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AGRICULTURAL TRADE PERFORMANCE AND POTENTIAL: A RETROSPECTIVE

PANEL DATA ANALYSIS OF U.S. EXPORTS OF CORN AND SOYBEANS

Grace E. Grossen

Thesis submitted to the faculty of the Virginia Polytechnic Institute and

State University in partial fulfilment of the requirements for the degree of

Master of Science

In

Agricultural and Applied Economics

Jason Grant, Chair

Mary Marchant

A. Ford Ramsey

May 30, 2019

Blacksburg, Virginia

Keywords: (Panel data, corn and soybeans, agricultural trade, gravity model)

Agricultural trade performance and potential: A retrospective panel data analysis of U.S. exports

of corn and soybeans

Grace E. Grossen

ABSTRACT

There are a variety of international issues that disrupt the global trade market, an

important one being national policies on the regulation of genetically modified organisms, or

GMOs. Many crops have been genetically modified for reasons from herbicide resistance to

correcting dietary shortfalls. This study evaluates the United States’ exports of corn and

soybeans from 1998 to 2016 to identify unusual shocks in trade values. In particular, this study

quantifies how the importers’ policy stance on the GMO issue impacts bilateral trade values. I

estimate a gravity model with both ordinary least squares (OLS) and Poisson pseudo maximum

likelihood (PPML) estimations. Residual analysis is used to assess the difference between actual

trade and the trade levels predicted by the models. The results suggest that anti-GMO policies

reduce trade values by an average of 11%. The largest difference between predictions and actual

trade values is seen in corn exports to the European Union. Between 1998 and 2016, this

forgone trade in corn was valued at $52.7 billion, which is $2.77 billion per year on average.

This value is similar to the annual average value of U.S. exports of corn to Japan in the same

period, $2.46 billion. The results have important implications for the agricultural industry. For

developing nations, adoption of GMO crops could increase productivity and help alleviate

poverty. Ultimately, the decision to adopt is up to the consumer, so the factors of consumer

knowledge and opinions of GMOs are not to be ignored.

Agricultural trade performance and potential: A retrospective panel data analysis of U.S. exports

of corn and soybeans

Grace E. Grossen

GENERAL AUDIENCE ABSTRACT

There are a variety of international issues that disrupt the global trade market, an

important one being national policies on the regulation of genetically modified organisms, or

GMOs. This study evaluates the United States’ exports of corn and soybeans from 1998 to 2016

to identify unusual drops in trade values. In particular, this study quantifies how the importers’

policy stance on the GMO issue impacts bilateral trade values. I estimate a gravity model with

various estimation methods. Residual analysis is used to assess the difference between actual

trade and the trade levels predicted by the models. The results suggest that anti-GMO policies

reduce trade values by an average of 11%. The largest difference between predictions and actual

trade values is seen in corn exports to the European Union. Between 1998 and 2016, this

forgone trade in corn was valued at $52.7 billion, which is $2.77 billion per year on average.

This value is similar to the annual average value of U.S. exports of corn to Japan in the same

period, $2.46 billion. The results have important implications for the agricultural industry. For

developing nations, adoption of GMO crops could increase productivity and help alleviate

poverty. Ultimately, the decision to adopt is up to the consumer, so the factors of consumer

knowledge and opinions of GMOs are not to be ignored.

iv

ACKNOWLEDGEMENTS

I would like to thank a number of people without whom this work would not be possible.

I appreciate all of the support from my advisor, Jason Grant, as well as my committee members,

Mary Marchant and Ford Ramsey, and many other professors in the ag econ department. I also

appreciate the support of my parents, family, and friends, especially my cohort. Lastly, I want to

thank my Marching Virginians family, including Dave McKee, Polly Middleton, Chad Reep, and

every member of the MV trumpet section.

v

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ..........................................................................................................................................iv

TABLE OF CONTENTS .............................................................................................................................................. v

LIST OF FIGURES ......................................................................................................................................................vi

LIST OF TABLES...................................................................................................................................................... vii

Chapter 1: Introduction .................................................................................................................................................. 1

Objectives and Approach .......................................................................................................................................... 7

Organization ........................................................................................................................................................... 10

Chapter 2: Background ................................................................................................................................................ 11

Grain and Oilseed Crops and Markets ................................................................................................................... 11

Policy Setting .......................................................................................................................................................... 18

Brief Historical Context .......................................................................................................................................... 19

Chapter 3: Theory and Methods .................................................................................................................................. 22

Theory ..................................................................................................................................................................... 22

Estimation Methods ................................................................................................................................................. 25

Chapter 4: Data ............................................................................................................................................................ 31

Chapter 5: Estimation and Results ............................................................................................................................... 38

Chapter 6: Conclusions and Discussion....................................................................................................................... 54

Future Work ............................................................................................................................................................ 55

References ................................................................................................................................................................... 57

vi

LIST OF FIGURES

1.1: Value of Total Trade in Corn and Soybeans, 1998-2016

1.2: GMO Google Image Result

1.3: Glyphosate Chemical Structure

1.4a, Percent Change in Imports of U.S. Soybeans (including China), 1998-2016

1.4b: Percent Change in Imports of U.S. Soybeans (not including China), 1998-2016

2.1: Global Export Share of Major Corn Producers, 1998-2016

2.2: Global Export Share of Major Soybeans Producers, 1998-2016

2.3: Corn for Grain 2017 Production by County for Selected States

2.4: Cultivation of Land in China

2.5: Share of Japan’s Corn Imports by Country, 1998-2016

2.6: Value of China’s Corn Imports by Country, 1998-2016

2.7: Adoption of genetically engineered crops in the United States, 1996-2018.

4.1: Share of U.S. Exports Represented by a Selected Group of Importers, 1998-2016

4.2: Share of U.S. Corn Exports by Country, 1998-2016

4.3a: Share of U.S. Soybean Exports by Country (including China), 1998-2016

4.3b: Share of U.S. Soybean Exports by Country (not including China), 1998-2016

5.1: Alternate Model Tree

5.2: Distribution of Errors for Model 2

5.3: Average Predicted and Actual Value of U.S. Corn Exports to the European Union (EU-15),

1998-2016

5.4: Average Predicted and Actual Value of U.S. Corn Exports to China, 1998-2016

5.5: Average Predicted and Actual Value of U.S. Corn Exports to Japan, 1998-2016

5.6: Average Predicted and Actual Value of U.S. Soybean Exports to the EU, 1998-2016

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LIST OF TABLES

1.1: Largest Net Importers of Corn and Soybeans, 1998-2016

3.1: GMO Variable Matrix

4.1: Selected Groups of Importers for Each Commodity

4.2: RTA Variable Matrix

5.1: Small Corn Sample Regression Results

5.2: Small Soybean Sample Regression Results

5.3: Small Combined Sample Regression Results

5.4: Large Corn Sample Regression Results

5.5: Large Soybean Sample Regression Results

5.6: Large Combined Sample Regression Results

5.7: Observations with Top Negative Residuals for each PPML Model

1

Chapter 1: Introduction

International trade in agricultural goods is one of the many threads that connects people

and nations around the world. A safe and reliable food supply is important to any population,

and people no longer consume only food grown and produced in close proximity. Fresh fruits

and vegetables (Harmonized System chapters 7 and 8) traded across global borders in 2016 alone

had a value of over $150 billion. Many of those products are foods that Americans have become

accustomed to having access to year-round. Yet many of these products cannot be produced

domestically, or cannot be supplied by domestic producers year-round due to climate limitations.

A well-known example is the avocado from Mexico.

With a vast and productive land base, Midwest agriculture in the U.S. has an advantage in

producing commodities such as corn and soybeans. A number of factors have led to increased

demand for these commodities. As median incomes in developing nations rise, demand for

protein from meat also increases, and with it, demand for crops to feed livestock. The value of

global trade in soybeans has grown from just over $8 billion in 1998 to over $51 billion in

2016—a more than 6-fold increase in less than 2 decades. The value of global trade in corn was

just under $8 billion in 1998, but surpassed $33 billion between 2011 and 2013 (author’s

calculations from CEPII’s BACI database). Figure 1.1 illustrates the trend in value traded as well as

volume traded. Starting under $10 million in the late 1990’s, the value of corn and soybean trade

has steadily climbed, with soybeans out-growing corn after 2006. Both see a downturn

following 2008, and have declined again in later years. This decline in value is due to softening

prices. The quantities increase steadily for the duration of the study period.

2

A variety of influences, both policy-related and other factors, impact the state of bilateral

trade relations and the effective functioning of trade agreements. These factors determine how

trade is distributed in the market. In the agricultural sector, trade policy tends to be more

protectionist than in other sectors. For example, the average applied MFN (most favored nation)

tariff on agricultural goods was 18.1% in 2013. For industrial products, that figure was 3.7%

(Bureau, Guimbard & Jean, 2017). A tariff on imported goods protects domestic industry by

increasing the consumer price of imported goods, making domestic products more favorable to

the consumer, all else being equal. In cases where domestic cost of production is high and

imports are cheaper, tariffs may serve to simply level out the playing field and protect a domestic

industry by raising the costs of competing imported goods. However, when the tariff increases

Figure 1.1: Quantity and Value of Total Corn and Soybeans Traded, 1998-2016.

Source: Author’s calculations from CEPII’s BACI database.

3

the imported good’s price above that of the domestic good, the domestic prices will also increase

because competition cannot drive them further down.

