An Economic Inquiry into Blending Rents at Prirnary Elevators in : A Spatial Approach

A Thesis Submitted to the College of

Graduate Studies and Research

in Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy

in the Department of Agricultural Economics

University of

BY

Andre Louis Eugene Hucq

Fall, 1997

O Copyright: Andre Louis Eugene Hucq, 1997. All rights reserved. National Library Bibliothèque nationale I*I ofCanada du Canada Acquisitions and Acquisitions et Bibliographie Services services bibliographiques 395 Wellington Street 395, rue WeIlington Ottawa ON K1A ON4 Ottawa ON KIA ON4 Canada Canada Ywr file Votre nilurenca

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College of Graduate Studies and Research

SUMMARY OF DISSERTATION

Submitted in partial fulfillment

of the requirements for the

DEGREE OF DOCTOR OF PHILOSOPHY

Andre LmEm Hucq

Department of Agricultural Economics

University of Saskatchewan

Fall, 1997

Examining Cornmittee:

Dr. Graham Scoles DWAMMlfW6~WDean'sChair, Chair College of Graduate Studies and Research

Dr. Jack Stabler Chair of Advisory Cornmittee. Department of Agricultural Economics

Dr. Richard Gray Supenrisor, Department of Agricultural Economics

Dr. Gary Storey Department of Agricultural Economics

Dr. Harvey Brooks Adjunct Professor, Department of Agricultural Economics

Dr. Eric Howe Department.of Economics

External Examiner

Dr. Daryl F. Kraft, Ph.D., Department Head, Department of Agricultural Economics and Farm Management University of m. 353 Agncultural and Food Sciences Building University of Manitoba , Manitoba, Canada R3T 2N2 Blendina Rents at Primarv Elevators in Western Canada: A S~atialA~~roach.

It is frequently the important function of an economic study to detemine whether any one of the market participant is acting in a non-cornpetitive manner. The reason for this is that when a firm is in a position to exert some market power, it is able to influence market conditions by, for exarnple, selling its product at a price that is higher than its marginal cost of production.

The maximum prices that elevator companies operating on the Canadian Prairies can charge for their services have for many years being regulated by the Canadian Grain Commission and these have also been uniformly set across al1 companies. Hence it is not approptiate to undertake a study of competitiveness of grain elevator companies by simply examining their tariff charges. One aspect of the trade that is, however, largely unregulated (at the primary elevator level at least) is grain blending. Revenues that come into being when different qualities of grain are blended represent the consequence of a non-price competitive action by the elevator manager when these are passed on to a farmer. Blending can therefore provide a platfon for grain companies to act in a competitive manner in an othennrise regulated market. Under certain circumstances, the opposite can also, of course, appiy.

The grading system in use in Canada establishes a financial incentive that influences a rational elevator agent to ship out grain with a quality level situated along a minimum quality line. This enables the elevator manager to generate rents by blending grain, but how much of the rents the agent keeps and how much is passed back to the producer depends to a large extent on the competition in place at the elevator's location. Thus the central issue becomes one of reflection and of deterrnining the conditions under which rents generated in the normal course ot an elevators daily operations are passed back to faners.

The issues that are examined in this study are as follows:

What is the nature of the competition that anses as elevating fims and famiers compete for rents?

O Who ultimately captures these rents?

0 The spatial configuration (structure) of the grain market on the Prairies is changing rapidly. These changes will effect the competition between elevators and the well- being of faners. By examining the nature of the competition in a cross-sectional setting, is it possible to draw some inference for the effects of this changing structure?

The study used data of deliveries to and from 130 elevators located on the Prairie provinces of Canada. The results of the study found that elevator managers most likely use blending rents as a tool to draw grain deliveries to their elevators. The highly cornpetitive nature of the industry suggests that non-competitive behaviour by elevator companies generally is not possible, except possibly in some remote areas where few elevators are present. This conclusion has obvious implications on the net returns to farmers as more elevators are being closed down to be replaced by fewer but larger elevators. No inferences could be drawn on whether or not elevators have an added ability to generate rents depending on the variability of the quality of the grain delivered to the elevator as too many unquantifiable variables play a part in this process. A strong inference wuld be drawn that High Throughput (HTP) elevators, in relation to other conventional elevators, have an effect on blending rent retention. This finding has obvious important implications for the future, since much of the grain elevation needs of Prairie farmers in the future will be met by HTP elevators. However, the fact that HTP elevators often provide farrners with altemate benefits such as trucking fees also needs to be taken into account. Any positive rents generated in these elevators must therefore be counter weighed by the other benefits that may flow to ;atmers.

BIOGRAPHICAL

Septem ber, 1948 Born in Namur, Belgium.

July, 1974 Diploma in Law, University of Cape Town. July, 1975 Admitted as Attorney at Law, Supreme Court of South Africa. October 1979 MBA (honors), Boston University.

PUBLICATIONS

Safdar Hosseini and Andre Hucq (1993) "Iranian self-sufficiency Policy through Price Support or Fertiiizer Subsidy". Selected paper for the Canadian Agricultural Economics Society Annual Meetings, July 11- 14.

Murray Fulton and Andre Hucq (1996) "The Economics of Grain Blending". 1996 Van Vliet Publicaiion Series; Depamnent of AgrkuItural Economics, University of Saskatchewan, Permission to Use

In presenting this thesis in partial fulfillment of the requirements for a postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University rnay make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholariy purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts therefore financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholariy use which may be made of the material in my thesis.

Requests for permission to copy or to make other use of material in this thesis in whole or part should be addressed to:

Head,

Department of Agricultural Econornics,

University of Saskatchewan,

Saskatoon. Saskatchewan,

Canada, S7N OWO. It is frequently the important function of an economic study to detemiine whether any one of the market participant is acting in a non-competitive manner. The reason for this is that when a fim is in a position to exert some market power, it is able to influence market conditions by, for example, selling its product at a price that is higher than its marginal cost of production.

The maximum prices that elevator companies operating on the Canadian Prairies can charge for their services have for many years being regulated by the Canadian Grain Commission and these have also been unifomly set across al1 companies. Hence it is not appropriate to undertake a study of cgmpetitiveness of grain elevator companies by simpIy examining their tanff charges. One aspect of the trade that is, however, largely unregulated (at the primary elevator level at least) is grain blending. Revenues that come into being when different qualities of grain are blended represent the consequence of a non-price competitive action by the elevator manager when these are passed on to a fanner. Blending can therefore provide a platform for grain companies to act in a competitive manner in an otherwise regulated market. Under certain circumstances, the opposite can also, of course, apply.

The grading system in use in Canada establishes a financial incentive that influences a rational elevator agent to ship out grain with a quality level situated along a minimum quality line. This enables the elevator manager to generate rents by blending grain, but how much of the rents the agent keeps and how much is passed back to the producer depends to a large extent on the competition in place at the elevatofs location. Thus the central issue becomes one of reflection and of determining the conditions under which rents generated in the nomal course of an elevators daily operations are passed back to famiers.

The issues that are examined in this study are as follows:

What is the nature of the competition that arises as elevating firms and fanners compete for rents? a Who ultimately captures these rents?

a The spatial configuration (structure) of the grain market on the Prairies is changing rapidly. These changes will effect the competition between elevators and the well-being of farmers. By examining the nature of the competition in a cross-sectional setting, is it possible to draw some inference for the effects of this changing structure?

The study used data of deliveries to and from 130 elevators located on the Prairie provinces of Canada. The results of the study found that elevator managers most likely use blending rents as a tool to draw grain deliveries to their elevators. The highly cornpetitive nature of the industry suggests that non-cornpetitive behaviour by elevator companies generally is not possible, except possibly in some remote areas where few elevators are present. This conclusion has obvious implications on the net retums to farmers as more elevators are being closed down to be replaced by fewer but larger elevators. No inferences could be drawn on whether or not elevators have an added ability to generate rents depending on the variability of the quality of the grain delivered to the elevator as too many unquantifiable variables play a part in this process. A strong inference could be drawn that High Throughput (HTP) elevators, in relation to other conventional elevators, have an effect on blending rent retention. This finding has obvious important implications for the future, since rnuch of the grain elevation needs of Prairie famers in the future will be met by HTP elevators. However, the fact that HTP elevators often provide faners with aiternate benefits such as trucking fees also needs to be taken into account. Any positive rents generated in these elevators must therefore be counter weighed by the other benefits that may flow to farrners.

iii Acknowledgrnents

I am grateful to my supewisor Dr. Richard Gray and to Drs. Jim Vercammen and Murray Fulton for their help and advice.

I thank the following who helped in the development of this thesis; Dr. Hartley Furtan, Dr. Gary Storey and Dr. Harvey Brooks. Their advice throughout my yean of leaming at the University of Saskatchewan were always especially useful.

I am grateful to the Canadian Wheat Board (and, of course, al1 those unknown farmefs who support the work of the Canadian Wheat Board) for the financial support in the form of the Canadian Wheat Board Fellowship.

I owe a debt of gratitude to Sonny Blazina of the Canadian Wheat Board for the help he gave me in gathenng the data.

Finally. I am indebted to my fellow students Raman, Tom, Safdar, Marv, Varghese and the others for their friendship during my stay in the department.

This thesis is dedicated to my wife Penny and my children Solange and Guy. Table of Contents

Permission to Use

Abstract

Acknowledgments

Table of Contents

List of Tables

List of Figures

Preface

Chapter 1

lntroduction and Problem Review 1.1 Problem Setting 1.1.1 lntroduction 1.1.2 Competition Between Elevators 1.1 -3 Effects of Competition 1.1 -4 Problem Statement 1.2 1 mportance of Study 1.2.1 Financial Retums from Blending 1.2.2 Effect on Gross Fann Returns 1.3 Purpose and Objectives of the Study 1.4 Scope and Limitations of the Study 1.5 Organization of the Thesis

Chapter 2

Grain Regulations. the CWB and Elevator Operations 2.1 The lnstitutional Framework 2.1.1 lntroduction 2.1.2 The Regulatory Environment 2.1 -2.1 The Canadian Grain Commission 2.1.2.2 Grain Grading 2.1 -2.3 Kemel Visual Distinguishability (KVD) 2.1.2.4 The Canadian Wheat Board (CWB) 2.1 -2.5 Elevators Tariffs and Fees 2.2 The Elevator Systern on the Prairies 2.2.1 Introduction 2.2.1.1 High Throughput (HTP)Elevators 2.2.1 -2 The Whole-Fam Approach 2.2.2 Grain Binning and Grading at a Primary Elevator 2.2-2.1 Introduction 2.2.2.2 Blending of Grain 2.2.2.3 Grain Distribution - Primary and Terminal Elevators 2.2.2.4 Delivery and Transportation Documentation 2.2.2.5 Auditing of Elevator Stocks 2.2.2.6 Elevator Managers 2.2.3 Elevator Competition 2.2.4 Elevator Sources of lncome and Costs 2.2.4.1 Explicit revenues 2.2.4.2 Hidden Revenues 2.2.4.3 Method of Payment 2.2.4.4 Operating Costs 2.3 Review and Summary 2.3.1 Review of the Incentives 2.3.1 .1 The Financial lncentives 2.3.1.2 Effects of the Price Pooling System 2.3.2 Summary Addendum 2.1 Minimum Kilograms per Hectoliter Requirements for Western Grain Grades (and Corresponding Grams per 0.5 Liter) Addendum 2.2 Maximum Tariffs (Wheat) - Primary Elevators (1993) Addendum 2.3 Grades of Red Spring Wheat - Some Other Primary Grade Deteminants Addendum 2.4 Grain Operations

Chapter 3

Literature Review and Spatial Models 3.1 Economic Literature 3.1 -1 Markets and Efficiency 3.1.2 Conjectural Variations 3.1.3 Principal-Agent and Strategic Behaviour 3.1.4 Rent Seeking 3.1 5. Market Power 3.1.6 Grading and Quality Perspectives 3.2 Blending Research 3.3 Spatial Theory and Models - 3.3.1 lntroduction 3.3.1.1 Use of Space to Describe Interaction of Firms. 3.3.2 Spatial Models 3.3.3 Linear Models 3.3.4 The Circular Models 3.3.5 More Linear Modvls 3.4 Surnmary

