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Estimating Import Demand for Fresh Tomatoes into the United States and the European Union

Permanent Link: http://ufdc.ufl.edu/UFE0021177/00001

Material Information

Title: Estimating Import Demand for Fresh Tomatoes into the United States and the European Union
Physical Description: 1 online resource (159 p.)
Language: english
Creator: Ali, Mohammad
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: demand, estimating, european, fresh, import, states, tomatoes, unied, union
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Continuous talks and negotiations initiated by the World Trade Organization (WTO), in recent years, on globalization and trade liberalization have made international trade a key issue for all nations as it has expanded the global markets with enhanced competitiveness. There exist both opportunities and costs as it expands exports on one hand and poses threats of competition from importers on the other. Agriculture being the major player in international trade has to cope with this changing trend of the global market situation. The European Union (EU-15) being more and more open should be of especial interest to the growers, traders and policy makers of all nations including the United States (U.S.). This is a research project for the analysis of import demand for fresh tomatoes into the U.S. and the EU-15 for the assessment and evaluation of competitiveness to enable the specialty crop industry to compete successfully. The sources of data for this research are the United Nations Statistics Division- Commodity Trade Statistics Database website, Food and Agriculture Organization Statistics Database website and other websites maintained by the United States Department of Agriculture (USDA). Data for the period 1963-2005 have been used in this research. A differential production approach has been used for estimating import demand for fresh tomatoes. Imports are considered as inputs to importing firms. The mode used in this research is derived from the basic principle of the theory of firm whereby the firm maximizes profit by determining a level of output and minimizing the cost of producing that level of output. The cost minimization stage is applied to get conditional factor demand equations in the estimation process. The differential production version of the Netherlands National Bureau of Research (NBR) specification is estimated by the iterative seemingly unrelated regression (SUR) method using the well known least square procedure (LSQ) in Time Series Processor (TSP). Results show that Mexico is the prominent supplier of fresh tomatoes in the U.S. import market facing no close competitor. Canada and EU-15 compete with each other for the U.S. import market whereby Canada is losing its relative share and EU-15 is gaining its relative share. For the EU-15 import demand for fresh tomatoes, Morocco is the major supplier with no close competitor. Israel and Rest of the World (ROW) are competing with each other in the EU-15 import market. Albania, Bulgaria and ROW are losing their relative share in the EU-15 import market with an indication of some kind of structural changes. Mexico and Morocco have significant influence and control over the U.S. and the EU-15 import markets. It is necessary for other participants to figure out the secrets and follow them to be competitive with these major players in their respective markets. Otherwise, the implications would be significant on both markets if there are some diseases or calamities in Mexico and Morocco.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Mohammad Ali.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Kilmer, Richard L.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021177:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021177/00001

Material Information

Title: Estimating Import Demand for Fresh Tomatoes into the United States and the European Union
Physical Description: 1 online resource (159 p.)
Language: english
Creator: Ali, Mohammad
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: demand, estimating, european, fresh, import, states, tomatoes, unied, union
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Continuous talks and negotiations initiated by the World Trade Organization (WTO), in recent years, on globalization and trade liberalization have made international trade a key issue for all nations as it has expanded the global markets with enhanced competitiveness. There exist both opportunities and costs as it expands exports on one hand and poses threats of competition from importers on the other. Agriculture being the major player in international trade has to cope with this changing trend of the global market situation. The European Union (EU-15) being more and more open should be of especial interest to the growers, traders and policy makers of all nations including the United States (U.S.). This is a research project for the analysis of import demand for fresh tomatoes into the U.S. and the EU-15 for the assessment and evaluation of competitiveness to enable the specialty crop industry to compete successfully. The sources of data for this research are the United Nations Statistics Division- Commodity Trade Statistics Database website, Food and Agriculture Organization Statistics Database website and other websites maintained by the United States Department of Agriculture (USDA). Data for the period 1963-2005 have been used in this research. A differential production approach has been used for estimating import demand for fresh tomatoes. Imports are considered as inputs to importing firms. The mode used in this research is derived from the basic principle of the theory of firm whereby the firm maximizes profit by determining a level of output and minimizing the cost of producing that level of output. The cost minimization stage is applied to get conditional factor demand equations in the estimation process. The differential production version of the Netherlands National Bureau of Research (NBR) specification is estimated by the iterative seemingly unrelated regression (SUR) method using the well known least square procedure (LSQ) in Time Series Processor (TSP). Results show that Mexico is the prominent supplier of fresh tomatoes in the U.S. import market facing no close competitor. Canada and EU-15 compete with each other for the U.S. import market whereby Canada is losing its relative share and EU-15 is gaining its relative share. For the EU-15 import demand for fresh tomatoes, Morocco is the major supplier with no close competitor. Israel and Rest of the World (ROW) are competing with each other in the EU-15 import market. Albania, Bulgaria and ROW are losing their relative share in the EU-15 import market with an indication of some kind of structural changes. Mexico and Morocco have significant influence and control over the U.S. and the EU-15 import markets. It is necessary for other participants to figure out the secrets and follow them to be competitive with these major players in their respective markets. Otherwise, the implications would be significant on both markets if there are some diseases or calamities in Mexico and Morocco.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Mohammad Ali.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Kilmer, Richard L.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021177:00001


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ESTIMATING IMPORT DEMAND FOR FRESH TOMATOES INTO THE UNITED STATES
AND THE EUROPEAN UNION

















By

MOHAMMAD ALI


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2007

































2007 Mohammad Ali






























To my parents, my wife Dr. Salina Parveen and my sons
Sakib M. Adnan and Adib M. Adnan









ACKNOWLEDGMENTS

I am very much grateful for the invaluable assistance, guidance and cooperation I received

from my committee chair, Dr. Richard L. Kilmer. His motivation and perseverance kept me

going when I had almost succumbed to complete frustration as I had to move to Delaware first

and then to Maryland with my family just after my course work. Indeed, it is difficult to continue

such a venture when someone is not on the spot. Moreover, I had to wait for complete dataset for

the intended study period until January of 2007. The patience and flexibility he awarded to me

with moral and financial support in the development of this dissertation were greatly appreciated.

His knowledge and experience have been very beneficial to me over the years. I remain ever

thankful for the time and trouble he took to meticulously review my work with helpful

comments.

I am also grateful to the members of my committee for their mentoring and support -both

technical and theoretical. Dr. Ronald W. Ward helped me in manipulating data through

mathematical programming that made the job easier and faster for me. Without his assistance, it

would take much of my time and energy to get the data in working format. He also provided

insights regarding the performance of the models used for my study and suggested some

improvement to explain any kind of structural change that is not included directly in the model. I

appreciate his enthusiasm for work which is very refreshing. Dr. Mark G. Brown offered

considerable support in selecting the model for my study. He helped me look at some alternative

models to check whether they fit better on the given data sets. Finally, my discussions with Dr.

Thomas H. Spreen and Dr. Lawrence W. Kenny helped me shape this research within the realm

of reality/practicality.

A number of other individuals contributed directly or indirectly through their helpful

comments and appreciation. I would like to recognize Dr. Ronald Jansen, United Nations,









Statistics Division and Gary Lucier, United States Department of Agriculture, Economic

Research Service for their input and clarifications on different aspects of the data used in this

research. I thank Carlos E. Jauregui (a doctoral candidate and friend in the same department) for

helping me out whenever I got stuck with the computer programming of my model and Carol

Fountain for her assistance in formatting my dissertation. I also thank the staff and members at

the department as well as at the UF Library for their overall assistance.

Now I would like to acknowledge some Bangladeshi friends and families for their

contributions. My friends Dr. Abu M. Khan (Sayem) and Dr. Murshed M. Chowdhury supported

me with accommodations (during their stay at the University of Florida (UF)) whenever I came

to Gainesville for a short time to work on my dissertation. After that, I started getting board and

lodging with transportation from our family friends Dr. Khandker A. Muttalib and Dr. Jaha A.

Hamida (husband and wife and both working in the Physics department) until I am thoroughly

done with my dissertation. I would like to express my extreme gratitude to them. I would also

like to thank all my Bangladeshi Sunday volleyball partners and their families for their support

and encouragement.

I would like to express my endless thanks and gratitude to my family members and other

relatives and well-wishers for their love, support and encouragement throughout my lifelong

educational endeavors. It is my wife who encouraged and supported me both emotionally and

financially to make this milestone possible. I thankfully appreciate her and my two sons Sakib

and Adib for their patience, understanding, and moral support through all these years of studies.

Finally, I would like to express my appreciation to the International Agricultural Trade and

Policy Center (IATPC) at the University of Florida and its Director Dr. John J. VanSickle for

partially funding this research project.









TABLE OF CONTENTS



A C K N O W L E D G M E N T S ..............................................................................................................4

L IS T O F T A B L E S ................................................................................................. ..................... 8

LIST OF FIGURES ............................................. .. .......... ............ ........... ....9

A B S T R A C T .......................................................................................................... ..................... 10

CHAPTER

1 INTRODUCTION .................................. .. ........... ..................................... 12

Problematic Situation ......... .... ................ .. ........... ............................... 13
P ro b le m S tate m en t ..................................................................................................................14
O b je ctiv e s ..................................................................................................... ..................... 14
C h a p te r S u m m a ry ...................................................................................................................14

2 B A C K G R O U N D ....................................................................................................................16

U .S T o m a to e s ........................................................................................................................1 6
E U T o m ato e s ..........................................................................................................................1 8
Data ..................................................... .............................. 20

3 LITERA TURE REVIEW ................................................................................................. 26

Arm ington Trade M odel .............................................................. ............................26
Differential Approach and the Rotterdam Models .................. ...................................28
Demand System and Functional Formulation ..................... ...................................32
Production Approach and Utility Approach........................................................................33
Import Dem and and the Producer Theory ...........................................................................37
Inverse D em and A analysis .................................................................................................. 44
D ifferential Production A approach ..................................................................... ...............47
Sum m ary of Literature R eview ...........................................................................................51

4 THEORETICAL AND EMPIRICAL MODEL ................................................... ...............53

T h e o retic al M o d e ls ................................................................................................................. 5 3
E m p iric a l M o d e ls....................................................................................................................6 0
Data Section ............................................... ............................... 61

5 EM PIRICAL RESULTS .................................................................................................. 65

Results for U.S. Tomato Import Demand Analysis.............................................................65
D descriptive Statistics ................................................................................................. 65



6









M odel R results .................................................................................. ....................... 65
Sum m ary for U .S A naly sis .............. ..... ............................................................ ................ 7 1
Results for EU-15 Tomato Import Demand Analysis ....................................... ................ 71
D escriptiv e Statistics .................................................. ............................................. 72
M odel R results .................................................................................. ....................... 72
Sum m ary for EU -15 A analysis .................... ................................................................. 79

6 CONCLUSION S .................... ..................... ............... ................... .. 96

O b se rv a tio n s ...........................................................................................................................9 6
Summary ....................................................... .................. 96
Conclusions ................................................. ............................. 98
Im p lic a tio n s ............................................................................................................................9 9

APPENDIX

A COMPUTER PRINTOUTS FOR U.S. ANALYSIS..............................................100

B COMPUTER PRINTOUTS FOR EU-15 ANALYSIS ............................................117

C SUPERFLUOU S M ATERIAL .......................................................................................140

L IS T O F R E F E R E N C E S .............................................................................................................152

B IO G R A P H IC A L SK E T C H .......................................................................................................158









LIST OF TABLES


Table page

2-1 P reduction of tom atoes .............. ....................................................................22

2-2 U.S. exports and imports of fresh and processed tomatoes compared to World ...............22

2-3 U .S. fresh tom ato exports to the EU ............................................................. ................ 23

2-4 U .S. fresh tom ato im ports from the EU ........................................................ ................ 23

2-5 EU's tomato exports compared with the U.S. and World (total tomatoes)....................24

2-6 EU's tomato imports compared with the U.S. and World (total tomatoes)....................24

2-7 EU's tomato production compared with the U.S. and World (total tomatoes)...............25

5-1 Import cost shares, quantity shares, and average prices by country of origin for U.S. .....80

5-2 Test results for the production differential AIDS, CBS, Rotterdam and NBR models
with first-order autocorrelation imposed for U.S. import demand analysis ...................80

5-3 Coefficient estimates of the production NBR model for the U.S.................................81

5-4 Demand parameter estimates and conditional elasticity of the production NBR model
fo r th e U .S ........................................................................................................ ........ .. 8 1

5-5 D ivisia elasticities over tim e for the U .S. analysis ....................................... ................ 82

5-6 Conditional own-price elasticities over time for the U.S. analysis...............................83

5-7 Import cost shares, quantity shares, and average prices by country of origin for EU-
15 .......................................................................................... ........................ .................. 8 7

5-8 Test results for model selection for EU-15 analysis .................................... ................ 87

5-9 Coefficient estimates of the production NBR model for EU-15...................................88

5-10 Demand parameter and conditional elasticity estimates of the production NBR model
for E U -15 ........................................................................................................ ....... .. 89

5-11 Divisia elasticities over tim e for EU -15 analysis.......................................... ................ 90

5-12 Conditional own-price elasticities over time for EU-15 analysis.................................91









LIST OF FIGURES


Figure page

5-1 Impact of structural change on U.S. demand for Canadian and Dominican Republic
fre sh to m ato e s ................................................................................................................. .. 8 4

5-2 Impact of structural change on U.S. demand for Mexican and EU-15 fresh tomatoes. ....85

5-3 Impact of structural change on U.S. demand for ROW fresh tomatoes. ...........................86

5-4 Impact of structural change on EU-15 demand for Albanian and Bulgarian fresh
to m ato es ........................................................................................................... ....... .. 9 2

5-5 Impact of structural change on EU-15 demand for Israeli and Morocco fresh
to m ato es ............. ..................................................................................... ....... .. 9 3

5-6 Impact of structural change on EU-15 demand for Romanian and Turkish fresh
to m ato es ........................................................................................................... ....... .. 9 4

5-7 Impact of structural change on EU-15 demand for ROW fresh tomatoes.........................95









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

ESTIMATING IMPORT DEMAND FOR FRESH TOMATOES INTO THE UNITED STATES
AND THE EUROPEAN UNION

By

Mohammad Ali

August 2007

Chair: Richard L. Kilmer
Major: Food and Resource Economics

Continuous talks and negotiations initiated by the World Trade Organization (WTO), in

recent years, on globalization and trade liberalization have made international trade a key issue

for all nations as it has expanded the global markets with enhanced competitiveness. There exist

both opportunities and costs as it expands exports on one hand and poses threats of competition

from importers on the other. Agriculture being the major player in international trade has to cope

with this changing trend of the global market situation. The European Union (EU-15) being more

and more open should be of especial interest to the growers, traders and policy makers of all

nations including the United States (U.S.). This is a research project for the analysis of import

demand for fresh tomatoes into the U.S. and the EU-15 for the assessment and evaluation of

competitiveness to enable the specialty crop industry to compete successfully. The sources of

data for this research are the United Nations Statistics Division- Commodity Trade Statistics

Database website, Food and Agriculture Organization Statistics Database website and other

websites maintained by the United States Department of Agriculture (USDA). Data for the

period 1963-2005 have been used in this research.

A differential production approach has been used for estimating import demand for fresh

tomatoes. Imports are considered as inputs to importing firms. The mode used in this research is









derived from the basic principle of the theory of firm whereby the firm maximizes profit by

determining a level of output and minimizing the cost of producing that level of output. The cost

minimization stage is applied to get conditional factor demand equations in the estimation

process. The differential production version of the Netherlands National Bureau of Research

(NBR) specification is estimated by the iterative seemingly unrelated regression (SUR) method

using the well known least square procedure (LSQ) in Time Series Processor (TSP).

Results show that Mexico is the prominent supplier of fresh tomatoes in the U.S. import

market facing no close competitor. Canada and EU-15 compete with each other for the U.S.

import market whereby Canada is losing its relative share and EU-15 is gaining its relative share.

For the EU-15 import demand for fresh tomatoes, Morocco is the major supplier with no close

competitor. Israel and Rest of the World (ROW) are competing with each other in the EU-15

import market. Albania, Bulgaria and ROW are losing their relative share in the EU-15 import

market with an indication of some kind of structural changes.

Mexico and Morocco have significant influence and control over the U.S. and the EU-15

import markets. It is necessary for other participants to figure out the secrets and follow them to

be competitive with these major players in their respective markets. Otherwise, the implications

would be significant on both markets if there are some diseases or calamities in Mexico and

Morocco.









CHAPTER 1
INTRODUCTION

Globalization, in a modem world, has emerged as a blessing for both developed and

developing countries. With globalization comes trade liberalization that reduces barriers to

international trade. According to the World Trade Organization (WTO), elimination of trade

barriers can result in annual welfare gains for the world ranging from US $250 billion to US

$620 billion. Trade liberalization can also help alleviate poverty by contributing to a more

efficient resource allocation and raising productivity. Thus, free trade can contribute to higher

wages and standard of living. So, it can be said that trade liberalization and poverty

reduction/income growth go hand in hand (WTO, 2002).

In the field of agriculture, increased globalization has both potential benefits and costs for

the United States (U.S.). In some cases, there will be opportunities to expand U.S. exports and in

others, there will be a threat of facing competition from importers with lower prices. The overall

effects on the U.S. may further be complicated by high income elasticities of demand. For

example, many of the specialty crops may have relatively higher income elasticities of demand

in the U.S. than other field crops. Hence, income growth will definitely have a positive effect on

the U.S. demand for those specialty crops; however, this may or may not neutralize the impact of

increased competition as well as the effect of potentially lower prices. Thus, the globalization of

markets along with the emphasis on international trade has increased interest on the

competitiveness of the U.S. in global markets (Institute of Food and Agricultural Sciences

(IFAS), 2002).

U.S. farm cash receipts are valued at more than $241 billion and Florida is the tenth

leading state in this respect with $6.84 billion in farm cash receipts as of 2004. Florida is also the

fifth leading state in crop production with $5.36 billion in cash receipts (National Agricultural









Statistics Service (NASS), 2006). Agriculture accounts for more than 314,000 jobs in the state of

Florida (IFAS, 2002). The U.S. is also one of the world's leading importers and exporters of

fruits, vegetables and nuts. The specialty crops are very important for Florida. The three leading

specialty crops in terms of value are greenhouse/nursery at $1.63 billion, oranges at $980 million

and tomatoes at $501 million (NASS, 2006). The specialty crops are important for feeding the

nation as well as the world. So, it is very important to look at the future of this industry both

domestically and globally

The International Agricultural Trade and Policy Center (IATPC) at the University of

Florida (UF) is entrusted with the responsibility to focus on research and education that will help

the industry understand the implications of trade and policy related issues. The Center has been

established to help growers, industry leaders and policy makers in understanding various impacts

of all these issues on the future of the industry (IATPC website). The proposed project looks at

the import demand for fresh tomatoes into the U.S and the European Union (EU).

Problematic Situation

The World Trade Organization (WTO), in recent times, is continuously initiating talks on

agricultural trade aimed at trade liberalization. Consequently, the globalization of markets places

enhanced emphasis on international trade and competitiveness among suppliers. It has become a

necessary venture for the producers/processors to gain a larger share of world agricultural

exports. The specialty crop sector also needs to keep pace with this trend of potentiality. One

way of doing so is to provide firms with the necessary information on global markets. Therefore,

demand studies for individual countries are needed for the specialty crop sector in order to

enable the sector to strategically plan to expand exports. Thus, the analysis of import demand for

tomatoes into the most potential markets like the EU is necessary for the U.S. specialty crop









industry for the assessment and evaluation of competitiveness that will enable the industry to

compete successfully in an increasingly growing domestic and global markets.

Problem Statement

What is the state of competition among the suppliers for imported tomatoes into the

United States and the European Union?

Objectives

The broad objective of this research is to estimate import demand for tomatoes into the

United States and the European Union and utilize estimated parameters in order to measure the

sensitivity of demand for tomatoes to changes in own price, prices of substitutes and quantity of

imports, and thereby look at competitiveness among suppliers. The specific objectives include

1) To look at international trade in tomatoes imported into the United States and the European
Union.

2) To review different demand models including import demand models.

3) To develop a model to estimate import demands for fresh tomatoes into the United States and
the European Union.

4) To determine the extent of competition among the suppliers of fresh tomato imports into the
United States and the European Union.

5) To look at if there is any structural influence on the import demand for fresh tomatoes.

Chapter Summary

Data on fresh tomato imports for this study have been obtained from the United Nations'

database. A differential production version of the Netherlands National Bureau of Research

(NBR) model has been used in estimating import demands. In Chapter 2 a background for this

study has been provided. Chapter 3 discusses a detailed literature review that is helpful for the

study. Then the theoretical and empirical models are discussed in Chapter 4 that also includes the









data section in detail. Chapter 5 provides a discussion on empirical results. The concluding

Chapter 6 includes observations, summary, conclusions, and implications and recommendations.









CHAPTER 2
BACKGROUND

U.S. Tomatoes

The United States is one of the world's largest producers of tomatoes. It is second in rank,

just behind China (Economic and Research Services (ERS)-United States Department of

Agriculture (USDA) website). Imports of tomatoes have also risen significantly since 1994. The

industry has been growing rapidly over last few decades. In terms of U.S. farm cash receipts, the

U.S. fresh and processed tomatoes are second only to potatoes among all vegetables in value.

Tomatoes accounted for $2.06 billion in farm cash receipts in 2004 (NASS, 2006), which is 12

percent of all vegetable and melon receipts. In terms of 2002 harvested acreage of tomatoes, the

five top states are California, Florida, Ohio, Indiana, and Michigan (ERS-USDA, 2003).

However, the leading producers of fresh market tomatoes were Florida (39 percent), California

(31 percent), Ohio (7 percent), Virginia (4 percent), and North Carolina (2.4 percent) in 2002. On

the other hand, the top processing tomato producing states were California (95.4 percent),

Indiana (1.7 percent), Ohio (1.7 percent), Michigan (0.7 percent), and Pensylvania (0.2 percent).

The average annual per capital consumption of fresh and processed tomatoes rose by 18

percent during the 1990's compared to the 1980's, amounting to 91 pounds on a fresh-weight

basis in 1999, with processed tomatoes accounting for about 80 percent (ERS-USDA, 2003). The

total domestic utilization of fresh market tomatoes in 2002 was 5.2 billion pounds (18.4 pounds

per person) while that of processed tomatoes totaled to 19.9 billion pounds (69.2 pounds per

person). The fresh market data provided above excludes domestic greenhouse or hydroponic

tomatoes that might add one more pound to fresh per capital consumption if included. Mexico

and Canada are the major suppliers of fresh market tomatoes to the U.S. while Canada is the

leading export market for U.S. fresh and processed tomatoes (ERS-USDA website).









With respect to tomato exports and imports, about 7 percent of the U.S. fresh market

tomato supply is exported. The top U.S. export markets in 2001 were Canada (78.6 percent),

Mexico (13.9 percent), Belgium (4.09 percent), Japan (1.38 percent), and France (0.67 percent)

(U.S. Trade Statistics website). For processed tomatoes, about 5 percent of the tomato product

supply was exported during the 1990's. The top U.S. export markets for processed tomatoes in

2001 were Canada (48.61 percent), Mexico (11.47 percent), Japan (8.88 percent), United

Kingdom (4.67 percent) and South Korea (4 percent) (U.S. Trade Statistics website).

Now, as for imported tomatoes, it is important to observe that more fresh tomatoes are

imported than processed tomatoes. In 2000, fresh tomato imports accounted for about 32 percent

of domestic consumption, which is down from 36 percent in 1998, but more than 19 percent in

1990 (ERS-USDA website). The U.S. normally imports most fresh tomatoes during the period

when domestic supply is low, i.e., late fall to early spring. In 2001, Mexico supplied 82.47

percent of the value of fresh tomato imports followed by Canada (12.8 percent), the Netherlands

(3.55 percent) and Israel (0.45 percent) (U.S. Trade Statistics website). As a net importer, the

United States imported 36% of total fresh tomato consumption and exported about 9% of total

domestic production in 2002 (ERS-USDA, 2003).

In the case of processed tomatoes, imports accounted for 4 percent of domestic

consumption during the 1990's, which is down from 7 percent in the 1980's (ERS-USDA

website). In 2001, Canada accounted for 40 percent of the imported processed tomatoes followed

by Italy (27.16 percent), Mexico (14.91 percent), Dominican Republic (5.32 percent), China

(3.84), and Israel (2.75 percent) (ERS-USDA website). The importance of the U.S. tomato

industry will be clear from the tables at the end of this chapter. Table 2-1 shows the U.S. tomato

production including Florida's share compared with the world's total production. Florida









production is mainly for the fresh market and it constitutes around 40% of U.S. fresh tomatoes.

Table 2-2 shows the U.S. exports and imports of tomatoes compared with the world exports and

imports. For the period 1991-2005, U.S. exports varied from 148,297 to 188,173 metric tons

whereas imports varied from 360,770 to 951,785 metric tons.

EU Tomatoes

The Common Agriculture Policy (CAP) of 1962 has laid the foundation for creating the

European Union (EU) through which Europe has taken a strong protective stance of its

agricultural markets. This is especially true for products like dairy, fresh fruits and vegetables

including tomatoes that are vulnerable to foreign competition as a result of domestic prices that

are higher than the world price levels. In fact, European consumers pay almost twice the

competitive world price for most of its agricultural products (Adams and Kilmer, 2003). In other

words, European producers receive almost twice the world price for many agricultural products

due to domestic farm programs. Thus, agricultural subsidies accounted for almost half of the

EU's total budget in 2000 (US $40 billion on agriculture) (ERS-USDA website). The domestic

policies for citrus and tomatoes include export refunds, product withdrawals from the market,

intervention thresholds, and direct producer aids.

Recent EU General Agreement on Trade and Tariff (GATT) and later the World Trade

Organization (WTO) membership has forced some changes to CAP, resulting in less domestic

support for European agricultural markets. Consequently, the EU, once called "Fortress Europe"

is now becoming more and more accessible to the world agricultural producers including the

U.S. producers (Adams and Kilmer, 2003). For example, the EU is now the third largest regional

export market for US agricultural products with imports of $6.4 billion in 2001 (Adams and

Kilmer, 2003). The EU was a net exporter to the U.S. with a surplus of Eur 2.63 billion in 2001

while the U.S. was a net importer from the EU with a deficit of Eur 5.65 billion. The values for









2000 were Eur 2.63 billion and Eur 5.51 billion respectively with an exchange rate of Eur 1= US

$1.12 (European Communities (EC) (2202)).

Although the level of EU support for agriculture is decreasing, it is still relatively high

compared to the U.S. and rest of the world. However, the recent changes have signaled a clear

trend away from market-distorting actions and toward direct payments to producers. In general,

the intention of the EU is to enhance European agricultural competitiveness by setting product

intervention as "a real safety net measure, allowing EU producers to respond to market signals

while protecting them from extreme price fluctuations," and promoting market oriented,

sustainable agriculture by finishing the transition from product support to producer support,

introducing a decoupledd system of payments per farm" which are supposedly not connected to

production (Adams and Kilmer, 2003). In fact, the EU wishes to allow flexibility in production,

but at the same time it also wants to guarantee income stability to producers. During the last 10

years, the EU has reduced price supports but increased direct payments to tomato, dairy and

citrus producers in order to compensate them for such reductions (Adams and Kilmer, 2003).

After the successful negotiation of the 1993 Uruguay Round of GATT, the EU has been

progressing towards liberalization of agricultural markets including citrus, tomatoes and dairy,

moving from market-distorting support to less market-distorting regulation. Consistent with this

market liberalization trend in Europe, the EU has made major changes to common market

organization for tomatoes in 2000 that were less market distorting. So, there exists more market

flexibility after 2000, a lesser amount of product can be removed from the market and supports

for the export of tomatoes have also declined (Adams and Kilmer, 2003). As of 1998, the EU

imports 4 percent of world tomato production and exports 7 percent of world tomato production.

In fact, very little of the world's fresh tomato production is exported fresh. The leading tomato









producers for processing in 1999/2000 were the U.S. (11.6 million tons vs. 8.5 million tons in

1998/99), the EU (9.1 million tons vs. 8.0 million tons) and Turkey (1.8 million tons vs. 1.7

million tons) (Adams and Kilmer, 2003). Therefore, the importance of the EU cannot be ignored

in terms of production, consumption and trade of tomatoes.

The members of the EU-15 are Austria, Belgium, Denmark, Finland, France, Germany,

Great Britain, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, and Sweden.

The export and import position of the EU in tomato trade can be visualized from Table 2-3 and

Table 2-4. The tables show that U.S. fresh tomato export and import trade with the EU is

important even though there are some up and down swings from year to year. They also depict

that EU's role in world's export and import trade in tomatoes as well as production is quite

significant (Table 2-5, Table 2-6 and Table 2-7).

As the World Trade Organization (WTO) is constantly debating on agricultural trade with

a vision towards trade liberalization and/or globalization of markets, emphasis on international

trade and competitiveness among suppliers has become a striking issue in order to ensure a larger

share of world agricultural exports. The specialty crop sector like tomatoes also needs to keep

pace with this potential trend. Therefore, the study of import demand for tomatoes into the most

potential markets like the EU is very much necessary and justified for the U.S. specialty crop

industry in order to compete successfully through the assessment and evaluation of

competitiveness in an increasingly growing domestic and global markets.

Data

The data on imports of fresh tomatoes have been used for 43 years i.e., for the period 1963-

2005. The source of data is mainly the United Nations (U.N.) Statistics Division-Commodity

Trade Statistics Database (UN-COMTRADE) website. The International Agricultural Trade and

Policy Center (IATPC) at the University of Florida has made arrangements for data availability.









Other sources of data are online websites maintained by the USDA and the U.N. Food and

Agriculture Organization (FAO) Statistics (UN-FAOSTAT) website. The required data are

collected and manipulated to fit in the model to be used for this research. The data are in both

volumes and values from which price/cost data can easily be calculated by dividing the value of

imports by the volume of imports.











Table 2-1. Production of tomatoes
Year U.S. Florida World U.S. Fresh Florida Fresh
Production Productiona Productiona Tomato Tomato
(1,000 cwt) (1,000 cwt) (1,000 cwt) Production Production
(1,000 cwt) (1,000 cwt)
1990 240,905 15,240 1,677,225 33,800 15,240
1991 251,448 16,170 1,670,099 33,988 16,170
1992 214,582 20,858 1,645,031 39,033 20,858
1993 230,196 17,160 1,710,874 36,663 17,160
1994 268,181 16,995 1,825,395 37,387 16,995
1995 259,798 15,035 1,911,961 34,098 15,035
1996 261,780 14,484 2,048,519 33,634 14,484
1997 232,242 13,720 1,968,839 32,777 13,720
1998 220,668 13,952 2,116,780 32,628 13,952
1999 293,455 15,820 2,352,832 36,735 15,820
2000 254,830 15,760 2,367,172 37,665 15,760
2001 220,501 14,908 2,316,384 35,527 14,908
2002 270,438 14,400 2,380,374 37,302 14,400
2003b 232,000 14,190 2,564,700 35,578 14,190
2004b 283,700 15,120 2,713,800 38,346 15,120
2005b 243,500 15,540 2,727,800 39,462 15,540
aTotal tomatoes include fresh and processed Source: National Agricultural Statistics Service,
U.S. Department of Agriculture, ERS-USDA website:
http://www.ers.usda.gov/Briefing/Tomatoes.and bhttp://www.nass.usda.gov/index.asp.
bAlso derived from data supplied by FAOSTAT (06/20/06), Food and Agriculture Organization,
United Nations. Note: 1 metric ton = 2,205 pounds and 1 cwt. = 100 pounds.



Table 2-2. U.S. exports and imports of fresh and processed tomatoes compared to World
Year U.S. Exports World Exports U.S. Imports World Imports
(Metric Tons) (Metric Tons) (Metric Tons) (Metric Tons)
1990 157,311 2,390,374 360,995 2,407,976
1991 148,297 2,437,142 360,770 2,438,764
1992 171,292 2,477,245 196,027 2,791,387
1993 169,142 2,951,351 418,395 2,973,351
1994 169,891 3,231,714 396,040 2,949,429
1995 155,951 3,452,170 620,944 3,101,528
1996 161,279 3,356,339 737,150 3,444,013
1997 179,093 3,752,235 742,464 3,629,123
1998 158,955 3,973,383 847,320 3,681,147
1999 170,873 3,975,530 740,656 3,579,430
2000 208,564 3,786,645 730,063 3,621,868
2001 205,486 4,235,422 823,541 3,918,901
2002a 182,286 4,272,195 860,098 4,120,920
2003a 180,713 4,526,953 939,257 4,362,962
2004a 212,280 4,843,480 931,970 4,649,342
2005a 188,173 4,894,498 951,785 4,684,459
Source: National Agricultural Statistics Service, U.S. Department of Agriculture plus ERS-
USDA and Bureau of the Census, U.S. Department of Commerce. aAlso Food and Agriculture
Organization, United Nations. UN-FAOSTAT website. Note: 1 metric ton=1.102 short
tons=2,205 pounds and 1 short ton=2000 pounds=0.907 metric tons.











Table 2-3. U.S. fresh tomato exports to the EU
Countries Values in 1,000 Dollars
1995 1996 1997 1998 1999 20


00 2001 2002a 2003a 2004a 2005a


Austria 0 0 0 0 0 0 0 0 0 0 0
Belgium- 44 2,737 3,811 3,729 5,689 8,706 5,978 6,372 3,461 2,845 399
Luxembourg
Denmark 0 0 0 0 0 0 0 0 0 0 0
Finland 0 0 0 0 0 0 0 0 0 0 0
France 44 0 0 87 44 792 975 0 208 3 0
Germany 261 0 348 223 541 0 24 0 0 5 7
Great Britain 415 522 1,962 2,496 1,896 2,193 75 1,087 703 659 0
Greece 0 0 0 0 0 0 0 0 0 0
Ireland 0 0 0 0 51 0 0 0 0 0 0
Italy 8 0 0 0 0 0 94 0 0 3 0
Netherlands 109 0 1,573 803 1,163 497 0 1,355 4,144 2,549 705
Portugal 0 0 0 55 476 209 39 0 0 0 0
Spain 0 44 131 179 155 305 29 0 0 0 0
Sweden 0 0 0 0 0 0 0 0 22 0 0
Total EU-15 881 3,303 7,825 7,572 10,015 12,702 7,214 8,814 8,538 6,064 1,111
EU-25 881 3,303 7,825 7,572 10,015 12,702 7,214 8,814 8,538 6,064 1,111
EU-27 881 3,303 7,825 7,572 10,015 12,702 7,214 8,814 8,538 6,064 1,111
Source: Department of Commerce, U.S. Census Bureau, Foreign Trade Statistics and aU.S.
Trade Statistics website.



Table2-4. U.S. fresh tomato imports from the EU


Countries


Austria
Belgium-
Luxembourg
Denmark
Finland
France
Germany
Great Britain
Greece
Ireland
Italy
Netherlands
Portugal
Spain
Sweden
Total EU-15
EU-25
EU-27


Values in 1,000 Dollars
1995 1996 1997 1998
0 0 0 0
2,766 4,114 5,097 5,555


0
0
0
4
0
0
0
0
21,131
0
1,982
0
25,883
25,883
25 883


0
0
0
0
23
0
0
0
42,646
0
3,879
0
50,662
50,662
50 662


6
0
0
18
15
0
0
23
52,909
0
7,829
0
65,897
65,897
65 897


17
0
160
0
11
0
0
3
64,487
0
10,894
0
81,127
81,127
81 127


1999
0
3,292

40
0
231
0
0
0
0
0
57,171
0
10,711
5
71,450
71,450
71 450


2000
0
2,056

0
0
0
0
6
0
0
0
46,392
0
10,698
0
59,152
59,152
59 152


2001
0
1,102

0
0
0
0
0
0
0
9
51,027
0
9,709
0
61,847
61,847
61 847


2002a
0
1,389

0
0
0
0
0
0
0
13
45,630
0
13,710
0
60,742
60,758
60758


2003a
0
2,046

0
0
0
0
0
0
0
0
33,908
0
7,025
9
42,988
43,051
43 051


2004a
0
3,997

0
0
0
0
0
0
0
0
27,721
0
6,001
0
37,719
37,732
37 732


2005a
0
2,139

0
0
0
0
0
0
0
4
16,065
0
820
0
19,028
19,053
19053


Source: Department of Commerce, U.S. Census Bureau, Foreign Trade Statistics and aU.S.
Trade Statistics website.










Table 2-5. EU's tomato exports compared with the U.S. and World (total tomatoes)
Year EU's Exports U.S. Exports World Exports
(In Metric Tons) (In Metric Tons) (In Metric Tons)
1990 1,187,299 157,311 2,390,374
1991 1,296,414 148,297 2,437,142
1992 1,425,689 171,292 2,477,245
1993 1,571,570 169,142 2,951,351
1994 1,897,143 169,891 3,231,714
1995 1,838,367 155,951 3,452,170
1996 1,778,314 161,279 3,356,339
1997 1,950,304 179,093 3,752,235
1998 1,805,104 158,955 3,973,383
1999 1,982,756 170,873 3,975,530
2000 1,800,303 208,564 3,786,645
2001 2,051,591 205,486 4,235,422
2002a 1,992,242 182,286 4,272,195
2003a 2,089,092 180,713 4,526,953
2004a 2,254,156 212,280 4,843,480
2005a 2,207,576 188,173 4,894,498
Source: U.S. Tomato Statistics, Economic Research Service (ERS), USDA and aFood &
Agriculture Organization, United Nations. UN-FAOSTAT website.



Table2-6. EU's tomato imports compared with the U.S. and World (total tomatoes)
Year EU's Imports U.S. Imports World Imports
(In Metric Tons) (In Metric Tons) (In Metric Tons)
1990 1,307,863 360,995 2,407,976
1991 1,401,731 360,770 2,438,764
1992 1,495,584 196,027 2,791,387
1993 1,576,035 418,395 2,973,351
1994 1,632,286 396,040 2,949,429
1995 1,577,282 620,944 3,101,528
1996 1,735,146 737,150 3,444,013
1997 1,807,677 742,464 3,629,123
1998 1,726,751 847,320 3,681,147
1999 1,743,478 740,656 3,579,430
2000 1,806,436 730,063 3,621,868
2001 1,919,086 823,541 3,918,901
2002a 1,928,620 860,098 4,120,920
2003a 2,068,209 939,257 4,362,962
2004a 2,155,020 931,970 4,649,342
2005a 2,124,053 951,785 4,684,459
Source: U.S. Tomato Statistics, Economic Research Service (ERS), USDA and aFood &
Agriculture Organization, United Nations. UN-FAOSTAT website.










Table 2-7. EU's tomato production compared with the U.S. and World (total tomatoes)
Year EU's Production U.S. Production World Production
(In 1,000 cwt.) (In 1,000 cwt.) (In 1,000 cwt.)
1990 298,035 240,899 1,677,225
1991 298,680 251,452 1,670,099
1992 279,777 214,510 1,645,031
1993 280,862 230,184 1,710,874
1994 299,553 268,192 1,825,395
1995 286,962 259,792 1,911,961
1996 324,208 261,777 2,048,519
1997 303,232 232,235 1,968,839
1998 326,787 220,660 2,116,780
1999 361,113 293,453 2,352,832
2000 358,844 254,830 2,367,172
2001 332,034 220,501 2,316,384
2002 346,133 270,438 2,380,374
2003a 333,250 232,000 2,564,700
2004a 379,494 283,700 2,713,800
2005a 373,221 243,500 2,727,800
Source: U.S. Tomato Statistics, Economic Research Service (ERS), USDA. aAlso derived
from data supplied by FAOSTAT (06/20/06), Food and Agriculture Organization, United
Nations. Note: 1 metric ton = 2,205 pounds and 1 cwt. = 100 pounds.









CHAPTER 3
LITERATURE REVIEW

In order to formulate a model to analyze the import demands for tomatoes into the

European Union (EU) and the United States (U.S.), it is necessary to look at the literature on

import demand estimation. The theory of demand has occupied a vast area in the field of

economics covering consumer theory as well as the theory of the firm. Globalization of markets

has increased the emphasis on international trade and on the competitiveness among exporters.

So, import demand analysis is a vital point in determining the competitiveness in international

trade.

Armington Trade Model

International trade flows are classified on three characteristics such as the kind of

merchandise, the country (region) of origin or the seller, and the country (region) of demand or

the buyer. The assumption frequently used in theories of demand is that merchandise of one kind

supplied by one country is a perfect substitute for merchandise of the same kind supplied by any

other country indicating infinite elasticities of substitution and constant price ratios. But, in

reality there are lags in buyers' responses as well as some other imperfections in their behaviors

that should not be overlooked. So, it is preferable to recognize that any feasible world model of

demand would find few, if any, merchandise with perfect substitutability. Bearing this in mind,

Armington (1969) has presented a general theory of demand for products not only distinguished

by kinds, but also distinguished by place of production. In his paper, a distinction has been made

between goods and products in the sense that goods are distinguished only by their kinds

whereas products are distinguished both by their kinds and place of production. Products are

differentiated from the buyers' viewpoint according to the area of the suppliers' residence and

they are assumed to be imperfect substitutes in demand. If there are 5 goods and 20 supply









regions, then the number of products distinguished will be 100. The geographic area not only

serves as a basis for distinguishing products by origin, but also as a basis for identifying sources

of demand.

Now the problem in Armington's (1969) model is to simplify the product demand

functions systematically for practical use in estimation and forecasting. Beginning with the

general Hicksian model, imposition of a sequence of more restrictive assumptions leads to a

highly simplified specification of the product demand function that reveals the relationships

between demand, income and prices. The basic modification to the Hicksian model is the

assumption of independence, meaning that the buyers' preferences for any given kind of product

are independent of their purchases of any other kind of product. With this assumption of

independence, it is possible, in principle, to measure the quantity of each good demanded by

each country. There are demands for different groups of competing products and each of these

demands can be considered to be a market and suppliers from different countries could be

expected to compete in that market. Thus, the demand for a particular product can be expressed

as a function of the size of the market for that product and the relative prices of competing

products.

Another assumption in Armington's (1969) analysis is that the market share of each

country is unaffected by changes in the size of the market as long as the relative prices remain

unchanged. This additional assumption implies that the size of the market is a function of money

income and the prices of different goods. Combining this with the earlier product demand

function, the demand for any product can be expressed as a function of money income, the price

of each good, and the price of that product relative to all other products in the same market. So,

the approach used in his study assumes that (a) elasticities of substitution between competing









products in any market are constant irrespective of market shares, and (b) elasticity of

substitution between any two products competing in a particular market is the same as the

elasticity of substitution between any two other products competing in the same market. Hence, a

specific type of relationship between the demand for a product, the size of the corresponding

market and the relative prices has been suggested by these assumptions. The price parameter in

this relation is the elasticity of substitution in that market. Analysis of changes in demand for any

product is possible by differentiating the demand functions. The change in demand for any

product will depend on the growth of the market and on the change in the product's market share

in that market collectively. The change in market share of a particular product will depend on the

change in its price relative to the average change in prices of all other products in that market.

The change in growth of the market depends on the change in income and income elasticity of

demand for the respective good. Thus, this study emphasizes on the relevancy of demand theory

to the research in some areas of trade analysis as well as forecasting.

Differential Approach and the Rotterdam Models

Seale et al. (1992) showed a Rotterdam application to international trade with a differential

approach. A Rotterdam import allocation model is used to estimate a geographic import demand

system for U.S. fresh apples in four importing markets -Canada, Hong Kong, Singapore and

United Kingdom. The differential approach is widely used in estimating consumer demand

systems, but in case of estimating import demand, it is used less frequently. Three of such studies

are Theil and Clements (1987), Clements and Theil (1978) and Lee, Seale and Jierwiriyapant

(1990). Theil and Clements (1978) estimated derived import demands for four aggregate import

groups using the differential approach in production theory. Clements and Theil (1978) used the

same approach and estimated import demands for 13 individual as well as four groups of

countries for three broad categories such as food, raw materials and manufactures with the









assumption of homothetic technology. Following Barnett (1979), Lee, Seale and Jierwiriyapant

(1990) used the differential approach in utility maximization in order to estimate Japanese import

demands for five kinds of fresh fruits and the geographic import demands for citrus juice. In all

of the above studies, the Rotterdam model has been used for estimation purpose.

The study by Seale et al. (1992) estimated geographic import demands using the

Rotterdam model. They treated each of the importing countries as an individual consumer,

following Mountain (1988) who showed that the Rotterdam model like other popular flexible

functional models is at least a second-order approximation of the underlying demand system. In

this study, multistage budgeting has been utilized where an importing country first allocates total

income (expenditures) between domestic and imported goods. Then, the total expenditure on

imports is allocated among various imported goods and finally, depending on the expenditure for

an import, it is allocated among different suppliers of each good. This is a method like the utility

tree approach (Barten 1977) and can easily accommodate the differential approach to the utility

maximization and is useful for estimating demands for disaggregated imports. Preferences for

imports are based on block-wise dependence that enables estimation of geographic import

demand subsystems independent of the import demands for all other goods. The conditional

import demand system is derived through the differential approach and then parameterized as per

a Rotterdam specification. The Rotterdam parameterization is attractive in this case, because it

permits nested testing for restrictions on homogeneity, symmetry, homotheticity and separability

(preferences being additive).

The Seale's (1992) study calculated expenditure and price elasticities from estimated

parameters using both the Rotterdam absolute price model (Rotterdam A.P.) and the Rotterdam

preference independence model (Rotterdam P.I.). The elasticities thus calculated, measure the









impacts on import shares among fresh apple suppliers as expenditures on total apple imports

change and as the prices of imported apples from various geographic locations change. They also

used Working's (1943) model for calculating income elasticities of demand for fresh imported

apples as a group and by sources of supply.

Lee et al. (1994) have discussed the choice of model in consumer behavior analysis in

Taiwan. Numerous specifications of a demand system in consumer demand analysis have

evolved through time. These include linear and quadratic expenditure systems, translog models,

the Working model, the Rotterdam model and the Almost Ideal Demand System (AIDS). Among

all of them, two systems have become popular in agricultural economics, the Rotterdam model

and the AIDS. However, the underlying assumptions for these two systems have different

implications. The marginal expenditure shares and the Slutsky terms are taken to be constants in

the Rotterdam model whereas they are considered to be functions of budget shares in AIDS. In

order to choose an appropriate demand system, statistical tests are conducted when the

underlying competing systems are nested (Amemiya, p. 142). On the other hand, when the

systems are not nested, an alternative testing procedure is needed. Deaton (1978) conducted a

non-nested testing procedure in order to compare competing demand systems with the same

dependent variables that are not applicable in comparing Rotterdam and AIDS, as the dependent

variables are different. However, Barten (1993) explained that the Rotterdam and the AIDS are

special cases of a more general demand system and also nested within that system. So, he

suggested pair-wise and higher-order testing procedures to choose the best fitted system.

Thus, the Lee et al. (1994) looked into how prices and income influenced Taiwanese consumer

demand for the period 1970-89 and how the elasticities of demand evolved through time. They

examined four different versions of the demand system indicated by Barten (1993) such as the









Rotterdam, a differential version of the AIDS, the Dutch Central Bureau of Statistics (CBS)

system and the Netherlands National Bureau of Research (NBR) system. Then, a general model

nesting all these four systems has been developed in order to facilitate choosing the best fitted

one.

The differential approach has been used by many other researchers. Rossi (1984) has made

use of this approach in the theory of multiproduct firm in order to analyze input demand and

output supply in Italian agriculture. In case of multiproduct and multifactor production structure,

the firm's technology is usually described by a production, cost or profit function. The

approximation process may be in variable space comprising prices, quantities, or price-quantity

ratio like in the case of classical translog specification. On the other hand, it may be in parameter

space as in the case of so-called differential approach. Since the technology of a firm is basically

unknown, the differential approach has an advantage in the sense that it does not specify any

particular form, but can accommodate different technologies not being bias to any particular

form. In this study, he actually followed Laitinen and Theil (1978, 1980) in estimating the

parameters of aggregate multiproduct technologies using differential approach. He extended the

works of Laitinen and Theil in case of short-run with fixed factors of production and then

considered the dynamic adjustments along with the variables to model the role of weather in

agriculture.

Laitinen and Theil (1978) considered the demand and supply of the multiproduct firm

without the usual specializing assumptions of input-output separability or constant elasticities of

substitution or scale. They estimated a system of input demand equations under the condition of

cost minimization and a system of output supply equations under the condition of profit









maximization. The prices are described in terms of substitution and complementary relation

among inputs and among outputs in the demand and supply equations.

Demand System and Functional Formulation

Barten (1977) has reviewed the work done on the formulation and estimation of complete

systems of consumer demand functions and delineated the related problems and issues that are of

both theoretical and empirical in nature. From the theoretical consideration, constraints are

imposed on such systems to deal with empirical problems like lack of sufficient data. There are

various alternative approaches to deal with the issue and it is yet to make a clear-cut choice.

Continuous research is going on concerning the problems of specifying and estimating such

systems. The present review emphasizes the nature of the approach, its possibilities and

limitations. It is basically an empirical approach since it aims at the formulation of the system

that is to be estimated using actual data.

Barten (1993) also discussed the choice of functional form in consumer allocation models,

which are based on microeconomic theory of consumer demand. Allocation models are

concerned with optimal allocation of given means for different alternatives, or as its dual, the

minimal use of these means to achieve a given set of objectives. These models are formulated

not only for consumer demand analysis, but also for demand for inputs in production, and

composition of imports by origin of supply. Four basic approaches to arrive at demand equations,

satisfying required properties, have been described. The first one is from a functionally specified,

increasing and quasi-concave utility function like u = u(qi, .......q) that is maximized subject to

a budget constraint ipq, = m. Then the first order conditions are solved to get quantities as a

function of prices and income. The parameters of the utility function become the constants of the

demand equations. An example of this approach is the linear expenditure system (L.E.S.) that









can be seen in Deaton (1975). The second approach starts from a functionally specified indirect

utility function like u* = u (m, p,......., p,) and using Roy's identity rule, one can obtain an

estimable demand equation such as


q, =-(m/ p, / (3-1)
Oln p, ln m

Example of this approach can be stated as the Indirect Translog Utility Function used by

Christensen et al. (1975).

Barten's (1993) third approach is based on a specified expenditure function expressed in

terms of utility and prices like e = e(u, p,......., p,) Now, applying Shephard's lemma (i.e.,

w, = 8 In el8 In p, ) provides Hicksian demand as

q, = h, (u, p ,........ p,P ) (3-2)

from where u can be eliminated by using an expenditure function in order to express it in terms

of m andp. A good example of this type of specification is the Almost Ideal Demand System

(AIDS) by Deaton and Muellbauer (1980). The fourth approach is related with some kind of

double-logarithmic specification. Many earlier empirical demand studies used a double-

logarithmic specification with constant elasticities and they seem to work well empirically, but

are not sufficiently adequate to explain theoretical restrictions. For example, the constant

elasticity restriction requires a constant budget share. So, researchers working with a double-

logarithmic system started imposing theoretical restrictions on the estimation process in order to

make it statistically efficient. Theil (1965) started such a double-logarithmic specification.

Production Approach and Utility Approach

Davis and Jensen (1994) discussed the supremacy of the production theory approach over

the utility approach in import demand estimation. They pointed out the drawbacks of the two-









stage utility maximization approach that has been widely applied in estimating agricultural

commodity import demand systems and elasticities, suggesting an alternative two-stage profit

maximization approach that can overcome those limitations, but still retains the advantages.

Since the nature of most imported commodities are inputs in the production process, the use of a

utility-based demand system in estimating import demands will have conceptual as well as

empirical disadvantages. The conceptual disadvantage of the two-stage utility maximization

approach arises from the fact that most imported agricultural commodities are inputs, and not

final goods entering consumers' utility function. This conceptual misspecification of an import

demand system has other empirical disadvantages. First, it is believed by most agricultural

economists that aggregation under utility-based import demand models (i.e., defining the first-

stage utility aggregates) is a difficult job to form a consensus. In most utility-based models, the

procedure is to pick a commodity and assume that it is weakly separable from all other goods

that should have been included in the utility function for logical consistency. Thus, this unique

condition of separability is not actually an intuitive one and the choice of first-stage aggregates

becomes confusing and debatable. Secondly, since most of the models are a conditional demand

system, the estimated elasticities are also conditional elasticities. The problem with these

conditional elasticities is that they do not explain all of the price effects captured by the

unconditional elasticities and hence, the use of conditional elasticities may lead to biased and

erroneous inferences and policy recommendations. Thirdly, the unconditional elasticities

obtained from such a misapplied utility-based import demand system are not structural estimates,

rather they are reduced form estimates and the regressors used are incorrect.

Under the above circumstances, Davis and Jensen (1994) prescribed an appealing

alternative conceptual approach that satisfies the criterion such as (a) defining the first-stage









aggregates and thereby the estimation of unconditional elasticities easily and more intuitively,

(b) making structural parameter estimation (derived demand) possible, and (c) retaining the

advantages of two-stage utility maximization procedure. This approach is to model the import

demand estimation in a two-stage, multiproduct, profit maximization framework under

production theory. As soon as the producer theory is applied, the conceptual problem of treating

imports as separate final goods is overcome and as a result, the parameters estimated thereon will

be structural. Again, as a two-stage method, it will also retain all the advantages of the two-stage

utility maximization procedure. Moreover, specifying the first-stage aggregates is more intuitive

in a producer theory model of profit maximization than in a consumer theory model of utility

maximization. Hence, the estimation of unconditional elasticities becomes less questionable

and/or debatable. The presentation, being the integrated efforts of various authors (Blackorby,

Primont, Russell (BPR) (1978); Bliss (1975); Chambers (1988); Fuss (1977); Lau (1972) and

Yuhn (1991)), has been given in the following ways.

First Stage

In Davis and Jensen (1994) the transformation function of the multiproduct industry is

assumed to be well behaved, intertemporally separable, and homothetically separable in input

partition In and can be represented by F (ql,.......,qm, X,........, X,) = 0. Here, q and Xare outputs

and aggregate inputs respectively. The aggregate input X, is defined as X, = X, ( x, ...... ,xn ), and

i= 1,....,.n where, the x,,'s are disaggregate inputs. Assuming perfect competition to prevail,

profit maximization may occur in two stages that will be consistent with single-stage

optimization (Bliss, chapter 7, property 3). The first-stage for a profit maximization problem

solves the objective function

H(p, W) = maX [pq'- WX': F(q,X) e T] (3-3)
q,x









where, p and q are 1 x m vectors of output prices and quantities respectively, X and Ware 1 x n

vectors of aggregated input quantities and price indices respectively. W, is defined as a linearly

homogeneous aggregator function in the form of W, = W, ( w ....... w, ) and it is dual to the X,.

The w,, is the factor price corresponding to a disaggregated input x,, and T represents the

technology set of the industry. From the above aggregate profit function ,(p, W), the output

supplies and input demands can be derived by applying Hotelling's (1932) lemma, which will be


n q, = q, (p,W), = 1,........ m, (3-4)


a = X, =X, (p,W), i= 1,..........n (3-5)
aw,

which are homogeneous of degree zero in p and W according to Euler's theorem.

Second Stage

Following Davis and Jensen (1994) as the transformation function is assumed to be

homothetically separable in the In partition, the sufficient conditions for two-stage optimization

are satisfied (Blackorby et al., 1978) and the conditional demands are derivable from Hotelling's

(1932) lemma by differentiating the aggregate profit function with respect to the disaggregated

input price w,,.

-= x i= 1,........,n; and j = 1,........ J,. (3-6)


In duality theory, there are two equivalent explanations for an optimal level of x,, because,

theoretically the second stage of the two-stage profit maximization problem can be expressed in

two equivalent ways. Even though these two forms are the same as those of the two-stage utility

maximization approach, their solutions to the second stage problem differ empirically. One of









the two forms is to minimize the cost of acquiring a predetermined level of aggregated input such

as


C ,X ) m in j :X = (x ,,.......x J, i= 1,......... ,n (3-7)
x j _J = 1

which can be solved for xhj = xj(w,, X,). Here, C,(w,, X1) is the cost function, w, is the vector of

prices, xhU is the Hicksian conditional input demand function since it is conditional on

predetermined aggregate input level (X,) from the first stage. The Hicksian input demand

function is homogeneous of degree zero in w, by Euler's theorem. Following duality theory, the

alternative form of the above minimization problem will be


X, k,C) =max X, x(x,,.....,x ):C, i= 1,...,n (3-8)


giving a solution for xm,, = x,(w,, C,). The X,(w,,C,) part of the Equation 3-8 represents the

indirect production function which can be considered as analogous to indirect utility function of

the utility maximization problem. By Euler's theorem the solution, xm, = xj(w,, C,) is

homogeneous of degree zero in w, and C, and this is the conditional Marshallian input demand

function that is conditional on the predetermined expenditure level C,. So, solving Equation 3-7

and (3-8) we can get Hicksian demand and Marshallian demand respectively and they will be

equivalent at the optimal point according to Chambers (1982) and Davis and Kruse (1993).

Therefore, it is possible to calculate the conditional and unconditional elasticies and their

relationship by applying the two-stage profit maximization approach to import demand analysis.

Import Demand and the Producer Theory

Burgess (1974a) explained the theory of import demand in a general equilibrium model.

The traditional general equilibrium model of international trade treated imports as final goods

with a perfect domestic substitute. So, the elasticity of demand for imports depends on domestic









supply and demand responses influenced by society's preferences, and the transformation

function indicating factor endowment and technology. But, this theoretical framework normally

collapses when estimation of the elasticity of import demand is sought. In fact, some favorable

functional forms have been chosen for facilitating estimation of the parameters of interest.

Hence, the logarithm of the quantity of imports is considered as a linear function of the logarithm

of income and the logarithm of the ratio of import price to a price index of all domestic goods.

Thus, the parameters are to measure the income elasticity and price elasticity of imports. But,

without imposing an arbitrary separability restriction on the consumer's choice between

domestic and imported goods, it is not possible to derive this estimating equation just from an

explicit model of rational behavior.

In Burgess (1974a), few attempts have subsequently been taken to develop a theory of

import demand from the microeconomic standpoint whereby restrictions have been imposed on

the underlying analytical structure. These restrictions need to be empirically justified rather than

assumed a priori. So, imports are regarded as final goods entering into the consumers' utility

function directly with no perfect domestic substitute. One such framework is developed by

Gregory (1971) and it assumes that a constant elasticity of substitution (CES) functional form

can represent society's preferences. Thus, the logarithm of the ratio of imports to domestic goods

is a linear function of the logarithm of the ratio of their prices. This model has an advantage in

the sense that the estimating equation gives an estimate of the elasticity of substitution between

imports and domestic goods and the estimate of own price elasticity can readily be obtained from

there. But, its major disadvantages are (1) the maintained hypothesis of separability between

imports and all domestic goods indicating that the partial elasticities of substitution between

imports and all other domestic goods are equal, and (2) ignorance of the fact that international









trade occurs in bulk as intermediary goods requiring further processing before reaching

consumers.

So, Burgess (1974a) developed a model of import demand without having these two major

difficulties. Moreover, he explicitly incorporates the theory of import demand into a general

equilibrium framework. The primary concern in this area has been to look at the responsiveness

of import demand to changes in price and income in a partial equilibrium framework for

predicting balance of payment consequences. But, in this general equilibrium framework of

import demand, the balance of payments adjustment process prevails and the primary concern

becomes the effect of changes in import prices, due to tariff policy, on the returns to primary

factors as well as on income distribution. He agrees that most of the imported goods require

further processing before delivery as a final product. The processing may be of complete

transformation, or it may simply involve storing, handling, transportation, distribution and other

marketing activities. These processing activities require domestic capital and labor. So, it is a

policy question to address how tariff policy changes the domestic price of imports and how this

will affect the competitive returns to the primary factors and also the distribution of income.

An assumption of this production theory model is that imports are purchased by firms

trying to minimize the cost of delivering a single product to the final consumer. Since a multi-

factor generalization of the Cobb-Douglas and CES functional forms are not flexible enough for

analyzing issues in this respect, the transcendental logarithmic functional form developed by

Christensen, Jorgenson and Lau (1973) has been used. It permits the Allen-Uzawa partial

elasticities of substitution between factors to differ and it does not impose any arbitrary

separability restrictions a priori. Thus, it enables the researcher to test the hypothesis about the

effects of tariff policy on real income and distribution of income.









Following a cost minimization approach, Burgess (1974b) estimated import demand

equations with a three-input, and two-output technology model. Imports are taken as an

additional input along with the primary inputs of labor and capital, and a joint cost function is

assumed to express technology. Now the producing unit combines labor and capital with the

imported materials in order to minimize the cost of production of a specified bundle of

consumption goods and investment goods. Technology, therefore, consists of two outputs, two

primary inputs and imported materials. The transcendental logarithmic joint cost function used in

this analysis is competent enough to test the maintained hypotheses of most of the previous

studies. The partial derivative of the joint cost function with respect to the price of an input gives

the cost minimizing level of that input demanded. This logarithmic derivation with respect to

factor prices will yield the cost shares. In the same way, the partial derivative of the joint cost

function with respect to output gives the marginal cost of the respective output and under the

condition of a perfectly competitive situation, the marginal costs equal output prices. So, the

logarithmic derivation with respect to output will provide an expression for revenue share.

Burgess (1974b) assumed a maintained hypothesis of constant returns to scale technology

and estimated the cost function using cost share and revenue share equations derived from the

logarithmic derivation of the joint cost function. It found convincing results against the

traditionally maintained hypothesis that technology is separable between inputs and outputs.

Changes in output composition significantly influence the optimum input mix at any given set of

factor prices. Therefore, the optimum level of input mix depends not only on factor prices, but

also on the composition of final demands. According to the study results, changes in composition

of output in favor of consumption goods will increase the demand for labor, but decrease the

demands for capital and imports. The study also rejected the traditionally maintained hypothesis









of separability between inputs and outputs since it has been found that when labor and capital,

and also labor and imports are substitutes, capital and imports are complements. This indicates

that any measure that reduces the price of capital will enhance the demands for imports that will

eventually have adverse effects on the country's balance of payments.

Diewert and Morrison (1989) used a producer theory approach to generate export supply

and import demand functions. The approach uses an economy-wide gross national product

(GNP) function or a restricted profit function with exports as outputs and imports as inputs. This

kind of approach in modeling trade functions using a production theory framework was

introduced by Kohli (1978). Unlike the traditional approach, imports are considered to be

intermediate inputs into the producing sector on one hand, and exports are regarded as

nondomestic outputs produced by the nation's private production sector. This approach has an

advantage over the traditional approach in the sense that one can model only the private

production sector of the economy, ignoring the complexities of modeling the consumer sector.

Duality theory can be applied in order to derive the producer supply and demand functions

conveniently based on the consistency with profit maximizing behavior.

Diewert and Morrison's (1989) model of producer behavior is based on the short run

competitive profit maximization motive, holding capital as fixed. Producers take the wage rate as

fixed and can hire as much labor as required at the going wage rate. So, the profit maximizing

firms, operating under conditions of perfect competition, actually face the domestic and

international price vectors and a vector of domestic primary factor stock, and they in fact, make

the domestic as well as foreign demand and supply decisions. The technology is represented by a

production possibility set from which a well-defined profit function or restricted profit function

can be obtained. Since the translog restricted profit function implemented by Kohli (1978)









frequently fails to maintain theoretical curvature conditions, a special case of the Biquadratic

Restricted Profit Function defined by Diewert and Wales (1987) has been used in this study.

Kohli (1978) has modeled the substitution possibilities between Canadian imports, exports

and domestic inputs or outputs with a similar approach followed by Burgess (1974a, 1974b) in

regard to the treatment of imports. In the study, imports are considered to be inputs like labor and

capital, and exports are considered to be an output of technology. Output consists of investment

goods and consumption goods. The technology represented by a restricted profit function having

labor and capital fixed in the short run, and prices of imports, exports, consumption goods and

investment goods as exogenous, is somewhat similar to Samuelson's (1953-4, 1958) GNP

function. The import demand and export supply functions, along with the supply equations for

consumption goods and investment goods and also the inverse demand functions of domestic

factors are simultaneously derived from this GNP function. In the estimation process the

symmetry and homogeneity restrictions are assumed to hold. The procedure can estimate import

and export functions without assuming that outputs and domestic inputs can be aggregated, and

at the same time it can focus on the substitution possibilities inherent in the production

technology.

Truett and Truett (1998) investigated the Korean demand for imports and the impacts of

trade liberalization on domestic factor inputs using a translog cost function using a production

theory approach. They also regarded imports as productive inputs. The advantage of looking at

imports as factors of production is that the impact of changes in import prices (due to tariff

and/or other trade restrictive policies) on domestic input demands, domestic output prices and on

the quantity of imports demanded, can be observed for appropriate decision making. Of course,

the impact will depend on whether the imports have a complementary relationship or substitute









relationship with domestic inputs. The model used by them assumes that aggregate output

consists of two types of goods consumption goods and investment goods whereas the inputs are

classified into three types labor, capital and imports. In the early studies where imports were

treated as final goods, the demand for imports was taken to be a function of national income, the

price of imports, and the price of domestic goods, with some exchange rate adjustments

(Houthakker and Magee (1969) is an example).

However, the idea of imports as a productive factor, as mentioned earlier, was

subsequently adopted by Burgess (1974b, p.225). The importance of treating imports as a

productive factor is that if they are a substitute for or have a complementary relationship with

one or more domestic inputs, then trade policy may affect domestic factor income and its

distribution. For example, when there is a substitute relationship between imports and domestic

inputs, any reduction in import restriction will decrease the demand for domestic inputs in the

short run. But, if they have a complementary relationship, the reduction in trade restriction will

have positive impact on the demand for domestic inputs.

In the Truett and Truett (1998) study, it is assumed that imports are combined with

domestic inputs (labor and capital) by the producer having an objective of minimizing cost of

producing a bundle of goods to be sold domestically or abroad. They used a translog cost

function with its corresponding input share and revenue share equations and estimated the cross

price elasticities of demand between different pairs of inputs and also the direct price elasticities

of demand for them. The question of separability of outputs (i.e., consumption goods and

investment goods) has been investigated to look at whether Korea's demand for imports is

affected by the composition of outputs or not.









Trutt and Truett (1998) followed the Zellner-efficient method (ZEF) (Zellner, 1962) in

estimating the cost function, cost share and revenue share equations by iterating on the estimated

covariance matrix until convergence is reached. It could be argued that both prices and quantities

may be considered as endogenous and it is more appropriate to use an iterative three-stage least

square method (I3 SLS) with some instrumental variables. The procedure has a problem in

selecting the instrument variable since there is no straightforward method of doing so and thus it

becomes somewhat arbitrary. As a result, the estimates may be too sensitive to the instrumental

variables chosen. This is the limitation of cost function approach. However, the results of this

procedure are found to be similar to those of the maximum likelihood method in various studies.

Truett and Truett (1998) focuses its attention on the hypothesis of input-output

separability. This means that the marginal rate of transformation between various products is

independent of the composition of inputs and the marginal rate of substitution between factor

pairs is independent of the composition of outputs. So, a sufficient condition for this is that all

the interacting terms are zero. It suggests that whenever there exists an input-output separability,

changes in output mix will not affect the cost minimizing input mix. The study also gives

estimates of different elasticities of interest such as direct and cross price elasticities of demand

for inputs as well as the inverse price elasticities of output supply and the elasticity of input

prices in response to output prices etc. The elasticities are expressed in estimated parameters and

cost and revenue shares.

Inverse Demand Analysis

Huang (1988) has provided a framework for estimating a complete price dependent

demand system i.e., an inverse demand system. An inverse demand system is one where prices

are functions of quantities demanded and income. It is as important as the quantity dependent

demand system. It explains price variations as functions of quantity variations and it has









analogous properties like the regular demand system. Agricultural economists (i.e., Fox, Houck,

Waugh) have long before recognized that lags between production decision and commodities

marketed may predetermine quantities with some price adjustments for market clearing.

Therefore, quantities rather than prices are more appropriate instrumental or control variables for

analysis of many types of agricultural policies and problems. An inverse demand system is also

theoretically justified in the classical demand theory framework. It is indicated by Hicks (1956,

p.83) that the Marshallian demand has two functions such as (1) it shows the amounts consumers

will take at given prices, and (2) it shows the prices at which consumers will buy given

quantities. Hence, the second function, "quantity into price" implies the inverse demand system.

Applications of inverse demand systems are found in Huang (1983, 1988), Barten and

Bettendorf (1989), and Moschini and Vissa (1992). From empirical standpoint, inverse demand

and regular demand systems are not equivalent. In order to avoid statistical inconsistencies, the

right-hand side variables in such systems should be ones not controlled by the decision maker.

Usually, the consumer in most industrialized economies is a price taker and quantity adjuster for

most goods and services purchased. In this case, a regular demand system is indicated. However,

for certain other goods like fresh fruits and vegetables, fresh fish etc., supply is very inelastic in

the short-run and the producers are eventually price takers. Price taking producers and price

taking consumers are linked by a group of traders who select a price that is expected to clear the

market. This means, for fixed quantities the wholesale traders practically offer prices that are low

enough to induce consumers to buy the entire available quantities. Hence, the traders set prices

as a function of quantities whereby causality goes from quantity to price. In such cases, inverse

demand systems are indicated.









Huang (1988) develops an inverse demand system and applies it to estimate price

flexibilities for thirteen U.S. aggregate food groups and one non-food sector for 1947 through

1983. The demand system is estimated using a constraint maximum likelihood method. The

concept of distance function and its related substitution effect and scale flexibilities are used. The

system explores the interdependent nature of food price variations in response to changes in

quantity. The price flexibilities indicate the change in commodity price needed to induce

consumers to absorb a marginal increase in the quantity of that commodity or others. The

estimated scale flexibilities provide the response of a commodity price to a proportionate change

in the quantity of all goods. They also give an important linkage between compensated and

uncompensated flexibilities. In order to understand an inverse demand system, one may illustrate

the price effects of a marginal change in quantities demanded considering Anderson's (1980)

suggestion that the "scale slope" of quantities demanded plays the same role as the income slope

in ordinary demand system.

Moschini and Vissa (1992) also presented an inverse demand system that can be estimated

in a linear form. They explained how to derive an inverse demand system that resembles one of

the most commonly used demand systems in applied demand analysis, i.e., the Almost Ideal

Demand System (AIDS) of Deaton and Muellbauer (1980) having its popularity for the

availability of an approximate linear version. So, they named their system as the Linear Inverse

Demand System (LIDS). In deriving an inverse demand system, one can start either from a direct

utility function and exploit Wold's identity yielding ordinary inverse demand system, or

alternatively start from a distance (transformation) function and exploit Shephard's theorem

yielding compensated inverse demand system. They derived the system (LIDS) from a distance









function and showed that it has good approximation properties compared with inverse translog

demand system (ITL) and nonlinear inverse demand system (NLIDS).

Barten and Bettendorf (1989) used inverse demand system for price formulation of fish

explaining why people pay for different types of fish the recorded prices. Gorman (1959) first

established fish as a respectable, interesting and challenging subject in demand analysis. He

started with the proposition that the price of fish depends in part on a function of its quantity

consumed and income, and in part on the shadow prices of fundamental characteristics shared by

all types of fish. Following Gorman, they related the price of each type of fish to its quantity

demanded and to total real expenditure on fish. Their study refers to eight major types of fresh

sea fish landed at Belgian fishery port. They assumed a weak separability of the total commodity

bundle into these types of fish on one hand and other types on the other hand. So, only the

quantities of these fish and their prices matter. They also assumed that collective consumer

behavior for fresh sea fish could be interpreted as that of the rational representative consumer.

Therefore, they expressed market demand by a system of Marshallian demand functions and

deduced the inverse demand system. For the estimation purpose, they estimated a Rotterdam

variant of inverse demand system.

Differential Production Approach

Washington and Kilmer (2002a, b) used a differential production approach to estimate

import demand of whey with a comparison to Rotterdam model. The application of production

theory to international trade is not a new concept. Previous studies using a production theory

approach to international trade include Burgess (1974a) and (1974b), Kohli (1978) Diewert and

Morrison (1989), and Truett and Truett (1998). Each of these studies recognized that most goods

in international trade require further processing before final delivery to consumers. Even though

a traded product is not physically altered, activities such as handling, storing, repackaging,









transportation, insurance, and retailing occur. Thus, a significant amount of domestic value is

added when the final product reaches the consumer. Therefore, it seems more appropriate to

view imported products as inputs rather than as final goods even if goods are not physically

transformed.

The production approach views imports as intermediary goods (inputs) and not as final

goods entering the consumer's utility function. Most of the imports arrive in a country in bulk

and consumers rarely buy commodities in bulk or directly from exporting countries. Following

the methodology of Laitinen and Theil (1978) and Theil (1980a, b), the model is derived from

the differential approach to the theory of the firm where firms maximize profit in a two-stage

procedure, i.e., in the first stage, determining the profit maximizing level of output to produce

and in the second stage minimizing the cost of producing that profit maximizing level of output.

According to Laitinen and Theil, and Davis and Jensen (1994), this procedure is consistent with

a one-step or direct profit maximization procedure. The first stage provides the output supply

equation and in the second stage, the conditional factor demand system is obtained. From the

results of both stages, a system of unconditional derived demand equations is derived.

The advantages of the production theory approach over the utility approach to import

demand estimation have been discussed by Davis and Jensen (1994), Kohli (1991) and

Washington and Kilmer (2002a, b). The striking points are the facts that (a) most imported

agricultural commodities are inputs and not final goods, (b) specifying the first stage aggregates

is more intuitive when using the production theory approach, (c) it is easier and more intuitive to

estimate unconditional elasticities using production theory, (d) the estimated parameters using

production theory will be structural parameters, and (e) viewing imports as intermediate goods

not only has its merits in correctness, but it also leads to substantial simplifications theoretically.









For example, the demand for imports can be derived from production theory and there is no need

to model final demand and as such it can avoid the difficulties that arise when we aggregate over

individual consumers.

Washington and Kilmer (2002a, b) articulated the system of equations for estimating such

import demand in the following manner. In fact, the objective of a competitive firm in the first

stage is to identify the profit-maximizing level of output by equating marginal cost with marginal

revenue. This procedure yields the differential output supply equation

N
d(log Q*) = (o d(log p*) + d(logw ) (3-9)
j=1

where Q*, p* and w, represent the output, output price and the price of inputs respectively; (p and

;rare the price elasticity of supply and the elasticity of supply with respect to input prices

respectively. Nis the total number of inputs used in production.

In the second stage, the firm minimizes its input costs/expenditure. Here, the differential

factor demand model is derived that will be used to estimate the system of source specific

derived demand equations. This is specified as (Washington and Kilmer (2002a,b))


f1 d(log x)= 0 d(log X) + Y"* d(log w) (3-10)
j=1


where f/ is the factor share of imported good x from source country i in total input cost; x, and w,

represent the quantity and price of inputs which include the price of each imported good from

n
source country i; d(log X) -= ft d(log x,) where d(log X) is the Divisia volume input
t=1

index; 06 is the mean share of the ith input in the marginal cost of the firm; r,* is the conditional









price coefficient between the ith andjth importing sources or inputs; n is the number of inputs in

the system, n e N.

It is important to mention that the differential factor demand model is required to meet the

following parameter restrictions in order for the model to conform to theoretical considerations:

Sn* -= 0 (homogeneity), and


,* = ,, (symmetry).

The second stage procedure results in the conditional own price/cross price elasticity

c d(logx,) (3-11)
exw =---o = (3-11)
d(logw) fw

and the conditional Divisia volume input elasticity,

E d(logx ) 0,* (3-12)
d(logX) f

Using the relationship between the Divisia volume input index and output,

d(logX) = y d(log Q*), Equation 3-9 can be substituted into Equation 3-10 to yield the

unconditional derived demand system (Washington and Kilmer (2002a,b))


/d(logx,)= ,*[(pd(logp)+ zj d(logwj)] + -, d(logwj). (3-13)
J=1 J=1

Now, dividing through Equation 3-13 by f and using Equations 3-11 and 3-12, one can

get the unconditional derived demand elasticities, the elasticity of input demand with respect to

output price

d(log x, )
d(logp )

and the unconditional own price/cross price elasticity of input demand









d(logx,) c
xw d(log w, )


Lastly, the unconditional elasticity of derived demand with respect to the price of an input

contained in Nbut not in n can be found as


d(log x)

Summary of Literature Review

It has been seen that Consumer Demand system deals with the allocation of a given total

budget over a set of commodities taking into account the effects of price variation. This is used

as a tool by the demand analyst to describe and predict empirical consumer behavior. The

demand system is derived from the theory of utility maximization. The differential approach to

consumer theory as proposed by Barten (1964) and Theil (1965) is an approximation of these

demand equations. Regarding the functional specification, four alternative approaches have been

derived, with well-known demand systems as an illustration. These are the Barten (1964) and

Theil's (1965) the Rotterdam model, Deaton and Muellbauer's (1980) the Almost Ideal Demand

System (AIDS) model, Keller and Van Driel's (1985) the Dutch Central Bureau of Statistics

(CBS) model, and Neves's (1994) AIDS income-variant the Netherlands National Bureau of

Research (NBR) model. Consumer demand allocation models have widely been used in import

demand studies by various researchers such as Lee, Seale, and Jierwiriyapant (1990), Seale,

Sparks, and Buxton (1992), Lee, Brown, and Seale (1992), and Satyanarayana, Wilson, and

Johnson (1999) and many more.