Not all countries have the capacity to produce all that they consume. Thus, many

countries depend on imports to meet demand. For example, Table 1.1 lists the largest net

importers of corn and soybeans for the years 1998-2016. On the soybean side, China becomes

the largest net importer in 2003, and their negative trade balance in soybeans grows to over $30

billion. Japan is usually the biggest net importer of corn, but it was passed up by the EU-15 in

2014. This refers to the original 15 European Union member states.

Year Soybeans Corn

Country Exports-Imports Country Exports-Imports

1998 EU-15 -$3.58 billion Japan -$1.8 billion





2003 China -$5 billion Japan -$2.07 billion

2004 China -$5.31 billion Japan -$2.49 billion










2014 China -$35 billion EU-15 -$3.42 billion


2016 China -$32 billion Japan -$2.56 billion

Likewise, not every country has sufficient demand to consume all that they produce, so they

depend on access to export markets for the sale of surplus production. In this way, trade is

Table 1.1: Largest Net Importers of Corn and Soybeans, 1998-2016.

Source: author’s calculations from CEPII’s BACI database.

4

critical to maintain national stability and reliable market outlets for both net importers and net

exporters. Looking at a specific sector like corn or soybeans, there are many more net importers

than net exporters. More specifically, out of 188 countries, 151, or 80%, were net importers of

corn in 2016. That figure was 77% for importers of soybeans in 2016.

Agriculture is not only an important global industry, it can also be a vulnerable one,

impacted by unpredictable natural circumstances such as weather, a changing climate, and policy

factors. The 2012 drought is a dramatic example of how weather trends can impact crop

production and global crop markets. In 2012, a warm spring allowed for early planting of corn,

but after a hot, dry, unpredictable season, the yields were far less than initially expected.

Production of corn in Illinois, usually the biggest corn producing state in the US, dropped by

34% from the year before. Early in the season, the USDA had predicted yields of 166 bushels

per acre, but at the end of the year, the average corn yield was 123.4 bushels per acre. At the

same time, demand for corn remained strong, leading to corn prices over $7 a bushel. (Pitt, 2013)

A more recent example is the spring of 2019. The Midwest is taking longer than usual to

get the corn crop planted because of an unusually wet spring planting season. Illinois,

Minnesota, Indiana, and South Dakota are all behind schedule. These 4 states usually produce

40% of the United States’ corn, and only have a small fraction of their intended corn acres

planted as of early May (Roach, 2019). If planting doesn’t catch up, the total acreage planted

may be far less than expected, resulting in a smaller crop come harvest, and volatile prices as the

season unfolds.

Farmers have lived with this kind of uncertainty for generations, but as our planet’s

climate changes, extreme weather events, including floods and droughts, become more frequent.

Increases in mean temperatures and number of high degree-days have the potential to alter

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growing seasons, and agricultural producers all over the world will have to adapt. (Rosenzweig

et al. 2001)

A development that has been causing international disagreement in the agricultural sector

for decades is the issue of genetically modified organisms, colloquially known (and feared) as

GMOs. GMO is a term with a hazy definition, so

it can be easily misunderstood. The World Health

Organization (WHO) defines a GMO as any

organism containing genetic material (DNA) that

has been modified in a manner that would not

occur under natural circumstances. A Google

image search yields images of unnaturally perfect vegetables being injected with unknown

chemicals (figure 1.2). However, this depiction is misleading, and the image is reused so widely,

it is difficult to trace it back to the original source. Genetic engineering allows specific genes to

be edited or transferred from one species to another. While the technology that is used to move

genes from one organism to another is relatively new, it has and does occur in nature in a process

called horizontal gene transfer (HGT). Bergthorsson et al.’s 2004 study observed this process in

the mitochondrial DNA of Amborella trichopoda, a plant with a genome containing foreign

genes.

Despite the arguments for it, many countries still ban the cultivation of GMO crops for

fear of contamination of their domestic food supply. Many countries accept GM imports for

animal feed uses, but prefer GMO-free in human food. Labeling and registration requirements

are common, based on the argument that the more information the consumer has, the

better. However, whether it is voluntary labeling of GMO-free foods, as the Non-GMO project

Figure 1.2: GMO Google Image Result.

6

aims to do, or mandatory labeling of foods containing GM ingredients, it is still incomplete

information.

The motivation behind the initial modification of crops is also not well

understood. There are many reasons a researcher may seek to modify an existing organism.

Modifying crops to be more hardy, pest resistant, and

productive is one of many solutions brought forward to

address both a growing global population and a

changing climate. Some of the best known GM crops

are Monsanto’s Bt corn and Roundup Ready soybeans.

Monsanto first patented Roundup Ready in 1996 and

the patent was up in 2015, but their next generation variety, Genuity Roundup Ready 2, was

released in 2009 and will hold a patent for many years to come. Roundup, a common herbicide

used on farms and in backyards alike, was also developed by Monsanto. The active chemical is

glyphosate, shown above.

Roundup ready plants are resistant to the herbicide. While Roundup use is fairly

common, both in industrial farming and backyard gardening, there are concerns about the safety

of the chemical. In 2019, a case was brought against Monsanto in California. A jury found that

Roundup was a substantial factor in a man developing cancer after using the weed killer on his

property for years, and he was awarded $80 million in damages. Monsanto is facing many more

cases like this across the United States, but they still stand beside studies which conclude that

their herbicides are not carcinogenic. (Tyko, 2019)

Bt corn takes its name from the organism that contains the desired gene: Bacillus

thuringiensis. Bt is a naturally occurring soil bacterium, and the gene causes the plant to contain

Figure 1.3: Glyphosate Chemical

Structure. (Wikimedia Commons)

7

a natural pesticide; when bugs try to eat the plant, they are poisoned and eat no more. It is

possible, however, that insects develop resistance to the pesticide. To slow this natural process,

Monsanto recommends Insect Resistance Management practices. One is to plant a “sanctuary”

section of non-Bt crop where susceptible insects can survive and stay in the gene pool, helping

keep resistance at bay.

Because Monsanto holds patents and has intellectual property rights to the genes and the

varieties, another concern is the power that they have over the farmers that plant their seeds. The

farmers aren’t able to save seeds to replant, but rather have to purchase new seed from Monsanto

every season. When a large portion of the acreage planted is in GM crops, as is the case in the

US, some worry about the amount of control over the food supply that one company holds. That

company is in the process of getting even bigger, as Monsanto is soon to be integrated into the

Bayer group (Monsanto, 2018). Biodiversity is also a concern in some nations where cultivation

is banned: if all of the crop is genetically identical, a single disease could destroy an entire

harvest.

Objectives and Approach

The purpose of my thesis is to identify unusual shocks for U.S. exports of corn and

soybeans and to quantify the effects of anti-GMO policies on trade. In principle, these goals

could be accomplished by comparing percent changes relative to a predetermined base

period. However, percentage changes reflect more than just change over time. They are

sensitive to the size of initial trade and the selection of the base period. For example, an increase

of one million dollars could be a 50% increase if the initial value was $2 million, or only 10% if

it was initially $10 million. The figures on the following page are to illustrate this bias. They

8

show percent changes in U.S. corn and soybean exports to major partners, using 1998 as the base

year to compare.

In the first figure, it is clear that China’s share grew consistently, increasing imports of

U.S. soy by over 4000% from 1998 to 2016, with a value of $330 million in 1998 and $14 billion

in 2016(CEPII’s BACI database). The second is rescaled to show the other four countries more

clearly. Compared to levels in 1998, U.S. exports of soybeans to Canada are highly variable

from year to year. What is not reported is the fact that Canada’s initial level of U.S. soy imports

in 1998 was just over $23 million, and it was among the smallest values in the sample, and the

following value in 1999 is over 200% of the initial value. Thus, percent changes can be

misleading because of small initial values or shares. Further, percentage changes do not control

for various economic and geographic factors that promote or impede trade, such as distance,

transport costs, and the relative sizes of national economies.

Figure 1.4a: Percent Change in Imports of U.S. Soybeans (including China), 1998-2016.


9

Various other approaches have been used in the empirical literature. Henseler et. al

(2013) used a partial equilibrium model and simulated a scenario where the U.S., Argentina, and

Brazil stop exports of soybeans to the European Union. With this method, they analyze the

negative impacts on the EU’s animal feed and meat markets. Nunes de Faria and Wieck (2015)

create indices describing the asynchronicity between the regulations of two countries in a pair.

They use this index in a gravity model to evaluate the impact that asynchronous approvals have

on trade. In this way, they evaluate the impact of the restrictiveness of a regulation, not just its

existence. They find that trade flows of cotton, corn, and soybeans have been negatively

impacted by asynchronous approvals. Kalaitzandonakes, Kaufman, and Miller (2014) use a

spatial equilibrium model to evaluate a zero threshold case study on the EU. They find that

completely stopping European soybean imports from Argentina, Brazil, and the U.S. would have

severe impacts on the prices of soybeans and soy products in the EU.

-100

0

100

200

300

400

500

600

% C

han

ge c

om

par

ed t

o 1

99

8

Percent Change in Imports of US Soybeans, 1998-2016

CAN

EUR

KOR

MEX

Figure 1.4b: Percent Change in Imports of U.S. Soybeans (not including China), 1998-2016.


10

In this study I use an empirical gravity model grounded in theory and estimate multiple

specifications in order to generate deviations between actual and predicted trade. I then use

residual analysis to identify the most significant negative shocks between actual and predicted

trade to understand some of the important natural and policy factors contributing to abnormal

shocks to bilateral trade flows. This is similar to Cassey and Zhao’s (2012) method for

identifying foreign markets underserved by the state of Washington’s agricultural sector.

My core hypotheses are as follows:

H1: U.S. corn and soybean exports to foreign markets with protectionist policies related to GMO

tolerance will be associated with the largest negative deviations from potential trade as predicted

by the model.

