 1.2 explicit the role of
policymakers in managing the commercial risk. Finally, section 1.3 concludes Chapter I.
1.1 Soybean Industry Organization
Soybean production has faced an upsurge in the last decades, mainly after the
1970s. The broad usage of soybean and its by-products – meal and oil – in several industrial
processes is surprising, as long as soybean production in large scale is a relatively new
activity. Historians usually consider the plant gained US farmers interest after 1940s and
South Americans only by the 1980s (HighQuest & Soytech, 2011).
Global consumption has been pushed by high economic growth of developing
world – especially East Asia – and the emergence of new uses, such as feedstock for
biodiesel fuel production. Increased demand along with institutional speculation has been
raising soybean prices remarkably in the last decades.
The primary product of soybean is soybean meal, whereas the RBD (refined,
bleached, deodorized) soybean oil is a secondary product. Indeed, soybean oil is a residual
product of soybean meal manufacturing after solvent extraction process is employed3. Over
the past five decades, soybean meal has become the most available and preferred source of
protein for animal feed manufacturers. The high level of protein (up to 50%), as well as low
fiber content, make it especially good for poultry, swine, aquaculture and dairy and livestock
cattle leading to rapid gain of muscle mass and weight.
Other efficient sources of protein such as fish, meat and bones in spite of high
protein contents have considerable drawbacks. Fishmeal, for example, is significantly more
3 A mechanical press of oil produces high quality oil but is less efficient in protein meal production.
17
expensive and supply is unsteady. Moreover, it is often claimed that poultry acquire fish taste
when feed with fishmeal. Meat and bone meals were the main source of protein for feeding
before soybean meal came into scene. However, many countries have prohibited the use of
these products in breeding especially after the so-called “mad cow” disease. In this way, we
can say that steady supply, some intrinsic characteristics and changes in institutional rules
related to food safety are the major factors behind sustained growth of consumption of
soybean meal. There is no a perfect substitute in meal markets worldwide4.
However, soybean is usually considered an inferior source of oil because of its
low content compared to other major oilseeds. Typically, the low prices and more reliable
supply drives food processors and food service operators to use oil as an ingredient for baked
and fried food, or for cooking oil production. However, drawbacks as trans fat content and
availability of other better sources of vegetal oil (sunflower, canola and rapeseed) make this
market less attractive than market for soybean meal. Noteworthy, developments in biodiesel
industry have attracted more attention to soybean oil.
Although competition for soybean is weak among other vegetal protein sources,
the crop compete for acreage with corn as they convey very similar growing conditions – hot,
wet and humid climate with fertile lands for the highest yields. But, intense competition
between corn and soybeans is more likely to be seen in the U.S. and Argentina, because of
climate issues, rather than other markets such as Brazil.
Agents in the supply chain are settled out all over the world according to the
nature of their activities. They are in general closer to large consuming and producing
markets to rip benefits of reduced transportation costs. However, different processes of
soybean production – such as seed production, growing, processing and manufacturing – can
occur in different countries.
Major soybean producing countries are the United States, Brazil and Argentina.
The larger crushers, feed and food manufacturers and a vast industry for animal breeding is
also based in these countries. However, China and the European Union are the largest
importers of soybean and have a small internal production when compared to volume
demanded by their processing industries.
In general, an input sector (seed and crop protection industries overall), growers,
logistic operators (elevators, crushers, trading companies, etc.) and consumers (feed millers,
food processors and other consumers such as biodegradable plastics and biodiesel producers)
4 The lack of substitutes will be reflected in our empirical exercise showing that imported quantity of other
goods to produce meals is positively related with imports of soybean.
18
are the agents in the soybean value chain. In the case of new conflicting technologies, such as
GMOs innovation, policymakers will also play a strategic role by establishing rules for
national production and consumption.
The combination of high specialization or international labor division, clashing
views of technology by different agents in the value chain and lack of multilateral regulation
are the roots of trade impacts in the case of GMOs.
Seed Industry
The modern and multinational seed industry can be seen as the centerpiece of the
GMO innovation. Actually, very often the history of GM-seeds in agriculture can overlap the
history of global seed industry itself. This industry passed through a drastic transformation in
terms of product portfolios and technology during the last decades.
Scientific and legal developments occurring in the 1980s brought forth a new
technological trajectory – from common methods of seed selection and reproductions to the
hybrid and the genetically modified techniques to selection of desirable intrinsic
characteristics for the seeds (see Wilkinson & Castelli, 2000). The new technology and
appropriability mechanisms required a completely new base of knowledge to the new
developments. Specialists usually agree that we migrated from chemistry to biology-based
innovation trajectories.
Unsurprisingly, the new challenges called for a general movement of merging and
acquisitions, as part of companies’ effort to be competitive in face of the new scientific and
economic requirements. Before 1970s, the seed industry was primary regional with small-
scale firms replicating seeds developed under public domain. A large fraction of famers used
to save their own seeds, since legal framework for appropriability or substantial scientific
innovations weren’t significant at the time. In the 1970s, after transgenia and appropriability
came into scene, seed industry turned out to be more concentrated and international, pursuing
profits not only from their day-to-day replication activities but also from royalties of specialty
varieties (Wilkinson & Castelli, 2000). However, the big clash will come to international
trade chains in 1996, with the first commercial release of Monsanto GM-soybean. Table 1
shows the major exporting countries of soybeans seed in 2014.
19
Table 1 – Exports of soybean seeds 2014
Exporter Trade Value (US) % of world
exports.
Accumulated %
USA 51,120,057.00 30.21 30.21
Argentina 28,041,752.00 16.57 46.78
Canada 26,145,221.00 15.45 62.23
Paraguay 12,242,793.00 7.24 69.47
Malaysia 7,269,400.00 4.30 73.77
Chile 6,242,947.00 3.69 77.46
Uruguay 4,696,756.00 2.78 80.23
Brazil 4,315,733.00 2.55 82.78
India 4,175,584.00 2.47 85.25
Zambia 2,891,612.00 1.71 86.96
Note: Code HS12 120110 was selected to return these data. It was only possible to segregate seed and grain data
internationally after the adoption of HS12 classification system.
Source: Elaborated by the authors based on Comtrade database (2015).
Top 5 countries exporting seeds accounted for 73.77% of world exports in 2014.
As it can be seen, seed companies with higher international presence are based in the United
States and they accounted for 30% of global exports of seed in 2014. The United States are
followed by Argentina (16.57%), Canada (15.45%), Paraguay (7.24%) and others (30%). To
a certain extent the global importance of seed industry in each country can be seen as a
reflection of how long countries took to allow production of GMOs in their territories. Brazil,
for instance, authorized the growth of GMOs a decade later the first commercial release and
stand for the 8th
position in the global seed exporters rank, accounting for only 2.55% of
world trade.
It is worth noting that seed exports are not expressive when compared to soybean
trade because much of the seeds grew in major producer countries are nationally produced.
From a broader perspective, the commercial seed market can be divided into
proprietary and non-proprietary. Simplistically, the first comprehend seeds owned and
marketed by brand companies while the second comprehends seeds traded by local farmers or
plant breeders – the common situation prior to the 1970s. Accordingly to estimates from
International Seed Organization (ISO), 85% of commercial seed market is proprietary
nowadays, a sign of the economic power of these companies in global food chains. Table 2
shows the Top ten firms operating in the seed industry, their annual sales and shares of global
proprietary seed market in 2010.
20
Table 2 -The World's Top 10 Seed Companies 2010
Company – 2010 Seed sales (USD billions) % of global proprietary seed market
Monsanto (US) 4.964 23%
DuPont (US) 3.300 15%
Syngenta (Switzerland) 2.018 9%
Groupe Limagrain (France) 1.226 6%
Land O' Lakes (US) 917 4%
KWS AG (Germany) 702 3%
Bayer Crop Science (Germany) 524 2%
Sakata (Japan) 396 <2%
DLF-Trifolium (Denmark) 391 <2%
Takii (Japan) 347 <2%
Top 10 Total 14,785m 67%
[of global proprietary seed market] Source: ETC Group
As it can be seen, the top 10 firms already accounted for more than 67% percent
of the global proprietary seed market in 2010. This structure has direct impacts on the pace of
technological diffusion, as trade in technology became globally available very quickly and
these companies could control the availability of conventional seeds in the largest agricultural
producing countries as large-scale farmers have been increasingly dependent on the
proprietary seed market. Moreover, they could put together a higher amount of resources to
lobby other agents including policymakers.
The predominant strategy used by giant seed companies has been both heavy
investments in R&D and merger and acquisitions of companies with know-how or large
market-share in areas of interest (Howard, 2009). Innovations have pursued to boost
production yields, cut down production costs and deliver nutritional profiles and value-added
traits desired by consumers – industrial and individual ones.
Monsanto, which had more than 23% of global seed markets in 2010, turns out to
be a key player only from the 1980s. Besides patenting technologies the company acquired
over 50 seed companies between 1996-2013. Some important acquisitions comprehend Delta
& Pine land (1.5 USD billions), Cargill’s5 International Seed Division (1.4 USD billions) and
Holdens’s Fondation Seeds (1.02 USD billions). DuPont major acquisition was Pionner Hi-
Bred, the world’s largest seed company at the time. However, DuPont strategy has involved
more customized agreements with some of the largest remaining independent seed companies
to share germplasm. The other companies adopted very similar strategies to gain market
shares and keep sustained increasing returns (Howard, 2009).
5 Cargill has ownership of companies operating in logistic, crushing and also in feed manufacturing.
21
The economic power of seed companies come not only from economic
concentration but also from their ability to coordinate activities between themselves (mainly
R&D) and vertical integration downstream and upstream to farming activities, such as
partnerships with processors, which allows easy identification of their needs and increased
proximity with farmers worldwide. Monsanto and Cargill partnership is only an example of
seed companies engaging in partnerships with processors. GreenLeaf Genetics is an example
of a partnership between two seed companies to sell foundation seeds6.
These companies also sell or develop partnerships with sellers of crop protection
inputs. Indeed, usually they don’t sell a particular seed but a technological package, meaning
herbicides and insecticides that work along with plants with specific traits.
But, even though seed companies have bargaining power to influence important
actors determining most of the direction and pace of technological innovation, they cannot
fully control end-consumers aversion to the technology and the design of the national
regulatory frameworks.
In sum, this partial power to coordinate agents all over the value chain and the
absence of effective multilateral regulation will be constant source of increased commercial
risk and/or opportunity costs for producers that serve markets with certain levels of
technology hate. In other words decisions taken by the seed companies in terms of the pace
and scope of the new developments, as well their ability to coordinate innovation and
adoption will impact the entire value chain in many ways.
Growers
The major producing countries – namely United States, Brazil7 and Argentina –
accounted for respectively 35.02%, 28.13% and 17.31% of world total soybean production in
2014. These three leading producers were followed by China (3.96%), India (3.41%)
Paraguay (3.23%), Canada (1.96%), Ukraine (1.06%) and Uruguay (1.03%) (FAOSTAT,
2015). Data on shares of world exports are provided in Table 3.
Besides the US, Brazil and Argentina (the G3 hereinafter) a sub-group of
countries also play considerable role in international markets of soya products, given their
expressive exporting and production volumes. This group is made up of Canada, Paraguay,
6 Foundation seed is seed so designated by an agriculture experiment station. Its production must be carefully
supervised or approved by representatives of an agricultural experiment station. It is the source of all other
certified seed classes, either directly or through registered seeds (Rice Knowledge Bank, 2015). 7 Note that Brazil is the only giant producer of soybeans that has no expressive role in international markets of
soybean seeds. As we have argued before, this may be a result of later approval for GMOs cultivation in the
country.
22
India, Ukraine, China, Bolivia and Uruguay - the G7 hereinafter. These countries together
accounted for 20.30% of world production of soybeans in 2013 (FAO, 2015).
Not surprisingly, besides being leaders in soybean exports, the G3 are also the
largest exporters of the two major by-products, soybean meal and oil. Argentina has a
noteworthy position in meal and oil markets, which can be partially explained by the country
tax policy, which favors exports of meal and oil instead of soybeans. On the other hand,
Brazil and US export more soybeans than oil and meal, supplying markets with large
crushing capacity such as European Union (EU) and China.
Table 3 – World Exports of Soy Products by Country (2014)
Soybeans (HS12 – 120190)
Exporter Trade Value (USD trillion) % of world share Accumulated
USA 24.206 41.0% 41.0%
Brazil 23.273 39.0% 80.0%
Argentina 3.748 6.0% 87.0%
Paraguay 2.292 4.0% 91.0%
Canada 1.756 3.0% 94.0%
Uruguay 1.616 3.0% 96.0%
Ukraine 0.701 1.0% 98.0%
Flour and Meal (HS12- 230400 and 120810)
Argentina 11.853 35.61% 35.61%
Brazil 7.000 21.03% 56.65%
USA 5.476 16.45% 73.10%
Netherlands* 2.151 6.46% 79.57%
India 1.292 3.88% 83.45%
China 1.181 3.55% 87.00%
Paraguay 1.109 3.33% 90.34%
Oil (HS12 - 150710 and 150790)
Argentina 3.467 41% 41%
Brazil 1.129 13% 54%
USA 0.806 9% 63%
Paraguay 0.481 6% 69%
Spain* 0.415 5% 74%
Netherlands* 0.375 4% 78%
Germany* 0.328 4% 82%
Bolivia 0.293 3% 85%
Notes: Both codes 230400 and 120810 are used in HS for meal and flour. Code 120190 was adopted after HS12 version and exclude
soybean seeds (120110), previously classified in a single code 120100. Oil different codes only discriminate between refined (150790) and crude (150710). Countries marked with “*” are processors or distribution ports as they don’t produce meaningful amounts of soybean.
Source: Elaborated by the authors based on Comtrade database (accessed in 2015).
23
Specialists usually agree that soybean production in Brazil and Argentina has cost
advantages when compared to production in the United States. That would be a reason behind
the increasing market share of Brazil and Argentina over the US shares (HighQuest &
Soytech, 2011). However, there are other differences explaining how importers (crushers and
processors) decide upon where to source their soybeans such as the level of foreign material,
moisture, beans integrity, protein and oil content.
The discrimination between GM and conventional seeds are the additional setback
the seed technology industry put to growers’ decision making. Actually, the modern version
of this problem is set in term of which GM variety is being produced and approved for
importation in different countries. Considering low variability of prices and traditional
quality standards across major producing countries, such a discrimination seems to have been
played an underlying role in deciding from where to source soybean in the last decades – as
we are arguing in this Ph.D. dissertation.
Brazil was the only country from the G3 group that has taken almost a decade to
approve production of GMOs internally. There are two equally valid explanations for that.
First, the legal moratorium was effective in prohibiting farmers to grow GM-seeds until 2005.
We know it can be only partially true on account of growers in south of Brazil have started
planting GM-seeds by the late-1990s, making use of pirate seeds smuggled from Argentina.
Second, the first GM-seeds weren’t good enough for tropical regions, performing poorly in
terms of yield. This explanation seems very plausible if we consider the lobby of large
farmers in Brazil. As soon as these farmers could see any significant rewards in growing
genetically modified seeds in Brazilian Middle West, they would fight for GMOs
liberalization – as happened in the early-2000s.
On the other hand, U.S. and Argentina growers are ripping the benefits and paying
the costs of planting GM-seeds since 1996. U.S. has notably higher average yields per hectare
than Brazil, as a result of more capital-intensive production means. The existence of a large
producer supplying large amounts of conventional soybean along with adverse markets
demanding GM-free products created a dual-market system – i.e. a market for GMOs and
another for conventional crops. However, with the Brazilian Biosafety Law of 2005, dual-
market basis will be significantly weakened by a severe reduction on supply of conventional
soybean. Adoption in Brazil was rapid in pace, making the country the second largest country
in terms of planted area with GM-crops already in 2013, as can been in Table 4.
24
Table 4. Hectare area planted with GM-seeds by country (2013)
Rank Country Area (millions of hectares 2013)
Area (millions of hectares 2013)
Crops
1 United States
70.1
73.1 Maize, soybeans, cotton, canola, sugar beet,
alfalfa, papaya, squash
2 Brazil 40.3 42.2 Soybean, maize, cotton
3 Argentina 22.9 24.3 Soybean, maize, cotton
4 India 11 11.6 Cotton
5 Canada 10.8 11.6 Canola, maize, soybean, sugar beet
6 China 4.2 3.9 Cotton, papaya, poplar, tomato, sweet pepper
7 Paraguay 3.6 3.9 Soybean, maize, cotton
8 Pakistan 2.8 2.9 Cotton
9 South Africa 2.9 2.7 Maize, soybeans, cotton
10 Uruguay 1.5 1.6 Soybean, maize
11 Bolivia 1 1 Soybean
12 Australia 0.6 0.5 Cotton, canola
13 Philippines 0.8 0.8 Maize
14 Myanmar 0.3 0.3 Cotton
15 Burkina
Faso 0.5
0.5 Cotton
16 Spain 0.1 0.1 Maize
17 México 0.1 0.2 Cotton, soybean
18 Colombia 0.1 0.1 Cotton, Maize
19 Chile <0.1 <0.1 Maize, soybean, canola
20 Honduras <0.1 <0.1 Maize
21 Portugal <0.1 <0.1 Maize
22 Czech
Republic <0.1 <0.1 Maize
23 Poland <0.1 <0.1 Maize
24 Egypt <0.1 <0.1 Maize
25 Slovakia <0.1 <0.1 Maize
26 Costa Rica <0.1 <0.1 Cotton, soybean
27 Romania <0.1 <0.1 Maize
Source: James (2014)
Table 4 shows that the three largest soybean producers also figures among the
largest world producers of GMOs. However, countries adopted technology in different times,
for different crops and varieties as we are going to discuss in more details in the next section.
25
Considering their strategic position in the supply chain, farmers acquire seeds
from input industries to carry out their core activities. Usually, along with seeds farmers are
also choosing part of other technologies. Although farmers can be substantially different
worldwide in terms of size or technology-level, the new shape of soybean value chain has
certainly decreased their scope of decision, especially in terms of choosing across different
varieties of the same crop.
Decisions are made considering legal approvals at home, costs – including the
overall costs of technological package – yield delivered given the environment conditions,
and crusher’s acceptance of available varieties. In other words, seed industry, crushers, food
and feed processors, and policymakers have been playing a more central role in technology
development, adoption and acceptance. By the end of the day these agents determine the
premiums and penalties related to different seeds adoption, and growers respond accordingly
to their rationale of higher returns. We can sum up the role of growers in global trade as to
produce as many as soybeans as possible.
Grain elevators, Domestic Crushers and Trading Companies
Grain elevators are usually complete receival points comprehending activities like
receiving, testing, weighting and storing grains until selling them to crushers or other
elevators. They are called this way because they scoop up grains from lower level into silos
or other storage facility. There are many types of elevators, but from a broader perspective
they can be classified as country and exporting elevators – also called exporting terminal.
Country elevators sell to other larger elevators in the country or processors
whereas export terminals sell beans to trading companies, international processors or end-
consumers in international markets. As this study deals with soybean trade we are mainly
concerned about GM-soybeans being exported to be processed in countries averse to the
technology.
Domestic processors or crushers are companies carrying out all activities from
handling and elevating to extraction of soybean meal and crude degummed oil. As cited
before, hexane extraction is the most common method employed to produce soybean meal
and have oil as a residue8. Domestic processors buy soybeans from country elevators and sell
soybean meal and oil to end-consumers in the country or abroad – directly or through trading
companies.
8 Other extraction methods like mechanical press can delivery a higher quality oil but affects productivity of
soybean meal.
26
Export terminals, in turn, acquire soybeans from country elevators or growers to
sell to trading companies or direct to countries with high crushing capacity – such as the EU
and China. Very often country elevators, crushers, exporting terminals and trading companies
are controlled by the same corporations making difficult to breakdown their activities and
commercial relationships. ADM, Bunge, Cargill, Louis Dreyfus, AGP and CHS are important
players controlling grain distribution activities all over the world.
Countries differ substantially in terms of presence and size of these agents. Brazil
and Argentina, for instance, are more likely to sell the grains to domestic processors or export
terminals as these countries have less complex logistic operations. Otherwise, U.S. growers
usually sell to country elevators, making soybean pass through at least 3 or 4 operators before
entering the exporting market.
The better infrastructure in the U.S. cuts down logistic costs but has some quality
drawbacks because of the higher levels of foreign materials and breakage of beans, resulting
from over handling. On the other hand, U.S. growers gain as they can sell FOB9 to elevators
operating nearby their farmers, which requires much less expertise in logistic issues. Many
specialists consider logistic advantages partially pay down lower production costs in South
America – due to relatively lower wages and land prices.
Just to provide a better picture of the relative importance of these agents, let’s take
into account some estimates from the American Soybean Association (ASA). According to
them, U.S. processors purchased on average 55% of all soybeans domestically produced from
2006 to 2010. Export terminals purchased 36% and cattle breeders 5%10
. Processors, likewise
country elevators, are mainly sited closer to large producing or consuming areas around the
world.
Export terminals, otherwise, are located in ports of easy access to growers or
elevators in major producing regions. They seek to minimize overall costs of transportation
and storage at the same time they mitigate the transaction costs related to moving soybean
from a country to another. Thus, these companies have offices, facilities and others branches
in both major producing and consuming countries and often sell to their own affiliates.
The concentration and hierarchical control in distribution can be partially
explained by high risks involved in these activities. Besides soybean is normally considered a
commodity, transactions in the segment can be often considered complex. Indeed,
9 Free on board price.
10 The remaining 2% was kept as elevators carryovers.
27
coordination in agro-food chains in general has been considered complex mainly because of
high risks involved in farming.
Considering the risks from the standpoint of transaction costs, the more
agriculture assets become specific the more complex transactions will be. Frequency and
uncertainty are also other two issues to be considered. Even before the GM-seeds came on
the scene, other qualities criteria, the significant economies of scale – calling for operating at
full capacity – overall level of uncertainty, and market concentration were far enough to
make soybean chains complex. Nonetheless, it is very clear that GM-seeds created one more
source of asset specificity. For each different variety of soybeans emerging a result of
innovation in seed industry, we have an increased range of similar but different products in
the marketplace11
.
Therefore, logistic operators and soybean processors – often the same agent – are
managing significant part of the risks related to production and trade of soybean. They
depend on many independent growers to keep their activities at full capacity. They usually
play a very important role in assuring or achieving quality standards and in IP (identity
preservation). They have a strategic position not only between end-consumer (domestic and
international ones) but also between input industry and farmers – being common to
processors and seed companies commit themselves in partnerships. Last but not least, they
are subjected to different national regulatory frameworks to carry their grains from producing
to processing countries.
In sum, these corporations are in charge of managing national and international
growers decisions to guarantee their supply of soybeans to operate at full capacity. In the
relationship with seed industry they signalize what their needs are, in terms of output traits –
higher oil or protein content for instance – and also input traits demanded by growers given
their closeness to them. Other important role is to align premiums they pay to growers that
are producing varieties they demand and royalties paid by growers to seed companies.
Moreover, they are also a key agent in monitoring royalties’ payment as they can easily test
varieties they acquire and request documents to proof that seeds were obtained by legal
means.
11
This idea is not only realistic but convenient for theoretical and empirical analysis, since monopolistic
competition is a good representation for markets like this.
28
External Processors, Feed millers and Food Processors
After been grown and/or processed soybean and by-products follow to internal
consumption or exports. As we have seen, soybeans are mostly used to produce feed and food
contents. Feed is produced from soybean meal and used as protein source especially for
poultry, swine, aquaculture and diary cattle breeding. Food ends include consumption of
beans, flours, oil, among others, and alternative source of protein. Many food products have
soybean products in their composition, for example the chocolate with soy lecithin.
More recently, soybean has been used as feedstock for biodiesel production and
raw material in biodegradable plastic. Noteworthy, sometimes companies operating elevators,
processing and trading activities also controls or have ownership of feed manufacturing
facilities and other end-consumers – e.g. the Cargill corporation operating in feed sector.
In general, countries with crushing capacity and low production of soybeans will
be largest consuming markets of soybeans. That is the case of the European Union and China.
Countries with large presence of feed processors, as a result of large animal breeding sectors,
but without crushing capacity will be key markets to soybean meal. Lastly, large presence of
food processors, biodiesel industry and low crushing capacity will determine the major
importers of soybean oil. Very often countries are important players for both soybean and by-
products markets given general gains of scope. Table 5 shows world largest importers, value
of trade, percentage and accumulated percentage of world imports for soybeans, soybean
meal and oil.
29
Table 5 – World Imports of Soya Product by Country (2014)
Soybeans (HS12 – 120190)
Exporter Trade Value (USD
billions)
% of world share Accumulated
China 40.265 69.13% 69.13%
EU-27 8.277 14.21% 83.34%
Mexico 2.071 3.56% 86.89%
Japan 1.831 3.14% 90.04%
USA 1.149 1.97% 92.01%
Turkey 1.119 1.92% 93.93%
Thailand 1.076 1.85% 95.78%
Egypt 1.057 1.82% 97.60%
Flour and Meal (HS12- 230400 and 120810)
EU-27 13.311
51.92% 51.92%
Thailand 1.676
6.54% 58.46%
Japan 1.057
4.12% 62.58%
Philippines 0.974
3.80% 66.38%
Mexico 0.826
3.22% 69.60%
Algeria 0.822
3.21% 72.81%
Malaysia 0.790
3.08% 75.89%
Egypt 0.644
2.51% 78.41%
Peru 0.602
2.35% 80.76%
Canada 0.515
2.01% 82.77%
Oil (HS12 - 150710 and 150790)
India 1.985
29.10% 29.10%
China 1.092
16.01% 45.11%
EU-27 0.999
14.65% 59.76%
Algeria 0.566
8.30% 68.06%
Peru 0.335
4.92% 72.98%
Mexico 0.190
2.79% 75.77%
South Africa 0.158
2.33% 78.10%
Dominican Rep. 0.129
1.90% 80.00%
Pakistan 0.119
1.76% 81.76%
Ecuador 0.119
1.75% 83.50%
Notes: Codes 230400 and 120810 have been used in international classification for meal and flour. Code 120190 was
adopted after HS12 version and exclude soybean seeds (120110), previously classified in a single code 120100. Oil
different codes only discriminate between refined (150790) and crude (150710)
Source: Elaborated by the authors based on Comtrade database (accessed in 2015).
30
The top 5 destinations together for each product accounted for 92.01% of
soybeans, 69.60% of meal and flour and 72.98% of oil world imports in 2014. This level of
concentration makes decisions in major markets a big deal for production and exporting
decisions taken in sourcing countries – what we are naming as commercial risk. In this way,
the EU large importing volumes combined with high levels of technology “hatred” is
undoubtedly a source of trade conflicts (see Anderson & Jackson, 2004).
China (69.13%), Europe Union (14.21%), Mexico (3.56%) and Japan (3.14%) are
currently the world major importers of soybeans. These countries have minor levels of
soybean production, excluded China that is the fourth largest producer in the world, but still a
net major importer. The European Union is also the major destination for soybean meal and
flour (51.92% of world’s imports in 2014). Major oil importers are India, China and
European Union, respectively.
Crushers in these countries are highly dependent on imports of soybean to supply
soybean meal to feed and food manufacturers operating internally. Usually, these countries
are also dependent on imports of soybean meal and oil, as national crushing capacity is not
enough to fulfill internal demand by feed and food manufacturers.
From the perspective of commercial risks, end-consumers opposing to the
technology in some of the major importing countries are the agents creating additional risks
for the supply chain given the presence of genetic engineered seeds in marketplace, even in
the absence of legal barriers. Again, because of contrary position of manufacturing industry
– a result consumers’ aversion of soy products along with strict government regulations –
logistic operators and processors in sourcing countries have to plan their production strategies
also based on technological rejection worldwide.
As we have been arguing consumers’ aversion to products of GMOs and
government policies towards the technology adoption will play an underlying role in the level
of commercial risks. Indeed, we defend that these two agents sustain many of the controversy
established in the production chain. If not by the commercial risk related to the law and the
end-consumer aversion to the technology we believe that processors and manufacturers
would have no reasons to keep skeptical to the technology. These two additional burdens will
be treated in the next section.
31
1.2 Countries’ Regulatory Frameworks and Public Opinion Towards GMOs
In this section we introduce the way in which the countries have been regulating
GM production and trade during the past three decades of agricultural biotechnology
developments. At first, regulation can be thought from the unilateral and multilateral
perspectives. The latter is especially necessary when norms placed by national authorities in
one country can potentially affect business and citizens as a whole in another country.
History has shown that many conflicts can emerge from international affairs,
including those propelled by environmental, social, economic, ethical matters. The nature of
the conflict should be taken into account, as the drivers and remedies for each type of dispute
will depend on it.
Trade agreements determining sanitary and other technical standards for example,
have mostly employed scientific-based approaches, whereas environment-based agreements
have employed the precautionary principle to establish international standards and norms
(Winham, 2009).
The precautionary principle states that the introduction of a new product or
process, whose ultimate effects are disputed or unknown, should be restrict. In the absence of
precautionary principle the matter can be treated under more pragmatic approaches, such as
the substantial equivalence principle. Substantial equivalence states that a product containing
comparable amounts of a few basic components, such as proteins, fats, and carbohydrates as
its counterpart, should be considered as safe as the comparable one.
Modern biotechnology is particularly complex because its multi-issue profile.
