The Effects of Exchange Rate and Commodity Price Volatilities on Trade Volumes of Major Agricultural Commodities

Home , Commodity market

A K Iftekharul Haque

A Thesis

presented to

The University of Guelph

In partial fulfillment of requirements

for the degree of

Master of Science

Food, Agricultural and Resource Economics

Guelph, Ontario, Canada © A K Iftekharul Haque, September 2012

ABSTRACT

The Effects of Exchange Rate and Commodity Price Volatilities on Trade Volumes of Major Agricultural Commodities

A K Iftekharul Haque Advisor: University of Guelph, 2012 Professor Getu Hailu

This thesis examines the effects of price and exchange rate volatilities on the volume of trade corn, soybean, wheat and rice. Empirical results indicate that price volatility and exchange rate volatilities do not have effects on Canada’s export of wheat and soybean, and Canada’s import of corn and rice. This thesis also examined the effects of exchange rate and commodity price volatilities on developed countries’ trade and developing countries’ trade separately. Results show that trade between developing countries is more sensitive to exchange rate and commodity price volatilities than trade between developed countries.

Acknowledgements

I would first like to thank my advisor, Dr. Getu Hailu, for countless reasons. His mentorship, continuous support and extreme level of patience throughout my research have been sources of encouragement for my professional and personal development. I would like to thank Professor Karl Meilke for agreeing to be in my advisory committee even after his retirement. I undoubtedly benefited from his vast knowledge of international trade policy. I am grateful to Professor Alan Ker, another member of my advisory committee, not only for his invaluable guidance but also taking care of all other issues of mine during my stay at the Department of Food, Agricultural and Resource

Economies. I would also like to thank all the faculty members and staffs of the

Department of Food, Agricultural and Resource Economics, for guidance throughout the coursework and completion of my thesis.

My sincere gratitude goes to the Canadian Agricultural Trade Policy and

Competitiveness Research Network (CATPRN) for providing me with the finances necessary for this research.

I would also like to thank my peer group for their continuous support to my work.

Notably Xin Xie, Rebecka Elskamp, Alex Cairns, Rob Anderson, Zongyuan Shang,

Johanna Wilkes, Tor Tolhurst and Di Ai for their valuable advice, support and criticism.

I would like to thank my parents for their unconditional love; and my wife, Tasnuva, for her extreme patience and encouragement to my work. Finally I must thank my son,

Shoummo, for being a source of joy and happiness.

iii Table of Contents

ACKNOWLEDGEMENTS III

TABLE OF CONTENTS IV

LIST OF TABLES VI

LIST OF FIGURES VII

CHAPTER 1: INTRODUCTION 1

1.1: Background 1

1.2 Economic Problem 3

1.3 Economic Research Problem 3

1.4 Purpose and Objectives 5

CHAPTER 2: RECENT TRENDS OF EXCHANGE RATES AND COMMODITY PRICES 6

2.1 Exchange Rate Volatility 6

2.2 Agricultural Commodity Price Volatility 8

2.3 Drivers of Agricultural Commodity Price Volatilities 13

2.4. Chapter Summary 20

CHAPTER 3: LITERATURE REVIEW 21

3.1: Effects of Exchange Rate Volatilities: Theoretical Background 21

3.2 Measuring Exchange Rate and Price Volatilities 22

3.3 Empirical Literature: Exchange rate Volatility and Trade 24

3.4 Empirical Literature: Exchange Rate Volatility and Agricultural Trade 26

3.5 Chapter summary 28

CHAPTER 4: CONCEPTUAL FRAMEWORK 29

4.1 Model Description 29

iv 4.2 Import Demand 29

4.3 Chapter Summary 33

CHAPTER 5: EMPIRICAL FRAMEWORK 34

5.1 Econometric specification 34

5.2 Variable Description 35

5.3 Data and sources 40

5.4 Model Selection 44

5.5 Diagnostics: Tests for Unit root, Heteroscedasticity, Serial Correlation and Multicollinearity 47

5.6 Chapter Summary 49

CHAPTER 6: RESULTS AND DISCUSSIONS 50

6.1 Introduction 50 6.2.1 Quarterly Imports of Wheat and Soybean from Canada 50 6.2.2. Quarterly import models of corn and rice 56

6.3 Annual Models 62 6.3.1 Top developed importers’ imports from Developed exporters 62 6.3.2 Top developing importers’ imports from developing exporters 69

6.4 Chapter Summary 75

CHAPTER 7: SUMMARY AND CONCLUSION 76

7.1 Summary 76

7.2 Policy implications 79

7.3 Limitations and further research 81

7.4 Research Contribution 82

REFERENCES 83

APPENDIX A 87

APPENDIX B 89

List of Tables

Table 2.1: Evolution of Exchange rate Arrangements, 1996-2007 7 Table 5.1: Summary of export and import data for quarterly models 41 Table 5.2: Summary of import data for annual models 41 Table 5.3: Summery of Data Frequency and Sources for exchange rate, GDP prices 42 Table 5.4: List of Countries for Quarterly Models 42 Table 5.5: List of importing countries considered for annual models 43 Table 6.1: Fisher’s unit root test for wheat and soybean 51 Table 6.2: VIF for wheat and soybean model 53 Table 6.3: Coefficient estimates of quarterly wheat and soybean imports from Canada from 2000 to 2009 55 Table 6.3a : Coefficient estimates of quarterly wheat and soybean imports from Canada from 2000 to 2009 (without expected price variable) 56 Table 6.4: Fisher’s unit root test for corn and rice model 57 Table 6.5: VIF for corn and rice model 59 Table 6.6: Coefficient estimates of Canada’s corn and rice import demand from 2000-2009 60 Table 6.7: Coefficient estimates of Canada’s corn and rice import demand from 2000-2009 (without percentage change of expected price) 61 Table 6.8: Fisher’s panel Unit Root Test 63 Table 6.9: Hausman Specification tests 64 Table 6.10: Friedman’s test for cross sectional independence 64 Table 6.11: Variance Inflation Factors 65 Table 6.12 : Wooldridge test for serial correlation 65 6.13: Coefficients estimates of developed countries’ wheat, soybean, corn and rice imports from developed importers from 1991 to 2009 67 6.13a: Coefficients estimates of developed countries’ wheat, soybean, corn and rice imports from developed countries from 1991 to 2009 (without percentage change of expected price) 68 Table 6.14: Fisher’s panel Unit Root Test 69 Table 6.15: Hausman’s Specification tests 70 Table 6.16: Friedman’s test for cross-sectional independence 71 Table 6.17: Variance Inflation Factors 71 Table 6.18 : Wooldridge test for serial correlation 72 6.19: Coefficient estimates of developing importers’ imports of wheat, soybean, corn and rice from developing exporters from 1991 to 2009 73 6.20: Coefficient estimates of developing importers’ imports of wheat, soybean, corn and rice from developing exporters from 1991 to 2009 (without percentage change of expected price) 74

List of Figures

Figure 2.1: Exchange Rate movements of major currencies 7 Figure 2.2a: Monthly Corn price (F.O.B) in selected market from January 2000 (USD/Ton) 8 Figure 2.2b: Historical volatility of corn price 9 Figure 2.3a: Monthly wheat price (F.O.B) in selected market from January 2000 (USD/Ton) 10 Figure 2.3b: Historical volatility of wheat price 10 Figure 2.4a: US Soybean monthly F.O.B. Price from January 2000 11 Figure 2.4b: Historical volatility of soybean price 11 Figure 2.5a: Monthly rice price (F.O.B) in selected market from January 2000 to January 2012 (USD/Ton) 12 Figure 2.5b: Historical volatility of rice price 12 Figure 2.6: Global Ethanol Production (in million Gallons) 14 Figure 2.7 : Share of US Corn used to produced ethanol, 1980-2011 14 Figure 2.8: Monthly Volume of Future Trades of Wheat, Maize and Soybeans at Chicago Board of Trade (CBOT) 15 Figure 2.9: Per Capita Income Level by Developing Region 17 Figure 2.10: GDP Per Capita of India and China (Constant US Dollar) 17 Figure 2.11 a: Major Exporters of Maize in 2008 18 Figure 2.11 b: Major Exporters of Wheat in 2008 18 Figure 2.11 c: Major Exporters of Rice in 2008 19 Figure 2.12: Stocks to cereal use ratio 20

vii

Chapter 1: Introduction

1.1: Background

Agricultural commodity price and exchange rate volatilities drew a global attention because of their potential effects on international trade and domestic food prices.

Although the effects of exchange rate volatilities on international agricultural trade have been examined for long time, the effects of price volatilities have not been examined at large. Most of the recent studies (IFPRI, 2011,; Braun and Tadesse, 2012; Weersink et al

2008; OECD and FAO, 2012 ) on commodity price volatilities reviewed the reasons for agricultural commodity price volatility. The purpose of this study is to examine the effects of both exchange rate and commodity price volatilities on international agricultural commodity trade and to estimate the effects of volatilities on developed and developing countries separately.

Effects of exchange rate volatilities on trade flows became a center of interest from early 1970 when a floating exchange rate regime began to replace the former fixed exchange rate regime. The floating exchange rate system allows the value of a currency to fluctuate based on the foreign exchange market fundamentals. Smith’s (1999) review show that a number of studies were conducted to determine the impact of exchange rate volatilities on trade flows, and find that the empirical evidence is mixed. For example,

Cushman (1983), Thursby and Thursby (1987) and Bini-Smaghi (1991) find that an increase in exchange rate volatility leads to a reduction in the volume of international trade. In contrast, Frankel and Wei (1995) and Sercu and Uppal (2003) claim that exchange rate volatilities may not have any effect on the volume of international trade.

1 While the effect of exchange rate volatility is still uncertain, agricultural commodity price volatility has recently received much attention after the unprecedented spike in crop prices and volatilities that occurred in 2007-08. The rise in the level of commodity prices and volatilities resulted in a number of countries adopting policies that restricted food imports and exports (IFPRI, 2011).

Commodity price volatility may have implications for the volume of agricultural

commodity trade when individual countries adopt policies that restrict imports or exports

(e.g., export bans) as a method of coping with price variations. Although the

consequences of exchange rate volatility on trade have extensively been examined and at

the centre of debate, research on the effects of commodity price volatility on international

trade (e.g., on volume) is limited. Volatility in the world market prices can have major

effects on agricultural trade since agricultural products and agricultural industry have

many characteristics, such as perishable nature of products and less supply

responsiveness to short term price fluctuation that distinguished them from other

industries. Uncertainty in the world agricultural market has a greater impact on farm income

in both developed and developing countries (Koo and Kennedy, 2007) and food security in

developing and low income countries (IFPRI, 2011).

In this study, I examine the effects of price and exchange rate volatilities on Canada’s

trade with its major trading partners using quarterly data for wheat, soybean, corn and

rice, and examine the effects of exchange rate and price volatilities on developed and

developing countries’ trade separately with annual models.

1.2 Economic Problem

Increased volatilities of exchange rate and commodity prices increase uncertainties over expected profit of firms (Hooper and Kohlhagen, 1978; Clark, 1973; IFPRI. 2011). Clark

(1973) argues that exporting firms reduce exports and charge higher price as risk premium when they expect such uncertainties over profit. The rise in price due to the risk premium directly affects consumers’ surplus (Bellemare et al. 2011). When importers decrease imports due to volatilities of exchange rate and commodity prices, excess demand decreases in international market and reduces the price of commodities in the international market which affects producers’ surplus of exporting countries. As a result, both consumers and producers in countries engaged in agricultural trade can be affected because of volatilities in exchange rate and commodity prices. The findings of this research will be useful for agricultural trading firms of both developed and low income countries; and central banks and trade ministries of low income countries.

1.3 Economic Research Problem

A number of studies examined the effects of exchange rate volatilities on commodity trade flows using aggregate data 1 (Akhtar and Hilton, 1984; Arize, 1995; Arize, 199;

Arize and Ghosh, 1997; Bahmani-Osookee, 2002; Chowdhury, 1993; Gotur 1985) and bilateral trade data (Bini-Smaghi, 1991; Cushman, 1983; Dell’ Ariccia, 1999; Hooper and

Kohlhagen, 1978; McKenzie and Brooks, 1997; Thursby and Thursby; 1997). Most of these studies examined the effect of exchange rate volatility on overall trade flows (i.e.,

1 measures the trade flow of a nation to all of its trading partners or to the rest of the world

3 total of trade in all sectors) rather than trade flows of a specific sector (e.g., agriculture) or specific commodity (e.g., wheat, corn). Sector specific studies mostly attempted to estimate the effects of exchange rate volatilities on trade of manufacturing goods (Di Vita and Abott, 2004; Klein, 1990; Maskus, 1986; Belanger et al. 1992, Chou, 2000). Only a few studies estimated the effects exchange rate volatilities on agricultural commodity trade flows (Cho et al. 2002, Sun et al. 2002, Kandilov, 2008; Giorgioni and Thompso,

2002, Villanueva and Sarker 2009). However, most of these studies (Cho et al 2002,

Kandilov 2008; Giorgioni and Thompson, 2002) used aggregated agricultural commodity trade data of countries. Research on the effects of exchange rate volatility on specific agricultural commodities is limited.

Meanwhile, research on the effects of commodity price volatilities on trade flows is also limited. Despite a few recent studies (Raddatz, 2011; FAO et al. 2011; Weersink et al. 2008, Wright, 2011) that a reviewed the effects of food price volatilities on food security, the effects of commodity price volatilities on trade flows remain unaddressed.

Zhang (2010) is one of the first studies to examine the effects of exchange rate, price and freight cost volatilities on the U.S. soybean exports.

This study explores the effects of both price and exchange rate volatilities on

Canada’s wheat, corn, soybeans and rice trade using quarterly data for the period 1999:1-

2010:4. It also examines the effects of exchange rate and commodity price volatilities on import demand of major developed and developing importers of wheat, soybean, corn and rice.

4 1.4 Purpose and Objectives

The purpose of this research is to estimate the effect of commodity price and exchange rate volatilities on Canada’s trade flows of wheat, corn, soybeans and rice.

The specific objectives are:

1. To estimate the effect of exchange rate and agricultural commodity price

volatilities on Canada’s exports of wheat and soybean; and Canada’s imports of

corn and rice.

2. To examine the effects of commodity price and exchange rate volatilities on the

agricultural commodity imports of developed and developing countries

separately.

After this brief introduction, chapter 2 presents recent trends of exchange rates and commodity prices, chapter 3 reviews the key literature, chapter 4 discusses the theoretical framework, chapter 5 presents the empirical framework of the study, chapter 6 provides the estimates of parameters of the regression models and finally chapter 7 provides summaries and conclusions.

