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Competitiveness of the B.C. food and beverage industry in the Pacific Rim: an empirical analysis of the.. Cain, Laura Lea-Anne 1995

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COMPETITIVENESS OF THE B.C. FOOD AND BEVERAGE INDUSTRY IN THE PACIFIC RIM: An Empirical Analysis of the Influencing Factors by LAURA LEA-ANNE CAIN B.Sc. (Agr.), University of British Columbia, 1992 A THESIS SUBMITTED IN P A R T I A L F U L F I L L M E N T OF T H E REQUIREMENTS FOR T H E D E G R E E OF M A S T E R OF SCIENCE in T H E F A C U L T Y OF G R A D U A T E STUDIES Department of Agricultural Economics We accept this thesis as conforming to the required standard  T H E UNIVERSITY OF BRITISH C O L U M B I A April 1995 © L a u r a Lea-Anne Cain, 1995  In  presenting  degree  at the  this  thesis  in  University of  partial  fulfilment  of  of  department  this thesis for or  by  his  or  scholarly purposes may be her  representatives.  It  permission.  of  /~f  1/Z< CULTo/C/ic  The University of British Columbia Vancouver, Canada  Date  DE-6 (2/88)  LyLp^A^T  for  an advanced  Library shall make  it  agree that permission for extensive  publication of this thesis for financial gain shall not  Department  requirements  British Columbia, I agree that the  freely available for reference and study. I further copying  the  AZCO^ACJCJ  is  granted  by the  understood  that  head of copying  my or  be allowed without my written  Abstract Factors or characteristics which influence the export competitiveness of British Columbia's food and beverage processing industries in the Pacific Rim markets (i.e., Japan, Hong Kong, Taiwan, China: Mainland, Singapore and South Korea) are studied using pooled time-series and cross-sectional data, for the years 1988 through 1992. Changes in exports and in export market share are explained by changes in systematic exogenous and endogenous differences amongst B.C. and competing provincial industries over the five year period. The results indicate that, converse to what is suggested in the literature, there is no statistical consistency in the explanatory capability of comparative cost, industrial organization, or firm strategy variables to explain competitiveness in Pacific Rim markets. Rather, it appears export success is due to many unique factors at the firm or provincial level.  Hence, it is not possible to make generalizations about the competitiveness  determinants of these industries in the Pacific Rim markets.  ii  Table of Contents Abstract  ii  Table of Contents  iii  List of Tables  v  Acknowledgements  v  Dedications  vi  1.0 INTRODUCTION 1.1 Problem Statement 1.2 Objective 1.3 Thesis Outline  1 3 4 4  2.0 OVERVIEW 2.1 Competitiveness Defined 2.2 Market Overview  6 6 9  3.0 THEORETICAL FRAMEWORK 3.1 Review of the Literatures 3.1.1 Traditional Trade Theory 3.1.2 New Trade Theory: Considering LO. Variables 3.1.3 Limitations of Trade and LO. Theories . . 3.1.4 Competitiveness: Linking Trade, LO., and Strategic Management Theories 3.2 Theoretical Considerations 3.2.1 Wage Rates 3.2.2 Interest Rates 3.2.3 Exchange Rates 3.2.4 Tariffs 3.2.5 Transportation Costs 3.2.6 Industrial Concentration 3.2.7 New Capital Expenditures 3.2.8 Labour Productivity 3.2.9 New Product Innovation  19 24 24 25 26 26 27 27 29 30 30  4.0 MODEL SPECIFICATION 4.1 Conceptual Model 4.2 Empirical Model  32 32 34  5.0 EMPIRICAL IMPLEMENTATION 5.1 DATA 5.1.1 Data Overview 5.1.2 Variable Specification  38 38 38 41 iii  14 14 14 16 17  5.2 Methodology  51  6.0 RESULTS AND ANALYSES 6.1 Statistical Analysis of the Variables 6.2 Regression Analysis  57 57 58  7.0 CONCLUSION  78  REFERENCES  81  APPENDIX 1.  Selected Definitions of Competitiveness  85  APPENDIX 2.  Provincial Domestic Shipments and Pacific Rim Exports, 19881992, by Industry Category  87  Provincial Market Shares in the Pacific Rim, 1988-1992, by Industry Category  92  Concordances Between S.I.C, H.S., and SITC-2 Data Classification Systems  97  APPENDIX 3. APPENDIX 4. APPENDIX 5.  Correlations Between Number of Establishments and Industry Concentration Ratios  104  APPENDIX 6.  Summary of Trademark Application Data  105  APPENDIX 7.  Correlation Matrix of Variables  106  iv  List of Tables Table 2.1 Table 2.2 Table 2.3 Table 6.1 Table 6.2 Table 6.2A Table 6.2B Table 6.2C Table 6.2D Table 6.2E Table 6.3A Table 6.2C Table 6.3D  Pacific R i m Population and Income Statistics Pacific Rim Total (World) Imports in Five Five Industry Categories, 1988 and 1992 B . C . Exports to the Pacific Rim: 1988-1992, by Food Industry Category Variable Statistics Provincial Industry Domestic Shipments and Value-Added Sales: Comparison of GLS and OLS Regression Results Provincial Industry Exports to Pacific Rim and U.S.A. Markets: Regression Results Provincial Industry Exports to Pacific Rim and U.S.A. Markets: Regression Results Using Exchange Rates Provincial Industry Stacked Aggregated Exports to Pacific Rim and U.S.A. Markets: Regression Results Provincial Industry Sum of Exports to Pacific Rim and U.S.A. Markets: Regression Results National (Canadian) Industry Exports to the Pacific Rim: Regression Results Provincial Industry Market Share in Pacific Rim and U.S.A. Markets: Regression Results Provincial Industry Stacked Aggregated Exports to Pacific Rim and U.S.A. Markets: Regression Results National (Canadian) Industry Market Share to the Pacific Rim: Regression Results  v  9 12 13 56 59 63 65 67 68 72 74 75 76  Acknowledgements I would like to take this opportunity to express my acknowledgements to my thesis commitee, Dr. Richard Barichello, John Schildroth, Dr. Mary Bohman, and Dr. Keith Head, for their endless support and encouragement of my work. A particular thanks is owed to Dr. Barichello, for the numerous hours he spent discussing and explaining various aspects of my thesis with me, over e-mail, while on sabbatical at Harvard. His continued interest in my research, and his keen insight into economic theory and its practical applications were of significant benefit to me over these past two years. A special additional thanks is also owed to John Schildroth, Director, Trade Competition Branch, B.C.M.A.F.F., and to the B.C. Ministry of Agriculture, Fisheries, and Food, for the generous funding of my research in lieu of the U.B.C. Food Research Institute. The support and ecouragement of Kathy Shynkaryk and Retha Gertsmar enrich the department and the lives of the students in it. They will always be held in the highest of regard. Finally, I would like to thank my family, my friends, and most importantly, my husband, Warren Munroe. Words cannot adequately express what it has meant to have these people in my life.  vi  Dedications  I dedicate this thesis to my mom and dad (in his memory). My memories of their years of hard work and endless love have guided me through many long hours of study and research. I can only hope to one day be the sort of people they have left impressioned in my heart.  vii  1  1.0 INTRODUCTION To the British Columbia (B.C.) food industry, the Asia-Pacific region represents a substantial and significant export market opportunity. Nemetz (1990, p.l) points out that: "with average growth in both Gross Domestic Product (GDP) and GDP per capita well in excess of the world's market economies (UN [1988], 1985/6 Statistical Yearbook: 10-11), the Asia-Pacific bloc is viewed as... an engine of growth that will play a major role in 1  sustaining continued global economic expansion". As the socio-economic conditions in these markets evolve, increased opportunities in trade arise. This would suggest, therefore, that B.C.'s productive and diversified agri-food sectors would stand to gain. The arising market opportunities in the Pacific Rim, however, signal an increase in competition from other (domestic and international) food and beverage industries, vying for shares in these same markets. Hence, there is a need to identify the factors that will influence the "competitiveness" of the "B.C. industry in the Pacific Rim. The term "competitiveness" has been subject to numerous interpretations over the last decade . One commonly cited definition is that of Agriculture Canada's "Task Force on 2  Competitiveness in Agri-food Industries" (1991) which states: "A competitive industry is one  which possesses the sustained ability to profitably gain and maintain domestic and/or expor  This includes Oceania, ASEAN, the newly industrializing states (NICs), Japan, island states, the People's Republic of China, and the four smaller independent countries of Kampuchea, Laos, North Korea, and Vietnam. (See Nemetz, 1990). 1  (These are discussed in more detail in Chapter Two.)  2 market share". Implicitly this definition underscores the objective of identifying factors or characteristics which determine market success. The current literature is inconclusive as to the factors that influence or determine an industry's ability to obtain and sustain market share, however. Traditional theory rests on the understanding that relative cost differences between trading nations , in addition to industry 3  structure variables (i.e., firm concentration and/or economies of scale production) determine industry success. Recent studies by business management schools, on the other hand, point to the importance of business strategies in explaining domestic or foreign market shares or sales. That is, the notion of competitiveness, while rooted in traditional trade theory, extends a step further to consider the influence of endogeneous firm strategy variables into the traditionally exogeneous comparative cost and industrial organization paradigms. Conceptually, the notion introduces firm-specific cost-reducing, revenue-enhancing, and other business practices into the array of existing trade theory variables. Despite this recent emphasis of the important influence of business strategy variables, however, firm-specific factors have typically been omitted from empirical analyses. This is largely due to a lack of available firm-level data; for confidentiality and/or feasibility reasons, such data is inaccessible. In the existing competitiveness literature, rather, there are a large number of conceptual models to be found which offer hypotheses on possible determining factors. Alternatively, there are case studies, which, while important for the purpose of identifying those variables pertinent to one or a few firms, do not permit for generalizations across all firms in an industry. In addition, the literature consists of a large number of highly aggregated analyses, which typically analyze the competitiveness of 'national industries' or of  3.  (Or industries or firms.)  3  nations themselves. Such highly aggregated data, however, makes it increasingly difficult to understand the influence of more firm-specific strategies on market success. This thesis attempts to address these data limitations, by undertaking an analysis of regional (i.e., provincial) industry market competitiveness in the Pacific Rim. This approach allows for consideration of those firm strategies which can be discussed and modelled at the regional industry level. Specifically, the study addresses the strategy of developing new products, as measured or represented by the number of new trademarks applied for by individual firms in an industry. Analysis of five provincial food and beverage industry sectors, in five provinces, over a five year period is carried out. Competitiveness measures are explained by changes in the systematic exogeneous and endogeneous differences amongst related provincial industries.  1.1 Problem Statement Although significant export opportunities are seen to exist for the B.C. agri-food industry in the Pacific Rim markets, there is nonetheless a lack of understanding of the driving factors behind gaining or maintaining the industry's competitiveness in this region. Specifically, it is unclear whether or not, or to what extent, competitiveness is determined by trade, industrial organization, and/or business strategy factors. Alternatively, it is unclear whether competitiveness is influenced by factors which are exogeneous to the firm versus those that are inherently a part of the firm's endogeneous decision-makings. And, to the extent that endogeneous characteristics may be important, the significance of investment in cost-reducing versus revenue-enhancing strategies on influencing export market competitiveness need to be understood.  4 1.2 Objective Employing a matrix of pooled, time-series industry variables, the objective of this thesis is to identify those characteristics or factors which positively influence the competitiveness of the B.C. food industry in the Pacific Rim. The direction of the research will be to extend the neo-classical trade and industrial organization theory variables to incorporate firm strategy characteristics. Hence, both those factors exogeneous, or beyond the influence of the firm, and those endogeneous, or controlled by the firm, are considered. In order to achieve this goal, the following specific objectives must be met: a model must be built which reflects, in addition to traditional factors, the role of cost-reducing and revenue-enhancing strategies undertaken by firms in food and beverage manufacturing industries; data required for estimation of the model must be collected; the statistically significant competitiveness variables must be identified, using the above model, by estimating the effects of the traditional and non-traditional variables on domestic market sales and export market shares; and the findings must be interpreted in terms of determining what is necessary to foster the competitiveness of the B.C. food and beverage processing industry in the Pacific Rim marketplace.  1.3 Thesis Outline The oudine of the study is as follows. Chapter Two provides an overview of the alternative competitiveness definitions, and outlines a working definition for the purpose of this research. Chapter Two also provides an overview of some of the market conditions in the Pacific R i m and in specific provincial food and beverage industries. The competitiveness  5  literature, followed by theoretical considerations are reviewed in Chapter Three. Chapter Four then explains the empirical model. This is followed by a discussion of the data, variable specifications and methodology, in Chapter Five. Results and analyses are presented in Chapter Six, and conclusions are given in Chapter Seven.  6  2.0 OVERVIEW 2.1 Competitiveness Defined In the current economic literature a certain degree of confusion surrounds the term "competitiveness". The concept lacks a standard, generally accepted definition . Robert 4  Reich (Wall Street Journal, July 2, 1992) is often quoted as having said, "rarely has a term in public discourse gone so directly from obscurity to meaninglessness without an intervening period of coherence." And yet, strictly translated, into its root word, "compete" and suffixes, "ive" and "ness" , competitiveness is simply defined as, "the state of having the ability to 5  compete". That is, competitiveness means "being able to compete". The real confusion behind the term's interpretation, then, rests not in its literal meaning, but rather in its "applied" meaning. The concern of economists and others is to identify the indicators that can or should be used to measure "compete," and, on understanding the factors or determinants that influence "being able to". Hence, the interest is in developing a "working" definition for use in empirical analyses. One noticeable stumbling block, upon which much of the confusion seems to have arisen, however, is that different levels of economic analysis (e.g., firm, industry, or nation) typically call for different indicators and determining factors. That is, for example, while balance of payments might be considered an appropriate indicator of competitiveness at the national level, it has little meaning at the firm or industry levels. Hence, it is very difficult to develop one standard working definition for competitiveness i f the approach being taken is dependent upon different measures. A range of definitions, rather, is, at this time, all that is  See Appendix 1 for a list of the different definitions, as outlined in Ash and Brink (1992) and in Abbott and Bredahl (1994). 4  See Webster's New World Dictionary, 2nd Ed., the suffix "ive" means "of or having the nature of; the suffix "ness" means "state, quality or instance of being". 5  7  available for empirical work. In this thesis the competitiveness definition developed by the Task Force on Competitiveness in Agri-food Industries (Agriculture Canada, 1991) is adopted. As identified in Chapter One, the definition states: A competitive industry is one which possesses the  sustained ability to profitably gain and maintain domestic and/or export market share. This  interpretation, which is employed rather extensively in the literature , is considered in this 6  study for a few reasons. First, the interest of this research lies in identifying those factors that will enable B.C.'s food manufacturing industries to gain and maintain market shares in the Pacific Rim. Implicitly, the phrase, "...possesses the sustained ability to...", emphasizes the objective of identifying factors or characteristics which influence competitiveness. Second, the Task Force's definition includes the phrase "to profitably gain and maintain" market share; this considers the "dynamics" inherent in a market economy. It implies that market share is not a static concept, which is of particular importance given today's market environment of decreasing tariff and subsidy protection. Once an industry obtains or gains a certain market share, its ability to maintain that share will determine its true competitive ability. Hence, inclusion of the phrase gain and maintain specifies the necessary and sufficient conditions of competitiveness. Third, the terms "domestic" versus "export" (market share) are important and require some discussion. While B.C.'s exports are of primary interest in the research at hand, the province's domestic sales may prove insightful in identifying general competitiveness factors. This view corresponds to the findings of Hazeldine (1994) whereby it was determined that the 1986 growth in the Canadian food and beverage manufacturing industries can be attributed to  Refer, for example, to Abbott and Bredahl (1994); Ash and Brink (1994); Hazeldine (1994); Ho and Beghin (1994); and van Duren, Martin, and Wesgren (1994). 6  8 these industries having developed their domestic markets first. This is to say that the ability to understand domestic market success is likely to have some bearing on our understanding of the factors that influence the competitiveness in the export markets. Market "share" is also an important component of the Task Force's definition and requires further dicussion. While an industry's (domestic or export) sales might be increasing in absolute terms, the size of the market to which it is selling may be increasing at an even greater rate; thus, the industry's sales could actually be declining in relative terms, in which case only the share of sales (i.e., as a percent of total market sales) would be considered an adequate measure or indicator of competitiveness. In the absence of adequate market share data, however, total sales or total exports, as competitiveness indicators, may still prove useful for analytical purposes. Fourth, related to market share notion is the concept of relative market share. While an industry's absolute market share may be increasing, this share increase may be very small relative to the market shares of other industries in that export region. The term "relative", then, highligts the need to consider the market shares of competing industries in any competitiveness assessment. Finally, the Task Force's definition refers explicitly to industries, rather than nations, which is important given the level of analysis undertaken here. New developments in the trade, and new, or "strategic trade", literatures point to the importance of firm level 7  characteristics in explaining export market competitiveness. Yet, as previously discussed, firm level data is not readily available for empirical analyses. Rather the approach taken in this thesis is to focus on specific industry sectors.  Trade theory combined w i t h industrial organization theory is referred to as "strategic" trade theory (see Abbott and B r e d a h l , 1994, p. 19). 7  9  2.2 Market Overview Tables 2.1 to 2.3 below provide a brief statistical overview of some of the economic and market conditions pertaining to the six Pacific Rim markets studied. Table 2.1 Overview of Population and Income Statistics: Pacific Rim GDP/capita In Own Currency  2  POP ('OOO)  1  Market Japan Hong Kong Taiwan P.R.C. S.Korea Singapore 1  2 3  1988  1994  Percent change  % change  1988-92  122,626  125,107  2.2%  19.9%  5,651  5,630  3  -0.5%  39.7%  20,004  21,299  6.1%  29.6%  1,088,169  1,190,431  8.6%  41.0%  42,773  45,083  5.1%  44.8%  2,645  2,859  7.5%  33.3%  Source: U.S. Dept. of Commerce. Statistical Abstract of the U.S., 1994. Table No. 1351. (unless otherwise specified). Based on mid-year populations. Source: International Monetary Fund. International Financial Statistics. May 1994. Source: Calculated from statistics in Asian Development Bank, Economics and Development Resource Centre. Key Indicators of Developing Asian and Pacific Countries. Volume XXIV, 1993.  Table 2.1 shows a general increase in population and in consumer incomes in the particular Pacific Rim economies, over the 1988 to 1992 period. Only Hong Kong exhibits a slight decline in population, but which is then accompanied by a significant increase in total GDP per capita. Theoretically, an increasing population base, faced with increased income per capita, signifies a greater demand for all consumption goods; and hence, greater potential market opportunities for those firms capable of supplying these markets.  10  Table 2.2 below provides an overview of the actual total (world) import patterns of the six Pacific Rim economies, in processed food and beverage products, over the 1988 to 1992 period. Corresponding to the general trend in increase in population and income, discussed above, the data in Table 2.2 reveal similar general trends in increased total imports. The only exception being imports in processed fruit and vegetable products and in processed cereal and grain products (excluding Japan's processed cereal and grain imports) for the 1992 calendar year. In general, these trends suggest, as expected, increased export opportunities in these markets. Table 2.3 shows the B.C. exports, by the same industry categories, to these six Pacific Rim economies. In addition, Table 2.3 reveals total domestic shipments (i.e., sales) and valueadded sales, for purpose of comparison with the export data. To supplement this, Appendix 2 provides an overview of the Pacific Rim exports of related provincial industries, including the provinces of Alberta, Manitoba, Ontario, and Quebec. Two things stand out in looking at these data. First, despite the general increase in Pacific Rim imports, discussed above, the B.C. industries' exports to the Pacific Rim do not necessarily follow the same smooth inclining trend. Rather, during some years, some industries exhibit increasing exports, while the next year these same industries exhibit a substantial decline in exports, followed by a further increase, etc. Hence, the B.C. industry export data reveals somewhat erratic trends. Moreover, to some Pacific Rim regions, notably South Korea and Singapore, B.C. exports over the 1988 to 1992 period are rmnimal, if not nil. Second, the data in Appendix 2 reveals BC's "strength" in Pacific Rim exports relative to the provincial industries' domestic shipments, and relative to the domestic shipments and exports  11  of the other provinces of Alberta, Manitoba, Ontario and Quebec. Even in comparison to Ontario, which has significantly larger industries, the B.C. industries' exports, as a share of total domestic shipments is relatively greater. This suggests that, in general, B.C. is more competitive than its competing provincial industries in these Asian markets.  