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Changes in industry selling prices of fourteen Canadian processed foods industries : effects of shifts.. Kim, Chung Dong 1991-12-31

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c. CHANGES IN INDUSTRY SELLING PRICES OF FOURTEEN CANADIAN PROCESSED FOODS INDUSTRIES: EFFECTS OF SHIFTS IN U.S.-CANADIAN EXCHANGE RATES (1971-1984) by CHUNG DONG KIM B.Sc, Korea University, 1981 A THESIS SUBMITTED, IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES Department of Agricultural Economics We accept this thesis as conforming to the required standard THE UNIVERSITY OF April <P Chung Dong BRITISH COLUMBIA 1991 Kim, 1991 43 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department The University of British Columbia Vancouver, Canada DE-6 (2/88) a ABSTRACT This thesis studies fourteen Canadian processed food industries and their pricing behaviour. Pricing models for each industry for the period of 1971-1977 and 1978-1984 have been established. This* study also tests wether changes in a pricing behaviour occurred in the middle of 1970s in which shifts in Canada-U.S. exchange rate occured. Food prices change for several reasons. The main reasons for changes in processed food prices are expected to be changes in input costs and demand factors. Input costs consist of material, labour, capital and fuel cost. Changes in demand side - import competition and excess demand - are are important factors. This study attepmts to establish, identify, and analyze pricing models by employing such variables for fourteen Canadian processed food industries at the wholesale level. Karikari (1988) has shown that the Canadian manufacturing industries changed their pricing behaviour as the U.S.-Canada exchange rate shifted in the middle of the 1970s. This study also tests if the changes (shift) in pricing behaviour of the food processing industries took place between two sub-periods: pre-depreciation of U.S.-Canada exchange rate (1971 to 1977), and post-depreciation of U.S.-Canada exchange rate (1977 to 1984). After analyzing the characteristics of the Canadian food processing industries and the distribution channel, three economic theories - which are considered to be appropropriate in i i reflecting the characteristics and the pricing behsviour - have been discussed. The Mark-up Pricing Theory is employed to explain the food processors' oligopolistic pricing behaviour. From the Mark-up Pricing Theory, relative changes in mark-up, material price, labour price, energy price, capital price, and productivity of each input are derived as independent variables in the pricing model while change in industrial selling price of processed foods is shown as a dependent variable. Excess demand and import competition are the main sources for the fluctuations in the mark-up factor. The Bilateral Monopoly Theory is applied to explain bargaining processes, from which prices of processed foods are determined, between processors and retailers. A shipment variable has been derived from the Bilateral Monopoly Theory as one of the substitutes for the mark-up variable. An International Trade Theory is discussed for the industries that face import competition. From this theory, it is concluded that import price would also influence Canadian food processors' mark up. Also discussed is a theory on how the pricing behaviour would change in a situation in which shifts in exchange rates occur. Quarterly data in rate of changes form are used for the estimation of the pricing model. Lags are allowed for independent variables to proferly reflect the characteristics of food processors. First, assuming changes in pricing behaviour, the pricing model is regressed for each industry in each sub-period, respectively. Variables for each industry in each sub-period are selected. It seems that the finalized regression results indicate a possibility of changes in pricing behaviour. A statistical test incorporating dummy variables is used to check if the changes in pricing behaviour which occurred in the middle of 1977 are statistically significant. The results can be summarized as follows. Different variables and different lags fit for each industry in each sub-period. The material prices-in different lag forms - are the main factors that influence changes in the industry selling price. In some industries in a certain period, the material prices are not important at all; only the U.S. prices are shown as important factors. The wage - current or lagged - is an important variable in some industries (at least in one period). The shipment variables are important in most industries with a positive or a negative sign, indicating the food processors' monopolistic pricing behaviour is influenced or interupted by the foods retailers' behaviour. The U.S. price variable(s) is a significant factor in most industries. The statistical test indicates that most of the industries have experienced structural changes and/or model changes between the two periods, except poultry, sugar cane & beet, vegetable oil, brewery, and winery industries. This study, however, does not necessarily conclude that the Canadian processed foods industries' pricing behaviour was changed according to the Karikari's hypothesis. TABLE OF CONTENTS ABSTRACT ii LIST OF TABLES viLIST OF FIGURES '. . .viii ACKNOWLEDGEMENTS ix CHAPTER I INTRODUCTION 1 1 .1 Introduction1.2 Problem Statement 3 1.2.1 Pricing Model1.2.2 Trade with U.S. 5 1.2.3 Exchange Rates between Canadian and U.S. Dollar 6 1.3 Objective of Thesis 10 1.4 Procedures of This Study 11 1.5 Thesis Structure 5 CHAPTER 2 SURVEY OF THE CANADIAN PROCESSED FOODS INDUSTRIES . 17 2.1 Introduction . . 12.2 Background Information 12.3 Structure of Processors 9 2.4 Structure of Distributors 23 2.5 Retailers' Power . . . 3CHAPTER 3 ECONOMIC THEORIES 45 3.1 Introduction3.2 Markup Pricing Theory 6 3.3 Excess Demand as Measurement of Markup Fluctuation . 52 3.4 Application of Bilateral Monopoly Theory for Mark-up 56 3.4.1 Justification for Use of Bilateral Monopoly Theory 53.4.2 Bilateral Monopoly Theory 58 3.4.3 Modification of Bilateral Monopoly Theory . . 65 3.5 Theories of International Trade 63.5.1 Introduction 63.5.2 Pricing Under Import Competition 71 3.6 Integration of Three Theories 80 3.6.1 Substitution for Mark-up3.7 Shift in Exchange Rates 83.7.1 Shift in Domestic and Foreign Price 81 3.7.2 Shift in International Price Competitiveness . 83 3.7.3 Shift in Trade Pattern 84 3.7.4 Shift in Importance Between Input Cost Variable and Import Competition Variable ........ 84 CHAPTER 4 EMPIRICAL DATA . . 87 4.1 Introduction 8v 4.2 Industry Selling Price (P ) 87 4.3 Price of Materials (P ) .x 90 4.4 Changes in Wages (P,)m 3 4.5 Changes in Demand (SHI) 5 4.6 Changes in the Import Price (P.) 96 4.7 Changes in Fuel Price (Pf) and1Capital Price (Pk) . . 98 4.7.1 Fuel Price (Pf) 94.7.2 Capital Price (P.) 9 4.8 Changes in Productivity; M/X, L/X, F/X, and K/X ... 100 CHAPTER 5 EMPIRICAL TEST AND RESULTS 101 5.1 Introduction 105.2 Distributed lag Model 105.2.1 Lags in Material and Labour Prices ....... 101 5.2.2 Lags in U.S. Price 103 5.2.3 Lags in Shipment Variable 105 5.3 Regressions and Results ..... 106 5.3.1 Regressions for Variable Selection 105.3 Tests of Changes in Pricing Behaviour 124 CHAPTER 6 SUMMARY AND CONCLUSIONS 136 6.1 Summary and Conclusion6.2 Limitations and Recommendations 140 APPENDIX 143 BIBLIOGRAPHY ..188 vi LIST OF TABLES Table 2-1 Cost Structures of Food Industries, Canada, 1980 . . 20 Table 2-2 Enterprise Concentration, Canada, 1980 ........ 22 Table 2-3 Supermarket Chains in Canada, 1983 25 Table 2-4 Convenience Store Groups (chain), 1983 ....... 26 Table 2-5 Food Store Sales Trend, Canada 30 Table 2-6 Sales of Major Distributors, Canada . 31 Table 2-7 Provincial Market Shares-The Top 5 32 Table 4-1 Final Data for Biscuit Industry (SIC 1071) ..... 88 Table 4-2 Material Weight of Biscuit Industry (SIC 1071) ... 91 Table 5-1 Regressions With Finally Selected Variables . . . . 110 Table 5-2 Tests of Structural Changes with Dummy Variables . . 127 Table 5-3 Summary of Results 13vii LIST OF FIGURES Figure 1-1 Canada/U.S. Exchange Rates (quarterly) ...... 8 Figure 3-1 Bilateral Monopoly Theory 59 Figure 3-2 Pricing with Import Competition 73 1 viii ACKNOWLEDGEMENTS I would like to express my appreciation to my major supervisor, Tim Hazledine, for his personal comment to this research, and for his encouragement and generosity. I would like to thank Marry Bohman and David Green for their suggestions and care in reviewing the final draft. I am grateful to Gwynne Sykes for her contribution in editing the English composition of this work. This thesis would not be completed without their assistance. Finally, I would like to thank my family for encouraging and supporting me to continue my education. ix CHAPTER I INTRODUCTION 1.1 Introduction This thesis constitutes a study of short-run price determination for the industry selling price of Canadian processed foods. Particular attention is given to the relationship between the Canadian and U.S. industry selling price, between the Canadian industry selling price and the exchange rates of the Canadian-U.S. dollar (especially devaluation), and between the Canadian industry selling price and the food retailers' price negotiation power. Food processors, connecting farmers and retail grocers, handle and prepare food products for marketing. The nature and the form of the final consumer product determine the extent of food processing. Even though the cost of food processing varies in accordance with the final level and type of processing, the basic set of input costs is the same. These input costs are usually in the form of materials, labour, capital and energy inputs. As the level of these input costs varies, so do the prices of the processed food products. Besides these input costs, demand pressures are also considered to be an influencing factor in the pricing of processed food products. These demand pressures are usually import competition and changes in demand. The product prices of the industries that face international trade are affected by variation in import prices. The variation of the demand level for 1 the products will change the prices. In particular, the Canadian grocers' market power, resulting from the high concentration in the food retail sector, is considered to be significant in setting food prices. Also, it is expected that the pricing behaviour of the Canadian food processors differs between the pre-depreciation and the after-depreciation period.1 Changes in the U.S. food prices should exert more influence toward changes in the Canadian processed food prices during the period in which the Canadian dollar is relatively strong against the U.S. dollar (appreciation) than during the period in which the Canadian dollar is relatively weak (depreciation). On the other hand, changes in the input costs exert more influence toward changes in the price of Canadian processed foods during periods of depreciation than those of appreciation. Therefore, the problems this thesis addresses are that of estimating the extent to which a change in any of these market factors will create a change in the wholesale price (industry selling price) of Canadian processed foods, and that of testing various models of pricing behaviour. This research employs quarterly data between 1971 and 1984 for the fourteen Canadian food processing industries. 1 Karikari, J. A.: "International Competitiveness and industry Pricing in Canadian Manufacturing", Canadian Jourmal of Economics, XXI No. 2, May 1988, pp. 410 - 426. 2 1.2 Problem Statement  1.2.1 Pricing Model Price efficiency is required for either an improvement in social welfare or the incremental development of an ideal economy. In achieving these objectives, price policies are often employed. The price policies are largely concerned with stabilising the price level. The results depend upon how changes in the price level are supposed to occur. It is usually said that prices are changed because either the demand situation fluctuates or input costs are changed. A quantitative analysis is sometimes required to facilitate a better understanding of price fluctuations. Food expenditures represented a 16% share of personal 2 disposable income for the average Canadian in 1984. Because of the significance of food expenditures, the increases in the price of food products are major concern to farmers, food processors, consumers, economists, and all levels of government. The rate of inflation in Canada, particularly with respect to food, was dramatic over the 1971-1984 period; especially 1973-1975 (44.05%) and 1978-1981 (35.33%). The retail food price index increased from 34.4 to 117.4, an increase of 241.3 percent, and the consumer price index for all items Agriculture Canada: Food Market Commentary, (Food Market Analysis Division of the Policy, Planning, and Economic Branch, Ottawa, December, 1984), p.3. 3 increased from 42.2 to 122.3, an increase of 189.8 percent. There are numerous reasons for the food price increases. They include developments in the agricultural marketing system, a decline in the value of the Canadian dollar with respect to other currencies (especially the U.S. dollar), increases in demand, increases in the imported food prices, decreases in the input productivities, and increases in the costs of inputs. Recognizing the adverse effects of these high rates of inflation on the Canadian economy, the government sometimes needs to launch an anti-inflation program to keep the overall rate of price increase 4 within a target level. In order to help the policy makers analyze food pricing behaviour, forecast food prices, study food trade-policy, or maintain food price controls for the improved social welfare and the incremental development of an ideal economy, certain quantitative information should be developed and presented to them. Defining the wholesale pricing mechanism of the Canadian processed food industries and developing a short-run systematic pricing model for the processed foods will provide background information. 3 Robbins, G.: Handbook of Food Expenditures, Prices and Consumption, Agriculture Canada, Ottawa, November 1986, pp.55-57. 4 In October 1975, an Antiinflation Board was established, whose main objective was to control movements in prices, profits, and wages in accordance with the guidelines set by the government. For example, it launched a three-year antiinflation program to keep the overall rate of price increase within a target level of eight percent in the first year, six percent in the second year and four percent in the third year. 4 Further, it will be necessary to identify the relationship between changes in the industry selling prices and changes in other factors: material costs, labour cost, capital cost, demand pressure, import prices, and the exchange rates. Also, special attention is given to industry shipments as one of the independent variables. As will be explained in chapter 2, the Canadian grocery retail sector is highly concentrated via a few large chain stores. Their market share is very high. As they are large in size and as they link consummers and processors in the distribution channel, Canadian food processors cannot ignore these retailers in pricing outputs. That is, the processed food prices cannot be determined soley by the processors 'monopolistic' pricing behaviour because there are so many factors (e.g., volume purchasing, brand name.order, and display shelve control) that lead to the retailers' price negotiation power against the processors. The 'Bilateral Monopoly Theory', as will be explained in chapter 3, explains the process of price negotiations and provides a basis for incoroporation of industry shipments as an important independent variable. If this shipments variable shows significance with a negative sign, it should not be said that the Canadian food processors soly or monopolistically determine output prices. 1.2.2 Trade with U.S. Pricing behaviour in other countries inevitably enters the debate on the causes of price increases in Canadian processed 5 foods. It is relevant not only as a standard of performance but also as a potential contributory source of Canadian price level movements. The term "import inflation" carries the essence of the phenomenon. It refers to increases in price levels at home which are induced by similar increases abroad. The extent to which this phenomenon explains the increases in Canadian processed food prices is important in determining appropriate price policies. The feasibility and desirability of various policy options depend on how Canadian prices interact with foreign prices. The trade of processed foods between Canada and U.S is a prominent factor. It is believed that Canadian food prices are influenced by U.S. prices because (1) the United States is the Canada's most important trading partner, (2) many food commodities move relatively freely across the border, and (3) the U.S. plays the dominant role because of its larger size. From this point of view, it is plausible that the movement of prices in the two countries will be close, especially for industries which are facing trade competition between each other. Therefore, a hypothesis that Canadian food prices are influenced by fluctuations in U.S. food prices can be raised. 1.2.3 Exchange Rates between Canadian and U.S. Dollar There are several studies on the price mechanism of Canadian manufacturing industries. Some include U.S. prices as an import competition variable in their pricing models. However, there has been no analysis done on how the two different sets of exchange 6 rates - the period of relatively strong Canadian dollar (appreciation, 1971 to 1977) and the period of relatively weak Canadian dollar (depreciation, 1978 to 1984) against the U.S. dollar - affect the pricing behaviour of the Canadian food processors. Over the last twenty years, Canadians have experienced a range of exchange rates for the Canadian dollar that is exceptionally 5 wide by historical standards, as shown in Figure -1-1. In the first half of the 1970s, in general, the Canadian dollar was relatively stronger than in the 1960s (See Byleveld 1980, pp. 34-40 for details). The middle of the 1970s was a transition period. There were up and down fluctuations due to the new floating rate system, the boom in exports, and the increased long-term foreign loans, followed by a substantial decrease in following years (See Byleveld 1980, pp. 34-40 for details). However, the situation was changed after the first quarter of 1977. The value of the Canadian dollar started to decline because of the decrease in long-term foreign loans, the election of the Parti Quebecois government in Quebec, the debt trouble caused by sectoral over-expansion, and the depression (Byleveld 1980, pp. 34-40) . 5 Byleveld, H.: "The Canadian dollar: Where From and Where to Now?," The Canadian Business Review, Vol.7, Winter 1980. Historically, the Canadian dollar was stronger than the U.S. dollar in the 1950s; one U.S. dollar was worth about 0.965 Canadian dollar. In the 1960s, the Canadian dollar was weaken and was kept at around the same level; one U.S. dollar was equivalent to about 1.075 Canadian dollar. 7 Figure 1-1 Canada/U.S. Exchange Rates (quarterly) Source: Bank of Canada The depreciation of Canadian dollar should provide Canadian manufacturing industries with gains in competitiveness in international markets. As the Canadian manufacturing industries' international competitiveness increases, the pricing behaviour of the Canaian manufacturing industries is expected to be changed 8 between the pre-depreciation and the post-depreciation periods. In the pre-depreciation period, the Canadian manufacturing sector domestic prices are expected to be set by both 'Eastman-Stykolt pricing hypothesis (ESPH)' and 'monopolistic pricing hypothesis (MPH)'. On the other hand, in the post-depreciation period Canadian manufacturing sector prices are expected to be determined soly by Canadian manufacturers' 'monopolistic pricing behaviour'. As explained above, there was a shift (depreciation) in Canadian-U.S. dollar exchange rates in the middle of the 1970s. Karikari (1988) tested these two pricing behaviour hypotheses for the periods of 1970-75 and 1975-80. He concluded that the Canadian manufacturers' pricing behaviour was according to both the ESPH and the MPH in the first half of the 70s. On the other hand, the MPH was the only pricing behaviour employed by the Canadian manufacturers in the second half of the 70s. By considering what Karikari (1988) claims and the relative weakness of Canadian dollar from the middle of the 1970s, it is also expected that Canadian food processors exhibit different priceing behaviour between the two sub-periods. The changes in behaviour coincide with Karikari's hypothesis that the impact of import prices on domestic prices in the first subperiod shoul be more important than in the second subperiod and that, on the other hand, the impact of input costs on domestic prices in the ^ Karikari, J. A.: "International Competitiveness and Industry Pricing in Canadian Manufacturing", Canadian Journal of Economics, XXI No.2, May 1988, pp.410 - 426. 9 second subperiod should be more significant than in the first subperiod. 1.3 Objective of Thesis This thesis has two objectives. The first objective is to provide a systematic analysis of the factors which determine the rate of changes in industry selling prices for Canadian processed 7 foods. The analysis places particular emphasis on the immediate determinants of the rate of industry selling price changes. This study will develop a regression model that explains the reasons and workings behind the industry selling price changes observed in fourteen separate food processing industries in a partial equilibrium for the sample period of 1971 to 1984 with quarterly data. The model is expected to determine the extent that the industry selling price changes are due to changes in input costs (material prices, labour price, energy prices, and capital prices) and demand pressure (demand and import price). Therefore, in the model, the industry selling price of each industry becomes the dependent variable and the input costs and demand pressure There are three levels of price indexes according to the Statistics Canada. These indexes are Farm Product Price Index, Industry Selling Price Index, and Consumer Price Index. What this paper will analyze is the Industry Selling Price of the Canadian food processing industries. The Industry Selling Price Index is defined as: the measure of prices received by manufacturers for goods they sell and, consequently, elements of the purchaser's price which accrue to other industries do not belong to the price quotation entering into the index. Thus freight, insurance, and taxes are excluded from price quotations (Statistics Canada, Industry Selling Price Indexes, Catalogue No.62-001). 10 factors become the independent variables. Also, the model should be able to distinguish between these market influences and identify the effects of each factor. The second objective is to test the hypothesis that the Canadian food processors exhibit changes in pricing behaviour due to a shift in the Canadian-U.S. exchange rates. The shift in the exchange rates between Canadian currency and U.S. currency occurred in 1977. This devaluation will shift international competitiveness. These shifts will cause changes in the processors' pricing behaviour. 1.4 Procedures of This Study The Standard Industrial Classification (SIC) is a Statistics Canada code that identifies, among other things, the nature of the manufacturing activity and the degree to which an industry is broken down and analyzed. In Statistics Canada publications, the Canadian food industry is divided into eighteen sections. These eighteen sections encompass every food processing industry that exists in Canada, to a level of dis-aggregation to the three or four digit Standard Industrial Classification. A four digit classification represents the lowest level of dis-aggregation performed by Statistics Canada and is one level of study below a three digit SIC. The three or four digit level of the eighteen Canadian food processing industries are: (1) S.I.C.1011 Slaughtering and Meat Processors (2) S.I.C.1012 Poultry Processors 11 (3) S.I .C.102 Fish Products Industry (4) S.I .C.1031 Fruit and Vegetable Canners and Preservers (5) S.I .C.1032 Frozen Fruit and Vegetable Processors (6) S.I .C.104 Dairy Products Industry (7) S.I .C.105 Flour and Breakfast Cereal Products Industry (8) S.I .C.106 Feed Industry (9) S.I .C.1071 Biscuit Manufacturers (10) S. I.C.1072 Bakeries (11) s. I.C.1081 Confectionery Manufacturers (12) s. I.C.1082 Cane and Beet Sugar Processors (13) s. I.C.1083 Vegetable Oil Mills (14) s. I.C.1089 Miscellaneous Food Processors (15) s. I.C.1091 Soft Drink Manufactures (16) s. I.C.1092 Distilleries (17) s. I.C.1093 Breweries (18) s. I.C . 1094 Wineries Due to lack of data, this study excludes four industies: Fish Product Industry (102), Fruit and Vegetable Canners and Preservers (SIC 1031), Frozen Fruit and Vegetable Processors (1032), and Miscellaneous Food Processors (SIC 1089). The remaining fourteen industries are of interest to this study. The following procedures will be employed to achieve the stated objectives for each industry. First, both the processors and the food retailers will be studied. For the food processors, the number of firms, volume of production, cost structures, and concentration ratio will be analyzed. The grocers (retailers) will be divided into chain stores r and individual stores and discussed (the number of retailers, sales volume of each chain retailer, each chain retailer's weight in the total retail sales volume, and market shares). By studying the characteristics of both processors and retailers, we will be able to identify the market structure and the pricing mechanism involved (i.e., oligopoly in supply and oligopsony in demand), as well as significant interaction processes between processors and retailers. Secondly, once the market structure and the pricing mechanism are identified, the fundamental economic theories related to the characteristics of the Canadian food processing industries will be discussed. The economic theories will be the Markup Pricing theory for non-perfect competition in supply side (oligopoly), the Bilateral Monopoly Theory for non-perfect competition in demand side (oligopsony), and the International Trade Theory for industries which are facing international competition. As well, a theory related to the impact of changes in exchange rate will be discussed. Thirdly, the three economic theories are combined and modified. The economic theories are combined to make a regression model which is suitable for the Canadian food processing industries. However, not all the variables in the model are empirically measurable. Accordingly, it is necessary to replace these non-measurable variables with variables which should be 13 measurable and representative of the original non-measurable variables. Some unimportant variables are omitted as well. After these procedures, an empirically testable regression model for Canadian food processing industries is derived. The model consists of the industry selling price as a dependent variable and material prices, wages, import prices, and shipments (or personal disposable income) as explanatory variables. Fourthly, the data for each variable is collected. The sources of the data, the manipulation of the raw data, and the limitations of data mining are discussed. After these procedures, a final form of the data sets will be created. Fifth, the model for each food processing industry, with the final set of data, will be estimated. All the variables will be regessed for each industry for the first and the second sub-periods, respectively. Variables will be selected for each period. The variables selected for the first period and the variables selected for the second period, together and with dummy variables, will be regressed for the whole sample period. The significance of structural changes between the two sub-period will be tested by using the dummy variables and chi-squared value. Having these results, comments on each industry will be written. Finally, we will have a section for conclusions and recommendat ions. 14 1.5 Thesis Structure The structure of this thesis consists of five parts; industry survey, discussion of theory and model, data, test of the model, and conclusions. Chapter 2 will contain a survey of selected Canadian processed food industries, describing general characteristics of the industries, as well as the structure bf the processors on the supply side. Also, retail food stores will be discussed in the demand side. This chapter will also deal with the industries' marketing and pricing behaviour. Chapter 3 will be a discussion of economic theories. Three fundamental economic theories will be considered; the Markup Pricing Theory, the Bilateral Monopoly Theory, and the International Trade Theory. All three theories are reviewed, modified, and combined for specific application to this empirical test for the Canadian processed food industries. At the end of this chapter, a testable model will be derived from the economic theories. Chapter 4 will deal with data for the testable model which is defined in chapter 3. In this chapter, the sources of the data will be discussed. By using the biscuit industry as an example, the methods of manipulation for the raw data will be explained. At the end of this chapter, as a result, the final data for the variables of the model will be presented. Chapter 5 will describe the empirical tests. The sample period will be divided into two sub-periods and estimasted for each sub-period separately. And then, the variables will be selected for 15 each period for different pricing behaviour. Dummy variables will be used to test if the changes in the pricing behaviour are statistically significant. The results will then be discussed. Finally, we will conclude this study in chapter 6 with a discussion of the resulting poli-cy implications, of their limitations, and of their role in future research. This chapter will also summarize the main findings of the thesis. . 16 CHAPTER 2 SURVEY OF THE CANADIAN PROCESSED FOODS INDUSTRIES 2.1 Introduction The choice among theories has important implications. The expected pattern of price-wage interactions, questions of bias and identification of the price equation, questions of import competitions, aspects of demand and supply side, and use of the price equation for forecasting should all be analyzed with respect to particular hypotheses concerning pricing behaviour (see Laden, 1970, for elaborate discussion). From this point of view, it is necessary to analyze the characteristics of the Canadian food processing industries before any econometric model is considered. This chapter will consider such characteristics of the Canadian food processing industries. 2.2 Background Information The food processing industry is the largest of the manufacturing industries in Canada, annually accounting for approximately 13% g of total manufacturing employment and 18% of total sales. It is also one of the most diverse of the manufacturing industries, producing within its own sub-sectors and plants, a wide range of products differing in the combination of inputs used, the nature and extent of processing, the technology applied, and the intended market. g Canada. Statistics Canada: Manufacturing Industries Canada, Catalogue No. 31-204, Table 3, 1967 to 1986. 17 The industry represents a link in the food chain between producer and consumers and provides the major market for primary agricultural products, as well as being a substantial consumer of packaging materials, energy, capital input, and transportation equipment and services. Unlike manufacturing in general, employment in the food processing industries is relatively evenly distributed across Canada in proportion to population. Total industry employment has been generally stable in recent years, although there have been some significant changes within individual sub-sectors, resulting from such factors as changing consumer demand, trade flows, and technical advances. Output of the industry has been expanding steadily, although at a somewhat slower rate than that of the total manufacturing sector. Growth has been dependent upon population increases, increased demand of more highly processed products and increased consumption or trade of certain items. About 90% of domestic 9 demand for processed foods is supplied by the industry. In most instances imports consist of products not produced domestically including processed tropical and semi-tropical items and items with special brand, quality, or geographic identification. However, imports of directly competitive items are significant in some product markets. Canada. Statistics Canada: Manufacturing Industries of Canada, Catalogue No. 31-204, Table 3, 1967 to 1986. 18 2.3 Structure of Processors Although a wide range of information is available for consideration, only the cost structures and concentration ratio of food processing industries - which are closely related to our pricing model - will be discussed in this section. By looking at both the number of total employees and the total industry shipments in 1980, as shown Table 2-1 and Table 2-2, the slaughtering and meat processing industry emerges as the largest of the food processing industries with shipments of 6.9 billion dollars and an employment figure of 35,000 workers. The smallest is the wine industry, which had shipments of only 169 million dollars and an employment figure of just 1,300 workers. Not surprisingly, the heaviest cost is that of materials and supplies. In some cases, e.g. vegetable oil refineries, the cost of material and supplies is as high as 88% of the value of shipments. This means that the gross margin is only 12% which is relatively low. On the other hand, in the brewery industry, the cost of material and supplies is only 29% of the value of shipments, resulting in higher gross margins (71%). The second greatest cost consists of wages for the production line workers which range from 20.7% (bakeries) to 2.4% (vegetable oil). Salaries paid to non-production-workers were usually smaller than wages paid to the production line workers. The contribution of energy to the shipments was not very ' significant. It ranged from only 0.8% (slaughtering and meat processors) to 3.6% (distilleries). 19 Table 2-1 Cost Structures of Food Industries, Canada, 1980 Industr ies Total Wages Salar ies Cost of Cost of Empl Energy Ma t e r i -oyees ($,000) ($,000) ($,000) als($000) Slaughtering & Meats 35,084 458,464 174,963 54 342 5,719,259 (6.6) (2.5) (0 8) (82.4) Poultry 10,130 116,842 25,620 1 2 652 740,680 (11.8) (2.6) (1 2) (74.9) Fish 27,084 299,973 68,855 33 289 937,788 (20.5) (4.5) (2 2) (64.0) Fruits & Vegetables 13,567 129,417 75,164 18 530 766,375 (10.7) (6.2) (1 5) (63.5) Dairy 26,028 246,487 216,095 64 1 97 3,321,690 (.5.7) (5.0) (1 5) (77. 1 ) Flour/Breakfast Cereals .5,168 60,771 37,884 9 708 669,106 (6.6) (4.1 ) (1 0) (72.5) Feed 9,646 92,528 64,604 28 587 1,844,283 (4.1) (2.8) (1 3) (80. 