5 Chapter 2: Recent Trends of Exchange Rates and Commodity Prices

2.1 Exchange Rate Volatility

From the early 1970s a floating exchange rate regime began to replace the former fixed exchange rate regime which was also known as Bretton Wood System. Most of the developing countries continued to peg their currencies either to a single important currency, e.g., the U.S. Dollar, or to a basket of currencies. For example, in 1975, 87% of the developing countries had some types of fixed exchange rate system. Later, countries gradually moved from fixed to a floating exchange rate regime (see Table 2.1).

Appendix A provides a more detailed list of countries according to their exchange rate system.

In a floating exchange rate system, exchange rates are determined by the demand and supply of currencies in the foreign exchange market. At the beginning of the floating exchange rate regime, exchange rates of major currencies experienced increased fluctuations (Clark 2004). The fluctuations of major currencies under floating exchange rate system made researchers and policy makers concerned about its potential effect on international trade.

Figure 2.1 Shows the exchange rates of major currencies over the last four decades. It is obvious from Figure 2.1 that key currencies of the world became unstable after the adoption of floating exchange rate regime.

6 Table 2.1: The Evolution of Exchange rate Arrangements, 1996-2007

Fixed Arrangements Floating Arrangements Total Number of Year (Number of Countries) (Number of Countries) Countries

1996 124 60 184

2001 93 93 186 2002 95 92 187

2003 94 93 187

2004 94 93 187 2005 98 89 187

2006 105 82 187

2007 105 83 188 Source: IMF (2007) Note: End of period data.

Figure 2.1: Exchange Rate movements of major currencies CAD/USD CAD/EUR

1.7 1.8 1.7 1.5 1.6 1.3 1.5 1.4 1.1 1.3 1.2 0.9 1.1 0.7 1

9 1 3 5 5 7 7 9 9 1 0.5 4 0 06 0 08 n-99 p-9 p-0 p-0 y-0 p-0 y- p-0 y- n-0 p-0 n-11 p-1 Ja e ay-00Jan-01e ay-02Jan-03 Jan- Jan- Ja e ay-10Ja e ay-12 8 0 4 8 0 2 4 7 9 5 9 S M S M Se Ma Se Ma Se Ma S M S M -7 -8 -8 -8 -9 -9 -9 95 9 -9 -07 0 n-71 n ec- g-03 n Ja Oct-76Sep Jul-82Jun Apr Feb Ja ov- Oct Sep-01 Jul-0Ju Apr-11 Dec-72Nov-74 Aug May-86 Mar D N Au May-

CAD/GBP USD/GBP 3 3

2.5 2.5 2 2 1.5

1.5 1

1 0.5 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11 ------Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan

Source: Thompsons-Reuters Datastream

7 2.2 Agricultural Commodity Price Volatility

Agricultural commodity price volatility drew much attention of economists, policymakers and media since the food price hike of 2007-2008(IFPRI, 2011). The food price upheaval experienced in 2007-08 was not observed since the early 1970s (Weersink et al. 2008).

Although the food price hike of 2007-08 and the most recent in 2011 were below the historical highest of 1970s, price volatility reached its highest level in the past 50 years

(IFPRI, 2011). This section briefly reviews the price fluctuations of major agricultural commodities over the last decade for four major agricultural commodities- corn, wheat, soybean and rice.

Corn

The food price crisis in 2007-08 began with a sharp rise in price of corn among the major agricultural commodities. Figure 2.2a shows that the level of corn price of major exporters started to rise from June 2006 and reached the peak in July 2008. It began to decrease after July 2008 and again reached a new peak in mid-June, 2011. Figure 2.2b suggests that corn price volatility also increased dramatically in 2007 and higher volatilities continued.

Figure 2.2a: Monthly Corn price (F.O.B) in selected market from January 2000 (USD/Ton)

Source: FAO GIEWS Database

Figure 2.2b: Historical volatility of corn price

45 40 35 30 25 20 15 10

Historical VolatilityHistorical (%) 5 0 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

Corn price volatiltiy

Source: CME group

Wheat

Wheat Prices quoted by the major international suppliers also became volatile from mid-

2007 (Figure 2.3b). From June 2006 wheat price in all major international markets started to increase and reached to record high level at 450 USD per ton in March 2008.

Then it started to fall until December 2008 and went through a volatile period until it reached USD 350 per tom in March 2011 (Figure 2.3a). Figure 2.3b shows the volatility of wheat price over last two decades. The figure reports that wheat price volatility began to increase from 2006. From 2007 to 2008, historical volatility of wheat price increased from 32.4% to 50.6%. Although it came down to 35% in 2010, it began to increased again in 2011.

Figure 2.3a: Monthly wheat price (F.O.B) in selected market from January 2000 (USD/Ton)

600 500 400 300 200 100 0 00 01 01 02 02 03 03 04 05 05 06 06 07 08 08 09 09 10 10 11 12 ------Jul Jul Jan Jan Jun Jun Oct Oct Apr Feb Sep Feb Dec Dec Aug Aug Nov Mar Mar May May

Argentina USA (no.2 red soft) USA (no.2 red hard

Source: FAO GIEWS Database

Figure 2.3b: Historical volatility of wheat price

Historical Volatility(%) Historical 10

0 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

Wheat price volatility

Source: CME group

Soybean

Soybean price began to rise slightly from fall 2006 but did not rise to the extent of corn price. It showed an upward trend from May 2007 and peaked at the level of 550USD/ton in June 2008 (Figure 2.4a). Soybean price volatility reported in figure 2.4b shows that like other major crops soybean price volatility also increased after 2006.

Weersink et al. (2008) report that the record high price of soybean was a spillover from the surge in corn price. The soybean price rise can also be attributed to the surge in demands of edible oil and reduction of soybean harvest. This decline of soybean production was not due to bad weather condition rather largely to decline in planted area in US as farmers shifted to corn from soybean. Volatility reached the peak in 2009 and then it began to come down (figure 2.4b).

Figure 2.4a: US Soybean monthly F.O.B. Price from January 2000

600 550 500 450 400 350 300 250 200 150 100 2000M11 2001M04 2001M09 2002M02 2002M07 2002M12 2003M05 2003M10 2004M03 2004M08 2005M01 2005M06 2005M11 2006M04 2006M09 2007M02 2007M07 2007M12 2008M05 2008M10 2009M03 2009M08 2010M01 2010M06 2010M11 2011M04 2011M09

US Soybean Price

Source: FAO GIEWS Database

Figure 2.4b: Historical volatility of soybean price

45 40 35 30 25 20 15 10 5 0 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

Soybean price volatility

Source: CME group

11 Rice Price hike of rice started little later compared to other agricultural commodities. Rice prices in major international sources were somewhat stable until the beginning of 2007.

Then it shows a slightly upward trend until the beginning g of 2008 and jumped to USD

1000 per ton in mid-2008 (Figure 2.5a). Figure 2.5b shows that rice price volatility was low until 2007. Between 2007 and 2008 volatility increased from 16% to 34%. It came down to 20% in 2009 but started to rise again after 2010.

Figure 2.5a: Monthly rice price (F.O.B) in selected market from January 2000 to January 2012 (USD/Ton)

1400 1200 1000 800 600 400 200 0 00 02 04 06 08 10 12 00 01 02 03 04 05 06 07 08 09 10 11 ------Jan Jan Jan Jan Jan Jan Jan Sep Sep Sep Sep Sep Sep May May May May May May

Pakistan - Rice (25% broken) - Export Thailand: Bangkok - Rice (Thai 100% B) USA - Rice (U.S. California Medium Grain)

Source: FAO GIEWS Database

Figure 2.5b: Historical volatility of rice price

40 35 30 25 20 15 10 5 Historical volatilityHistorical (%) 0

4 0 93 9 99 0 05 06 9 0 1991 1992 19 19 1995 1996 1997 1998 1 20 2001 2002 2003 2004 2 20 2007 2008 2009 2010 2011

Rice price volatility

Source: CME group

2.3 Drivers of Agricultural Commodity Price Volatilities

This section briefly discusses the reasons of recent agricultural commodity price volatilities from the recent literature.

Biofuel Policies

World ethanol production skyrocketed in the last decade from around 2000 million gallons in 2001 to more than 13000 gallon in 2010 because of the subsidization and biofuel mandates set by the United States and European Union (Figure 2.6). The primary motivation for biofuel support is that biofuels will reduce demand for imported oil. To comply with the mandate and support, farmers switched to production of biofuel crops , most of which are also used as food or feed. Figure 2.7 shows that in recent years more than 40 % of US maize is used for ethanol production. Moreover, input demand for biofuel crops increased recent years which contributed to the overall increase of cost of agricultural inputs (IFPRI, 2011).

Production of biofuel crops strengthens the links between two highly volatile

markets- energy market and food market (IFPRI, 2011). Since ethanol is the substitute of

fuel, when the price of one barrel of fuel increases, the demand for ethanol, a substitute

product of fuel, also increases. This eventually increases the demand and consequentially

the price for corn (Weersink et al. 2008).

Figure 2.6: Global Ethanol Production (in million Gallons)

14,000 12,000 10,000 8,000 6,000 4,000 2,000 -

0 1 2 3 5 6 7 8 9 1 2 3 4 5 7 8 9 0 9 9 9 9 9 0 0 0 0 0 0 99 99 00 01 19 19 1 199 1994 19 19 19 1 199 2000 20 20 20 2 200 2006 20 20 20 2

Ethanol Production (million gallons) Source: US Department of Energy

Figure 2.7 : Share of US Corn used to produced ethanol, 1980-2011

50.00

40.00

30.00

20.00

10.00

0.00

0 6 0 4 6 0 4 6 8 0 8 8 9 9 9 0 0 0 0 1 9 9 9 9 9 0 0 0 0 0 1 1982 1984 1 1988 1 1992 1 1 1998 2 2002 2 2 2 2

% of Corn used for Ethanol

Source: US Department of Energy

Speculation

Agricultural Commodity Future Trading was identified as one of the drivers of recent

volatility in agricultural commodity markets (IFPRI, 2011). After 2005, monthly volume

of futures trading of wheat, soybean and maize increased dramatically (Figure 2.8).

Futures trading for all three mentioned agricultural commodities continued to rise during

2010-11 also. IFPRI (2011) reports that although investors have increased their trading of

14 food commodity futures, only two per cent of these futures contract have resulted in delivery of real goods. For example, the volume of futures traded on exchange worldwide for maize is more than three times greater than the global production of maize.

Commodity index fund have became attractive for the investors as investment fund flowed from the equity market, to real estate and now to the commodity markets. This pattern of increasing agricultural commodity futures trading and higher prices for commodity futures can worsen the volatility of spot prices for food commodities to excessive levels (IFPRI, 2011).

Figure 2.8: Monthly Volume of Future Trades of Wheat, Maize and Soybeans at Chicago Board of Trade (CBOT)

Source: IFPRI (2011)

Speculative behavior by governments (i.e., export bans, large stock orders) has also played a role in increasing the volatility in agricultural commodity market. A number of countries adopted supply restraint policies at the beginning of the high price volatilities in

2007. For example, rice export was banned by India, Vietnam, Egypt and Cambodia; and

Argentina and Ukraine banned export of wheat. This supply cut from the major suppliers in the global grain market fueled the price volatilities of agricultural commodities even more.

15 Aside from supply restraint policies, foreign buyers started to stockpile food grains in response to food crisis and riots. Countries started to order for larger orders rather than purchase one or two month’s supply at a time regardless of price and scarcity of food grain (Weersink et al. 2008). This kind of speculative purchasing has also contributed to price spike and volatilities in agricultural commodity market (BIAC,

2011).

Demand from Developing Countries

In recent years, several developing countries experienced rapid economic growth . As a result of per capita income increase (Figure 2.9), consumers of developing countries are enjoying more purchasing power which ultimately results in increased demand of commodities.

Figure 2.10 shows the dramatic increase of per capita GDP in China and India.

Because of Spectacular economic growth in developing countries a big portion of their

population came out of poverty and demanding more grains. On the other hand, because

of the increased income, middle and upper income population of those countries shifted

their demand from grain to other high valued commodities such as meat, dairy, fruits,

vegetables and fish. The rise in demand for meat, in turn, boosts the demand for grains to

feed animals (Weersink et al 2008). As a result, it contributes to increase of food price.

Figure 2.9: Per Capita Income Level by Developing Region

3500 3000 2500 2000 1500 1000 500 0 Sub-Saharan Middle East and Southeast Asia South Asia East Asia Africa North Africa

1995 2020

Source: IFPRI Impact Simulation

Figure 2.10: GDP Per Capita of India and China (Constant US Dollar)

3000

2500

2000 China 1500 India 1000

500

0 1970 1975 1980 1985 1990 1995 2000 2005 2010

Source: WDI Database

Climatic Factors

Climate factors also contributed to the price volatilities in 2007-08 and again 2010.

Export markets for major agricultural commodities are highly concentrated. For example,

in 2008, 84% of maize was exported by only 5 countries, top five exporters of wheat

17 exported 63% of total wheat exports and 85% export share of milled rice were held by top 5 rice exporters (Figure 2.11a, 2.11b and 2.11c). Because of this high level of concentration, the world’s capacity to cope with shocks became limited (IFPRI, 2008).

Any incidence of poor weather in the major exporting countries or other types of production shocks immediately affect the international price and price volatilities. For example, wheat crop failure due to drought in Australia in 2008 and Russian federation in

2010 brought strong market reaction and soaring price.

Figure 2.11 a: Major Exporters of Maize in 2008

ROW India 16% 4% US France Argentina 6% Brazil France Brazil US India 6% 53% Others Argentina 15%

Source: FAOSATAT Database Figure 2.11 b: Major Exporters of Wheat in 2008

18 US US ROW 23% 37% France Canada France Russian Federation 12% Argentina Argentina Russian Canada Others 7% Federation 12% 9%

Source: FAOSATAT Database

Figure 2.11 c: Major Exporters of Rice in 2008

ROW

15% US Thailand Thailand 7% 37% Vietnam Pakistan India India 10% US Pakistan Others Vietnam 11% 20%

Source: FAOSATAT Database

Stocks of Cereals

Global stocks of cereal, measured as stocks to cereal use, came down to historically low level in 2007-08 and from then it always remains around 21 whereas before 2003-04 it used to remain more than 30. IFPRI (2011) reports that stock to use ratios of wheat were always low during the price spikes in of wheat in the 1970s, 1995-96, 2007-08 and 2010-

19 11. The current level of stocks to cereal use made the cereal market very vulnerable to any shock. A small dip in grain stocks may lead to major volatility in world cereal market. Wright (2012) argues that when stocks are already tight a minor shock can have major consequences on prices of agricultural commodities.