H2: Drought and weather-related shocks and disease will also be associated with large negative

deviations from potential trade as predicted by the model.

Organization

This thesis is organized as follows. Chapter 2 provides background on the global corn

and soybean markets, as well as some background on agricultural policy in China, who imports

significant amounts of U.S. corn and soybean exports. Chapter 2 also briefly describes relevant

historical events during the study period: 1998-2016. Chapter 3 discusses the theoretical

framework of the study and the methods used to empirically execute the analysis. Chapter 4

discusses the data set used for the analysis. This includes the original sources, data preparation,

and how the data were used. Chapter 5 presents the results and discusses their interpretation and

meaning, and Chapter 6 concludes with a summary of my main findings and policy implications,

as well as future applications of this work.

11

Chapter 2: Background

Grain and Oilseed Crops and Markets

Corn and soybeans are represented by the 4 digit Harmonized System (HS-4) codes 1005

and 1201, respectively. These categories specifically include whole or broken corn and

soybeans. My analysis does not directly include byproducts such as ethanol and distillers dried

grains (DDGs), as they are classified separately. Soybeans are crushed into two major products:

soybean meal and oil. Meal of oilseeds, including that of soybeans, is represented by the HS-4

code 1208. Soybean meal is a primary component of animal feed; 97% of the soybean meal in

the U.S. is used for animal feed. Soy oil is represented by HS-4 code 1507. It is commonly used

as cooking oil and as an ingredient in processed foods. For example, soy oil is the first

ingredient listed in Ken’s Caesar salad dressing.

Corn is converted into many industrial products and byproducts including corn meal,

corn starch, corn oil, corn syrup, and ethanol. One bushel of corn can be converted to 2.8 gallons

of ethanol (Radich, 2015). HS-4 1102 includes corn and other cereal flours, HS-4 1103 includes

corn meal, and HS-4 1108 includes corn starch. Corn oil, like soy oil, is in chapter 15,

specifically 1515. HS-4 2303 includes brewing and distilling waste. This includes DDGs, a

byproduct of alcohol production. DDGs are often used as an ingredient in livestock rations.

Despite a record drought that impacted U.S. corn production in 2012, the United States

has held a dominant market share in corn exports for decades. Figure 2.1 shows the U.S. market

share in corn compared to that of 4 other large producers: Argentina (ARG), Brazil (BRA),

China (CHN), and the Ukraine (UKR). The marketing years for corn and soybeans in the U.S.

starts in September, while Argentina and Brazil harvest in the opposite season. In 2003, China

reached a relative peak with almost 20% of the market share. This was the same year the U.S.

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reached their lowest market share since 1998. After 2008, China’s domestic support price for

corn was too high for exports to be worthwhile (Hejazi & Marchant, 2017), so its exports

dropped to essentially zero. Since then, China has relied almost exclusively on corn imports to

satisfy domestic food and feed requirements. Brazil and the Ukraine have both grown from

being fairly irrelevant in the late 1990’s to holding between 10% and 20% of the export market

in 2015. Argentina was the most stable of the group, staying between 10% and 20% for most of

the sample period. Argentina and Brazil both have small spikes in 2013 mirroring the decrease

in the United States’ share, likely as a result of the record 2012 drought that impacted corn

production in the US. The total market share represented by this group ranges between 70% and

90%. The decline of the U.S. share over time is also due to an increasing number of countries

entering the corn export market.

Figure 2.1: Global Export Share of Major Corn Producers, 1998-2016.


13

Figure 2.2 shows the U.S. market share in soybeans compared to that of 4 other large

producers: Argentina, Brazil, Canada (CAN), and Paraguay (PRY). Soybean production is

dominated globally by the United States and Brazil. Unlike corn, the U.S. doesn’t hold the top

global export market share of soybeans throughout the whole sample period, with the highest

share of just over 60% occuring in 1999. The United States’ export share increases and

decreases are mirrored by that of Brazil. When each holds about 40% of the market share, it

leaves only 20% divided between dozens of other countries in the soybean export

market. Argentina sometimes mirrors the U.S. and sometimes reflects Brazil’s movements, but

stays significantly below the levels of either country. Canada and Paraguay are stable between

1% and 10% for the whole sample period. This group consistently holds 94% or more of the

soybean export market share, but most of that is divided between the United States and Brazil.

14

As mentioned above, corn and soybeans are both important inputs to livestock

production. Feed alone is usually the most expensive input for a livestock operation, so the

prices of corn and soybeans are as important to a pork or beef grower as they are to a crop

farmer. Iowa is not only the top producer of pork in the US, it’s also one of the top producers of

both corn and soybeans, second only to its neighbor Illinois. This combination means Iowa has a

very interconnected and self-supporting agriculture sector. The following image from NASS

shows the acreage of corn planted in that part of the country, known as the Corn Belt. It should

be noted that the data is “for selected states,” meaning that a state being all blank does not mean

that it is a maize-free zone, but rather that its corn production isn’t large enough to include.

Figure 2.2: Global Export Share of Major Soybean Producers, 1998-2016.


15

Generally, more area is required to grow crops than to raise livestock, especially in the

case of hogs, which are often raised in an indoor system. Therefore, the available arable land in

a country is a good indicator of its ability to specialize in crop production. Corn and soybeans

alone make up a large portion of the planted acres in the United States, with 90 million acres of

each planted in 2018 according to USDA estimates (2018 USDA Ag Outlook). Because animal

feed is a major end use of both corn and soybeans, demand for meat is one driver of demand for

the crops. As incomes rise in developing parts of the world, people eat a more diverse diet

including more high quality animal proteins. Domestic demand for U.S. pork has remained

stagnant for some time, but the Chinese population, about a quarter of the global population, has

a high demand for pork and provides a growth market for anyone producing pork or pork

production inputs, particularly the United States. Figure 2.4 below shows the cultivation of land

in China, and it is clear that while China’s land mass is significant, much of the country is

unutilized.

Figure 2.3: Corn for Grain 2017 Production by County for Selected States

Source: NASS

16

Demand is also driven by processed products that use plant oils, such as salad dressing

and biofuels. Grain alcohol has long been produced for human consumption, but around 2008

that same ethanol molecule was valued quite differently. In 2007, President Bush signed a bill

that provided for increases in ethanol production through 2022, creating sudden new demand for

corn (Gustafson, 2010). U.S. corn prices shot up, but ethanol prices couldn’t keep up for long,

and soon expensive corn made ethanol production more expensive than it was worth. Today, the

fuel we put in our cars is generally about 10% ethanol (Radich, 2015). Though ethanol is a very

simple molecule, it has had a significant impact on the corn market.

For countries with populations larger than their land endowment can feed, imports of

corn, soybeans, and other agricultural products exceed exports. However, these countries often

Figure 2.4: Cultivation of Land in China.

Source: UT Austin Map Collection

17

have a trade surplus in industrial and manufactured goods. Japan is a good example of this; the

island nation holds very limited arable land and this limits expansion. Japan’s trade balance is

defined as exports minus imports, implying a negative trade balance when the former exceeds

the latter and the country is a net importer. The graph below shows the U.S. and two other major

exporters’ share of Japan’s corn import market. The U.S. holds nearly all of the share, staying

close to 90% up until 2012. When the United States’ share decreases in response to the 2012

American drought, Argentina and Brazil pick up some slack, but the total value of Japanese corn

imports also declined around the same time. Japan’s total imports are plotted in orange and

scaled on the right hand axis.

Figure 2.5: Share of Japan’s Corn Imports by Country, 1998-2016.


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Policy Setting

Since the early 2000’s, U.S. agricultural exports to China have been growing steadily,

correlating with China’s growing economy and incomes and increasing demand for meats and

processed food. U.S. agricultural exports to China increased in value by 25.6% per year from

2002 to 2013, and China accounted for 16% of all U.S. agricultural exports in 2016 (Marchant et

al., 2017). U.S. export growth was mostly in commodities that don’t conflict with China’s self-

sufficiency goals, such as soybeans (Hejazi and Marchant, 2017). China was the top destination

of total American agricultural exports as of 2016, followed by Canada, Mexico and Japan, but

this doesn’t mean that exports of every individual agricultural product increased. Corn did not

see the same impressive growth as soybeans did. Part of the reason for this was the deal that

China struck with the Ukraine in 2014, in part due to the Silk Road Initiative, to buy Ukrainian

corn, even though U.S. corn was lower priced. (Hansen et al., 2017) Even though they produce

and consume half of the world’s pork, China didn’t increase domestic production of soy for

soybean meal. Their soybeans are sourced from the U.S. as well as Brazil because it’s not

profitable to produce soybeans in China, especially compared to corn under the price support

policies. While China is a major destination for U.S. pork, the European Union leads in pork

exports (mainly frozen pork) to China, led by Germany and Spain. Figure 2.6 below shows the

increase in China’s corn imports after 2008, and the shift from American to Ukrainian majority

in market share.

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Brief Historical Context

The study period is 1998 through 2016. During this period, the WTO added 33 new

members, among them China, Taiwan, Vietnam, the Ukraine, Russia, and several Middle Eastern

countries and former Soviet states. In addition, the WTO launched the Doha Round in 2001. In

2001, China became a member of the WTO, which means it had to bring its policies in line with

the rules set by the WTO, including those regarding import protection and domestic market-

distorting policies.

The European Union also expanded during the study period. Ten new countries joined in

2004 (Cyprus, Czechia, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and

Slovenia), two joined in 2007 (Bulgaria and Romania), and one joined in 2013 (Croatia). With

these additions, the EU reached 28 members. By joining the EU, these mostly eastern European

countries gained freer access to markets in Western Europe, and some joined the Eurozone.