1 ) Biscuits 6,708 64,796 37,299 5,234 183,821 (17.4) (10.0) (T 4) (49.4) Bakery 26,065 246,477 146,959 28 525 545,552 (20.7) (12.4) (2 4) (45.9) Confect ionery 10,034 90,756 49,382 8 235 . 406,386 (11.6) (6.3) (1 1 ) (52.2) Sugar Cane & Beet 2,570 32,562 16,700 1 5 857 640,895 (4.2) (2.2) (2 0) (82.4) Vegetable oil 1 ,460 17,713 12,860 1 0 835 640,896 (2.4) (1.8) (1 5) (88. 1 ) Soft drinks 13,274 100,421 1 34,844 18 01 1 578,407 (10.0) (12.6) (1 6) (53.9) Distilleries 5,509 58,239 63,051 24,457 284,885 (8.8) (9.3) (3 6) (42.0) Breweries 12,342 173,956 127,214 25 069 347,125 (14.4) (10.5) (2 8) (28.8) Wineries 1,313 12,655 11,391 1 807 89,366 (7.5) (6.7) (1 1) (52.7) Source: Statistics Canada: Industrial Organization and Concentration in the Manufacturing, Mining and Logging Industries, Ca. No. 31-402, Table 2, 1980. - Numbers in brackets are in percentage form (proportion of industry shipments in Table 2-2) 20 Table 2-2 discloses the concentration ratio of each food processing industry. The third column shows the concentration ratio for four enterprises and the fourth column shows the concentration ratio for eight enterprises. The concentration ratio for four enterprises ranged from 25.7% (feed) to 99.0% (breweries), while that for eight enterprises ranged from 34.1% (feed) to 100% (sugar cane & beet, vegetable oil, and breweries). This concentration ratio is often used in discussing the power of leading firms in the industry. Generally, the higher ratio indicates the higher market power of the leading firms in the industry. Some researchers even use the term of voligopoly' for food processing industries that are exposed to 40%-50% of four firms concentration ratio, expecting that the leading firms can control the domestic market, especially prices. However, the critical value is not clearly defined. Certainly, it is easily said that flour and breakfast cereals, biscuits, sugar cane and beet, vegetable oil, distilleries, breweries, and the wine industry are oligopolistic. Therefore, it is reasonable to assume that the leading firms in these industries can control and lead the output price, pricing above costs.1^ In recognizing the oligopolistic market structure, it is not sufficient to define the market structure by considering only the concentration ratios. The environments of each industry should be 1^ However, because of the oligopoly, firms are quite often unwillingly forced to face the xprice-war'. 21 Table 2-2, Enterprise Concentration, Canada, 1980 Industries Establish Industry Leading enterprises -ments Shipments Concentration ($,000) 4 8 Slaughtering & Meats 547 6,944,216 43.3 53 .0 Poultry 90 988,813 36.3 50 .6 Fish 376 1,465,236 44.7 53 .5 Fruit & Vegetables 199 1,206,074 39.0 55 .7 Dairy 456 4,309,194 37.0 50 .5 Flour & Breakfast Cereals 49 923, 117 66.0 84 .7 Feeds 609 2,280,731 25.7 34 . 1 Biscuits 33 372,298 79.9 95 .7 Bakery 1487 1,189,419 33.5 47 .8 Confect ionery 109 778,962 5'0. 1 72 .9 Sugar Cane & Beets 1 3 777,385 na 100 .0 Vegetable oil 10 727,390 70.9 100 .0 Soft drinks 238 1,072,274 48.7 61 .4 Distilleries 33 679,091 94.5 99 .3 Breweries 41 1,205,530 99.0 100 .0 Winer ies 32 169,659 72.0 97 .0 Source; Statistics Canada: Industrial Organization and Concentration in the Manufacturing, Mining and Logging Industries, Ca. No. 31-402, Table 1, 1980. studied as well. For example, the poultry and the dairy industries show low concentration ratios. However, these two industries are controlled by marketing boards which manage the price and/or the quantity of the outputs, resembling a monopoly mechanism. In some industries, the concentration ratios do not appropriately reflect real circumstances. For example, the soft drinks industry's concentration ratio shows only 48.7% for four enterprises. This figure does not mean that the firms do not have 22 price control. The number does not correctly reflect the real circumstance that bottling firms are locally distributed and are given exclusive rights to bottle soft drinks for the district. In fact, about 90% of the total Canadian soft drinks industry is accounted for by both Coca Cola and Pepsi Cola companies which are franchisers (The Financial Times, Sep. 22, 1985, pp. B11—12). In the worst case, the bakery industry, with 1,400 establishments, has only 33.5% of shipments accredited to four enterprises. This low national concentration ratio is natural when industry characteristics are considered. That is, a few companies cannot control price across the provinces. However, when we consider the local situation, a bakery store exists as a combination of a producer, retailer, and wholesaler. From this point of view, a bakery store can act as a monopoly, a competitive-monopoly, or a oligopoly in that specific region - a price maker. Therefore, in general, the firms in each food processing industry can be assumed to be price setters. At least, they are riot price takers. 2.4 Structure of Distributors There were 33,000 food retailers in 1983 (not including specialty stores). They were categorized into supermarket chains, chain convenience stores, department stores, voluntary groups, cooperative stores, unaffiliated stores, and specialty food stores according to ownership, the number of stores under the 23 same ownership, and the characteristics of operation. In 1983, there were 41 supermarket chains containing 1,634 stores (Table 2-3). Among the supermarket chains, Canada Safeway had the largest number of stores (302 stores) followed by Steinberg/Miracle Food Mart (218 stores). The third largest retailer in terms of store numbers was Dominion (178 stores). When we look at the provinces, Ontario had the largest number of stores (727 stores). Table 2-4 shows that in 1983 there were 29 convenience chains containing 3,419 stores and 52 co-operative or voluntary groups containing 8,549 stores in 1983, and 19,200 unaffiliated independent stores. Mac's Milk had the largest number of stores (740 stores). The second one was Beckers (643 stores) and the third one was Seven-Eleven (360 stores). Ontario had the largest number of convenience stores (1,723 stores). However, this number is very volatile year by year. The total sales volume of foods in Canada, percentage changes of sales, and the shares of the sales between the chains and the independent stores during the period of 1971-1984 are presented in Table 2-5. The percentage changes of food sales are shown in the third column. The periods of 1973-1975 and 1978-1980 show relatively higher increases in food sales. This has two implications; one is that consumers purchased more foods, the other is that inflation was higher. 24 Table 2-3 Supermarket Chains in Canada, 1983 Name B.C. PRAIS. ONT. QUE. ATL. Prns Total • A & P - - 1 05 2 - 1 07 Allwesr (Associated Grocers) - 6 - - - 6 Ava/Heritage (Provigo) * - - - 8 - 8 Basics ^Basics Wholesase) - - - - 5 5 Bonanza - - 1 1 - - 1 1 Best for Less (Dominion) - - 1 3 - 1 5 28 Canada Safeway 180 29 - - - 302 Dominion (incl. Warehouse.Plus) - 7 141 - 30 1 78 Economart (see Westfair & 3 7 1 - - 1 1 Kelly, Douglas) Food City - - 45 - - 45 Gordons (Div. of Zehrmart) - - 1 3 - - 13 Jadis/Bas^cs (Steinberg) - - 1 5 - 6 Kwik Save * 1 1 - - - - 1 i L & M Foodmarket - - 1 2 - - 12 Loblaws (incl. Mo-Frills, - 2 1 22 - - 1 24 see Westfair) O.K.Economy (Westfair & National) - 29 2 - - 31 Overwaitea (incl. Save-on-foods) 52 - - - - 52 Provigo (Corporate) - - - 1 1 1 - 1 1 1 Shop Easy (Westfair) - 2 - - - 2 Sobeys - - - 9 87 96 Steiberg's/Miracle Food Mart - - 87 130 1 218 Super Valu (Westfair) - 6 - - - 6 Thrift (see Dominion) - - 2 - 3 5 Valdi*(see Steinberg) - 29 56 - - 85 Wades - - - - 10 10 Woodward 1 5 9 - - - 24 Zehr's - - 39 - - 39 Others 1 5 9 40 8 6 87 Totals 189 286 727 275 1 57 1 ,634 Source: "Survey of Chains and Groups," Canadian Grocer, Vol.98 No.8, Maclean Hunter, August,1984, p.31. * Independent 25 Table 2-4 Convenience Store Groups (chain), 1983 Name B.C. PRAIS . ONT. QUE. Atlc. Prns. Total Beckers - - 653 - - 653 Bonisoir (Hudon Et Deaudelin) - - - 146 - 1 46 Cantor Bakeries - - 1 7 52 - 69 Convenient Food Mart - - 8 - - 8 Dutch Girl - - 5 - - 5 Le Frigo/Ice Box - - 7 35 - 42 Green Gables Fine Foods - - - - 69 69 Hasty Market 5 3 1 5 - - 23 Irving - - - 58 58 Jug City (Oshawa Food) - - 26 - - 26 Kwick Way (T.R.A.,Johnson-MacD) - - - - 71 71 Kwikie Minit Markets - - 30 - - 30 Little Short Stop Stores - - 32 - - 32 La Maisonnee (Steinberg) - - - 58 - 58 Mac's Milk 79 167 471 23 - 740 Magic Mart - - 10 - 10 Mike's Milk - - 73 - - 73 Min A Mart - - 46 - - 46 Perrette - - - 150 - 1 50 Picadilly - - 9 - - 9 Pinto (Loeb, Mikes's) - - 75 19 - 94 Provi-Soir (Provigo) - - - 187 - 1 87 Q Marts (Alberta Grocers) - 38 - - - 38 Quick Mart (Atlantic Whisirs. - - - 80 80 Red Rooster (Home & Pitfield) - 1 58 - - - 1 58 7-11 (Southland) 94 188 78 - - 360 Shaw' Dairy Stores - - 33 - - 33 Sseedee Mart/Tags (Alta. Grocers)- 16 - - - 1 6 Top Value Gasmarts (Loeb) — — 1 35 — 1 35 Totals 1 78 570 1 ,723 670 278 3,419 Source: "Survey of Chains and Groups," Canadian Grocer, Vol.98 No.8, August,1984, p.39. 26 Total grocery sales increased by only 3.8% in 1983, which is the lowest increase during the period. This has two implications. Most importantly it means that Canadian consumers spent only 3.8% more dollars for groceries in 1983. On the other hand, it means that there was a smaller price increase in that year due to price wars. During this period, the shares of food sales through chains were higher than those through independents. The range is 53.3% in 1971 to 60.4% in 1979. However, the proportion of non-chain store shares become higher. The figures show that independents captured 42.8% of the total grocery store sales volume in the country in 1984. This is the fifth increase in a row since 1979. In other words, independents continue to increase their market share at the expense of chains. In 1983, total food sale through supermarkets, grocery stores and convenience stores was 25.8 billion dollar. The 1,634 chain supermarkets along with 3,419 chain convenience stores together accounted for 14.9 billion dollars in sales, which represents 57.7% of the total sales. Independent stores had sales of 10.9 billion dollars which was 42.3% of the total food sales.11 However, if we readjust the market share figures according to the retailers' characteristic that the retailers can exert purchasing . power against the processors, the results will be different. To 11 8,549 independent stores belonging to voluntary groups accounted for sales volume of 7.2 billion dollar or 27.8%. 19,200 unaffiliated independent stores - of all sizes including supermarkets, mom and pops, etc., but not belonging to voluntary groups - totalled 3.7 billion dollars in sales or 14.5%.). 27 illustrate, the actual dollar figures for each category for 1982 is presented. With these dollar figures we can make appropriate comparisons. Chain supermarkets (four or more stores with one owner) had sales of $14.0 billion in 1982. Chain convenience stores (four or more stores with one owner) had sales of $1.2 billion. Chain department stores had food sales of $686.4 million. Voluntary group independents had sales of $6.7 billion. Co-operative stores had sales of $1.3 billion. Unaffiliated independents had sales of $3.5 billion. Specialty and all other food stores had sales of $1.9 billion. The total of the above 1 o sales is $28.7 billion. If we reassemble these figures based on purchasing power, then chain supermarkets, chain convenience stores, chain department stores, co-operative stores, and voluntary group independents can be bound together. The total of these groups is $24. billion which is 83.7% of the total food sales in 1982. Chain supermarkets alone have 48.9% of the total food sales. Table 2-6 represents the sales and the earnings of major distributors for Canada in the 1982/1983 financial year. Loblow Companies Ltd. shows the highest figures followed by Provigo Inc., Steingerg's Inc. and Canada Safeway Ltd. However, the earnings of Canada Safeway Ltd. show the highest number followed by Dominion Stores Ltd. and Provigo Ltd. There are two things to be noticed from these figures: a) the ranks of sales volume and 12 "Chain Share:52% or 47.5%? Answer:Yes!," Canadian Grocer, Maclean Hunter, Toronto, Vol.97 No.6, June, 1983, p.5. 28 earnings are very volatile year by year among the biggest 29 Table 2-5 Food Store Sales Trend, Canada Years Total-Sales Chains Independent* ($M) changes(%) ($M) % in total ($M) % in total 1 971 7,260 + 6.0 3,868 53.3 3,392 46.7 1 972 7,721 6.4 4,410 57. 1 3,311 42.9 1 973 8,595 11.3 4,997 58. 1 3,598 41.9 1 974 10,263 19.4 6, 1 36 59.8 4, 1 27 40.2 1975 1 1 ,984 16.7 7,110 59.3 4,874 40.7 1 976 13,156 9.8 7,809 59.4 5, 346 40.6 1 977 14,371 9.2 8,639 60. 1 5,732 39.9 1 978 16,253 13.1 9,792 60.2 6,462 39.8 1 979 18,192 11.9 10,996 60.4 7, 196 39.6 1980 20,204 11.1 12,043 59.6 8,161 40.4 1 981 22,858 13.1 13,594 59.5 9,267 40.5 1 982 24,844 8.7 14,580 58.7 10,264 41.3 1983 25,799 3.8 14,887 57.7 10,912 42.3 1 984 27,606 7.0 15,790 57.2 11,815 42.8 Source: "A Touch & Flat Year; Chain Share Down," Canadian Grocer, Vol.98, No.2, Maclean Hunter Ltd, Toronto, February, 1984, p.75. Explanations: * Includes voluntary groups and unaffiliated independents - Included: Sales from grocery stores, supermarkets, and convenience stores. ' - Not included: Food sales through department stores, specialty stores (bakeries, butchers etc.), or co-op stores. - Chain Store: Four or more stores under single ownership. And includes convenience chain stores. - Voluntary Groups: Independents operating in major or secondary wholesale-sponsored group programs. 30 Table 2-6 Sales of Major Distributors, * Canada (1982/1983) Distributors Sales Earnings ($000) ($000) A & B Canada, Ltd. 946,333 -Alberta Grocers Wholesale Ltd. 117,962 993 Becker Milk Co. Ltd. 283 , 1 1 3 5,033 Canada Safeway Ltd.(Cons.) 3,300,252 68,539 Dominion Stores Ltd. 2,348,352 27,013 Federated Co-operatives Ltd 1,371,808 -Kelly, Douglas & Co. 1,713,815 16,691 Loblaw Companies Ltd.(Cda.) 3,847,255 -The Oshawa Group Ltd. 2,118,285 16,912 Provigo Inc. 3,682,954 24,146 Sobeys Stores Ltd. 555,251 5,524 Steinberg's Inc. 3,352,851 12,079 Westfair Foods Ltd. 1 ,045,621 11,529 Source: "Distributor Results at a Glance," Canadian Grocer, Vol.98, No.1, Maclean Hunter Ltd., Toronto,January, 1984, p.31. * Includes total sales of all items sold 31 Table 2-7 Provincial Market Shares-The Top 5 Provinces Jan -April Jan-Dec Provinces Jan-April Jan-Dec 1 984 1983 1 984 1983 Retailers Shares % Shares % Retailers Shares % Shares % Atlant ic Quebec Sobeys 1 9 20 Provigo Group 25 25 Dominion 1 4 1 6 Metro/Richel ieu 23 22 Co-op 1 4 1 5 Steinberg 18 19 Save Easy 12 9 Co-op 1 1 10 IGA 9 9 IGA 9 8 Total 68 69 Total 86 84 -Sobeys includes LoFood -Steinberg includes Jadis -Dominion includes BforL/Thrift Ontario Manitoba Dominion 1 3 16 Safeway 38 34 Loblaws 1 5 1 4 Super Valu 1 6 1 4 Steinberg/MFM 1 3 1 3 Co-op 9 1 0 A & P 1 2 1 2 IGA 7 7 IGA 1 1 10 Dominion 1 4 Total 64 65 Total 71 69 -Dominion includes BforL/Thrift/ -Safeway includes Food Barn Mr. Grocer -Super Valu price wars -Loblaw includes No Frills/ Superstore -Steinberg includes Basic/Valdi Saskatchewan Alberta Safeway 25 24 Safeway 42 41 Co-op 28 28 Co-op 21 22 OK Economy 1 1 1 1 IGA 8 8 Super Valu 6 5 Woodwards 6 6 IGA 4 5 Super Valu 2 2 Total 74 73 Total 79 79 -Safeway includes Food Barn -Safeway includes Food Barn British Columbia Safeway 30 31 Overwa i tea 18 1 5 Super Valu 10 1 0 Woodwards 10 1 0 Co-op 4 4 Total 72 70 -Safeway includes Food Barn -Oeverwaitea includes Save on Foods and SonF+3 Source: "Major Players," Canadian Grocer, Vol.98 No.9, Maclean Hunter, September, 1984, p.16. - This study was performed by International Surveys Ltd. 32 distributors, b) the rank of sales volume among distributors is not always the same as the rank of the sales volume among the distributors for provinces, suggesting that the food retail business is more likely local-oriented. Table 2-7 shows the top five retailers in each province for 1983 and 1984, the average weekly market share for each retailer. It explains local-oriented characteristics. In general, the 1 3 market shares of the five retailers are very high. Quebec shows the highest concentration ratio by top five retailers while Ontario shows the lowest ratio. The top five retailers are different in each province. For example, Safeway, Overwaitea, Super Valu, Woodwards, and Co-op are the five top retailers in B.C. while Provigo, Metro, Steiberg, Co-op, and IGA are the top five retailers in Quebec. Also, first place varies in provinces: Sobeys in the Atlantic Provinces, Provigo in Quebec, Dominion in Ontario, and Safeway in Manitoba, Alberta and B.C.. 2.5 Retailers' Power In this section, the interaction between processors and retailers in Canadian food industries will be discussed. The influence of the buyer side structure on pricing of the Canadian processed foods has generally been assumed neutral in previous studies. That is, the retail stage is an atomistic structure, and 13 . . . Entry barriers would be considered moderate to high in most metropolitan markets for multi-unit organizations. The scarcity of good store sites, economies of scale, and the enterprise differentiation of established retail firms are the major deterrents to new multi-firm entry. 33 consequently its bargaining power is nil in the pricing mechanism. It is, however, clear that this kind of perception ignores the power of buyers in the food industry market. This study claims that retailers do affect the market performance of the food industries because they possess market power in several ways which are not recognized in previous studies. In section 2.3 and 2.4, we analyzed the structure of processors and retailers, respectively. As we discussed in the last sections, the Canadian food retail channels are well concentrated. When such strong concentrations are exhibited one retailers' behaviour would be an influence in the pricing mechanism. Although each processor in each food processing industry determines the appropriate output prices because of non-perfect-competitive markets, the pricing is not solely under the processor's control. The retailers do participate in pricing and tend to control (reduce) the output price as the retailers' market structure is concentrated. The following partially explains and confirms this situation. David Leighton, chairman of Nabisco Brands, stated that 'The bulk of business now goes through six large buying groups. 1 4 Pressure for productivity is enormous.' 'Thus we have seen retailers take charge of the marketplace and dominate 1 4 "The 'Monster' of Marketing," Canadian Grocer, Vol.98, No.3, Maclean Hunter, Toronto, March, 1984, p.46. 34 merchandising.' Jean Rene Halde, who was the president of Groupe des Epiciers Unis Metro-Richelieu, stated that 'And we as retailers and you as manufacturers would benefit from sharing some perception...' at the annual Grocery Products Manufacturers of Canada convention.1^ Retailers exert their pricing power in several ways. First, and probably most important, is volume purchasing. The concentration in food distribution systems is very significant. As shown in Table 2-7, the top five retailers in each province are responsible for at least 60% of total food sales. Furthermore, as it was explained, when we readjust the national market shares according to the retailers with concentrated purchasing power, more than 80% of food is sold through these stores. From this standpoint, the retailers can be considered as oligopsony. That is, a retailer's purchasing volume for the' product is so large that processors cannot ignore retailer's purchases. Second, markets have become tighter. Over the past two decades Canadians have been spending a decreasing proportion of their disposable incomes in food stores. In 1983 Canadians spent only 9.6% of their after-tax dollars in the retail food trade - the lowest figure ever. Furthermore, the population increase is moderate. Food consumption will not expand faster than population 1 5 "The Dream of The Brand Comeback," Canadian Grocer, Vol.98, No.5, Maclean Hunter, Toronto, May 1984, p.5. 16 "Retailers/suppliers Needn't Always War," Canadian Grocer, Maclean Hunter, Toronto, Vol.97 No.6, June, 1983, p.5. 35 growth. Population has increased at approximately 1.1% annually for the past ten years. In order to gain greater than 1.1% increase in tonnage of foods, a redistribution of what is eaten by consumers is required among the processors. That is, the food processing industries have become relatively tighter. This tighter market has helped the retailers to gain power against the processors. Third, the food processors have responded to the increasing demands of 'value' shoppers. We demonstrate that the characteristics of consumer's buying behaviour varies in ways that fundamentally affect the nature of industry competition and pricing behaviour. These characteristics become partially embodied in the structure of the retail distribution system and create bargaining power for some retailers against the processors. In most cases product differentiation is a result of the manufacturers activities (such as advertising) engaged to raise market shares, to compete with rivals, to prohibit new entries, and to enjoy comfortable profits. However, it shouldn't be ignored that product differentiation is also a result of the process of consumers' choice which depends on the attributes of alternative products and their prices. Products have several "attributes" of value to the consumers: brand image, taste, ingredients, 'style of cooking, degree of necessity, etc., from which consumers make their purchase decisions. The consumers seek information about the various product 36 attributes and prices from several sources: manufacturers' advertising, previous experience, neighbors' words, and retailers' flyers. The consumers are then attracted to certain retailers with value shopping. Retailers compete with each other to attract more local consumers by providing cheaper prices, better selections, good quality, and good services. Consumers are interested in prices, patronizing retailers that can offer better prices. Therefore, retailers have to try to offer better prices to the consumers. One way to achieve this objective is to provide the consumers with generic or private label brands. The generic products are offered at cheaper prices to the consumers. These generic or bulk products are specially ordered from the processors which usually manufacture the same products with their own brand names. When the retailers order the generic products, they negotiate with the processors about a specific quantity and price. That is, ordering the private label brands helps the retailer to exert the oligopsony purchasing power. Private label brands influence price indirectly through their use as leverage in negotiating prices at the wholesale level. Regardless of whether retailers carry private label products, there is the constant threat of such held by the retailers. This constant threat may be enough to influence the price of the manufacturer's brand products. It is quite common that a price bargaining session is held between the representative of a large retail outlet and a 37 food processor who is fully aware of the consequences of the loss of that particular private label contract. Certainly, the use of private labels as a price competitive device has added a new dimension to price negotiations. This may represent a shift in bargaining power whereby the retailers assume the dominant position. Therefore, ordering the differentiated product induced by consumer behaviour will provide an additional basis for retailers' power. This argument seems to be well supported by the following statements. 1 7 Retailers are still pushing non-brands harder than brands. And Heinz President Tom Smyth told the Grocery products Manufacturers of Canada that the rest of the 1980s *will witness a massive move toward national name brands,' he was, perhaps,, speaking wishfully from the manufacturers' point of view It is clear that for some time now the great pendulum of trade power has been firmly stuck to the side favoring retailers. Thus we have seen retailers take charge of the marketplace and dominate merchandising. ... But at the same time the retailers have also dominated manufacturers, often dictating not only when to jump but also how high. It is this last area,..., that has led to a less than satisfactory distributor/supplier environment. The days of the consumer experimentation with bulk foods may be waning, but it is certainly no over. The single, most-important decline in bulk sales has been because of a conscious retailer decision to cut back. But generics remain with us and they will do so through the rest of the decade,... None of this means national brands will suddenly blast generics and private labels out of the food store. Far from it. But it is time for that pendulum to swing back a little closer to middle, to that neutral ground wherein both manufacturers and distributors can more cooperatively go about meeting their respective goals, which in both cases, is selling product to the consuming public. Manufacturers and distributors recently have had great difficulty getting their philosophies together for their "Competitive Weapons Used During 1982," Canadian Grocer, Maclean Hunter, Toronto, Vol.97 No.7, July, 1983, p.16. 38 common good. Smyth touched on the hope for the future when he said the consumer return to national brands 'will be fuelled by the demand for quality and value in both old and new products coupled by the recognition by the grocery trade that national ogme brands meet every test of the demanding consumer.' Fourth, the retailers can influence the sales volume of a certain brand. Since each retailer can withhold his selling efforts for a particular brand and in fact can influence consumers to purchase another brand, the retailing sector for the brand is in a position to bargain away profits from the processors. As the final link in the marketing channel, just getting self space may be an issue. The retailers have the control over shelf space and brand merchandising. The retailer can place favour on a specific brand thereby enticing greater sales of that brand. The retailers allow more shelf space for the brand and locate the brand at the best spot for exposure to consumers, e.g. near the check out counters and at the corner of shelves. For products sold through food stores, low price and frequent purchase of the product reduces the desire of the consumer to expend effort on comparison and search. The consumer demands a nearby retail outlet, is willing to shop around in the store, and needs no sales help, thus the consumer considers the food purchase relatively unimportant. Since the food purchase is not perceived to be important, the consumer is willing to rely on less objective criteria accordingly. Relatively more objective 1 p "The Dream of The Brand Comeback," Canadian Grocer, Vol.98, No.5, Maclean Hunter, Toronto, May 1984, p.5. 39 arid costly information sources, such as sales assistance by the retailer, direct shopping and comparison, are not utilized, unlike other consumer goods. Because of this characteristic, the consumer picks up the brand which is well exposed to him unless he is interested in a particular brand reflecting his strong image of that brand due to the processor's advertising and 1 9 personal preference. That is, the location and the amount of shelf space provided for a product have significant impacts on the sales of the brand. One study conducted by Burgoyne, Inc. of Cincinnati, Ohio, revealed that promotional labeling moved 44% more merchandise in conventional price marking stores, and that the promotional labeling increased item movement by 58% in non-pricing (scanning) stores. The corner locations at the end of aisles are an . . 20 extremely effective merchandising tool. Therefore, the processors try to display their products at the best place. The retailer must be persuaded to promote the product aggressively and to provide effective display. Again, this situation provides the retailer with more bargaining power. In order to occupy the best location the processors may provide discounted-price (as a promotion rebate) to the retailer. This will provide another form of bargaining power. 1 9 If there is a very strong demand by consumers for the particular product(or brand), the retailer is forced to meet the demand and to order the product. This situation will make the retailer's bargaining power weaker. 20 . "Promo Labeling Hikes Sales," Canadian Grocer, Vol.97 No.10, October, 1983, p.6. 40 Fifth, in our food industry model encompassing both processor and retail stages, it is obvious that entry barriers extend beyond the traditional barriers of advertising, economies of scale, and capital requirements. For the newly differentiated products or the innovated products stimulated by the consumers' 21 demands and/or from the reaction of competitors, gaining accessibility to the distribution channel is as important as overcoming the barriers to entry into the production stage. The processor's selling strategy may involve advertising, but must also include efforts to encourage retailers to stock and push the product. Although advertising by the processor is a relatively good measure of product-differentiation for the products sold through the retailers, account should also be taken of selling efforts by the processor directed toward the retailer. These efforts are very important in gaining support from retail channels in differentiating the product as well as influencing the division of profits between the processing and retail stages. Although advertising, as a partial substitute for processors' selling activity towards the retailer, is very effective, the food processors selling products through the food retail outlets must gain accessability to numerous retail outlets to achieve scale economies associated with dense coverage of a given geographic area because the consumer is unwilling to travel large distances to purchase the product. Where the processor has a brand name image established, the efforts needed to convince 21 Seven new food products are introduced a day. 41 retailers to stock the product is minimal. Furthermore, the processor need not convince the retailer to promote the sale of the product since the influence the retailer has over the consumer is limited. Hence the processor has less need to promote his product with retailers by means of processors' salesmen or intermediaries such as wholesalers. Where the processor is unable to develop a brand image through advertising, however, the retailer becomes very powerful, and the processor's ability to achieve product, differentiation in the eyes of the consumer is severely limited. Entry into retail channels becomes extremely difficult for an efficient density of market coverage. Although processors' salesmen (or paid substitutes) may aid the undifferentiated processor in gaining entry into retail outlets, their efforts will yield him little or no product differentiation because the retailer can do little to influence the consumer's buying decision. For foods, therefore, direct advertising to the consumer is the dominant form of selling effort by the processor. As well as leading to product differentiation in the eyes of the consumer, advertising determines the processor's power vis-a-vis the retailer and his ease of access to distribution. Where retailer power is high, the processor's rate of return will be bargained 22 down, ceteris paribus. 22 Alternate means of product differentiation available to the processor are likely to be ineffective. As a result, advertising is a relatively good measure of product differentiation for products sold through the food outlets. 42 Sixth, the consumers can and will very frequently change brands. Foods are consumption goods which only last, at most, a couple of months at home. That is, the frequency of the purchase of a food is relatively higher than with durable goods. Because of this fact, there is a greater chance of market share instability for food brands. It is very volatile, changing weekly, monthly, and yearly, and being heavily influenced by the local and regional situation. Also, as the study shows, the consumers are very sensitive to price. A study shows that 81% of consumers purchased two or more brands within a study period (7 2 3 weeks). This kind of consumer behaviour makes the processor more sensitive. That is, there are more chances that the processor will easily lose its current . market share if the processor does not maintain pricing and advertising very effectively. Because of this, the processor tries to cut the price to sustain the market share. This processor's behaviour frequently creates price cutting wars which, in turn, increases retailer strength at the bargaining table. In the above ways, relative power of retail distributors over processors is inevitable, and will exert influence on the 24 distribution of industry profits between stages. Consequently, the level of profits of the processing and 23 . "Consumers Respond to Pricing Strategies," Canadian Grocer, Vol.98, No.5, Maclean Hunter, May 1984, p.8. The studies was done by the A.C.Nielsen of Canada ltd. 2^ The after-tax profit of food and beverage manufacturers for the year of 1987 is 2.69% of sales, compared to 4.07% for other sectors of Canadian manufacturing. 43 retail stages depends simultaneously on the structure of the processing stage, the structure of the retail stage and the interaction between them rather than, as is commonly assumed, the structure of the processing stage alone. 44 CHAPTER 3 ECONOMIC THEORIES 3.1 Introduction General market structural characteristics of Canadian food processing industries, retail outlets, and the pricing behaviour between processors and retail stores have been discussed in chapter 2. As it was explained, the characteristics can be summarized as follows: (1) oligopoly or monopoly among the processors (2) oligopsony and monopolistic-competition among the retailers. The choice of theories has important implications. The expected pattern of price-wage interactions, questions of bias and identification of the price equation, questions of import competition, aspects of demand and supply side, and use of the price equation for forecasting should all be analyzed with 25 respect to particular hypotheses concerning pricing behaviour. Based on the characteristics of Canadian food processing industries, as discussed in chapter 2, we can now develop a econometric model that reflects these characteristics. The theoretical discussion presented in this chapter will be divided into four parts. Previous studies will be reviewed in the first section. In the second section, an economic theory reflecting the oligopoly market structure of the processing sector (Markup Pricing Theory) will be discussed. In the third 25 See Laden (1970) for elaborate discussions. 