Figure 2.12: Stocks to cereal use ratio

27.0

25.0

23.0

21.0

19.0

17.0

15.0 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13

Source: FAO Cereal Supply and Demand Brief

2.4. Chapter Summary

This chapter provides a brief overview of the recent trend of exchange rate of major currencies, price of major agricultural commodities and their volatilities. The descriptive statistics presented in this chapter suggests that volatilities of both exchange rate and commodity price increased in recent years. Therefore, it is important to investigate their effects on agricultural commodity trade. The next chapter provides a review of key literature on effects of exchange rate volatility and commodity price volatility on international trade.

20 Chapter 3: Literature Review

3.1: Effects of Exchange Rate Volatilities: Theoretical Background

Attention to exchange rate volatilities was first drawn after switching of the major currencies of the world to a floating exchange rate system from the previous fixed regime in 1973. Early theoretical contribution (Ethier, 1973) on effects of exchange rate volatilities on trade flows asserts that exchange rate volatilities have a negative effect on volume of trade if traders do not have idea about how exchange rate volatilities will affect their expected profit.

Clark (1973) argued that exposure to exchange rate volatility creates risk over firms’ profit and to insulate themselves from uncertainty over profit due to exchange rate volatilities firms tend to reduce trade. A competitive firm (i) with no market power , (ii) producing only one commodity which is sold entirely to one foreign market, (iii) receiving payments in foreign currency, (iv) operating in a condition where hedging through forward sales of the foreign currency export sales is not possible; and (v) unable to alter its output in response to favorable or unfavorable shifts in the profitability of its exports arising from movements in the exchange rate is adversely affected by greater volatility in the exchange rate. This leads to a reduction in output, and hence in exports, in order to reduce the exposure to risk (Clark, 1973).

However, in the presence of hedging possibility, the effect of exchange rate volatility largely depends on the firm’s response to risk and uncertainty. If traders are risk averse, an increase in exchange risk will unambiguously reduce the volume of trade whether the risk is borne by importers or exporters. It also depends on who bears the risk.

21 If importers bear the risk, the price will fall as import demand falls, whereas if exporters bear the risk, the price will raise as exporters charge an increasingly higher risk premium

(Hooper and Kohlhagen, 1978). With an inelastic supply (marginal cost) curve, a shift of aggregate import demand (and thus marginal revenue) to the left caused by an increase in exchange risk to the importer, will result in a relatively large drop in price and a relatively small drop in quantity. If exporters bear the risk and face inelastic demand for their output, an increase in exchange risk will shift their supply to the left and induce a relatively large increase in price and a small decrease in quantity.

Exposure of unforeseen movements in exchange rate is low in advanced economies where well developed forward market exists, i.e. a specific transaction can be easily hedged (IMF, 2004). However, such markets do not exist for the currencies of most developing and low income countries.

Opposite view of the effects of exchange rate volatilities on trade flows is also available in literature. Increased exchange rate volatility positively affects the value of exporting firms through the price and volume impacts of exchange rates, and also makes an exporting strategy more attractive relative to the direct investment. As a result, exchange rate volatility can be positively related to investment in export production capacity (Sercu and Vanhulle, 1992).

3.2 Measuring Exchange Rate and Price Volatilities

The methods of measuring exchanged rate volatilities went through a process of evolution in last three decades. However, still no single process dominates the approximation of exchange rate volatilities. The most commonly use measure of exchange rate volatilities are measure of variance. However the construction of the

22 measure of variance widely differed in from study to study (Bahmani-osokee and Hegerty

2007). Few approaches of measuring volatilities are historical volatility, implied volatility, rolling window, within period volatility, moving standard deviation, General

Autoregressive Conditional Heteroskedasticity (GARCH) etc.

Here we will briefly discuss different measures of volatilities used in exchange rate and trade related research in recent years.

The standard deviation of daily observations of the nominal exchange rate during each three-month period was one of the first measures of exchange rate volatilities in empirical literature (Akhter and Hilton, 1984). Later studies adopted the moving standard deviation of the monthly change in the exchange rate to measure the exchange rate volatilities (Kenen and Rodrik, 1986; Cushman, 1986; Chowdhury, 1993). This method had some advantage over other contemporary methods for being stationary.

Autoregressive Conditional Heteroskedasticity (ARCH) became very popular in measuring exchange rate volatilities afterwards. ARCH is a measure of volatility in time series errors. ARCH models assume the variance of the current error term to be a function of the actual sizes of the previous time periods' error terms. A number of studies (Arize et al. 2005; Cho et al. 2002; McKenzie and Brooks, 1997) used ARCH process to measure the exchange rate volatilities.

A further extension of ARCH process is Generalized Autoregressive Conditional

Heteroskedasticity (ARCH) which incorporates moving average process. GARCH became popular in measuring exchange rate volatilities in recent years.

23 3.3 Empirical Literature: Exchange rate Volatility and Trade

The findings of empirical literature on the effect of exchange rate volatilities on trade flows are mixed and not conclusive. This section briefly discusses the findings of some empirical studies on this issue.

From the earlier empirical studies it was evident that exchange volatilities have a negative impact on exports (Akhter and Hilton, 1984). The study used a polynomial distributed lag method in their OLS estimate of the effects of exchange rate volatility on trade flows. Their result confirms the theoretical assertion that exchange rate volatilities reduce international trade. According to their model the export volume is a function of foreign income, foreign capacity utilization and relative prices; and import volume is a function of domestic income, the ratio of foreign to domestic capacity utilization, and relative prices. They measure the using data for the USA and Germany; they estimated their models using quarterly data over the period 1974-1981 and found that volatility had a significantly negative effect on US imports, German exports and imports but no effect on US exports.

However, the findings of the above study (Akhter and Hilton, 1984) were challenged by a further study (Gotur, 1985) which used the same methodology as Akhter and Hilton with certain modification. It included France, the UK and Japan in the model, applied the Cochrane-Orcutt procedure to control for autocorrelation only when the

Durbin-Watson statistic calls for autocorrelation (Akhter and Hilton used it even in the cases in which the problem was not even present ), changed the sample period under investigation to account for lag structure and to incorporate the rate of change, rather than the level, of the exchange rate. After these modifications she found that German exports

24 and imports have been negatively impacted, and Japanese exports are positively affected, but the other seven trade flows were not affected.

However, both of the above mentioned studies suffer from spurious regression

problem because none of them accounted for integrating properties of variables

(Bahmani-Osokee and Hegerty, 2007).

A number of recent studies also found a negative relationship between exchange

rate volatilities and trade flows. For example, significant negative relationship between

real exchange rate volatility and export volume in short run and long run was found for

eight South American countries (Arize et al. 2008). By using Error Correction Model

Chou (2000) found significantly negative relationship between export volume and

volatility of real effective exchange rate (REER) for trade flows of industrial materials,

mineral and fuels; and manufactured good. However, the relation was not significant for

foodstuffs. Significant negative relationship was also found between exchange rate

volatilities and export supply for all the G7 countries and their partners for twenty one

industries (Peridy, 2003).

In contrast to the findings of the above mentioned studies, a positive relationship

between exchange rate volatilities and international trade flows was also found in few

empirical literature, mostly for bi-lateral trade (McKenzie and Brooks, 1997; Poon et al.

2005 ). While a number of studies found no significant effect of exchange rate volatilities

on trade flows (Kenen 1980, Thursby, 1980; IMF, 1984; Baily et al. 1986, Toneryo,

2004). As a result, the relationship between exchange rate volatilities and trade flows still

remain ambiguous.

25 The inconclusive relation between exchange rate volatilities and trade flows was explained by the argument that exchange rate volatility is an ‘inadequate indicator’ of price risk faced by firms since an increase in exchange rate volatilities may not necessarily increase real domestic currency price volatilities (Smith, 1991).

Another explanation behind not getting any systematic relationship between exchange rate volatilities and trade volume is undermining of a series of problems related to the methods of estimation which might lead to imprecise statistical results (Bini-

Smaghi, 1997).

IMF (2004) observed that while exchange rate fluctuations have increased in times of currency and balance of payments crises during the 1980s and 1990s, there has not been any increase, on average, in such volatility between the 1970s and the 1990s. It also found some empirical evidence of negative relationship between exchange rate volatility and trade. However, such a negative relationship is not robust and It concludes that if exchange rate volatility has a negative effect on trade, this effect would appear to be fairly small and is not robust.

3.4 Empirical Literature: Exchange Rate Volatility and Agricultural Trade

Despite an extensive literature on effect of exchange rate volatility on overall trade, very few studies (Cho et al. 2002; Kandilov, 2008; Zhang, 2010) explored the impact of exchange rate and other volatilities on agricultural trade. Compared to the other sectors, agriculture trade was found to be more sensitive to exchange rate uncertainties in developed countries. Using a sample of bilateral trade flows across ten developed countries (G 10 countries) Cho et al (2002) shows that the real exchange rate uncertainty

26 has had a significant negative effect on agricultural trade and the negative impact on agricultural trade was more significant compare to the other sectors.

Agricultural exports from developing countries are much more vulnerable to exchange rate volatilities compared to the exports from developed countries. Kandilov

(2008) found that the effect of exchange rate volatility is largest for developing country exporters and smallest for developed exporters. Since developing countries do trade with vehicle currency (US Dollar) only exchange rate volatility of the vehicle currency (U.S. dollar), and not the exporter-importer currency, matters for developing country exporters

(Kandilov, 2008).

Villanueva and Sarker (2009) conducted a study to examine the effects of exchange rate volatility on fresh tomato imports into the United States from Mexico.

They showed with the cointegration analysis that while changes in exchange rate have a positive effect on trade flows, volatility of the exchange rate has a significant negative effect on trade flows.

Although other volatilities (e.g. commodity price volatility and freight cost

volatility) may have much potential to affect international trade, only the effect of

exchange rate volatilities received much attention in the literature. Zhang et al (2010)

found that although commodity price and freight cost volatilities have no significant

impact on traded volume of soy bean between U.S. and Brazil, these two volatilities play

important roles in determining U.S. soy bean trade with China. The authors explained

that possibilities of hedging and market power are two important factors in determining

the effects of volatilities on trade.

3.5 Chapter summary

This chapter reviews the existing literature on the effects of exchange rate volatility and commodity price volatility on international trade flows. Although a number of literature exists on the effects of exchange rate on trade flows, literature on commodity price volatilities on international trade is scarce. The next chapter presents the theoretical framework used in this study.

28 Chapter 4: Conceptual Framework

4.1 Model Description

This study used Hooper and Kohlhagen’s (1978) trade model that derived the demand and supply functions for individual firms and then aggregated to derive market demand and supply to obtain reduced form equations for market equilibrium price and quantity.

Hooper and Kohlhagen developed the model for an individual firm importing a commodity under exchange rate uncertainty. This study extends Hooper and kohlhagen’s model by incorporating commodity price uncertainty into it. It is worth mentioning that

Zhang (2010) developed a model, based on Hopper and Kohlhagen’s model, which included exchange rate, price and ocean freight cost uncertainties.

4.2 Import Demand

According to Hopper and Kohlhagen’s (1978) trade model, suppose a firm uses imported commodity as inputs to produce final goods. The importer faces a linear demand function for its output (Q), which is an increasing function of domestic income (Y), the prices of substitutes (PD) and a decreasing function of its own price:

Q = aP + bPD + cY (4.1)

Following Hooper-Kohlhagen’s (1978) model, the model assumes a two period framework where in the first period the firm receives orders for its domestic output and places order for its imported input; and in the second period it receives the imported input and pays for it and ships and gets paid for its own output. The firm sets the level of its output to maximize its utility, which is an increasing function of its expected profits and a

29 decreasing function of the standard deviation of profits. The firm’s optimization problem can be written as:

max U (π ) = E(π ) − γ (V (π )) 2/1 (4.2) Q

where U is the total utility, π is profit, E is the expected value, V is the variance and γ is

the relative measure of risk preference where γ >0 implies risk aversion, γ =0 implies

risk neutrality and γ <0 represents risk loving.

The firm’s profit π in domestic currency can be formulated as:

π = Q * P(Q )−UC *Q − HM *iQ (4.3)

where Q is the amount of output, P is the domestic price per unit output faced by the firm,

UC is the unit cost of output, H is the weighted average cost of foreign exchange to the importer, M is the cost of imported inputs, i is the fixed ratio of imports to total output.

If q is the quantity of imports needed to produce Q amount of output then q can be

defined as

q = iQ (4.4)

In this study, I assume that the importer can hedge foreign exchange risk by purchasing

foreign exchange in advance and hedge commodity price in the future market. Suppose

the firm hedges a constant proportion ( α ) in the forward market at the futures exchange

~ rate, R ; the remain proportion (1-α ) of foreign exchange is purchased at the spot

exchange rate R. So, H can be defined as:

30 ~ H = 1( −α)R + α R (4.5)

~ P is the commodity future market price in foreign currency,

~ M= P (4.6)

By substituting Equations 5 and 6 into Equation 3, importer’s profit is obtained:

~ ~ π = Q * P(Q ) −UC *Q − [( 1− α)R + α R]P*iQ (4.7)

In which Q0 denotes, ……….It is assumed that, in Equation 7, all the variable except

~ ~ R and p are known with certainty on the contract date. Thus, the variance in the

importing firm’s profit is:

2 2 2 2 V (π ) = [( 1−α)R.iQ ] σ ~ + (αiQ ) σ ~ ~ (4.8) p R P

~ ~ ~ 2 2 where σ ~ and σ ` are the variances of P and R P , respectively. P ~ ~ R P

Substituting (8) into (2),

~ 2 2 2 2 2 2/1 U = QP (Q) −UC .Q − E(H )E(P .) iQ −[( 1−α) R σ ~ + α σ ~ ~ ] γ .iQ p R P

(4.9)

The first-order condition for equation with respect to output quantity (9) is:

~ 2 2 2 2 2 2/1 dU / dQ = Q(dP / dQ ) + P(Q) −UC − E(H )E(P)i −[( 1−α) R σ ~ + α σ ~ ~ ] γ .i = 0 p R P

(4.10)

31 Substituting for dP /dQ from Equation 1,

~ 2 2 2 2 2 2/1 (Q / a) + P(Q) −UC − E(H )E(P)i −[( 1−α) R σ ~ + α σ ~ ~ ] γ .i = 0 (4.11) p R P

Substituting q = iQ from (4) into (11)

~ 2 2 2 2 2 2/1 q = (i )(2/ aUC + bPD + cY ) + (ai )[2/ E(H )E(P)i + γ[( 1−α)R σ ~ + α σ ] ] P ~ ~ R P

(4.12)

~ ~ 2 2 2 2 2 2 2 Since σ = E(R) σ ~ + E(P) σ ~ + σ ~ σ ~ , (Bohrnstedt and Goldberger, 1969) ~ ~ P R P R R P

~ ~ ~ 2 2 2 2 2 2 q = (i )(2/ aUC + bPD + cY ) + (ai 2/ )[[( 1− α)R + α E(R)] E(P)i + γ[( 1− α)R σ ~ + α [E(R)] σ ~ P P ~ 2 2 2 2 2 2 + α [E(P)] σ ~ + α σ ~ σ ~ ]] R P R

(4.13)

If γ >0,

~ 2 2 2 2 2 2 2 dq / dσ ~ = (ai )2/ γ[( 1−α)R + α {[ E(R)] + α σ ~ }] < 0 (4.14) P R and

~ 2 2 2 2 2 dq / dσ ~ = (ai )2/ α γ [{ E(P)} + σ ~ ] <0 R p

(4.15)

Therefore, from equation (14) and (15) it can be asserted that if the importers are risk averse an increase in exchange rate or commodity price volatility will reduce the volume of import. If the importers are risk neutral ( γ =0), exchange rate or commodity price

32 volatility will not have any effect of import demand. In the case of risk-loving importers

(γ <0), an increase in exchange rate and commodity price volatility will increase the

import.