Chapter 4

Model Specifications 4.1 Spatial Competition - Implications on Elevator Actions 4.1.1 Shipping "On-the-Linen 4.1.1.1 An Example 4.1.2 Competition 4.1.3 Modeling Elevator Actions 4.1.3.1 Supply or Demand? 4.1.4 Use of Spatial Models in a Supply Setting 4.1.5 The Horizontal Axis 4.1 S.1 Sharing the Rents 4.1.5.2 A Circular Model 4.1.6 The Vertical Axis 4.1.7 Negative Blending Rents 4.1.7.1 Elasticity Considerations 4.2 Conclusion - Effects of Competition Addendum 4-1 Blending on the Line with Grain Protein

Chapter 5

The Spatial Model 5.1 The Model 5.1 .1 Introduction 5.1.2 Assumptions 5.1.3 The Objective Functions 5.1.4 The Complete Model 5.1.5 Reaction Functions 5.1.6 Other Scenarios. 5.2 Identification of Test Variables 5.2.1 lntroduction 5.2.2 Elevators Ability to Generate Rents 5.2.3 Elevator's Ability to Retain Rents 5.2.4 Aggressiveness of Manager to Seek Business 5.3 Surnmary of Economic Relationship

Chapter 6

Data Collection, Data Analysis and the EconornetrÎc Model - 6.1 Data Description and Collection 6.1.1 lntroduction

vii 6.1.2 The Sample 6.1.3 The Data Manipulation Process 6.1.3.1 The Blending Rents 6.1 -3.2 The Cornpetition 6.2 The Hypothesis, the Econometric Model and Review of the Test Results 6.2.1 - The Hypotheses. 6.2.2 The Econometric Model 6.2.3 Choice of Test Method 6.2.4 Test of Structural Change 6.2.5 Redundant Variables Test 6.2.6 Tesüng the Equation 6.2.7 A System of Equations 6.2.8 The Test Results 6.2.8.1 Statistical Test No. 1 6.2.8-2 Statistical Test No. 2 6.2.8.3 Sensitivity Tests 6.2.8.4 Sensitivity Test 1 6.2.8.5 Sensitivity Test 2 6.3 Summary of Results 6.3.1 Introduction 6.2.9 Effects of Manager's Experience Addendum 6.1 Regression Data

Chapter 7

Review of Results and Conchsion 7.1 Review of the Problem 7.1 .1 Introduction 7.1.2 References to Grain Blending 7.1.3 The Blending hues 7.1.4 The Blending of Grain in an Economic Framework 7.2 Review of Results 7.2.1 The Assumptions and the Hypotheses 7.2.2 The Methodology 7.2.3 The Results 7.2.3.1 Statistical Review 7.2.3.2 Previous Studies 7.2.3.3 The Current Study 7.2.4 Conclusions. 7.3 Limitations of the Study 7.4 Recomrnendation for Fufiher Research

Bibliography

viii Table 1.1 . Matrix of Possible Grade Gain/losses on One Tonne of Grain Delivered to a Country Elevator .1994 Crop Year ($/Tonne) ...... 8

Table 1.2 . Rents from Blending #1 High Protein Grains .Three Year Period...... 9

Addendum 2.1. Minimum Kg per Hectoliter requirements for Western Grades ...... 43

Addendum 2.2. Maximum Tariffs .Prirnary Elevators...... 44

Addendum 2.3. Grades of Red Spring Wheat .Other Prirnary Grade Deteminants ...... 45

Table 4.1 Notation Used in the Models ...... 83

Table 5.1 : Table of Values for Base Scenano*...... 121 Table 6.1 a: Listing of Elevator Ownership ...... -136

Table 6.1 b: Capacity Distribution of Elevators in the Sample ...... 137

Table 6.2. Average Cornpetition Faced by Elevators...... 141

Table 6.3 Summary Data on Deliveries to Elevators ...... 142

Table 6.4. Surnmary of Standard Deviation Results Over Three Years ...... 142

Table 6.5. Statistical Review of Data Used in Regression ...... 143

Addendum 6.1 Fiegression Data ...... ,...... 162

Table 7.1. Statistical Review of Data Used in Regression...... 173 List of Figures

Figure 1.1 : Preference Rating of Farmer for Grain Deliveries to Two Elevators . 5 Figure 2.1 : Theoretical Distribution of Quality .One Year and Three Years ..... 18 Figure 2.2. Location of HTP Elevators in Western Canada ...... 23 Figure 2.3 Layout of a Grain Elevator with the "Blending Bowln ...... 26 Figure 2.4.a. Deliveries To Elevators Broken Down into Grades (1994) ...... 28 Figure 2.4. b: Deliveries to Terrninals Broken Down into Grades (1994) ...... 28 Figure 2.5.a. Deliveries to Elevators Broken Down into Grades (1992) ...... 29 Figure 2.5.b. Deliveries to Teminals Broken Down into Grades (1 992) ...... 29 Figure 2.6. Theoretical Cost Structure of an Elevator ...... 38 Figure 3.1 : Eaton and Lipsey's Model ...... 67 Figure 3.2. A Circular Model with Firms on the Boundary...... 69 Figure 3.3. Three Firms Locating in a Plane...... 69 Figure 3.4. Section of a Circumference...... 70 Figure 3.5. Spatial and Spatial Cornpetition...... 72 Figure 3.6. A Spatial Pricing Rule...... 79 Figure 3.7. Net Marginal Revenue Cuwes for Firrn A and Firm B...... 80 Figure 3.8. Location of Firms ...... 80 Figure 4.1. Shipping on the Line with a Uniform Distribution ...... 85 Figure 4.2. Blending with a TwoGrade Scenario ...... 88 Figure 4.3. Farmer Delivery lndifference Points...... 94 Figure 4.5. Elevator's Market Area - A Circular View ...... 99 Figure 4.6. Manager's Willingness to Pay Higher Price...... 101 Figure 4.7. Negative Blending Rents ...... 103 Figure 4.8. Elasticity of Supply and Rents ...... 107 Figure 4.9. Blending on the Line with Grain Protein ...... 110 Figure 5.1 : Location of ya and g,the Elevator's Optimal Grade Break Points 113 Figure 5.2. The Vertical and Horizontal Axes ...... 116 Figure 5.3. Graph of Reaction Functions...... 120 Figure 5.4. Reaction Functions: h = O and base solution...... 122 Figure 5.5. Reaction Functions: h = -1 and base solution ...... 123 Figure 5.6. Reaction Functions: h = 1 and base solution...... 125 Figure 5.7. The Standard Deviation Function ...... 129 Figure 5.8. Ability of Manager to Retain Rents ...... 130 Preface

Warehousemen like Marsh and Cargill dealt with the faner in essentially two ways (circa 1871). The fanner could consign his grain to the warehouse, keeping ownership of the grain hirnself (if he had enough to fil1 a bin) paying the warehouseman a commission for the storage time and insurance coverage and then he himself making the decision when and where to sell, at the time he felt the price was right. Prices were typically lowest at harvest time with peak supply and highest when this supply "disappearancen brought shortages.

The other route for the warehouseman was to buy the farmer's output outright at harvest. Most farrnerç were on a shoestring in these times. Often they would become antagonized by the low price they received, particulariy when the grain was graded on the spot by the warehouseman (who, of course, had to meet a grade standard when the grain amved at Chicago or another grain consign city). Farrners often thought they were taken advantage of in this grading process. Almost al1 of the time warehousemen wanted to minimize price risk, for they essentially saw themselves as middlemen, taking a regularized commission for this service. Thus they established relationships with the railroader and the commission houses in the great trading centers. The farmers were particulariy unconvinced by the arguments advanced by the railroads, particularly because they felt that the grading methods of both the wheat buyers and the millers were being used to downgrade the famers wheat - "clinchingn was the terni farmers applied to this.

Most small grain corning in from the field (, wheat, rye etc.) did need to be cleaned to separate out bits of twigs, , Stones dirt etc., that inevitably were a part of harvest. The term for this is 'dockage', for the farmers price was and is still today docked for its presence. A series of screens could allow a grain Company to "cleann the grain. Screening also made it possible to separate by size of grain, one of the key elements in establishing grade. This allowed an elevator to "mix to grade" in other words, to calibrate right down to grade level by mixing premium and average qualities - producing "skin gradesn to use the vemacular of the Vade. This was (and is) a Iegitimate and profitable function of grain handling, one of the necessary functions a grain company must perfom. Farmerç, however, usually complained about this arguing that they were held to the lowest grade of a lot of grain. yet the grain company could then mix and gain an advantage from that extracted part which was premium. The logic of this argument was suspect - the farmer was not in a position to screen effectively. Nevertheless. grading has been for many years a source of considerable distention within the tradel."

Broehl, Wayne G.; (1 992); Camill: Tradina the World's Grain.

The grain market (circa 1974) as seen by Francis M. Riley, a poultry feeder from Alabama.

"1 think one of the first things involved here is Me blending practices by the grain cornpanies. For exarnple, they take 100 tons of Number 1 or Number 2 corn and then take 100 tons of No. 5 or sample grade corn that is simply not fit for consumption and blend it together, and in doing so they make Number 3 corn or corn that reaches the USDA standards of Number 3. It is a practice of taking some good and some bad and making it al1 good. Well, this simply does not work. There is NO way that you can take a bad apple and a good apple and put them together and have an apple that is acceptable. This, I think is the cause of the problems we have suffered. "

Extract from "Grain Grades and Standardsn bv Lowell D. Hill (1 990)

1 Discussions with various individuals involved in the trade suggest that different views still exist on what mixing of grades does. Some individuals consider that on the whole it reduces the quali of the mixed grain. Others think the effect is to increase the quality of the grain source: discussions with fanen and elevator Company em ploy ees) . ?'

xii Chapter 1

Introduction and Problem Review

This chapter sets out bnefly the nature of the problem that is the subject of this study, as well as its purpose and objectives.

The chapter is divided into five sections:

Problem setting;

Importance of the study;

Purpose and objectives of the study;

Scope and limitations of the study and;

Organization of the thesis.

1.1 Problem Setting

1.1.1 Introduction Within the Western Canadian context, an important question which has been inadequately studied is the nature of the competition between elevators with respect to their grain purchases. This study focuses on one small link in the lengthy chain that extends from farmer to end-user. As such, it represents an examination of the relationship between several individuals, namely a grain famer and one or more elevator managers. In ternis of an economic theme, it coufd be classed as a study of imperfect spatial competition and of limited opportunism in the presence of an asymmetric information set2. The economic theories that are used in developing the mode1 are themselves covered under the industnal organization paradigm3.

1.1 -2 Cornpetition BenHeen Elevators Elevator companies in Westem Canada typically act as grain merchants. As such, they purchase grain from famers and they seIl it to grain buyers. Due to the nature of the institutions which exists in Westem Canada, over 90 percent of the principal crop grown on the Prairies, wheat, is bought by these companies as agents of the Canadian Wheat Board (cwB)~. The CWB pays farmers for their grain in several installments beginning with an initial payment which is made when the grain is delivered. Other payments are made through the year, with a final payment being made once the final price obtained by the CWB for al1 the grain sold during the year has been calculated5. The wheat is marketed in both national and international markets by the CWB~.

ln terms of its dealings with farmers and elevator cornpanies, the CWB prescribes the initial payment that must be offered to producers for a specific grade and it pays elevator companies on the basis of grade delivered at termina1 positions.

Elevator companies compete for potential business through a variety of means - the most obvious being the price offered for grain, which is a gross price minus marketing charges. More subtle foms of competition include the grades offered for a product and the amount of services provided by the elevator. The numerous forms of competition make a study of competition difficult to measure.