12 Table 2.2  Pacific Rim Total (World) Imports In Five Industry Categories, 1988-1992. ('000 Constant 1990 $Cdn.) Japan  Hong Kong  Taiwan  Processed Meat and Fish Products 1988 18,845,080 1,651,775 1989 17,546,660 1,616,976 1990 17,144,090 1,743,426 1991 17,614,120 1,803,354 1992 20,380,710 2,104,288 Processed Fruit and Vegetable Products 1988 3,009,663 1,651,775 1989 3,248,692 1,616,976 1990 3,041,494 1,743,426 1991 3,106,436 1,803,354 1992 2,488,358 708,259 Processed Cereal and Grain Products 1988 1,609,167 528,603 1989 1,762,556 492,429 1990 1,665,905 465,724 1991 1,796,295 482,273 1992 2,735,642 447,274 Other Processed Food Products 1988 2,602,157 845,332 1989 2,338,157 769,140 1990 1,872,141 788,835 1991 1,858,349 837,588 1992 2,407,353 911,920 Beverage Products 1988 1,289,998 464,990 1989 1,595,719 524,245 1990 1,967,538 593,313 1991 2,091,789 656,537 1992 2,152,146 778,362  P.R.C.  S. Korea  Singapore  308,124 500,653 482,237 504,928 540,625  157,874 131,306 502,170 810,247 1,102,675  206,826 250,809 278,722 311,289 544,586  1,024,759 1,014,264 953,509 1,050,486 1,125,598  163,766 171,780 227,913 224,377 203,543  110,635 163,716 230,834 428,080 321,601  127,004 137,344 111,287 123,032 62,948  847,790 798,341 737,640 898,942 602,174  650,361 640,745 466,970 545,138 464,796  1,511,392 1,663,856 1,560,786 1,587,897 452,513  3,496,797 3,620,300 2,671,099 2,309,749 590,392  827,551 805,451 614,128 554,688 340,976  196,300 217,108 234,307 266,101 319,790  380,445 272,799 371,911 375,689 585,487  1,245,301 592,138 557,170 456,215 533,623  792,530 585,103 597,651 549,246 846,654  105,202 146,960 222,322 252,332 376,358  28,937 45,017 54,504 56,907 70,320  105,156 83,192 96,883 146,746 224,162  326,863 295,963 317,731 332,528 387,615  Source: Statistics Canada. World Trade Data Base (SITC-Revision 2), On CD-ROM. 1993  13  Table 2.3 B.C. Exports to the Pacific Rim: 1988 -1992, by Industry Category No. of Estab.  Domestic Shipments  ValueAdded  B.C. Processed Meat and Fish Products 1988 118 1,562,706.0 503,740.4  1989 1990 1991 1992  121 115 109 112  1,502,932.0 1,551,000.0 1,428,883.0 1,418,937.0  431,518.3 463,800.0 439,204.5 435,074.6  Japan  H.K.  207,906.0 229,521.2 140,755.8 179,629.7 153,910.9  9,654.7 4,893.9 6,355.0 5,039.8 5,244.3 89,926.8 7,012.0 64,263.0 16,859.8 47,998.8  Taiwan  P.R.C.  S. Korea  15,697.9 3.2 6,068.3 339.7 2,767.0 2,759.6 944.4 5,655.6 6,548.9 4,790.6  Sing. 141.8 177.7 418.2 204.4 46.0  B.C. Processed Fruit and Vegetable Products  1988 1989 1990 1991 1992  34 34 34 32 27  279,317.9 254,555.0 256,200.0 236,931.8 243,283.6  94,389.4 105,026.2 91,100.0 92,992.4 99,533.6  3,624.7 3,277.7 2,145.6 1,513.0 837.5  982.6 277.1 126.3 236.1 247.6  0.0 19.4 60.5 23.7 28.4  7,929.9 9,670.9 5,373.1 8,501.3 10,118.8  28.9 819.5 715.0 741.0 234.3  22.5 2,207.1 51.6 26.0 17.3  131,133.1 121,047.1 65,400.0 55,587.1 67,164.2  10,832.8 11,732.1 3,705.7 9,601.6 2,865.2  133.9 589.3 477.2 183.4 770.5  0.0 304.5 275.5 396.2 1,343.6  334.4 0.0 2,177.2 0.0 0.0  0.0 14.5 0.0 11.0 1.9  55.2 182.1 217.9 34.8 837.0  253,465.3 262,513.1 242,600.0 276,041.7 399,440.3  54,066.1 12,452.4 7,308.6 11,393.7 13,597.4  22.9 145.6 0.0 11.4 0.0  6.9 57.5 79.2 0.0 2,205.1  0 6.6 0.0 0.0 377.8  0.0 0.0 0.0 0.0 0.0  0.0 3.0 8.0 49.0 835.6  43.1 764.9 507.8 134.8 42.3  46.4 428.8 64.0 0.0 29.6  16.5 90.6 152.5 110.2 241.0  19,304.5 0.0 7,407.4 0.0 4,635.9 2,513.4 1,607.7 0.0 6,894.8 116.3  0.0 11.7 24.2 7.0 92.0  B.C. Processed Cereal and Grain Products  1988 1989 1990 1991 1992  88 93 90 87 86  395,819.6 436,858.6 422,100.0 382,291.7 414,272.4  143,454.3 148,691.1 170,900.0 153,787.9 170,522.4  Other B.C. Processed Food Products  1988 1989 1990 1991 1992  65 70 64 63 55  292,409.2 284,607.3 143,200.0 127,462.1 168,470.1  B.C. Beverage Products  1988 1989 1990 1991 1992  32 34 33 33 35  518,701.9 494,240.8 492,800.0 508,049.2 754,850.7  Source: Domestic shipments and value-added data: Statistics Canada, Annual Survey of Manufacturers (Cat. No. 31-203); Export trade data: Obtained through 'special request" of the International Trade Division, Statistics Canada. These data were organized into the specific five industry categories developed for the purpose of this thesis. (For less specific export data, refer to Cat. No. 65-003, Jan-Dec, annual.)  14  3.0 THEORETICAL FRAMEWORK  3.1 Review of the Literatures The notion of competitiveness is commonly discussed in the context of existing trade theories. Competitiveness can be considered as a further extension of the traditional theories. This chapter reviews some of the important concepts that have developed from the trade literatures, and discusses the new concepts arising from the competitiveness literatures. The chapter ends with theoretical considerations of some of the more common determinants identified.  3.1.1 Traditional Trade Theory Trade theory dates back at least as far as to Adam Smith's classical work, The Wealth of Nations (1773). In this, Smith developed the first theory on the gains from specialization and trade. He explained that when nations specialize in doing whatever it is they do best, and trade their wares with those of other nations, productivity increases, income increases, and overall consumption opportunities increase. Open trading between economies, the theory argues, brings about the most efficient allocation of resources. David Ricardo (1817), proceeded to further develop Smith's theory of the gains from trade. Ricardo determined that nations gain when each specializes in producing those commodities in which they have a "comparative cost advantage". His observations provided economists with the framework from which to determine what commodities would be traded. In the early 1900's, trade economists were then working to explain why it was that comparative production costs differed between nations. Eli Heckscher and Bertil Ohlin  15 (1909) determined that the resources required in production, (i.e., land, labour, capital, and technical know-how) exist in different proportions between different countries. The Heckschler-Ohlin (hereafter, H-O) model thus suggested that a country's relative endowment of these factor inputs -as reflected in their relative costs (e.g., land rents, wage rates, and interest rates)-- determines trade. Hence, it was argued that nations faced with lower relative input costs will tend to trade, or export, products that use these inputs in the production process. MacDougall (1951), whose findings were later supported by Balassa (1963) and Stern (1962), made the first serious attempt to empirically test the H-O model. MacDougall studied the 1937 exports of 25 U.S. and U.K. "industries" (i.e., specified as an aggregate of related products). He estimated a U.S./U.K. export ratio as a function of a labour productivity ratio (or, the ratio of output per U.S. worker to output per U.K. worker). Excluding the mutual trade between the two countries due to the existence of high U.S. and British tariffs, MacDougall determined that, in fact, increases in labour productivity ratios explained increases in the export market share. MacDougall, however, found that when one country had the comparative cost advantage, it did not capture the whole of the export market. He attributed the phenomena to "the existence of imperfect markets (oligopolistic and monopolistic), non-homogeneous products, transport costs, and the like" (Chacholiades, p.90). As Chacholiades points out, while MacDougall's findings do support the classical H-O theory, they highlight the theory's "greatest defect...that it does not shed any light on what determines comparative advantage and on how comparative advantage may be expected to change in the future" (Ibid.). In 1954, a study by Wassily Leontief contradicted the H-O theory. Using 1947 data, Leontief deteirnined that exports from the United States were more labour (and hence, less  16 capital) intensive than the goods it was importing. This conflicted with the presupposition that the U.S. was relatively more abundant in capital than in labour. Economists subsequently attempted to explain Leontief's paradox. Some of the explanations which developed include: (1) the influence of higher U.S. labour productivity; (2) the existence of tariff and non-tariff barriers in importing countries; and (3) the influence of the quality of labour, or "human capital" as a production input. As Abbott and Bredahl (1994) explain, "most of the explanations that emerged represented minor deviations from necessary assumptions or extensions into a world of multiple dimensions, ... and [so] a marriage of trade theory with modern industrial organization theory arose in order to introduce several new concepts into the analysis" (p. 19).  3.1.2 New Trade Theory: Considering LO. Variables Industrial organization theory is concerned with markets in which there are few sellers. That is, the theory addresses markets in which the standard perfect competition paradigm no longer holds. As Chamberlin (1933) points out, these markets, which are dominated by a relatively small number of firms and differentiated products arose at the turn of the century, with the development of manufacturing industries. The predominant focus of 1.0. theory has been to analyze these different market structures and to determine their consequences for industry performance (not to mention for consumer welfare purposes). Following the works of Bain (1959), much of 1.0. theory has rested on the "structure-conduct-performance" (SCP) model. This model considers how the market structure (i.e., as explained by the degree of competition amongst firms in an industry) determines the 'conduct' (or price setting and other behaviours) and, consequently, industry 'performance' (profitability, growth, etc.). Industry structure is typically measured in terms of the degree of economies of scale  17 in an industry, and/or the degree of firm concentration (where, one firm exhibits high concentration, or monopolistic type industry characteristics; and many firms exhibit low concentration, reflecting purely competitive industry type characteristics). Industries characterized by increasing returns, or large scale production --considered synonymous with more efficient production— were seen as determinants of international trade in certain sectors. The H-O model, however, assumed constant returns to scale. Consideration of the L O . framework in the traditional trade models therefore enabled this limitation of the applied H-O theory to be addressed. Moreover, Vernon (1966) determined that the "use of advanced technological production techniques and, especially, the investments in research and development to develop them, had not been incorporated into the H-O model" (Pool and Stamos, p.33). This related to the findings by Gruber and Vernon (1970) which suggested that "industries associated with a relatively high research effort, also tend to export a relatively high proportion of their output... [where] "research effort" is by measured by industry R & D expenditures as a percent of industry sales, or by technical personnel as a percentage of total industry employment" (p.235).  3.1.3 Limitations of Trade and LO. Theories While trade and/or L O . theories provide a good starting point for identifying competitiveness factors, they do not, or cannot, necessarily describe competitiveness conduct fully. The current literature suggests that trade theory is at too macro of a level to account for the micro (or firm or industry) level undertakings that in effect are driving trade. Research undertaken by various business schools ascertains the planning and marketing strategies of firms (or behaviours of management) do play an important role in influencing market  18 performance. Conventional trade theory explains export market share entirely by differences among countries, especially relative differences in the factor endowments. Empirical analyses typically require extensive data to depict all of the possible factor input price combinations both within and between nations. Such data makes this an empirically difficult task and the high level of aggregation provides little opportunity for analyses of a more complex set of variables. Moreover, "critics of trade theory point out that firms trade, nations don't; that firms make investment and marketing decisions, nations don't; and that firms compete in international markets, nations don't" (Bredahl, Abbott and Reed, p.4). Similarly, 1.0. theory, although employing more dis-aggregated data, places a great deal of emphasis on the nature of the industry rather than on the nature of the firms within the industry. It can be argued that the role of firm conduct, as relayed through the SCP model, is typically ignored. That is, "the SCP framework shifted its emphasis [from] Fellner's analysis ... on the cognitive and motivational properties of agents at the bargaining table ... to the structural environment that determines the opportunity set of each bargaining party. It concentrates on what determines the cards held by each bargainer rather than the skill and aggression with which he plays them" (Caves, Porter, Spence, and Scott, p.5). The elements of firm conduct are, rather, assumed to be "simultaneously determined" by the forces of industrial structure (Ibid.). The predominant focus of LO. theory on industrial structure has essentially left firms to be interpreted as "passive agents through which industrial structure works its influence on industrial performance" (Sawyer, 1985 p. 90) The notion of competitiveness then, extends beyond the traditional boundaries of macro trade theory, and, instead, attempts to account for the practices of the firm, by including such variables into the traditional models. As Porter (1990) points out "seeking to  19 explain competitiveness at the national level, ...is to answer the wrong question. What we must understand is the determinants of [competitiveness],... [by] focusing not on the economy as a whole, but on specific industries and industry segments." (p.6). 3.1.4 Competitiveness: Linking Trade, LO., and Strategic Management Theories Analyses of firm behaviour by some business schools have helped to bridge the gap between trade theory and the notion of competitiveness. Some of the earlier contributions to strategic management theory have come from the research findings of the Marketing Science Institute and Harvard Business School's "Profit Impact of Market Strategies" (PJJvIS) project. The PIMS project, an ongoing study, commencing in 1972, entailed detailed financial and business practices of 57 different major U.S. corporations, entailing over 600 diverse businesses . 8  The project established that strategic planning is linked to profit performance (Schoeffler, Buzzell, and Heany, 1974), as is market share (Buzzell, Gale, and Sultan, 1975). Schoeffler et al. accounted for more than 80% of the variation in profit in the more than 600 businesses analyzed using 37 different factors. Of the seven factors identified as being "most important," five can be regarded as strategic management characteristics. These include: 9  total marketing expenditures, product quality, R & D expenditures, investment intensity, and corporate diversity . Hence, Buzzell et al. then explain the market share-profitability link 10  by economies of scale, market power, and "quality of management" characteristics. . 11  8  See Schoeffler, Buzzel, and Heany (1974) or Buzzel, Gale and Sultan (1975).  Note that the authors do not explain what is meant by "most important", but it is my interpretation that the intended meaning is "statistically significant". 9  The two remaining factors, market share and return on investment, are really more indicators than determinants of competitiveness. 10  11  (These are not mutually exclusive.)  20 "Good managers (including, perhaps, lucky ones!)," they said, "are successful in achieving high shares of their respective markets" (p.98). According to the authors, 'good managers' encompass those "skilful in controlling costs, getting maximum productivity from employees, and ... [those capable of] achieving a leadership position —possibly by developing a new [product or market] field" (Ibid.). Later developments in the strategic management literature arose in studies by Porter (1980). Porter proposed that firms develop unique production and marketing strategies, based on the specific market environment in which they conduct business. The important market environment variables influencing firm decisions include the bargaining powers of buyers and of suppliers, the threat of potential entrants, and the existence of substitute goods. Porter contended that firms gain competitive advantage by reducing costs, differentiating product(s) or processes, and by focusing on a niche market. While a necessary condition for competitive advantage, Porter argued that these factors are not sufficient. He contended that a firm must also be effective in all components of its business— from product development, to production, to customer service. Porter (1985) then categorized the different business strategies into two groups: primary activities and support activities. The primary activities include: location and, hence, transportation logistics, production operations, marketing and sales, and service. The support activities include: firm infrastructure, human resource management, technological development, and procurement. In his latest work, The Competitive Advantage of Nations, (1990), Porter steps out of firm-level analyses and "contributes to understanding... the national attributes that foster competitive advantage in particular industries, and the implications for both firms and for government" (Porter, 1990a, p.xii). He undertakes an historical case study analysis of  21 important industries in ten major trading nations. The result of his work is a four-sided framework for competitive analyses, which Porter refers to as the "Diamond of National Competitive Advantage". To some extent a combination of the paradigms developed in his earlier works, Porter's competitiveness framework includes: (i) demand conditions; (ii) factor conditions; (iii) related and supporting industries; (iv) firm strategy, firm structure, and strong domestic rivalry amongst competing firms. He claims that productivity is the only meaningful measure of competitiveness at the national level: the productivity of a nation's labour and capital, produces a high and rising standard of living. In short, Porter's findings rest on the understanding that a "nation's competitiveness depends on the capacity of its industries to innovate and upgrade" (Ibid.). Porter argues that "national prosperity is created, not inherited," and that "it does not grow out of a country's natural endowments, its labour costs, its interest rates, or it's currency's value, as classical economics insists." He points to the role innovation plays in explaining competitive success in the global marketplace. Porter states that "innovation ... can be manifested in a new product design, a new production process, a new marketing approach, or a new way of conducting training"; that it "always involves investments in skill and knowledge, as well as physical assets and brand reputations"; and that "information plays a large role in [its] process" (1990a, p.74). Similarly, Beck (1992) recommends that innovative firms "turn out new products or services that people actually want to buy,"..."find new markets"...and, "develop new processes that boost quality while reducing costs" (pp. 107-109). Porter (1990) identifies the following specific company strategies as necessary for competitive advantage: create pressures for innovation  22 seek out the most capable competitors as motivators establish early warning systems (i.e., systems that help identify changes taking place in the market, so they can act on them, thereby getting a jump on competition); improve the national diamond; and welcome domestic rivalry...(Porter states that "to compete globally, a company needs capable, domestic rivals and vigorous domestic rivalry" (P.92) (1990a, pp.89-92). It is interesting to consider how closely linked the concept of innovation is with that of R & D . Although the two words have been used interchangeably, R & D has, in general, tended to be used to explain the development of new production technologies, while innovation has typically been associated with new product development. Rosenberg (1970) explains that economic theory "has always had a difficult time coming to grips with the problems posed by new products. Our analytical apparatus and our techniques of measurement have been notably deficient in the handling of product innovation as opposed to cost-reducing process innovation. But clearly product innovation has been playing, and will probably continue to play, a major role in the changing pattern of international trade" (p.72). Further consideration reveals, however, that both innovation and R & D entail research, and both entail development - o f new products, or of new production techniques. Yet, while the role of research may be explicit in the term ' R & D ' , its role in the concept of 'innovation' is more subtle. Understanding this role rests on the assumption that any new innovation is restricted to or, alternatively, guided by parameters which in themselves are determined by the innovator's a priori knowledge of the production or consumption demands pertaining to the particular innovation. A n innovator's knowledge of a growing demand for health food products, for example, influences his or her development of such foods. Similarly, an  23 innovator's knowledge of the food industry's need for a new production technique entices new technological developments in this area. Successful innovation or R & D , therefore, infers something about a firm's "market knowledge base". That is, one can assume that a firm which is successful in innovating or developing new products or processes implicitly has been successful in accessing, processing, and utilizing information pertaining to market conditions and production opportunities. Hence, it can be argued that the "research" aspect is crucial since it influences a firm's knowledge or information base. Addressing the competitiveness of the Canadian agri-food industry, the 57 member Task Force on Competitiveness in the Agri-Food Industry, employed Porter's diamond 12  framework, with some modifications. The Task Force replaced "firm strategy, structure, and rivalry with that of the industry", and created two other components: (i) government policy and programs; and (ii) innovations and productivity, as two specific firm strategies. With the firm being directly linked to the industry, and the industry itself, sitting in the centre of the amended diamond, the Task Force emphasized the importance of "linkages" between industries and supporting industries, related industries, consumers, and government, etc. The idea being that a certain degree of cooperation and sharing of information or knowledge amongst the industry players can work to enhance their competitive capabilities. Van Duren, Martin and Westgren (1992; 1994), Toffler (1990), and others, express similar views on the importance of firm linkages and sharing of information, van Duren et al. (1992) identify linkages as one of many important firm level strategies. They take the approach that a firm's relationships with its customers or suppliers are "often the basis for  (Established in 1989 by Minister Donald Mazinkowski.)  