Assuming that all firms are homogenous 2, firm level import demands can be summed into

the following aggregate import demand function.

~ ~ d Q = g(UC , PD ,Y , E(R), E(P ,) σ ~ ,σ ~ ,σ ~ σ ~ ) P R R P (4.16)

4.3 Chapter Summary

This chapter discusses the theoretical framework used in this thesis. I used a modified version of Hooper-Kohlhagen import demand model. Hooper and Kohlhagen (1978) derived this model to show the effect of exchange rate volatility on imports. I incorporated food price volatility to the original Hooper-Kohlhagen model and derived the effects of exchange rate volatility and commodity price volatility on import demand.

2 The limitation of this assumption is that all firms may not have the same level of risk preference.

33 Chapter 5: Empirical Framework

5.1 Econometric specification

This study used panel data models to estimates the effects of exchange rate and commodity price volatilities on trade flows using a panel data regression model.

In line with the modified Hooper-Kohlhagen model with price volatility presented in the chapter 4, I used the following empirical model to estimate the effects of exchange rate and commodity price volatilities on Canada’s export and import:

ln pimp it = β0 +β1 ln XV it +β2 ln PV it +β3 ln. PCGDPit +β4 ln Pit +β5 ln Et (Pit +1)+β6 ln ER ijt +β7D_Q+β8T +eit

(5.1) Where

Natural logarithm of country i’s per capita import at period t ln pimp it Natural logarithm of Country i’s exchange rate volatility at period t ln XV it Natural logarithm of price volatility country i faces at period t ln PV it Natural logarithm of country i’s per capita GDP at period t ln PCGDP it Natural logarithm of import price of country i at period t ln Pit Natural logarithm of expected price of import of country i at period t, ln Et (Pit +1) =lnF t,t+1

Ln F t,t+1 Natural logarithm of futures price Natural logarithm of Country i’s exchange rate with country j ln ER ijt D_Q2 Dummy variable 3 for Quarter 2 D_Q2 Dummy variable 4 for Quarter 3 D_Q4 Dummy variable 5 for Quarter 4 T Time trend

3 For quarterly models 4 For quarterly models 5 For quarterly models

34 5.2 Variable Description

Dependent Variable: Per capita volume of import

Quarterly and annual real per capita volumes of imports of commodities measured in metric ton are used as dependent variables respectively for quarterly and annual models.

Total import volumes are divided by the population to obtain per capita volume of import. Since I used per capita real GDP as an independent variable, dependent variable is also transformed to per capita. For quarterly models of wheat and soybean, per capita import volumes of wheat and soybean from Canada by its major importers are used as dependent variable. On the other hand, Canada’s per capita volume of import of corn and rice from its major importing sources are used as the dependent variable. For annual models, the per capita import volumes of top importers of each commodity are considered as the dependent variable.

Independent Variables

Exchange Rate

Quarterly and annual nominal exchange rates (exporters’ currency per unit of importer’s currency) of each period are used. Therefore, it is expected that if exchange rate appreciates, cost of imports will be cheaper for importers. Exchange rate data are obtained from Thompson-Reuters DataStream ( http://online.thomsonreuters.com ) through the University of Guelph Data Resource Centre.

35 Import price

Unit price of import is considered as import price. Unit price of import is calculated by dividing the value of import in U.S. dollar by the quantity of import measured in ton.

Since real volume of imports is used as dependent variable, nominal unit prices are converted to real prices using U.S. Consumer Price Index (Base year 1982=100). Note that unit price indices may create bias in estimation because of the compositional changes in quantities and quality mix of exports and imports. Even with best practice stratification the scope for reducing such bias is limited due to the sparse variable list available on customs documents (Silver, 2007). Despite this issue, unit value of import and export prices are widely used because of their relatively low cost availability compared with price surveys.

Expected price

The modified version of Hooper-Kohlhagen’s model presented in the Chapter 4 assumes that firms are capable of hedging the risk of price volatility by operating in the futures market. This assumption is valid for today’s world since real time futures price data are readily available and forms can buy and sell in the commodity exchanges. Given this backdrop, I used the Chicago Mercantile Exchange (CME) futures price, F it , of the next

period as expected cash price,ln Et (Pit +1 ) . Futures price data are obtained from

Thompson-Reuters DataStream (2012). Recently developed price forecast models, e.g., the World Bank’s commodity price forecast (World Bank 2012), and USDA’s season average price forecasts (USDA 2012), are also mainly based on futures price. Therefore, it is reasonable to use futures price as importers’ expected price.

36 Expected exchange rate

Although expected exchange rate is a variable in my theoretical framework for import demand in the Chapter 4, this variable was dropped from the empirical model. The reason of dropping this variable is that forward exchange rates are available for very few countries only. In their original work Hooper and Kohlhagen (1978) used next period’s realized exchange rate as expected exchange rate. However, by taking the realized exchange rate of the next period, one would violate one of the main assumptions of the model that exchange rates are uncertain and assumes that traders can forecast perfectly.

Exchange rate volatility

Exchange rate volatility is one of the main independent variables of the empirical model.

While a variety of exchange rate volatility measures have been used in the literature, there is still no consensus on which measure is the most appropriate (Clark et al. 2004).

The disagreement is partly due to the fact that there is no generally accepted theory of the impact of exchange rate volatility on firm behavior (Kandilv, 2008). Most often, some variant of the standard deviation of the annual or monthly exchange rate is used to measure volatility (Kandilov, 2008; Cho et al. 2002; Clark et al. 2004; Frankel and Wei,

1993; Rose, 2000; Tenreyro 2007 ).

Following Kandilov (2008) I measured the exchange rate volatility between

countries i and j in period t, XV ijt as the standard deviation of the first difference of the natural logarithm of the daily exchange rate between the two countries over a period 6:

6 Three months period for quarterly and one year period for annual

37 XV ijt =Std[ ln Xijt,d − ln Xijt,d-1 ], for d = 1,2,..., end of the period. (5.2)

where

XV = Exchange rate volatility between country i and j over period t; ij t

Std=Standard deviation

lnXijt, d =Natural logarithm of nominal exchange rate between country i and j on day d in period t

lnXijt, 1-d = Natural logarithm of nominal exchange rate between country i and j on day d-1 in period t

End of the period = the last day of the period; for example, for monthly data, the 30 th or 31st calendar day; for quarterly data, the 90 th day of the quarter.

Both real and nominal exchange rates were widely used in the previous studies. For

example, Pick (1990), Arize, et al. (2000), and Cho et al. (2002) used real exchange rate

while Tenreyro (2007) and some other studies used nominal exchange rate. Kandilov

(2008) noted that for all industrial and developing countries there is little difference

between the real and the nominal exchange rate volatility in practice. Thursby and

Thursby (1987) also showed that real and nominal exchange rate volatilities do not have a

different effect on trade flows. As a result, it is reasonable to use nominal exchange rate

in this study.

Price volatility

We used the same method as exchange rate volatility to calculate price volatility using monthly representative international prices for each commodity. Since daily data of prices is not available for each commodity and country, we used monthly real price data.

38 Monthly nominal price data in U.S. dollar are obtained from FAOSTATS (FAO 2012) and then deflated to real prices using U.S. Consumer Price Index (Base year 1982=100).

Following formula is used to compute price volatility:

PV= Std[ ln Pm − ln Pm-1 ] (5.3)

Where

PV denotes the price volatility

Std represents standard deviation;

lnP m =Natural logarithm of price at month m ;

lnP m 1- =Natural logarithm of price at month m-1

It should be mentioned here that the correlation between real and nominal price volatility is found to be very high, ranges 0.85 to 0.92.

Real Gross Domestic Product (GDP) per capita

Real GDP data for importing countries, obtained from USDA-Economic Research

Service (USDA-ERS 2012: http://www.ers.usda.gov/data-products/international- macroeconomic-data-set.aspx ), are divided by population to obtain real GDP per capita.

Since it is adjusted to the population of the importer, GDP per capita is a better measure

for country’s income and well being.

Quarterly Dummies

For our quarterly models, three quarterly dummies for second, third and fourth quarters were used for seasonality.

39 Time trend variable

Time trend variables account for any time-variant effects that are not captured in the regression. We used time trend variable in both quarterly and annual models.

5.3 Data and sources

The data used in this study comes from various sources such as the Canadian

International Merchandise Trade (CIMT) online database, the United States Department of Agriculture (USDA), Thompson-Reuters DataStream, EUROSTAT and FAOSTAT and Tri University Data Resources (TDR), University of Guelph. A detailed account of data sources are provided in this section.

Import and Export data

Quarterly data of Imports and exports data of crops (including corn, rice, soybean and wheat) ranging from 2000 to 2010 are obtained from the Canadian International

Merchandise Trade (CIMT: link) of Statistics Canada (CIMT, 2011). They are strongly balanced panel data, which denotes the real volumes (in metric tons) of export and import data for wheat, soybean, corn and rice. Table 5.1 summarizes the quarterly trade data used in the study:

Table 5.1: Summary of export and import data for quarterly models

Crop Trade Unit (‘000) Time Source Flow Period Corn Import Metric Ton 2000 Q1- Canadian International 2009 Q4 Merchandise Trade (CIMT): http://www.statcan.gc.ca/trade- commerce/data-donnee-eng.htm Rice Import Metric Ton 2000 Q1- CIMT: 2009 Q4 http://www.statcan.gc.ca/trade- commerce/data-donnee-eng.htm Soybean Export Metric Ton 2000 Q1- CIMT: 2009 Q4 http://www.statcan.gc.ca/trade- commerce/data-donnee-eng.htm Wheat Export Metric Ton 2000 Q1- CIMT: 2009 Q4 http://www.statcan.gc.ca/trade- commerce/data-donnee-eng.htm

For annual model, we use FAOSTATS annual detailed trade matrix to get annual imports data of wheat, soybean, corn and rice by for individual top developing and developed importers from 1991 to 2009. Table 5.2 summarizes the import data used for this study.

Table 5.2: Summary of import data for annual models

Crop Trade Unit (‘000) Time Period Source

Flow

Corn Import Metric Ton 1991-2009 FAOSTAT: http://faostat.fao.org/ Rice Import Metric Ton 1991-2009 FAOSTAT: http://faostat.fao.org/ Soybean Import Metric Ton 1991-2009 FAOSTAT: http://faostat.fao.org/ Wheat Import Metric Ton 1991-2009 FAOSTAT: http://faostat.fao.org/

41 Exchange rate, price and Gross domestic product (GDP) data

Daily exchange rate data was collected from Thompson-Reuters DataStream (2012).

Monthly price and per capita real GDP of countries are obtained of commodities are obtained from United States Department of Agriculture (hereafter ‘USDA ERS’) (2012) and Quarterly GDP data are collected from EUROSTAT (2012). Table 5.3 summarizes the sources and frequency of exchange rate, price, per capita GDP and GDP data uses in this study.

Table 5.3: Summery of Data Frequency and Sources for exchange rate, GDP prices

Data Frequency Data sources

Exchange Rate Daily Thompson-Reuters DataStream 7 (2012) (http://thomsonreuters.com/ ) Commodity Price Daily Tri University Data Resources(TDR), University of Guelph ( http://tdr.tug-libraries.on.ca/ )

Commodity Price Monthly United States Department of Agriculture (USDA ERS 2012); ( http://www.ers.usda.gov/data- products/international-macroeconomic-data- set.aspx ) Per capita real Annual USDA Economic Research Service International gross domestic macroeconomic data set (USDA ERS 2012) products Real gross Quarterly EUROSTAT 8(2012) domestic products (http://epp.eurostat.ec.europa.eu )and Division of Statistics of countries in the sample.

Countries

For quarterly models, the study considered Canada’s top and regular trading partners for wheat, soybean, rice and corn. However, few important trading partners e.g., Venezuela

for wheat, could not be incorporated in this study because of unavailability of quarterly

7 Data base of Thompson-Reuters 8 Database of European Union

42 data for few variables. Table 5.4 provides the list of Canadian trading partners included in each crop model.

Table 5.4: List of Countries for Quarterly Models

Commodities and Trade Flows Countries

Wheat Exports from Canada U.S., Italy. Japan, Morocco

Soybean Exports from Canada U.S., Japan, Germany, France, France, Netherlands, Belgium, Malaysia. Hong Kong, the Philippines and Italy Canada’s Corn Imports U.S. and Rest of the World

Canada’s Rice Imports Thailand, Pakistan, Italy and U.S.

For annual models, I included top importers of each commodity. The list of top importers is provided in the table 5.5.

Table 5.5: List of importing countries considered for annual models

Commodity Countries

Wheat Developed: Germany, Italy, Japan, The Netherlands, Republic of Korea, Spain and USA Developing : Algeria, Brazil, Indonesia, Malaysia, Mexico, Pakistan, the Philippines, Turkey Soybean Developed: Germany, Italy, Japan, The Netherlands, Norway, Portugal, Republic of Korea, Spain, United Kingdom Developing : Argentina, China, Colombia, Indonesia, Mexico, the Philippines, Thailand, Turkey Rice Developed : Canada, Hong Kong, France, Singapore, United States Developing : Brazil, China, Indonesia, Malaysia, the Philippines, Cameroon Corn Developed : Canada, France, Germany, Italy, Japan, the Netherlands, Republic of Korea, Spain, United Kingdom Developing : Algeria, China, Colombia, Indonesia, Malaysia, Mexico, Peru, Turkey

5.4 Model Selection

Balance Panel data were used for both our quarterly and annual models. This section discusses the selection process of estimation models for panel data regression from fixed effects model, random effects model and pooled OLS.

Fixed Effects Model

The fixed effects model assumes that the intercept term captures the individual heterogeneity which implies that every country gets it own intercept while the slope coefficients remain the same (Baltagi, 2005).