-- 2 Opportunistic behaviour is defined b Williamson as taking advantage of another when allowed by circumstances (WiY liamson, 1975). This behaviour by elevator managers is limited by the fact that they are, in theory, unable to consistently take advantage of blending opportunities to capture rents for themselves. 3 Industrial organization is defined b Carlton and Perioff (1994) as the study of the structure of firms and markets and of their interactions. 4 The nature of the on the Prairies will be descnbed more fully in Chapter 2. 5 The CWB also pools the price received for al1 sales into various pool accounts so that al1 farmers receive the same price for grain of similar quality irrespective of the tirne of delivery. 6 Feed wheat is excluded from the CWB monopoly. The maximum tariffs charged by elevators for various services have also historically been controlled by the Canadian Grain Commission (CGC)'. Even today, very little variation in tariffs across elevator is observede. Given this environment, elevators cornpete for business by offering producen higher grades than the intnnsic quality of the grain warrants or by offenng trucking premiag. At the same time, the systern under wtiich the CWB operates allows the grain companies an opportunity to generate income from the process of blending grain to achieve higher grades at the terminal than was purchased in the country".

1.1.3 Effects of Cornpetition The distribution of the income (or rents) that is generated from the blending of grain is not a simple issue". As the ideas developed in this thesis will indicate, an elevator manager may in fact, given in particular the competitive environment under which an elevator manager often works, be forced to pass back to the producer the rents generated in the blending of grain. This results in competitive markets because the elevator manager has to make use of some bargaining power by raising the grade on deliveries to generate a profit maximizing throughput. Thus, although a farmer cannot

7 Aside from the elevation of grain and its transfer on to rail cars for shipment to export points, elevators also offer a variety of other services. These may include the cleaning and dryin of grain and the selling of , chernicals, fertilizer and small farm equiprnent. Qhe maximum tariff allowed under CGC regulations for regulated services at the primary elevator is set out in addendum 2.1 at the end of Chapter 2. 8 The degree of regulation of tariffs has been changed. In the past (pnor to the commencement of this study) the tanff charged by each elevation company was uniform across ail elevators owned b that company. The elevator companies also charged less than the authorized tarii in the country but they charged the full allowable tariff (or even more) at the terminais. The companies can now charge varying tariffs depending on the competitive nature of the elevator. 9 Truckin premia are the exception rather than the rule. The fact that price is non- negotia% le leaves grade as the main source of bargaining between elevator managers and famiers. 10 Blending is defined by Kohls and Uhl (1990) as "A grain marketing strategy whereby twu different qualities of grain are mixed in such a way as to raise the total value of both lots." Blendin of grain (also known as commingling) is described more fully in "Grain Grading for i! fficiency and Profit (Canada Grains Council, 1982)". See also Hill "Grain Grades and Standards - Historical Issues Shaping the Future" (1990). 11 The word Vent" is sometimes considered in economic literature to be a retum to land. The meaning of rents in this study is as described by Mishan (1968) and Currie, Murphy and Schmitz (1971) as economic surplus. Whether the rents obtained from blending are used to cross-subsidize tanff charges and that in the final analysis no "economic surplus" is generated is not examined in this study. possibly know what grain is in an elevator's bins nor can helshe know the extent of the blending opportunity available to the elevator manager (an asymmetric information set) the farmer can still profit from the blending of grain done in the elevator".

Presumably farmers are quick to take advantage of any situation that forces elevator managers to bid against each other, and as a result sharp competition should be expected to develop between elevators. If a fanner has a number of elevators to which a delivery can be made, economic rationale dictates that a farmer will deliver to the elevator that offers the highest net price (net of transportation costs). In many locations in the prairie provinces, the spatial nature of the industry should generally make it very difficult for an elevator manager to consistently keep the rents that blending generate. To achieve a high throughput so as to operate in a region of profitability, an elevator manager needs to draw as many deliveries as possible to the e~evator'~.Hence al1 the elevators in a particular area will need to offer farmers in that area a higher grade than the grain warrants (or some other inducement) so as to entice those farrners to deliver to their e~evator'~.The elevator manager will blend up the value of the grain but in this case the farmers will have captured the blending rents. Little competition in an area should lead to a reversal of this position with the elevator retaining the rents?

In some ways, the decision variables of elevator managers and of famers that develop in the cornpetitive process can be modeled in ternis of a decision tree used in game theory by assuming for a moment a single famer who is deciding to which of two elevators (denoted 'a1and 'b') to deliver a truck load of grain that should be graded

12 Blending surplus information is generally held to be the pnvate domain of the elevator Company (or the elevator manager) - there are no regulations/legislation in place compelling the elevator manager to disclose exactly what he/she does with the grain once it is binned. 13 An elevator needs to tum over the grain at least five or six times to be profitable (Ross, 1970). 14 The principal inducement is probabl a transportation subsidy. Deliveries by farmers are usually done fob the elevator. 2' levator companies on occasion entice farmers to deliver to a particular elevator by paying some or al1 their transportation costs. 15 The fact that information about blending surplus is not symmetric does not therefore necessanly place the producer at a disadvantage. a #216. In Figure 1.1 below, lp and b represent the per unit costs of delivering grain to elevators 'aJ and 'b' respectively with:

g>t, and

The pn'ce of #1 > the price of #2

Net Price Preference Ratinca of farrner Elevator 'a' 7

Farmer O #1-t& (1)

Elevator 'b' Q #2-t,, (2)

Figure 1.1 : Preference Rating of Farmer for Grain Deliveries to Two Elevators

If both elevators offer the famer a #2 grade, the fanner delivers to elevator 'b' (who captures the blending rents plus the elevation fee, the dockage, whose value could be quite considerable when grain pnces are high, and any cleaning fee) and elevator 'a' loses the business. If elevator 'bJ offers the farmer #1, the farmer automatically goes to elevator 'b' who obtains the elevation fee etc. but loses the blending rents. Elevator 'a' can however be made to join the game by having elevator 'b' offering the farmer #2 and elevator 'a' offering #l. Whether the farmer delivers to elevator 'aJ or 'b' now depends on whether #2 - tt, < > #1 - ta. If #2 - t, = #1 - ta then the famer is indifferent

16 The grading system for prairie wheat is described more fully in Chapter 2. and either elevator could obtain the famer's grain". In al1 cases, the elevators will blend some (or all) of the grain - and the question becomes one of who gets the rents18?

What would make elevator 'a' willing to pay a higher price for lower quality grain must, of course, depend on its ability to blend the grain. If, for example, elevator 'a' is full of low quality #2 grain, there is little chance of being able to upgrade anything to #1 status. The loss to an elevator who buys grain under these circumstances could be substantial, leading especially to the loss of rail cars which is the life blood of the elevatorl9. Thus one would expect that elevator 'a' has (or expects to receive) grain with an even distribution of quality over a range that would allow for upgrades to take place.

The issue is simply one of competitiveness. Blending of grain in the elevator always takes place - the elevator manager blends to capture rents for the company's own account or to recoup overgrading to famers - and thus the distribution of rents must, to a large extent, depend on the degree of competition in an area.

17 This simple game represents a game in extensive forrn. Kreps (1990) and Tirole (1988) provide excellent reviews of game theory. 18 An interesting situation that is not covered in this study is the 'value' that accrues to the farmer who delivered the #1. When a farmer delivers a high quality #1 grain that is used in upgrading the #2 grain, no extra benefits are passed back to that famer. All that that farmer will ever receive is a #1 pnce for the grain (there may, of course, be some other methods used to reward the farmer - e.g. some discount on seeds or fertilizer or a trucking premium). The problem is similar in nature to the extemality created when bees owned by one famer fly across a fence and pollinate the blossoms in a neighbour's apple orchard - should the bee keeper (or the orchard owner) receive any extra payments? These types of extemality problems are usually studied within the sphere of welfare economics (see for example Bator, 1958). lgIt should be remembered that if a load of grain is graded a KI, the CWB will expect the elevator to ship out #1 grain. The elevator manager wPI ultimately face a difficult problem if that "#lnis actually a #2 that cannot be up raded. An elevator that delivers to a terminal a grade which is not the one cal7 ed for by the CWB will usually find itself "punished" by the withdrawal of spot car availability. For example, many elevators are usually assigned ei ht rail cars a week which represents approximately seven hundred tonnes of grain. I9 the number of rail cars is reduced, the elevator manager will have to reduce the deliveries to the CWB which would lead to very poor operating results for that elevator. Chapter 4 provides a model where a manager is willing to pay a higher price for lower quality grain. 1.1.4 Problem Statement

The problem that is specifically examined in this study can be summarized as follows. The grading system in use in Canada establishes a financial incentive that influences a rational elevator agent to ship out grain with a quality level situated along a minimum quality line2'. This enables the elevator manager to generate rents by blending grain, but how much of the rents the agent keeps and how much is passed back to the producer depends to a large extent on the competition in place at the elevator's location. Thus the central issue becomes one of reflection and of deterrnining the conditions under which rents generated in the normal course of an elevator's daily operations are passed back to farmers.

The issues that need to be examined are therefore as follows:

What is the nature of the competition that anses as elevating fims and famers compete for rents?

Who ultimately captures these rents?

The spatial configuration (structure) of the grain market on the Prairies is changing rapidly. These changes will effect the competition between elevators and the well-being of farmers. By examining the nature of the competition in a cross-sectional setting, is it possible to draw some inference for the effects of this changing structure?

1.2 Importance of Study

There are several aspects of blending at the country elevator level that make this study important in ternis of increasing Our understanding of agricultural issues.

1.2.1 Financial Returns from Blending The financial retums that flow from the blending of grain are significant and it is therefore important to generate a better understanding of the issue.

20 Rationality plays an important part in economic theory. Williamson (1975) who is a major proponent of transaction costs theories, describes bounded rationality as the limited human capacity to anticipate or solve complex problems. Kreps (1990) develops models where a "playef acts in the belief that the other "playet' acts irrationally. Table 1.1 below provides an idea of the blending profits/losses (based on 1994/1995 initial payments for wheat) that could have been generated by an elevator when blending grain2'. The column on the lof? represents the grade gben a tonne of grain when delivered to a primary elevator. The top row represents the grade of that tonne when it is shipped out of the elevator. The nurnbers in the matrix represent the gainlloss of valuep. Al1 values to the left of the diagonal are positive being the gains on upgrades (elevators gain) and al1 values to the nght of the diagonal are negative representing losses on downgrades (famers gain). The zero's along the diagonal indicate that the same grade was purchased as was shipped and no blending took place.

Table 1.l. Matrix of Possible Grade Gainiiosses on One Tonne of Grain Delivered to a Country Elevator - 1994 Crop Year (Wonne).

#1 14.0°h 12.50 O -12.50 -20.00 #1 13.5% 25.00 12.50 O -7.50 #2 13.5% 32.50 20.00 7.50 O #1 40.00 27.50 15.00 7.50 #2 46.00 33.50 21 .O0 13.50 #3 58.00 45.50 33.00 25.50 FEED 70.00 57.50 45..00 37.50

Source: Price data obtained from the CWB. Values as calculated.

21 Blending rents can also be captured by upgrading the protein level in grain. 22 The handling fee charged by elevator companies was approximately $8.00 per tonne and numerous other fees were also charged famen for terminal cleaning. dockage, freight and CGC weighing and inspection fees (see addendum 2.2 at the end of Chapter 2 for the allowable tariff rates in 1993). The CWB (and thus famiers, who ultimately pay for the whole cost of marketing the grain) was also charged a storage fee on grain delivered to elevators. Grade gains increase the retums to the elevator. Grade losses increase the retums to the fanner. When the fact that over 20 million tonnes of grain are delivered to primary elevators every year is taken into account, the potential of generating large rents cleariy existsn. The rent generating possibilities that develop as grain from one crop year is blended with grain from another crop year can ais0 be quite considerable and should also be taken into account when looking at an overall picture of an elevator's blending capabilitiesZ4. A cursoiy look through Table 1.2 indicates that the blending value of grain changes from year to year and from grade to grade. For example, in the 1992/ 1993 crop year, the rents that would have been generated by upgrading a #1 CWRS 14.0 percent grain to a #1 CWRS 14.5 percent grain was $2.50 per tonne (or $220 per 88 tonne car load). In the 1994/1995 year, blending of a tonne of the same quality grain would have generated rents of $12.50 per tonne (or $1 100 per 88 tonne car load) .