24  total quality management (TQM), joint ventures, and flexible vertical relationships" (p. 18). Toffler, on the other hand devotes a whole book to the importance of knowledge, and its influence on creating wealth. He points out that: Apart from the fact that no business could open its doors if there were no language, culture, data, information, and know-how, there is the deeper fact that of all the resources needed to create wealth, none is more versatile than these. In fact, knowledge (sometimes just information and data) can be used as a replacement for other sources. Knowledge —in principle inexhaustible— is the ultimate substitute. (P.83). One frequently noted limitation to the competitiveness studies by Porter and others is that they lack statistical analyses. Statistical analyses would provide some insight into the significance to which the suggested determinants actually influence competitiveness, and hence, give some measure of the degree of reliability. This is the objective of this study.  3.2 Theoretical Considerations The literature discusses traditional trade, industrial organization, and business management factors which influence competitiveness. In this section, the economic rationale and implications behind the influence of: wage rates, interest rates, exchange rates, import tariffs, transportation costs, industrial concentration, new capital expenditures, labour productivity, and new product innovations (the latter two of which exhibit cost-reducing and revenue-enhancing strategies of firms respectively) are considered.  3.2.1 Wage Rates. Wage rates are commonly used as measures to approximate the opportunity cost of employing labour. Hence, it is assumed that firms will employ labour up to, but not beyond their opportunity cost. Following the comparative advantage doctrine, relative wage rates are therefore inversely related to exports. Theory would suggest that firms  25 facing lower relative labour costs can employ more workers, and hence increase output — hence producing more per unit factor input cost than their competitors. Labour efficient firms are, ceteris paribus, more cost competitive and hence, will tend to export more. However, a study carried out by Kaldor (1978) and recently re-tested by McCorriston and Sheldon (1994) finds a paradox to this traditional comparative cost doctrine. Kaldor presented data on relative unit labour costs and unit export values for eleven major industrialized countries for the period 1963-1975 and compared them with changes in each country's global market share. He found that, in six of the eleven cases...rising (falling) relative labour costs or relative export values were matched with higher (lower) market shares. More recently, McCorriston and Sheldon determined that, during the 1966-1985 period, the market shares of West Germany, Japan and the United States appear to be positively correlated with relative labour costs. Essentially these findings imply that higher wage rates attract more skilled labour, resulting in greater cost efficiency and/or higher product value.  13  These findings would suggest, in turn, that higher wage rates could be  positively correlated with export sales.  3.2.2 Interest Rates. Real interest rates, adjusted for inflation affect industry costs through a dynamic process. Faced with lower real interest rates, firms are enticed to invest in new capital expenditures, and undertake financing for product and/or (domestic or export) market development purposes. This investment continues up to, but not beyond, the point where the marginal efficiency of investment equals the opportunity cost of capital, as represented by the real interest rate. Hence, assuming the marginal efficiency of investment is less than the  The resulting decrease in costs or increase in product value would typically depend on the type of highi labour skill employed. 13  26 opportunity cost of capital, when real interest rates fall, we can expect investment to increase, and, for those exporting firms, total exports to increase as well, although only for a limited time. A fall in real interest rates will be offset by a decline in foreign investment, resulting in an excess supply of Canadian dollars on the international market. This in turn will result in a depreciation of the exchange rate. Hence, the exchange rate (expressed in dollars Canadian per unit of foreign currency) rises, enticing firms to further increase exports. As exports increase, however, the value of the Canadian currency increases on the world market, and interest rates will begin to decline accordingly.  3.2.3 Exchange Rates. Real exchange rates measure the relative price of two goods. In the absence of export market price data (i.e., prices paid by consumers in the destination country) real exchange rates (RER) can be employed as proxy measures to reflect the value of selling goods to the particular export market. Expressed in units of domestic currency per unit of foreign currency, a depreciation, or rise in the RER reflects an increase in the price received by domestic manufacturers in the exporting country. Hence, a rise in the RER is expected to be accompanied by an increase in exports.  3.2.4 Tariffs. Tariffs enable an importing country to restrict the number of imports of one or many specific commodities. Hence, tariffs interfere with the free flow of goods into a nation. They adjust the prices at which commodities are traded on the world market to the price faced by consumers inside an importing country. The effect of an import tariff on Canadian exporting manufacturers is on the price of the Canadian good as seen by the consumers in that importing market. Thus, an increase in the rate of an import tariff will  27 have the effect of reducing exports to that country.  3.2.5  Transportation Costs. In the presence of transportation costs, manufacturers located  farthest from the final export market destination are more likely to be at a disadvantage than their competitors, since their goods will bear the transport cost. Depending on the elasticity of demand, increased costs result in increased prices paid by consumers in the importing country. This results in the goods being less desirable in this market, and total exports consequendy decline. The more inelastic the demand, the more one can expect transport costs to be embedded in the final consumer purchase price. While "food" typically exhibits an inelastic demand, value-added, processed food products, tend to be more elastic in nature. Yet this changes as personal income (or G D P per capita) rises. High income economies are more likely to be engaged in secondary or manufacturing industries, leaving less time for production of primary or non-value-added foods, and hence, greater reliance on imported, processed products. Given the relatively high average personal incomes in Japan, Hong Kong, and Taiwan, increased transport costs are assumed to be inversely related to market share. As transport costs relate to the markets of Singapore, South Korea, and China, on the other hand, one would hypothesize that transport costs, in general, are positively related to market share.  3.2.6  Industrial Concentration. Industrial organization theory emphasizes the importance of  the degree of industrial concentration or market power in influencing an industry's competitive performance. Market dominance by one or a few firms provides the opportunity to effectively set prices, and, consequently, realize significantly higher profits for a particular  28 product. This market power in turn acts as a type of barrier to new firms wishing to enter the market: Those firms that are able to obtain prices above their marginal cost of production have the resources required to invest in research and development towards new products, or new) production processes; increase plant size, and engage in large promotions or advertisements, etc. Thus firms in concentrated industries implicidy evoke some economies of scale dominance. There are two conflicting arguments in 1.0. theory, however, which pertain to how concentration may influence export competitiveness. Traditional 1.0. theory would infer that an industry dominated by a few, large firms is inefficient because there is not an 'infinite' number of competitors, forcing the firms to price at marginal cost. Firms are more profitable and consequently obtain "fat", comfortable positions in the domestic market. In doing, they maintain a sluggishness with respect to engaging in foreign competition. In such a situation, firm concentration is hypothesized to be negatively related to export market share. Dissenters of this traditional LO. theory, however, believe that such economy of scale firms have only become dominant because they are so efficient. Their costs of production are lower than their competitors and consequently they are able to sell at lower relative market prices. These firms use the profits made in their domestic markets to influence their share of foreign markets -- e.g., by "undercutting" foreign competitors' selling prices (perhaps even below the cost of production, for a period of time), and conducting large advertising or product promotions in order to "push out" competition and/or gain product loyalty. In this case, firm concentration is assumed to relate positively to export market share. Given today's decreasing domestic protection and thus more opportunity for foreign competition, most firms are unlikely to sit idle in their domestic markets, at least not for long. Rather, aware of the increasing foreign competition, it is hypothesized that firms in highly  29  concentrated industries will do all they can to increase their market shares both at home and abroad. Therefore, firm concentration relates positively to export market share.  3.2.7 New Capital Expenditures. The economic implication of firm investment in new capital expenditures (i.e., manufacturing plants, machinery, and equipment), can be interpreted in two alternative ways. First, firm output increases, and, in the longer run, profitability rises. Larger manufacturing plants, for example, enable greater economies of scale potential, hence, increasing total output, while typically holding inputs constant. Alternatively, or in addition to, capital investment can result in a decline in the capital to labour ratio, as firms substitute more capital for less labour. A final possible scenario to this sequence is that of an increase in the capital to labour efficiency ratio. With a fixed or given quantity of labour, new capital investments will increase the number of outputs per unit of labour input. In any case, we would hypothesize that investment in new capital expenditures relates positively to greater output, and at lower costs —in the long run. This, in turn, relates positively to export market share. Second, it can be assumed that new capital expenditures are being driven by increased firm profits. Firms, in a competitive market, producing at a marginal cost below their marginal revenues will be making pure economic profits in the short run. The firms in the industry thus have the necessary finances with which to invest in new expenditures; that is, before newfirmsenter the industry, reducing pure economic profit back to zero. In this case, new capital expenditures can be considered a proxy for profitability. As profitability increases, outputs increase (for the reasons given above), and exports will increase as well.  30 3.2.8 Labour Productivity. Labour productivity is a measure of labour efficiency. It is similar to the capital to labour efficiency ratio, discussed above, except that labour productivity is typically regarded as the output per unit of labour input ratio. Hence, given a fixed quantity of labour, increased output per unit of labour input results in lower costs of production. Alternatively, given afixedoutput for less labour inputs also results in lower costs of production. Firms are thus more cost competitive, and hence able to export more, gaining market share. Increased labour productivity, therefore, is assumed to be positively related to export market share.  3.2.9 New Product Innovation. As discussed in Chapter 3.1, the notion of "innovation" is closely linked with "R&D". Both require market or consumer research has been undertaken prior to the new product innovation or development.  Hence, embedded in the resulting  product is the information or knowledge in the process. In neoclassical economic theory, "perfect" or complete information is assumed to exist amongst all players in the market economy. In the real world, however, this is not always the case. Consumers rarely have perfect information on all of the products available to them, and producers rarely have perfect information on all consumers' demands and demand elasticities. The closer one is to perfect information, however, the closer they are to exacting a market premium price. In terms of the manufacturer, this is to say that the more the manufacturer knows about the consumer's preferences, the closer they are to pricing based on the consumer's elasticity, and hence achieving a price premium. In a perfectiy competitive world, this might be seen by arisein the innovating firm's marginal revenue function. In a less than perfectly competitive world, however, when other firms do not have the same market knowledge (or else they would have produced the same  31 product), this situation can be represented by the innovating firm facing the downward sloping demand function of the consumer (in the perfect case) or consumers (in the less than perfect case), and pricing accordingly. Hence, to the extent that new product innovation or R & D is synonymous with perfect information or market knowledge, we would expect to see an increase in firm or industry sales, given an increase in the number of new product innovations. Since rarely is it the case that a manufacturer has complete knowledge of the consumer demand, the innovations to sales relationship will tend to be less than perfect, although significant nonetheless.  32 4.0 MODEL SPECIFICATION 4.1 Conceptual Model A model employed in a study by Gruber and Vernon (1970), explains exports as a function of the economic characteristics of the industries that generate the exports. That is: (4.1) For exporting country i: n  £  where: Eijk a,b,c,....  *Mb,c,...)  specifies total exports from area / to area j in product category k; and specify various economic characteristics applicable to industries producing manufactured goods (p. 237).  Alternatively, equation (4.1) could be modified for the purpose of a competitiveness assessment. Equation 4.1 could be used to explain competitiveness as a function of the economic characteristics of the industries that generate the exports. That is,: (4.2) = fla,b,c,..)  where:  C  m m  = SALES-,  = E..  u  v  h  =  Imp  Jb  33  where, Sales E  = =  E Impjk,  = export market share (i.e., total exports over imports).  ijkt  ijkt  ijkt  domestic market sales; total exports; and  Conceptually, the statistical significance of one or more of the different competitiveness-influencing factors, proposed in the literature, and discussed in Chapter Three, could then be tested. The specific competitiveness measure and the choice of industry characteristics, for the purpose of empirical analyses, however, will ultimately depend on the particular research question(s) being addressed, and, more notably, on the available data. The underlying question or purpose of this study is to identify those factors or characteristics which may influence the competitiveness of B.C. processed food and beverage industries in the Pacific Rim, as well as the domestic market. Specifically, the interest lies in determining the influence of exogeneous factors, as suggested in comparative cost and industrial organization doctrines, and endogeneous factors, suggested in business school and, now, competitiveness literatures. The available data includes a matrix of pooled cross-sectional and time-series observations, encompassing five processed food and beverage exporting industry "categories," in five Canadian provinces, for the years, 1988 through 1992. The five 14  industry categories include: processed meats and fish (MF), fruits and vegetables (FV), cereals and grains (CG), other processed foods (OTH), and beverages (BEV). The exporting provinces include British Columbia (BC), Alberta (AB), Manitoba (MB), Ontario (ON), and  thorough description of the industry categories is given in Chapter Five, and is summarized in Appendix  34 Quebec (QU). The industry characteristics considered in the analysis are: exchange rates; wage rates; an industry concentration measure; a measure of new capital expenditures an endogeneous, cost-reducing measure, reflecting cost-saving practices of firms in the industry — specifically, changes in labour productivity; and TM - an endogeneous, revenue-enhancing measure, reflecting revenue-generating practices of firms in the industry; specifically, the number of new trademark applications (a proxy for investments in market research and product innovation).  XR WR CR NC LP -  Hence, given these data, model (4.2) can be re-specified as: (4.3) For exporting region i: = AXR,,WR CR NC ,LP TM ) ijp  ijp  it  itr  ¥  where ijkt on the applicable RHS variables, specify province /, in industry category j, in time (i = 1..5, provinces); 0 = 1..5, industry categories); (t = 1..5, years). 15  4.2 Empirical Model For empirical purposes, (4.3) is specified as follows: (4.4) For exporting region i:  Note that the introduction of exporting "region", as opposed to "country", "industry category" as opposed to "product category" and the change in notation between; and k in (4.3) is employed to permit the use of consistent notation throughout the rest of the paper.  35 C  m  = P, - PXK, 2  +  +  P  4  ^  +  P  5  ^ + P ^ + P 7M^ 7  +  + ^  where, specifies total exports from province i, in industry category period t; and  to area k; in time  where, e  i j t  equates epsilon, the error disturbance term in province /, in industry category j, in time t;  and,  It is assumed that the error term, epsilon, is distributed with a non-zero mean and variance sigma-squared, since it is highly likely that there will be certain individual provincial industry effects which will bias the estimated regression coefficients. For example, existing government export policies, and/or pre-arranged contractual trade agreements would be considered fixed effects not measured by the explanatory variables considered, however, embedded in e , , and therefore influencing or biasing the estimated beta coefficients. To ijt  account for this, a fixed effects covariance model is employed for estimating purposes. Described in Kmenta (1986) and Kennedy (1992), amongst others, the idea behind the fixed effects model is that a different intercept exists for each N cross-sectional units and each T time periods. As a result, (AM) + (1-1) dummy variables are introduced into the equation, hence, creating fixed effects in the model. In the general case, this model can be described as follows: (4.5)  36 For exporting region i:  Y  where,  « = Pi  +  h ita X  -  +  +  PA,*  Y  +  +  Z„,  =1 =0  for the ith cross-sectional unit, otherwise (i=2, 3,...,N);  W„,  =1 =0  for thefthtime period, otherwise (t=2, 3,...,T).  3  \  + 3  -  +  IN ** 2  Introducing the fixed effects covariance specification of (4.5) into (4.4), we arrive at the following empirical model: (4.6) For exporting region i:  m  c  = Pi  +  W  +  P  5  3  ^  +  P  5  C 4  ^ P ^ Q + P LP^ + P 7M^ +  5  5  6  7  + y DPj + 6DI + pDT + e i=2  ;=2  r=2  t  where, C  ijkt  WR CRjj, tJt  NC LP  it  ijt  TMij, DP  {  DIj  specifies competitiveness (measured in terms of domestic sales, total exports, or export market shares), from province /, in industry category j, to area k; in time period t; and is wage rates in province /, in industry product category j, in time t; is industry concentration in province /, in industry product category j, in time period t; is new capital expenditures in province /, in time period /; is labour productivity in province /, in industry product category j, in time period t.; and is the number of new trademark applications of firms in province i, in industry product category j, intimeperiod t. is the provincial cross-sectional dummy variable (i=2..5, includes BC, AB, ON, and QU); is the industry (product category) cross-sectional dummy variable  37  (j=2..5, includes MF, FV, CG, OTH, and BEV); and is thetimedummy (t=2..5, includes 1988, 1989, 1990, 1991, and 1992,); and is epsilon, the error term.  DT e  t  ijt  A final step in the specification of this empirical model deals with scaling the regression variables. The inclusion of cross sectional data-- in this case, industries of different sizes— introduces a likely skew in the statistical distribution of the dependent and explanatory variables. To account for this, the data are converted to log form, thus generating a distribution that is more in accord with the normal distribution assumption of the statistical significance test applied here. Hence, model (4.6) is re-specified: (4.7)  For exporting region i:  C  m  = Pi  +  VJogXRt  +  Ps&wWfe + IJosPR* +VJogNC  u +  5  5  5  + yDP + bDL + pDT + e i  iH  ^logLP^  +  ^logTM^  38 5.0 EMPIRICAL IMPLEMENTATION 5.1 DATA Data pertairiing to five explanatory variables, exchange rates (XR), wage rates (WR), industry concentration (CR), labour productivity (LP), and trademarks (TM), and two alternative dependent variables, total exports and export market share, are considered in this analysis. Section 5.1.1 provides an overview of the general data matrices, while 5.1.2 describes the sources and methods for calculating the individual variables.  5.1.1 Data Overview The data are defined for five processed food and beverage industry "categories," in 16  five Canadian provinces, for the years, 1988 through 1992. In addition, a matrix of traderelated exports from these provincial industries, to Pacific Rim countries, and total (world) imports into the Pacific Rim are defined. As discussed briefly in Section 4.1, the five industry categories include: processed meats and fish (MF), fruits and vegetables (FV), cereals and grains (CG), other processed foods (OTH), and beverages (BEV); the exporting provinces include: British Columbia (BC), Alberta (AB), Manitoba (MB), Ontario (ON), and Quebec (QU); and the Pacific Rim export markets considered include: Japan, Hong Kong, Taiwan, China-mainland, South Korea, Singapore, and the United States. In general, the industry categories entail groups of "similar" 3-digit SIC (i.e., "Standard Industrial Classification") food and beverage manufacturing industries. Similar  (Defined below.)  