45 part, another economic theory reflecting the oligopsony and monopolistic-competition in the retail sector (Bilateral Monopoly Theory) will be considered. In the fourth part, for the industries that have international trade competition, a relevant import competition theory will be discussed. 3.2 Markup Pricing Theory In chapter 2, the characteristics of each Canadian food processing industry were analyzed. When we considered the supply side, it was concluded that each industry, in general, was characterized as oligopolistic. Even if it is not quite true for certain industries, we can at least assume that each firm in an industry is taking extra profits above the costs since the firm is not facing a perfect competition (even in the case of the bakery industries). Because of this oligopolistic characteristic in the supply side of each industry, a markup pricing model (which will be developed in this section and which allows for a firm to enjoy abnormal profits) is the model that is deemed appropriate for each Canadian food processing industry. It is assumed that the input markets which the manufacturers are facing are perfectly competitive, that is, the input prices are not affected by a manufacturer's activity. This means that in our model all input cost variables are considered as exogenous to the manufacturers. A monopolistic or oligopolistic firm has the power to take some residual profits. That is, the firm is selling the product 46 at a price which includes costs and above-economic profits. Therefore, the total value of the outputs can be described as an identity (Hazledine and Luck (1980)) (3.2.1) Y = m*C where Y is the total value of outputs, m is the mark-up, and C is the total costs of the inputs to produce the total outputs. The magnitude of m is equal to or greater than one in a normal situation. In the perfect competition market in which no economic profit is allowed, the magnitude of m is one so that the total value for the total outputs are equal to the total costs. If the industry, on the other hand, is allowed to take some above-economic-profits for any reason, e.g., oligopoly, the magnitude is greater than one. In this identity Y can be explained in terms of the unit price and the quantity of output. (3.2.2) Y = P *X X where Px is the unit price of the output and X is the number of the output. In the identity (3.2.1), the total costs(C) can be also divided into several inputs, and can be expressed in terms of input unit price and the number of input. We can basically consider material, labour, fuel, and capital as the inputs. The total costs can be then expressed as (3.2.3) C = P *M + P *L + P *F + P *K mirk where P , P^, P^, and Pk are prices of material, labour, fuel, and capital, and M, L, F, and K are the number of units of 47 material, labour, fuel, and capital, respectively. Inserting (3.2.2) and (3.2.3) into the identity (3.2.1), and dividing' through by X gives us M L F K (3.2.4) P = m*P * - + m*P *- + m*P*- + m*P .*-x mX 1 X E X KX This new equation expresses the output price as the weighted sum of the of input prices.. Because we want to express this equation in terms of the rates of changes for the prices rather than the actual price levels, we 2 6 differentiate this equation with respect to time(t). After differentiating the equation, and by dividing through by P , we get dP dm P M dPm mM d(M/X) (3.2.5) ----- = + + *mP dtP dt P X dt P X dt xxx dm P,L dP, mL d(L/X) + __ _±_ + __± + *mP dt P X dt P X dt x x dm PfF dPf mF d(F/X) + __ _i_ + __i + *mP dt P X dt P X dt xx dm P,K dP. mK d(K/X) + 1S_ + ___ + *mP dt P X + dt P X dt X X Rearranging and summing (3.2.5) gives us dP dm (3.2.6) ----- = dtP dtm 2 6 This is a typical method of transforming the variables into the form of rate of change in the inflationary models or pricing models. Lipsey and Parkin (1970), Goldstein (1974), and Hazledine and Luck (1980) have employed this method. 48 mP M dP mP,L dP, mP,F dP- mP, K dP, __rn_ __m_ __1_ + + __k P X dtP P X dtP, P X dtPf P X dtP, x . m x 1 x fx \ mP M d(M/X) mP,L d(L/X) mPfF d(F/X) + ---- + --±- + __£_ P X dt(M/X) P X dt(L/X) P X dt(F/X) XXX mP.K d(K/X) + PvX dt(K/X) To simplify the equation, write V for the rates of changes, dV/dtV, for some variable V, then (3.2.6) can be rewritten as (3.2.7) Px = m + amPm + a^. + afPf + afcPk * • • . • + am(M/X) + a1(L/X) + af(F/X) + ak(K/X) where a = mP M/P X, a, = mP,L/P X, a. = mP,F/P X, and a, = m mxl lx t rx K mPRK/PkK. This equation now shows that the rate of change of the output price can be explained in three parts - changes in the markup, changes in the input prices, and changes in the productivity of the inputs. The rate of changes in the mark-up • • • • • • is expressed by m. The Px, Pm, Pi P^, and Pk represent the rate of change in the output price, material price, labour price, fuel price, and capital price, respectively. And these changes in the input prices are weighted by the corresponding input costs share. The final three terms in • • • • (3.2.7), (M/X), (L/X), (F/X), and (K/X), represent the rate of changes in productivity of each input. Each term is also weighted by the corresponding input costs share. When we consider the coefficients, a , a,, a., and a, , these m 1 t k have certain characteristics. These coefficients show the 49 multiplication of the mark-up and the share of the total cost of a corresponding input in the total output sales. In other words, am represents the share of material costs in the total costs since a = mP M/P X = mP M/Y = mP M/mC = P M/C. m mxm  m The signs of all the input price coefficients are expected to be positive. When the prices of the inputs are increased the output price is expected to be increased. When the productivity of an input increases the output price should be decreased. Kwack (1977) says that a productivity (e.g., labour productivity (X/L)) should carry a negative coefficient for two reasons: (a) with employment fixed, an increase in real output will reduce unit cost; and (b) with the flow of new orders (demand) fixed, an increase in real output will reduce the level of excess demand. However, it should be noticed that, in this equation, the sign of each coefficient of the productivity variable will be positive because the productivity variable- is expressed in terms of the changes in the input over the output not the changes in the output over the input. There are special characteristics of the coefficients - a , a-^, a^, and a^. One is that the sum of these coefficients should be one. This means that if the changes in all input prices impact on the output prices instantly and if the markup and the productivity of each input are constant, then the changes in the output prices should be fully explained only by the changes in the input prices. Another characteristics is that the coefficient of an input 50 price variable should have the same sign and the same magnitude with the coefficient of the corresponding input productivity variable in the equation. What this characteristic means is that, given the markup constant, the actual increases in output price may not occur even if the input prices are increased unless the magnitude of the proportional increases in the input prices exceed the proportional increases in the corresponding input 27 productivity. Now, in applying the markup pricing equation for the empirical test for the Canadian processed food industries, we need to modify the equation. In the equation (3.2.7), it is possible to measure all the variables in the empirical test except the markup (m). The changes in the markup cannot be measured. So it is necessary to determine some practical variables to substitute for changes in the markup. There are two methods for dealing with the markup. The simplest is to assume the markup to be constant. That is, the company does not change the markup throughout the period (Eckstein and Fromm (1968), Lipsey and Parkin (1970), Ball and Duffy (1972), and Goldstein (1974)). However, this assumption is not practically acceptable. As we discussed in chapter 2.3, in general, each food industry faces oligopoly in supply side and oligopsony in buyer side. Once the market structure is in this category, there are many market 27 For the input productivity variables in our equation, the proportional "decrease" would be the appropriate expression. 51 factors that can affect the markup; e.g., price wars among the processors, negotiations between the sellers and buyers, import competitions, and so on. Therefore, the markup will be changed by these various market forces. That is, the markup should be regarded as a non-constant term (McFetridge (1973), DeRosa and Goldstein (1981), and Hazledine and Luck (1980)). Therefore, it is necessary to find some variables which can measure the fluctuations of the markup. There are several variables used as substitution for the markup variable. The first one is use of excess demand. The second one is application of the Bilateral Monopoly Theory. And the third one is use of import competitions. 3.3 Excess Demand as Measurement of Markup Fluctuation The demand pressure variable, which affects the markup level, has been tried with various concepts in previous studies. Researchers have identified a potentially important market forces - shift in demand. The economic theory behind this concept is as follows. When there is excess demand, the demand curve which the firms face will shift out. Due to this outward shift of the demand curve, the elasticity of demand becomes smaller at that point. This decrease in the demand elasticity will induce monopolistic (oligopolistic) firms to increase the output price. In order to measure the level of the shift in demand, several alternative variables have been tried in the previous studies. 52 These variables are the unfilled orders-shipments, inventory-sales ratio, or capacity utilization ratios (Schultze and Tryon (1965), Eckstein and Fromm (1968), Laden (1972), Tobin (1972), McFetridge (1973), Popkin (19774), Hazledine (1980), and DeRosa and Goldstein (1981)). In some cases, unemployment rate (Kuh (1967), Spif'aller (1971), Kwack (1975), Kwack (1977)) and substitute prices and income (Laden (1972)) are used to catch changes in excess demand. One of the most common variables is the inventory-sales ratio. McFetridge developed and used this concept, in his 1973 study of the pricing model for Canadian manufacturers. The justification for use of this measurement is that when there is excess demand, the volume of the inventory would be down. And thus it is expected that the negative relationship with the price changes. Hazledine and Luck (1980), in their study of the pricing model for the Canadian processed foods industries, used a modified 2 8 version of this measurement after correcting for defects. They They state "A difficulty with the interpretation of inventory fluctuations in food processing industries is that there are often important seasonal variability in both demand and supply, which can be reasonably well predicted, and which lead to the use of variations in inventories as a deliberate instrument for matching production and demand cycles. Thus planned variations may obscure the effect of unanticipated-demand-induced fluctuations" (Hazledine, T. and Luck, D.: Explaining Quarterly  Changes in Prices of Twenty-One Canadian Processed Food Products,  1971 - 1977, Unpublished, October 1980, pp.13-15). 53 29 incorporated the estimated trend-shipment variable with the excess demand variable, expecting that the sign of the coefficient would be negative. Another common variable used for excess demand is capacity utilization (Hazledine and Luck 1980)). The ratio of actual to trend output is used to measure the shift in demand. Justification for use of this measurement is that when there is more demand, the firm should produce more product given the production capacity, resulting in a positive relationship with price changes. In addition to the above variables, ratio of unfilled-order to shipments and trend shipments (Hazledine and Luck (1980)) are ? n tried. Kuh (1967), Spita"ller (1971), Kwack (1975), Kwack (1977)) use unemployment rate to substitute prices. Income is also replaced by the unemployment rate (Laden (1972)). They expect, in general, that the signs of the coefficients for the variables of unfilled-order to shipment ratio, capacity utilization ratio, and personal income are positive, and that the signs of the coefficients in the variables of inventory to shipment ratio and unemployment rate are negative. However, the output prices may not be necessarily raised with 29 Using the estimated trend shipment will solve the problem that the fluctuation in shipments may result from seasonal productivity in the industries. 30 In some aggregate pricing models (Goldstein(1974) and Lipsey and Parkin(1970)), the "excess demand" variables were not used because increases in demand will cause increase in labour demand, thus increase in wages which is already incorporated as an input variable in the model. 54 increases in demand for the following reasons: 1) when firms are operating at less than full capacity, increases in output production will occur without changes in output price (Murray and Ginman (1976, p.75)); 2) if demand slackens, firms will cut production rather than output prices; 3) the rigidity in price may be due to such factors as a noncompetitive industry, (Canadian food processors are facing this, where firms fear the reaction of their competitors to any price reduction); 4) in an expending market a firm cannot raise prices until capacity has been reached in other firms, as this will price it out of the market. Kirman and Sobel (1974) hypothesize that a firm's profit maximizing inventory strategy causes the firm to react differently to a demand change depending on whether the firm perceives the change to be permanent or transitory. The firm changes its output in response to any demand change, permanent or transitory, but it changes its price only in response to permanent demand changes. Kawasaki et al. (1983, p.607) support this proposition, using German data. They concluded that firms tend to change both price and output in response to a permanent (perceived by the firms) change in demand, but only output in response to a transitory change. Also, if there are economies of scale, an increase in demand may not necessarily increase the output price. When there is more demand, the firm will produce more. The increases in production will result in lower unit cost because of the economies of scale. 55 The variables discussed above have been developed based on the fact that the consumers and/or the retailers (buyers) simply take the price and the quantity that are set up by the oligopolistic firms. However, as we discussed in chapter 2.5, the Canadian food industries are categorized as a bilateral monopoly market structure because the buyers can exert power to control the output price. Therefore, the direct use of the above variables may not be appropriate for the Canadian food processing industries. Rather, shipments are an appropriate variable to 31 measure the markup fluctuation. This is explained in the next sect ion. 3.4 Application of Bilateral Monopoly Theory for Mark-up  3.4.1 Justification for Use of Bilateral Monopoly Theory The concept that the use of shipments as an appropriate measurement for the markup of the Canadian processed food industries is derived from the application of the Bilateral Monopoly Theory. Application of the Bilateral Monopoly Theory to the Canadian food processing industries, even though there is more than one seller (processors) and there is more than one buyer (retailers) in each food industry, is reasonable because the supply side of 31 In fact, the shipment variable alone hasn't been used as the measurement of the demand pressure because of defects. When the positive sign of this variable is found in the equation, it could mean that the increase in shipments may not be because of the increase in excess demand but because of the expansion of the industry. But if it turns out as negative sign, this argument may not be valid. 56 each industry is oligopolistic and the demand side is oligopsonistic. Furthermore, the food retailers (and/or the wholesalers) are known to be typically competitive-monopolistic. Because they are oligopolistic, firms in the industry have at least some power to set output price similar to the monopoly in the bilateral monopoly model. On the other hand, oligopsony in the demand side means that they also have at least some capacity to play a role in the pricing mechanism as does monopsony in the Bilateral Monopoly Model. It is also assumed that the monopsonistic firm in the Bilateral Monopoly Model is a monopoly in its retail market so that it is facing a downward-sloped demand curve. The competitive-monopoly in the food retailers' output market indicates that they are facing a downward-sloped demand curve similar to the monopoly situation in the Bilateral Monopoly Theory. This is again consistent with the monopoly in the buyers' output market in the theory. That is, we can apply 32 the Bilateral Monopoly Theory for the Canadian food industries. In fact, the application of the theory for the oligopoly and oligopsony case weakens a criticism that there might be a failure in the bargaining process under the bilateral monopoly pricing mechanism. The bargaining process is a fight for profit. The bargain ends at a certain point where each party's willingness to risk is reflected. Criticism of the procedure claims that the two parties might end up without an agreement because they cannot get what they want at the bargaining table. But chances of this failure will be reduced in the case of the oligopoly and oligopsony because there is more than one firm on each side. A party might (or should) take the other party's offer, in the case of oligopsony and oligopoly, which would not be taken by the party if there were only one buyer and one seller. This is because he knows that, by rejecting the other party's offer, the profit he rejected might be taken by the rival. That is, one party's willingness to risk, in the case of oligopoly and oligopsony, will be reduced because more than one 57 Assuming the above conditions are acceptable, we can borrow the concept of the bargaining process in the Bilateral Monopoly Theory, and use it for the food pricing behaviour of the Canadian food processing industries. 33 3.4.2 Bilateral Monopoly Theory Bilateral monopoly is defined as a market in which one seller of an input confronts one buyer (see Fig. 3-1). The seller (processor, S) produces inputs(X) which is required by the buyer (retailer, B). S produces X at the cost of C(X)g and the cost function gives rise to the average cost, AC(X)g, and the marginal cost curve, MC(X)g. The buyer uses the input(X) to produce his output(Q) subject to the production function Q = f(X). If the market for Q is a monopoly situation, i.e., the demand curve is P = P(Q), then the total revenue, for the retailer, from the sale of Q is R(Q)b = Pq*Q = P(Q)*Q = P{f(X)}*f(X) = R{f(X)>b. From R{f(X)}, , the marginal revenue product of X, MRP , and the 34 average revenue product of X, ARP , are derived. The profits of X the buyer (B) and the seller (S) are: firm is participating. 3 3 This theory is based on the Gravelle and Rees (1981, pp.393-400): Microeconomics, Essex, UK. And see Henderson and Quandt (1980): Microeconomic Theory, McGraw-Hill, for the mathematical approach. 34 MRP : dR{f(X)},/dx = R',*f' = MR *MP x b' b xx ARP : R{f(X)}./X = P *Q(X)/X = P *APv X J3 CJ CJ X 58 Figure 3-1 Bilateral Monopoly Theory X2 Xi X* x (3.4.1) IIb = R{f(X)}fa - Px*X (3.4.2) II = Pv*X - C(X)c where P , existing between P and P 0, the price of X. A A I X ^ Based on these equations, we can consider several possible behaviours between S and B. First, if the seller (S) regards P X as a parameter, monopsony, then S produces X at which MC(X) s 59 P , resulting MC(X) is the seller's supply curve. If the buyer X s (B) faces the marginal buyer cost curve, MC^, then the buyer will choose a1 at which MC^ = MRPx to maximize profits, resulting in Px1 and X1. The monopsony solution is then at Pxl and X1 where the buyer is off his demand curve, MRPx, for X, but where the seller is on his supply curve, MC(X)s< On the other hand, if the buyer regards P as a parameter and therefore wants to buy X where MRP = P , then the MRP curve is XX X the buyer's demand curve for X. And this MRPx curve also becomes the demand curve that the monopolistic seller(S) is facing. So the seller will have a marginal curve, MR(X)g, based on the seller's demand curve, MRP . This reflects the fact that P must X X fall if more X is to be sold. The monopoly solution is a2 at which MR(X)g = MC(X)g to maximize profits, resulting in Px2 and X2. At these Px2 and X2, the buyer is on his demand curve (MRPx) and but the seller is off his supply curve, MC(X)g. In reality, each party knows that the price (P ) can be affected by his own action, and thus does not regard the price as a parameter. Since neither party will treat Px as a parameter, the buyer will not be on his demand curve (MRP ) and the seller X will not be on his supply curve,' MC(X) . Therefore, neither of a1 nor a2 will be a solution point. Then, let us discuss the possible solution point of X and P . Now we assume that they know both R{f(X)}^ and C(X)g, and that they want to maximize the joint profits. Because we assume that each party wants to maximize profits as defined by (3.4.1) and 60 (3.4.2). 11^ and IIs will be maximized subject to an agreement. Mathemat ically, (3.4.3) II, + II = R{f(X)}K - P *X + P *X - C(X) D S D X X S = R{f(X)}, - C(X) D S The first order condition to maximize joint profits is then (3.4.4) d(ll. + II ) §_ = R. {f (X)} f - (X) _ c'(X) = 0 dX Since R'{f(X)}. f'(X) = MRP and C'(X) = MC(X) , the above O • X s s equation will be (3.4.5) MRP = MC(X) This means that the quantity of X traded at which both parties * will reach the maximum profits will be X . Once they set up the quantity, they will negotiate the price. As we can see in (3.4.5), the price (Px) does not affect the sum of total combined profits. Instead, the price determines the share of combined profits. Differentiating (3.4.1) and (3.4.2) * with respect to P , holding X constant, gives us X dll, (3.4.6) 5 = -X dP x dIIs (3.4.7) = X dP x As P rises, the buyer's profit falls at the rate of X but the X seller's profit increases at the rate of X. Thus the buyer wants the lowest price while the seller wants the highest price. The low limit and the high limit of the price are established by the fact that neither party shows up for negotiation, which results 61 in less profit than the profit which he could get without any negotiation with the other party. From the theory, we know that each party gets no profit if there is no negotiation. Thus there must exist a negotiation and thus there should be a non-zero profit. (3.4.8) II, = R{f(X)}, - P *X > 0 b b x (3.4.9) II = P *X - C(X) > 0 DA 5 From the above equations, (3.4.10) R{f(X)}K > P *X > C(X)c b x s Dividing (3.4.10) by X, and because R{f(X)}, = P *Q, we get M (3.4.11) P *APv > Pv > AC(X) (J A A O where AP is a average production of X in producing Q for the buyer and AC(X)g is a average cost to produce X for the seller. From (3.4.11), we can see that, by negotiating, the possible agreed price, P , should lie below (or on) the P *AP (= ARP ) X CJ X X curve and above(or on) the AC(X) . Therefore, the locus between s the points a3 and a4 in Fig. 3-1 will be the sets of P x and So far, we have assumed that both parties maximize 11^ and IIg. With this assumption, if there is a successful negotiation, * the negotiated quantity of X will be X . However, this assumption does not provide the price at which the trade will occur. All we can know is that the price will stay between AC(X) and ARP S X curves, meanwhile other types of market structures provides us with both the equilibrium price and the quantities of the 62 35 product. That is, this model is indeterminate. * In reality, however, the agreed price( P ) exists in the A. market. There are several theories to explain how the agreed price is reached. One method is through the process of bargaining, where the price is set according to negotiation (reflecting each party's economical and(or) non-economical conditions) between the two parties. One model for this solution is the "Zeuthen's model of the bargaining process". With the assumption of full information, that both parties * already know the quantity of X , thus that both parties will need * to negotiate only about the level of P , Zeuthen shows the X process of negotiations. The buyer offers P° and the seller offers Ps . Because X was X X already agreed, 11^ and IIg depend on only the level of P*x» b s Because P is lower than P , x x' (3.4.12) IIb(Pbx) > IIb(PSx) (3.4.13) HS(PSX) > Hs(Pbx) In these equations II^P x) is the buyer's profit with certainty if he takes PS . On the other hand, II, (P^ ) is the buyer's x b x profit"with uncertainty when the buyer insists P because the X seller might reject P . Therefore, the buyer will insist P only if 35 It does not mean that there is a market failure. The market still exists. The economist simply does not have an economical concept to explain the existence of the market; * * existence of a equilibrium P and the quantity of X . 63 (3.4.14) (1 - qs)Hb(Pbx) > Hb(PSx) where qg is the buyer's expected-probabi1ity that the seller might reject Pb . Rearranging (3.4.14), X II (Pb) - IIb(psx) (3.4.15) qc < -5—5__ __b___x__ s IIb(P° ) That is, only if qg is less than the right hand side, the buyer b will stay on P . Also when there is an equality between both sides, the buyer shows indifference between insisting Pb and X taking Ps . Therefore, the right hand side of (3.4.15) can be X regarded as a critical value of the buyer's determination not to s take P . As to the seller, we can similarly write X II (ps ) -II (pb ) (3.4.16) q. < — § —x —x-b II (Ps ) s x where q^ is the seller's expected-probability that the buyer • • s might reject P . With the same reasoning, we can regard the right hand side as the critical value of the seller's b determination no to take P . Zeuthen assumes that the party with less willingness to take the risk of no agreement as measured in (3.4.15) and (3.4.16) will lower or increase his offering price. The final price (P ) will be reached at the level at which the combined profit is divided equally between the two parties (see Gravelle and Rees, P.P. 396 - 398). Hence the agreed price can be written as * R{f(X*)K - C(X*)e (3.4.17) P = 5_ § X 2X 64 3.4.3 Modification of Bilateral Monopoly Theory It has been discussed in the previous section that the price in the bilateral model depends not only on the seller's behaviour but also on the buyer's behaviour. That is, the firm tends to keep the markup (m) as large as possible by insisting on a higher 3 6 price while the buyer tries to lower the price (markup). That is, the fluctuation of the markup could be explained by the Bilateral Monopoly Theory, or by the buyer's behaviour. However, we have not explained which variables can then be used to measure the seller's and the buyer's willingness to avoid the risks of confrontation. This will be assessed in this section by modifying the Bilateral Mmonopoly Theory. In the past section, given full information to both sides, it * was determined that the equilibrium quantity (X ) of a good in the bilateral monopoly model is agreed at the point at which the buyer's marginal revenue product (MRP ) equaled the seller's marginal cost curve (MC(X)s). And, provided X , the agreed price ie (P ) of X is given as X * R{f(X*)k + C(X*) p = 5 s X 2X 36 There is one interesting point with regard to the markup. In the Bilateral Monopoly Theory, we may not expect that the magnitude of the markup factor becomes below one. But, on the other hand, the markup factor in the oligopoly and oligopsony could go below one in certain situations. One of the situations is a severe price-cutting competition with rivals for some reason (e.g., market share). In this case, at least for a short period., the firms might be pricing with a markup which is less than one. 65 However, in reality, it is more likely that each does not have full information about the other's curve; the buyer doesn't know the seller's marginal cost curve (MC(X)g) and the seller doesn't know the buyer's marginal revenue product curve (MRP ). Because of this situation, there are chances that each party more likely tries to release the incorrect information about his curve to the other party because, by doing so, the party who is providing the false information may get more profit even though the efficient * choice of X is not reached. The buyer will infer, at the bargain table, that his marginal revenue product curve is lower than the real curve, hoping to lower the price in order to increase his profit, meanwhile the seller will try to inflate his marginal cost curve for the same reason. For example, let the buyer (B) know the seller's cost function (C(X)s) presented to him correctly whereas the seller does not know the true demand function for the buyer's outputs(Q). And the true demand curve is P = k - q{f(X)}. Then the true revenue function for the buyer is (3.4.3.1) R = P *Q = [k - q{f(X)}] f(X) = R(k, X) where k is the constant term in the demand curve. Now, because the seller doesn't know the true demand curve, the. buyer is providing the seller with the false demand curve, P' = k'-q{f(X)}, where the constant term (k') is lower than the true constant term (k). Then the reported buyer's revenue curve is (3.4.3.2) R* = P *Q = [k' - q{f(X)}] f(X) = R'(k', X) Given the false demand curve, both parties maximize the joint 66 profit, II = II' +11 = R'(k', X) - C(X) , where II', is the r bs s b buyer's reported profit. By following the same steps as (3.4.3), * (3.4.4), and (3.4.5), we get the agreed level of X' as given at the point where MRP' = MC(X) , where MRP' is from R'(k', X). So X S X the agreed level of X' is a function of k', X' = z(k'). And * * thus P is a function of X' (see Gravelle and Rees, pp398-X 400). Under full information the joint profit maximizing point (X ) is known by both parties before or at negotiations. The buyer does know the seller's marginal cost curve, MC(X) , and the seller does know the buyer's marginal revenue product curve (MRP ). Therefore, both parties have an idea of the profit maximizing point since they know the point at which MC(X)g = * MRPx. In this case the equilibrium point X can not be considered as a exogenous variable from the seller's point of view. * Therefore, the seller may not consider the quantity of X in his pricing model. However, as discussed above, when full information is not * given to both parties, the equilibrium point X' is exogenous from the seller's point of view. The true MRPx is not revealed to the seller. The MRP'x which is presented to the seller depends on the buyer's honesty. So the seller can not guess where the * location of X' is going to be at the bargaining table. * Furthermore, the location of X' becomes more ambiguous because the seller will be involved in negotiations, the results of which will be determined not only by his bargaining skill but also the 67 ] other party's bargaining skill. Therefore, in the imperfect information market which more likely exists in the Canadian food * industries, the seller can consider the quantity of X' as a exogenous variable in his pricing behaviour. * So far it has been suggested that the agreed quantity of X' under the imperfect information market could be a measurement which is one of factors that will alter the level of the seller's markup (m) in the markup pricing model. That is, we can substitute the markup (m) in the markup pricing model with shipment levels of outputs instead of using the ratio of inventory to shipments, capacity utilization, ratio of unfilled-order to shipment, and so on. The expected sign of the coefficient of shipment ~(X) will either be negative or positive. Increases in shipments will give more powerful price-negotiating capability to the retailers against the processors. When the retailers have more power at the bargaining table, the price is more likely to be reduced rather than raised. On the other hand, the output price will be increased with more shipment if the processors have tight capacity utilization. 3.5 Theories of International Trade  3.5.1 Introduction In addition to the excess demand variable discussed in the previous sections, import competition is another factor that influences markup fluctuations; another measurement of market 68 force changes (Schultze and Tryon (1965), Eckstein and Fromm (1968), Tobin (1972), McFetridge (1973), Popkin (1974), and Hazledine and Luck (1980)). For the industries that are facing import competition, the impact of trade can not be ignored. Domestic prices are expected to be influenced by changes in the exchange rate and in foreign prices. The proposition that imports provide a competitive constraint on the pricing (mark-up) behaviour of domestic firms has long been a part of the case for a trade policy. Even if output in the domestic industry is concentrated among a few producers and even if there is little countervailing power from either consumers or organized labour, actual and potential competition from imports are said to be sufficient to discourage monopolistic (oligopolistic) pricing behaviour. For if domestic producers consistently maintain a price above the import prices (landed prices), they face the same prospective loss of outputs and profits as if there were effective internal price competition. As such, the expectation is that an increase in import competition will produce a decrease in the rate of domestic producers' prices (profitability). Several studies utilize the import competition variable. In 37 . . . some cases, it is used as a independent variable in models in 37 . ... Esposito, L. and Esposito, F. F.: "Foreign Competition and Domestic Industry Profitability," Review of Economics and Statistics, Vol. 53, November 1971, pp.343-353. Jones, J. C, Laudadio, L., and Percy, M,:. "Market Structure and Profitability in Canadian Manufacturing Industry: Some Cross-section Results," Canadian Journal of Economics, Vol. 6, August 1973, pp.356-368. 69 which the industries' profitability (markup) is a dependent 38 variable. In the other cases, import competition is used as an independent variable in models in which the domestic price is the dependent variable. There are also studies that compare pricing behaviour before and after large changes (shift) in import 39 competition. Some Canadian food processing industries are competing with imported goods. As to these industries, the price (markup) of domestically produced Canadian processed foods will be influenced by import competition. This section presents a theoretical discussion of how import competition influences the firms' markup (price) level in the markup pricing model developed in the Marvel, H. P.: "Foreign Trade and Domestic Competition," Economic Inquiry, Vol. 18. January 1980, pp.103-122. Pugel, T. A.: "Foreign Trade and U.S. Market Performance," Journal of Industrial Economics, Vol. 29, December 1980, pp.119-129. McFetridge, D. C: 'Short-run price adjustment in the Canadian manufacturing sector.' Essays on Price Changes, Prices and Incomes Commission, Ottawa, 1973. Hazledine, T. and Luck, D.: Explaining Quarterly Changes in  Prices of Twenty-One Canadian Processed Food Products, 1971- 1977, Unpublished, October 1980. Clayman, J.: "Exploding the 'Inflationary' Myth," Viewpoint, Vol.9, Spring 1979, pp.1-5. Karikari, J. A.: "International Competitiveness and Industry Pricing in Canadian Manufacturing", Canadian Journal of Economics, XXI No. 2, May 1988, pp. 410 - 426. Goldstein, M.: "Downward Price Inflexibility, Ratchet Effects, and the Inflationary Impact of Import Price Changes: Some Empirical Evidence," International Monetary Fund Staff Papers, Vol.24, 1977, pp.569-612. 