Consider a linear unobserved effects panel data model for N observations and T periods:

Y = X β + a + u ,t = 1,..., T;i = 1,...., N it it i it (5.4)

Where Yit is the dependable variable for country i at period t, X it is the N × K regressor

matrix with observable time-variant independent variables, β is a K ×1vector of

coefficients, ai is the unobserved time variant country effect and uit is the independent

and identically normal distributed error terms.

If we average the equation for each i, we get

− − − Y i = X i β1 + ai + u i (5.5)

− − − −1 T −1 T −1 T Where Y = T y , u i = T u and X = T X ∑t=1 it ∑t=1 it i ∑t=1 it

Since ai is fixed over time, it appears in both(5.4) and (5.5). If we subtract (5.4) from

(5.5) for each t we get,

− − .. − .. Yit − Y i = β1 (X it − X i ) + (uit − ui ) ⇒ Y it = X it + u it (5.6)

Random Effects Model

The random effects model assumes that the unobserved time variant individual effect, ai

in (5.2.1) is uncorrelated with each explanatory variable (Baltagi, 2005):

Cov (a , X ) = ,0 t = 2,1 ,..., T i it (5.7)

In fact, the ideal random effect assumptions include all the fixed effect assumptions plus

the additional requirement that ai is independent of all explanatory variables in all time

periods.

Hausman specification test

In order to determine whether to use fixed effects or random effects models, Hausman specification test can be performed. Hausman specification test shows how large the difference in estimates is in relation to the variances of estimates (Baltagi, 2005). The computation procedure of Hausman test is as follows:

^ FE ^ RE ^ FE ^ RE ^ FE ^ RE H = (β − β )′×[Var (β ) −Var (β )] −1 × (β − β ) (5.8)

^ FE ^ RE Where β is the coefficient estimate of fixed effects model and β is the coefficient estimate of random effects model.

The null hypothesis of the Hausman test is that there is no systematic difference between coefficients of fixed and random effects models models. Fixed effects models are chosen if the null hypothesis is rejected while random effects model are chosen otherwise (Hausman 1978).

45 Testing for random effects: Breusch-Pagan Lagrange multiplier (LM)

In order to decide between a random effects regression and a simple OLS regression,

Breusch-Pagan Lagrangian multiplier test is suggested (Breusch and Pagan, 1980). The null hypothesis in the LM test is that variances across entities are zero. That is, no significant difference across units (i.e., no panel effect). Rejecting the null hypothesis indicates the presence of unobserved effects and pooled OLS would not be efficient. We conducted Breusch-Pagan Lagrangian multiplier test to decide between random effects and pooled OLS regression model.

Test for Cross-sectional Dependence

In panel data regression analysis, it is typically assumed that disturbances in panel data models are cross-sectionally independent. This assumption is particularly true for panels with large cross section dimensions. However, macro panels with smaller cross section dimension and sufficiently large time periods may have the problem of cross section dependence (pesaran, 2004). Cross-sectional dependence may arise due to spatial or spillover effects, or due to unobservable common factors (Su and Zhang, 2010). Macro panels on countries or regions with long time series that do not account for cross-country dependence may lead to misleading inference (Baltagi, 2008). In this study, we conducted Pesaran’s cross-sectional dependence test (CD test). But, one of the possible drawbacks of the CD test is that adding up positive and negative correlations may result in failing to reject the null hypothesis of cross-sectional dependence even if there is plenty of cross-sectional dependence in the errors. Hoyos and Sarafidis (2006) suggest conducting Fees’ and Friedman’s CD test if the average absolute correlation of the

46 residuals is high in the Pesaran’s CD test. In this study, we conduct Friedman’s and

Frees’ CD test if the average absolute correlation of the residuals was high in Pesaran’s

CD test. In case of the presence of cross-sectional dependence in the panel, we presented regression results with Driscoll-Kraay standard errors (Hoechle 2007).

5.5 Diagnostics: Tests for Unit root, Heteroscedasticity, Serial Correlation and Multicollinearity

This section provides an overview of the diagnostics done in this study to test for unit root, heteroscedasticity, serial correlation and multicollinearity. Results of the test performed are reported in the following sections for each individual crop model.

Unit root test

It is now a common practice to test for unit root in time series econometrics (Baltagi,

2008). In panel data analysis, testing for unit root is relatively recent (Levin, Lin and Chu

2002, Im et al. 2003; Harris and Tzavalis, 1999; Maddala and Wu, 1999; Choi, 2001 and

Hadri, 2000). The stationarity or non-stationarity of a time series can strongly influence its behavior and properties. If the variables in the regression model are not stationary, the standard assumptions of asymptotic analysis will not be valid. Because of non- stationarity the usual ‘t-ratios’ do not follow t-distribution. Therefore, the hypothesis test cannot be considered valid. Such estimates are termed as ‘spurious regression’ by

Gramger and Newbold (1974) since they yield results with high R-squared values and high t-ration with no econometric meaning. The problem of non-stationarity can be treated by applying difference operator to the series (Kennedy, 2011).

47 In this study, we conducted Levin-Lin-Chu test for panel unit root in the cases where we find cross sectional independence. Levin, Lin and Chu (2002) argue that individual unit root tests have limited power against alternative hypotheses with highly persistent deviations from equilibrium. They suggested a more powerful panel unit root test than performing a separate unit root test for each cross section (Baltagi, 2008). The null hypothesis is that each individual time series contains a unit root against the alternative that each time series is stationary. In presence of cross sectional dependence, Fisher panel unit root test (Maddala and Wu, 1999) and Pesaran’s crossectionally augmented Dickey

Fuller panel unit root test was conducted to detect unit roots (Pesaran, 2007).

Test for Serial Correlation

The presence of serial correlation in panel data models potentially biases the standard errors (Drukker, 2003). As a result, it is important to test for serial correlation in the idiosyncratic error term. Although a number of tests have been proposed to test for autocorrelation in a panel data model, Wooldrige test (Wooldridge 2002) is the most attractive because it requires relatively fewer assumptions and easy to implement

(Drukke,r 2003). We performed Wooldridge test for serial correlation in our panel data models.

Test for Heteroscedasticity

Modified Wald test was applied to test the presence of heteroscedasticty in the fixed effect panel data models (Baum, 2001). Some of the regression models in this study are random effects models. Since there is no specific test to detect heteroscedasticty in

48 random effects model, we reported cluster-robust covariance estimators to avoid potential presence of heteroscedasticity in random effect model.

Test for multicollinearity

Variance Inflation Factor (VIF) was used to check the severity of multicollinearity. The

VIF shows us how much the variance of the coefficient estimate is being inflated by multicollinearity. As a rule of thumb, VIF greater than 10 suggests to concern about multicollinearity and VIF greater than 30 suggests severe multicollinearity ( Belsley, Kuh

and Welsh, 1980) .

5.6 Chapter Summary This chapter provides the model specification used in this study. It also introduced and described the variables to be used in the empirical model. The next chapter provides the coefficient estimates of the regression models.

49 Chapter 6: Results and Discussions

6.1 Introduction

This chapter provides the results and discussions of both quarterly and annual models.

Results of the diagnostic tests are also provided in this chapter. We estimated the effects of exchange rate and commodity price volatilities on Canada’s trade with its major trading partners for wheat, soybean, corn and rice with quarterly models. In addition, the effects of volatilities on major developed and developing importers according to their sources of imports were estimated with the annual models.

6.2 Quarterly Models

The quarterly models are estimated for Canada’s exports of wheat and soybean and imports of corn and rice over the period 2000:Q1 to 2010:Q4. The list of Canada’s trading partners for each commodity is provided in table 5.4 in chapter 5.

6.2.1 Quarterly Imports of Wheat and Soybean from Canada

The parameter estimates of quarterly export models of wheat and soybean are presented in table 6.3. Before estimation, unit root tests are conducted. Fisher’s unit root test statistics are presented in table 6.1. For both models, the unit root test suggests that log of price volatility and log of exchange rate volatility are level stationery. For wheat, all other variables are difference stationery. For soybean, log of real import price is level stationery but log of expected price and exchange rate variables are difference stationery.

Therefore, first differences of the difference stationery variables are used as independent variables.

50 Table 6.1: Fisher’s unit root test for wheat and soybean model

Wheat Model Soybean Model

Level First Difference Level First Difference

Ln Price 105.65*** 305.69*** 268.93*** 713.41*** volatility (0.000) (0.00) (0.000) (0.000) Ln Exchange 39.63*** 226.98*** 85.88*** 494.13*** rate volatility (0.000) (0.000) (0.000) (0.000) Ln Import Price 4.67 42.66*** 102.97*** 600.59 (0.78) (0.00) (0.000) (0.000) Ln Expected 2.754 77.39*** 25.67 194.45*** price (0.9488) (0.000) (0.1767) (0.000) Exchange rate 4.55 88.19*** 13.67 479.62*** (0.8037) (0.000) (0.8464) (0.000) Ln per capita 6.247 215.91*** 4.19 587.58*** real GDP (0.6195) (0.000) (0.518) (0.000) *,** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses denote P value

Based on the results of Fisher’s unit root test, my empirical framework is specified as:

ln pimp it = β 0 + β 1 ln XV it + β 2 ln PV it + β 3 .∆ ln PCGDP it + β 4 ln Pit +

β 5 ∆ ln E t ( Pit + 1 ) + β 6 ln ∆ ER ijt + β 7 D _ Q + β 8T + e i t (6.1) The Hausman test is performed to test whether a fixed or random effects model is appropriate. The Hausman's test statistic for the wheat regression is 31.68 with a p-value of 0.000, suggesting that a fixed effects model is preferred to a random effects model. To test for cross-sectional dependence the Friedman’s test is conducted. The test statistic for

Friedman’s test is 79.316 with a p-value of 0.000, which suggests the presence of cross- sectional dependence for wheat quarterly model. Therefore, a fixed effects regression with Driscoll Kraay robust standard error was chosen to estimate the wheat model.

The Hausman’s test statistic for the soybean model is 22.06 with a p-value of

0.001, suggesting that a fixed effects model is preferred to a random effects model. The

51 Friedman’s test statistic for the soybean regression model is 63.03 with a p-value of

0.000, which indicates the presence of cross-sectional dependence in the soybean regression model. Therefore, soybean export model was estimated by a fixed effect model with Driscoll-Kraay standard error.

Wooldridge test for autocorrelation in panel data is used to test if there is a serial correlation. The statistic of this test follows F-distribution. The null hypothesis under this test is that there is no first-order autocorrelation. For wheat model, the test statistics is

0.021 with a p-value of 0.893. Therefore, we fail to reject the null hypothesis of no serial correlation in the wheat model at a five percent significance level. For soybean model,

Wooldridge test statistics is 3.02 with a p-value 0.11, indicating that we fail to reject the null of no serial correlation in the soybean model at a five percent significance level.

Modified Wald test is commonly used to test if there is hetroscedasiticity in fixed effects model. The test statistics of the Wald test follows a Chi-square distribution. The computed statistics of Wald test statistic in the wheat model is 635 with a p-value of

0.000. Therefore, the null hypothesis of homoscedastic errors is rejected at a five percent significance level. For the soybean model the computed statistics of Wald test is 452.23 with a p-value of 0.000, suggesting heteroscedastic errors in the soybean model.

To check for multicollinearity issues, the variance inflation factor is used. The variance inflation factors (VIF) presented in the table 6.2 for both wheat and soybean model shows that VIF for all variables in both models are below 2. Therefore, multicollinearity should not be a concern for wheat and soybean model.

52 Table 6.2: VIF for wheat and soybean model

Variables VIF for wheat VIF for Soybean

Ln Price volatility 1.14 1.15 Ln Exchange rate volatility 1.41 1.21 Ln Import Price 1.44 1.81 Ln Expected price 1.06 1.03 Ln Exchange rate 1.54 1.56 Ln per capita real GDP 1.38 1.03 Time trend 1.23 1.17 Mean VIF 1.31 1.28

Table 6.3 reports the coefficients estimates of import demand of Canada’s wheat and soybean by its major trading partners. For wheat model, results show that log of price volatility and log of exchange rate volatility do not have significant effect on log of per capita wheat import by canada’s major trading partners. Percentage change in real import price has a negative and significant effect at a ten percent level of significance on log of wheat import volume from Canada which expected because as current import price of a commodity increases the demand for that commodity decreases. The positive and significant (at a ten percent level significant) coefficient of percentage change in expected price of the next period asserts that importers import more when they expect a price hike in future. Percent change in exchange rate has a positive and significant effect on log of per capita import of wheat at a one percent level of significance. It is expected because if the importer’s exchange rate appreciates the cost of imports becomes cheaper for the importer and import demand increases.

On the other hand, coefficients estimates of soybean model also yield similar results as the wheat model. Percent change in real import price and nominal exchange rate has a positive and significant effect on log of soybean import volume from Canada at a one

53 percent level of significance; and percentage change in expected price has a positive and significant effect on log of per capita import volume of soybean from Canada at a one percent level of significance. The positive and significant (at a one percent level of significance) coefficient of time trend variable confirms that imports of soybean from

Canada by its major trading partners increased overtime.

Since expected price is usually not included in a typical import demand model 9, to

check the robustness of the results presented in table 6.3 reported, another regression

results in table 6.3a excluding the percentage change of expected price from the right

hand side. For both wheat and soybean models, log of exchange rate volatility and log

price volatility do not have significant effect on log of import volumes of wheat and

soybean from Canada. The signs of the other variables remain the same in the results

presented in the table 6.3 a.

9 Some of the literature that examine the effects of exchange rate volatility and price volatility (e.g. Zhang 2010) includes only the expected price or exchange rate. For comparison purpose results with expected price are provided in the appendix B

Table 6.3: Coefficient estimates of quarterly wheat and soybean imports from Canada from 2000 to 2009 Commodities Dependent Variable: Log of per capita import Wheat Soybean Independent variables Fixed Effect Fixed Effect 0.181 -0.083 ln Price volatility (0.122) (0.113) -0.272 -0.1805 ln Exchange rate volatility (0.541) (0.191) -1.25* -0.7483*** ∆ ln Real Import Price 10 (0.722) (0.143) 3.951* 0.6600** ∆ ln Expected Price (2.18) (0.323) 6.241** 0.4716*** ∆ ln Exchange rate (4.19) (0.015) -5.639 -7.307 ∆ ln Per Capita Real GDP (4.86) (2.56) 1.254 -2.0599** Dummy_Quarter 2 (0.913) (0.559_ 1.0499 -0.7571 Dummy_ Quarter3 (1.10) (0.115) 0.8741 -0.0230 Dummy Quarter_4 (1.07) (0.134) -0.011 0.0501*** Timetrend (0.02) (0.003) -8.324* -6.4412*** Constant (4.63) (1.83) No. of Observation 171 390 R2 0.08 0.66 Prob > F 0.009 0.000 *. ** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll- Kraay robust standard errors

10 For soybean model, this variable was used at level since it is level stationery.

55 Table 6.3a : Coefficient estimates of quarterly wheat and soybean imports from Canada from 2000 to 2009 (without expected price variable) Commodities Dependent Variable: Log of per capita import Wheat Soybean Independent variables Fixed Effect Fixed Effect 0.211 -0.136 ln Price volatility (0.12) (0.115) 0.011 -0.149 ln Exchange rate volatility (0.74) (0.197) -0.935* -0.801*** ∆ ln Real Import Price (0.723) (0.142) 7.404 0.471*** ∆ ln Exchange rate (5.21) (0.015) -7.985 -8.207 ∆ ln Per Capita real GDP (5.87) (2.4) 1.925* -2.296*** Dummy_Quarter 2 (1.05) (0.52) 1.999* -0.816*** Dummy_ Quarter3 (1.05) (0.115) 1.551 0.021 Dummy Quarter_4 (1.13) (0.14) -0.004 0.051*** Timetrend (0.02) (0.003) -1.902 -6.135*** Constant (8.3) (1.84) No. of Observation 171 390 R2 0.05 0.62 Prob > F 0.03 0.000 *. ** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll- Kraay robust standard errors

6.2.2. Quarterly import models of corn and rice

This section presents and discusses the coefficient estimates of corn and rice import models. It also presents the results of regression diagnostics performed before the estimations.