Table 1-2. Rents from Blending #lHigh Protein Grains - Three Year Period:

GRADE 19921 1993 Rents 1993/ 1994 Rents 1994/ 1995 Rents Prices Prices Prices

O 150.00 O 6.00 137.50 12-50 O 125.00 12.50 4.00 117.50 7.50

Source: Price data obtained from the CWB. Values as calculated

23 When a large volume of blending takes place, it then of course becomes legitimate for producers to argue that while the receive a price cornmensurate with the lowest acceptable grade of the lot of rain t iat they deliver, the elevators, by rnixing or blending the grain, obtain for t9, emselves an unfair advantage from the premiurn that is generated on the upgraded grain (Hill, 1990). 24 Table 1.2 is provided to show the extent of rents that could be enerated if the elevator manager were to succeed in blendin the grain in a raiB car up one protein break (which until this year were set at the haB f percent level). The rents are given for prices for the three crop years 1992 - 1995. As can be noted, there exist a wide range of rent generatin possibilities - from $0 to $1 100 (per 88 t car) - so that rents can change considerab9 y from year to year. 1.2.2 Effect on Gross Farm Returns Since it is expected that in future there will be a reduced number of elevators on the Prairies, it is important to get a better understanding of the effect of competition on the distribution of blending rents since this has a direct effect on gross farm retums?

There are at present over one thousand delivery points in the three prairie provincesm. With the passage of time and as a result of corporate restnicturing and the elimination of many major farm support program, many of the elevators on the Prairies will be closed down (Weatherald, 1996). There will also be some swapping of elevators between elevating companies as they attempt to increase or alter their market share or reduce their costs and increase their profitability". Grain companies have been building High Throughput (HTP) elevators but their number represents only a small portion of the elevators that are being closed. The closure and the consolidation of elevators may thus mean that competition between grain elevating companies will diminish.

1.3 Purpose and Objectives of the Study

The purpose of the study is to develop a model that allows for various hypotheses to be formulated regarding the rents produced in the blending process, and to develop an econometric model which uses actual data from elevators situated in Western Canada to test these hypotheses.

The specific objectives of the study are:

1.) To develop a theoretical model that makes use of location (spatial) theory to attempt to explore the following issue - in an economic profit-maxirnizing setting,

25 For example, producers are usually treated as an homogeneous group in econornic models. Blending rents allows an elevator manager to treat fanners in different wa S. This applies to farrners in a single area (large farmers will be treated diX erently from small farmers) or across wide areas (a fanner delivenng to a remote elevator will be treated differently from a farmer delivering to a town with three or four elevators). 26 A delivery point in its usual context represents a town, city or village or other location where one or more elevators may be situated. n Grain Matters (Feb, 1994) provides a table of declining elevator capacity. Total elevator capacity for all grains was expected to decline from 11 % million tonnes in 1962 to 7 million tonnes in 1993. how does competition affect an elevator manager's need to pass blending rents back to the farmer.

2.) To collect and process the data required to test various hypotheses made in relation to elevator manager's actions.

3.) To test the hypothesis that the window in each grade (infra grade) and the fact that the delivery requirements of the Grain Commission at the terminais (the input of the teminal) allow for minimum quality within a grade enables the elevator manager to generate surplus for the elevating Company via rent generation and:

4.) To test the hypothesis that increased competition shifts the distribution of this blending surplus towards the producer.

In the normal course of events, only publicly available aggregate data could have been used since the buying and selfing of grain at a specific elevator is held to be pnvate (and confidential) information of grain companies. The critical data that makes this conceptual and practical study possible had to be obtained from the CWB. With this data, the test of the competition hypothesis (objective #4) could be camed out by comparing the value of grain deliveries to specific primary elevators in Western Canada against the value of deliveries to the grain elevator terminais from these same primary elevators and applying statistical formulation to the results.

The theoretical spatial model that is derived in this study is one in which a single fanner coexists with two elevators. Each is provided with a profit maximizing objective function which generates a solution of the extent to which an elevator manager would be willing to pay a higher pBce for grain of lower quality and then solving for the reaction function for both elevators. The cross-over point of the two reaction functions provide an equilibnum solution - one from which each elevator manager will not be willing to deviate given hidher assumptions of the reaction of their competitor - and this provides a view of an elevator's action as competition is assumed to change.

The basic assumption that is made in this study is that "spacen can be used as an analogue for economic phenomena (Greenhut et al, 1987) and that two forces are at work in detemining the grading (pricing) behaviour of an elevator manager, namely the demand effect which is related to basic demand convexities of farmers (the farmer 11 responds to the price incentive) and the competition effect which is related to the degree of competition faced by the elevator and the conjectural variation assumptions made in the model.

1.4 Scope and Limitations of the Study

This study represents a very simplified view of the actual operation of an elevator. A large number of effects can, for a number of reasons, not be incorporated in the model. These include:

The data that is used is comprised of grain delivered to the CWB (Board Wheat) only.

Other foms of non-price competition (e-g. service, trucking premium etc.) are not explicitly considered.

The risk attitudes of famers and elevator managers are not explored.

Measurement and blending errors by elevator managers are not explicitly modeled.

1.5 Organization of the Thesis

Chapter two describes the grain industry and the regulations under which elevators operate. A description of the process by which elevator managers blend grain is provided. Chapter three contains a review of the Iiterature on the economic foundations of the model. This chapter also incorporates a review of various location models and of the limited literature on blending. Chapter four and Chapter 5 provide details of the model and of the hypothesis to be tested. Chapter six sets out details of the data and the econometric model used to test the hypothesis and provides the results of the tests. Finally Chapter seven completes the thesis with a summary and conclusion. Chapter 2

Grain Regulations, the CWB and Elevator Operations

This chapter traces through the regulations that govem the flow of grain from farm to export terminal and reviews the operations and cost structure of a primary elevator.

The chapter is divided into three sections:

1. The Institutional Frarnework. This section examines briefly the regulatory framework under which the Canadian Grain Commission (CGC) supervises the production and transportation of grain on the rair ries". This section also examines the duties of the CGC in setting and regulating the grading system in use on the Prairies and it also reviews some of the functions of the Canadian Wheat Board (CWB);

2. The Elevator System on the Prairies;

28 This study is not intended to be a comprehensive analysis of the grading system in use in Westem Canada since the grading system does not represent the central issue of this study. There are several excellent books on the Canadian grading system. In particular, Grain Grading for Efficiency and Profit published by the Canada Grains Council (1 982); Wheat Gradin in Western Canada published by the CGC (1 983) ; A History of the CGC 1912 - 19% 7 by J. Blanchard (1987); Grains and Oilseeds - Handling, Marketing Processin published by the Canadian International Grains Institute (second edition, 1993). C3, arles F. Wilson's "a Century of Canadian Grain" (1978) provides a good review of Canadian government poli towards the grain industry. An excellent review of the U.S. grading system and Xt e problems encountered in that country is contained in Grain Grades and Standards b Lowell Hill (1 990). Further references are contained in the bibliography. The CG(!, the CWB and many grain companies and State and Provincial govemment departments also keep web sites on the intemet. See for example: uhttp://~.cgc.ca/"for the CGC and "http:/Iwww.cwb.ca/" for the CWB sites. 3. Review of lncentives and Summary. This section reviews the incentives that come into being as elevators purchase grain from famers and transfer it to the CWB and it also reviews the effects of price pooling;

2.1 The Institutional Framework

Many of the rules, laws and regulations that govem the grain trade in the Prairies are generally directed at ensuring that only high quality grain leaves Canada. The logistics of the market requires that a very large percentage of grain produced on the Prairies be moved thousands of kilometers before it reaches a rniller. Grain is handled many times - by the farrner, the primary elevator, the railway and the terminal elevator before it is exported.

2.1.2 The Regulatory Environment

2.1.2.1 The Canadian Grain Commission

The Canadian Grain Commission (CGC), a govemment agency created by the Canada Grain Act of 191229, oversees much of the movernent of grain for the Canadian Wheat Board (CWB). The CGC obtains its power of regulation from various acts of parliament with the first major piece of legislation being the General Inspection Act of 1874. Later legislation of importance are the Manitoba Grain Act of 1900 and the Canada Grain Acts of 1912, 1930, 1971 and 198830. Schedule III of the Canada Grain regulations establishes the regular grades for al1 grain grown in the prairie provinces3'.

29 Then known as the Board of Grain Commissioners. 30 References for al1 these Acts are contained in the Grains and Oilseeds Handbook (1 991 ). 31 The internet web site "http://www.cadvision.com/violetbooWcrop.ht* describes crop uality factors, how the end use is affected by these factors and displays how 8anadian grade standards reflect crop evaluation for markets. 2.1 -2.2 Grain Grading

One of the most important functions of the Grain Commission is the establishment and mntrol of grades for various classes of wheat produced in Westem canadaP. Although this study is not meant to be a review of the grading system in place in Canada, a brief analysis of grading is required since blending and grading are very much inter related.

Grading is defined (Grains and Oilseeds (1993) at page 292) as the segregation of grain into parcels of defined quality to facilitate price detemination. A successful grain grading system, the authors suggest, must achieve the following results:

Assure producers an equitable price relative to grain q~ality~~;

Facilitate eff iciency in grain handling by encouraging the collective storage of bulk lots of similar quality;

Establish a method of relating prices to quality to simplify trading;

Enable the buyer to consistently obtain the same quality of grain;

Separate grain into a sufficient number of quality divisions so that buyers have a choice of grades, but, at the same time, limit the number of grades to facilitate handling and storing in an efficient bulk-handling system.

32 There are two grading standards in use for prairie wheat. The Primary Standards Samples are prepared for most grains and represent as nearly as possible the minimum quality of each grade, considering the predominant grading factors of a crop. Export Standards Samples apply only to Canada Westem Grains. They are prepared for most grades of wheat and are intended to ensure that buyers will receive grain that is reasonably close in quali to the average of the grade. The export sample represents the quality that the %WB guarantees its foreign clients. The Export Standards sample and the Primary Standards sample are two different standards and should not be confused. This study is not concerned with Export Standards samples. Addendum 2.3 sets out some of the Primary Grade determinants for CWRS wheat for 1993. 33 Returning value to producers has alwa s been a key issue of the grading system set up in Canada. For example, from 1992' onwards, the value of protein came under review because the premium payable on protein became much more valuable that those payable on grade only and a scheme had to be devised whereby (amongst other reasons) farrners were to be better rewarded for delivering high protein grain. Discussion centered mostly on whether the protein breaks should be at the 1 percent or the 3 ercent breaks (See for example, the document entitled "A protein Pa ment Systern Por Western Canadian Producers - a Discussion Paper of the CWB (l&35)11. Grades therefore comprise a set of rules that provide the guidelines for infomation signals in the market? They are used to classify products with respect to selected characteristics deemed economically or aesthetically important in markets in which personal inspection and selection are neither physically nor economically feasible (Hill, 1990, Mehren, 1961). Over the past twenty yean, technology has dramatically increased the number of physical, biological and intrinsic characteristics that can econornically be measured. Furthemore, communication technology has made access to this infomation feasible to al1 participants (including famiers) so that price discovety and detemination can reflect this information almost immediately (Jones and Hill, 1994)".

The factors that make up a grade are obviously paramount. The Grain Grading Handbook for Western Canada (1995) defines a grading factor as follows:

"A grading factor is a physical condition of grain, the result of growing conditions, handling procedures or storage practices which visually indicates a reduction in quality, e.g. frost damage, sprouted or heated kemels."

The institution that is charged with creating and regulating the grading system must therefore decide upon some measurable determinants that give the product its inherent level of quality (Hill, 1990). In the case of a grain sample, there exists a large number of attributes that could be measured and which could therefore be used as a determinant of quality. These could be its protein or moisture content or the falling number or ash content of the produced by the particular grain or any of dozens of other measurable char acte ris tic^^^.