Ethical, environmental, economic and health issues have basically the same weight in terms
of pros and cons. Consequently, regulation was spread out in few different agreements based
on distinctive matters relating to this same technology.
These agreements, however, in spite of incomplete congruence in some specific
points, are equally valid and their principles and guidelines can be claimed accordingly to the
parts’ particular views of the technology. The contrasting scopes can partially explain the
mismatches between rules enacted in World Trade Organization (WTO) agreements and the
Cartagena Protocol for Biosafety (CPB). These are considered the main agreements on
international regulation of modern agricultural biotechnology.
The Cartagena Protocol, opened for signatures in May/2000, is primarily focused
on environmental risks. So, it is based on the precautionary approach and seeks to
32
“(…) contribute to ensuring an adequate level of protection in the field of
safe transfer, handling and use of living modified organisms resulting from
modern biotechnology that may have adverse effects on the conservation
and sustainable use of biological diversity (…)”(Secretariat of the
Convention on Biological Diversity, 2000).
Even though risks to human health are taken into consideration – as declared in
other parts of the protocol – focus is kept on environment issues and monitoring of living
modified organisms. Living modified organisms means that the organisms are capable of
transferring or replicating genetic material. Currently 170 countries have signed the protocol,
being worth noting that the United States and Argentina have not ratified it yet. This fact
alone is enough to bring out the weakness of the Protocol to reach compromise among big
players when it comes to commercial disputes.
On the other hand, the World Trade Organization (WTO) agreements and their
reference to the Codex Alimentarius are clearly more scientific-based. Unsurprisingly, they
are primarily focused on assuring harmonious trade of products involving sanitary,
phytosanitary and other technical standards instead of potential risks involving environment
and ethical issues.
Of course, these differences in scope and principles alone will be an obstacle to
achieve harmonization of norms and procedures. Some countries can base their regulatory
framework in a more scientific-based approach at the same time others can claim for the right
of enacting precautionary measures.
One example of these incongruences is the establishment of minimum and
maximum levels of protection and the possibility of claiming the so-called safeguard
measures. The scientific-based approaches under substantial equivalence principle tend to
offer a very limited space for safeguards and set up standard ceilings whereas precautionary
approach, by taking into account uncertainty, set up standard floors.
Both Technical Barriers to Trade (TBT agreement of WTO) and Sanitary and
Phytosanitary Measures (SPS agreement of WTO) came into force in 1995, at the very
beginning of commercial production of GMOs. Time mismatch of regulation and conflicts
may partially explains why GMOs are not straightly treated in the scope of these agreements.
The SPS establishes a “…multilateral framework of rules and disciplines to guide
the development, adoption and enforcement of sanitary and phytosanitary measures in order
to minimize their negatives effects on trade (SPS agreement text, emphasis mine) ”.
Furthermore, it is stated that relevant international organizations – such as the Codex
33
Alimentarius Commission – will provide the bases to guide the building of regulatory
frameworks. The TBT seeks to ensure that
…technical regulations and standards, including packaging, marking and
labeling requirements, and procedures for assessment of conformity with
technical regulations and standards do not create unnecessary obstacles to
international trade (TBT agreement text, emphasis mine).
The UN Food Agriculture Organization (FAO) and the World Health
Organization (WHO) established the Codex Alimentarius Commission or “Food Code” in
1962. Membership in Codex is open to all member nations of the United Nations (UN) and
currently 165 countries participate. It has been developing a series of guidelines, including
labeling rules to GMOs that may bring more harmonization to the field.
However, the current scenario makes clear that multilateral regulation failed in
fully organizing the production and trade of GMOs. The complexity involving biotechnology,
the lack of clear general standards for dealing with GMOs production and trade, the existence
of a couple of agreements not always in congruence with one another, as well as the lack of
more efficient sanction mechanisms, made country-level regulation the relevant part for
understanding the state of affairs of GMOs.
National regulations differs in restrictiveness degree, type of approach or principle
(scientific or precaution-based), type of basis (scientific or political), and liability (private or
public sector)(Josling, Orden, & Roberts, 2004). A range of questions is treated by national
regulations, such as approval process, coexistence rules, labeling regimes, traceability,
liability schemes, and others. For our purposes approval process in producing and importing
countries and labeling rules in importing countries in particular will be central questions.
We see countries regulatory profile as a result of internal disputes across different
groups of interest, such as firms, consumers, Non Governmental Organizations (NGOs) and
the government itself. That is why the largest agricultural countries tend to take more
pragmatic regulatory posture whereas net importers of agricultural goods with high-income
levels tend to implement more strict rules (P. R. S. Oliveira, Silveira, Magalhães, & Souza,
2013).
Accordingly, policymakers took two important dimensions into account when
considering commercial risks of producing GMOs. First, the approval of many varieties can
raise risks of producing events not approved in every destination countries. Second, even
34
with just a few approved varieties, if adoption rates are too high and logistic capacity for IP is
poor the country can have problems in exporting to countries that are strongly averse to
technology. Indeed, in the 1990s the pertinent dilemma was between the GM-Free vs GM as
many manufacturers preferred to import conventional products as a precautionary measure in
face of large supply amounts. After 2005, when Brazil finally allowed farmers to legally
grown GM crops, the dilemma became one of asymmetrical approval, i.e. a risk of approving
varieties not allowed for consumption in destination markets.
Needless to say, to keep IP of different GM varieties is a huge challenge
considering current transportation structure based on large gains of scale. From a
policymaking perspective, it is simpler to deny approval of varieties not accepted in the most
important destination markets.
These polices implications show how policymakers became very important to
keep commercial risks at a manageable level to middlemen – export elevators, processors
and trading companies being very often owned by the same corporations. In other words, the
absence of certain level of regulation and a less concentrate industry would make risk
management much more complicated than it actually was.
1.2.1 Producing Countries: Regulation, Adoption and Consumers’ Perception
As we have been arguing the aftermaths of ongoing debates in each country
resulted in different regulatory frameworks for global production and trade of GMOs. Only
by considering the G3 countries we can see differences in regulation that will shape the new
pattern of trade. The U.S. has approved a larger number of soybean varieties for cultivation
and other processing uses when compared to Brazil and Argentina. The country also accounts
for the highest global shares of GMOs production over the total agricultural outcome – i.e.
considering all crops to which GM varieties are available (see table 4).
Table 6 shows the events, traits, developing company, purpose and year of
approval for soybean varieties in the U.S..
35
Table 6 – United States’ Approved GM Soybean (2015)
Event Trait Company Authorized
For
Year
Food and
Feed
Year
Cultivation
ACS-GMØØ2-9*** Glufosinate herbicide
tolerance
Bayer CropScience
(including fully and partly
owned companies)
Cultivation n/a 1996
DD-Ø26ØØ5-3
Modified oil/fatty
acid, Antibiotic
resistance, Visual
marker
DuPont Pioneer
Food and
Feed/Cultiva
tion
1997 1997
A5547-127 Glufosinate herbicide
tolerance Bayer CropScience
Food and
Feed/Cultiva
tion
1998 1998
GU262***
Glufosinate herbicide
tolerance, Antibiotic
resistance
Bayer CropScience
(including fully and partly
owned companies)
Food and
Feed/Cultiva
tion
1998 1998
MON 89788 Herbicide Tolerant Monsanto
Food and
Feed/Cultiva
tion
2007 2007
DP-3Ø5423-1
Sulfonylurea
herbicide tolerance,
Modified oil/fatty
acid
DuPont Pioneer
Food and
Feed/Cultiva
tion
2009 2009
MON 87705 *
Herbicide Tolerance
+ Modified Product
Quality
Monsanto
Food and
Feed/Cultiva
tion
2011 2011
SYHTØH2 ***
Glufosinate herbicide
tolerance, Mesotrione
Herbicide Tolerance
Bayer CropScience and
Syngenta Cultivation n/a 2014
DAS-44406-6 *
Glufosinate herbicide
tolerance, Glyphosate
herbicide tolerance,
2,4-D herbicide
tolerance
Dow AgroSciences LLC
Food and
Feed/Cultiva
tion
2014 2014
DAS-81419-2 *
Glufosinate herbicide
tolerance,
Lepidopteran insect
resistance
Dow AgroSciences LLC
Food and
Feed/Cultiva
tion
2014 2014
40-3-2 Glyphosate herbicide
tolerance Monsanto
Food and
Feed/Cultiva
tion
1995 1993
A2704-12 Glufosinate herbicide
tolerance Bayer CropScience
Food and
Feed/Cultiva
tion
1996 1998
MON 87701 Lepidopteran insect
resistance Monsanto
Food and
Feed/Cultiva
tion
2010 2011
Continued….
36
Event Trait Company Authorized
For
Year
Food and
Feed
Year
Cultivation
MON 87708* Dicamba herbicide
tolerance Monsanto
Food and
Feed/Cultiva
tion
2011 2015
MON 87769*
Glyphosate herbicide
tolerance Modified
oil/fatty acid
Monsanto
Food and
Feed/Cultiva
tion
2012 2011
MST-FGØ72-3***
Glyphosate herbicide
tolerance, Isoxaflutole
herbicide tolerance
Bayer CropScience and
MS Technologies LLC
Food and
Feed/Cultiva
tion
2012 2013
DAS-68416-4*
Glufosinate herbicide
tolerance, 2,4-D
herbicide tolerance
Dow AgroSciences LLC
Food and
Feed/Cultiva
tion
2014 2011
DAS-68416-4 X
MON 89788*
Glufosinate herbicide
tolerance, Glyphosate
herbicide tolerance,
2,4-D herbicide
tolerance
Dow AgroSciences LLC
Refer to
Individual
Event Status
n/a n/a
DAS-81419-2 X
DAS-44406-6 *
Glufosinate herbicide
tolerance ,
Lepidopteran insect
resistance
Dow AgroSciences LLC
Refer to
Individual
Event Status
n/a n/a
DP-3Ø5423-1 X
GTS 40-3-2
Glyphosate herbicide
tolerance,
Sulfonylurea
herbicide tolerance,
Modified oil/fatty
acid
DuPont Pioneer
Food and
Feed/Cultiva
tion
n/a n/a
MON 87701 X
MON 89788 **
Glyphosate herbicide
tolerance Modified
oil/fatty acid
Monsanto
Refer to
Individual
Event Status
n/a n/a
MON 87705 x MON
89788
Glyphosate herbicide
tolerance
Modified oil/fatty
acid
Monsanto
Refer to
Individual
Event Status
n/a n/a
MON 87708 X
MON 89788 *
Glyphosate herbicide
tolerance
Dicamba herbicide
tolerance
Monsanto
Refer to
Individual
Event Status
n/a n/a
MON 87769 X
MON 89788
Glyphosate herbicide
tolerance
Modified oil/fatty
acid
Monsanto
Refer to
Individual
Event Status
n/a n/a
Note: * not commercialized ** imported ***status not reported
Source: Prepared by the authors based on Cera (2015), CropLife International (2015), ISAAA (2015)
and GMO Compass (2015)
From a total of 24 approved varieties, major seed companies have been reporting
that at least 10 are actually been produced in the United States12
. The U.S. is often
acknowledged as the major advocate in favor of increased marketing liberalization for
GMOs.
U.S. regulatory framework has a low degree of restrictiveness, reflecting the
adoption of substantial equivalence approach combined with well-aligned semi-autonomous
12 Commercial status is reported by seed companies participating in the CropLife International website – BASF
Plant Science, Bayer CropScience, Dow AgroScience LLC, DuPont Pioneer, Monsanto and Syngenta. There are cases in
which approved varieties are not updated in the website, and so, it is not possible to obtain the commercial status of them.
37
government bodies. An important characteristic of U.S. regulation is the basis on the nature
of the products, rather than the process in which they were produced –a natural result of
substantial equivalence principle.
Government regulates biotech plants through the Coordinated Framework for
Regulation of Biotechnology, established as a formal police in 1986. The government
agencies responsible for oversight of the products of modern biotechnology are the USDA’s
Animal and Plant Health Inspection Service (USDA-APHIS), the U.S. Environmental
Protection Agency (EPA) and the Department of Health and Human Services’ Food and Drug
Administration (FDA). Depending on its characteristics a product may be subject to the
jurisdiction of one or more of these agencies.
USDA-APHIS is primary concerned with potential plant diseases resulting from
genetically engineered plants and environmental impacts related to growing new varieties.
The Plant Protection Act is the underlying framework in which USDA decisions are based.
FDA is responsible for assuring food safety overall, including GM-food. The regulatory
framework is based upon the Federal Food, Drug, and Cosmetic Act and the Public Health
Service Act. EPA controls the use of all crop protection products regardless the form they are
presented. As GMOs comprises insect resistance or/and herbicide tolerance traits, the agency
also carry out mandatory analysis of new developments, under the Federal Insecticide,
Fungicide and Rodenticide Act and the Toxic Substances Control Act.
With regards to adoption, 54% of total soybean produced in 2000 was genetically
engineered according to data from the National Agricultural Statistics Service (NASS). This
share reached 87% in 2005 and 94% in 2015. High adoption shares and number of approved
varieties suggest that commercial risks didn’t prevent U.S. policymakers of keeping a
favoring scenario for GMOs in the country.
U.S. citizens’ opinion on GMOs has been considered mixed, but studies
considering polls carried out between 2001 and 2006 commonly state that:
i. Public knowledge and understanding of biotechnology remains relatively low;
ii. Consumers know little about the extent to which their foods include genetically
modified ingredients;
iii. While support for GM foods has been stable, opposition has softened and opinions
on safety remain split;
iv. Animal cloning evinces much stronger opposition than does modifications of
plants;
38
v. Consumers look to those closest to them as trusted sources of information on GM
foods and biotechnology;
vi. Religious belief has some impact but it is not a key source of variation in public
attitudes towards biotechnology (The Mellman Group, 2006).
According to these polls only 41% of U.S. citizens claimed to have heard a “great
deal” or “some” about genetically modified food in 2006. The remaining 59% said they heard
“not too much” or “nothing at all” about it – the share of people responding “nothing at all”
was on average 25% of the sample. Just 26% of Americans favored the introduction of
genetically modified foods into the U.S. food supply, and 58% opposed. In 2006, opposition
declined to 46% although support continued almost at the same level (27%)(The Mellman
Group, 2006).
On the other hand, farmers and scholars have been reporting positive views of
modern biotechnology advances. Groups in the US opposed to GMOs include some
organizations related to environmental, organic farming and consumers’ rights (LOC, 2014).
The question of mandatory labeling has been central accordingly to a recent poll
showing that 93% of consumers in U.S. advocates in favor of mandatory labeling (Kopicki,
2013). However, authorities are keeping the regulation in place, which allows industry to
voluntarily use labels indicating whether foods have or have not been derived from
genetically engineered plants. FDA provides guidance for voluntary labeling.
Argentina, in spite of passing by a rapid adoption of GM seeds in agriculture, has
approved just a few varieties of soybean for cultivation, decreasing the commercial risks in
terms of asymmetrical approval – i.e. a lag between cultivation approval at home and
food/feed approvals abroad. Table 7 presents the list of Argentina’s approved soybean
varieties for different purposes.
39
Table 7 – Argentina’s Approved Varieties of GM Soybean (2015)
Event Trait Company Authorized For
Year for
food and
feed
Year for
cultivation
40-3-2 Glyphosate herbicide
tolerance Monsanto
Food and
feed/Cultivation 1996 1996
A5547-127* Glufosinate herbicide
tolerance Bayer CropScience
Food and
feed/Cultivation 2008 2011
A2704-12* Glufosinate herbicide
tolerance Bayer CropScience
Food and
feed/Cultivation 2004 2011
MON 87701 X
MON 89788
Glyphosate herbicide
tolerance Monsanto
Food and
feed/Cultivation 2012 2012
BPS-CV127-9 ** Imidazolinone herbicide
tolerance. BASF Inc.
Food and
feed/Cultivation 2013 2013
DAS-44406-6*
Glufosinate herbicide
tolerance, 2,4-D herbicide
tolerance
Dow AgroSciences
LLC
Food and
feed/Cultivation 2015 2015
DP-3Ø5423-1
Sulfonylurea herbicide
tolerance, Modified
oil/fatty acid
DuPont Pioneer Food and
Feed/Cultivation 2015 2015
DP-3Ø5423-1 X
GTS 40-3-2*
Glyphosate herbicide
tolerance, Sulfonylurea
herbicide tolerance,
Modified oil/fatty acid
DuPont Pioneer Food and
feed/Cultivation 2015 2015
Note: * not commercialized ** status not reported
Source: Prepared by the authors based on Cera (2015), Biotechnology Industry Organization (2015),
ISAAA (2015) and GMO Compass (2015)
In spite of approving the Roundup Ready TM
(40-3-2) soybean from Monsanto in
1996, Argentina authorities took 15 years to approve cultivation of a second different variety,
the Liberty Link TM
(A5547-127 and A2704-12) from Bayer. This kept the asymmetry
between approved varieties in source and destination quite low, as Roundup Ready TM
was
broadly accepted worldwide, at least from the legal standpoint. Indeed, according to major
seed companies Argentina has been producing only the stacked variety MON 87701 X MON
89788, since 2003.
However, the GM soybean share reached 90% of the total produced in the first
seven seasons after 1996. Planted area with GM soybean was already 98% of total area with
the crop in 2004/2005 seasons. The great adoption rate is usually credited to weak
mechanisms to guarantee property rights and agricultural benefits perceived by Argentinean
farmers as a whole (Finger & Hartmann, 2009).
The Law on Seeds and Phytogenetic Creations (Ley de Semillas y Creaciones
Fitogéneticas), the Law on the Promotion of the Development and Production of Modern
Biotechnology (Ley de Promoción del Desarrollo y Producción de la Biotecnologia
Moderna) and administrative acts issued by the Secretary of Agriculture, Livestock, Fisheries
and Food (SAGPA) regulate GMOs issues in Argentina.
40
The SAGPA is responsible for oversight the release and commercialization of new
GMOs. Approval process count with assistance of an expert advisory committee, and
comprehends assessments of accomplishment with biosafety standards related to farming and
environment protection, food safety and commercial risks. Farming, environmental and food
safety risks are assessed under scientific-bases, but commercial risk analysis brings a political
perspective into the approval process.
The Biotechnology Directorate (BD) made up of experts from public and private
sectors are in charge of farming and environmental risk assessment. The National Service on
Safety and Quality of Farming Products carries out the food safety analyses. The Agriculture
Market Board assesses commercial risks of approving new varieties not approved in
destination markets.
Public opinion polls show that awareness level is also very low in Argentina. On
average 39% of people voiced they knew about genetically modified soybean being grown in
the country. On the other hand, 51% said they are willing to pay more to consume non-GMO
food, only 12% believe genetically modified crops can be beneficial for society as a whole
and 51% believe large multinational companies will rip benefits of technology adoption
(Diamante & Izquierdo, 2004).
Labeling is not mandatory in Argentina, and country authorities often argue that
the EU mandatory labeling is detrimental to exports of agricultural products from Argentina.
Brazilian government, instead, implemented strict rules for the commercialization
of GMOs. The policymakers nearly banned commercial growth of any GMO in the country
for almost a decade after 1996. Table 8 put together information on soybean events approved
for cultivation by Brazilian government.
41
Table 8 – Brazil’s Approved GM Events of Soybean
Event Trait Company Authorized For Year of
approval
40-3-2 Glyphosate herbicide tolerance Monsanto Food and
feed/Cultivation 1998
BPS-CV127-9 *** Imidazolinone herbicide tolerance. BASF Inc. Food and
feed/Cultivation 2009
A2704-12** Glufosinate herbicide tolerance Bayer
CropScience
Food and
feed/Cultivation** 2010
A5547-127** Glufosinate herbicide tolerance Bayer
CropScience
Food and
feed/Cultivation** 2010
MON 87701 X MON
89788 Glyphosate herbicide tolerance Monsanto
Food and
feed/Cultivation 2010
DAS-68416-4* Glufosinate herbicide tolerance, 2,4-
D herbicide tolerance
Dow
AgroSciences
LLC
Food and
feed/Cultivation* 2015
Note: * not commercialized, ** imported, *** status not reported
Source: Prepared by the authors based on Cera (2015), Biotechnology Industry Organization (2015),
ISAAA (2015) and GMO Compass (2015)
As it can be seen, Brazil was the last country from the G3 to approve the
cultivation of GMOs. It is worth noting that in spite of approval for cultivation of Roundup
Ready TM
(40-3-2) being granted in 1998, legal production would be actually possible only
after 2005, when the National Biosafety Law (no. 11 105 of March 24th
2005) came into
force.
However, as commonly cited GM soybean has been “informally” produced in the
south of Brazil since the mid-1990s. Technically, this prior production cannot be considered
illegal, since a prohibitive act will come into force only few years later due to a court claim
started by the Brazilian Institute for Consumer Defense (IDEC) in 1998. The moratorium
lasted officially until 2003, but remaining conflicts made adoption of GM seeds just take over
after 2005.
The Biosafety law established the creation of the National Technical Committee
(CTNBio), as a bureau under the Ministry of Science, Technology and Innovation, which is
responsible for oversight of all GMOs issues in the country. The CTNBio is made up of
experts and representatives from Brazilian Ministries, such as the Ministry of Agriculture,
Livestock and Food Supply (MAPA), Ministry of Health, Ministry of Environment, Ministry
of Agrarian Development (MDA), Ministry of Development, Industry and Trade (MDIC),
Ministry of Defense, Ministry of International Affairs (MRE) and Ministry of Fishing and
Aquaculture (MPA). This hybrid governance design ensures that political matters
(commercial risks included) are going to be considered in each approval process.
42
While in 2003/04 4.7 million of hectares was planted with genetically modified
seeds, in 2014/15 this share was of 29.1 million of hectares – about 93.2% of total area
planted with soybeans (Galvão, 2014). In spite of the rapid adoption, Brazilian authorities
approved the second variety of GM soybean (BPS-CV127-9) only by 2009. Also, in spite of
having approved 6 varieties of soybean, according to major seed companies’ declaration the
country actually grows only three genetically engineered varieties.
Unlike Argentina and US, Brazil hasn’t produced a meaningful amount of GM
soybean (Roundup ReadyTM
in this case) until the second half of the 2000s. This peculiarity
will play a very important role in the first decade of commercialization, when the major
distinction was made between GM and GM-free products.
Consumer awareness about GM food is surprisingly high in Brazil as indicated by
a recent poll. A survey with 1439 respondents revealed that 54.8% of respondents was aware
about the use of “transgenic plants” to produce medicines and 94.7 was aware of their use in
food production (Capalbo, Arantes, Maia, Borges, & Silveira, 2015).
One should consider that the above-mentioned poll was released online attracting
high proportion of people with college degree and somehow connected to farming activities.
However, even if these numbers are expected to be lower if more diverse respondents were
considered, awareness level may be expected to be higher when compared to the U.S. and
Argentina because of the existence of a mandatory labeling regime in place since 2003. Over
50% of respondents declared they believe transgenic plants are harmful to environment,
42.8% believe they are harmful to human health and 37.2% believe the new developments
are unethical13
.
Last but not least, let’s briefly consider other countries with smaller but still
noticeable presence in global markets, such as Paraguay, Canada, Uruguay and Ukraine.
Paraguay has approved and grown two GM soybean varieties – the Roundup
Ready TM
(40-3-2) and INTACTA RR2 PRO TM
from Monsanto (BIO, 2015; Yankelevich,
2011). Approval for Roundup Ready TM
was granted in 2004 (CERA, 2015). Although the
Ministry of Agriculture granted late approval for the first GMOs, there are strong evidences
of growers using GM seeds since the mid-1990s. Current GM-soybean adoption share of total
output is estimated in 97%, in Paraguay.
13
The survey also indicated that consumers have different perceptions of the words “biotechnology”,
“biosafety”, “genetic engineering”, “GMO” and “transgenic plants”. Perception tends to be more positive
towards biotechnology than biosafety, and transgenic plants have more negative connotation than GMO and
genetic engineering.
43
Canada, instead, have approved a large number of GMOs event for various crops.
The country has approved 23 varieties of GM soybean and at least 3 varieties have been
actually grown – namely Roundup Ready TM
(40-3-2 approved in 1995) LibertyLinkTM
(A2704-12 approved in 1999) and Genuity Roundup Ready 2 Yield (MON 89788 approved
in 2007). Other approved varieties comprehend “not commercialized” (12), “import”(2),
“closed loop cultivation” (2) and varieties not reported in the Biotradestatus database (4).
About 62% of soybean produced in Canada is genetically modified according to data from
Statistics Canada. A 2002 survey reported that only 31% of Canadians viewed genetically
modified fruits and vegetables as good, whereas 63% thought these products were bad (Pew
Reserch Center, 2003).
Uruguay has approved 4 varieties, from which only one is currently cultivated, the
INTACTA RR2 PRO TM
from Monsanto. Authorities have approved the first genetic
modified variety at the beginning of commercial production of GMOs. Planted hectares with
GM soybean are estimated in 99% of total harvested to this crop. Likewise Brazil, Uruguay
accounts with a national commission for biosafety (GNBio) operating with representatives of
industry, civil society and government. Public perception is mixed, but consumers have been
claiming for mandatory labeling. Today the country has a voluntary labeling regime in place
(Markley & Yankelevich, 2012).
Ukraine is the only noticeable exporter that has not approved cultivation of
GMOs. There are some rumors about pirate production of GM soybean in the country, but at
least for the exported amount it is very unlike the country would be able to access external
markets without any further official control – especially the EU markets.
Only recently Ukraine government approved importation of GM soybean meal to
be used by local feed manufacturers. Many specialists agree that the country continues to be
a challenging market for biotechnology promotion. The major drivers of this situation are the
generally negative public opinion, paper work and the gap between testing and approving
systems. Moreover, the industry (individual producers and traders) has not been very active
in supporting GMOs, unlike in other agricultural countries (Hager & Dubinyuk, 2014).
1.2.2 Importing Countries: Regulation, Adoption and Consumers’ Perception
European Markets
The European Union is one of the largest destinations of soybean and by-products
as we have seen previously. It accounted for 14.21% of soybean, 51.92% soybean meal and
44
14.65% of oil world imports in 2014 (FAO, 2015). Nevertheless, the bloc conjunct regulatory
framework is often considered one of the most restrictive in the world. The combination of
these two factors – a large market with a high degree of technology hatred – will become a
big deal for producing and processing industry as whole worldwide.
It is worth noting that in spite of the EU being often treated as a homogenous bloc
of countries, Member States (MS) can differ considerably in their views and attitudes towards
GMOs. From a broad perspective, the MS could be clustered into adopters, conflicted and
opposed countries (FAS-USDA, 2015).
Adopters usually account with a supportive industry and farming sectors with
little opposition from consumer organizations, which enable domestic cultivation of GMOs
once approved by the EU, significant R&D activities and importation of high volumes of
GMOs. Spain, Portugal, Czech Republic, Slovakia and Romania are part of this group.
Indeed, the only approved variety for cultivation in the EU is the Bt Corn (MON810) –
grown mainly in Spain.
Conflicted group has R&D activities, imports large amounts but has little or none
area planted with GMOs. Consumers’ aversion tends to be higher when compared to
“Adopters” prompting policymakers to put more restrictive rules into force. Industry and
retailers avoid using or selling GMOs fearing boycotts or even depredation of shops and
facilities by radical groups lead by some NGOs. Countries in this group are France, Germany,
Poland, Southern Belgium, Bulgaria, Ireland, Sweden and Lithuania.
Opposed group usually imports GMOs only when they need to fulfill domestic
demand, and private sector (farmers and industry), government and consumers intensely
oppose to GMOs. They tend to be more supportive to organic and traditional agricultural
sector marked by geographical indication products. Countries such as Austria, Croatia,
Cyprus, Greece, Hungary, Italy, Malta, Slovenia and Latvia make part of this more averse
group.
Notwithstanding contrasting views across MS, the Bloc has a common regulation
framework for approvals, labeling and traceability procedures for commercialization of
GMOs. Broadly based on precautionary principle, the EU regulatory framework has left
substantial room for most averse countries to ban production and importation of GMOs based
on the argument of lack of sufficient scientific evidences to assure safety in terms of human
and animal health and environment protection.
Indeed, many MS have raised bans against cultivation and importation of GMOs
during the past two decades of commercialization. None of these bans affected directly
45
soybean products, but the production and importation of corn varieties among other GM
crops.
The EU regulates GMOs through two major directives. Directive EC 1819/200314
establishes rules for importing, distributing and processing GMOs – including labeling rules
– and directive 2001/18/EC rules cultivation issues. Thus, approval process discriminates
between cultivation and importation (for food and feed). Taking into account the impacts on
trade, we are primarily concerned with approvals for importation of GMOs to be used as
food, feed and ingredients. A scheme of approvals for food and feed is provided in Figure 1.
In the EU, the approval process is the same for GMOs or products containing GM
ingredients for any purpose, such as processing, food and/or feed. First an interested part
submits an application containing all the relevant and available information to the MS
authority appreciation. Importers and seed companies are typically the applicants for this
type of approval.
Once submission is received, the MS authorities must send a dossier to be
assessed by the European Food Safety Authority (EFSA). EFSA core task is to independently
assess any possible risk of GM plants to human and animal health and the environment. The
European Community takes EFSA opinion as scientific advice and not as a final decision.
EFSA has being often considered supportive to technology adoption, since the analyses are
much more scientific-based than other phases in approval process.
14
It replaced the Directive 90/220/ECC – on the deliberate release into the environment of genetically modified
organisms – was based on the precautionary principle recommended by OECD (1986) to regulate the releases of
genetically modified organisms.