Table 6.4 reports the test statistics of Fisher’s unit root test. The null hypothesis of the presence of unit root is rejected for log of price volatility and log of exchange rate

56 volatility in both corn and rice models. Therefore, the log of price volatility and the log of exchange rate volatility are level stationery. All other variables of both models are difference stationery. Therefore, in the regression estimation, the log of price and the log of exchange rate volatilities were considered at level, and first differences of import price, expected price and exchange rate variables are used.

Table 6.4: Fisher’s unit root test for corn and rice model

Corn Model Rice Model

Level First Difference Level First Difference

Ln Price 38.08*** 112.78*** 54.346*** 174.042*** volatility (0.000) (0.000) (0.0000) (0.0000) Ln Exchange 14.516*** 120.90*** 30.366 *** 195.4121*** rate volatility (0.000) (0.000) (0.0002) (0.0000) Ln Import Price 2.33 41.44*** 4.170 77.1910*** (0.67) (0.000) (0.841) (0.0000) Ln Expected 1.54 49.63*** 3.5450 86.2790*** price (0.819) (0.000) (0.895) (0.0000) Exchange rate 0.555 32.44*** 5.3013 87.56*** (0.95) (0.000) (0.7249) (0.0000) Ln per capita 5.268 16.68*** 3.7771 19.7347*** real GDP (0.261) (0.0022) (0.876) (0.011) *,** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses denote P value

Test statistics of Hausman’s test for corn model is 18.23 with a p-value 0.000, suggesting that a fixed effects model is preferred to random effects model. The test statistics of Friedman’s test in the corn model is 26.400 with a p-value 0.000. Therefore, I reject the null hypothesis of cross-sectional independence in the panel. As a result, fixed effects regression with Driscoll-Kraay standard error is used to estimate the corn import model.

57 On the other hand, the Hausman’s test statistics computed for the rice model is

2490 with a p-value of 0.000. Thus, I reject the null hypothesis of that both estimates are consistent. A fixed effect model is preferred in this case. Test statistics computed for

Friedman's test of cross sectional independence is 30.07 with a p-value of 0.00.

Therefore, I reject the null hypothesis of cross-sectional independence. A fixed effects model with Driscoll-Kraay standard error is preferred in this case as well.

The test statistics of the Wooldridge test of serial correlation in the corn model is

16.81 with a p-value of 0.15. Therefore, I fail to reject the null hypothesis of no first order serial correlation. For rice model, the Wooldridge test statistics is 5.807 with p- value 0.09. Thus, I fail to reject the null hypothesis of no serial correlation at 5% significance level for rice model also.

The test statistics of the modified Wald test for the corn model is 42.07 with a p- value 0.000, suggesting to reject the null hypothesis of homoscedastic errors. For rice model, the statistics of the modified Wald test is 758.03 with a p-value 0.00. This, I reject the null hypothesis of no serial correlation.

VIFs for the explanatory variables of corn and rice model presented in the table

6.5 shows that the highest VIF for corn model is 1.97 (log of price volatility) and for rice model is 1.5 (log of Price volatility). Since no variable of any of the two models is more than 10, multicollinearity should not be a concern in corn and rice models.

58 Table 6.5: VIF for corn and rice model

Variables VIF for Corn VIF for Rice

Ln Price volatility 1.97 1.5 Ln Exchange rate volatility 1.42 1.47 Ln Import Price 1.37 1.1 Ln Expected price 1.35 1.09 Ln Exchange rate 1.34 1.06 Ln per capita real GDP 1.22 1.05 Time trend 1.57 1.01 Mean VIF 1.46 1.18

Table 6.6 presents the coefficient estimates of Canada’s corn and rice imports from its major import sources. Results show that percentage change in real price and nominal exchange rate volatilities do not have significant effects on log of per capita import of corn and rice. Other variables also have the expected signs. Among the significant variables, percentage change in real import price has a negative effect on the log of per capita corn import at a ten percent level of significance and on the log of per capita rice import at a one percent level of significance. Percentage change in per capita real GDP has a positive and significant effect on the log of per capita corn and rice import at a five percent and a one percent level of significance, respectively.

59 Table 6.6: Coefficient estimates of Canada’s corn and rice import demand from 2000-2009 Dependent Variable: Log of per capita import Corn Rice Independent variables Fixed Effect Fixed Effect 0.033 0.0307 ln Price volatility (0.067) (0.027) -0.002 0.0271 ln Exchange rate volatility (0.167) (0.066) -1.022* -0.460*** ∆ ln Real Import price (0.618) (0.085) 0.184 0.122 ∆ ln Expected Price (0.404) (0.211) 1.380 0.3294 ∆ ln Exchange rate (1.18) (0.48) 1.381** 7.101*** ∆ ln Per Capita real GDP (0.488) (1.86) -0.086 -1.36 Dummy_Quarter 2 (0.127) (0.358) 0.029 -1.686*** Dummy_ Quarter3 (0.124) (0.42) 0.117 -1.211*** Dummy Quarter_4 (0.136) (0.35) 8.438 0.003*** Timetrend (0.014) (0.002) 2.04 -7.292* Constant (0.139) (0.751) No. of Observation 78 152 R2 0.43 0.3257 Prob > F 0.03 0.000 *. ** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll- Kraay robust standard errors

Table 6.7 presents the coefficient estimates of corn and rice model excluding one independent variable, i.e.; percentage change in expected price. The results show that signs and significance of the main independent variables do not change significantly.

60 Table 6.7: Coefficient estimates of Canada’s corn and rice import demand from 2000-2009 (without percentage change of expected price) Commodities Dependent Variable: Log of per capita import Corn Rice Independent variables Fixed Effect Fixed Effect 0.023 0.030 ln Price volatility (0.06) (0.02) -0.014 0.024 ln Exchange rate volatility (0.16) (0.06) -1.070* -0.461*** ∆ ln Real Import Price (0.60) (0.08) -1.461 0.3292 ∆ ln Exchange rate (1.16) (0.21) 1.431*** 7.523*** ∆ ln Per Capita real GDP (0.46) (0.48) -0.100 -1.43**** Dummy_Quarter 2 (0.12) (0.35) 0.036 -1.777*** Dummy_ Quarter3 (0.121) (0.42) 0.135 -1.288*** Dummy Quarter_4 (0.129) (0.35) 0.13 0.003 Timetrend (0.12) (0.002) 8.750 -7.290*** Constant (3.25) (0.75) No. of Observation 78 152 R2 0.43 0.33 Prob > F 0.01 0.000 *. ** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll- Kraay robust standard errors

6.3 Annual Models The previous section presented the results of quarterly regression models to examine the effects of exchange rate and commodity price volatilities on Canada’s trade with its major trading partners for wheat, soybean, corn and rice. This section provides the results of the annual models which attempt to examine the effects of exchange rate commodity price volatilities on import demand of top developed and developing importers for each commodity. The following sections present the coefficients estimate of the following trade flows:

1. Top developed importers’ imports from developed countries

2. Top developing importers’ import from developing countries.

6.3.1 Top developed importers’ imports from Developed exporters

This section presents and discusses the results of top developed importers’ import

demand of wheat, soybean, corn and rice from their developed trading partners.

Table 6.8 provides Fisher’s panel unit root test statistics for each variable used in

the regression models. The test statistics of the Fisher’s test reports that log of price

volatility is level stationery in all crop models except Corn. For corn, log of price

volatility is difference stationery. Log of exchange rate volatility is level stationery for all

four crops. Log of real import price, log of expected price, log of exchange rate and log

of per capita real GDP are difference stationery.

Table 6.8: Fisher’s panel Unit Root Test

Wheat Soybean Corn Rice

Level First Level First Level First Level First Differen Difference Differen Difference ce ce Ln Price 213.30 229.51 0.2945 131.22 6.4049 286.589 40.79 99.825 Volatility *** *** (1.000) *** (0.994) *** *** *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Ln 55.439 173.32 68.363 228.87 58.018 202.984 0.521 34.41 Exchange *** *** *** *** *** *** *** *** rate (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.007) (0.000) Volatility Ln Real 17.38 57.645 17.07 82.77 14.99 170.49 13.73 109.792 Import (0.17) *** (0.518) *** (0.662) *** *** Price (0.000) (0.000) (0.000) (0.32) (0.000) Ln 21.645 158.96 8.29 71.006 11.34 145.33 13.95 87.927 Expected *** (0.97) *** (0.8791) *** ** *** Price (0.086) (0.000) (0.000) (0.000) (0.31) (0.000) Ln 5.11 93.20 8.437 126.86 7.60 122.43 0.825 28.329 Exchange (0.984) *** (0.971) *** (0.983) *** (0.991) *** rate (0.000) (0.000) (0.000) (0.001) Ln 6.725 17.519 10.412 19.435 6.92 18.75 3.6719 1.266 Percapita (0.944) *** (0.917) ** (0.990) ** ** real (0.045) (0.036) (0.04) (0.072) (0.009) GDP *,** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses denote P value

Table 6.9 and 6.10 presents the test statistics of Hausman model specification test and Friedman’s test for cross-sectional independence respectively. Table reports that the

Hausman test statistics computed in the wheat model rejects the null hypothesis that both estimates are consistent. As a result, a fixed effects model is preferred to a random effects model for wheat regression. The test statistics for the Friedman’s test for cross sectional independence in the wheat model rejects the null hypothesis of cross-sectional independence. In order to address this problem, I estimate the model with Driscoll-Kraay standard errors. Therefore, I estimate a fixed effects model with Driscoll-Kraay standard errors to estimate the wheat mode l.

63 The Hausman’s statistics calculated in the soybean model fails to reject the null hypothesis that both fixed effects and random effects estimates are consistent, suggesting to use a fixed effects model. The Friedman test statistics in table 6.11 rejects the null hypothesis of cross-sectional independence in the soybean model. Therefore, similar to wheat model I estimate a pooled OLS model with Driscoll-Kraay standard error for soybean model

The corn import model is also estimated as pooled OLS model with Driscoll-

Kraay standard errors since the Hausman’s test statistics rejects the null hypothesis that both estimates are consistent; and Fridman’s test statistics rejects the null hypothesis of cross-sectional independence in the panel.

The Hausman’s test statistics computed in the rice model rejects the null hypothesis that both fixed and random effects estimates are consistent. The Friedman’s test statistics in the table 6.11 rejects the null hypothesis of cross-sectional independence.

Thus, rice model is estimated with fixed effects model with Driscoll-Kraay standard errors.

Table 6.9: Hausman Specification tests

Wheat Soybean Corn Rice Test P Value Test P Value Test P Value Test P Statistics Statistics Statistics Statistics Value 12.59*** 0.000 0.34 0.98 28.33*** 0.001 20.97*** 0.00

*,** and *** denote significance level at 10%, 5% and 1% respectively.

Table 6.10: Friedman’s test for cross sectional independence

Wheat Soybean Corn Rice

Test P Value Test P Value Test P Value Test P Statistics Statistics Statistics Statistics Value 13.87** 0.03 35.956 0.000*** 24.548 0.0019** 6.181 0.04**

*,** and *** denote significance level at 10%, 5% and 1% respectively.

64 Table 6.11 reports the variance inflation factors (VIF) for all the independent variables used for each regression. The table reports that VIF for all variables are not high enough to be concerned about multicollinearity.

Table 6.11: Variance Inflation Factors

Wheat Soybean Corn Rice Ln Price volatility 1.93 1.57 1.96 3.03 Ln Exchange rate volatility 1.16 1.16 1.25 1.27 Ln Real Import Price 1.15 3.45 1.61 1.44 Ln Expected price 1.06 1.31 1.04 1.03 Ln Exchange rate 1.60 4.06 1.50 3.00 Ln per capita real GDP 1.30 1.26 1.19 1.35 Time trend 1.39 1.47 1.45 1.27 Mean VIF 1.37 2.04 1.43 1.77

Table 6.12 reports the test statistics for Wooldridge test for autocorrelation for wheat, soybean, corn and rice model. Test statistics of Wooldridge test for all the models rejects the null hypothesis of no first order serial correlation in all the models.

Table 6.12 : Wooldridge test for serial correlation

Wheat Soybean Corn Rice

Test P Test P Test P Test P Statistics Value Statistics Value Statistics Value Statistics Value 15.886*** 0.007 12.60*** 0.007 3.669*** 0.009 376.12*** 0.0026

*,** and *** denote significance level at 10%, 5% and 1% respectively.

65 Table 6.13 reports parameter estimates of the developed countries’ imports of major agricultural commodities from their developed counterparts. Results show that log of price volatility and log of exchange rate volatility do not have significant effect on log of per capita import volumes of wheat, soybean and rice. Only log of per capita corn import volume are positively affected by both log of exchange rate and log of commodity price volatilities at a one percent level of significance. Percentage change of real import price has a negative and significant effect on log of per capita imports of all four commodities. On the other hand, percentage change of expected price has a positive and significant effect on log of per capita import volumes of wheat, soybean and corn imports. Since the traders from developed countries have access to both commodities and financial futures, they are expected to hedge the risk of both commodity price and exchange rate volatilities (Cho et al 2002, Kandilov 2008, Zhang 2010). Coefficients estimates in table 6.13 also support this proposition for three out of four crops since price and exchange rate volatilities have no significant effect on log of per capita imports of wheat, soybean and rice. Moreover, positive and significant effect of percentage of expected price suggest that developed countries are developed countries are responsive to the price change in the futures market. Domestic policies of many developed countries may also play a role in minimizing the effects of volatilities on their trade. For example, variable import levy of the European Union (EU) insulates the EU countries from the price and exchange rate volatilities.