70 previous section. 3.5.2 Pricing Under Import Competition 3.5.2.1 Homogeneous Products A theory employing the price of the imported goods as the • • 40 import competitive measurement can be developed from a kinked demand schedule under a oligopolistic market condition. Now we assume that the Canadian firms (domestic firms) do not have any market power in the world market (U.S.) because the quantity produced by the domestic firms as a group is small in relation to 41 the total world production. And we assume that foreign and McFetridge, D. C: 'Short-run price adjustment in the Canadian manufacturing sector.' Essays on Price Changes, Prices and Incomes Commission, Ottawa, 1973. Hazledine, T. and Luck, D.: Explaining Quarterly Changes in Prices of Twenty-One Canadian Processed Food Products, 1971-1977, Unpublished, October 1980. 4 1 In fact, this assumption was proved to be true by Kohli (1979) in a study of Canadian-United States trade. Kohli tested the small-open-economy hypothesis in the case of Canada-United States trade by applying the framework developed by Appelbaum (1975 and 1979). This approach applies duality principles to noncompetitive markets and provides an explicit parametric test for the price taking hypothesis. In applying the principles of duality, he considers the economy's equilibrium is characterized by the solution to a profit maximization problem. He assumes, however, not that import and export price are given, but that Canadian firms face supply and demand functions for Canadian imports and exports respectively. Given this hypothesis, he tested for competitive behaviour, i.e., the market prices of imports and exports being equal to the corresponding shadow prices. In the context of Canada-United States trade he found that competitive behaviour could not be rejected when considering Canadian import decisions. Therefore he could not reject the assumption that Canada acts as a price taker and is a small open economy as far as its imports are concerned. Regarding Canadian exports to the U.S.,however, he found that departure from competitive behaviour are significant. Therefore, he rejected the 71 domestic firms produce homogeneous goods. Therefore, the domestic firms are likely, as a group, to be faced with highly elastic supply and demand for their product from abroad, provided that there are no quota controls on the amount of product imported or exported and that there is no international collusion in the form of market allocation. The price at which the demand from foreign country is elastic is lowered by transport costs and foreign tariffs on imports charged by foreign country, while the price at which supply from foreign country is elastic is raised by transport costs and domestic tariffs on imports. As transport costs and tariffs are generally not negligible, the domestic price at which foreign supply is elastic is at least somewhat above the domestic price at which foreign demand is elastic. In figure 3-2, line AB represents the demand curve which the domestic industry is facing. With the above assumptions, the demand curve becomes perfectly elastic at a price of C which is equal to the world (foreign) price, at which foreign supply of the product becomes perfectly elastic, plus transport costs from the relevant foreign producing locations, plus the tariff on imports to domestic country; that is, if the domestic production price exceed this price, the total market is taken by imports. Similarly, the demand curve becomes horizontal at the price G which is equal to the world(foreign) price, at which foreign demand for the product becomes infinitely elastic, less transport assumption that Canada is a price taker in its export market. 72 Figure 3-2 Pricing with Import Competition costs to the foreign markets and less the tariff at the foreign markets. These limits are shown by the lines CD and GF. As a result, the initial demand curve for domestic firms becomes CDEF, resulting in the kinked marginal revenue curve (CDHI). The profit maximizing strategy of domestic producers faced with a demand curve as depicted in figure 3-2 depends on the structure of the industry. If the industry is not highly concentrated, the firms can be 73 expected to accept the price as given and act independently. To maximize profits while acting independently, the firms choose levels of output such that the given price is equal to their marginal cost of production. Provided that the prices of inputs used in production are not affected by the amount of those inputs used in the industry, the supply curve for the domestic firms as a group is given by the horizontal summation of the individual firm's marginal cost curve. Such a supply curve is shown by MC in figure 3-2. The equilibrium price and quantity for the domestic firms as a group is then given by K and L respectively. If the firms operate in an industry which is highly concentrated, they need not accept the price as given. In such a situation the producers can be expected to recognize their mutual interdependence. An extreme form of response to mutual interdependence is for the firms to set prices so as to maximize their joint profits. The short run profit-maximizing policy for a group of firms is to set a price at which the quantity demanded is such that the marginal cost of production for each firm is equal to the marginal revenue for the firms as a group. In figure 3-2, provided that the horizontal summation of the marginal cost curves for the firms is given by MC, the price is C, and the corresponding quantity sold by the firms as a group is J. A comparison of C and K shows extra profits resulting from concentration. It is, however, possible that concentration does not result in a higher price. When horizontal summation of the marginal cost curves of the group of domestic firms is given by 74 MC, the profit maximizing price for the firms is C, regardless of whether prices are set independently or to maximize joint profits. When the horizontal summation of the marginal cost curves of the domestic firms is given by MC", the profit maximizing price is G, regardless of whether the prices are set independently or to maximize joint profits. Therefore, whether or not concentration influences the price can be seen to depend on the position of the horizontal summation of the marginal cost curves of the domestic firms at the price levels C and G. If the oligopolistic price varies with the levels of C and G, then the oligopolistic price depends on the level of foreign prices, tariffs, and transport costs. C depends on the level of foreign prices, tariff duties on imports into the home country, and transport costs. G depends on the level of foreign prices, tariff duties on imports into foreign country, and transport costs. The relationship between C and G and foreign prices are such that a reduction in foreign prices reduces the level of both C and G, holding the tariffs and the transport costs 42 constant. Figure 3-2 also shows that the influence of foreign prices on the domestic price depends on whether or not domestic sales are highly concentrated among a few firms. If the horizontal summation of the marginal cost curves of the firms is given by MC and the landed price of imports is in the range of OC, the 42 Because the exports are so small, the effects of exports are not considered or ignored. 75 profit maximizing price for the domestic firms is positively related to the level of foreign prices regardless of whether the firms act independently or act to maximize joint profits. If, however, the horizontal summation of the domestic firm's marginal cost curves is given by MC and the landed price of imports is again in the range of OC, the profit maximizing price for the domestic firms is positively related to the level of the foreign prices only when the domestic firms act to maximize joint profits. If the domestic firms act independently in this situation, there is no relationship between the profit maximizing price for the firms and the level of the foreign prices. It is noticed that moderate shifts in the domestic demand curve and the firms' marginal cost curve will not affect domestic price because of the characteristics of the kinked marginal revenue curve. Instead, the increases in C and G will shift the kink, thus inducing an increase in the domestic price. That is, the domestic price becomes a function of the import price of the competing goods. This tariff limit pricing theory can be applied to trade of homogeneous goods. 3.5.2.2 Heterogeneous Products Now let us consider a case where the products are not homogeneous. In the case of differentiated products, such that each firm perceives a downward-sloping demand curve, the industries can no longer, theoretically, be distinguished as 76 either import-competing, trade-sheltered, or exporting. Some domestic firms in an industry perceive little sensitivity of their demands to import prices while the sales of others are sensitive; some make substantial export sales while others find exporting unprofitable. Some industries who make substantial sales may also face severe import competition (intraindustry trade). Although the position of an industry's typical firm in relation to the import competition and export opportunities still depends on the relation between its costs curve and world prices, the concept of a single world price explained above no longer applies. Instead, we assume that, even though imported and domestic goods are not identical, both goods are at least substitutes for each other with a positive cross price elasticity of demand. In this case, the purchasers have a choice between domestically produced goods and imported goods. Consumption depends on their demand curves; personal income, import price, domestic price, and so on. Now suppose that an increase in import prices take place, then the purchasers will reduce consumption of imported goods and buy domestic goods as substitutes. This increase in consumption of domestic goods will shift out the demand curve of domestic goods. This outward shift means that the elasticity of demand becomes smaller (cited in Moffat, 1970, pp253-261).43 This decrease in 43 . . . Robinson, J.: The Economics of Imperfect Competition, MacMillan, London, 1933 , pp.. 70-71. 77 demand elasticity induces the firms to increase their price. That is, domestic price is a function of the import price when products are not identical. In other words, as previously discussed, import competition prices constrain the domestic firms' pricing behaviour with both homogeneous and nonhomogeneous goods. Therefore, the import price can now be used as another measurement for the markup fluctuations along with other market force variables. It is noted that import competition should be expected to exert greater restraint on prices in highly concentrated industries (DeRosa and Goldstein, 1981, p.602). The presumption here is that a competitive domestic market will have already kept price close to marginal cost, thereby eliminating much of the potential disciplinary force of imports. In contrast, the greater deviation of price from marginal cost in highly concentrated industries provides considerable scope for import discipline. For the sign of the coefficient in the import price variable, a positive relationship is generally expected. If all other factors related to output prices are held constant, but import prices are allowed to vary, we would expect the relationship between profits and import prices to be positive, i.e., the higher the import prices, the higher the profit. A number of alternative conjectures might, however, produce different results. For example, if the market is faced with a 44 ... Moffat, W. R.: "Taxes in the Price Equation: Textiles and Rubbers," Review of Econometrics and Statistics, Vol. 52, August 1970, pp.253-261. 78 disequilibrium situation in which increasing import price leads to an increase in domestic demand. The increase in the consumption of domestic goods provides the ratailers with more order volume. This increased order volume helps the retailers exert more negotiating power against the processors, resulting in lower industry selling prices. Also, if increases in domestic production due to increases in import prices provide more capacity utilization, the price of domestic goods may be decreased. Therefore, increases in import price will result in decreases in the domestic price. In this study, U.S. prices are used as the import price. There are several reasons for using the U.S. price. First, the U.S. economy is extremely large, both in absolute terms, and relative 45 to the Canadian economy (Hazledine, 1980). Second, the U.S. is Canada's biggest trading partner. Third, the sample period of this study comprises two sub-periods based on the shift in the exchange rate between the U.S and Canadian dollar in order to see if there are differing results in the estimated coefficients reflecting changes in the pricing behaviour after the shift in the exchange rate. This import price variable is used in the form of rate of changes to be consistent with other variables. Hazledine: 'Testing two models of pricing and protection with Canadian/United States data.', 1980. 79 3.6 Integration of Three Theories  3.6.1 Substitution for Mark-up In section 3.2, equation (3.2.7) was derived; (3.2.7.a) Px = m + am*Pm + a^ + af*Pf + ak*Pk + am(M/X) + ax(L/X) + af(F/X) + ak(K/X) In this equation m is not measurable as it is. In order to find substitutes for the mark-up variable, we discussed excess demand variables, the modification of the Bilateral Monopoly Theory, and the trade theory in the previous sections. It was then concluded that the quantity of shipments and the import prices became important factors for the firm's markup fluctuation. Now substituting these variables for theb markup (m) in the equation • • • • • (3.2.7.b) Pv = a + a *Pm + a *P, + a *Pf + a *P, x c mm 11 rr kk • • • • + am(M/X) + a1(L/X) + af(F/X) + ak(K/X) • • +a *SHI +a *P. + u x u 1 where u is the disturbance term, a is a constant, a is the ' c x coefficient of the changes in shipments, and au is the coefficient of the import price. 3.7 Shift in Exchange Rates In section 1.2.3 the relationship between the U.S. dollar and the Canadian dollar during the 1971-1984 was analyzed. It was explained that the exchange rate shifted in 1977. The Canadian dollar became relatively weaker after 1977 than prior to 1977. A proposition that the shift in the exchange rate between the Canadian and U.S. dollar would cause the shift in the Canadian 80 food processors' pricing behaviour has been raised. This has left us with two hypotheses: (1) the magnitude of import price coefficient after the devaluation is smaller (in absolute value) than the magnitude of import price coefficient before the devaluation, (2) the magnitude of input costs coefficient is bigger after the devaluation than the magnitude of input costs coefficient before the devaluation. The shift in the exchange rates will result in an important shift in the domestic firms' pricing behaviour; a shift in domestic and foreign price, a shift in international price competitiveness (relative price), a shift in the trade pattern, and thus a shift in relative importance between the input cost variables and the import competition variable in a pricing model. 3.7.1 Shift in Domestic and Foreign Price The analysis of the role of the exchange rate usually begins by assuming that most foreign trade is carried out by autonomous profit-maximizing firms. Decisions to import or export are made on the basis of profitability calculations in terms of domestic currency costs and prices. International commodity arbitrage is assumed, and as a result, the following familiar equilibrium Karikari, J. A.: "International Competitiveness and Industry Pricing in Canadian Manufacturing", Canadian Journal of Economics, XXI No.2, May 1988, pp.410 - 426. 81 condition holds for a product, holding other factors constant (3.7.1 ) P = P• (3.7.2) P. = P *e48 1 w where Px is the domestic price, P^ is the import price in domestic currency, Pw is the foreign price, and e is the exchange rate expressed as the domestic currency per unit foreign currency. From the domestic firms' point of view, Px is endogenous and Pw and e are exogenous. The equation (3.7.1) is an equilibrium condition because any differences between the actual domestic currency equivalent of the import price present the private sector with arbitrage profit opportunities, and the arbitrage itself will eliminate the price di f ferent ials. The equations can be rewritten as P (3.7.3) -- = e P w From, this equation, we can see the relationship between the domestic price and the exchange rate. This equation indicates that the increases (devaluation) in e will lead the increases in the domestic price. If the increases in e take place, the ratio 47 . The "law of one price" which merely means that the same good can not be sold at a difference price of the common currency in two countries as long as the two countries keep trading. Because of the "law of one price", given sufficient time, the changes in the exchange rates will be fully compensated for by changes in domestic prices. 48 This equation give us that P = P + e which is frequently employed in the pricing models. x w 82 of P to P should also increase so as to sustain the equality. A W The directions of the movements of the currencies are that P is x 49 increased while P is constant. w 3.7.2 Shift in International Price Competitiveness It is easy to see from (3.7.2) that the increases (depreciation) in the exchange rate will increase the import price to the home country while the foreign price is constant. On the other hand, the import price from the foreign country's point of view becomes cheaper. Also devaluation will lead to increases in wage in the home country. This higher wage will push the price of output produced at home higher while weakening international competitiveness. However, the wage in money terms will be adjusted significantly less than by the full amount of the changes in the exchange rate because nontraded goods'prices will not change by as much. Therefore, this will place firms in a better position relative to their foreign competitor (Artus, 1975, pp. 599-600). Subsequently, the price of a good which is domestically produced becomes relatively cheaper in the international market. Therefore, a result, the domestic firms become more price 50 competitive in international markets. 49 This can be explained by using diagrams. See, for example, Edwards (1987, pp.3-7) and Kost (1977, pp.99-106) for the explanation with diagrams. 50 In fact, this is not a quite right as an explanation of international price competitiveness. There are more complicated formula for the international price comparison with consideration 83 3.7.3 Shift in Trade Pattern The gain of international price competitiveness and the increases in the domestic and import prices due to devaluation will alter trade patterns. It is expected that the import volume will be reduced while the export volume will be increased after 51 . . . the devaluation. Devaluation results in increases in the import price and the output price in the domestic country and decreases in the foreign country's output price as described above. Increases in the import price and the output price will cause decreases in the demand for goods, resulting in deduction in import volume. However, the decreases in the output price in the foreign country will increase consumption of the goods, resulting increases in import volume (more export from the devaluing country). 3.7.4 Shift in Importance Between Input Cost Variable and Import  Competition Variable As a result of the above impacts, we can expect changes in the domestic firms' pricing behaviour. One of the changes in the pricing behaviour will be changes (shift) in for importance among the variables which affect the output price. Deppler and Ripley of the trade weights, periods, exchange rates, and so on; i.e. relative price index, price competitiveness index, and so on. But we use the above equation for explanation. 51 The magnitude of changes in the volume of a imported (or exported) good will depend on the magnitude of the import (or export) price elasticity. For example, the import volume of a good with higher price elasticity will be reduced more dramatically than that with lower price elasticity for devaluations. 84 (1978, p.154) identify this concept. Their price equation is P = 1/(1 + an)VC + an/(1 + an)CP + 1/(1 + an)Y, where VC, CP, Y, a, and n represent variable costs, competition prices, income, elasticity of marginal cost with respect to output, and absolute value of price elasticity of demand. From the equation, the coefficient of VC, 1/(1 + an), plus the coefficient of CP, an/(l x 52 + an), is one. The increase in the relative importance of one of these variables in the price formation process entails a reduction in the role of the other. This is because the relative responsiveness of price to the input cost and the import price depends upon the quantity a*n. If a*n is large because the firm's monopoly power is restricted (i.e., n is large) and/or because there are increasing marginal costs (i.e., a is large) then competitor prices tend to dominate the price formation process. For example, for a relatively smaller firm involved in international trade, n tends to be relatively large. And thus the coefficient of import price tends to be larger. As a result, the import price is relatively more important than input price. If, on the other hand, a*n is small because the firm as a large supplier enjoys a considerable monopoly power in the international market and/or because production is subject to 52 Though the equation above is not exactly identical with our model, his concept can be applied to our equation for the explanation. It is because the sum of the coefficient of all inputs (am + a-^ + a^ + a, in our equation, which is equivalent to 1/(1 + an) in his equation) and the coefficient of the import price (a in our equation is equivalent to a_/(1 + an)) is one. 85 constant or near constant returns to scale, pricing is dominated by supply considerations. It has been shown that the relative importance of input costs and competitor price variables in the price equation depend on the quantity a*n. Now let us apply this concept for the case of depreciation in exchange rates. When there is a devaluation, the volume of exports is increased and the volume of imports is decreased as explained above. This increase in exports and decrease in imports means that a domestic firm becomes relatively larger in the international trade, and that the size (or production) of the firm becomes bigger. This means that n and (or) a becomes smaller as explained above. That is, the import price becomes less important. 86 CHAPTER 4 EMPIRICAL DATA 4.1 Introduction In the previous chapter we modified and combined the variables in the pricing equation. These equations are • • • • • (4-1-l} Px,t = ac/ am*Pm,t + VPl,t + af*Pf,t + ak*Pk,t + am(M/X),t + *i^/X\t + af(F/X),t + ak(K/X),t • » + a *SHI , + a *P. . + u . x , t u 1, t , t where t means the current time. In this chapter we are going to explain each variable with respect to the empirical data. Quarterly data from 1971 to 1984 will be used, for 14 disaggregated Canadian food processing industries at the 3 or 4 digit SIC industry level will be tested (see Appendix 4.1 for quarterly data). In explaining the data, the biscuit industry will be used as an example. • 4.2 Industry Selling Price (P^) The industry selling price (P ) is the dependent variable in our pricing model. The data were collected from the Industry  Selling Price Index (62-011), Statistics Canada for two base years - 1971 and 1981. The data sets for 1981 were multiplied by an adjustment factor in order to make them consistent with the 1971 figures. After converting the row data from monthly to quarterly, a percentage change (DP) in Table 4-1. 87 Table 4-1 Final Data for Biscuit Industry (SIC 1071) Year DP DPM DW DSHI DPDIC DPUS 1971 .1 0.000 0.000 0.000 0.000 0.000 0.000 1971 .2 2.554 -0.891 -1.136 -0.304 4.684 2.877 1971 .3 0.299 -0.487 1.916 10.700 13.625 -0.425 1971 .4 0.298 1 .400 5.263 -2.486 -8.653 -1.309 1972.1 1 .683 5.822 0.714 -1 .885 1 .302 0.870 1972.2 1 . 1 68 -0.938 -1.773 5.938 8.248 0. 149 1972.3 0.385 -0.740 3.610 1 . 1 56 8.821 0.000 1972.4 0.959 2. 132 2.439 6.752 -4.979 -0.697 1973. 1 3.229 2.-237 1.361 -12.779 -0.931 3 . 1 43 1973.2 0.000 4.330 5.034 5.025 ' 11.257 1 .563 1973.3 6.716 15.843 2.236 -10.807 13.606 3.672 1973.4 10.086 9.603 2. 188 12.347 -5.614 6.875 1974. 1 7.048 18.064 2.752 -3.641 -2. 199 4.499 1974.2 9.949 11.576 2.381 3.793 1.0.731 6.852 1974.3 5.855 9.708 5.233 -16.771 17.494 8.402 1974.4 11.691 13.373 3.591 32.210 -10.082 12.886 1975. 1 4.389 -5.672 2.400 -16.100 0.808 8. 127 1975.2 0.916 -7.854 4.427 16.038 7.829 -2.322 1975.3 0. 1 60 -0.892 4.738 0.747 17.995 -0.215 1975.4 -0.533 -3.098 2.619 4.780 -9.798 -0.976 1976. 1 -3.700 -1.588 4.408 -10.531 -0.952 -1.418 1976.2 -1.225 -0.993 3.333 7.264 10.181 -1.160 1976.3 0.733 -2.069 3.656 -6.708 11.000 -0.143 1976.4 -1.343 -1.900 3.942 17.226 -8.179 1 .524 1977. 1 4.084 5.285 2. 1 96 -18.642 -5.377 7.609 1977.2 3.760 5.036 0.391 -3.034 12.947 5.473 1977.3 11.975 -1.656 2. 1 40 -10.938 1 0.823 2.652 1977.4 0.000 0.490 2.857 11.831 -8.855 10.217 DP: Percentage changes in Industry Selling Prices (Buiscuts) DPM: Percentage changes in material prices DW: Percentage changes in wages DSHI: Percentage changes in output shipments DPDIC: Percentage changes in personal disposable incomes DPUS: Percentage changes in U.S. wholesale prices 88 Table 4-1 Final Data for Biscuit Industry (SIC 1071) Year DP DPM DW DSHI DPDIC DPUS 1978.1 2.064 3.203 4.074 -0.715 -0.909 0.744 1978.2 1.103 -0.073 0.890 2. 1 59 1 1 .849 1 .267 1978.3 1 .091 2.390 0.882 -4.312 9.444 3.670 1978.4 4.002 4.313 3.322 -1.365 -8.131 5.303 1979. 1 6.701 6.980 1.861 -0.667 -2.786 2.451 1979.2 3.039 3.577 4.319 3.849 16.488 -0.331 1979.3 0.118 4.486 2.866 -2.396 6.298 1 .979 1979.4 -0.118 7.091 6.811 1 .072 -6.610 3.092 1980. 1 10.146 8.958 -3.333 -5.882 -2.223 4.479 1980.2 1 .250 8.396 2.849 -4.180 12.960 3.584 1980.3 2.116 4.783 3.499 7.368 1 1 .004 -0.201 1980.4 3.902 6.440 4.225 0.255 -9.208 8.052 1981 . 1 8.375 -1.235 -0.811 -10.212 1.211 3.425 1981 .2 3. 1 28 -4.122 1 .226 2.667 11.159 0.91-3 1981 .3 0.952 1.021 -0. 135 -0.581 15.314 2.975 1981 .4 1.031 -1.313 4.987 4.402 -1 1 .423 -1.651 1982. 1 3.528 0.657 1 . 1 55 -8.285 -1.645 0.240 1982.2 1 .718 -3.036 6.853 3.062 10.221 3.596 1982.3 0.028 0.797 4.988 -3.860 9.702 -0.101 1982.4 0.028 -0.233 -0.905 6.403 -12.265 -0.234 1983. 1 7.582 0.053 -0.457 -18.055 -3.423 -0.141 1983.2 0.617 4.200 -3.211 16.956 9.765 1 .744 1983.3 0.000 3. 173 -0.474 6.402 14.890 1 . 187 1983.4 0.026 0.285 4.524 -2.647 -12.043 3.215 1984.1 6.798 0.381 3. 189 -9.412 -3.896 1 .584 1984.2 0. 1 44 1 .688 -7.064 8.075 13. 139 4.078 1984.3 0.072 1 .043 4.751 -3.642 8.417 3.087 1984.4 -0.024 0.241 4.422 1 .588 -8.811 0.757 89 • 4.3 Price of Materials (P ) m— The price of material (P ) is a complicated variable. If biscuits were made of one material, e.g., wheat flour, the material price variable would simply be the percentage change of the price of wheat flour. However, because biscuits are made of various materials as shown in the table 4-2, it is necessary to find a way of combining the material prices. One method is to use the weight of each material cost in the total material costs. The steps are as follows: (1) The weight of each material cost in the total costs for 1971, 1974, 1977, 1978, 1981, and 1984 was calculated. Table 4-2 shows the weight of each material cost for the corresponding year. The row data were collected from the Biscuit Manufacturers  (32-202), Statistics Canada. (2) The average weight of each material cost for these years was calculated. The last column of Table 4-2 shows the average weight. (3) The price index of materials or material prices was 53 collected from various sources. There were some materials for which prices could not be found from any source. We dealt with 53 Industry Selling Price Index (62 - 011), Statistics Canada. Farm Data Bank, Agriculture Canada. Cereals and Oilseeds Review, Statistics Canada. Dairy Market Report, Agriculture Canada. Canadian Grains Industry - Statistics Handbook, Canada Grain Council. Livestocks and Animal Products, Statistics Canada. CANSIM, Statistic Canada. 90 Table 4-2 Material Weight of Biscuit Industry (SIC 1071) 1 j iMterials i 1 Materiel . Symbols 1971 1974 1977 1978 19S1 1984 1971-1984 Total (S'OOO) 1971-1984 % : Chocolate (cooking); sweetened D509129 163 559 1126 1717 4941 2756 11262 0.014 j Chocolate (cooking); unsweetened D509129 3 3 0.000 Chocolate (coating); sweetened D509129 558 2447 3620 2739 1849 5639 16852 0.021 Chocolate (coating); unsweetened D509129 1029 1723 2249 2862 1276 9139 0.011 Cocoa butter EKAF003 727 187 671 1585 0.002 Cocoa butter substitute EKAF003 385 479 864 0.001 Cocoa and Chocolate powder D509129 747 1331 4562 5092 1766 4073 17571 0.022 Cocoa and chocolate preparations D509129 648 927 2562 2352 2844 4144 13477 0.017 Coconut; shredded MCOCONU 820 1759 2182 2225 2433 2787 12206 0.015 Eggs AIDP074 426 625 811 619 1096 1883 5460 0.007 Flour; hard wheat (bread flour) D507302 1111 2105 2608 3645 8439 9161 27069 0.034 Flour; soft wheat (cake flour) D507302 8620 15138 15757 15386 21730 28515 105146 0.131 Flour based mixes D507302 1184 175 1359 0.002 Other flour D507302 18553 1290 1421 10222 5366 36852 0.046 Fruits; dried (rasins, currants, dates) FCFO001 691 1243 1678 1380 2955 1986 9933 0.012 Fruits; fresh FCFG001 48 74 122 0.000 Fruit; processed, frozen, jam, jellies FCFG001 334 796 1078 1500 1491 2189 7388 0.009 Milk; whole liquid D505848 128 63 191 0.000 Dairy powder; skim milk D506354 306 463 791 686 1115 1266 4627 0.006 Dairy powder; whey milk D505848 142 82 224 0.000 Dairy powder; skim milk and whey blend* D506354 47 47 0.000 Nuts; all kinds MPEANUT 339 725 777 795 1062 1349 5047 0.006 Oli and fat; 0 0.000 Butter D504802 226 465 197 607 1742 1175 4412 0.005 Lard D502571 2240 6007 5276 6314 5511 9112 34460 0.043 Mrgarine D510112 17 267 150 1012 1446 0.002 Shortening D502628 5814 12678 9927 8512 8189 17371 62491 0.078 Other oil and fat D502628 1670 4465 5897 8725 9430 8011 38198 0.048 Salt 357 357 0:000 | Starch FPC02 135 471 452 607 416 520 2601 0.003 ! Sweetening agents; ' 0 0.000 1 Sugar D509402 7765 28839 17820 16288 32249 19104 122065 0.152 i ! Invert sugar (sugar solid basis) D509402 1849 888 921 3670 1192 . 8520 0.011 | Liquid sucrose (sugar solid basis) D509402 551 871 857 2625 4904 0.006 j Other natural sweetening agents D509402 1953 2516 2089 1862 2623 1557 .12600 0.016 ! Artificial Sweentening agents D509402 - - 294 294 0.000 | Paper; all kinds D525055 3718 4486 3501 2740 2477 16922 0.021 i Paper bags; all kinds D525055 3491 6803 6458 8940 8902 34594 0.043 ! Folding and set-up boxes; paperboard D525801 4641 6834 10718 11145 16030 20427 69795" 0.087 ; Corrugated Boxes and cartons D526001 4372 7546 6927 7009 8224 10450 44528 0.055 j Bags; transparent film D526343 1535 1734 3169 3416 6619 5444 21967- 0.027 | Transparent film in sheets and rolls D526343 2740 4132 3282 4890 5073 8461 28583 0.036 j Label, tags, wrappers D525055 1797 868 967 1448 1549 1581 8210 0.010 1 .. 803371 1 i Source: Biscuit Manufacturers (32-202), Statictics Canada these materials in two ways. When the proportion of the material cost was very small, i.e. salt, the material was ignored and the weight was considered as zero. When the proportion was large, the material was combined with another material with similar characteristics, i.e. other oils and fats were regarded as shortening. The weight of each material cost was then readjusted accordingly. When the material prices are presented as a price index form, i.e. Industry Selling Price Index (62-011), there were two base years - 1971 and 1981. In this case, the 1981 based data sets were adjusted by multiplying an adjustment factor to make consistent with 1971-based data sets. The collected monthly data were converted into the quarterly data. The percentage changes from these data were calculated. When material prices were presented as unit prices, these unit prices were converted into index form for the base year of 1971. The price indices were converted into quarterly data and then into the percentage change form. The import price and the import volume figures were collected from Imports by Commodities (65-007), Statistics Canada. In order to get the prices of imported materials, e.g. coconuts, the total import value (Canadian dollar) 'of the material was divided by the total import volume for each month and was then converted into quarterly data. These quarterly coconuts prices were converted into a index form for the base year of 1971. The percentage changes from these quarterly data 92 were calculated. (4) Finally, by multiplying the corresponding weight by the percentage changes of each material price index and by adding these weighted percentage changes of each material price index, the weighted price change (Pm) for the quarter were obtained. For each quarter, the same procedure was applied to produce the data set. The following shows the formula for one quarter. P . = 0.094*dD509l29 + 0.003*dEKAF003 + 0.015*dMC0C0NUT + m, t 0.007*dAIDP074 + 0.094*dD507302 + 0 . 021*dFCFG001 + 0.006*dD506354 + 0.006*dMPEANUT + 0.005*dD504802 + 0.043*dD502571 + 0.126*dD502628 + 0.003*dFPCO2 + 0.185*dD509402 + 0.074*dD525055 + 0.087*dD52580l + 0.055*dD526001'+ 0.063*dD52634354 where D509129 ... are the quarterly price indices of the corresponding materials obtained from the above processes and where dD509l29 = {(D509l29fc - D509129fc_ )/D509129fc_1}* 100. The same method was applied to the other variables and the coefficients represent the corresponding weights. The third column (DPM) of Table 4-1 shows the material price changes. • 4.4 Changes in Wages (P^) Even though previous researchers didn't determine labour costs • • 55 to be significant in pricing behaviour, it was tried again in 54 Each variable represents simply each material. These symbols were used just for convenience. The material names and symbols are presented in Table 4-2. 55 Hazledine, T. and Luck, D.: Explaining Quarterly Changes  in Prices of Twenty-One Canadian Processed Food Products, 1971- 1977, Unpublished, October 1980. 