In these studies, imports are considered to be final goods entering directly into the

consumer's utility function. Even though Satyanarayana, Wilson, and Johnson (1999) use a

consumer demand theory model, they recognize that production theory could be used to estimate









the derived demand for malt; however, critical data was not available. As the nature of

international trade is such that traded goods are either used in production processes or go through

a number of domestic channels before reaching the consumer, the production processes and

domestic channels generate some value added to the product by the time it reaches the final

consumer. Therefore, it is more appropriate to view imported goods as inputs even if no

transformation takes place (Kohli, 1978; Diewert & Morrison, 1989, and Truett and Truett,

1998).

The other advantage of using an input demand model in import demand studies is related

to how traded goods are typically reported. Most traded commodities are typically reported in

bulk quantities and values at dockside. In fact, consumers almost never purchase commodities in

such quantities or at the port/dockside. So, with the assumption that importing decisions are

made by a profit-maximizing or cost-minimizing firm is more consistent with the way how trade

data is typically reported (Washington and Kilmer, 2002a, b). This study intends to use Laitinen

(1980) and Theil's (1980a, b) differential input demand model along with three other input

demand models which are the firm's version of the AIDS, CBS and NBR on the consumer side.

Of course, the ultimate choice of model specification has to be made depending on empirical

grounds.









CHAPTER 4
THEORETICAL AND EMPIRICAL MODEL

In this study, a production theory approach has been followed in estimating import demand

for tomatoes into the United States and the European Union. Liu, Kilmer and Lee (2007) have a

similar study on import demand with an emphasis on choice of appropriate functional forms. The

procedure and models used by them have been followed in this study.

This study uses Laitinen (1980) and Theil's (1980 a, b) differential input demand model

(Rotterdam) along with three additional input demand models which are the firm's version of

the AIDS, CBS and NBR on the consumer side. The choice of appropriate model amongst

different specifications for the input demand allocation models ultimately depends on empirical

grounds. This study examines the empirical performance of four similar input demand allocation

models in an econometric analysis of the import market for tomatoes into the U.S. and the EU.

The synthetic model developed by Barten (1993) has been used to compare the empirical results

of these four models. In case of EU import market analysis the Synthetic model itself has been

tried as a fifth model. Hence, the theoretical models for this study comprise the differential input

demand model and the firm's version of the AIDS model, CBS model, NBR model and Barten's

(1993) synthetic model.

Theoretical Models

Differential Input Demand Model

The differential input demand model (Laitinen (1980); Theil, (1980 a, b)) is based on the

firm's version of the fundamental matrix equation of consumer demand. The approach begins

with the traditional production optimization problem of choosing a bundle of inputs that will

Min C =Ypx (4-1)

Subject to z= h(x)









where, C is total cost; p, and x, are the price and quantity for the ith input; z is output which

is held constant; and x is a vector of n input quantities. The first order conditions are solved for

the input demand equations x, = x, (p.....p,,, z) The derivation of the differential input demand

model (Laitinen (1980); Theil (1980 a, b)) is an approximation of this set of input demand

equations resulting in the differential input demand system as represented by

fdlnx, =0,dlnX- rdlnp, (4-2)


where f = px,C is the share of total cost from input i; dln x, is the change in the ith input;

0, = 9(p, x, / 9z) /(tC / 9z) is ith input's share in marginal cost, d In X = Z fd In x, is the Divisia


volume index; and z, is the Slutsky coefficient. Equation 4-2 is the ith differential demand

equation of the firm and it indicates that changes in the decision to purchase the ith input depend

upon the changes in the total amount of inputs obtained and changes in input prices. Given the

data with sufficient variability in input prices and quantities, variables can be constructed

forf,dlnx,, dln X and dlnpj and the coefficients for 0, and z, can be estimated.

The restrictions on the above input demand Equation 4-2 are

adding-up: 0, = 1, j = 0, (4-3)


homogeneity: zj = 0, (4-4)


and Slutsky symmetry: z0j = Zjj, (4-5)

and the condition for curvature is (x'frx) > 0. Equation 4-2 also results in own price and cross

compensated price elasticities









EXP d ln (4-6)
dlnpj f

and the Divisia volume elasticity

dlnxx O8
Eg d (4-7)
dlnX f

The Production AIDS model

Unlike the consumer AIDS model having expenditure as a function of utility and prices for

a consumer as in Deaton and Muellbauer (1980), the production AIDS model has cost specified

as a function of output and prices of inputs for a firm as (Liu, Kilmer and Lee(2007))

In c(p, z) = (1 z) In a(p) + z In b(p) (4-8)


where In a(p) = a0 + a In pj + Z Z In p, In pj
2


and Inb(p) = Ina(p) +/0 pf ;
J

c is the total cost, p is a vector of n input prices and z is output that is held constant. When

there is no production, z = 0 and Equation 4-8 becomes

In c(p, z) = In a(p) (4-9)

which is the firm's fixed cost.

The consumer AIDS model in differential form (Barten, 1993) can be written as follows to

represent the production AIDS model (Liu, Kilmer and Lee (2007))

df, =/,dlnX + Idlnpj (4-10)


This model is similar to the differential input demand model (Equation 4-2) on the right-hand

side; however, the dependent variables are different on the left-hand side. The production AIDS









model explains the change in input i's share (marginal share) of total cost, while the differential

input demand model concerns with the change in input quantity.

Following Lee, Brown, and Seale's (1994) version of the consumer AIDS model, the

production AIDS model can be formulated as (Liu, Kilmer and Lee (2007))

fdlnx, = (/ + f)dlnX ( f A, + f,)dlnp, (4-11)


where A, is Kronecher's delta equal to unity if i = j and zero otherwise. It can be noticed that

the AIDS model (Equation 4-11) and the differential input demand (Equation 4-2) have the same

dependent variable. Hence, this will allow the use of Barten's (1993) synthetic model to

empirically test for the appropriate functional form.

The restrictions on the production AIDS model are

adding-up: A = 0, y,j = 0, (4-12)


homogeneity: y = 0, (4-13)


and Slutsky symmetry: y, = y (4-14)

Also, the curvature condition is (x''rx) > 0 where the 7c matrix is composed of the elements

S= rj -fA, +f,.

Equation 4-11 results in the own price and cross compensated price elasticities to be

dlnx, y,j- fA,+ff (4-15)
e d In p (4-15)
Sdlnpj f

and the Divisia volume elasticity as

dlnx, /, + f
Ex d nX (416)
d1nX f









The Production CBS Model

Keller and van Driel (1985) developed the Dutch Central Bureau of Statistics (CBS)

consumer demand model based on Working's Engel model. The production CBS model in

differential form is written as (Liu, Kilmer and Lee (2007))

f(dlnx, -dlnX)= /,dlnX- Yzrdlnp,. (4-17)


Following Lee, Brown, and Seale's (1994) version of the consumer CBS model and

rearranging Equation 4-17, the production CBS model can be formulated as (Liu, Kilmer and

Lee (2007))

fdlnx, (A8, +f,)dlnX- Z;,dlnp, (4-18)


which is another representation of the production CBS model and with this formulation, Barten's

(1993) synthetic model can be used to empirically test for the appropriate functional form. This

model has the production AIDS model's volume coefficients f/l (Equation 4-11) and the

differential input demand model's price coefficients zr (Equation 4-2). It shares the adding-up,

homogeneity and symmetry conditions with these two models. The constraints on the production

CBS model are

adding-up: f/8, = 1, Y ,j =0, (4-19)
1 1

homogeneity: ZrJ = 0, (4-20)


and Slutsky symmetry: zr = zJ (4-21)

Equation 4-18 also results in the own price and cross compensated price elasticities

S- dlnx (4-22)
Sdlnpp f









and the Divisia volume elasticity

dlnx, ,8, (4-23)
dlnX f

The Production NBR Model

Neves (1994) developed the consumer NBR as a consumer allocation model. On the

producer side, the production NBR model written in differential form is (Liu, Kilmer and Lee

(2007))

df + fdlnX =O,dlnX- 7,dlnp,. (4-24)


Following Lee, Brown, and Seale's (1994) version of the consumer NBR model, the

differential production NBR model has been written as (Liu, Kilmer and Lee (2007))

fdlnx, = OdlnX- (, fA, +f)dln p (4-25)


This model has the volume coefficient 0, as in the differential input demand model (Equation 4-

2) and the price coefficients y,j as in the production AIDS model (Equation 4-11). The

constraints are

adding-up: Y = 1, y,j = 0, (4-26)
1 1

homogeneity: y,j = 0, (4-27)


and Slutsky symmetry: y, = yj,. (4-28)

The curvature condition is (x''rx) > 0 where the elements of the 7r matrix are




Equation 4-25 results in the own price and cross compensated price elasticities as









d ln x 2,/ Aj f + f f (
dlnx = yj (4-29)
d1npj f

and the Divisia volume elasticity as

d In x (4-30)
dlnX f(

The Synthetic Input Demand Model

A synthetic model that contains all of the four input demand models was developed by

Barten (1993). This synthetic system is employed to assess and compare the empirical

performance of each of the four conditional demand systems. Following Lee, Brown, and Seale's

(1994) version of Barten's (1993) synthetic model, Barten's synthetic production model can be

written as follows (Liu, Kilmer and Lee (2007))

fd1nx = (d +, )d1nX- f(ez f(A f ))d1n pj (4-31)


where d, = 531/ + (1 ,1 )O, and ej = 2 Yj + (1 82)nrj. It is good to notice that Equation 4-31

becomes the differential input demand model when 1 = 2 =0, the production CBS model

when d1 =1 and 52 =0, the production AIDS model when 51 = 82 =1, and the production NBR

model when 51 =0 and 2 =1. The demand restrictions on Equation 4-31 are

adding-up: Yd, = 1- ,1 and ej = 0, (4-32)


homogeneity: Ye = 0, (4-33)
i

Slutsky symmetry: e = e j, (4-34)

and the curvature condition is (x''rx) > 0 when the elements of the r matrix are defined as

z = e 2f (A, f ).









Equation 4-31 also results in own-price and compensated cross-price elasticities as

dlnx, e, 2 f (Alj -f )
Sdlnpj f


and the Divisia volume elasticity as

dxx 1n x d, +,5
dl n = f(4-36)
dlnX f

Empirical Models

In order to obtain estimable forms of the five demand systems, all variables must have

dates attached. The standard practice of replacing the cost shares by their two-period moving

average (Barten, 1993) has been followed as


f +=(f +/f-i)/2. (4-37)

This study uses annual data and the log differentials are measured as annual differences of

the logarithmic value for time t and t-1. The differential input demand model (Equation 4-2) is

transformed into

f,tDx,, = 0,DX, rJ Dpjt + Elt (4-38)

n
where Dx, = ln x -lnx 1,' DX = -f,tDx,, Dpjt =ln pj -Inp 1, is the error term, and
1=1

6, and nrj are parameters to be estimated.

The production AIDS model (Equation 4-11) is transformed into


ft Dx,t = (A8 + f,)DX, (7,j I, A+j + jtjt)Dpjt + ,t (4-39)


where /, and y, are the parameters to be estimated.

The production CBS model (Equation 4-18) is transformed into











J

where f, and n-s are the parameters to be estimated.

The production NBR model (Equation 4-25) is transformed into

f,Dx, =0,DX, (y, f/, A, + t ,) Dpt + ,t (4-41)


and 0, and /,j are the parameters to be estimated.

Barten's (1993) synthetic demand model (Equation 4-31) is transformed into


ft Dx, = (d, + 8, ft )DXt (e d2 ft (A ft ))Dpt + s, (4-42)


where d8, 38, 52, and e, are the parameters to be estimated.

Data Section

The tomato data has been collected from various sources. Some of them are published and

some of them are on-line websites. The main source for tomato import data is the United Nations

(U.N.) Statistics Division-Commodity Trade Statistics Database (UN-COMTRADE) website.

The International Agricultural Trade and Policy Center (IATPC) at the University of Florida has

made necessary arrangements for the availability of this data. The U.S. tomato data are collected

mostly from the website maintained by the USDA. Some data are also collected from the U.N.

Food and Agriculture Organization (FAO) Statistics (UN-FAOSTAT) website. The data set for

this research have been completed for the period 1963 2005. In doing so, the U.N. commodity

specification SITC Rev. 1 with commodity code 0544 for fresh tomatoes has been used.

At present the EU has 27 members. This study is confined to EU-15. Because data for 12

new members (Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland,

Slovakia and Slovenia officially included on May 1, 2004, and Bulgaria and Romania included


/, Dx, = (A + /,,)DX, ;Dp,, + D ,


(4-40)









on January 1, 2007) are not available for the entire period under consideration. Therefore, they

are not included in this research as part of the EU dataset. Hence, the EU means EU-15 in this

study.

In order to get the EU member country's data as well as that of the EU as a whole, data for

tomato imports has been collected from 1963-2005 irrespective of the country's inclusion in the

EU. The data on the value (in U.S. dollar) of imports include cost, insurance and freight. The

price data have been derived by dividing the values by quantity (in kilograms) imported.

For a continuous time series data for the period under research (1963-2005), a few steps

have been taken. First, since Belgium and Luxembourg were Customs Union until 1998, data for

Belgium and Luxembourg have been added together for the years from 1999 through 2005 to

represent a continuous series for Belgium and Luxembourg. Second, the Federal Republic of

Germany (FRG) data for 1963-1990 has been added to Germany data for 1991-2005 to make the

Germany data series a continuous one. Third, the data for the United States of America (USA)

has been constituted by adding USA before 1981 (with code 841) data to USA (code 842) data

for 1981 through 2005. As the Customs area of the United States (U.S.) also includes the

territory of U.S. Virgin Islands, the trade data of U.S. Virgin Islands before 1981 was also

included in the USA data.

Moreover, most of their trade occurred within the EU member countries. In this study,

intra-trade among the EU members has been subtracted from total import trade figure of each

member in order for arriving at the actual trade involvement of them with the outside world.

Then, all the EU member countries' trades have been added together to get EU import trade data.

All these manipulations have been done through a mathematical programming in TSP (Time

Series Processor) software. Thus, two sets of working data have been created one for the EU-









15 as a whole with different participating partner countries and rest of the world (ROW) and the

other for the U.S. with different participating partner countries and ROW.

In selecting separate participating partner countries in the working dataset, their shares in

total import trade during the period under study have been taken into consideration. For EU-15,

about 95% of import trade has been covered by separate partners and remaining trade has been

placed under ROW. It should be mentioned here that Bulgaria and Romania are in the list of

separate partners according to their contribution shares even though subsequently they have

become EU members. In the same way, about 99.5% of total U.S. import trade is covered by

separate partners and remaining goes to ROW. Thus, for the U.S. import the partner/source

countries are Mexico, Canada, EU-15, Dominican Republic and ROW and those for the EU-15

are Morocco, Romania, Bulgaria, Israel, Albania, Turkey, USA and ROW. The U.S. is kept as a

separate partner in the working dataset for particular interest even if its share in EU-15 import is

very small (only 0.12%). All programs relating above mentioned data manipulation are provided

in Appendix C.

However, in the working dataset as created there remain some zeros in quantity and value

columns for a few partner countries for few years indicating no import taking place during those

years. In order to deal with this situation, the zero quantity is replaced by 1 being a small number

compared to millions as Wooldridge (2000) indicated a similar procedure. For the corresponding

number in the value column that ultimately is transformed into price (since price =

value/quantity), a separate regression procedure is followed. Theoretically, corresponding to very

low quantity, price should be very high. Keeping this in mind, an ordinary least square (OLS)

regression is done on price with quantity and time trend (years) as independent variables along

with a constant term. Then, zero value (price) is replaced by the highest price for the country of









origin in question plus twice the standard deviation of the dependent variable plus inflation as the

coefficient of the trend (years) variable with appropriate adjustment. The price regression models

for the U.S. and the EU-15 are provided in Appendix A and Appendix B respectively.









CHAPTER 5
EMPIRICAL RESULTS

Results for U.S. Tomato Import Demand Analysis

The U.S. import demand analysis has been performed on the data set with four separate

partners and ROW. The extent of their involvement and the empirical results are discussed

below.

Descriptive Statistics

Table 5-1 shows the cost share, quantity share and the average prices of imports from the

five sources of origin for 1964, the sample mean, and 2005. The largest cost share was for the

import of tomatoes from Mexico and the lowest was for imports from the Dominican Republic.

The cost share for imports from Mexico actually decreased from 98.78% in 1964 to 72.70% in

2005 whereas the decrease for imports from ROW was from 0.38% in 1964 to 0.25% in 2005.

During the same period the cost share for imports from Canada, EU-15 and Dominican Republic

increased from 0.79% to 24.40%, from 0.0% to 2,52% and from 0.05% to 0.13% respectively.

The average prices for imports from Mexico and Dominican Republic are the same, but the

average import price for EU-15 tomatoes is higher than other countries and increased over time.

Model Results

For the U.S. import demand analysis, the differential production version of all the five

models i.e., AIDS, CBS, NBR, differential input demand (DID) and Synthetic were estimated

with first order autocorrelation AR(1), homogeneity and symmetry imposed. As all of these

models satisfy adding-up conditions, only four equations have been estimated excluding the

ROW equation as indicated in Barten (1969). The parameters for the omitted equation are

established from the estimates of other equations using the adding-up conditions. The models

were estimated by the iterative seemingly unrelated regression (SUR) method that is performed









using the well known least square procedure (LSQ) in the Time Series Processor (TSP) (Hall and

Cummins, 1998). All models are found to have significant autocorrelation based on t-statistics

(Table 5-2). A likelihood ratio test is done comparing the log likelihood values of the first four

models to that of the Synthetic model in order to choose an appropriate model for the given data.

Table 5-2 shows the test result whereby the NBR model qualifies as a model to be used. So, only

results from the production NBR as corrected for autocorrelation (TSP website) have been

reported for further discussion. The model has been provided in Appendix A.

The coefficient estimates (y7,) for production NBR (Equation 4-25), the demand parameters

0, and 7r, and the conditional demand elasticity estimates (Equation 4-29 and Equation 4-30)

calculated at sample mean cost share are shown in Table 5-3 and Table 5-4. The property of the

Slutsky matrix to be positive semi-definite one is validated as all the eigen values are positive

except one which is zero. The eigen values for the production NBR Slutsky matrix are

1.20646D-17, 0.000940, 0.008208, 0.038217, and 0.10139. This is an indication that the import

firms are operating optimally.

Divisia Elasticities: The Divisia import volume elasticities for Canada, Dominican

Republic, Mexico, EU-15 and ROW are 0.172136, 0.173076, 1.06388, 1.24252 and -1.68288

respectively (Table 5-4). The Divisia import elasticities for Mexico, EU-15 and ROW are

significant and more than unity in absolute value terms whereas those for Canada and Dominican

Republic are not significant and less than unity. This means that if total imports of tomatoes into

the U.S. increases by 1%, other things remaining the same, imports from Canada or Dominican

Republic would not change and imports from Mexico and EU-15 would increase by more than

1%; however, the import from ROW (with a negative Divisia import elasticity) would cause a

decline by more than 1%. The reason for this may be the trivial share of ROW (only 0.37%) in









the U.S. total tomato import. So, when there is an increased volume of imports into the U.S., the

prominent partners with larger shares capture the opportunity and the insignificant players

aggregated in ROW loose their market share in competition.

Conditional Own-Price Elasticities: The conditional own-price elasticities for Canada,

Dominican Republic, Mexico, EU-15 and ROW are -0.804941, -0.736886, -0.081015, -0.878641

and -0.520927 respectively. All of them are statistically significant but inelastic. These

conditional elasticities indicate that the U.S. import demand for tomatoes from Mexico is the

most inelastic meaning the least responsive to price change followed by ROW. The import

demand for EU-15, Canada and the Dominican Republic tomatoes are more elastic, but still

inelastic. Among them the EU-15 has the least inelastic demand and the most responsive to price

change. This means that with a change in price the U.S. import demand for EU-15 tomatoes

would change the most. For example, with a 1% decrease in an import price (ceterius paribus),

the increases in the U.S. import demand for tomatoes would be 0.88% for EU-15 tomatoes;

0.80% for Canadian tomatoes, 0.74% for Dominican Republic tomatoes, 0.52% for ROW. The

responsiveness of the U.S. import quantity would be almost the same (i.e., almost "no change")

for Mexican tomatoes if their prices change.

Conditional Cross-Price Elasticities: It is noticeable from Table 5-4 that among the

conditional cross-price demand parameters fourteen (7 pairs) are statistically significant and

different from zero. They are between (1) Canada and Mexico, (2) Canada and EU-15, (3)

Dominican Republic and Mexico, (4) Dominican Republic and EU-15, (5) Dominican Republic

and ROW, (6) Mexico and EU-15, and (7) Mexico and ROW. All of these conditional cross-

price elasticity estimates are positive (implying input substitutes) except one involving ROW,

i.e., between Dominican Republic and ROW. Results indicate that (1) if price of Mexican









tomatoes increases by 1%, the U.S. import demand for Canadian tomatoes will increase by

0.76%. On the other hand, if the price of Canadian tomatoes increases by 1%, the U.S. import

demand for Mexican tomatoes will increase by 0.04% only. (2) If the price of EU-15 tomatoes is

increased by 1%, the U.S. import demand for Canadian tomatoes would increase by 0.10%;

however, if Canadian tomato price is increased by 1%, import demand for EU-15 tomato would

increase by 0.12%. (3) A 1% price increase in Mexican tomatoes will cause a 1.20% increase in

the U.S. import demand for Dominican Republic tomatoes whereas a 1% increase in the price of

Dominican Republic tomatoes will have 0.003% impact on the import demand for Mexican

tomatoes. (4) If the price of EU-15 tomatoes is increased by 1%, import demand for Dominican

Republic tomatoes would increase by 0.27%; on the other hand, if price of Dominican Republic

tomatoes is increased by 1%, the import demand for EU-15 tomatoes would increase by 0.02%.

(5) The conditional cross-price elasticities between Dominican Republic and ROW are negative

indicating complementary relation which is not expected. Most of the conditional cross-price

elasticities related to ROW are negative. The conditional cross-price elasticities show that if the

price of ROW tomatoes increases by 1%, the U.S. import demand for Dominican Republic

tomatoes would decline by 0.80% and if price of Dominican Republic tomatoes increases by 1%,

the import demand for ROW tomatoes would decline by 0.17%. (6) The conditional cross-price

elasticities between Mexico and EU-15 show that if the price of EU-15 tomatoes is increased by

1%, the U.S. import demand for Mexican tomatoes would increase by 0.03%, but if the price of

Mexican tomatoes is increased by 1%, the import demand for EU-15 tomatoes would increase by

0.73%. (7) Finally, the conditional cross-price elasticities suggest that if the price of ROW

increases by 1%, the U.S. import demand for Mexican tomatoes would increase by only 0.01%;









on the contrary, if the price of Mexican tomatoes increases by 1%, the import demand for ROW

tomatoes would increase by 0.92%.

The Divisia volume elasticities and the conditional own-price elasticities for the U.S.

analysis are calculated from 1995 to 2005 and the results are presented in Table 5-5 and Table 5-

6 respectively. Even though there is no variable in the model directly related to any sort of

structural change, it is to some extent accounted for through potential changes in the Divisia

volume index coefficient that enters directly in elasticity calculation. If the Divisia volume

elasticity and the conditional own-price elasticity change over time, this indicates that structural

change is occurring recognizing that the precise nature of this structural change is not known.

Therefore, these two elasticities are calculated over a period of 11 years with an initial sample

size of 32 years (1964-1995) and each time adding one more year forward with a block of 32

years and subtracting the first year from the previous block. In the top part of both the Tables 5-5

and 5-6, elasticities are calculated letting both estimated parameters (numerator Oi and 7ij) and

mean factor cost shares (denominator MFi) change along with the sample; but in the bottom part

of the tables, the denominator (MFi) is held constant at the initial sample block for the years

1964 to 1995 (2,33). The model for this simulation is provided in Appendix A. In the top part of

both the tables elasticities show changes over time, but these changes may be due to structural

influences (Oi and tij) or mean cost share influences (MFi) or both. On the other hand, in the

bottom part of Tables 5-5 and 5-6, the changes are due to (Oi and 7ij) representing the structural

changes more precisely.

Thus, Table 5-5 (bottom part) shows that Divisia elasticities for Canada increase for a

while and then decline, implying that Canada ultimately is losing shares of any increases in the

U.S. imports. The Dominican Republic has a difficulty in capturing the U.S. import market. The









Divisia elasticities for the Dominican Republic show that this country is having increasing

problems keeping its share of any increases in the U.S. imports of fresh tomatoes. Mexico holds

a more or less stable position with little evidence of structural adjustments; EU-15 has a little

improvement in capturing the U.S. import market; and the ROW has some insignificant mixed

impact of gaining and losing. Table 5-6 (bottom part) shows the conditional own-price elasticity

changes over time as a result of structural influences. The U.S. imports from Canada indicate

some variation in price sensitiveness due to structural adjustments; the Dominican Republic is

becoming more price sensitive; Mexico is becoming less price sensitive although it was already

highly insensitive to price changes; EU-15 is highly sensitive to price changes and getting

slightly more sensitive; and the ROW is, in fact, less sensitive to price changes.

Conditional price elasticities do not always behave as one might expect when underlying

structural changes are occurring. For example, conditional own-price elasticities in the U.S.

import demand analysis for Canadian fresh tomatoes for the years 2003, 2004 and 2005 are not

of the right sign (positive instead of negative). So, it seems more appropriate to look at the

change in the share of each partner country when the total import volume changes over time in

order for getting some idea about structural change. Moreover, import volume is an important

variable in the model. Thus, the shares of each participating country have been calculated over

the same period (i.e., 1995-2005) with the U.S. total import increases of 10%, 15%, 20%, 25%,

and 30% using the Divisia elasticities at the bottom part of Table 5-5. The variations/

fluctuations in each partner's market share of the U.S. import market over the time are the result

of structural change since the underlying variable values are kept fixed in the simulations. These

are shown in Figure 5-1 through Figure 5-3 and the related data are given in Appendix C.









Figure 5-1 shows the situations for Canada and Dominican Republic. Canada is losing its

relative shares of the growth in the U.S. import over time, but the losses are numerically quite

small. Dominican Republic was stable for a while at the beginning of the period and then lost a

small portion of its share in the U.S. import market and sustained the loss for the rest of the

period under consideration. For the most part the share changes were extremely small. In Figure

5-2, Mexico holds a very stable share in the U.S. fresh tomato import market throughout the

entire period with a little increase in its share toward the ending years. The case of EU-15 is

interesting because the shares have generally risen over the study period, as seen in the left

portion of Figure 5-2. Finally, Figure 5-3 indicates that the ROW is steadily losing its small share

of the U.S. import market for fresh tomatoes, but not by a magnitude leading to any particular

concern.

Summary for U.S. Analysis

From the above discussion and the conditional price elasticities in Table 5-4, it is clear that

the U.S. import demand for Mexican tomatoes is the most stable one meaning that it does not

change much with the change in its own price or the changes in other partners' prices. It implies

that the U.S. consumers prefer Mexican tomatoes. In other words, Mexico faces no close

competitors in the U.S. import market for fresh tomatoes. Results show that there is almost a

sharp competition between Canada and EU-15 while the Dominican Republic is not competitive

with EU-15. The market shares of different partners do not vary much indicating very little

structural change reflected through the estimated parameters measured across time.

Results for EU-15 Tomato Import Demand Analysis

The EU-15 analysis was ultimately done with a data set created for six partner countries

and the ROW. The empirical data status and model results are described below in detail.









Descriptive Statistics

The cost shares, quantity shares and the average prices of imports from all the seven

sources of origin for 1964, sample mean and 2005 are displayed in Table 5-7. Among the

separate countries, the largest import cost share was for the imports from Morocco and the

smallest was for imports from Turkey. ROW ranked second in terms of cost share. The cost

share for the imports from Morocco decreased from 84.12% in 1964 to 63.94% in 2005 and the

import cost shares for the imports from Albania, Bulgaria and Romania also decreased during the

same time period. On the other hand, import cost shares for the imports from Israel, Turkey and

ROW showed considerable increase during that time. The average prices for the imports from

Bulgaria and Romania are almost the same and those for Albania, Morocco, Turkey and ROW

are similar.

Model Results

In order to analyze EU-15 import demand, all of the five differential production models

were estimated in the same way as they were estimated for the U.S. import demand analysis. The

AIDS and CBS were estimated with first order autocorrelation (AR1) whereas the other three

models had no significant autocorrelation (Table 5-8). Symmetry and homogeneity conditions

were imposed in all of them. The likelihood ratio (LR) test (Table 5-8) shows that DID would be

the most appropriate model followed by the CBS model. So, DID was applied to estimate the

conditional demand parameters and demand elasticity estimates (Equation 4-6 and Equation 4-7).

But, four out of eight conditional own-price elasticities were of the wrong sign (not negative)

which is clearly unacceptable according to economic theory. Also the signs of the eigen values of

the Slutsky matrix were not correct. The first three eigen values came out negative instead of

positive.









Different trials were given with different years and with a different number of participating

partner countries even though in the original working data there were eight participating partners

including the ROW. However, all the conditional own-price elasticities were not found to be

negative. Then the CBS model was tried, but the results were of the same kind. So, with the

given data neither the DID nor CBS was working. Therefore, as a second choice the AIDS and

NBR were applied. For these two models all the conditional own-price demand elasticities are

found to be negative except the one related to the U.S. Finally, given that the U.S. import share is

very small (0.12%), the U.S. data were merged with ROW making seven partner countries

involved in EU-15 imports of fresh tomatoes.

At this stage, the AIDS, NBR and the Synthetic model itself were estimated and all the

own-price demand coefficients were found to be negative. However, they were not statistically

significant in the Synthetic model; but they were all significant except one related to the ROW in

both AIDS and NBR. These two models are almost equally likely for estimation purposes in case

of the given data. Of course, DID and CBS were also estimated again with seven partner

countries, but no fruitful results were found. The production NBR model has been selected

between them on the grounds that it generates the larger likelihood value, it has a closer LR test

statistics for acceptance (Table 5-8), eigen values are either zero or positive except one which is

-0.0065308, and conditional own-price elasticities are negative while most of the conditional

cross price elasticities are positive (Table 5-10). However, the data is not rich enough to give

theoretically precise results. Therefore, the estimates found will likely deviate from the correct

estimates; however, the estimates are an approximation of the correct estimates and will be

interpreted. The model is provided in Appendix B.









The estimates for the coefficients (y,j) of production NBR (Equation 4-25), the demand

parameters 0, and 7,, and the demand elasticity estimates (Equation 4-29 and Equation 4-30) as

calculated at sample mean cost shares are shown in Table 5-9 and Table 5-10. The property that

the Slutsky matrix be positive semi-definite is not validated as all the eigen values are positive or

zero except one that is -0.0065308. The eigen values for the production NBR Slutsky matrix are -

0.0065308, 9.47247D-18, 0.021110, 0.026703, 0.043189, 0.071218 and 0.13542. The -

0.0065308 eigen value is problematic, but is very close to zero.

Divisia Elasticities: The Divisia import volume elasticities for Albania, Bulgaria, Israel,

Morocco, Romania, Turkey and ROW are -1.08036, 0.58081, 0.33524, 1.09925, 0.18365,

0.74704, and 2.44395 respectively. The Divisia import volume elasticities for Albania and

Morocco are statistically significant and more than unity in absolute value terms. The Divisia

import volume elasticity for ROW is also more than unity, but not statistically significant. All

other Divisia import elasticities are insignificant and less than unity. This means that if total

tomato import into the EU-15 is increased by 1%, other things being equal, the import from

Albania (with negative volume elasticity) would decrease by 1.08% and imports from Bulgaria,

Israel, Morocco, Romania, Turkey and ROW would increase by 0%, 0%, 1.10%, 0%, 0% and0%

respectively. So, in terms of import volume increase, Morocco captures the opportunity as a

prominent partner.

Conditional Own-Price Elasticities: The conditional own-price elasticities for Albania,

Bulgaria, Israel, Morocco, Romania, Turkey and ROW are -1.07609, -0.72700, -0.83100, -

0.13009, -0.65609, -0.96215, and -0.89697 respectively (Table 5-10). All of them are statistically

significant except the one associated with ROW. These conditional elasticities show that EU-15

import demand for Albanian tomatoes is elastic and the others are inelastic even though the









import demand for Turkish tomatoes is almost unitary elastic. Among the partners, the

inelasticity is the most (i.e., the least responsive to price change) for the import demand for

tomatoes from Morocco. Conditional own-price elasticities indicate that for a 1% decrease in the

import prices, ceterius paribus, the increases in EU-15 import demand for tomatoes would be

1.08% for Albanian tomatoes, 0.73% for Bulgarian tomatoes, 0.83% for Israeli an tomatoes,

0.13% for Morocco tomatoes, 0.66% for Romanian tomatoes, 0.96% for Turkish tomatoes, and

0.90% for ROW tomatoes. So, EU-15 import quantity from Albania would increase the most

followed by Turkey, ROW, Israel, Bulgaria and Romania if their prices decrease. The import

quantity from Morocco would not change much with the change in price.

Conditional Cross-Price Elasticities: Among the conditional cross-price demand

parameters, only five were statistically significant (different from zero). They are between (1)

Albania and Morocco, (2) Bulgaria and Morocco, (3) Bulgaria and ROW, (4) Morocco and

Turkey, and (5) Turkey and ROW (Table 5-10). All of the corresponding conditional cross-price

elasticities are positive (input substitute relation) excepting the ones related to ROW, which

indicate a complementary relationship. (1) The conditional cross-price elasticity estimates

between Albania and Morocco are significant indicating that if the price of Morocco tomatoes is

increased by 1%, the EU-15 import demand for Albanian tomatoes would increase by 0.64%; on

the other hand, if the price of Albanian tomatoes increases by 1%, the EU-15 demand for

Morocco tomatoes would not change much (only increases by 0.02%). (2) The conditional cross-

price elasticity between Bulgaria and Morocco is significant, but the same between Morocco and

Bulgaria is not. These conditional elasticities show that if the price of Morocco tomatoes is

increased by 1%, the EU-15 import demand for Bulgarian tomatoes would increase by almost

1% whereas if the price of Bulgarian tomatoes is increased by 1%, the demand for Morocco









tomato would not change. (3) The conditional cross-price elasticities between Bulgaria and

ROW are significant and negative (input complement). If the price of ROW tomatoes increases

by 1%, the import demand for Bulgarian tomatoes would decrease by 0.54%, but when the price

of Bulgarian tomatoes is increased by 1%, the import demand for ROW tomatoes would

decrease by 0.38% (4) The conditional cross-price elasticities between Morocco and Turkey are

also significant and positive. If the price of Turkish tomatoes is increased by 1%, the EU-15

demand for Morocco tomato would increase by 0.03%; on the other hand, if the price of

Morocco tomatoes is increased by 1%, the EU-15 demand from Turkey would increase by 1.27%

(elasticity is more than unity). (5) The conditional cross-price elasticies between Turkey and

ROW show complementary relations. It means a 1% increase in the price of ROW tomatoes

would result 0.85% reduction in the EU-15 import demand for Turkish tomatoes whereas a 1%

increase in the price of Turkish tomatoes would result 0.29% reduction in import demand for

ROW tomatoes. (6) The conditional cross-price elasticities between Israel and ROW are

significant and positive that means a 1% increase in ROW tomato price would increase EU-15

import demand for Israeli tomato by 0.74% whereas a 1% increase in the price of Israeli

tomatoes would increase EU-15 import demand for ROW tomatoes by 0.64%. (7) The

conditional cross-price elasticities between Romania and Turkey are also significant indicating

that if the price of Turkish tomatoes is increased by 1%, the EU-15 demand for Romanian

tomatoes would increase by 0.09%; on the contrary, if the price of Romanian tomatoes is

increased by 1%, the import demand for Turkish tomatoes would increase by 0.36%.

In the same way as the U.S. analysis, the Divisia volume elasticities and the conditional

own-price elasticities for the EU-15 analysis are calculated from 1995 to 2005 and the results are

presented in Table 5-11 and Table 5-12 respectively. Even though there is no variable in the









model related directly to any sort of structural change, underlying structural change could be

accounted for in the Divisia volume index coefficient and in the elasticities. Similarly,

conditional own-price elasticities are derived from the own-price coefficients that could change

over time. Therefore, these two elasticities are calculated over a period of 11 years with an initial

sample size of 32 years (1964-1995) and then for each adjustment period one year forward is

added and the earliest year among the 32 years is dropped. This way the sample size is kept

fixed. In the top parts of both the Tables 5-11 and 5-12, elasticities are calculated letting both

estimated parameters (numerator Oi and tij) and mean factor cost shares (denominator MFi)

change along with the sample; but in the bottom part the denominator (MFi) is held constant at

the initial sample block for the years 1964 to 1995 (2,33) and the resulting changes are only

attributable to changes in the parameters Oi and tij representing the structural impacts. The model

for the above simulation is provided in Appendix B.

Table 5-11 shows the changes in the Divisia volume elasticities over time. The top part of

the table indicates that these changes may be structural or due to changes in mean cost shares or

both. The bottom part explains the changes as a result of changes in the parameters, thus

capturing potential structural change more precisely. The table indicates that Albania is losing in

the EU-15 import volume in anyway even if the loss is not as bad as it was at the beginning.

Bulgaria has some loss and gain in between with an ultimate loss at the end; Israel shows some

losses in the middle of the period under consideration but gains at the end while Morocco has a

somewhat stable position indicating very little structural impact; Romania has some ups and

downs in the EU-15 import volume; Turkey was gaining at the beginning with a sudden drop in

between and again gaining a little toward the end of the period; and the ROW is somehow

experiencing steady loss in the EU-15 import volume until the end of the period.









Table 5-12 (bottom part) tells the same story about structural impact in terms of own-price

sensitiveness. Albania is getting less price sensitive while Bulgaria is getting more price

sensitive; Israel is becoming less price sensitive; Morocco is becoming less price sensitive during

the more recent years; and Romania is becoming more price sensitive while Turkey is getting

less price sensitive. The ROW has mixed impacts even though it becomes less price sensitive

near the end of the study period.

As conditional price elasticities do not always behave the way they theoretically should (

as in case of conditional own-price elasticities for the EU-15 import demand for Israeli tomatoes

for the years 2003, 2004 and 2005), it is better to look at the changes in shares of each partner

country when the total import volume changes over time in order to explain the impact of

structural changes. So, the shares of each participating country have been calculated over the

same period (i.e., 1995-2005) with the EU-15 total import increases of 10%, 15%, 20%, 25%,

and 30% using the Divisia elasticities at the bottom part of Table 5-11. The variations/

fluctuations in each partner's market share of the EU-15 import market over the time are actually

the result of structural changes. These are shown in Figure 5-4 through Figure 5-7 and the related

data are provided in Appendix C.

In Figure 5-4, the stories for Albania and Bulgaria are almost the same. Both experience

losses (Albania having negative Divisia elasticities) in relative shares of the growth in the EU-15

imports over time. Even if the losses in share toward the end period are not as bad as the earlier

years, they are, in any case, going to lose EU-15 import market share unless they do something

to reverse the situation. Figure 5-5 explains the impact of some structural influence on Israel's

market share in EU-15 tomato import market. Although at the beginning it experienced little

stability and losses, it really picked up increasing shares toward the ending part of the period as a









result of some structural impact. On the other hand, Morocco has a stable and steady growth

(with a slight decrease at the end) in its market share in EU-15 fresh tomato import market, but

showing little sign of structural influence. In Figure 5-6, Romania's situation is somewhat stable

with little loss and gain over time. So, structural factor may not be in effect for this variation in

its share in EU-15 import market. Turkey faced some stability and a little growth in market share

at the beginning and then a sharp drop that may be due to structural adjustment even though

toward the end of the period there appears to be some recovery. Finally, Figure 5-7 shows that

from a stable situation ROW started experiencing a sharp drop in market share until the terminal

year of the time period when it picked up some gain again. This kind of disruption reflects

potential structural changes.

Summary for EU-15 Analysis

The above discussion and Table 5-10 summarize that the EU-15 import demand for

Morocco tomatoes is the most stable one. It means, the import quantity from Morocco does not

change much with the change in its own price or the prices of the other partner countries. This

implies that Morocco faces no close competitors and it is a prominent partner with the EU-15.

However, there exists some competition between Israel and ROW. As the EU-15 import market

grows over time, Albania is going to lose its share under any situation; Bulgaria's position is

somehow stable even though losing its share; Israel was losing initially, but toward the recent

time it is picking up its share; and Morocco's case is more stable showing no indication of

structural effect. Turkey shows a stable condition at the beginning and then some drops and pick

ups, but towards the end period it is gaining its share.













Table 5-1. Import cost shares,
Year Canada


1964
Average

2005


1964
Average

2005


1964
Average

2005


1964
Average

2005


1964
Average


0.79%
4.48%
(7.51)
24.40%


$219,358
39,753,000
(78,152,300)
274,699,840


0.50%
2.95%
(4.87)
14.88%


562,125
23,049,400
(43,161,800)
141,642,032


$0.39
1.02
(0.52)


quantity shares, and average prices by country of origin for U.S.
Dom. Rep. Mexico EU-15 ROW
Annual Cost Share
0.05% 98.78% 0% 0.38%
0.22% 90.59% 3.67% 1.04%
(0.27) (12.31) (5.21) (1.10)
0.13% 72.70% 2.52% 0.25%


$13,291
436,666
(724,383)
1,449,620


0.17%
0.31%
(0.41)
0.09%


189,476
1,066,709
(1,740,665)
856,968


$0.07
0.59
(0.50)


Annual Cost in U.S. Dollars
$27,354,888 $2.20833
271,616,000 25,298,800
(228,486,000) (41,842,800)
818,552,896 28,333,600

Annual Quantity Share
98.76% 0%
95.18% 1.20%
(6.12) (1.78)
84.20% 0.78%

Annual Quantity in Kilograms
111,638,936 1
398,402,000 8,394,931
(198,047,000) (13,874,400)
801,408,192 7,396,764


$105,323
5,254,056
(7,347,914)
2,857,037


0.57%
0.36%
(0.35)
0.05%


648,311
1,604,506
(1,523,853)
482,476


Annual Average Price (U.S. Dollar/Kilogram)
$0.25 $2.21 $0.16
0.59 2.17 2.05
(0.27) (1.19) (1.93)


2005 1.94 1.69 1.02 3.83 5.92


Table 5-2. Test results for the production differential AIDS, CBS, Rotterdam and NBR models
with first-order autocorrelation imposed for U.S. import demand analysis
Model Rho t-statistics P value Log Likelihood LR=2(Lsn-Lmodl)a


AIDS 0.410442 5.63482 0.000 593.24964 24.98326

CBS 0.345709 4.59926 0.000 567.39256 76.69742

NBR 0.493086 7.11773 0.000 604.56978 2.34298

DID 0.431854 6.02729 0.000 571.26802 68.9465

Synthetic 0.48838 6.91896 0.000 605.74127b
aThe table value for 2 = 5.99 at a= .05 level and 9.21 at a= .01 level. bThe estimates for 65
and 62 are -0.207467 and 1.14440 with standard errors 0.163098 and 0.100646 respectively.










Table 5-3. Coefficient estimates of the production NBR model for the U.S.


Equation 0, 7y-
Canada Dom.Rep. Mexico EU-15 ROW
Canada .007711 .006731 .000069 -.006387 .002778* -.003190*
(.009057)a (.413064) (.000643) (.004580) (.001516) (.001767)
Dom. Rep. .000384 .000578 .000646 .000508** -.001801**
(.001200) (.000545) (.000671) (.000218) (.000608)
Mexico .963753** .011868 -.006322** .000196
(.015325) (.006770) (.002633) (.005445)
EU-15 .045628** .003108* -.000071
(.009426) (.001694) (.001694)
ROW -.017475** .004867**
(.004185) (.001535)
aNumbers in parentheses are asymptotic standard errors estimated using the Delta method.
**Statistically different from zero at a = 0.05 level. *Statistically different from zero at a =
0.10 level.

Table 5-4. Demand parameter estimates and conditional elasticity of the production NBR model for
the U.S.

Equation 0 Canada Dom. Rep. Mexico EU-15 ROW

Canada .007711 -.036056** .000168 .034190** .004423** -.002725
(.009057)b (.004131) (.000643) (.004580) (.001516) (.001767)
Dom.Rep. .000384 -.001633** .002654** .000589** -.001778**
(.001200) (.000545) (.000672) (.000218) (.000525)
Mexico .963753** -.073390** .026944** .009603**
(.015325) (.006770) (.002633) (.001949)
EU-15 .045628** -.032265** .000310
(.009426) (.001694) (.000727)
ROW -.017475** -.005409**
(.004185) (.0015363)

Divisia Conditional price elasticity'
Equation Elasticity Canada Dom. Rep. Mexico EU-15 ROW

Canada .172136 -.804941** .003761 .763289** .098733** -.060841
(.202191)b (.092216) (.014353) (.102247) (.033835) (.039437)
Dom. Rep. .173076 .075999 -.736886** 1.19724** .265896** -.802252**
(.541235) (.290059) (.245827) (.302747) (.098172) (.236985)
Mexico 1.06388** .037742** .002930** -.081015** .029743** .010601**
(.016917) (.005056) (.000741) (.007474) (.002907) (.002152)
EU-15 1.24252** .120434** .016050** .733718** -.878641** .008440
(.256675) (.041271) (.005926) (.071706) (.046123) (.019792)
ROW -1.68288** -.262448 -.171249** .924778** .029847 -.520927**
(.402980) (.170118) (.050587) (.187730) (.069991) (.147788)
aThe eigen values for the Slutsky matrix are 1.20646D-17, 0.00093948, 0.0082077, 0.038217 and
0.10139. bNumbers in parentheses are asymptotic standard errors. Standard errors were estimated
using the delta method. CEstimated at sample mean cost shares. **Statistically different from zero at
S= 0.05 level. *Statistically different from zero at a = 0.10 level.












Table5-5. Divisia elasticities over time for the U.S. analysis
Divisia elasticities (E, )a with both parameter and mean change


Canada
0.453614
0.404887
0.302610
0.337869
0.275252
0.325307
0.251374
0.139138
0.111075
0.088835
0.008797


Dom. Rep.
0.272034
0.244738
0.240887
0.165349
0.175672
0.174524
0.156900
0.127674
0.115558
0.135994
0.220775


Mexico
1.000443
1.004255
1.012297
1.022119
1.028195
1.038350
1.049991
1.063852
1.076720
1.088653
1.103784


EU-15
3.554495
2.892485
2.248715
1.737735
1.534845
1.321553
1.207141
1.161990
1.074732
1.023946
1.022021


ROW
-2.46572
-2.38155
-2.28929
-2.13422
-1.80969
-1.74041
-1.62653
-1.66412
-1.59095
-1.52566
-1.58419


Divisia elasticities (E, m)b with parameter change but mean fixed at sample 2,33
Year Canada Dom. Rep Mexico EU-15 ROW
1995 0.453614 0.272034 1.000443 3.554495 -2.46572
1996 0.46222 0.244212 0.999473 3.631007 -2.46810
1997 0.415626 0.238535 1.000634 3.591645 -2.50041
1998 0.589547 0.162676 1.001227 3.470977 -2.52384
1999 0.625145 0.171781 0.996715 3.671049 -2.32388
2000 0.982030 0.168225 0.994714 3.641943 -2.38393
2001 0.977150 0.148069 0.993505 3.720346 -2.34837
2002 0.654073 0.118138 0.995146 3.909508 -2.53007
2003 0.611752 0.104625 0.996742 3.830233 -2.55266
2004 0.566848 0.119939 0.997732 3.783115 -2.55990
2005 0.064474 0.192196 1.001609 3.870090 -2.71122


a Ei (1+j), (32+j) = (1+j), (32+j) / MFi (+j)' (32+j) where j = 1, 2, ...

parameter and MFi is calculated from data. b Ei i(+j)' (32+j)


......... 11 and 0, is estimated
S (1j), (32+j) / MFi (2,33) where


j = 1, 2, ............ 11 and 0, is estimated parameter and MFi is calculated from data.


Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005












Table 5-6. Conditional own-price elasticities over time for the U.S. analysis
Conditional own-price elasticities (Eji)a with both parameter and mean change
Year Canada Dom. Rep. Mexico EU-15 ROW
1995 -0.82696 -0.54947 -0.02043 -0.74888 -0.47103
1996 -0.89775 -0.69700 -0.02642 -0.76510 -0.52577
1997 -0.98687 -0.78031 -0.03434 -0.79223 -0.56006
1998 -0.79147 -0.77489 -0.04130 -0.83624 -0.53747
1999 -0.80862 -0.80959 -0.05118 -0.85197 -0.59960
2000 -0.69272 -0.74529 -0.05758 -0.86696 -0.51387
2001 -0.72265 -0.85646 -0.06747 -0.87083 -0.48483
2002 -0.76935 -0.90842 -0.07703 -0.88850 -0.53174
2003 -0.77081 -0.93193 -0.08552 -0.89734 -0.57082
2004 -0.79078 -0.86940 -0.09113 -0.88896 -0.58124
2005 -0.78010 -0.86068 -0.09682 -0.89557 -0.61120

Conditional own-price elasticities (Ex m)b with parameter change but mean fixed at sample 2,33
Year Canada Dom. Rep. Mexico EU-15 ROW
1995 -0.82696 -0.54947 -0.02043 -0.74888 -0.47103
1996 -0.88563 -0.69764 -0.02185 -0.71244 -0.50910
1997 -0.98887 -0.78240 -0.02327 -0.68783 -0.52097
1998 -0.65205 -0.77844 -0.02169 -0.71085 -0.45608
1999 -0.59770 -0.81368 -0.02181 -0.70585 -0.49080
2000 -0.13554 -0.75429 -0.01745 -0.71706 -0.34083
2001 -0.03170 -0.86423 -0.01619 -0.70975 -0.26450
2002 -0.07995 -0.91485 -0.01552 -0.75583 -0.29811
2003 0.03398 -0.93785 -0.01484 -0.78265 -0.32343
2004 0.02589 -0.88419 -0.01194 -0.75031 -0.31126
2005 0.20133 -0.87803 -0.00933 -0.77383 -0.34936
aEii (1+j), (32 ) (1),(32+j)/M Fi (+j), (32+j) where j= 1, 2, .............11 and z, is the estimated
parameter and MFi is calculated from data. bEiim(1+j), (32+j) = (1+j), (32+j)/MFi (2,33) where
j = 1, 2, ............11 and z, is the estimated parameter and MFi is calculated from data.





































































Figure 5-1. Impact of structural change on U.S. demand for Canadian and Dominican Republic
fresh tomatoes. (A) Canadian tomatoes (B) Dominican Republic tomatoes.



84


Canada share of
US import market


0.0054 .................. .

0.0052 .......
0.0050 ......

0.0048
0.0046
0.0044 -..
0.0042 .


Dominican Republic share of
US import market





































































Figure 5-2. Impact of structural change on U.S. demand for Mexican and EU-15 fresh tomatoes.
(A) Mexican tomatoes (B) EU-15 tomatoes.


Mexico share of
US import market
0.9820 .0.9820
0 .9 1 8 ........................{ ... ,.,,: ............."._.............. .",,' ............... ... ......... ... ... ..
0.9818 0.9818
0.9816 0.9816
0.9814 ....................... 0.9814
0.9812 ................. .9812
0.9810 .0.9810
0.9808988
0..9808
0.98060.9806
0..9806
0.9806 ...... .. ,,,.......- .9 0
0.9804
0.9804 09802
0.9802 .0.9800
0.9800 10
-1 0
2004 2
2 0 0 2 ................ ,, .. 2 0
2000 ...25

1998 Percentage increase in
Structural change 199 total US imports
associated with the elasticities 1996 30 total US imports
A
1994


EU-15 share of
US import market













Rest of the World share of
US import market


Figure 5-3. Impact of structural change on U.S. demand for ROW fresh tomatoes.











Table 5-7. Import cost shares, quantity shares, and average prices by country of origin for EU-15
Year Albania Bulgaria Israel Morocco Romania Turkey ROW
Annual Cost Share
1964 0.13% 4.88% 0% 84.12% 3.61% 0% 7.26%
Average 2.16% 3.61% 4.36% 76.36% 6.73% 1.74% 5.04%
(2.56) (2.84) (4.82) (6.53) (6.10) (2.58) (3.34)
2005 0% 0.01% 12.44% 63.94% 0.01% 6.43% 17.17%

Annual Cost in U.S. Dollars
1964 $48,038 $1,840,474 $1,262 $31,730,108 $1,361,401 $1.06 $2,739,627
Average 2,017,004 2,911,697 7,888,280 9,241,680 5,643,429 3,526,344 7,098,639
(2,341,134) (2,319,369) (1,073,390) (4,802,280) (5,380,099) (6,423,904) (1,056,850)
2005 3.03 20,624 44,735,840 229945104 35,735 23,119,076 61,743,611

Annual Quantity Share
1964 0.37% 8.18% 0% 76.21% 6.83% 0% 8.41%
Average 3.10% 4.97% 2.81% 72.66% 10.04% 1.70% 4.72%
(3.70) (3.81) (2.64) (10.39) (8.53) (2.49) (3.74)
2005 0% 0.01% 7.60% 70.05% 0.01% 5.93% 16.40%

Annual Quantity in Kilograms
1964 572,000 12,660,747 3,312 118,009,256 10,570,330 1 13,023,064
Average 4,484,032 8,258,625 5,537,090 131,722,000 17,382,800 3,599,705 9,515,378
(4,616,481) (6,620,936) (6,407,523) (39,308,500) (17,403,900) (6,157,144) (10,678,700)
2005 1 19,526 25,201,496 232,239,648 37,440 19,664,766 54,367,024

Annual Average Price (U.S. $/Kg)
1964 $0.08 $0.15 $0.38 $0.27 $0.13 $1.06 $0.21
Average 0.77 0.55 1.04 0.69 0.54 0.80 0.78
(0.86) (0.41) (0.46) (0.25) (0.38) (0.35) (0.37)
2005 3.03 1.06 1.78 0.99 0.95 1.18 1.14


Table 5-8. Test results for model selection for EU-15 analysis.
Model Rho t-statistics P value Log Likelihood LR=2(Lsn-Lmodl)
AIDS -0.13526 -2.00077 0.045 685.31208 31.38804
CBS -0.12500 -1.85916 0.063 695.98166 10.04888
NBR -0.09441 -1.39541 0.163 688.35849 25.29522
DID -0.06767 -1.00291 0.316 700.27111 1.46998
Synthetic -0.08094 -1.19275 0.233 701.0061b
aThe table value for 2= 5.99 at a= .05 level and 9.21 at a= .01 level, bThe
estimates for 15i and 62 are 0.308207 and -0.030456 with standard errors 0.190839 and
0.127050 respectively.











Table 5-9. Coefficient estimates of the production NBR model for EU- 15
Equation yj

Albania Bulgaria Israel Morocco Romania Turkey ROW
Albania -.02328** -.00210 .00062 .00331 -.00259 .00045 -.00016 .00048
(.01092)a (.00277) (.00278) (.00311) (.00757) (.00395) (.00116) (.00640)
Bulgaria .02098 .00856 .00175 .00843 .00021 .00162 -.02118**
(.01544) (.00536) (.00429) (.01073) (.00555) (.00164) (.00947)
Israel .01462 .00547 -.04045** -.00011 -.00007 .03011
(.02276) (.00725) (.01416) (.00722) (.00235) (.01184)
Morocco .83943 .08116 -.04160** .00880 -.01375
(.07548) (.05321) (.02001) (.00767) (.04528)
Romania .01236 .01862 .00511 .01733
(.03242) (.01371) (.00338) (.01362)
Turkey .01299 .00036 -.01565**
(.02177) (.00246) (.00246)
ROW .12291 .00265
(.05040) (.03178)
aNumbers in parentheses are asymptotic standard errors computed with the Delta method.
**Statistically different from zero at a = 0.05 level. *Statistically different from zero at a =
0.10 level.











Table 5-10. Demand parameter
for EU-15


and conditional elasticity estimates of the production NBR model


Equation 9 _- a

Albania Bulgaria Israel Morocco Romania Turkey ROW
Albania -.02338** -.02319** .00140 .00425 .01387* .00190 .00021 .00157
(.01092)b (.00277) (.00278) (.00311) (.00757) (.00395) (.00116) (.00640)
Bulgaria .02098 -.02626** .00332 .03601** .00264 .00225 -.01936**
(.01544) (.00536) (.00429) (.01073) (.00555) (.00164) (..00920)
Israel .01462 -.03624** -.00715 .00282 .00069 .03231
(.02276) (.00725) (.01416) (.00722) (.00235) (.01102)
Morocco .83943 -.09934* .009811 .02207** .02473
(.07548) (.05321) (.02003) (.00767) (.03619)
Romania .01236 -.04417** .00628 .02072
(.03242) (.01371) (.00338) (.01457)
Turkey .01299 -.01673** -.01477**
(.02177) (.00246) (.00520)
ROW .12291 -.04519
(.05040) (.03178)

Equation Divisia Conditional price elasticity
Elasticityc
Albania Bulgaria Israel Morocco Romania Turkey ROW
Albania -1.08036** -1.07609** .06489 .19701 .64348* .08811 .00988 .07273
(50684)b (.12843) (.12911) (.14419) (.35142) (.18345) (.05375) (.29696)
Bulgaria .58081 .03871 -.72700** .09199 .99692** .07309 .06219 -.53590**
(.42734) (.07702) (.14849) (.11867) (.29696) (.15358) (.04549) (.25477)
Israel .33524 .09734 .07619 -.83100** -.16388 .06469 .01576 .74090**
(.52193) (.07125) (.09829) (.16614) (.32458) (.16557) (.05398) (.25270)
Morocco 1.09925** .01816* .04716 -.00936 -.13009* .01285 .02891** .03238
(.09884) (.00992) (.01405) (.01854) (.06968) (.02623) (.01004) (.04740)
Romania .18365 .02820 .03922 .04190 .14573 -.65609** .09330* .30774
(.48165) (.05872) (.08240) (.10726) (.29751) (.20370) (.05022) (.21643)
Turkey .74704 .01225 .12921 .03954 1.26971** .36129* -.96215** -.84985**
(1.25201) (.06662) (.09451) (.13542) (.44111) (.19446) (.14129) (.29890)
ROW 2.44395 .03110 -.38420** .64131** .49079 .41120 -.29323** -.89697
(1.00028) (.12701) (18265) (.21873) (.71836) (.28919) (.10313) (.63084)
aThe eigen values for the Slutsky matrix are -0.0065308, 9.47247D-18, 0.021110, 0.026703,
0.043189, 0.071218 and 0.13542. bNumbers in parentheses are asymptotic standard errors and
were estimated using the delta method. cEstimated at sample mean cost shares. **Statistically
different from zero at a = 0.05 level. *Statistically different from zero at a = 0.10 level.












Table 5-11. Divisia elasticities over time for EU-15 analysis
Divisia elasticities (El) with both parameter and mean change


Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005


Albania
-1.47386
-1.34877
-1.31760
-1.22725
-1.43440
-1.25003
-1.27236
-1.03652
-1.14369
-1.20686
-1.16712


Bulgaria
0.61053
0.37179
0.47812
0.45224
0.37988
0.66127
0.90023
0.81050
0.68212
0.76306
0.77719


Israel
0.19870
0.19081
0.02233
-0.11496
-0.21824
-0.24613
0.12357
0.19538
0.47961
0.52833
0.78517


Morocco
1.15429
1.15992
1.17049
1.16672
1.20907
1.20233
1.20443
1.29244
1.25276
1.26734
1.13938


Romania
0.02082
0.05682
-0.08559
0.11012
0.23150
0.45997
0.38382
0.01685
0.10092
0.10415
0.17902


Turkey
1.80311
1.76605
1.85061
1.50872
1.98134
0.66449
0.27793
-0.18537
0.60706
0.21202
0.48810


ROW
2.49313
2.55665
2.63550
2.47556
1.66476
1.32249
1.02599
-0.03721
0.02162
-0.22080
1.52012


Divisia elasticities E, M parameter change but mean fixed at sample 2,33
Year Albania Bulgaria Israel Morocco Romania Turkey ROW
1995 -1.47386 0.61053 0.19870 1.15429 0.02082 1.80311 2.49313
1996 -1.34721 0.35930 0.21725 1.16156 0.05617 1.91003 2.45700
1997 -1.31264 0.44402 0.03010 1.17133 -0.08321 2.12307 2.49529
1998 -1.21739 0.40056 -0.18171 1.16698 0.10477 1.76848 2.33258
1999 -1.41599 0.31833 -0.39158 1.21030 0.21533 2.39415 1.54427
2000 -1.22543 0.51199 -0.49715 1.20444 0.41229 0.90753 1.23111
2001 -1.23698 0.64172 0.27876 1.20766 0.32508 0.44179 0.96850
2002 -0.99987 0.53954 0.47998 1.30145 0.01339 -0.33920 -0.03349
2003 -1.09474 0.42501 1.26150 1.26548 0.07390 1.34488 0.01896
2004 -1.14249 0.44667 1.49365 1.28141 0.06871 0.55217 -0.19863
2005 -1.08704 0.42910 2.38274 1.15126 0.10583 1.40678 1.45360
a Ei (1+j),(32+j) (1+j), (32+j)/ MF (+j) (32+j) where j = 1, 2, ........... 11 and 0, is estimated


parameter and MFi is calculated from data. b Eim(1+j)' (32+j)


0 (1+j), (32+j) / MFi (2,33) where


j = 1, 2, ........... 11 and 0, is estimated parameter and MFi is calculated from data.












Table 5-12. Conditional own-price elasticities over time for EU-15 analysis
Conditional own-price elasticities (E11)a with both parameter and mean change


Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005


Albania
-1.33563
-1.34502
-1.27132
-1.28189
-1.32045
-1.29714
-1.27507
-1.18356
-1.06549
-1.06675
-1.07429


Bulgaria
-0.02298
-0.57405
-0.73028
-0.73225
-0.74338
-0.82131
-0.84578
-0.81415
-0.79684
-0.81324
-0.79961


Israel
-0.87042
-0.99189
-0.95801
-0.92933
-1.00634
-0.88734
-0.83289
-0.76029
-0.54959
-0.43974
-0.38157


Morocco
-0.14148
-0.11948
-0.10879
-0.11400
-0.15487
-0.18874
-0.20691
-0.14355
-0.08599
-0.08052
-0.05384


Romania
0.03344
-0.05058
-0.19846
-0.33547
-0.57981
-0.64474
-0.71041
-0.66842
-0.64424
-0.62588
-0.60391


Turkey
-1.03718
-1.04681
-1.05458
-1.03818
-0.96492
-0.82970
-0.85740
-0.86986
-0.95343
-0.84424
-0.82246


ROW
-1.59637
-1.42251
-2.42236
-2.32185
-1.67719
-1.62733
-1.95294
-1.23559
-1.13652
-1.09511
-0.48632


Conditional own-price elasticities (E, m)b with parameter change but mean fixed at sample 2,33
Year Albania Bulgaria Israel Morocco Romania Turkey ROW
1995 -1.33563 -0.02298 -0.87042 -0.14148 0.03344 -1.03718 -1.59637
1996 -1.34455 -0.58523 -0.99633 -0.12040 -0.05944 -1.05196 -1.40270
1997 -1.27009 -0.74302 -0.95876 -0.10924 -0.21590 -1.06512 -2.34216
1998 -1.27917 -0.75266 -0.91645 -0.11414 -0.35938 -1.04772 -2.24060
1999 -1.31562 -0.77087 -1.05308 -0.15557 -0.59727 -0.96125 -1.62209
2000 -1.29019 -0.84270 -0.83031 -0.19001 -0.66422 -0.77426 -1.57816
2001 -1.26587 -0.86681 -0.69987 -0.20890 -0.72978 -0.78541 -1.89479
2002 -1.17511 -0.84995 -0.50572 -0.14830 -0.70395 -0.78046 -1.20374
2003 -1.06032 -0.84448 0.07348 -0.09234 -0.69854 -0.92776 -1.10966
2004 -1.06026 -0.85959 0.45255 -0.08741 -0.70333 -0.64011 -1.07722
2005 -1.06545 -0.85648 0.72246 -0.06003 -0.70843 -0.54614 -0.50506
aEii (1+j), (32+) = J (+j), (32+j)/MFi (1+j), (32+j) where j = 1, 2, ............. 11 and z,, is the estimated

parameter and MFi is calculated from data. bEiim(1+j), (32+j) = (1+j), (32+j)/MFi (2,33) where
j = 1, 2, ............ 11 and tr,, is the estimated parameter and MFi is calculated from data.
































































Figure 5-4. Impact of structural change on EU-15 demand for Albanian and Bulgarian fresh
tomatoes. (A) Albanian tomatoes (B) Bulgarian tomatoes.


Albania share of
EU-15 import market


Bulgaria share of
EU-15 import market
0.0480

0 .0470 ..................... ......
0.0460 ...........................
0.0450..
0 .0440 .......................
0.0430
0.0420
0.0410






































































Figure 5-5. Impact of structural change on EU-15 demand for Israeli and Morocco fresh
tomatoes. (A) Israeli tomatoes (B) Morocco tomatoes.


Israel share of
EU-15 import market
0.0150 0.0150
0.0140 0.0140
0.0130 0.0130
0.0120 0.0120
0 .O LOO ....... ....................i....O L O
0.0110 0.0110
0.0100
0.0100
0.0090
0.0090
0.0080
0.0080 .................. 0.00 0
0.0070 10
15
20042002 20
2000 .... 25
Structural change1998 Percentage increase in
associated withathe elasticities 30 total EU-15 imports
associated with the elasticities 1 onnc3


Morocco share of
EU-15 import market






































































Figure 5-6. Impact of structural change on EU-15 demand for Romanian and Turkish fresh
tomatoes. (A) Romanian tomatoes (B) Turkish tomatoes.


Romania share of
EU-15 import market


Turkey share of
EU-15 import market











Rest-of-the World share of
EU- 15 import market


Figure 5-7. Impact of structural change on EU-15 demand for ROW fresh tomatoes









CHAPTER 6
CONCLUSIONS

Observations

In order to have a good demand analysis empirical data is very important. The empirical

data used in this research do not behave equally well for the U.S. import demand analysis and the

EU-15 analysis. In case of the U.S., the results are as per expectation according to economic

theory from the appropriate model at first instance (NBR as qualified by LR test). However, the

scenario is not the same for the EU-15 import demand analysis. The empirical data was not

giving a theoretically convinced results from the model that seemed appropriate by the LR test

(the DID). So, a different specification of model had to be chosen (NBR), as a second line of

choice, in order to analyze the given data set with a little manipulation (merging the U.S. data

with ROW). Hence, it is the data, not always the models that cause problems in generating

theoretically acceptable results.

Summary

Divisia volume elasticities show that when the U.S. total import volume is increased by

1%, fresh tomato imports from each of Mexico and the EU-15 would increase by more than 1%,

but imports from ROW would decrease by more than 1%. Conditional own-price elasticities

indicate that the U.S. demands for fresh tomatoes from all of the five sources under study are

inelastic. Inelasticity is the most for the demand for Mexican tomatoes (-0.08) and the least for

EU-15 tomatoes (-0.88). Among the significant conditional cross-price elasticities only the ones

between Dominican Republic and ROW and vice versa are negative indicating a complementary

relation. All others (six pairs) show a substitute relationship as expected. The conditional cross-

price elasticities suggest that the U.S. demand for Mexican tomatoes remains almost the same

when other countries individually change their price in either direction. On the other hand, when









Mexico increases/decreases its price by (say) 1%, the increase/decrease in quantity of import

from Canada, Dominican Republic, EU-15 and ROW would be 0.76%, 1.20%, 0.73%, and

0.92% respectively. So, Mexico is competitive with others, but others are not competitive with

Mexico. Conditional cross-price elasticities also show that Canada and EU-15 are competitive

with one another (0.10 against 0.12). None of these source specific import demands seem to be

influenced by structural changes even as Canada is gradually losing its market share while EU-

15 is gaining its share.

For EU-15 import demand, the only two significant (and more than unity) Divisia volume

elasticities indicate that with a 1% increase in total import volume EU-15 demand for Morocco

fresh tomatoes would increase by more than 1%, but the import demand for Albanian tomatoes

would decrease by more than 1%. All of the conditional own-price elasticities are significant

except the one related to ROW. Only the conditional own-price elasticity of the demand for

Albanian tomatoes is elastic (-1.08). Among the conditional inelastic demands, Morocco has the

most inelasticity (-0.13) and Turkey has the least inelasticity (almost unitary elastic). Of thirteen

significant conditional cross-price elasticities, four relating Bulgaria, Turkey and ROW show a

complementary relationship; others show a substitute relationship. The conditional cross-price

elasticities suggest that Morocco is competitive with others, but others are not competitive with

Morocco. If Morocco increases its price by 1%, Eu-15 import demand for Albanian tomatoes

increases by 0.64%, demand for Bulgarian tomatoes increases by 1% and that for Turkey

increases by 1.27%, but if others increase their price individually by 1%, demand for Morocco

tomatoes almost does not change. Conditional cross-price elasticities also show some

competition between Israel and ROW. There exists some structural influence on import demands

for fresh tomatoes into the EU-15 especially for Albania, Israel, Turkey and ROW.









Conclusions

Given the continuous changes in international as well as domestic trade policies and trade

liberalization/globalization, the potentiality of exporting countries/producers to achieve a larger

share in international trade is of greater interest. It has been found that for the U.S. import

demand of fresh tomatoes the prominent supplier is Mexico facing no close competitor. Canada

and EU-15 compete closely with each other for the U.S. imports of fresh tomatoes. Hence, the

two products seem to be homogeneous. On the other hand, Mexican fresh tomatoes have

heterogeneous characteristics compared with other partners'. The U.S. consumers have either

some preferential tastes for Mexican tomatoes, or Mexico has some especial strategies,

technologies and product qualities that make it a prominent one in the U.S. import market.

Canada is losing its share in the U.S. imports while EU-15 is gaining. However, no significant

symptom of structural change impact has been found in case of the U.S. import market for fresh

tomatoes.

Similarly, for the EU-15 import demand for fresh tomatoes Morocco is the major supplier

with no close competitor. Israel and ROW are somehow competing with each other for EU-15

imports indicating some homogeneous tendency in their products. However, Morocco seems to

enjoy some preferential treatment in the EU-15 fresh tomato import market that characterizes its

product as heterogeneous to others'. Albania, Bulgaria and ROW are losing their share in EU-15

imports. Some structural impacts have been found with respect to EU-15 import demand for

tomatoes from Albania, Bulgaria, Israel, Turkey and ROW. One interesting finding is that some

small exporters like Israel and Turkey are increasingly penetrating into the EU-15 import market

in recent years.









Implications

As the U.S. fresh tomato import market is dominated by Mexico and it has no competitor,

it is necessary for the other countries to figure out some measures that will make their

competition with Mexico closer in order to get a larger share in the U.S. import market. Canada

also needs to look into its overall situation and find out the causes of losing share of the U.S.

fresh tomato import market to the EU-15

In the same way, Morocco holds the dominating position in the EU-15 import market and

other partners except Bulgaria and Romania (have become EU members subsequently) have to

do something that will make them closer competitors to Morocco. Albania's losing situation is

very delicate and needs immediate attention. Israel and Turkey should explore further to keep on

increasing their market shares.

Since the main suppliers for the U.S. and EU-15 fresh tomato markets are Mexico and

Morocco respectively, in case of any calamities, diseases or disruption in their supply, there

could be significant impacts on these two markets. Unless other countries succeed in becoming

more competitive with these two prominent suppliers, the U.S. and the EU-15 import markets

will remain at some risk.

Thus, the import demand analysis of this research will help the participating countries

determine the level of competitiveness among other competitors and then make appropriate

decision thereby to ensure their own gains from trade. It is also expected to help the policy

makers to undertake changes/adjustments in policies and implement them effectively.













APPENDIX A

COMPUTER PRINTOUTS FOR U.S. ANALYSIS


The Model to deal with zero quantity and price for U.S. Analysis.


OPTIONS MEMORY-1500 Double; ?US#01 Price Calculation Model; ? To replace Zero price for U.S. Import Analysis;
TITLE 'TOMATO IMPORTS TO THE US';

smpl 1 43;


LOAD ZYRS ZIMP
1963 902
1964 902
1965 902
1966 902
1967 902
1968 902
1969 902
1970 902
1971 902
1972 902
1973 902
1974 902
1975 902
1976 902
1977 902
1978 902
1979 902
1980 902
1981 902
1982 902
1983 902
1984 902
1985 902
1986 902
1987 902
1988 902
1989 902
1990 902
1991 902
1992 902
1993 902
1994 902
1995 902
1996 902
1997 902


V1
189750
219358
298959
241757
306522
222304
216389
560946
393364
245434
143427
90716
111149
151567
159007
302144
263748
240082
331663
369382
616082
959659
748704
1298959
2107219
2573120
2888215
3345576
4560436
5664522
6490505
10479733
17997856
38849532
61045808


102889656
121800280
163877088
169921584
175542448
234794400
261605248
274699840


Q1 V2 Q2 V3 Q3


730125
562125
733875
562312
803312
575562
400250
977250
505250
409687
467187


2481
13291
33276
9319
41735
75850
144332
121233
203117
220130
169100


21320 20705768 1
189476 27354888
188640 29424864
50292 52008580 1
379875 42585292
508062 46973296


916687
797875
1523625
1345187
834375


135582 219628 1049062


247117
233824
288937
523625
468312
380875
448187
563750
788625
1086062
766812
1261125
1930875
2117875


68018008
94966856
84131328
88150080
115137936
64070732


375471 1656500 64131552
258069 1036375 72428816
632044 1977812 149405840
376993 894437 161319616
324980 835062 153871376
254595 586750 131475112
554520 1126937 23793849
117345 352375 173374416
243863 780500 226757760
392689 1111375 17113312
829172 2122750 168479888
4040176 9988785 3279031
2212019 5768554 1608818
1203746 2862687 1523564


2327812 477349 1266562 2241637


3075187 1019288
2672000 467383
5213699 618499
4733488 328571
7673394 15237
11655089 39438
21769264 94680
37504200 53591


61728728
79553504
101390248
105680184
100499128
130153808
133565936
141642032


1404125 3908248
418250 26946147
559437 14148201
326875 32545004


V4 Q4 V5
08846184 13928
111638936 0
120410128 0
62705440 703
164302752 123059
175722128 12883
202410752 17355
290759360 2768
258677808 55205
264119568 0
339795200 12065
267888560 11595
253600880 0
294192576 9574
0 356244928 0
369276320 0
322163936 0
294612672 1363
6 236592128 5234
267219568 88818
331736864 14583
0 369494048 20791
8 380314432 36128
68 430983424 3628
40 406785376 3911
80 362726880 3674
60 385940928 5684
64 352312128 3181
2 353543872 86580
6 183116320 85920
8 400494304 25714


16117 335774656 376031680 31977(
43191 434508160 593079616 45101
100296 613726272 685677632 8886,
49441 549398080 660608640 119103


Q5;
65690 13565 94217
0 105323 648311
0 167763 668557
6875 57697 231001
306330 23536 126579
47249 45275 197879
30914 32468 194149
17812 180445 796919
104367 34202 163878
0 129604 313238
18312 139288 484990
52601 135977 1137011
0 126452 1748431
16132 89110 874245
0 160421 674755
0 128911 236786
0 53016 89746
3062 28456 50561
4 58011 268097 312113
136482 632151 534929
6 71248 1384907 601291
76 1028974 1814396 1075765
47 1590097 1933597 1223189
705 1705323 1156736 1081471
491 1579579 1266919 1024096
326 1624760 3513976 1160022
585 2677385 3466587 1489777
770 1305635 3820630 2897837
34 3027499 5897411 1109323
61 2916249 10776134 4222087
519 9677932 8478949 3162569
;54 10490708 7133936 1827493
016 14822351 4221050 1343177
7703 27270029 10402293 2332987
3389 41025640 17126556 3275951


40701 28812 600902720 734053120 144522212 46619633 24440423 4889259
2398 1687 517601728 615063808 123059860 41901303 18251894 4135634
10665 14687 438421792 589954432 101363658 34695648 14394573 4008089
0 0 517007488 679187072 99481702 34809782 14115626 3864210
2290 2875 582243072 724015808 91658917 30983318 19301177 4595575
11978 21011 794276800 784988032 63798848 19163022 23735958 4930831
641605 807375 789782848 779020288 56011543 15406065 18641380 3172048
1449620 856968 818552896 801408192 28333599 7396764 2857037 482476


? V: value in US dollars; Q: quantity in kilograms;
? 1: Canada
? 2: Dominican Republic
? 3: Mexico
? 4: European Union-15
? 5: Rest of the World
? 902: United States as Importer;

select q2>0;
P2-V2/Q2;
olsq P2 C Q2 ZYRS;

select q4>0;











P4-V4/Q4;
olsq P4 C Q4 ZYRS;

SMPL 1, 43;

Q2-1*(Q2-0)+Q2*(Q2>0);
Q4-1*(Q4-0)+Q4*(Q4>0);

Print Q2 P2 Q4 P4;

END;



The United States NBR Import Demand Model.