46
Figure 1 – European Union Approval Process of New GMOs for Food and Feed
Source: FAS-USDA (2015)
In the following, the European Community send in a draft decision to the
Standing Committee on Plants, Animals, Food and Feed Meetings (PAFF) reflecting EFSA
opinion. PAFF committee must vote in favor or against the draft within 3 months. For
applications submitted after 2011, the verdict can be only valid if qualified majority were
achieved. In case of deadline expiration without any valid decision, European Commission
can send forward the draft to Appeal Committee of Member States. In case of lack of
qualified majority to take a decision within the deadline, the Commission can finally
deliberate upon the matter.
Although timeline for taking decisions should be no longer than 12 months it has
taken 47 months on average. Even if the entire process comes into a conclusion with an
approval grant, MS can invoke safeguard clauses arguing lack of needed scientific evidence
to discard risks to human and animal health and the environment. Approvals last for 10 years
and need to be renewed after expiration.
In recent years, a number of approvals of new soybean varieties for food and feed
and renewals have been made. Although not formally confirmed, there are indications that
the EU approved new varieties because of the increasing scarcity of conventional crops is
47
creating a production risk for the domestic livestock and processing industry (see Stein &
Rodriguez-Cerezo, 2009). Table 1 shows GM soybean currently approved in the EU.
Table 9- GM Soybean Approved in the EU for food and feed
Event Trait Company Approval Type Year
40-3-2 Herbicide Tolerant Monsanto Food and feed 1996
A2704-12 Herbicide Tolerant Bayer Food, Feed and
Processing 2008
MON89788 Herbicide Tolerant Monsanto Food, Feed and
Processing 2008
A5547-127 Herbicide Tolerant Bayer
CropScience
Food, Feed and
Processing 2012
DP3560423 Herbicide Tolerant
(stacked gene) DuPont
Food, Feed and
Processing 2012
MON87701* Insect Resistant
(Lepidoptera) Monsanto
Food, Feed and
Processing 2012
MON 87705*
Herbicide Tolerance
+ Modified Product
Quality
Monsanto Food, Feed and
Processing 2012
MON87701xMON89788 Insect Resistant and
herbicide tolerant Monsanto
Food, Feed and
Processing 2012
MON 87708* Glyphosate herbicide
tolerance
Dicamba
herbicide
tolerance
Food, Feed and
Processing 2015
MON 87769* Glyphosate herbicide
tolerance
Modified
oil/fatty acid
Food, Feed and
Processing 2015
MON87708 x MON89788* Herbicide tolerant Monsanto
Food, Feed and
Processing 2015
BPS-CV127-9** Imidazolinone
herbicide tolerance. BASF Inc. Food and feed 2015
Note: * not commercialized ** status not reported
Source: Prepared by the authors based on Cera (2015), Biotechnology Industry Organization (2015),
ISAAA (2015) and GMO Compass (2015)
Note that approval of GMOs, Roundup ReadyTM
aside, took more than 10 years to
occur as a result of de facto moratorium. Roundup ReadyTM
from Monsanto received its
second approval in 2005, after the company has submitted an application to do so. That was
the only approved soybean event prior to the unofficial moratorium on new approvals from
1998 to 2003.
Despite having granted approval to 12 different varieties of soybean, only 6 have
been actually imported by the EU. Compared to U.S. and Canada there is a lag of more than
48
10 varieties. On the other hand, Brazil, Argentina, Paraguay, Ukraine and Uruguay approved
has mostly approved varieties already approved by EU authorities.
Taking into account approval year and actually grown varieties in sourcing
countries, however, the controversy in the mid-1990s can be clearly see around the Roundup
Ready TM
soybean approvals. The EU approved the Roundup ReadyTM
in 1996, whereas
Argentina has approved in 1996, United States in 1994 and Canada in 1995. Brazil, unlikely,
will approve this event only by 1998, and intensive adoption – due to agronomic and
regulatory issues – will take over only after 2005. In spite of official approval, it is known
that the EU feed and food manufactures continued skeptical about the technology due to
strongly negative views of consumers.
In 2004, when regulation claims on GMO importation intensified, the labeling and
traceability of GMOs was implemented under EC 1829/2003, which replaced the previous
regulation. Traceability and labeling of GMOs are mandatory. Labeling rules define which
products must be labeled as "contains GMOs". The product must be labeled regardless of the
degree of processing, and when sold without packaging, for example, in restaurants, the
information "contains GMOs" should be visible. Any product with an adventitious level of
GM ingredients greater than allowed should be labeled as "GMO". Animal protein – meat,
diary products, and eggs - feed with GM products is the only exception to mandatory labeling
norms. However, there are signs that voluntary labeling with the information “GM-Free” has
been opening marketplaces for IP grains in feed sector.
There is a threshold of adventitious presence of 0.9% of GMOs to a product be
considered conventional. When adventitious presence involves an event that is not authorized
for consumption in the EU, the product cannot be placed on the market, despite being
labeled. It is worth noting that the level of adventitious presence tolerated by the EU is one of
the world’s lowest. For products not approved the tolerance level is virtually zero (0.1)15
.
Polls on consumer opinions provide indications of strong level of opposition to
GMOs in Europe as a whole. In November 2000, for example, the "Nordic Industrial Fund"
conducted a survey in Denmark, Finland, Norway and Sweden on consumers' opinion about
genetically modified foods and their derivatives. The survey found that conventional foods
have benefits simply because they are not genetically modified. A series of negative
associations, such as "unhealthy products" and "great uncertainty about the risks" were
15 This level can be a great problem to modern trade basis. The current logistic system for international transportation and
storage of grains is based on scale gains, complicating identity preservation and maintenance of low levels of adventitious
presence.
49
attributed to GMOs (Grimsrud, 2004). The same author pointed out that the Norwegians, who
the authors take as a sample for the European market, would be willing to buy a GM product
at an average discount of 50% in price.
The results of another survey revealed that highest level of formal education
positively affects the consumer’s acceptance in the same country, while women and older
respondents reacted negatively. Curiously, self-declared awareness about the GM technology
negatively affects the will to consume GM-food (Mccluskey, Grimsrud, & Wahl, 2006). It
could be evidencing that the way in which media has conveyed the issue contributed to the
negative perception among European consumers (see Brossard, Shanahan, & Nesbitt, 2007).
Surveys on the EU27 revealed that Europeans are quite aware about GM issues.
Results show that 46% of respondents talked about or searched for information occasionally,
9% talked about or searched for information frequently and 27% at least heard about GM-
food. The same study also revealed that levels of technology support for food production in
2010 were low and decreasing when compared to surveys carried out in 2005. In 2010, only
5% of respondents declared “totally agree” with technology and 18% declared that they “tend
to agree”. On the other hand, 33% declared “tend to disagree” and 16% “totally
disagree”(Gaskell, Stares, Allansdottir, & Allum, 2010).
However, empirical researches often point to a certain degree of inconsistences
between public opinion and effective decreases in total intakes of GM-food/feed. These
inconsistences may be related to the fact that consumers do not properly recognize labels
(Noussair, Robin, & Ruffieux, 2002). Finally, the role of retailers must be considered in the
EU case.
The other European countries not subjected to the official EU standards, have also
hold a number of rules contrary to GMOs. Actually, in some cases, they are even more
restrictive than EU countries in terms of national biosafety regulations. Switzerland, for
example, has passed several decrees establishing moratoriums on production of any GMOs in
the country. The two largest retailers in the country (Coop and Migros) representing around
70% of the whole market, advertises they only commercialize GM-free products – assuring
that even the livestock and diary products were not feed with GMOs. Coop published in a
press release in 2007 results of an in-house poll indicating that 87% of respondents told to be
unwilling to eat GM-food (Strossman, 2009).
To sum up, in European countries most opposed to technology, producers see
organic production as more promising and believe that the rules of coexistence will not be
able to ensure good farming practices to maintain identity preserved. The presence of
50
organizations such as Greenpeace and Friends of the Earth contributes to the formulation of
the concept of "Frankenstein foods". In other words, politicians, policy makers, agricultural
cooperatives and consumers share the same opinion, to say that, agricultural biotechnology
creates uncertainties, offers no clear benefits, and therefore is not required (Gaskell et al.,
2010).
China
China is also one of the largest markets for soybean and by-products as we have
seen in the first section of this chapter. The country accounted for 69.17% of global imports
of soybeans and 16.01% of soybean oil in 2014 (FAO, 2015). Currently, the country is not a
major importer of soybean meal. One central characteristic of country is the increasing
crushing capacity, which put Chinas in the first position of major importers rank. The history
of GMOs would be certainly different if China was not a big player, or if it had enacted more
strict regulation to these products.
The basis of the regulation in China is noticeably different from that observed in
the EU and other countries declared opposed to GM technology. According to the National
Biosafety Law, enacted in 1993, China aims to “promote research in biotechnology, consider
the adequate control of biosafety, ensure the maintenance of public health, prevent
contamination of the environment and maintain the balance of biodiversity” (Decree
304/2001).
Regulatory framework is outlined by the State Council and implemented by the
Ministry of Agriculture (MOA). The regulation of production, importing, processing among
other issues are broadly covered by Decrees 8, 9 and 10 of MOA. All the approval process
for cultivation and importation are virtually controlled by the Ministry of Agriculture and
bureaus under its coordination such as the National Biosafety Committee (NBC) formed by
(Lagos & Jie, 2013).
For the importation of product to be processed internally, a foreign seed developer
must apply for an agricultural biosafety certificate from Administrative Examination and
Approval Office of MOA. Applicants have to provide information and test results for the
varieties intended to be imported.
After receiving all paper work, MOA bureaus will carry out a number of tests in
different environments to testify safety issues. Once the test results are obtained MOA
bureaus send them forward to NBC for final opinion on safety matters. Based on the entire
process MOA issues the biosafety certificate, enough for importing products to be processed
but not for cultivation.
51
Indeed, China has not approved importation of any GM seed for domestic
cultivation in spite of being one of the largest GM producers in the world. Government has
invested large amounts of money in R&D while private research is very limited and foreign
capital for R&D is legally prohibited. This may be a strategy of infant industry protection
when the favoring position towards GMOs by Government is declared. The approval process
in China was often criticized in the past for lacking transparency. No written regulation or
guideline for approval procedures existed before May 2003. Currently three GM soybeans
have received market approval in China, as seen in table 10.
52
Table 10 - China’s approved GM soybeans
Event Trait Company Authorized for Year
GTS-40-3-2 Glyphosate herbicide
tolerance Monsanto Processing, Food and Feed 2002
A2704-12 Glufosinate herbicide
tolerance
Bayer
CropScience Processing, Food and Feed 2007
MON89788 Glyphosate herbicide
tolerance Monsanto Processing, Food and Feed 2008
DP356043
Glyphosate herbicide
tolerance, Sulfonylurea
herbicide tolerance
DuPont (Pioneer
Hi-Bred
International Inc.)
Processing, Food and Feed 2010
DP305423
Sulfonylurea herbicide
tolerance, Modified
oil/fatty acid
DuPont Processing, Food and Feed 2011
BPS -CV127-
9**
Sulfonylurea herbicide
tolerance Basf Processing and Feed 2013
MON87701 Lepidopteran insect
resistance Monsanto Processing and Feed 2013
MON87701 x
MON89788
Glyphosate herbicide
tolerance, Lepidopteran
insect resistance
Monsanto Processing and Feed 2013
A5547-127 Glufosinate herbicide
tolerance
Bayer
CropScience
Importing processing
material 2014
DP305423 x
GTS 40-3-2
Glyphosate herbicide
tolerance, Sulfonylurea
herbicide tolerance,
Modified oil/fatty acid
DuPont (Pioneer
Hi-Bred
International Inc.)
Importing processing
material 2014
Note: ** market status not reported
Source: Prepared by the authors based on Cera (2015), Biotechnology Industry Organization (2015),
ISAAA (2015) and GMO Compass (2015)
Note that the approvals of GM soybeans in China were delayed even for the
Roundup ReadyTM
from Monsanto. The lack of effective monitoring system in place is the
only explanation for large amounts of soybean coming from United States and Argentina that
have entered the Chinese marketplace. Indeed, many analysts consider that regulation of
53
imports was minimal in the 1990s. China has a system of temporary approvals, so granted
approvals expires each three years requiring renewals.
Although positive view and acceptance by Chinese government, it is worthy
noting that China’s import approval process takes on average 2-3 years and can only
commence when a submitter for import approval has already received full regulatory
approval in their country of origin, intensifying negative impacts of asynchronous approval (J
Huang, Yang, & Yang, 2012).
Labeling is mandatory in China since 2007, although policymakers have
established several exceptions and the criteria for exception are not clear. Government voices
that criteria to inclusion or removal of products from the list include socioeconomic factors,
political goals and national biosafety issues. The regulation is defined by Decree 10
(CH7053) and the list includes soybeans, soybean meal and soybean oil (Jikun Huang, Qiu,
Bai, & Pray, 2006).
Some issues related to labeling, and more specific procedures for export and
import, are decided by an inter-ministerial council formed by the State Council. Also in 2001,
the Ministry of Public Health promulgated the first guideline to GM food safety. These laws
came into force in June of 2002, bringing a number of important deliberations, such as the
requirement of field tests before market release, mandatory labeling, new standards for
importation and exportation of GM food and rules for regional monitoring (J Huang et al.,
2012).
Consumer participation in shaping the legal apparatus is often considered very
incipient, making it easier for the government to implement standards from the top down, and
so, reducing the importance of consumer opinion.
There are several studies focused on consumer opinion in China. They present
significant divergence in consumer acceptance in the country. On the one hand, a study
carried out by Greenpeace (2004) claimed that GM foods were generally not accepted by
Chinese consumers. On the other hand, some studies identified that Chinese consumers
would be willing to pay a premium for GM foods.
Mccluskey et al. (2006), for instance, analyzed the differences between the views
of consumers in both China and Japan. According to the authors, Chinese consumers would
be willing to pay a premium of 38% to consume a variety of GM rice (Golden Rice) and
16.4% for GM soybean oil. Although factors such as formal education and self-reported
knowledge have negative impacts on their willingness to consume GM products, the values
are always less significant than in Japan.
54
The explanation for divergences may be a change in opinion across the years.
There was a trend in studies pre-2008 indicate that consumers were very opened and
accepting biotechnology products. More recent studies, however, have revealed certain level
of concerns among Chinese consumers (Lagos & Jie, 2013). According to these authors
media and NGOs have constantly broadcasted more suspicious views towards GMOs and
some scientists have voiced concerns about long-term risks surrounding the technology.
Other importing countries
Finally, we could consider other importing countries to conclude our analysis of
country regulatory frameworks for GMOs. Mexico, Japan, Philippines, India and Algeria are
secondary markets, which could be considered briefly. We are also going to present a list of
countries taking part in the UNEP-GEF project to outline biosafety regulations.
Mexico has not being a challenge for exporting countries of GM soybeans.
Country legal framework, however, has a negative impact on importation of GM seeds not
approved for production in the country. Society as a whole doesn’t seem very participative in
GMOs debate and label isn’t mandatory for food and feed being produced with GM
ingredients (Otero, 2015). About 20 GM soybeans have been granted approval, but only 9
have been actually imported. Mexico country is a large importer from United States.
Japan’s regulatory framework has been considered very pragmatic. When
compared to other countries, Japan has one of the largest numbers of approvals for soybean
varieties, including those that are nutritionally modified. There are 19 different varieties
approved, being 8 actually imported. The approval process depends on the product’s purpose,
so not all events undergo the same process. Although almost all the varieties have been
approved for cultivation, according to data from the Biotechnology Industry Organization
(BIO), so far Japan has not produced any GM soybean. As in Europe, the food industry and
retailers have avoided selling products labeled as GM - but processed foods end up being
excluded from the list of mandatory labeling.
Japanese consumers’ attitude toward GM technology is closer to the attitude of
European consumers than their Asian neighbors. Consumers were willing to buy a GM
product if the discount on the final price of the product was, on average, 50%(Curtis &
Mccluskey, 2004). Variables such as knowledge of the subject, level of formal education, if
the individual is female or in an older age group, directly affect the rejection of the product at
an increasingly rate when compared with a sample of U.S. consumers.
Philippines, together other agricultural countries in Asia, has been more open to
55
biotechnology products. The country has the highest level of consumer awareness in Asia,
according to a poll (Asian Food Information Center, 2008). A total of 8 GM soybean varieties
is approved for food and feed uses in the country, being only 2 of them not commercialized
(BIO, 2015). India is also an agricultural country, but has approved only two GM varieties of
GM soybean (Roundup ReadyTM
and Genuity Roundup Ready 2 YieldTM
). Consumers in
both countries are more favorable towards the technology and a majority of consumers
believe that food biotechnology will bring benefits in the next few years. In Philippines 70%
of consumers believe in benefits, and in India this share is of 70% of consumers(Asian Food
Information Center, 2008). Expected benefits included better quality, improved yield,
healthier products and higher levels of food security. There is not much information on
situation of GMOs in Algeria, but in December 2000, the country banned the importation,
distribution, commercialization and utilization of any GM plant material (Moola & Munnik,
2007). National regulatory framework is being prepared with the cooperation of international
organizations such as the United Nations Environment Programme of the Global
Environment Facility Coordination (UNEP-GEF). The UNEP-GEF project was created to
support developing and underdeveloped countries in building national biosafety laws.
Participant countries can potentially impact trade flows in the near future when they fully
implement regulatory frameworks. The project has been inspired mainly by precautionary
approach being the regulations based on the case-by-case procedure. Table 11 shows
countries participating in the project.
56
Table 11. UNEP-GEF Countries
Africa Asia and the Pacific Central and Eastern Europe Latin America and the
Caribbean
Algeria Azerbaijan Albania Antigua and Barbuda
Benin Bangladesh Armenia Argentina
Botswana Bhutan Belarus Argentina
Burkina Faso Cambodia Croatia Bahamas
Burundi Cook Islands Czech Republic Barbados
Cape Verde Indonesia Estonia Belize
Central African Republic Iran, Islamic Republic of Georgia Chile
Chad Jordan Latvia Costa Rica
Comoros Kazakhstan Lithuania Dominica
Congo Kiribati Malta Dominican Republic
Congo, Democratic
Republic of the
Korea, Democratic People's
Republic of Moldova, Republic of Ecuador
Côte d'Ivoire Korea, Republic of Romania El Salvador
Djibouti Kyrgyzstan Serbia Grenada
Eritrea
Lao People's Democratic
Republic Slovakia Guatemala
Ethiopia Lebanon Slovenia Guyana
Gabon Micronesia
Macedonia, The Former
Yugoslav Republic of Haiti
Gambia Maldives Turkey Honduras
Ghana Marshall Islands Ukraine Jamaica
Guinea Mongolia Nicaragua
Guinea Bissau Myanmar Panama
Lesotho Nepal Paraguay
Liberia Niue Peru
Continued…
57
Africa Asia and the Pacific Central and Eastern Europe Latin America and the
Caribbean
Libya Arabic Palau
Saint Kitts and Nevis
Madagascar Papua New Guinea Saint Lucia
Mali Philippines
Saint Vincent and the
Grenadines
Mozambique Samoa Suriname
Morocco French Solomon Islands Uruguay
Niger Sri Lanka Venezuela
Nigeria Syrian Arab Republic
Trinidad and Tobago
Rwanda Tajikistan
Sao Tome and Principe Thailand
Senegal Tonga
Seychelles Tuvalu
Sierra Leone Vanuatu
Sudan Viet Nam
Swaziland Yemen
Tanzania, United
Republic of
Togo
Zimbabwe
Source: Unep-GEF (2014)
The quantity of countries establishing their regulatory frameworks reveals not
only potential risks related to strict regulations, but that regulations based on risk assessment
prior to the first importation or cultivation of a certain variety is a practice of few countries –
not disregarding the fact that these few countries accounts for more than 90% of global trade
of soybeans.
58
1.3 Remarks
In sum, the history of production and trade of GMOs can be told as an innovation
in the seed industry that increases operation risks across the whole value chain. Commercial
risk arises when end-consumers from important markets develop considerable levels of
“hatred” against the technology. When growers and other grain handlers sell to end-
consumers in other countries, the conflict became a problem of international trade. In this
context, processor and logistic operators will become central agents to manage the
commercial risks related to adoption of new developments. That is because they are
strategically between growers, willing to adopt the technology, and adverse feed and food
manufacturers demanding conventional grains in foreigners’ countries. Thus, the
concentrated ownership in grain handling and processing, and partnerships with the largest
seed corporations, is appropriate to manage gains of scale and mitigate commercial risks.
The government through it regulatory agencies ended up mitigating risks, as
private coordination of produced varieties in producing country would only by a chance
minimize all the production and commercial risks involved in growing varieties not approved
in destination markets. However, regulation responded more to technology constrains and
levels of hatred than to commercial risk itself. Anyway, if no control in variety approvals was
taken a large range of different varieties being produced in one country, and given the high
costs of IP, to segregate varieties by destination would be very costly.
The most known examples of international conflicts related to GMOs are the
technology rejection by the EU – both in terms of end-consumers and regulatory frameworks
– and high rates of technology adoption and large number of approval of GMOs varieties in
the U.S.. On the other hand, China was an alternative destination for GMOs produced in
United State. Brazil and Argentina instead, took more cautious decisions upon the production
of GMOs.
It is worth noting that first clear discrimination in international markets was
between conventional and genetically modified products. Along with decreasing availability
of conventional seeds, traits differences are setting the ground for differentiation within
different GM-seeds instead the former antagonism between GM and conventional crops.
From a broader perspective, when one looks back in history of agro-industry
innovations, we can see a lot of important developments on production techniques leading to
huge gains of productivity perceived by consumers only in terms of price cut-downs. But,
nothing ever made consumers believe that a soybean could actually be an imperfect substitute
59
for a soybean, not because they taste or look different but because they were produced from
two different seeds. By no means a consumer was concerned about seed used to produce
grains processed to produce food.
This point is noteworthy because increasing consumer power is an international
reality and it is creating a series of conflicts in agricultural markets. This is very important
issue to think future impacts on agricultural trade, as it seems a structural change in
consumption drove by increases in world income.
60
CHAPTER II – TECHNOLOGICAL EFFECTS AND TRADE THEORIES
In this chapter we pursue two related objectives. We start by seeking for empirical
evidences technological impacts on trade. Then, we seek to assess how existing theories of
trade and technology change can shed some lights on the framework drawn from systematic
observation of real data. This organization of the ideas is justified by relatively new set of
challenges we are dealing with, and the absence of a theoretical framework that fully applies
to a case marked by backwards technology effects on trade.
As we have seen in Chapter I, the EU and China figure among the major
importers of soybean and soybean by-products and they have taken different positions
regarding the regulation of technology as well as consumers in these countries tend to have
opposing views towards benefits of GMOs. On the other hand, Brazil, Argentina and the U.S.
also enacted divergent measures to regulate cultivation of GMOs in their farmlands.
We believe that the combination of giant markets rejecting the technology and
asynchronous international diffusion led to noteworthy changes in bilateral trade pattern.
Thus, a look at the trade flows between (non) adopters and (non)“haters” after 1996 seems a
good place to start from. In addition, a look at studies pointing out to the effects of
technology adoption and strict regulations on trade reinforces our thesis. These points are
addressed in the first part of this chapter ( section 2.1).
We dedicate the second part of this chapter to the theoretical investigation (section
2.2). We describe the multi-country Ricardian model presented by Eaton and Kortum (2002),
the firm heterogeneity by Helpman, Melitz and Rubinstein (2008) and, last but not least, the
set of main ideas from technological gap theorists, such as Posner (1961), Maggi (1993) and
Dosi et. al. (1998; 2015)
It is possible to note that an initial graphical analysis reveals the most outstanding
impacts of technology in terms of replacement effects in trade flows. Also, as expected, none
of the theoretical frameworks can fully addresses the case of trade in GMOs. Yet the models
provide several helpful insights and highlight the need for developments to consider a case of
adverse technological effects on trade. These and other findings are discussed in section 2.3.
61
2.1 Evidences of Technological Effect on International Trade of GMOs
At first, let’s focus on the big picture. As broadly known, the EU and other
Europeans countries, huge importers of soybean and by-products and hereinafter only the
EU, have raised several worries and trade barriers against the free trade of GMOs. Brazil, one
of the three giant producing countries, will delay cultivation approval of GMOs for almost a
decade. Argentina, instead, will approve just a few varieties for production, which is also
approved in the EU, but growers will rapid adopt the new technology. The U.S. will approve
more varieties of soybean and face a rapid internal adoption process during the first years of
commercial release. Our last protagonist will be China, a giant importer of soybean, which
makes lesser distinction between the “old” and “new” technology used to produce soybean
and by-products.
As we are dealing with big players and very marked positions towards the
technology, at least some flows rearrangement should be seen in bilateral trade series. Of
course, we are not saying that technological effects can explain alone trade patterns after
1996. Instead, we are arguing that besides traditional production and trade costs, the GMOs
rate of adoption should also be considered as one of the key drivers of preference for certain
sources of soybean.
Accordingly, the big picture depicted at the charts below reflects a twofold reason
for the same problem. On the one side, most opposing processors or consumers will demand
a GM-free product (IP) making minor distinction across different genetically modified
varieties. IP capability and low level of adoption will be key to avoid related commercial
losses. This first source of conflicts is primarily related to end-consumer perception seeing
GMOs as unsafe regardless of the type of gene inserted or technique used to produce them.
Policymakers played a key role here by enacting mandatory labeling rules that allow
discrimination among conventional and GM products, but consumers will keep denying the
product even if it can be legally imported and used in the manufacturing of food and feed.
On the other hand, the case-by-case approach for risk assessment will lead to
asymmetric and/or asynchronous approval, which will affect the commercialization even if a
processor is indifferent or in favor of using the technology in food and feed production – and
here is the second source of conflict. Put in another way, ships containing a non-approved
event will be simply refused at arrival port no matter what grains handlers and processors
think about the technology. Policymakers play a more conspicuous role in this type of
conflict as they dictate the pace of approval assessment and can ban entry of unauthorized
62
GMOs into their countries, no matter the general public perception of GMOs. An overlook at
the volumes and origin of imports of European countries16
from 1990 to 2014 (Chart 1) can
reveal some interesting trends. First of all, it is important to remember that much of the
imported soybean is intended to be crushed and sold domestically as soybean meal and/or oil
to feed and food manufacturers and biodiesel millers. As these end-consumers are differently
affected by labeling regulation in the EU, they also have different incentives to show the flag
in favor or against the technology depending on the general perception of society as a role of
the technology.
As we have mentioned before, animal protein feed with GMOs are exempt from
mandatory labeling in the EU. Also, there are likely much less worries about “transgenics”
when there are not intended to human consumption – having no records of public pressures
against the use of GMOs as feedstock, for instance.
Thus, both the strict regulation and end-consumer rejection is expected to impact
on trade. In presence of certain level of hatred, the domestic crushers will try to mitigate risks
by decreasing the amounts of GMOs in their facilities, and obviously, even if they desire,
they cannot purchase varieties not approved for importation in their countries – unless
monitoring systems are poor.
16
European countries comprise not only the European Union (EU) states members but also other economies in
the continent, such as Switzerland, Belarus, Rep. of Moldova, Russia Federation, Iceland, Norway, Ukraine,
Albania, Bosnia Herzegovina, Serbia, Montenegro and Macedonia (FYROM). European countries out of the
EU, in general adopted very similar frameworks when compared to EU members (P. R. S. Oliveira et al., 2013).
By adding all the countries in the continent we can disregard the year of entry into the bloc to the purposes of
this study.
63
Chart 1 – EU’s Soybeans Imports by Source (Billions of USD from 1990-2014)
Source: Elaborated by the authors based on COMTRADE and BACI data17.
Cutting down imports of GMOs to a possible extent is rational if we assume that
indifferent customers wouldn’t mind about buying conventional varieties at the same prices.
As we are going to see also in this section, relative prices equal to 1, considering
conventional and GM grains as imperfect substitutes, is expected under some simulation
conditions as well as it is predicted by some empirical works.
17
Data from 1995 to 2012—underlying years for this case study—were gathered from Baci database. This is a
reconciled world trade database developed by the CEPII at a high level of product disaggregation. The original
data come from United Nations Statistical Division (Comtrade database). CEPII uses original procedures to
reconcile declaration of exporters and importers (for details see Guillaume & Zignago, 2010). Additional years,
1990-1994 and 2013-2014, come from the original Comtrade database, since Baci do not comprise years before
1995 and post 2012.
64
Argentina’s shares of soybean markets are reduced not only in the EU, especially
because of the national strategy favoring exports of soybean meal and oil – using export
taxation for soybeans18
. Yet, data in Chart I show that Argentina’s exports to the EU felt
mainly after 1996. They dropped from a half billion of USD to virtually zero between 1996
and 2005.
Nonetheless, its clear that the U.S. lost market shares of European markets,
whereas Brazil increased its exports into the continent. As it can be seen, Brazil’s exports
have been increasing since the early 1990s, and continued growing after 1996. Exports spiked
from approximately 1 to 3 billion of USD between 1996 and 2005. Conversely, the U.S.
exports to Europe began falling away right by 1996, going from almost 3 to less than 1
billion of USD between 1996 and 2005.
Taking into account that the U.S. is the major global exporter of soybeans this
consistent decrease along with sustained growths of Brazilian market shares is a strong
indication of backward technological effects on trade – more precisely a negative effect of
demand rejection affecting the U.S. soybean exports.