39 groups are defined as industries with common primary inputs . Hence, SIC 105 (Flour, 17  Cereal Food and Feed Industry) is combined with SIC 106 (Vegetable Oil Mills) and with SIC 107 (Bakery Products Industries); SIC 108 (Sugar & Sugar Confectionery Industry) is combined with SIC 109 (Other Food Products Industries); SIC 111 (Soft Drink Industry) is combined with SIC 112 (Distillery Products Industry), SIC 113 (Brewery Products Industry); and SIC 114 (Wine Industry); while SIC 103 (Fruit and Vegetable Industries) is left unaggregated. The only exception to this "similar inputs" approach is the combination of SIC 101 (Meat & Poultry Products Industries) with SIC 102 (Fish Products Industry). While meat and fish arguably face dissimilar inputs, it has nonetheless been necessary to group these industries together in order to avoid excluding the fish products industry altogether . The 18  only other alternative would be to exclude those provinces lacking a fish products industry of any significant size; omitting these provinces from the analysis would, however, reduce the current data set by more than half. As alluded to in the above discussion, the specific provinces included in the analysis are chosen primarily on the basis of the size of their processed food and beverage manufacturing industries. Provinces which, on average, lack the necessary industry data, have been omitted. Provincial industry export and total (world) import data are included for the Pacific Rim markets of Japan, Hong Kong, Taiwan, China-mainland, South Korea, and Singapore. The decision to include these particular countries or economies, over others in the Asia Pacific bloc is due to their relative proximity to the B.C. region, and to their existing and/or  17 18  This was necessary in order to sum together those industries with m i n i m a l data. (The Fish Products industry in B.C. is an important component of the agri-food industry.)  40 increasing wealth (i.e., in terms of GDP per capita). It is assumed that a strong relationship exists between per capita GDP and consumption of imported goods. The U.S. export market is also included in the analysis for comparison, and data and model verification purposes, since a substantial proportion of provincial food and beverage product exports are currently destined for U.S. markets. One problem posed by the above data is that the industry data are categorized based on the SIC system of industry classification, the export data are categorized based on the "Harmonized Commodity Description and Coding System" (H.S.), and the import data are categorized based on the "Standard International Trade Classification System" for commodity coding, Revision-2 (SITC-2). Hence, it has been necessary to "harmonize" the industry classification codes with the commodity classification codes. A list of the concordances between the SICs and the H.S. codes has been obtained by special request from the Standards Division, Statistics Canada. These concordances are summarized in Appendix 2. The World Trade import data is at the 4-digit level of detail and consequently does not correspond exactly with the H.S. data. The SITC-2 data is, however, based on the H.S. system, and so the task of correlating the two is not wholly impossible. In the end, some allowances have had to be made in terms of permitting some SLTC-2 codes to incorrectiy concorde with the H.S. codes. The implications for this in terms of the thesis results are believed to be minimal, however, since: (1) the SITC-2 commodity categories are generally comparable to the export categories; (2) the concording data are kept consistent across all respective industry export statistics; and (3) since the SITC-2 data are used in the denominator to construct the market share variable, it acts to scale the dependent variable, hence taking the place of the log conversion procedure, which is otherwise used to scale the  41  total exports dependent variable.  19  5.1.2 Variable Specification This section explains the sources and methods employed in specifying the dependent and independent variables in this study. Where applicable, variables are expressed in natural logs. The log conversion procedure is used to scale the data, in order to avoid the statistical skew inherent in most cross sectional industry statistics. At the same time, when both dependent and independent variables are logged, conversion to logs permits comparison of the various provincial industry characteristics on exports in terms of a common elasticity unit. The estimated coefficients explain the percent change influence on the dependent variable given a percentage change in any one of the explanatory variables. For those variables comprising some zero values, the log approximation is used as the appropriate alternative scaling technique. That is, log(Y +1)  where,  if Y = 0,  log(Y+l)= 0  Since only the left hand side, total export data contains zeros, there are some implications for interpreting the effect of a percent change in an independent variable on the resulting change in the dependent variable. When the left hand side variable is expressed in log approximation form, and therighthand side variables are expressed in log form, the estimated coefficients identify the effect a percent change in X on an absolute change in Y. Hence, when Y is zero, as in many of the export data, the estimated coefficient on the X variable explains the absolute change in Y given a percent change in X. If the log approximation  19  (The market share variable and log specifications are discussed in more detail in section 5.2, below.)  42 were not used, any percent change in X would always be met with a 0% change in Y (since Y would be zero to begin with). Applying the log approximation to the dependent variable, however, permits one to recognize that when faced with, for example, a 10% drop in the exchange rate, a 0.2 coefficient on the exchange rate variable would imply a corresponding 2 unit absolute increase in the dependent variable. This is both important and applicable when the dependent variable is very small (i.e., approaches zero). In this case, the log (1+Y) approximates Y. When Y is very large, however, (1+Y) approximates Y, so hence the log(l+Y) is approximately equal to the log(Y). That is, in the latter case, when Y is very large, we move back into a constant elasticity approach for interpreting the estimated coefficients. When Y is very small, however, the coefficient estimate must be interpreted in terms of its absolute influence on Y, rather than its relative or percent change influence. Data are also, where applicable, expressed in real (1990) Canadian dollars ($Cdn.). The procedure for converting from nominal to real entails dividing by the Canadian CPI (1990=100). CPI are obtained from International Financial Statistics (IFS), February 1994 issue, for the countries of Canada, the United States, Japan, China, South Korea, and Singapore. The CPI and nominal exchange rate data for Hong Kong and Taiwan are obtained from Asian Development Bank, January 1995 issue. In both publications CPI are expressed in 1990 base year units. Total Exports (EXP) Total exports are specified as the log of the real (1990 $Cdn.) value of 8-digit H.S. commodities exported from the provinces in which they are manufactured to the specific Pacific Rim markets. Export documents collected by Canada Customs, and tabulated by Statistics Canada are the principal source of all export statistics for Canadian commodities. The statistics include "both goods which are wholly produced in Canada and goods previously  43 included in import statistics which have since been changed in form by further processing and then export" (Statistics Canada, Cat. No.65-003, Jan.-Dec. 1988, p.5). Furthermore, "exports are classified to the country to which they are consigned at the time the goods leave Canada, i.e. to the furthest known destination, and are recorded at the values declared on export documents, which usually reflect the actual selling price or, in the case of non-arms length transactions, the transfer price used for company accounting purposes" (Ibid., p.6). "Most exports are valued at the place in Canada where they are laden aboard a carrier for export (e.g. mine, farm or factory) but a significant proportion of exports by water or air reflect values which include transportation to the port of export" (Ibid.). Provincial export statistics are available through special request of the International Trade Division, Statistics Canada. The purchased data provides "province of origin" export 20  data, based on the H.S. system, at the eight digit level of detail.  21  These regional export  data are reported in nominal $Cdn. and are converted to real (1990) $Cdn.  Market Share (MS) Market Share, is calculated as the percent of total commodity exports produced by industry i in province j, and exported to Pacific Rim country k, expressed as a percent of the total (world) imports of these commodities into country k. Total world import data, for the six Pacific Rim countries, and the U.S., are obtained  Prior to January 1984, exporters were requested to identify the province in which goods were laden for export, regardless of where the goods were manufactured, grown, or extracted. As of January 1984, exporters have been required to identify the province of origin (i.e., where manufactured, grown, or extracted), instead of the province of lading. Full conversion from the old form to the new form was completed January 1987. (See B.C. Ministry of Industry and Small Business Development, B.C. External Trade Report, 1985, p. S2-S1.3.) 21  Effective January 1988, all commodity trade statistics were based on the H.S. system.  44 from Statistics Canada's World Trade Data Base (SITC-Revision 2), on CDrom , 1993. 22  The CDrom database provides a complete matrix of import and export statistics, created from data reported by United Nations member countries. Note that in constructing the World Trade Database (WTD), Statistics Canada "has performed a number of adjustments to alleviate inconsistencies in the data as reported to the United Nations. Relying on the principle that import statistics are generally more accurate than export statistics, the WTD uses imports as the basis for allocating international trade flows. Exports to countries, consequently, are reallocated according to what customer countries report as imports. ... Trade of non-reporting and late reporting countries are imputed using the trade data reported by their trading partners" (Statistics Canada, World Trade Database on CD-Rom, User Guide, March 1993, p.l). Furthermore, "the value of trade is measured consistendy in thousands of U.S. dollars and valuation adjustments are performed to ensure that the dollar value of exports will equal the dollar value of imports in all trade flows" (Ibid., p.2). These annual data are reported in nominal $U.S.; statistics are currently available for the period 1980-1992. The data are broken down by country and by commodity, whereby the latter is based on the Standard International Trade Classification, Revision 2, commodity coding system. This data classification system is based on the Harmonized System of commodity coding. The world trade data are harmonized (as closely as is possible) to the H.S.-based provincial export data. Appendix 1 provides a summary of the concordances.  Exchange Rates (XR) Exchange rates are specified in real terms, expressed as the log of Canadian dollars  (Produced and maintained by Statistics Canada's International Trade Division.)  45 per unit of foreign currency. Mathematically this is defined as follows:  eP RER ~ log(—-)  where e is the nominal exchange rate, in units of Canadian currency per unit of foreign currency; P  T  is the CPI in the foreign country of interest; and P  N  is the Canadian CPI.  CPI and nominal exchange rate data are obtained from International Financial Statistics (IPS), February 1994 issue, for the countries of Japan, China-mainland, South Korea, Singapore, the United States, and Canada. The CPI and nominal exchange rate data for Hong Kong and Taiwan are obtained from Asian Development Bank, January 1995 issue. Since, in both publications, nominal exchange rates are expressed in units of foreign currency per U.S. dollar, the rates are first inverted and then multiplied by the $Cdn./$U.S. exchange rate, in order that they be specified in units of foreign currency per Canadian dollar. The CPIs in both publications are indexed to the base year 1990.  Wage Rates (WR) Wage rates, specified in real terms and in logs, and are calculated as:  total wages paid total hours paid  Data on total wages paid and total hours paid, broken down by 4-digit SIC industry categories, are published in Statistics Canada's, Annual Survey of Manufacturers (Cat. No. 31203).  46 Industry Concentration (CR) Industry concentration ratios are typically specified as the percent of sales, shipments, or value-added, etc., accounted for by the largest n firms (or establishments) in the industry. The value of 'n' is commonly published as either 4, 8, 12, 16, 20 and 50. While these data are calculated and made available by Statistics Canada, Manufacturing and Primary Industries Division, recent data (i.e., for the years 1988 through 1992) are not currentiy published. As an alternative measure, the number of establishments in each provincial industry (based on the 3-digit SIC codes), is employed. The number of industry establishments are, in general, negatively correlated with the published CR4 statistics. To test the reliability of this proxy variable, correlation coefficients were determined where possible. The results of this test are shown in Appendix 5. Number of establishment data are available in the Annual Survey of Manufacturers (Catalogue No. 31-203).  New Capital Expenditures (NC) The statistics Canada publication, Private and Public Investment in Canada: Intentions, (Catalogue No. 61-205: annual for the years 1982 through 1994), Tables 13-19 (in Section ITJ: Provinces and Territories) provides data on actual investment expenditures on machinery and equipment and on new constructions. These annual data are published for "food and beverage" manufacturing industries (i.e., combined or aggregated 2-digit SIC data), for every province except Ontario and Quebec. In Ontario and Quebec, actual capital investment expenditure data are provided for the "food" and the "beverage" industries separately. Statistics Canada obtains these data via a survey questionnaire sent to the companies at  47  the end of each calendar year. The new capital expenditure data used in this study are derived by summing new equipment and machinery expenditures with new construction expenditures, in order to create a "new total capital expenditures" variable.  Labour Productivity (LP) Labour productivity is calculated as the log of the ratio of total value-added to total labour inputs, where "labour input" is measured in terms of "person hours worked". Personhours worked is the sum of person-hours spent at the place of employment by persons at work. The statistic differs from a measure of "person-hours paid" by excluding vacation time, holidays, time lost due to illness, accidents, etc., and is therefore considered to more accurately reflect productive efficiency.  23  Provincial industry value-added data, reported by 3-digit SIC, are obtained from the Statistics Canada publication, Annual Survey of Manufacturers (Cat. No.31-203). These data are converted to real $Cdn. by the method describe in Section 5.1 above. Person hours worked data are obtained by special request from the Information and Classification Section, Industry Division, Statistics Canada.  Trademarks/New Product Innovations (TM) The number of new trademark applications in each processed food and beverage industry category, in each province, and in each year are used to determine the trademark statistics variable. These data are obtained from the Trade-marks Journal, published by the Trade-marks Division, of the Canadian Intellectual Property Office (CIPO). The journal is a  Refer to the discussions regarding the benefit of using hours worked as the measure of labour input, rather than wages paid, in Statistics Canada catalogue #15-240E, Aggregate Productivity Measures, February, 1993. 2 3  48 weekly publication listing all new trade-mark applications. Each application is accompanied by a description of the type of "wares" and/or "services" being trademarked; a picture (if applicable); the name and address of the applying firm; and the (approximate) date of the product's entry into the Canadian marketplace. Since the Journal is not indexed, it is necessary to scan each page for related trademark applications. The following procedure was met: Only those trademarks listed as "wares" (i.e., as opposed to "services" or "wares and services") and pertaining to products of the processed food or beverage categories used in this study were gathered. 24  Only those wares listed as "proposed for use in Canada" or "used in Canada since., [not earlier than January 1988]" were considered. Trademark applications for products introduced into the Canadian economy prior to January 1988 were not included. Further, only those food and beverage manufacturers located in British Columbia, Alberta, Manitoba, Ontario, or Quebec were included. The result of this search was an annual total of approximately 800 new food and beverage trademark applications for all of the provinces and industries combined. These were then sorted according to the appropriate industry and province of manufacture.  The  procedure for this needs further explanation; the reason being that certain unforeseen difficulties arose during the data gathering-process. These are summarized in points 1-3 below: 1.  Some firms applying for the trade-marks were listed as being located in more than one province, in the Guide to Canadian Manufacturers (Statistics Canada, catalogue 32-  (Refer to Appendix 2.)  49 250) publication. For example, in the Statistics Canada catalogue 32-250, Thomas J. Lipton is classified as a "processed meat" manufacturingfirmlocated in BC, ON, and QU; a "processed fruit and vegetable" manufacturer in ON; and an "other food product" manufacturer, located in Manitoba, Ontario, and Quebec; 2.  Sometimes the firm applying for the trade-mark is identified as manufacturing in one SIC industry, while the commodity being trade-marked is distinctly in another. (Some firms thus manufacture products in industries that don't correspond with the industry under which they are classified by Statistics Canada.) For example, the Quaker Oats Company is classified as a manufacturer in the "cereal and grains industry". In 1988, however, thisfirmtrade-marked "fruit drinks", which are classified under the processed fruits and vegetable products industry.  3.  Somefirmswill use one trademark name to protect a number of commodities, which may be classified under different SIC industries. For example, in 1988 Nabisco Brands Canada Inc. trademarked the name "Dickson's" to cover mints, drink crystals, and crackers, amongst other things. The first two commodities would typically be classified as "other foods", while crackers would be classified as "cereals and grains". Hence, in accounting for these data difficulties (a good estimated guess would be that  approximately 15% of the trademark data faced one or more of the above limitations), the following systematic approach was followed: First, a list of allfirmsclassified as manufacturers in the processed food and beverage industry categories used in this study was created. These data were obtained from the Guide to Canadian Manufacturers (Statistics Canada, catalogue 32-250). This list provided a reference to identify firms producing in more than one industry and/or in more than one province.  50 Each trademark was then allocated or sorted by the commodity or "ware" and then by the province(s) in which the firm: a) is classified as being a manufacturer of that type of commodity; and (if the firm is not a classified as being a manufacturer of that commodity), b) the trademark is allocated to: (i) the appropriate industry category, and then to (ii) the province(s) in which the manufacturing firm is located. So, for example, when a firm registering a trade-mark is listed as being located in more than one province, the trade-mark is sorted first according to the type of ware(s) being trade-marked. If it is a "processed meat" product, for example, then it is allocated to the processed meat and fish industry category: hence the wares act as the primary determining factor. Secondly, the processed meat trademark is then allocated to the province(s) in which the firm is listed as a being processed meat manufacturing company. If the firm is not classified as processed meat company, but as a processed cereals and grain company for example (i.e., similar to the Quaker Oats problem), then the trademark is allocated to the province(s) in which the firm is manufacturing. Finally, when firms use one trademark name to protect a number of commodities, classified under different SIC industries, one trade-mark is allocated to each industry for which a ware has been identified. They are then sorted according to the province(s) in which the firm is classified as manufacturing that (those) type of wares; and secondly to the province(s) in which the firm is manufacturing (if it is not classified as manufacturing those type of wares). Approximately 4000 new food and beverage trademark applications have been gathered in total; that is, approximately 800 applications per annum, on average. are summarized in Appendix 6.  The results  51  5.2 Methodology To determine the statistical significance of the different exogeneous and endogeneous industry characteristics on influencing market shares in the Pacific Rim, the empirical model specified in equation (4.7) is estimated using the Generalized Least Squares estimating regression technique. Employing both cross-sectional and time-series datarisksintroducing heteroscedasticity (unequal variance of the error terms) and autocorrelation (correlation between the error terms over time) into the empirical regression analaysis. These conditions violate two of the critical, basic assumptions of the classical linear regression model. Specifically, these violations are: E(e ) = o] E( n i) °i# 2  it  e  e  =  heteroskedasticity; auto-correlation.  In the condition of heteroscedasticity or autocorrelation, use of the common Ordinary Least Squares (OLS) estimating technique, yields coefficient estimates, that, while still unbiased and consistent, are no longer efficient (i.e., have minimum variance). Hence, "the confidence intervals based on the estimators will be unnecessarily wide and the tests of significance less powerful" (Gujarati, p.342). This is because the variance-covariance matrix of the disturbance vector is incorrect. In such case, the method of OLS is no longer a suitable estimating technique. One common approach to overcome this is to employ the method of generalized least squares (GLS). The GLS method estimates a new variance-covariance matrix by "making use of the information (in the heteroscedasticity case) that some disturbances are likely to be large because their variances are large, or the information (in the auto-correlated disturbances case)  52  that when, for example, one disturbance is large and positive, then another disturbance is likely to be large and positive" (Kennedy, p. 114). Hence, "instead of minimizing the sum of squared residuals [as is the case with OLS], an appropriately weighted sum of squared residuals is minimized" (Ibid.).  