OPTIONS MEMORY-1500 Double; ? US 02NBR-SELEC













1984 902 959659 1086062 392689 1111375 171133120 369494048 2079176 1028974
1 14396 1075765
12 748704 766812 829172 2122750 168479888 380314432 3612847 1590097
1933597 1223189
19 2 1298959 1261125 4040176 9988785 327903168 430983424 3628705 1705323
11 101471
187 9107219 1930875 2212019 5768554 160881840 406785376 3911491 1579579
12 4
1 73120 2117875 1203746 2862687 152356480 362726880 3674326 1624760
3513976 11
1 2888215 2327812 477349 1266562 224163760 385940928 5684585 2677385
1489777
19 2 3345576 3075187 1019288 1404125 390824864 352312128 3181770 1305635
3820630 '897837
11 9 4 2672000 467383 418250 269461472 353543872 8658034 3027499
59411 11092
1 522 5213699 618499 559437 141482016 183116320 8592061 2916249
107761 422087
1993 902 6490505 4733488 328571 326875 325450048 400494304 25714519 9677932
8478949 3162569
1994 10479733 7673394 15237 16117 335774656 376031680 31977654 10490708
7133936 1827493
1995a 902 17997856 11655089 39438 43191 434508160 593079616 45101016 14822351
42 1050 1343177
1 532 21769264 94680 100296 613726272 685677632 88867703 27270029
1 4
19 4 37504200 53591 49441 549398080 660608640 119103389 41025640
171
19 61728728 40701 28812 600902720 734053120 144522212 46619633
244443 4895
19 1 79553504 2398 1687 517601728 615063808 123059860 41901303
18184 4135
1 101390248 10665 14687 438421792 589954432 101363658 34695648
143953 40C88
20101 99 4 105680184 0 0 517007488 679187072 99481702 34809782
1411 3864210
175542448 100499128 2290 2875 582243072 724015808 91658917 30983318
1 115
2 4400 130153808 11978 21011 794276800 784988032 63798848 19163022

2 605248 133565936 641605 807375 789782848 779020288 56011543 15406065
141 3172048
274699840 141642032 1449620 856968 818552896 801408192 28333599 7396764
2857037 482476

? V: value in US dollars; Q: quantity in kilograms;
? 1: Canada
? 2: Dominican Republic
? 3: Mexico
? 4: European Union-15
? 5: Rest of the World
? 902: United States as Importer;

? Eliminating zero values in Q;

Q2-1*(Q2-0)+Q2*(Q2>0);
Q4-1*(Q4-0)+Q4*(Q4>0);

PRINT ZYRS Q2 Q4;

? Eliminating Pi-0; ?? [Following Highest Price + Twice Std.Dev. + Inflation];

SELECT ZYRS-2001;
V2-2.40887;

SELECT ZYRS-1964;
V4-2.20833;
SELECT ZYRS-1965;
V4-2.30727;
SELECT ZYRS-1972;
V4-2.99985;




102


























bible;


able;























able;


b) ) ;


b) ) ;


b) );


b) ) ;



























ub eql resl;eqsub


2;eqsub













SET B45--B41-B42-B43-B44;

SET
B55-(-(-B11-B12-Bl3-Bl4)-(-B12-B22-B23-B24)-(-Bl3-B23-B33-B34)-(-Bl4-B24-B34-B44));

SET A5=1-A1-A2-A3-A4;

? Calculate the standard errors for ROW

frml rowl A5=1-Al-A2-A3-A4;
frml row2 B15--B11-B12-B13-B14;
frml row3 B25--B21-B22-B23-B24;
frml row4 B35--B31-B32-B33-B34;
frml row5 B45--B41-B42-B43-B44;
frml row6
B55-(-(-B11-Bl2-Bl3-Bl4)-(-B12-B22-B23-B24) -(-B13-B23-B33-B34)-(-B14-B24-B34-B44));
frml row7 B21-B12;
frml row8 B31-B13;
frml row9 B32-B23;
frml rowlO B41-B14;
frml rowll B42-B24;
frml rowl2 B43-B34;

analyz rowl-rowl2;

?? Calculate Eigenvalues

SET B51-B15;
SET B52-B25;
SET B53-B35;
SET B54-B45;
SET B55-B55;

? Tranform r to Pie

SET D11-(B11-MF1+MF1*MF1);
SET D12-(B12+MF1*MF2);
SET D13-(B13+MF1*MF3);
SET D14-(B14+MF1*MF4);
SET D15-(-B11-Bl2-Bl3-Bl4+MFl*MF5);

SET D21-(B12+MF2*MF1);
SET D22-(B22-MF2+MF2*MF2);
SET D23-(B23+MF2*MF3);
SET D24-(B24+MF2*MF4);
SET D25-(-Bl2-B22-B23-B24+MF2*MF5);

SET D31-(B13+MF3*MF1);
SET D32-(B23+MF3*MF2);
SET D33-(B33-MF3+MF3*MF3);
SET D34-(B34+MF3*MF4);
SET D35-(-Bl3-B23-B33-B34+MF3*MF5);

SET D41-(B14+MF4*MF1);
SET D42-(B24+MF4*MF2);
SET D43-(B34+MF4*MF3);
SET D44-(B44-MF4+MF4*MF4);
SET D45-(-Bl4-B24-B34-B44+MF4*MF5);

SET D51-(-B11-Bl2-Bl3-Bl4+MF5*MFl);
SET D52-(-Bl2-B22-B23-B24+MF5*MF2);
SET D53-(-Bl3-B23-B33-B34+MF5*MF3);
SET D54-(-Bl4-B24-B34-B44+MF5*MF4);
SET
D55-(-(-B11-Bl2-Bl3-Bl4)-(-B12-B22-B23-B24)-(-Bl3-B23-B33-B34)-(-Bl4-B24-B34-B44)-MF5+MF5*MF5);

? Create each row five;

MMAKE(VERT) El D11-D15;
MMAKE(VERT) E2 D21-D25;
MMAKE(VERT) E3 D31-D35;




106













MMAKE(VERT) E4 D41-D45;
MMAKE(VERT) E5 D51-D55;

? Creates square matrix

MAKE E E1-E5;

? Calculate Eigenvalues of E

MAT EV EIGVAL(E);
Print E EV;

? Creates standard errors for pi ij's where the pi ij's are D11, D12, etc

frml pil dll-(Bll-MFl+MFl*MFl);
frml pi2 dl2-(Bl2+MFl*MF2);
frml pi3 dl3-(Bl3+MFl*MF3);
frml pi4 dl4-(Bl4+MFl*MF4);
frml pi5 dl5-(-B11-Bl2-Bl3-Bl4+MFl*MF5);

frml pi6 d21-(Bl2+MF2*MFl);
frml pi7 d22-(B22-MF2+MF2*MF2);
frml pi8 d23-(B23+MF2*MF3);
frml pi9 d24-(B24+MF2*MF4);
frml pilO d25-(-Bl2-B22-B23-B24+MF2*MF5);

frml pill d31-(Bl3+MF3*MFl);
frml pil2 d32-(B23+MF3*MF2);
frml pil3 d33-(B33-MF3+MF3*MF3);
frml pil4 d34-(B34+MF3*MF4);
frml pil5 d35-(-Bl3-B23-B33-B34+MF3*MF5);

frml pil6 d41-(Bl4+MF4*MFl);
frml pil7 d42-(B24+MF4*MF2);
frml pil8 d43-(B34+MF4*MF3);
frml pil9 d44-(B44-MF4+MF4*MF4);
frml pi20 d45-(-Bl4-B24-B34-B44+MF4*MF5);

frml pi21 d51-(-B11-Bl2-Bl3-Bl4+MF5*MFl);
frml pi22 d52-(-Bl2-B22-B23-B24+MF5*MF2);
frml pi23 d53-(-Bl3-B23-B33-B34+MF5*MF3);
frml pi24 d54-(-Bl4-B24-B34-B44+MF5*MF4);
frml pi25 d55-(-(-B11-Bl2-Bl3-Bl4)-(-Bl2-B22-B23-B24)
-(-Bl3-B23-B33-B34)-(-Bl4-B24-B34-B44)-MF5+MF5*MF5);

analyz pil-pi25;

? Elasticities

? Divisia Input Index
FRML EL1 E1-A1/MFl;
FRML EL2 E2-A2/MF2;
FRML EL3 E3-A3/MF3;
FRML EL4 E4-A4/MF4;
FRML EL5 E5-(1-Al-A2-A3-A4)/MF5;

? Divisia Input Index WITH FIRST F
FRML EL6 EFl-A1/FFl;
FRML EL7 EF2-A2/FF2;
FRML EL8 EF3-A3/FF3;
FRML EL9 EF4-A4/FF4;
FRML EL10 EF5-(1-Al-A2-A3-A4)/FF5;

? Divisia Input Index WITH LAST F
FRML EL11 EL1-A1/FL1;
FRML EL12 EL2-A2/FL2;
FRML EL13 EL3-A3/FL3;
FRML EL14 EL4-A4/FL4;
FRML EL15 EL5-(1-Al-A2-A3-A4)/FL5;

? Compensated price elasticities




107


































































US NBR Simulation Model

OPTIONS MEMORY-1400 Double; ? US _02NBR-SELECTEDNEW2007-RECWW Models for US Import Analysis;
TITLE 'TOMATO IMPORTS TO THE US'; ? For Elasticity Trend over 11 years & Calculating Mean Quantity for sample 2,33;

? NBR Final Model with AR1 plus Homogeneity and Symmetry imposed (Automatic Rho selection);

smpl 1 43;


LOAD ZYRS ZIMP
1963 902
1964 902
1965 902
1966 902
1967 902
1968 902
1969 902
1970 902
1971 902
1972 902
1973 902
1974 902
1975 902
1976 902


V1
189750
219358
298959
241757
306522
222304
216389
560946
393364
245434
143427
90716
111149
151567


Q1 V2 Q2 V3 Q3 V4 Q4
730125 2481 21320 20705768 108846184
562125 13291 189476 27354888 111638936
733875 33276 188640 29424864 120410128
562312 9319 50292 52008580 162705440
803312 41735 379875 42585292 164302752
575562 75850 508062 46973296 175722128
400250 144332 916687 68018008 202410752
977250 121233 797875 94966856 290759360
505250 203117 1523625 84131328 258677808
409687 220130 1345187 88150080 264119568
467187 169100 834375 115137936 339795200
135582 219628 1049062 64070732 267888560
247117 375471 1656500 64131552 253600880
233824 258069 1036375 72428816 294192576


V5 Q5;
13928 65690 13565 94217
0 0 105323 648311
0 0 167763 668557
703 6875 57697 231001
123059 306330 23536 126579
12883 47249 45275 197879
17355 30914 32468 194149
2768 17812 180445 796919
55205 104367 34202 163878
0 0 129604 313238
12065 18312 139288 484990
11595 52601 135977 1137011
0 0 126452 1748431
9574 16132 89110 874245













159007
302144
263748
240082
331663
369382
616082
959659
748704
1298959
2107219
2573120
2888215
3345576
4560436
5664522
6490505
10479733
17997856
38849532
61045808


102889656
121800280
163877088
169921584
175542448
234794400
261605248
274699840


288937 632044 1977812 149405840 356244928
523625 376993 894437 161319616 369276320
468312 324980 835062 153871376 322163936
380875 254595 586750 131475112 294612672
448187 554520 1126937 237938496 236592128
563750 117345 352375 173374416 267219568
788625 243863 780500 226757760 331736864
1086062 392689 1111375 171133120 369494048
766812 829172 2122750 168479888 380314432
1261125 4040176 9988785 327903168 430983424
1930875 2212019 5768554 160881840 406785376
2117875 1203746 2862687 152356480 362726880
2327812 477349 1266562 224163760 385940928


3075187 1019288
2672000 467383
5213699 618499
4733488 328571
7673394 15237
11655089 39438
21769264 94680
37504200 53591


61728728
79553504
101390248
105680184
100499128
130153808
133565936
141642032


1404125 390824864 352312128
418250 269461472 353543872
559437 141482016 183116320
326875 325450048 400494304


0
0
0
1363
52344
88818
145836
2079176
3612847
362870
391149
367432
5684585
318177
8658034
8592061
25714519


16117 335774656 376031680 31977654
43191 434508160 593079616 4510101(
100296 613726272 685677632 8886770
49441 549398080 660608640 11910338


0 160421 674755
0 128911 236786
0 53016 89746
3062 28456 50561
58011 268097 312113
136482 632151 534929
71248 1384907 601291
1028974 1814396 1075765
1590097 1933597 1223189
5 1705323 1156736 1081471
1 1579579 1266919 1024096
6 1624760 3513976 1160022
2677385 3466587 1489777
0 1305635 3820630 2897837
3027499 5897411 1109323
2916249 10776134 4222087
9677932 8478949 3162569
10490708 7133936 1827493
i 14822351 4221050 1343177
3 27270029 10402293 2332987
9 41025640 17126556 3275951


40701 28812 600902720 734053120 144522212 46619633 24440423 4889259
2398 1687 517601728 615063808 123059860 41901303 18251894 4135634
10665 14687 438421792 589954432 101363658 34695648 14394573 4008089
0 0 517007488 679187072 99481702 34809782 14115626 3864210
2290 2875 582243072 724015808 91658917 30983318 19301177 4595575
11978 21011 794276800 784988032 63798848 19163022 23735958 4930831
641605 807375 789782848 779020288 56011543 15406065 18641380 3172048
1449620 856968 818552896 801408192 28333599 7396764 2857037 482476


? V: value in US dollars; Q: quantity in kilograms;
? 1: Canada
? 2: Dominican Republic
? 3: Mexico
? 4: European Union-15
? 5: Rest of the World
? 902: United States as Importer;

? Eliminating zero values in Q;

Q2 1*(Q2-0)+Q2*(Q2>0);
Q4 1*(Q4-0)+Q4*(Q4>0);

PRINT ZYRS Q2 Q4;


? Eliminating Pi-0; ?? [Following Highest Price + Twice Std.Dev. + Inflation];

SELECT ZYRS-2001;
V2-2.40887;

SELECT ZYRS-1964;
V4-2.20833;
SELECT ZYRS-1965;
V4-2.30727;
SELECT ZYRS-1972;
V4-2.99985;
SELECT ZYRS-1975;
V4-3.29667;
SELECT ZYRS-1977;
V4-3.49455;
SELECT ZYRS-1978;
V4-3.59349;
SELECT ZYRS-1979;
V4-3.69243;

?To find out Average and Annual Costs for making Table;
smpl 2, 43;
msd vl-v5;













smpl 2,2;
msd vl-v5;
print vl-v5;

smpl 43,43;
msd vl-v5;
print vl-v5;

? To find Average and Annual Import Quantity (kg) for making Table;
smpl 2,43;
msd ql-q5;

smpl 2,2;
msd ql-q5;
print ql-q5;

smpl 43,43;
msd ql-q5;
print ql-q5;
? End of Calculation for Table;

SMPL 1, 43;

? Defining Total Cost(S);

S-vl+v2+V3+V4+v5;

? Calculating prices (Pi)
Pl-vl/Ql; P2-v2/Q2;P3-v3/Q3;P4-v4/Q4; P5-v5/Q5;

PRINT ZYRS Q1 P1 Q2 P2 Q3 P3 Q4 P4 Q5 P5;

? To find Average and Annual Price (US $/Kg) for making Table;
smpl 2,43;
msd pl-p5;

smpl 2,2;
msd pl-p5;
print pl-p5;

smpl 43,43;
msd pl-p5;
print pl-p5;
? End of Calculation for Table;

SMPL 1,43;

? CALCULATION OF FACTOR COST SHARES (Fi-PRICE*QUANTITY/TOTAL COST)
Fl-vl/S; F2-v2/S; F3-v3/S; F4-v4/S; F5-v5/S;

? LOGGING ALL PRICES AND QUANTITIES(LPi,LQi)
LPl LOG(P1); LP2-LOG(P2);
LP3-LOG(P3); LP4-LOG(P4); LP5-LOG(P5);

LQ1 LOG(Q1);LQ2-LOG(Q2);
LQ3-LOG(Q3);LQ4-LOG(Q4); LQ5-LOG(Q5);

smpl 2 43;

? CALCULATION TWO PERIOD MEAN OF FACTOR SHARES( Fil)
Fl11(Fl+Fl(-: ,, 2 F21-(F2+F2(-1))/2;
F31-(F3+F3(-1 ,, 2 F41-(F4+F4(-1 ,, 2 F51-(F5+F5(-1 ,, 2

? Calculation of Total Quantity for finding Shares;
T ql+q2+q3+q4+q5;

? Calculation of Quantity Shares for making Table;
kl-Q1/T; k2-Q2/T; k3-Q3/T; k4-Q4/T; k5-Q5/T;
msd kl-k5;













smpl 2,2;
msd kl-k5;

smpl 43,43;
msd kl-k5;
? End of Quantity Share Calculation;

? Calculation of Mean Quantity for Structural Change;
smpl 2,33;
msd ql-q5;

msd T;

SMPL 2,43;

? CALCULATION: CHANGE IN LOGGED PRICES(DPi)
DP1 LP1-LP1(-1); DP2-LP2-LP2(-1);
DP3-LP3-LP3(-1); DP4-LP4-LP4(-1); DP5-LP5-LP5(-1);

? CALCULATION:CHANGE IN LOGGED QUANTITY(DQi)
DQ1-LQ1-LQ1(-1); DQ2-LQ2-LQ2(-1);
DQ3-LQ3-LQ3(-1); DQ4-LQ4-LQ4(-1); DQ5-LQ5-LQ5(-1);


? DEPENDENT VARIABLE fi*Dq and SUMMATION INDEX(FDQi)
FDQ1-F11*DQ1; FDQ2-F21*DQ2;
FDQ3-F31*DQ3; FDQ4-F41*DQ4; FDQ5-F51*DQ5;

DQ-FDQ1 +FDQ2+FDQ3+FDQ4+FDQ5;

?Fi*DP and SUMMATION INDEX(DP)
FDP1 F11*DP1; FDP2-F21*DP2;
FDP3-F31*DP3; FDP4-F41*DP4; FDP5-F51*DP5;


DP-FDP1 +FDP2+FDP3+FDP4+FDP5;


proc zzzz;

? DIFFERENTIAL NBR MODEL WITH AR1 PLUS HOMOGENEITY AND SYMMETRY;

trend obs;
dl -(obs-1);
frmlresl FDQ1-(Al*DQ+B11*DPl+B12*DP2+B13*DP3+B14*DP4+(-Bll-B12-B13-B14)*DP5-F11*(DP1-DP));
frml eql [dl*resl*sqrt(1-rho**2) + (1-dl)*(resl rho*resl(-1))]*
(1-rho**2)**(. i i....

trend obs;
dl -(obs-1);
frml res2 FDQ2-(A2*DQ+B12*DP1+B22*DP2+B23*DP3+B24*DP4+(-B12-B22-B23-B24)*DP5-F21*(DP2-DP));
frml eq2 [dl*res2*sqrt(1-rho**2) + (1-dl)*(res2 rho*res2(-1))]*
(1-rho**2)**(. i i....

trend obs;
dl -(obs-1);
frml res3 FDQ3-(A3*DQ+B13*DPl+B23*DP2+B33*DP3+B34*DP4+(-Bl3-B23-B33-B34)*DP5-F31*(DP3-DP));
frml eq3 [dl*res3*sqrt(1-rho**2) + (1-dl)*(res3 rho*res3(-1))]*
(1-rho**2)**(. i 2 .....

trend obs;
dl -(obs-1);
frml res4 FDQ4-(A4*DQ+Bl4*DPl+B24*DP2+B34*DP3+B44*DP4+(-Bl4-B24-B34-B44)*DP5-F41*(DP4-DP));
frml eq4 [dl*res4*sqrt(1-rho**2) + (1-dl)*(res4 rho*res4(-1))]*
(1-rho**2)**(. i 2 ...1. .


? mark significant variables with STARS;












REGOPT (STARS,STAR1-.10,STAR2 .05) T;

PARAM Al 0 A2 0 A3 0 A4 0
B 110 B12 0 B13 0 B14 0
B22 0 B23 0 B24 0
B33 0 B34 0
B44 0
rho 0;

eqsub eq resl;eqsub eq2 res2;eqsub eq3 res3;eqsub eq4 res4;
lsq(nodropmiss,tol le-7,maxit 1000) eql eq2 eq3 eq4;

COPY @LOGL LU;
LUl1LU;

SMPL NR1,NR2;

? Elasticities;

MSD F11 F21 F31 F41 F51;

?------------ MEAN FACTOR SHARES
SET MF1 @MEAN(l);
SET MF2-@MEAN(2);
SET MF3-@MEAN(3);
SET MF4-@MEAN(4);
SET MF5-@MEAN(5);

PRINT MF1-MF5;

SMPL NR1,NR1;

MSD F1 F2 F3 F4 F5;

?------------ MEAN FACTOR SHARES
SET FF1 @MEAN(1);
SET FF2-@MEAN(2);
SET FF3-@MEAN(3);
SET FF4-@MEAN(4);
SET FF5-@MEAN(5);

PRINT FF1-FF5;

SMPL NR2,NR2;

MSD F1 F2 F3 F4 F5;

?------------- MEAN FACTOR SHARES
SET FL1-@MEAN(1);
SET FL2-@MEAN(2);
SET FL3-@MEAN(3);
SET FL4-@MEAN(4);
SET FL5-@MEAN(5);

PRINT FL1-FL5;

SMPL NR1,NR2;

SET B15 -B11-B12-B13-B14;

SET B21 B12;
SET B25 -B21-B22-B23-B24;

SET B31-B13;
SET B32 B23;
SET B35 -B31-B32-B33-B34;

SET B41-B14;
SET B42 B24;
SET B43 B34;












SET B45 -B41-B42-B43-B44;


SET
B55-(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24)-(-B13-B23-B33-B34)-(-B14-B24-B34- 44,,

SET A5 1-A1-A2-A3-A4;

? Calculate the standard errors for ROW

frmlrowl A5 1-A1-A2-A3-A4;
frmlrow2 B15--B11-B12-B13-B14;
frml row3 B25 -B21-B22-B23-B24;
frml row4 B35 -B31-B32-B33-B34;
frml row5 B45 -B41-B42-B43-B44;
frml row6
B55-(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24)-(-B13-B23-B33-B34)-(-B14-B24-B34.i 44,,
frmlrow7 B21 B12;
frmlrow8 B31 B13;
frml row9 B32 B23;
frmlrowl0 B41 B14;
frml rowi 1 B42 B24;
frmlrowl2 B43 B34;

analyz row 1-row 12;

?? Calculate Eigenvalues

SET B51 B15;
SET B52 B25;
SET B53 B35;
SET B54 B45;
SET B55 B55;

? Tranform r to Pie

SET D11-(B11-MF1+MF1*MF1);
SET D12-(B12+MF1*MF2);
SET D13-(B13+MF1*MF3);
SET D14-(B14+MF1*MF4);
SET D15-(-B11-B12-B13-B14+MF1*MF5);

SET D21-(B12+MF2*MF1);
SET D22-(B22-MF2+MF2*MF2);
SET D23-(B23+MF2*MF3);
SET D24-(B24+MF2*MF4);
SET D25-(-B12-B22-B23-B24+MF2*MF5);

SET D31-(B13+MF3*MF1);
SET D32-(B23+MF3*MF2);
SET D33-(B33-MF3+MF3*MF3);
SET D34-(B34+MF3*MF4);
SET D35-(-B13-B23-B33-B34+MF3*MF5);

SET D41-(B14+MF4*MF1);
SET D42-(B24+MF4*MF2);
SET D43-(B34+MF4*MF3);
SET D44-(B44-MF4+MF4*MF4);
SET D45-(-B14-B24-B34-B44+MF4*MF5);

SET D51-(-B11-B12-B13-B14+MF5*MF1);
SET D52-(-B12-B22-B23-B24+MF5*MF2);
SET D53-(-B13-B23-B33-B34+MF5*MF3);
SET D54-(-B14-B24-B34-B44+MF5*MF4);
SET
D55-(-(-B11-B12-Bl3-Bl4)-(-B12-B22-B23-B24)-(-Bl3-B23-B33-B34)-(-Bl4-B24-B34-B44)-MF5+MF5*MF5);

? Create each row five;

MMAKE(VERT) El D11-D15;
MMAKE(VERT) E2 D21-D25;












MMAKE(VERT) E3 D31-D35;
MMAKE(VERT) E4 D41-D45;
MMAKE(VERT) E5 D51-D55;

? Creates square matrix

MAKE E E1-E5;

? Calculate Eigenvalues of E

MAT EV EIGVAL(E);
Print E EV;

? Creates standard errors for pi ij's where the pi ij's are Dll, D12, etc

frmlpil dll-(B11-MFl+MF1*MF1);
frml pi2 d12-(B12+MFl*MF2);
frml pi3 dl3-(B13+MF1*MF3);
frml pi4 dl4-(B14+MFl*MF4);
frmlpi5 d15 (-B11-B12-B13-B14+MFl*MF5);

frml pi6 d21 (B12+MF2*MF1);
frml pi7 d22-(B22-MF2+MF2*MF2);
frml pi8 d23-(B23+MF2*MF3);
frml pi9 d24-(B24+MF2*MF4);
frml pilO d25-(-B12-B22-B23-B24+MF2*MF5);

frml pill d31l(B13+MF3*MF1);
frml pil2 d32-(B23+MF3*MF2);
frml pil3 d33-(B33-MF3+MF3*MF3);
frml pil4 d34-(B34+MF3*MF4);
frml pil5 d35-(-B13-B23-B33-B34+MF3*MF5);

frml pil6 d41 (B14+MF4*MF1);
frml pil7 d42-(B24+MF4*MF2);
frml pil8 d43-(B34+MF4*MF3);
frml pil9 d44-(B44-MF4+MF4*MF4);
frml pi20 d45-(-B14-B24-B34-B44+MF4*MF5);

frmlpi21 d51 (-B11-B12-B13-B14+MF5*MF1);
frml pi22 d52-(-B12-B22-B23-B24+MF5*MF2);
frml pi23 d53-(-B13-B23-B33-B34+MF5*MF3);
frml pi24 d54-(-B14-B24-B34-B44+MF5*MF4);
frml pi25 d55-(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24)
-(-B13-B23-B33-B34)-(-B14-B24-B34-B44)-MF5+MF5*MF5);

analyz pil-pi25;

? Elasticities


? Divisia Input Index
FRML EL1 E1lA1/MF1; FRML EL1M ElM-A1/MMF1;
FRML EL2 E1lA2/MF2; FRML EL2M E2M-A2/MMF2;
FRML EL3 E3-A3/MF3; FRML EL3M E3M-A3/MMF3;
FRML EL4 E4-A4/MF4; FRML EL4M E4M-A4/MMF4;
FRML EL5 E5-(1-Al-A2-A3-A4)/MF5; FRML EL5M E5M-(1-Al-A2-A3-A4)/MMF5;

? Divisia Input Index WITH FIRST F
FRML EL6 EF1-A1/FF1; FRML EL6M EF1M-A1/MFF1;
FRML EL7 EF2-A2/FF2; FRML EL7M EF2M-A2/MFF2;
FRML EL8 EF3-A3/FF3; FRML EL8M EF3M-A3/MFF3;
FRML EL9 EF4-A4/FF4; FRML EL9M EF4M-A4/MFF4;
FRML EL10 EF5-(1-Al-A2-A3-A4)/FF5; FRML EL10M EF5M-(1-Al-A2-A3-A4)/MFF5;

? Divisia Input Index WITH LAST F
FRML EL11 EL1 A1/FL1; FRML EL11M EL1M-A1/MFL1;
FRML EL12 EL2-A2/FL2; FRML EL12M EL2M-A2/MFL2;
FRML EL13 EL3-A3/FL3; FRML EL13M EL3M-A3/MFL3;
FRML EL14 EL4-A4/FL4; FRML EL14M EL4M-A4/MFL4;












FRML EL15 EL5-(1-Al-A2-A3-A4)/FL5; FRML EL15M EL5M-(1-Al-A2-A3-A4)/MFL5;


? Compensated price elasticities


FRML EP16 E11
FRML EP17 E12
FRML EP18 E13
FRML EP19 E14
FRML EP20 E15

FRML EP21 E21
FRML EP22 E22
FRML EP23 E23-
FRML EP24 E24
FRML EP25 E25

FRML EP26 E31
FRML EP27 E32
FRML EP28 E33-
FRML EP29 E34
FRML EP30 E35

FRMLEP31 E41
FRML EP32 E42
FRML EP33 E43
FRML EP34 E44
FRML EP35 E45


(B11-MF1+MF1*MF1)/MF1; FRML EP16M E11M-(B11-MMF1+MMF1*MMF1)/MMF1;
(B12+MF1*MF2)/MF1; FRML EP17M E12M-(B12+MMF1*MMF2)/MMF1;
(B13+MF1*MF3)/MF1; FRML EP18M E13M-(B13+MMF1*MMF3)/MMF1;
(B14+MF1*MF4)/MF1; FRML EP19M E14M-(B14+MMF1*MMF4)/MMF1;
(-B11-B12-B13-B14+MF1*MF5)/MF1; FRML EP20M E15M (-B11-B12-B13-B14+MMF1*MMF5)/MMF1;

(B12+MF2*MF1)/MF2; FRML EP21M E21M-(B12+MMF2*MMF1)/MMF2;
(B22-MF2+MF2*MF2)/MF2; FRML EP22M E22M-(B22-MMF2+MMF2*MMF2)/MMF2;
(B23+MF2*MF3)/MF2; FRML EP23M E23M-(B23+MMF2*MMF3)/MMF2;
(B24+MF2*MF4)/MF2; FRML EP24M E24M-(B24+MMF2*MMF4)/MMF2;
(-B12-B22-B23-B24+MF2*MF5)/MF2; FRML EP25M E25M-(-B12-B22-B23-B24+MMF2*MMF5)/MMF2;

(B13+MF3*MF1)/MF3; FRML EP26M E31M-(B13+MMF3*MMF1)/MMF3;
(B23+MF3*MF2)/MF3; FRML EP27M E32M-(B23+MMF3*MMF2)/MMF3;
(B33-MF3+MF3*MF3)/MF3; FRML EP28M E33M-(B33-MMF3+MMF3*MMF3)/MMF3;
(B34+MF3*MF4)/MF3; FRML EP29M E34M-(B34+MMF3*MMF4) /MMF3;
(-B13-B23-B33-B34+MF3*MF5)/MF3; FRML EP30M E35M-(-B13-B23-B33-B34+MMF3*MMF5)/MMF3;

(B14+MF4*MF1)/MF4; FRML EP31M E41M-(B14+MMF4*MMF1) /MMF4;
(B24+MF4*MF2)/MF4; FRML EP32M E42M-(B24+MMF4*MMF2) /MMF4;
(B34+MF4*MF3)/MF4; FRML EP33M E43M-(B34+MMF4*MMF3) /MMF4;
(B44-MF4+MF4*MF4)/MF4; FRML EP34M E44M-(B44-MMF4+MMF4*MMF4)/MMF4;
(-B14-B24-B34-B44+MF4*MF5)/MF4; FRML EP35M E45M-(-B14-B24-B34-B44+MMF4*MMF5)/MMF4;


FRML EP36 E51-(-B11-B12-B13-B14+MF5*MF1)/MF5; FRML EP36M E51M-(-B11-B12-B13-B14+MMF5*MMF1)/MMF5;
FRML EP37 E52-(-B12-B22-B23-B24+MF5*MF2)/MF5; FRML EP37M E52M-(-B12-B22-B23-B24+MMF5*MMF2)/MMF5;
FRML EP38 E53-(-B13-B23-B33-B34+MF5*MF3)/MF5; FRML EP38M E53M-(-B13-B23-B33-B34+MMF5*MMF3)/MMF5;
FRML EP39 E54-(-B14-B24-B34-B44+MF5*MF4)/MF5; FRML EP39M E54M-(-B14-B24-B34-B44+MMF5*MMF4)/MMF5;
FRML EP40 E55-(-(-B 1-B12-Bl3-Bl4)-(-B12-B22-B23-B24)-(-Bl3-B23-B33-B34)-(-Bl4-B24-B34-B44)-MF5+MF5*MF5)/MF5; FRML
EP40M E55M-(-(-B 1-B12-Bl3-Bl4)-(-B12-B22-B23-B24)-(-Bl3-B23-B33-B34)-(-Bl4-B24-B34-B44)-MMF5+MMF5*MMF5)/MMF5;

ANALYZ ELl-EL5, EL1M-EL5M;
MAKE ELCOEF @COEFA;

ANALYZ EP16-EP40, EP16M-EP40M;
MAKE EPCOEF @COEFA;

MMAKE(VERTICAL) MBM ELCOEF EPCOEF;
MAKE MBETA MBETA MBM;

?smpl 2,2;
?print LU;

?mark significant variables with STARS;
?REGOPT (STARS,STAR1 .10,STAR2 .05,) T;

endproc zzzz;


MFORM(TYPE-GEN,NROW-60,NCOL-1) MBETA-0;

SMPL 2, 33;

MSD F11 F21 F31 F41 F51;
?------------ MEAN FACTOR SHARES


SET MMF1
SET MMF2-
SET MMF3-
SET MMF4-
SET MMF5-

SMPL 2,2;


@MEAN(1);
@MEAN(2);
@MEAN(3);
@MEAN(4);
@MEAN(5);


MSD F1 F2 F3 F4 F5;
?---------------
SET MFF1 @MEAN(1);
SET MFF2 @MEAN(2);


MEAN FACTOR SHARES












SET MFF3 a @MEAN(3);
SET MFF4a @MEAN(4);
SET MFF5a @MEAN(5);

SMPL 33,33;

MSD F1 F2 F3 F4 F5;
?------------ MEAN FACTOR SHARES
SET MFL1 @MEAN(1);
SET MFL2 a@MEAN(2);
SET MFL3 a@MEAN(3);
SET MFL4 a@MEAN(4);
SET MFL5 a @MEAN(5);

DO J-1 TO 11;
SET NR1-1+J;
SET NR2-32+J;
SMPL NR1,NR2;
ZZZZ;
ENDDO;

WRITE(FORMAT-EXCEL,FILE-'U:\TOMATORESEARCH\USNEWANALYSIS\ELEPELAS-RECWW.XLS') MBETA;

END;













APPENDIX B

COMPUTER PRINTOUTS FOR EU-15 ANALYSIS


The Model to deal with zero quantity and price for EU-15 Analysis


OPTIONS MEMORY 1500 signif=5 DOUBLE; ? EU15#01Price Calculation Model; ? To replace zero Price for EU-15 Import Analysis;
TITLE 'TOMATO IMPORTS TO THE EU';

SMPL 1 43;

?READ(FORMAT-EXCEL,FILE-'U:\TomatoResearch\TSPWORKS2006\EUAnalysis\EUonly.xls');

LOAD ZYRS ZIMP V1 Q1 V2 Q2 V3 Q3 V4 Q4 V5 Q5 V6 Q6 V7 Q7


V8 Q8;
1963 901 31653 451074 1773563 16782212
3376225 19971241
1964 901 48038 572000 1840474 12660747
13016627
1965 901 118653 1108312 2220545 15148287
1795230 12036480
1966901 187269 17480002856091 13152661
2539638 10580398
1967 901 207999 2220125 3753435 18649844
2665245 13813254
1968 901 310461 2266000 4405008 19895024
1488195 7481394
1969 901 368538 2496687 3916124 14940986
3636690 12882177
1970 901 457730 2397937 4464447 15889571
11442442 37441791
1971901 469036 2752398 4245967 16817272
4986064 14826093
1972 901 962086 4625350 4017254 16139896
7784385 19256671
1973 901 1594265 4437519 4893273 14262623
976072 1966077
1974 901 1797788 5708053 5692533 15417826
1433878 2602306
1975 901 3158736 7143835 7033987 15033790
42096 1391526 2230043
1976 901 2968861 7003612 5147297 12926526
132987 2215727 4062108


18133

1262

5152

85829

46886

26510

32359

40904

64882

35034

36545

21647

731299

183105


1977 901 3472400 9189788 7425601 15885014 232500
2066804 3061723
1978 901 2963215 6163585 7430996 14492037 230964
202229 2231462 2797116


4


.0123 26567148 122325848 1004321 9072714 7000 72000 3240 12187


3312 31730108 118009256 1361401 10570330 0

5562 27267956 125699712 1928476 15228634 (

107862 36479432 108006936 2691155 19047720

118310 36637392 122262104 2789153 18614500

101198 27803468 97863480 5120585 33277424

99740 36980408 129603024 7855349 34033604

68278 38635412 133623216 9460210 42568288 3

136795 44930860 129688160 14373836 59700256

42506 45938032 118869120 15097659 63092032

29147 77101920 170786640 19292240 54257208

3 219126 65580600 138533712 15978113 43303736

9 1000174 99121944 137464352 15771246 39263124

4 3315306 75478176 106260064 13685706 37747484

6 4158786 82794408 115851376 9554171 25682334

1 3208973 85231920 104367608 9191896 21285690


0

1000

0

115

0

1550

0

632

333

49

10(

375


0 2419 6437 2737208

0 17404 53152

12812 11245 21812

0 69348 154932

0 74419 93022

0 128959 220245

40398 260188 308561

0 284716 335143

1875 284469 401349

0 657057 745654

6 42800 108407 82604

43 187800 41024

57 17625 196696

00 1687 53600 51748

11 104073 200784


19799014026272 67988127907311 16267033 2373524 2974927 93011048 104508152 9071943 19847036 44832 82886 196614
141842 1673201 1711795


1980 9016052384 103625114767464 11321398 2157749 1940617 87930720
867395 513303 2614841 2395847
1981 901 5573419 10825753 4173807 10542792 1422141 1240105 74939560
246247 303185 2178646 2958982
1982 901 6942520 16861542 2793814 7765386 1335471 1713708 50565584
39146 36069 1739339 3025668
1983 901 5955306 13127198 3990447 10570612 635025 665686 48541384
69812 82299 1714422 3349194
1984901 4628415 11485112 2225929 6696144 1818868 2723827 49903220
73121 98443 1755933 3507856
1985 9013714979 96790742804760 8574710 1519295 2329586 56146208
66289 88741 1109962 2356596
19869014428861 96525742080354 5348562 2521897 2659235 68524448
84403 88221 1740140 2880545
19879014756993 96994762978113 6105456 2280814 2929812 87123544
141317 109611 2877196 3757298
1988 901 5344652 8643190 2402380 4767624 4903546 3937171 76347792


349851 243009 4564090 5078631
1989 9014125968 6758386 2317410
864997 3588237 4834089


91080208 11919843 23267114 102946 122824

89919392 6717752 15562404 115808 164500

66672692 6240809 14409339 89026 207127

67457464 753943720009412 74331 144311

82007208 8647271 24582260 410328 781058

91701904 4742369 15723088 140775 329186

96462176 4144346 11844698 670611 1127182

97076016 5764089 12027178 2554835 3132693

84892376 6708963 14960272 2264643 2524775


5286987 3193985 3638453 71171816 97750632 7488692 16598397 2450896 3062926 1633335












19909017360551 110201873029963 5708198 10650899 8021161 130990440 110714248 2765811 5380596 5190490 4332286 1125452
702779 6934463 5863059
1991901 1869942 2294788 5032627 8084701 8374074 6277594 145453872 140390832 3665304 6377014 5308010 5725206 2509599
1876646 5001624 4620865
1992 901 404674 585339 3651196 5088920 6577894 5570072 139864960 141403616 3295008 5192788 6377826 7713368 391317
410689 5869919 5966114
1993 901 116900 270875 1710821 2183913 5634468 5223672 143160640 177926880 2223115 3715382 3718712 4547761 188735
224006 4278326 4454055
1994901 43000 47199 357690 560199 5986108 5533320 120315416 160862048 1217387 2056625 4114383 5180666 155184 156425
3399291 3484049
1995 901 0 0 29111 43369 11579928 6364214 147584576 146905040 125053 151773 2661260 2334571 308488 239044
5825043 4559700
1996901 24187 11375 183522 69726 17264544 9271430 148078032 157396080 230793 297000 4508979 3476803 38016 20788
5271388 3221124
1997901 0 0 15124 2138620697980 10657880 104872824 154990608 38161 46226 1020557 927674 82651 103135
7566921 5781611
1998901 0 0 22636 3078822782924 12299150 147687616 186837792 29637 48398 905471 869349 92865 72220
7331996 5544938
1999901 0 0 60193 71125 22791080 14885635 141139536 206332992 0 0 2285333 2621314 205003 163351
5596276 5080246
2000901 10448 18000 54910 66112 23066744 16460790 117918632 149369664 0 0 10910094 11328141 39582 24734
12195800 10873083
2001 901 16528 57300 9909 16625 21346768 17480612 108504040 185144192 22387 33500 6927946 9118854 10763 9212
10767377 11338870
2002901 61870 101898 37526 55663 21751194 14590805 170802208 181180384 4604 3625 16571994 18724980 21009 14917
12378914 12102489
2003901 22075 26453 206498 198835 24412648 16800316 136954944 182067392 43642 46710 26240464 25322332 21415 17437
29699618 29466621
2004901 150833 169081 84115 8433831703530 18551426 172314848 192159904 190675 188753 19238032 17210976 12976 6761
27974391 29547420
2005 901 0 0 20624 19526 44735840 25201496 229945104 232239648 35735 37440 23119076 19664766 43605 31100
61700006 54335924

? 1: Albania
? 2: Bulgaria
? 3: Israel
? 4: Morocco
? 5: Romania
? 6: Turkey
? 7: United States
? 8: Rest of the World;
?901 EU-15 as importer;
select ql>0;
PlV1/Q1;
olsq P1 C Q1 ZYRS;

select q5>0;
P5-V5/Q5;
olsq P5 C Q5 ZYRS;

select q6>0;
P6-V6/Q6;
olsq P6 C Q6 ZYRS;

SMPL 1, 43;

Q11I*(Q1 0)+QI*(Q1>0);
Q5-1*(Q5-0)+Q5*(Q5>0);
Q6-1*(Q6-0)+Q6*(Q6>0);

Print Q1 P1 Q5 P5 Q6 P6;
END;

The EU-15 NBR Import Demand Model.

OPTIONS MEMORY-1500 Double; ? EU15 02NBR-NEW2007-FINAL SELECTED-V2 Models for EU













? NBR Model with Homogeneity and Symmetry imposed without Rho (since Rho--0.094410 with P value
0.163 & LU1-689.03834)& LU2-688.35849;

smpl 1 43;

?READ(FORMAT-EXCEL,FILE-'U:\TomatoResearch\TSPWORKS2006\EUAnalysis\EUonly.xls');

LOAD ZYRS ZIMP V1 Q1 V2 Q2 V3 Q3 V4 Q4 V5
Q5 V6 Q6 V7 Q7 V8 Q8;
1963 901 31653 451074 1773563 16782212 18133 4012 26567148 122325848 1004321
9072714 7000 72000 3240 12187 3376225 19971241
1964 901 48038 572000 1840474 12660747 1262 3312 31730108 118009256 1361401
1570330 0 0 2419 6437 2737208 13016627
165 901 118653 1108312 2220545 15148287 5152 5562 27267956 125699712 1928476
1228634 0 0 17404 53152 1795230 12036480
166 901 187269 1748000 2856091 13152661 85829 107862 36479432 108006936 2691155
1 0477 20 1000 12812 11245 21812 2539638 10580398
167 901 207999 2220125 3753435 18649844 46886 118310 36637392 122262104 2789153
1 614500 0 0 69348 154932 2665245 13813254
168 901 310461 2266000 4405008 19895024 26510 101198 27803468 97863480 5120585
3277424 0 0 74419 93022 1488195 7481394
169 901 368538 2496687 3916124 14940986 32359 99740 36980408 129603024 7855349
3033604 0 0 128959 220245 3636690 12882177
170 901 457730 2397937 4464447 15889571 40904 68278 38635412 133623216 9460210
2568288 3115 40398 260188 308561 11442442 37441791
71 901 469036 2752398 42459671 6817272 64882 136795 44930860 129688160 14373836
5700256 0 0 284716 335143 4986064 14826093
172 901 962086 4625350 4017254 16139896 35034 42506 45938032 118869120 15097659
63092032 1550 1875 284469 401349 7784385 19256671
1973 901 1594265 4437519 4893273 14262623 36545 29147 77101920 170786640 19292240
54257208 0 0 657057 745654 976072 1966077
174 901 1797788 5708053 5692533 15417826 216473 219126 65580600 138533712 15978113
43303736 6326 42800 108407 82604 1433878 2602306
175 901 3158736 7143835 7033987 15033790 731299 1000174 99121944 137464352 15771246
263124 33343 187800 41024 42096 1391526 2230043
76 901 2968861 7003612 5147297 12926526 1831054 3315306 75478176 106260064 13685706
37747484 4957 17625 196696 132987 2215727 4062108
177 901 3472400 9189788 7425601 15885014 2325006 4158786 82794408 115851376 9554171
2682334 1000 1687 53600 51748 2066804 3061723
78 901 2963215 6163585 7430996 14492037 2309641 3208973 85231920 104367608 9191896
285690 37511 104073 200784 202229 2231462 2797116
179 901 4026272 6798812 7907311 16267033 2373524 2974927 93011048 104508152 9071943
1847036 44832 82886 196614 141842 1673201 1711795
180 901 6052384 10362511 4767464 11321398 2157749 1940617 87930720 91080208 11919843
3267114 102946 122824 867395 513303 2614841 2395847
181 901 557341 10825753 4173807 10542792 1422141 1240105 74939560 89919392 6717752
1562404 115808 164500 246247 303185 2178646 2958982
82 901 6942520 16861542 2793814 7765386 1335471 1713708 50565584 66672692 6240809
14409339 89026 207127 39146 36069 1739339 3025668
83 901 5955306 13127198 3990447 10570612 635025 665686 48541384 67457464 7539437
00941 74331 144311 69812 82299 1714422 3349194
184 901 4628415 11485112 2225929 6696144 1818868 2723827 49903220 82007208 8647271
24582260 410328 781058 73121 98443 1755933 3507856
185 901 3714979 9679074 2804760 8574710 1519295 2329586 56146208 91701904 4742369
15723088 140775 329186 66289 88741 1109962 2356596
186 901 4428861 9652574 2080354 5348562 2521897 2659235 68524448 96462176 4144346
11844698 670611 1127182 84403 88221 1740140 2880545
187 901 4756993 9699476 2978113 6105456 2280814 2929812 87123544 97076016 5764089
12027178 2554835 3132693 14131 109611 2877196 3757298
188 901 5344652 8643190 2402380 4767624 4903546 3937171 76347792 84892376 6708963
14960272 2264643 2524775 349851 243009 4564090 5078631
189 901 4125968 6758386 2317410 5286987 3193985 3638453 71171816 97750632 7488692
16598397 2450896 3062926 1633335 864997 3588237 4834089
90 901 7360551 11020187 3029963 5708198 10650899 8021161 130990440 110714248 2765811
380596 5190490 4332286 1125452 702779 6934463 5863059
1 91 901 1869942 2294788 5032627 8084701 8374074 6277594 145453872 140390832 3665304
377014 5308010 5725206 2509599 1876646 5001624 4620865
1992 901 404674 585339 3651196 5088920 6577894 5570072 139864960 141403616 3295008
5192788 6377826 7713368 391317 410689 5869919 5966114
1993 901 116900 270875 1710821 2183913 5634468 5223672 143160640 177926880 2223115
3715382 3718712 4547761 188735 224006 4278326 4454055




119

















































L v vain
? 1 Albc
? 2 Bulc
? 3 Isrc
? 4 More
? 5 Rom=



























able;


bible;


ible;












































































b) ) ;


b) );


b) ) ;

















b) ) ;


ob );


1;?eqsub


';?eqsub


3;?eqsub


I;?eqsub


5;?eqsub













SET FL6-@MEAN(6);
SET FL7-@MEAN(7);

PRINT FL1-FL7;

SMPL 2, 43;

SET B17--B11-B12-B13-B14-B15-B16;

SET B21-B12;
SET B27--B21-B22-B23-B24-B25-B26;

SET B31-B13;
SET B32-B23;
SET B37--B31-B32-B33-B34-B35-B36;

SET B41-B14;
SET B42-B24;
SET B43-B34;
SET B47--B41-B42-B43-B44-B45-B46;

SET B51-B15;
SET B52-B25;
SET B53-B35;
SET B54-B45;
SET B57--B51-B52-B53-B54-B55-B56;

SET B61-B16;
SET B62-B26;
SET B63-B36;
SET B64-B46;
SET B65=B56;
SET B67--B61-B62-B63-B64-B65-B66;

SET B77-(-(-B11-Bl2-Bl3-Bl4-B15-Bl6)-(-Bl2-B22-B23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)-(-B14-
B24-B34-B44-B45-B46)
-(-B15-B25-B35-B45-B55-B56)-(-Bl6-B26-B36-B46-B56-B66));

SET A7=1-A1-A2-A3-A4-A5-A6;

? Calculate the standard errors for ROW

frml rowl A7=1-A1-A2-A3-A4-A5-A6;
frml row2 B17--B11-B12-B13-B14-B15-B16;
frml row3 B27--B21-B22-B23-B24-B25-B26;
frml row4 B37--B31-B32-B33-B34-B35-B36;
frml row5 B47--B41-B42-B43-B44-B45-B46;
frml row6 B57-B51-B52-B53-B54-B55-B56;
frml row7 B67--B61-B62-B63-B64-B65-B66;
frml row8 B77-(-(-B11-Bl2-Bl3-Bl4-B15-Bl6)-(-Bl2-B22-B23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)-
(-B14-B24-B34-B44-B45-B46)
-(-B15-B25-B35-B45-B55-B56)-(-Bl6-B26-B36-B46-B56-B66));

frml row9 B21-B12;
frml rowlt B31-B13;
frml row11 B32-B23;
frml rowl2 B41-B14;
frml rowl3 B42-B24;
frml rowl4 B43-B34;
frml rowl5 B51-B15;
frml rowl6 B52-B25;
frml rowl7 B53-B35;
frml rowl8 B54-B45;
frml rowl9 B61B16;
frml row20 B62-B26
frml row21 B63-B36
frml row22 B64B46;
frml row23 B65=B56

analyz rowl-row23;





124













?? Calculate Eigenvalues

SET B71-B17;
SET B72-B27;
SET B73-B37;
SET B74-B47;
SET B75-B57;
SET B76-B67;
SET B77-B77;

? Tranform r to Pie;

SET D11-(B11-MF1+MF1*MF1);
SET D12-(B12+MF1*MF2);
SET D13-(B13+MF1*MF3);
SET D14-(B14+MF1*MF4);
SET D15-(B15+MF1*MF5);
SET D16-(B16+MF1*MF6);
SET D17-(-B11-Bl2-Bl3-B14-B15-B16+MFl*MF7);

SET D21-(B12+MF2*MF1);
SET D22-(B22-MF2+MF2*MF2);
SET D23-(B23+MF2*MF3);
SET D24-(B24+MF2*MF4);
SET D25-(B25+MF2*MF5);
SET D26-(B26+MF2*MF6);
SET D27-(-Bl2-B22-B23-B24-B25-B26+MF2*MF7);

SET D31-(B13+MF3*MF1);
SET D32-(B23+MF3*MF2);
SET D33-(B33-MF3+MF3*MF3);
SET D34-(B34+MF3*MF4);
SET D35-(B35+MF3*MF5);
SET D36-(B36+MF3*MF6);
SET D37-(-Bl3-B23-B33-B34-B35-B36+MF3*MF7);

SET D41-(B14+MF4*MF1);
SET D42-(B24+MF4*MF2);
SET D43-(B34+MF4*MF3);
SET D44-(B44-MF4+MF4*MF4);
SET D45-(B45+MF4*MF5);
SET D46-(B46+MF4*MF6);
SET D47-(-Bl4-B24-B34-B44-B45-B46+MF4*MF7);

SET D51-(B15+MF5*MF1);
SET D52-(B25+MF5*MF2);
SET D53-(B35+MF5*MF3);
SET D54-(B45+MF5*MF4);
SET D55-(B55-MF5+MF5*MF5);
SET D56-(B56+MF5*MF6);
SET D57-(-B15-B25-B35-B45-B55-B56+MF5*MF7);

SET D61-(B16+MF6*MF1);
SET D62-(B26+MF6*MF2);
SET D63=(B36+MF6*MF3);
SET D64=(B46+MF6*MF4);
SET D65-(B56+MF6*MF5);
SET D66-(B66-MF6+MF6*MF6);
SET D67-(-Bl6-B26-B36-B46-B56-B66+MF6*MF7);

SET D71-(-B11-Bl2-Bl3-Bl4-B15-Bl6+MF7*MFl);
SET D72-(-Bl2-B22-B23-B24-B25-B26+MF7*MF2);
SET D73-(-Bl3-B23-B33-B34-B35-B36+MF7*MF3);
SET D74-(-Bl4-B24-B34-B44-B45-B46+MF7*MF4);
SET D75-(-B15-B25-B35-B45-B55-B56+MF7*MF5);
SET D76-(-Bl6-B26-B36-B46-B56-B66+MF7*MF6);
SET D77-(-(-B11-Bl2-Bl3-Bl4-B15-Bl6)-(-Bl2-B22-B23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)-(-B14-
B24-B34-B44-B45-B46)
-(-B15-B25-B35-B45-B55-B56)-(-Bl6-B26-B36-B46-B56-B66)-MF7+MF7*MF7);

? Create each row EIGHT;




125














MMAKE(VERT) El D11-D17;
MMAKE(VERT) E2 D21-D27;
MMAKE(VERT) E3 D31-D37;
MMAKE(VERT) E4 D41-D47;
MMAKE(VERT) E5 D51-D57;
MMAKE(VERT) E6 D61-D67;
MMAKE(VERT) E7 D71-D77;

? Creates square matrix

MAKE E E1-E7;

? Calculate Eigenvalues of E

MAT EV EIGVAL(E);
Print E EV;

? Creates standard errors for pi ij's where the pi ij's are Dll, D12, etc;

frml pil D11-(B11-MFl+MFl*MFl);
frml pi2 D12-(Bl2+MFl*MF2);
frml pi3 D13-(Bl3+MFl*MF3);
frml pi4 D14-(Bl4+MFl*MF4);
frml pi5 D15-(B15+MFl*MF5);
frml pi6 D16-(B16+MFl*MF6);
frml pi7 D17-(-B11-Bl2-Bl3-Bl4-B15-Bl6+MFl*MF7);

frml pi8 D21-(Bl2+MF2*MFl);
frml pi9 D22-(B22-MF2+MF2*MF2);
frml pilO D23-(B23+MF2*MF3);
frml pill D24(B24+MF2*MF4);
frml pil2 D25(B25+MF2*MF5);
frml pil3 D26(B26+MF2*MF6);
frml pil4 D27-(-Bl2-B22-B23-B24-B25-B26+MF2*MF7);

frml pil5 D31-(Bl3+MF3*MF1);
frml pil6 D32-(B23+MF3*MF2);
frml pil7 D33-(B33-MF3+MF3*MF3);
frml pil8 D34-(B34+MF3*MF4);
frml pil9 D35-(B35+MF3*MF5);
frml pi20 D36-(B36+MF3*MF6);
frml pi21 D37-(-Bl3-B23-B33-B34-B35-B36+MF3*MF7);

frml pi22 D41-(Bl4+MF4*MF1);
frml pi23 D42-(B24+MF4*MF2);
frml pi24 D43-(B34+MF4*MF3);
frml pi25 D44-(B44-MF4+MF4*MF4);
frml pi26 D45-(B45+MF4*MF5);
frml pi27 D46-(B46+MF4*MF6);
frml pi28 D47-(-Bl4-B24-B34-B44-B45-B46+MF4*MF7);

frml pi29 D51-(B15+MF5*MFl);
frml pi30 D52-(B25+MF5*MF2);
frml pi31 D53-(B35+MF5*MF3);
frml pi32 D54-(B45+MF5*MF4);
frml pi33 D55-(B55-MF5+MF5*MF5);
frml pi34 D56-(B56+MF5*MF6);
frml pi35 D57-(-B15-B25-B35-B45-B55-B56+MF5*MF7);

frml pi36 D61-(Bl6+MF6*MFl);
frml pi37 D62=(B26+MF6*MF2);
frml pi38 D63=(B36+MF6*MF3);
frml pi39 D64=(B46+MF6*MF4);
frml pi40 D65=(B56+MF6*MF5);
frml pi41 D66-(B66-MF6+MF6*MF6);
frml pi42 D67-(-Bl6-B26-B36-B46-B56-B66+MF6*MF7);

frml pi43 D71-(-B11-Bl2-Bl3-Bl4-B15-Bl6+MF7*MFl);
frml pi44 D72-(-Bl2-B22-B23-B24-B25-B26+MF7*MF2);
frml pi45 D73-(-Bl3-B23-B33-B34-B35-B36+MF7*MF3);




126













frml pi46 D74-(-Bl4-B24-B34-B44-B45-B46+MF7*MF4);
frml pi47 D75-(-B15-B25-B35-B45-B55-B56+MF7*MF5);
frml pi48 D76-(-Bl6-B26-B36-B46-B56-B66+MF7*MF6);
frml pi49 D77-(-(-B11-Bl2-Bl3-Bl4-B15-Bl6)-(-Bl2-B22-B23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)-
(-B14-B24-B34-B44-B45-B46)
-(-B15-B25-B35-B45-B55-B56)-(-Bl6-B26-B36-B46-B56-B66)-MF7+MF7*MF7);

analyz pil-pi49;

? Elasticities

? Divisia Input Index
FRML EL1 E1-A1/MFl;
FRML EL2 E2-A2/MF2;
FRML EL3 E3-A3/MF3;
FRML EL4 E4-A4/MF4;
FRML EL5 E5-A5/MF5;
FRML EL6 E6-A6/MF6;
FRML EL7 E7(1-A-A2-A3-A4-A5-A6)/MF7;

? Divisia Input Index WITH FIRST F
FRML EL8 EF1-A1/FF1;
FRML EL9 EF2-A2/FF2;
FRML EL10 EF3-A3/FF3;
FRML EL11 EF4-A4/FF4;
FRML EL12 EF5-A5/FF5;
FRML EL13 EF6-A6/FF6;
FRML EL14 EF7-(1-A1-A2-A3-A4-A5-A6)/FF7;

? Divisia Input Index WITH LAST F
FRML EL15 EL1-A1/FL1;
FRML EL16 EL2-A2/FL2;
FRML EL17 EL3-A3/FL3;
FRML EL18 EL4-A4/FL4;
FRML EL19 EL5-A5/FL5;
FRML EL20 EL6-A6/FL6;
FRML EL21 EL7-(1-A1-A2-A3-A4-A5-A6)/FL7;

? Compensated price elasticities

FRML EP11 E11-(B11-MF1+MF1*MF1)/MF1;
FRML EP12 E12-(B12+MF1*MF2)/MF1;
FRML EP13 E13-(B13+MF1*MF3)/MF1;
FRML EP14 E14-(B14+MF1*MF4)/MF1;
FRML EP15 E15-(B15+MF1*MF5)/MF1;
FRML EP16 E16-(B16+MF1*MF6)/MF1;
FRML EP17 E17-(-B11-Bl2-Bl3-Bl4-B15-Bl6+MFl*MF7)/MFl;

FRML EP21 E21-(B12+MF2*MF1)/MF2;
FRML EP22 E22-(B22-MF2+MF2*MF2)/MF2;
FRML EP23 E23-(B23+MF2*MF3)/MF2;
FRML EP24 E24(B24+MF2*MF4)/MF2;
FRML EP25 E25(B25+MF2*MF5)/MF2;
FRML EP26 E26-(B26+MF2*MF6)/MF2;
FRML EP27 E27-(-Bl2-B22-B23-B24-B25-B26+MF2*MF7)/MF2;

FRML EP31 E31-(B13+MF3*MF1)/MF3;
FRML EP32 E32-(B23+MF3*MF2)/MF3;
FRML EP33 E33-(B33-MF3+MF3*MF3)/MF3;
FRML EP34 E34-(B34+MF3*MF4)/MF3;
FRML EP35 E35-(B35+MF3*MF5)/MF3;
FRML EP36 E36-(B36+MF3*MF6)/MF3;
FRML EP37 E37-(-Bl3-B23-B33-B34-B35-B36+MF3*MF7)/MF3;

FRML EP41 E41-(B14+MF4*MF1)/MF4;
FRML EP42 E42-(B24+MF4*MF2)/MF4;
FRML EP43 E43-(B34+MF4*MF3)/MF4;
FRML EP44 E44-(B44-MF4+MF4*MF4)/MF4;
FRML EP45 E45-(B45+MF4*MF5)/MF4;
FRML EP46 E46-(B46+MF4*MF6)/MF4;
FRML EP47 E47-(-Bl4-B24-B34-B44-B45-B46+MF4*MF7)/MF4;




127






























































EU-15 NBR Simulation Model

OPTIONS MEMORY-1500 Double; ? EU15 02NBR-NEW2007SELECTED-RECW Models for EU Import Analysis without USA with
all calculations;
TITLE 'TOMATO IMPORTS TO THE US'; ? For Elasticity Trend over 11 years & Calculating Mean Quantity for sample 2,33;

? NBR Model with Homogeneity and Symmetry imposed without Rho (since Rho=-0.094410 with P value 0.163 & LUl=689.03834)&
LU2-688.35849;

smpl 1 43;

?READ(FORMAT-EXCEL,FILE-'U:\TomatoResearch\TSPWORKS2006\EUAnalysis\EUonly.xls');

LOADZYRS ZIMPV1 Q1 V2 Q2 V3 Q3 V4 Q4 V5 Q5 V6 Q6 V7 Q7
V8 Q8;
1963 901 31653 451074 1773563 16782212 18133 40123 26567148 122325848 1004321 9072714 7000 72000 3240 12187
3376225 19971241
1964901 48038 572000 1840474 12660747 1262 3312 31730108 118009256 1361401 10570330 0 0 2419 6437 2737208
13016627
1965 901 118653 1108312 2220545 15148287 5152 5562 27267956 125699712 1928476 15228634 0 0 17404 53152
1795230 12036480
1966901 187269 17480002856091 13152661 85829 107862 36479432 108006936 2691155 19047720 1000 12812 11245 21812
2539638 10580398













1967 901 207999 2220125 3753435 18649844
2665245 13813254
1968 901 310461 2266000 4405008 19895024
1488195 7481394
1969 901 368538 2496687 3916124 14940986
3636690 12882177
1970 901 457730 2397937 4464447 15889571
11442442 37441791
1971901 469036 2752398 4245967 16817272
4986064 14826093
1972 901 962086 4625350 4017254 16139896
7784385 19256671
1973 901 1594265 4437519 4893273 14262623
976072 1966077
1974 901 1797788 5708053 5692533 15417826
1433878 2602306
1975 901 3158736 7143835 7033987 15033790
42096 1391526 2230043
1976 901 2968861 7003612 5147297 12926526
132987 2215727 4062108


46886 118310 36637392 122262104 2789153 18614500

26510 101198 27803468 97863480 5120585 33277424

32359 99740 36980408 129603024 7855349 34033604

40904 68278 38635412 133623216 9460210 42568288 3

64882 136795 44930860 129688160 14373836 59700256

35034 42506 45938032 118869120 1509765963092032

36545 29147 77101920 170786640 19292240 54257208

216473 219126 65580600 138533712 15978113 43303736

731299 1000174 99121944 137464352 15771246 39263124

1831054 3315306 75478176 106260064 13685706 37747484


1977 901 3472400 9189788 7425601 15885014 2325006 4158786
2066804 3061723
1978 901 2963215 6163585 7430996 14492037 2309641 3208973
202229 2231462 2797116
19799014026272 67988127907311 16267033 2373524 2974927
141842 1673201 1711795


82794408 115851376 9554171 25682334

85231920 104367608 9191896 21285690

93011048 104508152 9071943 19847036


0

0

0

115

0

1550

0

632

333'

495

100

3751

4483


0 69348 154932

0 74419 93022

0 128959 220245

40398 260188 308561

0 284716 335143

1875 284469 401349

0 657057 745654

6 42800 108407 82604

43 187800 41024

57 17625 196696

0 1687 53600 51748

11 104073 200784

32 82886 196614


1980 9016052384 103625114767464 11321398 2157749 1940617 87930720 91080208 11919843 23267114 102946 122824
867395 513303 2614841 2395847
1981 901 5573419 10825753 4173807 10542792 1422141 1240105 74939560 89919392 6717752 15562404 115808 164500
246247 303185 2178646 2958982
1982 901 6942520 16861542 2793814 7765386 1335471 1713708 50565584 66672692 6240809 14409339 89026 207127
39146 36069 1739339 3025668
1983 901 5955306 13127198 3990447 10570612 635025 665686 48541384 67457464 7539437 20009412 74331 144311
69812 82299 1714422 3349194
19849014628415 114851122225929 6696144 1818868 2723827 49903220 82007208 864727124582260 410328 781058
73121 98443 1755933 3507856
1985 901 3714979 96790742804760 8574710 1519295 2329586 56146208 91701904 4742369 15723088 140775 329186
66289 88741 1109962 2356596
1986901 4428861 96525742080354 5348562 2521897 2659235 68524448 96462176 4144346 11844698 670611 1127182
84403 88221 1740140 2880545
19879014756993 96994762978113 6105456 2280814 2929812 87123544 97076016 5764089 12027178 2554835 3132693
141317 109611 2877196 3757298
1988 901 5344652 8643190 2402380 4767624 4903546 3937171 76347792 84892376 6708963 14960272 2264643 2524775
349851 243009 4564090 5078631
1989 9014125968 6758386 2317410 5286987 3193985 3638453 71171816 97750632 7488692 16598397 2450896 3062926 1633335
864997 3588237 4834089
1990 901 7360551 110201873029963 5708198 10650899 8021161 130990440 110714248 2765811 5380596 5190490 4332286 1125452
702779 6934463 5863059
1991901 1869942 2294788 5032627 8084701 8374074 6277594 145453872 140390832 3665304 6377014 5308010 5725206 2509599
1876646 5001624 4620865
1992 901 404674 585339 3651196 5088920 6577894 5570072 139864960 141403616 3295008 5192788 6377826 7713368 391317
410689 5869919 5966114
1993 901 116900 270875 1710821 2183913 5634468 5223672 143160640 177926880 2223115 3715382 3718712 4547761 188735
224006 4278326 4454055
1994901 43000 47199 357690 560199 5986108 5533320 120315416 160862048 1217387 2056625 4114383 5180666 155184 156425
3399291 3484049
1995 901 0 0 29111 43369 11579928 6364214 147584576 146905040 125053 151773 2661260 2334571 308488 239044
5825043 4559700
1996901 24187 11375 183522 69726 17264544 9271430 148078032 157396080 230793 297000 4508979 3476803 38016 20788
5271388 3221124
1997901 0 0 15124 2138620697980 10657880 104872824 154990608 38161 46226 1020557 927674 82651 103135
7566921 5781611
1998901 0 0 22636 30788 22782924 12299150 147687616 186837792 29637 48398 905471 869349 92865 72220
7331996 5544938
1999901 0 0 60193 71125 22791080 14885635 141139536 206332992 0 0 2285333 2621314 205003 163351
5596276 5080246
2000901 10448 18000 54910 66112 23066744 16460790 117918632 149369664 0 0 10910094 11328141 39582 24734
12195800 10873083
2001 901 16528 57300 9909 16625 21346768 17480612 108504040 185144192 22387 33500 6927946 9118854 10763 9212
10767377 11338870












2002 901 61870 101898 37526 55663 21751194 14590805 170802208 181180384 4604 3625 16571994 18724980 21009 14917
12378914 12102489
2003 901 22075 26453 206498 198835 24412648 16800316 136954944 182067392 43642 46710 26240464 25322332 21415 17437
29699618 29466621
2004901 150833 169081 84115 8433831703530 18551426 172314848 192159904 190675 188753 19238032 17210976 12976 6761
27974391 29547420
2005 901 0 0 20624 19526 44735840 25201496 229945104 232239648 35735 37440 23119076 19664766 43605 31100
61700006 54335924

? V: value in (not million) US dollars; Q: quantity in (not million) kilograms;
? 1: Albania
? 2: Bulgaria
? 3: Israel
? 4: Morocco
? 5: Romania
? 6: Turkey
? 7: United States:7 and Rest of the World:8;
?901 EU-15 as importer;

? Eliminating zero values in Q;

Q1 1*(Q1 0)+Q1*(Q1 >0);
Q5-1*(Q5-0)+Q5*(Q5>0);
Q6-1*(Q6-0)+Q6*(Q6>0);

PRINT ZYRS Q1 Q5 Q6;

? Adding USA with ROW;
v7-v7+v8;q7-q7+q8;
print v7 q7;

? Eliminating Pi-0; ?? [Following highest price+twice the Std.Dev.+inflation];

SELECT ZYRS-1995;
Vl-2.82813;
SELECT ZYRS-1997;
Vl-2.86838;
SELECT ZYRS-1998;
Vl-2.88851;
SELECT ZYRS-1999;
Vl-2.90863;
SELECT ZYRS-2005;
V1 3.02940;

SELECT ZYRS-1999;
V5-1.74572;
SELECT ZYRS-2000;
V5-1.76601;

SELECT ZYRS-1964;
V6-1.05760;
SELECT ZYRS-1965;
V6-1.08666;
SELECT ZYRS-1967;
V6-1.14477;
SELECT ZYRS-1968;
V6-1.17383;
SELECT ZYRS-1969;
V6-1.20289;
SELECT ZYRS-1971;
V6-1.26101;
SELECT ZYRS-1973;
V6-1.31912;

? To find out Average and Annual Costs for making Table;
smpl 2,43;
msd vl-v7;

smpl 2,2;
msd vl-v7;












print v1-v7;


smpl 43,43;
msd vl-v7;
print vl-v7;

? Calculation to find out Average and Annual Import Quantity (kg) for making Table;

smpl 2,43;
msd ql-q7;

smpl 2,2;
msd ql-q7;
print ql-q7;

smpl 43,43;
msd ql-q7;
print ql-q7;

? End of calculation for Table;

SMPL 1,43;

? Defining Total Cost(S);
S V1+V2+V3+V4+V5+V6+V7;

? Calculating prices (Pi)
PlV1/Q1; P2-V2/Q2; P3-V3/Q3; P4-V4/Q4; P5-V5/Q5; P6-V6/Q6; P7-V7/Q7;

PRINT ZYRS Q1 P1 Q2 P2 Q3 P3 Q4 P4 Q5 P5 Q6 P6 q7 p7;

? Calculation to find Average ans Annual Price (US$/Kg) for making Table;

smpl 2,43;
msd pl-p7;

smpl 2,2;
msd pl-p7;
print pl-p7;

smpl 43,43;
msd pl-p7;

? End of Calculation for Table;

SMPL 1,43;

? CALCULATION OF FACTOR COST SHARES (Fi-PRICE*QUANTITY/TOTAL COST)
FlV1/S; F2-V2/S; F3-V3/S; F4-V4/S; F5-V5/S; F6-V6/S; F7-V7/S;

? LOGGING ALL PRICES AND QUANTITIES(LPi,LQi)
LPl LOG(P1); LP2-LOG(P2); LP3-LOG(P3); LP4-LOG(P4);
LP5-LOG(P5); LP6-LOG(P6); LP7-LOG(P7);

LQ1 LOG(Q1); LQ2-LOG(Q2); LQ3-LOG(Q3); LQ4-LOG(Q4);
LQ5-LOG(Q5); LQ6-LOG(Q6); LQ7-LOG(Q7);

smpl 2 43;

? CALCULATION FOR TWO PERIOD MEAN OF FACTOR SHARES( Fil)
Fl11(Fl+Fl(-1 ,, 2 F21-(F2+F2(-1 ,, 2 F31-(F3+F3(-1))/2; F41-(F4+F4(-- ,, 2
F51-(F5+F5(-1 ,, 2 F61-(F6+F6(-1 ,, 2 F71-(F7+F7(-1))/2;


? Calculation of Quantity Share for making Table;
T ql+q2+q3+q4+q5+q6+q7;
kl-ql/T; k2-q2/T; k3-q3/T; k4-q4/T; k5-q5/T; k6-q6/T; k7-q7/T;

msd kl-k7;

smpl 2,2;












msd kl-k7;

smpl 43,43;
msd kl-k7;
? End of Quantity Share calculation;
smpl 2,33;
msd ql-q7;

msd T;

SMPL 2,43;

? CALCULATION: CHANGE IN LOGGED PRICES(DPi)
DPl-LP1-LP1(-1); DP2-LP2-LP2(-1); DP3-LP3-LP3(-1); DP4-LP4-LP4(-1);
DP5-LP5-LP5(-1); DP6-LP6-LP6(-1); DP7-LP7-LP7(-1);

? CALCULATION:CHANGE IN LOGGED QUANTITY(DQi)
DQ1 LQ1-LQ1(-1); DQ2-LQ2-LQ2(-1); DQ3-LQ3-LQ3(-1); DQ4-LQ4-LQ4(-1);
DQ5-LQ5-LQ5(-1); DQ6-LQ6-LQ6(-1); DQ7-LQ7-LQ7(-1);


? DEPENDENT VARIABLE fi*Dq and SUMMATION INDEX(FDQi)
FDQ1-F11*DQ1; FDQ2-F21*DQ2; FDQ3-F31*DQ3; FDQ4-F41*DQ4;
FDQ5-F51*DQ5; FDQ6-F61*DQ6; FDQ7-F71*DQ7;

DQ-FDQ1+FDQ2+FDQ3+FDQ4+FDQ5+FDQ6+FDQ7;

?Fi*DP and SUMMATION INDEX(DP)- NOT NEEDED FOR THIS MODEL;
FDP1 F11*DP1; FDP2-F21*DP2; FDP3-F31*DP3; FDP4-F41*DP4;
FDP5-F51*DP5; FDP6-F61*DP6; FDP7-F71*DP7;

DP-FDP1 +FDP2+FDP3+FDP4+FDP5+FDP6+FDP7;

proc zzzz;

? DIFFERENTIAL NBR MODEL WITH AR1 PLUS HOMOGENEITY AND SYMMETRY;

trend obs;
dl -(obs-1);
frmlresl FDQ1 (Al*DQ+B11*DPl+B12*DP2+B13*DP3+B14*DP4+B15*DP5+B16*DP6+(-B11-B12-Bl3-Bl4-B15-Bl6)*DP7-F11*(DP1-
DP));
?frml eql [dl*resl*sqrt(1-rho**2) + (1-dl)*(resl rho*resl(-ln I 1-rho**2)**(. i I 2 i-..h

trend obs;
dl -(obs-1);
frmlres2 FDQ2-(A2*DQ+B12*DPl+B22*DP2+B23*DP3+B24*DP4+B25*DP5+B26*DP6+(-B12-B22-B23-B24-B25-B26)*DP7-F21*(DP2-
DP));
?frml eq2 [dl*res2*sqrt(1-rho**2) + (1-dl)*(res2 rho*res2(-1 I [ 1-rho**2)**(. i i-..h

trend obs;
dl -(obs-1);
frmlres3 FDQ3-(A3*DQ+Bl3*DPl+B23*DP2+B33*DP3+B34*DP4+B35*DP5+B36*DP6+(-Bl3-B23-B33-B34-B35-B36)*DP7-F31*(DP3-
DP));
?frml eq3 [dl*res3*sqrt(1-rho**2) + (1-dl)*(res3 rho*res3(-1 I 1-rho**2)**(. i i-..h

trend obs;
dl -(obs-1);
frml res4 FDQ4-(A4*DQ+B14*DPl+B24*DP2+B34*DP3+B44*DP4+B45*DP5+B46*DP6+(-B 4-B24-B34-B44-B45-B46)*DP7-F41*(DP4-
DP));
?frml eq4 [dl*res4*sqrt(1-rho**2) + (1-dl)*(res4 rho*res4(-11 I 1-rho**2)**(. i ,...h

trend obs;
dl -(obs-1);
frmlres5 FDQ5-(A5*DQ+B15*DPl+B25*DP2+B35*DP3+B45*DP4+B55*DP5+B56*DP6+(-B15-B25-B35-B45-B55-B56)*DP7-F51*(DP5-
DP));
?frml eq5 [dl*res5*sqrt(1-rho**2) + (1-dl)*(res5 rho*res5(-1 I 1-rho**2)**(. i i-..h

trend obs;
dl (obs-1);












frml res6 FDQ6-(A6*DQ+Bl6*DPl+B26*DP2+B36*DP3+B46*DP4+B56*DP5+B66*DP6+(-Bl6-B26-B36-B46-B56-B66)*DP7-F61*(DP6-
DP));
?frml eq6 [dl*res6*sqrt(1-rho**2) +(1-dl)*(res6 rho*res6(-1 I 1-rho**2)**(. i ,...h