93 this study because it was believed that the variable would be significant since the proportion of the labour cost in the total production cost in some industries was quite large, as shown in the Table 2-1 in chapter 2. There are several alternative data sets for this variable: total monthly earnings per total employees, average hourly earnings of production workers, monthly earnings per production, and total monthly earnings per nonproduction workers. The use of each of these data sets has advantages and disadvantages.^ The data used in this study is the average hourly earnings of production workers from Employment, Earning and Hours (72-002), Statistics Canada. In this publication, data reporting procedures were changed in March of 1982. In order to maintain consistency with previous data, later sets were recalculated by multiplying an adjustment factor. The original monthly data were converted into quarterly data and percentage changes (DW in Table 4-1). Not all of the industries' monthly wage data were available from this siurce. For the industries with no wage data, the wage variable was not simply dropped off, because it was believed that the changes in the wage would affect on the changes in the price Dennis, K.: "Market power and the behaviour of industrial prices." Essays on Price Changes, Prices and Income Commission, Ottawa, 1973. 56 For detailed discussion, see Dennis, K.: "Market power and the behaviour of industrial prices." Essays on Price Changes, Prices.and Income Commission, Ottawa, 1973, P.60. 94 since the total wage (salary) paid represented a significant proportion of the total production cost; that is, dropping the wage variable could result in a biased estimation of the coefficients. In this case, the quarterly interpolation of annual earnings data from the Census of Manufactures, Statistics Canada, were used. In fact, there might exist a couple of problems in using the average hourly earnings of production workers. The increases in the salaried employee's payment were not reflected in the data. But this was not a serious matter with the Canadian food processing industries because the proportion of the salary payment in the total labour costs was substantially small. The other problem was that the data contained the increases in wages for overtime work. In this case the data dide not reflect the pure increases in the average hourly wages. • 4.5 Changes in Demand (SHI) The data were retrieved from the Inventories, shipments, and  orders in manufacturing industries (31-001), Statistics Canada. The monthly data of shipments was converted into quarterly data by summing the monthly shipments. And then the value of the quarterly shipments was divided by the quarterly price index to get the quantity-compatible shipment data. This data were converted into percentage change form (DSHI in Table 4-1). For the industries which did not have available data, personal disposable income was used. Changes in personal disposable income influence the consumers' food purchases at the retail level. The 95 retailers' purchases volume will be influenced by the changes in the consumers' purchases. The changes in the retailers' purchasing behaviour will affect the pricing mechanism between the retailers and the processors. Therefore, the personal disposable income can be used for an approximation of the shipments National Income and Expenditure Accounts (13-001), Statistics Canada, presents quarterly personal disposable income (seasonally adjusted) data with a national base. And Canadian Census, Statistics Canada, presents yearly Canadian population data. After the yearly census was interpolated into quarterly data, the quarterly personal disposable income was divided by the quarterly population to get the personal disposable income per capita. And then it was converted into the percentage change form (DPDIC in Table 4-1). • 4.6 Changes in the Import Price (P^) In collecting this data, each commodity's unit price calculated from the volume and the value of each imported commodity should be used. After these unit prices are obtained for each commodity, it is necessary to combine these unit prices by using appropriate weights in order to get prices compatible with the corresponding domestic industry selling price. However, because it is impossible to get the appropriate weights for this procedure, other sets of data are normally used. In this, study. the U.S. wholesale price, which is the 96 counterpart of the Canadian industry selling price was used. The data were collected from the U.S. wholesale prices and price indexes, USDA, and were converted to Canadian dollar by multiplying by the exchange rate which was expressed in terms of the Canadian dollar per the U.S.dollar. In doing this, the U.S. industry classifications were not 5 8 exactly matched with the Canadian industry classifications. For example, the U.S. biscuit industry (0211-21) was classified as a subcategory of the bakery industry (0211). Therefore, when the bakery industry price was quoted, the biscuit industry price was included. As a result, the U.S.bakery industry price was not exactly compatible with the Canadian bakery industry price. In assessing the effect of import competition on domestic output price, the exchange rate has been applied in two ways. First, it has been used to convert the U.S. price into the Canadian price with the converted price being applied in the equation. The second method has been to use the exchange rate as a separate variable. This study uses the first method, because we are interested in the different sets of exchange rates in the 57 Hazledine and Luck (1980) explain the reasons very well. 5 8 The following food industries are the counterpart of the Canadian Food Processed industries, respectively, and the Wholesale Price Index of these industries were used for the import prices. Bakery Products (0211), Cookies, crackers, and related products (0211-21), Flour and flour base mixes and dough (0212), Meats (0221), Processed poultry (0222), Dairy products (023), Raw cane sugar (0252-0101), Confectionery materials (0254), Malt beverages (0261-01), Distilled liquor, except brandy (0261-02), Wine (0261-03), Soft drinks (0262), Crude vegetable oils (0272), and Prepared animal feeds (029) 97 sample period. Some industries, such as dairy, feed, and brewery industries, do not face serious import competition with U.S. because of relatively small trade volume. Therefore, it would not have been harmful even if we had failed to incoporated the import price variable in the equations of those industries. However, we did use the import price variable for these industries. The monthly data were converted into quarterly data and percentage change form (DPUS in Table 4-1). • • 4.7 Changes in Fuel Price (P^) and Capital Price (Pk) 4.7.1 Fuel Price (P£) In this study this variable was omitted for two reasons. The first reason was that the proportion of fuel cost in total production cost was very small, as shown in Table 2-1. Therefore, in priori, it was assumed that changes in the prices of fuels would not affect the industry selling price. The second reason 60 was that when Hazledine and Luck tried this variable separately in the model, they found it to be insignificant. They also tried integrating this variable with the material costs and found that the results were, in most industries, rather worse. They 59 We also tried the exchange rate separately. However, the results were not significantly improved or different from the first method. A dummy variable were tried, too (after depreciation is 1). The results were not significantly different. 60 Hazledine and Luck: Explaining Quarterly Changes in  Prices of Twenty - one Canadian Processed Food Products, 1971-77, P. 17 - P. 19. 98 concluded that this was a noise variable. Therefore, it is believed that the omission of this variable will not result in a biased estimation. 4.7.2 Capital Price (P^) Capital investment was incorporated into the Mark-up Pricing Model and expressed as a percentage change in the model, showing the industry selling price change as a function of capital price. However, in the empirical test, this variable may not be an important factor (using the quarterly data). Capital can be categorized into three concepts: equipment and machinery, buildings and land, and money. Suppose that the firm rents equipment and machinery, and buildings and land. The firm will have contracts. The contracts will basically specify the amount of rent and the lease period. The firm will likely have long-term (one year or more) contracts with constant monthly payments. That is, the monthly rent will not be changed - within the leasing period. Even if the payments are scheduled as a escalating system, the rents are not usually changed monthly. Rather, they are changed yearly. On the other hand, suppose that the firm borrows some money from financial institutions to buy equipment and machinery, buildings and land or for use as working capital. The firm will ^1 In fact, they incorporated the wage and the fuel prices with the material prices, and then concluded that these were noise variables. But I believed that, as discussed, the wage might be important. 99 pay interest. The interest rate fluctuates according to economic situations. Accordingly, the monthly interest payment will change. However, when we consider the magnitude of interest payment fluctuations in the total cost of the Canadian food processing industries, the proportion is very small. As a result, the influence of interest rate changes to the industry selling price can be ignored. • • • • 4.8 Changes in Productivity; M/X, L/X, F/X, and K/X In dealing with quarterly data, all these productivity changes were included in a constant term in this study. Data for the change in productivity of materials (M/X), labour (L/X), and Fuel (F/X) are available only annually. No good data for productivity of capital is available. Productivity growth of materials (M/X) has been proxied as a constant in studies because the constant term in a price change model will pick up productivity growth at a constant rate. McFetridge, D. C: 'Short-run price adjustment in the Canadian manufacturing sector.' Essays on Price Changes, Prices and Incomes Commission, Ottawa, 1973. Hazledine, T. and Luck, D.: Explaining Quarterly Changes in  Prices of Twenty-One Canadian Processed Food Products, 1971- .1977, Unpublished, October 1980. 1 00 CHAPTER 5 EMPIRICAL TEST AND RESULTS 5.1 Introduction The final form of the equation which will be tested in this chapter was been derived in chapter 3. The equation is written as • • • (5.1) Pv . = a„ + a *Pm . + a *P, . x,t c mm,t 1 l,t • * + a *SHI + a *P. + u. x , t u I , t t 6 3 where each variable has been explained in previous chapters and ufc is a disturbance term. This chapter first discusses the introduction of lags into the model, and then presents the empirical results. 5.2 Distributed lag Model Before regressing the model on the data, we need to discuss specification of lags in the explanatory variables. 5.2.1 Lags in Material and Labour Prices There are several possible causes of lags in material prices. A processor may not instantly react to changes in material prices and labour costs because of: (1) the other processors' reaction to the changes. The industry is neither monopolistic nor in perfect competition. Each processor must carefully consider reactions by rivals to increase 63 This shipment variable (SHI) will be substituted with the personal disposable income for some industries as explained in chapter 4. 101 its output price. Each processor will be reluctant to increase the price instantly. This will induce the delay of price increase; (2) processing time. After the materials for which prices have been increased are purchased, it will take some time for the materials to be processed and finished as final products. Also the product will be stored for some time in the warehouse before it is sold; (3) the processor's perception of material price changes. If the processor considers the changes to be transitory, the processor will not increase the output price. However, some time later, if the processor realizes that the price changes are permanent, he will finally change the output price; (4) contractual obligations. If the processor has contracts with material suppliers for a certain period, the fluctuations in the material prices in the market will not be immediately reflected in the processor's output price; and (5) imperfect information. Even though the material prices are increased in the market, the processor might not recognize the price increases for some time because of imperfect information. Then, how many distributed lags should be allowed for the material price variable and the wage variable? There is no clear answer, though Hazledine and Luck (1980) found that only current material prices were important. The delay will depend on the particular situation of each industry. We, ad hoc, tried three '102 lags for the material price variable and one lag for the wage variable. Furthermore, unlike other economic techniques (such as the Koyck model, the partial adjustment model, and the adaptive model), the magnitude of coefficients of the current and of the each successive lagged variable will not necessarily be getting smaller as the lags become longer, as previously explained. 5.2.2 Lags in U.S. Price Lags between the change in domestic price and a change in the corresponding import price can occur for several reasons. The first reason is that: the volume traded in any given market period depends on prior expectations held about the exchange rate, and that the lags therefore occur,in the adjustment of trade volumes to their desired levels. The second reason is the time delay between the order, the delivery, and the distribution of the import goods. The third reason is willingness and ability of Canadian buyers to substitute other foreign imports for the U.S. products in the face of rising prices. The fourth reason is the practice of selling off inventories at former prices before passing on the price change. However, the lag periods cannot be determined a priori. Several studies consider the time lag relationship between 64 Driskill, Robert, and MacCafferty,S.: "Speculation, Rational Expectations, and Stability of the Foreign Exchange Market," Journal of International Economics, Vol. 10, February 1980, pp.91-102. 103 Canadian and U.S. prices. Burbidge and Harrison (1985, pp. 786-788) found that there is about three months time delay between the fluctuations of the U.S. variables and the Canadian variables, which includes the U.S. and the Canadian indices of industrial production, consumer price indices, three-month treasury-bill rates, money supplies, and the exchange rates. It takes about three months for the Canadian economy to reflect the impacts of U.S. fluctuations. Bonomo and Tanner (1972) applied spectral analysis to industrial production series for the two countries and found statistically significant relations between the U.S. and Canadian economic cycles of lengths between three to ten months. McPheters and Stronge (1976) did a cross spectral analysis test to test the nature of the relationship between two series of equal length for the two countries. The results indicated a significant degree of association between the price series of the two countries. The time delay was two to ten months by the U.S. consumer prices. As for the United Kingdom, Goldstein (1974) tried distributed lags and each time lag in his study. He concluded that the discrete lag of three-quarter was the best result, both in terms of the fit of the equation and the significance of the import • 65 price variable. Accordingly, different lags on the import price variable 6 5 Burrows and Hitiris (1972) also found that a three-quarter lag was the best. Smith (1968) and Lipsy and Parkin (1970) used a one-quarter lag for the different sample period from the above researchers' studies. 104 will be tried. However, it is expected that the lags will be shorter than those in the studies cited above because the price of domestic goods in their equations is not the industry selling price but the retail price. This means that their lags might be longer than in this study because there exists one or two quarters of time delay between the price behaviour in the retail sector and the wholesale sector. Discrete three-quarter and distributed lags will be tested for in this study. 5.2.3 Lags in Shipment Variable The processors always monitor their production activities and the activities of retailers, including shipment levels and retailers' orders. They apply their analyses from the market situation to the pricing activities. They will consider the current shipment levels and the past shipment levels as well. So, it is natural to allow lags for this variable.^ Finally, substituting the above lagged terms for each variable, equation (5.1) can be written as (5'2) Px,t = ac + am,t*Pm,t + am,t-1*Pm,t-1 • • + a *P + a *P m,t-2 m,t-2 m,t-3 m,t-3 + al,t*Pl,t + al,t-1*Pl,t-1 ^ Lags in income variables: Changes in personal income will affect consumption. These changes in consumption will change the retailers' purchasing activities. This change will be dictated by the pricing activities of the processors and the retailers. In this process, there will be a time sequence between the change in income and the change in the industry selling price. 105 + ax,t*SHI,t + ax,t-1*SHI,t-1 • • + a *P. . + a . *P. . , u 1,t u,t-1 1,t-1 • • + a . *P. , 0 + a , *P. , 0 + u, u,t-2 i,t-2 u,t-3 i,t-3 t 5.3 Regressions and Results 5.3.1 Regressions for Variable Selection For the regressions, a statistical program called vSHAZAM 6 7 6.0' was used with the 'MTS' computer system. First, the ordinary least squares method (OLS) was applied to regress equation (5.2) for each industry for the period of 1971-1977 and 1978-1984, respectively, incorporating all the above 68 variables. During the initial OLSs, the residuals were checked by using appropriate statistical methods in order to check whether heteroscedasticity and autocorrelation existed. By doing this, it was confirmed that there were no significant heteroscedasticity problems. However, autocorrelation was checked during the initial regressions. The D-W statistical test was not applied to check the autocorrelation because, a priori, higher order (especially fourth-order) autocorrelation was suspected as the data were quarterly (see, Thomas and Wallis, 1971, pp. 57-71). In order to check the autocorrelation between the current residual and the 6 7 The author is Dr. K. White in the Economics Department at University of British Columbia. 68 It was decided to place the sample period division at the fourth quarter of 1977 which may not match with the shift in the exchange rate (around 1976). It is because there are lagged variables (up to the three quarters). 106 lagged residuals: (1) the residuals from the initial OLS were obtained; (2) the residuals were lagged up to the fourth quarter; and (3) the current residual was regressed on the lagged residuals without the constant term (see, Johnston, 1984, pp. 69 304-325). There were autocorrelations - especially, the first and the fourth - in most industries. If there are autocorrelation problems, the estimated results will be biased and inconsistent. One way to deal with this problem is to apply the generalized least squares method (see Johnston, pp. 287-335). AUTO command with a option of '/order=n, n=1, 2, 3, 4, ...'in the 'SHAZAM' does the proper procedures automatically for GLS depending on the order (n) of autocorrelation. After running the OLSs or AUTOs (with the appropriate order) for the equations with all the above variables - for each industry and for 1971-1977 and 1978-1984, respectively, the results were obtained (see Appendix 5.3). Because the results included all the variables regardless of whether the variables were significant or not, it was necessary to review the variables for each industry and for each period, respectively. The variables which were statistically insignificant (at 95% with two-tail) for the corresponding industry were dropped via 69 The SHAZAM has a convenient command to check the autocorrelation. Command 'AUTO ,../order=4' prints the values of ROHs and the t-ratios up to the fourth lagged residuals. Therefore, it . is very easy to check that what order of autocorrelation is. 1 07 the processes of regressions. After the first regression, the t-ratios of variables were checked. The most insignificant variable(s) was dropped, and then regressed with the rest of variables. This process was repeated till the final variables 7 1 were selected. In each stage of the regression for variable dropping, autocorrelation problems were again checked and handled with the 'AUTO' command as explained the above. Another problem, in dropping the variables, was multicollinearity among the explanatory variables. Most of insignificant variables could be dropped without serious problems since multicollinearity problems were not detected. However, in dropping the insignificant variables, we had to pay attention to variables with a possibility of multicollinearity problems. First, the correlation matrix of coefficients was studied after the initial regression. When the value of correlation coefficient was equal to or larger than 0.70 between variables, e.g., A and B, it was considered that there was a multicollinearity. When the multicollinearity was detected between A and B, the handling of these variables was as follows: (1) If the t-ratio of A and the t-ratio of B were significant 70 Variable selections and the related criteria are very subjective. Further, because the outcomes of statistical tests for structural changes in a model between two sub-periods might be significantly different between a test with one set of variables and a test with the other set of variables, the methods with which the variables were handled and selected in this study have to be explicitly explained. 71 The critical alpha=0.025 (that is, 95% for two-tail test) was used in this study. 108 regardless of the high correlation coefficient, both A and B were included in the model; (2) If the t-ratio of A (B) was significant, and but the t-ratio of B (A) was insignificant, A (B) was included in the model. And then the dependent variable was regressed only on B (A). If the t-ratio of B (A) was turned out to be significant in this separate regression, B (A) was not dropped from the model even though the t-ratio of B (A) was low, assuming that the insignificant t-ratio of B (A) resulted from the multicollinearity problem with A (B); (3) If the t-ratio of A (B) was significant, but the t-ratio of B (A) was insignificant, A (B) was included in the model. And then the dependent variable was regressed only on B (A). If the t-ratio of B (A) turned out to be insignificant in this separate regression, B (A) was dropped from the model, assuming that the insignificant t-ratio of B (A) was due to the fact that B (A) was not a variable important to the dependent variable. The model was then regressed with the rest of variables; or (4) If the t-ratio of A and > the t-ratio of B were insignificant in the regression, the dependent variable was regressed on only A or B separately. From this separate regression, A (B) was included in the model regardless of the low t-ratio while B (A) was dropped if the t-ratio of A (B) was significant and but the t-ratio of B (A) was insignificant. If both t-ratios of A and B were insignificant in this separate 109 Table 5-1 Regressions With Finally Selected variables  (1971-1977 and 1978-1984) Industry Slaughtering Poultry Dairy & Meats SIC 1011 SIC 1012 SIC 104 Periods 1 2 1 2 1 2 Command AUTO AUTO AUTO AUTO AUTQ —- AUTO Order 4 2 4 4 4 4 Constant 1.43 -0.30 0.55 1.04 -1.98 1.36 (5.18) (-0.56) (0.83) (2.30) (-11.92) (3.52) DPMt 0. 62 0.66 0.53 0.88 0.51 0. 05 (11.94) (5.05) (3.58) (9.12) (21.01) (1.21) DPMt-1 0.16 0.09 (5.95) (4.27) DPMt-2 0.01 0.06 (3.24) (1.34) DPMt-3 -2.01 -0.36 (-6.21) (-4.24) DWt * * 0.45 0.17 (5.40) (2.33) DWt-1 -0.29 * * 0.65 f-4.09) (6.51) DSHIt -0.17 ** -0.04** 0.15 -2.72) (-2.73) (5.19) DSHIt-1 ** ** -0. 10 (-3.27) DPUSt 0.14 0.15 0.10 -0,20 (3.02) (2.14) (2.40) (-3.98) DPUSt-1 -0.32 0.16 (-3.80) (3.29) DPUSt-2 -0.14 0.29 (-3.16) (2.41) DPUSt-3 2 .47 -0.25 -0.42 16.67) (-2.26) (-5.01) 2 0.98 0.78 0.77 0.84 0.95 0.78 Ajtd-R 0.97 0.75 0.73 0.80 0.92 0.71 D.F. 18 24 20 22 14 21 - DPM: Rate of changes in material prices - DW: Rate of changes in wage - DSHI: Rate of changes, in shipments - DPUS: Rate of changes in import prices (U.S.) - Numbers in 'Order' row are the order of autocorrelation - 'AUTO' is a command in 'SHAZAM' to correct the autocorrelation problem - Period 1 is the first period (1971-1977) and period 2 is the second period (1978-1984) - t is the current time and t-1, t-2, and t-3 are lags Table 5-1 Regressions With Finally selected Variables (1971-1977 and 1978-1984) Industry Flour & Cereal SIC 105  Feed SIC 106 Biscuits SIC 1071 Periods Command Order AUTO 2 AUTO 3 AUTO 4 AUTO 4 AUTO 3 AUTO 4 Constant DPMt DPMt-1 DPMt-2 DPMt-3 1.33 (2.09) 0.83 (0.97) -0.39 (-2.83) 0.12 (3.81) -1.08 (-1.34) 0.99 (12.62) -0.22 (-2.66) 0.16 (4.17) -0.20 (-1.23) 0.45 (8.61) 0. 04 (2.17) 1.53 (15.61) 0.22 (9.65) 0.22 (3.93) -0.14 (-2.21) -0. 07 (-2.30) 2 . 11 (6.35) 0.24 (2.35) -0.37 (-2.05) 0.39 (3.13) DWt DWt-1 0.74 (3.14) 0.18 (3.05V -0.20 (-4.16) -0.26 (-3.33) -0.467 (-3.75) 0.14 (0.91) DSHIt DSHIt-1 ** ** -0.14** (-2.22) -0.53 (-5.4) ** 0.11 (4.19) -0. 01 (-0.21) -0.49 (-6.60) -0.16 (-2.48) DPUSt DPUSt-1 DPUSt-2 DPUSt-3 0.58 (3.75) 0.31 (2.10) 0.59 (2.25) 0.87 (4.26) -0.20 (-2.77) 0.26 (2.95) 0.11 (1-89) 0.28 (4.62) 0.34 (7.16) 0.66 (12.95) -0.19 (-4.20) 0.28 (2.02) -0.16 (-1.22) . 2 Ajtd-R P.F. 0.50 0.45 21 0.55 0.45 22 0.97 0.95 15 0.95 0.95 22 0.99 0.97 13 0. 69 0.55 18 - Critical point (95%) for two-tail test was used for t-ratios - Variables (with insignificant t-ratio) were left in the model, assuming that the low t-ratios were consequence of multicollinearity problem - Parentheses are t-ratios * Data was not available due to confidential ** Personal disposable income was used to substitute shipments /// Table 5-1 Regressions With Finally Selected Variables (1971-1977 and 1978-1984) Industry Bakery Confec Sugar Cane & tionary Beet SIC 1072 SIC 1081 SIC 1082 Periods 1 2 1 2 1 2 Command AUTO AUTO AUTO AUTO AUTO AUTO Order 4 4 3 4 4 4 Constant 0.66 1.40 -5.27 1.90 0.63 6.39 (2.15) (2.16) (-5.12) (1.93) (4.32) (2.67) DPMt 0.03 0.49 0. 42 0.12 0.96 1. 65 (1.18) (10.61) (10.60) (3.24) (18.77) (4.36) DPMt-1 0.04 0.14 -0. 62 -0.71 (1.10) (2.91) (-11.57) (-2.04 DPMt-2 0.24 0.26 0.21 (7.23) (4.94) (6.26) DPMt-3 DWt 0.15 0.50 * * (1.57) (2.20) DWt-1 -0.26 0.11 1.63 * * (-3.44) (2.19) (9.30) DSHIt 0.13** -0.06** -0.10 (4.20) (-5.02) (-2.57) DSHIt-1 0.13** ** 0.13 (2.91) (3.14) DPUSt 0.60 -0.44 -0.78 0. 64 (6.70) (-3.58) (-5.68) (12.00) DPUSt-1 -0.59 0.39 0.21 0.56 -0.47 (-7.71) (4.37) (8.54) (4.20) (-8.09) DPUSt-2 -0.42 0.35 -0.72 (-3.74) (8.52) (-2.05 DPUSt-3 0.26 (4.28) Rz 2 0.96 0.84 0.92 0.64 0.97 0.36 Ajtd-R | 0.94 0.78 0.89 0.55 0.96 0.28 D.F. 13 19 17 23 18 24 Table 5~l Regressions With Finally Selected Variables (1971-1977 and 1978-1984) Industry Vegetable Oil SIC 1083 Soft drink SIC 1091 Distillery SIC 1092 Periods Command Order OLS AUTO 4 AUTO 4 AUTO 3 AUTO 4 AUTO 4 Constant DPMt DPMt-1 DPMt-2 DPMt-3 0.09 (0.11) 0.86 (18.03) -0.37 (-0.59) 0.47 (5.72) -0.47 (-1.15) 0.39 (10.65) 0.17 (3.18) 0.17 (3.47) -0.24 (-0.24) 3.30 (7.88) 0.21 (9.76) -0.21 (-9.20) -0.23 (-0.67) -0.34 (-4.28) 0.21 (2.34) -0.24 (-3.33) DWt DWt-1 0.62 (4.45) -0.33 (-2.27) -0.76 (-12.47) 0. 60 (4.93) DSHIt DSHIt-1 -0.05 (-3.13) -0.04 (-16.16) DPUSt DPUSt-1 DPUSt-2 DPUSt-3 0.25 (3.45) -0.39 (-3.19) 0.25 (2.25) 0.88 (6.50) -0.17 (-4.91) 0.36 (6.27) -0.18 (-3.48) 0.48 (3.30) 0.72 (5.13) -0.40 (-2.30) R. 2 Ajtd-R P.F. 0.93 0.92 22 0.89 0.88 24 0.85 0.81 17 0.78 0.75 24 0.85 0.80 17 0. 64 0. 52 20 Table 5-1 Regressions With Finally Selected Variables  (1971-1977 and 1978-1984) Industry Brewery SIC 1093 Winery SIC 1094 Periods Command Order 1 2 OLS AUTO 3 1 2 AUTO AUTO 4 4 Constant DPMt DPMt-1 DPMt-2 DPMt-3 2.94. 3.78 (2.84) (2.13) -0. 54 (-2.48) 3.05 0.72 (4.41) (0.08) -0.33 (-4.05) 0.40 0.25 (4.51) (5.48) DWt DWt-1 -0.56 (-2.36) * * * * DSHIt DSHIt 0.09 (3.18) -0.89 (-2.69) -0.09 (-2.48) -0. 06 (-6.69) DPUSt DPUSt-1 DPUSt-2 DPUSt-3 0.48 (0.79) 0.51 (3.36) R2 2 Ajtd-R | D.F. 0.22 0.34 0.15 0.26 21 24 0.47 0.80 0.39 0.77 20 24 ii4 regression, both A and B were deleted from the model. In general, the model works very well inexplaining changes in industry selling prices for the two sample periods (1971-1977 and 1978-1984), with some exceptions. The model does not provide a good fit for the first period of the winery industry, the second period of biscuit, confectionary, sugar cane & beet, and distillery industries, both periods of flour & cereal and brewery industries (see Table 5-3 for summary). Especially, industries controlled and governed by the government, e.g, the brewery industry (the adjusted R is under 0.30), have lower adjusted R2s. For most of the industries the goodness of fit is very different between the first period and the second period. For slaughtering & meat, dairy, biscuits, bakery, confectionary, cane & beet sugar, vegetable oil, soft drink, and distillery industries (9 out of 14 industries), our pricing model explains pricing behaviour much better in the first period than in the 2 second period (higher adjusted R in the first period). There might be, for these industries, some other factors that influence the pricing behaviour in the second period. For example, 97% of slaughtering & meat industry in the first period could be explained with our equation. That is, 3% of the pricing behaviour 2 should be explained by other factors. However, the adjusted R in the in the second period was 0.75. Our equation could not catch 25% of the pricing behaviour in the second period. There must have been greater number of excluded variables or unexpected 115 factors in the second period; one example could be a price war. It could be said that, given that the industries behaved according to our pricing model and the variables, pricing mechanisms and behaviour in the first period were better, more stabilized, more expectable, more controllable, or more reliable for these industries. On the other hand, poultry, brewery, and 2 . winery industries had a higher adjusted R in the second period. If we can apply the same explanation, these industries had better, more reliable, more controllable, or more stabilized pricing behaviour. Interestingly, these industries are controlled by governments or marketing boards. It could be said, for the second period, that governments and marketing boards gave more weight or consideration to the changes in these variables when the time for the change of prices came. Two other industries, flour & cereal and feed industries, had the same or close 2 adjusted R for for periods. The outcome of the regressions more likely reflects the characteristics of eace industry. As explained in previous sections, particular variables which were significant in certain industries were not so in other industries. Furthermore, a r variable(s) which was significant in the first sub-period was not necessarily significant in the second sub-period, probably because of different processing characteristics among food processing industries. Most of the material prices were statistically significant in each industry. However, no material prices were significant in the first period for the flour & 1 16 cereal and brewery industries and the second period for the soft drink industry. There is a no explanation for this. It could be that: (a) the changes in material price could not be reflected due to other factors (e.g., price wars); (b) the weights of material price changes within the industry selling price changes were small because of other factors (e.g., tax increases); or (c) the industry selling price changes could follow the trends of U.S.'s counterpart industries as there were close business relationships between the two countries (e.g., franchise, use of the same materials (syrups for soft drink), and parent & subsidiary firms). The industry selling price for industries using perishable or live materials, e.g., slaughtering & meats, poultry, sugar cane & beets, and vegetable oil, were mostly influenced by only the current and the first lagged material price. On the hand, for industries which used non-perishable materials or which required longer material processing time, e.g., dairy, feed, biscuit, bakery, confectionary, and soft drink industries, the second and the third lagged material price were also significant. When the current and the lags were both significant, the coefficient of the current material price was the largest and the magnitude (in absolute value) became smaller as the lags were getting higher, except for the first period of the bakery and winery industries and the second period of the biscuit and the confectionary industries (these industries use non-perishable materials). All significant current material prices had positive signs, except for the second period of the 1 1 7 distillery industry and the first period of the winery industry. However, the negative sings were also shown in the lags. These negatives signs are a very common outcome in regressions with 72 lagged variables. In the long term, the coefficients of these current and lagged material price variables are added together. When this was done, except for the second period of the slaughtering & meat, the flour & cereal, the distillery, and the brewery industries, they were all, positive. When the two sub-periods were compared by using the net coefficient value (sum of positive and negative value in current and lags) of material price, there was no clear trend, i.e., impacts of changes in input costs on the industry selling price changes were greater in the second period as Karikari (1988) found. The coefficient (in absolute value) of the first period in Examples: DPfc PMt DPMt_1 DPMfc_ 2 DPMt -3 110- 105 100 - 95 95 - 99 99 - 93 93 - 1 00 1 05 95 99 93 100 + - + - + -110- 105 93 - 95 95 - 91 91 - 94 94 - 90 1 05 95 91 93 90 + - + - + 105- 110 95 - 100 100- 91 91 - 90 90 - 88 1 10 100 91 90 88 - - +• + + 1 18 dairy, feed, confectionary, vegetable oil, and soft drink industries was greater than that of the second period. That is, the impacts of material price changes on the industry selling price changes were greater in the first period for these industries. However, the coefficient, (in absolute value) for second period was greater in slaughtering. & meat, flour & cereal, bakery, sugar cane & beet, distillery, brewery, and winery industries. Also, the poultry and biscuit industries' coefficients were about the same. When wage and material price variables were added, the coefficients for the first period of biscuit and distillery industries became greater than those of the second period. Therefore, 7 of 14 industries had greater coefficient values (of input cost) in the first period while 6 of 14 industries had greater coefficients values in the second