Naturally, one should also consider other drivers for export growth or decrease,
such as the relatively lower costs of production in Brazil – as pointed in chapter I. In this
particular case, variable trade costs related to distance seem to play a less important role as
both countries have access to the Atlantic Ocean, and U.S. distance from Europe is smaller.
Exchange rates can also play a role in favor of South American traders as production costs
are expressed in local currencies and soybeans are traded in USD19
. But, in spite of these
other effects, we cannot deny that the replacement of market shares is very timely marked to
this case in particular.
In addition, some changes in data after 2005 – the first year of legal growth of
GMOs in Brazil – will also back up the idea of strong technological effects occurring in
trading with Europe. Data shows that at the same time the adoption of GMOs had increased
abruptly after 2005 in Brazil, other producing countries have increased their market shares in
Europe.
18
Mr. Macri, the new president elected in 2015 is lifting some taxes on grains’ exports that are expected to have
impacts on international grains markets. 19
Indeed, in spite of other analysis pointing to negative effects in U.S. exports related to currency devaluations
in South America, when we consider all the existing and potential flows of trade this effect changes sign and
decrease in significance.
65
Canada, Ukraine and Paraguay20
are the most noticeable examples of this trend.
This new replacement of sources seems to have a link with the technology adoption as these
countries have approved just a few or none GM varieties for cultivation and/or have
relatively low rate of technology adoption along with good IP capacity. Aggregated soybean
exports of these three countries summed up nearly 4 billions of USD in 2013, more than the
value of Brazilian exports at the same year.
On the other hand, the same clear replacement pattern does not hold for soybean
meal and oil trade, especially because the U.S. plays a secondary role in these markets –
making difficult to have counterfactual for large number of approved varieties from the
supply side.
Another reason for different pattern is that end-consumers in this case are mainly
feed manufacturers. The exemption of mandatory labeling for animal protein feed with
GMOs makes agents from the meat, poultry and diary industry less afraid of boycotts. Only a
small fraction of breeders demands GM-free ration, then the impact of importing meal
deriving from GM-soybean does not seem a “big deal”. IP soybean demand is estimated at
20% of all soybean consumption in Europe (FAS-USDA, 2015).
These facts together indicate that trade of soybean meal is especially impacted by
asymmetric approval instead of end-consumer rejection – especially by the absence of
mandatory labeling requirements.
Notwithstanding, some few indications of technological effects can still be drawn.
First, as there was no mismatch of approved varieties between Europe, Brazil and Argentina,
feed manufacturers were taking reduced risks by importing large amounts of soybean meal
from these countries (more than 8 billions of USD in 2014). Second, instead of a cut down in
market shares of major GM producers during the second half of the 1990s, Brazil lost market
shares after 1997, and lost the leading position in this market in 2005. On the other hand,
Argentina and United States enlarged their market shares of European markets after 1996.
This arrangement open a room for considering a second type of technological effect, which
we have been calling “technological gap” – or the positive effect of relative efficiency gains
or losses related to technology adoption.
20
As we have seen in chapter 1, Paraguay has approved only one variety of GM-soybean for cultivation as well
as has low levels of adoption. Canada has approved a plenty but has improved capacity to IP and adoption rate
is around 50%. Ukraine is considered a GM-Free country.
66
In the case of soybean oil, technological effects are even more ambiguous. The
relatively small Europe’s imports of soybean oil21
and minor participation of the U.S. in
global markets may be an explanation for such ambiguity. The EU experienced an oil-
importing boom during the 2000s, so data were split into two charts for clarity.
Argentina and the United States hold larger shares of European markets in the first
half of the 1990s. However, in spite of continued exports of the US and Argentina, Brazil was
the unique country of the G3 that enlarged its market share from 1996 to 1997.
Noteworthy, soybean oil demand will take off in the continent from 2002 to 2014,
and international prices will peak. Increased demand and prices are a result of new uses –
such as feedstock for biodiesel – and the outbreak of the international financial crisis. During
these years, Brazil and Argentina will take over the European markets while U.S. shares will
keep relatively low. Biodiesel production in Europe spiked from the worth of 2 to 10 billion
tons between 2004 and 2013 (European Biodiesel Board, 2015). It was not by chance that
soybean oil imports boomed at this period.
As we have discussed in Chapter I, actors in European countries differ in terms of
their views of technology benefits. From that, a look at how agents in the most opposing or
supporting countries choose their soybean suppliers can contribute to make some trends
clearer. Chart 2 shows importing patterns for different groups of countries in Europe.
21
In Chapter I we have seen how soybean oil is considered a residual product of soybean meal extraction and
how inferior it is considered for cooking purposes when compared to other vegetal sources of oil.
67
Chart 2 – European Countries’ Imports by Cluster
Source: Elaborated by the authors based on COMTRADE and BACI data
Although all the groups somehow replicate the pattern of aggregated data, i.e.
decreases in shares of U.S. soybean along with increases of Brazilian shares, the impacts
were different in terms of pace of replacement and differences between market shares of one
or another country.
During the second half of the 1990s, the Adopters reduced less their imports from
the U.S. and kept them up with imports from Brazil. After 2005, however, there was a clear
preference for the Brazilian soybean – remember that the second half of the 2000s was the
period of rapid adoption in Brazil. Imports from Canada and Ukraine have been increasing in
the last years, but smaller in pace and proportion when compared to most adverse countries in
Europe.
68
For the Conflicted group, however, decreases in imports from U.S. was sharper
from 1997 to 2000, in spite of the imports from Brazil and the U.S. have varied at the same
pace during the 2000s. After 2009, Paraguay and Canada gained expressive market shares in
these markets, and in greater proportion when compared to Adopters. This indicates that
more recent replacement of Brazilian imports for soybean coming from countries with higher
proportion of conventional soybean or reduced number of approved varieties is more
remarkable in this group than in the Adopters one.
For the most opposed countries two points outstand. First, as expected there is a
sharpen decrease of imports from the U.S. along with sustained increases of Brazil’s exports.
Second, there is a marked fall of Brazilian market shares after 2005 – excluded results for
2008 – at the same time Ukraine imports spike. This replacement may be related to high
levels of adoption in Brazil and the image of GM-free country of Ukraine – but here factors
as proximity and other trade barrier may be playing an important role as well.
Lastly, other European countries also reduced their U.S. imports replacing them
initially by Canadian soybean, but imports from Brazil spiked after 1999. More recently the
trend of importing more soybean from Canada and Paraguay emerged. Conversely, by
looking at trade between China and the major global suppliers we can see a different pattern
– see Chart 3.
69
Chart 3 – China’s Imports by source (tones 1990-2014)
Source: Elaborated by the authors based on COMTRADE and BACI data
China is much larger than Europe in terms of amounts of imported soybean, but
the high national crushing capacity reduces considerably the needs of importing soybean
meal and oil. China imported more than 40 billion of USD in soybeans in 2014, whereas
Europe imported 8.3 billion of USD.
In spite of differences in production and trade costs Brazil and U.S. exports into
China grew as long as the country expanded its internal crushing capacity. Noteworthy,
Argentina also played an important role in the Chinese market. As it can be seen, the
financial crisis affected less the processing industry in China in comparison with Europe22
.
22
It is beyond the focus of this study, but this consideration raises concern about specialization of Brazil in
serving a mature instead of a booming market and effects on sustained growth of exports.
70
Thus, it is not possible to drawn any visible parallel between technology adoption
and marked falls in imports from big adopters. Note that there is no replacement of sources of
soybean in 1996. Moreover, Brazil never took the leading position in soybean markets in
China. It didn’t happen because the U.S. not only has yield advantages over Brazil as well as
trade costs to export to China are in general reduced because of exit to the Pacific Ocean.
Therefore, the absence of major trade barriers, such as a demand rejection,
guaranteed the relatively common effect of “technological gap” – i.e. countries innovating or
adopting new and better technologies will have increased shares of international markets.
Another difference of China is the negligible shares of minor producers, such as
Paraguay, Canada and Ukraine. This is a result of large imported amounts and also from the
lack of a substantial market demand for conventional soybeans.
From 1995 to 1999 imports of soybean meal boomed in China. Brazil and
Argentina were the major suppliers during this period as well as in the following years. The
shares of Chinese markets, once more simply reflected the global participation of major
producing countries in world markets. Shares of soybean oil market in China also kept the
same proportion of major countries shares of global markets, i.e. Argentina kept the leading
position followed by Brazil and, then the U.S.
A simple Constant Market Share23
analysis carried out by Oliveira et al. (2012)
can give out some interesting issues about the drivers of the growth of soybean exports of
Brazil, the U.S. and Argentina across the years.
In the partial adoption period (1995-1997) competiveness effect was the key
driver of Brazilian exports growth whereas the world imports was the key driver of U.S.
exports growth. In the dual-market period (2000-2002) U.S. global exports of soybean grew
only by 11% while Brazil faced a growth of 178% and Argentina of 246%. While
competiveness effects can explain up to 69% of Brazilian exports growth, it can explain up to
(-389%) of U.S. exports growth in the same period. It means that if not by the strong growth
of global imports of soybean, which explains up to 442% of U.S. growth, the country could
have lost meaningful shares in global markets during this period because of the lack of
“competitiveness”. This can be seen as a strong sign of high levels of technology hatred
negatively affecting U.S. competitiveness. It is worth noting that the competitiveness effect is
23
The CMS technique assumes that a country keeps constant it market shares being any change in the trade-
flows a result of three basic effects: growth of world trade, destination market and competitiveness.
Competitiveness is a residual effect and can have a number of explanations such as reduced production or trade
costs or, in the case studied, certain level of hate against GMOs, see (Carvalho, 1995; Tomich & Leite, 1999).
71
the amount of growth variation that cannot be explained neither by growth of world imports
nor by growth of destination markets of a particular exporter.
However, the competiveness effect will change in favor of the U.S. after 2005. In
the post adoption period (2007-2009) competiveness effect will be positive to the U.S. (31%)
and negative to both Brazil (-39%) and Argentina (-47%).
Most of the empirical studies on trade of GMOs focused on regulation
mismatches between trade partners leading to changes in trade flows in terms of volume and
prices – as they analyzed the problem from the partner conflict perspectives. Indeed, the
standardization versus differentiation of international trade basis, including regulatory issues,
has been a particularly important starting point to address changes in trade flows since they
impact the overall transaction costs. It can be assumed that the goal of standardization is to
realize scale effects of world product mandates (Feinberg, 2000), reducing transaction costs
and risks or maximizing profits (III & Kashlak, 1999; Isaac, Perdikis, & Kerr, 2004; Meyer,
2001; Rugman, 1976). Achieving these benefits requires the centripetal forces of
international convergence, including production and process standardization (Griffith, Hu, &
Ryans, 2000). Recent research has focused on the general challenges facing the international
standardization of technology, as well as the challenges facing the international
standardization of biotechnology (Madhok & Osegowitsch, 2000).
From a neoclassical perspective, it can be said that competing with the centripetal
forces of standardization are the countervailing centrifugal forces of public and private
policies that threaten the scale benefits by fragmenting foreign markets (Isaac et al., 2004).
The idea of attracting and repealing forces of trade is key from the empirical and
theoretical perspective. In empirical works they are considered in the form of iceberg costs
and cultural and regulatory differences among trade partners. In theoretical models they are
usually referred as geographical costs – as we are going to see latter on this chapter. Many
empirical studies have been pointing to considerable impacts of regulatory heterogeneity on
bilateral trade. These studies also built index and techniques to better measure this
heterogeneity. In agriculture, in particular, authors usually consider these differences as non-
tariff barriers (NTB) increasing trade costs for most dissimilar partners (Burnquist, Shutes,
Rau, Souza, & Faria, 2011; Vigani & Olper, 2013; Winchester et al., 2012).
Vigani et al. (2012) performed a gravity model to analyze the impact of
technology on bilateral trade in 2005, 2006 and 2007. The gravity variable was the gap
between the regulatory frameworks of trade partners measured by an index estimated by the
authors.
72
The magnitude of the estimated coefficient implies that one standard deviation
decrease in the GMO dissimilarity index (=0.188) increases exports by 33%, all else
remaining equal. Thus, the effect is not only statistically significant but appears also relevant
from an economic point of view. Moreover, the results suggest that labeling is the most
detrimental dimension to trade followed by slow and complex approval process and
traceability. Yet, it is important to say that some studies point to low-level of allowed
adventitious presence and asynchronous24
approval as key drivers of conflicts in international
markets (Faria & Wieck, 2015; Gruere, 2011; Kalaitzandonakes, Kaufman, & Miller, 2014;
Stein & Rodriguez-Cerezo, 2009).
Although very aligned with their purpose of analyzing “regulatory distances” and
trade, Vigani et.al. (2012)25
estimated a cross-section model and their index do not vary
across the time. Thus, they lost the dynamical effect of a change in regulation and the impact
on trade. We are primary interested in these effects of a dynamical increase or decrease of
proximity, which we only can assess with panel data – as we are going to see in Chapter 3.
A more complete index, taking into account not only biosafety regulatory
dissimilarity index but also the gap between approved varieties were built in a recent study,
also pointing to significant impacts on trade. In this study, however, the index varies across
the time, capturing the continued tension between importer and exporters. Considering the
years of 2000, 2009 and 2012 it is possible to see that restrictiveness index is higher for
European countries and South America, and lower for the U.S. (Faria & Wieck, 2015).
Disdie & Fontagné (2010), in turn, studied the impact of the EU de facto
moratorium and bans of other European countries on the exports of complainants (Canada,
Argentina and US) and non-complainants in the WTO dispute from 1995-2005. For all
agricultural products considered26
, estimated coefficients on the “EU moratorium and/or
product-specific measures” variable are negative and statistically significant. As a result, this
econometric specification shows that EU measures on GMOs reduced Argentina, Canada and
US exports of maize seeds by 89.4% on average. Regarding national bans, it appears that
only the Austrian ones on maize (seeds and other) and the Italian one on maize seeds do not
have a significant impact. All other national safeguard measures affected Argentinean,
24
Asynchronous approval means the short-term gap between years of approval across different countries. Long-
term gaps may be seen as asymmetric approval. 25
A more recent work found that regulatory strictness seems to be endogenous. Lack of comparative advantage
in agriculture, strong presence of rural population, stringent environmental laws and spread media ownership in
rich countries led to strict regulation of GMOs (Vigani & Olper, 2013). 26
Maize seeds, maize, oilseed rape, cottonseeds, starch residues and other preparations of a kind used in animal
feeding.
73
Canadian and US exports. Noteworthy, recent studies show that policymakers from different
member states have kept their positions regarding the technology by voting in a favor or
against new approvals in a steady way (Smart, Blum, & Wesseler, 2015).
However Disdier & Fontagné (2010) did not analyzed the soybean trade because
they focused on potential regulatory effects, and soybean was the only crop that was
approved before the de facto moratorium initiated in 199827
. Our study goes to another
direction, showing that impacts beyond the differences in regulatory positions can give out
many interesting stylized effects of technology adoption under certain levels of hatred.
Moreover, the soybean case seems a unique opportunity to highlight how asymmetries in
adoption and acceptance can impact bilateral trade given the international concentration of
markets.
Anderson & Jackson (2004), by using a GTAP model with neoclassical closure,
pointed out that since 1998 when the EU implemented the moratorium, GM adopting
countries have lost EU market shares to GM free suppliers, particularly Brazil for maize and
soybean and Australia and Central Europe for rapeseed.
On the other hand, there are evidences that Canada’s rapeseed and US corn sales
to the EU were successfully shifted to other markets. Market losses occurred but only over a
short period, and globalization quickly offered new export opportunities to GM producers
avoiding exporters incurring in major economic losses via demand diversification (Smyth,
Kerr, & Davey, 2006; Stein & Rodriguez-Cerezo, 2009). This shift to less adverse markets
seems to be the case of soybean markets when we consider the replacement of U.S. by
Brazilian exports in the second half of 1990s, along with growing imports of China.
There are also special concerns in literature about bans and technology diffusion
especially for the case of developing countries. The general idea is that GM technology
diffusion was hampered to a certain extent by major markets rejection (K. Anderson, 2010).
Our description of regulatory frameworks outlining risk assessment procedures in Brazil and
Argentina corroborates this assertion to the extent that these countries deliberately used
political issues to approve new varieties of GMOs afraid of high commercial risks. However,
consider that only the commercial risk was driving the approval process is a very simplistic
assumption.
27 The EU pressured by national interest groups did not approve any new event between 1998 and 2003. This period was
defined by literature as the de facto moratorium. The controversies and conflicts that arose from this period were discussed
at the DSB (Dispute Settlement Body) under WTO.
74
Besides effects on bilateral market attraction and repealing one can think of
premium prices for IP grains. Foster (2010), Parcell & Kalaitzandonakes (2004) and Bullock
& Desquilbet (2002) carried out empirical analyses on prices and dual-market system28
.
Foster (2010) points out that apart from consumer attitudes, the key driver of price
premiums are mandatory labeling of GM products in some key grain consuming countries
(particularly high-income countries) higher production costs for non-GM crops and the cost
of IP. The author examines whether premiums exist for some crops and countries, assuming
European Union and Japan as major markets for certified non-GM soybeans, while Brazil,
United States and Canada are the major suppliers. But accordingly to data presented by the
author, Brazil certifies only a small amount of total conventional soybean internally
produced29
. Taking into account the increases of Canada shares in Europe, it is possible that
averse importers are choosing more reliable and with higher certifying capacity sources for IP
soybeans.
Based on data for premiums paid for Illinois growers, EU import prices of
soybean meals and Tokyo Grain Exchange (TGE) future markets, Foster (2010) argues that
there is enough evidence to assume that premiums were paid for non-GM grains. Illinois
growers traded their grains over and above normal cash prices at harvest time in autumn
between 2004 and 2008. Moreover, the author argues, based on United Nations (UN) data,
that imports price of Brazilian soybean meal into the EU had averaged 4 to 9 per cent higher
than soybean meal imports from Argentina between 1996 and 2008. Considering that from
February 2001 to August 2009 the future prices for IP soybeans had exceeded 30% of the
GM-soybean, author concludes that demand for IP product was increasing at that time.
Although there are no many studies looking for actual demand size for non-GM, the
persistent voting position of the EU towards technology can be seen as an indication that
conflicts are not going to be solved in the near future.
Bullock & Desquilbet (2002) analysis of TGE data also found similar results.
According to the authors, conventional soybean prices per ton averaged $27.5 higher than
GM-soybeans price between May 2000 and September 2001 – calculated as the difference
between monthly prices of non-GM and GM-soybeans. Noteworthy, in accordance with our
discussion in Chapter I, the authors found that $7.50 on average was the premium paid to
28
Unfortunately, to the best of our knowledge there is publication of recent study looking at empirical evidences
of price premiums. This is likely because of lack of macro-level evidences of premiums as highlighted by an
unpublished work by Oliveira et al. (2013). 29
Brazil certified only 2.5% of all non-GMO grain domestically produced in 2008, according to data presented
by Foster (2010).
75
contracted farmers while $20 remained with grain handlers. This can be seen as a reflection
of the higher complexity involved in intermediary activities, as we have showed in chapter I.
Parcell & Kalaitzandonakes (2004) carried out a slightly different analysis. They
studied shifts on prices by analyzing responses of Chicago Board of Trade (CBT) and TGE
future prices of non-GMO soybeans to large food manufacturers and retailers announcements
intentions to remove bioengineered ingredients from their branded products. They agree upon
the thesis that small demand shifts in niche markets with limited size would result in only a
small price impact on the conventional commodity. However, if the demand shift is
significantly large, then price impact may be noteworthy. Three models were estimated
following a GARCH (1, 1) framework – which is more indicated for periods of varying
volatility. In Models I and II the dependent variable is the percentage rate of return of CBOT
futures price between open on day t+1 and settlement on day t-1. Model three has as
dependent variable the percentage rate of return of TGE non-GMO soybean futures price
between settlement on day t+1 and settlement on day t-1. Empirical results from “Model I”
suggest that soybean futures prices did not respond to ban announcements. The joint F-test on
the summation of the coefficients for the five days prior to and five days after the
announcement is statistically significant; however, the summation of price changes around
the announcement is not statistically significant. This further finding suggests that while there
is some evidence of a soybeans future price reaction, the market quickly filtered out the
information.
In model II, when each announcement is analyzed separately, they found that
there were no significant differences in the impact of individual bans and so no individual
effects of firms can be seen. In model III, estimation returned significant positive coefficients
(at p<0,01) to TGE conventional rate of return and futures contracts rollover. Neither the firm
ban announcement coefficient nor the summation of coefficients accounting for the rate of
return the five days prior to and the five days after the announcement are statistically
significant. This indicates that the impact of ban announcements by key food companies, as a
proxy for the size of the non-bioengineered soybean market, was not considered large enough
by the market to matter.
Besides empirical studies dealing with the problem of trade of GMOs, there are
also some simulation models.
76
Moschini (2004)30
developed a partial-equilibrium model to analyze implications
from the introduction of genetically modified products into international markets. Results
show that by imposing mandatory labeling regimes, GMOs exports into Europe decrease. In
other words, labeling could become a ban on imports depending on the level of segregation
costs.
Choi (2010), Lence & Hayes (2001) and Desquilbet & Bullock (2009)
investigated the international trade of genetically modified products, modeling a market for
close substitutes under market cleaning and rational agent assumptions. Choi (2010) and
Lence & Hayes (2001) made use of comparative statics while Desquilbet & Bullock (2009)
estimated a simulation model allowing for multiple equilibriums.
Choi (2010) set the United States as a monopolist GM producer exporting into
Europe, which is an importer of GM food, and a producer of conventional crops. Even
though the author is mainly concerned with the effect of a ban on the land rental prices31
,
some interesting intermediate propositions arise from the paper. According to author, GM
crops require extensive R&D and are not easily copied by others. On the other hand, many
firms grow traditional crops given that there is not entry barrier for this market. However, due
to the close substitutability between the two goods, the U.S. has only a weak monopoly
power in GMOs market.
The author argues that a restrictive quota imposed by European markets on GMOs
imports makes the price of GM food higher and decreases consumer surplus in Europe. Since
goods are close substitutes, quota on GM product also makes the price of traditional food
higher – via cross price elasticity of demand. Thus, the quota increases the producers’
surpluses in the USA, via GM-food increased price, but doesn’t increase the surpluses for
traditional producers in Europe given the perfect competition in traditional farming. This
framework can be very close to what happen with crops grown in Europe, but that is not the
case for soybean.
Lence & Hayes (2001)32
, also considering goods as imperfect substitutes, state
that for certified grains and fixed supply, i.e. short run, the relative prices adjust for market
cleaning. They structured a market with two types of consumers – one is indifferent and the
30 In the model, innovators hold property rights, farmers are competitive, and some consumers believe that GM food is
inferior in quality when compared with traditional food. Identity preservation generates additional costs to the whole market
(GM and traditional) via segregation costs. 31
The author concludes that given perfect competition in the market for traditional crops only land rental prices
can be maintained high in the long run. 32
They also assume that consumer preferences categorize consumers into broad homogenous groups, for
example, feed industry is indifferent to GMO and non-GMO and food industry prefer non-GMO grains.
77
other is willing to buy non-GM grain at any relative prices – and two types of firms – one
producing GMO and the other producing non-GMO. Authors also explicitly bring IP costs
into the model.
They found that premiums only exist when the GM output is relatively large when
compared to non-GM output and demand for Non-GM grains are also relatively higher.
When the conventional supply is relatively large, the equilibrium conditions call for relative
prices to be equal to 1. Premium prices arise as a required incentive to sustain conventional
production under higher costs – i.e. IP and production costs – when there are consumers with
strong preference for Non-GM grains and supply is relatively small. Moreover, IP costs may
lead to part of non-GM product being commercialized without certification.
Desquilbet & Bullock (2009), by exploring who pays the costs and who reaps the
benefits of maintaining a dual-market system, estimated a simulation model in which both
type of grains are produced as well as a third good (alternative good) is also produced. The
model allowed for six equilibriums classes differing in which type of goods are produced and
if premiums are positive or zero. They explicitly considered directed and indirect externality
costs33
of transportation in the model as well as the endogenous production costs of each type
of grain. By externality costs they mean the scale diseconomies emerging from higher
segregation.
According to authors, producers take into account production costs, externality
costs of transportation, direct IP (identity preservation) costs, the technology fee and prices of
GM and non-GM products to make their decisions on production levels. Net prices, instead,
strongly depend on the level of hatred and IP costs, since it is the market price less total IP
costs.
If GMO technology is already being commercialized, the introduction of a small
amount of “hatred” causes the IP demand curve to “appear” and the regular demand curve
(i.e. the curve representing indifferent consumers) to shift-in. If there are no costs of IP, the
IP grain price and regular grain price – i.e. non-segregated grains - remain equal to the
regular price brought about in the equilibrium with GMO technology and without hatred.
Thus, given that GMO technology exists, and there are no costs to identity preservation, the
economy moves from a state with no hatred to a state with a small amount of hatred without
affecting prices, producer welfare, or consumer welfare (Desquilbet & Bullock, 2009b) .
33 See Oliveira, Silveira, & Alvin (2012)for further information on segregation and logistic costs effects
resulting from dual-market systems.
78
The high IP costs, instead, allow multiple competitive equilibriums. Generally, the
equilibrium depends on the size of the channels and premium prices may be positive or zero,
depending on the total costs (IP + technology fee + endogenous costs) and the level of hatred.
High level of hatred in comparison to total costs may lead to equilibria with both regular
producers – i.e. GM producers and Non-GM producers whose don’t segregate grains – and IP
producers and price premiums.
When externality costs are too high, only very high levels of hatred could allow
for a dual-market system, being equilibrium only possible with premium prices regime to pay
off high IP and opportunity costs. Otherwise, too high opportunity costs for non-GM
producers may lead dual-market to fade in spite of the level of hatred. Equilibria with zero
price premiums – i.e. relative prices equal to 1 – may occur when seed market is a monopoly
and IP costs are the same for IP producers and regular producers34
– given the significant
output of IP grains.
In terms of benefits of planting GMOs there are many studies corroborating
economic gains ( see Bärwald Bohm et al., 2014; Brookes & Barfoot, 2014; Chavas, Shi, &
Lauer, 2014; Qaim & Zilberman, 2003; Sturges et al., 2003). Authors usually point to less
expansive and easy control of weed, higher yields and reduction of adoption of tillage
systems.
Yield gains are the most questionable benefit being possible to find evidences of
negligible or negative effects of technology. However, many studies points to higher yields
especially for developing countries in which prior pest controls were poor(Qaim &
Zilberman, 2003).
A recent studied estimated that economic gains reached 116.6 billion of USD
from 1996 to 2012. For the soybean case, there was a cut down in production costs, mainly
through reduced expenditure on weed control (herbicides). In South America, additionally,
there were gains associated with the adoption of no tillage production systems, shortening the
production cycle, so enabling famers to rip benefits of growing a second crop in the interval
of two seasons. They estimate that gains for farm incomes amounted 4.8 billion in 2012
(Brookes & Barfoot, 2014).
It is important to note that technology costs vary across countries and so cost
savings also differ across countries. In Argentina technology costs vary from 2-4 dollars per
34
Authors also discuss the different equilibria when seed market is monopolist or competitive. Zero price
premiums equilibrium is possible only when seed industry is a monopoly and technology fee is set at profit-
maximizing value. They found numerically, that monopoly maximizes when it avoid equilibriums with price
premiums – when IP costs for regular producers are too high.
79
hectare, whereas in Brazil it is 11-25 and in US 15-39. Yield gains are more likely to be seen
in Brazil and Argentina where insect resistant varieties improved considerably pest control
(Bärwald Bohm et al., 2014; Brookes & Barfoot, 2014).
In sum, on the one hand we have major producing and exporting countries
regulating technology in different ways and, so altering the market forces of international
technological diffusion. On the other hand, we have major importers taking different
regulatory positions towards this same technology. In addition, consumers all over the word
will have different views towards the consumption of products deriving from GMOs.
The combination of this initial scenario will become a unique experiment for
studying the interactions of technical change and trade. Overall, a set of empirical and
theoretical analysis has been pointing to negative effects of regulatory heterogeneity on trade
– mainly through asynchronous approval, mandatory labeling and LLP of unauthorized event.
However, empirical analyses have often not considered the effects of technological gap on
trade, and this effect is important once not all markets developed levels of hatred against the
GMOs technology.
It is the same of saying that for each approval of a new variety, the countries are
facing not only a commercial risk but also an opportunity costs defined as the distance a
country is taking from the most innovative markets.
At first, the aftermath of these interactions can be drawn from a dual-market
system for closer substitutes. Explicitly defining the market structure, as usually done by
authors carrying out simulations, is important since premiums will highly depend on the
preference of a good over the other. Last but not least, we are considering a period of
constant technical change meaning that innovation and adoption is also constant. This is
important since from our perspective the differences in approval and changes in regulatory
framework is the key drivers of negative or positive effects on trade – not the general level of
technology available in a country, although this also affect trade volumes.
Thus, technical change instead of overall level of technology seems to be the
underlying forces determining bilateral trade in this model. Next section brings how trade
theories, developed based in most general frameworks can shed some light on this case study.