34 See, for example, Storey et al. 1994) for a review of the effects of the grading system in place on the quality O1 products (in this case hogs). 35 Informative articles on the Arnerican grading systern that appear in Jones and Hill (1994) are: "Grading systems in the Pork Industriesnby M. Khayenga and J. Kliebenstein; "Are Standards of Identity Obsolete or Redundant" by D.I. Padberg and P. Kaufman and "Assessing Federal Grade Criteria for Fruit and Vegetablesn by C. Zulauf and T. Sporleder. 36 In this chapter the terni characteristics is used rather loosely but this is done intentionally to describe the problern because one can easily get sidetracked by the problem of defining precisely what the word characteristic means in a grading model. Lancaster (1966, 1971) and rnany other researchers (Rosen (1 974), Lucas (1975), Ladd and Martin (1 976). Brown and Rosen (1982), Veeman (1987)) have examined the issue of characteristics in ternis of models developed under the title of 'Hedonic Pricing Modelsn. The CGC determines the grade of grain by measuring five basic attnbutes of a sample - its weight (kilograms per hectoliter), its percentage by weight of Hard Vitreous Kemels, the amount of foreign material and classes other than wheat present in a fixed quantity sample, the number of heated, broken or shninken kemels, and the number of diseased, damaged or sprouted kemels in the sample (Grain Grading Handbook, 1995)37. The presence of moisture is not considered a grading factor nor is the level of protein in the grainY>.

Canadian grain grades are based on a lowest factor approach. This means that the numerical grade assigned to a sample is detemined by the factor with the lowest quality level. This results in a minimum quality process and, as is examined in Section 2.3 and in Chapter 4, the incentive that comes into being from ttiis minimum requirement is to blend grain to the minimum level of each grade factor (Bockstael, 1984).

In theory, the levels of the quality in a grain crop year, may be expected to follow the fom of a "normaln distribution as indicated in Figure 2.1 (Grain Grading for Efficiency and Profit, 1982). Most of what is produced tends to be of "averagen quality, the remainder being of either higher or lower quality.

37 Hard vitreous kernels (H.V.K) - These are whole, reasonably sound kernels that even though moderately bleached, show clear evidence of vitreousness, Le., the natural translucent colouring which is an extemally visible sign of hardness. Vitreous kemels of of other classes that blend are included in the percentage of H.V.K. for grade detemination. Non-vitreous kernels - Kernels having a starch spot of any size, broken or otherwise damaged kemels, severely bleached kernels and kemels of contrastin wheat classes are al1 considered non-vitreous (Grain G fading Handbook for &stem Canada, 1993). 38 A farmer has, to some extent, some degree of control over the grade of the crop that the land will produce by simply using good farming practices (such as proper storing of the grain after harvest) or by using desirable quantities of chemicals to kill weeds etc. This will limit the amount of foreign material or diseased kemels. But there are also a number of grading factors over which the famer has more limited control - such as, to some extent, frost damage at the time of harvest. Thus the "quality of grain" decision of the fanner could be modeled either an endogenous and as an exogenous variable. Volume Volume

Low Quality High One Year LOWQuality High Three Years Figure 2.1 : Theoretical Distribution of Quality - One Year and Three Yean Source: Grain Grading for Efficiency and Profit, 1982

2.1.2.3 Kernel Visual Distinguishability (KVD)

The Canadian grains industry has some particular characteristics which make Canadian wheat unique and desirable. In particular, a very elaborate system has been built up over the last 80 years which is concentrated on retaining a high level of quality in the wheat. This quality characteristic is aimed both at the milling (flour making quality) value of the wheat as well as the level of quality of the produced by the flour (the baking quality)? The ability of the Canadian system to retain a high quality product is achieved by regulating production and through a system known as KVD or Kemel Visual Distinguishability, where every individual lot of grain has to be identifiable visu al^^^^. This means that officiais on the Canadian side of the market systern must be able to determine, by just scooping a handful of grain and effecting

39 The reliance on producing consistently high quality wheat has been the subject of much debate. Studies have shown that prairie farmers could increase their income if the focus of the regulations shifted to yield rather than qualiv. Some attempt is now being made to provide producers with some dqree of flexibility. An important study in this regard is the study by Ulrich et al (1987) in which the authors compared the possible retums from high yield (lower quality) wheat from those received from registered seeds. 40 The varieties of grain that are licensed go through very strenuous trials before they are accepted for use. The process is described more full in the Government Publication "Varieties of Grain Crops for Saskatchewan, Y994". For a review of the Canadian quality system, see 'The Future Quality System for Canadian Wheat' discussion paper (June, 1996). See also "The Canadian Quali Advantage. What is it? Can we maintain it" by Dr. K.H. Tipples of the CGC (1 993 some simple visual tests4', the class and the grade of the grainu. This visual requirement rnakes it necessary (if not critical) for elevator managers to be very proficient at instantly determining a number of the characteristics of grain that have been established by the CGC as being some desired attribute that gives grain its value. This point is obviously important in allowing an elevator manager to make a quick decision when dealing with a faner who has a load of grain to deliverG.

2.1.2.4 The Canadian Wheat Board (CWB)

There exists a particularity in the prairie grains industry that makes the relationship between prairie farmer and buyer unique - and that is the existence of a monopsonist buyer. Price detetmination as far as a Canadian grain producer is concemed is predetemined and exogenous since payments made to an individual producer are set by the CWB - which decides unilaterally both the amount of the interim payment made to producers when they deliver grain as also the amount of any other payment madeM.

Most of the wheat produced in Westem Canada is sold through the CWB since it has the monopoly rights over al1 the wheat exports4'. Some 95 percent of the annual wheat production enters the primary elevator system and of this number some 97

4t The elevator manager will, for example, count out the number of kemels in the sample which are sprouted or diseased and determine the degree of matufity of the kemels. For other tests, the manager uses equipment which will weigh the sample and determine its moisture and protein content. Some of the grade deteminants are set out in Addendum 2.1 at the end of this Chapter. 42 Several classes of wheat have been developed over the years, the principal one bein the Canada Western Red Sprin wheat (CWRS) class. Other classes include the 8anada Prairie Spnng wheat (CP8 ) class and Canada Western Amber (CWAD) class. All registered seed varieties rnust fall within one of the approved classes of wheat. 43 Other methods of varietal identification include Non-Visual Varietal ID and the Affidavit system used in Australia. 44 The Mission Statement of the CWB is defined in the Canadian Wheat Board Act (R.S.,c. C-12) as follows: 'The Canadian Wheat Board markets quality products and sewice in order to maximize returns to prairie famiers." Kraft et al (1 996) provide a performance evaluation of the CWB and review extensively the pncing policies of the CWB and the benefitdcosts that flow from what is commonly known as "single-desk marketingn. Althou h the CWB is descnbed here as a monopsonist, it may technically be that t9, e CWB is simpl the collective marketing amof Prairie farmers and as such it is not a "monopsonis tX buyer of grain. 45 Feed wheat excepted. Prairie wheat and bariey which are marketed under the control of the CWB are referred to as "Boardn grains. Non-Board refers to grain marketed through the open market system, such as feed wheat and bariey, rye, oilseeds and specialty crops. percent is delivered to the CWB~~.The short harvesting season, the constrained export capacity of the railways and the substantial on fami storage that takes place in Canada has compelled the CWB to regulate the flow of grain across the system through the use of quota books and con tract^^^.

The Canadian Wheat Board ~ct~allows the CWB to operate elevators. either directly or by means of agents, and to pay such agents compensation as may be agreed upon. Pursuant to this authority, the CWB normally enters into contracts with primary elevator companies to act as its agents and to purchase and take deliveiy of, and store and deliver to terminal on behalf of the CWB al1 Board wheat that is delivered by prod~cers~~.To achieve this end, the elevator Company is required to grade the grain offered by producers and to issue to the producer a cash purchase ticket for the initial payment posted by the CWB less authorized deductions and tanff charges".

2.1.2.5 Elevators Tariffs and Fees

The CGC is responsible for the regulation of elevator tanffs. Until recently, this included the setting of maximum tariffs5'. Until the regulations were changed. the Commission conducted an annual tariff analysis and review. lt then recommended a maximum tariff for federal govemment approval. The objective of the annual tariff review was to estirnate the revenue and costs of grain handling and storage at licensed primary and terminal elevators and to calculate measures of financial performance which were then used to recommend the allowable tariff.

46 Source: CGC data. 47 A new method of contracting for grain was introduced by the CWB in the 1993 crop year. The changes were intended to allow the CWB to better meet its sales commitments and to improve the timeliness of deliveries (for details, see for example the Information guide "A New Approach to Delive Policy and Contracts - Elevator Manager's Information Guiden issued by the CM/$ 48 TheCWBAct. R.S., c. C-12, S. 1. 49 The terrns of the contract between the CWB and elevator companies are set out in a Memorandum of Agreement which has a duration of one year and which must accordingl be renewed every year. The description of the contract given here are from a dral't of the 1993/1994 agreement. Presumably these standard clauses are renewed unchanged each year except possibly for minor modifications. 50 Section 6 of the draft Agreement between the CWB and elevator companies. 51 The Commission no longer sets a maximum tariff but it still keeps tariff charges under review. Revenue from elevation and storage at pnmary elevators was, until this year, calculated from the previous yeats annual reports on grain handled which were submitted to the commission by al1 the primary companies. The figure representing net tonnes shipped by province was multiplied by the average filed elevation tatiff for each province to amve at total elevation revenue. A figure representing additional revenue (cleaning, drying, grain gains and losses, overagedshortages etc.) was also derived for the previous year from financial estimates supplied by selected companies. Revenues generated from blending was only calculated for operations effected at the terminal elevators.

The regulations have now been changed to allow elevator operators to be somewhat more flexible and competitive in pncing their services

2.2 The Elevator System on the Prairies

2.2.1 Introduction

The first primary elevator in Western Canada was built in 1879. For the next fifty years primary elevator construction continued to accelerate until 1934 when the number of elevators on the Prairies reached its peak (5746 elevators). Thereafter their numbers declined annually so that by 1991 the total had decreased to 1539, composed of 258 in Manitoba, 807 in Saskatchewan 463 in and 11 in (Berry, 1993). Elevator operations are big business with the value of grain inventory in any elevator often exceeding $1,000,000 at any one time. It is not unusual for an elevator manager to buy over 25,000 tonnes of grain in a single year, representing financial transactions in the many millions of dollars52.

2.2.1.1 High Throughput (HTP) Elevators

New types of elevators are now being built on the Prairies. These are large elevators (over 10,000 tonnes capacity) with ample car spotting facilities to take advantage of

52 These numbers represent values for al1 rains bought by an elevator - these would include CWB and non-Board wheat and i! arley, rye, canola and many other specialty crops. lower freight rates? These elevators are usually made of concrete and they are designed with high throughput in mind.

The effects of elevator closure and their replacement by fewer but larger HTP elevators is at this stage an unknown. The consequence of HTP elevators on cornpetition will very rnuch depend on their location and size and on the delivery decisions of famiers. High throughput elevators need to draw grain from a larger area to achieve the efficient throughput levels that make them commercially viable entities? As these elevators increase their catchment area, the variability of the grain going through the elevator should increase. This, at least theoretically, should increase the elevator manager's ability to blend various grades as is modeled in Chapters 4 and 5. Countering this effect is the limited number of bins in an HTP (which are usually built with fewer but larger bins) and the fact that the engineers who designed these elevators were generally looking for high loading and unloading speed and not necessanly blending abilitys.