period. Consequently, the hypothesis that monopolistic pricing behaviour had a more dominant role in the second period seems to lose ground. One interesting result to be discussed is the shipment variables. This study has introduced shipment variables from the Bilateral Monopoly Theory, in which the Canadian processed food retailers have price negotiation power against the food processors. The shipment variables became a measurement of the willingness to accept risk for both retailers and processors. Therefore, the significance (with a negative sign) of these variables could mean that the processors' monopolistic (or oligopolistic) pricing power was weaker, that is, the processors 1 19 could not change the prices solely based on the changes in input costs. These variables (current or lagged) were significant in most industries (at least in one period), except for sugar cane & beet and vegetable oil industries, indicating that the retailers' negotiation power significantly contributed to the industry selling price changes. This means the loss of monopolistic pricing behaviour with respect to retailers. The sugar cane & beet and vegetable oil industries' pricing behaviour was not influenced by the retailers' pricing behaviour. Interestingly, the concentration rates for 8 enterprises of these two industries are shown as 100% in Table 2-2. However, we could not see any significant relationship between the magnitude of the coefficients and the concentration rates. In previous studies, the signs of the shipment variables (or other demand variables) were expected to be positive as these variables were regarded as measurements of 'excess demand'. However, the signs of shipment variables could not be determined in this study and, as discussed in section 3.4.3, could be positive or negative depending on the situation. The signs of net effects of these variables were mostly negative, except for the first period of the dairy and the bakery industries, and the second period of the feed and the brewery industries. This means that the price came down as retailers purchased more (volume purchasing). Because the retailers try to cut prices at the bargaining table, these negative signs also support the strong retailers' role in the pricing mechanism; the negative signs 120 probably mean that the retailers' negotiating power is stronger or more powerful than the processors'. On the other hand, the processors probably had more negotiating power in the first period for the dairy and bakery industries, and in the second period for the feed and brewery industries as the signs were positive. The positive sign could occur under the full production capacity utilization. When the two sub-periods are compared, it seems that slaughtering & meat, dairy, feed, bakery, confectionary, distillery, brewery, and winery industries enjoyed greater negotiating power in the first period as shipment variables were significant (or had greater magnitude of coefficients after adding the current and the lags, in absolute value), while the poultry, flour & cereal, biscuits, and soft drink industries had greater power in the second period. The import variable provided another interesting result. Even though there were industries (e.g., dairy and poultry) that had no or very minor trade competition with U.S., the import price variables were, ad hoc, regressed for all the industries. All the industries except for the first period for vegetable oil and winery industries and the second period for the brewery industry were influenced by import prices (U.S. prices). Like material prices, the current and' lagged variables turned out to be significant with a mixture of positive and negative signs. As explained at the end of 3.5.2.2, the expected signs for the import variable could be positive or negative, depending on the 121 characteristics of the corresponding industry. The coefficients for the current and lagged variables were added to determine the long term effects. Both periods for the poultry and dairy industries, the first period for the soft drink industry, and the second period . for the bakery, confectionary, and sugar cane & beet industries had negative signs. These negative signs can be explained as follows: (a) an increase in the import price results in a reduction in the consumption of imported goods, which increases the consumption of domestic goods. An increase in the consumption of domestic goods will give more negotiating power to the retailers, which in turn decreases the industry selling prices; or (b) depreciation in the Canadian dollar will result in greater export of domestic goods, increasing in the price elasticity of domestic goods, which will decrease the industry selling price (Karikari, 1988). When the two sub-periods were compared, unlike what Karikari (1988) claimed, the role of import prices in the Canadian processed foods industries' pricing behaviour was not greater in the first period. Only the dairy, biscuit, and brewery industries had greater coefficient magnitudes (in absolute value after adding the current and lagged variables), indicating that they were more strongly influenced by import prices in the first perod. On the other hand, the slaughtering & meats, flour & cereal, feed, bakery, sugar, cane & beet, vegetable oil, soft drink, distillery, and winery industries had greater coefficient magnitudes in the second period. The poultry and confectionary 1 22 industries had about the same magnitude in the both periods. Consequently, it appears that the Canadian processed foods industries were more influenced in the second period by import prices regardless of depreciation in the exchange rate in the second period, contrary to the results of Karikari's (1988) study. It is questionable whether the role of import price changes (input cost changes) in the Canadian processed foods industries' pricing behaviour was increased as the weight of the input cost (import price) variables in the pricing behaviour became smaller as the period changed, or vice-versa. Of the nine industries (slaughtering & meats, flour & cereal, feed, bakery, sugar cane & beet, vegetable oil, soft drink, distillery, and winery industries as discussed in the above paragraph) which had smaller coefficient magnitudes for import prices in the first period, becoming larger in the second period, only the feed, vegetable oil, soft drink, and distillery industries's coefficient magnitudes for material prices became smaller in the second period. On the other hand, the slaughtering & meat, flour & cereal, bakery, sugar cane & beet, and winery industry's coefficient magnitudes for material prices became larger as the coefficients of import prices became larger in the second period. Of the dairy, biscuit, and brewery industries, which had larger import price coefficients in the first period, and which became smaller in the second period, only the brewery industry's material price coefficient became larger in the second period as 123 the import price coefficients became smaller while the dairy and biscuit industries' material price coefficients were larger in the first period. From this evidence, it canot be said that the importance of import prices (input cost) in the Canadian processed foods industries' pricing behaviour was weaker (stronger) as the exchange rates shifted in the middle of the 70s. 5.3 Tests of Changes in Pricing Behaviour As discussed in section 3.7, it is hypothesized that the importance of the input costs or the importance of import price in the Canadian food processors' pricing behaviour could change according to Karikari' hypothesis as the exchange rate shifts. This hypothesis turned out to be untrue as explained in the last section even though it seems that the regression results provide some evidence for the changes in pricing behaviour. In the last section the model was regressed for each sub-period and the variables were selected for each industry, assuming that changes in pricing behaviour occurred in the mid-703. As shown in Table 5-1, it was then concluded that, for most industries, the pricing behaviour differs between the two sub-periods even thogh the result is far from the Karikari's. In this section a statistical test will be performed in order-to verify if the changes are statistically significant. Usually two statistical techniques, dummy variables and Chow-test, have been used for the test of structural changes. As shown in Table 5-1, most equations had autocorrelation problems, 1 24 especially of the fourth order. When there are autocorrelation problems, the Chow-test is not valid unless the RHOs for the two sub-periods are the same. Therefore, use of dummy variables seems to be more appropriate for tests in this study. The procedures are as follows. Variables were selected in the previous section for each industry and each period. The variables selected for the first period and the second period were incorporated in a equation. Dummy variables (Z) were set up for each variable, including a dummy variable for the constant term. Dummy variables are equal to 1 for the second period; 0 otherwise. Then this new equation was regressed for the whole sample period (1971-1984). For example, DPMfc and DPMfc_1 were selected for the first period and the second period, respectively. The new equation, incorporating both DPMfc and DPMfc_1, with dummy variables (Z) is DP = a1 + b^Z + a2*DPMt + b2*Z*DPMfc +a3*DPMt_1 + b3*Z*DPMfc_1 + u The hypothesis is that b1=b2=b3=0. After regressing the equation for the combined sample period (1971-1984), we compared the calculated chi-square value and the critical chi-square values for the given degree of freedom (number of restricted variables). If the calculated chi-square value falls in between the two critical values (lower bound and upper bound), the hypothesis may be accepted, indicating that there are no structural changes between the two periods. In this study, however, the error terms for most of estimated equations with dummy variables were autocorrelated. Similar to 125 the AUTO command used in the variable selection, this autocorrelation problem was handled with the same AUTO command with options of ORDER, ML, DN, and GS was used, where ORDER sets the number of autocorrelation orders, ML does the maximum likelihood estimation for RHOs, DN computes the estimated variance of the regression line by dividing the residual sum of squares by n instead of n-k, and GS does the grid search to estimate RHOs (see SHAZAM manual). The regression results are presented in Table 5-2. Table 5-2 provides the calculated chi-square values, the critical values of the lower and upper bounds (at lower bound = 0.975 and upper bound = 0.025) and the degree of freedom (number of restricted variables). The poultry, sugar cane & beet, vegetable oil, brewery, and winery industries' calculated chi-square values were within the critical boundaries, respectively, accepting the hypothesis that there are no structural changes between the two sub-periods. For the rest of industries, it could be said that the pricing behaviour was changed in the mid-70s as the calculated chi-square values exceeded the critical values. However, as discussed in the above section, it cannot be said that industries' pricing behaviour was, as a general rule, changed according to Karikari's hypothesis. Rather, it could be concluded that the behavioral changes occurred in a way that reflects the characteristics of the corresponding industry. 1 26 Table 5-2 Tests of Structural Changes with Dummy Variables (1971 - 1984) Industry Slaughtering Poultry Dairy Flour & & Meats Cereal SIC 1011 SIC 1011 SIC 104 SIC 105 Command AUTO AUTO AUTO AUTO Order 2 4 4 2 Constant 0.69. (0.69) 0.52 (0. 91) -1.67(-7. 93) 0.91 (1. 23) Z 0.32 (0.28) 0.02 (0. 03) 2.49 (8. 04) 0.28 (0. 28) DPMt 0.62 (2.96) 0.61 (6. 05) 0.45(12. 52) ZDPMt 0.29 (0.44) 0.23 (1. 05) -0.18(-2. 77) DPMt-1 0.09 (0.89) 0.04 (1. 11) ZDPMt-1 -0.12(-0.72) 0.30 (4. 56) DPMt-2 0.09 (2. 22) -0.00(-1. 35) ZDPMt-2 0.15 (1. 70) -0.18(-1. 34) DPMt-3 0.00 (0.01) -0.00(-0. 04) -0.00(-1. 28) ZDPMt-3 -2.02(-6.55) -0.14(-0. 76) 0.79 (5. 10) DWt 0.41 (5. 47) ZDWt -0.44(-3. 15) DWt-1 -0.18(-0.74) 0.50 (5. 47) ZDWt-1 -0.47(-1.32) -0.40(-2. 83) DSHIt 0.05 (0.20) 0.01 (0. 21) 0.18 (5. 88) 0.16 (1 .22 ZDSHIt 0.01 (0.02) -0.03(-0. 70) -0.14(-2. 52) -0.20(-1. 22) DSHIt-1 -0.06(-2. 40) ZSHIt-1 0.15 (3. 30) DPUSt 0.09 (0.61) 0.09 (1. 89) 0.07 (0. 89) 0.58 (4 . 34 ZDPUSt -0.46(-0.72) 0.09 (1. 24) -0.23(-1. 89) -0.43(-1. 09) DPUSt-1 -0.37(-4. 98) 0.22 (1. 64) ZDPUSt-1 0.28 (2. 12) -0.37(-1. 12) DPUSt-2 0.02 (0. 52) 0.29 (3. 13) ZDPUSt-2 -0.00(-0. 04) -0.40(-3. 14) DPUSt-3 0.01 (0.06) 0.09 (1. 72) -0.34(-3. 89) ZDPUSt-3 2.56 (7.62) -o.12 r-i. 53) 0.22 (1. 97) Ry 2 0.82 0.75 0.88 0.59 Ajtd-R | 0.75 0.67 0.79 0.47 D.F. Asymptotic Asymptotic Asymptotic Asymptotic Chi-square 81.05 12.58 225.15 31.42 D.F. 8 7 12 6 Lower-bound 2.700 1.690 4.404 1.237 Upper-bound 19.023 16.013 23.337 14.449 - Zs are dummy, where Z=l for the second period (1978-1984) and Z=0 for the first period (1971-1977) - Critical bounds: Lower-bound at 0.975 and Upper-bound at 0.025 - t is the current time and t-l, t-2, and t-3 are the time lags. 1^7 Table 5-2 Tests of Structural Changes with Dummy Variables  (1971 - 1977 and 1978 - 1984) Industry Feed Biscuits Bakery Confec tionary SIC 106 SIC 1071 SIC 1072 SIC 1081 Command AUTO AUTO AUTO AUTO Order 4 4 4 3 Constant -0.71(-1 .69) 1.52 (5. 56) 1.58 (3. 55) -1.73(-2. 44) Z 0.41 (0 .79) 0.22 (0. 60) 0.73 (1. 23) 5.16 (4. 20) DPMt 0.95(12 .85) 0.13 (2. 50) 0.16 (4. 77) 0.19 (4. 21) ZDPMt -0.49(-3 .31) -0.35(-3. 01) 0.33 (5. 49) 0.01 (0. 18) DPMt-1 -0.23(-4 .46) 0.40 (3. 76) 0.09 (2. 65) ZDPMt-1 0.30 (2 .58) -0.20(-0. 91) -0.01(-0. 27) DPMt-2 0.13 (5 .23) -0.20(-l. 79) 0.04 (1. 24) 0.10 (2. 40) ZDPMt-2 -0.09(-1 .49) 0.65 (2. 79) 0.02 (0. 35) 0.07 (1. 20) DPMt-3 -0.01(-0. 06) ZDPMt-3 -0.2K-1. 45) DWt -0.22(-1. 78) -0.71(-0. 63) 0.29 (1 .22 ZDWt -0.03(-0. 23) 0.28 (2. 18) -0.61(-2. 16) DWt-1 0.57 (4 .17) -0.08(-0. 43) -0.39(-3. 81) 1.01 (5. 31) ZDWt-1 -0.33(-1 .60) 0.18 (0. 97) 0.40 (3. 55) -0.63(-2. 72) DSHIt -0.42(-6 .24) -0.09(-l. 50) 0.08 (4. 35) -0.14(-3. 26) ZDSHIt 0.42 (3 .95) 0.11 (1. 09) -0.11(-4. 41) 0.13 (2. 39) DSHIt-1 0.08 (0 .97) -0.06(-1. 01) 0.04 (1. 67) 0.06 (1. 46) ZSHIt-1 0.06 (0 .50) -0.14(-1. 75) -0.04(-1. 55) -0.0K-0. 16) DPUSt -0.17(-2 .67) 0.07 (0. 64) 0.01 (0. 12) 0.11 (2. 90) ZDPUSt 0.43 (2 .88) -0.16(-0. 88) -0.14(-0. 81) -1.15(-5. 23) DPUSt-1 0.23 (4 .38) 0.26 (2. 45) 0.14 (3. 48) ZDPUSt-1 -0.26(-2 .51) -0.38(-2. 30) 0.12 (0. 70) DPUSt-2 0.92 (6. 93) -0.17(-1. 94) ZDPUSt-2 -1.07(-5. 26) -0.06(-0. 48) DPUSt-3 0.07 (2 .27) -0.41(-3. 79) 0.43 (5. 52) ZDPUSt-3 -0.12(-1 .74) 0.90 (4. 83) -0.67(-5. 04) * 2 0.95 0.85 0.87 0.81 Ajtd-R | 0.93 0.73 0.76 0.72 D.F. Asymptotic Asymptotic Asymptotic Asymptotic Chi-square 82.96 195.53 126.07 57. 08 D.F. 10 12 12 9 Lower-bound 3.247 4.404 4.404 2.700 Upper-bound 20.483 23 .337 23.337 19.023 - Parenthesis are t-ratio - D.F. for t-ratio is asymptotic - D.F. for Chi-square test is the number of restricted variables - 'AUTO' means a command in 'SHAZAM* to correct the autocorrelation problem - Numbers in 'Order' row means the order of autocorrelation VX1 Table 5-2 Testa of Structural Changes with Dummy Variables (1971 - 1977 and 1978 - 1984) Industry Sugar Cane & Vegetable Soft drink Distillery Beet Oil SIC 1081 SIC 1083 SIC 1091 SIC 1092 Command AUTO AUTO AUTO AUTO Order 1 1 4 4 Constant 0.49 (0.23) 0.04 (0.05) 0.39 (0.42) 3.84 (5.59) Z 3.94. (1-24) 0.11 (0.09) 1.38 (1.08) -4.00(-4.78) DPMt 0.69 (3.04) 0.83(13.09) -0.02(-0.42) ZDPMt 0.27 (0.64) -0.31(-2.41) -0.27(-2.72) DPMt-1 -0.38(-1.56) 0.30 (5.36) -0.13(-1.96) ZDPMt-1 0.03 (0.08) -0.35(-5.00) 0.34 (2.72 DPMt-2 -0.02(-0.37) 0.07 (1.62) ZDPMt-2 0.07 (0.75) -0.43(-3.88) DPMt-3 0.15 (2.50) 0.09 (1.22) ZDPMt-3 -0.17 r-l.61) -0.10(-0.80) DWt -0.11(-0.43) -0.77(-4.28) ZDWt 0.11 (0.43) 0.75 (2.72) DWt-1 0.18 (0.63) -0.14(-0.99) ZDWt-1 -0.73(-2.05) 0.63 (2.73) DSHIt -0.03(-2.33) ZDSHIt -0.44(2.50) DSHIt-1 0.00 (1.23) ZSHIt-1 -0.07(-3.51) DPUSt 0.60 (3.81) 0.00 (0.07) 0.22 (1.94) 0.33 (3.50) ZDPUSt -0.37(-1.01) 0.22 (1.86) 0.66 (2.17) 0.42 (2.19) DPUSt-1 -0.25(-1.23) -0.05(-0.48) ZDPUSt-1 0.36 (0.91) 0.05 (0.20) DPUSt-2 0.15 (0.70) -0.21(-2.70) ZDPUSt-2 -0.58(-1.75) 0.88 (5.21) DPUSt-3 -0.08(-2.00) -0.37(-4.04) ZDPUSt-3 -0.10(-1.39) -0.03(-0.14) # 2 0.56 0.92 0.79 0.72 Ajtd-R | 0.43 0.91 0.68 0.53 D.F. Asymptotic Asymptotic Asymptotic *•. Asymptotic Chi-square 4.16 6.99 45.31 232.65 D.F. 6 4 9 11 Lower-bound 1.237 0.484 2.700 3.816 Upper-bound 14.449 11.143 19.023 21.920 Table 5-2 Tests of Structural Changes with Dummy Variables  (1971 - 1977 and 1978 - 1984) Industry Brewery SIC 1093 Winery SIC 1094 Command Order AUTO 1 AUTO 3 Constant Z DPMt ZDPMt DPMt-1 ZDPMt-1 DPMt-2 ZDPMt-2 DPMt-3 ZDPMt-3 1.96 (2.45) -1.07(-3.03) 0.00 (0.00) -0.55(-0.25) 2.12 (3.00) -0.20(-0.16) -0.17(-1.86) 0.12 (0.69) 0.21 (2.33) -0.07(-0.39) DWt ZDWt DWt-1 ZDWt-1 0.29 (0.54) 0.03 (0.72) DSHIt ZDSHIt DSHIt-1 ZSHIt-1 0.00 (0.05) 0.07 (0.06) -0.08(-2.32) 0.14 (2.27) -0.00(-0.35) -0.00(-0.18) -0.00(-0.35) -0.07(-2.34) DPUSt ZDPUSt DPUSt-1 ZDPUSt-1 DPUSt-2 ZDPUSt-2 DPUSt-3 ZDPUSt-3 0.66 (0.85) 0.04 (0.05) -0.46(-0.72) -0.0K-0.12) 0.18 (1.28) 0.02 (0.09) * 2 Ajtd-R | D.F. 0.28 0.04 Asymptotic 0.47 0.32 Asymptotic Chi-square D.F. Lower-bound Upper-bound 11.24 7 1.690 16.013 13.74 6 1.237 14.449 ' 2)0 Table 5-3 Summary of Results - P - L Effect Effect Effect Signs Signs e o from from from of of r w DPMs DPMt DPMt-2 DPMt Sum of i DPMt-1 DPMt-3 DPMs o R 1 1 I I | | d Slaughter 1 y y + + ing & meat 2 y y + — Poultry 1 y y - + 2 y y — + Dairy 1 y y + + 2 y y + + Flour & 1 * no nil nil nil cereal 2 y y + — Feed 1 y y + + 2 y y + + Biscuit 1 y y + + 2 * y y + + Bakery 1 y y + + 2 y y + + Confec 1 * y y + + tionary 2 y y + + Sugar cane 1 y y + + & beet 2 * y y + Vegetable 1 y y + + oil 2 y y + + Soft drink 1 y y + + 2 no nil nil nil Distillery 1 * y y + + 2 * y y — — Brewery 1 * no nil nil nil 2 * y y + — Winery 1 * y y - + 2 y y + + 131 Table 5-3 Summary of Results - P -Larger Larger Effect Signs Larger Larger e Effect Effect from of Effect Effect r from from SHIs Sum from from i DPMs DPMs of SHIs SHIs o in in SHIs in in d Prd. 1 Prd. 2 Prd. 1 Prd. 2 Slaughter 1- y - * ing & meat 2 y — Poultry 1 y ' -2 y — * Dairy 1 * y + 2 y — Flour & 1 y — cereal 2 * y — * Feed 1 * y — * 2 y + Biscuit 1 y — 2 y — * Bakery 1 y + * 2 * y — Confec 1 * y — tionary 2 y — Sugar cane 1 no nil & beet 2 * no nil Vegetable 1 * no nil oil 2 no nil Soft drink 1 * y -2 y — * Distillery 1 y -2 * y — Brewery 1 y -2 * y + Winery 1 y - * 2 * y — IS % Table 5-3 Summary of Results - P -Effect Signs Larger Larger Struc e from of Effect Effect tural r Import Sum of from from Change i Prices Import Import Import o Prices in in d Prd. 1 Prd. 2 Slaughter 1 y + ing & meat 2 y + * y Poultry 1 y -2 y no Dairy 1 y • -2 y — y Flour & 1 y + cereal 2 y + * y Feed 1 y + 2 y + * y Biscuit 1 y + 2 y + y Bakery 1 y + 2 y — * y Confec 1 y + tionary 2 y — y Sugar cane 1 y + & beet 2 y — no Vegetable 1 no nil oil 2 y nil no Soft drink 1 y — 2 y + * y Distillery 1 y + 2 y + * y Brewery 1 y + * 2 no nil no Winery 1 no nil 2 y + * no |3H 15S CHAPTER 6 SUMMARY AND CONCLUSIONS This thesis has studied fourteen Canadian processed food industries and their pricing behaviour, has derived pricing models for each industry for the period of 1971-1984, and has tested whether changes in pricing behaviour has taken place in the mid-70s due to a shift in the exchange rate. In this chapter, summaries, conclusions, and recommendations will be drawn. 6.1 Summary and Conclusion As food expenditures represent a significant proportion of the Canadian disposable income, it is important to understand the process of food price formation. Quantitative analyses for the Canadian food processors' pricing behaviour, such as econometric models, are valuable to economists and policy makers. Food prices change for several reasons. The main reason for changes in processed food prices are probably changes in input costs and demand factors. Input costs consist of material, labour, capital and fuel cost. As for changes in the demand side, import competition and excess demand are important variables. This study has attempted to establish, identify, and analyze pricing models by employing such variables for fourteen Canadian processed food industries at the wholesale level. As Karikari (1985) has shown, the Canadian manufacturing industries changed their pricing behaviour as the U.S. - Canada exchange rate shifted in the mid-70s. This study also has tested 136 whether the changes (shift) in pricing behaviour of the food processing industries took place between two sub-periods; pre-depreciation of U.S.-Canada exchange rate (1971 to 1977) and post-depreciation of U.S.-Canada exchange rate (1977 to 1984). In order to establish and identify a proper pricing model that reflects Canadian food processing industries' pricing mechanism and behaviour, discussions of the characteristics of Canadian food processors and food retail distributors were necessary. From the analysis of food processors, in chapter 2, it was found that material cost was the highest of the food processors' input costs. Labour cost was the second highest followed by energy cost. It was concluded, by analyzing the 'enterprise concentration' and 'concentration in food retail distribution', that Canadian food processors were characterized as oligopoly and retail food distributors as oligopsony. It was also demonstrated that food prices could not be determined by only the food processors. Since the retail distributors were oligopsonic, had control of shelf spaces, and retained other factors, the retail distributors could exert price negotiating power through volume purchases and generic label orders. Based on the characteristics discussed above, three economic theories were discussed in chapter 3. The Mark-up Pricing Theory was employed to explain the food processors' oligopolistic pricing behaviour. From the Mark-up Pricing Theory, percentage changes in mark-up, material cost, labour cost, energy cost, capital cost, and productivity of each input were derived as 1 37 independent variables in the pricing model while change in industry selling price of processed foods was a dependent variable. Excess demand and import competition were the main factors which caused fluctuations in the mark-up factor. The Bilateral Monopoly Theory was applied to explain bargaining processes between processors and retailers, in determining the price of processed foods, . A shipment variable was derived from the Bilateral Monopoly Theory to substitute for the mark-up variable. A International Trade Theory was discussed for industries that faced import competition. From this theory, it has been concluded that import competition would also influence Canadian food processors' mark-up. It was discussed how the pricing behaviour would change in a situation in which shifts in exchange rates occurred. Chapter 4 explained the methods of collecting data, sources of data, and modifications of data. In the final equation, percentage changes in material price, wages, import prices, and shipments were employed while energy cost, capital cost, and productivity of each input were dropped due to lack of data and/or unsuitable application. All the data were converted into quarterly form. Before the data were estimated for the equation, lags for independent variables were introduced in chapter 5 in order to reflect the characteristics of the food processing industries properly. First, the equation was estimated for each period to identify which variables were significant for each industry's 1 38 price changes. It seemed that the regression results for each industry were different for the first and the second period. After the variable selection for each sub-period, a statistical test using dummy variables was performed to check whether structural changes had occurred in the mid-70s were statistically significant. The above regression results can be summarized as follows: (1) The pricing model, in general, fits the sample data quite well with some exceptions; (2) Different variables and different lags should be applied to each industry for the different periods; (3) The material prices-in different lag forms-are the main factors that influence changes in the industry selling price. The signs of the long term effects are mostly positive; (4) In the industries, e.g., slaughtering & meat, poultry, and sugar cane & beet industries, which use perishable or live materials, the current material price is usually significant. Otherwise, the lagged material prices are more important; (5) In some industries, the material prices are not important at all. Only the U.S. prices are shown as important factors, e.g.,. the first period for brewery and flour & cereal industries; (6) The wage - current or lagged - is an important variable in some industries; (7) The shipment (or the income) variables are important in most industries. The signs of long term effects are mostly negative, indicating that the industry selling price was not 1 39 determined by only the food processors' monopolistic pricing behaviour; (8) The U.S. price variable(s) is a significant factor in most industries with a mixture of positive and negative signs. Also, this variable is significant in the industries that seem to have minimum or small trade'competitions with U.S. industries; (9) Most of the industries had structural changes and/or model changes between the two periods. However, the poultry, sugar cane & beet, vegetable oil, brewery, and winery industries were not changed. However, the pricing behaviour was not necessarily changed according to Karikari's hypothesis; and (10) Pricing behaviour was changed in a way that reflects the characteristic of each industry. 6.2 Limitations and Recommendations After deliniating general the characteristics of the food processing industries' pricing mechanism, a basic model developed from the Mark-up Pricing Model was applied to 14 food processing industries. This provided poor estimation results for certain industries, such as the brewery and distillery industries. -It is recommended that each industry be studied more closely and that the characteristics and the market mechanism of the food processing industries should be identified before a specific pricing model is applied for the industry in order to better reflect each industry's pricing behaviour. As is a common problem in research of the food processing 1 40 industry, collection and modification of data was also very difficult in this study, particularly, when dealing with material costs. In order to create a material price index, more than 100 materials are required in certain food processing industries. An atempt was made to collect and employ as many as material inputs as possible. This means that relatively less important materials were used in calculating material price index, which could cause noises which produced a poor relationship with the industry selling prices in some industries. It is recommended that material price index be derived by combining only major material components in the future studies. It is very difficult to separate the net effect of U.S. price changes on Canadian price changes from the coefficients of U.S. price variables. There are various reasons for this. Canada and U.S. often face common input markets in the world; i.e., nuts, sugar canes, and so forth. When prices of these input materials are increased in the U.S. market, the prices of these materials go up in Canada as well because the materials used by Canadian food processors are purchased in U.S. future markets. If material prices are increased in U.S., then output prices will go up in U.S.. Since material prices in U.S. go up, the material prices in Canada will go up because these materials are purchased in the U.S. market, resulting in increases in Canadian output prices. Therefore, it is difficult to identify the net effect of U.S. output price changes, which result from import competition, on Canadian output price changes. 141 U.S. and Canadian firms are related in various ways: franchises, multinational firms, and licenses. In the case of the soft-drink industry, an increase in the price of a soft-drink syrup in the U.S. market means an increase in the price of soft-drink syrup in Canada as well because most Canadian soft-drink bottlers purchase soft-drink syrup from U.S. franchisers under the franchise agreement. Therefore, the positive relationship with the U.S. price variable, to some extent, results from input price increases in both countries. This study has concluded that the Canadian food processors' pricing behaviour, to a great extent, has been changed/or shifted in the middle of the 70s. This shift in pricing behaviour could have been caused by a shift in the U.S.-Canadian dollar exchange rate or could be the results of some other economic phenomenon. Unfortunately, this study was not able to provide a clear answer to this question. Finally, even though this study provides some evidence of changes in pricing behaviour, this study does not conclude an agreement with Karikari's hypothesis. Since only 14 industries were tested, this disagreement may not be credible. It is recommended that more individual industries be tested for better validity. 1 42 APPENDIX Appendix 4.1 Quarterly Data for the 14 Industries YEAR: Sample periods, quarterly P??: Industry Selling Prices PM??: Material Prices for the coresponding industry W??: Wages for the coresponding industry SHI??: Shipments for the coresponding industry PDIC: Personal Diposable Icome PUSSL: U.S. wholesale prices of the U.S. counterpart industry Slaughtering Meat YEAR PSL PMSL WSL SHISL PDIC PUSSL 71 1 97 .80 100. 00 3. 47 449610. 00 646 .94 111. 04 2 98 .30 99. 00 3. 58 473126. 00 677 .24 115. 02 3 100 .50 102. 96 3. 55 485873. 00 769 .52 117. 53 4 1 03 .40 105. 65 3. 58 488011 . 00 702 .93 117. 29 721 1 10 .50 115. 20 3. 61 538922. 00 712 .08 1 26. 58 2 1 1 5 .70 118. 22 3. 82 597948. 00 770 .82 1 24. 57 3 1 19 .20 1 27. 1 1 3. 86 602224. 00 838 .81 1 30. 05 4 1 21 .40 129. 91 3. 91 633114. 00 797 .05 1 28. 1 7 731 1 34 .50 1 48. 38 3. 90 679861 . 00 789 .63 1 52. 56 2 1 43 .70 156. 87 4. 1 5 764204. 00 878 .52 1 59. 67 3 1 69 .00 188. 21 4. 18 815272. 00 998 .05 180. 48 4 159 .90 173. 64 4. 29 826239. 00 942 .02 161. 1 5 741 155 .10 167. 20 4. 33 814571 . 00 921 .30 1 67. 58 2 1 46 .60 1 53. 31 4. 44 817493. 00 1 020 . 1 7 1 39. 97 3 155 .50 1 65. 63 4. 80 805845. 00 1 1 98 .63 1 58. 38 4 156 .10 163. 85 4. 98 839485. 00 1 077 .79 1 55. 01 751 1 48 .40 1 54. 84 5. 07 858365. 00 1 086 .50 1 59. 76 2 1 59 .10 170. 42 5. 57 917092. 00 1171 .56 1 90. 99 3 1 77 .50 196. 08 5. 73 985724. 00 1 382 .38 213. 25 4 175 .50 188. 65 5. 81 999882. 00 1246 .93 204. 62 761 167 .50 1 77. 75 5. 89 958359. 00 1235 .06 1 79. 52 2 167 .40 178. 92 6. 03 1014491. 00 1 360 .80 1 78. 94 3 161 .60 1 70. 1 7 6. 28 990656. 00 1510 .49 1 64. 90 4 1 53 .40 1 53. 83 6. 34 956755. 00 1386 .95 160. 67 771 1 54 .80 156. 44 6. 44 977771 . 00 1312 .38 167. 98 2 1 60 .60 1 67. 88 6. 53 1075860. 00 1 482 .29 1 76. 59 3 • 168 .30 175. 53 6. 59 1083481 . 00 1 642 .72 1 86. 02 4 1 72 .90 176. 54 6. 72 1120213. 00 1 497 .25 1 96. 10 781 183 .80 182. 65 6. 86 1196835. 00 1483 .64 215. 87 2 208 .50 213. 49 6. 86 1404483. 00 1 659 .44 238. 78 3 218 .50 221 . 55 7. 1 1 1350307. 00 1816 . 1 5 243. 38 4 325 .20 235. 89 7. 26 1562444. 00 1 668 .49 250. 98 791 247 .40 259. 46 7. 41 1585452. 00 1 622 .01 284. 02 2 247 .30 256. 1 7 7. 61 1704697. 00 1889 .44 278. 64 3 238 .10 242. 95 7. 91 1658789. 00 2008 .43 262. 79 1 43 4 239.60 240. 90 8 801 239.00 236. 20 8 2 226.00 222. 58 8 3 247.80 250. 93 8 4 262.00 264. 46 8 811 250.50 249. 50 9 2 253.60 252. 81 9 3 264.00 268. 1 3 9 4 256.20 248. 76 9 821 251.60 244. 50 10 2 281.90 284. 42 10 3 286.70 289. 00 11 4 271.80 265. 1 6 1 1 831 286.70 266. 06 1 1 2 274.30 260. 85 10 3 266.60 250. 1 4 1 1 4 262.10 242. 07 1 1 841 275. 10 257. 46 1 1 2 285. 10 269. 78 1 1 3 291.30 276. 46 1 1 4 283.80 265. 93 1 1 Poultry YEAR PPO PMPO 71 1 94 .60 100. 00 2 97 .70 103. 1 2 3 1 04 .80 107. 58 4 103 .40 100. 67 721 1 1 1 .60 108. 51 2 1 18 .80 112. 89 3 1 18 .20 116. 60 4 121 .60 115. 97 731 1 36 .30 1 30. 63 2 1 50 .70 1 52. 10 3 173 .00 179. 83 4 1 73 .40 180. 54 741 - 174 .00 179. 93 2 171 .70 1 75. 52 3 172 .90 172. 1 7 4 1 70 .20 1 74. 1 4 751 1 73 .10 175. 46 2 191 .70 1 75. 98 3 200 .20 182. 55 4 213 .20 186. 68 761 207 .30 182. 33 2 198 .00 1 77. 83 3 199 .20 188. 63 4 188 .70 183. 35 771 186 .20 175. 59 2 1 93 .20 187. 28 .00 1678209. 00 1875. 67 270. 09 .09 1579758. 00 1833. 98 268. 1 4 .28 1655205. 00 2071 . 67 256. 1 3 .60 1738762. 00 2299. 64 287. 22 .77 1855717. 00 2087. 89 291 . 38 .01 1802610. 00 2113. 19 280. 97 .29 1935126. 00 2348. 99 282. 27 .75 1962758. 00 2708. 73 302. 80 .95 1902632. 00 2399. 30 280. 41 . 1 4 1805085. 00 2359. 82 290. 38 .56 2062902. 00 2601 . 02 324. 24 .09 2072705. 00 2885. 68 323. 35 .06 1987263. 00 2524. 61 299. 1 1 .19 1908184. 00 2431 . 34 302. 41 .93 2016088. 00 2660. 18 301 . 96 .22 1985039. 00 3046. 52 285. 89 .13 1944351. 00 2672. 1 4 277. 37 .24 1911612. 00 2560. 89 301 . 55 .21 2084011 . 00 2888. 19 305. 29 .30 2019380. 00 31 22. 64 314. 94 .60 2092012. 00 2838. 52 303. 89 WPO PDIC PUSPO 646. 94 109. 1 2 677. 24 115. 22 7 69. 52 121. 29 702. 93 105. 26 712. 08 114. 34 7 70. 82 108. 97 838. 81 118. 16 797. 05 114. 93 789. 63 148. 67 878. 52 180. 76 998. 05 216. 72 942. 02 162. 95 921 . 30 1 59. 15 1020. 1 7 139. 29 1 1 98. 63 1 54. 07 1 077. 79 1 63. 10 1086. 50 168. 05 1171. 56 1 79. 65 1382. 38 206. 35 1 246. 93 195. 36 1 235. 06 1 69. 89 1 360. 80 165. 73 1510. 49 1 70. 96 11386. 95 1 48. 56 1312. 38 174. 67 1482. 29 188. 1 7 1 44 3 1 93 .00 188 .47 1642. 72 192.87 4 1 92 .20 182 .93 1497. 25 181.12 781 1 92 .40 182 .02 1483. 64 199.39 2 206 .70 193 .26 1659. 44 219.28 3 223 .60 201 .73 1816. 15 225.65 4 231 .90 204 .99 1668. 49 228.59 791 238 .40 210 .48 1 622. 01 241.31 2 240 .60 214 .37 1889. 44 225.95 3 227 .60 212 .67 2008. 43 203.42 4 227 .20 215 .17 1875. 67 213.34 801 229 .90 221 .10 1833. 98 210.27 2 220 .50 222 .21 2071 . 67 193.77 3 232 .70 227 .18 2299. 64 253.62 4 252 .80 247 .57 2087. 89 247.34 81 1 259 .80 256 .87 2113. 19 245.88 2 264 .20 260 .95 2348. 99 233.97 3 280 .70 271 .41 2708. 73 241.49 4 280 .00 275 .71 2399. 30 205.93 821 281 .40 269 .17 2359. 82 211.44 2 272 .40 259 .98 2601 . 02 225.79 3 280 .20 263 .29 2885. 68 229.73 4 269 .60 253 .65 2524. 61 215.74 831 272 .00 245 .00 2431 . 34 214.41 2 272 .60 254 .53 2660. 18 213.70 3 289 .20 264 .40 3046. 52 237.44 4 305 .60 279 .45 2672. 1 4 249.91 841 309 .00 283 .30 2560. 89 271 .42 2 305 .50 283 .93 2888. 19 266.64 3 310 .50 288 .62 3122. 64 265.15 4 312 .80 282 .57 2838. 52 264.08 •y YEAR PDA PMDA WDA SHI DA PDIC PUSDA 71 1 96 .30 100 .00 3 .05 328542. 00 646.94 114. 36 2 97 .80 94 .03 3 .06 391049. 00 677.24 117. 35 3 101 .20 96 .91 3 .08 417737. 00 769.52 1 17. 53 4 1 04 .80 1 06 .60 3 .24 413991 . 00 702.93 116. 99 721 1 05 .30 108 .22 3 .26 369749. 00 712.08 117. 95 2 1 06 .00 1 03 .05 3 .30 434220. 00 770.82 115. 29 3 1 06 .40 103 .62 3 .36 456815. 