Figure 2.6: Value of China’s Corn Imports by Country.


20

The financial crisis of 2008 had an impact on all international trade, agricultural and

otherwise. In 2012, the United States experienced a severe drought. This and the production

losses resulting from it impacted the global markets because the United States holds a huge

market share in agricultural goods and therefore has great market influence. Low yields

combined with stable demand led to high corn prices, so even though the yield was low in 2012,

it was actually one of the most valuable harvests in U.S. history (Pitt, 2013).

Finally, most of the corn and soybean produced in the U.S are produced using GMO

technology. Figure 2.7 shows the adoption trends in the U.S. since 1996. However, while

GMO’s are accepted domestically and in several foreign markets, a number of markets prohibit

the sale of corn or soybeans produced using GMO technology for either food or feed use. For

example, most member states of the European Union do not allow domestic cultivation of GMO

crops, and imports of GMO corn and soybeans into the EU are limited and mainly used for

animal feed. Less is known about the extent to which the U.S. is under-trading in these markets

due to weather, financial or policy related factors. This study fills this void.

21

Figure 2.7: Adoption of genetically engineered crops in the United States, 1996-2018.

Source: Economic Research Service

22

Chapter 3: Theory and Methods

Theory

International trade in agricultural products is vital to the prosperity of U.S. agriculture

and food industries and likewise to the well-being of U.S. food consumers. Producers gain access

to foreign markets and, if there are economies of scale, enlarged markets allow firms to move

down their long-run average cost curves and expand their sales. Consumers benefit from lower

prices, greater variety, and more consistent supplies of goods throughout the year.

Based on theoretical models of trade going back to Ricardo, it is reasonable for a nation

to seek trade with its neighbors. Further, it is logical for firms in trading countries to seek out

marketing opportunities beyond their own borders. Along with theoretical trade benefits, this

also provides an incentive to maintain relative world peace. At the same time, ensuring a safe

and nutritious food supply has created incentives for some nations to become self-sufficient in

key industries, including agriculture.

The model I am using is the gravity model. While it was based on Newton’s equation of

universal gravity in physics, it has also been shown to describe an importing country’s demand

for exports from a foreign country (Head and Mayer 2014; Grant and Lambert 2008; Peterson et

al. 2013). The original equation from physics is below:

[1] 𝐹 = 𝐺𝑚1𝑚2

𝑟2

The left side is the force of gravity, F. The right side is the gravitational constant, G, multiplied

by the product of the masses (m) over the squared distance (r) between the two bodies. The

equation was first used in an economic context by Tinbergen (1964). This model equated GDP

23

(gross domestic product) with economic “mass” and used this and distance to predict the value of

trade between two nations. While physical gravity is a force exerted in both directions, the

gravity model of trade estimates the value of expenditures in one direction. Anderson (2010)

prescribed a theoretical foundation for the traditional model as follows (see also Anderson and

van Wincoop 2003):

[2] 𝑋𝑖𝑗 = 𝑌𝑖𝐸𝑗 𝑑𝑖𝑗2⁄

Where Xij is the value of bilateral trade from i to j, Yi is the total production value of the exporting

country, Ei is the total expenditure of the importing country, and dij is the distance between

them. When looking at total aggregate trade, GDP is a reasonable value for both Y and E since it

can be measured as either output-based or expenditure-based GDP. Geographical distance

accounts for frictions that increase trade costs the more distant countries are (i.e., transportation

costs), and can also include factors such as language barriers, cultural differences in institutions,

governance, and tastes and preferences, and lack of common historical experience (Head and

Mayer 2014).

This model has performed well empirically (Anderson and van Wincoop, 2003;

Bergstrand and Egger, 2009); it is generally stated that this basic model can explain 60% or more

of the variation in trade flows. While many theoretical justifications have been suggested

(they’re briefly described in “The Gravity Model”), this study adopts Anderson (2010) as its

basis. The model is theoretically anchored in an expenditure equation, or the inverse of indirect

utility. In gravity, the expenditure is dependent on the availability (production) and accessibility

24

(distance) of a product given a “budget” (GDP). Anderson’s demand side structural gravity

equation is as follows:

[3] 𝑋𝑖𝑗 =𝐸𝑗𝑌𝑖

𝑌(

𝑡𝑖𝑗

𝑃𝑗𝛱𝑖)

1−𝜎

Where Xij is bilateral trade from country i to country j, Ej is the total expenditure on tradeable

goods by the importing country (capacity to spend), Yi is the total production of the exporting

country, (capacity to produce) and Y is total global expenditure. In this way, the first term is the

share of total global expenditures that is composed of j’s expenditure’s on i’s goods. σ is the

elasticity of substitution between varieties from different countries, and Pi and Πj represent

inward and outward multilateral resistance. These explain a country’s preference for one partner

over all others, as trade doesn’t occur in a bilateral vacuum. The greater the multilateral

resistance for a pair, the greater is the propensity for them to trade with each other. The trade

costs associated with the country pair are indicated by tij. This may include language barriers,

cultural differences, and historical relationships as well as physical distance. To estimate a

multiplicative model, it can be estimated in a log-linearized form with panel data as follows:

[4] 𝑙𝑛(𝑋𝑖𝑗𝑡) = 𝛽0 + 𝛽1𝑙𝑛(𝐺𝐷𝑃𝑖𝑡) + 𝛽2𝑙𝑛(𝐺𝐷𝑃𝑗𝑡) + 𝛽3𝑙𝑛(𝐷𝐼𝑆𝑇𝑖𝑗) + 𝑢𝑖𝑗 + 𝑢𝑡 + 𝜀𝑖𝑗𝑡

Where 𝜀𝑖𝑗𝑡 is the error term, and Pi and Πj are controlled by pair fixed effects. Equation [4] also

includes year fixed effects to control for changes in world prices over the sample period and

other period-specific shocks that are common across all trading pairs. The above equations

25

describe aggregate trade of a generic good. The model can also allow for disaggregation by

product with the subscript k:

[5] 𝑙𝑛(𝑋𝑖𝑗𝑡𝑘) = 𝛽0 + 𝛽1𝑙𝑛(𝑃𝑅𝑂𝐷𝑖𝑡𝑘) + 𝛽2𝑙𝑛(𝐺𝐷𝑃𝑗𝑡) + 𝛽3𝑙𝑛(𝐷𝐼𝑆𝑇𝑖𝑗) + 𝑢𝑖𝑗 + 𝑢𝑡 + 𝑢𝑘 + 𝜀𝑖𝑗𝑡𝑘

Where 𝑃𝑅𝑂𝐷𝑖𝑡𝑘 is exporter production, representing the exporting country i’s capacity to

produce good k in year t. Commodity fixed effects are represented by uk.

In this case, my k subscript refers to whole and broken corn and soybeans at the HS-4

digit level, 1005 and 1201. The exporter’s annual production of the good reflects the capacity to

produce, while the importer’s GDP, as before, represents the demand capacity in the importing

country. If both countries in a pair produce similar bundles of goods, it is probable that they

won’t have much incentive to trade unless one has a clear comparative advantage in production

of one good. In this study, I use regressions based on equation [5] where i = US, such that

regressions are performed on the sub-sample in which the U.S. is the only exporter.

Estimation Methods

The general framework of my methods are as follows: the first step is to estimate a

gravity model using a state of the art panel data set. The next step is to determine the impacts of

policy in variation between predicted and actual trade values.

I estimated many iterations of the gravity model with a panel data set. A panel data set

looks at the same panel of subjects over time (i.e., repeatedly). In this case, the panel ID

contains country pair and commodity. Therefore the index on the dependent variable, v, is tijk,

where t denotes the year and ijk is the panel ID, representing exporter, importer, and commodity

respectively. When looking at only U.S. exports, there is no variation in i, so the panel ID

26

becomes jk. In the model specifications looking at an individual commodity, k does not vary, so

the major source of variation to identify a policy effect comes from variation across destination

markets, j.

Iterations of the model in the applied research literature now include variables, often

dummies, which aim to capture the effects of other factors relevant to bilateral trade

relationships. These include colonial history, common language, shared border, and shared

membership in regional trade agreements. For example, the U.S. has entered into a free trade

agreement with 20 other countries. Thus, an FTA binary variable is included in the regressions

to control for potentially higher trade flows with partners where trade has been liberalized due to

the FTA. However, inclusion of importer fixed effects rules out variables that vary only by the

importer and not over time, such as colonial history, shared border, and shared language. For

this study, I also created a dummy variable for GMO policies. It equals one for any importer and

year wherein there was a policy, in place to prevent or reduce the production or use of GMO

crops and foods, whether it was an import ban or a moratorium on cultivation. Cultivation bans

don’t directly impact trade, but they reflect tastes and preferences that do not favor GMO

products. In some cases, the importing of GMO crops is allowed, but the cultivation is

prohibited. There are also cases where imports are allowed “with authorization,” but the actual

difficulty of the authorization process is unclear. Table 3.1 shows those countries which have

had anti-GMO policies at any point during the sample period. The countries included in the

table are abbreviated as follows: Azerbaijan (AZE), Belize (BLZ), Bhutan (BTN), Switzerland

(CHE), Colombia (COL), Algeria (DZA), Ecuador (ECU), EU-151 (EUR), Indonesia (IDN),

1 The EU-15 data was aggregated by the author

27

Kyrgyzstan (KGZ), Madagascar (MDG), Peru (PER), Russia (RUS), Saudi Arabia (SAU),

Thailand (THA), Turkey (TUR), the Ukraine (UKR), and Venezuela (VEN).