REGOPT (STARS,STAR1-.10,STAR2 .05) T;

PARAM Al 0 A2 0 A3 0 A4 0 A5 0 A6 0
Bl110 B12 0 B13 0 B14 0 B15 0 B16 0
B22 0 B23 0 B24 0 B25 0 B26 0
B33 0 B34 0 B35 0 B36 0
B44 0 B45 0 B46 0
B55 0 B56 0
B66 0;
?rho 0;

?eqsub eql resl;?eqsub eq2 res2;?eqsub eq3 res3;?eqsub eq4 res4;?eqsub eq5 res5;?eqsub eq6 res6;
?lsq(nodropmiss,tol le-7,maxit 1000) eql eq2 eq3 eq4 eq5 eq6;
lsq(nodropmiss,tol le-7,maxit 1000) resl res2 res3 res4 res5 res6;

COPY @LOGL LU;
LUl1LU;

SMPL NR1, NR2;

? Elasticities;

MSD F11 F21 F31 F41 F51 F61 F71;


SETMF1-
SET MF2-
SET MF3-
SET MF4-
SET MF5-
SET MF6-
SET MF7-


-------- MEAN FACTOR SHARES
@MEAN(1);
@MEAN(2);
d@MEAN(3);
a@MEAN(4);
d@MEAN(5);
d@MEAN(6);
@dMEAN(7);


PRINT MF1-MF7;

SMPL NR1,NR1;

MSD F1 F2 F3 F4 F5 F6 F7;


MEAN FACTOR SHARES


SET FF1
SET FF2
SET FF3
SET FF4
SET FF5
SET FF6
SET FF7


@MEAN(1);
a MEAN(2);
@MEAN(3);
@MEAN(4);
@MEAN(5);
@MEAN(6);
@MEAN(7);


PRINT FF1-FF7;

SMPL NR2,NR2;

MSD F1 F2 F3 F4 F5 F6 F7;


MEAN FACTOR SHARES


SET FL1
SET FL2
SET FL3
SET FL4
SET FL5
SET FL6
SET FL7


@MEAN(1);
@MEAN(2);
@MEAN(3);
@MEAN(4);
@MEAN(5);
@MEAN(6);
@MEAN(7);


PRINT FL1-FL7;

SMPL NR1, NR2;













SET B17 -B11-B12-B13-B14-B15-B16;


SET B21 B12;
SET B27--B21-B22-B23-B24-B25-B26;

SET B31-B13;
SET B32 B23;
SET B37 -B31-B32-B33-B34-B35-B36;

SET B41-B14;
SET B42 B24;
SET B43 B34;
SET B47 -B41-B42-B43-B44-B45-B46;

SET B51 B15;
SET B52 B25;
SET B53 B35;
SET B54 B45;
SET B57 -B51-B52-B53-B54-B55-B56;

SET B61-B16;
SET B62 B26;
SET B63 B36;
SET B64 B46;
SET B65 B56;
SET B67--B61-B62-B63-B64-B65-B66;

SET B77-(-(-B11-B12-B13-B14-B15-B16)-(-B12-B22-B23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)-(-B14-B24-B34-B44-B45-B46)
-(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66));

SET A7-1-A1-A2-A3-A4-A5-A6;

? Calculate the standard errors for ROW

frml rowl A7-1-A1-A2-A3-A4-A5-A6;
frmlrow2 B17 -B11-B12-B13-B14-B15-B16;
frml row3 B27 -B21-B22-B23-B24-B25-B26;
frml row4 B37--B31-B32-B33-B34-B35-B36;
frml row5 B47 -B41-B42-B43-B44-B45-B46;
frml row6 B57 -B51-B52-B53-B54-B55-B56;
frml row7 B67 -B61-B62-B63-B64-B65-B66;
frml row8 B77-(-(-B11-B12-B13-B14-B15-Bl6)-(-B12-B22-B23-B24-B25-B26)-(-Bl3-B23-B33-B34-B35-B36)-(-Bl4-B24-B34-B44-B45-
B46)
-(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66));

frmlrow9 B21 B12;
frmlrowl0 B31 B13;
frml rowl 1 B32 B23;
frmlrowl2 B41 B14;
frml rowl3 B42 B24;
frml rowl4 B43 B34;
frmlrowl5 B51 B15;
frml rowl6 B52 B25;
frml rowl7 B53 B35;
frmlrowl8 B54 B45;
frmlrowl9 B61 B16;
frml row20 B62 B26;
frml row21 B63 B36;
frml row22 B64 B46;
frml row23 B65 B56;

analyz rowl-row23;

?? Calculate Eigenvalues

SET B71-B17;
SET B72 B27;
SET B73 B37;
SET B74 B47;













SET B75 B57;
SET B76 B67;
SET B77 B77;

? Tranform r to Pie;

SET D11-(B11-MF1+MF1*MF1);
SET D12-(B12+MF1*MF2);
SET D13-(B13+MF1*MF3);
SET D14-(B14+MF1*MF4);
SET D15-(B15+MF1*MF5);
SET D16-(B16+MF1*MF6);
SET D17-(-B11-B12-B13-B14-B15-B16+MF1*MF7);

SET D21-(B12+MF2*MF1);
SET D22-(B22-MF2+MF2*MF2);
SET D23-(B23+MF2*MF3);
SET D24-(B24+MF2*MF4);
SET D25-(B25+MF2*MF5);
SET D26-(B26+MF2*MF6);
SET D27-(-B12-B22-B23-B24-B25-B26+MF2*MF7);

SET D31-(B13+MF3*MF1);
SET D32-(B23+MF3*MF2);
SET D33-(B33-MF3+MF3*MF3);
SET D34-(B34+MF3*MF4);
SET D35-(B35+MF3*MF5);
SET D36-(B36+MF3*MF6);
SET D37-(-B13-B23-B33-B34-B35-B36+MF3*MF7);

SET D41-(B14+MF4*MF1);
SET D42-(B24+MF4*MF2);
SET D43-(B34+MF4*MF3);
SET D44-(B44-MF4+MF4*MF4);
SET D45-(B45+MF4*MF5);
SET D46-(B46+MF4*MF6);
SET D47-(-B14-B24-B34-B44-B45-B46+MF4*MF7);

SET D51-(B15+MF5*MF1);
SET D52-(B25+MF5*MF2);
SET D53-(B35+MF5*MF3);
SET D54-(B45+MF5*MF4);
SET D55-(B55-MF5+MF5*MF5);
SET D56-(B56+MF5*MF6);
SET D57-(-B15-B25-B35-B45-B55-B56+MF5*MF7);

SET D61-(B16+MF6*MF1);
SET D62-(B26+MF6*MF2);
SET D63-(B36+MF6*MF3);
SET D64-(B46+MF6*MF4);
SET D65-(B56+MF6*MF5);
SET D66-(B66-MF6+MF6*MF6);
SET D67-(-B16-B26-B36-B46-B56-B66+MF6*MF7);

SET D71-(-B11-B12-B13-B14-B15-B16+MF7*MF1);
SET D72-(-B12-B22-B23-B24-B25-B26+MF7*MF2);
SET D73-(-B13-B23-B33-B34-B35-B36+MF7*MF3);
SET D74-(-B14-B24-B34-B44-B45-B46+MF7*MF4);
SET D75-(-B15-B25-B35-B45-B55-B56+MF7*MF5);
SET D76-(-B16-B26-B36-B46-B56-B66+MF7*MF6);
SET D77-(-(-B11-B12-B13-B14-B15-Bl6)-(-B12-B22-B23-B24-B25-B26)-(-Bl3-B23-B33-B34-B35-B36)-(-Bl4-B24-B34-B44-B45-B46)
-(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)-MF7+MF7*MF7);

? Create each row EIGHT;

MMAKE(VERT) El D11-D17;
MMAKE(VERT) E2 D21-D27;
MMAKE(VERT) E3 D31-D37;
MMAKE(VERT) E4 D41-D47;
MMAKE(VERT) E5 D51-D57;












MMAKE(VERT) E6 D61-D67;
MMAKE(VERT) E7 D71-D77;

? Creates square matrix

MAKE E E1-E7;

? Calculate Eigenvalues of E

MAT EV EIGVAL(E);
Print E EV;

? Creates standard errors for pi ij's where the pi ij's are Dll, D12, etc;

frmlpil D1(B11-MF1+MF1*MF1);
frml pi2 D12-(B12+MF1*MF2);
frml pi3 D13-(B13+MF1*MF3);
frml pi4 D14-(B14+MF1*MF4);
frml pi5 D15-(B15+MF1*MF5);
frml pi6 D16-(B16+MFl*MF6);
frml pi7 D17-(-B11-B12-B13-B14-B15-B16+MFl*MF7);

frml pi8 D21-(B12+MF2*MF1);
frml pi9 D22-(B22-MF2+MF2*MF2);
frml pilO D23-(B23+MF2*MF3);
frml pill D24-(B24+MF2*MF4);
frml pil2 D25-(B25+MF2*MF5);
frml pil3 D26-(B26+MF2*MF6);
frml pil4 D27-(-B12-B22-B23-B24-B25-B26+MF2*MF7);

frmlpil5 D31-(B13+MF3*MF1);
frml pil6 D32-(B23+MF3*MF2);
frmi pil7 D33-(B33-MF3+MF3*MF3);
frml pil8 D34-(B34+MF3*MF4);
frml pil9 D35-(B35+MF3*MF5);
frml pi20 D36-(B36+MF3*MF6);
frml pi21 D37-(-B13-B23-B33-B34-B35-B36+MF3*MF7);

frml pi22 D41 (B14+MF4*MF1);
frml pi23 D42-(B24+MF4*MF2);
frml pi24 D43-(B34+MF4*MF3);
frml pi25 D44-(B44-MF4+MF4*MF4);
frml pi26 D45-(B45+MF4*MF5);
frml pi27 D46-(B46+MF4*MF6);
frml pi28 D47-(-B14-B24-B34-B44-B45-B46+MF4*MF7);

frmlpi29 D51 (B15+MF5*MF1);
frml pi30 D52-(B25+MF5*MF2);
frmlpi31 D53-(B35+MF5*MF3);
frml pi32 D54-(B45+MF5*MF4);
frml pi33 D55-(B55-MF5+MF5*MF5);
frml pi34 D56-(B56+MF5*MF6);
frml pi35 D57-(-B15-B25-B35-B45-B55-B56+MF5*MF7);

frml pi36 D61-(B16+MF6*MF1);
frml pi37 D62-(B26+MF6*MF2);
frml pi38 D63-(B36+MF6*MF3);
frml pi39 D64-(B46+MF6*MF4);
frml pi40 D65-(B56+MF6*MF5);
frml pi41 D66-(B66-MF6+MF6*MF6);
frml pi42 D67-(-B16-B26-B36-B46-B56-B66+MF6*MF7);

frml pi43 D71-(-B11-B12-B13-B14-B15-B16+MF7*MFl);
frml pi44 D72-(-B12-B22-B23-B24-B25-B26+MF7*MF2);
frml pi45 D73-(-B13-B23-B33-B34-B35-B36+MF7*MF3);
frml pi46 D74-(-B14-B24-B34-B44-B45-B46+MF7*MF4);
frml pi47 D75-(-B15-B25-B35-B45-B55-B56+MF7*MF5);
frml pi48 D76-(-B16-B26-B36-B46-B56-B66+MF7*MF6);
frml pi49 D77-(-(-Bl -B12-Bl3-Bl4-Bl5-Bl6)-(-B12-B22-B23-B24-B25-B26)-(-Bl3-B23-B33-B34-B35-B36)-(-Bl4-B24-B34-B44-B45-
B46)











-(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)-MF7+MF7*MF7);


analyz pil-pi49;

? Elasticities

? Divisia Input Index ???????CHANGE WILL START HERE FOR RECURSIVE MODEL;
FRML EL1 E1lA1/MFl; FRML EL1M ElM-A1/MMFl;
FRML EL2 E2-A2/MF2; FRML EL2M E2M-A2/MMF2;
FRML EL3 E3-A3/MF3; FRML EL3M E3M-A3/MMF3;
FRML EL4 E4-A4/MF4; FRML EL4M E4M-A4/MMF4;
FRML EL5 E5-A5/MF5; FRML EL5M E5M-A5/MMF5;
FRML EL6 E6-A6/MF6; FRML EL6M E6M-A6/MMF6;
FRML EL7 E7-(1-Al-A2-A3-A4-A5-A6)/MF7; FRML EL7M E7M-(1-Al-A2-A3-A4-A5-A6)/MMF7;

? Divisia Input Index WITH FIRST F
FRML EL8 EFl1A1/FF1; FRML EL8M EF1M-A1/MFFl;
FRML EL9 EF2-A2/FF2; FRML EL9M EF2M-A2/MFF2;
FRML EL10 EF3-A3/FF3; FRML EL10M EF3M-A3/MFF3;
FRML EL11 EF4-A4/FF4; FRML EL11M EF4M-A4/MFF4;
FRML EL12 EF5-A5/FF5; FRML EL12M EF5M-A5/MFF5;
FRML EL13 EF6-A6/FF6; FRML EL13M EF6M-A6/MFF6;
FRML EL14 EF7-(1-Al-A2-A3-A4-A5-A6)/FF7; FRML EL14M EF7M-(1-Al-A2-A3-A4-A5-A6)/MFF7;

? Divisia Input Index WITH LAST F
FRML EL15 EL1lA1/FL1; FRML EL15M EL1M-A1/MFL1;
FRML EL16 EL2-A2/FL2; FRML EL16M EL2M-A2/MFL2;
FRML EL17 EL3-A3/FL3; FRML EL17M EL3M-A3/MFL3;
FRML EL18 EL4-A4/FL4; FRML EL18M EL4M-A4/MFL4;
FRML EL19 EL5-A5/FL5; FRML EL19M EL5M-A5/MFL5;
FRML EL20 EL6-A6/FL6; FRML EL20M EL6M-A6/MFL6;
FRML EL21 EL7-(1-Al-A2-A3-A4-A5-A6)/FL7; FRML EL21M EL7M-(1-Al-A2-A3-A4-A5-A6)/MFL7;

? Compensated price elasticities

FRML EP1 El 1(Bll-MF1+MF1*MF1)/MFl; FRML EP1M EllM-(B11-MMF1+MMF1*MMF1)/MMFl;
FRML EP2 E12-(B12+MF1*MF2)/MFl; FRML EP2M E12M-(B12+MMF1*MMF2)/MMFl;
FRML EP3 E13-(B13+MF1*MF3)/MFl; FRML EP3M E13M-(B13+MMF1*MMF3)/MMFl;
FRML EP4 E14-(B14+MF1*MF4)/MFl; FRML EP4M E14M-(B14+MMF1*MMF4)/MMFl;
FRML EP5 E15-(B15+MF1*MF5)/MFl; FRML EP5M E15M-(B15+MMF1*MMF5)/MMFl;
FRML EP6 E16-(B16+MF1*MF6)/MFl; FRML EP6M E16M-(B16+MMF1*MMF6)/MMFl;
FRML EP7 E17-(-B11-B12-B13-B14-B15-B16+MF1*MF7)/MFl; FRML EP7M E17M-(-Bll-B12-B13-B14-B15-
B16+MMF1*MMF7)/MMFl;

FRML EP8 E21-(B12+MF2*MF1)/MF2; FRML EP8M E21M-(B12+MMF2*MMF1)/MMF2;
FRML EP9 E22-(B22-MF2+MF2*MF2)/MF2; FRML EP9M E22M-(B22-MMF2+MMF2*MMF2)/MMF2;
FRML EP10 E23-(B23+MF2*MF3)/MF2; FRML EP10M E23M-(B23+MMF2*MMF3)/MMF2;
FRML EP11 E24-(B24+MF2*MF4)/MF2; FRML EP11M E24M-(B24+MMF2*MMF4)/MMF2;
FRML EP12 E25-(B25+MF2*MF5)/MF2; FRML EP12M E25M-(B25+MMF2*MMF5)/MMF2;
FRML EP13 E26-(B26+MF2*MF6)/MF2; FRML EP13M E26M-(B26+MMF2*MMF6)/MMF2;
FRML EP14 E27-(-B12-B22-B23-B24-B25-B26+MF2*MF7)/MF2; FRML EP14M E27M-(-B12-B22-B23-B24-B25-
B26+MMF2*MMF7)/MMF2;

FRML EP15 E31-(B13+MF3*MF1)/MF3; FRML EP15M E31M-(B13+MMF3*MMF1)/MMF3;
FRML EP16 E32-(B23+MF3*MF2)/MF3; FRML EP16M E32M-(B23+MMF3*MMF2)/MMF3;
FRML EP17 E33-(B33-MF3+MF3*MF3)/MF3; FRML EP17M E33M-(B33-MMF3+MMF3*MMF3)/MMF3;
FRML EP18 E34-(B34+MF3*MF4)/MF3; FRML EP18M E34M-(B34+MMF3*MMF4)/MMF3;
FRML EP19 E35-(B35+MF3*MF5)/MF3; FRML EP19M E35M-(B35+MMF3*MMF5)/MMF3;
FRML EP20 E36-(B36+MF3*MF6)/MF3; FRML EP20M E36M-(B36+MMF3*MMF6)/MMF3;
FRML EP21 E37-(-B13-B23-B33-B34-B35-B36+MF3*MF7)/MF3; FRML EP21M E37M-(-B13-B23-B33-B34-B35-
B36+MMF3*MMF7)/MMF3;

FRML EP22 E41-(B14+MF4*MF1)/MF4; FRML EP22M E41M-(B14+MMF4*MMF1)/MMF4;
FRML EP23 E42-(B24+MF4*MF2)/MF4; FRML EP23M E42M-(B24+MMF4*MMF2)/MMF4;
FRML EP24 E43-(B34+MF4*MF3)/MF4; FRML EP24M E43M-(B34+MMF4*MMF3)/MMF4;
FRML EP25 E44-(B44-MF4+MF4*MF4)/MF4; FRML EP25M E44M-(B44-MMF4+MMF4*MMF4)/MMF4;
FRML EP26 E45-(B45+MF4*MF5)/MF4; FRML EP26M E45M-(B45+MMF4*MMF5)/MMF4;
FRML EP27 E46-(B46+MF4*MF6)/MF4; FRML EP27M E46M-(B46+MMF4*MMF6)/MMF4;
FRML EP28 E47-(-B14-B24-B34-B44-B45-B46+MF4*MF7)/MF4; FRML EP28M E47M-(-B14-B24-B34-B44-B45-
B46+MMF4*MMF7)/MMF4;













FRML EP29 E51-(B15+MF5*MF1)/MF5; FRML EP29M E51M-(B15+MMF5*MMF1)/MMF5;
FRML EP30 E52-(B25+MF5*MF2)/MF5; FRML EP30M E52M-(B25+MMF5*MMF2)/MMF5;
FRML EP31 E53-(B35+MF5*MF3)/MF5; FRML EP31M E53M-(B35+MMF5*MMF3)/MMF5;
FRML EP32 E54-(B45+MF5*MF4)/MF5; FRML EP32M E54M-(B45+MMF5*MMF4)/MMF5;
FRML EP33 E55-(B55-MF5+MF5*MF5)/MF5; FRML EP33M E55M-(B55-MMF5+MMF5*MMF5)/MMF5;
FRML EP34 E56-(B56+MF5*MF6)/MF5; FRML EP34M E56M-(B56+MMF5*MMF6)/MMF5;
FRML EP35 E57-(-B15-B25-B35-B45-B55-B56+MF5*MF7)/MF5; FRML EP35M E57M-(-B15-B25-B35-B45-B55-
B56+MMF5*MMF7)/MMF5;

FRML EP36 E61-(B16+MF6*MF1)/MF6; FRML EP36M E61M-(B16+MMF6*MMF1)/MMF6;
FRML EP37 E62-(B26+MF6*MF2)/MF6; FRML EP37M E62M-(B26+MMF6*MMF2)/MMF6;
FRML EP38 E63-(B36+MF6*MF3)/MF6; FRML EP38M E63M-(B36+MMF6*MMF3)/MMF6;
FRML EP39 E64-(B46+MF6*MF4)/MF6; FRML EP39M E64M-(B46+MMF6*MMF4)/MMF6;
FRML EP40 E65-(B56+MF6*MF5)/MF6; FRML EP40M E65M-(B56+MMF6*MMF5)/MMF6;
FRML EP41 E66-(B66-MF6+MF6*MF6)/MF6; FRML EP41M E66M-(B66-MMF6+MMF6*MMF6)/MMF6;
FRML EP42 E67-(-B16-B26-B36-B46-B56-B66+MF6*MF7)/MF6; FRML EP42M E67M-(-B16-B26-B36-B46-B56-
B66+MMF6*MMF7)/MMF6;

FRML EP43 E71-(-B11-B12-B13-B14-B15-B16+MF7*MF1)/MF7; FRML EP43M E71M-(-B11-B12-B13-B14-B15-
B16+MMF7*MMF1)/MMF7;
FRML EP44 E72-(-B12-B22-B23-B24-B25-B26+MF7*MF2)/MF7; FRML EP44M E72M-(-B12-B22-B23-B24-B25-
B26+MMF7*MMF2)/MMF7;
FRML EP45 E73-(-B13-B23-B33-B34-B35-B36+MF7*MF3)/MF7; FRML EP45M E73M-(-B13-B23-B33-B34-B35-
B36+MMF7*MMF3)/MMF7;
FRML EP46 E74-(-B14-B24-B34-B44-B45-B46+MF7*MF4)/MF7; FRML EP46M E74M-(-B14-B24-B34-B44-B45-
B46+MMF7*MMF4)/MMF7;
FRML EP47 E75-(-B15-B25-B35-B45-B55-B56+MF7*MF5)/MF7; FRML EP47M E75M-(-B15-B25-B35-B45-B55-
B56+MMF7*MMF5)/MMF7;
FRML EP48 E76-(-B16-B26-B36-B46-B56-B66+MF7*MF6)/MF7; FRML EP48M E76M-(-B16-B26-B36-B46-B56-
B66+MMF7*MMF6)/MMF7;
FRML EP49 E77-(-(-B 1-B12-Bl3-Bl4-B15-Bl6)-(-B12-B22-B23-B24-B25-B26)-(-Bl3-B23-B33-B34-B35-B36)-(-Bl4-B24-B34-B44-B45-
B46)
-(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)-MF7+MF7*MF7)/MF7;
FRML EP49M E77M-(-(-B 1-B12-Bl3-Bl4-B15-Bl6)-(-B12-B22-B23-B24-B25-B26)-(-Bl3-B23-B33-B34-B35-B36)-(-Bl4-B24-B34-B44-
B45-B46)
-(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)-MMF7+MMF7*MMF7)/MMF7;

ANALYZ EL1-EL7, EL1M-EL7M;
MAKE ELCOEF @COEFA;

ANALYZ EP1-EP49, EP1M-EP49M;
MAKE EPCOEF @COEFA;

MMAKE(VERTICAL) MBM ELCOEF EPCOEF;
MAKE MBETA MBETA MBM;

endproc zzzz;

MFORM(TYPE-GEN,NROW-112,NCOL-1) MBETA-0;

SMPL 2, 33;

MSD F11 F21 F31 F41 F51 F61 F71;
?------------ MEAN FACTOR SHARES
SET MMF1 @MEAN(1);
SET MMF2 @MEAN(2);
SET MMF3 @MEAN(3);
SET MMF4 @MEAN(4);
SET MMF5 @MEAN(5);
SET MMF6-@MEAN(6);
SET MMF7-@MEAN(7);

SMPL 2 2;

MSD F1 F2 F3 F4 F5 F6 F7;
?------------ MEAN FACTOR SHARES
SET MFF1 @MEAN(1);
SET MFF2a @MEAN(2);
SET MFF3a @MEAN(3);












SET MFF4 a @MEAN(4);
SET MFF5a @MEAN(5);
SET MFF6a @MEAN(6);
SET MFF7 @MEAN(7);

SMPL 33,33;

MSD F1 F2 F3 F4 F5 F6 F7;
?------------ MEAN FACTOR SHARES
SET MFL1 @MEAN(1);
SET MFL2 a @MEAN(2);
SET MFL3 a @MEAN(3);
SET MFL4 a @MEAN(4);
SET MFL5 a @MEAN(5);
SET MFL6 @MEAN(6);
SET MFL7 @MEAN(7);

DO J-1 TO 11;
SET NR1-1+J;
SET NR2-32+J;
SMPL NR1, NR2;
ZZZZ;
ENDDO;

WRITE(FORMAT-EXCEL,FILE-'U:\TOMATORESEARCH\EUNEWANALYSIS\ELEPELAS-RECW.XLS') MBETA;
END;













APPENDIX C
SUPERFLUOUS MATERIAL


Program Creating TLB Dataset for 1963-2005 (TOMDATA2005.TLB)

OPTIONS MEMORY-500 LIMPRN-120 LINLIM-60;

? TOM2005#01.TSP CREATING NEW TLB DATABASE WITH 2005 DATA INCLUDED WITH Q & V
? ----------------------------------------------------------------------- I
FREQ NONE;
TITLE 'TOMATO EXPORTS TO AND FROM EEC THROUGH 2005';
?IN 'H:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005';
OUT 'H:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005';
READ(FORMAT-EXCEL,FILE-'H:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005.XLS');
DOC NUM 'COUNTER';
DOC YRS 'YEAR 1963-2005';
DOC IMP 'IMPORTING COUNTRY';
DOC EPX 'EXPORTING COUNTRY';
DOC COM 'TOMATOES 544';
DOC V 'VALUE OF TOMATOES TRADED $US';
DOC Q 'QUANTITY OF TOMATOES TRADED (KG)';
DOC UNT 'UNIT-2 FOR KILOGRAMS';
OUT;

?PRINT @NOB;

?DBLIST 'H:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005';
END;

Program Creating Individual Country's Value and Quantity (V. Q.) for EU-15 (TOMDATA2005_15.TLB)

OPTIONS MEMORY-1500 signif=0;
? TOM2005#02.TSP;
TITLE 'TOMATO EXPORTS TO AND FROM EEC 15 COUNTRIES';
IN'H:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005';

SUPRES SMPL;


? INITALIZING ALL PARTNER COUNTRIES TO ZERO;
? -------------------------------------------------------------- I

? -------------------------------------------------------------- I
? ALL IMPORTING AND EXPORTING COUNTRIES USE EITHER ZIMPZ15 OR ZIMPZ27 ;
LIST ZIPZ15 40 58208246251 276 300 372 381 528 620 724 752 826 842; ? ORIGINAL 15 EEC COUNTRIES
LIST ZIMPZ15 40 58 208 246 251 276 300 372 381 528 620 724 752 826 842; ? ORIGINAL 15 EEC COUNTRIES;
LIST ZIMPZ27 40 58 208 246 251 276 300 372 381 528 620 724 752 826 842
100 196 203 233 348 428 440 470 616 642 703 705; ? ADDED 12 ADDITIONAL COUNTRIES;


? IMPORTING COUNTRIES WITH NEW COUNTIES ADDED

LISTZEPXZ 08 12 20 24 28 31 32 36 40 44 50 51 52 56 58
68 70 76 80 84 86 90 92 96 100 104 108 120 124 129 132
136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 208 212
214 218 222 226 230 231 232 233 234 246 251 266 270 275 276 288
292 296 300 304 308 312 320 324 332 340 344 348 352 360 364 372
376 381 384 388 392 400 404 408 410 414 418 422 428 430 434 440
442 446 450 454 458 462 466 470 474 478 480 484 492 496 498
500 504 508 516 520 524 528 530 532 533 536 554 558 562 566
579 583 584 586 591 604 608 616 620 624 638 642 643 646 658
659 678 682 686 690 694 699 702 703 704 705 706 710 711 716 717
724 732 736 740 752 757 760 764 768 780 784 788 792 796 800 804
807 810 818 826 834 837 838 839 842 849 854 858 860 862 879 882 887 890 891 894 899 900
568 798 112 174 178 184 795 762;

? EXPORTING COUNTRIES;














? THE NEW DATA IS CREATED AND THEN THE PROCEDURE IS TURNED OFF


DOT(VALUE-I) ZEPXZ;
V.-0; Q.-0;
print i;
ENDDOT;


? FOR EACH IMPORTER CREATE A V. AND Q. WITH THE PARTNER TRADE;
? - - - - - - - - - - - - - - -


DOT(CHAR-#, VALUE-J) ZIMPZ15; ? <--- HER
DOT(CHAR-%, VALUE I) ZEPXZ;
DO K-1963 TO 2005;
PRINT J I K;
SELECT IMP-J & EPX I & YRS-K; PRINT @NOB;
IF @NOB>0; THEN; DO;
V.% V; Q.% Q; ENDDO;
ELSE; SET IDD-1;
ENDDO; ENDDOT; ENDDOT;
SELECT 1;


E WE ARE CREATING FOR THE 15 EEC COUNTRIES;


DOT ZEPXZ;
V. V.; Q. Q.;
ENDDOT;


? CREATING 901 AND 902
? = =- = =- = =- = =- = = =-


SELECT 1;
SET NRR-@NOB;
PRINT NRR;
DEU (IMP^ 842); ?1-EU 0US;
EU US (DEU-1)*901 + (DEU 0)*902; ? 901-EU CODE AND 902-NEW CODE FOR US;
SET M-0;

DO L-901 TO 902;
DO K-1963 TO 2005;
SET MM+1;
SMPL 1,NRR;
SET NR2=NRR+M;
DOT(CHAR-%, VALUE I) ZEPXZ;
SMPL 1,NRR;
SELECT EU US-L & YRS-K;
MSD(NOPRINT) V. Q.;
SMPL NR2,NR2;
V.-@SUM(1);Q. -I '[1,1
YRS-K; IMP-L; UNT-2; COM-544;
ENDDOT;
ENDDO;
ENDDO;

PRINT NR2;
SET NR3=NRR+1;
print nr3 nr2;
SMPL NR3, NR2;
SMPL 1,NR2;


OUT 'H:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005 15'; ? <-
YRS-YRS; IMP-IMP; COM-COM; EPX-EPX; UNT-UNT; V-V; Q-Q;
DOT(CHAR-%, VALUE I) ZEPXZ;
V.-V.; Q.-Q.; ENDDOT;
OUT;
DBLIST 'H:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005 15'; ?
END;


MUST CHANGE TO 15 OR 27;




-- MUST CHANGE TO 15 OR 27;


-------------













Program Creating Partners for EU-15 in Excel

OPTIONS MEMORY 1500 signif=0 DOUBLE; ? Creating partners for EU-15;
TITLE 'TOMATO EXPORTS TO AND FROM EEC';
?IN 'k:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005 15';
IN'U:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005 15';
?dblist 'k:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005 15';
?dblist 'U:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005 15';

SUPRES SMPL;


? INITALIZING ALL PARTNER COUNTRIES TO ZERO;


LIST ZIMPZ 901 902;
? IMPORTING COUNTRIES;

LISTZEPXZ 08 12 20 24 28 31 32 36 40 44 50 51 52 56 58
68 70 76 80 84 86 90 92 96 100 104 108 120 124 129 132
136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 208 212
214 218 222 226 230 231 232 233 234 246 251 266 270 275 276 288
292 296 300 304 308 312 320 324 332 340 344 348 352 360 364 372
376 381 384 388 392 400 404 408 410 414 418 422 428 430 434 440
442 446 450 454 458 462 466 470 474 478 480 484 492 496 498
500 504 508 516 520 524 528 530 532 533 536 554 558 562 566
579 583 584 586 591 604 608 616 620 624 638 642 643 646 658
659 678 682 686 690 694 699 702 703 704 705 706 710 711 716 717
724 732 736 740 752 757 760 764 768 780 784 788 792 796 800 804
807 810 818 826 834 837 838 839 842 849 854 858 860 862 879 882
887 890 891 894 899 900 568 798 112 174 178 184 795 762;
? EXPORTING COUNTRIES;

? LIST OF VARIABLES NOW START WITH THE Z IN FRONT OF THE NAMES;
? ZYRS ZIMP ZVO ZQO ZV8 ZQ8

LISTZNEECZ 01901 8 12 20 24 28 31 32 36 44 50 51 52
68 70 76 80 84 86 90 92 96 100 104 108 120 124 129 132
136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 212
214 218 222 226 230 231 232 233 234 266 270 275 288
292 296 304 308 312 320 324 332 340 344 348 352 360 364
376 384 388 392 400 404 408 410 414 418 422 428 430 434 440
446 450 454 458 462 466 470 474 478 480 484 492 496 498
500 504 508 516 520 524 530 532 533 536 554 558 562 566
579 583 584 586 591 604 608 616 624 638 642 643 646 658
659 678 682 686 690 694 699 702 703 704 705 706 710 711 716 717
732 736 740 757 760 764 768 780 784 788 792 796 800 804
807 810 818 834 837 838 839 842 849 854 858 860 862 879 882 887 890
891 894 899 900 568 798 112 174 178 184 795 762;

DOT ZEPXZ; ZV. V.; ZQ.-Q.; ENDDOT;
ZYRS-YRS; ZIMP-IMP;

ZV901-0;ZQ901-0;
DOT 40 58 56 442 208 246 251 276 300 372 381 528 620 724 752 826;
ZV901-ZV901+ZV.;
ZQ901-ZQ901+ZQ.;
ENDDOT;
ZV1-ZVO-ZV901; ? VALUE NET OF EEC SUBSTRACTING INTERTRADE;
ZQ1-ZQO-ZQ901; ? QUANTITY NET OF EEC SUBSTRACTING INTERTRADE;

? COUNTRIES TO INCLUDE AS SEPARATE TRADING PARTNERS;
? 8 100 376 504 642 792 842;

LISTZROW EUZ 1220 24 28 31 32 36 44 50 51 52 68 70
76 80 84 86 90 92 96 104 108 120 124 129 132 136 140 144 152
156 166 170 180 188 191 192 196 200 203 204 212 214 218 222 226 230 231
232 233 234 266 270 275 288 292 296 304 308 312 320 324 332 340 344
348 352 360 364 384 388 392 400 404 408 410 414 418 422 428 430 434 440












442 446 450 454 458 462 466 470 474 478 480 484 492 496 498 500 508
516 520 524 530 532 533 536 554 558 562 566 579 583 584 586 591 604
608 616 624 638 643 646 658 659 678 682 686 690 694 699 702 703 704
705 706 710 711 716 717 732 736 740 757 760 764 768 780 784 788 796
800 804 807 810 818 834 837 838 839 849 854 858 860 862 879 882 887
890 891 894 899 900 568 798 112 174 178 184 795 762;

ZV998-0; ZQ998-0; ? REST OF THE WORLD FOR EU;
DOT ZROW EUZ;
ZV998-ZV998 + ZV.;
ZQ998-ZQ998 + ZQ.;
ENDDOT;

? DOT 8 100 376 504 642 792 842 998;
? PRINT ZYRS ZIMP ZV. ZQ.;
? ENDDOT;


? TURNING OFF THE FOLLOWING PROCEDURE;


PROC DONTDO;
MFORM(TYPE-GEN,NROW-400,NCOL-10) MEECM-0;
DOT(INDEX-I,VALUE-K) ZNEECZ;

MAT MEECM(I,1)-K;
SELECT ZIMP-901;
MSD(NOPRINT) ZV. ZQ.;
MAT MEECM(I,2)-901;
MAT MEECM(I,3)-,- '" [1 i
MAT MEECM(I,4)-,- T [h 2,

SELECT ZIMP-902;
MSD(NOPRINT) ZV. ZQ.;
MAT MEECM(I,5)-902;
MAT MEECM(I,6)-,- [, ii1
MAT MEECM(I,7)-,- T [h 2,
SELECT 1;
ENDDOT;

WRITE(FORMAT-EXCEL,FILE-'U:\tomato research\TSP WORKS 2006\RESTOFWORLD.XLS') MEECM;
ENDPROC DONTDO;


SELECT ZIMP-901;
?WRITE(FORMAT-EXCEL,FILE-'k:\Zstudents\MohAli\TSP2007\ANALYSIS\EU-15PARTNER#01.XLS') ZYRS ZIMP
? ZVO ZQO ZV1 ZQ1 ZV8 ZQ8 ZV100 ZQ100 ZV376 ZQ376 ZV504 ZQ504 ZV642 ZQ642
? ZV792 ZQ792 ZV842 ZQ842 ZV998 ZQ998;
WRITE(FORMAT-EXCEL,FILE-'U:\Zstudents\MohAli\TSP2007\ANALYSIS\EU-15PARTNER#01.XLS') ZYRS ZIMP
ZVO ZQO ZV1 ZQ1 ZV8 ZQ8 ZV100 ZQ100 ZV376 ZQ376 ZV504 ZQ504 ZV642 ZQ642
ZV792 ZQ792 ZV842 ZQ842 ZV998 ZQ998;

END;


Program Creating Partners for the U.S. in Excel

OPTIONS MEMORY 1500 signif=0 DOUBLE; ? Creating partners for EU-15;
TITLE 'TOMATO EXPORTS TO AND FROM EEC';
?IN 'k:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005 15';
IN'U:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005 15';
?dblist 'k:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005 15';
?dblist 'U:\Zstudents\MohAli\TSP2007\DATA2005\TOMDATA2005 15';

SUPRES SMPL;


? INITALIZING ALL PARTNER COUNTRIES TO ZERO;
?9 - - - - - - - - - - - - - - - - - - -













LIST ZIMPZ 901 902;
? IMPORTING COUNTRIES;

LISTZEPXZ 08 12 20 24 28 31 32 36 40 44 50 51 52 56 58
68 70 76 80 84 86 90 92 96 100 104 108 120 124 129 132
136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 208 212
214 218 222 226 230 231 232 233 234 246 251 266 270 275 276 288
292 296 300 304 308 312 320 324 332 340 344 348 352 360 364 372
376 381 384 388 392 400 404 408 410 414 418 422 428 430 434 440
442 446 450 454 458 462 466 470 474 478 480 484 492 496 498
500 504 508 516 520 524 528 530 532 533 536 554 558 562 566
579 583 584 586 591 604 608 616 620 624 638 642 643 646 658
659 678 682 686 690 694699 702 703 704 705 706 710 711 716 717
724 732 736 740 752 757 760 764 768 780 784 788 792 796 800 804
807 810 818 826 834 837 838 839 842 849 854 858 860 862 879 882
887 890 891 894 899 900 568 798 112 174 178 184 795 762;
? EXPORTING COUNTRIES;

? LIST OF VARIABLES NOW START WITH THE Z IN FRONT OF THE NAMES;
? ZYRS ZIMP ZVO ZQO ZV8 ZQ8

LISTZNEECZ 01901 8 12 20 24 28 31 32 36 44 50 51 52
68 70 76 80 84 86 90 92 96 100 104 108 120 124 129 132
136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 212
214 218 222 226 230 231 232 233 234 266 270 275 288
292 296 304 308 312 320 324 332 340 344 348 352 360 364
376 384 388 392 400 404 408 410 414 418 422 428 430 434 440
446 450 454 458 462 466 470 474 478 480 484 492 496 498
500 504 508 516 520 524 530 532 533 536 554 558 562 566
579 583 584 586 591 604 608 616 624 638 642 643 646 658
659 678 682 686 690 694699 702 703 704 705 706 710 711 716 717
732 736 740 757 760 764 768 780 784 788 792 796 800 804
807 810 818 834 837 838 839 842 849 854 858 860 862 879 882 887 890
891 894 899 900 568 798 112 174 178 184 795 762;

DOT ZEPXZ; ZV.-V.; ZQ. Q.; ENDDOT;
ZYRS-YRS; ZIMP-IMP;

ZV901-0;ZQ901-0;
DOT 40 58 56 442 208 246 251 276 300 372 381 528 620 724 752 826;
ZV901-ZV901+ZV.;
ZQ901-ZQ901+ZQ.;
ENDDOT;
ZV1-ZVO-ZV901; ? VALUE NET OF EEC SUBSTRACTING INTERTRADE;
ZQ1-ZQO-ZQ901; ? QUANTITY NET OF EEC SUBSTRACTING INTERTRADE;

? COUNTRIES TO INCLUDE AS SEPARATE TRADING PARTNERS;
? 8 100 376 504 642 792 842;

LISTZROW EUZ 1220 24 28 31 32 36 44 50 51 52 68 70
76 80 84 86 90 92 96 104 108 120 124 129 132 136 140 144 152
156 166 170 180 188 191 192 196 200 203 204 212 214 218 222 226 230 231
232 233 234 266 270 275 288 292 296 304 308 312 320 324 332 340 344
348 352 360 364 384 388 392 400 404 408 410 414 418 422 428 430 434 440
442 446 450 454 458 462 466 470 474 478 480 484 492 496 498 500 508
516 520 524 530 532 533 536 554 558 562 566 579 583 584 586 591 604
608 616 624 638 643 646 658 659 678 682 686 690 694 699 702 703 704
705 706 710 711716 717 732 736 740 757 760 764 768 780 784 788 796
800 804 807 810 818 834 837 838 839 849 854 858 860 862 879 882 887
890 891 894 899 900 568 798 112 174 178 184 795 762;

ZV998-0; ZQ998-0; ? REST OF THE WORLD FOR EU;
DOT ZROW EUZ;
ZV998-ZV998 +ZV.;
ZQ998-ZQ998 +ZQ.;
ENDDOT;

? DOT 8 100 376 504 642 792 842 998;
? PRINT ZYRS ZIMP ZV. ZQ.;
? ENDDOT;













? TURNING OFF THE FOLLOWING PROCEDURE;


PROC DONTDO;
MFORM(TYPE-GEN,NROW-400,NCOL-10) MEECM-0;
DOT(INDEX-I,VALUE-K) ZNEECZ;

MAT MEECM(I,1)-K;
SELECT ZIMP-901;
MSD(NOPRINT) ZV. ZQ.;
MAT MEECM(I,2)-901;
MAT MEECM(I,3), ',T [1 i
MAT MEECM(I,4)-,- h [, 2,

SELECT ZIMP-902;
MSD(NOPRINT) ZV. ZQ.;
MAT MEECM(I,5)-902;
MAT MEECM(I,6) -,- ',T [ i
MAT MEECM(I,7)-,- T [h 2,
SELECT 1;
ENDDOT;


WRITE(FORMAT-EXCEL,FILE-
ENDPROC DONTDO;


'U:\tomato research\TSP WORKS 2006\RESTOFWORLD.XLS') MEECM;


SELECT ZIMP-901;
?WRITE(FORMAT-EXCEL,FILE-'k:\Zstudents\MohAli\TSP2007\ANALYSIS\EU-15PARTNER#01.XLS') ZYRS ZIMP
? ZVO ZQ0 ZV1 ZQ1 ZV8 ZQ8 ZV100 ZQ100 ZV376 ZQ376 ZV504 ZQ504 ZV642 ZQ642
? ZV792 ZQ792 ZV842 ZQ842 ZV998 ZQ998;
WRITE(FORMAT-EXCEL,FILE-'U:\Zstudents\MohAli\TSP2007\ANALYSIS\EU-15PARTNER#01.XLS')ZYRS ZIMP
ZVO ZQ0 ZV1 ZQ1 ZV8 ZQ8 ZV100 ZQ100 ZV376 ZQ376 ZV504 ZQ504 ZV642 ZQ642
ZV792 ZQ792 ZV842 ZQ842 ZV998 ZQ998;

END;


U.S. 3D Chart Data for Structural Change


Canada's Share


1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005


Percentage increase in U.S. total imports


0.1
0.0053
0.0053
0.0052
0.0053
0.0053
0.0055
0.0055
0.0054
0.0053
0.0053
0.0051


Dom. Rep.'s Share
1995
1996
1997
1998


0.1
0.004
0.004
0.004
0.004


0.15
0.0051
0.0051
0.0051
0.0052
0.0053
0.0055
0.0055
0.0053
0.0053
0.0052
0.0049


0.2
0.0050
0.0050
0.0050
0.0052
0.0052
0.0055
0.0055
0.0052
0.0052
0.0051
0.0047


Percentage increase in U.S. total imports
0.15 0.2
1 0.0039 0.0038
1 0.0039 0.0038
1 0.0039 0.0038
0 0.0039 0.0037


0.25
0.0049
0.0049
0.0049
0.0051
0.0051
0.0055
0.0055
0.0052
0.0051
0.0051
0.0045



).25
).0037
).0037
).0037
).0036


0.3
0.0048
0.0048
0.0048
0.0050
0.0051
0.0055
0.0055
0.0051
0.0050
0.0050
0.0043



0.3
0.0036
0.0036
0.0036
0.0035











1999 0.0040 0.0039 0.0038 0.0036 0.0035
2000 0.0040 0.0039 0.0037 0.0036 0.0035
2001 0.0040 0.0039 0.0037 0.0036 0.0035
2002 0.0040 0.0039 0.0037 0.0036 0.0035
2003 0.0040 0.0038 0.0037 0.0036 0.0035
2004 0.0040 0.0039 0.0037 0.0036 0.0035
2005 0.0040 0.0039 0.0038 0.0036 0.0035

Percentage increase in U.S. total imports
Mexico's Share 0.1 0.15 0.2 0.25 0.3
1995 0.9815 0.9815 0.9816 0.9816 0.9816
1996 0.9814 0.9814 0.9814 0.9814 0.9814
1997 0.9815 0.9816 0.9816 0.9816 0.9816
1998 0.9816 0.9816 0.9817 0.9817 0.9818
1999 0.9812 0.9811 0.9810 0.9808 0.9807
2000 0.9810 0.9808 0.9806 0.9805 0.9803
2001 0.9809 0.9807 0.9804 0.9802 0.9800
2002 0.9811 0.9809 0.9807 0.9805 0.9804
2003 0.9812 0.9811 0.9810 0.9809 0.9808
2004 0.9813 0.9812 0.9811 0.9810 0.9810
2005 0.9816 0.9817 0.9818 0.9818 0.9819

Percentage increase in U.S. total imports
EU-15's Share 0.1 0.15 0.2 0.25 0.3
1995 0.0067 0.0072 0.0077 0.0082 0.0086
1996 0.0067 0.0073 0.0078 0.0083 0.0087
1997 0.0067 0.0072 0.0077 0.0082 0.0086
1998 0.0066 0.0071 0.0076 0.0081 0.0085
1999 0.0067 0.0073 0.0078 0.0083 0.0087
2000 0.0067 0.0073 0.0078 0.0083 0.0087
2001 0.0067 0.0073 0.0079 0.0083 0.0088
2002 0.0068 0.0075 0.0080 0.0086 0.0090
2003 0.0068 0.0074 0.0080 0.0085 0.0089
2004 0.0068 0.0074 0.0079 0.0084 0.0089
2005 0.0068 0.0074 0.0080 0.0085 0.0090

Percentage increase in U.S. total imports
ROW's 0.1 0.15 0.2 0.25 0.3
1995 0.0022 0.0018 0.0014 0.0010 0.0006
1996 0.0022 0.0018 0.0014 0.0010 0.0006
1997 0.0022 0.0017 0.0013 0.0010 0.0006
1998 0.0022 0.0017 0.0013 0.0009 0.0006
1999 0.0022 0.0018 0.0014 0.0011 0.0007
2000 0.0022 0.0018 0.0014 0.0010 0.0007
2001 0.0022 0.0018 0.0014 0.0011 0.0007
2002 0.0022 0.0017 0.0013 0.0009 0.0006
2003 0.0022 0.0017 0.0013 0.0009 0.0006
2004 0.0022 0.0017 0.0013 0.0009 0.0006
2005 0.0021 0.0017 0.0012 0.0008 0.0005











EU-15 3D Chart Data for Structural Change.
Albania's Share Percentage increase in EU-15 total imports
0.1 0.15 0.2 0.25 0.3
1995 0.2078 0.1816 0.1576 0.1355 0.1151
1996 0.2109 0.1860 0.1632 0.1423 0.1229
1997 0.2117 0.1872 0.1648 0.1441 0.1250
1998 0.2141 0.1906 0.1690 0.1492 0.1309
1999 0.2092 0.1836 0.1602 0.1386 0.1186
2000 0.2139 0.1903 0.1687 0.1488 0.1304
2001 0.2136 0.1899 0.1682 0.1482 0.1297
2002 0.2194 0.1982 0.1787 0.1609 0.1444
2003 0.2171 0.1949 0.1745 0.1558 0.1385
2004 0.2159 0.1932 0.1724 0.1532 0.1356
2005 0.2172 0.1951 0.1749 0.1562 0.1390

Bulgaria's Share 0.1 0.15 0.2 0.25 0.3
1995 0.0476 0.0469 0.0462 0.0455 0.0450
1996 0.0465 0.0453 0.0441 0.0431 0.0421
1997 0.0469 0.0458 0.0448 0.0439 0.0431
1998 0.0467 0.0455 0.0445 0.0435 0.0426
1999 0.0463 0.0450 0.0438 0.0427 0.0416
2000 0.0472 0.0462 0.0454 0.0446 0.0438
2001 0.0478 0.0471 0.0464 0.0459 0.0453
2002 0.0473 0.0464 0.0456 0.0448 0.0441
2003 0.0468 0.0457 0.0447 0.0437 0.0428
2004 0.0469 0.0458 0.0448 0.0439 0.0431
2005 0.0468 0.0457 0.0447 0.0438 0.0429

Isreal's Share 0.1 0.15 0.2 0.25 0.3
1995 0.0101 0.0098 0.0094 0.0091 0.0089
1996 0.0101 0.0098 0.0095 0.0092 0.0089
1997 0.0099 0.0095 0.0091 0.0088 0.0085
1998 0.0097 0.0092 0.0087 0.0083 0.0079
1999 0.0095 0.0089 0.0084 0.0079 0.0074
2000 0.0094 0.0088 0.0082 0.0076 0.0071
2001 0.0102 0.0099 0.0096 0.0093 0.0091
2002 0.0104 0.0102 0.0099 0.0098 0.0096
2003 0.0112 0.0113 0.0114 0.0115 0.0116
2004 0.0114 0.0116 0.0118 0.0120 0.0121
2005 0.0123 0.0129 0.0134 0.0139 0.0144

Morocco's Share 0.1 0.15 0.2 0.25 0.3
1995 0.5359 0.5391 0.5421 0.5448 0.5473
1996 0.5363 0.5396 0.5427 0.5456 0.5482
1997 0.5367 0.5403 0.5436 0.5466 0.5494
1998 0.5365 0.5400 0.5432 0.5461 0.5489
1999 0.5386 0.5430 0.5470 0.5507 0.5541
2000 0.5383 0.5426 0.5465 0.5501 0.5534
2001 0.5385 0.5428 0.5468 0.5504 0.5538
2002 0.5430 0.5493 0.5550 0.5604 0.5653











2003 0.5412 0.5468 0.5519 0.5565 0.5609
2004 0.5420 0.5479 0.5533 0.5582 0.5628
2005 0.5358 0.5389 0.5418 0.5445 0.5469

Romania's Share 0.1 0.15 0.2 0.25 0.3
1995 0.0948 0.0908 0.0871 0.0837 0.0805
1996 0.0951 0.0912 0.0877 0.0844 0.0814
1997 0.0938 0.0893 0.0853 0.0815 0.0780
1998 0.0956 0.0919 0.0885 0.0854 0.0826
1999 0.0966 0.0934 0.0904 0.0877 0.0852
2000 0.0985 0.0961 0.0939 0.0918 0.0899
2001 0.0977 0.0949 0.0923 0.0900 0.0878
2002 0.0947 0.0907 0.0869 0.0835 0.0804
2003 0.0953 0.0915 0.0880 0.0848 0.0818
2004 0.0952 0.0914 0.0879 0.0847 0.0817
2005 0.0956 0.0919 0.0885 0.0854 0.0826

Turkey's Share 0.1 0.15 0.2 0.25 0.3
1995 0.0064 0.0066 0.0068 0.0069 0.0071
1996 0.0065 0.0067 0.0069 0.0071 0.0072
1997 0.0066 0.0069 0.0071 0.0073 0.0075
1998 0.0064 0.0066 0.0067 0.0069 0.0070
1999 0.0067 0.0071 0.0074 0.0076 0.0079
2000 0.0059 0.0059 0.0059 0.0059 0.0059
2001 0.0057 0.0055 0.0054 0.0053 0.0052
2002 0.0053 0.0049 0.0046 0.0044 0.0041
2003 0.0062 0.0062 0.0063 0.0064 0.0065
2004 0.0057 0.0056 0.0055 0.0054 0.0054
2005 0.0062 0.0063 0.0064 0.0065 0.0065

ROW's Share 0.1 0.15 0.2 0.25 0.3
1995 0.0376 0.0395 0.0413 0.0430 0.0445
1996 0.0375 0.0394 0.0411 0.0427 0.0442
1997 0.0376 0.0395 0.0413 0.0430 0.0445
1998 0.0371 0.0388 0.0404 0.0419 0.0433
1999 0.0347 0.0354 0.0361 0.0367 0.0372
2000 0.0338 0.0341 0.0344 0.0346 0.0348
2001 0.0330 0.0329 0.0329 0.0329 0.0328
2002 0.0300 0.0286 0.0274 0.0262 0.0252
2003 0.0301 0.0288 0.0277 0.0266 0.0256
2004 0.0295 0.0279 0.0265 0.0252 0.0239
2005 0.0344 0.0350 0.0356 0.0361 0.0365












Code Number used for Data Manipulation (As of January, 2007)
Item Code Item


Albania
Algeria
Andorra
Angola
Antigua and Barbuda
Areas, nes
Argentina
Armenia
Aruba
Australia
Austria
Azerbaijan
Bahamas
Bangladesh
Barbados
Belarus
Belgium
Belgium-Luxembourg
Belize
Benin
Bolivia
Bosnia Herzegovina
Br. Antarctic Terr.
Br. Indian Ocean Terr.
Br. Virgin Isds
Brazil
Brunei Darussalam
Bulgaria
Bunkers
Burkina Faso
Burundi
Cameroon
Canada
Cape Verde
Caribbean, nes
Cayman Isds

Central African Rep.
China, Hong Kong SAR


8
12
20
24
28
899
32
51
533
36
40
31
44
50
52
112
56
58
84
204
68
70
80
86
92
76
96
100
837
854
108
120
124
132
129
136

140
344


Lao People's Dem. Rep.
Latvia
Lebanon
Liberia
Libya
Lithuania
Luxembourg
Madagascar
Malawi
Malaysia
Maldives
Mali
Malta
Marshall Isds
Martinique
Mauritania
Mauritius
Mexico
Mongolia
Montserrat
Morocco
Mozambique
Myanmar
Namibia
Nauru
Nepal
Neth. Antilles
Neth. Antilles and Aruba
Netherlands
Neutral Zone
New Zealand
Nicaragua
Niger
Nigeria
Norway
Occ. Palestinian Terr.
Other Asia, nes
(Created)
Other Eurpe, nes


418
428
422
430
434
440
442
450
454
458
462
466
470
584
474
478
480
484
496
500
504
508
104
516
520
524
530
532
528
536
554
558
562
566
579
275

900
568


Code









Chile
China
China, Macao SAR
Cocos Isds
Colombia
Comoros
Congo
Cook Isds
Costa Rica
C6te d'Ivoire
Croatia
Cuba
Cyprus
Czech Rep.
Czechoslovakia

Dem. People's Rep. Of Korea
Dem. Rep. of the Congo
Denmark
Dominica
Dominican Rep.
Ecuador
Egypt
El Salvador
Equatorial Guinea
Eritrea
Estonia
Ethiopia
EU-15 (Created)
Europe EU, nes
Faeroe Isds
Finland
Fmr Ethiopia
Fmr Rhodesia Nyas
Fmr USSR
Fmr Yugoslavia
France
Free Zones
FS Micronesia
Gabon
Gambia
Germany
Ghana
Gibraltar
Greece
Greenland


152
156
446
166
170
174
178
184
188
384
191
192
196
203
200

408
180
208
212
214
218
818
222
226
232
233
231
901
492
234
246
230
717
810
890
251
838
583
266
270
276
288
292
300
304


Pakistan
Panama
Peru
Philippines
Poland
Portugal
Rep. of Korea
Rep. of Moldova
Reunion
Romania
ROW for EU- 15
ROW for U.S.
Russian Federation
Rwanda
Saint Kitts and Nevis
Saint Kitts, Nevis and
Anguilla
Samoa
Sao Tome and Principe
Saudi Arabia
Senegal
Serbia and Montenegro
Seychelles
Sierra Leone
Singapore
Slovakia
Slovenia
So. African Customs Union
Solomon Isds
Somalia
South Africa
Spain
Special Categories
Sri Lanka
Sudan
Suriname
Sweden
Switzerland
Syria
Tajikistan
TFYR of Macedonia
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey


586
591
604
608
616
620
410
498
638
642
998
999
643
646
659

658
882
678
682
686
891
690
694
702
703
705
711
90
706
710
724
839
144
736
740
752
757
760
762
807
764
768
780
788
792









Grenada
Guadeloupe
Guatemala
Guinea
Guinea-Bissau
Haiti
Honduras
Hungary
Iceland
Import (*Used only to read
data)
India
Indonesia

Iran
Ireland
Israel
Italy
Jamaica
Japan
Jordan

Kenya
Kiribati
Kuwait


308
312
320
324
624
332
340
348
352

1
699
360

364
372
376
381
388
392
400

404
296
414


Turkmenistan
Turks and Caicos Isds
Tuvalu
Uganda
Ukraine
Unit (Kg.)
United Arab Emirates
United Kingdom
United Rep. of Tanzania

Uruguay
US Misc. Pacific Isds
USA
USA-New
(Created)
Uzbekistan
Venezuela
Viet Nam
Western Asia, nes
Western Sahara
World
World without EU
(Created)
Yemen
Zambia
Zimbabwe


795
796
798
800
804
2
784
826
834

858
849
842

902
860
862
704
879
732
0









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BIOGRAPHICAL SKETCH

Born in 1955 to the parents Mr. Md. Lal Miah and Mrs. Amena Khatoon, Mohammad Ali

started his elementary education in his own village at Puthai Primary School under the police

station (P.S.) of Brahmanbaria in the former district of Comilla, Bangladesh. After third grade,

his youngest (among five) maternal uncle, Dr. Nurul Islam Bhuiyan (a national physician) of

village South Tarua under the same P.S. and district, brought him to a better school called

Khulapara Primary School from where he got a non-residential scholarship in fifth grade in

1965. Then, he started his high school studies at Brahmanbaria Annada Government High

School from where he got a residential scholarship in eighth grade and stood sixth position in the

Humanities Group with a distinction in mathematics in Secondary School Certificate (SSC)

examination (tenth grade) under the Comilla Board in 1970. After that the financial crisis of his

family compelled him to study at Jhenidah Cadet College (free of cost) under the district of

Jessore, Bangladesh from where he passed his Higher Secondary Certificate (HSC) examination

(twelfth grade) in first division with a board scholarship in 1972. During his studies at Jhenidah

Cadet College, he joined the Liberation War, war of independence of Bangladesh from Pakistan

in 1971. As a freedom fighter, he fought in Sector 3 along with the regular army unit and also as

a group commander.

Then, Mr. Ali started his university studies and took his bachelor's and master's degrees in

economics in 1977 and 1979 respectively from the University of Dhaka, Bangladesh. He also

took an M.B.A. degree from the same university in the year 1982. During his studies at the

university, he worked on a part-time basis in Dhaka university library as a Library Assistant and

in Bangladesh Biman, the national airlines as a Junior Sales Assistant. He had to do that for

supporting himself and the family consisting of three younger brothers and two sisters and the

parents since his father, a government employee (Block Supervisor) in Agricultural Extension









Division, lost his job as a result of prolonged absence from duty due to illness (throat cancer,

surviving 19 years after detection until 1992 when he died). Then, he served as an Assistant

Statistical Officer, Assistant Director and Financial Analyst in the central bank of his country,

the Bangladesh Bank for about 6 years. After that, he worked as a member of the Bangladesh

Civil Service (BCS) Cadre in the Audit and Accounts Department for more than 12 years. Before

starting his further studies at the University of Florida (UF), Gainesville, Florida, he was in the

position of a Deputy Accountant General of Bangladesh. He earned his M.S. in Food and

Resource Economics from the University of Florida in 2000.

Mr. Ali started his higher studies in the United States (U.S.) with an intention of going

back to his country to continue his superior service job from where he was on leave without pay.

He got admitted for higher studies with a verbal consent of his superior authority, but the

authority did not want him to finish his M.S. Towards the close of his M.S., he was called back

to return and join his job within 2 weeks and after that they actually fired him. So, he decided to

continue his studies after M.S. and stay in the U.S. permanently without going back to

Bangladesh. Thus, he enrolled in the Ph.D. program of the same department at UF. While

pursuing his Ph.D., he has been teaching microeconomics and macroeconomics at the under

graduate level in the University of Maryland Eastern Shore (UMES) since spring 2004 as a part-

time Lecturer.





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1 ESTIMATING IMPORT DEMAND FOR FRESH TOMATOES INTO TH E UNITED STATES AND THE EUROPEAN UNION By MOHAMMAD ALI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2 2007 Mohammad Ali

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3 To my parents, my wife Dr. Salina Parveen and my sons Sakib M. Adnan and Adib M. Adnan

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4 ACKNOWLEDGMENTS I am very much grateful for the invaluable assistance, guidance and cooperation I received from my committee chair, Dr. Richard L. Kilm er. His motivation and perseverance kept me going when I had almost succumbed to complete fr ustration as I had to move to Delaware first and then to Maryland with my family just after my course work. Indeed, it is difficult to continue such a venture when someone is not on the spot. Moreover, I had to wait for complete dataset for the intended study period until January of 2007. The patience and flexibility he awarded to me with moral and financial support in the developmen t of this dissertation we re greatly appreciated. His knowledge and experience have been very bene ficial to me over the years. I remain ever thankful for the time and trouble he took to meticulously review my work with helpful comments. I am also grateful to the members of my committee for their mentoring and support -both technical and theoretical. Dr. Ronald W. Ward helped me in manipulating data through mathematical programming that made the job easier and faster for me. Wit hout his assistance, it would take much of my time and energy to get the data in working format. He also provided insights regarding the performan ce of the models used for my study and suggested some improvement to explain any kind of structural change that is not in cluded directly in the model. I appreciate his enthusiasm for work which is very refreshing. Dr. Mark G. Brown offered considerable support in selecting the model for my study. He helped me look at some alternative models to check whether they f it better on the given da ta sets. Finally, my discussions with Dr. Thomas H. Spreen and Dr. Lawrence W. Kenny help ed me shape this research within the realm of reality/practicality. A number of other individuals contributed directly or i ndirectly through their helpful comments and appreciation. I would like to r ecognize Dr. Ronald Jansen, United Nations,

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5 Statistics Division and Gary Lucier, United St ates Department of Agriculture, Economic Research Service for their input a nd clarifications on different aspe cts of the data used in this research. I thank Carlos E. Jaur egui (a doctoral candidate and fr iend in the same department) for helping me out whenever I got stuck with the computer programming of my model and Carol Fountain for her assistance in formatting my disse rtation. I also thank the staff and members at the department as well as at the UF Library for their overall assistance. Now I would like to acknowledge some Bangladeshi friends and families for their contributions. My friends Dr. Abu M. Khan (Sayem) and Dr. Murshed M. Chowdhury supported me with accommodations (during th eir stay at the University of Florida (UF)) whenever I came to Gainesville for a short time to work on my di ssertation. After that, I started getting board and lodging with transportation from our family frie nds Dr. Khandker A. Muttalib and Dr. Jaha A. Hamida (husband and wife and both working in the Physics department) until I am thoroughly done with my dissertation. I would like to express my extreme gr atitude to them. I would also like to thank all my Bangladeshi Sunday volleyba ll partners and their families for their support and encouragement. I would like to express my endless thanks and gratitude to my family members and other relatives and well-wishers for their love, support and encouragement throughout my lifelong educational endeavors. It is my wife who en couraged and supported me both emotionally and financially to make this milest one possible. I thankfully appreci ate her and my two sons Sakib and Adib for their patience, unde rstanding, and moral s upport through all thes e years of studies. Finally, I would like to express my appreciation to the Intern ational Agricultural Trade and Policy Center (IATPC) at the University of Flor ida and its Director Dr. John J. VanSickle for partially funding this research project.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF FIGURES................................................................................................................ .........9 ABSTRACT....................................................................................................................... ............10 CHAPTER 1 INTRODUCTION..................................................................................................................12 Problematic Situation.......................................................................................................... ....13 Problem Statement.............................................................................................................. ....14 Objectives..................................................................................................................... ..........14 Chapter Summary................................................................................................................ ...14 2 BACKGROUND....................................................................................................................16 U.S. Tomatoes.................................................................................................................. ......16 EU Tomatoes.................................................................................................................... ......18 Data........................................................................................................................... ..............20 3 LITERATURE REVIEW.......................................................................................................26 Armington Trade Model.........................................................................................................26 Differential Approach and the Rotterdam Models.................................................................28 Demand System and Functional Formulation........................................................................32 Production Approach and Utility Approach...........................................................................33 Import Demand and the Producer Theory..............................................................................37 Inverse Demand Analysis.......................................................................................................44 Differential Production Approach..........................................................................................47 Summary of Literature Review..............................................................................................51 4 THEORETICAL AND EMPIRICAL MODEL.....................................................................53 Theoretical Models............................................................................................................. ....53 Empirical Models............................................................................................................... .....60 Data Section................................................................................................................... .........61 5 EMPIRICAL RESULTS........................................................................................................65 Results for U.S. Tomato Import Demand Analysis................................................................65 Descriptive Statistics.......................................................................................................65

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7 Model Results..................................................................................................................65 Summary for U.S. Analysis....................................................................................................71 Results for EU-15 Tomato Import Demand Analysis............................................................71 Descriptive Statistics.......................................................................................................72 Model Results..................................................................................................................72 Summary for EU-15 Analysis................................................................................................79 6 CONCLUSIONS....................................................................................................................96 Observations................................................................................................................... ........96 Summary........................................................................................................................ .........96 Conclusions.................................................................................................................... .........98 Implications................................................................................................................... .........99 APPENDIX A COMPUTER PRINTOUTS FOR U.S. ANALYSIS............................................................100 B COMPUTER PRINTOUTS FOR EU-15 ANALYSIS........................................................117 C SUPERFLUOUS MATERIAL.............................................................................................140 LIST OF REFERENCES.............................................................................................................152 BIOGRAPHICAL SKETCH.......................................................................................................158

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8 LIST OF TABLES Table page 2-1 Production of tomatoes..................................................................................................... .22 2-2 U.S. exports and imports of fresh and processed tomatoes compared to World...............22 2-3 U.S. fresh tomato exports to the EU..................................................................................23 2-4 U.S. fresh tomato imports from the EU.............................................................................23 2-5 EUs tomato exports compared with the U.S. and World (total tomatoes).......................24 2-6 EUs tomato imports compared with the U.S. and World (total tomatoes).......................24 2-7 EUs tomato production compared with the U.S. and World (total tomatoes)..................25 5-1 Import cost shares, quantity shares, and av erage prices by country of origin for U.S......80 5-2 Test results for the production differe ntial AIDS, CBS, Rotterdam and NBR models with first-order autocorrelation impos ed for U.S. import demand analysis......................80 5-3 Coefficient estimates of the production NBR model for the U.S......................................81 5-4 Demand parameter estimates and conditiona l elasticity of the production NBR model for the U.S.................................................................................................................... ......81 5-5 Divisia elasticities over time for the U.S. analysis............................................................82 5-6 Conditional own-price elasticities over time for the U.S. analysis....................................83 5-7 Import cost shares, quantity shares, and average prices by country of origin for EU15............................................................................................................................. ...........87 5-8 Test results for model se lection for EU-15 analysis..........................................................87 5-9 Coefficient estimates of th e production NBR model for EU-15........................................88 5-10 Demand parameter and conditional elastic ity estimates of the production NBR model for EU-15...................................................................................................................... .....89 5-11 Divisia elasticities over time for EU-15 analysis...............................................................90 5-12 Conditional own-price elasticities over time for EU-15 analysis......................................91

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9 LIST OF FIGURES Figure page 5-1 Impact of structural change on U.S. demand for Canadian and Dominican Republic fresh tomatoes................................................................................................................. ...84 5-2 Impact of structural change on U.S. de mand for Mexican and EU-15 fresh tomatoes.....85 5-3 Impact of structural change on U.S. demand for ROW fresh tomatoes............................86 5-4 Impact of structural change on EU15 demand for Albanian and Bulgarian fresh tomatoes....................................................................................................................... ......92 5-5 Impact of structural change on EU -15 demand for Israeli and Morocco fresh tomatoes....................................................................................................................... ......93 5-6 Impact of structural change on EU15 demand for Romanian and Turkish fresh tomatoes....................................................................................................................... ......94 5-7 Impact of structural change on EU-15 demand for ROW fresh tomatoes.........................95

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10 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ESTIMATING IMPORT DEMAND FOR FRESH TOMATOES INTO TH E UNITED STATES AND THE EUROPEAN UNION By Mohammad Ali August 2007 Chair: Richard L. Kilmer Major: Food and Resource Economics Continuous talks and negotiati ons initiated by the World Tr ade Organization (WTO), in recent years, on globalization and trade liberaliza tion have made international trade a key issue for all nations as it has expanded the global markets with enhanced competitiveness. There exist both opportunities and costs as it expands exports on one hand and poses threats of competition from importers on the other. Agricu lture being the major player in international trade has to cope with this changing trend of the global market situation. The Eur opean Union (EU-15) being more and more open should be of especial interest to the growers, traders a nd policy makers of all nations including the United States (U.S.). This is a research project for the analysis of import demand for fresh tomatoes into the U.S. and the EU-15 for the assessment and evaluation of competitiveness to enable the specialty crop indu stry to compete successfully. The sources of data for this research are the United Nations St atistics DivisionCommodity Trade Statistics Database website, Food and Agriculture Organiza tion Statistics Databa se website and other websites maintained by the United States Depart ment of Agriculture (USDA). Data for the period 1963-2005 have been used in this research. A differential production approach has been used for estimating import demand for fresh tomatoes. Imports are considered as inputs to importing firms. The m ode used in this research is

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11 derived from the basic principl e of the theory of firm wher eby the firm maximizes profit by determining a level of output and minimizing the co st of producing that level of output. The cost minimization stage is applied to get conditional factor demand equations in the estimation process. The differential producti on version of the Netherlands National Bureau of Research (NBR) specification is estimated by the iterative seemingly unrelated regression (SUR) method using the well known least square procedure (LSQ) in Time Series Processor (TSP). Results show that Mexico is the prominent supplier of fresh tomatoes in the U.S. import market facing no close competitor. Canada and EU-15 compete with each other for the U.S. import market whereby Canada is losing its relative share and EU-15 is gain ing its relative share. For the EU-15 import demand for fresh tomatoes, Morocco is the major supplier with no close competitor. Israel and Rest of the World (ROW ) are competing with each other in the EU-15 import market. Albania, Bulgaria and ROW are losing their relati ve share in the EU-15 import market with an indication of so me kind of structural changes. Mexico and Morocco have significant influen ce and control over the U.S. and the EU-15 import markets. It is necessary for other particip ants to figure out the secrets and follow them to be competitive with these major players in thei r respective markets. Otherwise, the implications would be significant on both markets if there are some diseases or calamities in Mexico and Morocco.

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12 CHAPTER 1 INTRODUCTION Globalization, in a modern world, has emer ged as a blessing for both developed and developing countries. With global ization comes trade liberaliza tion that reduces barriers to international trade. According to the World Tr ade Organization (WTO), elimination of trade barriers can result in annual welfare gains fo r the world ranging from US $250 billion to US $620 billion. Trade liberalizati on can also help alleviate pove rty by contributing to a more efficient resource allocation and raising productivity. Thus, free trade can contribute to higher wages and standard of living. So, it can be said that trade liberalization and poverty reduction/income growth go hand in hand (WTO, 2002). In the field of agriculture, increased globali zation has both potential benefits and costs for the United States (U.S.). In some cases, there will be opportunities to expand U.S. exports and in others, there will be a threat of facing compe tition from importers with lower prices. The overall effects on the U.S. may further be complicated by high income elasticities of demand. For example, many of the specialty cr ops may have relatively higher income elasticities of demand in the U.S. than other field crops. Hence, income growth will definitely have a positive effect on the U.S. demand for those specialty crops; however, this may or may not neutralize the impact of increased competition as well as the effect of pot entially lower prices. Thus, the globalization of markets along with the emphasis on internat ional trade has increased interest on the competitiveness of the U.S. in global markets (Institute of Food and Agricultural Sciences (IFAS), 2002). U.S. farm cash receipts are valued at more than $241 billion and Florida is the tenth leading state in this respect with $6.84 billion in farm cash receipts as of 2004. Florida is also the fifth leading state in crop production with $5.36 billion in cash receipts (National Agricultural

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13 Statistics Service (NASS), 2006). Agriculture accounts for more th an 314,000 jobs in the state of Florida (IFAS, 2002). The U.S. is also one of th e worlds leading importers and exporters of fruits, vegetables and nuts. The specialty crops ar e very important for Florida. The three leading specialty crops in terms of value are greenhouse/ nursery at $1.63 billion, oranges at $980 million and tomatoes at $501 million (NASS, 2006). The specialty crops are important for feeding the nation as well as the world. So, it is very important to look at the future of this industry both domestically and globally The International Agricultural Trade and Po licy Center (IATPC) at the University of Florida (UF) is entrusted with th e responsibility to focus on research and education that will help the industry understand the implicat ions of trade and policy relate d issues. The Center has been established to help growers, industry leaders and policy makers in understanding various impacts of all these issues on the future of the industry (IATPC website). The proposed project looks at the import demand for fresh tomatoes into the U.S and the European Union (EU). Problematic Situation The World Trade Organization (WTO), in rece nt times, is continuous ly initiating talks on agricultural trade aimed at trade liberalization. Consequently, the globalization of markets places enhanced emphasis on internati onal trade and competitiveness am ong suppliers. It has become a necessary venture for the producers/processors to gain a larger share of world agricultural exports. The specialty crop sector also needs to keep pace with this trend of potentiality. One way of doing so is to provide firms with the n ecessary information on global markets. Therefore, demand studies for individual countries are needed for the specialty crop sector in order to enable the sector to strategica lly plan to expand exports. Thus, the analysis of import demand for tomatoes into the most potential markets like the EU is necessary for the U.S. specialty crop

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14 industry for the assessment and evaluation of co mpetitiveness that will enable the industry to compete successfully in an increasingly growing domestic and global markets. Problem Statement What is the state of competition among the suppliers for imported tomatoes into the United States and the European Union? Objectives The broad objective of this research is to estimate import demand for tomatoes into the United States and the European Union and utilize estimated parameters in order to measure the sensitivity of demand for tomatoes to changes in own price, prices of substitutes and quantity of imports, and thereby look at competitiveness among suppliers. The specific objectives include 1) To look at international trade in tomatoes imported into the United States and the European Union. 2) To review different demand mode ls including import demand models. 3) To develop a model to estimate import demands for fresh tomatoes into the United States and the European Union. 4) To determine the extent of competition among the suppliers of fresh tomato imports into the United States and the European Union. 5) To look at if there is any structural influence on the import demand for fresh tomatoes. Chapter Summary Data on fresh tomato imports for this study have been obtained from the United Nations database. A differential producti on version of the Netherlands National Bureau of Research (NBR) model has been used in estimating import demands. In Chapter 2 a background for this study has been provided. Chapter 3 discusses a detail ed literature review that is helpful for the study. Then the theoretical and empirical models ar e discussed in Chapter 4 that also includes the

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15 data section in detail. Chapte r 5 provides a discussion on empi rical results. The concluding Chapter 6 includes observations, summary, conc lusions, and implications and recommendations.