2.2.1.2 The Whole-Farm Approach

Cornbined with the introduction of HTP elevators, the elevating companies are also gearing their marketing plans towards a "whole-fami" approach so that in future they will supply al1 of a famer's needs - from advise on what crops to plant and seed to use, to what chemicals to spray, equipment to use etc. This means that a local elevator will only represent but a small part of the services that the elevating company will provide to its customersS6. They will also develop their grain gathering capability so that grain will be picked up on farm by the elevator company and then delivered to an elevator of their (not the famer's) own choice. The presence of HTP elevators and the

53 Car spotting is a term that indicates the number of raiiway cars that an elevator can handle in any one period (usually a week). Variable freight rates are (as of 1997) not yet being offered by the railways. 54 See Section 2.2.4. 55 See for exarnple the lnland Terminal Study (Grains Group, 1970) where the blending of grain is mentioned in passing and does not form part of the design parameters of these elevators. However some experimentation has been done with automated computerized blending systems and for cleaning to export standards on the Prairies. One can expect that new generations of HTP will be better equipped to blend grain. 56 Source: Discussion with elevator managers. Mole fam* approach should increase the elevator companies information gathering

Figure 2.2: Location of HTP Elevators in Western Canada

The presence of HTP elevators alongside srnall low-throughput elevators could provide a base to test some economic conjectures. For example. Iimit pricing may be of some concem. A large HTP elevator with many bins engineered for both speed and blending could always pa a higher price for each rade of grain and prevent al1 other srnailer (higher cost elevators from being in t9, is market. Once cornpetition has been eliminated. ther elevator could sirnply revert to rading correctly thus capturin all blendin rents. The competing corn anies would ta en need to enter the maR et with simi3 ar-built HTP elevators, whic R would not necessarily be in their best interest. 2.2.2 Grain Binning and Grading at a Primary Elevator

2.2.2.1 Introduction

When a farmer delivers a load of grain to an elevator, a 500 gram sample is drawn from the load and cleaned. The clean grain is then visually inspected by the elevator manager? If the manager thinks that the grain has a high moisture content, the sample may be tested in a moisture meter to determine whether or not the grain is tough or darnp by Grain Commission definition". Following this visual examination and the moisture test, the elevator manager assigns a grade to the grainM. If the producer agrees with the grade and dockage6' assigned, the producer is given a Cash Purchase Ticket negotiable at any bank for the grain delivered6*. A copy of the document is fonivarded to the head office of the Company and a second copy is sent to

58 Assigning a grade to a delivery is something that elevator managers have to be very proficient at or they will probably not survive long in the job. The determinants to look for in a sample and the tests to be done are laid out clearly in the Grain Grading Handbook for Western Canada. A few of these determinants are tabled in Addendum 2.1. 59 There are many problems associated with the use of equipment in testing moisture (as also protein content). The equipment has to be calibrated very carefully. Results are very sensitive to weather conditions at the time of the tests and to other factors such as the length of time that the machine has been turned on. Hence results can va considerably from one reading to another (source: discussion with elevator officia7 s). 60 There are currently four grain grades into which CWRS wheat can be segregated (#IV#2, #3 and feed). Two of these grades are further divided into different (non- grade affecting) protein level segments (at the .O1 percent level) so that when grain is delivered to an elevator there are a large number of classification (grades and protein levels) that can be assigned by an elevator manager for a unit load of grain. Grain that has a high moisture content or that is diseased also receives a different classification (Grain Grading Handbook, 1994). 61 The elevator manager uses 0.5 kg of grain from the average of a delivery for a dockage test. The manager places the sample in a dockage tester to remove the foreign material. Docka e is the foreign material in the grain that can be easily removed by cleaning. Ta e grain is re-weighed and the amount of dockage is determined as a percentage of the grain's weight. It is deducted from the gross weight to give the net weight delivered by the producer (Grains and Oilseeds, 1993). 62 If the farmer and the elevator mana er disagree on an assigned grade, a sample is sent to the nearest office of the CG 8 whose decision on the rade is final. Pepper and Hill (1977) surve ed U.S. farmers on their attitude towa 3s grain grades and standards. When as1: ed what methods they used to venfy grading accuracy, over half the farrners reported that they relied solely on the honesty of the elevator manager. Of those faners who reported efforts to veri grading accuracy, some used personal observation of the grading procedure whi?' e others (less than twenty percent) said they submitted samples to several buyers or used personal testing equipment. See also the survey by Devine et al. (1979). the CWB which then becomes aware of the quantity and quality of the grain that is available for shipment from that elevator.

After the elevator manager buys the grain, the grain has to be stored until shipment is ananged. Binning grain requires special skills. Generally speaking a grain buyer cannot pay full price for grain not meeting grade limits without running the nsk of selling grain at a loss at the next stage of the market channel. Thus an elevator manager must ensure that small lots, which are purchased over a period of time and placed in a bin to accumulate until car lot quantities have been obtained, receive the same grade and dockage when unloaded at the terminal as was at least given the producer when the grain was originally purchased. Any difference in value between the grade given by the elevator manager to a producer and the grade given by the Commission is absorbedkaptured by the elevator Company (Grain and Oilseeds, 1992). Thus what concems the elevator manager is not the quality of a sample of grain when it is being graded at the primary elevator, but rather its grade when it will be exarnined by a CGC agent at a terminal.

2.2.2.2 Blending of Grain

There are several methods used by elevator managers to blend grain?

All elevators ultimately make use of gravity to move grain around and in and out of the elevator. Grain is moved from one point to another through "legsn or spouts that can be directed to any bin as required. When grain is received, the elevator manager can decide to which bin it should be assigned. One way to blend grain is simply to send it directly into a bin containing different grain so that blending takes place immediately on delivery. Another method is to direct the best grain to a "special" bin. When rail cars are loaded, the elevator manager makes use of what is called "the blending bowl" (essentially a gamer into which the grain is dropped to be weighed) to

63 It should be noted that the lar e-scale blending of grain is not necessarily a world wide phenornena. For examp7 e, the vanety of wheat is extremely important to the French wheat industry, especially at fams, country elevator and flour millers. When it cornes off the fam, French wheat is usually placed in bins by groups of varieties according to milling yield and baking characteristics - good, average, feed wheat etc. Although some blending does occur on wheat moving to export channels in France, there does not seem to be the desire or the necessity to blend wide margins of different qualities (Office of Technology Assessment, 1989). Figure 2.3: Typical Layout of an Elevator with the wBlendingBowln blend grain from different bins6". The elevator manager will mechanically swing the spout over to one bin, empty out a desired quantity of grain and then shift the spout over to another bin and empty out another quantity of grain until the desired blend is achieved. The blended grain is then weighed and Ioaded into a railway car with the use of a spoutB5.

2.2.2.3 Grain Distribution - Primary and Terminal Elevators What actually happens to grain between the time it is received and the time it is shipped from an elevator can be seen by examining the distribution of grain from some randomly selected elevators. The graphs shown in Figures 2.4 and 2.5 on the following pages were generated by examining the grade variability of shipments from data obtained from the CWB. Two elevators were picked at random from the one hundred and thirty elevators that make up the total sample used in testing the model. Figure 2.4.a depicts the distribution of grain delivered to a terminal from an elevator in 1994 (here the grading is done by an officia1 of the CGC) and Figure 2.4.b depicts the grade distribution of grain delivered to that same elevator by famers (here the grading

is by the elevator manager) 66. It can be obsewed that the peaks of the distributions are very pronounced at the grade (and protein) breaks in the deliveries to terminals, in particular the peak that takes place right on the 100 mark (The number 100 is used by the CGC as the grade nurnber assigned to #1 with no ).

Figures 2.5.a and 2.5.b in tum represent deliveries to and from an elevator but these deliveries are for 1992 from a different elevator. Here there exists much greater similarity between the grades at the primary elevator and those at the terminal, but again there is a distinctive movement of grade peaks towards the grade breaks67.

64 See #9 on the elevator in Figure 2.3. 65 Source: Persona[ discussions with elevator Company officials. 66 The Grade Code shown on the horizontal axis is a four-digit numerical code which is used throughout the grain industry, identifying each grade of each class of grain. 67 The 1994 crop was grown and hawested under favourable conditions in sharp contrast to the 1992 crop (Preston, Dexter et al, 1994). - Tonnes per Grade

Figure 2.4.a: Deliveries To Elevators Broken Down into Grades (1994)

Tonnes oer Grade

gtngm""" 8 O U) .-.-.-NNNfNhjN ~3$~~~~8GO! Grade Assigned to Delivery by Code Number z

Figure 2.4.b: Deliveries to Terminals Broken Down into Grades (1994)

Source: Graphed from data supplied by the CWB Tonnes per Grade

2.5.a: Deliveries to Elevators Broken Down into Grades (1992)

Tonnes per Grade

Ooco~mNOmNmNNOb~"000 O2$!2fZNBP8-"c~~g O O Grade Assigned to Delivery by Code NumbeF T I

Figure 2.5.b: Deliveries to Terminais Broken Down into Grades (1992) In both sets of deliveries, it is interesting to note that the elevator manager applies fewer grades to deliveries from famersa. The elevator manager simply utilizes one grade for ail deliveries for which one price applies. The deliveries to tenninals are however graded accurately by the CGC so that there is a very wide range of deliveries to terminals. For exarnple, the elevator manager for the elevator used in the 1992 graphs used 10 grades for al1 deliveries to that elevator whereas there were 25 grades used by the CGC to grade deliveries from cars shipped from that e~evator~~.

The changes that occur in grades between primary elevator and terminal elevators was also examined in the study by Giannakas et al (Giannakas, Gray & Lavoie, 1996) who showed using aggregate data that the distribution of CWRS #1 wheat was significantly different at the elevator than at the terminal. It would, in general, be very difficult for an elevator manager to consistently blend grain to its maximum potential. Hence, the most that one could expect is a movement of the distribution of grade peaks towards the grade breaks.

2.2.2.4 Delivery and Transportation Documentation

Once a rail car is loaded with grain, the elevator manager completes a set of documents which include a shipment report for the company and the CWB" and a Bulk Grain Waybill for the railway company. The shipment report sent to the company sets out the quantity of grain shipped, its weight, the protein content of the grain, the dockage percentage deducted, its moisture content and an estimate of the purchased grade from the farmer. The elevator manager keeps a sample of al1 the grain shipped

68 The elevator manager paid ail farmers who delivered grain whose protein level was below 13.5 percent one price ($1 10 for #1) and then paid al1 famers who delivered rain whose protein level was between 13.5 percent and 14 percent the next price 9$1 25 for #1, 13.5 percent protein). 69 The issue of identity preserve (aiso known as close loop systems or special bin grain) has become important in the grain trade. It essentially means that al1 the characteristics of a shipment of grain are retained as the shipment (e.g. a one truck load from a fam) makes its way along the market chain from farmer to miller. For example, some companies spend a great deal of money engineering seeds that have specific attributes (BI 602 from the Anheuser Bush Brewin company is an example of this). The grain grown from these seeds rnust there? ore be kept separate at al1 times (Jones et ai, 1994). ldentity presewe has important ramifications in this study since it represents the inverse of blending. With identity presewe, the characteristics of the grain from a particular field or farm are retained; with blending, the characteristics are altered. 70 This information may be sent by the Head Office of the company. so that it can be used later if there is a discrepancy between the grade of the elevator manager and the Grain Commission grade assigned to each rail car. At some stage after delivery to a terminal, a report of the grade given a railcar by the CGC is received by the elevator company and in due course the elevator manager receives a report of unload inspections which matches the grade at the terminal with the grade at the elevator so that the elevator manager is made aware of any grade gains and loses on the shipments made.

2.2.2.5 Auditing of Elevator Stocks

Audits of elevator grain holdings are made approximately every two years7'. What will usually happen is that an elevator manager will be infotmed of the arriva1 of an audit team at some given time in the future. The elevator manager is then required to make sure that the elevator will contain little grain when the audit is done so that the elevator is closed for a short period only. The purpose of the audit is more to confinn that no theft of grain takes place rather than to check grades. An elevator company knows full well of the blending that takes place in al1 its elevator so that an elevator manager who is not proficient at blending is therefore considered as doing a poor job. In this respect, the CE0 of the Canada's largest grain elevator company, the , made the following comment at a recent teleconference:

"Overgrading by the competition occurs, but we must recognize that at times we also provide "blending" opportunities to producers. We see our competition provide a significant amount of flexibility to their country staff which often results in staff changes when an excessive grade liability is discovered. There is a financial price to pay for such flexibility in product integrity. The SWP has been fortunate that due to the integrity and judgment country staff have shown with respect to blending, we have not suffered unduly financia~ly"~~. The following also appears on page 28 of the Saskatchewan Wheat Pool prospectus of December 19,1995:

71 The audit process is known as a "cut-off" in elevator circles. Audits at terminal points are directed by CGC personnel, whereas primary elevators need only supply the CGC with stock reports. 72 Source: Dialogue with the CE0 - Satellite uplink, 1996. 'The company can also earn revenue from grade and weight gains on grain handling. This can result from blending stocks in the primary elevator, from incumng less dockage at port terminal unload than assessed at purchase and from losing less grain during handling than provided for by the shrinkage allowancen.