28

2 Country AZE BLZ BTN CHE COL DZA ECU EUR IDN KGZ MDG PER RUS SAU THA TUR UKR VEN

1998 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1

1999 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1

2000 0 1 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 1

2001 0 1 0 0 1 1 0 1 1 0 0 0 0 1 1 0 0 1

2002 0 1 0 0 1 1 0 1 1 0 0 0 0 1 1 0 0 1

2003 0 1 0 0 1 1 0 1 1 0 0 0 0 1 1 0 0 1

2004 0 1 0 0 1 1 0 1 1 0 0 0 0 1 1 0 0 1

2005 0 1 0 1 1 1 0 1 1 0 0 0 0 0 1 0 0 1

2006 0 1 0 1 1 1 0 1 1 0 0 0 0 0 1 0 0 1

2007 0 1 0 1 0 1 0 1 1 0 0 0 0 0 1 0 1 1

2008 0 1 0 1 0 1 1 1 1 0 0 0 0 0 1 0 1 1

2009 0 1 0 1 0 1 1 1 1 0 0 0 0 0 1 1 1 1

2010 0 1 0 1 0 1 1 1 1 0 0 0 0 0 1 1 1 1

2011 0 1 0 1 0 1 1 1 1 0 1 0 0 0 1 1 1 1

2012 0 1 0 1 0 1 1 1 1 0 1 1 0 0 1 1 1 1

2013 0 1 0 1 0 1 1 1 1 0 1 1 0 0 1 1 1 1

2014 0 1 0 1 0 1 1 1 0 1 1 1 0 0 1 1 1 1

2015 0 1 1 1 0 1 1 1 0 1 1 1 0 0 1 1 1 1

2016 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1

Source: The information was gathered from a variety of sources; some web links can be found in the footnote.

2 Sources: https://sustainablepulse.com/2015/10/22/gm-crops-now-banned-in-36-countries-worldwide-sustainable-pulse-research/#.XMhb_OhKhPY http://www.fao.org/food/food-safety-quality/gm-foods-platform/browse-information-by/country/en/#st https://gmo.geneticliteracyproject.org/FAQ/where-are-gmos-grown-and-banned/

Table 3.1: GMO Variable Matrix.

29

In order to see the impact of GMO policies on U.S. exports of corn and soybeans, I ran

models both with and without the GMO dummy variable. When the GMO dummy was left out,

I assumed that the effects would appear in the error term and used residual analysis to identify

shocks. However, the error term also reflects shocks due to other omitted factors. Residual

analysis involves calculating the difference between actual and predicted trade for each

observation and ranking the observations based on this value. The largest negative residual

values represent the most significant deviations from the model. In this case, I assume the

predicted values are a reasonable expectation of what trade should be conditional on all

covariates included, and deviations from actual trade flows represent trade inefficiency due to

policy factors.

The estimation methods I use are ordinary least squares (OLS), as used throughout the

gravity literature (Rose, 2004; Cassey & Zhao, 2017) and a Poisson pseudo maximum likelihood

(PPML) estimation, as recommended by Santos Silva and Tenreyro (2006). As explained in

“The Log of Gravity,” estimating a log-linearized gravity model with OLS presents a few

issues. The first and most basic is that trade data often includes many zero values in the

dependent variable. These may be a result of measurement error or because of trade policy

factors. Either way, however, the omission of zero trade flows due to logarithmic transformation

of the dependent variable is likely to create sample selection bias (Grant and Boys 2012).

In addition, the OLS model predicts trade in logs rather than levels. In order to gain

practical inference, it must be transformed back to real values. Jensen’s inequality (below) states

that the log of the expected value is not equal to the expected value of the log, making the

estimates obtained with OLS biased.

30

[6] 𝐸[𝑙𝑛(𝑥)] ≠ 𝑙𝑛 (𝐸[𝑥])

While the OLS estimator is statistically problematic for this reason, it is still used widely

in the trade literature to estimate the gravity model, often alongside alternatives such as PPML,

the new workhorse of gravity model estimation, as introduced by “The Log of Gravity” in

2006. Although Poisson distributions are used for count data, that is not required in this case for

the estimator to be consistent. I estimate PPML both with and without zero values

included. While it is an added benefit, keeping all the zero dependent variable values isn’t the

main purpose of using PPML. To run the regressions in Stata, I used OLS regression and PPML

commands created by Sergio Correia (2014) which allow the model to absorb multiple levels of

fixed effects, such as importer and year.

31

Chapter 4: Data

Global trade data is readily available in the UN Comtrade database. However, this

database includes trade values as reported by both importers and exporters. Not every individual

trade observation has information reported by both sides and they often are imperfectly

aligned. The bulk of my data is sourced from the BACI data set, distributed by CEPII. CEPII is

a French center for research and expertise on international trade, migration, finance, and

macroeconomics. In the BACI set, the pairs of reported values are reconciled based on

reliability indicators for exporter and importer. The set includes trade in both quantity and value

in $1,000 units. The values are observed on a yearly basis at the country level. BACI reports the

values by product at the HS-6 level, so I aggregated the values to the HS-4 digit level. The

original BACI data includes only positive trade values. However, zero trade flows are just as

important (Grant and Boys, 2012). For example, if a strict GMO policy leads to zero export

values for U.S. corn exports, omitting zero trade flows through logarithmic transformation of the

dependent variable will lead to underestimation of the GMO impact. Thus, I added zeros to the

data set. To do this, I reshaped the data from long to wide format, and replaced missing trade

values with zeros. Stata code for these steps and the aggregation from HS-6 to HS-4 can be

found in the appendix.

While in most trade literature, indeed most research literature in general, it seems that

more data is always better, that isn’t necessarily the case here. Large data sets may contain many

zeros and very small observations that aren’t relevant or influential in the market of interest and

pull down the mean of the sample. Because I am looking specifically at U.S. exports of corn

(1005) and soybeans (1201), I sought to narrow the data set to those countries that imported

significant amounts of the products from the United States. Because the USDA considers a trade

32

relationship significant when it is valued at over $100 million dollars in a given year, I identified

countries that imported over $100 million of either product from the United States in at least 10

of the 19 years in the sample. The table below describes the groups. The graph following it

shows the share represented by these groups in total U.S. exports corn and soybean markets.

Limiting the importer group to the largest importers of the U.S. products results in higher mean

GDP and trade values, as well as all positive values. The 9 countries in the soy group hold a

consistently larger portion of that market than the 8 countries in the corn group held in theirs.

Both groups represent significant amounts of the U.S. export market. One downside of these

selected groups is the dramatically reduced sample size.

Corn Group Soy Group All

Importers

Importers Canada (CAN), Colombia

(COL), Egypt (EGY), Japan

(JPN), Korea (KOR), Mexico

(MEX), Saudi Arabia (SAU),

Venezuela (VEN)

Canada (CAN), China

(CHN), EU-15 (EUR),

Indonesia (IDN), Japan

(JPN), Korea (KOR), Mexico

(MEX), Thailand (THA),

Turkey (TUR)

219

importers

Mean, median

GDP

$1.1 bil, $0.61 mil $5.2 bil, $1.1bil $0.47 bil,

$20.6 mil

Mean, median

trade value

$30.7 mil, $0.5 mil $38.1 mil, $0.5 mil $3.7 mil,

$3,050

Sample Size 152 171

% of

Observations

where v = 0

0% 0% 58%

Table 4.1: Selected Groups of Relevant Importers for Each Commodity.


33

For soybeans, the selected group of countries has a much higher average GDP than that

of the corn group countries, but both have a mean GDP a factor of ten larger than that of the

whole group. The average values of trade are similar. The soy group’s share of the U.S. export

market was both larger and more constant than that of the corn group. The following graphs

show the shares of U.S. exports for each individual country. For corn, Japan is the top

destination for most of the period, but it is decreasing while Mexico’s share increases. South

Korea’s share seems the least stable. For soybeans, China’s share soars from under 10% to over

60% while the other countries in the group stay stable at or decline to less than 10%. A third

graph shows the soybean group without China and rescaled to make changes over time

Figure 4.1: Share of U.S. Exports represented by a selected group of importers, 1998-2016.


34

clearer. In this graph, you can see that the shares of Europe, Japan, and Mexico steadily decline.

Europe imported over 35% of U.S. soybean exports in 1998, but fell to about 5% in 2009 and

stayed below 10% for the rest of the period.

Figure 4.2: Share of U.S. Corn Exports by Country, 1998-2016.


35

Figure 4.3a: Share of U.S. Soybean Exports by Country (including China), 1998-2016.


Figure 4.3b: Share of U.S. Soybean Exports by Country (not including China), 1998-2016.


36

As mentioned in chapter 3, the data also includes an RTA dummy variable. The variable

equals 1 if the importer and exporter are part of the same trade agreement in that year, or in this

case, if the importer is part of an RTA with the United States in that year. The U.S. has trade

agreements with 20 different countries, shown table 4.2. The countries included in the table are

abbreviated as follows: Australia (AUS), Bahrain (BHR), Canada (CAN), Chile (CHL),

Colombia (COL), Costa Rica (CRI), Dominican Republic (DOM), Guatemala (GTM), Honduras

(HND), Israel (ISR), Jordan (JOR), Korea (KOR), Morocco (MAR), Mexico (MEX), Nicaragua

(NIC), Oman (OMN), Panama (PAN), Peru (PER), Singapore (SGP), and El Salvador (SLV).