Table 6.13a reports the coefficients estimates of the developed countries’ imports of major agricultural commodities from their developed counterparts excluding the

66 percentage change in expected price variable. Results suggest no drastic change in the signs and significances of the variables.

6.13: Coefficients estimates of developed countries’ wheat, soybean, corn and rice imports from developed importers from 1991 to 2009 Commodities

Dependent Variable: Wheat Soybean Corn Rice Log of per capita import Fixed Independent Variables Effects Pooled OLS Fixed effects Fixed effects -0.098 -0.104 0.20** -0.055 ln Price volatility (0.05) (0.83) (0.102) (0.191) -0.162 0.316 0.24*** 0.280 ln Exchange rate volatility (0.112) (0.333) (0.08) (0.240) -0.039* -3.571*** -0.19* -2.398** ∆ ln Real Import Price (0.109) (0.91) (0.103) (0.905) 0.117* 3.503*** 0.24** -0.045 ∆ ln Expected Price (0.06) (0.909) (0.37) 0.1638 0.031*** -0.041 0.06* -0.046 ∆ ln Exchange rate (0.006) (0.034) (0.008) (0.179) -3.941 3.566 1.02 -51.728** ∆ln Per Capita real GDP (2.27) (9.96) (1.88) (21.87) 0.003 -0.085** -0.002** 0.028 Time trend (0.003) (0.025) (0.881) (0.02) -4.453*** -0.282* -6.69* -7.580*** Constant (0.70) (3.16) (7.9) (0.82) 0.07 0.128 0.97 0.61 R squared 0.000 0.000 0.000 0.000 Prob > F 126 153 162 51 Number of Observation *,** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll-Kraay robust standard errors

6.13a: Coefficients estimates of developed countries’ wheat, soybean, corn and rice imports from developed countries from 1991 to 2009 (without percentage change of expected price) Commodities

Dependent Variable: Wheat Soybean Corn Rice Log of per capita Import Fixed Independent Variables Effects Pooled OLS Fixed effects Fixed effects -0.079 -0.120 0.797** -0.328 ln Price volatility (0.05) (0.07) (0.31) (0.264) ln Exchange rate -0.158 0.1059 0.369 0.1531 volatility (0.114) (0.35) (0.23) (0.182) -0.045* -0.258* -2.152*** -0.097 ∆ ln Real Import Price (0.116) (0.59) (0.19) (0.196) 0.0294** -0.004 -0.032 0.1196 ∆ ln Exchange rate (0.006) (0.04) (0.04) (0.209) ∆ ln Per Capita real -3.487 2.4312 0.757 -50.86 GDP (2.27) (11.31) (0.74) (0.016) 0.0030 -0.101 -0.035 -0.027 Time trend (0.003) (0.02) 0.033) (0.041) -4.309*** -2.429 5.884* -7.501 Constant (0.807) (3.16) (3.4) (0.98) 0.32 R squared 0.06 0.10 0.503 0.000 Prob > F 0.000 0.000 0.000 Number of 51 Observation 126 153 162 *,** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll- Kraay robust standard errors

6.3.2 Top developing importers’ imports from developing exporters

This section presents the coefficient estimates regression models aimed to examine the effects of exchange rate volatility and commodity price volatility on top developing importers’ imports of wheat, soybean, rice and corn from their developing trade partners.

Table 6.14 presents the test statistics of Fisher’s panel unit root test for wheat, soybean, corn and rice models. Test statistics suggests that price volatility and exchange rate volatility variables are level stationery whereas import price, expected price, exchange rate and GDP per capita variables are difference stationery.

Table 6.14: Fisher’s panel Unit Root Test

Wheat Soybean Corn Rice

Level First Level First Level First Level First Difference Difference Difference Difference Ln Price 10.63 36.54 5.693 254.746 5.69 254.74 81.58 199.65 Volatility *** *** (0.991) *** (0.991) *** *** *** (0.056) (0.000) (0.000) (0.000) (0.000) (0.000) Ln 74.92 205.05 108.57 245.61 108.57 245.61 52.640 186.187 Exchange *** *** *** *** *** *** *** *** rate (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Volatility Ln real 2.34 38.32 4.789 121.23 20.23 149.08 11.06 58.58 Import (0.12) *** *** Price (0.000) (0.232) (0.000) (0.205) (0.000) (0.19) (0.00)

Ln 18.55 136.25 10.084 129.190 10.084 129.19 27.91 175.85 Expected (0.099) *** (0.862) *** (0.862) *** *** *** Price (0.000) (0.000) (0.000) (0.5) (0.000) Ln 31.40 48.57 13.48 169.02 14.18 166.33 14.573 48.729 Exchange *** *** *** *** *** (0.265) *** rate (0.11) (0.000) (0.24) (0.000) (0.57) (0.000) (0.000) Ln 2.48 55.288 2.433 46.07 2.43 46.075 1.32 37.58 Percapita (1.00) *** (1.00) *** (1.000) *** (0.999) *** real (0.000) (0.001) (0.000) (0.002) GDP *,** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses denote P value

69 Table 6.15 and 6.16 present the test statistic of Hausman’s model specification

test and Friedman’ test for cross-sectional dependence respectively. The test statistics of

Hausman’s model specification test computed in the wheat model is1.16 with a p-value

0.97. Therefore I fail to reject the null hypothesis that both fixed and random effects

estimates are consistent. A random effects model is appropriate in this case. Friedman’s

test statistics of cross-sectional independence presented in table 6.18 is 12.32 with a p-

value 0.03 for wheat model. Thus, I reject the null hypothesis of cross-sectional

independence. On the basis of these two tests, the wheat import model is estimated as a

pooled OLS model with Driscoll-Kraay robust standard error.

Hausman’s test statistics presented in the Table 6.15 for soybean and corn

indicate that the null hypothesis that both fixed and random effects models are consistent

is rejected for these two models. Therefore, random effects model are preferred for

soybean, corn and rice models. Friedman’s test statistics presented in the table

6.186suggest that null hypothesis of cross-sectional independence is rejected for soybean,

corn models. Therefore, we estimated the soybean and corn model as pooled OLS model

with Driscoll-Kraay standard errors. Rice model is estimated as fixed effects model with

Driscoll-Kraay robust standard error.

Table 6.15: Hausman’s Specification tests

Wheat Soybean Corn Rice

Test P Test P Test P Test P Statistics Value Statistics Value Statistics Value Statistics Value 1.6 0.97 1.16 0.998 2.34 .8009 194*** 0.000

*,** and *** denote significance level at 10%, 5% and 1% respectively.

70 Table 6.16: Friedman’s test for cross-sectional independence

Wheat Soybean Corn Rice

Test P Test P Test P Test P Statistics Value Statistics Value Statistics Value Statistics Value 12.32** 0.037 18.77*** 0.0089 15.15** 0.03 20.33*** 0.001

*,** and *** denote significance level at 10%, 5% and 1% respectively.

Table 6.19 provides the VIF for all the explanatory variables of wheat, soybean, corn and rice models. Low VIF values of variables in each model confirm that multicollinearity is not an issue to be concerned in any of the models.

Table 6.17: Variance Inflation Factors

Wheat Soybean Corn Rice Ln Price Volatility 1.28 2.3 2.30 2.07 Ln Exchange rate 2.18 1.11 1.12 1.06 Volatility Ln Real Import Price 1.19 2.47 2.47 1.36 Ln Expected Price 1.10 1.01 2.47 1.08

Ln Exchange rate 1.42 1.91 1.91 2.13

Ln Percapita real 1.06 1.15 1.14 1.10 GDP Ln Price Volatility 1.42 1.09 1.10 1.56 Mean VIF 2.09 1.58 1.58 1.48

Test statistics of Wooldridge test of serial correlation for wheat, soybean,. Corn and rice model are presented in the table 6.18. Test statistics for wheat, soybean and corn model reject the null hypothesis of no first order serial correlation. Test statistics of

Wooldridge test computed for rice model is 3.23 with a p-value 0.146. Therefore, I fail to reject the null hypothesis of no first order serial correlation in rice model.

Table 6.18 : Wooldridge test for serial correlation Wheat Soybean Corn Rice Test P Value Test P Value Test P Value Test P Value Statistics Statistics Statistics Statistics 18.21*** 0.008 9.080** 0.01 12.20** 0.01 3.23 0.1467

*,** and *** denote significance level at 10%, 5% and 1% respectively.

Table 6.19 presents the coefficients estimates of the imports of wheat, soybean, corn and rice by developing countries from their developing counterparts. Estimates show that log of price volatility has a positive and significant effect on log of per capita wheat import volume at a five percent level of significance. This result is supported by

IFPRI(2011) which reports that wheat importing developing countries have a tendency to import more than the required amount of wheat when they face volatility so that they can have a considerable buffer stock to avoid domestic food riots. It may be mentioned here that wheat is the staple food in majority of the developing countries. Although log of price volatility affects wheat import positively, it has a negative and significant effect on log of per capita soybean import volume (at one percent level of significance) and imports of corn and rice at five percent level of significance. Since the majority of developing countries are unable to hedge the risk by operating in the futures market due to financial constraints, regulation and limited storage capacity, they are more likely to be affected negatively by price volatility. On the other hand, log of exchange rate volatility has no effect on developing countries’ log of import volume from other developing countries. Since a majority of developing countries follow a ‘managed floating’ exchange rate system they are more likely to protect their traders from exchange rate volatility. As expected, percentage change in real import price has negative and significant effects on

72 log of import volume of wheat, soybean and corn. But percentage change in expected price has a positive and significant effect (at five percent level) on log of rice rice import only. Log of per capita import volumes of all other crops are unaffected by expected price. This result suggests that developing countries are less responsive to the movement in futures market.

6.19: Coefficient estimates of developing importers’ imports of wheat, soybean, corn and rice from developing exporters from 1991 to 2009 Commodities

Dependent Variable: Wheat Soybean Corn Rice Log of per capita Import Independent variables Pooled OLS Pooled OLS Pooled OLS Fixed Effects

0.687*** -0.2826*** -0.03** -0.242* ln Price volatility (0.215) (0.079) (0.012) (0.128) ln Exchange rate -0.069 0.334 0.224 0.026 volatility (0.09) (0.36) (0.12) (1.21) -0.83** -2.78*** -0.862*** -0.107 ∆ ln Real Import Price (0.38) (0.75) (0.23) (0.07) 0.16 0.2159 -1.49 -0.153 ∆ ln Expected Price (0.32) (0.80) (0.685) (0.227) 0.344* 0.2051 0.306* -1.020 ∆ ln Exchange rate (0.12) (0.53) (0.84) (0.799) ∆ ln Per Capita real 12.25* 0.797 16.014 11.614 GDP (0.257) (10.22) (11.28) (0.61) 0.1087*** 0.261*** 0.1299** 0.069* Time trend (0.01) (0.03) (0.02) (.037) -3.13** 3.265* -1.9* -6.956*** Constant (1.18) (2.73) (1.19) (0.75) 0.33 0.3114 0.2762 0.1534 R squared 0.000 0.000 0.004 0.000 Prob > F 108 144 144 72 Number of Observation *,** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll-Kraay robust standard errors

73 Table 6.20 provides the estimates of coefficients without the percentage change in expected price variable. The table shows that signs and significance level remain the same for most of the variables.

6.20: Coefficient estimates of developing importers’ imports of wheat, soybean, corn and rice from developing exporters from 1991 to 2009 (without percentage change of expected price) Commodities

Dependent Variable: Wheat Soybean Corn Rice Log of per capita Import Pooled Pooled OLS Pooled OLS Fixed Effects Independent variables OLS 0.679** -0.274*** -0.036** -0.3691*** ln Price volatility (0.232) (0.07) (0.013) (0.089) ln Exchange rate 0.0880 0.351 0.208 0.5389 volatility (0.135) (0.366) (0.121) (0.141) -0.980** -2.697*** -0.582 -0.366 ∆ ln Real Import Price (0.34) (0.55) (0.327) (0.813) 0.3118* 0.198 0.234 -0.061 ∆ ln Exchange rate (0.14) (0.518) (0.86) (1.41) ∆ ln Per Capita real 21.30** 1.295 13.696 2.809** GDP (9.79) (9.9) (11.44) (0.79) 0.099*** 0.265*** 0.114*** 0.0184 Time trend (0.016) (0.03) (0.03) (0.02) -2.394* 3.961** -3.240* -11.54*** Constant (1.18) (3.96) (1.5) (3.7) 0.3273 0.3112 0.2365 0.2453 R squared 0.000 0.000 0.004 0.000 Prob > F 108 143 144 72 Number of Observation *,** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll-Kraay robust standard errors

6.4 Chapter Summary

This chapter presented the estimates of coefficients of both quarterly and annual models for wheat, soybean, corn and rice. The quarterly models attempted to examine the effects of exchange rate and commodity price volatilities on Canada’s wheat and soybean export; and corn and rice imports. The estimated coefficients suggest that price and exchange rate volatilities do not have a significant effect on Canada’s export of wheat and soybean; and imports of rice and corn. On the other hand, the annual models examined the effects of exchange rate and commodity price volatilities on imports of top developed and developing importers. Results suggest that trade between developing countries are more affected than trade between developed countries. Next chapter provides a summary of the study and discusses policy implications and limitations of this study

75 Chapter 7: Summary and Conclusion

This chapter provides a summary of the motivation and objectives of this study, theoretical framework used for analysis, empirical results and discussions, limitations of the study and future directions of research in this area.

7.1 Summary

The purpose of this thesis is to examine the effects of both exchange rate volatility and commodity price volatility on specific agricultural commodity trade. Previous literature on the effects of exchange rate volatility on international agricultural commodity trade came up with mixed results (Cho et al. 2002; Kandilov, 2008; Zhang, 2010; Dell Ariccia,

1999; Villanueva and Sarker, 2009). Most of the studies on agricultural trade focused on overall agricultural commodity trade, not specific commodity trade. At the same time, research on the effects of commodity price volatilities on commodity trade flows is also scarce. In recent years, agricultural commodity price volatilities also drew much attention of the researchers. Few studies (IFPRI, 2011; World Bank, 2011, FAO, 2011) predicted that price volatilities may have implication on agricultural commodity trade too. This backdrop motivated a study to examine the effects of both exchange rate and commodity price volatilities on trade of four major agricultural commodities: wheat, soybean, corn and rice. The specific objectives of the study are (i) to estimate the effects of both exchange rate and commodity price volatilities on agricultural trade and (ii) to examine the effects of commodity price and exchange rate volatilities on developed and developing countries separately.