Separate regressions are then run, using domestic sales, total Pacific Rim exports, and Pacific Rim market shares as alternative competitiveness indicators, or dependent variable measures. As discussed in Chapter Two, it is believed that determinination of the factors that influence competitiveness in the domestic market may provide some insight into the "general" (i.e., including export market) competitiveness influences. Moreover, the "richness" of the domestic market data (since the dependent and independent variables are largely derived from the same Statistics Canada firm survey questionnaires) permits some simple analyses to ensure correct model specification. In the total exports and export market share analyses, separate regressions are carried out for the Japan, Hong Kong, Taiwan, P.R.C., South Korea, and Singapore markets. In addition, a regression is also undertaken using U.S. export market data, for comparison purposes. To the extent that the significant lack of non-zero dependent variable (or export) statistics, exhibited in the research data at hand, may limit the explanatory capabilities of the independent variables, and hence significantly reduce the usefulness of the results of this study, alternative specifications of the export and export market share data are employed. The domestic market, export, and export market share regressions are organized into three sets. Specifically, these are as follows:  53  Regression Set 1: Provincial Industry Domestic Shipments and Value-Added Sales. Tw separate regressions are first run using the method of GLS on both domestic shipments (i.e., total plant sales) and value-added (i.e., sales less the cost of materials and supplies used) as alternative dependent variables. Domestic shipments reflect total industry sales, whereas value-added reflects only that portion of industry sales to which the firms actually manufactured or added value. Domestic shipment and value-added data are obtained from the Statistics Canada, Cat. No. 31-203, Annual Survey of Manufacturers. The same models are then run, however this time the OLS estimating technique is employed. Comparing OLS coefficient estimates with GLS estimates enables one to check for correct specification of the model. Despite the fact the method of OLS is expected to be inefficient in the presence of the data used for this study, both the OLS and GLS procedures should yield "qualitatively" similar results. That is, if GLS produces "generally large and positive" coefficient estimates, in a correctly specificied model, OLS should yeild the same. In the final component of Regression Set 1, the provincial industry and time dummy variables are excluded from the the original GLS domestic sales models discussed above. This is carried out simply for interest and comparison purposes; exclusion of the "fixed effects" represented by the dummy variables should result in biased coefficient estimates, if there are in fact fixed regional, industry and time effects.  Regression Set 2: Provincial Industry Pacific Rim Exports. Analysis of the statistical significance of the explanatory variables on total exports to the Pacific Rim, by provincial industry, is then carried out in Regression Set 2. Five alternative specifications of this model are employed; the latter four of which aim to address the typically "thin" or scant value of exports by various provincial industries in various years. Each of these alternative models are  54 discussed in turn below.  Provincial Industry Exports, Using Exchange Rates. Exchange rates are not included in the base export model since the analysis is limited to five years of data, in which case exchange rates must therefore be aggregated to an annual level. Hence, this is likely to reduce the explanatory capabilities of these data. There is some interest, however, to determining the explanatory powers exchange rates may pose, in comparison to the time dummy variables, in determining exports in the Pacific Rim. For this reason, the above Provincial Industry Exports Model is re-specified, using exchange rates in place of the time dummy variables.  Aggregated Dependent Variable Specification: "Stacked" Aggregated Exports. In this regression, each of the six Asian Pacific Rim market export data (i.e., excluding the U.S.), are "stacked" atop one another, in order to create an (export) dependent variable with 750 observations (as opposed to 125).  The interest in this approach lie in detenriining the effect  that enriching the dependent variable data, by significandy increasing the degrees of freedom, might have on the explanatory capabilities of the independent variables. Moreover, Pacific Rim market dummy variables (i.e., a dummy variable for each Japan, Hong Kong, Taiwan, China, South Korea, and Singapore markets) are introduced in this model. To this extent, exchange rates are included in place of the time dummies, for the purpose of attempting to maintain relatively the same degrees of freedom. The reason for introducing the market dummy variables is simply to ascertain the influence any one market may be imposing on the regression as a whole.  55 Alternative Dependent Variable Specification: Summed Export Data. As an alternative approach to account for thinness in the dependent variable data, the export market data (excluding the U.S A . ) are summed together. That is, rather than analyzing provincial industry exports to specific Pacific Rim markets, provincial industry exports to the entire (or "summed") Pacific Rim market is studied. This approach is an attempt to enrich the "quality" of the export data. Two separate summed dependent variables are specified in this case: the first includes Japan, along with Hong Kong, Taiwan, China-mainland, South Korea, and Singapore; the second specification omits the Japanese export statistics.  Alternative Explanatory Variable Specifications: National (Canadian) Industry Exports to Pacific Rim Markets. In this national exports model, all explanatory variables pertaining to the same industries, across all of the five provinces, each year, are summed together. Similarly, the corresponding exports to these explanatory variables are summed. This then creates a proxy of total "Canadian" industry characteristics to explain Pacific Rim exports. This is an important methodological approach to take since many of the empirical competitiveness studies undertaken to date employ highly aggregated, national-level data. Yet, at the same time, the direction of the literature is towards more disaggregated, regional or (ideally) firm level analyses.  Regression Set 3: Export Market Shares. This set of regressions closely mimics those regressions carried out in Regression Set 2 above; Pacific Rim market shares, however, are specified in place of total Pacific Rim export market sales. The alternative specifications of the market share regressions include: Provincial Industry Market Shares: Using Exchange Rates; National (Canadian) Industry Market Share in Pacific Rim Markets; and "Stacked" Aggregated Export Market Shares.  57 6.0 RESULTS AND ANALYSES 6.1 Statistical Analysis of the Variables Table 6.1 below provides an overview of the descriptive statistics that have been generated for each of the specified variables. Table 6.1 Variable Statistics NAME  #  MEAN  ST. DEV  VARIANCE  MINIMUM  MAXIMUM  MSJA MSHK MSTA MSCH MSSK MSSI  125 125 125 125 125 125  1.3843 0.3659 3.8593 2.2538 0.4303 0.1219  4.3799 1.0615 21.537 10.021 2.0330 0.3202  19.184 1.1268 463.85 100.41 4.1330 0.1025  0 0 0 0 0 0  41.912 8.0121 186.48 99.433 18.168 2.3779  logXPJA logXPHK logXPTA logXPCH logXPSK logXPSI  125 125 125 125 125 125  5.0266 3.0758 2.8648 2.8402 1.3247 2.3327  3.4117 2.7687 2.8193 3.0283 2.3481 2.2199  11.640 7.6659 7.9486 9.1704 5.5137 4.9279  0 0 0 0 0 0  12.252 9.8718 11.407 9.6773 9.3603 7.1467  logWR logCR logLP logTM logNC  125 125 125 125 125  2.5712 4.1351 4.0835 3.5197 11.885  0.1995 1.1218 0.4783 1.0927 0.9729  0.0398 1.2585 0.2287 1.1941 0.9466  2.1360 1.0986 3.3378 0 10.357  3.0641 6.1399 5.3619 5.3230 13.378  logship logvalad  125 125  13.440 12.492  1.1587 1.1470  1.3426 1.3157  10.944 9.8521  15.222 14.557  "MS" = market share; "XP" = exports; "log" = logged variable; "ship" = domestic shipments; and "valad" = domestic shipments in terms of dollars of value-added sales.  Table 6.1 shows no apparent outliers or errors within the data set. Further statistical analysis of the explanatory variables reveals "moderate" correlation exists between the following variables: - LP and WR, labour productivity and wage rates: +0.63 - TM and CR, new product innovations and industry concentration: +0.61  58 - NC and CR, capital expenditures and industry concentration: +0.62 - BEV and WR, the beverage industry dummy and wage rates: -0.85 - BEV and LP, the beverage industry dummy and labour productivity: +0.75 (The complete correlation matrix of variables is provided in Appendix 7.) While the linear relationship between these explanatory variables is less than perfect (i.e., <1), the standard errors (estimated in the regressions discussed below) are small, and hence we can expect that the coefficients are nonetheless estimated efficiently.  6.2 Regression Analysis Tables 6.1, followed by Tables 6.2A through 6.3B present the results of the regressions estimated in this study. These are discussed in turn below. Regression Set 1: Domestic Shipments and Value-added Sales (Table 6.1). The results of the (GLS run) domestic industry shipments and value-added model reveal generally significant and correctly signed variables. In the shipments model, the wage rate, industry concentration, and labour productivity variables all possess positive and statistically significant coefficient estimates. In the value-added model, the estimated coefficient on the trademark variable is also shown to be statistically signifcant in explaining changes in value-addded sales. Furthermore, the regional dummy variables, AB, ON, and QU, the industry dummy variable, MF, and thetimedummies, dT90, dT91, and dT92 also exhibit significant coefficient estimates. The results suggest, however, that changes in shipments and value-added are not at all explained by changes in new capital expenditures (NC). Since the wage rate variable, as specified, does not account for differences in the quality of labour, theoretically a significant, positively-signed wage rate coefficient could be considered to be "picking up" the influence of industries with high skilled labour. That is to  59 say, the results suggest that industries with high skilled labour tend to have higher domestic sales --both in terms of total shipment dollars and in terms of total dollars of value-added sales. The significant and positively signed industry concentration variable suggests that the tendency towards less concentrated, or alternatively, more competitive industries significantly explains increases in total industry shipments and value added sales. Further, industries with higher labour productivity and those exhibiting more innovative tendencies (as exhibited by the trademark variable, TM), also generally tend to play a role in explaining changes in domestic sales of the specific industries considered. The trademark variable in the domestic shipments model, however, is not statistically significant, but with a t-statistic of 1.45, the variable is explaining some (although niinimal) changes in domestic shipments. The relative inelasticities of the industry concentration, labour productivity, and trademark variables, as exhibited by the respective coefficients, indicate that domestic shipments and value-added are relatively unresponsive to changes in the number of firms in an industry, the productivity of labour, and the number of new products developed. This stands to reason since a percent change in the sales generated by an additional percent change in the number of firms, units of output per hour worked, or by an percent change in the number of new products developed in the industry, while significant, will likely be small relative to the existing total industry sales. The relatively large coefficient on the wage rate variable, while still inelastic, however, suggests that industry sales are 'less unresponsive' to increases in labour quality. The highly significant and positively signed Alberta, Ontario, and Quebec provincial dummy variable coefficient estimates, and the MF-industry dummy variables suggest that other regional and industry specific factors, not identified in the model, also influence domestic sales. Further, the elasticity exhibited by the Ontario regional dummy implies that,  H Z  co  co p  o  CO  u fS  S S  Os  H  0  Pi  O  r- o o -* d rs  T J  d  8P  H  r-H  O c T J  d  CB CO  J  O u  CS  a E  o U Iri  d  in  +  CO  H  ft  u  o  ^ in  T J  Q,  S  ^  —< • *  d —'  Tt  O  -H  O  -H  in  CN  y-,  d  d  13  ^  d  C  CS y - ,  in in  o  0.  d  s^  00  en.  /-v  cs in O cs  QS  O so  d  OS  00  d  in  in  o  d  CO  CO  -1  d  d -<  d.  d <s  vo  d  T J  w  Os  —i in  o  1  Os o O O  cs O cs  d co  ©  w  .-H  Os y-v OO i H O  c  C3  S S  d *-<  H  2  oo  oo Os — < cs d *-<'  O  -H  O  -H  o  d co. co r~ co C N  d  -H'  II  CD.  + T J  «  Os,  oo cs  2s  EL, CN  Os  in S\  S  00  oq ^ d C-  £ 5?  CO  s  T J  a>  T J T J  «! C8  o  ^ s + T  cm  in >- n  tj b  CS  o  +  &  z *  O Jo  3 <  T J  8  S i  wi  —I 1  S  ^5 Os  co  9  in  b  « S  d d  S  CO  PH  CS  VO  rco  > — 2 *-  1  00  Os  d  II 01 co r~  00  83  d S-  5"  CO.  d  Os  co  Os  »  s  ^. in  22 R  m  T J  CO. CU  E o Q  II  z  ^ co  8  CO  O O  co  J  •>1  cs ©  u  «° u  o  w  oo  a,  8  E o Q  .9-d co  v© ZS cs H  o  0 0  VO  &  3  00  1  E vo .9" d  es co  »  - C N  ^ CO  •o  T J  CU  T J  J2  I  1  is  U  "5  00  ®  o  i§5  S in ooP  E ^  T J  <!  _ScT 0C0J  cs  G-  ,  0  ^  O  £ °°  m  •° 12  1  ^ in  T J  ^  2  I s *  T J  S  ^2  p  iJ oo  *-  o  cs in CS -  CS  09  P £  O  0  Q  T J  <  a  ac >n  T J  SO  P  CO  E  T J  E m  cs  CO  O cs  cn.  V  o u Q.  P co 9 J  S  CO  CO  «8  CS  so  given the cumulative existence of related regional data, changes in this variable evokes a responsive change in domestic sales. This is likely due to the large size of the Ontario market, when it exports, relative to the other provinces. The one variable which does not prove to significandy explain changes in domestic output, is that of new capital expenditures. While it is assumed that new capital expenditures reflect industry profitability, and therefore should be an important determinant, the nonsignificance of the capital expenditure coefficient is not completely unexpected, since the data for this variable is specified at a total, provincial "food and beverage" industry level, rather than at the same 3-digit level of detail which the other variables are specified. The degree to which this variable has been aggregated then, hinders its ability to explain changes in the more disaggregated, 3-digit industry data. The high Buse R  225  regression coefficients suggests that changes in total industry  shipments and in value-added are significandy explained by changes in the explanatory variables identified. Furthermore, the significant F-statistics associated with each of these regressions indicates that, joindy, the explanatory variables (excluding the constant) do explain the variation in industry shipment dollar and in industry value added shipment dollars.  Comparing the regressions estimated using GLS, to the same regressions using OLS, in Table 6.1, reveals "qualitatively" similar results. The sign and general size of the coeficient estimates in the GLS models are paralled by the same coeficient estimates in the OLS models. These results indicate that the model is correctly specified. Since we expect  Following the discussion in Judge, Griffiths, Hill, Lutkepohl, and Lee (1985) the Buse R2 [1973] goodness of fit measure will be "between zero and one and is monotonically related to the F statistic" (pp.477-78). Furthermore, it is interesting to note that the same regressions were run using the OLS technique (although the results are not provided here) with similar regression coefficient estimates being calculated. 25  62 the OLS estimates to be biased as a result of heteroskedasticity and auto-correlation, however, direct comparison of the t-statistics is not possible. Comparison of the two coefficients of determination (Buse R and R or R -Adjusted), while not directiy comparable, given the 2  2  2  different methods of calculation, can very generally be compared since both attempt to measure the proportion of the variation in the dependent variable associated with variation in the explanatory variables. In either the GLS or OLS approaches, the results in Table 6.1 show very high coefficients of determination, thus indicating correctly specified models, with dependent variables that explain a very high proportion of the variation in the competitiveness measures.  In the final component of Regression Set 1, the provincial industry and time dummy variables are excluded from the GLS domestic sales models. The results show lower R  2  estimates and highly significant, i.e., t-statistics of 17.19 and 18.27, on the industry concentration coefficients in shipments and value-added sales models respectively. The lower R estimates suggest that, despite the exclusion of the dummy variables, the explicit 2  independent variables (i.e., the trade, LO., and firm strategy variables) identified do explain a siginificant proportion of the variation in the competitiveness measure(s). This has important implications in terms of the confidence it indicates in the model's specification: that being that it is correct. Furthermore, the new capital expenditures variable (NC) proves to be stastically significant in the non-dummy value-added model, whereas this is not the case in the value-added model, whereby the dummy variables or "fixed effects" are included. These results do lend support to the presupposition that exclusion of the fixed effects accounted for by the dummy variables do result in biased coefficient estimates. However, to the extent that the model is comprised of only five explicit non-dummy variables, the ability to identify the  63 true biasedness imposed upon these estimates is limiting.  Regression Set 2: Provincial Industry Pacific Rim Exports Model (Tables 6.2).  The Buse R coefficients in each export model, excluding South Korea, suggest that a 2  significant proportion of the variation in exports is being explained by changes in the explanatory variables considered. In addition, the calculated F-statistics for the Japan, Hong Kong, Taiwan, China, Singapore, and U.S.A. models indicate that, jointly, the explanatory variables considered do significantly explain the variation in exports to these regions. Results of the exports model (Table 6.2A below) reveal few statistically significant coeficient estimates arising from the trade, LO., or firm strategy explanatory variables. In the Japan exports model, decreasing firm concentration (i.e., increases in the number of firms in an industry) and increasing capital expenditures are shown to explain some of the increases in provincial industry exports to Japan. Declining relative wage rates, however, and increasing firm concentration (or a decrease in the number of firms) explains increases in exports to Taiwan; and, increasing wage rates, or an increase of skilled labour, corresponds to an increase in exports to China. None of the explicit RHS explanatory variables (i.e, trade, LO. or firm strategy variables), however, are stastically significant in explaining changes to exports in Hong Kong, South Korea, Singapore or even the U.S.A. markets. Explanatory powers are evident, however, in a noticeable number of the regional and industry dummy variables. These dummies exhibit highly significant and highly elastic coeficient estimates. In particular, the B.C. regional dummy variable proves to be very consistendy significant. In each of the Pacific Rim and U.S.A. export regressions, the B.C. dummy is attributed with a high t-statistic, and a large and positive coefficient estimate.  H  cn  CO  Z  r~ oo  o  ^ m cs  r^ co in O co CN  o <n  CN Os  H ©s H  f  d  •a  o ©  8  S  8£ CO CN  O  ^-s  o  ©  co  CN  M  0  o  •f-i  > W  OA «u  in >n r~-  0 0  0 0  35 H  +  OS  o  a)  00  CB  0  a  00  0 0 VO 3 CN  VO  ^  ™  CO  d << CN  d  Cl O in fill c£  3 If ©s  •a B  ' in >-11  © r-  00  £  fN  d  z  o •a  CS  CO Os  PH  o >n -H' O  VO  S  d  C  d  dd  CN ON  vo  CN  TH  CN  S  in  •  © CO  CN  P  S So  vo S51 co is o\  in  ^  VO CN  £ j CN  2  d  co C i  in C i  ^  >n  VO 00  VO  co ao in co  1  S  00  00  o  CO  ©  m 00 o— 1 d d  E n ^  o *—'  90  5c  in 0 0 T + m r- vo cs co d d  CO  $  VO  0 0  d  m os  w  CN  S  •  o  5d  ^ CN CN CN <N  w 90  N  ft  CN  d  CN  o  ^ cl  00  JN  2 ©  m  ^  ^  5  VO CO  d  VO CN .vo l>  ;_,  S5 ^  S  d  ©  2© S3  o d  O  CN  r- o in co  rf  CN  q  I r»-j  T-H CO CN  Ov II  s  2g .  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CO  O --i CN  W  S  u _ O" rr^ co  x a j W co C  A n additional, important observation of the regression results exhibited in Table 6.2A is that coefficient estimates determined to be statistically significant do vary from one export market to another. Moreover, the sign of a particular significant variable may vary for different markets. For example, the processed meat and fish industry dummy is significant and positively-signed in the Japan, Hong Kong, Taiwan, and China export market models, hence explaining changes in total industry exports to these markets. This variable does not, however, prove to significantly explain changes in exports to South Korea, Singapore, or the United States markets. In addition, the estimated coefficients on the wage rates and industry concentration variables are significant and negatively-signed in the Taiwan export market model, while only wage rates are significant, yet positively signed, in the China model. The observed results suggest that competitiveness-influencing factors or determinants depend on the particular market(s) in question. Secondly, the dummy variables (and in particular, the B.C. regional dummy) suggest that other influences, not accounted for by the explicit independent variables specified, are important in explaining changes in competitiveness measures. There is some concern, as well, however, that the lack of non-zero statistics occuring in the export market data (recall Appendices 2 and 3) may be inhibiting the explanatory capabilities of the independent variables specified, and hence affecting the above results. In Tables 6.2C, 6.2D, and 6.2E below, alternative specifications of the independent and dependent variable data are undertaken in attempt to account for this possibility. First, however, in Table 6.2B which follows, exchange rates are used in place of the time dummy variables in order to determine if the one specification is more "revealing" than the other.  f o  H  CO.  CO  JS  !  Cd 90  CO.  CU  en  e  es  u  C8  H O  00  CN  s  o o •o  o  —I  Tt  00  CO CN  vo  co  .'  d d  00  ^  d.  S 9  o  >0  Tf  oo  co  •  y-^  VO r H  o ©  CO  §8  0^  CN  p.  CO  d ©  CO  —•  —i  Os  vq  '—s  Os  Tt  co  co  d  •d  •1-1  $ O  into  •s 1/1  J»-  Os i-H  P  CN CO  CO iri CN CO O s s o OS  o  II  P P.  II CO y-v  "H  3  co PH  U-)  CN  Tf  CO  d d  Os  8  i-i  d; TT  W  li  tf  CS PH  Os  o  —I  CN  d• roo co Tt  3  CN  CO.  vo  + CO.  P OS  3d  •  Os  r-- T t d d  CN  *  Os CN  II  s  VO Tt 00!  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CN V I CN l H  ©  2  CN Q 0 col oo • Os CO CO  1  2  oo  d  S P  Tt  oo  d ©  P.  - CN CO  co Tt  rr  CO  d tf u  5 3  EL,  II  •1-1  d ©  m  5  zr  CN  cW  00  co Ov  oS d d  1  CN CO  CO  d  jq  CN VO fN,  ^9 Tt  &  3  r i  T3  T3  .2 "0  3d  CO CN  TS  u  T3  Os  O Tt OS IT)  £s  i o d  ^  OS CN  CN CN  '—.  Os" CN  2.327  +  o  s  d d  CO CO  Tt  I  fa  T3  w  os  vo  x  y-N  oo  CN  y-,  ir>  d -  1  co P  o a  Os  P "j CN C  stat  CU  =  or  r-  PP.  •i-i  cu  tf is  r-~  9  +  'in  d.  