2.2 Trade theories and Dual-market System
In this section we bring together some theoretical foundations of trade economics
and technology and how they contribute to better understanding a case of adverse
80
technological effects on trade. Noteworthy, we don’t intend to exhaust the topic but bring
some elements to move on with our empirical analysis in Chapter III. Instead of formalizing a
model to deal with major stylized facts identified above, we advance in identifying some
parts of theory that can contribute to explaining the case of GMOs and others that can be
treated in future research.
We depart from a review of how neoclassical35
models, especially the Ricardo’s
one, analyzing how them treat the relationship between technology and trade. In the
following, we introduce the most recent developments, which brought out issues such as
increasing returns, under the scope of firm heterogeneity models. Third, we focus on less
conventional developments presenting the ideas of technology-gap approach – which focus
mainly on impacts of technical change on bilateral trade. Although gravity is discussed along
with theories presentation, further discussion on this topic will be provided in the last Chapter
of this dissertation along with the final results.
2.2.1 The Ricardian Models of Trade and underlying role of technology
Mainstream economics often see trade as general equilibrium model with
hierarchical differences between factors – usually immobile across countries – and goods that
are perfectly mobile across countries. In trade, theory was mainly developed to answer
questions about the sources of gains countries could rip by trading.
In terms of gains, models can return very different results depending on structural
assumptions36
. But, more related to our goals, classical models of trade are aligned in saying
that trade comes from comparative advantage. By comparative advantage they mean
differences between autarky and integrated economy prices, which drive trade specialization
patterns (see Deardorff, 1980). Nonetheless, from a broader perspective there are two major
roots for modern theory of trade with different views about the source of comparative
advantage, namely Ricardian and Heckscher-Ohlin (HO) model.
HO model assumes that the proportion of factors is key for CA, i.e. countries
have different endowments of factors, which lead to different input prices. As countries differ
35
By Neoclassical model we mean models in which producers in country 𝑖 maximize revenue, usually by
choosing optimal level of output given the prices – perfect competition – and representative household
maximize utility by optimally allocating income. 36
Literature is plenty of studies about the gains and possible losses from trade. One classical example is the
debate over specialization in agricultural goods and the impacts on innovation rates and terms of trade discussed
by Latin America economists from CEPAL. Other questions as adjustment mechanism, as the wage decrease as
a necessary adjustment for deficit in balance of payments are commonly a natural result from some general
equilibrium models. As we are focused on the relationship between trade and technology we explicitly ignore
welfare and growth issues in this analysis.
81
only by factors proportion, technology is the same all over the world37
. In other words,
difference in technologies cannot be a source of trade as we have been arguing in the case of
soybean trade. On the other hand, the Ricardian model is strongly based on the assumption
that technology differences across countries are the bases for comparative advantages. As it
stands to reason Ricardian model is more appropriated for our purpose of studying
technological effects in trade when compared to HO-based models.
The model presented here to illustrate the underlying Ricardo’s ideas is the
seminal paper by Eaton & Kortum (2002) – a multi-country model based in the two-country
version by Dornbusch, Fischer, & Samuelson (1977). The model, as usual, is focused on
issues of supply side, simply assuming homothetic preferences in the form of a CES utility
function38
. This functional form is the most used by international economists because of the
operational treatment provided by Dixit-Stiglitz (1977) allowing for treatment to the
preference for variety under monopolistic competition. Preference for variety is a good
explanation for intra-industry trade. The general idea of “love for variety” is based on
Argmington assumption.
Eaton and Kortum (2002) depart from a world with 𝑁 countries {𝑖 = 1, … , 𝑁}
producing a continuum of goods 𝑗 ∈ [0,1]. As in Ricardo technology is country-specific.
Thus, authors denote country 𝑖′𝑠 efficiency in producing good 𝑗 as 𝑧𝑖(𝑗).
Unit input costs 𝑐𝑖 differ across countries39
, but are the same within a country
because they are mobile across activities and activities do not differ in terms of inputs shares.
Considering the productivity level of firms in country 𝑖 and input costs 𝑐𝑖 , the cost of
producing a unit good 𝑗 in country 𝑖 is then 𝑐𝑖/𝑧𝑖(𝑗).
As common in the most recent developments in the field, authors also consider
geographical barriers, which they claim to be a new development in Ricardian tradition at
that time. As usual, the operational grounds for geographical barriers are provided by
Samuelson’s standard and convenient “iceberg” assumption. According to this, delivering a
unit of a good from country 𝑖 to country 𝑛 requires producing 𝑑𝑛𝑖 units in 𝑖. As there is no
trade cost to serving domestic markets, 𝑑𝑛𝑛 = 1 and 𝑑𝑛𝑖 > 1 for any 𝑛 ≠ 𝑖. So,
37
Noteworthy but beyond the scope of this study one should consider some developments to plug technological
differences into HO models (see Fisher, 2011). 38
The CES mathematical form was developed by Hardy, Littlewood and Polya (1934). It was introduced in
economics by Arrow, Minhas and Solow (1961). In the field o f international trade, it was used in the form of
monopolistic competition by Dixit-Stiglitz (1977) and Spence (1977). 39
Authors also advance and break 𝑐𝑖 into intermediate inputs and cost of labor.
82
𝑝𝑛𝑖(𝑗) = (𝑐𝑖
𝑧𝑖(𝑗)) 𝑑𝑛𝑖
(1.a)
This would be the price country 𝑛 would pay if it choose to buy a good from 𝑖,
but considering perfect competition, it will be a unique price for good 𝑗 in country 𝑛, and this
will be the minimum international price for this good, represented as
𝑝𝑛(𝑗) = min{𝑝𝑛𝑖(𝑗); 𝑖 = 1, … , 𝑁}. (2.a)
As consumers have a CES utility functions with elasticity 𝜎 > 0, they purchase
individual goods in amounts 𝑄(𝑗) to maximize their utilities
𝑈𝑖 = (∫ 𝑞𝑖(𝑢)𝜎−1
𝜎
1
0
𝑑𝑢 )
𝜎𝜎−1
.
(3.a)
Maximization is subject to a budget constraint 𝑋𝑛, which is the country 𝑛′𝑠 total
spending. Note that consumers all over the world are represented by a representative
consumer, letting no room for difference in tastes within and across countries. In this case,
consumers have full access to information about all prices and the quality of all goods. As the
reader may have noticed this is important because in the soybean case empirical literature
have emphasized that consumers took different positions towards the product of the
innovation.
To make the model operational and coherent with underlying assumptions in
Ricardo, the authors pursue a probabilistic representation of technologies that can relate trade
flows to underlying parameters for an arbitrary number of countries across the continuum of
goods.
In doing so, they take country 𝑖′𝑠 efficiency in producing good 𝑗 as the realization
of a random variable 𝑍𝑖 (drawn independently for each 𝑗 ) from its country-specific
probability distribution 𝐹𝑖(𝑧) = Pr [𝑍𝑖 ≤ 𝑧]. Taking into account the law of large numbers,
this probability can also be considered the proportion of goods for which country 𝑖′𝑠
efficiency is bellow a cutoff 𝑧.40
Note that productivity is product and country-specific and
not related to firms as we are going to see later.
40
Eaton & Kortum (2002) do not explicitly consider the existence of a cutoff. However, the idea is pretty much
the same developed by HMR (2008) as we are going to see latter in this chapter.
83
By assuming that all markets are perfectly competitive, firm heterogeneity is
wiped out. This only makes sense if technology diffusion rate is instantaneous within
countries. This is also equivalent to saying that all firms producing a homogenous good
within country 𝑗 will export, as goods can only be differentiated by productivities reflected on
prices.
From the model, the cost of purchasing a particular good from country 𝑖 in
country 𝑛 will be 𝑃𝑛𝑖 = 𝑐𝑖𝑑𝑛𝑖/𝑍 – it is, the trade price conditional to efficiency probability –
and the existence of a lowest price 𝑃𝑛 resulting from perfect competition, the likelihood that 𝑖
will serve 𝑛 is the joint probability 𝜋𝑛𝑖 that 𝑖′𝑠 price turns out to be the lowest. Thus, the
Fréchet – or inverse Weibull distribution – is a good representation to the distribution of
efficiencies and prices.
𝐹𝑖(𝑧) = 𝑒−𝑇𝑖𝑧−𝜃
,
(4.a)
where 𝑇𝑖 > 0 and 𝜃 > 1.41
The country-specific parameter 𝑇𝑖 represents country 𝑖′𝑠 general
technology state. In terms of the shape this parameter regulates the location of the
distribution. Thus, the higher this parameter the greater is the chance of getting a higher
efficiency level for any good 𝑗 produced in country 𝑖 . From Ricardo’s perspective the
parameter 𝑇𝑖 is the absolute advantage of this country across the continuum of goods.
Parameter 𝜃, in turn, reflects heterogeneity across goods in countries’ relative
efficiencies – or the spread of the distribution of efficiencies. The bigger this parameter the
smaller is the variability across efficiency of different goods produced within the same
country. In a trade context 𝜃 governs comparative advantage within the continuum of goods
𝑗 ∈ [0,1]. In this particular model authors set this parameter as common to all countries.
Moreover, given the assumptions about the efficiency distribution, the authors
have drawn some interesting results about price distributions. Substituting the equation (1.a)
into the equation (4.a) we have the trade prices (costs) at which country 𝑖 could export goods
41
This restriction is important to make cross-elasticity of demand elastic in the CES.
84
into country 𝑛 – 𝐺𝑛𝑖(𝑝) = Pr[𝑃𝑛𝑖 ≤ 𝑝] = 1 − 𝐹𝑖(𝑐𝑖𝑑𝑛𝑖/𝑝) or
𝐺𝑛𝑖(𝑝) = 1 − 𝑒−[𝑇𝑖(𝑐𝑖𝑑𝑛𝑖)−𝜃]𝑝𝜃
.
(5.a)
However, country 𝑛 will actually by only the range of goods that country 𝑖 can
supply with a price lower than 𝑝. So, the amount of goods that country 𝑛 actually buys from
abroad, can be expressed by
𝐺𝑛(𝑝) = 1 − ∏[1 − 𝐺𝑛𝑖(𝑝)].
𝑁
𝑖=1
(6.a)
That is, by shopping around the globe, the good’s price in 𝑛 will have a domestic
distribution representing the lowest prices for each good internationally and domestically
supplied. We can get a more general formulation by inserting equation (5.a) into equation
(6.a).
𝐺𝑛(𝑝) = 1 − 𝑒−𝛷𝑛𝑝𝜃
,
(7.a)
where the parameter 𝛷𝑛 of country 𝑛′𝑠 price distribution is
𝛷𝑛 = ∑ 𝑇𝑖(𝑐𝑖𝑑𝑛𝑖)
−𝜃𝑁𝑖=1 .
(8.a)
Note that this price parameter summarizes the world state of technology, input
costs and geographical costs – being a measure for multilateral trade resistance (MTR) as we
are going to see in chapter III. The state of technology comprehends not only the gap between
general stocks of innovation across countries but also the gap across efficiencies within the
continuum of goods. This version of the model doesn’t advance on international technology
diffusion.42
Importantly, at the end of the day, countries trade technologies – in the form of
goods – discounted by input and geographic/trade costs. The aftermath is an increased access
to technology through trade goods perceived only by productivity differences. At this point it
42
Eaton and Kortum (1999) showed how a process of innovation and diffusion can give rise to a Fréchet
distribution.
85
must be clear that technology in this context is the set of techniques that a country use to
produce goods. These techniques or means of production are different in terms of quality,
resulting in different productivities.
Finally, three underlying properties can be drawn of the price distributions. First,
the probability that country 𝑖 provides a good at the lowest price in country 𝑛 is simply
𝜋𝑛𝑖 = 𝑇𝑖(𝑐𝑖𝑑𝑛𝑖)−𝜃/𝛷𝑛 . This is the 𝑖′𝑠 contribution to country 𝑛′𝑠 price parameter and the
fraction of goods that country 𝑛 actually buys from country 𝑖 - remember that we are dealing
with a continuum of goods and perfect competition.
Second, for goods that are actually purchased by country 𝑛 the source has no
bearing on the good’s price. Countries with better technology, reduced production and trade
costs will trade a wider range of goods, exactly to the point at which the distribution of prices
for what it sells in 𝑛 is the same as 𝑛′𝑠 overall price distribution. Note that the relation of
country 𝑖′𝑠 technology and input and trade costs relatively to an overall state of technology
and costs around the world will be the driver of country 𝑖′𝑠 exports.
Although authors haven’t developed this point, the idea of an international
technological frontier is somehow represented here. Coeteris Paribus, as much as the
countries get far from it more they will become inexpressive in international markets as a
result of low technological dynamism.
Third, the exact price index for the CES utility function, assuming 𝜎 < 1 + 𝜃 is
𝑃𝑛 = 𝛾𝛷𝑛
−1/𝜃.
(9.a)
Here
𝛾 = [𝛤 (
𝜃+1−𝜎
𝜃)]
1/(1−𝜎)
,
(10.a)
where 𝛤 is the Gamma function. This expression shows how geographic barriers lead to
deviations in the purchasing power parity. Note that 𝜎 < 1 + 𝜃 is necessary to have a well
defined .
It is possible to solve the model to equilibrium by considering labor as the unique
86
factor of production, as in Ricardo, and trade balances.43
More importantly, the gravity
equation can also be derived from the model under basic assumptions of equilibrium of the
balance of payments. Let 𝑌𝑖 = ∑ 𝑋𝑛𝑖𝑛 be country 𝑖′𝑠 total exports, then
𝑌𝑖 = ∑ 𝑇𝑖(𝑐𝑖𝑑𝑛𝑖)−𝜃𝑋𝑛
𝑛
= 𝑇𝑖𝑐𝑖−𝜃𝛺𝑖
−𝜃
(11.a)
where
𝛺𝑖−𝜃 ≡ ∑
𝑑𝑛𝑖−𝜃𝑋𝑛
𝛷𝑛𝑛
Solving equation (11.a) to 𝑇𝑖𝑐𝑖−𝜃 and plugging into 𝑋𝑛𝑖 = 𝑇𝑖(𝑐𝑖𝑑𝑛𝑖)
−𝜃𝑋𝑛
and applying clearing market condition, we get
(12.a)
𝑋𝑛𝑖 =𝑋𝑛𝑌𝑖𝑑𝑛𝑖
−𝜃𝛺𝑖𝜃
𝛷𝑛 .
(13.a)
Using 𝑝𝑛 = 𝛾𝛷𝑛−1/𝜃
from consumer assumptions, we finally have
𝑋𝑛𝑖 = 𝛾−𝜃𝑋𝑛𝑌𝑖𝑑𝑛𝑖
−𝜃(𝑝𝑛𝛺𝑖)𝜃.
(14.a)
This can be considered a standard gravity equation since bilateral resistance 𝑑𝑛𝑖
and multilateral resistance terms 𝑝𝑛 and 𝛺𝑖 were considered. From this, it is possible to see
that the model seeks to predict the role of comparative advantage meaning the differences in
productivity across goods within countries – i.e. parameter 𝜃. 44
43
Solving to equilibrium we should consider 𝑋𝑛𝑖 as the total spending in country 𝑛 on goods from country 𝑖.
Income or total spending of country 𝑛, therefore, will be 𝑋𝑛𝑖 ≡ ∑ 𝑋𝑛𝑖𝑖 . We know that 𝑋𝑛𝑖
𝑋𝑛= 𝜋𝑛𝑖 , so 𝑋𝑛𝑖 =
𝑇𝑖(𝑐𝑖𝑑𝑛𝑖)−𝜃𝑋𝑛. Considering the simplest case of no intermediate goods we can assume that 𝑐𝑖 = 𝜔𝑖, or the cost
of a unit of labor. In equilibrium total income in country 𝑖 must be equal to total spending on goods from
country 𝑖 . 𝜔𝑖𝐿𝑖 = ∑ 𝑋𝑛𝑖𝑛 Similarly, trade balance requires 𝑋𝑛 = 𝜔𝑛𝐿𝑛 so that 𝜔𝑖𝐿𝑖 = ∑𝑇𝑖(𝜔𝑖𝑑𝑛𝑖)−𝜃
∑ 𝑇𝑗(𝜔𝑗𝑑𝑛𝑗)−𝜃
𝑗𝑛 𝜔𝑛𝐿𝑛 .
This is like an exchange economy, where countries trade labor units. By choosing a numeraire we can solve for
wages applying the Walras’ Law. Fréchet distributions imply that labor demands are iso-elastic. 44
Authors used relative prices of a range of commodities data to obtain 𝑑𝑛𝑖 , since it couldn’t be estimated
simply as the distance between importer and exporter country as usual. So, they obtained trade elasticity of
“comparative advantage”, parameter 𝜃 for their sample of countries. Their main point is to estimate how much
trade depend on Ricardo’s “comparative advantage”(Jonathan Eaton & Kortum, 2002).
87
It is worth noting that perfect competition will wipe out the taste for varieties by
making goods in a category homogenous with their counterparts. The only distinction
consumers only tell the goods apart by the degree of efficiency at which the goods were
produced. In the case of GMOs, such assumption wouldn’t leave to the situation of a dual-
market system, since products should be considered the same – innovations are strictly
changing processes instead of products.
Before advancing in a more comprehensive analysis of our case under this
framework let’s see how firm heterogeneity theories and technological–gap framework
contributes to our case.
2.2.2 Firm Heterogeneity Models
From the early-2000s we have seen the rise of influential developments in the
field of international trade. The firm heterogeneity models have advanced from many
developments of the New Trade Theory, as increasing returns to scale (IRTS) and
monopolistic competition. It is not by chance that many specialists have usually named this
family of models as the New New Trade Theory (NNTT).
The central contribution of these models is the deliberate consideration of firm-
specific factors as underlying for trade analysis – assuming that countries don’t trade firms
do.
Studies have pointing to the fact that exporting is extremely rare as well as
exporters are larger, more productive, they use factors differently and they pay higher wages
(see Aw, Roberts, & Xu, 2008; Bernard, Eaton, Jensen, & Kortum, 2003; Kugler &
Verhoogen, 2008).
Here, we are going to present and discuss works by Melitz (2003) and Helpman,
Melitz, & Rubestein (2008) – hereinafter HMR(2008) – which will bring some additional
considerations to the Ricardian model discussed in the last section. We are going to see how
models differ in terms of type of competition (monopolistic vs. perfect competition), returns
to scale (increasing vs. constant) and trade costs (variable vs. fixed and variable).
On the other hand, they also resorted to tools like the homothetic preferences
(derived from a CES utility function), identical tastes across countries and distribution
functions to represent heterogeneity in efficiency levels. But, in this matter, HMR (2008)
88
used a Pareto distribution to represent efficiency across firms instead of Fréchet distribution
across countries.
Beforehand, we can say monopolistic competition provides us with a better
framework to think of a market for imperfect substitutes goods, fixed cost of trade are closer
to the type of regulation costs we are dealing with and IRTS can be a reason for country and
firm-level concentration in the supply chain of grains.
Let’s start with the assumptions about production and consumption. For clarity
we adopt the same notation used in the HMR (2008) paper with minor modification.
Consider a word with 𝐽 countries, indexed by 𝑗 = [1,2, … , 𝐽]. As usual, consider
that every country produces and consume a continuum of goods 𝐵𝑗 = [𝑙, … , 𝑙𝑛], where 𝐵𝑗 is
the set of goods available for consumption in country 𝑗 . Utility function of a world
representative consumer will be
𝑢𝑗 = [∫ 𝑥𝑗(𝑙)𝛼 𝑑𝑙𝑙∈𝐵𝑗
]
1𝛼
, 0 < 𝜎 < 1.
(1.b)
Here, 𝑥𝑗(𝑙) is the consumption of product 𝑙 and the parameter 𝛼 is cross elasticity
of demand, defined by the authors as 휀 = 1/(1 − 𝛼). Unlike in Eaton and Kortum (2002)
here this parameter will determine the mark-up each monopolist firm will charge. Goods will
differ not only by the techniques employed to manufacture them but also by consumers’
perception about substitutability of goods being supplied.
Noteworthy, homothetic preferences and identical tastes will make 𝛼 unique for
all firms worldwide. If 𝑌𝑗 is the income of country 𝑗 and consumers maximize utility, product
𝑙’s demand in 𝑗 will be
𝑥𝑗(𝑙) =
��𝑗(𝑙)− 𝑌𝑗
𝑃𝑗1− . 45
(2.b)
Here, ��𝑗(𝑙) is the price for a particular variety 𝑙 in country 𝑗 and 𝑃𝑗 is the
country’s ideal price given by
45
Although authors haven’t derived demand function step-by-step, demand functions from standard CES utility
functions with a continuous of good can be obtained by taking some standard procedures. Most of the grounds
for deriving it are in the Dixit-Stiglitz seminal paper. A simple solution however can be obtained from taking
the ratio of Frisch demands for two varieties, then getting Marshallian demand functions. Some further algebraic
manipulation will return equations (2.b) and (3.b).
89
𝑃𝑗 = [∫ ��𝑗(𝑙)1− 𝑑𝑙𝑙∈𝐵𝑗
]
1/(1− )
.
(3.b)
As we have mentioned above, firms are different within and across countries and
they produce a unique product. Thus, in country 𝑗 exists 𝑁𝑗 firms, and in the world economy
we have a set of ∑ 𝑁𝑗𝐽𝑗=1 firms. Monopolistic competition assumption will make the number
of firms equal to the number of products. Differently from the perfect competition assumed
by Kortum and Eaton (2002), here products are essentially different from one another. In
other words, goods are substitutes to a greater or lesser extent depending on the cross
elasticity of demand and not the degree of efficiency of production.
Each good is produced with a different combination of inputs 𝑎. Note that 𝑎 is
firm specific and varies between the lowest and the highest amount of inputs used in
production [𝑎𝐿 , … , 𝑎𝐻]. Thus, 1/𝑎 is the individual firms’ productivity level. This differences
in technology can be represented by a distribution function 𝐺(𝑎) with support [𝑎𝐿 , 𝑎𝐻] where
𝑎𝐻 > 𝑎𝐿 > 0. For simplicity and without loss of generality authors consider that spread,
location and shape of this distribution are the same for every 𝑗. Differences across firms are
captured by 𝑎′𝑠 and aggregate differences across countries are subsumed in the different
input costs considered for each 𝑗.
With respect to that, let 𝑐𝑗 be the unit cost of a bundle of inputs. In a world with
free mobility of inputs within the countries but not across countries 𝑐𝑖 will be country-
specific. Noteworthy, although authors do not extol this insight, 𝑐𝑗 is capturing all the sources
of country comparative advantage as reported by classical theory. It can account for
differences in the state of technology – 𝑇𝑗 parameter by Eaton and Kortum (2002) – along
with other sources of input costs differences such as factors’ endowments. Thus, 𝑐𝑗 here, by
reflecting all the country-level heterogeneity covers a wider range of sources of comparative
advantage when compared to Eaton and Kortum (2002).
In autarky the combination of heterogeneous firms and input prices under
monopolistic competition and CES preferences will determine prices in country 𝑗 as being
𝑝𝑗(𝑙) = 𝑐𝑗𝑎/𝛼 . This is the standard markup pricing equation, with a smaller markup
associated to a large cross elasticity of demand, or the Mill’s price. The markup is a result of
preference for variety inexistent in Eaton and Kortum (2002).
Trading, however, will bring additional costs to exporting firms, such as tariffs,
regulation, and transportation, among others. These costs can be breakdown into fixed (𝑐𝑗𝑓𝑖𝑗)
90
and variable (𝜏𝑖𝑗) costs. Transportation costs, for instance are expected to vary accordingly to
the amount of trade, whereas regulation as approval of a variety in the importing country,
expenses with labeling requirements, among others, can be considered as fixed cost. The
intuition behind the form of the fixed costs 𝑐𝑗𝑓𝑖𝑗 is that these costs are fueled by internal costs
of exporting country.
For the variable costs will be convenient and simple also make use of the
“melting iceberg” costs formulation. Therefore,
��𝑖𝑗(𝑙) = 𝜏𝑖𝑗
𝑐𝑗𝑎
𝛼 . 46
(4.b)
However, exporters from country 𝑗 will also incur fixed costs of trade. Fixed costs
will be key to determine the profitability of a particular exporter. Given its efficiency level
and country-specific input prices a firm can have no capability to serve a certain market by
trading.
Taking into account the demand as in equation (2b) and variables and fixed costs
of trade we can express the operating profits from any firm in 𝑗 to serve 𝑖 as
𝜋𝑖𝑗(𝑎) = (1 − 𝛼) (𝜏𝑖𝑗𝑐𝑗𝑎
𝛼𝑃𝑖)
1−
𝑌𝑖 − 𝑐𝑗𝑓𝑖𝑗. (5.b)
From that, to carry out a profitable sale to any market 𝑖 a firm must export a
minimum amount of goods to at least pay off the fixed costs of trade. Revenue, however, will
depend on the efficiency level – or technology – employed in manufacturing. In other words,
there is a minimum level of productivity required to serve a market 𝑖 defined as 𝑎𝑖𝑗. Exports
will be profitable only if 𝑎 ≤ 𝑎𝑖𝑗. This cutoff can be derived from the zero profit condition.
(1 − 𝛼) (𝜏𝑖𝑗𝑐𝑗𝑎𝑖𝑗
𝛼𝑃𝑖)
1−
𝑌𝑖 = 𝑐𝑗𝑓𝑖𝑗. (6.b)
Note that both type of trade costs are exogenous to the firm. By considering
variable and fixed costs of trade we also bring into the model the influence of IRTS, since
certain degree of specialization will turn out to be a gain of trade (see Krugman, 1985).
Noteworthy, domestic sells have no further costs from trade, so 𝑓𝑗𝑗 = 0, ∀ 𝑗 and 𝑓𝑖𝑗 > 0, ∀ 𝑖 ≠
𝑗. Analogously 𝜏𝑗𝑗 = 0, ∀ 𝑗 and 𝜏𝑖𝑗 > 0, ∀ 𝑖 ≠ 𝑗. The Ricardian model presented above, by
assuming only variable costs of trade, can neither explicitly treat IRTS as a source of trade
nor consider regulatory costs as fixed – as the empirical literature usually does. These two
46
HMR (2008) have not used the subscript 𝑖 in ��𝑖𝑗 , but as this exporting price will depend on trade-level
variables it is more accurate to include it.
91
considerations put this model closer to the underlying features of our case.
As a result, not all firms will entry into the game of international trade, just those
with efficiency high enough to overcome trade costs. This relatively simple assertion and
natural result from the model is an important feature of HMR (2008). As we mentioned
above, many studies in this field have been pointing to differences between exporting and
non-exporting firms, and high presence of zeroes in bilateral trade data. Indeed, zeroes have
been a challenge not only from the theoretical perspective but also to estimate the gravity
equation, as we are going to see in detail in Chapter III.
All that considered, the bilateral trade volumes can be characterized as
𝑉𝑖𝑗 = {∫ 𝑎1− 𝑑𝐺(𝑎)
𝑎𝐻
𝑎𝐿
for 𝑎𝑖𝑗 ≥ 𝑎𝐿
0 otherwise.
(7.b)
The demand function (2.b) and pricing equation (4.b) determine the value of trade
between 𝑖 and 𝑗.
𝑀𝑖𝑗 = (𝜏𝑖𝑗𝑐𝑗
𝛼𝑃𝑖)
1−
𝑌𝑖𝑁𝑗𝑉𝑖𝑗.
(8.b)
Note that 𝑉𝑖𝑗 = 0 will result in 𝑀𝑖𝑗 = 0. That is, if no firm in country 𝑗 is efficient
enough to export into 𝑖 bilateral trade will be zero. Taking into account the price index and
the definition of equation (7.b) authors define the ideal price index in country 𝑖.
𝑃𝑖1− = ∑ (
𝜏𝑖𝑗𝑐𝑗
𝛼)
1−
𝑁𝑗𝑉𝑖𝑗𝐽𝑗=1 .
(9.b)
The model provides a mapping from the income levels 𝑌𝑖, the number of firms 𝑁𝑖,
the unit costs 𝑐𝑖 , the fixed costs 𝑓𝑖𝑗 and the transport costs 𝜏𝑖𝑗 to the bilateral trade 𝑀𝑖𝑗 .
Authors do not solve to equilibrium. To have a closed form solution further assumptions from
Melitz (2003) will be needed. The author basically resorted to a labor market formulation as
in Krugman (2008), the entry and exit model by Hopenhayn (1992) and common market
clearing conditions to obtain autarky and trade equilibrium. We are not going to advance on
this here, since we are mostly concerned with the general results of the model and the
empirical results we can drawn from the relations between trade and technology innovation47
.
However, gravity can be derived from the model by assuming a Pareto
47
As we mentioned in somewhere dissertation, a model embodying most of the empirical stylized facts should
be developed in the near future.
92
distribution to represent firm heterogeneity 𝐺(𝑎) = (𝑎𝑘 − 𝑎𝐿𝑘)/(𝑎𝐻
𝑘 − 𝑎𝐿𝐾), 𝑘 > (휀 − 1) .
Again, this framework is interesting since it explicitly allows non-positive trade flows and
asymmetric flows between countries.