This study used a modified version of Hooper and Kohlhagen’s trade model

(Hooper and Kohlhagen, 1978). The basic Hooper and Kohlhagen model theoretically estimated the effects of exchange rate volatilities on import demand. We incorporated price volatility into the model. The theoretical framework used in this study asserts that the effects of exchange rate and commodity price volatilities on agricultural commodity trade largely depends on the risk preference and ability to hedge the market risk of the traders. Exchange rate volatility and commodity price volatility may have a negative effect on trade if the traders are risk averse and less capable to hedge the market risk. On the other hand, exchange rate volatility may have a positive effect on trade volume if the traders are risk lovers and more capable to hedge the market risks.

The empirical part of this study estimates the effects of exchange rate and commodity price volatilities in two different ways:

First, the effects of exchange rate and commodity price volatilities on Canada’s export of wheat and soybean; and imports of corn and rice with its major trading partners are estimated using quarterly data.

Second, the effects of exchange rate and price volatilities on wheat, soybean, corn and rice imports of on trade between developed and developed countries; and developing and developing countries are estimated using annual data.

The coefficient estimates of the quarterly models of Canada’s trade with its major trading partners suggest that price volatility does not have a significant effect on Canada’s export of wheat and soybean; whereas exchange rate volatility has a negative and significant

77 effect on Canada’s import of rice. Exchange rate volatility does not have a significant effect on import of corn and export of wheat and soybeans.

The annual models are estimated to examine the effects of exchange rate and commodity price volatilities on developed and developing countries’ imports separately.

Since developed countries’ traders have much wider access to commodities and financial futures market, it is expected that trade between developed countries will remain unaffected by volatilities. On the other hand, because of limited access to futures market

(and other derivative instruments) and the tendency towards “speculative behaviors” we expected that trade between developing countries will be affected negatively by price and exchange rate volatilities. Coefficients estimates of import demand of developed countries from their developed counterparts are largely consistent with our expectations.

Price volatility and exchange rate volatility have a positive and significant effect on developed countries’ corn imports only. But price and exchange rate volatilities do not have a significant effect on soybean, corn and rice imports.

Developing countries’ imports from developing countries are mostly affected by price and exchange rate volatilities. Price and exchange rate volatilities have a negative and significant effect on soybean, corn and rice imports of developing countries’ import from their developing trading partners. These results are expected because of developing countries’ limited access to commodity and financial futures market; and their tendency towards trade restrictive policies, such as export ban during the periods of volatilities

(IFPRI 2011). Previous studies, such as, Arize et al (2005), Arize et al ( 2003), Bahmani-

Oskoee(1996), and Kandilov (2008) found similar negative effects of exchange rate volatilities on developing countries’ trade with their developing counterparts. For

78 example, Arieze et al (2003) found negative effect of exchange rate volatilities on trade of Turkey, Korea, Malaysia, Indonesia, and Pakistan, Another study by Arize (2000) found a negative effect of exchange rate volatility on export volume of 13 LDCs.

Kandilov (2009) showed with a gravity model that exchange rate volatility has a negative effect on trade between developing countries.

7.2 Policy implications

The finding of this study suggests that the effects of exchange rate and commodity price volatilities vary across the countries studied. It may largely depend on countries’ domestic policies, access to futures market and financial services and traders’ risk preferences. In general, the finding suggests that developing countries’ trade are more affected because of exchange rate and commodity price volatilities. Therefore, exchange rate and commodity price volatilities may have an impact on agriculture and food security of developing countries. Since a number of developing countries are already food insecure because of the burden of population and low agricultural productivity, increase in price and exchange rate and commodity price volatilities may have the potential to further trigger food insecurity in many developing countries.

Restricting imports or reducing import tariffs in short run are very popular policy options for many developing countries to cope with volatilities. During the periods of volatilities in 2007-08, 43 out of 81 developing countries reduced import taxes and 25 banned exports for specific products or increased export taxes for agricultural commodities (BIAC, 2011). Some countries, being speculative, began to import more food than the requirement to create a buffer stock. These short term abrupt changes of

79 policies often discourages the necessary additional investment required for agricultural production and have potential to increase volatility further. Although these policies may help to stabilize the situation in a single country in short run, they are often counter productive and expensive in the long run. These policies may have implications on food securities of other countries as well. For example, ban on rice export by India during

2007-08, destabilize the world rice market and threatened food security of many countries that are dependent on rice.

One of the findings of this study is that generally commodity price and exchange rate volatility do not have a significant effect on developed countries’ trade. This finding suggests that farmers and all agents in the marketing chain in developed countries may be well protected from risk of exchange rate and price volatilities by a variety of market based instrument. They may be able to manage the risk they face with these instruments.

Sarris (2011) says that producers and consumers of developed countries have developed sophisticated market-based risk management system (e.g., insurance) to deal with commodities risk. In the last three decades, they also developed a variety of innovative financial instruments (futures, options, and other derivatives) to hedge the risk of price and exchange rates. On the contrary, most of the developing countries do not have a well developed futures market and their financial markets are also underdeveloped. Although the modern markets of risk management instrument are accessible to all, traders of most of the developing countries are unable to take this advantage because of a variety of institutional imperfections and financial constraints. Developing countries may consider establishing well-organized commodity exchange market. Developed countries may

80 extend their technical support in building commodity exchange markets in developing countries.

7.3 Limitations and further research

In our quarterly models, we tried to estimate the effects of exchange rate and commodity price volatilities with Canada’s major trading partners. Since quarterly data on GDP, exchange rate and price were not available for many countries, specially developing countries, we were unable to incorporate all the trading partners. If data become available, the estimates can be done with larger sample.

The assumptions about the accessibility of importers to financial and commodity

futures could be verified with data. Although most of the developing countries do not

have access to financial and commodities futures market, some major traders of

agricultural commodities do have access to these markets. Because of the unavailability

of information about access to futures market for all countries in the panels, I was unable

consider this factor in the model.

Unit price was used as import price in this study. Unit price indices may create

bias in estimation because of the compositional changes in quantities and quality mix of

exports and imports. One can use domestic price as an independent variable since this

variable is an important component of an import demand model. As domestic prices of

all commodities for all countries in the panels are not available, we used the unit value.

Another limitation of this study is that the effects of exchange rate and

commodity piece volatilities are estimated at the country level whereas the theoretical

framework was developed for an individual firm first and then aggregated for the

81 country. Further study may also consider panel co-integration and estimating pane cointegration regression.

7.4 Research Contribution

The purpose of this thesis was to examine the effects of both commodity price and exchange rate volatilities on trade flows. Overwhelming percentage of previous studies only examined the effects of exchange rate volatilities on trade flows. Inclusion of price volatility’s effect along with exchange rate volatilities is the key contribution of this thesis. Another important contribution of this thesis is the estimation of the effects of exchange rate and commodity price volatilities on trade of developed and developing countries separately.

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86 Appendix A

Table A1 : Exchange Rate Arrangements of Countries

Exchange Rate Regime Countries

(Number of Countries)

Exchange arrangements with Ecuador, El Salvador, Kiribati, Marshall Islands, no separate legal tender (41) Micronesia, Fed. States of Palau, Panama, San Marino , Timor-Leste, Dem. Rep. of Antigua and Barbuda, Dominica,, Grenada, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Benin,, Burkina Faso, Côte d'Ivoire, Guinea-Bissau, Mali,Niger, Senegal, Togo, Cameroon, Central African Rep., Chad, Congo, Rep. of Equatorial Guinea, Gabon

Euro Area Currency board Bosnia and Herzegovina, Brunei Darussalam, Bulgaria, arrangements (7) Hong Kong SAR, Djibouti, Estonia, Lithuania Other conventional fixed peg Aruba, Bahamas, Bahrain, Barbados, Belarus,, Belize, arrangements (52) Bhutan, Bolivia, Cape Verde, China , Comoros, Egypt, Eritrea, Ethiopia, Guyana, Honduras, Iraq, Jordan, Kuwait, Latvia, Lebanon, Lesotho, Macedonia, Maldives, Malta, Mauritania, Namibia, Nepal, Netherlands Antilles, Oman, Pakistan, Qatar , Rwanda, Saudi Arabia, Seychelles, Sierra Leone, Solomon Islands, Suriname, Swaziland, Syrian Arab Rep, Trinidad and Tobago, Turkmenistan, Ukraine, United Arab Emirates, Venezuela, Rep. Bolivariana , Vietnam, Zimbabwe Pegged exchange rates within Cyprus, Denmark, Slovak Rep., Slovenia, Hungary horizontal Tonga bands (6)

87 Exchange Rate Regime Countries

(Number of Countries) Crawling pegs Azerbaijan, Botswana, Costa Rica, Iran, Nicaragua Managed floating with no pre- Argentina, Bangladesh, Cambodia, Gambia, Ghana, Haiti, determined path for the Jamaica, exchange rate (51) Lao P.D.R., Madagascar, Malawi, Mauritius, Moldova, Mongolia, Sri Lanka, Sudan , Tajikistan, Tunisia, Uruguay, Yemen, Rep. of, Zambia, Colombia Czech Rep., Guatemala, Peru, Romania, Serbia, Rep. of, Thailand, Afghanistan, Armenia, Georgia, Kenya,Kyrgyz Rep, Mozambique, Algeria, Angola, Burundi, Croatia, Dominican Rep, Guinea, India, Kazakhstan, Liberia, Malaysia, Myanmar, Nigeria, Papua New Guinea, Paraguay, Russian Federation , São Tomé and Príncipe, Singapore, Uzbekistan Independently floating (25) Albania, Congo, Dem. Rep. of, Indonesia, Uganda, Australia, Brazil, Canada, Chile , Iceland, Israel, Korea, Mexico, New Zealand, Norway, Philippines, Poland, South Africa, Sweden, Turkey, United Kingdom, Tanzania, Japan, Somalia, Switzerland United States Source: IMF ( http://www.imf.org/external/np/mfd/er/2006/eng/0706.htm )

88 Appendix B

Table B1: Coefficient estimates of quarterly wheat and soybean imports from Canada from 2000 to 2009 (without percentage change of real import price variable) Commodities Dependent Variable: Log of per capita import Wheat Soybean Independent variables Fixed Effect Fixed Effect 0.134 -0.12 ln Price volatility (0.107) (0.10) -0.068 -0.43** ln Exchange rate volatility (0.62) (0.15) 3.992* 1.01 ∆ ln Expected Price (3.99) (0.38) 5.181 0.50*** ∆ ln Exchange rate (4.18) (0.014) -4.658 -7.58*** ∆ ln Per Capita real GDP (4.97) (2.57) 1.136 -2.15*** Dummy_Quarter 2 (0.90) (0.56) 0.943 -0.75*** Dummy_ Quarter3 (1.07) (0.126) 0.731 0.05 Dummy Quarter_4 (1.06) (0.121) -0.012 Timetrend (0.02) 0.05*** -7.205 -12.62*** Constant (5.05) (0.003) No. of Observation 171 390 R2 0.07 0.000 Prob > F 0.000 0.63 *. ** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll- Kraay robust standard errors

89 Table B2: Coefficient estimates of quarterly corn and rice imports by Canada from 2000 to 2009 (without percentage change of real import price variable) Commodities Dependent Variable: Log of per capita import Corn Rice Independent variables Fixed Effect Fixed Effect 0.018 0.0398 ln Price volatility (0.06) (0.03) 0.069 0.0229 ln Exchange rate volatility (0.16) (0.08) 0.298 -0.1358 ∆ ln Expected Price (0.40) (0.20) -2.074* 0.0695 ∆ ln Exchange rate (1.14) (0.60) 1.367*** 6.0774 ∆ ln Per Capita real GDP (0.50) (1.93) -0.060 -1.1617 Dummy_Quarter 2 (0.13) (0.37) 0.000 -1.4721 Dummy_ Quarter3 (0.12) (0.43) 0.087 -0.9803 Dummy Quarter_4 (0.13) (0.36) 8.275 0.0026 Timetrend (3.46) (0.002) -7.3362 Constant 0.018 (1.02) No. of Observation 78 152 R2 0.383 0.15 Prob > F 0.04 0.00 *. ** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll- Kraay robust standard errors

Table B3: Coefficients estimates of developed countries’ wheat, soybean, corn and rice imports from developed countries from 1991 to 2009 (without percentage change of real import price) Commodities

Dependent Variable: Soybean Corn Rice Log of per capita Wheat Import Fixed Independent Variables Effects Pooled OLS Pooled OLS Pooled OLS -0.11 -0.150 -0.358* -0.9502 ln Price volatility (0.05) (0.10) (0.17) (0.54) ln Exchange rate -0.17 0.147 -0.726** 0.5890 volatility (0.12) (0.34) (0.258) (1.08) 0.12* 0.597 -0.022 -0.6340 ∆ ln Expected Price (0.07) (0.392) (0.117) (0.70) 0.03*** 0.008 -0.140*** 1.1629 ∆ ln Exchange rate (0.006) (0.04) (0.018) (0.811) ∆ ln Per Capita real -3.88* 1.731 -1.443 -44.39 GDP (2.19) (11.89) (0.866) (85.60) 0.00 -0.103 0.048*** 0.0142** Time trend (0.002) (0.02) (0.008) (0.111) -4.75 -2.166* -1.742 -12.313*** Constant (0.88) (1.97) (4.99) (2.64) 0.14 R squared 0.065 0.109 0.11 0.00 Prob > F 0.000 0.00 0.000 Number of 57 Observation 126 153 162 *,** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll- Kraay robust standard errors

91 Table B4: Coefficients estimates of developing countries’ wheat, soybean, corn and rice imports from developing countries from 1991 to 2009 (without percentage change of real import price) Commodities

Dependent Variable: Soybean Corn Rice Log of per capita Wheat Import Fixed Independent Variables Effects Pooled OLS Pooled OLS Pooled OLS 0.453 -0.10* -0.037*** -0.370** ln Price volatility (.41) (0.24) (0.012) (0.166) ln Exchange rate 0.070 -0.02 0.175 0.299 volatility (0.14) (0.377) (0.144) (0.167) -0.049 -1.74 -1.246 0.481** ∆ ln Expected Price (0.25) (1.38) (0.74) (0.182) 0.316* 0.07 0.356 -0.702 ∆ ln Exchange rate (0.15) (0.69) (0.824) (1.5) ∆ ln Per Capita real 16.708 13.23 13.883 -56.013*** GDP (10.15) (15.61) (11.823) (16.13) 0.105*** 0.23*** 0.109 0.050* Time trend (0.015) (5.14) (0.026) (0.02) -3.692 -10.09*** -6.258 -3.004* Constant (1.97) (0.75) (0.621) (1.57) 0.2566 R squared 0.29 0.20 0.2556 0.000 Prob > F 0.000 0.000 0.000 Number of 72 Observation 108 144 144 *,** and *** denote significance level at 10%, 5% and 1% respectively. Numbers in parentheses are Driscoll- Kraay robust standard errors