2.2.2.6 Elevator Managers

The elevator manager is often an elevator companyJsonly link with the farn~er~~.The manager of a primary elevator (who is by the very nature of his/her occupation, a grain buyer) faces the same economic pressures as any other buyer - one of which is, of course, caveat emptor; buyer beware.

Elevator managers are generally reasonably well paid individuals and bonus incentives are provided for good performance7". These performance bonuses are paid. for example, for high elevator turnover rates and good achievement on grade gains. Bonuses can take many foms, including paid foreign trips or paid fishing holidays7'.

The largest elevator company on the Prairies (the Saskatchewan Wheat Pool) describes its incentive program with primary elevator managers as follows (Prospectus of Dec. 19, 1995):

"The company has a performance based incentive program for its primary elevator managers and assistants which entitles them to additional compensation if they exceed targeted handling of grain or sales of farm supplies."

73 Over twenty elevator managers, area managers, head office personnel and grain traders were interviewed to obtain information relating to: - the environment in which grain is traded; - managers competency to grade grain effectively; - salary and bonus structure. It is difficult to generalize because some managers are better than others at their work and some are more motivated than others at achieving superior results. Some area mana ers also appeared to be more demanding than others. On the whole, the indivi1 uals met had been sufficiently trained that they were able to do their job with a high level of competence. 74 Personal communications with elevator managers. 75 Personal communications with elevator managers. Elevator managers will nonnally work their way up the organization, starting as warehouse workers and then moving on to become assistant managers and then managers. They therefore receive on the job training over a period of time and they may also be required to follow instruction on grading at vaflous training programs that are set up by their emp~oye?~. In a large part, these individuals usually have a farming background and they are often grain farmers themselves. Experience rnay also be gained elsewhere. For example, the individual cunently in charge of grading at AGPRO grain in Saskatoon (a large HTP elevator that was formally a Canadian Govemment owned lnland Terminal) is a former grader at the CGC".

The grain trade is cunently undergoing many major structural changes and one can expect that the level of education that al1 employees of the grain companies will need to have will rise considerably in the future. Many elevator managers are now required to take at least Certificate level classes at Colleges and universities7'.

Proficiency in grading is only one aspect of the training individuals need to become elevator managers. Grading per se is not a difficult undertaking in that the rules are clearly set out in the Grain Grading Handbooks published by the CGC~'.

2.2.3 Elevator Cornpetition Based on the number of elevators, delivery points can be divided into two general classes - cornpetitive and noncornpetitive (Devine et al, 1979). The former can be regarded as being those with only one elevator and the latter those at which there are two or more elevators operated by different companiesaO.

76 For example, a grading course is currently being offered by the Kelsey lnstitute in conjunction with AGPRO Grain in Saskatoon, Saskatchewan. n See the attached Addendum 2.4 "Cargill Grain Operationsn taken from the Cargill Home Page on the internet (http://www.cargill.com/). 78 Source: Discussions with elevator managers. '' The grade assigned to a lot is some extent a subjective one so that differences of opinion are, of course, possible. 80 There is also presumably competition between elevator managers who are emplo ed by the same Company (for example, Saskatchewan Wheat Pool managers could be considered as competing with each other) but the degree of competition is assumed to be lower than the competition between individual companies. Though there are a number of single elevator points which may be regarded as occupying a monopolistic position, many single elevators also face competition provided by elevators at other near-by towns8'. Whatever the situation or geographical location of an individual elevator, policies as to grades and dockage engaged in by elevator managers have to be influenced to some degree by the conditions prevailing at other near-by elevators. This is due to the fact that on the Prairies, the structure of the market is such that many famers can haul their grain to more than one delivery point with a minimal increase in cost.

Charges levied for cleaning, elevation, and storage by primary elevators are likewise subject to competition and they may also be subject to some other arrangement or understanding between the parties. Thus, although a tariff fee is authorized by the CGC for cleaning or drying, any one or more of these services may be done either for free or at lower than the usual rate (or on some other basis) whenever the elevator manager thinks that a cornpetitive advantage can be attained by following this policye2. The fact that elevator companies have not, in the past, charged the full tariff that is allowed them seems to indicate that, between elevator companies, competition is very active in the prairiesa3.

Another form of competition is mentioned by Forbes (1 982) who wntes: When there is competition between elevators, it is usually in the fon of willingness to accept grain delivery rather than in price. Where two or more elevator companies locate close to each other, they compete by being open for more deliveries than would be the case for elevators in monopoly situations."

- 81 Economic literature suggests that firms facing downward sloping demand curves are non-competitive (Varian. 1992). Some elevators do face downward sloping demand cuwes (non-competitive situations). For example, The Pas, Manitoba has one elevator with the closest cornpetitor several hundred kilometers away. 82 Discussions with elevator cornpany personnel indicate that elevators companies will generally not allow their managers to reduce the posted elevation tariffs. 83 The tariff fee that was authorized by the CGC averaged $1 1.O0 a tonne for country elevators in 1994. The actual elevation fees charged by elevator companies were much lower, averaging about $8.00 per tonne (the fees are slightly different for each elevator cornpany). Source - Posted elevator tariffs and CGC rnfomation. 2.2.4 Elevator Sources of lncome and Costs

2.2.4.1 Explicit revenues

Elevators generate their income from several sources, some transparent and some non-transparent. An elevation tanff (as mentioned in Section 2.1) is charged the farmer when the grain is delivered. This tariff is usually calculated as dollars per tonne of grain delivered. The elevator may also charge a fee for drying or cleaning the grain and it may also seIl some of the screenings that result from the cleaning process". Furthemore, the elevator companies charge the CWB a storage fee which is calculated in a formula based on a length of storage.

2.2.4.2 Hidden Revenues

Practices used by elevators to generate "hiddenn revenues include the blending of grade classes as well as the blending of grain through its conditioning, where, for example, a small quantity of wet grain (on which a discount has been levied), is mixed with a much larger amount of dry grain of a similar grade as the grain goes into the rail car. By the time the car reaches the terminal the damp grain is usually dried out, the moisture having been absorbed and distributed throughout the dry grain and the full price is then paid to the elevator (Rosaasen. 1990)".

2.2.4.3 Method of Payment

The method of payment that is used in settlement of grain deliveries by famers is critical to the distribution of blending rents. The CWB pays the elevator company, on presentation of an invoice, the price for the wheat which is delivered by the company to the CWB at a terminal point after receipt by the CWB of the unload documents relating to such wheata6. The risk for al1 CWB wheat delivered by producers to the

84 See Gray et al (1996) for a review of the revenue obtained by elevator companies on dockage. 85 Other sources of "hiddenn revenue include returns on overages which appears as differences between quantities paid for and quantities actually in the elevator. Rosaasen (1992) provides a description of how this discrepancy occu rs. 86 Recall that the elevating compan already paid the fanner when the grain was delivered to the country elevator &ee Section 2.1.2.4). elevator company is retained by the company until terminal receipts are delivered to the CWB (Section 11").

The wheat delivered to elevators is owned by the elevating company (who act as agents of the CWB) and, under this arrangement, the benefits (as also the loss) of blending are lawfully passed to the elevating company. Section 8 of the Memorandum of Agreement, however, is interesting in that there is a restriction placed on the elevator companies. Section 8 reads as follows:

Section 8.a.

The (elevator) Company will deliver to the CWB at teminal points the full amount of tonnes of wheat purchased for the CWB and received from producers and is responsible for and assumes the risk of grade on such wheat and will deliver to the CWB at terminal points wheat equal to the quantities received.

Section 8.b.

The company will ship from each primary elevator and deliver to the CWB as nearly as possible tne same quantities and grades of wheat purchased for the CWB as were received from producers at such primary elevator.

Since, under the ternis of Section 9, the elevator company must provide the CWB with a daily report of al1 wheat received at each primary elevator as soon as such information is obtained at the head office of the company, the CWB is not necessarily aware, to the extent that the grain is overhnder graded on delivery, that what is purchased on its behalf by the elevators is not the same as what is actually received".

2.2.4.4 Operating Costs

The costs of running an elevator depend to a large extent on the age of the elevator and on its size (Ross, 1971; Kulshreshtha, 1975). Once fixed costs have been covered, an elevator can quickly become very profitable because marginal costs are

87 The section numbers mentioned here refer to the draft agreement between the CWB and the elevating companies - see footnote 49. 80 i.e. if an elevator manager grades as #1 a #2 grain, the CWB is incorrectly infomed as to what is in the bins. low. A study prepared for the Federal Govemment detemined that at high volumes a primary elevator shows only moderate sensitivity to volume changes (Ross, 1971). This results in unit costs nsing or falling rather sharply in an inverse relationship to volumes handled*. This study also showed that throughput and turnover are cntical to elevator profitabilitygO.The marginal costs of operation fall considerably as throughput increase to about 2% cent per bushel (taking inflation into account this is equivalent to about $2.00 a tonne). The key to the profitable operation of an eievator therefore lies in being able to achieve a high tumover rate by increasing throughput (Craddock, 1974). An elevator that can generate a high throughput rate will find its marginal costs to be less than $2.00 per tonne with the tariff fee charged for that last unit of grain, being the same as that charged for the first unit that was delivered (see the doted line of Figure 2.6). Research by Devine et al (Devine, 1979) also detemined that a key variable in maintaining a low-cost primary grain handling system is the efficient operation of primary elevators, namely that they tum over as much grain as possible. Thus, elevators live or die on the amount of turnover that they generate. Srnalier elevators that tum over four or five times may have significantly lower costs per tonne than larger elevators turning over less frequently.

Figure 2.6 on page 38 represents the theoretical cost structure of an elevator. The small elevator has a lower fixed cost than the large elevator but the variable cost of operation is higher. The elevation tariffg' is such that the HTP elevator needs to achieve a level of throughput of at least point 'b' to become profitable whereas the srnaller elevator has become profitable at throughput 'a' (blending rents and other sources of revenue are excluded for the moment).

At throughputs higher than point 'c', the HTP elevator is more profitable than the small elevator and a lower elevation tariff would be justified.

89 The costs of running a country elevator was categorized as follows: Salaries and wages for a manager and helpers including bonus, overtime, vacation pay, CPP, insurance, etc.; Fuel, li ht and power; Repairs and maintenance; Telephone and communication costs; 8ther variable costs such as travel, stationary etc.; Property and business tax; District mana er's expenses; Divisional expenses for administration and other head O9r ice expenses such as grain accounting, research etc.; Depreciation. 90 The average cost fell from over $3.00 a tonne for average operation to less than $1.60 a tonne for an elevator tuming over more than 7 times a year. Pnce and AC

Elevation Tanff

Throughput (tonnes/year)

Figure 2.6: Theoretical Cost Structure of an Elevator

where:

AC = Average Costs VC = Marginal Costs FC = Fixed Costs

91 Forbes et al (1982) provide a short review of the problems faced by elevators with their tariff structure. 38 2.3 Review and Summary

2.3.1 Review of the lncentives

2.3.1.1 The Financial lncentives

The financial incentives that are created as the grain flows from faner to termina1 elevator can be summaflzed as follows:

The prairie grain grading regulations, which ultimately are designed to generate an export quality at the average of the export standard sample, provide an incentive for an elevator manager to mix shipments received from farmers in order to attain a quality standard which approaches or exceeds minimum grade level of the primary standard. As outlined in Chapter 4, once a grade window which is accepted by the "buyef (in this case the CWB through the CGC officials at an export terminal) is established, the "sellef (a primary elevator manager) can fix the quality of the shipment within that window. Naturally, that quality will then tend to become the minimum allowable under the regulationgz. Since the primary elevator is the first receiver of grain from famiers, the elevator receives the grain with its ex-fam identity preserved - it is only after receipt that much of the initial blending takes place, with the result that the wheat quality is ultimately determined by the elevator Company at this point.