37

YEAR AUS BHR CAN CHL COL CRI DOM GTM HND ISR JOR KOR MAR MEX NIC OMN PAN PER SGP SLV

1998 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0

1999 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0

2000 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0

2001 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0

2002 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0

2003 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0

2004 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0

2005 1 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0

2006 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 0 0 0 1 1

2007 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1

2008 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1

2009 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1

2010 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1

2011 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1

2012 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2013 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2014 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2015 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2016 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Table 4.2: RTA Variable Matrix

Source: WTO

38

Chapter 5: Estimation and Results

Many variations of equation [5] in Chapter 3 were estimated, varying the sample size, the

fixed effects, the estimation method, and the inclusion of the GMO variable. I ran regressions

for corn alone, soybeans alone, and both commodities together. For each group, I ran variations

with the large or small sample size (small groups from Table 4.1 in Chapter 4), with OLS or

PPML, with importer fixed effects or importer and year fixed effects, and with or without the

GMO dummy variable. When using the PPML estimator, the dependent variable is not logged.

For the large samples, there are zero values, so I ran the PPML estimation both with and without

the zero values. Because the small groups have few countries, I only used importer fixed effects,

as the U.S. is the only exporter, the sample size is small, and using both importer and year would

take up too many degrees of freedom. Commodity fixed effects were used for all of the

combined sample variations. With all of these possible combinations, there are 44 alternative

models. A few of them are shown below, and the breakdown is illustrated in Figure 5.1. The

Stata commands for all of the PPML regressions can be found in the appendix. The following

are some of the variations on the model, shown for a single commodity group and labeled

alphabetically.

[A] 𝑙𝑛(𝑋𝑖𝑗𝑡) = 𝛽0 + 𝛽1𝑙𝑛(𝑃𝑅𝑂𝐷𝑖𝑡) + 𝛽2𝑙𝑛(𝐺𝐷𝑃𝑗𝑡) + 𝛽3𝑅𝑇𝐴 + 𝛽4𝐺𝑀𝑂 + 𝑢𝑖𝑗 + 𝜀𝑖𝑗𝑡

[B] 𝑋𝑖𝑗𝑡 = 𝛽0 + 𝛽1𝑙𝑛(𝑃𝑅𝑂𝐷𝑖𝑡) + 𝛽2𝑙𝑛(𝐺𝐷𝑃𝑗𝑡) + 𝛽3𝑅𝑇𝐴 + 𝛽4𝐺𝑀𝑂 + 𝑢𝑖𝑗 + 𝜀𝑖𝑗𝑡

[C] 𝑙𝑛(𝑋𝑖𝑗𝑡) = 𝛽0 + 𝛽1𝑙𝑛(𝑃𝑅𝑂𝐷𝑖𝑡) + 𝛽2𝑙𝑛(𝐺𝐷𝑃𝑗𝑡) + 𝛽3𝑅𝑇𝐴 + 𝑢𝑖𝑗 + 𝜀𝑖𝑗𝑡

39

[D] 𝑋𝑖𝑗𝑡 = 𝛽0 + 𝛽1𝑙𝑛(𝑃𝑅𝑂𝐷𝑖𝑡) + 𝛽2𝑙𝑛(𝐺𝐷𝑃𝑗𝑡) + 𝛽3𝑅𝑇𝐴 + 𝑢𝑖𝑗 + 𝜀𝑖𝑗𝑡

Each of these has importer fixed effects, with two including the GMO variable and the other two

excluding it. Two of the models have logged dependent variables for OLS, and the other two

have the dependent variable in levels for PPML. Tables 5.1-6 show the outcomes of all 44

variations of these regressions, separated by commodity and sample size3. The residuals for the

30 PPML regressions are calculated below in table 5.7.

In the small corn sample table, there are higher R square values and more statistical

significance in the PPML regressions, although the GMO variable is never significant. With the

exception of the RTA variable, the signs are as expected. The small soybean sample has similar

results, with RTA and exporter production having a different sign than expected. Results of the

small combined sample, shown in Table 5.3, are also similar to the small corn sample: the PPML

3 For all tables, * refers to statistical significance at the 10% level, ** at 5%, and *** at 1%.

Figure 5.1: Alternate Model Tree

40

estimations show more statistical significance, the GMO variable was never significant, and with

the exception of the RTA variable, all of the signs were as expected.

In the large corn sample table, the PPML regressions with only importer fixed effects

have greater significance than the OLS regressions, but the GMO variable is only significant in

the OLS cases. In all of the cases with both fixed effects, the results are less significant, likely

because the sample size doesn’t leave enough degrees of freedom after the fixed effects. Three

of the 4 OLS models have a negative sign for importer GDP, though they are not statistically

significant, and the RTA variable is only significant and positive in the OLS cases. All of the

PPML estimations with importer fixed effects have a negative sign on RTA, but all other signs in

those models are as expected.

Table 5.5 is the large soybean sample results. In this table, the GMO variable is always

negative, but is larger and more significant in the OLS models. There is the most statistical

significance in the PPML models with both fixed effects. This is different than the corn case

above, even though soy has smaller sample sizes. The coefficients on the RTA variable are

sometimes negative and generally very small. All but the first OLS model have a negative sign

on exporter production. This is not what was expected, but with only one exporter, the

production value only varies by year, so there are only 19 unique values for each commodity.

5.6 shows the large combined sample regression results. Here, the GMO variable is

always negative, but it is larger and more statistically significant in the OLS cases. The

coefficient on the RTA variable was only positive and significant with OLS. The rest of the

signs in the rest of the models are as expected. No model has statistical significance on every

coefficient, and the exporter production coefficient was always insignificant. The RTA variable

is consistently negative. This is counterintuitive, but it is likely because the sample includes only

41

U.S. exports, and the U.S. ships a lot to countries like Japan where a trade agreement is not in

place.

GMO No GMO

OLS PPML OLS PPML

Constant -25.646***

(8.333)

-21.852***

(4.610)

-26.288***

(8.302)

-21.865***

(4.591)

Ln(GDP IMP) 0.055

(0.218)

0.669***

(0.192)

0.141

(0.198)

0.670***

(0.176)

Ln(PROD EXP) 1.765***

(0.573)

1.115***

(0.335)

1.732***

(0.572)

1.114***

(0.333)

RTA -0.200

(0.279)

-0.423*

(0.256)

-0.168

(0.277)

-0.423

(0.256)

GMO -0.266

(0.281)

-0.009

(0.234)

Importer Fixed

Effects

YES YES YES YES

R square 0.71 0.83 0.71 0.83

N 152 152 152 152

GMO No GMO

OLS PPML OLS PPML

Constant -4.472

(3.924)

-0.548

(3.678)

-4.473

(3.908)

-0.857

(3.642)

Ln(GDP

IMP)

0.989***

(0.075)

0.163***

(0.083)

0.989***

(0.074)

1.160***

(0.083)

Ln(PROD

EXP)

-0.189

(0.261)

-0.529**

(0.244)

-0.188

(0.260)

-0.510**

(0.242)

RTA -0.320

(0.199)

-0.292**

(0.141)

-0.320

(0.199)

-0.295**

(0.141)

GMO 0.001

(0.119)

-0.189

(0.150)

Importer

Fixed

Effects

YES YES YES YES

R square 0.92 0.96 0.92 0.96

N 171 171 171 171

Table 5.1: Small corn sample regression results.

Table 5.2: Small soybean sample regression results.

42

GMO No GMO

OLS PPML OLS PPML

Constant -25.646***

(8.333)

-21.852***

(4.610)

-26.288***

(8.302)

-21.865***

(4.591)

Ln(GDP

IMP)

0.055

(0.218)

0.669***

(0.192)

0.141

(0.198)

0.670***

(0.176)

Ln(PROD

EXP)

1.765***

(0.573)

1.115***

(0.335)

1.732***

(0.572)

1.114***

(0.333)

RTA -0.200

(0.279)

-0.423*

(0.256)

-0.168

(0.277)

-0.423

(0.256)

GMO -0.266

(0.281)

-0.009

(0.234)

Importer

Fixed

Effects

YES YES YES YES

R square 0.71 0.83 0.71 0.83

N 152 152 152 152

Table 5.3: Small combined sample regression results.

43

GMO No GMO

OLS OLS PPML PPML PPML pos.

only

PPML

pos.

only

OLS OLS PPML PPML PPML pos.

only

PPML

pos.

only

Constant -6.416

(5.247)

0.027

(1.461)

-16.787***

(4.404)

5.153**

(2.041)

-16.763***

(4.392)

4.886**

(2.029)

-6.545

(5.222)

-0.110

(1.460)

-16.856***

(4.395)

5.039*

(0.149)

-16.815***

(4.385)

4.790**

(1.993)

Ln(GDP

IMP)

-0.142

(0.102)

-0.006

(0.142)

0.517***

(0.150)

0.094

(0.151)

0.550***

(0.149)

0.115

(0.150)

-0.161

(0.102)

0.004

(0.142)

0.525***

(0.149)

0.102

(0.149)

0.557***

(0.148)

0.121

(0.148)

Ln(PROD

EXP)

0.449

(0.341)

0.919***

(0.324)

0.893***

(0.323)

0.463

(0.339)

0.916***

(0.323)

0.890***

(0.322)

RTA 0.549***

(0.186)

0.601***

(0.187)

-0.135

(0.171)

0.043

(0.144)

-0.150

(0.171)

0.022

(0.145)

0.558***

(0.186)

0.611***

(0.187)

-0.131

(0.173)

0.046

(0.145)

-0.147

(0.172)

0.024

(0.145)

GMO -0.485**

(0.230)

-0.439*

(0.229)

-0.182

(0.176)

-0.119

(0.187)

-0.158

(0.171)

-0.102

(0.183)

Importer

Fixed

Effects

YES YES YES YES YES YES YES YES YES YES YES YES

Year Fixed

Effects

NO YES NO YES NO YES NO YES NO YES NO YES

R square 0.81 0.82 0.93 0.94 0.93 0.94 0.81 0.81 0.93 0.94 0.92 0.94

N 2,264 2,275 3,185 3,185 2,275 2,275 2,275 2,275 3,185 3,185 2,275 2,275

Table 5.4: Large Corn sample regression results.