An overlook at data for soybean trade will return a lot of flows with these
characteristics. If firm heterogeneity is represented by a Pareto distribution, trade volume 𝑉𝑖𝑗
can be expressed as
𝑉𝑖𝑗 =𝑘𝑎𝐿
𝑘− +1
(𝑘 − 휀 + 1)(𝑎𝐻𝑘 − 𝑎𝐿
𝑘)𝑊𝑖𝑗,
(10.b)
where
𝑊𝑖𝑗 = max {(𝑎𝑖𝑗
𝑎𝐿)
𝑘− +1
− 1, 0} . (11.b)
Note that 𝑎𝑖𝑗 is determined by the zero profit condition and free entry, and
equations (10.b) and (11.b) are monotonic functions. Taking this into account and expressing
𝑀𝑖𝑗 in its log-linear form, we have the following estimating equation
𝑚𝑖𝑗 = 𝛽0 + 𝜆𝑗 + 𝜒𝑖 − 𝛾𝑑𝑖𝑗 + 𝑤𝑖𝑗 + 𝑢𝑖𝑗,
(12.b)
where 𝜒𝑖 = (휀 − 1)𝑝𝑖 + 𝑦𝑖 is a fixed effect of the importing country, 𝜆𝑗 = −(휀 − 1) ln 𝑐𝑗 +
𝑛𝑗 is a fixed effect of the exporting country, 𝑑𝑖𝑗 are the variable costs of trade usually
measured as the symmetric distance between country pairs and, finally, 𝑤𝑖𝑗 is the proportion
of firms from 𝑗 exporting to 𝑖. By relating importer and exporter variables controlling for
sizes and trade costs to determine trade flows this can be considered a standard gravity
equation.
Noteworthy authors developed a technique to estimate 𝑤𝑖𝑗 based on the
observable variable 𝑚𝑖𝑗. The intuition behind this is that trade is only observed if at least one
firm is productive enough to do so. Also, trade increases as much as firms are relatively more
productive within a specific country. As we are going to use these insights to estimate most
of our empirical model, we are going to advance in this topic in Chapter III.
In sum, the main contributions of HMR(2008) are the monopolistic competition
assumption and treatment for firm heterogeneity and fixed costs of trade. In this framework,
we could consider GMOs trade as a process and product innovation and the existence of a
dual-market system as a consequence of love for variety. But we still cannot considerer
different tastes across countries.
93
2.2.3 Technological Gap and Trade
This strand of literature was developed under the umbrella of the evolutionary
economics48
. The most influential paper in the field of international trade and the
technological gap was written by Posner (1961), which brought forward the general idea of
continued technology differences among countries as a persistent source of trade.
Given that the developments in evolutionary economics are relatively new it is
often a hard task to outline a pattern or defined focus for the field49
. Therefore, we are going
to focus on some stylized facts which have being discussed in the literature and is related to
the issues we are developing in this study. As these models explicitly abandon the ties of the
equilibrium approach presented in neoclassical models, they bring forth a number of new
ideas, which often contradicts adjustment mechanisms from neoclassical models.
The underlying assumption in these models is the centrality and endogeneity of
technical change as a driver of economic activity, including trade patterns. In an evolutionary
world, firms continuously innovate causing instability and uncertainty in the economic
system. Technological gap theorists are more concerned with the relations between technical
change and trade than with trade equilibrium after a technological shock. The distinction
between technology and technical change is not merely a question of word’s choice.
Technical or technology change has the intrinsic meaning of a economic system marked by
continued breakups arising from innovation, as it may be clear from Rosenberg words:
…in a world where rapid technological change is taking place we may need
an analytical apparatus with focuses in a central way upon the process of
technological change itself, rather than treating it simply as an exogenous
force which leads to disturbances from equilibrium situations and thereby
sets in motion an adjustment process leading to a new equilibrium
(Rosemberg, N. (1970) apud Giovanni Dosi & Soete, 1988 pag. 402).
As uncertainty is present in addition to risk and agents are not fully rational,
instead of maximizing profits, firms adopt and develop routines according to their
experiences in the marketplace, investments in R&D, access to credit markets and random
effects inherent to economic activity. Routines are selected according to their “fitness” to the
48
See Nelson and Winter (1985) for a rich introduction of the field. 49
A detailed description of the modeling approach and concepts of these theories is beyond the scope of this
study, but we will shortly introduce the underlying ideas of these models, especially those related to the case we
are considering. Such an introduction is deeply based on papers by Dosi & Soete (1983), Dosi, (1982);
Giovanni Dosi, Grazzi, & Moschella (2015) and Maggi (1993).
94
actual market conditions. In this way, firms not fitting in will be dropped out of marketplace.
Although departing from very distinct and more realistic assumptions, note that this
framework allows for firms’ selection coming from technological heterogeneity. This idea is
central to understand the pattern of trade after GMOs came into scene and some markets
selected sources based on technological criteria.
Posner (1961) by not assuming identical preferences – an important assumption
for neoclassical model closures50
– explicitly considered the likelihood of a demand lag
impacting on the time needed for a technology adoption rate became critical to the
maintenance of market shares. However, theorists in the field somehow forgot this concept
during these years, perhaps because of the lack of a case in which this lag had such a sharp
effect on trade.
At this point, it is possible to see that these concepts, even if not fully formalized,
are insightful for our analysis of the soybean trade. Asymmetric and asynchronous approval
of new varieties is a persistent and institutionalized source of demand lag and innovation is a
continuous process led by seed companies. Even if we considerer this process trough a set of
partial equilibrium analysis suffering successive technological shocks, as we are more
interested in determining a relationship between technology gaps and trade patterns, the idea
of a technical change seems a more fruitful start point.
To prove this point Posner (1961) departs from very limiting assumptions such as
identical endowment of factors and zero costs of trade. Yet, central and pioneer contributions
lay on the concepts of international imitation lag.
When a firm in country 𝑗 innovates this technology won’t be readily available or
of concern by firms in country 𝑖 , creating a lapse between innovation and imitation or
adoption – or in HMR (2008) terms an increased level of firms’ heterogeneity. The time
elapsed until other firms could fully enjoy the technological benefits will be a result of four
effects. Note that if Eaton and Kortum are implicitly dealing with a world where industry
specialization is expected, being inter-industry trade more relevant, Posner (1961) by
considering that exporting firms can compete with other exporting firms from other countries
wipe out specialization leading to greater relevance of inter-industry trade. Naturally, by
assuming the other extreme of firm specialization in producing a unique product, we also are
assuming that all firms in everywhere are competing with one another.
50
Noteworthy, some neoclassical models have been considering non-homothetic preferences mainly after
Krugman (1979). Usually these models consider that demand in rich countries differs from that of poor
countries see (Dalgin, Mitra, & Trindade, 2004; Fieler, 2011; Hallak, 2006; Hunter, 1991).
95
Anyway, the first effect impacting on the imitation lag is the domestic reaction
lag 𝑙1, defined as the time required to innovation become central in dropping later-mover out
of marketplace51
. Second, the foreign reaction lag 𝑙2 , defined as the time needed to country
𝑖′𝑠 producers feel threaten by importation of country 𝑗′𝑠 products in domestic markets will
play a role. Third, one should also consider that imitation is not a “plug-and-play” process,
thus, agents will need some time to fully enjoy technology benefits as they faces a learning
curve. In other words, there is a learning to learn effect 𝑙3 that also affects the imitation lag.
Fourth and most important, the author considers that it is possible that some consuming
markets took some time to see any advantage in a new product or process, the demand lag 𝑙4.
Thus, this framework allows us to consider a case in which demand lag is strong enough to
make the innovation less destructive in terms of trade restructuration. From our perspective,
that is precisely what evidences are pointing to in the case of GMOs.
Some developments will also consider differences in “endowments” across
different countries (Maggi, 1993). This literature usually calls differences not related to the
rate of innovation across countries as the forces of Ricardian comparative advantage. That is,
these forces are related to an overall level of technology driving domestic factors and inputs
prices instead of technical change itself. We see no reason to not consider immobile factors
“endowment” as an additional driver of domestic prices. Anyway, the general conclusion
about countervailing forces of technical change holds for any source of trade not related to
technical change.
In other words, trade advantages coming from innovation and adoption can be
offset by advantages not related to technical changes – as the reduced cost of labor or land in
other producing countries. Breaking down these two different sources of trade has been a
current concern of literature, to prove the argument of prevalence of technical change as a
source of trade in detriment of labor costs adjustment – a natural result of Ricardian model
under equilibrium conditions (see Giovanni Dosi, Grazzi, & Moschella, 2015).
Another force acting over the advantages of the technical change is the rate of
international diffusion of technology. The intuition behind this is that the benefits of an
innovation made by country 𝑗, can be offset if the international diffusion and adoption by
other competitors is rapid enough.
This framework can shed some lights on questions related to the asymmetric
adoption and absence of strong decreases of market-shares of late-adopters. Countervailing
51
The idea of creative destruction by Schumpeter, first published in 1943, predicts similar patterns of
technological impacts on the economic system (Schumpeter, 2003).
96
forces such as non-technological advantages and pace of adoption itself can explain the
sustainability of late-movers in international markets.
Last but not least, this framework by considering that technical progress will
impact on countries’ market shares, open a room for considering international competition
between firms and countries in supplying destination markets. In this way, it is possible to
consider multi-specialization and trade as a result of international competition across
countries in common markets, instead of thinking of patterns based only on trade pair
characteristics. In other words, if countries 𝑗 and 𝑘 are serving a market 𝑖 with a similar
product, differences between 𝑗 and 𝑘 will also be a source of trade and determine the market
shares.
2.3 Theories of Trade and the Case of GMOs
Finally, we can draw a more specific parallel between the three frameworks
presented and the case of GMOs. We can think this question from three perspectives, i.e. the
market structures, trade and technical change impacts.
In terms of market structure, the monopolistic competition makes sense, since we
can think of a continuum of similar goods to describe soybean varieties. That will be true
especially after the asymmetric technology change, which will increase the range of products
available from the consumer’s perspective. Also, we cannot consider that differences are
enough to allow pure monopoly behavior, being the varieties at the best close substitutes of
one another. Of course, in the real word will be difficult to think of a markup equal to every
country, especially because varieties can enter utility asymmetrically, and preferences can
vary across consumers and countries. Indeed, the weakest argument from the theories
considered, in terms of pursing our goals, is the assumption of identical tastes across
countries.
In this sense, HMR (2008) by implicitly assuming that innovation may affect both
the productivity and cross elasticity of substitution, fits better to answer some questions
raised in this study. In addition, the imitation lag predicted by Posner (1961) also allows for
firm heterogeneity and differences in preferences for “technology”, providing foundation for
our two interest variables, namely the demand lag and technology gap.
In terms of trade, we can think that our case is marked by multi-specialization
with high concentration of exporting and importing countries. Firm, country and trade
specific variables affect the total volume of bilateral trade. Countries with higher levels of
97
technology stock (𝑇𝑖 from Ricardian Model), more efficient firms (𝛼 from HMR), reduced
input costs (𝑐𝑖 from HMR and Ricardian models) and competitive trade costs (𝑓𝑖𝑗 and 𝜏𝑖𝑗
from HMR) will trade more and higher volumes – all else being equal.
The technology change however, will have a double effect in the case of soybean
trade. It will affect firms’ relative levels of productivity (𝐺(𝑎) from HMR and 𝐹(𝑧) from
EK) through the innovation and adoption processes, and consumers’ perceptions through a
kind of preference for “old” production techniques. By being two-way asymmetric, the
innovation will increase firm and good heterogeneity (𝑎) , impacting also on the cross
elasticity of demand (휀) as the new product isn’t considered a better or equivalent substitute
for the old one in everywhere.
It’s also important to consider that regulatory costs were established at the
country-level. So, policymakers will also impact on countries general state of technology,
firms’ heterogeneity, but mainly on fixed costs of trade between adopters and opposing
countries. Considering HMR model, we could consider that 𝑐𝑖𝑓𝑖𝑗 could capture this effect of
increased regulatory costs.
In addition, during the period of analysis new events and asymmetric approval
across countries created a continued technological gap between the countries. Also, that
demand lag was persistent during the time leading to a dual market system accommodating
old and new technologies based on the existence of more than one consuming market.
Adoption process and other non-technological variables were important to keep late adopters
in international markets. This continuous interaction of technological change and trade under
the context of technological diffusion and other factors creating comparative advantages can
be only considered within the technology-gap framework.
These points can be considered into an empirical exercise by assuming that
technical change impacted trade mainly through two variables. First, the non-adoption
created a group of firms producing with an inferior technology, incurring in opportunity costs
of non-adoption, the technology-gap. Second, adoption created a problem for exporter in
markets under demand-lag (or technology hatred).
As most of the asymmetries of trade were created at county-level, with
uncoordinated case-by-case approach to approve new varieties, uncovered approvals – i.e.
varieties being approved in country 𝑗 but not in country 𝑖 – is a good proxy for measuring the
demand-lag. Opportunity costs of delaying approvals, in terms of distance of a technological
frontier given by world rate of innovation and adoption, can be proxied by the difference of
98
approved varieties in country 𝑗 and general state of world’s approvals for production – the
technological-gap. Developing a theoretical model to deal with the major stylized facts of our
case, although important, is a long-term task to be developed in future research.
99
CHAPTER III - EMPIRICAL ESTIMATION
Based primary on the technological-gap and firm heterogeneity frameworks, in
this chapter we estimate a gravity model to analyze the effects of innovation adoption in
terms of both the technology-gap and demand-lag from 1996 to 2012. This chapter is divided
into 4 more sections. In section 3.1 we discuss the method employed and the relationship
with theoretical points presented in Chapter II. In section 3.2 we introduce data used in this
experiment. In Section 3.3 we introduce and discuss the results. In section 5 we conclude and
put together the key findings.
3.1 Method
The gravity equation is a workhorse of international trade analysis. The method
has been used in empirical international economics at least since the seminal work of
Tinbergen (1962). However, it was needed many years before the model could have a
theoretical foundation. This lack of grounds, therefore, made the model a target for several
criticisms. For many authors, the paper by Anderson & Wincoop (2003) filled the gap
between an empirically stable relationship of bilateral trade, sizes and distances, and the
theory of international trade.
In terms of classical theory, it is important to have in mind that HO models is an
attempt to explain the existence of trade flows, which establishes a relationship between
factors proportions (endowment) and trade, whereas Ricardo resorted to technological
differences to explain why do countries trade. In both approaches countries size and trade
costs, made a minor contribution to understanding the stability of trade between similar
countries, the so-called intra-industry trade. On the contrary, this literature was dedicated to
prove that trade exists because of significant effects of complementarities and efficiency
gains of specialization.
A general formulation of the gravity equation can be written as
𝑀𝑖𝑗 = 𝑋𝜆𝑗𝜒𝑖𝜙𝑖𝑗 (1.c)
where, 𝑀𝑖𝑗 is the nominal value of country 𝑗′𝑠 exports to country 𝑖 , 𝜆𝑖 is the importer-effects
affecting trade flows, 𝜒𝑗 are the exporter effects and 𝜙𝑖𝑗 variables defined at the trade level
increasing or decreasing economic costs of trade. 𝑋 represents a general state of international
markets which is unrelated with countries characteristic, or the constant.
100
Importer and exporter effects have been commonly proxied by countries’ nominal
GDPs in aggregated data studies. More recently, many works have advanced on the topic
seeking to add other country-level variables into the model such as the state of technology,
rate of technical progress, regulatory issues, and other factors creating comparative
advantages for a particular country (Gómez-Herrera, 2012; Teh & Piermartini, 2009;
Winchester et al., 2012; WTO & UNCTAD, 2012). Very often, when the research objectives
allow, analysts make use of countries’ fixed effects52
to control for country heterogeneity. In
the particular case of estimating effects for a particular industry or good, it is recommended
to use the gross production in country 𝑗 of this particular good and the consumption
(imported and produced amount) in country 𝑖.
Additional to the general formulation in equation (1.c), many authors consider the
need of controls for multilateral trade resistance (MTR) in order to have a so-called
structured model53
. Actually, neglecting the MTR effects on trade is defined as the gold
medal of gravity misspecification (J. E. Anderson & Wincoop, 2003; Fieler, 2011; WTO &
UNCTAD, 2012). The intuitive concept behind it is that countries with higher MTR or
difficult to access the world markets, will trade less than countries with easier access to them.
Thus, increased trade activity can be a simple result of being closer to the most relevant
markets, for example.
From a most micro-founded perspective, MTR can also been seen as a result of a
model with CES utility function and monopolistic competition. In these models trading
around the world will result in an internal price index. The price index formation, in this case,
is the proxy for MTR in the theoretical model, as it represents an average price of all
suppliers of country 𝑗 weighted by their economic position. If not considered, the MTR
coefficient will be correlated with trade costs, so the others coefficients will be biased.
Virtually all the relevant works after Anderson and Wincoop (2003) have somehow
considered controls for MRT.
A common proxy for MRT has been the “remoteness” index described by Head &
Mayer (2013)54
. The calculation consists of creating a spatial weighted distance by dividing
geographical distance by the countries GDP shares of total world production – or the
52
By country-fixed effects we mean the use of dummy variables to estimate models on cross-sectional data, thus
controlling for country heterogeneity. The term is very common among trade economists but can lead to
misinterpretation if one thinks of fixed effect models in panel data. Importantly, the use of fixed effects for
country avoids the gold medal of misspecification since it controls for remoteness. 53
By structured we mean a model with micro-foundations. 54
Indeed, this index was developed in Head (2003).
101
summation over GPDs of countries in sample. That is, 𝑅𝑒𝑚𝑖 = ∑ 𝑑𝑖𝑗/(𝐺𝐷𝑃𝑗/𝐺𝐷𝑃𝑤)𝑗 , where
𝑑𝑖𝑗 is the geographical distance between countries 𝑖 and 𝑗 , 𝐺𝐷𝑃𝑗 is the country 𝑗′𝑠 gross
domestic product and 𝐺𝐷𝑃𝑤 is the sum of all GDPs of the countries considered (WTO &
UNCTAD, 2012). We have used this procedure to calculate or remoteness index of importers
and exporters.
The trade costs 𝜙𝑖𝑗 have been usually computed as the well-known Samuelson’s
“iceberg costs” formulation. The general idea is that the costs of sending a product from 𝑗 to 𝑖
increase alongside distance55
between this pair of countries. Thus, to a unit of good 𝑙 to arrive
it is necessary to send 𝜏𝑖𝑗 units to country 𝑖, as ad valorem tax. Usually, other variables are
used along with distance to capture other trade costs such as tariffs, cultural and regulatory
differences, and others (Burnquist et al., 2011; Samuelson, 1952; Vigani et al., 2012).
In this particular study, we are going to estimate a gravity equation to analyze
trade of one product and breakdown effects of technological gap and demand lag, as we are
primary concerned with the relationship between trade and over the time, in the context of
certain levels of technology “hatred”.
Thus, our challenge is to estimate a model of trade for one good – or more
precisely a short continuum of goods – and include our technology variables to drawn
conclusions about this relationship.
In doing so, we cannot resort to the convenience of using country fixed effects, as
it would hamper the estimation of technological gap, a country-level variable. But we can
alternatively use country 𝑖′𝑠 production and country 𝑗′𝑠 consumption of these commodities to
control for size. This procedure is not only recommended but also desired when working with
industrial data (Head & Mayer, 2013). The advantage of this procedure is to avoid the
problems of aggregation bias, getting more straightforward coefficients in terms of
interpretation. Noteworthy, without country’s fixed-effect, we need to control for RMT in
order to have a structured gravity equation.
One of the major drawbacks of using industry or one good data is the higher
shares of zero-valued flows, making the standard estimate of log-linear equation tricky.
Simply cutting out zero-valued flows from the sample is not the best solution since it can
potentially create a problem of strong sample selection bias. Yet, zeroes can be meaningful in
some situations as when impeditive fixed costs are playing a role in the chance of a country 𝑖
55
Commonly, distance is calculated by means of the great circle formula, which uses latitudes and longitudes of
the most important cities/agglomerations (in terms of population(Mayer & Zignago, 2011).
102
export to country 𝑗 – what may happen in our case if levels of “hatred” are big enough to
cripple some trade flows.
Econometric tests show that censored or truncated regressions and replacement of
zeroes by arbitrary numbers are biased and also not preferred to two-stage selection models
(Linders & De Groot, 2006). That is the reason why many economists have been using
Heckman (1979) two-stage model to correct for sample bias. In addition, the problem of firm
heterogeneity and impeditive costs was formally treated only recently by HMR (2008).
Also, we want to explicitly evaluate the effects of technology change in trade,
instead of considering only the state of technology in each country. As seen in chapter II, the
technology gap and the demand lag will change over time, and will impact differently on
trade for each period 𝑡. As we have panel-structured data, at first, it is not a problem to get
coefficients adjusted by the changes in these variables. But two others difficulties arise from
using panel data with gravity equation for one product.
First, Heckman type correction for selection bias is not straightforwardly
applicable for panel data. Second and related, HMR (2008) developed a whole model to
assess cross-sectional or pooled data, making the calculation of the controls for firm
heterogeneity and selection bias not ready to go with panel data analysis as well. Several
papers attempted to provide a final solution for Heckman type correction in panels, but until
the present there is no optimal solving for this puzzle (Charbonneau, 2014; Gómez-Herrera,
2012; Martínez-zarzoso, Vidovic, & Voicu, 2014).
All things considered, we decided to go further with HMR (2008) two-stage
adapted approach on pooled data and a FE model on panel data. With this, we can also assess
the stability of the coefficients in both models.
Our final models can account for the potential problems of sample selection,
omitted firm heterogeneity effects and dynamic effects56
of technology change predicted by
Posner (1961). In addition we could also include variables to test for other interesting effects,
such as technology state (Eaton and Kortum 2002) – measured by the countries’ average
productivity – and differences in land availability, since factor endowments can be a
significant source of agricultural goods.
56
By dynamic we mean an effect changing over time.
103
Original model by HMR (2008) can be obtained from the simple linearization of
equation 8.b57
.
𝑚𝑖𝑗 = 𝛽𝑜 + 𝜆𝑖 + 𝜒𝑗 − 𝛾𝑑𝑖𝑗 + 𝑤𝑖𝑗 + 𝑢𝑖𝑗 . (1.d)
For this specification, with lowercase letters representing the natural logarithms of
original variables, 𝜒𝑗 = −(휀 − 1) ln 𝑐𝑗 + 𝑛𝑗 is a fixed effect of the exporting country,
𝜆𝑖 = (휀 − 1)𝑝𝑖 + 𝑦𝑖 is a fixed effect of the importing country and 𝑑𝑖𝑗 is the symmetric
distance between 𝑖 and 𝑗 - with 𝜏𝑖𝑗−1 ≡ 𝐷𝑖𝑗
𝛾휀−𝑢𝑖𝑗 . The new variable 𝑤𝑖𝑗 controls for the
fraction of firms that exports from 𝑗 to 𝑖, possibly zero.
Taking into account our objectives and estimation strategy we adjusted the model
to
𝑚𝑖𝑗𝑡 = 𝛽𝑜 + 𝜑𝑐𝑜𝑛𝑠𝑖𝑡 + 𝛿𝑝𝑖𝑡 + 𝜓𝑝𝑟𝑜𝑑𝑗𝑡 − 𝜚𝑐𝑗𝑡 − 𝛾𝑑𝑖𝑗𝑡 + 𝑤𝑖𝑗𝑡 + 𝑢𝑖𝑗𝑡 , (2.d)
where, we directly control for country 𝑖′𝑠 consumption (𝑐𝑜𝑛𝑠𝑖𝑡) and variables
impacting on price index or remoteness (𝑝𝑖𝑡), and for country 𝑗′𝑠 outcome (𝑝𝑟𝑜𝑑𝑗𝑡 )and
production costs variables (𝑐𝑖𝑗). The control 𝑤𝑖𝑗𝑡 is defined as in HMR (2008) except for the
fact we are controlling firms’ fraction for each period 𝑡, explicitly assuming that the number
of exporting firms can change over time. Note that with this formulation we can compute the
technological gap as a country 𝑗′𝑠 specific-cost affecting the overall production costs. The
demand lag, in turn, will be computed as a type of variable cost of trade, making 𝑑𝑖𝑗 change
over the time58
.
As in HMR (2008), our first-stage consists of estimating a Probit model to
calculate both, the sample selection and the firm heterogeneity controls to be added into the
gravity equation at the second-stage. In addition to returning the controls for firm
heterogeneity and sample bias, by the means of the Probit model we can breakdown the
effects on trade into extensive and intensive margins. That is, how much each variable
impacts on the probability of trade and how much it increases the volume traded between 𝑖
and 𝑗.
57
Equation 8.b delivers the gravity equation from HMR (2008) model.
𝑀𝑖𝑗 = (
𝜏𝑖𝑗𝑐𝑗
𝛼𝑃𝑖)
1−
𝑌𝑖𝑁𝑗𝑉𝑖𝑗. (8.b)
58 For clarity, we are not saying that geographical distance changes over the time, but we are saying that some
variable costs, such as “demand lag” can vary across the t’s.
104
Thus, if firms’ productivity differs within countries in an interval (𝑎𝐿 , . . , 𝑎𝐻), in
which 𝑎𝐿 is the most productive and 𝑎𝐻 the least productive firm. Assume that productivities
variation can be represented by a Pareto distribution 𝐺(𝑎) = (𝑎𝑘 − 𝑎𝐿𝑘)/(𝑎𝐻
𝑘 − 𝑎𝐿𝐾), 𝑘 >
(휀 − 1). Though, only a share of firms will export – those with productivity high enough to
serve a market 𝑗 and breakeven fixed costs of trade.
The selection of firms into exporting markets is determined by a cutoff 𝑎𝑖𝑗, which
is implicitly defined by the zero profit condition – see equation (6.b)59
. Thus, we can define a
latent variable 𝑍𝑖𝑗 as (omitting time subscript for simplicity)
𝑍𝑖𝑗 =
(1 − 𝛼) (𝑃𝑖𝛼
𝑐𝑗𝜏𝑖𝑗)
−1
𝐶𝑂𝑁𝑆𝑖𝑎𝐿(1− )
𝑐𝑗𝑓𝑖𝑗 .
(3.d)
Note that 𝑍𝑖𝑗 is the ratio of variable export profits to the fixed costs for exports
from 𝑗 to 𝑖 by the most productive firm in country j. Positive exports are observed if and only
if 𝑍𝑖𝑗 > 1. Otherwise, if the most productive firm in 𝑗 cannot export to 𝑖, then no other firm
can. As a result, trade will be 0.
In this case, 𝑊𝑖𝑗60 is a monotonic function of 𝑍𝑖𝑗, that is, 𝑊𝑖𝑗 = 𝑍𝑖𝑗
(𝑘− +1)/( −1)−
1. 61
Fixed export costs are stochastic due to unmeasured trade frictions 𝑣𝑖𝑗 that are i.i.d., but
may be correlated with the residuals of the second stage estimation 𝑢𝑖𝑗′𝑠 . Let 𝑓𝑖𝑗 ≡
exp (𝑘𝜙𝑖𝑗 − 𝑣𝑖𝑗), where 𝑣𝑖𝑗~𝑁(0, 𝜎𝑣2), and 𝑘𝜙𝑖𝑗 is an observed measure of any country-pair
specific fixed trade costs. Note that unlike HMR (2008) we are assuming that fixed trade
costs only exist because of the interaction between 𝑖 and 𝑗. This assumption is appropriate for
our purposes since we are assuming that fixed costs will be a result of differences in
approved varieties for commercialization in 𝑖 and for production in 𝑗.
59
Remember we are assuming a Pareto distribution for firm heterogeneity with productivities varying from 𝑎𝐿
to 𝑎𝐻. Zero profit condition was defined as
(1 − 𝛼) (
𝜏𝑖𝑗𝑐𝑗𝑎𝑖𝑗
𝛼𝑃𝑖)
1−
= 𝑐𝑗𝑓𝑖𝑗. (6.b)
60
From the zero profit condition (6.b), and equation (11.b) presented below:
Wij = max {(aij
aL
)k−ε+1
− 1, 0} . (11.b)
61 See equations 4 and 8 in HMR(2008) or the theoretical model presentation in Chapter 2.
105
The number of approved varieties between different 𝑗𝑠 , that is the exporting
countries, may have an impact on average costs and yield, but is mainly a proxy for
innovation or imitation capacity. In other words, approvals are a proxy for countries 𝑗′𝑠
technological gap in relation to a technological frontier defined by technologies available and
adopted by other important exporters operating in the same industry. It is a new concept in
the literature, and future theoretical treatment can consider it as type of “technological
remoteness” determining countries’ importance given the level of technology and technical
progress.
Using this specification together with (휀 − 1) ln 𝜋𝑖𝑗 ≡ 𝛾𝑑𝑖𝑗 − 𝑢𝑖𝑗 , the latent
variable 𝑧𝑖𝑗𝑡 ≡ ln 𝑍𝑖𝑗𝑡 can be expressed as
𝑧𝑖𝑗𝑡 = 𝛾0 + 𝜑𝑐𝑜𝑛𝑠𝑖𝑡 + 𝜍𝑝𝑖𝑡 + 𝜓𝑝𝑟𝑜𝑑𝑗𝑡 − 𝜚𝑐𝑗𝑡 − 𝛾𝑑𝑖𝑗𝑡 − 𝑘𝜙𝑖𝑗𝑡 + 𝜂𝑖𝑗𝑡 . (4.d)
where 𝜂𝑖𝑗𝑡 ≡ 𝑢𝑖𝑗𝑡 + 𝑣𝑖𝑗𝑡 ~ 𝑁(0, 𝜎𝑢𝑡2 + 𝜎𝑣𝑡
2 ) is i.i.d. (yet correlated with the error term 𝑢𝑖𝑗𝑡 in
the gravity equation).