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16 CHAPTER 2 BACKGROUND U.S. Tomatoes The United States is one of th e worlds largest producers of to matoes. It is second in rank, just behind China (Economic and Research Se rvices (ERS)-United States Department of Agriculture (USDA) website). Imports of tomato es have also risen si gnificantly since 1994. The industry has been growing rapidly over last few d ecades. In terms of U.S. farm cash receipts, the U.S. fresh and processed tomatoes are second only to potatoes among all vegetables in value. Tomatoes accounted for $2.06 billio n in farm cash receipts in 2004 (NASS, 2006), which is 12 percent of all vegetable and mel on receipts. In terms of 2002 harv ested acreage of tomatoes, the five top states are California, Florida, Oh io, Indiana, and Michigan (ERS-USDA, 2003). However, the leading producers of fresh market tomatoes were Florida (39 percent), California (31 percent), Ohio (7 percent), Virginia (4 per cent), and North Carolina (2 .4 percent) in 2002. On the other hand, the top processing tomato produc ing states were California (95.4 percent), Indiana (1.7 percent), Ohio (1.7 pe rcent), Michigan (0.7 percent), and Pensylvania (0.2 percent). The average annual per capita consumption of fresh and processed tomatoes rose by 18 percent during the 1990s compared to the 1980s, amounting to 91 pounds on a fresh-weight basis in 1999, with processed tomatoes accoun ting for about 80 percent (ERS-USDA, 2003). The total domestic utilization of fr esh market tomatoes in 2002 was 5.2 billion pounds (18.4 pounds per person) while that of processed tomato es totaled to 19.9 bill ion pounds (69.2 pounds per person). The fresh market data provided a bove excludes domestic greenhouse or hydroponic tomatoes that might add one more pound to fr esh per capita consumption if included. Mexico and Canada are the major suppliers of fresh mark et tomatoes to the U.S. while Canada is the leading export market for U.S. fresh a nd processed tomatoes (ERS-USDA website).

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17 With respect to tomato exports and imports, about 7 percent of the U.S. fresh market tomato supply is exported. The top U.S. expor t markets in 2001 were Canada (78.6 percent), Mexico (13.9 percent), Belgium (4.09 percent), Japan (1.38 percent) and France (0.67 percent) (U.S. Trade Statistics website). For processed tomatoes, about 5 percen t of the tomato product supply was exported during the 1990 s. The top U.S. export markets for processed tomatoes in 2001 were Canada (48.61 percent), Mexico ( 11.47 percent), Japan (8.88 percent), United Kingdom (4.67 percent) and Sout h Korea (4 percent) (U.S. Trade Statistics website). Now, as for imported tomatoes, it is important to observe that more fresh tomatoes are imported than processed tomatoes. In 2000, fresh tomato imports accounted for about 32 percent of domestic consumption, which is down from 36 percent in 1998, but mo re than 19 percent in 1990 (ERS-USDA website). The U.S. normally impor ts most fresh tomatoes during the period when domestic supply is low, i.e., late fall to early spring. In 2001, Mexico supplied 82.47 percent of the value of fresh tomato imports followed by Canada (12.8 percent), the Netherlands (3.55 percent) and Israel (0.45 perc ent) (U.S. Trade Statistics we bsite). As a net importer, the United States imported 36% of total fresh tomato consumption and exported about 9% of total domestic production in 2002 (ERS-USDA, 2003). In the case of processed tomatoes, impor ts accounted for 4 percent of domestic consumption during the 1990s, which is down from 7 percent in the 1980s (ERS-USDA website). In 2001, Canada accounted for 40 percent of the imported processed tomatoes followed by Italy (27.16 percent), Mexico (14.91 percent), Dominican Re public (5.32 percent), China (3.84), and Israel (2.75 percent) (ERS-USDA website). The importance of the U.S. tomato industry will be clear from the ta bles at the end of this chapter. Table 2-1 shows the U.S. tomato production including Floridas share compared with the worlds total production. Florida

PAGE 18

18 production is mainly for the fresh market and it constitutes around 40% of U.S. fresh tomatoes. Table 2-2 shows the U.S. exports and imports of tomatoes compared with the world exports and imports. For the period 1991-2005, U.S. exports varied from 148,297 to 188,173 metric tons whereas imports varied from 360,770 to 951,785 metric tons. EU Tomatoes The Common Agriculture Policy (CAP) of 1962 has laid the foundation for creating the European Union (EU) through which Europe ha s taken a strong protective stance of its agricultural markets. This is es pecially true for products like dairy, fresh fruits and vegetables including tomatoes that are vulnerable to foreign competition as a result of domestic prices that are higher than the world price levels. In f act, European consumers pay almost twice the competitive world price for most of its agricult ural products (Adams and Kilmer, 2003). In other words, European producers receive almost twice the world price for many agricultural products due to domestic farm programs. Thus, agricultura l subsidies accounted for almost half of the EUs total budget in 2000 (US $40 billion on agriculture) (ERS -USDA website). The domestic policies for citrus and tomatoes include export refunds, product withdrawals from the market, intervention thresholds, a nd direct producer aids. Recent EU General Agreement on Trade and Tariff (GATT) and la ter the World Trade Organization (WTO) membership ha s forced some changes to CAP, resulting in less domestic support for European agricultural markets. Cons equently, the EU, once ca lled Fortress Europe is now becoming more and more accessible to the world agricu ltural producers including the U.S. producers (Adams and Kilmer, 2003). For exampl e, the EU is now the third largest regional export market for US agricultural products with imports of $6.4 billion in 2001 (Adams and Kilmer, 2003). The EU was a net ex porter to the U.S. with a su rplus of Eur 2.63 billion in 2001 while the U.S. was a net importer from the EU with a deficit of Eur 5.65 billion. The values for

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19 2000 were Eur 2.63 billion and Eur 5.51 billion respectiv ely with an exchange rate of Eur 1= US $1.12 (European Communities (EC) (2202)). Although the level of EU support for agricultur e is decreasing, it is still relatively high compared to the U.S. and rest of the world. Ho wever, the recent change s have signaled a clear trend away from market-distorti ng actions and toward direct paym ents to producers. In general, the intention of the EU is to enhance Europ ean agricultural competitiveness by setting product intervention as a real safety net measure, al lowing EU producers to re spond to market signals while protecting them from extreme price fluc tuations, and promo ting market oriented, sustainable agriculture by finishing the tran sition from product support to producer support, introducing a decoupled system of payments pe r farm which are supposedly not connected to production (Adams and Kilmer, 2003). In fact, the EU wishes to allow flexibility in production, but at the same time it also wants to guarantee income stability to producers. During the last 10 years, the EU has reduced price supports but increased direct payments to tomato, dairy and citrus producers in order to compensate them for such reductions (A dams and Kilmer, 2003). After the successful negotiation of the 1993 Uruguay Round of GATT, the EU has been progressing towards liberalization of agricultural markets includi ng citrus, tomatoes and dairy, moving from market-distorting sup port to less market-distorting re gulation. Consistent with this market liberalization trend in Europe, the EU has made major changes to common market organization for tomatoes in 2000 that were less ma rket distorting. So, there exists more market flexibility after 2000, a lesser amount of product can be removed from the market and supports for the export of tomatoes have also declin ed (Adams and Kilmer, 2003). As of 1998, the EU imports 4 percent of world tomato production an d exports 7 percent of world tomato production. In fact, very little of the worl ds fresh tomato production is expor ted fresh. The leading tomato

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20 producers for processing in 1999/2000 were the U. S. (11.6 million tons vs. 8.5 million tons in 1998/99), the EU (9.1 million tons vs. 8.0 milli on tons) and Turkey (1.8 million tons vs. 1.7 million tons) (Adams and Kilmer, 2003). Therefore, the importance of the EU cannot be ignored in terms of production, consump tion and trade of tomatoes. The members of the EU-15 are Austria, Be lgium, Denmark, Finland, France, Germany, Great Britain, Greece, Ireland, Ital y, Luxembourg, the Netherlands, Portugal, Spain, and Sweden. The export and import position of the EU in tomato trade can be visualized from Table 2-3 and Table 2-4. The tables show that U.S. fresh to mato export and import trade with the EU is important even though there are some up and down sw ings from year to year. They also depict that EUs role in worlds export and import tr ade in tomatoes as well as production is quite significant (Table 2-5, Tabl e 2-6 and Table 2-7). As the World Trade Organization (WTO) is c onstantly debating on agricultural trade with a vision towards trade liberaliza tion and/or globalization of mark ets, emphasis on international trade and competitiveness among suppliers has become a striking issue in order to ensure a larger share of world agricultural exports. The specialty crop sector like tomatoes also needs to keep pace with this potential trend. Therefore, the stud y of import demand for tomatoes into the most potential markets like the EU is very much neces sary and justified for the U.S. specialty crop industry in order to compete successfully through the assessmen t and evaluation of competitiveness in an increasingly growing domestic and global markets. Data The data on imports of fresh tomatoes have been used for 43 years i.e., for the period 19632005. The source of data is mainly the United Na tions (U.N.) Statisti cs Division-Commodity Trade Statistics Database (UN-COMTRADE) webs ite. The International Ag ricultural Trade and Policy Center (IATPC) at the Univ ersity of Florida has made arra ngements for data availability.

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21 Other sources of data are online websites main tained by the USDA and the U.N. Food and Agriculture Organization (FAO) Statistics (UN-FAOSTAT) we bsite. The required data are collected and manipulated to fit in the model to be used for this research. The data are in both volumes and values from which price/cost data can easily be calculated by dividing the value of imports by the volume of imports.

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22 Table 2-1. Production of tomatoes Year U.S. Productiona (1,000 cwt) Florida Productiona (1,000 cwt) World Productiona (1,000 cwt) U.S. Fresh Tomato Production (1,000 cwt) Florida Fresh Tomato Production (1,000 cwt) 1990 240,905 15,240 1,677,225 33,800 15,240 1991 251,448 16,170 1,670,099 33,988 16,170 1992 214,582 20,858 1,645,031 39,033 20,858 1993 230,196 17,160 1,710,874 36,663 17,160 1994 268,181 16,995 1,825,395 37,387 16,995 1995 259,798 15,035 1,911,961 34,098 15,035 1996 261,780 14,484 2,048,519 33,634 14,484 1997 232,242 13,720 1,968,839 32,777 13,720 1998 220,668 13,952 2,116,780 32,628 13,952 1999 293,455 15,820 2,352,832 36,735 15,820 2000 254,830 15,760 2,367,172 37,665 15,760 2001 220,501 14,908 2,316,384 35,527 14,908 2002 270,438 14,400 2,380,374 37,302 14,400 2003b 232,000 14,190 2,564,700 35,578 14,190 2004b 283,700 15,120 2,713,800 38,346 15,120 2005b 243,500 15,540 2,727,800 39,462 15,540 aTotal tomatoes include fresh and processed Sour ce: National Agricultural Statistics Service, U.S. Department of Agricu lture, ERS-USDA website: http://www.ers.usda.gov/Briefing/Tomatoes.and bhttp://www.nass.usda.gov/index.asp. bAlso derived from data supplied by FAOSTAT (06/20/06), Food and Agriculture Organization, United Nations. Note: 1 metric ton = 2,205 pounds and 1 cwt. = 100 pounds. Table 2-2. U.S. exports and imports of fresh and processed tomatoes compared to World Year U.S. Exports (Metric Tons) World Exports (Metric Tons) U.S. Imports (Metric Tons) World Imports (Metric Tons) 1990 157,311 2,390,374 360,995 2,407,976 1991 148,297 2,437,142 360,770 2,438,764 1992 171,292 2,477,245 196,027 2,791,387 1993 169,142 2,951,351 418,395 2,973,351 1994 169,891 3,231,714 396,040 2,949,429 1995 155,951 3,452,170 620,944 3,101,528 1996 161,279 3,356,339 737,150 3,444,013 1997 179,093 3,752,235 742,464 3,629,123 1998 158,955 3,973,383 847,320 3,681,147 1999 170,873 3,975,530 740,656 3,579,430 2000 208,564 3,786,645 730,063 3,621,868 2001 205,486 4,235,422 823,541 3,918,901 2002a 182,286 4,272,195 860,098 4,120,920 2003a 180,713 4,526,953 939,257 4,362,962 2004a 212,280 4,843,480 931,970 4,649,342 2005a 188,173 4,894,498 951,785 4,684,459 Source: National Agricultural Statistics Service, U.S. Department of Agriculture plus ERSUSDA and Bureau of the Census, U.S. Department of Commerce. aAlso Food and Agriculture Organization, United Nations. UN-FAOSTAT website. Note: 1 metric ton=1.102 short tons=2,205 pounds and 1 short to n=2000 pounds=0.907 metric tons.

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23 Table 2-3. U.S. fresh tomato exports to the EU Countries Values in 1,000 Dollars 1995 1996 1997 1998 1999 2000 2001 2002a 2003a 2004a 2005a Austria 0 0 0 0 0 0 0 0 0 0 0 BelgiumLuxembourg 44 2,737 3,811 3,729 5,689 8,706 5,978 6,372 3,461 2,845 399 Denmark 0 0 0 0 0 0 0 0 0 0 0 Finland 0 0 0 0 0 0 0 0 0 0 0 France 44 0 0 87 44 792 975 0 208 3 0 Germany 261 0 348 223 541 0 24 0 0 5 7 Great Britain 415 522 1,962 2,496 1,896 2,193 75 1,087 703 659 0 Greece 0 0 0 0 0 0 0 0 0 0 Ireland 0 0 0 0 51 0 0 0 0 0 0 Italy 8 0 0 0 0 0 94 0 0 3 0 Netherlands 109 0 1,573 803 1,163 497 0 1,355 4,144 2,549 705 Portugal 0 0 0 55 476 209 39 0 0 0 0 Spain 0 44 131 179 155 305 29 0 0 0 0 Sweden 0 0 0 0 0 0 0 0 22 0 0 Total EU-15 881 3,303 7,825 7,572 10,015 12,702 7,214 8,814 8,538 6,064 1,111 EU-25 881 3,303 7,825 7,572 10,015 12,702 7,214 8,814 8,538 6,064 1,111 EU-27 881 3,303 7,825 7,572 10,015 12,702 7,214 8,814 8,538 6,064 1,111 Source: Department of Commerce, U.S. Census Bureau, Foreign Trade Statistics and aU.S. Trade Statistics website. Table2-4. U.S. fresh tomato imports from the EU Countries Values in 1,000 Dollars 1995 1996 1997 1998 1999 2000 2001 2002a 2003a 2004a 2005a Austria 0 0 0 0 0 0 0 0 0 0 0 BelgiumLuxembourg 2,766 4,114 5,097 5,555 3,292 2, 056 1,102 1,389 2,046 3,997 2,139 Denmark 0 0 6 17 40 0 0 0 0 0 0 Finland 0 0 0 0 0 0 0 0 0 0 0 France 0 0 0 160 231 0 0 0 0 0 0 Germany 4 0 18 0 0 0 0 0 0 0 0 Great Britain 0 23 15 11 0 6 0 0 0 0 0 Greece 0 0 0 0 0 0 0 0 0 0 0 Ireland 0 0 0 0 0 0 0 0 0 0 0 Italy 0 0 23 3 0 0 9 13 0 0 4 Netherlands 21,131 42,646 52,909 64,487 57,171 46, 392 51,027 45,630 33,908 27,721 16,065 Portugal 0 0 0 0 0 0 0 0 0 0 0 Spain 1,982 3,879 7,829 10,894 10,711 10, 698 9,709 13,710 7,025 6,001 820 Sweden 0 0 0 0 5 0 0 0 9 0 0 Total EU-15 25,883 50,662 65,897 81,127 71,450 59, 152 61,847 60,742 42,988 37,719 19,028 EU-25 25,883 50,662 65,897 81,127 71,450 59, 152 61,847 60,758 43,051 37,732 19,053 EU-27 25,883 50,662 65,897 81,127 71,450 59, 152 61,847 60,758 43,051 37,732 19,053 Source: Department of Commerce, U.S. Cens us Bureau, Foreign Trade Statistics and aU.S. Trade Statistics website.

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24 Table 2-5. EUs tomato exports compared with the U.S. and World (total tomatoes) Year EUs Exports (In Metric Tons) U.S. Exports (In Metric Tons) World Exports (In Metric Tons) 1990 1,187,299 157,311 2,390,374 1991 1,296,414 148,297 2,437,142 1992 1,425,689 171,292 2,477,245 1993 1,571,570 169,142 2,951,351 1994 1,897,143 169,891 3,231,714 1995 1,838,367 155,951 3,452,170 1996 1,778,314 161,279 3,356,339 1997 1,950,304 179,093 3,752,235 1998 1,805,104 158,955 3,973,383 1999 1,982,756 170,873 3,975,530 2000 1,800,303 208,564 3,786,645 2001 2,051,591 205,486 4,235,422 2002a 1,992,242 182,286 4,272,195 2003a 2,089,092 180,713 4,526,953 2004a 2,254,156 212,280 4,843,480 2005a 2,207,576 188,173 4,894,498 Source: U.S. Tomato Statistics, Econom ic Research Service (ERS), USDA and aFood & Agriculture Organization, Unite d Nations. UN-FAOSTAT website. Table2-6. EUs tomato imports compared w ith the U.S. and World (total tomatoes) Year EUs Imports (In Metric Tons) U.S. Imports (In Metric Tons) World Imports (In Metric Tons) 1990 1,307,863 360,995 2,407,976 1991 1,401,731 360,770 2,438,764 1992 1,495,584 196,027 2,791,387 1993 1,576,035 418,395 2,973,351 1994 1,632,286 396,040 2,949,429 1995 1,577,282 620,944 3,101,528 1996 1,735,146 737,150 3,444,013 1997 1,807,677 742,464 3,629,123 1998 1,726,751 847,320 3,681,147 1999 1,743,478 740,656 3,579,430 2000 1,806,436 730,063 3,621,868 2001 1,919,086 823,541 3,918,901 2002a 1,928,620 860,098 4,120,920 2003a 2,068,209 939,257 4,362,962 2004a 2,155,020 931,970 4,649,342 2005a 2,124,053 951,785 4,684,459 Source: U.S. Tomato Statistics, Econom ic Research Service (ERS), USDA and aFood & Agriculture Organization, Unite d Nations. UN-FAOSTAT website.

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25 Table 2-7. EUs tomato production compared wi th the U.S. and World (total tomatoes) Year EUs Production (In 1,000 cwt.) U.S. Production (In 1,000 cwt.) World Production (In 1,000 cwt.) 1990 298,035 240,899 1,677,225 1991 298,680 251,452 1,670,099 1992 279,777 214,510 1,645,031 1993 280,862 230,184 1,710,874 1994 299,553 268,192 1,825,395 1995 286,962 259,792 1,911,961 1996 324,208 261,777 2,048,519 1997 303,232 232,235 1,968,839 1998 326,787 220,660 2,116,780 1999 361,113 293,453 2,352,832 2000 358,844 254,830 2,367,172 2001 332,034 220,501 2,316,384 2002 346,133 270,438 2,380,374 2003a 333,250 232,000 2,564,700 2004a 379,494 283,700 2,713,800 2005a 373,221 243,500 2,727,800 Source: U.S. Tomato Statistics, Econ omic Research Service (ERS), USDA. aAlso derived from data supplied by FAOSTAT (06/20/06), Food and Agriculture Organization, United Nations. Note: 1 metric ton = 2,205 pounds and 1 cwt. = 100 pounds.

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26 CHAPTER 3 LITERATURE REVIEW In order to formulate a model to analyze the import demands for tomatoes into the European Union (EU) and the United States (U.S.) it is necessary to look at the literature on import demand estimation. The theory of demand has occupied a vast area in the field of economics covering consumer theory as well as th e theory of the firm. Globalization of markets has increased the emphasis on international trade and on the competitiveness among exporters. So, import demand analysis is a vital point in determining the competitiveness in international trade. Armington Trade Model International trade flows are classified on three characteristics such as the kind of merchandise, the country (region) of origin or th e seller, and the country (region) of demand or the buyer. The assumption frequently used in theo ries of demand is that merchandise of one kind supplied by one country is a perfect substitute fo r merchandise of the same kind supplied by any other country indicating infinite elasticities of substitution and constant price ratios. But, in reality there are lags in buyers responses as well as some other imperfec tions in their behaviors that should not be overlooked. So, it is preferable to recognize th at any feasible world model of demand would find few, if any, me rchandise with perfect substitutability. Bearing this in mind, Armington (1969) has presented a general theory of demand for products not only distinguished by kinds, but also distinguished by place of producti on. In his paper, a distinction has been made between goods and products in the sense that goods are distinguished only by their kinds whereas products are distinguished both by th eir kinds and place of production. Products are differentiated from the buyers vi ewpoint according to the area of the suppliers residence and they are assumed to be imperfect substitutes in demand. If there are 5 goods and 20 supply

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27 regions, then the number of products distingu ished will be 100. The geographic area not only serves as a basis for distinguishing products by orig in, but also as a basis for identifying sources of demand. Now the problem in Armingtons (1969) model is to simplify the product demand functions systematically for practical use in estimation and forecasting. Beginning with the general Hicksian model, imposition of a sequence of more restrictive as sumptions leads to a highly simplified specification of the product de mand function that reve als the relationships between demand, income and prices. The basic modification to the Hicksian model is the assumption of independence, meaning that the b uyers preferences for any given kind of product are independent of their purchases of any ot her kind of product. With this assumption of independence, it is possible, in principle, to measure the quantity of each good demanded by each country. There are demands for different gr oups of competing products and each of these demands can be considered to be a market a nd suppliers from different countries could be expected to compete in that market. Thus, the demand for a particular product can be expressed as a function of the size of the market for that product and the relative prices of competing products. Another assumption in Armingt ons (1969) analysis is that the market share of each country is unaffected by changes in the size of th e market as long as the relative prices remain unchanged. This additional assumption implies that th e size of the market is a function of money income and the prices of different goods. Co mbining this with the earlier product demand function, the demand for any product can be expre ssed as a function of money income, the price of each good, and the price of that product relative to all other products in the same market. So, the approach used in his study assumes that (a ) elasticities of substitution between competing

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28 products in any market are cons tant irrespective of market shares, and (b) elasticity of substitution between any two products competing in a particular market is the same as the elasticity of substitution between any two other products competing in the same market. Hence, a specific type of relationship between the demand for a product, the size of the corresponding market and the relative prices has been suggest ed by these assumptions. The price parameter in this relation is the elasticity of substitution in th at market. Analysis of changes in demand for any product is possible by differentiating the dema nd functions. The change in demand for any product will depend on the growth of the market a nd on the change in the products market share in that market collectively. The change in market share of a particular product will depend on the change in its price relative to the average change in prices of all other pr oducts in that market. The change in growth of the market depends on the change in income and income elasticity of demand for the respective good. Thus, this study em phasizes on the relevancy of demand theory to the research in some areas of tr ade analysis as well as forecasting. Differential Approach and the Rotterdam Models Seale et al. (1992) showed a Ro tterdam application to interna tional trade with a differential approach. A Rotterdam import allocation model is used to estimate a geographic import demand system for U.S. fresh apples in four im porting markets Canada, Hong Kong, Singapore and United Kingdom. The differential approach is widely used in estimating consumer demand systems, but in case of estimating import demand, it is used less frequently. Three of such studies are Theil and Clements (1987), Clements and Th eil (1978) and Lee, Seal e and Jierwiriyapant (1990). Theil and Clements (1978) estimated deri ved import demands for four aggregate import groups using the differential appr oach in production theory. Clemen ts and Theil (1978) used the same approach and estimated import demands fo r 13 individual as well as four groups of countries for three broad categor ies such as food, raw material s and manufactures with the

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29 assumption of homothetic technology. Following Ba rnett (1979), Lee, Seale and Jierwiriyapant (1990) used the differential appro ach in utility maximization in or der to estimate Japanese import demands for five kinds of fresh fruits and the ge ographic import demands for citrus juice. In all of the above studies, the Rotterdam mode l has been used for estimation purpose. The study by Seale et al. (1992) estima ted geographic import demands using the Rotterdam model. They treated each of the importi ng countries as an individual consumer, following Mountain (1988) who showed that th e Rotterdam model like other popular flexible functional models is at least a second-order approximation of the underlying demand system. In this study, multistage budgeting has been utilized where an importing country first allocates total income (expenditures) between domestic and im ported goods. Then, the total expenditure on imports is allocated among various imported goods and finally, depending on the expenditure for an import, it is allocated among different suppliers of each good. Th is is a method like the utility tree approach (Barten 1977) and can easily accommoda te the differential approach to the utility maximization and is useful for estimating demands for disaggregated imports. Preferences for imports are based on block-wise dependence th at enables estimation of geographic import demand subsystems independent of the import demands for all other goods. The conditional import demand system is derived through the differe ntial approach and then parameterized as per a Rotterdam specification. The Rotterdam parameteri zation is attractive in this case, because it permits nested testing for restrictions on hom ogeneity, symmetry, homotheticity and separability (preferences being additive). The Seales (1992) study calcu lated expenditure and price elasticities from estimated parameters using both the Rotterdam absolute pr ice model (Rotterdam A.P.) and the Rotterdam preference independence model (Ro tterdam P.I.). The elasticities thus calculated, measure the

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30 impacts on import shares among fresh apple supp liers as expenditures on total apple imports change and as the prices of imported apples from various geographic locations change. They also used Workings (1943) model for calculating income elasticities of demand for fresh imported apples as a group and by sources of supply. Lee et al. (1994) have discussed the choice of model in consumer behavior analysis in Taiwan. Numerous specifications of a demand sy stem in consumer demand analysis have evolved through time. These include linear and quadratic expenditure systems, translog models, the Working model, the Rotterdam model and th e Almost Ideal Demand System (AIDS). Among all of them, two systems have become popular in agricultural economics, the Rotterdam model and the AIDS. However, the underlying assumpti ons for these two systems have different implications. The marginal expend iture shares and the Slutsky term s are taken to be constants in the Rotterdam model whereas they are considered to be functions of budget shares in AIDS. In order to choose an appropriate demand system statistical tests are conducted when the underlying competing systems are nested (A memiya, p.142). On the other hand, when the systems are not nested, an alternative testing procedure is needed. De aton (1978) conducted a non-nested testing procedure in order to comp are competing demand systems with the same dependent variables that are not applicable in comparing Rotter dam and AIDS, as the dependent variables are different. However, Barten (1993) explained that the Rotterdam and the AIDS are special cases of a more general demand system and also nested within that system. So, he suggested pair-wise and higher-ord er testing procedures to choos e the best fitted system. Thus, the Lee et al. (1994) looked into how pri ces and income influenced Taiwanese consumer demand for the period 1970-89 and how the elasti cities of demand evolved through time. They examined four different versions of the demand system indicated by Barten (1993) such as the

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31 Rotterdam, a differential version of the AIDS, th e Dutch Central Bureau of Statistics (CBS) system and the Netherlands National Bureau of Research (NBR) system. Then, a general model nesting all these four systems has been developed in order to fac ilitate choosing the best fitted one. The differential approach has been used by ma ny other researchers. Rossi (1984) has made use of this approach in the th eory of multiproduct firm in order to analyze input demand and output supply in Italian agriculture. In case of multiproduct and multifactor production structure, the firms technology is usually described by a production, cost or profit function. The approximation process may be in variable space co mprising prices, quantitie s, or price-quantity ratio like in the case of classical translog specific ation. On the other hand, it may be in parameter space as in the case of so-called differential approach. Since the te chnology of a firm is basically unknown, the differential approach has an advantag e in the sense that it does not specify any particular form, but can accommodate different te chnologies not being bias to any particular form. In this study, he actually followed La itinen and Theil (1978, 1980) in estimating the parameters of aggregate multiproduct technologie s using differential approach. He extended the works of Laitinen and Theil in case of shortrun with fixed factors of production and then considered the dynamic adjustments along with the variables to model the role of weather in agriculture. Laitinen and Theil (1978) considered the demand and supply of the multiproduct firm without the usual spec ializing assumptions of inpu t-output separability or constant elasticities of substitution or scale. They estimated a system of input demand equations under the condition of cost minimization and a system of output supply equations under the condition of profit

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32 maximization. The prices are described in te rms of substitution and complementary relation among inputs and among outputs in the demand and supply equations. Demand System and Functional Formulation Barten (1977) has reviewed the work done on the formulation and estimation of complete systems of consumer demand functions and delineated the related problems a nd issues that are of both theoretical and empirical in nature. From the theoretical consideration, constraints are imposed on such systems to deal with empirical problems like lack of sufficient data. There are various alternative approaches to deal with the issue and it is yet to make a clear-cut choice. Continuous research is going on concerning the problems of sp ecifying and estimating such systems. The present review emphasizes the nature of the approach, its possibilities and limitations. It is basically an em pirical approach since it aims at the formulation of the system that is to be estimated using actual data. Barten (1993) also discussed the choice of f unctional form in consumer allocation models, which are based on microeconomic theory of consumer demand. Allocation models are concerned with optimal allocation of given means for different alternatives, or as its dual, the minimal use of these means to achieve a given set of objectives. These models are formulated not only for consumer demand analysis, but also for demand for i nputs in production, and composition of imports by origin of supply. Four ba sic approaches to arrive at demand equations, satisfying required properties, have been described. The first one is from a functionally specified, increasing and quasi-concav e utility function like u = u ( q1, qn) that is maximized subject to a budget constraint i piqi = m Then the first order conditions ar e solved to get quantities as a function of prices and income. Th e parameters of the utility func tion become the constants of the demand equations. An example of this approach is the linear expenditure system (L.E.S.) that

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33 can be seen in Deaton (1975). The second approach starts from a functionally specified indirect utility function like np p m u u ......, *1 and using Roys identity rule, one can obtain an estimable demand equation such as m u p u p m qi i iln / ln /. (3-1) Example of this approach can be stated as the Indirect Translog Utility Function used by Christensen et al. (1975). Bartens (1993) third approach is based on a specified expenditure function expressed in terms of utility and prices like np p u e e ......., ,1 Now, applying She phards lemma (i.e., j ip e w ln ln ) provides Hicksian demand as n i ip p u h q ........, ,1 (3-2) from where u can be eliminated by using an expenditure function in order to express it in terms of m and p A good example of this type of specifica tion is the Almost Ideal Demand System (AIDS) by Deaton and Muellbauer (1980). The fourth approach is related with some kind of double-logarithmic specification. Many earlier empirical demand studies used a doublelogarithmic specification with constant elasticiti es and they seem to work well empirically, but are not sufficiently adequate to explain theore tical restrictions. For example, the constant elasticity restriction requires a constant budget share. So, re searchers working with a doublelogarithmic system started imposing theoretical rest rictions on the estimation process in order to make it statistically efficient. Theil (1965) st arted such a double-loga rithmic specification. Production Approach and Utility Approach Davis and Jensen (1994) discussed the suprem acy of the production theory approach over the utility approach in import demand estimation. They pointed out the drawbacks of the two-

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34 stage utility maximization approach that has be en widely applied in estimating agricultural commodity import demand systems and elasticities suggesting an altern ative two-stage profit maximization approach that can overcome those limitations, but still retains the advantages. Since the nature of most imported commodities are inputs in the production process, the use of a utility-based demand system in estimating impor t demands will have conceptual as well as empirical disadvantages. The conceptual disadva ntage of the two-stage utility maximization approach arises from the fact that most im ported agricultural commodities are inputs, and not final goods entering consumers u tility function. This conceptual misspecification of an import demand system has other empirical disadvantages First, it is believed by most agricultural economists that aggregation under utility-based import demand models (i.e., defining the firststage utility aggregates) is a diffi cult job to form a consensus. In most utility-based models, the procedure is to pick a commodity and assume that it is w eakly separable from all other goods that should have been included in the utility function for logical consistency. Thus, this unique condition of separability is not actually an intu itive one and the choice of first-stage aggregates becomes confusing and debatable. Secondly, sin ce most of the models are a conditional demand system, the estimated elasticities are also conditional elasticities. The problem with these conditional elasticities is that they do not explain all of th e price effects captured by the unconditional elasticities and hence, the use of c onditional elasticities may lead to biased and erroneous inferences and policy recommendati ons. Thirdly, the unconditional elasticities obtained from such a misapplied utility-based impor t demand system are not structural estimates, rather they are reduced form estimates a nd the regressors used are incorrect. Under the above circumstances, Davis and Jensen (1994) prescribed an appealing alternative conceptual approach that satisfies th e criterion such as (a) defining the first-stage

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35 aggregates and thereby the estimation of unconditional elasticities easily and more intuitively, (b) making structural parameter estimation (der ived demand) possible, and (c) retaining the advantages of two-stage utility maximization procedure. This a pproach is to model the import demand estimation in a two-stage, multiproduct, profit maximization framework under production theory. As soon as the producer theory is applied, the conceptu al problem of treating imports as separate final goods is overcome and as a result, the parameters estimated thereon will be structural. Again, as a two-st age method, it will also retain all the advantages of the two-stage utility maximization procedure. Moreover, specifyi ng the first-stage aggregates is more intuitive in a producer theory model of profit maximization than in a consumer theory model of utility maximization. Hence, the estimation of unconditi onal elasticities becomes less questionable and/or debatable. The presentation, being the in tegrated efforts of va rious authors (Blackorby, Primont, Russell (BPR) (1978); Bliss (1975); Chambers (1988); Fuss (1977); Lau (1972) and Yuhn (1991)), has been given in the following ways. First Stage In Davis and Jensen (1994) the transforma tion function of the mu ltiproduct industry is assumed to be well behaved, intertemporally sepa rable, and homothetically separable in input partition In and can be represented by F ( q1,, qm, X1,., Xn) = 0. Here, q and X are outputs and aggregate inputs respec tively. The aggregate input Xi is defined as Xi = Xi ( xi1,, xin ), and i = 1,.,. n where, the xijs are disaggregate inputs. Assuming perfect competition to prevail, profit maximization may occur in two stages th at will be consiste nt with single-stage optimization (Bliss, chapter 7, pr operty 3). The first-stage fo r a profit maximization problem solves the objective function T X q F WX pq W px q : ,max, (3-3)

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36 where, p and q are 1 m vectors of output prices and quantities respectively, X and W are 1 n vectors of aggregated input quantiti es and price indices respectively. Wi is defined as a linearly homogeneous aggregator function in the form of Wi = Wi ( wi1. win ) and it is dual to the Xi. The wij is the factor price correspon ding to a disaggregated input xij and T represents the technology set of the industry. From the above aggregate profit function ( p W ), the output supplies and input demands can be derived by applying Hotellings (1932) lemma, which will be ), ( W p q q pl l l l = 1,.., m (3-4) ), ( W p X X Wi i i i = 1,. n (3-5) which are homogeneous of degree zero in p and W according to Eulers theorem. Second Stage Following Davis and Jensen (1994) as the tr ansformation function is assumed to be homothetically separable in the In partition, the sufficient conditi ons for two-stage optimization are satisfied (Blackorby et al., 1978) and the cond itional demands are derivable from Hotellings (1932) lemma by differentiating the aggregate prof it function with respect to the disaggregated input price wij. ij ijx w i = 1,.., n ; and j = 1,.., Ji. (3-6) In duality theory, there are two equivalent explanations for an optimal level of xij because, theoretically the second stage of the two-stage profit maximi zation problem can be expressed in two equivalent ways. Even though these two forms ar e the same as those of the two-stage utility maximization approach, their solutions to the se cond stage problem differ empirically. One of

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37 the two forms is to minimize the cost of acquiring a predetermined level of aggregated input such as ....... : ,1 1min in i i i ij J j ij x i i ix x X X x w X w Ci ij i = 1,.., n (3-7) which can be solved for xh ij = xij( wi, Xi). Here, Ci( wi, Xi) is the cost function, wi is the vector of prices, xh ij is the Hicksian conditional input de mand function since it is conditional on predetermined aggregate input level ( Xi) from the first stage. The Hicksian input demand function is homogeneous of degree zero in wi by Eulers theorem. Following duality theory, the alternative form of the above minimization problem will be : ,....., ,1 1max i i ijJ j ij ij i iJ i i i x i i ix w C x x X X C w X i = 1,, n (3-8) giving a solution for xm ij = xij( wi, Ci). The Xi( wi, Ci) part of the Equation 3-8 represents the indirect production function which can be considered as analogous to indirect utility function of the utility maximization problem. By Eulers theorem the solution, xm ij = xij( wi, Ci) is homogeneous of degree zero in wi and Ci and this is the conditional Marshallian input demand function that is conditional on the predetermined expenditure level Ci. So, solving Equation 3-7 and (3-8) we can get Hicksian demand and Marshallian demand respectively and they will be equivalent at the optimal point according to Chambers (1982) and Davis and Kruse (1993). Therefore, it is possible to calculate the c onditional and unconditional elasticies and their relationship by applying the twostage profit maximization appro ach to import demand analysis. Import Demand and the Producer Theory Burgess (1974a) explained the theory of import demand in a general equilibrium model. The traditional general equilibrium model of inte rnational trade treated imports as final goods with a perfect domestic substitute. So, the elastic ity of demand for imports depends on domestic

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38 supply and demand responses influenced by soci etys preferences, a nd the transformation function indicating factor endowment and techno logy. But, this theoreti cal framework normally collapses when estimation of the elasticity of import demand is sought. In fact, some favorable functional forms have been chosen for facilita ting estimation of the parameters of interest. Hence, the logarithm of th e quantity of imports is considered as a linear function of the logarithm of income and the logarithm of the ratio of import price to a price index of all domestic goods. Thus, the parameters are to measure the income elasticity and price elas ticity of imports. But, without imposing an arbitrary separability restriction on the cons umers choice between domestic and imported goods, it is not possible to derive this estimating equation just from an explicit model of rational behavior. In Burgess (1974a), few attempts have subse quently been taken to develop a theory of import demand from the microeconomic standpoint whereby restrictions have been imposed on the underlying analytical structure. These restrictions need to be em pirically justified rather than assumed a priori. So, imports are regarded as final goods entering into the consumers utility function directly with no perfect domestic s ubstitute. One such framework is developed by Gregory (1971) and it assumes that a constant elasticity of substitution (CES) functional form can represent societys preference s. Thus, the logarithm of the ra tio of imports to domestic goods is a linear function of the logarithm of the ratio of their prices. This model has an advantage in the sense that the estimating equation gives an es timate of the elasticity of substitution between imports and domestic goods and the estimate of own price elasticity can readily be obtained from there. But, its major disadvantag es are (1) the maintained hypot hesis of separability between imports and all domestic goods indicating that th e partial elasticities of substitution between imports and all other domestic goods are equal, a nd (2) ignorance of the f act that international

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39 trade occurs in bulk as inte rmediary goods requiring further processing before reaching consumers. So, Burgess (1974a) developed a model of import demand without having these two major difficulties. Moreover, he explicitly incorporat es the theory of import demand into a general equilibrium framework. The primary concern in this area has been to look at the responsiveness of import demand to changes in price and inco me in a partial equilibrium framework for predicting balance of payment consequences. But, in this general equilibrium framework of import demand the balance of payments adjustme nt process prevails and the primary concern becomes the effect of changes in import prices due to tariff policy, on the returns to primary factors as well as on income distribution. He ag rees that most of the imported goods require further processing before delivery as a final product. The processing may be of complete transformation, or it may simply involve stori ng, handling, transportation, distribution and other marketing activities. These processing activities require domestic capital and labor. So, it is a policy question to address how ta riff policy changes the domestic pr ice of imports and how this will affect the competitive returns to the primar y factors and also the distribution of income. An assumption of this production theory mode l is that imports are purchased by firms trying to minimize the cost of delivering a si ngle product to the final consumer. Since a multifactor generalization of the Cobb-Douglas and CES functional forms are not flexible enough for analyzing issues in this respect, the transce ndental logarithmic func tional form developed by Christensen, Jorgenson and Lau (1973) has been used. It permits the Allen-Uzawa partial elasticities of substitution between factors to differ and it does not impose any arbitrary separability restrictions a prio ri. Thus, it enables the researcher to test the hypothesis about the effects of tariff policy on real in come and distribution of income.

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40 Following a cost minimization approach, Burgess (1974b) estimated import demand equations with a three-input, and two-output technology model. Imports are taken as an additional input along with the prim ary inputs of labor and capital, and a joint cost function is assumed to express technology. Now the produci ng unit combines labor and capital with the imported materials in order to minimize the cost of production of a specified bundle of consumption goods and investment goods. Technology, therefore, consists of two outputs, two primary inputs and imported materials. The transce ndental logarithmic joint cost function used in this analysis is competent e nough to test the maintained hypothe ses of most of the previous studies. The partial derivativ e of the joint cost function with resp ect to the price of an input gives the cost minimizing level of that input demande d. This logarithmic derivation with respect to factor prices will yield the cost shares. In the same way, the partial derivative of the joint cost function with respect to output gives the marginal cost of the respective output and under the condition of a perfectly competitive situation, th e marginal costs equal output prices. So, the logarithmic derivation with respect to output wi ll provide an expression for revenue share. Burgess (1974b) assumed a maintained hypothesi s of constant returns to scale technology and estimated the cost function using cost shar e and revenue share equations derived from the logarithmic derivation of the joint cost function. It found c onvincing results against the traditionally maintained hypothesis that technolo gy is separable between inputs and outputs. Changes in output composition sign ificantly influence the optimum input mix at any given set of factor prices. Therefore, the opt imum level of input mix depends not only on factor prices, but also on the composition of final demands. Accord ing to the study results, changes in composition of output in favor of consumption goods will in crease the demand for labor, but decrease the demands for capital and imports. The study also re jected the traditionally maintained hypothesis

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41 of separability between inputs a nd outputs since it has been f ound that when labor and capital, and also labor and imports are substitutes, capit al and imports are complements. This indicates that any measure that reduces the price of capital will enhance the demands for imports that will eventually have adverse effects on the countrys balance of payments. Diewert and Morrison (1989) used a producer th eory approach to generate export supply and import demand functions. The approach us es an economy-wide gross national product (GNP) function or a restricted pr ofit function with exports as out puts and imports as inputs. This kind of approach in modeling trade functi ons using a production theory framework was introduced by Kohli (1978). Unlike the traditi onal approach, imports ar e considered to be intermediate inputs into the producing sector on one hand, an d exports are regarded as nondomestic outputs produced by the nations privat e production sector. This approach has an advantage over the traditional approach in th e sense that one can model only the private production sector of the economy, ignoring the complexities of modeling the consumer sector. Duality theory can be applied in order to derive the producer suppl y and demand functions conveniently based on the consistency with profit maximizing behavior. Diewert and Morrisons (1989) model of produc er behavior is based on the short run competitive profit maximization motive, holding capit al as fixed. Producers take the wage rate as fixed and can hire as much labor as required at the going wage rate. So, the profit maximizing firms, operating under conditions of perfect co mpetition, actually face the domestic and international price vectors and a ve ctor of domestic primary factor stock, and they in fact, make the domestic as well as foreign demand and suppl y decisions. The technology is represented by a production possibility set from which a well-define d profit function or restricted profit function can be obtained. Since the translog restricted profit function implemented by Kohli (1978)

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42 frequently fails to maintain theoretical curvat ure conditions, a special case of the Biquadratic Restricted Profit Function defined by Diewert an d Wales (1987) has been used in this study. Kohli (1978) has modeled the substitution possi bilities between Canadi an imports, exports and domestic inputs or outputs with a simila r approach followed by Burgess (1974a, 1974b) in regard to the treatment of imports. In the study, im ports are considered to be inputs like labor and capital, and exports are considered to be an output of technology. Output consists of investment goods and consumption goods. The technology represen ted by a restricted profit function having labor and capital fixed in the short run, and prices of imports, exports, consumption goods and investment goods as exogenous, is somewhat similar to Samuelsons (1953-4, 1958) GNP function. The import demand and export supply func tions, along with the supply equations for consumption goods and investment goods and also the inverse demand functions of domestic factors are simultaneously derived from this GNP function. In the estimation process the symmetry and homogeneity restrict ions are assumed to hold. The procedure can estimate import and export functions without assuming that outp uts and domestic inputs can be aggregated, and at the same time it can focus on the substituti on possibilities inherent in the production technology. Truett and Truett (1998) invest igated the Korean demand for imports and the impacts of trade liberalization on domestic factor inputs using a translog cost f unction using a production theory approach. They also regarded imports as productive inputs. The advantage of looking at imports as factors of production is that the impact of changes in import prices (due to tariff and/or other trade restrictive policies) on domestic input demands, domestic output prices and on the quantity of imports demanded, can be observe d for appropriate decision making. Of course, the impact will depend on whether the imports have a complementary relationship or substitute

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43 relationship with domestic inpu ts. The model used by them assumes that aggregate output consists of two types of goods consumption goods and investment goods whereas the inputs are classified into three types labor, capital and im ports. In the early stud ies where imports were treated as final goods, the demand for imports was taken to be a function of national income, the price of imports, and the price of domestic goods, with some exchange rate adjustments (Houthakker and Magee ( 1969) is an example). However, the idea of imports as a produc tive factor, as mentioned earlier, was subsequently adopted by Burgess (1974b, p.225). The importance of treating imports as a productive factor is that if they are a substitute for or have a complementary relationship with one or more domestic inputs, then trade polic y may affect domestic factor income and its distribution. For example, when there is a substi tute relationship between imports and domestic inputs, any reduction in import re striction will decrease the demand for domestic inputs in the short run. But, if they have a complementary re lationship, the reduction in trade restriction will have positive impact on the demand for domestic inputs. In the Truett and Truett (1998) study, it is assumed that imports are combined with domestic inputs (labor and capital) by the produc er having an objective of minimizing cost of producing a bundle of goods to be sold domestical ly or abroad. They used a translog cost function with its corresponding input share and revenue share equa tions and estimated the cross price elasticities of dema nd between different pairs of inputs and also the di rect price elasticities of demand for them. The question of separa bility of outputs (i.e., consumption goods and investment goods) has been inve stigated to look at whether Koreas demand for imports is affected by the composition of outputs or not.

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44 Trutt and Truett (1998) followed the Zellner efficient method (ZEF) (Zellner, 1962) in estimating the cost function, cost share and reve nue share equations by iterating on the estimated covariance matrix until convergence is reached. It c ould be argued that both prices and quantities may be considered as endogenous and it is more a ppropriate to use an iterative three-stage least square method (I3SLS) with some instrumental variables. The procedure has a problem in selecting the instrument variable since there is no straightforwar d method of doing so and thus it becomes somewhat arbitrary. As a result, the esti mates may be too sensitive to the instrumental variables chosen. This is the li mitation of cost function approac h. However, the results of this procedure are found to be simila r to those of the maximum likelihood method in various studies. Truett and Truett (1998) focuses its a ttention on the hypothesis of input-output separability. This means that the marginal rate of transformation between various products is independent of the composition of inputs and the marginal rate of substitution between factor pairs is independent of the composition of outputs. So, a sufficient condition for this is that all the interacting terms are zero. It suggests that wh enever there exists an input-output separability, changes in output mix will not affect the co st minimizing input mix. The study also gives estimates of different elasticities of interest such as direct and cross pr ice elasticities of demand for inputs as well as the invers e price elasticities of output supply and the elasticity of input prices in response to output prices etc. The elasticities are expre ssed in estimated parameters and cost and revenue shares. Inverse Demand Analysis Huang (1988) has provided a framework for estimating a complete price dependent demand system i.e., an inverse demand system. An inverse demand system is one where prices are functions of quantities demanded and income. It is as important as the quantity dependent demand system. It explains price variations as functions of quantity variations and it has

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45 analogous properties like the regular demand syst em. Agricultural economists (i.e., Fox, Houck, Waugh) have long before recogn ized that lags between produc tion decision and commodities marketed may predetermine quantities with so me price adjustments for market clearing. Therefore, quantities rather than prices are more appropriate instrumental or control variables for analysis of many types of agricu ltural policies and problems. An inverse demand system is also theoretically justified in the classical demand theory framewor k. It is indicated by Hicks (1956, p.83) that the Marshallian demand has two functions such as (1) it shows the amounts consumers will take at given prices, and (2) it shows the prices at which consumers will buy given quantities. Hence, the second function, quantit y into price implies the inverse demand system. Applications of inverse demand systems are found in Huang (1983, 1988), Barten and Bettendorf (1989), and Moschini an d Vissa (1992). From empirical standpoint, inverse demand and regular demand systems are not equivalent. In order to avoid statistical inconsistencies, the right-hand side variables in such systems shoul d be ones not controlled by the decision maker. Usually, the consumer in most i ndustrialized economies is a price taker and quantity adjuster for most goods and services purchased. In this case, a regular demand system is indicated. However, for certain other goods like fresh fr uits and vegetables, fresh fish et c., supply is very inelastic in the short-run and the producers are eventually price takers. Price taking producers and price taking consumers are linked by a group of traders who select a price that is expected to clear the market. This means, for fixed quantities the wholes ale traders practically o ffer prices that are low enough to induce consumers to buy the entire availa ble quantities. Hence, the traders set prices as a function of quantities whereby causality goes fr om quantity to price. In such cases, inverse demand systems are indicated.

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46 Huang (1988) develops an inverse demand system and applies it to estimate price flexibilities for thirteen U.S. aggregate f ood groups and one non-food sector for 1947 through 1983. The demand system is estimated using a constraint maximum likelihood method. The concept of distance function and its related substitu tion effect and scale flexibilities are used. The system explores the interdependent nature of food price variations in response to changes in quantity. The price flexibilities indicate the ch ange in commodity price needed to induce consumers to absorb a marginal increase in th e quantity of that comm odity or others. The estimated scale flexibilities prov ide the response of a commodity price to a proportionate change in the quantity of all goods. They also give an important linkage between compensated and uncompensated flexibilities. In order to understand an inverse demand system, one may illustrate the price effects of a marginal change in quantities demanded considering Andersons (1980) suggestion that the scale slope of quantities de manded plays the same role as the income slope in ordinary demand system. Moschini and Vissa (1992) also presented an inverse demand system that can be estimated in a linear form. They explained how to derive an inverse demand system that resembles one of the most commonly used demand systems in app lied demand analysis, i.e., the Almost Ideal Demand System (AIDS) of Deaton and Muellb auer (1980) having its popularity for the availability of an approximate linear version. So, they named thei r system as the Linear Inverse Demand System (LIDS). In deriving an inverse de mand system, one can start either from a direct utility function and exploit Wolds identity yielding ordinary inve rse demand system, or alternatively start from a dist ance (transformation) function an d exploit Shephards theorem yielding compensated inverse demand system. They derived the system (LID S) from a distance

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47 function and showed that it has good approximati on properties compared with inverse translog demand system (ITL) and nonlinear inverse demand system (NLIDS). Barten and Bettendorf (1989) used inverse dema nd system for price formulation of fish explaining why people pay for diffe rent types of fish the record ed prices. Gorman (1959) first established fish as a respectable, interesting and challenging subject in demand analysis. He started with the proposition that the price of fish depends in pa rt on a function of its quantity consumed and income, and in part on the shadow prices of fundamental characteristics shared by all types of fish. Following Gorman, they related th e price of each type of fish to its quantity demanded and to total real expenditure on fish. Th eir study refers to eight major types of fresh sea fish landed at Belgian fishery port. They a ssumed a weak separability of the total commodity bundle into these types of fish on one hand a nd other types on the other hand. So, only the quantities of these fish and their prices matter. They also assumed that collective consumer behavior for fresh sea fish could be interpreted as that of the rational representative consumer. Therefore, they expressed market demand by a system of Marshallian demand functions and deduced the inverse demand system. For the es timation purpose, they estimated a Rotterdam variant of inverse demand system. Differential Production Approach Washington and Kilmer (2002a, b) used a di fferential production appr oach to estimate import demand of whey with a comparison to Ro tterdam model. The application of production theory to international trade is not a new concept. Previous st udies using a production theory approach to international trade include Burg ess (1974a) and (1974b), Kohli (1978) Diewert and Morrison (1989), and Truett and Truett (1998). Each of these studies reco gnized that most goods in international trade require further processing before final delivery to consumers. Even though a traded product is no t physically altered, activities such as handling, storing, repackaging,

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48 transportation, insurance, and reta iling occur. Thus, a significant amount of domestic value is added when the final product reaches the consum er. Therefore, it seems more appropriate to view imported products as inputs rather than as final goods even if goods are not physically transformed. The production approach views imports as in termediary goods (inputs) and not as final goods entering the consumers utility function. Most of the imports arrive in a country in bulk and consumers rarely buy commodities in bulk or directly from exporting countries. Following the methodology of Laitinen and Th eil (1978) and Theil (1980a, b) the model is derived from the differential approach to the theory of the firm where firms maximize profit in a two-stage procedure, i.e., in the first st age, determining the profit maximizing level of output to produce and in the second stage minimizing the cost of producing that profit maxi mizing level of output. According to Laitinen and Theil, and Davis and Jens en (1994), this procedure is consistent with a one-step or direct profit maximization procedur e. The first stage provides the output supply equation and in the second stage, the conditional factor demand system is obtained. From the results of both stages, a system of uncondi tional derived demand e quations is derived. The advantages of the production theory appr oach over the utility approach to import demand estimation have been discussed by Davis and Jensen (1994), Kohli (1991) and Washington and Kilmer (2002a, b). The striking points are the f acts that (a) most imported agricultural commodities are inputs and not final goods, (b) specifying the first stage aggregates is more intuitive when using the production theory approach, (c) it is easier and more intuitive to estimate unconditional elasticities using production theory, (d) the estimated parameters using production theory will be struct ural parameters, and (e) viewing imports as intermediate goods not only has its merits in correctne ss, but it also leads to substan tial simplifications theoretically.

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49 For example, the demand for imports can be deri ved from production theory and there is no need to model final demand and as such it can avoid th e difficulties that arise when we aggregate over individual consumers. Washington and Kilmer (2002a, b) articulated th e system of equations for estimating such import demand in the following manner. In fact, th e objective of a competitive firm in the first stage is to identify the profit-maximizing level of output by equating marginal cost with marginal revenue. This procedure yields the differential output supply equation N j j jw d p d Q d1 *) (log ) (log ) (log (3-9) where Q*, p* and wj represent the output, output price and the price of inpu ts respectively; and are the price elasticity of s upply and the elasticity of supply with respect to input prices respectively. N is the total number of i nputs used in production. In the second stage, the firm minimizes its input costs/expenditure. Here, the differential factor demand model is derived that will be us ed to estimate the system of source specific derived demand equations. This is speci fied as (Washington and Kilmer (2002a,b)) n j j ij i i iw d X d x d f1 *) (log ) (log ) (log (3-10) where if is the factor share of imported good x from source country i in total input cost; xi and wi represent the quantity and pri ce of inputs which include the price of each imported good from source country i ; n i t itx d f X d1) (log ) (logwhere) (log X d is the Divisia volume input index;i is the mean share of the ith input in the marginal cost of the firm;ij is the conditional

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50 price coefficient between the ith and jth importing sources or inputs; n is the number of inputs in the system, n N It is important to mention that the differentia l factor demand model is required to meet the following parameter restrictions in order for the model to conform to theo retical considerations: 0*ij j (homogeneity), and ji ij (symmetry). The second stage procedure re sults in the conditional own price/cross price elasticity i ij j i c xwf w d x d*) (log ) (log (3-11) and the conditional Divisia volume input elasticity, i i i xXf X d x d*) (log ) (log (3-12) Using the relationship between the Divi sia volume input index and output, ) (log ) (log*Q d X d Equation 3-9 can be substituted into Equation 3-10 to yield the unconditional derived demand system (Washington and Kilmer (2002a,b)) n j j ij j n j j i i iw d w d p d x d f1 1 *) (log )] (log ) (log [ ) (log (3-13) Now, dividing through Equation 3-13 by if and using Equations 3-11 and 3-12, one can get the unconditional derived demand elasticities, th e elasticity of input demand with respect to output price xX i xpp d x d ) (log ) (log*, (3-14) and the unconditional own price/cros s price elasticity of input demand

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51 c xw j xX j i xww d x d ) (log ) (log (3-15) Lastly, the unconditional elasticity of derived de mand with respect to the price of an input contained in N but not in n can be found as j xX j i xww d x d ) (log ) (log (3-16) Summary of Literature Review It has been seen that Consumer Demand system deals with the allocation of a given total budget over a set of commodities taki ng into account the effects of pr ice variation. This is used as a tool by the demand analyst to describe and predict empirical consumer behavior. The demand system is derived from the theory of u tility maximization. The differential approach to consumer theory as proposed by Barten (1964) and Theil (1965) is an approximation of these demand equations. Regarding the func tional specification, four alte rnative approaches have been derived, with well-known demand systems as an i llustration. These are th e Barten (1964) and Theil's (1965) the Rotterdam model, Deaton and Muellbauer's (1980) the Almost Ideal Demand System (AIDS) model, Keller and Van Driels ( 1985) the Dutch Central Bureau of Statistics (CBS) model, and Nevess (1994) AIDS income-v ariant the Netherlands National Bureau of Research (NBR) model. Consumer demand allocati on models have widely been used in import demand studies by various researchers such as L ee, Seale, and Jierwiri yapant (1990), Seale, Sparks, and Buxton (1992), Lee, Brown, and S eale (1992), and Satyanarayana, Wilson, and Johnson (1999) and many more. In these studies, imports ar e considered to be final goods entering direc tly into the consumers utility function. Even though Satyanarayana, Wilson, and Johnson (1999) use a consumer demand theory model, they recognize th at production theory could be used to estimate

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52 the derived demand for malt; however, critical da ta was not available. As the nature of international trade is such that traded goods are either used in production processes or go through a number of domestic channels before reach ing the consumer, the production processes and domestic channels generate some value added to the product by the time it reaches the final consumer. Therefore, it is more appropriate to view imported goods as inputs even if no transformation takes place (Kohli, 1978; Diew ert & Morrison, 1989, and Truett and Truett, 1998). The other advantage of using an input demand model in import demand studies is related to how traded goods are typically reported. Most traded commodities are typically reported in bulk quantities and values at dockside. In fact, consumers almost never purchase commodities in such quantities or at the port/d ockside. So, with the assumption that importing decisions are made by a profit-maximizing or cost-minimizing firm is more consistent with the way how trade data is typically reported (Washington and Kilmer 2002a, b). This study intends to use Laitinen (1980) and Theils (1980a, b) differential i nput demand model along wi th three other input demand models which are the firms version of the AIDS, CBS and NBR on the consumer side. Of course, the ultimate choice of model specif ication has to be made depending on empirical grounds.

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53 CHAPTER 4 THEORETICAL AND EMPIRICAL MODEL In this study, a production theory approach has been followed in estimating import demand for tomatoes into the United States and the Eur opean Union. Liu, Kilmer and Lee (2007) have a similar study on import demand with an emphasis on choice of appropriate functional forms. The procedure and models used by them have been followed in this study. This study uses Laitinen (1980) and Theils (1980 a, b) differen tial input demand model (Rotterdam) along with three a dditional input demand models wh ich are the firms version of the AIDS, CBS and NBR on the consumer side The choice of appropriate model amongst different specifications for th e input demand allocation models ultimately depends on empirical grounds. This study examines the empirical perform ance of four similar input demand allocation models in an econometric analysis of the import ma rket for tomatoes into the U.S. and the EU. The synthetic model developed by Barten (1993) ha s been used to compare the empirical results of these four models. In case of EU import market analysis the Synthetic model itself has been tried as a fifth model. Hence, the theoretical m odels for this study comprise the differential input demand model and the firms version of the AIDS model, CBS model, N BR model and Bartens (1993) synthetic model. Theoretical Models Differential Input Demand Model The differential input demand model (Laitinen (1980); Theil, (1980 a, b)) is based on the firms version of the fundamental matrix equa tion of consumer demand. The approach begins with the traditional production optimization prob lem of choosing a bundle of inputs that will Min i ix p C (4-1) Subject to ) ( h z

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54 where, C is total cost; ipand ix are the price and quantity for the thi input; z is output which is held constant; and is a vector of n input quantities. The first orde r conditions are solved for the input demand equations 1(.....,)iin x xppz The derivation of the differential input demand model (Laitinen (1980); Theil (1980 a, b)) is an approximation of this set of input demand equations resulting in the differential input demand system as represented by j j ij i i ip d X d x d f ln ln ln (4-2) where C x p fi i i/ is the share of total cost from input i; ix d lnis the change in the thi input; ) / /( ) / ( z C z x pi i i is ith inputs share in marginal cost, i i ix d f X d ln ln is the Divisia volume index; and ij is the Slutsky coeffici ent. Equation 4-2 is the thi differential demand equation of the firm and it indicates that changes in the decision to purchase the thi input depend upon the changes in the total amount of inputs obt ained and changes in input prices. Given the data with sufficient variability in input prices and quantities, variables can be constructed for X d x d fi iln lnand jp d ln and the coefficients for i and ij can be estimated. The restrictions on the above input demand Equation 4-2 are adding-up: 0 1 i ij i i (4-3) homogeneity: 0j ij, (4-4) and Slutsky symmetry: ji ij (4-5) and the condition for curvature is 0 ) ( x x Equation 4-2 also result s in own price and cross compensated price elasticities

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55 i ij j i xpf p d x d ln ln (4-6) and the Divisia volume elasticity i i i xXf X d x d ln ln (4-7) The Production AIDS model Unlike the consumer AIDS model having expenditu re as a function of utility and prices for a consumer as in Deaton and Muellbauer (1980), the production AIDS model has cost specified as a function of output and prices of input s for a firm as (Liu, Kilmer and Lee(2007)) ) ( ln ) ( ln ) 1 ( ) ( ln b z a z z c (4-8) where j i ij ij j j jp p p a a a ln ln 2 1 ln ) ( ln* 0 and jj jp a b 0) ( ln ) ( ln ; c is the total cost, is a vector of n input prices andzis output that is held constant. When there is no production,0 z and Equation 4-8 becomes ) ( ln ) ( ln a z c (4-9) which is the firms fixed cost. The consumer AIDS model in differential form (Barten, 1993) can be written as follows to represent the production AIDS model (Liu, Kilmer and Lee (2007)) j j ij i ip d X d dfln ln (4-10) This model is similar to the differential i nput demand model (Equation 4-2) on the right-hand side; however, the dependent vari ables are different on the left -hand side. The production AIDS

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56 model explains the change in input is share (marginal share) of total cost, while the differential input demand model concerns with the change in input quantity. Following Lee, Brown, and Seale's (1994) ve rsion of the consumer AIDS model, the production AIDS model can be formulat ed as (Liu, Kilmer and Lee (2007)) j j j i ij i ij i i i ip d f f f X d f x d fln ) ( ln ) ( ln (4-11) where ij is Kronecher's delta equal to unity if j i and zero otherwise. It can be noticed that the AIDS model (Equation 4-11) and the differen tial input demand (Equation 4-2) have the same dependent variable. Hence, this will allow the use of Barten's (1993) synthetic model to empirically test for the a ppropriate functional form. The restrictions on the production AIDS model are adding-up: 0 i i 0 i ij, (4-12) homogeneity: 0 j ij, (4-13) and Slutsky symmetry:ji ij (4-14) Also, the curvature condition is 0 ) ( x x where the matrix is composed of the elements j i ij i ij ijf f f Equation 4-11 results in the own price and cros s compensated price elasticities to be i j i ij i ij j i xpf f f f p d x d ln ln (4-15) and the Divisia volume elasticity as i i i i xXf f X d x d ln ln (416)

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57 The Production CBS Model Keller and van Driel (1985) developed the Dutch Central Bureau of Statistics (CBS) consumer demand model based on Workings En gel model. The production CBS model in differential form is written as (Liu, Kilmer and Lee (2007)) j j ij i i ip d X d X d x d f ln ln ) ln ln ( (4-17) Following Lee, Brown, and Seale's (1994) version of the consumer CBS model and rearranging Equation 4-17, the pr oduction CBS model can be formulated as (Liu, Kilmer and Lee (2007)) j j ij i i i ip d X d f x d f ln ln ) ( ln (4-18) which is another representation of the production CBS model and with this formulation, Barten's (1993) synthetic model can be used to empirically test for the appropriate functional form. This model has the production AIDS models volume coefficients i (Equation 4-11) and the differential input demand models price coefficients ij (Equation 4-2). It shares the adding-up, homogeneity and symmetry condit ions with these two models. The constraints on the production CBS model are adding-up: 0 1 i ij i i (4-19) homogeneity: 0 j ij, (4-20) and Slutsky symmetry: ji ij (4-21) Equation 4-18 also results in the own price and cross compensated price elasticities i ij j i xpf p d x d ln ln (4-22)

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58 and the Divisia volume elasticity i i i i xXf f X d x d ln ln. (4-23) The Production NBR Model Neves (1994) developed the consumer NBR as a consumer allocation model. On the producer side, the production NBR model written in differential form is (Liu, Kilmer and Lee (2007)) j j ij i i ip d X d X d f df ln ln ln (4-24) Following Lee, Brown, and Seale's (1994) ve rsion of the consumer NBR model, the differential production NBR model has been written as (Liu, Kilmer and Lee (2007)) j j j i ij i ij i i ip d f f f X d x d f ln ) ( ln ln (4-25) This model has the volume coefficient i as in the differential input demand model (Equation 42) and the price coefficients ij as in the production AIDS model (Equation 4-11). The constraints are adding-up: 0 1 i ij i i (4-26) homogeneity: 0 j ij, (4-27) and Slutsky symmetry: ji ij (4-28) The curvature condition is 0 ) ( x x where the elements of the matrix are j i ij i ij ijf f f Equation 4-25 results in the own price and cr oss compensated price elasticities as

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59 i j i i ij ij j i xpf f f f p d x d ln ln (4-29) and the Divisia volume elasticity as i i i xXf X d x d ln ln (4-30) The Synthetic Input Demand Model A synthetic model that contai ns all of the four input de mand models was developed by Barten (1993). This synthetic system is employed to assess and compare the empirical performance of each of the four conditional dema nd systems. Following Lee, Brown, and Seale's (1994) version of Bartens (1993) synthetic mode l, Barten's synthetic production model can be written as follows (Liu, Kilmer and Lee (2007)) j j j ij i ij i i i ip d f f e X d f d x d f ln )) ( ( ln ) ( ln2 1 (4-31) where i i id ) 1 (1 1 and ij ij ije ) 1 (2 2 It is good to noti ce that Equation 4-31 becomes the differential input demand model when 1 =2 =0, the production CBS model when1 =1 and 2 =0, the production AIDS model when 1 =2 =1, and the production NBR model when 1 =0 and 2 =1. The demand restric tions on Equation 4-31 are adding-up: 11 i id and 0 i ije (4-32) homogeneity: 0 j ije (4-33) Slutsky symmetry: ije =jie (4-34) and the curvature condition is 0 ) ( x x when the elements of the matrix are defined as ) (2j ij i ij ijf f e

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60 Equation 4-31 also results in own-price a nd compensated cross-price elasticities as i j ij i ij j i xpf f f e p d x d ) ( ln ln2 (4-35) and the Divisia volume elasticity as i i i i xxf f d X d x d1ln ln (4-36) Empirical Models In order to obtain estimable forms of the five demand systems, all variables must have dates attached. The standard pr actice of replacing the cost shares by their two-period moving average (Barten, 1993) has been followed as 2 / ) (1 it it itf f f. (4-37) This study uses annual data and the log different ials are measured as annual differences of the logarithmic value for time t and t -1. The differential input de mand model (Equation 4-2) is transformed into it j jt ij t i it itDp DX Dx f (4-38) where 1ln ln it it itx x Dx, it n i it tDx f DX 1, 1ln ln jt jt jtp p Dp, it is the error term, and i and ij are parameters to be estimated. The production AIDS model (Equation 4-11) is transformed into it jt j jt it ij it ij t it i it itDp f f f DX f Dx f ) ( ) ( (4-39) whereit and it are the parameters to be estimated. The production CBS model (Equation 4-18) is transformed into

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61 it jt j it t it i it itDp DX f Dx f ) ( (4-40) whereit and ij are the parameters to be estimated. The production NBR model (Equation 4-25) is transformed into it jt j jt it ij it ij t i it itDp f f f DX Dx f ) ( (4-41) and i and ij are the parameters to be estimated. Barten's (1993) synthetic demand model (Equation 4-31) is transformed into it jt jt ij it j ij t it i it itDp f f e DX f d Dx f )) ( ( ) (2 1 (4-42) where 12,,,andiijde are the parameters to be estimated. Data Section The tomato data has been collected from va rious sources. Some of them are published and some of them are on-line websites. The main source for tomato import data is the United Nations (U.N.) Statistics DivisionCommodity Trade Statistics Database (UN-COMTRADE) website. The International Agricultural Trade and Policy Cent er (IATPC) at the University of Florida has made necessary arrangements for the availability of this data. The U.S. tomato data are collected mostly from the website maintained by the USDA. Some data are also collected from the U.N. Food and Agriculture Organizati on (FAO) Statistics (UN-FAOSTA T) website. The data set for this research have been completed for the period 1963 2005. In doing so, the U.N. commodity specification SITC Rev.1 with commodity code 0544 for fresh tomatoes has been used. At present the EU has 27 members. This study is confined to EU. Because data for 12 new members (Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia and Slovenia officially included on May 1, 2004, and Bu lgaria and Romania included

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62 on January 1, 2007) are not available for the entire period under considera tion. Therefore, they are not included in this research as part of th e EU dataset. Hence, the EU means EU-15 in this study. In order to get the EU member countrys data as well as that of the EU as a whole, data for tomato imports has been collected from 1963-2005 i rrespective of the countr ys inclusion in the EU. The data on the value (in U.S. dollar) of im ports include cost, insurance and freight. The price data have been derived by dividing the values by quantity (in kilograms) imported. For a continuous time series data for the pe riod under research (1963-2005), a few steps have been taken. First, since Belgium and Luxe mbourg were Customs Uni on until 1998, data for Belgium and Luxembourg have been added toge ther for the years from 1999 through 2005 to represent a continuous series for Belgium and Luxembourg. Second, the Federal Republic of Germany (FRG) data for 1963-1990 has been adde d to Germany data for 1991-2005 to make the Germany data series a continuous one. Third, th e data for the United States of America (USA) has been constituted by adding USA before 1981 (w ith code 841) data to USA (code 842) data for 1981 through 2005. As the Customs area of th e United States (U.S.) also includes the territory of U.S. Virgin Islands the trade data of U.S. Virg in Islands before 1981 was also included in the USA data. Moreover, most of their trade occurred within the EU member countri es. In this study, intra-trade among the EU members has been subtracted from total import trade figure of each member in order for arriving at the actual trad e involvement of them with the outside world. Then, all the EU member countries trades have be en added together to get EU import trade data. All these manipulations have been done through a mathemati cal programming in TSP (Time Series Processor) software. Thus, two sets of wo rking data have been created one for the EU-

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63 15 as a whole with different par ticipating partner count ries and rest of the world (ROW) and the other for the U.S. with different par ticipating partner countries and ROW. In selecting separate participa ting partner countries in the working dataset, their shares in total import trade during the peri od under study have been taken into consideration. For EU-15, about 95% of import trade has been covered by se parate partners and remaining trade has been placed under ROW. It should be mentioned here th at Bulgaria and Romania are in the list of separate partners according to their contribution shares even though subsequently they have become EU members. In the same way, about 99.5% of total U.S. im port trade is covered by separate partners and remaining goes to ROW. Thus, for the U.S. import the partner/source countries are Mexico, Canada, EU-15, Domini can Republic and ROW and those for the EU-15 are Morocco, Romania, Bulgaria, Israel, Albania, Turkey, USA and ROW. The U.S. is kept as a separate partner in the working da taset for particular interest even if its share in EU-15 import is very small (only 0.12%). All programs relating a bove mentioned data mani pulation are provided in Appendix C. However, in the working dataset as created th ere remain some zeros in quantity and value columns for a few partner countries for few year s indicating no import taking place during those years. In order to deal with th is situation, the zero quantity is replaced by 1 being a small number compared to millions as Wooldridge (2000) indi cated a similar procedure. For the corresponding number in the value column that ultimately is transformed into price (since price = value/quantity), a separate regr ession procedure is followed. Theoretically, corresponding to very low quantity, price should be very high. Keeping this in mind, an ordina ry least square (OLS) regression is done on price with quantity and time trend (years) as independent variables along with a constant term. Then, zero value (price) is replaced by the highest price for the country of

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64 origin in question plus twice the standard deviatio n of the dependent variable plus inflation as the coefficient of the trend (years) variable with ap propriate adjustment. The price regression models for the U.S. and the EU-15 are provided in Appendix A and Appendix B respectively.

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65 CHAPTER 5 EMPIRICAL RESULTS Results for U.S. Tomato Import Demand Analysis The U.S. import demand analysis has been perf ormed on the data set with four separate partners and ROW. The extent of their involv ement and the empirical results are discussed below. Descriptive Statistics Table 5-1 shows the cost share, quantity share and the average prices of imports from the five sources of origin for 1964, the sample m ean, and 2005. The largest cost share was for the import of tomatoes from Mexico and the lowest was for imports from the Dominican Republic. The cost share for imports from Mexico actua lly decreased from 98.78% in 1964 to 72.70% in 2005 whereas the decrease for imports from RO W was from 0.38% in 1964 to 0.25% in 2005. During the same period the cost share for impor ts from Canada, EU-15 and Dominican Republic increased from 0.79% to 24.40%, from 0.0% to 2,52% and from 0.05% to 0.13% respectively. The average prices for imports from Mexico and Dominican Republic are the same, but the average import price for EU-15 tomatoes is highe r than other countries and increased over time. Model Results For the U.S. import demand analysis, the diffe rential production versi on of all the five models i.e., AIDS, CBS, NBR, differential i nput demand (DID) and Synthetic were estimated with first order autocorrelation AR(1), homoge neity and symmetry imposed. As all of these models satisfy adding-up conditi ons, only four equations have been estimated excluding the ROW equation as indicated in Barten (1969). The parameters for the omitted equation are established from the estimates of other equa tions using the adding-up conditions. The models were estimated by the iterative seemingly unrelat ed regression (SUR) method that is performed

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66 using the well known least square procedure (LSQ) in the Time Se ries Processor (TSP) (Hall and Cummins, 1998). All models are found to have significant autocorrelatio n based on t-statistics (Table 5-2). A likelihood ratio te st is done comparing the log like lihood values of the first four models to that of the Synthetic model in order to choose an appropriate model for the given data. Table 5-2 shows the test result whereby the NBR model qualifies as a model to be used. So, only results from the production NBR as corrected for autocorrelation (T SP website) have been reported for further discussion. The m odel has been provided in Appendix A. The coefficient estimates ( ij) for production NBR (Equation 4-25), the demand parameters i and ij and the conditional demand elasticity estim ates (Equation 4-29 and Equation 4-30) calculated at sample mean cost share are shown in Table 5-3 and Table 5-4. The property of the Slutsky matrix to be positive semi-definite one is validated as all the eigen values are positive except one which is zero. The eigen values for the production NBR Slutsky matrix are 1.20646D-17, 0.000940, 0.008208, 0.038217, and 0.10139. Th is is an indication that the import firms are operating optimally. Divisia Elasticities: The Divisia import volume elas ticities for Canada, Dominican Republic, Mexico, EU-15 and ROW are 0.172136, 0.173076, 1.06388, 1.24252 and -1.68288 respectively (Table 5-4). The Divisia import elasticities for Mexico, EU-15 and ROW are significant and more than unity in absolute valu e terms whereas those for Canada and Dominican Republic are not significant and less than unity. This means that if total imports of tomatoes into the U.S. increases by 1%, other things remaini ng the same, imports from Canada or Dominican Republic would not change and imports from Me xico and EU-15 would increase by more than 1%; however, the import from ROW (with a nega tive Divisia import elasticity) would cause a decline by more than 1%. The reason for this ma y be the trivial share of ROW (only 0.37%) in

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67 the U.S. total tomato import. So, when there is an increased volume of imports into the U.S., the prominent partners with larger shares capture the opportunity and the insignificant players aggregated in ROW loose their market share in competition. Conditional Own-Price Elasticities: The conditional own-price elasticities for Canada, Dominican Republic, Mexico, EU-15 and RO W are -0.804941, -0.736886, -0.081015, -0.878641 and -0.520927 respectively. All of them are statistically si gnificant but inelastic. These conditional elasticities i ndicate that the U.S. import demand for tomatoes from Mexico is the most inelastic meaning the least responsive to price change followed by ROW. The import demand for EU-15, Canada and the Dominican Re public tomatoes are more elastic, but still inelastic. Among them the EU-15 ha s the least inelastic demand and the most responsive to price change. This means that with a change in pr ice the U.S. import demand for EU-15 tomatoes would change the most. For example, with a 1% decrease in an import price (ceterius paribus), the increases in the U.S. import demand for tomatoes would be 0.88% for EU-15 tomatoes; 0.80% for Canadian tomatoes, 0.74% for Domi nican Republic tomatoes, 0.52% for ROW. The responsiveness of the U.S. import quantity would be almost the same (i.e., almost no change) for Mexican tomatoes if their prices change. Conditional Cross-Price Elasticities: It is noticeable from Table 5-4 that among the conditional cross-price demand parameters fourteen (7 pairs) are statistically significant and different from zero. They are between (1) Ca nada and Mexico, (2) Canada and EU-15, (3) Dominican Republic and Mexico, (4) Dominican Republic and EU-15, (5) Dominican Republic and ROW, (6) Mexico and EU-15, and (7) Mexi co and ROW. All of these conditional crossprice elasticity estimates are pos itive (implying input substitutes) except one involving ROW, i.e., between Dominican Republic and ROW. Resu lts indicate that (1) if price of Mexican

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68 tomatoes increases by 1%, the U.S. import demand for Canadian tomatoes will increase by 0.76%. On the other hand, if the price of Cana dian tomatoes increases by 1%, the U.S. import demand for Mexican tomatoes will increase by 0.04% only. (2) If the price of EU-15 tomatoes is increased by 1%, the U.S. import demand for Canadian tomatoes would increase by 0.10%; however, if Canadian tomato price is increase d by 1%, import demand for EU-15 tomato would increase by 0.12%. (3) A 1% price increase in Me xican tomatoes will cause a 1.20% increase in the U.S. import demand for Dominican Republic to matoes whereas a 1% increase in the price of Dominican Republic tomatoes will have 0.003% impact on the import demand for Mexican tomatoes. (4) If the price of EU-15 tomatoes is increased by 1%, import demand for Dominican Republic tomatoes would increase by 0.27%; on th e other hand, if price of Dominican Republic tomatoes is increased by 1%, the import demand for EU-15 tomatoes would increase by 0.02%. (5) The conditional cross-price elasticities betw een Dominican Republic and ROW are negative indicating complementary relation which is not expected. Most of the conditional cross-price elasticities related to ROW are negative. The cond itional cross-price elasticities show that if the price of ROW tomatoes increases by 1%, th e U.S. import demand for Dominican Republic tomatoes would decline by 0.80% and if price of Dominican Repub lic tomatoes increases by 1%, the import demand for ROW tomatoes would dec line by 0.17%. (6) The conditional cross-price elasticities between Mexico and EU -15 show that if th e price of EU-15 tomatoes is increased by 1%, the U.S. import demand for Mexican tomatoes would increase by 0.03%, but if the price of Mexican tomatoes is increased by 1%, the import demand for EU-15 tomatoes would increase by 0.73%. (7) Finally, the conditional cross-price elasticities suggest that if the price of ROW increases by 1%, the U.S. import demand for Me xican tomatoes would increase by only 0.01%;

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69 on the contrary, if the price of Mexican tomato es increases by 1%, the import demand for ROW tomatoes would increase by 0.92%. The Divisia volume elasticities and the condi tional own-price elastic ities for the U.S. analysis are calculated from 1995 to 2005 and the results are presen ted in Table 5-5 and Table 56 respectively. Even though there is no variable in the model directly related to any sort of structural change, it is to some extent account ed for through potential changes in the Divisia volume index coefficient that enters directly in elasticity calculation. If the Divisia volume elasticity and the conditiona l own-price elasticity change over tim e, this indicates that structural change is occurring recognizing that the precise natu re of this structural change is not known. Therefore, these two elasticities are calculated over a period of 11 years with an initial sample size of 32 years (1964-1995) and each time adding one more year forward with a block of 32 years and subtracting the first year from the previous block. In the top part of both the Tables 5-5 and 5-6, elasticities are ca lculated letting both estima ted parameters (numerator i and ij) and mean factor cost shares (denominator MFi) change along with the samp le; but in the bottom part of the tables, the denominator (MFi) is held constant at the ini tial sample block for the years 1964 to 1995 (2,33). The model for this simulation is provided in Appendix A. In the top part of both the tables elasticities show changes over ti me, but these changes may be due to structural influences ( i and ij) or mean cost share influences (MFi) or both. On the other hand, in the bottom part of Tables 5-5 and 5-6, the changes are due to ( i and ij) representing the structural changes more precisely. Thus, Table 5-5 (bottom part) shows that Di visia elasticities for Canada increase for a while and then decline, implying that Canada ultim ately is losing shares of any increases in the U.S. imports. The Dominican Republic has a diffi culty in capturing the U.S. import market. The

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70 Divisia elasticities for the Domi nican Republic show that this country is having increasing problems keeping its share of any increases in th e U.S. imports of fresh tomatoes. Mexico holds a more or less stable position with little eviden ce of structural adjustme nts; EU-15 has a little improvement in capturing the U.S. import mark et; and the ROW has some insignificant mixed impact of gaining and losing. Table 5-6 (bottom part) shows th e conditional ownprice elasticity changes over time as a result of structural infl uences. The U.S. imports from Canada indicate some variation in price sensitiv eness due to structural adjustme nts; the Dominican Republic is becoming more price sensitive; Mexico is beco ming less price sensitive although it was already highly insensitive to price changes; EU-15 is highly sensitive to price changes and getting slightly more sensitive; and the ROW is, in fact, less sensitive to price changes. Conditional price elastic ities do not always behave as one might expect when underlying structural changes are occurring. For example, conditional own-price elasticities in the U.S. import demand analysis for Canadian fresh to matoes for the years 2003, 2004 and 2005 are not of the right sign (positive inst ead of negative). So, it seems mo re appropriate to look at the change in the share of each partner country when the total import volume changes over time in order for getting some idea about structural ch ange. Moreover, import volume is an important variable in the model. Thus, the shares of each participating country have been calculated over the same period (i.e., 1995-2005) with the U.S. total import increases of 10%, 15%, 20%, 25%, and 30% using the Divisia elasticities at the bottom part of Table 5-5. The variations/ fluctuations in each partners ma rket share of the U.S. import market over the time are the result of structural change since the underlying variable values are kept fixed in the simulations. These are shown in Figure 5-1 through Figure 5-3 and the related data are given in Appendix C.