44

GMO No GMO

OLS OLS PPML PPML PPML

pos. only

PPML pos.

only


pos. only

PPML

pos. only

Constant -8.227

(8.206)

-8.700***

(3.122)

-1.609

(3.448)

-9.144***

(0.086)

-1.210

(3.431)

-9.148***

(1.315)

-6.958

(8.232)

-9.336***

(3.128)

-1.877

(3.425)

-9.277***

(1.333)

-1.475

(3.409)

-9.278***

(1.350)

Ln(GDP

IMP)

0.728***

(0.164)

0.810***

(0.279)

1.169***

(0.079)

1.126***

(0.086)

1.167***

(0.079)

1.127***

(0.087)

0.718***

(0.165)

0.856***

(0.279)

1.167***

(0.079)

1.132***

(0.088)

1.165***

(0.079)

1.133***

(0.089)

Ln(PRO

D EXP)

0.026

(0.542)

-0.474**

(0.228)

-0.495**

(0.228)

-0.049

(0.544)

-0.458**

(0.227)

-0.479**

(0.226)

RTA -0.024

(0.266)

-0.012

(0.268)

-0.186*

(0.111)

-0.220**

(0.106)

-0.188*

(0.110)

-0.224**

(0.106)

0.000

(0.267)

0.021

(0.269)

-0.178

(0.112)

-0.210*

(0.108)

-0.180

(0.112)

-0.212**

(0.107)

GMO -1.036***

(0.321)

-0.999***

(0.321)

-0.191

(0.138)

-0.215*

(0.120)

-0.190

(0.138)

-0.207*

(0.119)

Importer

Fixed

Effects


Year

Fixed

Effects


R square 0.81 0.82 0.98 0.98 0.98 0.98 0.79 0.82 0.98 0.98 0.98 0.98

N 1,188 1,188 2,564 2,564 1,188 1,188 1,188 1,188 2,564 2,564 1,188 1,188

Table 5.5: Large soybean sample regression results.

45

GMO No GMO


pos. only

PPML

pos. only


pos. only

PPML

pos. only

Constant -9.901

(5.287)

-0.955

(1.695)

-12.712**

(6.457)

-6.397**

(2.610)

-9.477

(6.556)

-6.312**

(2.580)

-8.759*

(5.288)

-1.267

(1.696)

-12.792**

(6.451)

-6.489**

(2.592)

-9.528

(6.558)

-6.399**

(2.565)

Ln(GDP

IMP)

0.026

(0.105)

0.098

(0.160)

0.964

(0.152)

0.925***

(0.181)

1.010***

(0.150)

0.921***

(0.179)

0.021

(0.106)

0.120

(0.160)

0.965***

(0.151)

0.930***

(0.180)

1.010***

(0.150)

0.925***

(0.178)

Ln(PROD

EXP)

0.559

(0.347)

0.329

(0.442)

0.108

(0.447)

0.491

(0.347)

0.332

(0.441)

0.108

(0.447)

RTA 0.90**

(0.188)

0.408**

(0.191)

-0.287

(0.191)

-0.261

(0.200)

-0.281

(0.193)

-0.279

(0.201)

0.405**

(0.189)

0.429**

(0.191)

-0.283

(0.193)

-0.257

(0.201)

-0.276

(0.194)

-0.274

(0.202)

GMO -0.856***

(0.233)

-0.825***

(0.233)

-0.151

(0.197)

-0.163

(0.206)

-0.149

(0.190)

-0.158

(0.198)

Importer

Fixed

Effects


Year Fixed

Effects


R square 0.70 0.70 0.87 0.87 0.86 0.86 0.70 0.70 0.87 0.87 0.86 0.86

N 3,484 3,484 5,765 5,756 3,484 3,484 3,484 3,484 5,765 5,765 3,484 3,484

Table 5.6: Large combined sample regression results.

46

The coefficients on logged independent variables can be interpreted as elasticities, or the

percent change in the dependent variable for every one percent change in the independent

variable. A value over one indicates that a change in the independent variable results in a larger

than proportional increase or decrease in the dependent variable. Exporter production generally

has a larger elasticity than the importer’s GDP, meaning that a change in the exporter’s

production, or their capacity to produce, has a larger impact on the trade value than a change in

the importer’s capacity to consume. This is logical, as the production values used are specific to

the commodity, and GDP reflects capacity to consume all goods. Only a fraction of an increase

in overall demand accounts for an increase in demand for corn or soybeans in particular.

Many of the significant coefficients on logged production or GDP are very close to one,

meaning that the elasticity of the trade value with respect to production or GDP is close to unit

elasticity, where the dependent variable’s response to a change in the independent variable is

directly proportional. In Table 5.6, the combined large sample results, most of the PPML models

have a significant positive coefficient on importer GDP. For the positive only samples with only

importer fixed effects, the coefficient is 1.01. This means that for a 10% increase in importer

GDP, there is a 1% increase in U.S. exports of the good to the importer.

The dummy variables are not logged and have only two possible values, so the

coefficients cannot be interpreted as elasticities. To interpret the coefficient on the GMO

dummy variable as a percentage change in the dependent variable, trade value, I exponentiate the

coefficient, subtract 1, and multiply by 100, as follows:

[7] %Δ due to GMO policy = ((exp (β4) - 1)*100)

47

Fifteen of the 30 PPML regressions included the GMO dummy, and the average effect of the

GMO policy dummy in all of these models is an 11% decrease in trade value with a minimum

decrease of 0.85% and a maximum decrease of 17.9%.

The next step is calculation of residual values. I use the method recommended by Santos

Silva and Tenreyro on the Log of Gravity page to calculate over and under trading4. The code I

used for the operation can be found in the appendix. Each model has a set of error terms with a

mean of zero. The distribution of the errors for each model is close to normally distributed

around zero. As an example, the distribution of errors from Model 2 is shown below in Figure

5.2.

4 http://personal.lse.ac.uk/tenreyro/lgw.html

Figure 5.2: Distribution of Errors for Model 2.


http://personal.lse.ac.uk/tenreyro/lgw.html

48

Table 5.7 shows the top negative residuals for the PPML models. When the GMO

variable is included, I expect the error to include different excluded factors, and the top negative

residual to reflect a different shock. When the GMO variable is excluded, I assume that its

effects are captured in the error.

Model

#

Description

Observation with Top Negative

Residual

Actual minus

predicted trade

value in million

dollars

1 Small Corn Sample, importer fixed

effects, GMO

Japan

2016

-$1,935

2 Small Corn Sample, importer fixed

effects, No GMO

Japan

2016

-$1,907

3 Small Soy Sample, importer fixed

effects, GMO

European Union (EU-15)

2012

-$11,801

4 Small Soy Sample, importer fixed

effects, No GMO


2012

-$12,477

5 Small Combined Sample, importer fixed

effects, GMO


2014, soy

-$14,972

6 Small Combined Sample, importer fixed

effects, No GMO


2014, soy

-$15,552

7 Large Corn Sample, importer fixed

effects, GMO


2008

-$4,193


effects, No GMO


2008

-$4,769

9 Large Corn Sample, importer and year

fixed effects, GMO

China

2008

-$134


fixed effects, No GMO

China

2008

-$318


effects, GMO, pos. only


2008

-$4,730


effects, No GMO, pos. only


2008

-$5,241


fixed effects, GMO, pos. only

China

2008

-$187

Table 5.7: Observations with Top Negative Residuals for each PPML model.

49


fixed effects, No GMO, pos. only

China

2008

-$191

15 Large Soy Sample, importer fixed

effects, GMO


2012

-$9,636


effects, No GMO


2012

-$10,503

17 Large Soy Sample, importer and year

fixed effects, GMO


2012

-$10,076


fixed effects, No GMO


2012

-$11,284




2012

-$10,870




2012

-$11,735


fixed effects, GMO, pos. only


2012

-$11,369


fixed effects, No GMO, pos. only


2012

-$12,520

23 Large Combined Sample, importer fixed

effects, GMO


2008, corn

-$7,912


effects, No GMO


2008, corn

-$8,446

25 Large Combined Sample, importer and

year fixed effects, GMO


2008, corn

-$6,523


year fixed effects, No GMO


2008, corn

-$7,132




2008, corn

-$8,349




2008, corn

-$8,864


year fixed effects, GMO, pos. only


2008, corn

-$6,939


year fixed effects, No GMO, pos. only


2008, corn

-$7,539

The top ranked observations by negative residuals represented only 3 importers in all

thirty PPML models, those importers being Japan, China, and the European Union. U.S. exports

to the EU showed the most significant under-trading in more models than Japan and China

combined. While it was expected that models controlling for the GMO policies would capture

different shocks in the residual, the observation top ranked by negative residuals was the same

50

for each pair of model variations that only differed by inclusion of the dummy variable. In most

cases, the inclusion of the GMO dummy correlates with a smaller value of the top negative error.

The following figure shows average predicted trade values (average of �̂� from all relevant

models) compared to the actual trade values over time for corn exported to the European Union

from the US. The predicted value reported in the graph is the average of the trade values

predicted by all of the PPML corn models.

The graph shows a dramatic difference between actual and predicted trade levels. While

actual trade stays in the hundreds of millions range, predicted exports of corn to the EU are

measured in billions. U.S. corn exports to the EU underperformed by $2.77 billion per year on

average, totaling $52.7 billion in forgone trade in corn alone between 1998 and 2016. The

Figure 5.3: Average Predicted and Actual Value of U.S. Corn Exports to the European Union

(EU-15), 1998-2016.