Tt  VP  G  c© 8 K ex  u  o  u IX  cu CS  v$  o  03 tf  VO y — r~- os r- CN  Os" OS >T>  Z  O CN  o d  P.  d co P i  o CN CN  Os sq co  lO  vq  d  67 Provincial Industry Exports, Using Exchange Rates Comparing the results presented in Table 6.2B with those in Table 6.2A, very little difference is seen to exist between the two model specifications. A non-significant exchange rate variable commonly coincides with non-significant time-dummy variables, except in the case of China and Singapore, where significant coefficient estimates on the time-dummy variables are not matched by significant exchange rate coefficient estimates. This would suggest that something other than the annual exchange rate is embedded in thetimedummy. Hence, this observation warrants specifying the model to include thetimedummy variables wherever possible. Of the three "alternative specification" models which follow, time dummy variables are specified in two of these models. As discussed in Section 5.2, in the "stacked aggregated exports" specification (Table 6.2C), exchange rates are still included in the model, in place of the time dummies, since Pacific Rim export market dummies have been introduced, and the intention is to attempt to maintain relatively the same degrees of freedom. Again, the reason for introducing the market dummy variables is to determine the influence any one market may be imposing on the regression as a whole.  b  X  ON  d  U  CO  o co O  H  vo  vo CS  d.  t/3 *  VD  2  00  Z  CO.  v^  8* 2 S  3  tfl CU  Ov T t ^  c  aa « a  c  cu u  M  cu  OS  is  =  O M  +  "~i  TH  2 -*  CU  CS  "3.  o  —I  1  Lh  ca.  Q. II  ui  '0 CS CM  H  a  0  QJ  c  o u  CN  & M  CX  Z  n  o pa  •a  u z  II  0  1  a CO  u 3  cs H  89  .1-  CO  £  8. 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O lm  s *S o-! o^ « _ ^ 1  w  S  rH  P  '5  in  .  5  J r». T t  as  fa  /-N  so r-  B x in U d C  H  fai f5 ^  u B  ?  • cn  rf o —*  cn  r-  bj  T J  p  '"•s  CO  m  cn  0. cn oo  11  P. cn  ^  Os  CM  Os  P cn  P .  SO  fa  u  "rf rf  W  PH OS / - N  cn  as  rH  3 cn  CN  cu  rH  CO O  T t  H O  1  i n sq  rH  ^  .>  CN Os  SH  ae  d  00  -J  Tt  IN  =  0  CN CN <T)  cn so  Os  c?  c  _• o  cn  OS  OS  Tl"  «  d So  CN  i  r i cn  P  CN  d  Q  T>  d  fi  CN  SO  3  ^) J8  H  69  CO  d.  _x r- ^  70  Alternative Dependent Variable Specifications: "Stacked" Aggregated Exports and Summed Export Data In Tables 6.2C and 6.2D above, the results of two regressions using alternative dependent variable specifications are given. In the first regression, each of the six Asian Pacific Rim market export data (i.e., excluding the U.S.), have been "stacked" atop one other, in order to create a model with 750 observations, as opposed to 125. The results show very littie significant influence by the independent variables considered in this study. Only the estimated coefficient on the industry concentration variable is shown to explain a fairly significant proportion of the variation in exports (i.e., a t-statstic of 1.75). The sign on this coefficient estimate is negative, and the size (0.350) indicates its inelastic responsiveness. That is to say, the results suggest that relatively concentrated industries (i.e., industries with few, or monopolistic firms) generally explain increases in exports to markets in the Pacific Rim, yet a percent increase in exports is relatively unresponsive to a corresponding percent decrease in the number of firms. The estimated coefficients on the provincial and industry dummy variables —excluding the beverage industry dummy, however— are all very significant and generally very elastic. Again, the BC dummy variable, relative to the other regional dummy variables, and relative to the industry dummy variables, reveals a highly significant t-statistic (14.44) and a highly elastic coefficient estimate (5.119). The Ontario regional dummy and the meat & fish industry dummy are also highly statistically significant and very elastic; in comparison to the BC dummy, however, Ontario and meat & fish exhibit relatively less powerful explanatory capabilities. As discussed earlier, the continued significance exhibited by the dummy variable estimates imply that something specific to the region or industry, but not explicitly accounted for in the model is important in explaining export sales.  71 Of the Pacific Rim market dummy variables included in this model (i.e., the Japan dummy, Hong Kong, Taiwan, China, and Singapore dummies), only Japan proves to be significant (t-statistic of 5.19). This coefficient estimate is 2.525, which suggests that this market is highly responsive. Given the size and wealth of this market, the results would appear to be accurate. That is to say that one would assume that the value of a contract awarded to export a certain processed agri-food product to Japan would be significant, relative to the value of a similar contract to any of the other Pacific Rim markets, because of the number of consumers in Japan and the price these consumers are typically prepared to pay. In Table 6.2D, the Asian Pacific Rim market export data are summed together. While not increasing the number of observations, this approach is meant to enrich the quality of these data. Two separate summed dependent variables are specified in this case: the first includes Japan, along with Hong Kong, Taiwan, China-mainland, South Korea, and Singapore; the second excludes the Japanese statistics. The results of the first model indicate that increased labour productivity, new product innovations, and factors embedded in the BC dummy and the meat & fish industry dummy variables significantly explain increases in exports to all of the six Pacific Rim markets combined. On the other hand, increases in labour productivity and factors embedded in most all of the regional, industry, and time dummy variables (excluding only the Other Processed Food Industry dummy and the 1989 time dummy) significantly explain increases in exports the five Pacific Rim markets (i.e., excluding Japan). Interestingly, the coefficient of determination is higher in that model in which Japan is included, yet fewer explanatory variables are identified as being statistically significant in determining changes in exports. Moreover, only the BC dummy and the meat &fishindustry dummy are shown to be significant in this model. Looking back at Tables 2.2, 2.3 and  Appendices 2 and 3, suggests that perhaps B.C.'s meat & fish industry exports to Japan (and likely, quite predominantly, the province'sfish exports) are "overpowering" or masking the influence of other variables or factors; this is, of course, despite the fact that the variables are specified in logarithms.  I  H  O  o  co  Os CO  Z  VD  CO.  r-^  o  CN CN i  u  f~ cs  OS  d s CO  "a  (A  o  e OC cu  H  e>  +  CO.  is  +  cu  in Q .  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S 00 CN co  o %  £ d S o  CO  cu oo ^ u r- o es co r>  S  « H  *• — HH  lS T t  ^ - ^  CO  es cs os  T  2  <  o co  .a d —<'  CO o  9  S  0 0  112  S  CN  d  d  '8  ^  CO rN  CO  Tt  co  S  o  ^  Tt  U fi C  d  SO CO O SO  >o So  CO  .IS ~  rf S  tN  5.  P.  ^  O CN CO CN  IS  OO SO Os  cN r~  d  w  00 00 in  (gip  00  SO  SO  d ll  d  rf  in SO ^-v in | co in HH co cs  HH  cu r- .^ u so CN es co T t  co  1/1  ri i-I  HH  cs f> £-» * co o  §51  d d II  8° e  -* d.  CO  CO T t CO CO 00 CN oo  St ~  co  00 00  CO  CO P r -  R  so S rf p H H  00  Os  Os f ?  ia. O  oo  r> Os r t sq • d in C il  tN  ca  II  T-H  d —< '  3  d  ©  ©  in co co o  P  In CN  , ,  —i CS  H  8 5 CN in  so rd d d  S  1  CO  cs  Os 90  b>  1  d  d i-5  H  >n CN cs  in  -H  & 5?  Os EN"  T»  d  o c?  o  >o i-5 SO  £j S  H  tu  +  in  Os ^ CS cs O —«  in  CO.  Os Os  rin o  o  vo  IT)  00  -3.7 (0.7  o  ©  •  s^  cu SO u in es —i  r7  O  CO  i  cu  cu CS Os  3  HH  w  t2 N I?^  5  'I? -  1  ^-s  S2 ©  77  Regression Set 3: Export Market Share Regressions The coefficient of determination in the market share regression (Buse R ) pertaining to 2  each market regression, except Japan, is noticeably low. Hence, very little of the variation in market share in Hong Kong, Taiwan, China, South Korea, Singapore, or the U.S.A. is explained by variations in the explanatory variables considered. In fact, the results in Tables 6.3 reveal very few of the explanatory variables as being statistically significant. Hence, the results suggest that changes in market shares of processed food and beverage industry categories, in individual Pacific Rim markets, are typically not explained by either of the exogeneous or endogeneous factors included in the study. The only coefficient estimate that does reveal some degree of consistency in statistical significance is the B.C. dummmy variable. In the Japan, Singapore, and U.S.A. model runs, the positively signed B.C. dummy is highly significant (i.e., t-statistics of 8.93, 3.78, and 5.07 respectively). This suggests that certain factors "embedded within" the B.C. dummy variable, but not explicidy identified in the model, are positively influencing shares in these Pacific Rim markets. Moreover, in the Japan, China, and U.S.A. market models the coefficients of the B.C. dummy variables are very large and positive, implying that the onset of a relative change, specific to the B.C. region, evokes a responsive absolute change in Japanese and U.S.A. market shares. A final note of interest is the changing sign on the estimated coefficients of some of the variables in the market share models (e.g., alternative positive and negatively signed coefficients of the industry concentration proxy (CR) variable). Although insignificant, these differences do begin to suggest that changes in different factors my influence individual export markets differently.  78 7.0 CONCLUSION Significant export opportunities are seen to exist for the B.C. agri-food industry in Pacific Rim markets. There is, however, a lack of understanding of the driving factors behind the industry's ability to be "competitive" (i.e., profitably gain and maintain market share) in these regions. The purpose of this study has been to attempt to develop an understanding of those factors or characteristics which influence the B.C. industries' competitiveness in the Pacific Rim. Specifically, the interest has been to determine the influence of exogenous factors, as suggested in the traditional comparative cost and industrial organization doctrines, and endogenous factors, suggested in business school and, now, competitiveness literatures. Using cross-sectional, time series data, systematic differences amongst related industries in B.C. and four other Canadian provinces are studied to explain changes in export market share and changes in total exports to Japan, Hong Kong, Taiwan, China, South Korea, Singapore and the United States. Converse to what is suggested in the literature, the findings show no statistical consistency in the explanatory capabilities of comparative cost, industrial organization, or firm strategy variables in explaining competitiveness in Pacific Rim markets. Rather, it appears that export success of the provincial industries is due to many unique factors at thefirmor provincial level. While low wage rates and industries with few or monopolisticfirmsexplain changes in exports to Taiwan, high wage rates (or skilled labour) explain changes in exports to China. On the other hand, changes in labour productivity and new product innovations are shown to be statistically significant in explaining changes in exports to the sum total of the Pacific Rim countries. Yet, when Japan is excluded from the "total" Pacific Rim market, only changes in labour productivity significantly explain changes in Pacific Rim exports. Moreover, analysis of the sum of all of thefiveprovince's industry exports to the individual Pacific Rim countries (i.e., a proxy for "Canadian industry" exports)  79  shows that less concentrated or more competitive industry structures and new product innovations explain changes in exports to Japan, while only competitive industry structures explain changes in market share in Japan; less concentrated industries and new product innovations, explain changes in exports to Taiwan; concentrated industries significantly explain changes in exports to China; and high wage rates (or skilled labour) and low labour productivity explain changes in exports to Singapore. Despite these findings, which differ depending on the particular export market in question, the regional dummy variables, most notably the B.C. regional dummy, proves to be very consistent in explaining export market competitiveness. The consistent statistical significance exhibited by the B.C. dummy coefficient estimates, compared to other regional and industry dummy estimates, and compared to the non-dummy explanatory variables, suggests that there are regional influences which impact on exports and export market shares of processed food and beverages to the Pacific Rim. As Kennedy notes, "The dummy variable coefficients reflect ignorance — they are inserted merely for the purpose of measuring shifts in the regression line arising from unknown variables" (p.222). Hence, these findings indicate that an important variable, explaining B.C.'s significance in these markets has not been correctly addressed by the model. Since it is only the B.C. regional dummy variable, and not any of the industry dummy variables, nor the remaining regional variables, that is so consistently significant throughout all of the export and export market share models, one is left to infer that export successs is due to many unique factors at the B.C. firm and/or B.C. provincial level. In terms of policy implications, these findings would suggest that any program or policy which increases the access, opportunities, or abilities of B.C. exporting firms to make business contacts/connections in the Pacific Rim markets would increase industry export  80 capability. This parallels what is currently discussed in business magazines and on radio and television talk shows, etc.; that is, "getting out there, or going to the markets, and getting immersed in the culture and business practices" is what matters most. Hence, neither the relative comparative cost advantages betweenfirms,industries or nations, nor the industrial structure of the domestic markets in which firms operate, nor the "innovativeness" or other such specific firm strategies will be solely and consistently responsible for their competitive export success. These theories would imply that B.C. and other Canadianfirmsneed merely wait for the markets to come to them, attracted to the comparative advantages offered. The findings in this study indicate, however, that gaining and maintaining shares in the Pacific Rim markets instead requires that domesticfirmsmight be more well advised to aggressively search out their markets, and thatfirmsin British Columbia, for reasons not wholly clear, are already at an advantage.  81 REFERENCES Abbott, Philip C , and Maury E. Bredahl. "Competitiveness: Definitions, Useful Concepts, and Issues." In Competitiveness in International Food Markets, eds. M.E. Bredahl, P.C.  Abbott, and M.R. Reed. 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Shifting Gears: Thriving in the New Economy. Toronto: Harper Collins, 1992. Belassa, Bela. "An Empirical Demonstration of Classical Comparative Cost Theory," Review of Economics and Statistics. Volume 45. Cambridge, Mass.: Harvard College, 1963. Bredahl, Maury E., Philip C. Abbott, and Michael R. Reed. Competitiveness in International Food Markets. Boulder CO: Westview Press, 1994. Buzzell, Robert D., Bradley T. Gale and Ralph G.M. Sultan. "Market Share - A Key to Profitability," Harvard Business Review. Volume 53, January-December. Boston, Mass.: Harvard College, 1975. Canadian Intellectual Property Office (CIPO). Trademarks Journal. Ottawa. Weekly editions, 1988-1992. Caves, Richard E., Michael E. Porter, A. Michael Spence, and John T. Scott. Competition in the Open Economy - A Model Applied to Canada. Cambridge, Mass.: Harvard University  Press, 1980. Chamberlin, E.H. The Theory of Monopolistic Competition. Cambridge, Mass.: Harvard University Press, 1933.  82 D'Cruz and Rugman (1992) New Compacts for Canadian Competitiveness. A study commissioned by Kodak Canada Inc., and published as part of the Kodak Series. Kodak Canada Inc., Toronto Ontario, 1992. Gruber, William H. and Raymond Vernon. "The Technology Factor in a World Trade Matrix," The Technology Factor in International Trade, edited by Raymond Vernon. New York: National Bureau of Economic Research, 1970. Gujarati, Domadar N. Basic Econometrics. Second Edition. New York: McGraw-Hill Publishing Company, 1988. Hazeldine, Tim. "Market Mass Competitiveness in the Canadian Food Industry." In Competitiveness in International Food Markets, eds. M.E. Bredahl, P.C. Abbott, and M.R.  Reed. Boulder CO: Westview Press, 1994. Heckscher, Eli F. and Bertil Ohlin. Heckscher-Ohlin Trade Theory, eds. Harry Flam and June M. Flanders. Cambridge, Mass.: The MIT Press, 1991. Ho Fan and John Beghin. 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New York: Harper Collins, 1966.  85 APPENDIX 1.  Selected Definitions of Competitiveness  The following list of definitions are meant to provide an overview of the diversity of interpretations of the term "competitiveness". These definitions are taken from lists provided in Ash and Brink (1992; Appendix 1), and in Abbott and Bredahl, (1994). Competitiveness is "the ability of a nation to produce, distribute, and service goods in the international economy in competition with goods and services produced in other countries and to do so in a way that earns a rising standard of living" (Scott and Lodge, 1985). Competitiveness is the "...ability to deliver goods and services at the time, place and form sought by overseas buyers at prices as good or better than those of other potential suppliers whilst earning at least opportunity cost returns on resources employed" (Freebairn, 1986). Competitiveness is "a national ability to produce and market products in international trade while earning a level of returns to the resources (both human and physical) used to produce those products which is at least comparable to what those resources could earn in alternative activities" (Langley, 1986). What we should mean by competitiveness, and thus the principal goal a of our economic policy, is the ability to sustain, in a global economy, an acceptable growth in the real standard of living of the population with an acceptably fair distribution, while efficiently providing employment for substantially all who can and wish to work, and doing so without reducing the growth potential in the standard of living of future generations (Landau, 1992, p.6). "Competitiveness can be broadly defined as the ability to sell commodities to overseas buyers at prices as low as or lower than those of other potential suppliers while earning at least opportunity cost returns on domestic resources used to produce and market these commodities" (Vollrath, 1989) "For a firm, competitiveness is the ability to design, develop, manufacture and market products at home and in other nations in competition with other firms. For a nation, it means doing all this without a decline in the real standards of living of its citizens." (U.S. Congress, Office of Technology Assessment (1988,p.25), quoted in Industry, Science and Technology Canada, 1991, p.3) Competitiveness is the "...ability to design, produce and market goods and services, the price and non-price characteristics of which form a more attractive package than those of competitors" (IMD and World Economic Forum, 1990).  "The only meaningful concept of competitiveness at the national level is national productivity" (Porter, 1990).  86 "Competitiveness is the ability to profitably gain and maintain market share in the domestic and/or export market" (Task Force on Competitiveness in the Agri-Food Industry, 1990) "Competitiveness is a structural quality built into [a country's] public and private institutions and ultimately woven into its social, economic and political fabric. [...} Competitiveness depends on competition, and economic efficiency and innovation are the result" (Purchase, 1991). "National competitiveness is better defined by reference to broader indicators that show the extent to which a country's involvement in global markets through trade, investment, and technology flows to growth in real income" (Economic Council of Canada, 1992).  87  APPENDIX 2. Provincial Domestic Shipments and Pacific Rim Exports, 1988-1992, by Industry Category ('000 Real 1990 $ Cdn.) 2. A Total Provincial Shipments and Exports of Processed Meat and Fish Products ('000 Real 1990 $Cdn.) No. of  Domestic  Value-  Estab. British Columbia 1988 118 1989 121 1990 115 1991 109 1992 112 Alberta 1988 80 1989 78 1990 79 1991 74 1992 69 Manitoba 1988 47 1989 45 1990 41 1991 42 1992 39 Ontario 1988 246 1989 249 1990 236 1991 220 1992 212 Quebec 1988 209 1989 201 1990 225 1991 217 1992 206  Shipments  Added  Japan  H.K.  Taiwan  4,893.9 15,697.9 5,039.8 6,068.3 89,926.8 2,767.0 64,263.0 944.4 47,998.8 6,548.9  3.2 339.7 2,759.6 5,655.6 4,790.6  141.8 177.7 418.2 204.4 46.0  56,683.2 517,166.1 22,460.0 25,483.3 39,247.9  P.R.C.  S. Korea  Singapore  U.S.A.  1,562,706.0 1,502,932.0 1,551,000.0 1,428,883.0 1,418,937.0  503,740.4 431,518.3 463,800.0 439,204.5 435,074.6  207,906.0 229,521.2 140,755.8 179,629.7 153,910.9  9,654.7 6,355.0 5,244.3 7,012.0 16,859.8  2,589,989.0 2,594,764.0 2,811,400.0 2,375,758.0 2,156,250.0  365,786.6 314,031.4 360,300.0 376,231.1 391,138.1  69.0 157.1 20.7 2,218.0 15,785.9  298.2 0.3 163.3 2,907.4 154.9  0.0 161.9 234.8 0.0 6.7  254.1 778.4 0.0 0.0 0.0  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 0.9 0.0  2,358.0 14,616.2 32,513.3 1,610.7 2,075.9  696,919.7 582,722.5 501,800.0 418,750.0 368,470.1  178,767.9 147,644.0 159,500.0 131,628.8 108,675.4  192.8 138.0 222.1 90.1 2,705.8  0.2 64.6 128.9 272.0 56.6  0.0 0.0 0.0 0.0 0.0  0.0 0.0 9.8 375.7 0.0  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 41.4 16.5  7,434.2 3,212.2 6,514.5 8,273.9 17,984.4  4,080,418.0 3,940,314.0 3,706,600.0 3,308,428.0 3,387,780.0  1,009,351.0 946,911.0 927,900.0 909,185.6 889,272.4  4,622.3 8,207.6 12,224.4 2,522.6 1,492.0  844.4 326.0 248.7 216.7 108.3  989.4 367.9 438.2 236.5 355.4  175.9 106.7 0.0 0.0 114.9  234.8 61.0 0.0 306.1 267.6  372.6 248.9 216.8 11.3 6.0  13,031.6 2,547.0 5,158.9 12,078.2 17,295.3  2,836,854.0 2,671,937.0 2,649,200.0 2,633,902.0 2,503,731.0  648,844.9 605,340.3 657,100.0 715,909.1 668,470.1  99.9 114.9 619.8 121.8 213.6  10.3 0.8 2.3 267.6 77.2  120.9 114.6 0.0 199.2 185.0  64.5 347.9 371.8 45.3 60.8  0.0 2.9 64.0 54.1 0.0  710.2 17.4 8.9 0.0 0.0  300.4 343.1 1,814.5 2,689.0 241.4  88  APPENDIX 2 (continued) 2. B Total Provincial Shipments and Exports of Processed Fruit and Vegetable Products C000 1990 $Cdn No. of Estab. British Columbia 1988 34 1989 34 1990 34 1991 32 1992 27 Alberta 1988 8 1989 11  Domestic  Value-  Shipments  Added  Japan  H.K.  Taiwan  P.R.C.  S. Korea  Singapore  U.S.A.  279,317.9 254,555.0 256,200.0 236,931.8 243,283.6  94,389.4 105,026.2 91,100.0 92,992.4 99,533.6  3,624.7 3,277.7 2,145.6 1,513.0 837.5  982.6 277.1 126.3 236.1 247.6  0.0 19.4 60.5 23.7 28.4  43.1 764.9 507.8 134.8 42.3  46.4 428.8 64.0 0.0 29.6  16.5 90.6 152.5 110.2 241.0  20,995.7 16,738.7 16,555.4 8,691.4 13,645.3  22,992.3 27,225.1 21,300.0  0.0 34.2  14.9 107.4  0.0 0.0 0.0  0.0 0.0  0.0 24.7  330.4 533.5  0.0  92.6  448.3  0.0 0.0  0.0 0.0  23.7 15.7  1,149.3 58.4  1990  10  72,057.2 92,146.6 56,600.0  0.0  15.5 20.8 0.4  1991 1992 Manitoba 1988 1989 1990 1991 1992 Ontario  10 11  107,102.3 105,503.7  42,140.2 34,141.8  0.0 0.0  0.0 0.0  0.0 4.2 5.2  5 4 4 3  203,080.3 156,300.0 141,856.1 104,244.4  116,171.6 79,800.0 75,947.0 56,110.1  0.3 0.0 0.0 124.9 0.0  2.8 0.0 0.0 0.0 0.0  37.9 44.0 415.5 0.0 10.1  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 0.0 0.0  0.0 3.9 0.0 0.0 0.0  183.0 0.0 939.9 0.0 0.0  1988 1989 1990  103 96 90  1,919,802.0 1,880,733.0 1,871,800.0  885,478.5 798,743.5 803,900.0  149.2 383.6 159.3  18.4  9.5 0.0 15.3  1,424.5 2,210.4 189.4  0.0 0.0 10.2  0.0 0.0  4,764.1  89.5 19.3  1991 1992 Quebec  80 79  1,815,530.0 1,752,052.0  871,401.5  164.6 497.4  99.9 188.8  142.6 0.0  0.0  0.0  0.0  7,545.9  825,000.0  0.0  13.9  50.7  6,318.8  1988 1989 1990 1991 1992  63 58 68 61 62  477,117.7 467,958.1 481,300.0 417,140.2 380,037.3  206,820.7 210,261.8 209,700.0 175,094.7 154,384.3  61.3 371.1 10.5 0.0 0.0  7.4 0.0 42.8 69.9 5.2  0.0 8.1 2.6 0.0 0.0  0.0 0.0 0.0 0.0 0.0  147.8 0.0 0.0 0.0 15.2  41.3 13.6 1.0 0.0 1.2  61.2  37.7  3,931.7 6,370.1  15.5 79.5 82.2 11.7  89  APPENDIX 2 (continued) 2. C Total Provincial Shipments and Exports of Processed Cereal and Grain Products ('000 1990 $Cdn) No. of Estab. British Columbia 1988 88 1989 93 1990 90 1991 87 1992 86 Alberta 1988 123 1989 133 1990 123 1991 122 1992 119 Manitoba 1988 61 1989 64 1990 57 1991 58 1992 55 Ontario 1988 367 1989 385 1990 366 1991 341 1992 334 Quebec 1988 365 1989 360 1990 464 1991 399 1992 387  Domestic  Value-  Shipments  Added  Japan  H.K.  