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71 Figure 5-1 shows the situations for Canada and Dominican Republic. Canada is losing its relative shares of the growth in the U.S. import over time, but the losses are numerically quite small. Dominican Republic was stable for a while at the beginning of the period and then lost a small portion of its share in the U.S. import market and sustained the loss for the rest of the period under consideration. For the most part the share changes we re extremely small. In Figure 5-2, Mexico holds a very stable share in the U.S. fresh tomato import market throughout the entire period with a little incr ease in its share toward the endi ng years. The case of EU-15 is interesting because the shares have generally ri sen over the study period, as seen in the left portion of Figure 5-2. Finally, Figu re 5-3 indicates that the ROW is steadily losing its small share of the U.S. import market for fresh tomatoes, but not by a magnitude leading to any particular concern. Summary for U.S. Analysis From the above discussion and the conditional pri ce elasticities in Table 5-4, it is clear that the U.S. import demand for Mexican tomatoes is the most stable one meaning that it does not change much with the change in its own price or the changes in other part ners prices. It implies that the U.S. consumers prefer Mexican toma toes. In other words, Mexico faces no close competitors in the U.S. import market for fresh tomatoes. Results show that there is almost a sharp competition between Canada and EU-15 wh ile the Dominican Republic is not competitive with EU-15. The market shares of different pa rtners do not vary much indicating very little structural change reflecte d through the estimated parameters measured across time. Results for EU-15 Tomato Import Demand Analysis The EU-15 analysis was ultimately done with a data set created for six partner countries and the ROW. The empirical data status and model results are described below in detail.

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72 Descriptive Statistics The cost shares, quantity shares and the average prices of imports from all the seven sources of origin for 1964, sample mean a nd 2005 are displayed in Table 5-7. Among the separate countries, the largest import cost sh are was for the imports from Morocco and the smallest was for imports from Turkey. ROW ranke d second in terms of cost share. The cost share for the imports from Morocco decreased from 84.12% in 1964 to 63.94% in 2005 and the import cost shares for the imports from Albania, Bulgaria and Romania also decreased during the same time period. On the other hand, import cost shares for the imports from Israel, Turkey and ROW showed considerable increas e during that time. The average prices for the imports from Bulgaria and Romania are almost the same and those for Albania, Morocco, Turkey and ROW are similar. Model Results In order to analyze EU-15 import demand, all of the five differential production models were estimated in the same way as they were estimated for the U.S. import demand analysis. The AIDS and CBS were estimated with first orde r autocorrelation (AR1) whereas the other three models had no significant auto correlation (Table 5-8). Symmet ry and homogeneity conditions were imposed in all of them. The likelihood ratio (L R) test (Table 5-8) shows that DID would be the most appropriate model followed by the CBS model. So, DID was applied to estimate the conditional demand parameters and demand elastic ity estimates (Equation 4-6 and Equation 4-7). But, four out of eight conditional own-price el asticities were of the wrong sign (not negative) which is clearly unacceptable according to economic th eory. Also the signs of the eigen values of the Slutsky matrix were not correct. The first th ree eigen values came out negative instead of positive.

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73 Different trials were given with different years and with a di fferent number of participating partner countries even though in th e original working data there we re eight participating partners including the ROW. However, al l the conditional ownprice elasticities we re not found to be negative. Then the CBS model was tried, but the results were of the same kind. So, with the given data neither the DID nor CBS was working. Therefore, as a second choice the AIDS and NBR were applied. For these two models all the conditional own-price demand elasticities are found to be negative except the one related to the U. S. Finally, given that the U.S. import share is very small (0.12%), the U.S. data were me rged with ROW making seven partner countries involved in EU-15 import s of fresh tomatoes. At this stage, the AIDS, NBR and the Synthe tic model itself were estimated and all the own-price demand coefficients were found to be ne gative. However, they were not statistically significant in the Synthetic model; but they were all significant except one related to the ROW in both AIDS and NBR. These two models are almost equally likely for estimation purposes in case of the given data. Of course, DID and CBS we re also estimated again with seven partner countries, but no fruitful results were found. The production NBR mode l has been selected between them on the grounds that it generates the larger likelihood va lue, it has a closer LR test statistics for acceptance (Table 5-8), eigen values are either zero or positive except one which is .0065308 and conditional own-price elasticities are negative while most of the conditional cross price elasticities ar e positive (Table 5-10). However, th e data is not rich enough to give theoretically precise results. Ther efore, the estimates found will li kely deviate from the correct estimates; however, the estimates are an approximation of the correct estimates and will be interpreted. The model is provided in Appendix B.

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74 The estimates for the coefficients ( ij) of production NBR (Equation 4-25), the demand parameters i and ij and the demand elasticity estimates (Equation 4-29 and Equation 4-30) as calculated at sample mean cost shares are show n in Table 5-9 and Table 5-10. The property that the Slutsky matrix be positive semi-definite is not validated as all the eigen values are positive or zero except one that is -0.0065308. The eigen values for the produc tion NBR Slutsky matrix are 0.0065308, 9.47247D-18, 0.021110, 0.026703, 0.043189, 0.071218 and 0.13542. The 0.0065308 eigen value is problematic, but is very close to zero. Divisia Elasticities: The Divisia import volume elastici ties for Albania, Bulgaria, Israel, Morocco, Romania, Turkey and ROW are -1.08036, 0.58081, 0.33524, 1.09925, 0.18365, 0.74704, and 2.44395 respectively. The Divisia import volume elasticities for Albania and Morocco are statistically significant and more than unity in absolute value terms. The Divisia import volume elasticity for ROW is also more than unity, but not statistically significant. All other Divisia import elasticities are insignificant and less than unity. This means that if total tomato import into the EU-15 is increased by 1%, other things being equal, the import from Albania (with negative volume elasticity) would decrease by 1.08% and imports from Bulgaria, Israel, Morocco, Romania, Turkey and ROW wo uld increase by 0%, 0%, 1.10%, 0%, 0% and0% respectively. So, in terms of import volume in crease, Morocco captures the opportunity as a prominent partner. Conditional Own-Price Elasticities: The conditional own-price elasticities for Albania, Bulgaria, Israel, Morocco, Romania, Turk ey and ROW are -1.07609, -0.72700, -0.83100, 0.13009, -0.65609, -0.96215, and -0.89697 respectively (T able 5-10). All of th em are statistically significant except the one associat ed with ROW. These conditional elasticities show that EU-15 import demand for Albanian tomatoes is elastic and the others are inel astic even though the

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75 import demand for Turkish tomatoes is almo st unitary elastic. Am ong the partners, the inelasticity is the most (i.e., the least res ponsive to price change) for the import demand for tomatoes from Morocco. Conditional own-price elas ticities indicate that fo r a 1% decrease in the import prices, ceterius paribus, the increases in EU-15 import demand for tomatoes would be 1.08% for Albanian tomatoes, 0.73% for Bulgar ian tomatoes, 0.83% for Israeli an tomatoes, 0.13% for Morocco tomatoes, 0.66% for Romanian tomatoes, 0.96% for Turkish tomatoes, and 0.90% for ROW tomatoes. So, EU-15 import quantity from Albania would increase the most followed by Turkey, ROW, Israel, Bulgaria and Romania if their prices decrease. The import quantity from Morocco would not change much with the change in price. Conditional Cross-Price Elasticities: Among the conditional cross-price demand parameters, only five were stat istically significant (different from zero). They are between (1) Albania and Morocco, (2) Bulgaria and Morocco (3) Bulgaria and ROW, (4) Morocco and Turkey, and (5) Turkey and ROW (Table 5-10). All of the corresponding conditional cross-price elasticities are positive (input substitute relation) excepti ng the ones related to ROW, which indicate a complementary relationship. (1) The conditional cross-price elasticity estimates between Albania and Morocco are significant indica ting that if the price of Morocco tomatoes is increased by 1%, the EU-15 import demand for Al banian tomatoes would increase by 0.64%; on the other hand, if the price of Albanian tomatoes increases by 1%, the EU-15 demand for Morocco tomatoes would not change much (only increases by 0.02%). (2 ) The conditional crossprice elasticity between Bulgaria and Morocco is significant, but the sa me between Morocco and Bulgaria is not. These conditional elasticities sh ow that if the price of Morocco tomatoes is increased by 1%, the EU-15 import demand for Bu lgarian tomatoes would increase by almost 1% whereas if the price of Bulgarian tomato es is increased by 1%, the demand for Morocco

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76 tomato would not change. (3) The conditional cross-price elasticities between Bulgaria and ROW are significant and negative (input complement ). If the price of ROW tomatoes increases by 1%, the import demand for Bulgarian tomatoes would decrease by 0.54%, but when the price of Bulgarian tomatoes is increased by 1% the import demand for ROW tomatoes would decrease by 0.38% (4) The conditi onal cross-price elas ticities between Morocco and Turkey are also significant and positive. If the price of Turkish tomatoes is increased by 1%, the EU-15 demand for Morocco tomato would increase by 0.03%; on the other hand, if the price of Morocco tomatoes is increased by 1%, the EU-1 5 demand from Turkey would increase by 1.27% (elasticity is more than unity). (5) The condi tional cross-price elastic ies between Turkey and ROW show complementary relations. It means a 1% increase in the price of ROW tomatoes would result 0.85% reduction in the EU-15 impo rt demand for Turkish tomatoes whereas a 1% increase in the price of Turkish tomatoes w ould result 0.29% reduction in import demand for ROW tomatoes. (6) The conditi onal cross-price elasticities between Israel and ROW are significant and positive that means a 1% increase in ROW tomato price would increase EU-15 import demand for Israeli tomato by 0.74% wher eas a 1% increase in the price of Israeli tomatoes would increase EU-15 import de mand for ROW tomatoes by 0.64%. (7) The conditional cross-price elasticitie s between Romania and Turkey are also significant indicating that if the price of Turkish tomatoes is increased by 1%, the EU-15 demand for Romanian tomatoes would increase by 0.09%; on the contra ry, if the price of Romanian tomatoes is increased by 1%, the import demand for Turkish tomatoes would increase by 0.36%. In the same way as the U.S. analysis, the Divisia volume elasticit ies and the conditional own-price elasticities for the EU -15 analysis are calculated fr om 1995 to 2005 and the results are presented in Table 5-11 and Table 5-12 respectivel y. Even though there is no variable in the

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77 model related directly to any sort of structural change, unde rlying structural change could be accounted for in the Divisia volume index coe fficient and in the elasticities. Similarly, conditional own-price elasticities are derived from the own-price co efficients that could change over time. Therefore, these two elasticities are ca lculated over a period of 11 years with an initial sample size of 32 years (1964-1995) and then for each adjustment period one year forward is added and the earliest year among the 32 years is dropped. This way the sample size is kept fixed. In the top parts of both the Tables 5-11 an d 5-12, elasticities are calculated letting both estimated parameters (numerator i and ij) and mean factor cost shares (denominator MFi) change along with the sample; but in the bottom part th e denominator (MFi) is held constant at the initial sample block for the years 1964 to 1995 (2,33) and the resulting changes are only attributable to changes in the parameters i and ij representing the structural impacts. The model for the above simulation is provided in Appendix B. Table 5-11 shows the changes in the Divisia volume elasticitie s over time. The top part of the table indicates that these changes may be struct ural or due to changes in mean cost shares or both. The bottom part explains the changes as a result of chan ges in the parameters, thus capturing potential structural cha nge more precisely. The table indi cates that Albania is losing in the EU-15 import volume in anyway even if the lo ss is not as bad as it was at the beginning. Bulgaria has some loss and gain in between with an ultimate loss at the end; Israel shows some losses in the middle of the peri od under consideration but gains at the end while Morocco has a somewhat stable position indicating very little structural impact; Romania has some ups and downs in the EU-15 import volume; Turkey was gaining at the beginning with a sudden drop in between and again gaining a little toward the end of the period; and the ROW is somehow experiencing steady loss in the EU-15 import volume until the end of the period.

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78 Table 5-12 (bottom part) tells the same story a bout structural impact in terms of own-price sensitiveness. Albania is gett ing less price sensitive while Bulgaria is getting more price sensitive; Israel is becoming less price sensitiv e; Morocco is becoming less price sensitive during the more recent years; and Romania is becoming more price sensitive while Turkey is getting less price sensitive. The ROW has mixed impacts even though it becomes less price sensitive near the end of the study period. As conditional price elasticities do not always behave the way they theoretically should ( as in case of conditional own-pri ce elasticities for the EU-15 im port demand for Israeli tomatoes for the years 2003, 2004 and 2005), it is better to l ook at the changes in shares of each partner country when the total import volume changes ove r time in order to explain the impact of structural changes. So, the shares of each par ticipating country have b een calculated over the same period (i.e., 1995-2005) with the EU-15 to tal import increases of 10%, 15%, 20%, 25%, and 30% using the Divisia elasticities at the bottom part of Table 5-11. The variations/ fluctuations in each partners mark et share of the EU-15 import ma rket over the time are actually the result of structural changes. These are shown in Figure 5-4 through Figure 5-7 and the related data are provided in Appendix C. In Figure 5-4, the stories for Albania and Bulgaria are almost the same. Both experience losses (Albania having negative Divi sia elasticities) in relative shar es of the growth in the EU-15 imports over time. Even if the losses in share to ward the end period are not as bad as the earlier years, they are, in any case, going to lose EU -15 import market share unless they do something to reverse the situation. Figure 5-5 explains the impact of some structural influence on Israels market share in EU-15 tomato import market Although at the beginni ng it experienced little stability and losses, it really picked up increasing shares toward the ending part of the period as a

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79 result of some structural impact. On the othe r hand, Morocco has a st able and steady growth (with a slight decrease at the end) in its market share in EU-15 fresh tomato import market, but showing little sign of structural influence. In Figu re 5-6, Romanias situa tion is somewhat stable with little loss and gain over time. So, structural factor may not be in effect for this variation in its share in EU-15 import market. Turkey faced some stability and a little growth in market share at the beginning and then a sharp drop that ma y be due to structural adjustment even though toward the end of the period there appears to be some recovery. Finally, Figure 5-7 shows that from a stable situation ROW started experiencing a sharp drop in market share until the terminal year of the time period when it picked up some gain again. This kind of disruption reflects potential structural changes. Summary for EU-15 Analysis The above discussion and Table 5-10 su mmarize that the EU-15 import demand for Morocco tomatoes is the most stable one. It m eans, the import quantity from Morocco does not change much with the change in its own price or the prices of the other partner countries. This implies that Morocco faces no close competitors and it is a prominent pa rtner with the EU-15. However, there exists some competition between Israel and ROW. As the EU-15 import market grows over time, Albania is going to lose its sh are under any situation; Bulgarias position is somehow stable even though losing its share; Isr ael was losing initially, but toward the recent time it is picking up its share; and Moroccos case is more stable s howing no indication of structural effect. Turkey shows a stable conditio n at the beginning and then some drops and pick ups, but towards the end peri od it is gaining its share.

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80 Table 5-1. Import cost shares, qua ntity shares, and average prices by country of origin for U.S. Year Canada Dom. Rep. Mexico EU-15 ROW Annual Cost Share 1964 0.79% 0.05% 98.78% 0% 0.38% Average 4.48% 0.22% 90.59% 3.67% 1.04% (7.51) (0.27) (12.31) (5.21) (1.10) 2005 24.40% 0.13% 72.70% 2.52% 0.25% Annual Cost in U.S. Dollars 1964 $219,358 $13,291 $27,354,888 $2.20833 $105,323 Average 39,753,000 436,666 271,616,000 25,298,800 5,254,056 (78,152,300) (724,383) (228,486,000) (41,842,800) (7,347,914) 2005 274,699,840 1,449,620 818,552,896 28,333,600 2,857,037 Annual Quantity Share 1964 0.50% 0.17% 98.76% 0% 0.57% Average 2.95% 0.31% 95.18% 1.20% 0.36% (4.87) (0.41) (6.12) (1.78) (0.35) 2005 14.88% 0.09% 84.20% 0.78% 0.05% Annual Quantity in Kilograms 1964 562,125 189,476 111,638,936 1 648,311 Average 23,049,400 1,066,709 398,402,000 8,394,931 1,604,506 (43,161,800) (1,740,665) (198,047,000) (13,874,400) (1,523,853) 2005 141,642,032 856,968 801,408,192 7,396,764 482,476 Annual Average Pr ice (U.S. Dollar/Kilogram) 1964 $0.39 $0.07 $0.25 $2.21 $0.16 Average 1.02 0.59 0.59 2.17 2.05 (0.52) (0.50) (0.27) (1.19) (1.93) 2005 1.94 1.69 1.02 3.83 5.92 Table 5-2. Test results for the production di fferential AIDS, CBS, Rotterdam and NBR models with first-order autocorrelation impos ed for U.S. import demand analysis Model Rho t-statistics P value Log Likelihood LR=2(Lsn-Lmodl)a AIDS 0.410442 5.63482 0.000 593.24964 24.98326 CBS 0.345709 4.59926 0.000 567.39256 76.69742 NBR 0.493086 7.11773 0.000 604.56978 2.34298 DID 0.431854 6.02729 0.000 571.26802 68.9465 Synthetic 0.48838 6.91896 0.000 605.74127b aThe table value for 2 2 = 5.99 at = .05 level and 9.21 at = .01 level. bThe estimates for 1 and 2 are -0.207467 and 1.14440 with standard errors 0.163098 and 0.100646 respectively.

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81 Table 5-3. Coefficient estimates of th e production NBR model for the U.S. ij Equation i Canada Dom.Rep. Mexico EU-15 ROW Canada .007711 .006731 .000069 -.006387 .002778* -.003190* (.009057)a (.413064) (.000643) (.004580) (.001516) (.001767) Dom. Rep. .000384 .000578 .000646 .000508** -.001801** (.001200) (.000545) (.000671) (.000218) (.000608) Mexico .963753** .011868 -.006322** .000196 (.015325) (.006770) (.002633) (.005445) EU-15 .045628** .003108* -.000071 (.009426) (.001694) (.001694) ROW -.017475** .004867** (.004185) (.001535) aNumbers in parentheses are asymptotic standard errors estimated using the Delta method. **Statistically different from zero at = 0.05 level. *Statistically different from zero at = 0.10 level. Table 5-4. Demand parameter estimates and cond itional elasticity of the production NBR model for the U.S. -ij a Equation i Canada Dom. Rep. Mexico EU-15 ROW Canada .007711 -.036056** .000168 .034190** .004423** -.002725 (.009057)b (.004131) (.000643) (.004580) (.001516) (.001767) Dom. Rep. .000384 -.001633** .002654** .000589** -.001778** (.001200) (.000545) (.000672) (.000218) (.000525) Mexico .963753** -.073390** .026944** .009603** (.015325) (.006770) (.002633) (.001949) EU-15 .045628** -.032265** .000310 (.009426) (.001694) (.000727) ROW -.017475** -.005409** (.004185) (.0015363) Conditional price elasticityc Equation Divisia Elasticity Canada Dom. Rep. Mexico EU-15 ROW Canada .172136 -.804941** .003761 .763289** .098733** -.060841 (.202191)b (.092216) (.014353) (.102247) (.033835) (.039437) Dom. Rep. .173076 .075999 -.736886** 1.19724** .265896** -.802252** (.541235) (.290059) (.245827) (.302747) (.098172) (.236985) Mexico 1.06388** .037742** .002930** -.081015** .029743** .010601** (.016917) (.005056) (.000741) (.007474) (.002907) (.002152) EU-15 1.24252** .120434** .016050** .733718** -.878641** .008440 (.256675) (.041271) (.005926) (.071706) (.046123) (.019792) ROW -1.68288** -.262448 -.171249** .924778** .029847 -.520927** (.402980) (.170118) (.050587) (.187730) (.069991) (.147788) aThe eigen values for the Slutsky matrix are 1.20646D-17, 0.00093948, 0.0082077, 0.038217 and 0.10139. bNumbers in parentheses are asymptotic standa rd errors. Standard errors were estimated using the delta method. cEstimated at sample mean cost shares. **Statistically different from zero at = 0.05 level. *Statistically different from zero at = 0.10 level.

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82 Table5-5. Divisia elasticities over time for the U.S. analysis Divisia elasticities (Ei )a with both parameter and mean change Year Canada Dom. Rep. Mexico EU-15 ROW 1995 0.453614 0.272034 1.000443 3.554495 -2.46572 1996 0.404887 0.244738 1.004255 2.892485 -2.38155 1997 0.302610 0.240887 1.012297 2.248715 -2.28929 1998 0.337869 0.165349 1.022119 1.737735 -2.13422 1999 0.275252 0.175672 1.028195 1.534845 -1.80969 2000 0.325307 0.174524 1.038350 1.321553 -1.74041 2001 0.251374 0.156900 1.049991 1.207141 -1.62653 2002 0.139138 0.127674 1.063852 1.161990 -1.66412 2003 0.111075 0.115558 1.076720 1.074732 -1.59095 2004 0.088835 0.135994 1.088653 1.023946 -1.52566 2005 0.008797 0.220775 1.103784 1.022021 -1.58419 Divisia elasticities (Ei m)b with parameter change but mean fixed at sample 2,33 Year Canada Dom. Rep Mexico EU-15 ROW 1995 0.453614 0.272034 1.000443 3.554495 -2.46572 1996 0.46222 0.244212 0.999473 3.631007 -2.46810 1997 0.415626 0.238535 1.000634 3.591645 -2.50041 1998 0.589547 0.162676 1.001227 3.470977 -2.52384 1999 0.625145 0.171781 0.996715 3.671049 -2.32388 2000 0.982030 0.168225 0.994714 3.641943 -2.38393 2001 0.977150 0.148069 0.993505 3.720346 -2.34837 2002 0.654073 0.118138 0.995146 3.909508 -2.53007 2003 0.611752 0.104625 0.996742 3.830233 -2.55266 2004 0.566848 0.119939 0.997732 3.783115 -2.55990 2005 0.064474 0.192196 1.001609 3.870090 -2.71122 a Ei (1+j), (32+j) = i (1+j), (32+j) / MFi (1+j), (32+j) where j = 1, 2, and i is estimated parameter and MFi is calculated from data. b Ei m (1+j), (32+j) = i (1+j), (32+j) / MFi (2,33) where j = 1, 2, and i is estimated parameter and MFi is calculated from data.

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83 Table 5-6. Conditional own-price elasticities over time for the U.S. analysis Conditional own-price elasticities (Eii)a with both parameter and mean change Year Canada Dom. Rep. Mexico EU-15 ROW 1995 -0.82696 -0.54947 -0.02043 -0.74888 -0.47103 1996 -0.89775 -0.69700 -0.02642 -0.76510 -0.52577 1997 -0.98687 -0.78031 -0.03434 -0.79223 -0.56006 1998 -0.79147 -0.77489 -0.04130 -0.83624 -0.53747 1999 -0.80862 -0.80959 -0.05118 -0.85197 -0.59960 2000 -0.69272 -0.74529 -0.05758 -0.86696 -0.51387 2001 -0.72265 -0.85646 -0.06747 -0.87083 -0.48483 2002 -0.76935 -0.90842 -0.07703 -0.88850 -0.53174 2003 -0.77081 -0.93193 -0.08552 -0.89734 -0.57082 2004 -0.79078 -0.86940 -0.09113 -0.88896 -0.58124 2005 -0.78010 -0.86068 -0.09682 -0.89557 -0.61120 Conditional own-price elasticities (Eiim)b with parameter change but mean fixed at sample 2,33 Year Canada Dom. Rep. Mexico EU-15 ROW 1995 -0.82696 -0.54947 -0.02043 -0.74888 -0.47103 1996 -0.88563 -0.69764 -0.02185 -0.71244 -0.50910 1997 -0.98887 -0.78240 -0.02327 -0.68783 -0.52097 1998 -0.65205 -0.77844 -0.02169 -0.71085 -0.45608 1999 -0.59770 -0.81368 -0.02181 -0.70585 -0.49080 2000 -0.13554 -0.75429 -0.01745 -0.71706 -0.34083 2001 -0.03170 -0.86423 -0.01619 -0.70975 -0.26450 2002 -0.07995 -0.91485 -0.01552 -0.75583 -0.29811 2003 0.03398 -0.93785 -0.01484 -0.78265 -0.32343 2004 0.02589 -0.88419 -0.01194 -0.75031 -0.31126 2005 0.20133 -0.87803 -0.00933 -0.77383 -0.34936 aEii (1+j), (32+j) = ij (1+j), (32+j)/MFi (1+j), (32+j) where j= 1, 2, .11 and ij is the estimated parameter and MFi is calculated from data. bEiim(1+j), (32+j) = ij (1+j), (32+j)/MFi (2,33) where j = 1, 2, .11 and ij is the estimated parameter and MFi is calculated from data.

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84 Figure 5-1. Impact of structur al change on U.S. demand for Ca nadian and Dominican Republic fresh tomatoes. (A) Canadian tomatoes (B) Dominican Republic tomatoes. 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total US imports 0.0042 0.0044 0.0046 0.0048 0.0050 0.0052 0.0054 0.0042 0.0044 0.0046 0.0048 0.0050 0.0052 0.0054 C a n a d a s h a r e o f U S i m p o r t m a r k e t 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total US imports 0.0035 0.0036 0.0037 0.0038 0.0039 0.0040 0.0041 0.0035 0.0036 0.0037 0.0038 0.0039 0.0040 0.0041 D o m i n i c a n R e p u b l i c s h a r e o f U S i m p o r t m a r k e t A B

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85 Figure 5-2. Impact of structur al change on U.S. demand for Mexican and EU-15 fresh tomatoes. (A) Mexican tomatoes (B) EU-15 tomatoes. 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total US imports 0.9800 0.9802 0.9804 0.9806 0.9808 0.9810 0.9812 0.9814 0.9816 0.9818 0.9820 0.9800 0.9802 0.9804 0.9806 0.9808 0.9810 0.9812 0.9814 0.9816 0.9818 0.9820 M e x i c o s h a r e o f U S i m p o r t m a r k e t 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total US imports 0.0065 0.0070 0.0075 0.0080 0.0085 0.0090 0.0065 0.0070 0.0075 0.0080 0.0085 0.0090 E U 1 5 s h a r e o f U S i m p o r t m a r k e t A B

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86 Figure 5-3. Impact of stru ctural change on U.S. demand for ROW fresh tomatoes. 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total US imports 0.0004 0.0006 0.0008 0.0010 0.0012 0.0014 0.0016 0.0018 0.0020 0.0022 0.0004 0.0006 0.0008 0.0010 0.0012 0.0014 0.0016 0.0018 0.0020 0.0022 R e s t o f t h e W o r l d s h a r e o f U S i m p o r t m a r k e t

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87 Table 5-7. Import cost shares, qua ntity shares, and average prices by country of origin for EU-15 Year Albania Bulgaria Israel Morocco Romania Turkey ROW Annual Cost Share 1964 0.13% 4.88% 0% 84.12% 3.61% 0% 7.26% Average 2.16% 3.61% 4.36% 76.36% 6.73% 1.74% 5.04% (2.56) (2.84) (4.82) (6.53) (6.10) (2.58) (3.34) 2005 0% 0.01% 12.44% 63.94% 0.01% 6.43% 17.17% Annual Cost in U.S. Dollars 1964 $48,038 $1,840,474 $1,262 $31, 730,108 $1,361,401 $1.06 $2,739,627 Average 2,017,004 2,911,697 7,888,280 9,241,680 5,643,429 3,526,344 7,098,639 (2,341,134) (2,319,369) (1,073,390) (4,802 ,280) (5,380,099) (6,423,904) (1,056,850) 2005 3.03 20,624 44,735,840 2299451 04 35,735 23,119,076 61,743,611 Annual Quantity Share 1964 0.37% 8.18% 0% 76.21% 6.83% 0% 8.41% Average 3.10% 4.97% 2.81% 72.66% 10.04% 1.70% 4.72% (3.70) (3.81) (2.64) (10.39) (8.53) (2.49) (3.74) 2005 0% 0.01% 7.60% 70.05% 0.01% 5.93% 16.40% Annual Quantity in Kilograms 1964 572,000 12,660,747 3,312 118,009,256 10,570,330 1 13,023,064 Average 4,484,032 8,258,625 5,537,090 131,722,000 17,382,800 3,599,705 9,515,378 (4,616,481) (6,620,936) (6,407,523) (39,30 8,500) (17,403,900) (6,157,144) (10,678,700) 2005 1 19,526 25,201,496 232,239, 648 37,440 19,664,766 54,367,024 Annual Average Price (U.S. $/Kg) 1964 $0.08 $0.15 $0.38 $0.27 $0.13 $1.06 $0.21 Average 0.77 0.55 1.04 0.69 0.54 0.80 0.78 (0.86) (0.41) (0.46) (0.25) (0.38) (0.35) (0.37) 2005 3.03 1.06 1.78 0.99 0.95 1.18 1.14 Table 5-8. Test results for model selection for EU-15 analysis. Model Rho t-statistics P value Log Likelihood LR=2(Lsn-Lmodl) AIDS -0.13526 -2.00077 0.045 685.31208 31.38804 CBS -0.12500 -1.85916 0.063 695.98166 10.04888 NBR -0.09441 -1.39541 0.163 688.35849 25.29522 DID -0.06767 -1.00291 0.316 700.27111 1.46998 Synthetic -0.08094 -1.19275 0.233 701.0061b aThe table value for 2 2 = 5.99 at = .05 level and 9.21 at = .01 level. bThe estimates for 1 and 2 are 0.308207 and -0.030456 with standard errors 0.190839 and 0.127050 respectively.

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88 Table 5-9. Coefficient estimates of the production NBR model for EU-15 ij Equation i Albania Bulgaria Israel Mo rocco Romania Turkey ROW Albania -.02328** -.00210 .00062 .00331 -.00259 .00045 -.00016 .00048 (.01092)a (.00277) (.00278) (.00311) (.00757) (.00395) (.00116) (.00640) Bulgaria .02098 .00856 .00175 .00843 .00021 .00162 -.02118** (.01544) (.00536) (.00429) (.01073) (.00555) (.00164) (.00947) Israel .01462 .00547 -.04045** -.00011 -.00007 .03011 (.02276) (.00725) (.01416) (.00722) (.00235) (.01184) Morocco .83943 .08116 -.04160** .00880 -.01375 (.07548) (.05321) (.02001) (.00767) (.04528) Romania .01236 .01862 .00511 .01733 (.03242) (.01371) (.00338) (.01362) Turkey .01299 .00036 -.01565** (.02177) (.00246) (.00246) ROW .12291 .00265 (.05040) (.03178) aNumbers in parentheses are asymptotic standard errors computed with the Delta method. **Statistically different from zero at = 0.05 level. *Statistically different from zero at = 0.10 level.

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89 Table 5-10. Demand parameter and conditional elas ticity estimates of the production NBR model for EU-15 -ij a Equation i Albania Bulgaria Israel Morocco Romania Turkey ROW Albania -.02338** -.02319** .00140 00425 .01387* .00190 .00021 .00157 (.01092)b (.00277) (.00278) (.00311) (.00757) (.00395) (.00116) (.00640) Bulgaria .02098 -.02626** 00332 .03601** .00264 .00225 -.01936** (.01544) (.00536) (.00429) (.01073) (.00555) (.00164) (..00920) Israel .01462 -.03624** -.00715 .00282 .00069 .03231 (.02276) (.00725) (.01416) (.00722) (.00235) (.01102) Morocco .83943 -.09934* .009811 .02207** .02473 (.07548) (.05321) (.02003) (.00767) (.03619) Romania .01236 -.04417** .00628 .02072 (.03242) ( .01371) (.00338) (.01457) Turkey .01299 -.01673** -.01477** (.02177) (.00246) (.00520) ROW .12291 -.04519 (.05040) (.03178) Conditional price elasticityc Equation Divisia Elasticityc Albania Bulgaria Israel Morocco Romania Turkey ROW Albania -1.08036** -1.07609** 06489 .19701 .64348* .08811 .00988 .07273 ( 50684)b (.12843) (.12911) (.14419) (.35142) (.18345) (.05375) (.29696) Bulgaria .58081 .03871 -. 72700** .09199 .99692** .07309 .06219 -.53590** (.42734) (.07702) (.14849) (.11867) (.29696) (.15358) (.04549) (.25477) Israel .33524 .09734 07619 -.83100** -.16388 .06469 .01576 .74090** (.52193) (.07125) (.09829) (.16614) (.32458) (.16557) (.05398) (.25270) Morocco 1.09925** .01816* .04716 -.00936 -.13009* .01285 .02891** .03238 (.09884) (.00992) (.01405) (.01854) (.06968) (.02623) (.01004) (.04740) Romania .18365 .02820 03922 .04190 .14573 -.65609** .09330* .30774 (.48165) (.05872) (.08240) (.10726) (.29751) (.20370) (.05022) (.21643) Turkey .74704 .01225 12921 .03954 1.26971** .36129* -.96215** -.84985** (1.25201) (.06662) (.09451) (.13542) (.44111) (.19446) (.14129) (.29890) ROW 2.44395 .03110 -.38420** 64131** .49079 .41120 -.29323** -.89697 (1.00028) (.12701) (18265) (.21873) (.71836) (.28919) (.10313) (.63084) aThe eigen values for the Slutsky matrix are -0.0065308, 9.47247D-18, 0.021110, 0.026703, 0.043189, 0.071218 and 0.13542. bNumbers in parentheses are asym ptotic standard errors and were estimated using the delta method. cEstimated at sample mean cost shares. **Statistically different from zero at = 0.05 level. *Statistical ly different from zero at = 0.10 level.

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90 Table 5-11. Divisia elasticities over time for EU-15 analysis Divisia elasticities (Ei ) with both parameter and mean change Year Albania Bulgaria Israel Mo rocco Romania Turkey ROW 1995 -1.47386 0.61053 0.19870 1.15429 0.02082 1.80311 2.49313 1996 -1.34877 0.37179 0.19081 1.15992 0.05682 1.76605 2.55665 1997 -1.31760 0.47812 0.02233 1.17049 -0.08559 1.85061 2.63550 1998 -1.22725 0.45224 -0.11496 1.16672 0.11012 1.50872 2.47556 1999 -1.43440 0.37988 -0.21824 1.20907 0.23150 1.98134 1.66476 2000 -1.25003 0.66127 -0.24613 1.20233 0.45997 0.66449 1.32249 2001 -1.27236 0.90023 0.12357 1.20443 0.38382 0.27793 1.02599 2002 -1.03652 0.81050 0.19538 1.29244 0.01685 -0.18537 -0.03721 2003 -1.14369 0.68212 0.47961 1.25276 0.10092 0.60706 0.02162 2004 -1.20686 0.76306 0.52833 1.26734 0.10415 0.21202 -0.22080 2005 -1.16712 0.77719 0.78517 1.13938 0.17902 0.48810 1.52012 Divisia elasticities Ei M parameter change but mean fixed at sample 2,33 Year Albania Bulgaria Israel Mo rocco Romania Turkey ROW 1995 -1.47386 0.61053 0.19870 1.15429 0.02082 1.80311 2.49313 1996 -1.34721 0.35930 0.21725 1.16156 0.05617 1.91003 2.45700 1997 -1.31264 0.44402 0.03010 1.17133 -0.08321 2.12307 2.49529 1998 -1.21739 0.40056 -0.18171 1.16698 0.10477 1.76848 2.33258 1999 -1.41599 0.31833 -0.39158 1.21030 0.21533 2.39415 1.54427 2000 -1.22543 0.51199 -0.49715 1.20444 0.41229 0.90753 1.23111 2001 -1.23698 0.64172 0.27876 1.20766 0.32508 0.44179 0.96850 2002 -0.99987 0.53954 0.47998 1.30145 0.01339 -0.33920 -0.03349 2003 -1.09474 0.42501 1.26150 1.26548 0.07390 1.34488 0.01896 2004 -1.14249 0.44667 1.49365 1.28141 0.06871 0.55217 -0.19863 2005 -1.08704 0.42910 2.38274 1.15126 0.10583 1.40678 1.45360 a Ei (1+j), (32+j) = i (1+j), (32+j) / MFi (1+j), (32+j) where j = 1, 2, and i is estimated parameter and MFi is calculated from data. b Ei m (1+j), (32+j) = i (1+j), (32+j) / MFi (2,33) where j = 1, 2, and i is estimated parameter and MFi is calculated from data.

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91 Table 5-12. Conditional ownprice elasticities over time for EU-15 analysis Conditional own-price elasticities (Eii)a with both parameter and mean change Year Albania Bulgaria Israel Mo rocco Romania Turkey ROW 1995 -1.33563 -0.02298 -0.87042 -0 .14148 0.03344 -1.03718 -1.59637 1996 -1.34502 -0.57405 -0.99189 -0 .11948 -0.05058 -1.04681 -1.42251 1997 -1.27132 -0.73028 -0.95801 -0 .10879 -0.19846 -1.05458 -2.42236 1998 -1.28189 -0.73225 -0.92933 -0 .11400 -0.33547 -1.03818 -2.32185 1999 -1.32045 -0.74338 -1.00634 -0 .15487 -0.57981 -0.96492 -1.67719 2000 -1.29714 -0.82131 -0.88734 -0 .18874 -0.64474 -0.82970 -1.62733 2001 -1.27507 -0.84578 -0.83289 -0 .20691 -0.71041 -0.85740 -1.95294 2002 -1.18356 -0.81415 -0.76029 -0 .14355 -0.66842 -0.86986 -1.23559 2003 -1.06549 -0.79684 -0.54959 -0 .08599 -0.64424 -0.95343 -1.13652 2004 -1.06675 -0.81324 -0.43974 -0 .08052 -0.62588 -0.84424 -1.09511 2005 -1.07429 -0.79961 -0.38157 -0 .05384 -0.60391 -0.82246 -0.48632 Conditional own-price elasticities (Eii m)b with parameter change but mean fixed at sample 2,33 Year Albania Bulgaria Israel Mo rocco Romania Turkey ROW 1995 -1.33563 -0.02298 -0.87042 -0 .14148 0.03344 -1.03718 -1.59637 1996 -1.34455 -0.58523 -0.99633 -0 .12040 -0.05944 -1.05196 -1.40270 1997 -1.27009 -0.74302 -0.95876 -0 .10924 -0.21590 -1.06512 -2.34216 1998 -1.27917 -0.75266 -0.91645 -0 .11414 -0.35938 -1.04772 -2.24060 1999 -1.31562 -0.77087 -1.05308 -0 .15557 -0.59727 -0.96125 -1.62209 2000 -1.29019 -0.84270 -0.83031 -0 .19001 -0.66422 -0.77426 -1.57816 2001 -1.26587 -0.86681 -0.69987 -0 .20890 -0.72978 -0.78541 -1.89479 2002 -1.17511 -0.84995 -0.50572 -0 .14830 -0.70395 -0.78046 -1.20374 2003 -1.06032 -0.84448 0.07348 -0. 09234 -0.69854 -0.92776 -1.10966 2004 -1.06026 -0.85959 0.45255 -0. 08741 -0.70333 -0.64011 -1.07722 2005 -1.06545 -0.85648 0.72246 -0. 06003 -0.70843 -0.54614 -0.50506 aEii (1+j), (32+j) = ij (1+j), (32+j)/MFi (1+j), (32+j) where j = 1, 2, .11 and ij is the estimated parameter and MFi is calculated from data. bEiim(1+j), (32+j) = ij (1+j), (32+j)/MFi (2,33) where j = 1, 2, .11 and ij is the estimated parameter and MFi is calculated from data.

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92 Figure 5-4. Impact of structur al change on EU-15 demand for Albanian and Bulgarian fresh tomatoes. (A) Albanian tomatoes (B) Bulgarian tomatoes. 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total EU-15 imports 0.1000 0.1200 0.1400 0.1600 0.1800 0.2000 0.2200 0.1000 0.1200 0.1400 0.1600 0.1800 0.2000 0.2200 A l b a n i a s h a r e o f E U 1 5 i m p o r t m a r k e t 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total EU-15 imports 0.0410 0.0420 0.0430 0.0440 0.0450 0.0460 0.0470 0.0480 0.0410 0.0420 0.0430 0.0440 0.0450 0.0460 0.0470 0.0480 B u l g a r i a s h a r e o f E U 1 5 i m p o r t m a r k e t A B

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93 Figure 5-5. Impact of struct ural change on EU-15 demand fo r Israeli and Morocco fresh tomatoes. (A) Israeli tomatoes (B) Morocco tomatoes. 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total EU-15 imports 0.0070 0.0080 0.0090 0.0100 0.0110 0.0120 0.0130 0.0140 0.0150 0.0070 0.0080 0.0090 0.0100 0.0110 0.0120 0.0130 0.0140 0.0150 I s r a e l s h a r e o f E U 1 5 i m p o r t m a r k e t 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total EU-15 imports 0.5350 0.5400 0.5450 0.5500 0.5550 0.5600 0.5650 0.5700 0.5350 0.5400 0.5450 0.5500 0.5550 0.5600 0.5650 0.5700 M o r o c c o s h a r e o f E U 1 5 i m p o r t m a r k e t A B

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94 Figure 5-6. Impact of struct ural change on EU-15 demand fo r Romanian and Turkish fresh tomatoes. (A) Romanian tomatoes (B) Turkish tomatoes. 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total EU-15 imports 0.0750 0.0800 0.0850 0.0900 0.0950 0.1000 0.0750 0.0800 0.0850 0.0900 0.0950 0.1000 R o m a n i a s h a r e o f E U 1 5 i m p o r t m a r k e t 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total EU-15 imports 0.0040 0.0045 0.0050 0.0055 0.0060 0.0065 0.0070 0.0075 0.0080 0.0040 0.0045 0.0050 0.0055 0.0060 0.0065 0.0070 0.0075 0.0080 T u r k e y s h a r e o f E U 1 5 i m p o r t m a r k e t A B

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95 Figure 5-7. Impact of stru ctural change on EU-15 dema nd for ROW fresh tomatoes 1994 1996 1998 2000 2002 2004 Structural change associated with the elasticities 10 15 20 25 30 Percentage increase in total EU-15 imports 0.0200 0.0250 0.0300 0.0350 0.0400 0.0450 0.0200 0.0250 0.0300 0.0350 0.0400 0.0450 R e s t o f t h e W o r l d s h a r e o f E U 1 5 i m p o r t m a r k e t

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96 CHAPTER 6 CONCLUSIONS Observations In order to have a good demand analysis empiri cal data is very important. The empirical data used in this research do not behave equally well for the U.S. import demand analysis and the EU-15 analysis. In case of the U.S., the results are as per expectation according to economic theory from the appropriate model at first inst ance (NBR as qualified by LR test). However, the scenario is not the same for the EU-15 import demand analysis. The empirical data was not giving a theoretically convinced results from the model that seemed appr opriate by the LR test (the DID). So, a different specif ication of model had to be ch osen (NBR), as a second line of choice, in order to analyze the given data set with a little manipulation (merging the U.S. data with ROW). Hence, it is the data, not always the models that cause problems in generating theoretically acceptable results. Summary Divisia volume elasticities show that when the U.S. total import volume is increased by 1%, fresh tomato imports from each of Mexico and the EU-15 would increase by more than 1%, but imports from ROW would decrease by more than 1%. Conditional own-price elasticities indicate that the U.S. demands for fresh tomato es from all of the five sources under study are inelastic. Inelasticity is the most for the dema nd for Mexican tomatoes (-0.08) and the least for EU-15 tomatoes (-0.88). Among the significant co nditional cross-price elasticities only the ones between Dominican Republic and ROW and vice ve rsa are negative indicating a complementary relation. All others (six pairs) show a substitute relationship as expected. The conditional crossprice elasticities suggest that the U.S. demand for Mexican tomatoes remains almost the same when other countries individually ch ange their price in either dir ection. On the other hand, when

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97 Mexico increases/decreases its price by (say) 1%, the increase/ decrease in quantity of import from Canada, Dominican Republic, EU-15 a nd ROW would be 0.76%, 1.20%, 0.73%, and 0.92% respectively. So, Mexico is competitive with others, but others are not competitive with Mexico. Conditional cross-price elasticities also show that Canada and EU-15 are competitive with one another (0.10 against 0. 12). None of these source specific import demands seem to be influenced by structural change s even as Canada is gradually losing its market share while EU15 is gaining its share. For EU-15 import demand, the only two significa nt (and more than unity) Divisia volume elasticities indicate that with a 1% increase in total im port volume EU-15 demand for Morocco fresh tomatoes would increase by more than 1%, but the import demand fo r Albanian tomatoes would decrease by more than 1%. All of the c onditional own-price elastic ities are significant except the one related to ROW. Only the condi tional own-price elasticity of the demand for Albanian tomatoes is elastic (-1.08). Among th e conditional inelastic demands, Morocco has the most inelasticity (-0.13) and Turkey has the least inelasticity (almost unitary elastic). Of thirteen significant conditional cross-price elasticities, four relating Bulgaria, Turkey and ROW show a complementary relationship; others show a subs titute relationship. The conditional cross-price elasticities suggest that Morocco is competitive with others, but ot hers are not competitive with Morocco. If Morocco increases its price by 1% Eu-15 import demand for Albanian tomatoes increases by 0.64%, demand for Bulgarian toma toes increases by 1% and that for Turkey increases by 1.27%, but if othe rs increase their price individually by 1%, demand for Morocco tomatoes almost does not change. Conditional cross-price elasticities also show some competition between Israel and ROW. There exists some structural influence on import demands for fresh tomatoes into the EU-15 especia lly for Albania, Israel, Turkey and ROW.

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98 Conclusions Given the continuous changes in international as well as dom estic trade policies and trade liberalization/globalization, the potentiality of exporting countries/ producers to achieve a larger share in international trade is of greater interest. It has been found that for the U.S. import demand of fresh tomatoes the prominent supplier is Mexico facing no close competitor. Canada and EU-15 compete closely with each other for th e U.S. imports of fresh tomatoes. Hence, the two products seem to be homogeneous. On th e other hand, Mexican fresh tomatoes have heterogeneous characteristics compared with othe r partners. The U.S. consumers have either some preferential tastes for Me xican tomatoes, or Mexico has some especial strategies, technologies and product qualities that make it a prominent one in the U.S. import market. Canada is losing its share in the U.S. imports while EU-15 is gaining. However, no significant symptom of structural change im pact has been found in case of the U.S. import market for fresh tomatoes. Similarly, for the EU-15 import demand for fr esh tomatoes Morocco is the major supplier with no close competitor. Israel and ROW are somehow competing with each other for EU-15 imports indicating some homogeneous tendency in their products. However, Morocco seems to enjoy some preferential treatment in the EU-15 fr esh tomato import market that characterizes its product as heterogeneous to others. Albania, Bu lgaria and ROW are losing their share in EU-15 imports. Some structural impacts have been found with respect to EU-15 import demand for tomatoes from Albania, Bulgaria, Israel, Turkey and ROW. One interesting finding is that some small exporters like Israel and Turkey are increa singly penetrating into th e EU-15 import market in recent years.

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99 Implications As the U.S. fresh tomato import market is dominated by Mexico and it has no competitor, it is necessary for the other countries to figur e out some measures that will make their competition with Mexico closer in order to get a larger share in the U.S. import market. Canada also needs to look into its overa ll situation and find out the causes of losing share of the U.S. fresh tomato import market to the EU-15 In the same way, Morocco holds the domina ting position in the EU-15 import market and other partners except Bulgaria and Romania (hav e become EU members subsequently) have to do something that will make them closer compe titors to Morocco. Albanias losing situation is very delicate and needs immediate attention. Isra el and Turkey should explore further to keep on increasing their market shares. Since the main suppliers for the U.S. and EU-15 fresh tomato markets are Mexico and Morocco respectively, in case of any calamities, diseases or disruption in their supply, there could be significant impacts on these two markets. Unless othe r countries succeed in becoming more competitive with these tw o prominent suppliers, the U.S. and the EU-15 import markets will remain at some risk. Thus, the import demand analysis of this res earch will help the pa rticipating countries determine the level of competitiveness among ot her competitors and then make appropriate decision thereby to ensure their own gains from tr ade. It is also expected to help the policy makers to undertake changes/adjustments in policies and implement them effectively.

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100 APPENDIX A COMPUTER PRINTOUTS FOR U.S. ANALYSIS The Model to deal with zero quan tity and price for U.S. Analysis. OPTIONS MEMORY=1500 Double; ?US#01 Price Calculation Mode l; ? To replace Zero price for U.S. Import Analysis; TITLE 'TOMATO IMPORTS TO THE US'; smpl 1 43; LOAD ZYRS ZIMP V1 Q1 V2 Q2 V3 Q3 V4 Q4 V5 Q5; 1963 902 189750 730125 2481 21320 20705768 108846184 13928 65690 13565 94217 1964 902 219358 562125 13291 189476 27354888 111638936 0 0 105323 648311 1965 902 298959 733875 33276 188640 29424864 120410128 0 0 167763 668557 1966 902 241757 562312 9319 50292 52008580 162705440 703 6875 57697 231001 1967 902 306522 803312 41735 379875 42585292 164302752 123059 306330 23536 126579 1968 902 222304 575562 75850 508062 46973296 175722128 12883 47249 45275 197879 1969 902 216389 400250 144332 916687 68018008 202410752 17355 30914 32468 194149 1970 902 560946 977250 121233 797875 94966856 290759360 2768 17812 180445 796919 1971 902 393364 505250 203117 1523625 84131328 258677808 55205 104367 34202 163878 1972 902 245434 409687 220130 1345187 88150080 264119568 0 0 129604 313238 1973 902 143427 467187 169100 834375 115137936 339795200 12065 18312 139288 484990 1974 902 90716 135582 219628 1049062 64070732 267888560 11595 52601 135977 1137011 1975 902 111149 247117 375471 1656500 64131552 253600880 0 0 126452 1748431 1976 902 151567 233824 258069 1036375 72428816 294192576 9574 16132 89110 874245 1977 902 159007 288937 632044 1977812 149405840 356244928 0 0 160421 674755 1978 902 302144 523625 376993 894437 161319616 369276320 0 0 128911 236786 1979 902 263748 468312 324980 835062 153871376 322163936 0 0 53016 89746 1980 902 240082 380875 254595 586750 131475112 294612672 1363 3062 28456 50561 1981 902 331663 448187 554520 1126937 237938496 236592128 52344 58011 268097 312113 1982 902 369382 563750 117345 352375 173374416 267219568 88818 136482 632151 534929 1983 902 616082 788625 243863 780500 226757760 331736864 145836 71248 1384907 601291 1984 902 959659 1086062 392689 1111375 171133120 369494048 2079176 1028974 1814396 1075765 1985 902 748704 766812 829172 2122750 168479888 380314432 3612847 1590097 1933597 1223189 1986 902 1298959 1261125 4040176 9988785 327903168 430983424 3628705 1705323 1156736 1081471 1987 902 2107219 1930875 2212019 5768554 160881840 406785376 3911491 1579579 1266919 1024096 1988 902 2573120 2117875 1203746 2862687 152356480 362726880 3674326 1624760 3513976 1160022 1989 902 2888215 2327812 477349 1266562 224163760 385940928 5684585 2677385 3466587 1489777 1990 902 3345576 3075187 1019288 1404125 390824864 352312128 3181770 1305635 3820630 2897837 1991 902 4560436 2672000 467383 418250 269461472 353543872 8658034 3027499 5897411 1109323 1992 902 5664522 5213699 618499 559437 141482016 183116320 8592061 2916249 10776134 4222087 1993 902 6490505 4733488 328571 326875 325450048 400494304 25714519 9677932 8478949 3162569 1994 902 10479733 7673394 15237 16117 335774656 376031680 31977654 10490708 7133936 1827493 1995 902 17997856 11655089 39438 43191 434508160 593079616 45101016 14822351 4221050 1343177 1996 902 38849532 21769264 94680 100296 613726272 685677632 88867703 27270029 10402293 2332987 1997 902 61045808 37504200 53591 49441 549398080 660608640 119103389 41025640 17126556 3275951 1998 902 102889656 61728728 40701 28812 600902720 734053120 144522212 46619633 24440423 4889259 1999 902 121800280 79553504 2398 1687 517601728 615063808 123059860 41901303 18251894 4135634 2000 902 163877088 101390248 10665 14687 438421792 589954432 101363658 34695648 14394573 4008089 2001 902 169921584 105680184 0 0 517007488 679187072 99481702 34809782 14115626 3864210 2002 902 175542448 100499128 2290 2875 582243072 724015808 91658917 30983318 19301177 4595575 2003 902 234794400 130153808 11978 21011 794276800 784988032 63798848 19163022 23735958 4930831 2004 902 261605248 133565936 641605 807375 789782848 779020288 56011543 15406065 18641380 3172048 2005 902 274699840 141642032 1449620 856968 818552896 801408192 28333599 7396764 2857037 482476 ? V: value in US dollars; Q: quantity in kilograms; ? 1: Canada ? 2: Dominican Republic ? 3: Mexico ? 4: European Union-15 ? 5: Rest of the World ? 902: United States as Importer; select q2>0; P2=V2/Q2; olsq P2 C Q2 ZYRS; select q4>0;

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101 P4=V4/Q4; olsq P4 C Q4 ZYRS; SMPL 1, 43; Q2=1*(Q2=0)+Q2*(Q2>0); Q4=1*(Q4=0)+Q4*(Q4>0); Print Q2 P2 Q4 P4; END; The United States NBR Import Demand Model. OPTIONS MEMORY=1500 Double; ? US_02NBR-SELECTEDNEW2007-V2 Models for US Import Analysis with EU-15; TITLE 'TOMATO IMPORTS TO THE US'; ? For Cost Share, Quantity Share and Average Price calculation; ? NBR Final Model with AR1 plus Homogeneity and Symmetry imposed (Automatic Rho selection); smpl 1 43; LOAD ZYRS ZIMP V1 Q1 V2 Q2 V3 Q3 V4 Q4 V5 Q5; 1963 902 189750 730125 2481 21320 20705768 108846184 13928 65690 13565 94217 1964 902 219358 562125 13291 189476 27354888 111638936 0 0 105323 648311 1965 902 298959 733875 33276 188640 29424864 120410128 0 0 167763 668557 1966 902 241757 562312 9319 50292 52008580 162705440 703 6875 57697 231001 1967 902 306522 803312 41735 379875 42585292 164302752 123059 306330 23536 126579 1968 902 222304 575562 75850 508062 46973296 175722128 12883 47249 45275 197879 1969 902 216389 400250 144332 916687 68018008 202410752 17355 30914 32468 194149 1970 902 560946 977250 121233 797875 94966856 290759360 2768 17812 180445 796919 1971 902 393364 505250 203117 1523625 84131328 258677808 55205 104367 34202 163878 1972 902 245434 409687 220130 1345187 88150080 264119568 0 0 129604 313238 1973 902 143427 467187 169100 834375 115137936 339795200 12065 18312 139288 484990 1974 902 90716 135582 219628 1049062 64070732 267888560 11595 52601 135977 1137011 1975 902 111149 247117 375471 1656500 64131552 253600880 0 0 126452 1748431 1976 902 151567 233824 258069 1036375 72428816 294192576 9574 16132 89110 874245 1977 902 159007 288937 632044 1977812 149405840 356244928 0 0 160421 674755 1978 902 302144 523625 376993 894437 161319616 369276320 0 0 128911 236786 1979 902 263748 468312 324980 835062 153871376 322163936 0 0 53016 89746 1980 902 240082 380875 254595 586750 131475112 294612672 1363 3062 28456 50561 1981 902 331663 448187 554520 1126937 237938496 236592128 52344 58011 268097 312113 1982 902 369382 563750 117345 352375 173374416 267219568 88818 136482 632151 534929 1983 902 616082 788625 243863 780500 226757760 331736864 145836 71248 1384907 601291

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102 1984 902 959659 1086062 392689 1111375 171133120 369494048 2079176 1028974 1814396 1075765 1985 902 748704 766812 829172 2122750 168479888 380314432 3612847 1590097 1933597 1223189 1986 902 1298959 1261125 4040176 9988785 327903168 430983424 3628705 1705323 1156736 1081471 1987 902 2107219 1930875 2212019 5768554 160881840 406785376 3911491 1579579 1266919 1024096 1988 902 2573120 2117875 1203746 2862687 152356480 362726880 3674326 1624760 3513976 1160022 1989 902 2888215 2327812 477349 1266562 224163760 385940928 5684585 2677385 3466587 1489777 1990 902 3345576 3075187 1019288 1404125 390824864 352312128 3181770 1305635 3820630 2897837 1991 902 4560436 2672000 467383 418250 269461472 353543872 8658034 3027499 5897411 1109323 1992 902 5664522 5213699 618499 559437 141482016 183116320 8592061 2916249 10776134 4222087 1993 902 6490505 4733488 328571 326875 325450048 400494304 25714519 9677932 8478949 3162569 1994 902 10479733 7673394 15237 16117 335774656 376031680 31977654 10490708 7133936 1827493 1995 902 17997856 11655089 39438 43191 434508160 593079616 45101016 14822351 4221050 1343177 1996 902 38849532 21769264 94680 100296 613726272 685677632 88867703 27270029 10402293 2332987 1997 902 61045808 37504200 53591 49441 549398080 660608640 119103389 41025640 17126556 3275951 1998 902 102889656 61728728 40701 28812 600902720 734053120 144522212 46619633 24440423 4889259 1999 902 121800280 79553504 2398 1687 517601728 615063808 123059860 41901303 18251894 4135634 2000 902 163877088 101390248 10665 14687 438421792 589954432 101363658 34695648 14394573 4008089 2001 902 169921584 105680184 0 0 517007488 679187072 99481702 34809782 14115626 3864210 2002 902 175542448 100499128 2290 2875 582243072 724015808 91658917 30983318 19301177 4595575 2003 902 234794400 130153808 11978 21011 794276800 784988032 63798848 19163022 23735958 4930831 2004 902 261605248 133565936 641605 807375 789782848 779020288 56011543 15406065 18641380 3172048 2005 902 274699840 141642032 1449620 856968 818552896 801408192 28333599 7396764 2857037 482476 ? V: value in US dollars; Q: quantity in kilograms; ? 1: Canada ? 2: Dominican Republic ? 3: Mexico ? 4: European Union-15 ? 5: Rest of the World ? 902: United States as Importer; ? Eliminating zero values in Q; Q2=1*(Q2=0)+Q2*(Q2>0); Q4=1*(Q4=0)+Q4*(Q4>0); PRINT ZYRS Q2 Q4; ? Eliminating Pi=0; ?? [Following Highest Price + Twice Std.Dev. + Inflation]; SELECT ZYRS=2001; V2=2.40887; SELECT ZYRS=1964; V4=2.20833; SELECT ZYRS=1965; V4=2.30727; SELECT ZYRS=1972; V4=2.99985;

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103 SELECT ZYRS=1975; V4=3.29667; SELECT ZYRS=1977; V4=3.49455; SELECT ZYRS=1978; V4=3.59349; SELECT ZYRS=1979; V4=3.69243; ?To find out Average and Annual Costs for making Table; smpl 2, 43; msd v1-v5; smpl 2,2; msd v1-v5; print v1-v5; smpl 43,43; msd v1-v5; print v1-v5; ? To find Average and Annual Import Quantity (kg) for making Table; smpl 2,43; msd q1-q5; smpl 2,2; msd q1-q5; print q1-q5; smpl 43,43; msd q1-q5; print q1-q5; ? End of Calculation for Table; SMPL 1, 43; ? Defining Total Cost(S); S=v1+v2+V3+V4+v5; ? Calculating prices (Pi) P1=v1/Q1; P2=v2/Q2;P3=v3/Q3;P4=v4/Q4; P5=v5/Q5; PRINT ZYRS Q1 P1 Q2 P2 Q3 P3 Q4 P4 Q5 P5; ? To find Average and Annual Price (US $/Kg) for making Table; smpl 2,43; msd p1-p5; smpl 2,2; msd p1-p5; print p1-p5; smpl 43,43; msd p1-p5; print p1-p5; ? End of Calculation for Table; SMPL 1,43; ? CALCULATION OF FACTOR COST SHARES (Fi=PRICE*QUANTITY/TOTAL COST) F1=v1/S; F2=v2/S; F3=v3/S; F4=v4/S; F5=v5/S; ? LOGGING ALL PRICES AND QUANTITIES(LPi,LQi) LP1=LOG(P1); LP2=LOG(P2); LP3=LOG(P3); LP4=LOG(P4); LP5=LOG(P5); LQ1=LOG(Q1);LQ2=LOG(Q2); LQ3=LOG(Q3);LQ4=LOG(Q4); LQ5=LOG(Q5); smpl 2 43;

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104 ? CALCULATION TWO PERIOD MEAN OF FACTOR SHARES( Fi1) F11=(F1+F1(-1))/2; F21=(F2+F2(-1))/2; F31=(F3+F3(-1))/2; F41=(F4+F4(-1))/2; F51=(F5+F5(-1))/2; ? Calculation of Total Quantity for finding Shares; T=q1+q2+q3+q4+q5; ? Calculation of Quantity Shares for making Table; k1=Q1/T; k2=Q2/T; k3=Q3/T; k4=Q4/T; k5=Q5/T; msd k1-k5; smpl 2,2; msd k1-k5; smpl 43,43; msd k1-k5; ? End of Quantity Share Calculation; SMPL 2,43; ? CALCULATION: CHANGE IN LOGGED PRICES(DPi) DP1=LP1-LP1(-1); DP2=LP2-LP2(-1); DP3=LP3-LP3(-1); DP4=LP4-LP4(-1); DP5=LP5-LP5(-1); ? CALCULATION:CHANGE IN LOGGED QUANTITY(DQi) DQ1=LQ1-LQ1(-1); DQ2=LQ2-LQ2(-1); DQ3=LQ3-LQ3(-1); DQ4=LQ4-LQ4(-1); DQ5=LQ5-LQ5(-1); ? DEPENDENT VARIABLE fi*Dq and SUMMATION INDEX(FDQi) FDQ1=F11*DQ1; FDQ2=F21*DQ2; FDQ3=F31*DQ3; FDQ4=F41*DQ4; FDQ5=F51*DQ5; DQ=FDQ1+FDQ2+FDQ3+FDQ4+FDQ5; ?Fi*DP and SUMMATION INDEX(DP) FDP1=F11*DP1; FDP2=F21*DP2; FDP3=F31*DP3; FDP4=F41*DP4; FDP5=F51*DP5; DP=FDP1+FDP2+FDP3+FDP4+FDP5; SMPL 2, 43; ? DIFFEREENTIAL NBR MODEL WITH AR1 PLUS HOMOGENEITY AND SYMMETRY; trend obs; d1 = (obs=1); frml res1 FDQ1-(A1*DQ+B11*DP1+B12*DP2+B13*DP3+B14*DP4+(-B11-B12-B13-B14)*DP5-F11*(DP1-DP)); frml eq1 [d1*res1*sqrt(1-rho**2) + (1-d1)*(res1 rho*res1(-1))]* (1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res2 FDQ2-(A2*DQ+B12*DP1+B22*DP2+B23*DP3+B24*DP4+(-B12-B22-B23-B24)*DP5-F21*(DP2-DP)); frml eq2 [d1*res2*sqrt(1-rho**2) + (1-d1)*(res2 rho*res2(-1))]* (1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res3 FDQ3-(A3*DQ+B13*DP1+B23*DP2+B33*DP3+B34*DP4+(-B13-B23-B33-B34)*DP5-F31*(DP3-DP)); frml eq3 [d1*res3*sqrt(1-rho**2) + (1-d1)*(res3 rho*res3(-1))]* (1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res4 FDQ4-(A4*DQ+B14*DP1+B24*DP2+B34*DP3+B44*DP4+(-B14-B24-B34-B44)*DP5-F41*(DP4-DP)); frml eq4 [d1*res4*sqrt(1-rho**2) + (1-d1)*(res4 rho*res4(-1))]* (1-rho**2)**(-1/(2*@nob)); ? mark significant variables with STARS;

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105 REGOPT (STARS,STAR1=.10,STAR2=.05) T; PARAM A1 0 A2 0 A3 0 A4 0 B11 0 B12 0 B13 0 B14 0 B22 0 B23 0 B24 0 B33 0 B34 0 B44 0 rho 0; eqsub eq1 res1;eqsub eq2 res2;eqsub eq3 res3;eqsub eq4 res4; lsq(nodropmiss,tol=1e-7,maxit=1000) eq1 eq2 eq3 eq4; COPY @LOGL LU; LU1=LU; SMPL 2, 43; ? Elasticities; MSD F11 F21 F31 F41 F51; ?================= MEAN FACTOR SHARES SET MF1=@MEAN(1); SET MF2=@MEAN(2); SET MF3=@MEAN(3); SET MF4=@MEAN(4); SET MF5=@MEAN(5); PRINT MF1-MF5; SMPL 2,2; MSD F1 F2 F3 F4 F5; ?================= MEAN FACTOR SHARES SET FF1=@MEAN(1); SET FF2=@MEAN(2); SET FF3=@MEAN(3); SET FF4=@MEAN(4); SET FF5=@MEAN(5); PRINT FF1-FF5; SMPL 43,43; MSD F1 F2 F3 F4 F5; ?================= MEAN FACTOR SHARES SET FL1=@MEAN(1); SET FL2=@MEAN(2); SET FL3=@MEAN(3); SET FL4=@MEAN(4); SET FL5=@MEAN(5); PRINT FL1-FL5; SMPL 2, 43; SET B15=-B11-B12-B13-B14; SET B21=B12; SET B25=-B21-B22-B23-B24; SET B31=B13; SET B32=B23; SET B35=-B31-B32-B33-B34; SET B41=B14; SET B42=B24; SET B43=B34;

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106 SET B45=-B41-B42-B43-B44; SET B55=(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24)-(-B13-B23-B33-B34)-(-B14-B24-B34-B44)); SET A5=1-A1-A2-A3-A4; ? Calculate the standard errors for ROW frml row1 A5=1-A1-A2-A3-A4; frml row2 B15=-B11-B12-B13-B14; frml row3 B25=-B21-B22-B23-B24; frml row4 B35=-B31-B32-B33-B34; frml row5 B45=-B41-B42-B43-B44; frml row6 B55=(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24)-(-B13-B23-B33-B34)-(-B14-B24-B34-B44)); frml row7 B21=B12; frml row8 B31=B13; frml row9 B32=B23; frml row10 B41=B14; frml row11 B42=B24; frml row12 B43=B34; analyz row1-row12; ?? Calculate Eigenvalues SET B51=B15; SET B52=B25; SET B53=B35; SET B54=B45; SET B55=B55; ? Tranform r to Pie SET D11=(B11-MF1+MF1*MF1); SET D12=(B12+MF1*MF2); SET D13=(B13+MF1*MF3); SET D14=(B14+MF1*MF4); SET D15=(-B11-B12-B13-B14+MF1*MF5); SET D21=(B12+MF2*MF1); SET D22=(B22-MF2+MF2*MF2); SET D23=(B23+MF2*MF3); SET D24=(B24+MF2*MF4); SET D25=(-B12-B22-B23-B24+MF2*MF5); SET D31=(B13+MF3*MF1); SET D32=(B23+MF3*MF2); SET D33=(B33-MF3+MF3*MF3); SET D34=(B34+MF3*MF4); SET D35=(-B13-B23-B33-B34+MF3*MF5); SET D41=(B14+MF4*MF1); SET D42=(B24+MF4*MF2); SET D43=(B34+MF4*MF3); SET D44=(B44-MF4+MF4*MF4); SET D45=(-B14-B24-B34-B44+MF4*MF5); SET D51=(-B11-B12-B13-B14+MF5*MF1); SET D52=(-B12-B22-B23-B24+MF5*MF2); SET D53=(-B13-B23-B33-B34+MF5*MF3); SET D54=(-B14-B24-B34-B44+MF5*MF4); SET D55=(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24)-(-B13-B23-B33-B34)-(-B14-B24-B34-B44)-MF5+MF5*MF5); ? Create each row five; MMAKE(VERT) E1 D11-D15; MMAKE(VERT) E2 D21-D25; MMAKE(VERT) E3 D31-D35;

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107 MMAKE(VERT) E4 D41-D45; MMAKE(VERT) E5 D51-D55; ? Creates square matrix MMAKE E E1-E5; ? Calculate Eigenvalues of E MAT EV = EIGVAL(E); Print E EV; ? Creates standard errors for pi ij's where the pi ij's are D11, D12, etc frml pi1 d11=(B11-MF1+MF1*MF1); frml pi2 d12=(B12+MF1*MF2); frml pi3 d13=(B13+MF1*MF3); frml pi4 d14=(B14+MF1*MF4); frml pi5 d15=(-B11-B12-B13-B14+MF1*MF5); frml pi6 d21=(B12+MF2*MF1); frml pi7 d22=(B22-MF2+MF2*MF2); frml pi8 d23=(B23+MF2*MF3); frml pi9 d24=(B24+MF2*MF4); frml pi10 d25=(-B12-B22-B23-B24+MF2*MF5); frml pi11 d31=(B13+MF3*MF1); frml pi12 d32=(B23+MF3*MF2); frml pi13 d33=(B33-MF3+MF3*MF3); frml pi14 d34=(B34+MF3*MF4); frml pi15 d35=(-B13-B23-B33-B34+MF3*MF5); frml pi16 d41=(B14+MF4*MF1); frml pi17 d42=(B24+MF4*MF2); frml pi18 d43=(B34+MF4*MF3); frml pi19 d44=(B44-MF4+MF4*MF4); frml pi20 d45=(-B14-B24-B34-B44+MF4*MF5); frml pi21 d51=(-B11-B12-B13-B14+MF5*MF1); frml pi22 d52=(-B12-B22-B23-B24+MF5*MF2); frml pi23 d53=(-B13-B23-B33-B34+MF5*MF3); frml pi24 d54=(-B14-B24-B34-B44+MF5*MF4); frml pi25 d55=(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24) -(-B13-B23-B33-B34)-(-B14-B24-B34-B44)-MF5+MF5*MF5); analyz pi1-pi25; ? Elasticities ? Divisia Input Index FRML EL1 E1=A1/MF1; FRML EL2 E2=A2/MF2; FRML EL3 E3=A3/MF3; FRML EL4 E4=A4/MF4; FRML EL5 E5=(1-A1-A2-A3-A4)/MF5; ? Divisia Input Index WITH FIRST F FRML EL6 EF1=A1/FF1; FRML EL7 EF2=A2/FF2; FRML EL8 EF3=A3/FF3; FRML EL9 EF4=A4/FF4; FRML EL10 EF5=(1-A1-A2-A3-A4)/FF5; ? Divisia Input Index WITH LAST F FRML EL11 EL1=A1/FL1; FRML EL12 EL2=A2/FL2; FRML EL13 EL3=A3/FL3; FRML EL14 EL4=A4/FL4; FRML EL15 EL5=(1-A1-A2-A3-A4)/FL5; ? Compensated price elasticities