51

European Union is the most extreme comparison. The two years that appear most in Table 5.7

are 2008 and 2012. In Figure 5.3, 2008 displays a small spike in the predicted value rather than a

drop in the actual values as might be expected in a year with market-altering circumstances.

In Figures 5.4 and 5.5 below, I make the same comparison with U.S. corn exports to

China and to Japan. The gap between actual and predicted exports to China is under a billion for

most of the sample. China, like the EU, did not have a trade deal with the U.S. during the

sample period, but it did allow imports and sales of GMO crops, which the EU did not. While

the largest difference between average predicted and actual export values in this graph is in 2016,

the observation with the highest ranked negative residual in models 13 and 14 was corn exports

to China in 2008. The average predicted value from these two models is also included on this

graph. Models 13 and 14 used a positive-only large sample set and importer and year fixed

effects.

In Figure 5.5, the actual value of U.S. corn exports to Japan is greater than the average

predicted value for the entire sample. Japan’s corn imports in 2016 had the highest ranking

negative residuals in the first two models, which used a small sample of relevant corn importers

and importer fixed effects. The average predicted value from these two models is also included

in the graph. These predictions are much closer to the actual values than the average of all the

models. Japan, like the EU and China, had no trade deal with the U.S. during the sample period.

Despite China not having a trade agreement with the United States, it became the top destination

for U.S. agricultural exports by 2016 (Hejazi and Marchant, 2017).

52

Figure 5.4: Average Predicted and Actual Value of U.S. Corn Exports China, 1998-2016.


Figure 5.5: Average Predicted and Actual Value of U.S. Corn Exports Japan, 1998-2016.


53

While most of the largest shocks are in corn, the European Union also under-traded in

soybeans. Figure 5.6 below illustrates this relationship. Similar to the corn graph, in 2008 and

2012 there are small increases in the average predicted value rather than drops in the actual

value.

Figure 5.6: Average Predicted and Actual Value of U.S. Soybean Exports to the EU, 1998-2016.


54

Chapter 6: Conclusions and Discussion

The purpose of this thesis is to identify the largest shocks in U.S. exports of corn and

soybeans and quantify the effect of the anti-GMO policies of importing partners. European

imports of U.S. corn did appear to be the most under traded category, having the top negative

error in most of the models, especially in the years 2008 and 2012. This was expected, as the

European Union has been resisting imports of GMO crops for many years. Inclusion of an anti-

GMO policy variable in the model did not change this result. The inclusion of the variable did

impact the size of the errors, but not the ranking of underperformance.

Additionally, the fact that there are many observations with top negative residuals in the

years 2008 and 2012 is important. These correlate with the 2008 financial crisis and the 2012

drought. 2008 is also the year of the ethanol boom and bust in the United States, when domestic

corn prices jumped and the industry rushed to meet the new source of demand. Both an increase

in domestic consumption and a decrease in global demand due to the recession would cause

export values to decrease. The 2012 drought decreased the United States’ capacity to produce,

leaving less surplus supply to export. It is clear that the events of these years had a major impact

on America’s agricultural exports.

Looking at the errors for exports to the EU, forgone corn trade was worth $52.7 billion

over 19 years. On average, that is $2.77 billion per year. For context, over the same period, the

U.S. exported an average of $1.45 billion worth of corn to Mexico, and $2.46 billion to Japan

each year. Forgone corn trade to China was valued at $1.45 billion over all 19 years, with

average under-trading of $450 million per year and maximum difference between predicted and

actual valued at $8.5 billion in 2016.

55

There are important policy implications to these findings. When cultivation and trade of

GMO crops is limited, the international market falls behind the technology frontier. It is

important to keep up with the frontier in order to meet some of the global challenges facing the

agriculture sector, such as a growing population and increasing demand for animal-sourced

proteins requiring feed. However, the decision to adopt or not to adopt is ultimately up to the

consumer. In the United States, GMO crops are hidden in many processed food products, and

alternatives made without GMO ingredients sell for a premium. While the voluntary non-GMO

labeling is done in an effort to help consumers be better informed, it doesn’t actually educate

them on what a GMO is or why it exists. More complete consumer knowledge may lead to more

widespread adoption and trade values closer to model predictions. Widespread adoption of

GMO crops would also lead to more efficient production, potentially freeing up resources for

other purposes.

Future Work

On May 30th, 2019, news broke that China would cease purchases of U.S. Soybeans

(Picchi, 2019). This research has potential to inform the U.S. soybean industry regarding

potential alternative markets. From 2012 through 2016, the U.S. exported an average of $13.6

billion worth of soybeans to China. In 2016, there was underutilized potential for soybean

exports to over 100 countries, but the total deviation between actual and predicted (based on

average errors from all soy and combined commodity models) is only about $12.4 billion, not

quite enough to fully make up for the loss of the Chinese market.

The most significant potential market is that of Europe, with potential for $8.5 billion in

soybean imports from the U.S. However, Europe’s aversion to GMOs is a major reason that the

United Sates don’t already send more soybeans there. The next two biggest potential markets are

56

India and Brazil, with potential for $567 million and $434 million worth of soybeans,

respectively, in 2016. However, Brazil stopped accepting imports of GMOs from the U.S. in

2017 (Jacobo, 2017). There is potential for the U.S. to expand soybean exports into other

markets, but upon initial inspection, there isn’t adequate realistic potential to make up for the

loss of the Chinese market.

57

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Appendix

Adding zeros to the set, referenced in Chapter 4:

reshape wide v q, i(i j hs4) j(t)

forvalue year = 1998 (1) 2016 {

replace v`year' = 0 if v`year' ==.

replace q`year' = 0 if q`year' ==.

}

reshape long v q, i(i j hs4) j(t)

Aggregating observations to HS-4 Level, referenced in Chapter 4:

tostring hs6, replace

gen l = length(hs6)

replace hs6 = "0" + hs6 if l == 5

gen hs4 = substr(hs6, 1, 4)

drop l

collapse(sum) v q, by(i j hs4 t)

PPML Regression Commands, referenced in Chapter 5:

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA gmo if CORN_GROUP==1 & hs4=="1005",

abs(imp)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA if CORN_GROUP==1 & hs4=="1005", abs(imp)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA gmo if SOY_GROUP==1 & hs4=="1201",

abs(imp)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA if SOY_GROUP==1 & hs4=="1201", abs(imp)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA gmo if SOY_GROUP==1 & hs4=="1201" |

CORN_GROUP==1 & hs4=="1005", abs(imp hs4)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA if SOY_GROUP==1 & hs4=="1201" |

CORN_GROUP==1 & hs4=="1005", abs(imp hs4)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA gmo if hs4=="1005", abs(imp)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA if hs4=="1005", abs(imp)

*ppmlhdfe v_mil LGDP_IMP RTA gmo if hs4=="1005", abs(imp year)

*ppmlhdfe v_mil LGDP_IMP RTA if hs4=="1005", abs(imp year)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA gmo if hs4=="1005" & v>0, abs(imp)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA if hs4=="1005" & v>0, abs(imp)

*ppmlhdfe v_mil LGDP_IMP RTA gmo if hs4=="1005" & v>0, abs(imp year)

*ppmlhdfe v_mil LGDP_IMP RTA if hs4=="1005" & v>0, abs(imp year)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA gmo if hs4=="1201", abs(imp)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA if hs4=="1201", abs(imp)

*ppmlhdfe v_mil LGDP_IMP RTA gmo if hs4=="1201", abs(imp year)

64

*ppmlhdfe v_mil LGDP_IMP RTA if hs4=="1201", abs(imp year)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA gmo if hs4=="1201" & v>0, abs(imp)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA if hs4=="1201" & v>0, abs(imp)

*ppmlhdfe v_mil LGDP_IMP RTA gmo if hs4=="1201" & v>0, abs(imp year)

*ppmlhdfe v_mil LGDP_IMP RTA if hs4=="1201" & v>0, abs(imp year)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA gmo if hs4=="1201" | hs4=="1005", abs(imp hs4)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA if hs4=="1201" | hs4=="1005", abs(imp hs4)

*ppmlhdfe v_mil LGDP_IMP RTA gmo if hs4=="1201" | hs4=="1005", abs(imp year hs4)

*ppmlhdfe v_mil LGDP_IMP RTA if hs4=="1201" | hs4=="1005", abs(imp year hs4)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA gmo if hs4=="1201" & v>0 | hs4=="1005" &

v>0, abs(imp hs4)

*ppmlhdfe v_mil LGDP_IMP lexp_prod RTA if hs4=="1201" & v>0 | hs4=="1005" & v>0,

abs(imp hs4)

*ppmlhdfe v_mil LGDP_IMP RTA gmo if hs4=="1201" & v>0 | hs4=="1005" & v>0, abs(imp

year hs4)

*ppmlhdfe v_mil LGDP_IMP RTA if hs4=="1201" & v>0 | hs4=="1005" & v>0, abs(imp year

hs4)

Calculating Residuals, referenced in Chapter 5:

predict fit_1 if e(sample), xb

gen yhat_1 = exp(fit_1) if e(sample)

egen meany1 = mean(v_mil) if yhat_1 !=. , by(year)

egen meanyhat_1 = mean(yhat_1) if e(sample), by(year)

gen alpha_1 = meany1/meanyhat_1 if e(sample)

gen error_1 = v_mil - yhat_1*alpha_1 if e(sample)

sum error_1

sum error_1 if e(sample)

sort error_1

agricultural trade performance and potential: a ... · countries depend on imports to meet demand....

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