00 838.81 115. 21 4 106 .60 1 1 0 .47 3 .49 404893. 00 797.05 1 20. 1 7 731 108 .20 1 1 4 .34 3 .52 372869. 00 789.63 124. 54 2 1 14 .40 1 1 5 .83 3 .60 436621 . 00 878.52 127. 07 3 1 1 6 .30 1 1 9 . 1 1 3 .68 469916. 00 998.05 1 32. 40 4 1 18 .00 1 33 .74 3 .89 445222. 00 942.02 1 40. 56 741 1 23 .20 142 .22 3 .93 444354. 00 921 .30 1 45. 04 2 1 33 .20 1 48 .84 4 .02 507404. 00 1 020. 17 1 42. 86 3 1 38 .50 1 57 .39 4 .32 583350. 00 1 1.98.63 1 40. 24 4 1 47 .70 176 .53 4 .57 539472. 00 1077.79 1 44. 56 751 1 56 .90 183 .24 4 .70 509470. 00 1086.50 1 48. 28 1 45 2 171 .50 186 .58 4. 91 650447. 00 1171. 56 1 53 .29 3 1 73 .20 186 .75 5. 12 697717. 00 1 382. 38 161 .61 4 1 73 .30 1 93 .25 . 5. 35 642014. 00 1 246. 93 171 .25 761 1 73 .60 1 99 .67 5. 46 632129. 00 1 235. 06 1 65 .42 2 1 76 .80 180 .34 5. 76 707792. 00 1 360. 80 163 .77 3 1 79 .30 183 .86 5. 88 704153. 00 1510. 49 167 .54 4 182 .70 201 .01 6. 03 647949. 00 1386. 95 167 .02 771 1 84 .10 200 .93 6. 16 680643. 00 1312. 38 1 72 .30 2 191 .60 1 98 .72 6. 36 796217. 00 1482. 29 183 . 12 3 1 92 .00 200 .40 6. 46 833021 . 00 1642. 72 1 87 .63 4 .1 93 .50 215 .05 6. 56 772834. 00 1497. 25 195 .00 781 1 99 .10 236 .88 6. 65 761332. 00 1483. 64 199 .28 2 203 .10 230 .65 6. 73 884648. 00 1 659. 44 206 .20 3 204 .60 231 .50 6. 85 909037. 00 1816. 1 5 216 .62 4 207 .70 252 .99 7. 00 844816. 00 1668. 49 232 .95 791 213 .90 262 .08 7. 27 845286. 00 1622. 01 241 .79 2 220 .80 265 .96 7. 36 990423. 00 1889. 44 240 .54 3 225 .60 269 .53 7. 57 1029692. 00 2008. 43 249 .84 4 231 .50 290 .62 7. 77 1007118. 00 1875. 67 257 . 1 6 801 240 .90 306 . 1 4 7. 99 939671 . 00 1833. 98 258 .47 2 249 .80 294 .93 8. 20 1104283. 00 2071 . 67 267 .84 3 257 .40 307 .31 8. 38 1158420. 00 2299. 64 269 .37 4 263 .70 334 .06 8. 65 1112827. 00 2087. 89 284 .87 81 1 275 .10 341 .84 8. 93 1077285. 00 2113. 19 292 .31 2 283 .70 334 .32 9. 26 1263523. 00 2348. 99 293 .78 3 290 .40 345 . 1 5 9. 54 1323020. 00 2708. 73 297 . 1 1 4 297 .00 366 .64 9. 72 1219000. 00 2399. 30 294 .35 821 307 .40 374 .76 10. 18 1196109. 00 2359. 82 299 .69 2 31 1 .90 360 .86 10. 37 1389321. 00 2601 . 02 309 .31 3 316 .80 364 .38 10. 64 1432353. 00 2885. 68 31 1 .23 4 325 .70 385 .36 10. 80 1327003. 00 2524. 61 308 .22 831 327 .70 371 .49 10. 98 1294597. 00 2431 . 34 307 .81 2 329 .60 359 .93 1 1 . 01 1456890. 00 2660. 18 308 .73 3 333 .30 364 .74 10. 87 1478782. 00 3046. 52 308 .69 4 337 .90 388 .41 1 1 . 40 1384714. 00 2672. 1 4 312 .57 841 347 . 1 0 391 .40 1 1 . 74 1396871 . 00 2560. 89 312 .22 2 353 .00 380 .45 12. 09 1570481. 00 2888. 19 322 .09 3 356 .80 383 .75 12. 08 1566736. 00 3122. 64 331 .89 4 362 .40 415 .45 1 1 . 88 1471873. 00 2838. 52 338 .43 Cereal YEAR PFL PMFL WFL PDIC PUSFL 71 1 99.90 106.51 3.11 646.94 106.50 2 100.60 106.39 3.16 677.24 106.72 3 99.20 106.22 3.17 769.52 105.95 4 100.30 106.05 3.23 702.93 104.86 721 101.10 106.95 3.26 712.08 105.01 2 100.90 106.95 3.32 770.82 103.24 3 1 02. 10 107.22 3.47 838.81 102.63 4 105.60 160.82 3.46 797.05 116.11 1 46 731 109. 20 166. 84 3. 56 789. 63 121 .25 2 111. 40 168. 71 3. 63 878. 52 1 24 .28 3 151. 20 173. 04 3. 77 998. 05 1 45 .75 4 169. 50 176. 02 3. 79 942. 02 1 64 .45 741 171 . 20 180. 72 3. 88 921 . 30 1 90 .51 2 170. 00 181 . 54 3. 94 1020. 1 7 1 55 .80 3 173. 30 184. 39 4. 20 1 198. 63 166 .92 4 179. 90 189. 28 4. 45 1 077. 79 184 .20 751 1 77. 20 185. 10 4. 66 1 086. 50 1 74 .04 2 1 77. 30 180. 82 4. 85 1171. 56 1 62 .89 3 1 77. 50 180. 66 5. 15 1 382. 38 171 .61 4 180. 60 1 79. 06 5. 1 6 1 246. 93 1 70 .33 761 180. 00 1 78. 89 5. 37 1 235. 06 1 60 .75 2 1 78. 70 178. 92 5. 52 1360. 80 1 54 .96 3 177. 30 178. 30 5. 65 1510. 49 1 48 .38 4 1 79. 60 1 76. 96 5. 85 1 386. 95 1 39 .93 771 181 . 70 5755. 28 5. 90 1312. 38 1 43 .16 2 184. 00 1 79. 39 5. 98 1 482. 29 1 38 .81 3 179. 50 1 77. 44 5. 81 1 642. 72 141 .52 4 182. 80 178. 49 6. 20 1497. 25 152 .59 781 1 85. 50 202. 34 6. 21 1 483. 64 1 59 .98 2 188. 90 247. 28 6. 31 1659. 44 169 .22 3 1 92. 80 248. 30 6. 48 1816. 1 5 1 79 .56 4 196. 30 256. 94 6. 67 1 668. 49 191 . 1 2 791 239. 40 260. 1 5 6. 88 1 622. 01 1 96 .35 2 240. 10 261 . 77 7. 01 1889. 44 201 .86 3 251 . 00 263. 91 7. 1 7 2008. 43 223 .13 4 256. 00 293. 60 7. 31 1875. 67 225 .91 801 259. 00 332 . 56 7. 39 1833. 98 224 .24 2 261 . 40 361 . 58 7. 66 2071 . 67 225 .24 3 286. 90 350. 1 7 7. 94 2299. 64 233 .23 3 323. 10 336. 66 8. 1 1 2087. 89 245 .68 81 1 339. 00 328. 22 8. 50 2113. 1 9 246 .48 2 336. 10 325. 68 8. 77 2348. 99 247 .87 3 325. 20 299. 1 5 9. 17 2708. 73 249 .00 4 322. 70 303. 1 2 9. 27 2399. 30 242 . 1 5 821 326. 80 296. 30 9. 40 2359. 82 246 .62 2 318. 80 298. 33 9. 75 2601 . 02 251 .55 3 323. 30 302 . 10 9. 99 2885. 68 249 .98 4 321 . 70 300. 39 10. 27 2524. 61 245 .91 831 326. 10 304. 76 10. 66 2431 . 34 244 .36 2 330. 10 316. 27 9. 66 2660. 18 247 .43 3 331 . 70 313. 40 9. 87 3046. 52 253 .22 4 348. 50 316. 67 10. 1 6 2672. 1 4 253 .03 841 348. 50 312. 20 10. 51 2560. 89 252 .21 2 348. 20 333 . 50 10. 70 2888. 19 267 .03 3 350. 40 336. 69 10. 65 31 22. 64 269 .88 4 352. 20 320. 48 10. 77 2838. 52 270 .27 Feed 1 47 YEAR PFE PMFE WFE SHIFE PDIC PUSFE 71 1 1 00. 70 100 .00 3. 1 1 147650 .00 646. 94 107.61 2 101. 10 97 .83 3. 16 167650 .00 677. 24 106.82 3 101. 10 95 .88 3. 1 7 167650 .00 769. 52 105.95 4 97. 10 90 .31 3. 23 157650 .00 702. 93 101.45 721 99. 10 95 .46 3. 26 157475 .00 712. 08 105.62 2 100. 1 0 98 .40 3. 32 167475 .00 770. 82 106.89 3 102. 50 105 .02 3. 47 1 77475 .00 838.. 81 108.23 4 111. 90 121 .96 3. 46 187475 .00 797. 05 135.28 731 1 34. 30 151 . 1 1 3. 56 202167 .00 789. 63 176.49 2 1 56. 40 1 90 .99 3. 63 230468 .00 878. 52 211.86 3 191 . 50 230 .91 3. 77 251963 .00 998. 05 217.12 4 163. 40 182 .42 3. 79 257204 .00 942. 02 189.54 741 1 70. 80 193 .74 3. 88 282650 .00 921 . 30 187.87 2 1 57. 00 174 .01 3. 94 279477 .00 1020. 1 7 152.52 3 175. 80 198 .78 4. 20 267846 .00 1 1 98. 63 182.41 4 183. 50 206 .38 4. 45 285080 .00 1077. 79 197.52 751 177. 40 180 .99 4. 66 299007 .00 1086. 50 170.54 2 1 66. 50 1 73 . 1 2 4. 85 320297 .00 1171. 56 171.99 3 171 . 80 1 84 .24 5. 1 5 326330 .00 1382. 38 180.06 4 1 69. 40 1 78 .35 5. 1 6 318406 .00 1 246. 93 177.66 761 1 67. 20 1 77 .59 5. 37 309567 .00 1235. 06 174.45 2 170. 00 182 .42 5. 52 329264 .00 1 360. 80 185.60 3 183. 60 188 .79 5. 65 324381 .00 1510. 49 203.71 4 174. 00 175 .30 5. 85 328669 .00 1 386. 95 203.34 771 188. 20 191 .09 5. 90 342496 .00 1312. 38 226.68 2 200. 30 210 .98 5. 98 365409 .00 1482. 29 248.58 3 174. 80 161 .47 5. 81 320603 .00 1 642. 72 194.79 4 1 69. 20 1 65 .21 6. 20 338238 .00 1497. 25 203.04 781 1 77. 70 171 .41 6. 21 357366 .00 1483. 64 215.31 2 188. 70 190 .92 6. 31 394172 .00 1659. 44 217.59 3 186. 10 185 .73 6. 48 374332 .00 1816. 1 5 220.39 4 199. 10 200 .24 6. 67 406920 .00 1668. 49 238.25 791 206. 40 . 21 1 .27 6. 88 438020 .00 1 622. 01 255.08 2 214. 70 221 .40 7. 01 467100 .00 1889. 44 249.80 3 229. 70 226 .78 7. 1 7 477346 .00 2008. 43 260.92 4 225. 20 229 .52 7. 31 505233 .00 1875. 67 263.27 801 225. 40 226 .52 7. 39 536110 .00 1833. 98 254.63 2 225. 50 226 .02 7. 66 544767 .00 2071 . 67 241.04 3 244. 20 261 .03 7. 94 560153 .00 2299. 64 268.22 4 267. 40 281 .77 8. 1 1 639699 .00 2087. 89 295.76 81 1 271 . 10 280 .43 8. 50 627479 .00 2113. 19 284.91 2 267. 80 274 .83 8. 77 659299 .00 2348. 99 284.91 3 262. 60 261 .48 9. 1 7 607666 .00 2708. 73 276.27 4 248. 80 240 .25 9. 27 629816 .00 2399. 30 258.24 821 249. 20 244 .41 9. 40 593089 .00 2359. 82 259.43 2 251 . 20 251 .61 9. 75 657899 .00 2601 . 02 269.73 3 250. 40 234 .31 9. 99 588737 .00 2885. 68 260.73 4 235. 90 217 .19 10. 27 577606 .00 2524. 61 252.81 831 244. 70 232 .02 10. 66 587855 .00 2431 . 34 260.31 2 257. 40 245 .76 9. 66 640662 .00 2660. 18 • 271.31 3 271 . 10 270 .91 9. 87 606561 .00 3046. 52 288.60 1 48 4 282 .00 280. 28 10 .16 670101 .00 2672. 1 4 310. 58 841 279 .70 276. 33 10 .51 672662 .00 2560. 89 298. 16 2 285 .00 280. 16 10 .70 727527 .00 2888. 1 9 299. 21 3 279 .20 270. 78 10 .65 656905 .00 31 22. 64 280. 1 2 4 261 .60 256. 71 10 .77 684830 .00 2838. 52 264. 08 Biscuit YEAR PBI PMB I WBI SHIBI PDIC PUSBI 71 1 97 .90 1 00. 00 2. 64 33188. 00 646. 94 118. 40 2 1 00 .40 99. 1 1 2. 61 33932. 00 677. 24 121. 80 3 1 00 .70 98. 68 2. 66 37675. 00 769. 52 121 . 29 4 101 .00 1 00. 00 2. 80 36848. 00 702. 93 , 1 19. 70 721 1 02 .70 1 05. 84 2. 82 36762. 00 712. 08 121. 06 2 1 03 .90 1 04. 76 2. 77 39400. 00 770. 82 1 20. 92 3 1 04 .30 1 03. 71 2. 87 40009. 00 838. 81 1 20. 91 4 105 .30 1 06. 1 1 2. 94 43120. 00 797. 05 1 20. 07 731 108 .70 1 08. 33 2. 98 38824. 00 789. 63 1 23. 84 2 1 08 .70 113. 05 3. 1 3 40775. 00 878. 52 125. 77 3 1 16 .00 1 30. 1 3 • 3. 20 3881 1 . 00 998. 05 1 30. 39 4 1 27 .70 1 43. 00 3. 27 48001 . 00 942. 02 1 39. 36 741 1 36 .70 169. 84 3. 36 49513. 00 921 . 30 1 45. 63 2 1 50 .30 1 90. 05 3. 44 56504. 00 1 020. 1 7 1 55. 61 3 159 .10 212. 1 2 3. 62 49781. 00 1 198. 63 168. 68 4 1 77 .70 256. 04 3. 75 73510. 00 1077. 79 190. 42 751 185 .50 227 . 60 3. 84 64382. 00 1086. 50 205. 89 2 1 87 .20 1 93. 35 4. 01 75392. 00 1171. 56 201 . 1 1 3 1 87 .50 190. 91 4. 20 76077. 00 1382. 38 200. 68 4 1 86 .50 182. 93 4. 31 79288. 00 1 246. 93 198. 72 761 1 79 .60 179. 33 4. 50 68314. 00 1235. 06 195. 90 2 177 .40 1 77. 35 4. 65 72379. 00 1 360. 80 193. 63 3 1 78 .70 1 72. 28 4. 82 68019. 00 1510. 49 1 93. 35 4 1 76 .30 1 68. 30 5. 01 78665. 00 1 386. 95 196. 30 771 183 .50 1 77. 71 5. 1 2 66614. 00 1312. 38 211. 23 2 190 .40 187. 1 2 5. 1 4 67022. 00 1 482. 29 222. 79 3 213 .20 1 84. 02 5. 25 66839. 00 1642. 72 228. 70 4 213 .20 185. 27 5. 40 74747. 00 1497. 25 252. 07 781 217 .60 191 . 03 5. 62 75744. 00 1 483. 64 253. 94 2 220 .00 190. 55 5. 67 78233. 00 1659. 44 257. 16 3 222 .40 195. 01 5. 72 75676. 00 1816. 1 5 266. 60 4 231 .30 203. 02 5. 91 77630. 00 1668. 49 237. 43 791 246 .80 216. 19 6. 02 82280. 00 1 622. 01 287. 46 2 254 .30 224. 44 6. 28 88044. 00 1889. 44 286. 51 3 254 .60 233. 59 6. 46 86036. 00 2008. 43 292. 18 4 254 .30 248. 28 6. 90 86856. 00 1875. 67 301 . 22 , 801 280 .10 270 . 18 6. 67 90041 . 00 1833. 98 314. 71 2 283 .60 297. 56 6. 86 87355. 00 2071 . 67 325. 99 3 289 .60 312. 58 7. 10 95776. 00 2299. 64 325. 33 4 300 .90 334. 08 7. 40 99767. 00 2087. 89 351 . 53 811 326 .10 319. 73 7. 34 97081 . 00 2113. 19 363. 57 2 336 .30 300 . 1 2 7. 43 102788. 00 2348. 99 366. 89 1 49 3 339 .50 301 . 97 7 .42 103163 .00 2708. 73 377 .81 4 343 .00 296. 84 7 .79 108815 .00 2399. 30 371 .57 821 355 .10 298. 61 7 .88 103320 .00 2359. 82 372 .46 2 361 .20 289. 1 5 8 .42 1 08313 .00 2601 . 02 385 .86 3 361 .30 291 . 60 8 .84 104161 .00 2885. 68 385 .47 4 361 .40 291 . 45 8 .76 110861 .00 2524. 61 384 .57 831 388 .80 291 . 44 8 .72 97733 .00 2431 . 34 384 .02 2 391 .20 302. 46 8 .44 115010 .00 2660. 18 390 .72 3 391 .20 312. 24 8 .40 122373 .00 3046. 52 395 .36 4 391 .30 312. 85 8 .78 119164 .00 2672. 1 4 408 .07 841 417 .90 314. 42 9 .06 115287 .00 2560. 89 414 .53 2 418 .50 319. 81 8 .42 124775 .00 2888. 19 431 .44 3 418 .80 324. 46 8 .82 120317 .00 3122. 64 444 .76 4 418 .70 325. 29 9 .21 122199 .00 2838. 52 448 .12 Bakery YEAR PBA PMBA WBA PDIC PUSBA 71 1 99.30 100.00 2.68 646.94 114.57 2 1 00.20 99. 1 3 2.78 677.24 115.83 3 100.30 98.50 2.84 769.52 116.82 4 1 00.30 99. 18 2.88 702.93 115.29 721 101.30 102.62 2.95 712.08 116.75 2 1 02.20 102.33 3.05 770.82 116.28 3 105.60 102.48 3.08 838.81 116.58 4 106.30 104.47 3.09 797.05 117.00 731 111.10 107.05 3.16 789.63 121.65 2 111.90 110.14 3.42 878.52 122.58 3 1 17.40 139.68 3.43 998.05 131 .40 4 1 27.20 155.40 3.52 942.02 143.26 741 1 32.90 168.61 3.59 921.30 147.20 2 142.20 176.47 3.84 1020.17 153.00 3 152.20 187.30 4.02 1 198.63 159.76 4 160.80 211.40 4.12 . 1077.79 170. 10 751 1 65.60 196.46 4.23 1086.50 181.33 2 169.10 179.52 4.47 1171 .56 182.51 3 170.00 177.17 4.67 1382.38 181.82 4 170.50 173.31 4.78 1246.93 180.71 761 170.80 171.98 4.86 1235.06 177.93 2 172.60 171.02 5.11 . 1360.80 175.52 3 175.50 169.92 5.20 1510.49 175.66 4 177. 10 170.23 5.28 1386.95 180.02 771 181.00 174.68 5.43 1312.38 188.68 2 184.00 181.07 5.90 1482.29 195.01 3 185.20 178.76 5.87 1642.72 200. 14 4 186.60 182.92 5.98 1497.25 209.76 781 190.70 185.34 6.05 1483.64 215.76 2 194.40 188. 1 5 6.24 1659.44 221.20 3 196.90 193. 16 6.L19 1816.15 232.86 4 203.50 198.41 6.34 1668.49 247.33 791 224.90 231.31 6.53 1622.01 253.77 150 2 230. 10 233 .73 6 .60 1889. 44 250. 61 3 235. 60 241 .37 6 .74 2008. 43 261 . 16 4 239. 20 248 .89 6 .90 1875. 67 273. 02 801 246. 60 260 .73 7 . 1 3 1833 . 98 280. 71 2 252. 30 277 .75 7 .56 2071 . 67 287. 96 3 264. 10 293 .71 7 .49 2299. 64 287. 91 4 282. 90 322 .31 7 .63 2087. 89 302. 51 81 1 297. 10 324 .15 7 .95 2113. 1 9 313. 92 2 302. 70 313 .59 8 .20 2348. 99 319. 43 3 304. 80 314 .43 8 .46 2708. 73 328. 25 4 311. 90 31 1 .85 8 .67 2399. 30 324. 74 821 315. 70 312 .93 8 .89 2359. 82 331 . 1 2 2 324. 00 307 .80 9 .32 2601 . 02 341 . 30 3 325. 90 313 .10 9 .20 2885. 68 344. 47 4 332. 90 314 .64 9 .26 2524. 61 342. 08 831 337. 80 315 . 1 3 9 .42 2431. 34 345. 61 2 341 . 80 320 .92 8 .90 2660. 18 349. 60 3 345. 00 326 .51 8 .66 3046. 52 353 . 44 4 354. 50 330 .31 8 .93 2672. 1 4 362. 88 841 362. 00 337 .22 9 .58 2560. 89 369. 59 2 366. 30 340 .75 8 .82 2888. 19 383. 1 0 3 369. 60 348 .42 8 .92 31 22. 64 395. 75 4 375. 90 349 .49 9 .34 2838. 52 401 . 19 Confectionary YEAR PCO PMCO WCO SHI CO PDIC PUSCO 71 1 100 .20 100.00 2 .43 59090. 00 646. 94 119.71 2 100 .00 97.45 2 .52 54730. 00 677. 24 120.39 3 100 .10 98. 10 2 .51 57743. 00 769. 52 121.79 4 99 .70 91 .36 2 .50 63498. 00 702. 93 119.60 721 99 .90 98. 18 2 .60 63682. 00 712. 08 121 .36 2 99 .90 95.31 2 .71 62999. 00 770. 82 118.65 3 100 .90 .95.46 2 .70 69155. 00 838. 81 119.63 4 101 .90 103.84 2 .69 69387. 00 797. 05 120.66 731 1 05 .90 114.47 2 .79 78764. 00 789. 63 123.54 2 111 .60 124.63 2 .90 72732. 00 878. 52 128.97 3 1 1 3 .00 135.08 2 .97 , 80397. 00 998. 05 135.11 4 1 1 4 .60 152.03 3 .01 92539. 00 942. 02 141 .86 741 133 .20 182.02 3 .09 82378. 00 921 . 30 166.31 2 1 47 .30 202.24 3 .26 81256. 00 1020. 1 7 207.93 3 1 73 .10 220.62 3 .36 90963. 00 1 1 98. 63 266.46 4 185 .90 250.51 3 .49 97664. 00 1 077. 79 373.53 751 1 98 .60 222.04 3 .70 103327. 00 1086. 50 330.70 2 201 .00 202.88 3 .93 99153. 00 1171. 56 251 . 18 3 202 .70 188.40 4 .02 116238. 00 1 382. 38 237.37 4 201 .20 186. 17 4 .07 114047. 00 1 246. 93 208.59 761 199 .90 194.25 4 .23 109361 . 00 1235. 06 202.06 2 200 .80 199.47 4 .37 107308. 00 1 360. 80 198.62 3 201 .80 202.62 4 .50 119156. 00 1510. 49 180.35 4 202 .30 223.09 4 .55 136302. 00 1 386. 95 171.49 151 Suga 771 222 .60 266 .67 4 .60 120596 .00 1312. 38 181 . 88 2 227 .30 303 .83 4 .77 123756 .00 1.482. 29 191 . 96 3 232 .90 303 . 1 5 4 .77 144189 .00 1 642. 72 187. 95 4 233 .50 338 .47 4 .74 138201 .00 1497. 25 193. 46 781 240 .10 333 .98 4 .80 147990 .00 1483. 64 199. 06 2 245 .80 318 .51 4 .95 130016 .00 1659. 44 205. 07 3 249 .00 314 .16 5 .03 159976 .00 1816. 15 209. 53 4 250 .80 337 .27 4 .92 164686 .00 1668. 49 214. 33 791 254 .10 352 .54 5 .07 166924 .00 1 622. 01 224. 23 2 274 .20 330 .55 5 .20 136294 .00 1889. 44 219. 34 3 274 .50 335 .42 5 .48 169592 .00 2008. 43 226. 40 4 274 .80 308 .03 5 .71 196236 .00 1875. 67 232. 85 801 296 .70 341 .46 6 .01 182340 .00 1833. 98 234. 1 4 2 303 .80 392 .00 6 .21 158175 .00 2071 . 67 236. 1 3 3 31 1 .80 363 .10 6 . 1 9 220105 .00 2299. 64 240. 06 4 319 .90 389 .69 6 .34 218337 .00 2087. 89 253. 85 81 1 342 .20 387 .97 6 .64 215275 .00 2113. 19 257. 94 2 359 .50 370 .54 6 .87 192390 .00 2348. 99 257. 10 3 365 .40 358 .20 6 .89 241861 .00 2708. 73 258. 94 4 366 .00 331 .39 7 .02 256780 .00 2399. 30 254. 67 821 368 .90 320 .65 7 .56 208455 .00 2359. 82 258. 34 2 369 .00 329 .31 7 .82 197201 .00 2601 . 02 265. 99 3 367 .40 283 .33 7 .79 251963 .00 2885. 68 276. 60 4 366 .50 283 . 1 0 8 .07 288422 .00 2524. 61 287. 1 6 831 366 .80 282 .73 8 .17 244764 .00 2431 . 34 292. 71 2 381 .70 294 .32 7 .91 226631 .00 2660. 18 298. 89 3 393 .20 313 .64 7 .87 268426 .00 3046. 52 308. 45 4 394 .40 321 .85 8 .23 283106 .00 2672. 1 4 314. 82 841 407 .10 319 . 1 1 8 .49 254540 .00 2560. 89 324. 1 4 2 413 .00 349 .78 8 .25 241381 .00 2888. 19 342. 00 3 413 .50 348 .90 8 .30 279997 .00 31 22. 64 349. 89 4 413 .60 340 .23 8 .63 319175 .00 2838. 52 350. 30 r Cane Beet YEAR PSU PMSU WSU SHISU PDIC PUSSU 71 1 101 .30 1 00 .60 48570 .00 646. 94 116. 48 2 98 .30 110 . 1 6 53687 .00 677. 24 117. 25 3 96 .30 96 .73 62900 .00 7 69. 52 119. 97 4 1 04 .10 100 .49 57547 .00 702. 93 119. 1 0 721 1 30 .30 1 1 2 .29 58771 .00 712. 08 126. 08 2 1 27 .00 141 .50 68980 .00 770. 82 119. 93 3 119 .00 1 40 .10 74097 .00 838. 81 1 22. 78 4 1 23 .60 1 38 .97 6901 5 .00 797. 05 1 24. 81 731 1 29 .20 1 44 .02 70524 .00 789. 63 1 27. 93 2 1 34 .80 1 56 .60 69916 .00 878. 52 1 36. 97 3 1 36 .40 1 54 .72 89232 .00 998. 05 1 45. 75 4 1 58 .20 161 .39 96591 .00 942. 02 1 52. 65 741 263 .60 257 .05 92531 .00 921 . 30 212. 1 7 2 320 .10 388 .09 132510 .00 1 020. 1 7 299. 73 3 389 .00 448 .73 217153 .00 1 1 98. 63 411. 21 1 52 4 570. .70 615 .57 232368. 00 1 077 .79 71 1 . 77 751 448 .10 778 .12 154432. 00 1 086 .50 486. 1 7 2 280 .80 541 .63 162635. 00 1171 .56 304. 42 3 267 .50 397 .24 156046. 00 1 382 .38 282. 72 4 226 .90 339 .05 122006. 00 1246 .93 209. 30 761 227 .70 296 .01 122677. 00 1235 .06 213. 37 2 221 .90 283 .49 142173. 00 1 360 .80 209. 48 3 187 .90 271 . 1 3 144766. 00 1510 .49 163. 54 4 159 .40 218 .60 123976. 00 1 386 .95 1 44. 89 771 1 74 .10 189 .61 100965. 00 1312 .38 1 57. 99 2 182 .60 205 .01 108871 . 00 1 482 .29 1 63. 1 2 3 1 70 .90 203 .93 113687. 00 1 642 .72 155. 43 4 1 66 .30 184 .60 114041 . 00 1 497 .25 147. 63 781 1 79 .50 1 72 .80 101337. 00 1 483 .64 203. 07 2 170 .30 183 .58 109694. 00 1 659 .44 213. 98 3 171 .00 181 .07 116615. 00 1816 .15 219. 25 4 90 .80 190 .93 116326. 00 1 668 .49 228. 94 791 190 .40 201 .78 117217. 00 1 622 .01 230. 99 2 1 92 .80 219 .86 122217. 00 1889 .44 231 . 04 3 21 1 .80 210 .04 129044. 00 2008 .43 249. 1 4 4 271 .50 240 .60 14201 1 . 00 1875 .67 271 . 85 801 361 .50 317 .36 152405. 00 1 833 .98 352. 78 2 483 .40 388 .85 193761 . 00 2071 .67 458. 45 3 525 .80 569 .95 213464. 00 2299 .64 510. 25 4 569 .60 746 .92 217752. 00 2087 .89 612. 01 811 447 .50 786 .33 212993. 00 2113 . 1 9 438. 05 2 329 .90 609 .56 230554. 00 2348 .99 304. 32 3 322 .60 473 .96 224322. 00 2708 .73 297. 71 4 290 .60 353 . 1 1 190609. 00 2399 .30 267. 42 821 298 .90 367 .19 166438. 00 2359 .82 291 . 59 2 258 .00 316 .10 181959. 00 2601 .02 330. 47 3 249 .80 259 .39 172644. 00 2885 .68 389. 47 4 238 .00 191 .91 167085. 00 2524 .61 364. 00 831 241 .80 182 .99 149859. 00 2431 .34 378. 99 2 278 .10 190 .72 168100. 00 2660 .18 396. 63 3 299 .70 215 .33 169759. 00 3046 .52 393. 51 4 283 .90 231 .21 153893. 00 2672 . 1 4 391 . 47 841 264 .40 216 .02 153598. 00 2560 .89 393. 32 2 259 .20 204 .83 165521 . 00 2888 .19 407. 27 3 .237 .60 183 .59 163649. 00 31 22 .64 41 1 . 1 2 4 240 .20 163 .03 178606. 00 2838 .52 404. 49 Vegetable Oil YEAR 71 1 2 3 4 721 2 PVE 97.40 98.80 1 05, 98, 98 100 30 50 40 80 PMVE 100.00 100.01 101.83 94.81 94.42 101.47 WVE SHIVE 28558.00 32634 33399 361 81 281 91 37448 00 00 00 00 00 PDIC 646.94 677.24 769.52 702.93 712.08 770.82 PUSVE 1 28.18 124.23 1 42 1 25 1 1 5 1 1 3 1 1 51 85 21 1 53 3 99. 00 1 00. 71 31 525 .00 838. 81 1 05. 87 4 116. 40 1 07. 45 40959 .00 797. 05 94. 77 731 1 56. 80 1 55. 71 47887 .00 789. 63 1 19. 25 2 220. 10 231 . 21 51 102 .00 878. 52 1 55. 57 3 271 . 60 258. 00 54590 .00 998. 05 217. 32 4 187. 70 189. 87 64875 .00 942. 02 206. 64 741 215. 30 225. 25 72170 .00 921 . 30 248. 72 2 1 90. 90 205. 71 63279 .00 1020. 17 242. 10 3 243. 00 259. 66 78407 .00 1 1 98. 63 329. 81 4 253. 20 278. 1 7 85631 .00 1077. 79 319. 1 0 751 212. 20 204. 20 66030 .00 1 086. 50 250. 82 2 1 96. 20 186. 24 61 1 20 .00 1171. 56 201 . 31 3 208. 50 202. 25 61 952 .00 1 382. 38 227. 89 4 1 84. 90 161. 02 70031 .00 1 246. 93 1 66. 06 761 180. 40 155. 74 67247 .00 1 235. 06 148. 24 2 190. 20 171 . 64 71 467 .00 1 360. 80 1 40 . 37 3 209. 30 200. 24 77850 .00 1510. 49 175. 07 4 204. 10 200. 84 81 103 .00 1 386. 95 1 76. 94 771 233. 80 245. 15 106597 .00 1312. 38 200. 73 2 275. 80 292. 69 116452 .00 1 482. 29 256. 79 3 199. 10 200. 63 108076 .00 1 642. 72 1 84. 31 4 199. 50 200. 48 106735 .00 1497. 25 1 96. 76 781 200. 10 215. 09 120722 .00 1483. 64 220. 54 2 226. 50- 251 . 60 125743 .00 1 659. 44 239. 1 2 3 216. 60 234. 44 110659 .00 1816. 15 265. 91 4 227. 40 248. 98 136133 .00 1 668. 49 260. 64 791 239. 60 266. 03 126343 .00 1 622. 01 281 . 65 2 237. 60 265. 35 152424 .00 1889. 44 282. 23 3 241 . 60 268. 36 162605 .00 2008. 43 302. 45 4 235. 20 242. 81 167230 .00 1875. 67 275. 26 801 217. 60 228. 80 186623 .00 1833. 98 235. 19 2 204. 70 223. 27 179913 .00 2071 . 67 209. 92 3 243. 80 263. 27 174560 .00 2299. 64 240. 29 4 255. 00 287. 28 197733 .00 2087. 89 249. 35 811 243. 50 269. 50 222431 .00 2113. 19 230. 1 3 2 239. 90 271 . 36 226690 .00 2348. 99 226. 54 3 235. 30 258. 1 1 171072 .00 2708. 73 227. 92 4 215. 60 246. 70 208838 .00 2399. 30 204. 85 821 214. 40 246. 92 186969 .00 2359. 82 195. 36 2 225. 80 255. 59 180366 .00 2601 . 02 209. 48 3 211. 20 231 . 41 169045 .00 2885. 68 1 97. 36 4 1 98. 70 225. 01 186078 .00 2524. 61 186. 68 831 206. 20 232. 33 185540 .00 2431 . 34 178. 69 2 212. 50 242. 75 187129 .00 2660. 18 206. 68 3 262. 30 302. 58 185693 .00 3046. 52 285. 15 4 276. 10 316. 94 275818 .00 2672. 1 4 289. 36 841 261 . 50 312. 57 279691 .00 2560. 89 296. 90 2 272. 50 380. 47 266081 .00 2888. 19 369. 91 3 256. 10 300. 41 174465 .00 31 22. 64 347. 92 4 235. 50 275. 40 214960 .00 2838. 52 343. 05 1 54 Soft Drink YEAR PSO PMSO WSO SHI SO PDIC PUSSO 71 1 94 .80 100. 00 2. 90 98728 .00 646. 94 123. 94 2 100 .10 98. 08 3. 05 98728 .00 677. 24 1 25. 75 3 100 .90 97. 94 3. 05 98728 .00 769. 52 126. 47 4 1 04 .20 102. 36 3. 1 7 98728 .00 702. 93 1 24. 81 721 106 .70 107. 60 3. 26 98728 .00 712. 08 1 25. 1 7 2 109 .30 106. 78 3. 35 98728 .00 770. 82 1 25. 1 7 3 109 .30 101. 29 3. 30 98728 .00 838. 81 1 24. 45 4 109 .20 1 02. 47 3. 44 98728 .00 797. 05 1 25. 80 731 1 1 1 .20 1 06. 67 3. 41 98728 .00 789. 63 1 26. 93 2 1 1 4 .80 108. 84 3. 55 1 22335 .00 878. 52 126. 27 3 1 1 5 .70 107. 49 3. 57 136087 .00 998. 05 123. 67 4 1 1 5 .70 119. 54 3. 81 126467 .00 942. 02 1 25. 36 741 1 23 .50 147. 98 3. 86 112602 .00 921 . 30 1 24. 26 2 141 .60 162. 88 4. 05 144646 .00 1020. 1 7 133. 69 3 1 47 .60 1 75. 62 4. 20 164914 .00 1 1 98. 63 155. 05 4 1 72 .10 224. 36 4. 37 142426 .00 1 077 . 79 172. 96 751 183 .50 1 95. 20 4. 55 136638 .00 1 086. 50 187. 62 2 1 93 .10 171 . 34 4. 63 181283 .00 1171. 56 1 93. 24 3 1 93 .30 169. 00 4. 75 203994 .00 1382. 38 188. 31 4 193 .20 158. 20 5. 08 176873 .00 1 246. 93 187. 93 761 1 92 .80 155. 48 5. 21 159185 .00 1 235. 06 184. 78 2 190 .20 163. 50 5. 58 196084 .00 1 360. 80 182. 37 3 191 .20 1 58. 74 5. 72 199245 .00 1510. 49 182. 40 4 1 92 .00 1 52. 64 5. 86 185115 .00 1 386. 95 1 87. 86 771 1 92 .10 158. 1 6 6. 1 3 161064 .00 1312. 38 1 98. 46 2 1 94 .50 1 65. 41 6. 36 200861 .00 •1 482. 29 206. 48 3 1 97 .20 1 65. 95 6. 39 216238 .00 1 642. 72 214. 47 4 199 .20 1 73. 78 6. 55 196879 .00 1 497. 25 223 . 65 781 202 .30 1 77. 04 6. 48 164368 .00 1 483. 64 229. 79 2 207 .30 178. 62 6. 54 224478 .00 1 659. 44 236. 75 3 210 .30 183. 42 6. 70 225268 .00 1816. 15 242. 1 2 4 215 .70 186. 32 7. 08 246615 .00 1 668. 49 252. 98 791 222 .90 201 . 1 5 7. 19 197602 .00 1 622. 01 264. 69 2 228 .60 210. 47 7. 24 233671 .00 1889. 44 260. 1 1 3 232 .10 210. 08 7. 39 242051 .00 2008. 43 267. 69 4 238 .80 236. 70 7. 54 206291 .00 1875. 67 271 . 61 801 249 .20 261 . 25 7. 88 205401 .00 1833. 98 282. 69 2 272 .00 315. 48 8. 1 5 294701 .00 2071 . 67 298. 84 3 254 .20 312. 30 8. 1 2 305353 .00 2299. 64 305. 52 4 297 .40 339. 68 8. 45 266811 .00 2087. 89 333. 89 81 1 302 .80 324. 60 8. 80 245578 .00 2113. 19 356. 53 2 316 .50 288. 70 8. 82 343454 .00 2348. 99 362. 10 3 319 .90 291 . 48 8. 87 360786 .00 2708. 73 372. 84 4 327 .10 307. 35 9. 55 309943 .00 2399. 30 371 . 69 821 332 .10 323. 65 9. 84 271740 .00 2359. 82 383. 10 2 353 .40 295. 91 10. 04 360970 .00 2601 . 02 395. 94 3 355 .10 291 . 1 4 10. 10 351906 .00 2885. 68 399. 72 4 • 358 .40 337. 78 10. 52 334020 .00 2524. 61 395. 90 1 55 831 359 .90 295. 44 10 .87 293319 .00 2431 . 34 399. 85 2 364 .00 313. 68 " 10 .74 383272 .00 2660. 18 402. 04 3 364 .70 298. 57 10 .91 407868 .00 3046. 52 403. 13 4 366 .70 335. 98 1 1 .25 350599 .00 2672. 1 4 412. 06 841 367 .60 332. 43 1 1 .89 309562 .00 2560. 89 420. 18 2 373 .30 345. 1 4 1 1 .77 405147 .00 2888. 19 437. 77 3 374 .80 384. 07 1 1 .57 439362 .00 3122. 64 449. 49 4 374 .70 447. 01 11 .99 386143 .00 2838. 52 456. 43 .illry YEAR PDI PMDI WD I SHIDI PDIC PUSDI 71 1 100 .00 100. 00 3 .86 66551 .00 646. 94 105. 59 2 100 .20 98. 90 4 .05 80694 .00 677. 24 1 06. 01 3 100 .20 97. 06 4 . 1 0 76873 .00 769. 52 1 06. 35 4 99 .60 94. 30 4 .03 107971 .00 702 . 93 104. 46 721 99 .70 97. 23 4 .20 76947 .00 712. 08 104. 31 2 101 .70 98. 19 4 .34 92342 .00 770. 82 1 02. 74 3 102 .80 100. 99 4 .33 82629 .00 838. 81 102. 23 4 104 .20 1 04. 60 4 .41 1 28611 .00 797. 05 103. 86 731 106 .60 110. 16 4 .47 851 37 .00 789. 63 105. 39 2 106 .90 118. 03 4 .78 109454 .00 878. 52 1 05. 68 3 107 .20 1 30. 42 4 .91 108800 .00 998. 05 1 06. 10 4 106 .90 1 30. 88 4 .99 164757 .00 942. 02 105. 67 741 105 .70 1 45. 1 7 5 .09 103951 .00 921 . 30 103. 59 2 104 .80 1 44. 68 5 .26 1 30537 .00 1020. 1 7 1 02. 03 3 1 1 3 .60 163. 34 5 .28 136458 .00 1 198. 63 105. 72 4 1 1 6 .50 1 67. 1 7 5 .57 151578 .00 1077. 79 107. 88 751 1 1 7 .30 160. 21 5 .89 110121 .00 1 086. 50 109. 1 4 2 1 19 .10 1 53. 1 6 6 .08 136167 .00 1171. 56 123. 45 3 1 1 9 .60 1 58. 96 6 .20 122122 .00 1 382. 38 131 . 93 4 118 .90 1 50. 36 6 .24 164259 .00 1 246. 93 1 32. 1 7 761 1 1 7 .20 151. 08 6 .40 1 03078 .00 1 235. 06 1 28. 98 2 1 16 .40 1 55. 57 6 .62 1 24998 .00 1 360. 80 127. 16 3 1 16 .20 1 58. 53 6 .75 1 12889 .00 1510. 49 126. 98 4 1 16 .90 1 48. 38 6 .89 149900 .00 1 386. 95 129. 31 771 119 .60 155. 1 4 7 .20 102156 .00 1312. 38 134. 20 2 124 .50 158. 40 7 .42 149403 .00 1 482. 29 137. 1 3 3 125 .90 1 50. 55 7 .47 135240 .00 1 642. 72 140. 45 4 131 .90 1 52. 57 7 .54 180345 .00 1 497. 25 146. 09 781 1 32 .60 •1 62. 05 7 .55 122037 .00 1 483. 64 151 . 97 2 136 .20 171. 58 7 .48 131125 .00 1 659. 44 1 56. 1 4 3 1 37 .20 171 . 36 7 .71 142564 .00 1816. 1 5 158. 86 4 139 .80 1 74. 18 7 .97 196512 .00 1668. 49 165. 08 791 1 44 .80 186. 19 8 .27 146377 .00 1 622. 01 1 68. 1 1 2 1 47 .70 1 95. 51 8 .62 138547 .00 1889. 44 168. 62 3 148 .10 203. 06 8 .92 138311 .00 2008. 43 175. 08 4 148 .90 201 . 07 9 .03 213453 .00 1 875. 67 179. 74 801 148 .30 208. 35 9 .44 1 32916 .00 1833. 98 179. 30 2 1 58 .70. 217. 74 9 .72 153560 .00 2071 . 67 180. 31 3 1 58 .40 232. 39 9 .92 167182 .00 2299. 64 187. 46 156 4 166. 80 243. 97 1 0 81 1 168. 80 249. 31 1 0 2 175. 80 253. 67 10 3 179. 1 0 249. 56 1 1 4 188. 30 236. 50 1 1 821 189. 80 247. 50 1 1 2 200. 90 253. 13 1 2 3 202. 90 255. 15 1 2 4 204. 20 243. 72 1 2 831 202. 00 255. 1 5 1 3 2 202. 30 269. 94 1 2 3 205. 7 0 280. 05 1 3 4 210. 00 279. 70 1 3 841 210. 60 285. 85 1 4 2 214. 00 292. 39 1 4 3 216. 20 292. 08 1 3 4 216. 80 275. 29 1 4 Brewery YEAR PBR PMBR 71 1 97.30 100.02 3 2 99.50 95.25 4 3 101.10 93. 1 4 4 4 102.10 90.21 4 721 1 02. 10 92.59 4 2 107.70 93.66 4 3 109.30 96.43 4 4 109.30 101.35 4 731 110.40 109.04 4 2 110.90 1 18.34 5 3 111.10 140.09 4 4 111.10 138. 19 5 741 1 37.60 158.99 5 2 145.20 159.61 5 3 146.20 180. 15 5 4 148.90 1 88.21 5 751 156.20 175.69 6 2 169.90 163.37 6 3 171.70 169.70 6 4 171.70 166.85 6 761 1 74.40 165.45 7 2 182.00 165. 15 7 3 183.10 163.03 7 4 185.70 157.95 7 771 191.40 3314.97 7 2 198.40 165.59 7 3 200. 10 151.63 7 4 200. 10 152.63 7 781 199.90 161.24 8 2 208.20 160.67 8 .26 225432 .00 2087. 89 1 97 .49 .57 156134 .00 2113. 1 9 204 .82 .82 186748 .00 2348. 99 208 .68 .23 200086 .00 2708. 73 212 .29 .40 239132 .00 2399. 30 21 1 .77 .91 158054 .00 2359. 82 218 .33 .21 184622 .00 2601 . 02 225 .54 .30 210061 .00 2885. 68 226 .86 .76 254487 .00 2524. 61 224 .85 .07 156216 .00 2431 . 34 224 .60 .91 190104 .00 2660. 18 226 .63 . 1 1 215633 .00 3046. 52 229 .55 .52 251600 .00 2672. 14 233 .06 .29 172739 .00 2560. 89 234 .01 .16 203269 .00 2888. 1 9 239 . 1 1 .91 201561 .00 31 22. 64 245 .83 .42 256995 .00 2838. 52 245 .75 WBR SHI BR PDIC PUSBR .69 75456 .00 646. 94 1 10 .53 .04 110755 .00 677. 24 1 1 1 .58 .25 122337 .00 769. 52 1 1 1 .94 .34 104594 .00 702 . 93 "1 10 .88 .67 88491 .00 712. 08 111 .03 .69 127805 .00 770. 82 109 .46 .74 1 35233 .00 838. 81 108 .92 .75 114539 .00 797. 05 109 .59 .93 102889 .00 789. 63 1 10 .68 .03 136646 .00 878. 52 1 1 1 .18 .91 151773 .00 998. 05 1 1 2 .02 .02 119410 .00 942. 02 1 1 2 .47 .20 109078 .00 921 . 30 1 1 2 .99 .46 164775 .00 1 020 . 1 7 1 1 4 .10 .77 194866 .00 1 198. 63 1 22 .10 .83 174133 .00 1077. 79 128 .00 .41 135788 .00 1 086. 50 1 35 .50 .52 201635 .00 1171. 56 1 39 .49 .65 216312 .00 1 382. 38 1 40 .48 .77 187641 .00 1 246. 93 1 39 .91 .24 144638 .00 1 235. 06 1 37 .12 .44 209450 .00 1 360. 80 1 35 .68 .46 213903 .00 1510. 49 1 35 .97 .68 190005 .00 1386. 95 1 38 .24 .79 162416 .00 1312. 38 1 44 .08 .75 240222 .00 1482. 29 148 .39 .62 243520 .00 1 642. 72 1 49 .87 .96 199309 .00 1 497. 25 1 55 .56 .24 173256 .00 1 483. 64 162 .10 .05 255997 .00 1659. 44 164 .71 1 57 3 208. 60 156. 08 4 208. 40 161. 01 791 211. 50 168. 86 2 228. 10 185. 94 3 234. 00 185. 43 4 233. 90 205. 48 801 253. 40 211. 50 2 261 . 20 226. 48 3 263. 10 239. 96 4 278. 90 255. 88 81 1 282. 20 265. 60 2 285. 50 262. 26 3 307. 90 250. 59 4 322 . 40 241 . 79 821 332. 10 250. 41 2 356. 70 249. 58 3 359. 80 232. 71 4 365. 40 232. 74 831 383 . 80 238. 34 2 384. 60 239. 56 3 387. 70 251 . 39 4 385. 60 268. 51 841 290. 30 275. 20 2 296. 60 277 . 89 3 295. 30 270. 49 4 295. 30 270 . 83 Winery YEAR PWI PMWI 71 1 99 .40 100. 00 2 99 .40 103. 55 3 99 .40 101. 91 4 101 .90 101. 90 721 101 .90 107. 97 2 109 .20 113. 29 3 1 09 .20 110. 38 4 1 09 .20 111. 53 731 1 09 .20 • 114. 33 2 1 13 .80 121. 90 3 1 14 .30 118. 70 4 1 1 4 .30 1 20. 94 741 1 1 4 .30 136. 39 2 1 1 2 .80 1 57. 62 3 1 22 .00 1 65. 30 4 1 22 .00 180. 99 751 1 32 .20 1 76. 42 2 141 .20 1 70. 53 3 141 .20 1 65. 07 4 141 .20 1 57. 74 761 1 54 .50 164. 36 8 .23 252152 .00 1816 .15 169. 73 8 .73 239427 .00 1 668 .49 180. 40 9 .30 199608 .00 1 622 .01 185. 79 9 .48 289308 .00 1889 .44 185. 64 9 .82 300728 .00 2008 .43 190. 47 0 .23 258301 .00 1875 .67 197. 01 0 .45 230831 .00 1833 .98 199. 91 0 .52 352445 .00 2071 .67 205. 24 0 .82 318192 .00 2299 .64 206. 23 1 .30 304058 .00 2087 .89 212. 53 2 .18 252689 .00 2113 .19 217. 71 1 .89 383550 .00 2348 .99 224. 98 1 .87 436739 .00 2708 .73 229. 62 2 .30 371137 .00 2399 .30 225. 1 1 3 . 1 5 301360 .00 2359 .82 230. 30 2 .87 466728 .00 2601 .02 242. 34 2 .56 486915 .00 2885 .68 244. 1 1 3 .44 412046 .00 2524 .61 239. 75 3 .58 348393 .00 2431 .34 243. 01 3 .34 491621 .00 2660 .18 251 . 99 3 .54 54281 6 .00 3046 .52 254. 08 3 .97 436057 .00 2672 . 1 4 256. 03 4 .76 367828 .00 2560 .89 260. 62 4 .62 544581 .00 2888 .19 271 . 94 4 .37 579599 .00 3122 .64 277. 89 4 .89 487124 .00 2838 .52 278. 05 WWI SHIWI PDIC PUSWI 69500 .00 646 .94 116. 28 1 0255 .00 677 .24 116. 74 39093 .00 769 .52 1 22. 61 1 4046 .00 702 .93 120. 60 10546 .00 712 .08 123. 77 1 3764 .00 770 .82 1 23. 78 1 3299 .00 838 .81 1 23. 76 16461 .00 797 .05 124. 71 1 281 0 .00 789 .63 128. 1 3 1 4979 .00 878 .52 142. 47 1 5045 .00 998 .05 1 34. 51 18942 .00 942 .02 139. 86 1 3387 .00 921 .30 141 . 02 18045 .00 1020 .17 141 . 03 1 7760 .00 1 1 98 .63 1 46. 61 2431 3 .00 1 077 .79 1 48. 21 1 4223 .00 1 086 .50 1 52. 87 1 9488 .00 1171 .56 1 59. 93 201 77 .00 1 382 .38 1 62. 64 25674 .00 1 246 .93 1 60. 56 15324 .00 1235 .06 1 54. 59 1 58 2 1 54. 50 1 64. 32 20791. 00 1 360. 80 151. 1 4 3 1 54. 50 158. 1 0 19540. 00 1510. 49 150. 63 4 165. 40 1 54. 08 25582. 00 1386. 95 1 53. 03 771 165. 40 1 64. 69 17561. 00 1312. 38 1 56. 65 2 166. 00 1 75. 21 23682. 00 1 482. 29 1 59. 02 3 1 75. 50 1 70. 37 25630. 00 1 642. 72 1 63. 45 4 180. 20 187. 42 30479. 00 1 497. 25 171 . 64 781 183. 00 1 77. 51 19279. 00 1 483. 64 .180. 02 2 1 82. 90 190. 45 27670. 00 1 659. 44 186. 1 3 3 183. 10 187. 32 34240. 00 1816. 15 1 94. 89 4 183. 20 1 85. 05 39073. 00 1 668. 49 206. 32 791 184. 90 1 99. 06 22229. 00 1 622. 01 216. 64 2 1 94. 00 207. 77 30945. 00 1889. 44 215. 41 3 1 94. 80 202. 60 35894. 00 2008. 43 219. 98 4 195. 00 208. 27 44481 . 00 1875. 67 224. 62 801 1 96. 00 234. 54 26725. 00 1833. 98 231 . 93 2 212. 70 260. 81 39476. 00 2071 . 67 238. 93 3 213. 00 261 . 30 44976. 00 2299. 64 247. 25 4 213. 40 264. 72 58480. 00 2087. 89 260. 1 2 81 1 220. 40 266. 91 32063. 00 2113. 19 272. 62 2 243. 50 274. 71 45179. 00 2348. 99 279. 87 3 249. 20 267. 04 51132. 00 2708. 73 286. 08 4 254. 20 261 . 73 62711. 00 2399. 30 291 . 25 821 254. 30 285. 09 37296. 00 2359. 82 298772 2 277. 90 296. 54 54504. 00 2601 . 02 316. 15 3 282. 50 288. 61 58481. 00 2885. 68 321 . 60 4 286. 10 284. 07 69188. 00 2524. 61 319. 30 831 286. 50 300. 65 40077. 00 2431 . 34 318. 36 2 295. 00 315. 67 56347. 00 2660. 18 318. 46 3 291 . 1 0 307 . 06 57966. 00 3046. 52 317. 20 4 288. 80 303. 97 68282. 00 2672. 1 4 325. 18 841 290. 30 319. 20 37489. 00 2560. 89 328. 16 2 299. 60 309. 69 55961 . 00 2888. 19 336. 83 3 295. 30 299. 81 61890. 00 31 22. 64 346. 21 4 294. 80 . 297. 10 63666. 00 2838. 52 346. 74 1 59 Appendix 5.3 Regressions with All Variables Before Variable Selection for Each Industry and Each Period, respectively Slaughtering Meats; 1971-1977 ASYMPTOTIC RHO 1 RHO 2 RHO 3 RHO 4 ESTIMATE •0.86759 -1 .09777 -0.84677 •0.78904 VARIANCE 0.01934 0.02615 0.02729 0.02916 ST.ERROR 0.13908 0.16170 0.16521 0.17077 T-RATIO -6.23819 -6.78917 -5.12540 -4.62050 R-SQUARE = 0.9862 R-SQUARE ADJUSTED = 0.9711 VARIANCE OF THE ESTIMATE-SIGMA**2 = 1.0605 STANDARD ERROR OF THE ESTIMATE-SIGMA = 1.0298 SUM OF SQUARED ERRORS"SSE= 11.665 MEAN OF DEPENDENT VARIABLE = 2.3403 VARIABLE ESTIMATED STANDARD T-RATIO PARTIAL STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR 11 DF CORR. COEFFICIENT AT MEANS 0.13087 0.80090E-01 5.3407* ** 0.8495 1.2657 0.3566 0.30588*** 0.99720 DPM 0.69893 0.75783 L1DPM 0.10137 0.10890 L2DPM 0.33240E-01 0.10867 0.43226E-01 0.30794E-01 L3DPM 0.54060E-01 0.54212E-01 0.70408E-01 0.48447E-01 DW 0.73390E-01 0.11291 0.65001 0.30730E-01 0.82799E-01 L1DW -0.30337 0.10837 -2.7994 -0.6450 -0.33278 DSHI -0.14363 0.92348E-01 -1.5554 -0.21497 L1DSHI -0.86846E-01 0.16397 -0.67825E-01 -0.13305 DPUS 0.62283E-01 0.10227 0.94096E-01 0.61954E-01 L1DPUS 0.53885E-01 0.73172E-01 0.81209E-01 0.50570E-01 L2DPUS 0.16154E-01 0.57769E-01 0.24303E-01 0.14718E-01 L3DPUS -0.62928E-01 0.58368E-01 -0.94616E-01 -0.52235E-01 CONSTANT 1.3683 0.50544 0.00000E+00 0.58465 -0.4246 • -0.52964** 0.60898* ? 0.73641*** 0.27964 -1.0781 2.7071 0.91382 0.13245 0.0918 0.2879 0. 1923 0.13083 0.11297 ?-0. 1577 0. 