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108 FRML EP16 E11=(B11-MF1+MF1*MF1)/MF1; FRML EP17 E12=(B12+MF1*MF2)/MF1; FRML EP18 E13=(B13+MF1*MF3)/MF1; FRML EP19 E14=(B14+MF1*MF4)/MF1; FRML EP20 E15=(-B11-B12-B13-B14+MF1*MF5)/MF1; FRML EP21 E21=(B12+MF2*MF1)/MF2; FRML EP22 E22=(B22-MF2+MF2*MF2)/MF2; FRML EP23 E23=(B23+MF2*MF3)/MF2; FRML EP24 E24=(B24+MF2*MF4)/MF2; FRML EP25 E25=(-B12-B22-B23-B24+MF2*MF5)/MF2; FRML EP26 E31=(B13+MF3*MF1)/MF3; FRML EP27 E32=(B23+MF3*MF2)/MF3; FRML EP28 E33=(B33-MF3+MF3*MF3)/MF3; FRML EP29 E34=(B34+MF3*MF4)/MF3; FRML EP30 E35=(-B13-B23-B33-B34+MF3*MF5)/MF3; FRML EP31 E41=(B14+MF4*MF1)/MF4; FRML EP32 E42=(B24+MF4*MF2)/MF4; FRML EP33 E43=(B34+MF4*MF3)/MF4; FRML EP34 E44=(B44-MF4+MF4*MF4)/MF4; FRML EP35 E45=(-B14-B24-B34-B44+MF4*MF5)/MF4; FRML EP36 E51=(-B11-B12-B13-B14+MF5*MF1)/MF5; FRML EP37 E52=(-B12-B22-B23-B24+MF5*MF2)/MF5; FRML EP38 E53=(-B13-B23-B33-B34+MF5*MF3)/MF5; FRML EP39 E54=(-B14-B24-B34-B44+MF5*MF4)/MF5; FRML EP40 E55=(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24)-(-B13-B23-B33-B34)-(-B14-B24-B34-B44)MF5+MF5*MF5)/MF5; ANALYZ EL1-EL15; ANALYZ EP16-EP40; PRINT; smpl 2,2; print LU; ?mark significant variables with STARS; REGOPT (STARS,STAR1=.10,STAR2=.05,) T; END; US NBR Simulation Model OPTIONS MEMORY=1400 Double; ? US_02N BR-SELECTEDNEW2007-RECWW Models for US Import Analysis; TITLE 'TOMATO IMPORTS TO THE US'; ? For Elasticity Trend over 11 years & Calculating Mean Quantity for sample 2,33; ? NBR Final Model with AR1 plus Homogeneity a nd Symmetry imposed (Automatic Rho selection); smpl 1 43; LOAD ZYRS ZIMP V1 Q1 V2 Q2 V3 Q3 V4 Q4 V5 Q5; 1963 902 189750 730125 2481 21320 20705768 108846184 13928 65690 13565 94217 1964 902 219358 562125 13291 189476 27354888 111638936 0 0 105323 648311 1965 902 298959 733875 33276 188640 29424864 120410128 0 0 167763 668557 1966 902 241757 562312 9319 50292 52008580 162705440 703 6875 57697 231001 1967 902 306522 803312 41735 379875 42585292 164302752 123059 306330 23536 126579 1968 902 222304 575562 75850 508062 46973296 175722128 12883 47249 45275 197879 1969 902 216389 400250 144332 916687 68018008 202410752 17355 30914 32468 194149 1970 902 560946 977250 121233 797875 94966856 290759360 2768 17812 180445 796919 1971 902 393364 505250 203117 1523625 84131328 258677808 55205 104367 34202 163878 1972 902 245434 409687 220130 1345187 88150080 264119568 0 0 129604 313238 1973 902 143427 467187 169100 834375 115137936 339795200 12065 18312 139288 484990 1974 902 90716 135582 219628 1049062 64070732 267888560 11595 52601 135977 1137011 1975 902 111149 247117 375471 1656500 64131552 253600880 0 0 126452 1748431 1976 902 151567 233824 258069 1036375 72428816 294192576 9574 16132 89110 874245

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109 1977 902 159007 288937 632044 1977812 149405840 356244928 0 0 160421 674755 1978 902 302144 523625 376993 894437 161319616 369276320 0 0 128911 236786 1979 902 263748 468312 324980 835062 153871376 322163936 0 0 53016 89746 1980 902 240082 380875 254595 586750 131475112 294612672 1363 3062 28456 50561 1981 902 331663 448187 554520 1126937 237938496 236592128 52344 58011 268097 312113 1982 902 369382 563750 117345 352375 173374416 267219568 88818 136482 632151 534929 1983 902 616082 788625 243863 780500 226757760 331736864 145836 71248 1384907 601291 1984 902 959659 1086062 392689 1111375 171133120 369494048 2079176 1028974 1814396 1075765 1985 902 748704 766812 829172 2122750 168479888 380314432 3612847 1590097 1933597 1223189 1986 902 1298959 1261125 4040176 9988785 327903168 430983424 3628705 1705323 1156736 1081471 1987 902 2107219 1930875 2212019 5768554 160881840 406785376 3911491 1579579 1266919 1024096 1988 902 2573120 2117875 1203746 2862687 152356480 362726880 3674326 1624760 3513976 1160022 1989 902 2888215 2327812 477349 1266562 224163760 385940928 5684585 2677385 3466587 1489777 1990 902 3345576 3075187 1019288 1404125 390824864 352312128 3181770 1305635 3820630 2897837 1991 902 4560436 2672000 467383 418250 269461472 353543872 8658034 3027499 5897411 1109323 1992 902 5664522 5213699 618499 559437 141482016 183116320 8592061 2916249 10776134 4222087 1993 902 6490505 4733488 328571 326875 325450048 400494304 25714519 9677932 8478949 3162569 1994 902 10479733 7673394 15237 16117 335774656 376031680 31977654 10490708 7133936 1827493 1995 902 17997856 11655089 39438 43191 434508160 593079616 45101016 14822351 4221050 1343177 1996 902 38849532 21769264 94680 100296 613726272 685677632 88867703 27270029 10402293 2332987 1997 902 61045808 37504200 53591 49441 549398080 660608640 119103389 41025640 17126556 3275951 1998 902 102889656 61728728 40701 28812 600902720 734053120 144522212 46619633 24440423 4889259 1999 902 121800280 79553504 2398 1687 517601728 615063808 123059860 41901303 18251894 4135634 2000 902 163877088 101390248 10665 14687 438421792 589954432 101363658 34695648 14394573 4008089 2001 902 169921584 105680184 0 0 517007488 679187072 99481702 34809782 14115626 3864210 2002 902 175542448 100499128 2290 2875 582243072 724015808 91658917 30983318 19301177 4595575 2003 902 234794400 130153808 11978 21011 794276800 784988032 63798848 19163022 23735958 4930831 2004 902 261605248 133565936 641605 807375 789782848 779020288 56011543 15406065 18641380 3172048 2005 902 274699840 141642032 1449620 856968 818552896 801408192 28333599 7396764 2857037 482476 ? V: value in US dollars; Q: quantity in kilograms; ? 1: Canada ? 2: Dominican Republic ? 3: Mexico ? 4: European Union-15 ? 5: Rest of the World ? 902: United States as Importer; ? Eliminating zero values in Q; Q2=1*(Q2=0)+Q2*(Q2>0); Q4=1*(Q4=0)+Q4*(Q4>0); PRINT ZYRS Q2 Q4; ? Eliminating Pi=0; ?? [Following Highest Price + Twice Std.Dev. + Inflation]; SELECT ZYRS=2001; V2=2.40887; SELECT ZYRS=1964; V4=2.20833; SELECT ZYRS=1965; V4=2.30727; SELECT ZYRS=1972; V4=2.99985; SELECT ZYRS=1975; V4=3.29667; SELECT ZYRS=1977; V4=3.49455; SELECT ZYRS=1978; V4=3.59349; SELECT ZYRS=1979; V4=3.69243; ?To find out Average and Annual Costs for making Table; smpl 2, 43; msd v1-v5;

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110 smpl 2,2; msd v1-v5; print v1-v5; smpl 43,43; msd v1-v5; print v1-v5; ? To find Average and Annual Import Quantity (kg) for making Table; smpl 2,43; msd q1-q5; smpl 2,2; msd q1-q5; print q1-q5; smpl 43,43; msd q1-q5; print q1-q5; ? End of Calculation for Table; SMPL 1, 43; ? Defining Total Cost(S); S=v1+v2+V3+V4+v5; ? Calculating prices (Pi) P1=v1/Q1; P2=v2/Q2;P3=v3/Q3;P4=v4/Q4; P5=v5/Q5; PRINT ZYRS Q1 P1 Q2 P2 Q3 P3 Q4 P4 Q5 P5; ? To find Average and Annual Price (US $/Kg) for making Table; smpl 2,43; msd p1-p5; smpl 2,2; msd p1-p5; print p1-p5; smpl 43,43; msd p1-p5; print p1-p5; ? End of Calculation for Table; SMPL 1,43; ? CALCULATION OF FACTOR COST SHARES (Fi=PRICE*QUANTITY/TOTAL COST) F1=v1/S; F2=v2/S; F3=v3/S; F4=v4/S; F5=v5/S; ? LOGGING ALL PRICES AND QUANTITIES(LPi,LQi) LP1=LOG(P1); LP2=LOG(P2); LP3=LOG(P3); LP4=LOG(P4); LP5=LOG(P5); LQ1=LOG(Q1);LQ2=LOG(Q2); LQ3=LOG(Q3);LQ4=LOG(Q4); LQ5=LOG(Q5); smpl 2 43; ? CALCULATION TWO PERIOD MEAN OF FACTOR SHARES( Fi1) F11=(F1+F1(-1))/2; F21=(F2+F2(-1))/2; F31=(F3+F3(-1))/2; F41=(F4+F4(-1))/2; F51=(F5+F5(-1))/2; ? Calculation of Total Quantity for finding Shares; T=q1+q2+q3+q4+q5; ? Calculation of Quantity Shares for making Table; k1=Q1/T; k2=Q2/T; k3=Q3 /T; k4=Q4/T; k5=Q5/T; msd k1-k5;

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111 smpl 2,2; msd k1-k5; smpl 43,43; msd k1-k5; ? End of Quantity Share Calculation; ? Calculation of Mean Quantity for Structural Change; smpl 2,33; msd q1-q5; msd T; SMPL 2,43; ? CALCULATION: CHANGE IN LOGGED PRICES(DPi) DP1=LP1-LP1(-1); DP2=LP2-LP2(-1); DP3=LP3-LP3(-1); DP4=LP4-LP4(-1); DP5=LP5-LP5(-1); ? CALCULATION:CHANGE IN LOGGED QUANTITY(DQi) DQ1=LQ1-LQ1(-1); DQ2=LQ2-LQ2(-1); DQ3=LQ3-LQ3(-1); DQ4=LQ4-LQ4(-1); DQ5=LQ5-LQ5(-1); ? DEPENDENT VARIABLE fi*Dq and SUMMATION INDEX(FDQi) FDQ1=F11*DQ1; FDQ2=F21*DQ2; FDQ3=F31*DQ3; FDQ4=F 41*DQ4; FDQ5=F51*DQ5; DQ=FDQ1+FDQ2+FDQ3+FDQ4+FDQ5; ?Fi*DP and SUMMATION INDEX(DP) FDP1=F11*DP1; FDP2=F21*DP2; FDP3=F31*DP3; FDP4=F41*DP4; FDP5=F51*DP5; DP=FDP1+FDP2+FDP3+FDP4+FDP5; proc zzzz; ? DIFFEREENTIAL NBR MODEL WITH AR1 PLUS HOMOGENEITY AND SYMMETRY; trend obs; d1 = (obs=1); frml res1 FDQ1-(A1*DQ+B11*DP1+B12*DP2+B13*DP3+B 14*DP4+(-B11-B12-B13-B14)*DP5-F11*(DP1-DP)); frml eq1 [d1*res1*sqrt(1-rho**2) + (1-d1)*(res1 rho*res1(-1))]* (1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res2 FDQ2-(A2*DQ+B12*DP1+B22*DP2+B23*DP3+B 24*DP4+(-B12-B22-B23-B24)*DP5-F21*(DP2-DP)); frml eq2 [d1*res2*sqrt(1-rho**2) + (1-d1)*(res2 rho*res2(-1))]* (1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res3 FDQ3-(A3*DQ+B13*DP1+B23*DP2+B33*DP3+B 34*DP4+(-B13-B23-B33-B34)*DP5-F31*(DP3-DP)); frml eq3 [d1*res3*sqrt(1-rho**2) + (1-d1)*(res3 rho*res3(-1))]* (1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res4 FDQ4-(A4*DQ+B14*DP1+B24*DP2+B34*DP3+B 44*DP4+(-B14-B24-B34-B44)*DP5-F41*(DP4-DP)); frml eq4 [d1*res4*sqrt(1-rho**2) + (1-d1)*(res4 rho*res4(-1))]* (1-rho**2)**(-1/(2*@nob)); ? mark significant variables with STARS;

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112 REGOPT (STARS,STAR1=.10,STAR2=.05) T; PARAM A1 0 A2 0 A3 0 A4 0 B11 0 B12 0 B13 0 B14 0 B22 0 B23 0 B24 0 B33 0 B34 0 B44 0 rho 0; eqsub eq1 res1;eqsub eq2 res2;eqsub eq3 res3;eqsub eq4 res4; lsq(nodropmiss,tol=1e-7,max it=1000) eq1 eq2 eq3 eq4; COPY @LOGL LU; LU1=LU; SMPL NR1,NR2; ? Elasticities; MSD F11 F21 F31 F41 F51; ?================= MEAN FACTOR SHARES SET MF1=@MEAN(1); SET MF2=@MEAN(2); SET MF3=@MEAN(3); SET MF4=@MEAN(4); SET MF5=@MEAN(5); PRINT MF1-MF5; SMPL NR1,NR1; MSD F1 F2 F3 F4 F5; ?================= MEAN FACTOR SHARES SET FF1=@MEAN(1); SET FF2=@MEAN(2); SET FF3=@MEAN(3); SET FF4=@MEAN(4); SET FF5=@MEAN(5); PRINT FF1-FF5; SMPL NR2,NR2; MSD F1 F2 F3 F4 F5; ?================= MEAN FACTOR SHARES SET FL1=@MEAN(1); SET FL2=@MEAN(2); SET FL3=@MEAN(3); SET FL4=@MEAN(4); SET FL5=@MEAN(5); PRINT FL1-FL5; SMPL NR1,NR2; SET B15=-B11-B12-B13-B14; SET B21=B12; SET B25=-B21-B22-B23-B24; SET B31=B13; SET B32=B23; SET B35=-B31-B32-B33-B34; SET B41=B14; SET B42=B24; SET B43=B34;

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113 SET B45=-B41-B42-B43-B44; SET B55=(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24 )-(-B13-B23-B33-B34)-(-B14-B24-B34-B44)); SET A5=1-A1-A2-A3-A4; ? Calculate the standard errors for ROW frml row1 A5=1-A1-A2-A3-A4; frml row2 B15=-B11-B12-B13-B14; frml row3 B25=-B21-B22-B23-B24; frml row4 B35=-B31-B32-B33-B34; frml row5 B45=-B41-B42-B43-B44; frml row6 B55=(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24 )-(-B13-B23-B33-B34)-(-B14-B24-B34-B44)); frml row7 B21=B12; frml row8 B31=B13; frml row9 B32=B23; frml row10 B41=B14; frml row11 B42=B24; frml row12 B43=B34; analyz row1-row12; ?? Calculate Eigenvalues SET B51=B15; SET B52=B25; SET B53=B35; SET B54=B45; SET B55=B55; ? Tranform r to Pie SET D11=(B11-MF1+MF1*MF1); SET D12=(B12+MF1*MF2); SET D13=(B13+MF1*MF3); SET D14=(B14+MF1*MF4); SET D15=(-B11-B12-B13-B14+MF1*MF5); SET D21=(B12+MF2*MF1); SET D22=(B22-MF2+MF2*MF2); SET D23=(B23+MF2*MF3); SET D24=(B24+MF2*MF4); SET D25=(-B12-B22-B23-B24+MF2*MF5); SET D31=(B13+MF3*MF1); SET D32=(B23+MF3*MF2); SET D33=(B33-MF3+MF3*MF3); SET D34=(B34+MF3*MF4); SET D35=(-B13-B23-B33-B34+MF3*MF5); SET D41=(B14+MF4*MF1); SET D42=(B24+MF4*MF2); SET D43=(B34+MF4*MF3); SET D44=(B44-MF4+MF4*MF4); SET D45=(-B14-B24-B34-B44+MF4*MF5); SET D51=(-B11-B12-B13-B14+MF5*MF1); SET D52=(-B12-B22-B23-B24+MF5*MF2); SET D53=(-B13-B23-B33-B34+MF5*MF3); SET D54=(-B14-B24-B34-B44+MF5*MF4); SET D55=(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24)-(-B13-B23B33-B34)-(-B14-B24-B34-B44)-MF5+MF5*MF5); ? Create each row five; MMAKE(VERT) E1 D11-D15; MMAKE(VERT) E2 D21-D25;

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114 MMAKE(VERT) E3 D31-D35; MMAKE(VERT) E4 D41-D45; MMAKE(VERT) E5 D51-D55; ? Creates square matrix MMAKE E E1-E5; ? Calculate Eigenvalues of E MAT EV = EIGVAL(E); Print E EV; ? Creates standard errors for pi ij's where the pi ij's are D11, D12, etc frml pi1 d11=(B11-MF1+MF1*MF1); frml pi2 d12=(B12+MF1*MF2); frml pi3 d13=(B13+MF1*MF3); frml pi4 d14=(B14+MF1*MF4); frml pi5 d15=(-B11-B12-B13-B14+MF1*MF5); frml pi6 d21=(B12+MF2*MF1); frml pi7 d22=(B22-MF2+MF2*MF2); frml pi8 d23=(B23+MF2*MF3); frml pi9 d24=(B24+MF2*MF4); frml pi10 d25=(-B12-B22-B23-B24+MF2*MF5); frml pi11 d31=(B13+MF3*MF1); frml pi12 d32=(B23+MF3*MF2); frml pi13 d33=(B33-MF3+MF3*MF3); frml pi14 d34=(B34+MF3*MF4); frml pi15 d35=(-B13-B23-B33-B34+MF3*MF5); frml pi16 d41=(B14+MF4*MF1); frml pi17 d42=(B24+MF4*MF2); frml pi18 d43=(B34+MF4*MF3); frml pi19 d44=(B44-MF4+MF4*MF4); frml pi20 d45=(-B14-B24-B34-B44+MF4*MF5); frml pi21 d51=(-B11-B12-B13-B14+MF5*MF1); frml pi22 d52=(-B12-B22-B23-B24+MF5*MF2); frml pi23 d53=(-B13-B23-B33-B34+MF5*MF3); frml pi24 d54=(-B14-B24-B34-B44+MF5*MF4); frml pi25 d55=(-(-B11-B12-B13B14)-(-B12-B22-B23-B24) -(-B13-B23-B33-B34)-(-B14-B24-B34-B44)-MF5+MF5*MF5); analyz pi1-pi25; ? Elasticities ? Divisia Input Index FRML EL1 E1=A1/MF1; FRML EL1M E1M=A1/MMF1; FRML EL2 E1=A2/MF2; FRML EL2M E2M=A2/MMF2; FRML EL3 E3=A3/MF3; FRML EL3M E3M=A3/MMF3; FRML EL4 E4=A4/MF4; FRML EL4M E4M=A4/MMF4; FRML EL5 E5=(1-A1-A2-A3-A4)/MF5; FRML EL5M E5M=(1-A1-A2-A3-A4)/MMF5; ? Divisia Input Index WITH FIRST F FRML EL6 EF1=A1/FF1; FRML EL6M EF1M=A1/MFF1; FRML EL7 EF2=A2/FF2; FRML EL7M EF2M=A2/MFF2; FRML EL8 EF3=A3/FF3; FRML EL8M EF3M=A3/MFF3; FRML EL9 EF4=A4/FF4; FRML EL9M EF4M=A4/MFF4; FRML EL10 EF5=(1-A1-A2-A3-A4)/FF5; FRML EL10M EF5M=(1-A1-A2-A3-A4)/MFF5; ? Divisia Input Index WITH LAST F FRML EL11 EL1=A1/FL1; FRML EL11M EL1M=A1/MFL1; FRML EL12 EL2=A2/FL2; FRML EL12M EL2M=A2/MFL2; FRML EL13 EL3=A3/FL3; FRML EL13M EL3M=A3/MFL3; FRML EL14 EL4=A4/FL4; FRML EL14M EL4M=A4/MFL4;

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115 FRML EL15 EL5=(1-A1-A2-A3-A4)/FL5; FRML EL15M EL5M=(1-A1-A2-A3-A4)/MFL5; ? Compensated price elasticities FRML EP16 E11=(B11-MF1+MF1*MF1)/MF1; FRML EP16M E11M=(B11-MMF1+MMF1*MMF1)/MMF1; FRML EP17 E12=(B12+MF1*MF2)/MF1; FRML EP17M E12M=(B12+MMF1*MMF2)/MMF1; FRML EP18 E13=(B13+MF1*MF3)/MF1; FRML EP18M E13M=(B13+MMF1*MMF3)/MMF1; FRML EP19 E14=(B14+MF1*MF4)/MF1; FRML EP19M E14M=(B14+MMF1*MMF4)/MMF1; FRML EP20 E15=(-B11-B12-B13-B14+MF1*MF5)/MF1; FRML EP20M E15M=(-B11-B12-B13-B14+MMF1*MMF5)/MMF1; FRML EP21 E21=(B12+MF2*MF1)/MF2; FRML EP21M E21M=(B12+MMF2*MMF1)/MMF2; FRML EP22 E22=(B22-MF2+MF2*MF2)/MF2; FRML EP22M E22M=(B22-MMF2+MMF2*MMF2)/MMF2; FRML EP23 E23=(B23+MF2*MF3)/MF2; FRML EP23M E23M=(B23+MMF2*MMF3)/MMF2; FRML EP24 E24=(B24+MF2*MF4)/MF2; FRML EP24M E24M=(B24+MMF2*MMF4)/MMF2; FRML EP25 E25=(-B12-B22-B23-B24+MF2*MF5)/MF2; FRML EP25M E25M=(-B12-B22-B23-B24+MMF2*MMF5)/MMF2; FRML EP26 E31=(B13+MF3*MF1)/MF3; FRML EP26M E31M=(B13+MMF3*MMF1)/MMF3; FRML EP27 E32=(B23+MF3*MF2)/MF3; FRML EP27M E32M=(B23+MMF3*MMF2)/MMF3; FRML EP28 E33=(B33-MF3+MF3*MF3)/MF3; FRML EP28M E33M=(B33-MMF3+MMF3*MMF3)/MMF3; FRML EP29 E34=(B34+MF3*MF4)/MF3; FRML EP29M E34M=(B34+MMF3*MMF4)/MMF3; FRML EP30 E35=(-B13-B23-B33-B34+MF3*MF5)/MF3; FRML EP30M E35M=(-B13-B23-B33-B34+MMF3*MMF5)/MMF3; FRML EP31 E41=(B14+MF4*MF1)/MF4; FRML EP31M E41M=(B14+MMF4*MMF1)/MMF4; FRML EP32 E42=(B24+MF4*MF2)/MF4; FRML EP32M E42M=(B24+MMF4*MMF2)/MMF4; FRML EP33 E43=(B34+MF4*MF3)/MF4; FRML EP33M E43M=(B34+MMF4*MMF3)/MMF4; FRML EP34 E44=(B44-MF4+MF4*MF4)/MF4; FRML EP34M E44M=(B44-MMF4+MMF4*MMF4)/MMF4; FRML EP35 E45=(-B14-B24-B34-B44+MF4*MF5)/MF4; FRML EP35M E45M=(-B14-B24-B34-B44+MMF4*MMF5)/MMF4; FRML EP36 E51=(-B11-B12-B13-B14+MF5*MF1)/MF5; FRML EP36M E51M=(-B11-B12-B13-B14+MMF5*MMF1)/MMF5; FRML EP37 E52=(-B12-B22-B23-B24+MF5*MF2)/MF5; FRML EP37M E52M=(-B12-B22-B23-B24+MMF5*MMF2)/MMF5; FRML EP38 E53=(-B13-B23-B33-B34+MF5*MF3)/MF5; FRML EP38M E53M=(-B13-B23-B33-B34+MMF5*MMF3)/MMF5; FRML EP39 E54=(-B14-B24-B34-B44+MF5*MF4)/MF5; FRML EP39M E54M=(-B14-B24-B34-B44+MMF5*MMF4)/MMF5; FRML EP40 E55=(-(-B11-B12-B13-B14)-(-B12B22-B23-B24)-(-B13-B23-B33-B34)-(-B14-B24B34-B44)-MF5+MF5*MF5)/MF5; FRML EP40M E55M=(-(-B11-B12-B13-B14)-(-B12-B22-B23-B24)-(-B13B23-B33-B34)-(-B14-B24-B34-B 44)-MMF5+MMF5*MMF5)/MMF5; ANALYZ EL1-EL5, EL1M-EL5M; MMAKE ELCOEF @COEFA; ANALYZ EP16-EP40, EP16M-EP40M; MMAKE EPCOEF @COEFA; MMAKE(VERTICAL) MBM ELCOEF EPCOEF; MMAKE MBETA MBETA MBM; ?smpl 2,2; ?print LU; ?mark significant variables with STARS; ?REGOPT (STARS,STAR1=.10,STAR2=.05,) T; endproc zzzz; MFORM(TYPE=GEN,NROW=60,NCOL=1) MBETA=0; SMPL 2, 33; MSD F11 F21 F31 F41 F51; ?================= MEAN FACTOR SHARES SET MMF1=@MEAN(1); SET MMF2=@MEAN(2); SET MMF3=@MEAN(3); SET MMF4=@MEAN(4); SET MMF5=@MEAN(5); SMPL 2,2; MSD F1 F2 F3 F4 F5; ?================= MEAN FACTOR SHARES SET MFF1=@MEAN(1); SET MFF2=@MEAN(2);

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116 SET MFF3=@MEAN(3); SET MFF4=@MEAN(4); SET MFF5=@MEAN(5); SMPL 33,33; MSD F1 F2 F3 F4 F5; ?================= MEAN FACTOR SHARES SET MFL1=@MEAN(1); SET MFL2=@MEAN(2); SET MFL3=@MEAN(3); SET MFL4=@MEAN(4); SET MFL5=@MEAN(5); DO J=1 TO 11; SET NR1=1+J; SET NR2=32+J; SMPL NR1,NR2; ZZZZ; ENDDO; WRITE(FORMAT=EXCEL,FILE='U:\TOMATORESEARCH\USNEWANALYSIS\ELEPELAS-RECWW.XLS') MBETA; END;

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117 APPENDIX B COMPUTER PRINTOUTS FOR EU-15 ANALYSIS The Model to deal with zero quan tity and price for EU-15 Analysis OPTIONS MEMORY=1500 signif=5 DOUBLE; ? EU15#01Price Calculation Model; ? To replace zero Price for EU-15 Import Analysis ; TITLE 'TOMATO IMPORTS TO THE EU'; SMPL 1 43; ?READ(FORMAT=EXCEL,FILE='U :\TomatoResearch\TSPWORKS2006\EUAnalysis\EUonly.xls'); LOAD ZYRS ZIMP V1 Q1 V2 Q2 V3 Q3 V4 Q4 V5 Q5 V6 Q6 V7 Q7 V8 Q8; 1963 901 31653 451074 1773563 16782212 18133 40123 26567148 122325848 1004321 9072714 7000 72000 3240 12187 3376225 19971241 1964 901 48038 572000 1840474 12660747 1262 3312 31730108 118009256 1361401 10570330 0 0 2419 6437 2737208 13016627 1965 901 118653 1108312 2220545 15148287 5152 5562 27267956 125699712 1928476 15228634 0 0 17404 53152 1795230 12036480 1966 901 187269 1748000 2856091 13152661 85829 107862 36479432 108006936 2691155 19047720 1000 12812 11245 21812 2539638 10580398 1967 901 207999 2220125 3753435 18649844 46886 118310 36637392 122262104 2789153 18614500 0 0 69348 1 54932 2665245 13813254 1968 901 310461 2266000 4405008 19895024 26510 101198 27803468 97863480 5120585 33277424 0 0 74419 9 3022 1488195 7481394 1969 901 368538 2496687 3916124 14940986 32359 99740 36980408 129603024 7855349 34033604 0 0 128959 2 20245 3636690 12882177 1970 901 457730 2397937 4464447 15889571 40904 68278 38635412 133623216 9460210 42568288 3115 40398 260188 3 08561 11442442 37441791 1971 901 469036 2752398 4245967 16817272 64882 136795 44930860 129688160 14373836 59700256 0 0 284716 3 35143 4986064 14826093 1972 901 962086 4625350 4017254 16139896 35034 42506 45938032 118869120 15097659 63092032 1550 1875 284469 4 01349 7784385 19256671 1973 901 1594265 4437519 4893273 14262623 36545 29147 77101920 170786640 19292240 54257208 0 0 657057 7 45654 976072 1966077 1974 901 1797788 5708053 5692533 15417826 216473 219126 65580600 138533712 15978113 43303736 6326 42800 108407 82604 1433878 2602306 1975 901 3158736 7143835 7033987 15033790 731299 1000174 99121944 137464352 15771246 39263124 33343 187800 41024 42096 1391526 2230043 1976 901 2968861 7003612 5147297 12926526 1831054 3315306 75478176 106260064 13685706 37747484 4957 17625 196696 132987 2215727 4062108 1977 901 3472400 9189788 7425601 15885014 2325006 4158786 82794408 115851376 9554171 25682334 1000 1687 53600 51748 2066804 3061723 1978 901 2963215 6163585 7430996 14492037 2309641 3208973 85231920 104367608 9191896 21285690 37511 104073 200784 202229 2231462 2797116 1979 901 4026272 6798812 7907311 16267033 2373524 2974927 93011048 104508152 9071943 19847036 44832 82886 196614 141842 1673201 1711795 1980 901 6052384 10362511 4767464 11321398 2157749 1940617 87930720 91080208 11919843 23267114 102946 122824 867395 513303 2614841 2395847 1981 901 5573419 10825753 4173807 10542792 1422141 1240105 74939560 89919392 6717752 15562404 115808 164500 246247 303185 2178646 2958982 1982 901 6942520 16861542 2793814 7765386 1335471 1713708 50565584 66672692 6240809 14409339 89026 207127 39146 36069 1739339 3025668 1983 901 5955306 13127198 3990447 10570612 635025 665686 48541384 67457464 7539437 20009412 74331 144311 69812 82299 1714422 3349194 1984 901 4628415 11485112 2225929 6696144 1818868 2723827 49903220 82007208 8647271 24582260 410328 781058 73121 98443 1755933 3507856 1985 901 3714979 9679074 2804760 8574710 1519295 2329586 56146208 91701904 4742369 15723088 140775 329186 66289 88741 1109962 2356596 1986 901 4428861 9652574 2080354 5348562 2521897 2659235 68524448 96462176 4144346 11844698 670611 1127182 84403 88221 1740140 2880545 1987 901 4756993 9699476 2978113 6105456 2280814 2929812 87123544 97076016 5764089 12027178 2554835 3132693 141317 109611 2877196 3757298 1988 901 5344652 8643190 2402380 4767624 4903546 3937171 76347792 84892376 6708963 14960272 2264643 2524775 349851 243009 4564090 5078631 1989 901 4125968 6758386 2317410 5286987 3193985 3638453 71171816 97750632 7488692 16598397 2450896 3062926 1633335 864997 3588237 4834089

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118 1990 901 7360551 11020187 3029963 5708198 10650899 8021161 130990440 110714248 2765811 5380596 5190490 4332286 1125452 702779 6934463 5863059 1991 901 1869942 2294788 5032627 8084701 8374074 6277594 145453872 140390832 3665304 6377014 5308010 5725206 2509599 1876646 5001624 4620865 1992 901 404674 585339 3651196 5088920 6577894 5570072 139864960 141403616 3295008 5192788 6377826 7713368 391317 410689 5869919 5966114 1993 901 116900 270875 1710821 2183913 5634468 5223672 143160640 177926880 2223115 3715382 3718712 4547761 188735 224006 4278326 4454055 1994 901 43000 47199 357690 560199 5986108 5533320 120315416 160862048 1217387 2056625 4114383 5180666 155184 1 56425 3399291 3484049 1995 901 0 0 29111 43369 11579928 6364214 147584576 146905040 125053 151773 2661260 2334571 308488 239044 5825043 4559700 1996 901 24187 11375 183522 69726 17264544 9271430 148078032 157396080 230793 297000 4508979 3476803 38016 20788 5271388 3221124 1997 901 0 0 15124 21386 20697980 10657880 104872824 154990608 38161 46226 1020557 927674 82651 103135 7566921 5781611 1998 901 0 0 22636 30788 22782924 12299150 147687616 186837792 29637 48398 905471 869349 92865 72220 7331996 5544938 1999 901 0 0 60193 71125 22791080 14885635 141139536 206332992 0 0 2285333 2621314 205003 163351 5596276 5080246 2000 901 10448 18000 54910 66112 23066744 16460790 117918632 149369664 0 0 10910094 11328141 39582 24734 12195800 10873083 2001 901 16528 57300 9909 16625 21346768 17480612 108504040 185144192 22387 33500 6927946 9118854 10763 9212 10767377 11338870 2002 901 61870 101898 37526 55663 21751194 14590805 170802208 181180384 4604 3625 16571994 18724980 21009 14917 12378914 12102489 2003 901 22075 26453 206498 198835 24412648 16800316 136954944 182067392 43642 46710 26240464 25322332 21415 17437 29699618 29466621 2004 901 150833 169081 84115 84338 31703530 18551426 172314848 192159904 190675 188753 19238032 17210976 12976 6761 27974391 29547420 2005 901 0 0 20624 19526 44735840 25201496 229945104 232239648 35735 37440 23119076 19664766 43605 31100 61700006 54335924 ? 1: Albania ? 2: Bulgaria ? 3: Israel ? 4: Morocco ? 5: Romania ? 6: Turkey ? 7: United States ? 8: Rest of the World; ?901 EU-15 as importer; select q1>0; P1=V1/Q1; olsq P1 C Q1 ZYRS; select q5>0; P5=V5/Q5; olsq P5 C Q5 ZYRS; select q6>0; P6=V6/Q6; olsq P6 C Q6 ZYRS; SMPL 1, 43; Q1=1*(Q1=0)+Q1*(Q1>0); Q5=1*(Q5=0)+Q5*(Q5>0); Q6=1*(Q6=0)+Q6*(Q6>0); Print Q1 P1 Q5 P5 Q6 P6; END; The EU-15 NBR Import Demand Model. OPTIONS MEMORY=1500 Double; ? EU15_02NBR-NEW2007-FINAL SELECTED-V2 Models for EU Import Analysis Without USA ; TITLE 'TOMATO IMPORTS TO THE US'; ? For Cost Share, Quantity Share and Average Price calculation;

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119 ? NBR Model with Homogeneity and Symmetry imposed without Rho (since Rho=-0.094410 with P value 0.163 & LU1=689.03834)& LU2=688.35849; smpl 1 43; ?READ(FORMAT=EXCEL,FILE='U:\TomatoResearch\TSPWORKS2006\EUAnalysis\EUonly.xls'); LOAD ZYRS ZIMP V1 Q1 V2 Q2 V3 Q3 V4 Q4 V5 Q5 V6 Q6 V7 Q7 V8 Q8; 1963 901 31653 451074 1773563 16782212 18133 40123 26567148 122325848 1004321 9072714 7000 72000 3240 12187 3376225 19971241 1964 901 48038 572000 1840474 12660747 1262 3312 31730108 118009256 1361401 10570330 0 0 2419 6437 2737208 13016627 1965 901 118653 1108312 2220545 15148287 5152 5562 27267956 125699712 1928476 15228634 0 0 17404 53152 1795230 12036480 1966 901 187269 1748000 2856091 13152661 85829 107862 36479432 108006936 2691155 19047720 1000 12812 11245 21812 2539638 10580398 1967 901 207999 2220125 3753435 18649844 46886 118310 36637392 122262104 2789153 18614500 0 0 69348 154932 2665245 13813254 1968 901 310461 2266000 4405008 19895024 26510 101198 27803468 97863480 5120585 33277424 0 0 74419 93022 1488195 7481394 1969 901 368538 2496687 3916124 14940986 32359 99740 36980408 129603024 7855349 34033604 0 0 128959 220245 3636690 12882177 1970 901 457730 2397937 4464447 15889571 40904 68278 38635412 133623216 9460210 42568288 3115 40398 260188 308561 11442442 37441791 1971 901 469036 2752398 4245967 16817272 64882 136795 44930860 129688160 14373836 59700256 0 0 284716 335143 4986064 14826093 1972 901 962086 4625350 4017254 16139896 35034 42506 45938032 118869120 15097659 63092032 1550 1875 284469 401349 7784385 19256671 1973 901 1594265 4437519 4893273 14262623 36545 29147 77101920 170786640 19292240 54257208 0 0 657057 745654 976072 1966077 1974 901 1797788 5708053 5692533 15417826 216473 219126 65580600 138533712 15978113 43303736 6326 42800 108407 82604 1433878 2602306 1975 901 3158736 7143835 7033987 15033790 731299 1000174 99121944 137464352 15771246 39263124 33343 187800 41024 42096 1391526 2230043 1976 901 2968861 7003612 5147297 12926526 1831054 3315306 75478176 106260064 13685706 37747484 4957 17625 196696 132987 2215727 4062108 1977 901 3472400 9189788 7425601 15885014 2325006 4158786 82794408 115851376 9554171 25682334 1000 1687 53600 51748 2066804 3061723 1978 901 2963215 6163585 7430996 14492037 2309641 3208973 85231920 104367608 9191896 21285690 37511 104073 200784 202229 2231462 2797116 1979 901 4026272 6798812 7907311 16267033 2373524 2974927 93011048 104508152 9071943 19847036 44832 82886 196614 141842 1673201 1711795 1980 901 6052384 10362511 4767464 11321398 2157749 1940617 87930720 91080208 11919843 23267114 102946 122824 867395 513303 2614841 2395847 1981 901 5573419 10825753 4173807 10542792 1422141 1240105 74939560 89919392 6717752 15562404 115808 164500 246247 303185 2178646 2958982 1982 901 6942520 16861542 2793814 7765386 1335471 1713708 50565584 66672692 6240809 14409339 89026 207127 39146 36069 1739339 3025668 1983 901 5955306 13127198 3990447 10570612 635025 665686 48541384 67457464 7539437 20009412 74331 144311 69812 82299 1714422 3349194 1984 901 4628415 11485112 2225929 6696144 1818868 2723827 49903220 82007208 8647271 24582260 410328 781058 73121 98443 1755933 3507856 1985 901 3714979 9679074 2804760 8574710 1519295 2329586 56146208 91701904 4742369 15723088 140775 329186 66289 88741 1109962 2356596 1986 901 4428861 9652574 2080354 5348562 2521897 2659235 68524448 96462176 4144346 11844698 670611 1127182 84403 88221 1740140 2880545 1987 901 4756993 9699476 2978113 6105456 2280814 2929812 87123544 97076016 5764089 12027178 2554835 3132693 141317 109611 2877196 3757298 1988 901 5344652 8643190 2402380 4767624 4903546 3937171 76347792 84892376 6708963 14960272 2264643 2524775 349851 243009 4564090 5078631 1989 901 4125968 6758386 2317410 5286987 3193985 3638453 71171816 97750632 7488692 16598397 2450896 3062926 1633335 864997 3588237 4834089 1990 901 7360551 11020187 3029963 5708198 10650899 8021161 130990440 110714248 2765811 5380596 5190490 4332286 1125452 702779 6934463 5863059 1991 901 1869942 2294788 5032627 8084701 8374074 6277594 145453872 140390832 3665304 6377014 5308010 5725206 2509599 1876646 5001624 4620865 1992 901 404674 585339 3651196 5088920 6577894 5570072 139864960 141403616 3295008 5192788 6377826 7713368 391317 410689 5869919 5966114 1993 901 116900 270875 1710821 2183913 5634468 5223672 143160640 177926880 2223115 3715382 3718712 4547761 188735 224006 4278326 4454055

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120 1994 901 43000 47199 357690 560199 5986108 5533320 120315416 160862048 1217387 2056625 4114383 5180666 155184 156425 3399291 3484049 1995 901 0 0 29111 43369 11579928 6364214 147584576 146905040 125053 151773 2661260 2334571 308488 239044 5825043 4559700 1996 901 24187 11375 183522 69726 17264544 9271430 148078032 157396080 230793 297000 4508979 3476803 38016 20788 5271388 3221124 1997 901 0 0 15124 21386 20697980 10657880 104872824 154990608 38161 46226 1020557 927674 82651 103135 7566921 5781611 1998 901 0 0 22636 30788 22782924 12299150 147687616 186837792 29637 48398 905471 869349 92865 72220 7331996 5544938 1999 901 0 0 60193 71125 22791080 14885635 141139536 206332992 0 0 2285333 2621314 205003 163351 5596276 5080246 2000 901 10448 18000 54910 66112 23066744 16460790 117918632 149369664 0 0 10910094 11328141 39582 24734 12195800 10873083 2001 901 16528 57300 9909 16625 21346768 17480612 108504040 185144192 22387 33500 6927946 9118854 10763 9212 10767377 11338870 2002 901 61870 101898 37526 55663 21751194 14590805 170802208 181180384 4604 3625 16571994 18724980 21009 14917 12378914 12102489 2003 901 22075 26453 206498 198835 24412648 16800316 136954944 182067392 43642 46710 26240464 25322332 21415 17437 29699618 29466621 2004 901 150833 169081 84115 84338 31703530 18551426 172314848 192159904 190675 188753 19238032 17210976 12976 6761 27974391 29547420 2005 901 0 0 20624 19526 44735840 25201496 229945104 232239648 35735 37440 23119076 19664766 43605 31100 61700006 54335924 ? V: value in (not million) US dollars; Q: quantity in (not million) kilograms; ? 1: Albania ? 2: Bulgaria ? 3: Israel ? 4: Morocco ? 5: Romania ? 6: Turkey ? 7: United States:7 and Rest of the World:8; ?901 EU-15 as importer; ? Eliminating zero values in Q; Q1=1*(Q1=0)+Q1*(Q1>0); Q5=1*(Q5=0)+Q5*(Q5>0); Q6=1*(Q6=0)+Q6*(Q6>0); PRINT ZYRS Q1 Q5 Q6; ? Adding USA with ROW; v7=v7+v8; q7=q7+q8; print v7 q7; ? Eliminating Pi=0; ?? [Following highest price+twice the Std.Dev.+inflation]; SELECT ZYRS=1995; V1=2.82813; SELECT ZYRS=1997; V1=2.86838; SELECT ZYRS=1998; V1=2.88851; SELECT ZYRS=1999; V1=2.90863; SELECT ZYRS=2005; V1=3.02940; SELECT ZYRS=1999; V5=1.74572; SELECT ZYRS=2000; V5=1.76601; SELECT ZYRS=1964; V6=1.05760; SELECT ZYRS=1965; V6=1.08666; SELECT ZYRS=1967; V6=1.14477;

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121 SELECT ZYRS=1968; V6=1.17383; SELECT ZYRS=1969; V6=1.20289; SELECT ZYRS=1971; V6=1.26101; SELECT ZYRS=1973; V6=1.31912; ? To find out Average and Annual Costs for making Table; smpl 2,43; msd v1-v7; smpl 2,2; msd v1-v7; print v1-v7; smpl 43,43; msd v1-v7; print v1-v7; ? Calculation to find out Average and Annual Import Quantity (kg) for making Table; smpl 2,43; msd q1-q7; smpl 2,2; msd q1-q7; print q1-q7; smpl 43,43; msd q1-q7; print q1-q7; ? End of calculation for Table; SMPL 1,43; ? Defining Total Cost(S); S=V1+V2+V3+V4+V5+V6+V7; ? Calculating prices (Pi) P1=V1/Q1; P2=V2/Q2; P3=V3/Q3; P4=V4/Q4; P5=V5/Q5; P6=V6/Q6; P7=V7/Q7; PRINT ZYRS Q1 P1 Q2 P2 Q3 P3 Q4 P4 Q5 P5 Q6 P6 q7 p7; ? Calculation to find Average ans Annual Price (US$/Kg) for making Table; smpl 2,43; msd p1-p7; smpl 2,2; msd p1-p7; print p1-p7; smpl 43,43; msd p1-p7; ? End of Calculation for Table; SMPL 1,43; ? CALCULATION OF FACTOR COST SHARES (Fi=PRICE*QUANTITY/TOTAL COST) F1=V1/S; F2=V2/S; F3=V3/S; F4=V4/S; F5=V5/S; F6=V6/S; F7=V7/S; ? LOGGING ALL PRICES AND QUANTITIES(LPi,LQi) LP1=LOG(P1); LP2=LOG(P2); LP3=LOG(P3); LP4=LOG(P4); LP5=LOG(P5); LP6=LOG(P6); LP7=LOG(P7); LQ1=LOG(Q1); LQ2=LOG(Q2); LQ3=LOG(Q3); LQ4=LOG(Q4); LQ5=LOG(Q5); LQ6=LOG(Q6); LQ7=LOG(Q7);

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122 smpl 2 43; ? CALCULATION FOR TWO PERIOD MEAN OF FACTOR SHARES( Fi1) F11=(F1+F1(-1))/2; F21=(F2+F2(-1))/2; F31=(F3+F3(-1))/2; F41=(F4+F4(-1))/2; F51=(F5+F5(-1))/2; F61=(F6+F6(-1))/2; F71=(F7+F7(-1))/2; ? Calculation of Quantity Share for making Table; T=q1+q2+q3+q4+q5+q6+q7; k1=q1/T; k2=q2/T; k3=q3/T; k4=q4/T; k5=q5/T; k6=q6/T; k7=q7/T; msd k1-k7; smpl 2,2; msd k1-k7; smpl 43,43; msd k1-k7; ? End of Quantity Share calculation; SMPL 2,43; ? CALCULATION: CHANGE IN LOGGED PRICES(DPi) DP1=LP1-LP1(-1); DP2=LP2-LP2(-1); DP3=LP3-LP3(-1); DP4=LP4-LP4(-1); DP5=LP5-LP5(-1); DP6=LP6-LP6(-1); DP7=LP7-LP7(-1); ? CALCULATION:CHANGE IN LOGGED QUANTITY(DQi) DQ1=LQ1-LQ1(-1); DQ2=LQ2-LQ2(-1); DQ3=LQ3-LQ3(-1); DQ4=LQ4-LQ4(-1); DQ5=LQ5-LQ5(-1); DQ6=LQ6-LQ6(-1); DQ7=LQ7-LQ7(-1); ? DEPENDENT VARIABLE fi*Dq and SUMMATION INDEX(FDQi) FDQ1=F11*DQ1; FDQ2=F21*DQ2; FDQ3=F31*DQ3; FDQ4=F41*DQ4; FDQ5=F51*DQ5; FDQ6=F61*DQ6; FDQ7=F71*DQ7; DQ=FDQ1+FDQ2+FDQ3+FDQ4+FDQ5+FDQ6+FDQ7; ?Fi*DP and SUMMATION INDEX(DP)NOT NEEDED FOR THIS MODEL; FDP1=F11*DP1; FDP2=F21*DP2; FDP3=F31*DP3; FDP4=F41*DP4; FDP5=F51*DP5; FDP6=F61*DP6; FDP7=F71*DP7; DP=FDP1+FDP2+FDP3+FDP4+FDP5+FDP6+FDP7; SMPL 2, 43; ? DIFFEREENTIAL NBR MODEL WITH AR1 PLUS HOMOGENEITY AND SYMMETRY; trend obs; d1 = (obs=1); frml res1 FDQ1=(A1*DQ+B11*DP1+B12*DP2+B13*DP3+B14*DP4+B15*DP5+B16*DP6+(-B11-B12-B13-B14-B15B16)*DP7-F11*(DP1-DP)); ?frml eq1 [d1*res1*sqrt(1-rho**2) + (1-d1)*(res1 rho*res1(-1))]*(1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res2 FDQ2=(A2*DQ+B12*DP1+B22*DP2+B23*DP3+B24*DP4+B25*DP5+B26*DP6+(-B12-B22-B23-B24-B25B26)*DP7-F21*(DP2-DP)); ?frml eq2 [d1*res2*sqrt(1-rho**2) + (1-d1)*(res2 rho*res2(-1))]*(1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res3 FDQ3=(A3*DQ+B13*DP1+B23*DP2+B33*DP3+B34*DP4+B35*DP5+B36*DP6+(-B13-B23-B33-B34-B35B36)*DP7-F31*(DP3-DP)); ?frml eq3 [d1*res3*sqrt(1-rho**2) + (1-d1)*(res3 rho*res3(-1))]*(1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res4 FDQ4=(A4*DQ+B14*DP1+B24*DP2+B34*DP3+B44*DP4+B45*DP5+B46*DP6+(-B14-B24-B34-B44-B45B46)*DP7-F41*(DP4-DP)); ?frml eq4 [d1*res4*sqrt(1-rho**2) + (1-d1)*(res4 rho*res4(-1))]*(1-rho**2)**(-1/(2*@nob)); trend obs;

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123 d1 = (obs=1); frml res5 FDQ5=(A5*DQ+B15*DP1+B25*DP2+B35*DP3+B45*DP4+B55*DP5+B56*DP6+(-B15-B25-B35-B45-B55B56)*DP7-F51*(DP5-DP)); ?frml eq5 [d1*res5*sqrt(1-rho**2) + (1-d1)*(res5 rho*res5(-1))]*(1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res6 FDQ6=(A6*DQ+B16*DP1+B26*DP2+B36*DP3+B46*DP4+B56*DP5+B66*DP6+(-B16-B26-B36-B46-B56B66)*DP7-F61*(DP6-DP)); ?frml eq6 [d1*res6*sqrt(1-rho**2) + (1-d1)*(res6 rho*res6(-1))]*(1-rho**2)**(-1/(2*@nob)); REGOPT (STARS,STAR1=.10,STAR2=.05) T; PARAM A1 0 A2 0 A3 0 A4 0 A5 0 A6 0 B11 0 B12 0 B13 0 B14 0 B15 0 B16 0 B22 0 B23 0 B24 0 B25 0 B26 0 B33 0 B34 0 B35 0 B36 0 B44 0 B45 0 B46 0 B55 0 B56 0 B66 0; ?rho 0; ?eqsub eq1 res1;?eqsub eq2 res2;?eqsub eq3 res3;?eqsub eq4 res4;?eqsub eq5 res5;?eqsub eq6 res6; ?lsq(nodropmiss,tol=1e-7,maxit=1000) eq1 eq2 eq3 eq4 eq5 eq6; lsq(nodropmiss,tol=1e-7,maxit=1000) res1 res2 res3 res4 res5 res6; COPY @LOGL LU; LU1=LU; SMPL 2, 43; ? Elasticities; MSD F11 F21 F31 F41 F51 F61 F71; ?================= MEAN FACTOR SHARES SET MF1=@MEAN(1); SET MF2=@MEAN(2); SET MF3=@MEAN(3); SET MF4=@MEAN(4); SET MF5=@MEAN(5); SET MF6=@MEAN(6); SET MF7=@MEAN(7); PRINT MF1-MF7; SMPL 2,2; MSD F1 F2 F3 F4 F5 F6 F7; ?================= MEAN FACTOR SHARES SET FF1=@MEAN(1); SET FF2=@MEAN(2); SET FF3=@MEAN(3); SET FF4=@MEAN(4); SET FF5=@MEAN(5); SET FF6=@MEAN(6); SET FF7=@MEAN(7); PRINT FF1-FF7; SMPL 43,43; MSD F1 F2 F3 F4 F5 F6 F7; ?================= MEAN FACTOR SHARES SET FL1=@MEAN(1); SET FL2=@MEAN(2); SET FL3=@MEAN(3); SET FL4=@MEAN(4); SET FL5=@MEAN(5);

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124 SET FL6=@MEAN(6); SET FL7=@MEAN(7); PRINT FL1-FL7; SMPL 2, 43; SET B17=-B11-B12-B13-B14-B15-B16; SET B21=B12; SET B27=-B21-B22-B23-B24-B25-B26; SET B31=B13; SET B32=B23; SET B37=-B31-B32-B33-B34-B35-B36; SET B41=B14; SET B42=B24; SET B43=B34; SET B47=-B41-B42-B43-B44-B45-B46; SET B51=B15; SET B52=B25; SET B53=B35; SET B54=B45; SET B57=-B51-B52-B53-B54-B55-B56; SET B61=B16; SET B62=B26; SET B63=B36; SET B64=B46; SET B65=B56; SET B67=-B61-B62-B63-B64-B65-B66; SET B77=(-(-B11-B12-B13-B14-B15-B16)-(-B12-B22-B23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)-(-B14B24-B34-B44-B45-B46) -(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)); SET A7=1-A1-A2-A3-A4-A5-A6; ? Calculate the standard errors for ROW frml row1 A7=1-A1-A2-A3-A4-A5-A6; frml row2 B17=-B11-B12-B13-B14-B15-B16; frml row3 B27=-B21-B22-B23-B24-B25-B26; frml row4 B37=-B31-B32-B33-B34-B35-B36; frml row5 B47=-B41-B42-B43-B44-B45-B46; frml row6 B57=-B51-B52-B53-B54-B55-B56; frml row7 B67=-B61-B62-B63-B64-B65-B66; frml row8 B77=(-(-B11-B12-B13-B14-B15-B16)-(-B12-B22-B23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)(-B14-B24-B34-B44-B45-B46) -(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)); frml row9 B21=B12; frml row10 B31=B13; frml row11 B32=B23; frml row12 B41=B14; frml row13 B42=B24; frml row14 B43=B34; frml row15 B51=B15; frml row16 B52=B25; frml row17 B53=B35; frml row18 B54=B45; frml row19 B61=B16; frml row20 B62=B26; frml row21 B63=B36; frml row22 B64=B46; frml row23 B65=B56; analyz row1-row23;

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125 ?? Calculate Eigenvalues SET B71=B17; SET B72=B27; SET B73=B37; SET B74=B47; SET B75=B57; SET B76=B67; SET B77=B77; ? Tranform r to Pie; SET D11=(B11-MF1+MF1*MF1); SET D12=(B12+MF1*MF2); SET D13=(B13+MF1*MF3); SET D14=(B14+MF1*MF4); SET D15=(B15+MF1*MF5); SET D16=(B16+MF1*MF6); SET D17=(-B11-B12-B13-B14-B15-B16+MF1*MF7); SET D21=(B12+MF2*MF1); SET D22=(B22-MF2+MF2*MF2); SET D23=(B23+MF2*MF3); SET D24=(B24+MF2*MF4); SET D25=(B25+MF2*MF5); SET D26=(B26+MF2*MF6); SET D27=(-B12-B22-B23-B24-B25-B26+MF2*MF7); SET D31=(B13+MF3*MF1); SET D32=(B23+MF3*MF2); SET D33=(B33-MF3+MF3*MF3); SET D34=(B34+MF3*MF4); SET D35=(B35+MF3*MF5); SET D36=(B36+MF3*MF6); SET D37=(-B13-B23-B33-B34-B35-B36+MF3*MF7); SET D41=(B14+MF4*MF1); SET D42=(B24+MF4*MF2); SET D43=(B34+MF4*MF3); SET D44=(B44-MF4+MF4*MF4); SET D45=(B45+MF4*MF5); SET D46=(B46+MF4*MF6); SET D47=(-B14-B24-B34-B44-B45-B46+MF4*MF7); SET D51=(B15+MF5*MF1); SET D52=(B25+MF5*MF2); SET D53=(B35+MF5*MF3); SET D54=(B45+MF5*MF4); SET D55=(B55-MF5+MF5*MF5); SET D56=(B56+MF5*MF6); SET D57=(-B15-B25-B35-B45-B55-B56+MF5*MF7); SET D61=(B16+MF6*MF1); SET D62=(B26+MF6*MF2); SET D63=(B36+MF6*MF3); SET D64=(B46+MF6*MF4); SET D65=(B56+MF6*MF5); SET D66=(B66-MF6+MF6*MF6); SET D67=(-B16-B26-B36-B46-B56-B66+MF6*MF7); SET D71=(-B11-B12-B13-B14-B15-B16+MF7*MF1); SET D72=(-B12-B22-B23-B24-B25-B26+MF7*MF2); SET D73=(-B13-B23-B33-B34-B35-B36+MF7*MF3); SET D74=(-B14-B24-B34-B44-B45-B46+MF7*MF4); SET D75=(-B15-B25-B35-B45-B55-B56+MF7*MF5); SET D76=(-B16-B26-B36-B46-B56-B66+MF7*MF6); SET D77=(-(-B11-B12-B13-B14-B15-B16)-(-B12-B22-B23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)-(-B14B24-B34-B44-B45-B46) -(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)-MF7+MF7*MF7); ? Create each row EIGHT;

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126 MMAKE(VERT) E1 D11-D17; MMAKE(VERT) E2 D21-D27; MMAKE(VERT) E3 D31-D37; MMAKE(VERT) E4 D41-D47; MMAKE(VERT) E5 D51-D57; MMAKE(VERT) E6 D61-D67; MMAKE(VERT) E7 D71-D77; ? Creates square matrix MMAKE E E1-E7; ? Calculate Eigenvalues of E MAT EV = EIGVAL(E); Print E EV; ? Creates standard errors for pi ij's where the pi ij's are D11, D12, etc; frml pi1 D11=(B11-MF1+MF1*MF1); frml pi2 D12=(B12+MF1*MF2); frml pi3 D13=(B13+MF1*MF3); frml pi4 D14=(B14+MF1*MF4); frml pi5 D15=(B15+MF1*MF5); frml pi6 D16=(B16+MF1*MF6); frml pi7 D17=(-B11-B12-B13-B14-B15-B16+MF1*MF7); frml pi8 D21=(B12+MF2*MF1); frml pi9 D22=(B22-MF2+MF2*MF2); frml pi10 D23=(B23+MF2*MF3); frml pi11 D24=(B24+MF2*MF4); frml pi12 D25=(B25+MF2*MF5); frml pi13 D26=(B26+MF2*MF6); frml pi14 D27=(-B12-B22-B23-B24-B25-B26+MF2*MF7); frml pi15 D31=(B13+MF3*MF1); frml pi16 D32=(B23+MF3*MF2); frml pi17 D33=(B33-MF3+MF3*MF3); frml pi18 D34=(B34+MF3*MF4); frml pi19 D35=(B35+MF3*MF5); frml pi20 D36=(B36+MF3*MF6); frml pi21 D37=(-B13-B23-B33-B34-B35-B36+MF3*MF7); frml pi22 D41=(B14+MF4*MF1); frml pi23 D42=(B24+MF4*MF2); frml pi24 D43=(B34+MF4*MF3); frml pi25 D44=(B44-MF4+MF4*MF4); frml pi26 D45=(B45+MF4*MF5); frml pi27 D46=(B46+MF4*MF6); frml pi28 D47=(-B14-B24-B34-B44-B45-B46+MF4*MF7); frml pi29 D51=(B15+MF5*MF1); frml pi30 D52=(B25+MF5*MF2); frml pi31 D53=(B35+MF5*MF3); frml pi32 D54=(B45+MF5*MF4); frml pi33 D55=(B55-MF5+MF5*MF5); frml pi34 D56=(B56+MF5*MF6); frml pi35 D57=(-B15-B25-B35-B45-B55-B56+MF5*MF7); frml pi36 D61=(B16+MF6*MF1); frml pi37 D62=(B26+MF6*MF2); frml pi38 D63=(B36+MF6*MF3); frml pi39 D64=(B46+MF6*MF4); frml pi40 D65=(B56+MF6*MF5); frml pi41 D66=(B66-MF6+MF6*MF6); frml pi42 D67=(-B16-B26-B36-B46-B56-B66+MF6*MF7); frml pi43 D71=(-B11-B12-B13-B14-B15-B16+MF7*MF1); frml pi44 D72=(-B12-B22-B23-B24-B25-B26+MF7*MF2); frml pi45 D73=(-B13-B23-B33-B34-B35-B36+MF7*MF3);

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127 frml pi46 D74=(-B14-B24-B34-B44-B45-B46+MF7*MF4); frml pi47 D75=(-B15-B25-B35-B45-B55-B56+MF7*MF5); frml pi48 D76=(-B16-B26-B36-B46-B56-B66+MF7*MF6); frml pi49 D77=(-(-B11-B12-B13-B14-B15-B16)-(-B12-B22-B23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)(-B14-B24-B34-B44-B45-B46) -(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)-MF7+MF7*MF7); analyz pi1-pi49; ? Elasticities ? Divisia Input Index FRML EL1 E1=A1/MF1; FRML EL2 E2=A2/MF2; FRML EL3 E3=A3/MF3; FRML EL4 E4=A4/MF4; FRML EL5 E5=A5/MF5; FRML EL6 E6=A6/MF6; FRML EL7 E7=(1-A1-A2-A3-A4-A5-A6)/MF7; ? Divisia Input Index WITH FIRST F FRML EL8 EF1=A1/FF1; FRML EL9 EF2=A2/FF2; FRML EL10 EF3=A3/FF3; FRML EL11 EF4=A4/FF4; FRML EL12 EF5=A5/FF5; FRML EL13 EF6=A6/FF6; FRML EL14 EF7=(1-A1-A2-A3-A4-A5-A6)/FF7; ? Divisia Input Index WITH LAST F FRML EL15 EL1=A1/FL1; FRML EL16 EL2=A2/FL2; FRML EL17 EL3=A3/FL3; FRML EL18 EL4=A4/FL4; FRML EL19 EL5=A5/FL5; FRML EL20 EL6=A6/FL6; FRML EL21 EL7=(1-A1-A2-A3-A4-A5-A6)/FL7; ? Compensated price elasticities FRML EP11 E11=(B11-MF1+MF1*MF1)/MF1; FRML EP12 E12=(B12+MF1*MF2)/MF1; FRML EP13 E13=(B13+MF1*MF3)/MF1; FRML EP14 E14=(B14+MF1*MF4)/MF1; FRML EP15 E15=(B15+MF1*MF5)/MF1; FRML EP16 E16=(B16+MF1*MF6)/MF1; FRML EP17 E17=(-B11-B12-B13-B14-B15-B16+MF1*MF7)/MF1; FRML EP21 E21=(B12+MF2*MF1)/MF2; FRML EP22 E22=(B22-MF2+MF2*MF2)/MF2; FRML EP23 E23=(B23+MF2*MF3)/MF2; FRML EP24 E24=(B24+MF2*MF4)/MF2; FRML EP25 E25=(B25+MF2*MF5)/MF2; FRML EP26 E26=(B26+MF2*MF6)/MF2; FRML EP27 E27=(-B12-B22-B23-B24-B25-B26+MF2*MF7)/MF2; FRML EP31 E31=(B13+MF3*MF1)/MF3; FRML EP32 E32=(B23+MF3*MF2)/MF3; FRML EP33 E33=(B33-MF3+MF3*MF3)/MF3; FRML EP34 E34=(B34+MF3*MF4)/MF3; FRML EP35 E35=(B35+MF3*MF5)/MF3; FRML EP36 E36=(B36+MF3*MF6)/MF3; FRML EP37 E37=(-B13-B23-B33-B34-B35-B36+MF3*MF7)/MF3; FRML EP41 E41=(B14+MF4*MF1)/MF4; FRML EP42 E42=(B24+MF4*MF2)/MF4; FRML EP43 E43=(B34+MF4*MF3)/MF4; FRML EP44 E44=(B44-MF4+MF4*MF4)/MF4; FRML EP45 E45=(B45+MF4*MF5)/MF4; FRML EP46 E46=(B46+MF4*MF6)/MF4; FRML EP47 E47=(-B14-B24-B34-B44-B45-B46+MF4*MF7)/MF4;

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128 FRML EP51 E51=(B15+MF5*MF1)/MF5; FRML EP52 E52=(B25+MF5*MF2)/MF5; FRML EP53 E53=(B35+MF5*MF3)/MF5; FRML EP54 E54=(B45+MF5*MF4)/MF5; FRML EP55 E55=(B55-MF5+MF5*MF5)/MF5; FRML EP56 E56=(B56+MF5*MF6)/MF5; FRML EP57 E57=(-B15-B25-B35-B45-B55-B56+MF5*MF7)/MF5; FRML EP61 E61=(B16+MF6*MF1)/MF6; FRML EP62 E62=(B26+MF6*MF2)/MF6; FRML EP63 E63=(B36+MF6*MF3)/MF6; FRML EP64 E64=(B46+MF6*MF4)/MF6; FRML EP65 E65=(B56+MF6*MF5)/MF6; FRML EP66 E66=(B66-MF6+MF6*MF6)/MF6; FRML EP67 E67=(-B16-B26-B36-B46-B56-B66+MF6*MF7)/MF6; FRML EP71 E71=(-B11-B12-B13-B14-B15-B16+MF7*MF1)/MF7; FRML EP72 E72=(-B12-B22-B23-B24-B25-B26+MF7*MF2)/MF7; FRML EP73 E73=(-B13-B23-B33-B34-B35-B36+MF7*MF3)/MF7; FRML EP74 E74=(-B14-B24-B34-B44-B45-B46+MF7*MF4)/MF7; FRML EP75 E75=(-B15-B25-B35-B45-B55-B56+MF7*MF5)/MF7; FRML EP76 E76=(-B16-B26-B36-B46-B56-B66+MF7*MF6)/MF7; FRML EP77 E77=(-(-B11-B12-B13-B14-B15-B16)-(-B12-B22-B23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)(-B14-B24-B34-B44-B45-B46) -(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)-MF7+MF7*MF7)/MF7; ANALYZ EL1-EL21; ANALYZ EP11-EP17; ANALYZ EP21-EP27; ANALYZ EP31-EP37; ANALYZ EP41-EP47; ANALYZ EP51-EP57; ANALYZ EP61-EP67; ANALYZ EP71-EP77; PRINT; smpl 2,2; print LU; ?mark significant variables with STARS; REGOPT (STARS,STAR1=.10,STAR2=.05,) T; END; EU-15 NBR Simulation Model OPTIONS MEMORY=1500 Double; ? EU15_02NBR-NEW 2007SELECTED-RECW Models for EU Import Analysis without USA with all calculations; TITLE 'TOMATO IMPORTS TO THE US'; ? For Elasticity Tr end over 11 years & Calculating Mean Quantity for sample 2,33; ? NBR Model with Homogeneity and Symmetry imposed without Rho (since Rho=-0.094410 with P value 0.163 & LU1=689.03834)& LU2=688.35849; smpl 1 43; ?READ(FORMAT=EXCEL,FILE='U :\TomatoResearch\TSPWORKS2006\EUAnalysis\EUonly.xls'); LOAD ZYRS ZIMP V1 Q1 V2 Q2 V3 Q3 V4 Q4 V5 Q5 V6 Q6 V7 Q7 V8 Q8; 1963 901 31653 451074 1773563 16782212 18133 40123 26567148 122325848 1004321 9072714 7000 72000 3240 12187 3376225 19971241 1964 901 48038 572000 1840474 12660747 1262 3312 31730108 118009256 1361401 10570330 0 0 2419 6437 2737208 13016627 1965 901 118653 1108312 2220545 15148287 5152 5562 27267956 125699712 1928476 15228634 0 0 17404 53152 1795230 12036480 1966 901 187269 1748000 2856091 13152661 85829 107862 36479432 108006936 2691155 19047720 1000 12812 11245 21812 2539638 10580398

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129 1967 901 207999 2220125 3753435 18649844 46886 118310 36637392 122262104 2789153 18614500 0 0 69348 1 54932 2665245 13813254 1968 901 310461 2266000 4405008 19895024 26510 101198 27803468 97863480 5120585 33277424 0 0 74419 9 3022 1488195 7481394 1969 901 368538 2496687 3916124 14940986 32359 99740 36980408 129603024 7855349 34033604 0 0 128959 2 20245 3636690 12882177 1970 901 457730 2397937 4464447 15889571 40904 68278 38635412 133623216 9460210 42568288 3115 40398 260188 3 08561 11442442 37441791 1971 901 469036 2752398 4245967 16817272 64882 136795 44930860 129688160 14373836 59700256 0 0 284716 3 35143 4986064 14826093 1972 901 962086 4625350 4017254 16139896 35034 42506 45938032 118869120 15097659 63092032 1550 1875 284469 4 01349 7784385 19256671 1973 901 1594265 4437519 4893273 14262623 36545 29147 77101920 170786640 19292240 54257208 0 0 657057 7 45654 976072 1966077 1974 901 1797788 5708053 5692533 15417826 216473 219126 65580600 138533712 15978113 43303736 6326 42800 108407 82604 1433878 2602306 1975 901 3158736 7143835 7033987 15033790 731299 1000174 99121944 137464352 15771246 39263124 33343 187800 41024 42096 1391526 2230043 1976 901 2968861 7003612 5147297 12926526 1831054 3315306 75478176 106260064 13685706 37747484 4957 17625 196696 132987 2215727 4062108 1977 901 3472400 9189788 7425601 15885014 2325006 4158786 82794408 115851376 9554171 25682334 1000 1687 53600 51748 2066804 3061723 1978 901 2963215 6163585 7430996 14492037 2309641 3208973 85231920 104367608 9191896 21285690 37511 104073 200784 202229 2231462 2797116 1979 901 4026272 6798812 7907311 16267033 2373524 2974927 93011048 104508152 9071943 19847036 44832 82886 196614 141842 1673201 1711795 1980 901 6052384 10362511 4767464 11321398 2157749 1940617 87930720 91080208 11919843 23267114 102946 122824 867395 513303 2614841 2395847 1981 901 5573419 10825753 4173807 10542792 1422141 1240105 74939560 89919392 6717752 15562404 115808 164500 246247 303185 2178646 2958982 1982 901 6942520 16861542 2793814 7765386 1335471 1713708 50565584 66672692 6240809 14409339 89026 207127 39146 36069 1739339 3025668 1983 901 5955306 13127198 3990447 10570612 635025 665686 48541384 67457464 7539437 20009412 74331 144311 69812 82299 1714422 3349194 1984 901 4628415 11485112 2225929 6696144 1818868 2723827 49903220 82007208 8647271 24582260 410328 781058 73121 98443 1755933 3507856 1985 901 3714979 9679074 2804760 8574710 1519295 2329586 56146208 91701904 4742369 15723088 140775 329186 66289 88741 1109962 2356596 1986 901 4428861 9652574 2080354 5348562 2521897 2659235 68524448 96462176 4144346 11844698 670611 1127182 84403 88221 1740140 2880545 1987 901 4756993 9699476 2978113 6105456 2280814 2929812 87123544 97076016 5764089 12027178 2554835 3132693 141317 109611 2877196 3757298 1988 901 5344652 8643190 2402380 4767624 4903546 3937171 76347792 84892376 6708963 14960272 2264643 2524775 349851 243009 4564090 5078631 1989 901 4125968 6758386 2317410 5286987 3193985 3638453 71171816 97750632 7488692 16598397 2450896 3062926 1633335 864997 3588237 4834089 1990 901 7360551 11020187 3029963 5708198 10650899 8021161 130990440 110714248 2765811 5380596 5190490 4332286 1125452 702779 6934463 5863059 1991 901 1869942 2294788 5032627 8084701 8374074 6277594 145453872 140390832 3665304 6377014 5308010 5725206 2509599 1876646 5001624 4620865 1992 901 404674 585339 3651196 5088920 6577894 5570072 139864960 141403616 3295008 5192788 6377826 7713368 391317 410689 5869919 5966114 1993 901 116900 270875 1710821 2183913 5634468 5223672 143160640 177926880 2223115 3715382 3718712 4547761 188735 224006 4278326 4454055 1994 901 43000 47199 357690 560199 5986108 5533320 120315416 160862048 1217387 2056625 4114383 5180666 155184 1 56425 3399291 3484049 1995 901 0 0 29111 43369 11579928 6364214 147584576 146905040 125053 151773 2661260 2334571 308488 239044 5825043 4559700 1996 901 24187 11375 183522 69726 17264544 9271430 148078032 157396080 230793 297000 4508979 3476803 38016 20788 5271388 3221124 1997 901 0 0 15124 21386 20697980 10657880 104872824 154990608 38161 46226 1020557 927674 82651 103135 7566921 5781611 1998 901 0 0 22636 30788 22782924 12299150 147687616 186837792 29637 48398 905471 869349 92865 72220 7331996 5544938 1999 901 0 0 60193 71125 22791080 14885635 141139536 206332992 0 0 2285333 2621314 205003 163351 5596276 5080246 2000 901 10448 18000 54910 66112 23066744 16460790 117918632 149369664 0 0 10910094 11328141 39582 24734 12195800 10873083 2001 901 16528 57300 9909 16625 21346768 17480612 108504040 185144192 22387 33500 6927946 9118854 10763 9212 10767377 11338870

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130 2002 901 61870 101898 37526 55663 21751194 14590805 170802208 181180384 4604 3625 16571994 18724980 21009 14917 12378914 12102489 2003 901 22075 26453 206498 198835 24412648 16800316 136954944 182067392 43642 46710 26240464 25322332 21415 17437 29699618 29466621 2004 901 150833 169081 84115 84338 31703530 18551426 172314848 192159904 190675 188753 19238032 17210976 12976 6761 27974391 29547420 2005 901 0 0 20624 19526 44735840 25201496 229945104 232239648 35735 37440 23119076 19664766 43605 31100 61700006 54335924 ? V: value in (not million) US dollars; Q: quantity in (not million) kilograms; ? 1: Albania ? 2: Bulgaria ? 3: Israel ? 4: Morocco ? 5: Romania ? 6: Turkey ? 7: United States:7 and Rest of the World:8; ?901 EU-15 as importer; ? Eliminating zero values in Q; Q1=1*(Q1=0)+Q1*(Q1>0); Q5=1*(Q5=0)+Q5*(Q5>0); Q6=1*(Q6=0)+Q6*(Q6>0); PRINT ZYRS Q1 Q5 Q6; ? Adding USA with ROW; v7=v7+v8; q7=q7+q8; print v7 q7; ? Eliminating Pi=0; ?? [Following highes t price+twice the Std.Dev.+inflation]; SELECT ZYRS=1995; V1=2.82813; SELECT ZYRS=1997; V1=2.86838; SELECT ZYRS=1998; V1=2.88851; SELECT ZYRS=1999; V1=2.90863; SELECT ZYRS=2005; V1=3.02940; SELECT ZYRS=1999; V5=1.74572; SELECT ZYRS=2000; V5=1.76601; SELECT ZYRS=1964; V6=1.05760; SELECT ZYRS=1965; V6=1.08666; SELECT ZYRS=1967; V6=1.14477; SELECT ZYRS=1968; V6=1.17383; SELECT ZYRS=1969; V6=1.20289; SELECT ZYRS=1971; V6=1.26101; SELECT ZYRS=1973; V6=1.31912; ? To find out Average and Annual Costs for making Table; smpl 2,43; msd v1-v7; smpl 2,2; msd v1-v7;

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131 print v1-v7; smpl 43,43; msd v1-v7; print v1-v7; ? Calculation to find out Average and Annua l Import Quantity (kg) for making Table; smpl 2,43; msd q1-q7; smpl 2,2; msd q1-q7; print q1-q7; smpl 43,43; msd q1-q7; print q1-q7; ? End of calculation for Table; SMPL 1,43; ? Defining Total Cost(S); S=V1+V2+V3+V4+V5+V6+V7; ? Calculating prices (Pi) P1=V1/Q1; P2=V2/Q2; P3=V3/Q3; P4=V4/ Q4; P5=V5/Q5; P6=V6/Q6; P7=V7/Q7; PRINT ZYRS Q1 P1 Q2 P2 Q3 P3 Q4 P4 Q5 P5 Q6 P6 q7 p7; ? Calculation to find Average ans Annual Price (US$/Kg) for making Table; smpl 2,43; msd p1-p7; smpl 2,2; msd p1-p7; print p1-p7; smpl 43,43; msd p1-p7; ? End of Calculation for Table; SMPL 1,43; ? CALCULATION OF FACTOR COST SHARES (Fi=PRICE*QUANTITY/TOTAL COST) F1=V1/S; F2=V2/S; F3=V3/S; F4=V4/ S; F5=V5/S; F6=V6/S; F7=V7/S; ? LOGGING ALL PRICES AND QUANTITIES(LPi,LQi) LP1=LOG(P1); LP2=LOG(P2); LP3=LOG(P3); LP4=LOG(P4); LP5=LOG(P5); LP6=LOG(P6); LP7=LOG(P7); LQ1=LOG(Q1); LQ2=LOG(Q2); LQ 3=LOG(Q3); LQ4=LOG(Q4); LQ5=LOG(Q5); LQ6=LOG(Q6); LQ7=LOG(Q7); smpl 2 43; ? CALCULATION FOR TWO PERIOD MEAN OF FACTOR SHARES( Fi1) F11=(F1+F1(-1))/2; F21=(F2+F2(-1))/2; F31=(F3+F3(-1))/2; F41=(F4+F4(-1))/2; F51=(F5+F5(-1))/2; F61=(F6+F6(-1))/2; F71=(F7+F7(-1))/2; ? Calculation of Quantity Share for making Table; T=q1+q2+q3+q4+q5+q6+q7; k1=q1/T; k2=q2/T; k3=q3/T; k4=q4/ T; k5=q5/T; k6=q6/T; k7=q7/T; msd k1-k7; smpl 2,2;