We know that 𝑧𝑖𝑗𝑡 > 0 when 𝑗 exports to 𝑖 , and 𝑧𝑖𝑗𝑡 = 0 when it does not.
Moreover, the value of 𝑧𝑖𝑗𝑡 affects the export volume. Thus, let’s define the indicator variable
𝑇𝑖𝑗𝑡 to equal 1 when country 𝑗 exports to 𝑖 and 0 when it doesn’t. Let 𝜌𝑖𝑗𝑡 be the probability
that 𝑗 exports to 𝑖, conditional on the observed variables62
. Thus we can specify the Probit
model as
𝜌𝑖𝑗𝑡 = Pr(𝑇𝑖𝑗𝑡 = 1|𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠)
= 𝛷(𝛾0 + 𝜑𝑐𝑜𝑛𝑠𝑖𝑡 + 𝜍𝑝𝑖𝑡 + 𝜓𝑝𝑟𝑜𝑑𝑗𝑡 − 𝜚𝑐𝑗𝑡 − 𝛾𝑑𝑖𝑗𝑡 − 𝑘𝜙𝑖𝑗𝑡)
(5.d)
where 𝛷(. ) is the cdf of the unit-normal distribution. According to HMR (2008) this
selection equation was derived from a firm level decision, and it therefore does not contain
the unobserved and endogenous variable 𝑊𝑖𝑗 that is related to the fraction of exporting firms.
Moreover, the Probit can be used to derive consistent estimates of 𝑊𝑖𝑗𝑡.
62
As in HMR (2008) we divided equation (5.d) by the standard deviation 𝜎𝜂 before specifying the Probit
equation to avoid imposing conjunct normality (𝜎𝜂2 ≡ 𝜎𝑢
2 + 𝜎𝑣2 = 1) . However we omitted the star as a
superscript to indicate it. Empirical estimations, as suggested by WTO & UNCTAD (2012), usually ignores this
step. We run models with and without this procedure and we have found no meaningful differences in the model
coefficients.
106
Let ��𝑖𝑗𝑡 be the predicted probability of exports from 𝑗 to 𝑖, using the estimates
from the Probit equation, and let ��𝑖𝑗𝑡 = 𝛷−1(��𝑖𝑗𝑡) be the predicted value of the latent variable
𝑧𝑖𝑗𝑡. Then a consistent estimate for 𝑊𝑖𝑗 can be obtained from
𝑊𝑖𝑗𝑡 = max {(𝑍𝑖𝑗𝑡)𝛿𝑡
− 1, 0},
where 𝛿𝑡 ≡ 𝜎𝑛𝑡(𝑘 − 휀 + 1)/(휀 − 1).
(6.d)
According to our assumptions, consistent estimation of the log-linear model
requires control for both the endogenous number of exporters (via 𝑤𝑖𝑗) and the selection of
country pairs into trading partners (which generates a correlation between the unobserved 𝑢𝑖𝑗
and the dependent variables). Thus, estimates for 𝐸[𝑤𝑖𝑗|. , 𝑇𝑖𝑗 = 1] and 𝐸[𝑢𝑖𝑗𝑡|. , 𝑇𝑖𝑗𝑡 = 1] are
needed.
Additionally, 𝐸[𝑢𝑖𝑗𝑡|. , 𝑇𝑖𝑗𝑡 = 1] = 𝑐𝑜𝑟𝑟(𝑢𝑖𝑗𝑡, 𝜂𝑖𝑗𝑡) (𝜎𝑢𝑡
𝜎𝜂𝑡) ��𝑖𝑗𝑡 . As 𝜂𝑖𝑗𝑡 has a unit
normal distribution, the inverse Mills ratio, as in Heckman (1979) seminal paper, is thus a
consistent estimation of ��𝑖𝑗𝑡63 . Therefore, 𝑧𝑖𝑗𝑡 ≡ ��𝑖𝑗𝑡 + ��𝑖𝑗𝑡 is a consistent estimate for
𝐸[𝑧𝑖𝑗𝑡|. , 𝑇𝑖𝑗𝑡 = 1] and ��𝑖𝑗𝑡 ≡ ln{exp[𝛿(��𝑖𝑗𝑡 + ��𝑖𝑗𝑡)] − 1} is a consistent estimate for
[𝑤𝑖𝑗𝑡|. , 𝑇𝑖𝑗𝑡 = 1] . Thus the model can be estimate using the transformation
𝑚𝑖𝑗𝑡 = 𝛽0 + 𝜑𝑐𝑜𝑛𝑠𝑖𝑡 + 𝜍𝑝𝑖𝑡 + 𝜓𝑝𝑟𝑜𝑑𝑗𝑡 − 𝜚𝑐𝑗𝑡 − 𝛾𝑑𝑖𝑗𝑡 + ln{exp[𝛿(��𝑖𝑗𝑡 + ��𝑖𝑗𝑡)] − 1}
+ 𝛽𝑢𝜂 ��𝑖𝑗𝑡 + ℯ𝑖𝑗𝑡
(7.d)
where 𝛽𝑢𝜂 ≡ 𝑐𝑜𝑟𝑟(𝑢𝑖𝑗𝑡 , 𝜂𝑖𝑗𝑡) (𝜎𝑢𝑡
𝜎𝜂𝑡) and ℯ𝑖𝑗𝑡 is an i.i.d error term satisfying 𝐸[𝑒𝑖𝑗| . , 𝑇𝑖𝑗 =
1] = 0. Note that equation (7.d) is nonlinear in 𝛿𝑡. However, HMR (2008) tests indicate that
a linear model can be estimated. Following the paper specifications we dropped the Pareto
assumption 𝐺(. ) and revert it to the general specification for 𝑉𝑖𝑗𝑡64. Thus, 𝑣𝑖𝑗𝑡 ≡ 𝑣(𝑧𝑖𝑗𝑡) is
now an arbitrary and increasing function of 𝑧𝑖𝑗𝑡 . Now, it is possible to control for
𝐸[𝑉𝑖𝑗𝑡 |. , 𝑇𝑖𝑗𝑡 = 1] using 𝑣(𝑧𝑖𝑗𝑡), approximated with a polynomial in 𝑧𝑖𝑗𝑡 replacing ��𝑖𝑗𝑡 ≡
63
The inverse Mills ratio is written as ��𝑖𝑗 = 𝜙(��𝑖𝑗)/𝛷(��𝑖𝑗), that is, the probability density function (pdf) over
the cumulative density function (cdf).
64 𝑉𝑖𝑗 = {∫ 𝑎
𝑎𝑖𝑗
𝑎𝐿
1−for aij ≥ aL
0 otherwise is presented and discussed in the previous chapter.
107
ln{exp[𝛿𝑡(��𝑖𝑗𝑡 + ��𝑖𝑗𝑡)] − 1} in the equation at the second-stage. Accordingly, our final
estimate equation will be
𝑚𝑖𝑗𝑡 = 𝛽0 + 𝜑𝑐𝑜𝑛𝑠𝑗𝑡 + 𝜍𝑝𝑗𝑡 + 𝜓𝑝𝑟𝑜𝑑𝑖𝑡 − 𝜚𝑐𝑖𝑡 − 𝛾𝑑i𝑗𝑡 + 𝛽1𝑧𝑖𝑗𝑡
+𝛽𝑢𝜂 ��𝑖𝑗𝑡 + ℯ𝑖𝑗𝑡.
(8.d)
As we mentioned before, the estimation of the controls for sample selection and
firm heterogeneity are not straightforward in the case of panel data. There are a few empirical
studies that tackle sample selection models for panel data, but none of them are conclusive in
terms of what is an optimal estimation approach. Among them, an even smaller number of
papers deal with control for firm heterogeneity as in HMR (2008), and again, none of them is
conclusive about optimal estimation procedure.
Most of the papers apply the solution proposed by Wooldridge (1995, p.121-130)
which makes use of a Chamberlain-Mundlak approach (see works by Egger et al (2009);
Egger and Pfaffermayr (2011)). Of course, the approach is focused in treating sample bias
instead of dealing with the firm heterogeneity control proposed by HMR (2008). Additional
tests can be used to diagnosis sample selection bias prior to employing corrections, which are
in general more complicated (Semykina & Wooldridge, 2010; Wooldridge, 1995).
In this study we chose to estimate a Probit for each 𝑡. In this way, we also have a
𝑧𝑖𝑗 and ��𝑖𝑗 for each 𝑡. This approach was used by Martínez-zarzoso, Vidovic, & Voicu (2014)
returning results aligned with other studies, although authors are not stickily employing the
corrections procedures presented in Wooldridge (1995). Instead, they are applying the testing
procedures for sample bias to have the controls. Our estimates of different models with and
without the controls provide more robustness to coefficient values as we are not strictly
employing the strategy used by HMR (2008). But, as HMR (2008) explicitly assume the
existence of firm heterogeneity, the estimates could be considered inconsistent without these
controls.
Testing for sample bias comprises estimating a Probit model – from equation
(4.d) – for each period 𝑡 and, then estimate an OLS with pooled data with controls for the
selection biases (Wooldridge 1995, p.121-130). If the controls are significant in the pooled
model, thus estimates of fixed effect on panel data isn’t efficient without the correction for
sample selection bias. It is important to note that this test was developed to assess sample
selection bias, but we are including HMR (2008) firm heterogeneity control in the same way,
108
as the calculation of firm heterogeneity controls has the inverse Mills ratio as a component.
Test results can be seen in Table 12.
Table 12 – Test for sample selection and firm heterogeneity Biases
Dependent variable: Bilateral trade
Polled – Test
��𝑖𝑗𝑡 -0.394
(0.257)
��𝑖𝑗𝑡 -0.690
(1.279)
��𝑖𝑗𝑡2 0.081
(0.541)
��𝑖𝑗𝑡3 -0.010
(0.063)
Constant -19.618***
(2.617) Observations 6,634
R2 0.420
Adjusted R2 0.418
F Statistic 199.352*** (df = 24; 6609)
Note: *p<0.1; **p<0.05; ***p<0.01
Independent variables omitted
for clarity. Robust stand errors.
Note that neither ηijt nor zijt
coefficients are significant in the pooled data model,
meaning that estimate via FE model on panel data is consistent accordingly to this test. Our
FE model has as individuals the country-pairs and time dimension from 1996 to 2012. Taking
country-pairs as ids is very common in the literature (Baltagi, Egger, & Pfaffermayr, 2014;
Gómez-Herrera, 2012).
We carried out some tests to confirm that FE model is preferred over alternatives.
An F test for FE model and OLS, with the null hypothesis of non-significant effect of
heterogeneity across individuals, returned a p-value < 2.2e-16. In the presence of significant
effects for individuals heterogeneity FE model is preferred over a simple OLS with pooled
data.
FE is also a better choice when compared to Random effects, if we use a
Houseman test with the null hypothesis of non-correlation between errors and regressors, we
get a p-value =1.914e-13. Thus, the Random Effect model is inconsistent (see Green, 2008,
chapter 9).
A Lagrange Multiplier Test (Breusch-Pagan), with null hypothesis of no need for
fixed time effect, pointed to the need for including time fixed effect in our FE model (p-
value< 2.2e-16).
109
Regarding model fitness, we tested for serial correlation, heteroskedasticity and
multi-collinearity. Breusch-Godfrey/Woodridge test don’t allow us to reject the hypothesis
for serial correlation (p-value = 5.117e-08). Also, the Breusch-Pagan test for panel data
revealed the problem of heteroskedasticity (p-value < 2.2e-16). We treated both problems
with “arellano” correction in R cran. Thus we got a robust covariance matrix of parameters
for fixed effects panel data according to White method(Arellano, 1987; White, 1980).
Finally, testing for multi-collinearity (for polled data) returned no Variation
Inflation Factor (VIF) higher then 5, except for controls and powered variables.
All the models and tests were run in the Comprehensive R Archive Network (R
Cran) making use of plm, glm, sandwich, lmtest, sampleSelection, car and tseries. Box 1
summarizes the major estimates steps.
Box 1 – Summary of Major Estimates Steps
Step # Name Description
1 Database Org. Data gathering and organization.
2 1st Stage - Probit Estimation of equation 5.d to obtain controls for firm heterogeneity
and sample bias. We estimated one model per year – 18 in total.
3 HMR-Model Estimation of cross sectional data (OLS) with fixed effect per year
(equation 7.d)
4 Test for Sample Selection Estimation of pooled data, id= country pair, with controls for firm
heterogeneity and sample bias based on Wooldridge (1995), p.121-
130. Test returned no significant coefficient for both controls.
5 FE or OLS? F test, H0=non-significant heterogeneity across individuals( p-value <
2.2e-16).
6 FE or Random Effects? Houseman Test, H0= non-correlation between errors and regressors
(p-value =1.914e-13).
7 FE with fixed effect for
time?
Lagrange Multiplier Test (Breusch-Pagan), H0=no need for fixed
time effect (p-value< 2.2e-16).
8 FE + time fixed effect
(Table 14 - column 4)
Baseline model without the controls from step 2 and technological
variables.
9 FE (Table 14 - column 5) Baseline model + interest variables Tech. Gap and Demand Lag.
10 FE (Table 14 - column 6) Baseline model + variables Tech. Gap + Demand Lag + controls
estimated in step 2.
11 FE (6) serial correlation? Breusch-Godfrey/Woodridge test, H0= no serial correlation. p-value
= 5.117e-08
12 FE (6)
heteroskedasticity?
Breusch-Pagan test, H0= no heteroskedasticity. p-value < 2.2e-16.
13 Multi-collinearity
(pooled data)?
Test carried out on pooled data. No Variation Inflation Factor (VIF)
higher then 5, except for controls and powered variables.
14 FE (6) + arellano (Table
14 - column 7)
We treated problems in step 11 and 12 with “arellano” correction.
Thus we got a robust covariance matrix of parameters for fixed
effects panel data according to White method(Arellano, 1987; White,
1980).
Source: Prepared by the authors.
110
3.2 Data
Although trade data availability is increasing, to extend analysis out of OCDE
countries are still a considerable challenge since a lot of countries have not organized
information that easily integrates with trade-level data from some structured sources.
Data on soybean trade flows used in this study comes from BACI database
developed by CEPII at a high level of product disaggregation. This database is based on
original data reported by the United Nations Statistical Division (COMTRADE database).
BACI was built using an original approach that reconciles the declarations of the exporter
and importer – enabling to considerably extend the number of countries in the dataset (for
detailed information see Guillaume & Zignago, 2010). General gravity data – distance,
colonial ties, common language, contiguous or landlocked territories, among others, are from
GeoDist database also by CEPII (for detailed information see Mayer & Zignago, 2011).
Agricultural related data, such as production, average yield, arable-land and others
were collected from the FAOSTAT database. Data on exchange rates comes from the
International Monetary Fund (IMF), and prices data from World Bank Commodity Price Data
(The Pink Sheet).
Country-level data on biosafety used to build our technological variables comes
from different sources. Data on approval of genetically modified varieties comes mainly from
International Service for the Acquisition of Agri-biotech Applications (ISAAA) database.
However, when needed – because of missing or incomplete information – data was fulfilled
by information from Global Agricultural Information Network (GAINS) report by the
Foreign Agricultural Service of the USDA (FAS-USDA) and data from Biosafety-Clearing
House (BCH) databases.
The GAINS reports are country-specific reports prepared by country authorities
relating the general state of technology regulation, public view and adoption and other related
issues. The BCH databases includes notification of first transboundary movements of living
modified organisms (LMO), and countries’ profile containing number of experts, number of
laws and other regulations on the topic, and copy of regulatory documents for public
consultation.
Complementary information was required mainly because not all countries have
passed regulation to prior risk analysis before approving a new GM variety for any purpose.
Thus, a zero approval could mean unrestricted imports (approval of all varieties being
produced) or a general ban on importing GM food/feed. In general, GAINS reports for
111
several years were consulted to build the variable for mandatory labeling, and when required
(because of missing of information) we consulted legal documents issued by country
authorities to fulfill the blanks.
To estimate sample selection and firm-heterogeneity biases it was necessary to
add to database all possible combinations of trade partners. Reasonably countries that have
no commercial relation with any of the other countries for a given year were dropped from
the sample. These type of non-positive flows have no meaning information to our study.
The final database used for the estimation of the Probit model contained 39,751
observations for 16 years, 42 variables and 84 countries. From those, only 6,634 observations
had positive trade flows. The significant reduction of the database calls for a correct assess of
sample selection bias to assure that estimates are consistent. However, as we have seen in 3.1,
according to Wooldridge (1995) test, estimates of FE model isn’t inconsistent without these
controls in our case. Nonetheless, these controls enter the HMR (2008) model via the
specification of the theoretical model.
Final panel database, using the controls generated in first stage has 6,634
observations (id=country-pairs) and 44 variables, containing trade flows of soybean between
1995 and 2012 for 84 countries. In Box 2 we briefly describe the variables actually used in
the models.
Box 2– Model’s Variables Descriptions
Variable Name Description Source
Bilateral Trade Natural logarithm of annual exports of soybean (HS6-120100) from country 𝑗 to
country 𝑖, calculated from the original variable “v”.
BACI-CEPII
Production j
Natural logarithm of annual outcome of soybean of country 𝑗, calculated from the
original variable “Production Quantity” given in metric tons. It includes declared
and estimated data.
FAOSTAT
Consumption i
Natural logarithm of annual domestic supply (imports-exports+stocks) of soybeans
in country 𝑖, including uses as food, feed, seed, processing, waste and others given
in metric tons.
FAOSTAT
Distance
Natural logarithm of geodesic distances between most populated cities in 𝑗 and 𝑖. Distance (or dist in original database) was calculated following the great circle
formula.
GeoDist -
CEPII
Land Border Dummy variable assuming value 1 if 𝑖 and 𝑗 are contiguous, 0 otherwise. GeoDist -
CEPII
Language Dummy variable assuming value 1 if 𝑖 and 𝑗 share a common language spoken for
at least by 20% of the population, 0 otherwise.
GeoDist -
CEPII
Colony Dummy variable assuming value 1 if 𝑖 and 𝑗 had/have a colonial tie, 0 otherwise. GeoDist -
CEPII
Same Country
Dummy variable assuming value 1 if 𝑖 and 𝑗 is considered the same country. Value
1 is settled when countries were/are considered the same state or the same
administrative entity for a long period (25-50 years in the twentieth century, 75 year
in the ninetieth and 100 years before).
GeoDist -
CEPII
Continued…
112
Variable Name Description Source
Exch. Rate 𝑖 Natural logarithm of the inverse of country 𝑖′s official annual exchange rate
given in value of local currencies in terms of 1 US dollar (1/ex_rate).
FMI database
Exch. rate 𝑗 Natural logarithm of the inverse of country 𝑗′s official annual exchange rate
given in value of local currencies in terms of 1 US dollar (1/ex_rate).
FMI database
Landlocked j Dummy variables assuming value 1 if a country 𝑗 is landlocked. GeoDist -CEPII
Landlocked i Dummy variables assuming value 1 if a country 𝑖 is landlocked. GeoDist -CEPII
Yield j
Natural logarithm of country 𝑗’𝑠 average yield per unit of harvested area for
crop products. In most of the cases yield data are not recorded but obtained
by dividing the production data by the data on area harvested.
FAOSTAT
Land
Natural logarithm of differences between 𝑖 and 𝑗 in terms of land under
temporary agricultural crops (multiple-cropped areas are counted only
once), temporary meadows for mowing or pasture, land under market and
kitchen gardens and land temporarily fallow (less than five years). Variable
named as arable-land in the original database.
FAOSTAT
RTA
Dummy variable assuming value 1 if countries 𝑖 and 𝑗 make part of
Regional Trade Agreement (RTA), and 0 otherwise.
International
Economics Data and
Programs - (Sousa,
2012)
Other Goods 𝑖
Natural logarithm of an index built by aggregating annual imports of major
substitutes goods for soybean meal by 𝑖, such as meat meal (HS6-230110),
fishmeal (HS6-230120), cottonseed meal (HS6-230610), linseed meal
(HS6-230620) and groundnut meal (HS6-230500). Value given in tones.
BACI-CEPII
Mand. Label Dummy variable assuming value 1 if country 𝑖 has implemented mandatory
labeling rules and country 𝑗 hasn’t, and 0 otherwise.
GAINS Report -
USDA
Price
Natural logarithm of average annual prices of soybean in global markets
given in nominal USD.
World Bank
Commodity Price Data
( The Pink Sheet)
Remoteness 𝑖 Natural logarithm of country 𝑖’𝑠 𝑎𝑛𝑛𝑢𝑎𝑙 remoteness index as proposed by
Head (2003) – i.e. 𝑅𝑒𝑚𝑖𝑡 = ∑ 𝑑𝑖𝑗/(𝐺𝐷𝑃𝑗𝑡/𝐺𝐷𝑃𝑤𝑡)𝑖𝑡 . Annual nominal GDP
data comes from IMF databases and distance from GeoDist.
IMF Data
GeoDist -CEPII
Remoteness 𝑗
Natural logarithm of country 𝑗′𝑠 remoteness index as proposed by Head
(2003) – i.e. 𝑅𝑒𝑚𝑖𝑡 = ∑ 𝑑𝑖𝑗/(𝐺𝐷𝑃𝑖𝑡/𝐺𝐷𝑃𝑤𝑡)𝑗𝑡 . Annual nominal GDP data
comes from IMF databases and distance from GeoDist.
IMF Data
GeoDist -CEPII
Tech. Gap
Variable calculated as the difference between numbers of approved varieties
for production in country 𝑗and the total varieties of GM-soybean adopted by
other 𝑗’𝑠. Raw data includes approval of GM-variety in producing countries.
ISAAA approval
database
Demand Lag
Variable calculated as the difference between number of approved varieties
for production in country 𝑗 and the total varieties approved for consumption
in country 𝑖. Original data includes approval of GM-variety in producing
and importing countries.
ISAAA approval
database
Source: Prepared by the author.
3.2.1 Variables Transformation
In this subsection we shortly provide further information on transformation we
have made in some interest variables.
General approach to create the variable Demand lag consisted of three steps. First,
we gathered data on approvals for cultivation, food and feed for all the varieties of soybean
available, as reported in ISAAA database. Second, for each 𝑡 and variety we created a
dummy variable (𝑎𝑠𝑠𝑖𝑗) assuming value 1 when country 𝑖 has approved a variety for
113
cultivation not approved for consumption as food, feed or both in country 𝑗65. Third, we
aggregated the values of dummy variables per year to have the total of demand lag for each
pair of countries in year 𝑡.
Further steps were necessary to solve the problem of including countries with
“zero” approved events into the model. We know that many countries have not implemented
regulatory frameworks, or have no assessment capacity to carry out tests for identification of
imports of unauthorized events. On the other hand, we also know that some countries had
banned importation of any GMOs into their territories. The combination of these issues turns
out to lead to two equally factual interpretations for zero approval. Some of them should be
considered as a general approval for any event available, and others should be considered as
actual bans. A related problem emerges when a country implements risk assessments
measures after 1996 – the first year of commercial release of GMOs.
Therefore, we analyzed all zero approvals case-by-case to determine if the value
of total events approved for each year should be ascribed, or zero approval actually means a
ban. Underlying information to discriminate between these two types of zero came from
GAINS reports, but also from BCH archives and the Africa Centre for Biosafety report
(Moola & Munnik, 2007; BCH, 2015; FAS, 2015). As bans to GMOs were usually of public
concerns and well disseminated across specialist and media, we created a dummy for bans to
correct for false zero approvals (𝑏𝑎𝑛𝑗). If a ban were reported by one of the data sources for
a specific 𝑡, so the dummy variable was set to 1. Otherwise, if no ban was reported, the
dummy is set to 0. By multiplying both we get the adjusted asymmetry between 𝑖 and 𝑗 in
terms of approved varieties – i.e. (𝑎𝑠𝑠𝑖𝑗 ∗ 𝑏𝑎𝑛𝑗 = 𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑_𝑎𝑠𝑠_𝑖𝑗).
In the case of Tech. Gap we took a more pragmatic approach. We simply found
the total number of different varieties available and approved for production in at least one
country 𝑗 for a given 𝑡 and subtracted the number of approved varieties in particular country 𝑗
to have a “distance” between country varieties and the state of the art of technology. That was
possible because major producing countries have taken very clear legal positions towards the
technology since the early-1990s. It’s worth noting that we haven’t used logarithm
transformation to these variables, as they are derived of dummy variables.
Lastly, it is important to relate some minors handling on independent variables,
beyond linearization, intended to increase the sample size. We have summed a constant
(0.01) to arable-land and Other Goods 𝑖 original entries to avoid a significant loss of
65
This database will be used to analyze impacts of individual approvals in trade in a future analysis.
114
observations. Although not formally proved as consistent, analysts broadly use censoring
techniques in econometric studies, especially in the case of independent variables. From the
economic perspective we can assume that most of zeroes are missing data for countries with
insignificant share of lands classified as arable-land or imports of meals derived of other
vegetable sources.
3.3 Results and Discussion
In this section we present and discuss our findings. First, it will be helpful to
retake our model and our main assumptions to go further on the analysis. As in equation (8.d)
our model predicts trade flows as a function of countries 𝑖 and 𝑗′𝑠 sizes, country j's costs of
production, country i's index price or remoteness, fixed and variables costs of trade and the
additional controls for firm heterogeneity (or fraction of exporting firms) and sample bias.
Our interest variables are defined as the Demand Lag between trade partners and Tech. Gap
between exporting countries in international markets.
As usual, country’s size is expected to increase bilateral trade, whereas production
and trade costs and remoteness are expected to decrease. Firm heterogeneity is expected to be
a significant control when variability of individual firms productivity are high, making with
some countries have lower fraction of firms productive enough to breakeven the fixed cost of
trade. Sample selection bias will be significant when biases generated by the unobserved
country-pair level shocks 𝑢𝑖𝑗 and 𝜂𝑖𝑗 are high. As our interest variables and the case study
itself point to the likelihood of strong negative effects of high level of “technology hatred”, it
is possible that some flows dropped to zero after adoption in some producing countries.
Finally, we expect that asymmetry of approval between trade partners decreases
trade since it increases the overall costs of trading. On the other hand, we expect that
opportunity costs of delaying adoption will also decreases overall bilateral trade as adoption
can cut down production costs and not all countries are averse to the technology. Noteworthy,
none of the studies we had access controlled for the opportunity costs of not adopting the
technology. Descriptive statistics can be consulted in Table 13. See variables’ description in
Box 2 for more details on variables transformations.
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Table 13 – Descriptive Statistics
Statistic N Mean St. Dev. Min Max
t 6,634 2,003.9 5.0 1,995 2,012
Bilateral Trade 6,634 5.6 3.6 0.0 16.5
Production 𝑗 6,634 13.7 3.5 3.4 18.3
Distance 6,634 8.3 1.2 4.1 9.9
Land Border 6,634 0.2 0.4 0 1
Language 6,634 0.2 0.4 0 1
Colony 6,634 0.1 0.2 0 1
Same Country 6,634 0.05 0.2 0 1
Price 6,634 5.8 0.3 5.3 6.4
Land 6,634 1.3 2.3 -6.1 16.6
Yield 𝑗 6,634 10.0 0.3 8.1 10.6
Tech. Gap 6,634 7.4 3.9 0 15
Mand. Label 6,634 0.3 0.4 0 1
Demand Lag 6,634 1.5 2.9 0 15
RTA 6,634 0.3 0.5 0 1
Exch. Rate 𝑖 6,634 -1.8 2.5 -9.9 3.1
Exch. Rate 𝑗 6,634 -1.9 2.5 -9.9 3.1
Consumption 𝑖 6,634 5.6 2.9 -2.3 11.2
Other Goods 𝑖 6,634 10.8 2.0 -3.9 14.3
Landlocked 𝑗 6,634 0.2 0.4 0 1
Landlocked 𝑖 6,634 0.1 0.3 0 1
Remoteness 𝑗 6,634 18.0 1.0 16.5 22.3
Remoteness 𝑖 6,634 17.8 0.9 15.7 24.3
��∗𝑖𝑗𝑡
6,634 1.0 0.6 0.0 3.5
��∗𝑖𝑗𝑡
6,634 0.9 0.5 0.3 6.3
��∗𝑖𝑗𝑡
2 6,634 1.0 1.7 0.1 39.3
��∗𝑖𝑗𝑡
3 6,634 1.5 7.3 0.02 246.2
Source: prepared by the authors.
As seen in the method section, we estimated the model in the second stage using
two different estimators (Two-Stage HMR and FE model). For the FE model, we
progressively introduced our interest variables and controls to better assess the coefficients
stability. Results can be seen in Table 14 below.