Taiwan  395,819.6 436,858.6 422,100.0 382,291.7 414,272.4  143,454.3 7,929.9 148,691.1 9,670.9 170,900.0 5,373.1 153,787.9 ^ 8,501.3 170,522.4 10,118.8  28.9 819.5 715.0 741.0 234.3  565,676.6 829,528.8 737,300.0 706,155.3 755,690.3  144,554.5 189,633.5 198,100.0 201,704.5 201,492.5  600.5 13.2 491.5 52.4 1,185.8  1.5 33.4 0.0 0.0 119.8  112.6 0.0 0.0 0.0 312.3  291,529.2 293,926.7 256,700.0 230,871.2 233,582.1  84,048.4 85,130.9 88,900.0 86,742.4 76,679.1  101.4 31.4 0.0 0.0 0.0  0.0 0.0 0.0 0.0 0.0  3,736,524.0 3,249,319.0 3,033,300.0 2,786,837.0 2,954,478.0  1,310,891.0 1,273,613.0 1,327,000.0 1,224,527.0 1,422,015.0  68.7 170.4 540.7 127.4 241.0  1,864,356.0 1,774,660.0 1,818,900.0 1,580,682.0 1,622,481.0  667,656.8 622,827.2 690,500.0 618,560.6 648,787.3  0.0 0.0 0.0 0.0 0.0  P.R.C.  22.5 19,304.5 2,207.1 7,407.4 51.6 4,635.9 26.0 1,607.7 17.3 6,894.8  S. Korea  Singapore  U.S.A.  0.0 0.0 2,513.4 0.0 116.3  0.0 11.7 24.2 7.0 92.0  37,375.1 30,783.9 52,023.6 66,572.2 145,547.4  0.0 0.0 0.0 0.0 0.0  16.6 12,736.9 0.0 0.0 0.0  0.0 0.0 0.0 0.0 0.0  2,015.1 2,749.3 183.7 6,323.3 13,968.9  22.0 5.7 45.5 607.8 128.6  0.0 0.0 0.0 165.3 33.4  118.0 0.0 0.0 0.0 0.0  231.4 448.7 551.8 0.0 23.2  372.2 154.3 2,381.8 741.2 518.2  59.8 44.2 397.8 168.6 429.7  0.0 0.0 122.4 45.3 10.5  0.0 11.1 547.0 849.4 163.8  0.0 62.6 344.0 1,269.0 187.8  0.0 33.0 17.8 133.1 78.5  1,394.1 1,279.9 1,622.8 8,209.9 3,744.1  0.0 0.4 0.0 0.0 0.0  31.8 34.9 0.0 4.4 0.0  17.4 1.6 0.0 0.0 14.4  7.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 7.3 10.1  r  90  APPENDIX 2 (continued) 2.D Total Provincial Shipments and Exports of Other ProcessedFood Products ('000 Real 1990 $Cdn No. of  Domestic  Value-  Estab.  Shipments  Added  Japan  292,409.2 284,607.3 143,200.0 127,462.1 168,470.1  131,133.1 121,047.1 65,400.0 55,587.1 67,164.2  10,832.8 11,732.1 3,705.7 9,601.6 2,865.2  133.9 589.3 477.2 183.4 770.5  0.0 304.5 275.5 396.2 1,343.6  334.4 0.0 2,177.2 0.0 0.0  0.0 14.5 0.0 11.0 1.9  55.2 182.1 217.9 34.8 837.0  45,164.2 92,459.8 93,407.3 95,623.3 115,118.9  306,160.6 335,288.0 282,400.0 336,363.6 339,552.2  108,910.9 115,811.5 117,200.0 155,397.7 161,100.7  94.2 71.1 55.2 109.1 17.8  101.5 617.0 0.0 0.0 0.0  1,560.1 0.0 0.0 0.0 0.0  0.0 72.7 112.5 153.6 91.9  0.0 0.0 0.0 0.0 2.8  6.0 0.0 61.5 39.9 99.7  1,625.3 208.5 80.9 161.6 108,757.9  130,803.1 135,078.5 147,700.0 146,969.7 124,347.0  49,505.0 47,644.0 58,600.0 54,166.7 54,104.5  555.7 371.3 303.1 426.6 398.3  1,163.5 0.0 0.0 0.0 17.9  24.4 0.0 5.1 9.0 413.5  0.0 0.0 0.0 0.0 82.7  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.3 0.0 28.4  22,675.0 30,367.1 1,058.4 510.5 674.5  3,737,624.0 3,578,115.0 3,434,400.0 3,374,527.0 3,551,679.0  1,789,989.0 1,684,817.0 1,639,100.0 1,721,875.0 1,827,425.0  438.8 856.9 561.4 719.2 394.5  167.9 22.3 314.3 58.7 236.6  237.6 11.5 5.8 90.2 393.6  512.4 475.7 79.7 626.3 583.8  0.0 0^0 14.9 0.0 11.5  64.9 1,391.3 20.3 0.0 38.2  964210.9 50,189.9 39,049.8 177,157.1 160,885.6  777,777.8 1,196,754.0 1,197,400.0 1,189,015.0 1,208,862.0  365,896.6 593,193.7 616,000.0 635,890.2 630,503.7  665.1 73.0 2,341.9 224.6 682.3  2.9 5.3 34.1 89.2 0.0  566.4 0.0 0.0 11.4 0.0  20.3 124.7 203.9 67.2 149.9  0.0 0.0 0.0 0.0 0.0  22.3 152.4 27.0 179.8 5.9  100.1 9,713.1 91.3 355.0 102.3  British Columbia 1988 65 1989 70 1990 64 1991 63 1992 55 Alberta 1988 37 1989 43 1990 36 1991 35 1992 32 Manitoba 1988 24 1989 24 1990 23 1991 20 1992 18 Ontario 1988 216 1989 213 1990 204 1991 200 1992 189 Quebec 1988 155 1989 156 1990 185 1991 168 1992 160  H.K.  Taiwan  P.R.C.  S. Korea Singapore  U.S.A.  91  A P P E N D I X 2 (continued) 2. E Total Provincial Shipments and Exports of Beverage Products ('000 Real 1990 $Cdn No. of  Domestic  Value-  Estab.  Shipments  Added  Japan  518,701.9 494,240.8 492,800.0 508,049.2 754,850.7  253,465.3 262,513.1 242,600.0 276,041.7 399,440.3  54,066.1 12,452.4 7,308.6 11,393.7 13,597.4  22.9 145.6 0.0 11.4 0.0  6.9 57.5 79.2 0.0 2,205.1  0 6.6 0.0 0.0 377.8  0.0 0.0 0.0 0.0 0.0  0.0 3.0 8.0 49.0 835.6  88,592.1 33,734.3 31,501.9 34,805.2 103,023.1  459,295.9 403,350.8 398,200.0 417,803.0 412,500.0  230,693.1 186,282.7 206,400.0 230,871.2 236,287.3  0.0 5,522.5 27.3 171.4 60.8  0.0 0.0 87.5 0.0 0.0  0.0 0.0 0.0 0.0 0.0  20.8 0.0 211.6 0.0 8.6  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 0.0 0.0  0.0 266.5 0.0 0.0 0.0  191,859.2 170,052.4 152,600.0 143,939.4 174,347.0  99,229.9 94,031.4 87,800.0 79,924.2 97,481.3  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 0.0 0.0  0.0 0.0 0.0 0.0 9.3  0.0 0.0 0.0 0.0 36.0  2,979,208.0 2,770,995.0 2,507,600.0 2,491,856.0 2,502,892.0  1,725,633.0 1,629,005.0 1,528,500.0 1,538,163.0 1,640,112.0  68.2 132.0 172.3 0.0 0.0  0.0 329.8 0.0 177.0 185.0  43.4 257.3 178.6 357.7 40.0  0 74.9 0.0 420.9 0.0  9.3 0.0 0 18.6 0.0  36 68.8 68.3 57.2 58.8  0.0 31.0 3,616.6 2,004.9 47.6  1,680,528.0 1,621,990.0 1,579,300.0 1,421,307.0 1,488,526.0  1,017,602.0 1,005,131.0 1,029,400.0 892,424.2 999,626.9  33.9 0.2 15.6 0.0 89.6  0.0 0.0 17.8 41.4 0.0  0.0 0.0 0.0 0.0 15.4  3 24.0 31.5 0.0 48.2  0 0.2 0.0 0.0 0.0  0.2 44.2 10.0 0.0 9.7  0.0 0.0 3,674.9 0.0 617.0  British Columbia 1988 32 1989 34 1990 33 1991 33 1992 35 Alberta 1988 25 1989 26 24 1990 1991 21 1992 21 Manitoba 1988 10 1989 11 1990 9 1991 8 1992 8 Ontario 1988 99 ^ 1989 94 1990 87 1991 72 1992 65 Quebec 1988 76 1989 66 1990 70 1991 64 1992 54  H.K.  Taiwan  P.R.C.  S. Korea  Singapore  U.S.A.  APPENDIX 3. Provincial Market Shares in the Pacific Rim 1988 -1992, by Industry Category ('000 1990 $ Cdn.) Japan j Hong Kong j Taiwan P.R.C. S. Korea Singapore 6. C. Processed Meat & Fish Products 1988 1.10E-02 5.85E-03 1.59 E-02 9.94E-02 1.57E-05 1.38E-04 1989 1.31E-02 3.93E-03 1.01 E-02 4.62E-02 1.35E-03 1.75E-04 1990 8.21 E-03 3.01 E-03 1.86E-01 5.51 E-03 9.90E-03 4.39E-04 1991 1.02E-02 3.89E-03 1.27E-01 1.17E-03 1.82E-02 1.95E-04 1992 7.55E-03 8.01 E-03 8.88E-02 5.94E-03 8.80E-03 4.09E-05 Alberta Processed Meat & Fish Products 1988 3.66E-06 1.81 E-04 0.00E+00 1.61 E-03 0.00E+00 0.00E+00 1989 8.95E-06 1.77E-07 3.23E-04 5.93E-03 0.00E+00I O.dOE+00 1990 1.21E-06 9.37E-05 4.87E-04 0.00E+00 0.00E+00 0.00E+00 1991 1.26E-04 1.61 E-03 0.00E+00 0.00E+00 0.00E+00 9.03E-07 1992 1.01 E-03 7.36E-05 1.24E-05 0.00E+00 0.00E+00 0.00E+00 Manitoba Processed Meat & Fish Products 1988 1.02E-05 1.23E-07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1989 7.86E-06 4.00E-05 0.00E+00 0.00E+00 0.00E+00 o.boE+od 1990 1.30E-05 7.40E-05 0.00E+00 1.96E-05 0.00E+00 0.00E+00 1991 5.11E-06 1.51 E-04 0.00E+00 4.64E-04 0.00E+00 3.94E-05 1992 1.72E-04 2.69E-05 0.00E+00 0.00E+00 0.00E+00 2.35E-05 Ontario Processed Meat & Fish Products 1988 2.45E-04 5.11 E-04 3.21 E-03 1.11 E-03 1.14E-03 3.64E-04 1989 4.68E-04 2.02E-04 7.35E-04 8.12E-04 2.43E-04 2.45E-04 1990 7.13E-04 1.43E-04 9.09E-04 0.00E+00 0.00E+00 2.27E-04 1991 1.43E-04 1.20 E-04 4.68E-04 0.00E+00 9.83E-04 1.07E-05 1992 9.51 E-05 5.14E-05 6.57E-04 1.04E-04 4.91 E-04 8.56E-06 Quebec Processed Meat & Fish Products 1988 5.30E-06 6.25E-06 3.92E-04 4.08E-04 0.00E+00 6.93 E-04 1989 6.55E-06 5.15E-07 2.29E-04 2.65E-03 1.17E-05 1.72 E-05 1990 3.62E-05 1.32E-06 0.00E+00 7.40E-04 2.30E-04 9.30E-06 1991 6.91 E-06 1.48E-04 3.94E-04 5.60E-05 1.74E-04 0.00E+00 1992 1.36E-05 3.67E-05 3.42E-04 5.51 E-05 0.00E+00 0.00E+00 [  h  U.S.A.  4.96E-03 5.12E-02 2.26E-03 2.63E-03 3.93E-03 2.06E-04 1.45E-03 3.27E-03 1.66E-04 2.08E-04 6.51 E-04 3.18E-04 6.55E-04 8.55E-04 1.80E-03 1.14E-03 2.52E-04 5.19E-04 1.25E-03 1.73E-03 2.63E-05 3.39E-05 1.82E-04 2.78E-04 2.42 E-05  APPENDIX 3 (continued) Japan Hong Kong Taiwan P.R.C. B.C. Processed Fruit & Vegetable Products  1988 1.20E-03 1989 1.01E-03 1990 7.05E-04 1991 4.87E-04 1992 3.37E-04  5.95E-04 0.00E+00 1.71 E-04 1.13E-04 7.25E-05 2.65E-04 1.31 E-04 1.06E-04 3.50E-04 1.40E-04  3.90E-04 4.67E-03 2.20E-03 3.15E-04 1.32E-04  3.65E-04 3.12E-03 5.75E-04 O.OOE+OO 4.71 E-04  4.38E-03 3.50E-03 3.39E-03 1.94E-03 2.63E-03  0.00E+00 O.OOE+OO O.OOE+OO 0.00E+00 O.OOE+OO 3.09E-05 0.00E+00^O.OOE+OO 1.26E-04 0.00E+00 O.OOE+OO 2.63E-05 0.00E+00 O.OOE+OO 2.60E-05  6.89E-05 1.12E-04 9.17E-05 2.57E-04 1.12E-05  1.67E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00  2.31 E-04 0.00E+00*" O.OOE+OO O.OOE+OO 2.56E-04 0.00E+00 O.OOE+OO 4.87E-06 1.82 E-03 0.00E+00 O.OOE+OO O.OOE+OO 0.00E+00 0.00E+00 O.OOE+OO O.OOE+OO 4.97E-05 0.00E+00 O.OOE+OO O.OOE+OO  3.82E-05 O.OOE+OO 1.92E-04 O.OOE+OO O.OOE+OO  1.12E-05 5.53E-05 1.11E-05 5.54E-05 2.67E-04  5.78E-05 0.00E+00 6.69E-05 6.35E-04 O.OOE+00  0.00E+00 9.36E-06 9.12E-05 1.05E-05 1.28E-05 6.25E-04 0.00E+00f 2.53E-07 0.00E+00 0.00E+00 0.00E+00 1.86E-05 0.00E+00 0.00E+00 2.58E-05  Manitoba Processed Fruit & Vegetable Products  1988 1989 1990 1991 1992  8.59E-08 0.00E+00 0.00E+00 4.02E-05 O.OOE+OO  h  Ontario Processed Fruit & Vegetable Products  1988 4.96E-05 1989 1.18E-04 1990 5.24E-05 1991 5.30E-05 1992 2.00E-04  U.S.A.  1.95E-05 1.13E-04 2.07E-04 1.23E-04 4.00E-04  Alberta Processed Fruit & Vegetable Products  1988 1989 1990 1991 1992  S. Korea Singapore  1  1  1.29E-02 O.OOE+OO O.OOE+OO 9.94E-04 1.35E-02 O.OOE+OO O.OOE+OO 8.22E-04 8.21 E-04 9.14E-05 5.11 E-05 1.30E-03 0.00E+00 O.OOE+OO O.OOE+OO 1.69E-03 0.00E+00 2.20E-04 8.41 E-05 1.22E-03  Quebec Processed Fruit & Vegetable Products  1988 2.04E-05 1989 1.14E-04 1990 3.46E-06 1991 0.00E+00 1992 0.00E+00  4.50E-06 0.00E+00 2.45E-05 3.88E-05 7.29E-06  0.00E+00 0.00E+00 4.71 E-05 0.00E+00 1.15E-05 0.00E+00 0.00E+00 O.OOE+OO 0.00E+00 O.OOE+OO  1.16E-03 4.87E-05 O.OOE+OO 1.71 E-05 O.OOE+OO 1.29E-06 O.OOE+OO O.OOE+OO 2.42E-04 1.92E-06  1.28E-05 3.23E-06 1.63E-05 1.84E-05 2.26E-06  APPENDIX 3 (continued) Japan Hong Kong Taiwan P.R.C. B.C. Processed Cereal & Grain Products  S. Korea Singapore  U.S.A.  1988  4.93E-03  5.47E-05  3.46E-05  1.28E-02  O.OOE+00  O.OOE+00  3.75E-02  1989  5.49E-03  1.66 E - 0 3  3.44E-03  4.45E-03  O.OOE+00  1.46 E - 0 5  2.83E-02  1990  3.23E-03  1.54E-03  1.10E-04  2.97E-03  9.41 E - 0 4  R  3.93E-05  5.03E-02  1991  4.73E-03  1.54E-03  4.77E-05  1.01 E - 0 3 O.OOE+00  R  1.27E-05  6.42E-02  1992  3.70E-03  5.24E-04  3.73E-05  1.52E-02  1.97E-04  2.70E-04  1.20E-01  4.74E-06  O.OOE+OO  2.02E-03  3.52E-03  O.OOE+00  2.53E-03  O.OOE+OO I O.OOE+00 O.OOE+00 O.OOE+00 O.OOE+OO O.OOE+00 O.OOE+00 2 . 6 8 E - 0 4 I 6 . 7 2 E - 0 4 O.OOE+00 O.OOE+OO O.OOE+OO  6.10E-03  Alberta Processed Cereal & Grain Products 1988 1989  3.73E-04  2.84E-06  7.50E-06  6.78E-05  O.OOE+00  O.OOE+OO  1990  2.95E-04  O.OOE+OO  O.OOE+00  O.OOE+00  1991  2.92E-05  1992  8.85E-04  R  1 . 7 3 E - 0 4 O.OOE+00  Manitoba Processed Cereal & Grain Products 1988  6.30E-05  O.OOE+OO  3.39E-05  1989  1.78 E - 0 5  O.OOE+00  8.97E-06  O.OOE+00 L J 3 . 3 8 E - 0 5 O.OOE+00 O.OOE+OO  1.78E-04 1.15E-02  2.80E-04 5.57E-04  3.74E-04 1.42E-04  1990  0.00E+00  O.OOE+00  9.74E-05  O.OOE+OO  O.OOE+00  8.99E-04  1991  O.OOE+OO  O.OOE+00  1.11 E - 0 3  1.04E-04  O.OOE+OO  O.OOE+OO  2.30E-03 7.15E-04  1992  O.OOE+00  6.00E+00  2.77E-04  7.37E-05  O.OOE+00  4.08E-05  4.26E-04  1  Ontario Processed Cereal & Grain Products 1988  4.27E-05  1.13E-04  O.OOE+00  O.OOE+00  O.OOE+OO  O.OOE+OO  1.40E-03  1989  9.67E-05  8.97E-05  O.OOE+00  6.70E-06  1.73E-05  4.10E-05  1.18E-03  1990 1991  3.25E-04  2.62E-04  7.09E-05  8.54E-04 3.49E-04  8.31 E - 0 5  3.50E-04 5.35E-04  1 29E-04 5.49E-04  2.89E-05 2.40E-04  7.92E-03  1992  1.80 E - 0 4  9.61 E - 0 4  2.25E-05  3.62E-04  3.18E-04  1.38 E - 0 4  3.08E-03  Quebec Processed Cereal & Grain Products 1988  O.OOE+OO  O.OOE+OO  1989  O.OOE+00  7.12E-07  4.90E-05  1990  O.OOE+OO  O.OOE+OO  1991  O.OOE+00  O.OOE+00  1.15E-05 1.991-06 9 . 4 4 E - 0 7 O.OOE+00 O.OOE+OO O.OOE+00 O.OOE+OO 8 . 0 4 E - 0 6 O.OOE+OO O.OOE+00  1992  O.OOE+OO  O.OOE+OO  O.OOE+OO  5.45E-05  3.19E-05  O.OOE+OO  1.57E-03  O.OOE+OO O.OOE+OO O.OOE+00 O.OOE+00 O.OOE+00  7.05E-06  O.OOE+OO  8.32E-06  O.OOE+OO O.OOE+00  A P P E N D I X 3 (continued) Japan Hong Kong Taiwan B. C. Other Processed Food Products 1988 1989 1990 1991 1992  4.16E-03 5.02E-03 1.98E-03 5.17E-03 1.19E-03  P.R.C.  S. Korea Singapore  1.58E-04 0.00E+00 8.79E-04 0.00E+00 7.66E-04 1.40E-03 0.00E+00 2.44E-05 6.05E-04 1.18E-03 5.85E-03 0.00E+00 2.19E-04 1.49E-03 0.00E+00 2.42E-05 8.45E-04 4.20E-03 0.00E+00 3.50E-06  U.S.A.  6.96E-05 3.11 E-04 3.65E-04 6.34E-05 9.89E-04  6.41 E-03 1.38E-02 1.48E-02 1.66E-02 1.79E-02  7.58E-06 0.00E+00 1.03E-04 7.26E-05 1.18E-04  2.31 E-04 3.12E-05 1.28E-05 2.80E-05 1.69E-02  0.00E+00 0.00E+00 5.56E-07 0.00E+00 3.36E-05  3.22E-03 4.55E-03 1.68E-04 8.85E-05 1.05E-04  1.35E-03 0.00E+00 1.74E-03 0.00E+00 2.14E-04 2.68E-05 1.67E-03 0.00E+00 9.97E-04 2.15E-05  8.18E-05 2.38E-03 3.40E-05 0.00E+00 4.51 E-05  1.37E-01 7.52E-03 6.19E-03 3.07E-02 2.50E-02  5.33E-05 4.57E-04 5.48E-04 1.79E-04 2.56E-04  2.82E-05 2.60E-04 4.51 E-05 3.27E-04 7.02 E-06  1.42E-05 1.45E-03 1.45E-05 6.15E-05 1.59E-05  Alberta Other Processed Food Products 1988 1989 1990 1991 1992  3.62 E-05 3.04E-05 2.95E-05 5.87E-05 7.37E-06  1.20E-04 8.02 E-04 0.00E+00 0.00E+00 0.00E+00  1  7.95E-03 0.00E+00 0.00E+00 0.00E+00 2.67E-04 0.00E+00 •D.OOE+OO 3.02E-04 0.00E+00 0.00E+00 4.09E-04 0.00E+00 0.00E+00 1.57E-04 5.16E-06  r  Manitoba Other Processed Food 1988 1989 1990 1991 1992  2.14E-04 1.59E-04 1.62 E-04 2.30E-04 1.65E-04  1.38E-03 1.24E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.17E-05 0.00E+00 0.00E+00 0.00E+00 3.37E-05 0.00E+00 0.00E+00 1.96E-05 1.29E-03 1.41 E-04 0.00E+00  Ontario Other Processed Food Products 1988 1989 1990 1991 1992  1.69E-04 3.66E-04 3.00E-04 3.87E-04 1.64E-04  1.99E-04 2.90E-05 3.98E-04 7.01 E-05 2.59E-04  1.21 E-03 5.30E-05 2.46E-05 3.39E-04 1.23E-03  Quebec Other Processed Food Products 1988 1989 1990 1991 1992  2.56E-04 3.12E-05 1.25E-03 1.21 E-04 2.83E-04  3.44E-06 2.89E-03 6.91 E-06 0.00E+00 4.32 E-05 0.00E+00 1.06E-04 4.28E-05 0.00E+00 0.00E+00  0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00  APPENDIX 3 (continued) Japan j Hong Kong Taiwan B.C. Beverage Products  1988 1989 1990 1991 1992  4.19E-02 4.93E-05 7.80E-03 2.78E-04 3.71 E-03 O.OOE+00 5.45E-03 1.73E-05 6.32E-03 O.OOE+OO  Alberta Beverage Products  1988 0.00E+00 1989 3.46E-03 1990 1.39E-05 1991 8.19E-05 1992 2.83E-05  O.OOE+00 O.OOE+OO 1.47E-04 O.OOE+00 O.OOE+00  Manitoba Beverage Products  1988 1989 1990 1991 1992  0.00E+00 0.00E+00 0.00E+00 0.00E+00 O.OOE+00  O.OOE+00 O.OOE+00 O.OOE+00 O.OOE+OO O.OOE+00  Ontario Beverage Products  1988 5.29E-05 1989 8.27E-05 1990 8.76E-05 1991 O.OOE+00 1992 O.OOE+00  O.OOE+00 6.29E-04 O.OOE+OO 2.70E-04 2.38E-04  Quebec Beverage Products  P.R.C.  S. Korea Singapore  U.S.A.  6.56E-05^ O.OOE+OO O.OOE+00 O.OOE+00 3.91 E-04 1.48E-04 O.OOE+OO 1.03E-05 3.56 E-04 O.OOE+00 O.OOE+00 2.51 E-05 O.OOE+00 O.OOE+00 O.OOE+00 1.47E-04 5.86E-03 5.37E-03 O.OOE+00 2.16E-03  1.87E-02 7.65E-03 6.75E-03 8.62E-03 2.19E-02  O.OOE+00 O.OOE+00 O.OOE+00 O.OOE+00 O.OOE+00  7.17E-04 O.OOE+00 3.88E-03 O.OOE+00 1.22E-04  O.OOE+OO O.OOE+00 O.OOE+00 O.OOE+00 O.OOE+OO  O.OOE+00 O.OOE+00 O.OOE+OO O.OOE+00 O.OOE+00  O.OOE+00 6.04E-05 O.OOE+00 O.OOE+OO O.OOE+00  O.OOE+OO O.OOE+OO O.OOE+00 O.OOE+00 O.OOE+OO"o.OOE+00 O.OOE+00 O.OOE+00 O.OOE+00 O.OOE+00  O.OOE+00 O.OOE+00 O.OOE+00 O.OOE+00 O.OOE+OO  O.OOE+OO O.OOE+00 O.OOE+00 O.OOE+OO O.OOE+00  O.OOE+OO O.OOE+00 O.OOE+OO O.OOE+00 O.OOE+OO  4.13E-04 O.OOE+OO 1.75E-03 1.66E-03 8.03E-04 O.OOE+00 1.42 E-03 7.40E-03 1.06E-04 O.OOE+00  8.87E-05 O.OOE+00 O.OOE+00 1.27E-04 O.OOE+OO  1.10E-04 O.OOE+00 2.32E-04 7.03E-06 2.15E-04 7.75E-04 1.72 E-04 4.97E-04 1.52E-04 1.01 E-05  1988 2.63E-05 O.OOE+OO O.OOE+00 1989 1.43E-07 O.OOE+00 O.OOE+00 1990 7.91 E-06 3.00E-05 O.OOE+OO 1991 O.OOE+00 6.31 E-05 O.OOE+00 1992 4.16E-05 O.OOE+OO 4.10E-05  1.04E-04 5.32E-04 5.79E-04 O.OOE+00 6.85E-04  O.OOE+00 4.71 E-07 O.OOE+00 2.00E-06 1.49E-04 O.OOE+OO O.OOE+OO 3.16E-05 7.87E-04 O.OOE+00 O.OOE+00 O.OOE+00 O.OOE+00 2.51 E-05 1.31 E-04  97 APPENDIX 4. Concordances Between S.I.C., H.S., and SITC-2 Classification Systems Appendices 4.A - 4.E below provide an overview of the1980 Canadian Standard Industrial Classification (SIC) , the Harmonized System of Commodity Classification (H.S.) , the Standard International Trade Classification, Revision 2 (SITC2) concordances used in this thesis. 1  2  3  4.A PROCESSED MEATand FISH PRODUCTS INDUSTRY (MF) H.S. 02 0201 0202 0203 0204 0205 0206 0207 0208 0209 0210 03 0302.70 0303 0304 0305 0306 (0306.21) (0306.22) 0307  H.S. Commodity Description meat of bovine animals,freshor chilled meat of bovine animals, frozen meat of swine,fresh,chilled or frozen meat of sheep or goats,fresh,chilled or frozen meat of horses, mules or ninnies,fresh,chilled or frozen edible offal of bovine animals, swine, sheep, goats, horses, asses, mules or ninnies,fresh,chilled or frozen meat and edible offal, of poultry other meat and edible meat offal, fresh, chilled or frozen pig fat free of lean meat and poultry fat (not rendered), fresh, chilled,frozen,salted, in brine, dried or smoked meat and edible meat ofal, salted, in brine, dried or smoked; edible flours and meals of meat or meat offal livers and roes -ofherring and other fish fish,frozen,excludingfishfilletsand otherfishmeat of heading No. 0304 fish fillets and otherfishmeat (whether or not miiiCBd),fresh,chilled, or frozen fish, dried, salted or in brine; smokedfish,whether or not cooked before or during the smoking process; flours, meals and pellets of fish,fitfor human consumption crustaceans, whether in shell or not, live, fresh, chilled, frozen, dried, salted or in brine; crustaceans, in shell, cooked by steaming or by boiling in water, whether or not chilled,frozen,dried, salted or in bnne;flours,meals and peUets of crustacean  molluscs, whether in shell or not, live, fresh, chilled,frozen,dried, salted or in brine; aquatic invertebrates other than crustaceans and molluscs, live, fresh, chilled, frozen, dried, salted or in brine;  (0307.10.10) (0307.10.21) (0307.21) (0307.31) (0307.41) (030751) (0307.60) (0307.91) flours, meals and pellets of aquatic invertebrates other than crustaceans,fitfor human consumption 05 0502.10.10 pigs', hogs' or boars' bristles and hair and waste thereof, not processed 0503.00.91 horse hair and horsehair waste, not proecessed 0504 guts, bladders and stomachs of animals (other thanfish),whole and pieces thereof (e.g., sausage casings) 0505 skins and other parts of birds, with their feathers or down (i.e., feathers of a kind used for stuffing; feather meal for the manufacture of animal 0506 0507 0508 (0508.00.1) 0510 (0510.00.12) 0511 (0511.10) (0511.99.40) 16 1601 1602 (1602.31.1)  ossein and bones treated with acidfarthe use in the manufacture ofgelatin and other rjroducts; bore ivory, tortoise-shell, whalebone hair, horns, antlers, hooves, nails, clsws and beaks, wnworked or simply prepared but not cut to shape; powder and waste of these products coral and similar materials, unworked or simply prepared but not otherwise worked; shells of molluscs, crustaceans or echinodersm and cuttlebone, unwroked or simply prepared but not cut to shape, powder and waste thereof glands and other animal products used in the preparation of pharmaceutical products, fresh, chilled, frozen or otherwise provisionally preserved (excludin urine) animal products not elsewhere specified or included (eg.,fish,crustaceans, molluscs, or other aquatic invertebrates for bait; meat waste and scrap for animal feed)  sausages and similar products, of meat oflal or blood; food preparations based on these products other prepared or preserved meat, meat offal or blood (eg., poultry liver paste; canned turkey; turkey pies; turkey cooked, in rolls or in pieces; canned ham; boiled, ready-to-serve ham; luncheon meats; other canned meats; beef stews; or beef prepared meals)  Source: Statistics Canada. Cat. No. ??? Source: Stats Canada? 1987? Note that determination of the correct H.S. commodity codes, in addition to a provision of their descriptions, are necessary for determination and collection of industry export data (see Stats. Can, Cat. No. 65-OOX?) and of industry trademark data. Source: U.N. International Trade Code Classification Index. 1983?. Note that the SITC-2 codes are necessary for determination and collection of import data (i.e., from the respective Pacific Rim countires/regions under study); this data is used to calculate market share. 1  3  98  US. Commodity Description  as.  (160231.99) (160239.1) (1602.49.10) (160230.19) (1602.50.29) (160230.90) (1602.90.10) (1602.90.90) 1603 1604 1605 Other 1302.31.10 1302.39.10 1501 1502 1503 1504 1505 1506 2301.10 4101 4102 4103  extracts and juices of meat,fishor crustaceans, molluscs or other aquatic invertebrates, canned or otherwise prepared or preservedfish;caviar and caviar substitutes prepared firm fish eggs (includesfishwhole or in pieces, but not minced; pickled herrings; kipper snacks; sandines, anchovies, etc) crustaceans, molluscs and other aquatic invertebrates, prepared or preserved agar-agar, crude carrageenan (Irish moss extract) lard; other pig fat and poultryfet,rendered wr«thercffnm pressed or solvent-extracted fats of bovine animals, sheep or goats, raw or rendered, whetownm pressed wsolveffl-eixtracted lard stearin, lard oil, oleostearin, oleo-oil and tallow oil, not emulsified or mixedOTotherwise prepared fats and oils and their fractions, offish or marine mammals, whether or not refined, but not chemically modified wool grease and fatty substances derived therefrom (including lanolin) other animal fats and oils and their fractions, whether or not refined, but not chemically modified flours, meals and pellets, of meat or meat offal, greaves (tankage for feeding, or otherwise) raw hides and skins of bovine or equine animals (fresh, or salted, dried, limed, pickled or otherwise preserved, but not tanned, parchment-dressed or further prepared), whether or not dehaired or split raw skins of sheep or lambs other raw hides and skins  SITC-2 Commodity Codes (and Descriptions) Corresponding to the Above 0111 0113 0112 0115 0116 0114 0118 0121 0129  Meat of bovine animals, fresh, chilled or frozen Meat of swine,fresh, chilled or frozen Meat of sheep and goats,fresh,chilled or frozen Meat ofhorses, asses, mules and ninnies,fresh,chiUed or frozen Edible offals of bovine, swine sheep, goats, horses, asses, mules andhirmies Poultry, dead (i.e., fowls, ducks, geese, turkeys, and guineafowls)and edible offals thereof (except liver), fresh, chilled, or frozen Other fresh, chilled orfrozenmeat or edible meat offals Bacon, ham and other dried, salted or smoked meat of domestic swine Meat and edible oflals,n.e.s., salted, in brine, dried or smoked  0341 0342 0343 0344 0350 0360  Fish, fresh (live or dead) or chilled (excluding fillets) Fish, frozen (exduding fillets) Fishfillets,freshor chilled Fishfillets,freshor chilled Fishfillets,frozen Crustaceans and molluscs, whether in shell or not,fres(live or dead), chilled,frozen,salted, in brine or dried; crustaceans, in shell, simply boiled in water  0141 Meat extracts and meat juices; fish extracts 0142 Sausages and the like, of meat meat offal or animal blood 0149 Other prepared or preserved meat or met offals 0371 Fish, prepared or preserved, ae.s. (including caviar and caviar substitutes) 0372 Crustaceans and molluscs, prepared or preserved, n.e.s,  99 4.B PROCESSED FRUIT AND V E G E T A B L E PRODUCTS INDUSTRY (FV) H.S. 20 2001* (2001.90.10) 2002 2003 2004 2005*  (2005.2030) (2005.80.90) 2007* (2007.99.3) 2008* (2008.11) (2008.19) 2009 Other 0710 0711 0712 0803.00.20 0804.10.20 0804.20.20 080430.20 0804.40 0804.50.20 0805.10.20 080530.20 0805.40 0805.90.20 0806.20 0811* (0811.90.60) 0812 0813* (0813.50.10) (0813.50.30) 0814 1212.30 2103* (210330) (2103.90.1) (2103.9030) 2104.10.10 2106.90.92 2202.10.9 2202.90.10 2209  H.S. Commodity Description vegetables, fruit, nuts and other edible parts of plants, prepared or preserved by vinegar or acetic acid (includes pickled cucumbers, onions, olives, relishes, and others). tomatoes prepared or preserved otherwise than by vinegar or acetic acidd (e.g., whole or in pieces, canned; canned tomatoe paste, tomato pulp and puree; etc.) mushrooms and truffles, prep, or preser. otherwise than vinegar or acetic add (eg., earned, frozen^ other vegetables prepared or preserved otherwise than by vinegar or acetic acid, frozen (eg., frozenflenchfrie potatoes; beans, com, peas, asparagus, etc. and mixtures thereof, frozen) other vegetables prepared or preserved otherwise than by vinegar or acetic acid, not frozen (eg., canned potatoes; potato salad, not in airtight containers; canned/bottled sauerkraut; canned peas, baked, canned beans, etc.; pimento, horseradish; and mixtures of vegetables (including salads))  jams, fruit jellies, marmalades, fruit or nut puree, and fruit or nut pastes, being cooked preparations, whether or not containing added sugar or other sweetening matter fruit, nuts and other edible parts of plants, otherwise prepared or preserved, whether or not containing added sugar or other sweetening matter or spirit, not elsewhere specified or included  fruit juices (including grape must) and vegetable juices, unfermented and notremainingadded spirit, whether or not containing added sugar or other sweetening matter (includes frozen, bottled, dehydrated juices) vegetables (uncooked or cooked by steaming or boiling in water), frozen (includes: french frie potatoes, and beans, peas, etc) vegetables provisionally preserved (for example, by sulphur dioxide gas, in brine, in sulphur water or in other preservative solutions), but unsuitable in that state for immediate consumption —includes onions, olives, capers, cukes, and other vegetables and mixtures of vegetables dried vegetables, whole, cut, sliced, broken or in powder, but not further prepared (includes: potatoes whether or not cut or sliced but not further prepared; onion powder and other dehydrated onion products, dehydrated mushrooms, dried garlic, tarragon, sweet marjoram and savory) dried bananas dried dates dried figs dried pineapples avacados, fresh or dried dried guavas dried mandarines (including tangerines and satsumas); dried Clementines dried lemons (citrus limon, citrus limonum) and limes (citrus aurantifolia) grapefruit, fresh or dried other dried fruit dried grapes blueberries, cherries, cranberries, apples, uncooked or cooked by steaming or boiling in water,frozen,whether or not containing added sugar or other sweetening matter — excluding nuts fruit and nuts provisionally preserved (eg. by sulphur dioxide gs, in brine, in sulphur water or in other preservative solutions), but unsuitable in that state for immediate consumption (e.g., cherries, strawberries, melons, apples, etc) apricots, prunes, apples, and other fruit dried; mixtures of nuts or dried fruits  peel of citrusfruitor melons (including watermelons), fresh, frozen, dried or provisionaUy preserved in sulphur water or in other preservative solutions apricot, peach or plum stones and kernels sauces and preparations therefor; mixed condiments and mixed seasonings  soups and broths and preparations therefor, in airtight containers mincemeat, canned yyyy yyyy vinegar and substitutes for vinegar obtained from acetic acid  SITC-2 Commodity Codes (and Descriptions) Corresponding to the Above. 0565 0582 0583 0589 0585  100 4.C 19  PROCESSED C E R E A L AND GRAIN PRODUCTS INDUSTRY (CG)  ns.  1901.20 1901.20 1904 1905*  RS. Commodity Description preparationsforinfant use, based on malt extract dairy-related foods cereal cake mixes; doughnut, pancake and pastry mixes, prepared; other doughs preparedfoo&obtained by the swelling or roastu^ bread, pastry, cakes, bisucuits and other bakers' wares, wheuier or nmcxaitaining cocoa (indudes crisp b^ sweet bisucuits; ice cream cones; graham wafers; products madefromwaffles or wafers; rolls, buns; pizza ousts and other pizza*; pies (other thanfruitpies) cooked, doughnuts, & quiche)  (190530.22) (1905.40) (1905.9051) (1905.90.70) (1905.90.9) 23 2302* bran, sharps and other residues, whether or not in theformof pellets, dereivedfromthe sifting, milling or other working of maize (corn), wheat, or other cereals (2302.20) 2304 oil-cake and other solid residues, whether or not ground cir in the form offsets, resulting frcm the ex^ 2305 oil-cake and other solid residues, whether or not grcmnd or in U K form of pellets, resulting frcnn the e ^ 2306* oil-cake and other solid residues, whether or not gnwnd or in theformof peUets, resulting f r ^ cottonseeds, linseed, sunflower seed, rape or colza seed, coconut or copra, or palm nuts. 2309 preparations of a kind used in animal feeding: e.g., dog or catfood,biscuits or other concentrates; other animal feeds (complete), milk replacers, micro premixes, macro premixes,feedsupplements, niinerals; and other bird and rabbit feed, etc. Other 1101 wheat or meslin flour (hard spring, durum (semolina and flour), whole wheat or graham) 1102* cereal flours other than of wheat or meslin (eg., ryeflour,com flour) (110230) 1103* groats and meal of oats,forfeed use; corn meal and com grits used otfjer than for uterranufacture of s respectively; cereal groats, meal and pellets, of buckwheat (1103.12.20) (1103.13.11) (1103.13.20) (1103.14) 1208 flours and meals of soya beans, and other oil seeds or oleaginous fruits, other than those of mustard 1214.10 lucerne (alfalfa) meal and pellets (forage product) 1214.90.40 grass meal (forage product) 1507 soya-bean oil and its fractions, whether or not refined, but not chemically modified 1508.10 ground-nut oil and itsfractions,crude virgin olive oil and its fractions 1509.10 1512.11 crude sunflower-seed or safflower oil and fractions thereof crude com oil and its fractions 1515.21 crude coconut (copra) oil and its fractions 1513.11 crude palm kernel or babassu oil and fractions thereof 1513.21 crude rape or colza, or mustard seed oil 1514.10 crude linseed oil and its fractions 1515.11 1515.30.10 crude castor oil and its fractions lung oil and its fractions 1515.40 jojoba oil and its fractions 1515.60 1515.90.10 1515.90.20 1515.90.31 1515.90.40 1905.90.82  illipe butter, shea butter and oiticica oil and their fractions cashew nut shell oil and its fractions crude wheat germ oil cocoa butter equivalent communion wafers  SITC-2 Commodity Codes (and Descriptions) Corresponding to the Above 0460 0470 0482 0488  Meal and flour ofwheat and flour of meslin (includes flour ofwheat or of meslin; groats, meal and pellets, of wheat) Other cereal meals andflours(includes cereal flours other uian of wrieat or of meslin; cereal groats, meal and p ^ Malt, roasted wnm (including malt flour) Malt extract; preparations offlour,meal, starch or malt extract, of a kind used as infam food cirfordietetic or caliria^ by weight of cocoa 0564 Flours, meals andflakesof potatoes, fruits and vegetables, ne.s. (including sago and tapioca) 0481 Cereal grains, worked or prepared ina manner not eslewherespecmed("prerjared breakfast fcxxis'') 0484 Bakery products (e.g.. bread, biscuits, cakes) and other baked goods madefromflour or starch pastes (e.g., communion wafers) 0814 Flours and meals, of meat, offals,fish,crustaceans or nioltuscs, unfit fa hunmconsurrpion; greaves 0812 Bran, sharps and other residues derived from the sifting, milling or working of cereals or of leguminous vegetables 0819 .93 Food wastes and prepared animal fees, ae..s. (in particular, beet-pulp, bagasse and other wastes of sugar manufacture; brewing and distilling dregs a ^ waste; residues of starch marmfacture and similar residues 0813 31 oil cake andother residues (except dregs) resulting from the extraction of vegetable oils of soyabeans 0813 32 of groundnuts 0813 33 of cotton seeds, linseed, sunflower seeds, rape seeds, ccx»rmt(c»rOT), palm rmts and k e r r ^ etc. 0819 .94 wine lees; argol  101 HJS. HJS. Commodity Description 0819 .99 svreetened forage; other preparation of a kind used in animalfeeding,aes.  4.D OTHER PROCESSED FOOD PRODUCTS INDUSTRY (OTH) H.S. H.S. Commodity Description 09 0901.12 coffee not roasted, decaffeinated 0901.21 coffee, roasted, not decaffeinated 0901.22 coffee, roasted, decaffeinated 0901.40 coffee, substitutes containing coffee 0902. tea, whether or not flavoured. *Note: see exclusions below (0902.10) (0902.20) (090230.9) 0903 mate 0904 pepper, (includes chilli peppers, and paprika), crushed or ground (0904.11) 0905 vanilla 0906 (0906.10) cinnamon and cinnamon-tree flowers, crushed or ground 0907 (0907.00.10) cloves, crushed or ground 0908 (0908.10.10) (0908.20.10) nutmeg, mace and cardamoms, crushed or ground (090830.10) 0909 seeds of anise, badian,fennel,coriander, cumin or caraway, juniper berries, crushed or ground (0909.10.10) (0909.20.10) (090930.10) (0909.40.10) (0909.50.10) 0910 ginger, thyme, bay leaves, & other spices, crushed or ground; saffron, turmeric (curcuma), curry and other spices (0910.10.10) (0910.40.10) (0910.91.10) (0910.99.1) (0910.99.91) 11 110230 rice flour 1103.12.20 groats and meal of oats,forfood use 1103.13.11 groats and meal streamlets, of corn,forpuffing 1103.13.20 com gritsforuse in the manufacture of cornflour 1103.14 groats and meal of rice 1104 cereal grains otherwise wotked (e.g., hulled, rolled, flaked, pearled sliced), or germs of these cereals of barley, oats, com, rye, wheat, etc.) 1105 flour, meal,flakes,granules, and pellets of potatoes 1106 flour and meal of dried peas, chickpeas (garbanzos), and beans; flour and meal of sago and cassava 1107 malt, whether or not roasted, screened or unscreened 1108 starches of wheat, corn, potato, manioc (cassava),rice,sago, arrowroot; irtulin 1109 wheat gluten,whether or not dried 17 1701 cane sugar, beet sugar, brown sugar, granulated sugar, icing sugar 1702 other sugars, including glucose and glucose syrup, com syrup, fructose, invert sugar, colouring caramels etc., but not including lactose and lactose syurp, or maple sugar or maple syrup (1702.10) (1702.20)  18 1803 1804 1805 1806 Other 0407.00.90 0408 0409.00.10 0410 0801.10 0801.20.20  cocoa paste, whether or not defatted cocoa butter, fat and oil cocoa powder, not attaining added sugar or ther sweetening matter chocolate and other food preparations containing cocoa (e.g., chocolate powder, chocolate crumb, chocolate in blocks, slabs or bars, other chocolate ajnfectionery, boxed chocolates, chocolate coated nuts, instant chocolate, hot chocolate (powder) birds' eggs, preserved or cooked bird's eggs, not in shell, and egg yolks, fresh, dried, awked by stearniningcir by boiling m not containing added sugar or other sweetening matter natural honey, pasteurized edible products of animal origin, not elsewhere specified or included coconuts, fresh or dried, whether or not shelled or peeled brazil nuts, shelled  j  102 H.S. 0801.30.20 0802.12 0802.22 0802.32 0802.40.20 080250.20 0802.90.12 0802.90.92 0811.90.60 081350.10 081350.30 1006.30 1006.40 1202.20 1203 1210.20 1212.92 1212.99 1302.12 1302.13 1302.20 1404.90.10 1508.90 1509.90 1510 1511 1512.19 1512.29 1513.19 1513.29 1514.90 1515.19 1515.21 1515.29 151550 1515.90.91 1516 1517 160231.1 160231.99 160239.1 1602.49.10 160250.19 160250.29 160250.90 1602.90.10 1602.90.90 1902* 1905.90.70 1905.90.8 (1905.90.82) 1905.90.9 1901.10 1901.20.12 1901.20.14 1901.90.2 1901.9031 1902 1902.20 190230 1902.40 1903 190530.22 1905.9051 2001.90.10 2005.2030 2006 2007.993 2008.11 2008.19  US. Commodity Description cashew nuts, shelled almonds, shelled hazelnuts orfilberts,shelled walnuts, shelled chestnuts, shelled pistachios, shelled pecans, shelled other nuts, shelled nuts, uncooked or cooked by steaming or boiling in water, frozen, whether or not containing added sugar or other sweetening matter mixtures of nuts mixtures of nuts and dried fruits s e m M n i l l e d or wholly milled rice, whether or not polished or glazed, includes parboiled, long, med, & short grains broken rice ground nuts, not roasted or otherwise cooked, srieUed wnether or not broken copra hop cones, ground, powdered or in the form of pellets; hipulin sugar cane, fresh or dried other vegetable saps and extracts of liquorice vegetable saps and extracts of hops pectin substances, pectinates and pectates vegetable flour peanut ou, and other ground nut ou and itsfractions,refined olive oil and itsfractions,not virgin other oils and their fractions, obtained solely from olives, whether or not refined, but not chemically modified palm oil and its fractions, whether or not refined, butrMttchemicaUynradified sunflower seed and safflower oils and fractions thereof, whether or not refined, but not chem modified other  coconut oil and itsfractions,refined palm kernel or babassu oil andfractionsthereof; refined rape, colza or mustard oil and fractions thereof, refined linseed oil, deodorized or refined maize (com) oil and its fractions, crude maize (com) oil and itsfractions,deodorized or refined sesame oil and itsfractions,crude or refined other oils, crude animal or vegetable fats and oils and theirfractions,partly or wholly hydrogenated, imer-esterified, re-esterified or daidinised, whether or not refined, but not further prepared margarine, excluding liquid margarine; imitation lard, shortening, blended salad oils, etc. prepared meals of turkeys other preserved products of turkey prepared meals and other preserved products of ducks, geese, and guineau fowls prepared meals of swine (induding mixtures) other prepared meals (apartfromstews) of bovine animals other preserved preparations of bovine animals (apartfromluncheon meats, corrie^ other preserved preparations of bovine, animals, not necessarily in airtight containers prepared meals of meat offal or blood other prepared meals of meat offal or blood, not riecessaruyta airtight containers pretzds rice paper, sealing wafers, and cheese sucks a m based food snacks; other food snacks preparations for infant use, put up for sale bread and batter mixes and doughs pizza mix, complete food preparations offlour,meal, starch or malt extract prepared pudding uncooked pasta, attaining eggs or nt,fresh,frozen,or dried stuffed pasta, whether or not cooked or otherwise prepared other pasta, with or without meat, in or not in airtight container couscous tapioca and substitutes therefor prepared from starch, m the form crfflakes, grams, pe^ waffles, pre-cooked, frozen frozen pizza fruit and nuts, prepared or preserved by vinegar or acetic add potatoe chips,flakes,frills fruit, nuts,fruit-pedand other parts of plants, preserved by sugar (drained, glace or crystallized) nut puree and nut pastes peanut butter and other ground nuts almonds, pistachio nuts, cashews, pecans, walnuts, pignolia nuts, macadamia nuts, etc.; and mixtures of two or more kinds of nuts, ground-nuts or seeds.  103 H.S. 2101 2101.10 2101.20 2101.30 210330 2103.90.1 2103.9030 2104 (2104.10.10) 2105.0030 2106.90.6 2106.90.91 2202.90.90 2302.20 2303.10 2303.20 230330.20 2306.90.10 2501.00.20 3502.10 3503.00.1  H.S. Commodity Description yeasts (active or inactive); prepared baking powders instant coffee instant tea; tea extracts, essences and preparations roasted chicory and other roasted coffee substimtes, and extracts, essences ardconcenmi mustardflewand meal and prepared mustard mayonnaise and salad dressing sauces for meat and fish  sweets, gums and the like, cemtaining syrtthetic sweetening agents popcorn, popped (excluding candied) other non-alcoholic beverages, aside from nectars, chocolate partially skimmed milk, eggnog, & non-alcoholic beer, bran, sharps, and otherresidues,whether cir not in ttefonnofpellets, derived from tte residues and starch manufacture and similar residues (e.g., gluten meal, com gluten feed, etc.) beet pulp, bagasse and other waste of sugar manufacture malt sprouts, from brewing or baling dregs and waste oil cake and other solid residues, whether or not ground in the fonnofpeUets, resulting from the exu^ table salt, made by an admixture of otha ingredients egg albumin edible gelatin  SITC-2 Commodity Codes (and Descriptions) Corresponding to the Above 0611 0612 0619 0615 0620 0721 0723 0722 0730 0980 0712  sugars, beat and cane, raw, solid refined sugars and oatrier prexiuctscrrefirurig beet and cane sugar, soU other sugars; sugar syrups; artificial honey (whether or not mixed with natural honey); caramel molasses, whether or not decolourized sugar confectionery (except chocolate confectionery) and other sugar prepartions cocoa beans, whole or broken, raw or roasted cocoa powder, unsweetened chocolate and other food prepartions containing cocoa, aes. chocolate and other focri reparations coniaini^ edible products and preparations, aes. extracts, essences or concentrates of coffee and preparations with a rjasis of thrjse extracts, essence substitutes and extracts, essences and concentrates thereof  4.E B E V E R A G E PRODUCTS INDUSTRY (BEV) H.S.  US. Commodity Description  22* 2201 (2201.90) 2202* (2202.10.9) (2202.90) 2203 2204 2205 2206 2207 2208 Other 1901.90.10 2106.90.31 2106.90.32 2106.90.33 2106.90.34 230330.10 2303.30.90 2307  mineral waters and aerated waters, natural or otherwise, not containing added sugar or cither sweetening matter nor flavoured mineral waters and aerated waters, containing added sugar or other sweetening matter orflavoured(includes carbonated soft drinks; and non-alcoholic beer)  beer (bottled, canned, draught, other), made from malt sparkling wine, champagne;redwine, white wine, grape must vermouth and other wine of fresh grapes other fermented beverages (cider, perry, mead); mixtures of fermented beverages (prune wine, perry sparkling); and mixtures of fermented beverages and non-alcholic beverages, not elsewhere specified or included (ginger beer and herbal beer, wine, beer, cider and other coolers) ethyl alcohol and other spirits, undenatured, of any strength spirits obtained by distilling grape wine or grape marc; whiskies (rye, scotch, Irish, bourbon, etc); mm and tafia; gin and geneva; vodka, tequila, liquers, spirit coolers, spirit fruit juices; angostura bitters malt extract soft drink syrup soft drink concentrates low calorie carbonated soft drink rx>strnixes regular carbonated soft drink postmixes brewers' and distillers' spend grains other brewing and distilling dregs and waste (aside from 2303.30.10 and malt spouts) wine lees; argol  SITC-2 Commodity Codes (and Descriptions) Corresponding to the Above 1110 non-alcoholic beverages, aes. (induding waters, induding spa waters and aerated waters; ice and snow, lemonade; flavoured spa waters; and flavoured aerated waters, and other non-alcoholic beverages, aes.) 1123 beer mad^frcmn^t(inctodingale,stout and porter)  104 APPENDIX 5.  Correlations Between Number of Establishments and Industry Concentration Ratios 1  Canadian Industry (4-digit SIC) Meat and Meat Products (1011) Poultry Products Industry (1012) Fish Products Industry (1021) Canned & Preserved Fruit & Veg.(1031) Frozen Fruit and Vegetable (1031) Cereal Grain Flour Industry (1051) Prep. Flour Mixes & Cereal Foods (1052) Feed industry (1053) Vegetable Oil Mills (1061) Biscuit Industry (1071) Bread and Other Bakery Products (1072) Cane and Beet Sugar Industry (1081) Chewing Gum Industry (1082) Sugar & Chocolate Confectionery (1083) Tea and Coffee Industry (1091) Dry Pasta Products Industry (1092) Potato Chip, Pretzel & Popcorn (1093) Other Food Products Ind. NEC (1098) Soft Drink Industry (1111) Distillery Products Industry (1121) Brewery Products Industry (1131) Wine Industry (1141)  CR4  CR6  CR8  -.80 -.88 -.07 +.04 -.08  -.89 -.77 -.4 +.26 -.03  -.92 -.72 -.67 +.02 +.70  —  ...  ...  —  ...  —  +.82 +.34 -.91  +.75 -.69 -.94  +.76 +.69 -.89  —  ...  ...  -.2  +.57  na  —  —  ...  —  ...  ...  —  ...  ...  —  ...  ...  —  ...  ...  ...  ...  ...  -.68 -.76 -.05 -.3  -.87 -.26 +.12 -.90  -.93 +.77 +.54 +.6  Observatio ns 17 15 15 13 13 4 4 15 17 17 4 17 4 4 4 4 4 4 17 17 17 14  Source: Statistics calculated from bi-annual (1954-80) and annual (1980-86) data. Industrial Organization and Concentration in the Manufacturing, Mining and Logging Industries: Unconsolidated Enterprise Concentration Data, (special request), Analysis and Development Div., Stats. Canada. 1  Data are expressed as correlation coefficients of the number of establishments and CR4, CR6, and CR8 industry concentration ratios.  105 APPENDIX 6. Summary of Trademark Application Data Province: Year - # of trademarks Meat & Fish  Fruits & Veg.  Cereals & Grains  Other Processed Foods  BC:  88-24 89-19 90-36 91-22 92-21  BC:  88-14 89-12 90-18 91-16 92-14  BC:  88-10 89-13 90-15 91-16 92-24  BC:  88-19 89-17 90-41 91-35 92-24  BC:  88-118 89-51 90-92 91-97 92-63  AB:  88-09 89-20 90-46 91-20 92-29  AB:  88-01 89-08 90-08 91-08 92-03  AB:  88-10 89-29 90-25 91-37 92-23  AB:  88-25 89-11 90-19 91-11 92-13  AB:  88-73 89-42 90-57 91-57 92-55  MB:  88-08 89-15 90-34 91-12 92-8  MB:  88-04 89-01 90-01 91-10 92-03  MB:  88-19 89-47 90-69 91-74 92-47  MB:  88-41 89-19 90-19 91-23 92-22  MB:  88-45 89-17 90-28 91-50 92-51  ON:  88-53 89-71 90-96 91-60 92-67  ON:  88-46 89-43 90-71 91-54 92-35  ON:  88-93 89-126 90-205 91-185 92-144  ON:  88-164 89-138 90-204 91-190 92-162  ON:  88-188 89-152 90-148 91-110 92-76  QU:  88-23 89-25 90-42 91-48 92-37  QU:  88-23 89-18 90-29 91-30 92-23  QU:  88-57 89-59 90-99 91-106 92-91  QU:  88-114 89-63 90-77 91-73 92-75  QU:  88-165 89-90 90-95 91-110 92-76  Beverages  APPENDIX 7. Correlation Matrix of Variables (Based on 125 Observations) LWR  1.0000  LCR  -0.11492  LLP  0.63037  1.0000 -0.31750  1.0000  LTM  0.37022  0.61993  0.28821  LNC  0.04025  0.62016  0.06452  BC  0.14147  AB  -0.00834  1.0000 0.56411  1.0000  -0.02836  -0.09019  -0.22254  0.01156  -0.29382  -0.39327  0.13383  0.51765  0.70220  0.22832  0.39398  -0.13060  -0.28304  1.0000 -0.25000 ON  0.17239 -0.25000  QU  -0.23060 -0.25000  MF  -0.10261  1.0000 0.43609 -0.25000 0.36435 -0.25000 0.26433  1.0000 -0.02578 -0.25000  1.0000  -0.53982  -0.09586  -0.41633E-17 -0.41633E-17 0.41633E-17 CG  -0.04989  0.40825  -0.29293  0.016726  0.  1.0000  0.13093  0.01694  0.69389E-17 -0.41633E-17 0.41633E-17 -0.11102E-16 -0.25000 1.0000 OTH  -0.35013  0.50406E-01 0.13981  0.09227  -0.06754  -0.97145E-17 0.69389E-17 -0.69389E-17 -0.55511E-17 -0.25000 -0.25000 BEV  0.85099  1.0000 -0.28755  0.74808  0.37092  -0.55511E-17 -0.55511E-17 0.55511E-17 -0.25000 T89  0.01591 0. 0.  T90  T91  -0.01058  -0.25000 0.02677  0.  0.  0.  -0.08595 0.  0.  0.  0.  0.  0.  -0.016114  0. 0.08388  0. -0.25000 0.016832  0.  0.  0.  0.  0.  0.  0.09141  1.0000  0.06089  0. -0.025626  -0.25000  1.0000 -0.04353  0. 0.01189  0.  0.01694  0. -0.25000  -0.00831  0. 1.0000 0.083106  -0.13605  0. -0.25000  1.0000 T92  -0.56488E-02 -0.40366E-01 -0.23300E-01 -0.18684E-01 -0.50723E-02 0.  0.  0.  0.  0.  0.  -0.25000  logCR  dBC  dAB  T91  -0.25000  0. -0.25000  1.0000  logWR dCG  0.  dOTH T92  logLP dON dBEV  logTM dQU T89  logNC dMF T90  

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