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132 msd k1-k7; smpl 43,43; msd k1-k7; ? End of Quantity Share calculation; smpl 2,33; msd q1-q7; msd T; SMPL 2,43; ? CALCULATION: CHANGE IN LOGGED PRICES(DPi) DP1=LP1-LP1(-1); DP2=LP2-LP2(-1); DP 3=LP3-LP3(-1); DP4=LP4-LP4(-1); DP5=LP5-LP5(-1); DP6=LP6-LP6(-1); DP7=LP7-LP7(-1); ? CALCULATION:CHANGE IN LOGGED QUANTITY(DQi) DQ1=LQ1-LQ1(-1); DQ2 =LQ2-LQ2(-1); DQ3=L Q3-LQ3(-1); DQ4=LQ4-LQ4(-1); DQ5=LQ5-LQ5(-1); DQ6 =LQ6-LQ6(-1); DQ7=L Q7-LQ7(-1); ? DEPENDENT VARIABLE fi*Dq and SUMMATION INDEX(FDQi) FDQ1=F11*DQ1; FDQ2=F21*DQ2; F DQ3=F31*DQ3; FDQ4=F41*DQ4; FDQ5=F51*DQ5; FDQ6=F61* DQ6; FDQ7=F71*DQ7; DQ=FDQ1+FDQ2+FDQ3+FDQ4+FDQ5+FDQ6+FDQ7; ?Fi*DP and SUMMATION INDEX(DP)NOT NEEDED FOR THIS MODEL; FDP1=F11*DP1; FDP2=F21*DP2; FD P3=F31*DP3; FDP4=F41*DP4; FDP5=F51*DP5; FDP6=F61*DP6; FDP7=F71*DP7; DP=FDP1+FDP2+FDP3+FDP4+FDP5+FDP6+FDP7; proc zzzz; ? DIFFEREENTIAL NBR MODEL WITH AR1 PLUS HOMOGENEITY AND SYMMETRY; trend obs; d1 = (obs=1); frml res1 FDQ1=(A1*DQ+B11*DP1+B12*DP2 +B13*DP3+B14*DP4+B15*DP5+B16*DP6+(-B11B12-B13-B14-B15-B16)*DP7-F11*(DP1DP)); ?frml eq1 [d1*res1*sqrt(1-rho**2) + (1-d1)*(res1 rho*res1(-1))]*(1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res2 FDQ2=(A2*DQ+B12*DP1+B22*DP2 +B23*DP3+B24*DP4+B25*DP5+B26*DP6+(-B12B22-B23-B24-B25-B26)*DP7-F21*(DP2DP)); ?frml eq2 [d1*res2*sqrt(1-rho**2) + (1-d1)*(res2 rho*res2(-1))]*(1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res3 FDQ3=(A3*DQ+B13*DP1+B23*DP2 +B33*DP3+B34*DP4+B35*DP5+B36*DP6+(-B13B23-B33-B34-B35-B36)*DP7-F31*(DP3DP)); ?frml eq3 [d1*res3*sqrt(1-rho**2) + (1-d1)*(res3 rho*res3(-1))]*(1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res4 FDQ4=(A4*DQ+B14*DP1+B24*DP2 +B34*DP3+B44*DP4+B45*DP5+B46*DP6+(-B14B24-B34-B44-B45-B46)*DP7-F41*(DP4DP)); ?frml eq4 [d1*res4*sqrt(1-rho**2) + (1-d1)*(res4 rho*res4(-1))]*(1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1); frml res5 FDQ5=(A5*DQ+B15*DP1+B25*DP2 +B35*DP3+B45*DP4+B55*DP5+B56*DP6+(-B15B25-B35-B45-B55-B56)*DP7-F51*(DP5DP)); ?frml eq5 [d1*res5*sqrt(1-rho**2) + (1-d1)*(res5 rho*res5(-1))]*(1-rho**2)**(-1/(2*@nob)); trend obs; d1 = (obs=1);

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133 frml res6 FDQ6=(A6*DQ+B16*DP1+B26*DP2 +B36*DP3+B46*DP4+B56*DP5+B66*DP6+(-B16B26-B36-B46-B56-B66)*DP7-F61*(DP6DP)); ?frml eq6 [d1*res6*sqrt(1-rho**2) + (1-d1)*(res6 rho*res6(-1))]*(1-rho**2)**(-1/(2*@nob)); REGOPT (STARS,STAR1=.10,STAR2=.05) T; PARAM A1 0 A2 0 A3 0 A4 0 A5 0 A6 0 B11 0 B12 0 B13 0 B14 0 B15 0 B16 0 B22 0 B23 0 B24 0 B25 0 B26 0 B33 0 B34 0 B35 0 B36 0 B44 0 B45 0 B46 0 B55 0 B56 0 B66 0; ?rho 0; ?eqsub eq1 res1;?eqsub eq2 res2;?eqsub eq3 res3;?eqsub eq4 res4;?eqsub eq5 res5;?eqsub eq6 res6; ?lsq(nodropmiss,tol=1e-7,maxit=1000) eq1 eq2 eq3 eq4 eq5 eq6; lsq(nodropmiss,tol=1e-7,maxit=1000) res1 res2 res3 res4 res5 res6; COPY @LOGL LU; LU1=LU; SMPL NR1, NR2; ? Elasticities; MSD F11 F21 F31 F41 F51 F61 F71; ?================= MEAN FACTOR SHARES SET MF1=@MEAN(1); SET MF2=@MEAN(2); SET MF3=@MEAN(3); SET MF4=@MEAN(4); SET MF5=@MEAN(5); SET MF6=@MEAN(6); SET MF7=@MEAN(7); PRINT MF1-MF7; SMPL NR1,NR1; MSD F1 F2 F3 F4 F5 F6 F7; ?================= MEAN FACTOR SHARES SET FF1=@MEAN(1); SET FF2=@MEAN(2); SET FF3=@MEAN(3); SET FF4=@MEAN(4); SET FF5=@MEAN(5); SET FF6=@MEAN(6); SET FF7=@MEAN(7); PRINT FF1-FF7; SMPL NR2,NR2; MSD F1 F2 F3 F4 F5 F6 F7; ?================= MEAN FACTOR SHARES SET FL1=@MEAN(1); SET FL2=@MEAN(2); SET FL3=@MEAN(3); SET FL4=@MEAN(4); SET FL5=@MEAN(5); SET FL6=@MEAN(6); SET FL7=@MEAN(7); PRINT FL1-FL7; SMPL NR1, NR2;

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134 SET B17=-B11-B12-B13-B14-B15-B16; SET B21=B12; SET B27=-B21-B22-B23-B24-B25-B26; SET B31=B13; SET B32=B23; SET B37=-B31-B32-B33-B34-B35-B36; SET B41=B14; SET B42=B24; SET B43=B34; SET B47=-B41-B42-B43-B44-B45-B46; SET B51=B15; SET B52=B25; SET B53=B35; SET B54=B45; SET B57=-B51-B52-B53-B54-B55-B56; SET B61=B16; SET B62=B26; SET B63=B36; SET B64=B46; SET B65=B56; SET B67=-B61-B62-B63-B64-B65-B66; SET B77=(-(-B11-B12-B13-B14-B15-B16)-(-B12B22-B23-B24-B25-B26)-(-B13-B23-B33-B 34-B35-B36)-(-B14-B24-B34-B44-B45-B46) -(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)); SET A7=1-A1-A2-A3-A4-A5-A6; ? Calculate the standard errors for ROW frml row1 A7=1-A1A2-A3-A4-A5-A6; frml row2 B17=-B11-B12-B13-B14-B15-B16; frml row3 B27=-B21-B22-B23-B24-B25-B26; frml row4 B37=-B31-B32-B33-B34-B35-B36; frml row5 B47=-B41-B42-B43-B44-B45-B46; frml row6 B57=-B51-B52-B53-B54-B55-B56; frml row7 B67=-B61-B62-B63-B64-B65-B66; frml row8 B77=(-(-B11-B12-B13-B14-B15-B16)-(-B12-B22-B23-B 24-B25-B26)-(-B13-B23-B33-B34-B35-B36)-(-B14-B24-B34-B44-B45B46) -(-B15-B25-B35-B45-B55-B56)-(-B16-B26-B36-B46-B56-B66)); frml row9 B21=B12; frml row10 B31=B13; frml row11 B32=B23; frml row12 B41=B14; frml row13 B42=B24; frml row14 B43=B34; frml row15 B51=B15; frml row16 B52=B25; frml row17 B53=B35; frml row18 B54=B45; frml row19 B61=B16; frml row20 B62=B26; frml row21 B63=B36; frml row22 B64=B46; frml row23 B65=B56; analyz row1-row23; ?? Calculate Eigenvalues SET B71=B17; SET B72=B27; SET B73=B37; SET B74=B47;

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135 SET B75=B57; SET B76=B67; SET B77=B77; ? Tranform r to Pie; SET D11=(B11-MF1+MF1*MF1); SET D12=(B12+MF1*MF2); SET D13=(B13+MF1*MF3); SET D14=(B14+MF1*MF4); SET D15=(B15+MF1*MF5); SET D16=(B16+MF1*MF6); SET D17=(-B11-B12-B13-B14-B15-B16+MF1*MF7); SET D21=(B12+MF2*MF1); SET D22=(B22-MF2+MF2*MF2); SET D23=(B23+MF2*MF3); SET D24=(B24+MF2*MF4); SET D25=(B25+MF2*MF5); SET D26=(B26+MF2*MF6); SET D27=(-B12-B22-B23-B24-B25-B26+MF2*MF7); SET D31=(B13+MF3*MF1); SET D32=(B23+MF3*MF2); SET D33=(B33-MF3+MF3*MF3); SET D34=(B34+MF3*MF4); SET D35=(B35+MF3*MF5); SET D36=(B36+MF3*MF6); SET D37=(-B13-B23-B33-B34-B35-B36+MF3*MF7); SET D41=(B14+MF4*MF1); SET D42=(B24+MF4*MF2); SET D43=(B34+MF4*MF3); SET D44=(B44-MF4+MF4*MF4); SET D45=(B45+MF4*MF5); SET D46=(B46+MF4*MF6); SET D47=(-B14-B24-B34-B44-B45-B46+MF4*MF7); SET D51=(B15+MF5*MF1); SET D52=(B25+MF5*MF2); SET D53=(B35+MF5*MF3); SET D54=(B45+MF5*MF4); SET D55=(B55-MF5+MF5*MF5); SET D56=(B56+MF5*MF6); SET D57=(-B15-B25-B35-B45-B55-B56+MF5*MF7); SET D61=(B16+MF6*MF1); SET D62=(B26+MF6*MF2); SET D63=(B36+MF6*MF3); SET D64=(B46+MF6*MF4); SET D65=(B56+MF6*MF5); SET D66=(B66-MF6+MF6*MF6); SET D67=(-B16-B26-B36-B46-B56-B66+MF6*MF7); SET D71=(-B11-B12-B13-B14-B15-B16+MF7*MF1); SET D72=(-B12-B22-B23-B24-B25-B26+MF7*MF2); SET D73=(-B13-B23-B33-B34-B35-B36+MF7*MF3); SET D74=(-B14-B24-B34-B44-B45-B46+MF7*MF4); SET D75=(-B15-B25-B35-B45-B55-B56+MF7*MF5); SET D76=(-B16-B26-B36-B46-B56-B66+MF7*MF6); SET D77=(-(-B11-B12-B13-B14-B15-B16)-(-B12B22-B23-B24-B25-B26)-(-B13-B23-B33-B 34-B35-B36)-(-B14-B24-B34-B44-B45-B46) -(-B15-B25-B35-B45-B55-B56 )-(-B16-B26-B36-B46-B56-B66)-MF7+MF7*MF7); ? Create each row EIGHT; MMAKE(VERT) E1 D11-D17; MMAKE(VERT) E2 D21-D27; MMAKE(VERT) E3 D31-D37; MMAKE(VERT) E4 D41-D47; MMAKE(VERT) E5 D51-D57;

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136 MMAKE(VERT) E6 D61-D67; MMAKE(VERT) E7 D71-D77; ? Creates square matrix MMAKE E E1-E7; ? Calculate Eigenvalues of E MAT EV = EIGVAL(E); Print E EV; ? Creates standard errors for pi ij's where the pi ij's are D11, D12, etc; frml pi1 D11=(B11-MF1+MF1*MF1); frml pi2 D12=(B12+MF1*MF2); frml pi3 D13=(B13+MF1*MF3); frml pi4 D14=(B14+MF1*MF4); frml pi5 D15=(B15+MF1*MF5); frml pi6 D16=(B16+MF1*MF6); frml pi7 D17=(-B11-B12-B13-B14-B15-B16+MF1*MF7); frml pi8 D21=(B12+MF2*MF1); frml pi9 D22=(B22-MF2+MF2*MF2); frml pi10 D23=(B23+MF2*MF3); frml pi11 D24=(B24+MF2*MF4); frml pi12 D25=(B25+MF2*MF5); frml pi13 D26=(B26+MF2*MF6); frml pi14 D27=(-B12-B22-B23-B24-B25-B26+MF2*MF7); frml pi15 D31=(B13+MF3*MF1); frml pi16 D32=(B23+MF3*MF2); frml pi17 D33=(B33-MF3+MF3*MF3); frml pi18 D34=(B34+MF3*MF4); frml pi19 D35=(B35+MF3*MF5); frml pi20 D36=(B36+MF3*MF6); frml pi21 D37=(-B13-B23-B33-B34-B35-B36+MF3*MF7); frml pi22 D41=(B14+MF4*MF1); frml pi23 D42=(B24+MF4*MF2); frml pi24 D43=(B34+MF4*MF3); frml pi25 D44=(B44-MF4+MF4*MF4); frml pi26 D45=(B45+MF4*MF5); frml pi27 D46=(B46+MF4*MF6); frml pi28 D47=(-B14-B24-B34-B44-B45-B46+MF4*MF7); frml pi29 D51=(B15+MF5*MF1); frml pi30 D52=(B25+MF5*MF2); frml pi31 D53=(B35+MF5*MF3); frml pi32 D54=(B45+MF5*MF4); frml pi33 D55=(B55-MF5+MF5*MF5); frml pi34 D56=(B56+MF5*MF6); frml pi35 D57=(-B15-B25-B35-B45-B55-B56+MF5*MF7); frml pi36 D61=(B16+MF6*MF1); frml pi37 D62=(B26+MF6*MF2); frml pi38 D63=(B36+MF6*MF3); frml pi39 D64=(B46+MF6*MF4); frml pi40 D65=(B56+MF6*MF5); frml pi41 D66=(B66-MF6+MF6*MF6); frml pi42 D67=(-B16-B26-B36-B46-B56-B66+MF6*MF7); frml pi43 D71=(-B11-B12-B13-B14-B15-B16+MF7*MF1); frml pi44 D72=(-B12-B22-B23-B24-B25-B26+MF7*MF2); frml pi45 D73=(-B13-B23-B33-B34-B35-B36+MF7*MF3); frml pi46 D74=(-B14-B24-B34-B44-B45-B46+MF7*MF4); frml pi47 D75=(-B15-B25-B35-B45-B55-B56+MF7*MF5); frml pi48 D76=(-B16-B26-B36-B46-B56-B66+MF7*MF6); frml pi49 D77=(-(-B11-B12-B13-B14-B15-B16)-(-B12-B22-B23-B24-B 25-B26)-(-B13-B23-B33-B34-B35B36)-(-B14-B24-B34-B44-B45B46)

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137 -(-B15-B25-B35-B45-B55-B56 )-(-B16-B26-B36-B46-B56-B66)-MF7+MF7*MF7); analyz pi1-pi49; ? Elasticities ? Divisia Input Index ???????CHANGE WILL START HE RE FOR RECURSIVE MODEL; FRML EL1 E1=A1/MF1; FRML EL1M E1M=A1/MMF1; FRML EL2 E2=A2/MF2; FRML EL2M E2M=A2/MMF2; FRML EL3 E3=A3/MF3; FRML EL3M E3M=A3/MMF3; FRML EL4 E4=A4/MF4; FRML EL4M E4M=A4/MMF4; FRML EL5 E5=A5/MF5; FRML EL5M E5M=A5/MMF5; FRML EL6 E6=A6/MF6; FRML EL6M E6M=A6/MMF6; FRML EL7 E7=(1-A1-A2-A3-A4-A5-A6)/MF7; FRML EL7M E7M=(1-A1-A2-A3-A4-A5-A6)/MMF7; ? Divisia Input Index WITH FIRST F FRML EL8 EF1=A1/FF1; FRML EL8M EF1M=A1/MFF1; FRML EL9 EF2=A2/FF2; FRML EL9M EF2M=A2/MFF2; FRML EL10 EF3=A3/FF3; FRML EL10M EF3M=A3/MFF3; FRML EL11 EF4=A4/FF4; FRML EL11M EF4M=A4/MFF4; FRML EL12 EF5=A5/FF5; FRML EL12M EF5M=A5/MFF5; FRML EL13 EF6=A6/FF6; FRML EL13M EF6M=A6/MFF6; FRML EL14 EF7=(1-A1-A2-A3-A4-A5-A6)/FF7; FRML EL14M EF7M=(1-A1-A2-A3-A4-A5-A6)/MFF7; ? Divisia Input Index WITH LAST F FRML EL15 EL1=A1/FL1; FRML EL15M EL1M=A1/MFL1; FRML EL16 EL2=A2/FL2; FRML EL16M EL2M=A2/MFL2; FRML EL17 EL3=A3/FL3; FRML EL17M EL3M=A3/MFL3; FRML EL18 EL4=A4/FL4; FRML EL18M EL4M=A4/MFL4; FRML EL19 EL5=A5/FL5; FRML EL19M EL5M=A5/MFL5; FRML EL20 EL6=A6/FL6; FRML EL20M EL6M=A6/MFL6; FRML EL21 EL7=(1-A1-A2-A3-A4-A5-A6)/FL7; FRML EL21M EL7M=(1-A1-A2-A3-A4-A5-A6)/MFL7; ? Compensated price elasticities FRML EP1 E11=(B11-MF1+MF1*MF1)/MF1; FRML EP1M E11M=(B11-MMF1+MMF1*MMF1)/MMF1; FRML EP2 E12=(B12+MF1*MF2)/MF1; FRML EP2M E12M=(B12+MMF1*MMF2)/MMF1; FRML EP3 E13=(B13+MF1*MF3)/MF1; FRML EP3M E13M=(B13+MMF1*MMF3)/MMF1; FRML EP4 E14=(B14+MF1*MF4)/MF1; FRML EP4M E14M=(B14+MMF1*MMF4)/MMF1; FRML EP5 E15=(B15+MF1*MF5)/MF1; FRML EP5M E15M=(B15+MMF1*MMF5)/MMF1; FRML EP6 E16=(B16+MF1*MF6)/MF1; FRML EP6M E16M=(B16+MMF1*MMF6)/MMF1; FRML EP7 E17=(-B11-B12-B13-B14-B15-B16+MF1*MF 7)/MF1; FRML EP7M E17M=(-B11-B12-B13-B14-B15B16+MMF1*MMF7)/MMF1; FRML EP8 E21=(B12+MF2*MF1)/MF2; FRML EP8M E21M=(B12+MMF2*MMF1)/MMF2; FRML EP9 E22=(B22-MF2+MF2*MF2)/MF2; FRML EP9M E22M=(B22-MMF2+MMF2*MMF2)/MMF2; FRML EP10 E23=(B23+MF2*MF3)/MF2; FRML EP10M E23M=(B23+MMF2*MMF3)/MMF2; FRML EP11 E24=(B24+MF2*MF4)/MF2; FRML EP11M E24M=(B24+MMF2*MMF4)/MMF2; FRML EP12 E25=(B25+MF2*MF5)/MF2; FRML EP12M E25M=(B25+MMF2*MMF5)/MMF2; FRML EP13 E26=(B26+MF2*MF6)/MF2; FRML EP13M E26M=(B26+MMF2*MMF6)/MMF2; FRML EP14 E27=(-B12-B22-B23-B24-B25-B26+MF2*MF7)/MF2; FRML EP14M E27M=(-B12-B22-B23-B24-B25B26+MMF2*MMF7)/MMF2; FRML EP15 E31=(B13+MF3*MF1)/MF3; FRML EP15M E31M=(B13+MMF3*MMF1)/MMF3; FRML EP16 E32=(B23+MF3*MF2)/MF3; FRML EP16M E32M=(B23+MMF3*MMF2)/MMF3; FRML EP17 E33=(B33-MF3+MF3*MF3)/MF3; FRML EP17M E33M=(B33-MMF3+MMF3*MMF3)/MMF3; FRML EP18 E34=(B34+MF3*MF4)/MF3; FRML EP18M E34M=(B34+MMF3*MMF4)/MMF3; FRML EP19 E35=(B35+MF3*MF5)/MF3; FRML EP19M E35M=(B35+MMF3*MMF5)/MMF3; FRML EP20 E36=(B36+MF3*MF6)/MF3; FRML EP20M E36M=(B36+MMF3*MMF6)/MMF3; FRML EP21 E37=(-B13-B23-B33-B34-B35-B36+MF3*MF7)/MF3; FRML EP21M E37M=(-B13-B23-B33-B34-B35B36+MMF3*MMF7)/MMF3; FRML EP22 E41=(B14+MF4*MF1)/MF4; FRML EP22M E41M=(B14+MMF4*MMF1)/MMF4; FRML EP23 E42=(B24+MF4*MF2)/MF4; FRML EP23M E42M=(B24+MMF4*MMF2)/MMF4; FRML EP24 E43=(B34+MF4*MF3)/MF4; FRML EP24M E43M=(B34+MMF4*MMF3)/MMF4; FRML EP25 E44=(B44-MF4+MF4*MF4)/MF4; FRML EP25M E44M=(B44-MMF4+MMF4*MMF4)/MMF4; FRML EP26 E45=(B45+MF4*MF5)/MF4; FRML EP26M E45M=(B45+MMF4*MMF5)/MMF4; FRML EP27 E46=(B46+MF4*MF6)/MF4; FRML EP27M E46M=(B46+MMF4*MMF6)/MMF4; FRML EP28 E47=(-B14-B24-B34-B44-B45-B46+MF4*MF7)/MF4; FRML EP28M E47M=(-B14-B24-B34-B44-B45B46+MMF4*MMF7)/MMF4;

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138 FRML EP29 E51=(B15+MF5*MF1)/MF5; FRML EP29M E51M=(B15+MMF5*MMF1)/MMF5; FRML EP30 E52=(B25+MF5*MF2)/MF5; FRML EP30M E52M=(B25+MMF5*MMF2)/MMF5; FRML EP31 E53=(B35+MF5*MF3)/MF5; FRML EP31M E53M=(B35+MMF5*MMF3)/MMF5; FRML EP32 E54=(B45+MF5*MF4)/MF5; FRML EP32M E54M=(B45+MMF5*MMF4)/MMF5; FRML EP33 E55=(B55-MF5+MF5*MF5)/MF5; FRML EP33M E55M=(B55-MMF5+MMF5*MMF5)/MMF5; FRML EP34 E56=(B56+MF5*MF6)/MF5; FRML EP34M E56M=(B56+MMF5*MMF6)/MMF5; FRML EP35 E57=(-B15-B25-B35-B45-B55-B56+MF5*MF7)/MF5; FRML EP35M E57M=(-B15-B25-B35-B45-B55B56+MMF5*MMF7)/MMF5; FRML EP36 E61=(B16+MF6*MF1)/MF6; FRML EP36M E61M=(B16+MMF6*MMF1)/MMF6; FRML EP37 E62=(B26+MF6*MF2)/MF6; FRML EP37M E62M=(B26+MMF6*MMF2)/MMF6; FRML EP38 E63=(B36+MF6*MF3)/MF6; FRML EP38M E63M=(B36+MMF6*MMF3)/MMF6; FRML EP39 E64=(B46+MF6*MF4)/MF6; FRML EP39M E64M=(B46+MMF6*MMF4)/MMF6; FRML EP40 E65=(B56+MF6*MF5)/MF6; FRML EP40M E65M=(B56+MMF6*MMF5)/MMF6; FRML EP41 E66=(B66-MF6+MF6*MF6)/MF6; FRML EP41M E66M=(B66-MMF6+MMF6*MMF6)/MMF6; FRML EP42 E67=(-B16-B26-B36-B46-B56-B66+MF6*MF7)/MF6; FRML EP42M E67M=(-B16-B26-B36-B46-B56B66+MMF6*MMF7)/MMF6; FRML EP43 E71=(-B11-B12-B13-B14-B15-B16+MF7*MF1)/MF7; FRML EP43M E71M=(-B11-B12-B13-B14-B15B16+MMF7*MMF1)/MMF7; FRML EP44 E72=(-B12-B22-B23-B24-B25-B26+MF7*MF2)/MF7; FRML EP44M E72M=(-B12-B22-B23-B24-B25B26+MMF7*MMF2)/MMF7; FRML EP45 E73=(-B13-B23-B33-B34-B35-B36+MF7*MF3)/MF7; FRML EP45M E73M=(-B13-B23-B33-B34-B35B36+MMF7*MMF3)/MMF7; FRML EP46 E74=(-B14-B24-B34-B44-B45-B46+MF7*MF4)/MF7; FRML EP46M E74M=(-B14-B24-B34-B44-B45B46+MMF7*MMF4)/MMF7; FRML EP47 E75=(-B15-B25-B35-B45-B55-B56+MF7*MF5)/MF7; FRML EP47M E75M=(-B15-B25-B35-B45-B55B56+MMF7*MMF5)/MMF7; FRML EP48 E76=(-B16-B26-B36-B46-B56-B66+MF7*MF6)/MF7; FRML EP48M E76M=(-B16-B26-B36-B46-B56B66+MMF7*MMF6)/MMF7; FRML EP49 E77=(-(-B11-B12-B13-B14-B15-B16)-(-B12-B22-B23-B24B25-B26)-(-B13-B23-B33-B34-B35-B36)-(-B14-B24-B34-B44-B45B46) -(-B15-B25-B35-B45-B5 5-B56)-(-B16-B26-B36-B46-B56-B66)-MF7+MF7*MF7)/MF7; FRML EP49M E77M=(-(-B11-B12-B13-B14-B15-B16)-(-B12-B22-B 23-B24-B25-B26)-(-B13-B23-B33-B34-B35-B36)-(-B14-B24-B34-B44B45-B46) -(-B15-B25-B35-B45-B5 5-B56)-(-B16-B26-B36-B46-B56-B66)-MMF7+MMF7*MMF7)/MMF7; ANALYZ EL1-EL7, EL1M-EL7M; MMAKE ELCOEF @COEFA; ANALYZ EP1-EP49, EP1M-EP49M; MMAKE EPCOEF @COEFA; MMAKE(VERTICAL) MBM ELCOEF EPCOEF; MMAKE MBETA MBETA MBM; endproc zzzz; MFORM(TYPE=GEN,NROW=112,NCOL=1) MBETA=0; SMPL 2, 33; MSD F11 F21 F31 F41 F51 F61 F71; ?================= MEAN FACTOR SHARES SET MMF1=@MEAN(1); SET MMF2=@MEAN(2); SET MMF3=@MEAN(3); SET MMF4=@MEAN(4); SET MMF5=@MEAN(5); SET MMF6=@MEAN(6); SET MMF7=@MEAN(7); SMPL 2 2; MSD F1 F2 F3 F4 F5 F6 F7; ?================= MEAN FACTOR SHARES SET MFF1=@MEAN(1); SET MFF2=@MEAN(2); SET MFF3=@MEAN(3);

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139 SET MFF4=@MEAN(4); SET MFF5=@MEAN(5); SET MFF6=@MEAN(6); SET MFF7=@MEAN(7); SMPL 33,33; MSD F1 F2 F3 F4 F5 F6 F7; ?================= MEAN FACTOR SHARES SET MFL1=@MEAN(1); SET MFL2=@MEAN(2); SET MFL3=@MEAN(3); SET MFL4=@MEAN(4); SET MFL5=@MEAN(5); SET MFL6=@MEAN(6); SET MFL7=@MEAN(7); DO J=1 TO 11; SET NR1=1+J; SET NR2=32+J; SMPL NR1, NR2; ZZZZ; ENDDO; WRITE(FORMAT=EXCEL,FILE='U:\TOMATORESEARCH\EUNEWANALYSIS\ELEPELAS-RECW.XLS') MBETA; END;

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140 APPENDIX C SUPERFLUOUS MATERIAL Program Creating TLB Datase t for 1963-2005 (TOMDATA2005.TLB) OPTIONS MEMORY=500 LIMPRN=120 LINLIM=60; ?=====================================================================================; ? TOM2005#01.TSP CREATING NEW TLB DATABASE WI TH 2005 DATA INCLUDED WITH Q & V ; ?=====================================================================================; FREQ NONE; TITLE 'TOMATO EXPORTS TO AND FROM EEC THROUGH 2005'; ?IN 'H:\Zstudents\MohAli\T SP2007\DATA2005\TOMDATA2005'; OUT 'H:\Zstudents\MohAli\T SP2007\DATA2005\TOMDATA2005'; READ(FORMAT=EXCEL,FILE='H :\Zstudents\MohAli\TSP2007\DAT A2005\TOMDATA2005.XLS'); DOC NUM 'COUNTER'; DOC YRS 'YEAR 1963-2005'; DOC IMP 'IMPORTING COUNTRY'; DOC EPX 'EXPORTING COUNTRY'; DOC COM 'TOMATOES = 544'; DOC V 'VALUE OF TOMATOES TRADED $US'; DOC Q 'QUANTITY OF TOMATOES TRADED (KG)'; DOC UNT 'UNIT=2 FOR KILOGRAMS'; OUT; ?PRINT @NOB; ?DBLIST 'H:\Zstudents\MohA li\TSP2007\DATA2005\TOMDATA2005'; END; Program Creating Individual Coun trys Value and Quantity (V. Q.) for EU-15 (TOMDATA2005_15.TLB) OPTIONS MEMORY=1500 signif=0; ? TOM2005#02.TSP; TITLE 'TOMATO EXPORTS TO AND FROM EEC 15 COUNTRIES'; IN 'H:\Zstudents\MohAli\T SP2007\DATA2005\TOMDATA2005'; SUPRES SMPL; ?==========================================================================; ? INITALIZING ALL PARTNER COUNTRIES TO ZERO; ?==========================================================================; ?==========================================================================; ? ALL IMPORTING AND EXPORTING COUNTRIES USE EITHER ZIMPZ15 OR ZIMPZ27 ; ?==========================================================================; LIST ZIMPZ15 40 58 208 246 251 276 300 372 381 528 620 724 752 826 842; ? ORIGINAL 15 EEC COUNTRIES; LIST ZIMPZ27 40 58 208 246 251 276 300 372 381 528 620 724 752 826 842 100 196 203 233 348 428 440 470 616 642 703 705; ? ADDED 12 ADDITIONAL COUNTRIES; ?==========================================================================; ? IMPORTING COUNTRIES WITH NEW COUNTIRES ADDED ; ?==========================================================================; LIST ZEPXZ 0 8 12 20 24 28 31 32 36 40 44 50 51 52 56 58 68 70 76 80 84 86 90 92 96 100 104 108 120 124 129 132 136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 208 212 214 218 222 226 230 231 232 233 234 246 251 266 270 275 276 288 292 296 300 304 308 312 320 324 332 340 344 348 352 360 364 372 376 381 384 388 392 400 404 408 410 414 418 422 428 430 434 440 442 446 450 454 458 462 466 470 474 478 480 484 492 496 498 500 504 508 516 520 524 528 530 532 533 536 554 558 562 566 579 583 584 586 591 604 608 616 620 624 638 642 643 646 658 659 678 682 686 690 694 699 702 703 704 705 706 710 711 716 717 724 732 736 740 752 757 760 764 768 780 784 788 792 796 800 804 807 810 818 826 834 837 838 839 842 849 854 858 860 862 879 882 887 890 891 894 899 900 568 798 112 174 178 184 795 762; ? EXPORTING COUNTRIES;

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141 ?==========================================================================; ? THE NEW DATA IS CREATED AND THEN THE PROCEDURE IS TURNED OFF ?==========================================================================; DOT(VALUE=I) ZEPXZ; V.=0; Q.=0; print i; ENDDOT; ?==========================================================================; ? FOR EACH IMPORTER CREATE A V. AND Q. WITH THE PARTNER TRADE; ?==========================================================================; DOT(CHAR=#, VALUE=J) ZIMPZ15; ? <==== HERE WE ARE CREATING FOR THE 15 EEC COUNTRIES; DOT(CHAR=%, VALUE=I) ZEPXZ; DO K=1963 TO 2005; PRINT J I K; SELECT IMP=J & EPX=I & YRS=K; PRINT @NOB; IF @NOB>0; THEN; DO; V.%=V; Q.%=Q; ENDDO; ELSE; SET IDD=1; ENDDO; ENDDOT; ENDDOT; SELECT 1; DOT ZEPXZ; V.=V.; Q.=Q.; ENDDOT; ?==========================================================================; ? CREATING 901 AND 902 ; ?==========================================================================; SELECT 1; SET NRR=@NOB; PRINT NRR; DEU= (IMP^=842); ?1=EU 0=US; EU_US = (DEU=1)*901 + (DEU=0)*902; ? 901=EU CODE AND 902=NEW CODE FOR US; SET M=0; DO L=901 TO 902; DO K=1963 TO 2005; SET M=M+1; SMPL 1,NRR; SET NR2=NRR+M; DOT(CHAR=%, VALUE=I) ZEPXZ; SMPL 1,NRR; SELECT EU_US=L & YRS=K; MSD(NOPRINT) V. Q.; SMPL NR2,NR2; V.=@SUM(1); Q.=@SUM(2); YRS=K; IMP=L; UNT=2; COM=544; ENDDOT; ENDDO; ENDDO; PRINT NR2; SET NR3=NRR+1; print nr3 nr2; SMPL NR3, NR2; SMPL 1,NR2; OUT 'H:\Zstudents\MohAli\TSP2007\ DATA2005\TOMDATA2005_15'; ? <==== MUST CHANGE TO 15 OR 27; YRS=YRS; IMP=IMP; COM=COM; EPX=EPX; UNT=UNT; V=V; Q=Q; DOT(CHAR=%, VALUE=I) ZEPXZ; V.=V.; Q.=Q.; ENDDOT; OUT; DBLIST 'H:\Zstudents\MohAli\TSP2007\DATA2005\TOMDAT A2005_15'; ? <==== MUST CHANGE TO 15 OR 27; END;

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142 Program Creating Partners for EU-15 in Excel OPTIONS MEMORY=1500 signif=0 DOUBLE; ? Creating partners for EU-15; TITLE 'TOMATO EXPORTS TO AND FROM EEC'; ?IN 'k:\Zstudents\MohAli\T SP2007\DATA2005\TOMDATA2005_15'; IN 'U:\Zstudents\MohAli\T SP2007\DATA2005\TOMDATA2005_15'; ?dblist 'k:\Zstudents\MohAli\T SP2007\DATA2005\TO MDATA2005_15'; ?dblist 'U:\Zstudents\MohAli\T SP2007\DATA2005\TO MDATA2005_15'; SUPRES SMPL; ?==========================================================================; ? INITALIZING ALL PARTNER COUNTRIES TO ZERO; ?==========================================================================; LIST ZIMPZ 901 902; ? IMPORTING COUNTRIES; LIST ZEPXZ 0 8 12 20 24 28 31 32 36 40 44 50 51 52 56 58 68 70 76 80 84 86 90 92 96 100 104 108 120 124 129 132 136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 208 212 214 218 222 226 230 231 232 233 234 246 251 266 270 275 276 288 292 296 300 304 308 312 320 324 332 340 344 348 352 360 364 372 376 381 384 388 392 400 404 408 410 414 418 422 428 430 434 440 442 446 450 454 458 462 466 470 474 478 480 484 492 496 498 500 504 508 516 520 524 528 530 532 533 536 554 558 562 566 579 583 584 586 591 604 608 616 620 624 638 642 643 646 658 659 678 682 686 690 694 699 702 703 704 705 706 710 711 716 717 724 732 736 740 752 757 760 764 768 780 784 788 792 796 800 804 807 810 818 826 834 837 838 839 842 849 854 858 860 862 879 882 887 890 891 894 899 900 568 798 112 174 178 184 795 762; ? EXPORTING COUNTRIES; ? LIST OF VARIABLES NOW START WITH THE Z IN FRONT OF THE NAMES; ? ZYRS ZIMP ZV0 ZQ0 ZV8 ZQ8 LIST ZNEECZ 0 1 901 8 12 20 24 28 31 32 36 44 50 51 52 68 70 76 80 84 86 90 92 96 100 104 108 120 124 129 132 136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 212 214 218 222 226 230 231 232 233 234 266 270 275 288 292 296 304 308 312 320 324 332 340 344 348 352 360 364 376 384 388 392 400 404 408 410 414 418 422 428 430 434 440 446 450 454 458 462 466 470 474 478 480 484 492 496 498 500 504 508 516 520 524 530 532 533 536 554 558 562 566 579 583 584 586 591 604 608 616 624 638 642 643 646 658 659 678 682 686 690 694 699 702 703 704 705 706 710 711 716 717 732 736 740 757 760 764 768 780 784 788 792 796 800 804 807 810 818 834 837 838 839 842 849 854 858 860 862 879 882 887 890 891 894 899 900 568 798 112 174 178 184 795 762; DOT ZEPXZ; ZV.=V.; ZQ.=Q.; ENDDOT; ZYRS=YRS; ZIMP=IMP; ZV901=0; ZQ901=0; DOT 40 58 56 442 208 246 251 276 300 372 381 528 620 724 752 826; ZV901=ZV901+ZV.; ZQ901=ZQ901+ZQ.; ENDDOT; ZV1=ZV0-ZV901; ? VALUE NET OF EEC SUBSTRACTING INTERTRADE; ZQ1=ZQ0-ZQ901; ? QUANTITY NET OF EEC SUBSTRACTING INTERTRADE; ? COUNTRIES TO INCLUDE AS SEPARATE TRADING PARTNERS; ? 8 100 376 504 642 792 842; LIST ZROW_EUZ 12 20 24 28 31 32 36 44 50 51 52 68 70 76 80 84 86 90 92 96 104 108 120 124 129 132 136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 212 214 218 222 226 230 231 232 233 234 266 270 275 288 292 296 304 308 312 320 324 332 340 344 348 352 360 364 384 388 392 400 404 408 410 414 418 422 428 430 434 440

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143 442 446 450 454 458 462 466 470 474 478 480 484 492 496 498 500 508 516 520 524 530 532 533 536 554 558 562 566 579 583 584 586 591 604 608 616 624 638 643 646 658 659 678 682 686 690 694 699 702 703 704 705 706 710 711 716 717 732 736 740 757 760 764 768 780 784 788 796 800 804 807 810 818 834 837 838 839 849 854 858 860 862 879 882 887 890 891 894 899 900 568 798 112 174 178 184 795 762; ZV998=0; ZQ998=0; ? REST OF THE WORLD FOR EU; DOT ZROW_EUZ; ZV998=ZV998 + ZV.; ZQ998=ZQ998 + ZQ.; ENDDOT; ? DOT 8 100 376 504 642 792 842 998; ? PRINT ZYRS ZIMP ZV. ZQ.; ? ENDDOT; ?=============================================================; ? TURNNING OFF THE FOLLOWING PROCEDURE; ?=============================================================; PROC DONTDO; MFORM(TYPE=GEN,NROW=400,NCOL=10) MEECM=0; DOT(INDEX=I,VALUE=K) ZNEECZ; MAT MEECM(I,1)=K; SELECT ZIMP=901; MSD(NOPRINT) ZV. ZQ.; MAT MEECM(I,2)=901; MAT MEECM(I,3)=@SUM(1); MAT MEECM(I,4)=@SUM(2); SELECT ZIMP=902; MSD(NOPRINT) ZV. ZQ.; MAT MEECM(I,5)=902; MAT MEECM(I,6)=@SUM(1); MAT MEECM(I,7)=@SUM(2); SELECT 1; ENDDOT; WRITE(FORMAT=EXCEL,F ILE='U:\tomato research\TSP WORK S 2006\RESTOFWORLD.XLS') MEECM; ENDPROC DONTDO; ?==================================================================================; SELECT ZIMP=901; ?WRITE(FORMAT=EXCEL,FILE='k:\Zstudents\MohAli\TSP 2007\ANALYSIS\EU-15PARTNER#01.XLS') ZYRS ZIMP ? ZV0 ZQ0 ZV1 ZQ1 ZV8 ZQ8 ZV100 ZQ100 ZV376 ZQ376 ZV504 ZQ504 ZV642 ZQ642 ? ZV792 ZQ792 ZV842 ZQ842 ZV998 ZQ998; WRITE(FORMAT=EXCEL,FILE='U:\Zst udents\MohAli\TSP2007\ANALYSIS\EU-15P ARTNER#01.XLS') ZYRS ZIMP ZV0 ZQ0 ZV1 ZQ1 ZV8 ZQ8 ZV100 ZQ100 ZV376 ZQ376 ZV504 ZQ504 ZV642 ZQ642 ZV792 ZQ792 ZV842 ZQ842 ZV998 ZQ998; END; Program Creating Partners for the U.S. in Excel OPTIONS MEMORY=1500 signif=0 DOUBLE; ? Creating partners for EU-15; TITLE 'TOMATO EXPORTS TO AND FROM EEC'; ?IN 'k:\Zstudents\MohAli\T SP2007\DATA2005\TOMDATA2005_15'; IN 'U:\Zstudents\MohAli\T SP2007\DATA2005\TOMDATA2005_15'; ?dblist 'k:\Zstudents\MohAli\T SP2007\DATA2005\TO MDATA2005_15'; ?dblist 'U:\Zstudents\MohAli\T SP2007\DATA2005\TO MDATA2005_15'; SUPRES SMPL; ?==========================================================================; ? INITALIZING ALL PARTNER COUNTRIES TO ZERO; ?==========================================================================;

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144 LIST ZIMPZ 901 902; ? IMPORTING COUNTRIES; LIST ZEPXZ 0 8 12 20 24 28 31 32 36 40 44 50 51 52 56 58 68 70 76 80 84 86 90 92 96 100 104 108 120 124 129 132 136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 208 212 214 218 222 226 230 231 232 233 234 246 251 266 270 275 276 288 292 296 300 304 308 312 320 324 332 340 344 348 352 360 364 372 376 381 384 388 392 400 404 408 410 414 418 422 428 430 434 440 442 446 450 454 458 462 466 470 474 478 480 484 492 496 498 500 504 508 516 520 524 528 530 532 533 536 554 558 562 566 579 583 584 586 591 604 608 616 620 624 638 642 643 646 658 659 678 682 686 690 694 699 702 703 704 705 706 710 711 716 717 724 732 736 740 752 757 760 764 768 780 784 788 792 796 800 804 807 810 818 826 834 837 838 839 842 849 854 858 860 862 879 882 887 890 891 894 899 900 568 798 112 174 178 184 795 762; ? EXPORTING COUNTRIES; ? LIST OF VARIABLES NOW START WITH THE Z IN FRONT OF THE NAMES; ? ZYRS ZIMP ZV0 ZQ0 ZV8 ZQ8 LIST ZNEECZ 0 1 901 8 12 20 24 28 31 32 36 44 50 51 52 68 70 76 80 84 86 90 92 96 100 104 108 120 124 129 132 136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 212 214 218 222 226 230 231 232 233 234 266 270 275 288 292 296 304 308 312 320 324 332 340 344 348 352 360 364 376 384 388 392 400 404 408 410 414 418 422 428 430 434 440 446 450 454 458 462 466 470 474 478 480 484 492 496 498 500 504 508 516 520 524 530 532 533 536 554 558 562 566 579 583 584 586 591 604 608 616 624 638 642 643 646 658 659 678 682 686 690 694 699 702 703 704 705 706 710 711 716 717 732 736 740 757 760 764 768 780 784 788 792 796 800 804 807 810 818 834 837 838 839 842 849 854 858 860 862 879 882 887 890 891 894 899 900 568 798 112 174 178 184 795 762; DOT ZEPXZ; ZV.=V.; ZQ.=Q.; ENDDOT; ZYRS=YRS; ZIMP=IMP; ZV901=0; ZQ901=0; DOT 40 58 56 442 208 246 251 276 300 372 381 528 620 724 752 826; ZV901=ZV901+ZV.; ZQ901=ZQ901+ZQ.; ENDDOT; ZV1=ZV0-ZV901; ? VALUE NET OF EEC SUBSTRACTING INTERTRADE; ZQ1=ZQ0-ZQ901; ? QUANTITY NET OF EEC SUBSTRACTING INTERTRADE; ? COUNTRIES TO INCLUDE AS SEPARATE TRADING PARTNERS; ? 8 100 376 504 642 792 842; LIST ZROW_EUZ 12 20 24 28 31 32 36 44 50 51 52 68 70 76 80 84 86 90 92 96 104 108 120 124 129 132 136 140 144 152 156 166 170 180 188 191 192 196 200 203 204 212 214 218 222 226 230 231 232 233 234 266 270 275 288 292 296 304 308 312 320 324 332 340 344 348 352 360 364 384 388 392 400 404 408 410 414 418 422 428 430 434 440 442 446 450 454 458 462 466 470 474 478 480 484 492 496 498 500 508 516 520 524 530 532 533 536 554 558 562 566 579 583 584 586 591 604 608 616 624 638 643 646 658 659 678 682 686 690 694 699 702 703 704 705 706 710 711 716 717 732 736 740 757 760 764 768 780 784 788 796 800 804 807 810 818 834 837 838 839 849 854 858 860 862 879 882 887 890 891 894 899 900 568 798 112 174 178 184 795 762; ZV998=0; ZQ998=0; ? REST OF THE WORLD FOR EU; DOT ZROW_EUZ; ZV998=ZV998 + ZV.; ZQ998=ZQ998 + ZQ.; ENDDOT; ? DOT 8 100 376 504 642 792 842 998; ? PRINT ZYRS ZIMP ZV. ZQ.; ? ENDDOT;

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145 ?=============================================================; ? TURNNING OFF THE FOLLOWING PROCEDURE; ?=============================================================; PROC DONTDO; MFORM(TYPE=GEN,NROW=400,NCOL=10) MEECM=0; DOT(INDEX=I,VALUE=K) ZNEECZ; MAT MEECM(I,1)=K; SELECT ZIMP=901; MSD(NOPRINT) ZV. ZQ.; MAT MEECM(I,2)=901; MAT MEECM(I,3)=@SUM(1); MAT MEECM(I,4)=@SUM(2); SELECT ZIMP=902; MSD(NOPRINT) ZV. ZQ.; MAT MEECM(I,5)=902; MAT MEECM(I,6)=@SUM(1); MAT MEECM(I,7)=@SUM(2); SELECT 1; ENDDOT; WRITE(FORMAT=EXCEL,F ILE='U:\tomato research\TSP WORK S 2006\RESTOFWORLD.XLS') MEECM; ENDPROC DONTDO; ?==================================================================================; SELECT ZIMP=901; ?WRITE(FORMAT=EXCEL,FILE='k:\Zstudents\MohAli\TSP 2007\ANALYSIS\EU-15PARTNER#01.XLS') ZYRS ZIMP ? ZV0 ZQ0 ZV1 ZQ1 ZV8 ZQ8 ZV100 ZQ100 ZV376 ZQ376 ZV504 ZQ504 ZV642 ZQ642 ? ZV792 ZQ792 ZV842 ZQ842 ZV998 ZQ998; WRITE(FORMAT=EXCEL,FILE='U:\Zst udents\MohAli\TSP2007\ANALYSIS\EU-15P ARTNER#01.XLS') ZYRS ZIMP ZV0 ZQ0 ZV1 ZQ1 ZV8 ZQ8 ZV100 ZQ100 ZV376 ZQ376 ZV504 ZQ504 ZV642 ZQ642 ZV792 ZQ792 ZV842 ZQ842 ZV998 ZQ998; END; U.S. 3D Chart Data for Structural Change Canada's Share Perc entage increase in U.S. total imports 0.1 0.15 0.2 0.25 0.3 1995 0.0053 0.0051 0.0050 0.0049 0.0048 1996 0.0053 0.0051 0.0050 0.0049 0.0048 1997 0.0052 0.0051 0.0050 0.0049 0.0048 1998 0.0053 0.0052 0.0052 0.0051 0.0050 1999 0.0053 0.0053 0.0052 0.0051 0.0051 2000 0.0055 0.0055 0.0055 0.0055 0.0055 2001 0.0055 0.0055 0.0055 0.0055 0.0055 2002 0.0054 0.0053 0.0052 0.0052 0.0051 2003 0.0053 0.0053 0.0052 0.0051 0.0050 2004 0.0053 0.0052 0.0051 0.0051 0.0050 2005 0.0051 0.0049 0.0047 0.0045 0.0043 Percentage increase in U.S. total imports Dom. Rep.'s Share 0.1 0.15 0.2 0.25 0.3 1995 0.0041 0.0039 0.0038 0.0037 0.0036 1996 0.0041 0.0039 0.0038 0.0037 0.0036 1997 0.0041 0.0039 0.0038 0.0037 0.0036 1998 0.0040 0.0039 0.0037 0.0036 0.0035

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146 1999 0.0040 0.0039 0.0038 0.0036 0.0035 2000 0.0040 0.0039 0.0037 0.0036 0.0035 2001 0.0040 0.0039 0.0037 0.0036 0.0035 2002 0.0040 0.0039 0.0037 0.0036 0.0035 2003 0.0040 0.0038 0.0037 0.0036 0.0035 2004 0.0040 0.0039 0.0037 0.0036 0.0035 2005 0.0040 0.0039 0.0038 0.0036 0.0035 Percentage increase in U.S. total imports Mexico's Share 0.1 0.15 0.2 0.25 0.3 1995 0.9815 0.9815 0.9816 0.9816 0.9816 1996 0.9814 0.9814 0.9814 0.9814 0.9814 1997 0.9815 0.9816 0.9816 0.9816 0.9816 1998 0.9816 0.9816 0.9817 0.9817 0.9818 1999 0.9812 0.9811 0.9810 0.9808 0.9807 2000 0.9810 0.9808 0.9806 0.9805 0.9803 2001 0.9809 0.9807 0.9804 0.9802 0.9800 2002 0.9811 0.9809 0.9807 0.9805 0.9804 2003 0.9812 0.9811 0.9810 0.9809 0.9808 2004 0.9813 0.9812 0.9811 0.9810 0.9810 2005 0.9816 0.9817 0.9818 0.9818 0.9819 Percentage increase in U.S. total imports EU-15's Share 0.1 0.15 0.2 0.25 0.3 1995 0.0067 0.0072 0.0077 0.0082 0.0086 1996 0.0067 0.0073 0.0078 0.0083 0.0087 1997 0.0067 0.0072 0.0077 0.0082 0.0086 1998 0.0066 0.0071 0.0076 0.0081 0.0085 1999 0.0067 0.0073 0.0078 0.0083 0.0087 2000 0.0067 0.0073 0.0078 0.0083 0.0087 2001 0.0067 0.0073 0.0079 0.0083 0.0088 2002 0.0068 0.0075 0.0080 0.0086 0.0090 2003 0.0068 0.0074 0.0080 0.0085 0.0089 2004 0.0068 0.0074 0.0079 0.0084 0.0089 2005 0.0068 0.0074 0.0080 0.0085 0.0090 Percentage increase in U.S. total imports ROW's 0.1 0.15 0.2 0.25 0.3 1995 0.0022 0.0018 0.0014 0.0010 0.0006 1996 0.0022 0.0018 0.0014 0.0010 0.0006 1997 0.0022 0.0017 0.0013 0.0010 0.0006 1998 0.0022 0.0017 0.0013 0.0009 0.0006 1999 0.0022 0.0018 0.0014 0.0011 0.0007 2000 0.0022 0.0018 0.0014 0.0010 0.0007 2001 0.0022 0.0018 0.0014 0.0011 0.0007 2002 0.0022 0.0017 0.0013 0.0009 0.0006 2003 0.0022 0.0017 0.0013 0.0009 0.0006 2004 0.0022 0.0017 0.0013 0.0009 0.0006 2005 0.0021 0.0017 0.0012 0.0008 0.0005

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147 EU-15 3D Chart Data for Structural Change. Albania's Share Perc entage increase in EU-15 total imports 0.1 0.15 0.2 0.25 0.3 1995 0.2078 0.1816 0.1576 0.1355 0.1151 1996 0.2109 0.1860 0.1632 0.1423 0.1229 1997 0.2117 0.1872 0.1648 0.1441 0.1250 1998 0.2141 0.1906 0.1690 0.1492 0.1309 1999 0.2092 0.1836 0.1602 0.1386 0.1186 2000 0.2139 0.1903 0.1687 0.1488 0.1304 2001 0.2136 0.1899 0.1682 0.1482 0.1297 2002 0.2194 0.1982 0.1787 0.1609 0.1444 2003 0.2171 0.1949 0.1745 0.1558 0.1385 2004 0.2159 0.1932 0.1724 0.1532 0.1356 2005 0.2172 0.1951 0.1749 0.1562 0.1390 Bulgaria's Share 0.1 0.15 0.2 0.25 0.3 1995 0.0476 0.0469 0.0462 0.0455 0.0450 1996 0.0465 0.0453 0.0441 0.0431 0.0421 1997 0.0469 0.0458 0.0448 0.0439 0.0431 1998 0.0467 0.0455 0.0445 0.0435 0.0426 1999 0.0463 0.0450 0.0438 0.0427 0.0416 2000 0.0472 0.0462 0.0454 0.0446 0.0438 2001 0.0478 0.0471 0.0464 0.0459 0.0453 2002 0.0473 0.0464 0.0456 0.0448 0.0441 2003 0.0468 0.0457 0.0447 0.0437 0.0428 2004 0.0469 0.0458 0.0448 0.0439 0.0431 2005 0.0468 0.0457 0.0447 0.0438 0.0429 Isreal's Share 0.1 0.15 0.2 0.25 0.3 1995 0.0101 0.0098 0.0094 0.0091 0.0089 1996 0.0101 0.0098 0.0095 0.0092 0.0089 1997 0.0099 0.0095 0.0091 0.0088 0.0085 1998 0.0097 0.0092 0.0087 0.0083 0.0079 1999 0.0095 0.0089 0.0084 0.0079 0.0074 2000 0.0094 0.0088 0.0082 0.0076 0.0071 2001 0.0102 0.0099 0.0096 0.0093 0.0091 2002 0.0104 0.0102 0.0099 0.0098 0.0096 2003 0.0112 0.0113 0.0114 0.0115 0.0116 2004 0.0114 0.0116 0.0118 0.0120 0.0121 2005 0.0123 0.0129 0.0134 0.0139 0.0144 Morocco's Share 0.1 0.15 0.2 0.25 0.3 1995 0.5359 0.5391 0.5421 0.5448 0.5473 1996 0.5363 0.5396 0.5427 0.5456 0.5482 1997 0.5367 0.5403 0.5436 0.5466 0.5494 1998 0.5365 0.5400 0.5432 0.5461 0.5489 1999 0.5386 0.5430 0.5470 0.5507 0.5541 2000 0.5383 0.5426 0.5465 0.5501 0.5534 2001 0.5385 0.5428 0.5468 0.5504 0.5538 2002 0.5430 0.5493 0.5550 0.5604 0.5653

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148 2003 0.5412 0.5468 0.5519 0.5565 0.5609 2004 0.5420 0.5479 0.5533 0.5582 0.5628 2005 0.5358 0.5389 0.5418 0.5445 0.5469 Romania's Share 0.1 0.15 0.2 0.25 0.3 1995 0.0948 0.0908 0.0871 0.0837 0.0805 1996 0.0951 0.0912 0.0877 0.0844 0.0814 1997 0.0938 0.0893 0.0853 0.0815 0.0780 1998 0.0956 0.0919 0.0885 0.0854 0.0826 1999 0.0966 0.0934 0.0904 0.0877 0.0852 2000 0.0985 0.0961 0.0939 0.0918 0.0899 2001 0.0977 0.0949 0.0923 0.0900 0.0878 2002 0.0947 0.0907 0.0869 0.0835 0.0804 2003 0.0953 0.0915 0.0880 0.0848 0.0818 2004 0.0952 0.0914 0.0879 0.0847 0.0817 2005 0.0956 0.0919 0.0885 0.0854 0.0826 Turkey's Share 0.1 0.15 0.2 0.25 0.3 1995 0.0064 0.0066 0.0068 0.0069 0.0071 1996 0.0065 0.0067 0.0069 0.0071 0.0072 1997 0.0066 0.0069 0.0071 0.0073 0.0075 1998 0.0064 0.0066 0.0067 0.0069 0.0070 1999 0.0067 0.0071 0.0074 0.0076 0.0079 2000 0.0059 0.0059 0.0059 0.0059 0.0059 2001 0.0057 0.0055 0.0054 0.0053 0.0052 2002 0.0053 0.0049 0.0046 0.0044 0.0041 2003 0.0062 0.0062 0.0063 0.0064 0.0065 2004 0.0057 0.0056 0.0055 0.0054 0.0054 2005 0.0062 0.0063 0.0064 0.0065 0.0065 ROW's Share 0.1 0.15 0.2 0.25 0.3 1995 0.0376 0.0395 0.0413 0.0430 0.0445 1996 0.0375 0.0394 0.0411 0.0427 0.0442 1997 0.0376 0.0395 0.0413 0.0430 0.0445 1998 0.0371 0.0388 0.0404 0.0419 0.0433 1999 0.0347 0.0354 0.0361 0.0367 0.0372 2000 0.0338 0.0341 0.0344 0.0346 0.0348 2001 0.0330 0.0329 0.0329 0.0329 0.0328 2002 0.0300 0.0286 0.0274 0.0262 0.0252 2003 0.0301 0.0288 0.0277 0.0266 0.0256 2004 0.0295 0.0279 0.0265 0.0252 0.0239 2005 0.0344 0.0350 0.0356 0.0361 0.0365

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149 Code Number used for Data Ma nipulation (As of January, 2007) Item Code Item Code Albania 8 Lao People's Dem. Rep. 418 Algeria 12 Latvia 428 Andorra 20 Lebanon 422 Angola 24 Liberia 430 Antigua and Barbuda 28 Libya 434 Areas, nes 899 Lithuania 440 Argentina 32 Luxembourg 442 Armenia 51 Madagascar 450 Aruba 533 Malawi 454 Australia 36 Malaysia 458 Austria 40 Maldives 462 Azerbaijan 31 Mali 466 Bahamas 44 Malta 470 Bangladesh 50 Marshall Isds 584 Barbados 52 Martinique 474 Belarus 112 Mauritania 478 Belgium 56 Mauritius 480 Belgium-Luxembourg 58 Mexico 484 Belize 84 Mongolia 496 Benin 204 Montserrat 500 Bolivia 68 Morocco 504 Bosnia Herzegovina 70 Mozambique 508 Br. Antarctic Terr. 80 Myanmar 104 Br. Indian Ocean Terr. 86 Namibia 516 Br. Virgin Isds 92 Nauru 520 Brazil 76 Nepal 524 Brunei Darussalam 96 Neth. Antilles 530 Bulgaria 100 Neth. Antilles and Aruba 532 Bunkers 837 Netherlands 528 Burkina Faso 854 Neutral Zone 536 Burundi 108 New Zealand 554 Cameroon 120 Nicaragua 558 Canada 124 Niger 562 Cape Verde 132 Nigeria 566 Caribbean, nes 129 Norway 579 Cayman Isds 136 Occ. Palestinian Terr. 275 Central African Rep. 140 Other Asia, nes (Created) 900 China, Hong Kong SAR 344 Other Eurpe, nes 568

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150 Chile 152 Pakistan 586 China 156 Panama 591 China, Macao SAR 446 Peru 604 Cocos Isds 166 Philippines 608 Colombia 170 Poland 616 Comoros 174 Portugal 620 Congo 178 Rep. of Korea 410 Cook Isds 184 Rep. of Moldova 498 Costa Rica 188 Runion 638 Cte d'Ivoire 384 Romania 642 Croatia 191 ROW for EU-15 998 Cuba 192 ROW for U.S. 999 Cyprus 196 Russian Federation 643 Czech Rep. 203 Rwanda 646 Czechoslovakia 200 Saint Kitts and Nevis 659 Dem. People's Rep. Of Korea 408 Saint Kitts, Nevis and Anguilla 658 Dem. Rep. of the Congo 180 Samoa 882 Denmark 208 Sao Tome and Principe 678 Dominica 212 Saudi Arabia 682 Dominican Rep. 214 Senegal 686 Ecuador 218 Serbia and Montenegro 891 Egypt 818 Seychelles 690 El Salvador 222 Sierra Leone 694 Equatorial Guinea 226 Singapore 702 Eritrea 232 Slovakia 703 Estonia 233 Slovenia 705 Ethiopia 231 So. African Customs Union 711 EU-15 (Created) 901 Solomon Isds 90 Europe EU, nes 492 Somalia 706 Faeroe Isds 234 South Africa 710 Finland 246 Spain 724 Fmr Ethiopia 230 Special Categories 839 Fmr Rhodesia Nyas 717 Sri Lanka 144 Fmr USSR 810 Sudan 736 Fmr Yugoslavia 890 Suriname 740 France 251 Sweden 752 Free Zones 838 Switzerland 757 FS Micronesia 583 Syria 760 Gabon 266 Tajikistan 762 Gambia 270 TFYR of Macedonia 807 Germany 276 Thailand 764 Ghana 288 Togo 768 Gibraltar 292 Trinidad and Tobago 780 Greece 300 Tunisia 788 Greenland 304 Turkey 792

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151 Grenada 308 Turkmenistan 795 Guadeloupe 312 Turks and Caicos Isds 796 Guatemala 320 Tuvalu 798 Guinea 324 Uganda 800 Guinea-Bissau 624 Ukraine 804 Haiti 332 Unit (Kg.) 2 Honduras 340 United Arab Emirates 784 Hungary 348 United Kingdom 826 Iceland 352 United Rep. of Tanzania 834 Import (*Used only to read data) 1 Uruguay 858 India 699 US Misc. Pacific Isds 849 Indonesia 360 USA 842 Iran 364 USA-New (Created) 902 Ireland 372 Uzbekistan 860 Israel 376 Venezuela 862 Italy 381 Viet Nam 704 Jamaica 388 Western Asia, nes 879 Japan 392 Western Sahara 732 Jordan 400 World 0 Kenya 404 World without EU (Created) 1 Kiribati 296 Yemen 887 Kuwait 414 Zambia 894 Zimbabwe 716

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152 LIST OF REFERENCES Adams, Damian C., J.D. and R.L. Kilmer. Eur opean Union Farm Policy for Citrus, Tomatoes, and Dairy. IATPC Policy Brief Series Pub lication PBTC 03-1, IFAS, University of Florida, Gainesville, FL 32611, January 2003. Amemiya, T. Advanced Econometrics. Cambridge: Harvard University Press, 1985. Anderson, R.W. Some Theory of Inverse Demand for Applied Demand Analysis. Eur. Econ. Rev. 14(1980): 281-90. Armington, P.S. A Theory of Demand for Produc t Distinguished by Place of Production. Staff Papers, International Mone tary Fund 16(1969): 159-76. Barnett, W.A. Theoretical Foundations of the Rotterdam Model. Rev. Econ. Stud. 46(1979): 109-30. Barten, A.P. Family Composition, Pr ices and Expenditu re Pattern. In Econometric Analysis for National Economic Planning. P.E. Hart, G. Mills, and J.K. Whitaker, eds., London: Butterworth, 1964. _____. Maximum Likelihood Estimation of a Co mplete System of Demand Equations. European Economic Review 1(1969): 7-73. _____. Consumer Allocation Model: Choice of Functional Form. Empirical Economics 18(1993): 129-58. _____. The Systems of Consumer Dema nd Functions Approach: A Review. Econometrica 45(1) (1977): 25-51. Barten, A.P. and L.J. Bettendorf. Price Inform ation of Fish: An Application of an Inverse Demand System. European Economic Review 33(1989): 1509-25. Blackorby,C., D. Primont, and R. Russell (BPR). Duality, Separability, and Functional Structure: Theory and Economic Applications. New York: North-Holland, 1978. Bliss, C. Capital Theory and the Distribution of Income Amsterdam: North-Holland, 1975. Burgess, D.F. Production Theory and the Derived Demand for Imports. Journal of International Economics 4(2) (1974a): 103-17. _____. A Cost Minimization Approach to Import Demand Equations. The Review of Economics and Statistics 56(4) (1974b): 225-34. Chambers, R. Applied Production Analysis: A Dual Approach. Cambridge: Cambridge University Press, 1988. _____. Duality, the Output Effect, a nd Applied Comparative Statics. Amer. J. Agr. Econ. 64(1982): 152-56.

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153 Christensen, L.R., D.W. Jorgenson, and L.J. Lau. Transcendental Logarithmic Production Frontiers. Review of Economics and Statistics 55(Feb. 1973): 28-45. _____. Transcendental Logar ithmic Utility Functions. American Economic review 65(1975): 367-82. Clements, K.W. and H. Theil. A Simple Method of Estimating Price Elasticities in International Trade. Econ. Letters 1(1978): 133-37. Davis, G.C. and K.L. Jensen. Two-Stage Utility Maximization and Import Demand Systems Revisited: Limitations and an Alternative. Journal of Agricultural and Resource Economics 19(2) (1994): 409-24. Davis, G.C. and N.C. Kruse. Consistent Estimation of Armington Demand Models. Amr. J. Agr. Econ. 75(1993): 719-23. Deaton, A.S. Models and Projections of Demand in Post-War Britain. London: Chapman and Hall, 1975. _____. Specification and Testing in Applied Demand Analysis. Econ. J. 88(September 1978): 524-36. Deaton, A.S. and J. Muellbauer. An Almost Ideal Demand System. Amer. Econ. Rev. 70(June 1980): 312-26. Diewert, W.E. and C.J. Morrison. Export Supply and Import Demand Functions: A Production Theory Approach. Empirical Methods for International Trade. Robert C. Feenstra ed. Cambridge, MA: MIT Press, 1989. Diewert, W.E. and T.J. Wales. Flexible F unctional Forms and Global Curvature Conditions. Econometrica 55(Jan. 1987): 43-68. EC (European Communities) Commi ssion. The Agricultural Situation in the European Union, 2000 Report, Commission Regula tion (EC) No.1512/2000(2002). ERS-USDA (Economic Research Service-U.S. Depa rtment of Agriculture). Washington, D.C. Vegetables and Melons S ituation and Outlook Yearbook. VGS-2003. July 2003. Website: http://usda.mannlib.cornell.edu/report s/erssor/specialty/v gs-bb/2003/vgs2003.pdf. (Accessed March 2007). _____. Briefing Room: Tomatoes Website: http://ers.usda.gov/briefing/tomatoes/index.htm. (Accessed March 2007). _____. Briefing Room: European Union Website: http://www.ers.usda.gov/Briefing/EuropeanUn ion/basicinfo.htm ( Accessed June 2006). _____. U.S. Tomato Statistics, 19602002. March 2003. Website: http://usda.mannlib.cornell.edu/data-sets/specialty/92010/ (Accessed March 2005).

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154 Fuss, M. The Demand for Energy in Canadian Manufacturing. J. Econometrics 5(1977): 89116. Gorman, W.M. The Demand for Fish, an Applicati on of Factor Analysis, research Paper No. 6, Series A (Faculty of Commerce and Social Science, University of Birmingham), Abstracted in Econometrica 28(1959): 649-50. Gregory, R.G. United States Imports a nd Internal Pressure of Demand: 1948-68. American Economic Review 61(Mar. 1971): 28-47. Hall, B.H. and C. Cummins. TSP Reference Manual, Version 4.5. Palo Alto, California: TSP International, 1998. Hicks, J.R. A Revision of Demand Theory. Oxford: Oxford University Press, 1956. Hotelling, H. Edgeworths Taxation Paradox an d the Nature of Demand and Supply Functions. Journal of Political Economy 40(1932): 577-616. Houthakker, H.S. and S.P. Magee. Income and Price Elasticities in World Trade. Review of Economics and Statistics 51(May, 1969): 111-25. Huang, K.S. The Family of Inverse Demand Systems. Eur. Econ. Rev. 23(1983): 329-37. _____. An Inverse Demand System for U.S. Composite Foods. American Journal of Agricultural Economics 70(4) (1988): 902-909. IATPC (International Agricultural Trade and Policy Center). University of Florida, Gainesville, FL 32611. News on Center Plan Website http://www.iatpc.ifas.ufl.edu/index.php. (Accessed January 2006). IFAS (Institute of Food and Agricultural Scie nces), Food and Resource Economics Department, University of Florida, Gainesville, FL 32611. IATPC Medium Term Plan, 2002-05, July, 2002. Keller, W.J. and J. van Driel. Differential Consumer Demand Systems. Eur. Econ. Rev. 27(April 1985): 375-90. Kohli, U.R. A Gross National Product Functi on and the Derived Demand for Imports and Supply of Exports. The Canadian Journal of Economics 11(2) (1978): 167-82. _____. Technology, Duality and Foreign Trade: Th e GNP Function Approach to Modeling Imports and Exports. New York: Harvester, Wheatsheat, 1991. Laitinen, K. A Theory of the Multiproduct Firm. Amsterdam: North-Holland Publishing Co., 1980. Laitinen, K. and H. Theil. Supply and Demand of the Multiproduct Firm. European Economic Review 11(1978): 107-54.

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155 Lau, L. Profit Functions of Technologies with Multiple Inputs and Outputs. Rev. Econ. And Statis. 54(1972): 281-89. Lee, J.Y., M.G. Brown, and J.L. Seale, Jr. D emand Relationships among Fresh Fruit and Juices in Canada. Review of Agricultural Economics 14(1992): _____. Model Choice in Consumer Analysis: Taiwan, 1970-89. American Journal of Agricultural Economics 76(1994): 504-12. Lee, J.Y., J.L. Seale, Jr., and P.A. Jierwiriya pant. Do Trade Agreements Help U.S. Exports? A Study of The Japanese Citrus Industry. Agribusiness 6(1990): 505-14. Liu, Y., R.L. Kilmer and J.Y. Lee. Canadia n Orange Juice Imports and Production Level Import Demand. Journal of Agribusiness 25(1) (Spring 2007): 17-29. Moschini, G. and A. Vissa. A Linear Inverse Demand System. Journal of Agricultural and Resource Economics 17(2) (1992): 294-302. Mountain, D.C. The Rotterdam Model: An Approximation in Variable Space. Econometrica 56(1988): 477-84. NASS (National Agricultural Statistics Service) United States Department of Agriculture. Orlando, Florida. Annual Statistical Bulletin (2006) Website: http://www.nass.usda.gov/Statistics_by_State/F lorida/Publications/A nnual_Statistical_Bull etin/index.asp (Accessed June 2007). NASS (National Agricultural Statistics Service) United States Department of Agriculture. Washington, D.C. Statistical Highlights (2006). Website: http://www.nass.usda.gov/Publications/Statis tical_Highlights/index.asp (Accessed June 2007). _____. Data and Statistics. Website: http://www.nass.usda.gov/index.asp (Accessed March 2007). Neves, P.D. A Class of Differential Demand Systems. Economics Letters. 44(1994): 83-86. Rossi, N. The Estimation of Product Supply and Input Demand by the Differential Approach. American Journal of Agricultural Economics 66(3) (1984): 368-75. Samuelson, P.A. Prices of Factors and Goods in General Equilibrium. Review of Economic Studies 21(1953-4):1-20. _____. Frank Knights theore m in linear programming. Zeitschrift fur Nationalokonomie 18(1958): 310-317. Satyanarayana, V., W.W. Wilson, and D.D. J ohnson. Import Demand for Malt in Selected Countries: A Linear A pproximation of AIDS. Canadian Journal of Agricultural Economics 47(1999): 137-49.

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156 Seale, J.L. Jr., A.L. Sparks, and B.M. Buxton. A Rotterdam Application to International Trade in Fresh Apples: A Differential Approach. Journal of Agricultural and Resource Economics 17(1) (1992): 138-49. Theil, H. The Information Approach to Demand Analysis. Econometrica 33(1) (1965): 67-87. _____. System wide Explorations in Internat ional Economics, Input-Output Analysis and Marketing Research New York: Amsterdam NorthHolland Publishing Co. 1980a _____. The System Wide Approach to Microeconomics Chicago: University of Chicago Press 1980b. Theil, H. and K.W. Clements. Applied Demand Analysis: Results from System-Wide Approaches. Cambridge MA: Ballinger Publishing Co., 1987. _____. A Differential Approach to U.S. Import Demand. Econ. Letters 1(1978): 249-52. Truett, L.J. and D.B. Truett. The Demand fo r Imports in Korea: A Production Analysis Approach. Journal of Development Economics 56(1998): 97-114. TSP (Time Series Processor). TSP International. Palo Alto, California. Advanced Examples Website: http://www.tspintl.com/examples/index.htm (Accessed March 2007). UN-COMTRADE (United Nations, Statistics Divi sion-Commodity Trade Statistics). New York. Data base Website: http://comtrade.un.org/db/default.aspx (Accessed January 2007) UN-FAOSTAT (United Nations, Food and Agricultu re Organization Statistics). New York. Faostat-Production Data/Trade Data Website: http://faostat.fao .org/default.aspx (Accessed April 2007). U.S. Trade Statistics. Foreign Agricultural Servi ce. United States Department of Agriculture. Washington, D.C. U.S. Trade Imports/Exports-FATUS Commodity Aggregations Website: http://www.fas.usda.gov/ustrade/USTImF atus.asp?QI= (Accessed January 2004). Washington, A.A. and R.L. Kilmer. The Derived Demand for Imported Cheese in Hong Kong. International Food and Agri business Management Review 5(2002a): 75-86. _____. The Production Theory Approach to Im port Demand Analysis: A Comparison of the Rotterdam Model and the Differential Approach. Journal of Agricultural and Applied Economics 34(3) (2002b): 431-443. Wooldridge, J.M. Introductory Econometrics : A Modern Approach Cincinnati, OH: SouthWestern College, 2000. Working, H. Statistical Laws of Family Expenditure. J. Amer. Statist. Assoc. 38(1943): 43-56. WTO (World Trade Organization). Annua l Report WT/TPR/OV/8, November 2002.

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157 Yuhn, K. Functional Separabili ty and the Existence of Cons istent Aggregates in U.S. Manufacturing. Internat. Econ. Rev. 32(1991): 229-50. Zellner, A. An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. J. Amr. Stat. Assoc. 57(1962): 348-68.

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158 BIOGRAPHICAL SKETCH Born in 1955 to the parents Mr. Md. Lal Mi ah and Mrs. Amena Khatoon, Mohammad Ali started his elementary educati on in his own village at Puthai Primary School under the police station (P.S.) of Brahmanbaria in the former dist rict of Comilla, Bangladesh. After third grade, his youngest (among five) maternal uncle, Dr. Nu rul Islam Bhuiyan (a national physician) of village South Tarua under the same P.S. and district, brought him to a better school called Khulapara Primary School from where he got a n on-residential scholarship in fifth grade in 1965. Then, he started his high school studies at Brahmanbaria Annada Government High School from where he got a residential scholarship in eighth grade and stood sixth position in the Humanities Group with a distinction in mathem atics in Secondary School Certificate (SSC) examination (tenth grade) under the Comilla Board in 1970. After that the financial crisis of his family compelled him to study at Jhenidah Cadet College (free of cost) under the district of Jessore, Bangladesh from where he passed his Higher Secondary Certificate (HSC) examination (twelfth grade) in first division with a board sc holarship in 1972. During his studies at Jhenidah Cadet College, he joined the Liberation War, wa r of independence of Bangladesh from Pakistan in 1971. As a freedom fighter, he fought in Sector 3 along with the regular army unit and also as a group commander. Then, Mr. Ali started his university studies and took his bachelors and masters degrees in economics in 1977 and 1979 respectively from the Un iversity of Dhaka, Bangladesh. He also took an M.B.A. degree from the same university in the year 1982. During his studies at the university, he worked on a part-time basis in Dhak a university library as a Library Assistant and in Bangladesh Biman, the national airlines as a Junior Sales Assistant. He had to do that for supporting himself and the family consisting of three younger brothers and two sisters and the parents since his father, a government employee (Block Supervisor) in Agricultural Extension

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159 Division, lost his job as a result of prolonged absence from duty due to illness (throat cancer, surviving 19 years after detection until 1992 when he died). Then, he served as an Assistant Statistical Officer, Assistant Dire ctor and Financial Analyst in the central bank of his country, the Bangladesh Bank for about 6 years. After that, he worked as a member of the Bangladesh Civil Service (BCS) Cadre in the Audit and Account s Department for more than 12 years. Before starting his further studies at the University of Florida (UF), Gainesville, Florida, he was in the position of a Deputy Accountant General of Bang ladesh. He earned his M.S. in Food and Resource Economics from the University of Florida in 2000. Mr. Ali started his higher studies in the Unite d States (U.S.) with an intention of going back to his country to continue his superior service job from wh ere he was on leave without pay. He got admitted for higher studies with a verb al consent of his superior authority, but the authority did not want him to finish his M.S. To wards the close of his M.S., he was called back to return and join his job within 2 weeks and after that they actually fired him. So, he decided to continue his studies after M.S. and stay in the U.S. permanently without going back to Bangladesh. Thus, he enrolled in the Ph.D. program of the same department at UF. While pursuing his Ph.D., he has been teaching micr oeconomics and macroeconomics at the under graduate level in the University of Maryland Ea stern Shore (UMES) since spring 2004 as a parttime Lecturer.