1806 0.2168 0.0840 -0.3091 0.6323 1 60 Slaughtering Meat; 1978-1984 ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T-RATIO RHO 1 -0.96974 0.04116 0.20287 -4.78000 RHO 2 -0.59685 0.08932 0.29886 -1.99711 RHO 3 -0.13950 0.13533 0.36787 -0.37922 RHO 4 -0.19593 0.07291 0.27002 -0.72562 R-SQUARE = 0.8261 R-SQUARE ADJUSTED = 0.6870 VARIANCE OF THE ESTIMATE-SIGMA**2 = 40.861 STANDARD ERROR OF THE ESTIMATE-SIGMA = 6.3923 SUM OF SQUARED ERRORS~SSE= 612.92 MEAN OF DEPENDENT VARIABLE = 2.5327 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS T-RATIO 1 5 DF PARTIAL CORR. 0.60422 0.80338 1.0512 0.85669 0.61159 DPM 0.43833 L1DPM 0.91861 0.69340 L2DPM -0.47143 -0.37262 L3DPM -1.5549 -1.1269 DW 0.23788 0.35982E-01 0.18397 L1DW -0.79054 -0.61267 DSHI 0.30646 0.29503 L1DSHI -0.63136 -0.64172 DPUS -0.95419E-02 -0.50208E-02 -0.77896E-02 L1DPUS -0.78832 1.0367 -0.66775 L2DPUS 0.71796 0.91317 0.64752 L3DPUS 2.0056 0.71008 1.6910 CONSTANT 1.2411 O.OOOOOE+00 0.49001 0.75209* ** 0.1906 0.34953 0.87383***??0.2201 0.53294 -0.55029? -0.1407 -0.27587 -2.5423" -0.5488 -0.90244 1.1805 0.20151 0.0520 1.1851 -0.66709 -0.1697 -0.11939 0.50938 0.60164** 0.1535 0.17269 0.54687 -1.1545* -0.2857 -0.35126 0.70389 -0.13556E-01-0.0035 -0.76041*** ??-0.1927 -0.41679 0.78623? ?? 0.1989 0.38104 2.8245" 0.5892 1.0445 1.2030 1.0317 0.2574 161 Poultry; 1971-1977 ASYMPTOTIC RHO 1 RHO 2 RHO 3 RHO 4 ESTIMATE 0.12528 -0. 1 4243 0.62138 •0.54276 VARIANCE 0.03615 0.02363 0.02500 0.05186 ST.ERROR 0.19012 0.15372 0.15813 0.22773 T-RATIO 0.65892 -0.92653 3.92964 -2.38337 R-SQUARE = 0.8451 R-SQUARE ADJUSTED = 0.7 260 VARIANCE OF THE ESTIMATE-SIGMA**2 = 7.9268 STANDARD ERROR OF THE ESTIMATE-SIGMA = 2.8155 SUM OF SQUARED ERRORS"SSE= 103.05 MEAN OF DEPENDENT VARIABLE = 2.7488 VARIABLE ESTIMATED STANDARDIZED ELASTICITY NAME COEFFICIENT COEFFICIENT AT MEANS STANDARD ERROR T-RATIO 1 3 DF 0.20325 0.23554 0.21531 0.17048 DPM 0.26061 0.25427 L1DPM 0.37857 0.34938 L2DPM -0.38768 -0.37945 L3DPM -0.41574E-01 -0.47531E-01 -0.38463E-01 DPDIC -0.81703E-01 0.62686E-01 -0.10660 L1DPDIC 0.11821 0.62988E-01 0.15460 DPUS 0.38898 0.10420 0.43080 L1DPUS -0.15578 0.11065 -0.15572 L2DPUS 0.94403E-01 0.90949E-01 0.98320E-01 L3DPUS 0.29862 0.91135E-01 0.30135 CONSTANT 0.36976 0.88343 0.00000E+00 0.13451 1.2822* 1.6072& •1 .8006~ •1 .3034 1 .8767 3.7332* -1.4080& 1 .0380~ 3.2767 0.3351 0.4071 -0.4468 -0.24386 -0.3400 • 0.4617 0.7193 -0.3637 0.2766 0.6726 0.41855 PARTIAL CORR. 0.29214 0.43705 -0.44731 -0.0675 0.13812 0.19957 0.91417 -0.37491 0.22737 0.71770 0.1153 1 62 Poultry; 1978-1984 ASYMPTOTIC RHO 1 RHO 2 RHO 3 RHO 4 ESTIMATE -0. 1 2288 1.17183 •0.25333 -0.69548 VARIANCE 0.02195 0.02105 0.02224 0.02427 ST.ERROR 0.14816 0.14507 0.14912 0.15579 T-RATIO -0.82937 8.07749 -1.69881 -4.46407 R-SQUARE = 0.9060 R-SQUARE ADJUSTED = 0.8507 VARIANCE OF THE ESTIMATE-SIGMA**2 = 2.0069 STANDARD ERROR OF THE ESTIMATE-SIGMA = 1.4166 SUM OF SQUARED ERRORS"SSE= 34.117 MEAN OF DEPENDENT VARIABLE = 1.8182 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS T-RATIO 1 7 DF 4.1933 0.7131 1.2372* 0.2874 -0.72688 -0.5335 DPM 0.65432 0.15604 0.57867 L1DPM 0.18836 0.15225 0.16346 L2DPM -0.80134E-01 0.11024 -0.66820E-01 -0.67938E-01 L3DPM -0.26004 0.99988E-01 -2.6007* -0.25334 DPDIC -0.34959E-01 0.11836E-01 -2.9536 -0.91639E-01 -0.52897E-01 L1DPDIC 0.30772E-01 0.14117E-01 2.1798 0.80571E-01 0.46709E-01 DPUS 0.83086E-01 0.33336E-01 2.4924 0.5173 0.76653E-01 L1DPUS 0.11826 0.37877E-01 3.1223 0.6037 0.95896E-01 L2DPUS -0.14934 0.41565E-01 -3.5930 -0.6570 -0.13007 L3DPUS 0.11196 0.43146E-01 2.5949 0.5327 0. 11837 CONSTANT 0.73833 0.51348 1.4379 0.00000E+00 0.40607 PARTIAL CORR. 0.53712 0.15680 -0. 1736 -0.22649 -0.5823 0.4674 0.19131 0.27617 0.34844 0.26276 0.3293 163 Dairy; 1971-1977 ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T-RATIO RHO 1 -1.08223 0.02475 0.15732 -6.87924 RHO 2 -1.68813 0.03445 0.18561 -9.09514 RHO 3 -1.10184 0.04070 0.20173 -5.46181 RHO 4 -0.81176 0.03180 0.17834 -4.55183 R-SQUARE = 0.9510 R-SQUARE ADJUSTED = VARIANCE OF THE ESTIMATE-SIGMA**2 = 0.77680 STANDARD ERROR OF THE ESTIMATE-SIGMA = 0.88136 SUM OF SQUARED ERRORS"SSE= 8.5448 MEAN OF DEPENDENT VARIABLE = 2.6228 0.8975 VARIABLE ESTIMATED STANDARDIZED ELASTICITY NAME COEFFICIENT COEFFICIENT AT MEANS STANDARD ERROR 0.57619E-01 T-RATIO 1 1 DF DPM 0.50124 0.88096 0.58813 L1DPM 0.15031E-02 0.68787E-01 0.27138E-02 0.18279E-02 L2DPM 0.87316E-01 0.66755E-01 1.3080 0.10925 L3DPM 0.20040E-01 0.71360E-01 0.28083$ 0.38035E-01 0.23527E-01 DW 0.39555 0.18434 8.6992*&~ 0.02185* 0.3669 0.45213 L1DW 0.66195 0.79500 DSHI 0.14858 0.18019 L1DSHI -0.10979 -0.14418 DPUS 0.10142 0.84628E-01 L1DPUS -0.40128 -0.30686 L2DPUS 0.29450 0.21440 L3DPUS -0.34932 -0.23397 CONSTANT -2.0252 0.00000E+00 -0.77218 0.15505 0.43787E-01 2.1457$" 4.2694&" 3.3933 0.47358E-01 -2.3182 0.13407 0.15984 0.15460 0.13513 0.20490 0.75651~ -2.5.105 1.9049 -2.5851 0.5432 0.7897 0.7151 -0.5729 0.2224 -0.6035 0.4980 -0.6148 -9.8840 PARTIAL CORR. 0.9344 0.0066 0.15686 0.0844 0.25986 0.44111 0.59184 •0.42986 0.10295 •0.41087 0.30401 -0.34151 -0.9480 1 64 Dairy; 1978-1984 ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T -RATIO RHO 1 0.24383 0.03538 0 . 18810 1 .29630 RHO 2 0.19436 0.02524 0.15887 1 .22343 RHO 3 0.57095 0.02403 0.15500 3 .68356 RHO 4 -0.34865 0.04264 0.20650 -1 .68842 R-SQUARE = 0.7908 R-SQUARE ADJUSTED = 0.6234 VARIANCE OF THE ESTIMATE-SIGMA**2 = 0.40232 STANDARD ERROR OF THE ESTIMATE-SIGMA = 0.63428 SUM OF SQUARED ERRORS"SSE= 6.0347 MEAN OF DEPENDENT VARIABLE = 2.2713 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS T-RATIO 1 5 DF PARTIAL CORR. 1.3099 1.6470 0.78804 0.3204 0.3913 0.1994 -0.15879* 0.3492 0.1610 DPM 0.79463E-01 0.60662E-01 0.86270E-01 L1DPM 0.10165 0.61717E-01 0.10884 L2DPM 0.55398E-01 0.70298E-01 0.59297E-01 L3DPM -0.14181E-01 0.89305E-01 -0.57291E-01 -0.15557E-01 DW 0.15661 0.10852 1.4432 0.14858 L1DW 0.82231E-01 0.13019 0.63162 0.82155E-01 DSHI 0.95234E-02 0.26538E-01 0.35885 0.78303E-01 0.11140E-01 L1DSHI 0.11660E-01 0.31338E-01 0.37206* 0.96401E-01 0.13425E-01 DPUS -0.17241 0.93362E-01 -1.8467 -0.4304 -0.15253 L1DPUS 0.12592 0.74005E-01 1.7015 0.4022 0.11528 L2DPUS 0.73030E-02 0.82070E-01 0.88985E-01 0.15077E-01 0.66192E-02 L3DPUS -0.23934E-01 0.74587E-01 -0.32089 -0.0826 -0.52574E-01 -0.22865E-01 CONSTANT 1.1333 0.60713 1.8666 0.4342 0.00000E+00 0.49895 0.32945 0.41710 0.22734 -0.0410 0.23235 0.10714 0.0923 0.0956 -0.35193 0.26080 0.0230 1 65 Flour Cereal; 1971-1977 ASYMPTOTIC ESTIMATE VARIANCE ST . ERROR T -RATIO RHO 1 0. 16493 0.02935 0. 17131 0 .96272 RHO 2 -0.80752 0.02808 0. 1 6759 -4 .81859 RHO 3 0.23182 0.04315 0. 20771 1 . 11607 RHO 4 -0.60084 0.03855 0. 19635 -3 .06006 R-SQUARE = 0.7047 R-SQUARE ADJUSTED = 0.3826 VARIANCE OF THE ESTIMATE-SIGMA**2 = 35.168 STANDARD ERROR OF THE ESTIMATE-SIGMA = 5.9302 SUM OF SQUARED ERRORS"SSE= 386.84 MEAN OF DEPENDENT VARIABLE = 2.7594 VARIABLE ESTIMATED STANDARDIZED ELASTICITY NAME COEFFICIENT COEFFICIENT AT MEANS STANDARD ERROR T-RATIO 1 1 DF 0.2052 DPM 0.17247E-02 0.24800E-02 0.69544 0.81148E-01 L1 DPM 0.11353E-02 0.23917E-02 0.47470 0.96909E-01 0.53404E-01 L2DPM -0.21163E-02 0.31115E-02 -0.68017 -0.2009 -0.99580E-01 L3DPM -0.65016E-02 0.37253E-02 -1.7452 -0.4657 -0.31542 DW 0.53673 0.59420 0.90329 0.2628 0.54082 L1DW -1.6058 0.98754 -1.6261* -0.4402 -1.5012 DPDIC 0.41912E-01 0.29161 0.50498E-01 0.54475E-01 L1DPDIC 0.39260 0.28733 0.51149 DPUS 0.46428 0.24775 1.8740*? 0.4919 0.32045 L1DPUS 0.25202 0.20065 1.2560 0.3542 0.14030 L2DPUS -0.10214 0.25448 -0.40137? -0.1201 -0.52736E-.01 L3DPUS 0.14097 0.22319 0.63162 0.1871 0.79686E-01 CONSTANT 3.2674 x3.9290 0.83160 0.00000E+00 1.1841 0.14373 1.3664 0.3809 PARTIAL CORR. 0.14722 0.1417 •0. 18064 •0.55394 0.16838 -0.47224 0.0433 0.47241 0.51221 0.27545 -0.11180 0. 15337 0.2432 166 Flour Cereal; 1978-1984 ASYMPTOTIC ESTIMATE VARIANCE ST .ERROR T -RATIO RHO 1 0.34465 0.03550 0. 18843 . 1 .82908 RHO 2 -0.08927 0.03304 0. 18177 -o .49114 RHO 3 -0.67955 0.03318 0. 18215 -3 .73070 RHO 4 0.28598 0.04869 0. 22067 1 .29597 R-SQUARE = 0.6642 R-SQUARE ADJUSTED = 0.3956 VARIANCE OF THE ESTIMATE-SIGMA**2 = 15.224 STANDARD ERROR OF THE ESTIMATE-SIGMA = 3.9018 SUM OF SQUARED ERRORS"SSE= 228.36 MEAN OF DEPENDENT VARIABLE = 2.4799 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS 1 5 0. 17373 0.45159E-01 0.34313 0.29504 DPM 0.37535E-01 0.15854 0.48077E-01 0.34778E-01 L1DPM 0.28839 0.18012 0.28967 L2DPM -0.36415 -0.35503 L3DPM 0.16363 -0.84847E-01 DW 0.42411 0.34620 L1DW 0.48066 0.43103 DPDIC -0.17763 -0.19706 L1DPDIC -0.82593E-01 0.96867E-01 -0.-91 91 8E-0-1 DPUS 0.85132 0.72250 L1DPUS 0.74798 0.27653 0.71743 L2DPUS -0.17572 0.27678 -0.17079 L3DPUS -0.45186 0.36284 -0.381 19 CONSTANT -0.47554 0.00O00E+OO -0.19176 1.6011 -2.0960 3.62.35 1 .2360 1.6291 0.89621E-01 -1.9820 0.85265 0.34569 2.4627 2.7049 -0.63488 -1.2453 1.4753 T-RATIO DF 0.23675 0.3821 -0.4760 0.6832 0.3040 0.3877 -0.4556 -0.2150 0.5366 0.5726 -0.1618 -0.3061 -0.32234 PARTIAL CORR. 0.0610 0.36124 -0.45833 0.64403 0.21580 0.25813 -0.34019 -0.15800 0.50921 0.47131 -0.11040 -0.29162 -0.0829 1 67 Feed; 1971-1977 ASYMPTOTIC RHO 1 RHO 2 RHO 3 RHO 4 ESTIMATE -0.01372 -0.22954 -0.02070 -0.85649 VARIANCE 0.01349 0.01454 0.01419 0.01464 ST.ERROR 0.11616 0.12057 0.11912 0.12099 T-RATIO -0. 11809 -1.90380 -0.17378 -7.07879 R-SQUARE = 0.9758 R-SQUARE ADJUSTED = 0.9495 VARIANCE OF THE ESTIMATE-SIGMA**2 = 4.3731 STANDARD ERROR OF THE ESTIMATE-SIGMA = 2.0912 SUM OF SQUARED ERRORS~SSE= 48.104 MEAN OF DEPENDENT VARIABLE = 2.7418 VARIABLE ESTIMATED STANDARD T-RATIO PARTIAL STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR 11 DF CORR. COEFFICIENT AT MEANS DPM 0.97201 0.11542 8. 421 5 0.9304 1 .3123 1 . 1727 L 1 DPM -0.20427 0.87272E-01 -2 .3406 -0.5766 -o. 27877 -0.22124 L2DPM 0.24194 0.63364E-01 3. 8182 0.7550 0. 29771 0.34097 L3DPM -0. 15934 0.99555E-01 -1 .6005 -0.4346 -o. 19564 -0.19412 DW -0.50566E- 01 0.23510 -o. 21 508 -0.0647 0.12869E-01 -0.51277E -01 L1 DW 0.54689 0.27784 1 .9684 0.5104 0. 1 3047 0.51453 DSHI 0.34092 0. 14935 -2. 2826 -0.5669 -o. 20491 -0.42044 L1DSHI 0.49149E-01 0.11107 0.44251 0. 1322 I.31 090E-01 0.52049E- 01 DPUS 0.27672; 0.11100 -2. 4928 -0.6008 -o. 37648 -0.37203 L1DPUS 0.25757 0.94355E-01 2 .7298 0.6355 0. 35325 0.31311 L2DPUS •0,16091 0.88685E-01 -1 . 8143 -0.4799 -o. 201 44 -0.24651 L3DPUS 0.22155 0 . 98701E-01 2 .2447 0.5605 0. 27692 0.30444 CONSTANT - 0.43034 1 .2598 -o. 341 59 0. 1024 0.00000E+00 -0.15695 1 68 Feed; 1978-1984 ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T-RATIO RHO 1 -0.63678 0.02499 0.15809 -4.02807 RHO 2 -0.79124 0.03789 0.19466 -4.06477 RHO 3 -0.29342 0.04299 0.20733 -1.41523 RHO 4 -0.62776 0.02815 0.16778 -3.74156 R-SQUARE = 0.9671 R-SQUARE ADJUSTED = 0.9407 VARIANCE OF THE ESTIMATE-SIGMA**2 = 1.0576 STANDARD ERROR OF THE ESTIMATE-SIGMA = 1.0284 SUM OF SQUARED ERRORS"SSE= 15.864 MEAN OF DEPENDENT VARIABLE = 1.6532 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS T-RATIO 1 5 DF PARTIAL CORR. DPM 0.39163 0.41455 L1DPM 0.11111E-02 0.15094E-02 0.13565E-02. L2DPM 0.85875E-01 0.78573E-01 0.67516E-01 L3DPM -0.28344E-01 0.65889E-01 -0.43018 -0.51256E-01 -0.27807E-01 DW -0.19073 0.13036 -1.4632 -0.23355 L1DW 0.28227 0.11718 2.4090 0.37971 DSHI 0.50703E-02 0.57368E-01 0.79664E-02 0.84729E-02 L1DSHI 0.16638 0.63732E-01 2.6106 0.28252 DPUS 0.31894 0.10508 3.0353 0.20682 L1DPUS -0.38997E-01 0.69550E-01 -0.56070 -0.46883E-01 -0.33683E-01 L2DPUS 0.25066E-02 0.80931E~01 0.30972E-01 0.0080 0.38882E-02 0.13389E-02 L3DPUS -0.20185E-01 0.76973E-01 -0.26223 -0.0676 -0.32290E-01 -0.14839E-01 CONSTANT -0.70106E-01 0.25487 -0.27506 -0.0708 0.00000E+00 -0.42405E-01 0.10652 3.6766 0.6885 0.54674 0.76020E-01 0.14616E-01 0.0038 1.0929 0.2716 0.15130 -0. 1104 -0.3534 -0.11533 0.5282 0.18014 0.88383E-01 0.0228 0.5589 0.26199 0.6168 0.39425 -0. 1433 169 Biscuits; 1971 -1977 ASYMPTOTIC RHO 1 RHO 2 RHO 3 RHO 4 ESTIMATE •1 .83072 •1 .9691 2 -1 .77546 -0.68172 VARIANCE 0.02179 0.04527 0.05298 0.02940 ST.ERROR T-RATIO 0.14760 -12.40293 0.21277 -9.25447 0.23017 -7.71384 0.17145 -3.97618 R-SQUARE = 0.9890 R-SQUARE ADJUSTED = 0.9770 VARIANCE OF THE ESTIMATE-SIGMA**2 = 0.44037 STANDARD ERROR OF THE ESTIMATE-SIGMA = 0.66360 SUM OF SQUARED ERRORS"SSE= 4.8440 MEAN OF DEPENDENT VARIABLE = 3.2494 VARIABLE ESTIMATED STANDARD T-RATIO STANDARDIZED ELASTICITY NAME COEFFICIENT COEFFICIENT AT MEANS DPM 0.23790 0.27471E-01 0.23214 L1DPM 0.17146 0.59637E-01 0.16931 L2DPM -0.78228E-01 0.65417E-01 -0.78422E-01 L3DPM -0.96907E-01 0.37288E-01 -0.89782E-01 DW -0.23213 -0.84651E-01 -0.19906 L1DW -0.21967 -0.84033E-01 -0.19515 DSHI -0.18213E-01 -0.51639E-01 -0.30994E-02 L1DSHI 0.46436E-01 0.27904E-01 -0.62283E-03 DPUS 0.38876 0.63594E-01 0.38675 L1DPUS -0.60262E-01 0.10143 -0.55286E-01 -0.51044E-01 L2DPUS 0.67764 0.10241 0.54725 L3DPUS -0.19703 0.55229E-01 -0.15256 CONSTANT 1.4685 0.10881 0.00000E+00 0.45192 PARTIAL ERROR 11 DF CORR. 8.6599 0.9339 0.37076 2.8751* 0.6550 0.26670 -1.1958* -0.3392 -0.12111 -2.5989 -0.6168 -0.15092 0.8012 0.6262 0. 1809 1.6641 0.4485 0.12928 6.1132 0.8790 0.37250 -0.59410 -0.1763 6.6169 0.8940 0.62975 -3.5675 -0.7324 -0.18109 13.495 0.9711 0.52267E-01 -4.4413 0.82456E-01 -2.6641** -0.29851E-01 -0.61015** 1 70 Biscuits; 1978-1984 RHO 1 RHO 2 RHO 3 RHO 4 ESTIMATE -0.52689 -0.20655 -0.70186 -0.76905 ASYMPTOTIC VARIANCE ST.ERROR T-RATIO 0.02871 0.16945 -3.10946 0.02135 0.14610 -1.41375 0.02798 0.16726 -4.19615 0.03496 0.18697 -4.11313 R-SQUARE = 0.7109 R-SQUARE ADJUSTED = 0.4797 VARIANCE OF THE ESTIMATETSIGMA**2 = 4.4392 STANDARD ERROR OF THE ESTIMATE-SIGMA = 2.1070 SUM OF SQUARED ERRORS"SSE= 66.589 MEAN OF DEPENDENT VARIABLE = 2.4792 VARIABLE ESTIMATED STANDARD T-RATIO STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR 15 DF COEFFICIENT AT MEANS DPM 0.17702 0.16358 L1DPM 0.51959E-01 0.59021E-01 0.48198E-01 L2DPM -0.38587 -0.34294 L3DPM 0.34680 0.32495 DW -0.43543 -0.34690 L1DW 0.18895 0.14628 DSHI -0.47851 0.82395E-01 L1DSHI -0.16289 0.40126E-02 DPUS 0.21721 0.18387 L1DPUS -0.45954E-01 -0.40616E-01 -0.45163E-01 L2DPUS 0.30946 0.21456 0.30220 L3DPUS -0.20125 0.16561 -0.20057 CONSTANT 1.8181 0.66462 0.00000E+00 0.73335 0.21768 0.27626 0.33172 0.23904 0. 14089 0.21351 0. 10807 0.73734E-01 0.27399 0.20136 0.81324 0.2055 0.18808 -1.1632 -0.2877 1.4508 0.3508 -3.0906 -0.6237 0.88499 0.2228 -4.4279 -0.7527 -2.2092 -0.4955 • 0.79275 0.2005 -0.22821 1.4423 0.3490 -1.2152 -0.2994 • 2.7356 PARTIAL CORR. 0.20141 0.0485 -0.44838 0.40773 -0.48046 0.20652 -1 . 1 125 0.39979 0.15614 -0.0588 0.27322 •0. 18087 0.5769 171 Bakery; 1971-1977 ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T-RATIO RHO 1 -1.40961 0.02565 0.16017 -8.80072 RHO 2 -1.50019 0.03121 0.17667 -8.49162 RHO 3 -1.44737 0.04361 0.20883 -6.93082 RHO 4 -0.79660 0.02817 0.16785 -4.74599 R-SQUARE = 0.9723 R-SQUARE ADJUSTED = VARIANCE OF THE ESTIMATE-SIGMA**2 = 0.35420 STANDARD ERROR OF THE ESTIMATE-SIGMA = 0.59515 SUM OF SQUARED ERRORS-SSE= 3.8962 MEAN OF DEPENDENT VARIABLE = 2.6164 0.9421 VARIABLE ESTIMATED STANDARDIZED ELASTICITY NAME COEFFICIENT COEFFICIENT AT MEANS STANDARD ERROR T-RATIO 1 1 DF DPM 0.29554E-01 0.34905E-01 0.84441E-01 0.30872E-01 L1DPM 0.20858E-01 0.52491E-01 0.59470E-01 0.22001E-01 L2DPM 0.29455 0.54752E-01 5.3798" 0.28947 L3DPM -0.48424E-01 0.49146E-01 -0.98529 ( -0.45572E-01 DW 0.25432 0.30113 L1DW -0.42307 -0.51892 DPDIC 0.11811 0.16228 L1DPDIC 0.13771 0.19536 DPUS 0.58738 0.51865 L1DPUS -0.47564 -0.40651 L2DPUS -0.14757 -0.12083 L3DPUS 0.37645 0.27940 CONSTANT 0.76074 0.00000E+00 0.29076 0.11557 0. 13909 0.27942E-01 0.54367E-01 0.12661 0.17193 0.15135 0.78899E-01 0.26802 2.2005 -3.0416 4.2270 2.5330 4.6393( -2.7665* •0.975027& 4.7713 0.84671*? 0.39735 &~ 0.8512 -0.2848 0.5529 -0.6759 -0.7867 0.6070. 0.8135 -0.6406 -0.2820 0.8211 2.8384 PARTIAL CORR. 0.2474 0.1190 0.84406 -0. 13914 0.24758 0.39222 0.43360 0.51191 0.71049 -0.57797 -0.17924 0.45338 0.6502 1 72 1 Bakery; 1978-1984 ESTIMATE VARIANCE RHO 1 0.37925 0.01723 RHO 2 0.17957 RHO 3 0.49011 RHO 4 -0.78932 ASYMPTOTIC ST.ERROR T-RATIO 0.13126 2.88928 0.01666 0.12908 1.39115 0.01815 0.13473 3.63775 0.01873 0.13685 -5.76758 R-SQUARE = 0.8742 R-SQUARE ADJUSTED = 0.7735 VARIANCE OF THE ESTIMATE-SIGMA**2 = 1.0278 STANDARD ERROR OF THE ESTIMATE-SIGMA = 1.0138 SUM OF SQUARED ERRORS"SSE= 15.417 MEAN OF DEPENDENT VARIABLE = 2.5192 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS T-RATIO 1 5 DF 0.48971 0.50826E-01 0.53088E-01 0.57225E-01 9.6350 2.9110 3.9927 DPM 0.4811 1 L1DPM 0.15454 0.14409 L2DPM 0.22848 0.22149 L3DPM -0.77932E-01 0.60139E-01 -1.2959 -0.76126E-01 DW 0.38090E-01 0.67651E-01 0.56436E-01 0.23499E-01 L1DW 0.13899 0.63560E-01 2.1867 0.82509E-01 DPDIC -0.60243E-01 0.15007E-01 -4.0145 -0.65999E-01 L1DPDIC 0.63052E-02 0.16884E-01 0.37345 0.28621E-01 0.71495E-02 DPUS -0.40802 0.13163 -0.40196 L1DPUS 0.34255 0.11927 0.33420 L2DPUS -0.47766 0.12020 -0.46400 L3DPUS -0.89709E-01 0.11560 -0.81578E-01 -0.90908E-01 CONSTANT 1.9855 0.73798 0.00000E+00 0.78815 0.9278 0.6008 0.7178 -0.3173 0.56303 0.4917 -0.7197 -3.0997 2.8719 -3.9741 -0.6249 0.5956 -0.7162 -0.77601 2.6905 PARTIAL CORR. 0.86014 0.27627 0.40834 •0. 1 3926 0. 1439 0.20745 •0.27150 0.0960 -0.36437 0.30485 -0.42372 -0.1965 0.5705 1 73 Confectionary; 1971 -1977 ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T -RATIO RHO 1 -0.00560 0.08370 0.28931 -0 .01937 RHO 2 0.04890 0.00894 0.09454 0 .51718 RHO 3 -0.94072 0.00972 0.09860 -9 .54107 RHO 4 0.00478 0.07576 0.27524 0 .01735 R-SQUARE = 0.9333 R-SQUARE ADJUSTED = 0.8605 VARIANCE OF THE ESTIMATE~SIGMA**2 = 3.7314 STANDARD ERROR OF THE ESTIMATE-SIGMA = 1.9317 SUM OF SQUARED ERRORS-SSE= 41.046 MEAN OF DEPENDENT VARIABLE = 3.7275 VARIABLE ESTIMATED STANDARDIZED ELASTICITY NAME COEFFICIENT COEFFICIENT AT MEANS STANDARD ERROR T-RATIO 1 1 DF 0.23791 2.4113*() 0.89075E-01 -0.90657E-0.44843&" -0.2382 0.4321 DPM 0.57367 0.92122 0.91244 L1DPM -0.80753E-02 -0.13436E-01 -0.11172E-01 L2DPM 0.56247E-01 0.12543 0.93347E-01 0.78375E-01 L3DPM -0.75021E-01 0.92239E-01 -0.81333 -0.90713E-01 DW 0.62328 0.39223 1.5891&% 0.45473 L1DW 1.4915 0.43402 3.4364)/# 0.56187 1.0920 DSHI -0.66115E-01 0.47061E-01 -1.4049 -0.3900 -0.64886E-01 L1DSHI 0.12826 0.60438E-01 2.1222 0.5390 0.14612 DPUS -0.86939E-01 0.12618 -0.68903*%--0.23002 -0.66495E-01 L1DPUS 0.19619 0.82340E-01 2.3826(#-0.52031 0.13967 L2DPUS -0.21690E-02 0.73976E-01 -0.029321"% -0.57388E-02 -0.16232E-02 L3DPUS 0.11428 0.70683E-01 1.6168 0.4382 0.79165E-01 CONSTANT -6.2040 2.0839 -2.9771 0.00000E+00 -1.6644 PARTIAL CORR. 0.5880 01-0.0273 0.1340 -0.12348 0.23681 0.7195 -0.11325 0.21790 -0.2034 0.5834 -0.0088 0.30223 -0.6680 174 Confectionary; 1978-1984 ASYMPTOTIC RHO 1 RHO 2 RHO 3 RHO 4 ESTIMATE 0.29571 0.38543 •0.26051 0.40176 VARIANCE 0.04332 0.06482 0.05475 0.07492 ST.ERROR 0.20813 0.25459 0.23398 0.27371 T-RATIO 1.42078 1.51391 -1 . 1 1 341 1.46781 R-SQUARE = 0.7059 R-SQUARE ADJUSTED = 0.4707 VARIANCE OF THE ESTIMATE-SIGMA**2 = 3.0726 STANDARD ERROR OF THE ESTIMATE-SIGMA = 1.7529 SUM OF SQUARED ERRORS"SSE= 46.089 MEAN OF DEPENDENT VARIABLE = 2.0897 VARIABLE ESTIMATED STANDARD T-RATIO PARTIAL STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR 15 DF CORR. COEFFICIENT AT MEANS DPM 0.10464 0.70558E-01 1 .4830 0.3576 0. 27940 0.10864E-01 L1DPM -0.52576E-01 0.60884E-01 - 0.86354 -0.2176 -0. 1 4751 -0. 18162E-01 L2DPM 0.20997 0.56494E-01 3 .71 66 0.6924 0. 58908 0.72621E-01 L3DPM -0.10323E-01 0.59663E -01 -o. 1 7302 -0.0446 0.30055E-01 -0.43331E-02 DW -0.17654 0.16707 -1 .0567 -0.2632 -o. 18925 -0.18541 L1DW -0.92367E-02 0.16401 -0.56319E-•01- 0.0145 0.10012E-01 -0.89739E-02 DSHI -0.68925E-01 0.29053E-01 -2 .3724 -0.5223 -o. 44762 -0.13671 L1DSHI -0.43495E-01 0.29358E-01 -1 . 4815 -0.3573 -o. 281 62 -0.72783E-01 DPUS -0.65351 0.19602 -3 . 3339 -0.6524 -o. 50246 -0.67528 L1DPUS 0.65246 0.23875 2. 7328 0.5765 0. 491 09 0.70559 L2DPUS -0.21258 0.19100 -1 . 1 1 30 -0.2762 -o. 17566 -0.21391 L3DPUS 0.55083E-01 0.19769 0 .27863 0.0718 I.45566E-01 0.55460E-01 CONSTANT 2.5667 1.9390 1.3238 0.3234 0.00000E+00 1.2282 175 Sugar Cane Beet ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T- RATIO RHO 1 -1 .83108 0.03993 0.19981 -9. 16390 RHO 2 -2.16848 0.14295 0.37808 -5. 73543 RHO 3 -1.55780 0. 14780 0.38445 -4. 05200 RHO 4 -0.67455 0.04200 0.20494 -3. 29145 R-SQUARE = 0.9765 R-SQUARE ADJUSTED = 0.9585 VARIANCE OF THE ESTIMATE-SIGMA**2 = 19.644 STANDARD ERROR OF THE ESTIMATE-SIGMA = 4.4322 SUM OF SQUARED ERRORS"SSE= 255.37 MEAN OF DEPENDENT VARIABLE = 4.0049 VARIABLE ESTIMATED STANDARD T-RATIO PARTIAL STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR 13 DF CORR. COEFFICIENT AT MEANS DPM 0 .92247 •0.90112E-01 10.237* 0.9432 0.95140 1 .0882 L1 DPM -0.53546 0.16369 -3.2711*&"% -0.6719 -0.54726 -0.70617 L2DPM -0 .10863 0.18614 -0.58359&"?/( -0.1598 -0.11231 -0. 13008 L3DPM 0.74183E-01 0.97860E -01 0.75805"" 0.2057 0.76735E-01 0.89897E- 01 -DSHI -0.17101E-02 0.12209 -0.140077E- 01-0.0039 -0.15789E-02 -0. 19742E -02 L1DSHI -0. 19033E -01 0.10366 -0. 18360/ -0.0509 -0.17714E-01 -0.20224E -01 DPUS 0 .65146 0. 1 0700 6.0887%( 0.8605 0.73164 0.56549 L1DPUS 0.43564 0.13966 -3.1192 -0.6543 -0.48827 -0.39761 L2DPUS 0 .32373 0.12243 2.6442 0.5914 0.36190 0.31917 L3DPUS -0.73376E-02 0.76228E-01 -0.96259E- 01-0.0267 -0.82059E-02 -0.70362E -02 CONSTANT 0 .56715 0.21529 2.6344 0.5899 0.00000E+00 0.14161 176 Sugar Cane Beet; 1978-1984 ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T-RATIO RHO 1 -0.55483 0.02862 0.16917 -3.27970 RHO 2 0.00302 0.03187 0.17851 0.01689 RHO 3 -0.57309 0.03529 0.18786 -3.05063 RHO 4 -0.52346 0.03298 0.18160 -2.88250 R-SQUARE = 0.4506 R-SQUARE ADJUSTED = 0.1275 VARIANCE OF THE ESTIMATE-SIGMA**2 = 615.74 STANDARD ERROR OF THE ESTIMATE-SIGMA = 24.814 SUM OF SQUARED ERRORS"SSE= 10468. MEAN OF DEPENDENT VARIABLE = 4.0622 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS T-RATIO 17 DF PARTIAL CORR. DPM 1.6883 0 .51668 3.2676 0.6211 1 .1299 0.42177 L1DPM -0. 79630 0. 601 38 -1 .3241 -0.3058 -0. 531 72 -0.21094 L2DPM -0 .49546 0 .69710 . -0.71075 -0.1699 -o. 32826 -0. 17411 L3DPM 0. 27184 0. 44542 0.61031 0.1464 0. 18005 .0.1 2732 DSHI 0 .67798 0 .58367 1.1616 0.2712 0. 23276 0.33489 L1DSHI -0.48235E -01 0.59444 -0.81145E-•01- 0.0197 0. 16371E-01 -0.20082E -01 DPUS -0 .28112 0 .44666 -0.62937 -0.1509 -o. 1 5541 -0.32587 L1DPUS 0. 461 1 5 0. 43224 1.0669 0.2505 0. 2561 1 0.52076 L2DPUS -0 .88561 0 .42539 -2.0819 -0.4507 -o. 49486 -0.95599 L3DPUS 0. 90088E-01 0 .39850 o. 22607 0.0547 I.50342E-01 0.97012E- 01 CONSTANT 5.0738 3 .8940 1.3030 0.3013 0.00000E+00 1.2490 L2DPUS L3DPUS CONSTANT 1 77 Vegetable Oil; 1971-1977 RHO 1 RHO 2 RHO 3 RHO 4 ESTIMATE -0.29140 0. 12193 0.21778 -0.08749 ASYMPTOTIC VARIANCE ST.ERROR 0.04515 0.06200 0.07769 0.07445 0.21249 0.24900 0.27873 0.27285 T-RATIO -1.37137 0.48969 0.78135 -0.32067 R-SQUARE = 0.9543 R-SQUARE ADJUSTED = 0.9192 VARIANCE OF THE ESTIMATE-SIGMA**2 = 25.372 STANDARD ERROR OF THE ESTIMATE-SIGMA = 5.0371 SUM OF SQUARED ERRORS"SSE= 329.84 MEAN OF DEPENDENT VARIABLE = 4.4689 VARIABLE ESTIMATED STANDARDIZED ELASTICITY NAME COEFFICIENT COEFFICIENT AT MEANS STANDARD ERROR T-RATIO 1 3 DF DPM 0.77548 0.17060 0.87880 L1DPM 0.20198 0.22358 0.21603 L2DPM 0.63107E-01 0.20989 0.66971E-01 0.87078E-01 L3DPM -0.11680 0.15300 -0.14005 DSHI 0.75876E-01 0.12403 0.66929E-01 0.97581E-01 L1DSHI -0.15892E-01 0.12918 -0.13961E-01 -0.21856E-01 DPUS -0.48942E-02 0.12806 -0.55070E-02 -0.40792E-02 L1DPUS -0.19820 0.17929 -0.13112 L2DPUS 0.71027E-01 0.17223 0.76783E-01 0.75210E-01 L3DPUS 0.39171E-01 0.14601 0.41027E-01 0.30154E-01 CONSTANT -0.49342 1.5288 0.00000E+00 -0.11041 4.5456 0.7835 0.90337 0.2430 0.30066 -0.76338 -0.2071 0.61176 -0.12303 -0.38219E--1.1055 -0.2931 0.41241 0.26827 -0.32274 PARTIAL CORR. 0.88329 0.23146 0.0831 -0.12278 0.1673 -0.0341 01-0.0106 -0.22561 0.1136 0.0742 -0.0892 1 78 Vegetable Oil; 1978-1984 ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T-RATIO RHO 1 -0.02069 0.06425 0.25349 -0.08164 RHO 2 -0.48375 0.04302 0.20741 -2.33238 RHO 3 0.62005 0.05183 0.22766 2.72359 RHO 4 -0.18428 0.06570 0.25632 -0.71894 R-SQUARE = 0.9146 R-SQUARE ADJUSTED = 0.8643 VARIANCE OF THE ESTIMATE~SIGMA**2 = 8.3511 STANDARD ERROR OF THE ESTIMATE-SIGMA = 2.8898 SUM OF SQUARED ERRORS"SSE= 141.97 MEAN OF DEPENDENT VARIABLE = 0.87281 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS T-RATIO 1 7 DF DPM. 0.42886 0.12961 3.3089 0.6259 0.79624 L1DPM -0.17698 0.12076 -1.4656 -0.3349 -0.38835 L2DPM 0.24427 0.10594 2.3057 0.4881 0.43196 L3DPM -0.72693E-01 0.89579E-01 -0.81150 -0.1931 -0.12162 DSHI -0.41238E-01 0.60352E-01 -0.68329 -0.84338E-01 -0.17711 0.60121E-01 LiDSHI 0.10590 0.34887 DPUS 0.27536 0.81 579 L1DPUS 0.11891 0.39198 L2DPUS -0.23978 -0.57182 L3DPUS -0.12622 -0.31825 CONSTANT -0.28734 0.00000E+00 -0.32921 0.94935E-01 0.96842E-01 0. 10379 0.73205E-01 0.60307 1.7615 2.9005 1.2279 -2.3102 •1 .7243 0.3929 0.5754 0.2854 -0.4888 -0.3858 0.47646 PARTIAL CORR. 0.55396 •0.22451 0.34195 •0. 10037 -0.1635 0.21066 0.40325 0.17412 -0.39170 -0.20991 -0.1148 179 Soft Drink; 1971-1978 ASYMPTOTIC RHO 1 RHO 2 RHO 3 RHO 4 ESTIMATE •0.94549 -0.52926 -0.97527 -0.77944 VARIANCE 0.02647 0.02217 0.02171 0.02584 ST.ERROR 0.16271 0.14891 0.14736 0. 16074 T-RATIO -5.81103 -3.55434 -6.61843 -4.84910 R-SQUARE = 0.9176 R-SQUARE ADJUSTED = 0.8276 VARIANCE OF THE ESTIMATE-SIGMA**2 = 3.4941 STANDARD ERROR OF THE ESTIMATE-SIGMA = 1.8692 SUM OF SQUARED ERRORS"SSE= 38.435 MEAN OF DEPENDENT VARIABLE = 2.8263 VARIABLE ESTIMATED STANDARDIZED ELASTICITY NAME COEFFICIENT COEFFICIENT AT MEANS STANDARD ERROR T-RATIO 1 1 DF PARTIAL CORR. DPM 0.70841E-01 0.72884E-01 0. 971 97 0.2812 0 .14698 0.65665E-01 L1DPM 0.41288 0.84392E-01 4.8924 0.8277 0 .85644 0.38145 L2DPM 0.16068 0.85692E-01 1 .8751 0.4922 0 .33349 0. 14734 L3DPM 0.76849E-01 0.81907E-01 0 .93824 0.2722 0 .16008 0.63098E-01 DW 0.48542 0.30647 1 .5839 0.4309 0 .23754 0.53117 L1DW -0.16000 0. 41 243 -0.38795 -0.1162 -0.78392E-01 -0.17846 DSHI -0.97895E-07 0.21678E- 06 -0. 451 59 -0. 1349 -0.43817E-01 -0.14249E- 01 L1DSHI -0.71136E -07 0.28376E- 06 -0. 25069 -0.0754 -0.31840E-01 -0.10354E- 01 DPUS -0.63935 0.30006 -2 . 1 308 -0.5405 -0 .64968 -0.57780 L1DPUS 0.83378 0.47641 1 .7501 0.4667 0 .85658 0.68486 L2DPUS -0.46144 0.49517 -o. 93188 -0.2705 -0 .47416 -0.35655 L3DPUS 0.27536 0.25590 1.0760 0.3086 0 .28203 0.20229 CONSTANT 0.27584 1.0075 0. 27378 0.0823 O.OOO00E+0O 0.97597E-01 180 Soft Drink; 1971-1977 ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T-RATIO RHO 1 -0.31232 0.04640 0.21541 -1.44988 RHO 2 0.83980 0.05284 0.22986 3.65345 RHO 3 0.41234 0.04027 0.20068 2.05468 RHO 4 -0.05462 0.05383 0.23200 -0.23542 R-SQUARE = 0.8258 R-SQUARE ADJUSTED = 0.6864 VARIANCE OF THE ESTIMATE-SIGMA**2 = 4.6950 STANDARD ERROR OF THE ESTIMATE-SIGMA = 2.1668 SUM OF SQUARED ERRORS"SSE= 70.426 MEAN OF DEPENDENT VARIABLE = 2.3499 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS T-RATIO 1 5 DF DPM 0.61281E-01 0.62133E-01 0.98630 0.97642E-01 L1DPM -0.91654E-01 0.61851E-01 -1.4819 -0.12978 L2DPM 0.51900E-01 0.85061E-01 0.61016 0.64850E-01 L3DPM 0.37397E-02 0.98422E-01 0.74006E-02 0.47161E-02 DW 0.74780E-01 0.21522 0.44807E-01 0.70266E-01 L1DW -0.43019 0.23606 -1.8224 -0.39686 DSHI 0.91020E-02 0.20645E-01 0.45355E-01 0.15666E-01 L1DSHI -0.73013E-01 0.18972E-01 -3.8485 -0.12917 DPUS 0.96009 1.0645 L1DPUS -0.14981 -0.89987E-01 -0.17231 L2DPUS 0.13108 0.79094E-01 0.15316 L3DPUS -0.15519 -0.93512E-01 -0.18098 CONSTANT -0.25323 0.00000E+00 -0.10776 0.29132 3.2957 0.24211 0.20293 0.20061. 1.9114 0.2468 -0.3573 -0.1556 0.37997E-01 0.34745 -0.4258 -0.44089 -0.7049 -0.6481 -0.61877 0.64595 -0.77359 -0.13249 PARTIAL CORR. 0. 12944 0. 18459 0.10264 0.0098 0.0894 0.25600 0.1131 0.36214 0.57393 -0.1578 0.1645 -0.1959 -0.0342 181 Distillery; 1971-1977 ASYMPTOTIC ESTIMATE VARIANCE ST .ERROR T -RATIO RHO 1 1.60755 0.01991 0. 14112 1 1 .39141 RHO 2 -1.92672 0.04049 0. 201 22 -9 .57499 RHO 3 1.60265 0.04431 0. 21 051 7 .61314 RHO 4 -0.86831 0.02329 0. 1 5260 -5 .69014 R-SQUARE = 0.8842 R-SQUARE ADJUSTED = 0.7 579 VARIANCE OF THE ESTIMATE-SIGMA**2 = 1.1517 STANDARD ERROR OF THE ESTIMATE-SIGMA = 1.0732 SUM OF SQUARED ERRORS"SSE= 12.669 MEAN OF DEPENDENT VARIABLE = 1.1992 VARIABLE ESTIMATED STANDARDIZED ELASTICITY NAME COEFFICIENT COEFFICIENT AT MEANS STANDARD ERROR T-RATIO 1 1 DF 0.62014E-01 4.3855 1.1713 0.7976 0.3330 -0.2029 -0.7874 -0.8403 •0.24848 DPM 0.27196 0.48623 L1 DPM 0.93946E-01 0.80207E-01 0. 15431 L2DPM -0.46261E-01 0.67306E-01 -0.68731 -0.80955E-01 L3DPM -0.32063 0.75686E-01 -4.2364 -0.52546 DW -0.73970 0.14388 -5.1411 -1.6403 L1DW -0.26909E-01 0.10829 -0.24282E-01 -0.57199E-01 DSHI 0.57889E-02 0.2781OE-01 0.20816 0.78649E-01 0.30604E-01 L1DSHI -0.33059E-01 0.31479E-01 -1.0502 -0.3019 -0.18293 DPUS 0.47127 0.11652 4.0445 0.7733 0.57264 L1DPUS -0.36076E-01 0.11285 .-0.31967 -0.55112E-01 -0.36576E-01 L2DPUS -0.22351 0.11499 -1.9438 -0.5056 -0.21032 L3DPUS -0.27948E-01 0.81180E-01 -0.34428 -0.42565E-01 -0.24562E-01 CONSTANT 3.1721 0.96421 3.2899 0.00000E+00 2.6451 PARTIAL CORR. 0.63014 0.22198 •0.1 0625 •0.74265 •0.60538 -0.0747 0.0626 •0.45392 0.71633 -0.0959 •0.34089 -0. 1032 0.7042 1 82 Distillery; 1978-1984 ASYMPTOTIC ESTIMATE VARIANCE ST .ERROR T- RATIO RHO 1 -0.54061 0.03119 0. 17659 -3. 061 32 RHO 2 -0.04679 0.01988 0. 14101 -o. 33181 RHO 3 -0.72073 0.02060 0. 1 4353 -5. 02164 RHO 4 -0.64309 0.04213 0. 20526 -3. 13299 R-SQUARE = 0.6477 R-SQUARE ADJUSTED = 0.3658 VARIANCE OF THE ESTIMATE-SIGMA**2 = 2.6239 STANDARD ERROR OF THE ESTIMATE-SIGMA = 1.6198 SUM OF SQUARED ERRORS-SSE= 39.359 MEAN OF DEPENDENT VARIABLE = 1.8099 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS 1 5 T-RATIO DF PARTIAL CORR. DPM -0.37121 0.15822 -2.3462 -0.44904 L1DPM 0.26025 0.22659 1.1486 0.35121 L2DPM -0.28107 0.17571 -1.5997 -0.35241 L3DPM 0.65877E-02 0.17176 0.11066E-01 0.82359E-02 DW 0.49385E-01 0.26572 0.46378E-01 0.64395E-01 L1DW 0.59397 0.17392 3.4151 0.74251 DSHI -0.34877E-02 0.25739E-01 -0. -0.42940E-01 -0.86734E-02 L1DSHI 0.16243E-02 0.19907E-01 0.20175E-01 0.42268E-02 DPUS 0.52957 0.30397 1.7422 0.55227 L1DPUS -0.42636E-01 0.28935 -0 -0.34283E-01 -0.47866E-01 L2DPUS 0.70863 0.33421 2.1203 0.79018 L3DPUS -0.40626 0.29756 -1.3653 -0.45303 CONSTANT -0.27719 0.55926 0.00000E+00 -0.15315 -0.5181 -0.63980 0.2843 0.40292 -0.3817 -0.47213 0.38355E-01 0.0099 0.18585 0.0479 0.6614 0.55796 13550 -0.0350 0.81595E-01 0.0211 0.4102 0.42512 .14735 -0.0380 0.4802 0.56801 -0.3325 -0.32564 -0.49564 -0.1269 183 Brewery; 1971-1977 ASYMPTOTIC ESTIMATE VARIANCE ST.ERROR T-RATIO RHO 1 1.18261 0.01663 0.12895 9.17095 RHO 2 -1.79107 0.02916 0.17076 -10.48882 RHO 3 1.12443 0.02870 0.16941 6.63745 RHO 4 -0.82859 0.01920 0.13858 -5.97927 R-SQUARE = 0.7477 R-SQUARE ADJUSTED = 0.4724 VARIANCE OF THE ESTIMATE-SIGMA**2 = 13.247 STANDARD ERROR OF THE ESTIMATE-SIGMA = 3.6396 SUM OF SQUARED ERRORS~SSE= 145.72 MEAN OF DEPENDENT VARIABLE = 2.9497 VARIABLE ESTIMATED STANDARD T-RATIO PARTIAL STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR 11 DF CORR. COEFFICIENT AT MEANS DPM -0 .86172E-02 0.26200E-02 -3.2889 -0.7041 -o. 7031 9 -0.23820 L 1 DPM 0.611 59E-03 0.35389E-02 0 . 1 7282 0.0520 .49912E-01 0. 16873E -01 L2DPM -o. 28334E-03 0. 36005E-02 -0.78694E- 01- 0.0237 0.23120E-01 -0.78420E- 02 L3DPM 0.23658E- 02 0.48267E-02 0.49016 0. 1462 0. 1 9245 0.68495E- 01 DW 1 .2218 0.45969 -2.6579 -0.6254 -o. 71218 -1.0765 L1 DW 2.0599 0.51150 . 4.0272 0.7719 1 .1901 1 .7467 DSHI -0 .19401 0.54320E-01 -3.5716 -0.7328 -o. 99546 -0.36148 L1DSHI 0.32865E- 01 0.65798E-01 0.49949 0.1489 0. 1 6774 0.62931E- 01 DPUS 1 .2473 0.59100 2.1105 0.5369 0. 57309 0.61131 L1DPUS 0.55701 0.47216 -1 . 1797 -0.3351 -o. 25514 -0.23560 L2DPUS 1.2662 0.54215 2.3356 0.5758 0. 58185 0.52363 L3DPUS -1.7705 0.57643 -3.0714 -0.6795 -o. 80271 -0.68107 CONSTANT 1.8427 2.2569 0. 81 648 0.2390 0.00000E+00 0.62472 184 Brewery; 1978-1984 ASYMPTOTIC ESTIMATE VARIANCE ST .ERROR T -RATIO RHO 1 0.37553 0.03216 0. 1 7933 2 .09406 RHO 2 0.53207 0.02324 0. 15245 3 .49005 RHO 3 0.56544 0.02630 0. 16219 3 .48640 RHO 4 -0.86144 0.08474 0. 291 1 0 -2 .95926 R-SQUARE = 0.5954 R-SQUARE ADJUSTED = 0.2717 VARIANCE OF THE ESTIMATE-SIGMA**2 = 25.044 STANDARD ERROR OF THE ESTIMATE-SIGMA = 5.0044 SUM OF SQUARED ERRORS"SSE= 375.66 MEAN OF DEPENDENT VARIABLE = 1.5875 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS T-RATIO 1 5 DF 0.29749 -0.50840 -3.0050 DPM 0.12088 0.88606E-01 0.16421 L1DPM -0.14974 0.29454 -0.20521 L2DPM -0.74988 0.24954 -0.93032 L3DPM 0.39751E-01 0.46155E-01 0.86125 -0.36516E-01 DW -0.77257E-01 0.63312 -0.41809E-01 -0.11238 L1DW -0.47864 0.64928 -0.73718 -0.70535 DSHI 0.80175E-01 0.61007E-01 1.3142 0.31064 L1DSHI 0.14361E-01 0.63237E-01 0.22710 0.65501E-01 0.54931E-01 DPUS -0.24155 0.78513 -0.77522E-01 -0.32145 0.72182 -1.7682 0.40634 -0.1302 -0.6130 0.2171 0.12203 -0. 1870 0.3213 L1DPUS -1.2763 -1.8059 L2DPUS -0.11059 -0.35423E-01 -0.15351 L3DPUS - 1 . 68 1 *6 -2.2830 CONSTANT 12.060 0.00000E+00 7.5969 0.70572 0.77869 -2.1596 4.9004 0.30766 -0.4153 0.15670 -0.4870 2.4611 PARTIAL CORR. 0. 1043 •0. 10955 •0.5951 5 0. 12821 -0.0315 •0.26040 0.36471 0.0585 -0.0792 •0.40558 -0.0404 •0.52714 0.5363 185 Winery; 1971-1977 RHO 1 RHO 2 RHO 3 RHO 4 ESTIMATE -0.85362 -0.03064 0.75017 0.83747 ASYMPTOTIC VARIANCE ST.ERROR 0.03314 0.04305 0.04345 0.03413 0.18203 0.20750 0.20844 0.18475 T-RATIO -4.68936 -0.14767 3.59907 4.53301 R-SQUARE = 0.6139 R-SQUARE ADJUSTED = 0.3170 VARIANCE OF THE ESTIMATE~SIGMA**2 = 8.5211 STANDARD ERROR OF THE ESTIMATE-SIGMA = 2.9191 SUM OF SQUARED ERRORS~SSE= 110.77 MEAN OF DEPENDENT VARIABLE = 2.4611 VARIABLE ESTIMATED STANDARDIZED ELASTICITY NAME COEFFICIENT COEFFICIENT AT MEANS STANDARD ERROR T-RATIO 1 3 DF DPM -0.31006 0.11678 -2.6551 -0.5930 -0.34287 L1DPM 0.35013 0.18223 1.9213 0.4703 0.32780 L2DPM 0.69064E-01 0.17612 0.39215 0.1081 0.66032E-01 L3DPM 0.33366E-02 0.13457 0.24794E-0.51220E-02 0.30298E-02 DSHI -0.10814 0.45137E-01 -2.3959 -0.5535 -0.30226 L1DSHI -0.29275E-01 0.21726E-01 -1.3474 -0.3501 -0.40689E-01 DPUS -0.67426E-01 0.19203 -0.35112 -0.62507E-01 -0.41962E-01 L1DPUS 0.28562 0.19272 1.4820 0.3802 0.14559 L2DPUS -0.15085 0.17048 -0.88487 -0.2383 -0.82612E-01 L3DPUS 0.88143E-01 0.16943 0.52023 0.83117E-01 0.46604E-01 CONSTANT 2.7720 1.0225 2.7109 O.OOOOOE+00 1.1263 PARTIAL CORR. -0.50184 0.54762 0. 10720 01 0.0069 -0.81356 -0.24949 -0.0969 0.26266 -0.14202 0.1428 0.6010 186 Winery; 1978-1984 ASYMPTOTIC ESTIMATE VARIANCE ST .ERROR T- RATIO RHO 1 0.62322 0.01992 0. 14114 4. 41 548 RHO 2 0.04782 0.03176 0. 1 7820 0. 26834 RHO 3 -0.37990 0.03475 0. 18641 -2. 03795 RHO 4 0.70886 0.02493 0. 15790 4. 48938 R-SQUARE = 0.8517 R-SQUARE ADJUSTED = 0.7644 VARIANCE OF THE ESTIMATE-SIGMA**2 = 2.2043 STANDARD ERROR OF THE ESTIMATE-SIGMA = 1.4847 SUM OF SQUARED ERRORS"SSE= 37.474 MEAN OF DEPENDENT VARIABLE = 1.8164 VARIABLE ESTIMATED STANDARD STANDARDIZED ELASTICITY NAME COEFFICIENT ERROR COEFFICIENT AT MEANS T-RATIO 1 7 DF DPM -0.17009E-01 0.79081E~01 -0.21509 -0.26507E-01 -0.16525E-01 L1DPM 0.28052 0.82965E-01 3.3812 0.6341 0.33269 L2DPM -0.43259E-03 0.88732E-01 -0.48753E--0.70216E-03 -0.51675E-03 L3DPM -0.54431E-01 0.64007E-01 -0.85039 -0.87560E-01 -0.75042E-01 DSHI -0.21525E-01 0.15862E-01 -1.3570 -0.98190E-01 L1DSHI -0.69222E-01 0.17380E-01 -3.9829 -0.33761 DPUS 0.14756 0.95585E-01 0.20809 L1DPUS 0.20033 0.15279 0.30165 L2DPUS 0.14467 0.93432E-01 0.21784 L3DPUS 0.56680 0.19038 0.84091 CONSTANT -2.9553 0.00000E+00 -1.6270 0.15682 1.3111 0.15994 2.9773 1.9673 -0.3126 -0.6948 0.94095 0.3030 0.90455 0.5854 -1.5022 PARTIAL CORR. -0.0521 0.45685 02-0.0012 -0.2020 -0.22725 -0.73177 0.2225 0. 12938 0.2143 0.36855 -0.3423 1 87 BIBLIOGRAPHY Agriculture Canada: Food Market Commentary, Food Market Analysis Division of the Policy, Planning, and Economic Branch, Ottawa, December, 1984, p. 3. 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