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Table 14 – Estimates Results
Dependent variable: Bilateral Trade
Probit OLS-HMR OLS-HMR I FE FE-I FE-II FE-III
(1) (2) (3) (4) (5) (6) (7)
Production j 0.027*** 0.408*** 0.302*** 0.395*** 0.382*** 0.381*** 0.381***
(0.001) (0.016) (0.025) (0.030) (0.032) (0.026) (0.032)
Remoteness 𝑗 0.003* -0.073* -0.120*** -0.045 -0.049 -0.043 -0.043
(0.002) (0.039) (0.039) (0.097) (0.096) (0.071) (0.096)
Exch. Rate 𝑗 0.008*** 0.122*** 0.082*** 0.041 0.043 0.045* 0.045
(0.001) (0.017) (0.018) (0.034) (0.034) (0.027) (0.034) Landlocked j -0.029*** 1.455*** 1.678*** 1.141*** 1.165*** 1.169*** 1.169***
(0.004) (0.116) (0.117) (0.244) (0.243) (0.183) (0.243)
Yield j 0.054*** 1.226*** 1.001*** 1.138*** 1.141*** 1.143*** 1.143*** (0.005) (0.127) (0.130) (0.270) (0.270) (0.196) (0.270)
Consumption i 0.009*** 0.317*** 0.267*** 0.302*** 0.300*** 0.301*** 0.301***
(0.001) (0.017) (0.018) (0.020) (0.020) (0.017) (0.020)
Remoteness 𝑖 -0.003** -0.110*** -0.086** -0.102** -0.101** -0.105*** -0.105***
(0.010) (0.038) (0.037) (0.040) (0.040) (0.038) (0.040)
Exch. Rate 𝑖 0.008*** -0.075*** -0.111*** -0.066*** -0.071*** -0.072*** -0.072***
(0.001) (0.014) (0.015) (0.014) (0.014) (0.014) (0.014)
Landlocked i 0.010* -0.623*** -0.573*** -0.589*** -0.604*** -0.607*** -0.607***
(0.006) (0.116) (0.116) (0.117) (0.117) (0.118) (0.117)
Other Goods 𝑖 0.028*** 0.162*** 0.060** 0.217*** 0.213*** 0.212*** 0.212***
(0.001) (0.023) (0.030) (0.027) (0.027) (0.024) (0.027)
Distance -0.063*** 0.058 0.370*** -0.303*** -0.287*** -0.289*** -0.289***
(0.002) (0.061) (0.075) (0.067) (0.067) (0.069) (0.067) Land Border 0.193*** 1.916*** 0.884*** 1.543*** 1.533*** 1.534*** 1.534***
(0.014) (0.128) (0.165) (0.141) (0.141) (0.132) (0.141)
Language 0.059*** 0.019 -0.344*** -0.014 -0.019 -0.015 -0.015 (0.006) (0.092) (0.099) (0.105) (0.105) (0.097) (0.105)
Colony 0.006 1.022*** 0.917*** 0.950*** 0.953*** 0.949*** 0.949***
(0.010) (0.160) (0.159) (0.143) (0.142) (0.164) (0.142) Same Country 0.032** -0.210 -0.360* -0.389* -0.381* -0.382* -0.382*
(0.015) (0.201) (0.201) (0.213) (0.213) (0.208) (0.213)
Land 0.008*** 0.211*** 0.179*** 0.182*** 0.186*** 0.187*** 0.187*** (0.001) (0.022) (0.023) (0.027) (0.027) (0.024) (0.027)
RTA 0.033*** -0.210* -0.327*** -0.401*** -0.379*** -0.390*** -0.390***
(0.005) (0.127) (0.126) (0.143) (0.142) (0.131) (0.142)
Price 5.223** 5.229** 5.246** 5.246**
(2.273) (2.313) (2.518) (2.317)
Mand. Label -0.029*** (0.023)
Tech. Gap -0.034*** -0.376*** -0.222*** -0.163** -0.164*** -0.164**
(0.003) (0.046) (0.050) (0.065) (0.060) (0.065) Demand Lag -0.021*** -0.316*** -0.249*** -0.159*** -0.160*** -0.160***
(0.003) (0.046) (0.049) (0.061) (0.057) (0.061)
��∗𝑖𝑗𝑡
1.164*** -0.022 -0.022
(0.261) (0.433) (0.438)
��∗𝑖𝑗𝑡
7.995*** -0.512 -0.512
(1.069) (1.737) (1.576)
��∗𝑖𝑗𝑡
2 -2.249*** 0.082 0.082
(0.439) (0.673) (0.599)
��∗𝑖𝑗𝑡
3 0.209*** -0.018 -0.018
(0.052) (0.073) (0.063)
Constant -4.689*** -13.107*** -16.403***
(0.384) (1.611) (1.857)
Observations 39,751 6,634 6,634 6,634 6,634 6,634 6,634
R2 0.425 0.438 0.253 0.254 0.255 0.255 Akaike Inf. Crit. 24,191.690
F Statistic
135.398***
(df = 36;
6597)
128.565***
(df = 40; 6593)
47.974***
(df = 39;
5522)
53.285***
(df = 35;
5526)
Note: *p<0.1; **p<0.05; ***p<0.01 With the exception of Model (5) all models have robust stand errors (arellano). Dummies for year fixed effects were used to all models and
are omitted.
117
As tests indicates the consistence of FE models we are going to set model (7) as
our benchmark – hereinafter FE (7). But in order to check coefficients stability, we are also
going to consider results from HMR-I (column 3) along with the Probit results (column 1) –
hereinafter HMR-I (3). HMR-I (3) is particularly interesting to breakdown the effects into
intensive and extensive margins, as well as the impacts of firm heterogeneity. Considering
models together do not lead to contradictory interpretations.
The Probit was estimated by pooling the data with fixed effects for years. Note
that it is not the procedure used to estimate ��𝒊𝒋𝒕 and ��𝒊𝒋𝒕
controls. The same procedure is used
in HMR (2008) when authors seek to demonstrate their results are not particular to a single
year.
Note that log-linear form allows us to interpret the effects as trade elasticity for
continuous variables, whereas the coefficients for technological variables should be
interpreted in a different way, since they are reported as integers. Also, the dummy controls
for trade costs, such as language, colony, same country, land and RTA, cannot have their
coefficients interpreted as trade elasticities.
Sample Selection and Firm Heterogeneity Biases
As expected, the controls for firm heterogeneity and selection bias play a role only
in the HMR-I (3). Firm heterogeneity is related to prohibitive fixed costs leading to the lack
of trade between country pairs. This latter effect is important to us since we are assuming that
adoption of GM technology by Argentina and the US along with non-adoption by Brazil from
1996-2005 may have caused changes in the extensive and intensive margin of soybean trade.
In other words, zero trade flows could occur because of strong rejection of GM technology
by some important markets – increased demand lags.
A comparison between models in columns (2) and (3) provides a picture about the
general impact the proposed controls have on the coefficients. The absence of controls will
cause an upward bias for some coefficients. Overall, variables controlling for sizes, country
𝑖′𝑠 remoteness and bilateral trade costs returned upward biased coefficients, whereas country
𝑗’s remoteness is downward. Interestingly, coefficients with higher importance for extensive
margin of trade not only return downward biases but also became significant after the
controls introduction (see coefficients for Language66
, Same Country, and RTA in columns
(1) to (3)).
66
Language will become statistically insignificant in the benchmark model (7).
118
Given the high Variation Inflation Factor (VIF), the value of the coefficients of
these controls cannot be interpreted as consistent in terms of trade elasticities impacts.
However, they are consistent with results from HMR (2008) showing that firm heterogeneity
overcomes the effects of selection bias. Also, the coefficients are positive, meaning that
countries with higher proportion of exporting firms and unobserved characteristic of
countries determining positive trade flows will also impact positively in the volume of trade.
Consequently, the measures of the effects of trade frictions in model (2) can be
considered upward biased, as they confound the true effect of trade frictions with their
indirect effect on the proportion of exporting firms (Helpman et al., 2008). Note that the most
impacted coefficients are those from variables representing trade frictions with predominant
effects in the margins of soybean trade (Land Border, Same Country, Distance, RTA,
Language), coherent with results reported by HMR (2008).
Note that none of the controls are significant in FE (7). Given the test for sample
selection bias proposed by Wooldridge (1995) we can still consider the estimates consistent.
Comparing model HMR-I (3) with FE (7) we can see that some coefficients will return
greater coefficients and others smaller ones. However, remoteness and exchange rate of
exporters and common language will be statistically insignificant in (7) but significant in (3).
Impacts of country-level variables
We have country-level variables for countries 𝑖 and 𝑗 in the model. Country 𝑗′𝑠
variables controlling for country specificities – technological variables excluded – are
Production 𝑗, Exch. Rate 𝑗, Landlocked 𝑗, Yield 𝑗 and Remoteness 𝑗. Country 𝑖′𝑠 variables are
Consumption 𝑖, Exch. Rate 𝑖, Landlocked 𝑖, Other Goods 𝑖 and Remoteness 𝑖.
From the supply side, based on theory and studies on aggregated data, we can
expect that soybean trade will increase along with higher outcomes of soybean and average
yields of exports. Conversely, trade volumes tend to be decreased by higher exchange rates,
lack of maritime exits and remoteness.
Production and yield returned the expected impact on trade flows as we can see in
columns (3) and (7). Given the production elasticity of trade reported in FE (3), for each
increase of say, 10%, in production, trade will increase in 3.8%. Trade elasticity of
production is not higher because of internal consumption of soybean in major producing
countries (e.g. China) and some of the larger exporters producing small quantities of soybean
(eg. international hubs). Note that production also impacts on the probability of exporting,
119
but the relevant impact will be at the intensive margin of trade. Exporter with higher average
yields, which can be seen as a result of technologies available for growers at the country
level, also increases the volume of trade significantly. The elasticity in this case, will make
with an increase in average productivity level of 10 % make exports increase by 11.4%. The
parameter in 𝑇𝑖 in Eaton and Kortum (2002) model, representing the general level of
technology in a country, can be a theoretical explanation for the high yield elasticity of
exports. If 𝑇𝑗 is high it will increase the chances of having more productive firms in every
industry.
Other variables for countries 𝑗 , however, need further considerations.
Contradictorily, landlocked exporters tend to export considerably higher volumes when
compared to countries with maritime access67
. However, this effect has a negative impact on
the extensive margin of trade, indicating that landlocked country has on average less trade
connections than others. A look at the data will make clear that this seeming odd result is
valid particularly for the soybean case, given intra-bloc trade in the European Union and
significant shares of world exports of landlocked countries like Paraguay, Switzerland,
Bolivia, Zambia, Austria and Hungary drive this coefficient up. Noteworthy, many of these
countries are large importers of soybeans from other producing countries.
Remoteness and variations of the exchange rate in exporting countries are not
statistically significant in FE(7), and have small coefficient values in HMR-I (3) models. This
result can be explained by the concentrated international supply of soybeans – primarily in
the Americas. In other words, as growers in Brazil, USA and Argentina produce almost the
totality of global traded soybeans, the remoteness of exporting countries tends to be
insignificant because of geographical issues – remember that remoteness index is build based
on distances and GDPs.
Variations in exchange rate in exporting countries are expected to impact trade in
two different ways in the case of soybeans. On the one hand it makes soybean export prices
in countries with devaluated currency cheaper. On the other hand, it makes the inputs used in
production more expensive. The net gains of devaluation will depend on this two-fold impact
on relative prices. Some studies show that currency devaluations in South America displace
exports of US in international markets (Andino & Koo, 2005). However, our results show
67
Landlocked countries tend to trade less (extensive and intensive margins) due to their remoteness leading to
increased trade costs – including transaction and transportation costs. According to World Bank they trade on
average 60% less when compared to countries with maritime boundaries.
120
that impacts on the likelihood of exports is possible very low and positive (0.08%), meaning
that currency valuation in country 𝑗 increases the likelihood of positive trade flows,
marginally. Regarding the intensive margin of trade, HMR-I (3) returned an elasticity of
0.082 for this variable. It can be a result of the predominance of increases of imported inputs
relative prices as well as an indication of the weakness of this variable to explain bilateral
trade of soybeans as the international prices are always given in USD dollars regardless
variation of local currencies.
From the demand side, theory predicts that bilateral trade tends to increase along
with higher demand levels, valuated exchange rates, absence or high imperfectness of
substitute goods, easy access to the sea and low levels of remoteness.
Results show that bilateral trade of soybean actually increases along with total
consumption (or size). One increase of 10% in consumption, will lead to an increase of 3% of
imports. Similar to suppliers’ size control, i.e. production, this number isn’t higher because of
larger consuming markets also producing certain amounts of soybean, such as China. The
effect of substitutes goods (Other Goods i) is interesting, since it shows that soybean has no a
close substitute for meal production in international markets – that is the major end product.
For each increase of 10% in imports of other meals feedstock, such as meat and bone meals,
fishmeal, among others, imports of soybean increase by 2.12% on average. HMR-I (3)
returns coefficients with the same sign and significance but with smaller values.
Importing countries with high degrees of remoteness, devaluated currencies and
landlocked will actually import less volumes of soybean. Landlocked countries import on
average 60% less when compared to countries with maritime access. Currency devaluations
and increases in the remoteness index have smaller impacts, but considering the magnitude of
small percentages applied on huge amounts of soybean traded, the economic significance can
be considerable to some cases. HMR-I (3) has similar impacts in terms of sign, magnitude
and significance of the coefficients. Noteworthy, landlocked country tends to have more
positive trade inflows when compared to others, accordingly to results of model (1).
In sum, we can say size and state of technology in producing countries are the key
drivers of exports volume. On the demand side, size, remoteness, valuated currencies and
access to maritime routes are the most important variables impacting trade inflows. In
addition, the lack of international close substitutes for soybean will also play a role in
determining demand levels.
121
Impact of trade-level variables
At the trade-level we have considered the usual Distance, Land Border, Language,
Colony, RTA and Same Country variables. In addition, seeking to control to factor
endowments and their impacts on input prices, we also considered the difference in land
endowment between trade partners through the variable Land.
First, it is important to note that common language is not significant according to
our benchmark model FE (7). On the other hand, this variable is highly significant in the
HMR-I (3) model, in which countries sharing a common language do trade 41.05% less than
others, but they have 5.9% more chances of trading among them. As in our benchmark model
this variable has no significance, we cannot say much about the effect of language on
international trade of soybean. Thinking of Brazil, Argentina and the United States, it is
difficult to think about an unambiguous effect, as countries speaking Portuguese are few and
with reduced economic importance. On the other hand, the number of countries speaking
English and Spanish will be higher and their economic importance too. This and other
qualifications peculiar to our case, such as the importance of China as a consumer market,
and absence of larger producers speaking Mandarin, will make the analysis of language effect
ambiguous.
A colonial tie, on the other hand, has a positive and meaningful impact on the
trade of soybean. Countries with colonial ties trade approx. 158% more when compared to
others. This can be seen as a shadow of former colonial economic relations, in which the
colonies provided primary products to the industrialized colonists. Interestingly, this effect is
seen exclusively at the intensive margin of trade.
On the other hand, countries sharing a same colonist for long periods will trade
reduced volumes of soybean (46.5% approx.). It can be a result of agricultural specialization
of colonies, making the trade of agricultural products smaller between two agricultural-based
countries, but intense between industrialized and agricultural countries. Impact on extensive
margins, however, will be positive (see column (1). As expected, RTAs will have the very
same type of impact on trade of soybeans. They do connect countries (extensive margin) but
they don’t increase volumes exported – remember that RTA does not mean a bilateral trade
agreement for soybean trade.
Distance, as a proxy for variable costs of trade based on the Samuelson’s “iceberg
costs” is expected to decrease trade volumes. According to our estimates, a 10% increase in
distance will lead to 28.9% decrease in the volume of trade (see column (7)). That is a
122
consistent result, as international freights tend to be a significant component of trade costs.
The access to the Pacific Ocean by the U.S. will make “iceberg costs” of exporting to China
smaller when compared to costs of Brazilian exports to the same market. Graphical analysis
shows that the absence of market rejection made of the U.S. the major supplier of soybean in
China. Additionally, results from HMR-I (3) suggest that distance has a negative impact on
the probability of trade, but a positive effect on volumes. This may be a simple result of gains
of scale. As distant markets will imply in higher costs of trade, higher volumes of trade can
represent some decreases on total costs.
Related to distance effects, the estimates show that countries sharing a land border
will trade significantly more than others. It is an additional indication that distance is a very
important variable to explain the soybean trade. Along with the fact handlers usually operates
closer to major producing or importing markets, we can say that this effects are merely
confirming that transportation costs have a large share in overall costs of soybean production
and distribution.
Finally, as colonial ties has also indicated, we can expect that endowment of land
will be important to partially explain the formation and direction of trade flows. This variable
is a proxy for differences in input costs 𝑐𝑗 and 𝑐𝑖 that will lead to certain degree of industry
specialization, ceteris paribus. It is a source of trade mostly thought from the HO models, but
also treatable in the framework developed by Eaton and Kortum (2002), if we assume that
relative endowments of land will impact the production costs of agricultural goods. The
difference in land endowment will impact mostly on the extensive margin of trade. If this
difference increases by 10%, trade will increase by approximately 1.87%. Note that the
coefficients are very similar across simulations.
Not related to bilateral trade costs but also important enough, the coefficient for
soybean prices in international markets (Price) is statically and economically significant. If
internal annual prices are increased by a very small amount the value of exports tend to
increase considerably (elasticity =5.2). It reinforces the thesis of lack of close substitutes for
soybeans in international markets, showing that imports are inelastic to certain ranges of
price variation.
In sum, the trade between countries located geographically closer, sharing a
border, with colonial ties and differences in factors endowment (land) is more frequent and
higher in volume when compared to others. Remarkably, countries under regional trade
agreements and considered as the same country trade more often but in lower volumes.
123
Impacts of technological variables
Finally, we can discuss the effects of the technological variables, which we are
assuming to have significant impacts on trade. We have three technology-related variables in
our model - Mand. Labeling, Tech. Gap, Demand Lag.
Precisely, differences in mandatory labeling regimes are more a proxy for
regulation asymmetry than for technological change itself, impacting mostly on the extensive
margin of trade. This assumption is based on several studies, which points out mandatory
labeling as one of the most detrimental factors impacting trade of GMOs(Carter & Gruère,
2006; Foster, 2010; Regimes & Giannakas, 2013). Indeed, labels enable consumers to tell
apart food originating from conventional and GM grains. In other words, without the label
consumers could not identify or decide about technologies of production.
There are evidences that countries with mandatory labeling imported soybeans
primary from Brazil, at least until the end of 2012 (P. R. S. Oliveira et al., 2013). Authors
estimated that when mandatory labeling regimes are increased by one year, imports from
Brazil increase by 4.5%.
The Demand Lag variable, in turn, was defined based on the general idea of
imitation lag developed by Posner (1961). Although many empirical works have been strictly
analyzing effects of divergences in regulation or acceptance of the technology in consuming
markets and impacts on producing ones, they rarely contribute to theoretical advances in this
field. In addition, the problem of the demand lag as a variable impacting on trade and
depending on the pace of technology adoption and acceptance has been ignored even among
evolutionary economists – perhaps because of the absence of a case in which this effect were
remarkable enough.
In this study we assume that the lag will enlarge market shares of countries
employing conventional (old) technology in those markets averse to the technology. The
duration and the proportion of the lag will determine the intensity of backward effects of
technology. As major players have passed regulations on risk assessment of new varieties
mainly based on the case-by-case approach, asymmetric and asynchronous approval is a good
proxy for the actual demand lag. That is especially true when we assume that policymakers
are voting new approvals accordingly to public opinion in their countries.
Moreover, approval asymmetry will be drastic as much as adverse countries have
good monitoring capacity to control entry of agricultural products into their territories. As we
have discussed in Chapter II, policymakers had/have a central role in keeping commercial
124
risks at a manageable level. Thus, if importing countries can keep effective bans on
unapproved varieties, an increase of uncovered approvals in exporting countries can
potentially affect or even cripple some trade flows.
The technological gap, on the other hand, has received considerable attention
among the evolutionary economists, but it is usually thought from the perspective of
technological differences between a country-pair. In this work we explicitly take a different
interpretation of the gap. We believe that a relative position of exporting countries in terms of
technology innovation and adoption when compared to general state of technology
worldwide is key to determine trade volumes.
We believe that international competition and innovation rate will create an
international technology frontier, which is constantly moving forward given the pace and
nature of innovation in the economic system. The distance of a country 𝑗, in terms of
technology employed in production, from this technological frontier is the technological gap.
As we consider the relative innovation and adoption rate around the world, this control is
somehow similar to the seminal concept of remoteness put by J. E. Anderson & Wincoop
(2003).
As technology will impact on the overall production costs and not all the countries
developed technology “hatred”, there will be gains of adoption that should be considered in
the models in order to think an overall technological impact. From that, trade should increase
as countries adopted newer and better technologies of production, in markets without the
drawback effects of technology rejection. Conversely, an increase of the technological gap
will decrease trade both in extensive and intensive margins, in those markets without high
levels of technology “hatred”. That is because in “normal” markets, “normal” technological
effects are expected to stand out – i.e. the positive impact of technology on overall production
costs will be a source of comparative advantage.
Considering the relative importance of technology gap and demand lag, we used
the costs related to mandatory labeling as the excludable variable required for estimating the
Probit at the first stage. The existence of a mandatory labeling regime in destination and
absence in sourcing countries decreases the probability of trade almost by 3%.
Beforehand, we can see that the coefficients of our other two technological
variables behavior steadily in all models. FE (7) points out that opportunity costs of not
adopting available technology are not only statistically but also economically significant. For
each variety that a producing country doesn’t grant approval for any reason trade will
decrease by 16.4% on average. Looking at the coefficients of HMR-I (3) we could say that
125
impacts could be even larger – 3.4% in the probability of trade and 22.2% in the volume of
trade.
Interestingly, the demand lag will have a very similar impact on trade volumes. A
difference in approved varieties between exporting and importing countries will decrease
trade on average by 16%. Again HMR-I (3) returns greater coefficients - 2.1% decreases in
the trade probability and 24.9% in trade volume.
The similarity of the impacts of both variables is an indication of the existence of
certain market rationality in approving new varieties, which balances opportunity costs of not
approving new and better varieties, and the commercial risks involved in adopting too many
varieties in the context of technology “hatred” in important markets. From this perspective,
the pace of adoption in Brazil and Argentina ended up balancing the opportunity costs of
non-adoption and the commercial risks of adoption in a way that the impacts on international
trade were not catastrophic.
The high coefficients for the technological variables are merely reflecting the
replacement effect we have seen in graphical analysis in Chapter II. As the EU is a giant
consuming market, the average impact of uncovered approvals is expected to have huge
impacts on trade pattern. These results indicate that, indeed, regulation and aversion in
developed countries can impact production and well fare in producing countries. Vigani et al
(2012) found an impact of 33% in trade flows to an increase of regulatory dissimilarity. This
can be an indication that not considering the effects of the opportunity costs or the
technological gap can bias upward the coefficient for the demand lag.
It is important to highlight that the first seeds developed in second half of the
1990s didn’t have great impacts on yield or overall cost reductions in tropical areas. In this
case, opportunity costs were kept relatively low in Brazil during this period. As the demand
lag was more remarkable during the first period, commercial risks were more relevant back
there. Thus, the effect of demand lag and small advantage of new technology were central to
hold Brazilian competitiveness during the national official ban on cultivation of GMOs.
Notwithstanding, the technological advance and development of new and better
varieties increased considerably the opportunity costs of non-adoption. From a more
evolutionary perspective technological advantage can reach up an irreversible degree for
later-movers. In other words, in the absence of non-technological advantages if a proved
better technology is available and adopted by other producing countries, the late-mover can
fail to catch-up, loosing significant shares of the market or even leaving the marketplace.
126
In sum, at the same time countries with more similar pace of approvals – smaller
demand-lags – tend to trade more (extensive and intensive margins) there are opportunity
costs of not approving better varieties that can decrease trade. In other others, we are saying
that countries faced a kind of technological trade-off in deciding about adoption under
technology rejection.
This results are paramount to police design, since it brings on scene
counterintuitive reasoning. First, adoption of new technologies will not be always desirable at
any cost. Second, if rejection is not even across different markets producing countries should
care not only about the commercial risks coming from demand lag but also those coming
from a technological gap, which can equally be detrimental to trade.
127
IV. CONCLUSIONS
The central aim of this dissertation was to study the relationship of technology
and trade under high levels of “hatred” toward a “new” product resulting from innovation.
The case of GM-soy is a good experiment, since both the adoption rate in producing
countries and the acceptance of the “new” product in international markets were asymmetric
and asynchronous across time and countries. Moreover, the production and consumption of
soybeans are concentrated at country level, mitigating the problem of the lack of trade data
disaggregated into conventional and GM grains.
To do so, we carried out a systematic observation of the soybean industry,
collected evidences of technological effects in trade of soybeans, put together theoretical
developments relating technology and trade, and finally, we have estimated a gravity
equation to assess our central hypotheses of a dual-technology effect on trade.
The case of GMOs starts with a true revolution in the seed industry, which will be
questioned by consumers – individual and industry – across different countries, in terms of
the new technology benefits and risks. This asymmetry of perceptions and interests on the
two edges of the supply chain will create tensions and risks to be managed by the middlemen.
As very often grains are produced in a country and processed in another, trade conflicts will
arise when producing countries adopt technologies that will be rejected by consumers in
importing countries.
In this scenario, policymakers will have a central role in keeping risks at a
manageable level to avoid collapse of trade flows, even if they are considering also other
production benefits related to the technology. Interesting, the vertical integration of the
supply chain, via ownership of enterprises operating at all logistic levels, the partnerships
between seed companies and grain handlers and high levels of industry concentration were
appropriate and necessary to avoid commercial collapses. That is because without
coordination the industry would not be able to keep full capacity in a system strongly
dependent on high economies of scale.
From the end-consumers’ perspective, the growths of per capita income
worldwide and the emergence of the certification industry will represent a structural change
in the consumption behavior. For the first time in the history of agriculture, it was possible to
see a technology intended to impact primary production process turns out to be of great
concern to society as whole. Consumers from high-income countries are, somehow,
developing a “taste for technology” that can impact significantly the agricultural production,
128
especially in the developing countries. In other words, they don’t demand only food, but they
demand food produced through one or another technology – e.g. organics, legal and fair
employment contracts, etc. Noteworthy, the industrial consumer, in turn, is also demanding
varieties that deliver higher values in terms of intrinsic characteristics – standard sizes, high
oil content, etc.
In the international arena, Brazil, Argentina and the United States at the supply
side, and China and the EU members at the demand side, are the protagonists of the history of
GM-soybean trade. Brazil adopted the technology ten years after growers in Argentina and
the U.S. planted genetically modified seeds for the first time. The EU members, the world
second largest destination of soybeans, developed very adverse regulatory frameworks for
approving and labeling GMOs. But, China has not raised significant bans against the
consumption of GMOs, being an important player to mitigate global commercial risks.
Graphical analysis and findings of other applied and theoretical studies can
illustrate the existence of significant technological effects in trade. In addition, they suggest
that impacts are based on different perception of benefits that consumers have in different
countries. However, many of these studies contribute very few to understanding and
investigating a general relation of “demand lag” and trade, although they usually focus on
estimating the effect of regulatory frictions on bilateral trade. The lack of theoretical interest
by the majority of the studies is a drawback to advance in many important unsolved questions
about the interaction of technology change and trade.
Seeking to shed some lights on the main stylized facts observed on applied studies
in the field, we have revisited three models of trade considering technological effects. The
natural starting point was the Ricardo model of trade as described by Eaton and Kortum
(2012). A second interesting contribution came from Helpman et al (2008). Lastly,
developments of evolutionary economists, departing from Posner (1961), contributed
considerably to our understanding of the case studied and specification of our empirical
model.
Considering our purposes, a clear inadequacy of the neoclassical models is the
lack of a treatment for different tastes across countries. In this respect, we can say that theory
need to advance in this matter to better approach cases in which technological or other
backward effects based on tastes matter.
Assuming that technology differences creates comparative advantages, the theory
of firm heterogeneity and its analytical developments, such as the Two-Stage estimation of
the gravity equation, and the central concepts of demand lag and technological gap, we
129
structured a model in which bilateral trade is a function of sizes, costs of trade and
technological variables.
The inclusion of a type of opportunity costs of no-adoption, namely the
technology-gap, is our main contribution in the matter of correct specification of empirical
models for the case of technology backward effects. Without this control, the analysis is
partial, since rejection is not verified in everywhere. Thus, in part of the marketplace, the
common effects of innovation leading to efficiency gains and, thus, comparative advantage
will be predominant.
We found that both the technological gap and the demand lag had significant
impacts on GM-soybean trade. Impacts can be verified in both the extensive and intensive
margin of trade. Interestingly, both effects are very similar showing that the trade-off
between the opportunity costs of non-approval and the increased commercial risks of
approving varieties not approved in destination markets were in someway balanced. For each
difference in approval considering the technological frontier – technological gap – countries
faced a volume of trade 16.4% lower. For each difference in approval considering varieties
approved in destination markets – demand lag – a country suffered a cut down of 16% in the
volume of trade.
We believe results can contribute to better designs of technological and trade
policies, since they provide a broader perspective of technological effects on trade. We also
believe that other similar cases marked by technological frictions can emerge at anytime, as
the “preference for technology” seems to be a structural change in consumption patterns.
The increased concern about the production means and their relation with ethical,
environmental, social and economic factors will likely increase trade conflicts if multilateral
bodies have reduced coordination power – as seen in the case of modern biotechnology and
trade.
Clearly, impacts on agriculture tend to be more noteworthy, given the especial
features faced by this sector, as intense competition in international markets and increased
awareness of consumers about food safety.
In addition, the lack of marked effects in other industries does not mean that
technological gap and demand lag is not operating in trade patterns in reduced proportions –
as Posner have already being pointing since the 1960s.
In conclusion, future research on the relationship of technological change and
trade is needed to advance in theoretical developments, especially to relax some of the strong
assumptions on consumers’ behavior. Empirical studies of other products and industries is
130
also required to investigate if this effect is specific to agricultural goods subjected to
commercialization approval, or the technological effect can be thought from a more general
perspective.
131
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