The CWB (and the CGC) cannot possibly rnonitor al1 that is going on in elevator operations. Thus what both the CWB and the CGC ultimately attempt to do is to provide an "informaln incentive to elevators to maintain the value of the grain. It is these incentives which yield rents and generate the opportunistic behaviour of the elevators that is modeled later.

92 lndividuals at both ends of the market chain i.e. farmers and millers have on occasion use the word "fraud" to describe this process (see for example Hill, 1990 at page 23). 2.3.1.2 Effects of the Price Pooling System

The total payment made to al1 wheat producers represents a pooling of al1 the sales (foreign and domestic) made throughout a crop year for grain of a particular gradeQ3. Pooling of prices within Pool accounts at the CWB lead to some trade-off. On the one hand the CWB wants al1 famers to be treated equally. On the other hand it also wants to promote the incentives that retain or produce a larger quantity of higher quality grain than would othenvise exist.

In other wheat markets such as that in the , where the elevator manager purchases the grain, the manager might be able to actually negotiate a higher pnce for any grade of grain? The fact that the CWB buys the grain and that al1 faners are paid a pooled price precludes the manager from negotiating any price for grain. This point is important in understanding the reasons for the extensive amount of grade changes that take place in elevators on the Prairies.

A prairie fannefs ability to negotiate a better deal with an elevator manager who, in these negotiations, acts simply as an agent of the CWB, is restricted to issues outside of price (or basis). The farmer may be able to improve on the grade of the delivery or obtain a transportation subsidy of some sort or arrange a side deal on fertilizer or equipment but he/she is unable to negotiate on the actual price of the grain. A Canadian wheat faner has very little contact with the actual buyer of the grain (the CWB) and the faner actually knows very little about what happens to the grain once it disappears down the elevator chute. By comparison, an American farmer (for example) negotiates a delivery directly with a wheat buyer and hence grade and price are negotiable as is the case for a Canadian faner delivering any non-CWB grain to an elevatoP5. This means, for example, that if a Canadian famer delivers a lot of

93 Each grade has its own Pool account. The pnce pooling system also results in a distortion of market signals in a way similar to blending. The pooling of grain to equilibrate ayments to faners is discussed in numerous articles including, for example, "8 rain transportation Reforms Update, Issue #2: CWB Pooling (1995)" published by Manitoba Agriculture. 44 It is interesting to note here that the vast majority of U.S. wheat moves directly from the fields to the country elevator whereas most of the Canadian wheat crop for any particular year remains on the farm until the CWB issues a cal1 for it. This means that as the wheat within an area ripens within a relatively short time, a large volume of grain must be handled by U.S. country elevaton within a few days which is not the case in Canada (Fams, 1964). 95 The same applies to any prairie fanner who delivers non-CWB grain. grain with a grade or protein level that cannot be improved upon (e-g. a #1 CWRS with a 15.5 percent protein level) the ability of the parties to negotiate becomes sornewhat limited (hence the preponderance of trucking prernia offered to farrner~)'~.

2.3.2 Summary There is no doubt that elevator managers can create rents from blending or conditioning because they are generally well trained individuals who keep accurate records of their grain purchases and who can instantly judge the value of grain when delivered. Their ability to capture these rents should result from the existence of an asymmetnc information set between farmer and elevator manager. The farmer is often unable to ascertain the true quality of the grain in relation to the grade unless the farmer becomes familiar with grading numbers and the farmer certainly cannot know what the elevator manager has in the bins and how much blending will ultimately be done. Competition however should eliminate this advantageg7.

In the blending/conditioning process, elevator managers are assisted by elevator design (the presence of the gamer or "blending bowl" in many elevators enables elevator managers to blend grain very easily and at no cost), the presence of a single buyer that limits the ability of the farmer to negotiate price (hence the introduction of other incentives such as trucking premia), a regulatory agency that is concerned with deliveries that is aimed at simply achieving a contract's minimum quality obligation (since the blending of #1 grain is prohibited under law at the terminal elevator, the blending of grain at the pnmary elevator seems to be the obvious other choice), by the presence of two different grading samples with a lower "qualityn being required from pnmary elevators and by a rather involved regulatory and grading system that makes the ramifications of what is going on difficult to interpret for many farmeng8.

96 In the United States (as contrasted to the Canadian situation) one can expect that active competition in country grain buying occurs in the prices offered for grain. But competition is of course not confined to prices and practically every other phase of country grain business is subject to competition to a varying degree. 97 See Section 1.1.3 for details on this point. 98 Forbes et al (1982) suggest that the problem may well be due to the fact that the system places total reliance on quantity (quality) controls rather than price differentiation as the regulatory mechanism. The incentive wtiich drives much of the activity inside a primary elevator can be sumrnarized as follows. The minimum obligation for the quality of the grain that a seller (in this case the elevator manager) needs to deliver provides a "built-inn incentive to attempt not to exceed this obligation, so that shipments always tend to the minimum quality allowed under the contract or regulationgg. This modus operandi applies al1 the way down the marketing chain from port terminal (which, as mentioned, is only required by regulation to ship out at the average of the export standard sample) to primary elevator (which is only required by regulation to ship out al the minimum of the primary standard sample).

Chapter 3 now examines some of the economic literature on markets and market efficiency and introduce some of the developrnent that has taken place in spatial models .

99 The method of payment used by the CWB also provides an incentive to elevator companies to blend. This point is discussed earlier. 42 Addendum 2.1

Minimum Kilograms per Hectoliter Requirements for Western

Grain Grades (and Corresponding Grams per 0.5 Liter)

Wheat Class arams Der 0.5 L

Red Spring no. 1 cwrs no. 2 cwrs no. 3 cwrs cw feed

no. 1 cw ad no. 2 cw ad no. 3 cw ad no. 4 cw ad

Red Winter no. 1 cw tw no. 2 cw tw no. 3 cw nn, cw feed

Soft White Spring no. 1 cw sws no. 2 cw sws no. 3 cw sws cw feed

Extra Strong Red no. 1 cw ex. Spring no. 2 cw ex cw feed

Canada Prairie Spring no. 1 cps no. 2 cps cw Feed

Experimental no. I CW no. 2 CW

Source: Grain Grading Handbook for Western Canada (1 993) Addendum 2.2

Maximum Tariffs (Wheat) - Primary Elevators (1993)

Service Basis of Fee -Rate 1. Eievation Receiving, elevating and loading out

Additional charges Removal of dockage terminal cleaning Administration consigned cars

2. Storaae With respect to graded storage receipt and interirn elevator receipts, for each succeeding day or part thereof after the fist ten days With respect to al1 other storage, for each day or part thereof

3. Custom cleanina as reauested bv the owner of the cirain (including receiving, elevating and It 13.8 loading out) With respect to grain not retumed to the It 3.05 owner

4. Custom drvina as reauested bv the owner of touah cirain

With respect to tough grain returned to owner

With respect to grain not retumed to the owner

Source: Grains and Oilseeds (1 993) Maximum tariffs were discontinued in 1995 Addendum 2.3

Grades of Red Spring Wheat - Some Other Primary Grade Determinants

Maximum Limits of Foreian Material

Min. Variety Minimum Degree of Foreign Material Wheat of Test Hard Soundness Other Weigh Vitreous Classes or t (kg Kemels Varieties

Matter Total Contrasting Total Other Classes than Cereal Grains 75 Any variety 65.00% Reasonably well About of red spring matured, 0.2% wheat equal reasonably free to or better from damaged than kernels Neepawa 72 Any variety 35.00% Fairly well matured, About of red spring may be moderately 0.3% wheat equaI bleached or frost to or better damaged, but than reasonably f ree Neepawa from severely damaged kernels.

69 Anyvariety No May be frost About of red spnng Minimum damaged, immature 0.5% wheat equal or weathered, but to or better moderately free t han from severely Neepawa damaged kemels. rSource: Grain Gradina Handbook. 1993 Addendum 2.4

Cargill Grain Operations

"When you work for Cargill, the community sees you as someone honest, trustworthy and full of integrity." - Julie Reynolds, Production Supervisor

The Cargill Grain Division is the largest grain trading business in the country (in the USA). The roots of Cargill's diverse, global businesses are in the grain industry- As Cargill has grown and expanded, we continue to recognize the grain business as one of our core strengths. We buy, seIl and transport grain from famers to manufacturer's of feed and food. Our customers are global food and agricultural organizations, as weli as other Cargill divisions.

We offer three distinct career opportunities: plant operations management, country elevator management and fertilizer plant management. Each of the positions allows you to immediately apply your background, training and manageflal skills in a hands- on role.

Plant O~erationsManaaer

As a Plant Operations Manager, you will be assigned to work at one of our larger inland grain elevators or an export facility located in a major port region. You will be responsible for the handling, storage and transportation of grain shipments. You will manage sophisticated monitoring systems and complex transportation logistics You will have first line supervision responsibility for production employees at our facility.

Countrv Elevator Manaaer

As a Country Elevator Manager, you will work at one of our elevators located in rural communities across the United States. You will be expected to perfot-m the day- to-day activities involved in moving and storing grain, maintaining quality standards and managing productivity. At the same time, you will learn grain merchandising and facilities operations. You will have ail the managerial responsibilities of a small business owner including accounting, credit management and employee supervision.

Fertilizer Plant Manaaer As a Fertilizer Plant Manager, you will apply your knowledge to produce and seIl crop protection products. Located at a country elevator, you will oversee operations such as fertilizer blending and product handling as well as employee supervision. Sales and marketing responsibility will give you the opportunity to develop relationships with fam customers. Your initial experience will give you a broad knowledge of fertilizer, agricultural and chemical operations.

Training for these positions in the Cargill Grain Division is immediate and on-the-job. You will work closely with your manager as you leam the business and will be given significant autonomy very early. You can expect to stay in your first assignment for 1-2 years and then relocate to different facilities as you build your career. Each move will increase your responsibility and may ultimately lead to a significant management role in the division.

Em~loveeProfile

Marshal Alsaker, Manager

"1 joined Cargill at the Bloomingburg, Ohio facility as a Country Elevator

Management Trainee. A year and a half later, 1 was promoted to Manager of a small country elevator in Russelville, Kentucky. Then 1 moved up to the Alberta, Minnesota facility where I was an Assistant Manager and recently promoted to Manager. I oversee merchandising, plant operations and a team of eleven employees. During spring fertilizer and fall harvest seasons, I can be on cal1 24 hours a day to take advantage of freight and rail cost savings. I function as a consultant to the customers and advise them on how to improve their bottom line on a commodity. I build custorner relations by encouraging famers to be aggressive rather than reactive to the market. 1 help them develop a marketing strategy with a break-even point and a plan to seIl a certain number of bushels by a certain date for a stated price. I enjoy hunting, fishing, iarming with my father and water sports. I participate in the Company fitness challenge and the corn pany-sponsored fam safety program for children."

O Copyright 1997 Cargill, Incorporated.

(Source: The CARGILL Home Page - ttp://www.cargill.com/hr/recruit~GrainOp.html) Chapter 3

Literature Review and Spatial Models

This chapter presents a review of the literature on the economic theories of importance to this study, a review of some of the limited literature on blending research and a review of various models which have used location (spatial) theory as their foundation. The relationship between the Canadian Wheat Board (CWB) and elevators is covered in the principal-agent literature and the relationship between elevators involves some aspects of strategic behaviourlw.

This chapter is divided into three main sections:

Economic Literature;

Blending Studies;

Spatial Theory and Models.

3.1 Economic Litefature

3.1.1 Markets and Eff iciency

It has been suggested that grading must be considered within the context of the total marketing system including handling and transportation (Grain Grading for Efficiency and Profit. 1982). Richard Kohl (1 972) describes marketing as "the performance of al1 business activities in the flow of goods and services from the point of initial agricultural production until they are in the hands of the ultimate consumer." In ternis of this definition, the grading of grain by an elevator manager is a part of the marketing process.

100 Schelling (1960) defines a strategic move as a move designed to influence the behaviour of others. 48 Marketing efficiency is a ten that describes how we!l marketing tasks are accomplished. Marketing efficiency can be separated into two parts, namely operational efficiency and exchange efficiency (Warlock et al, 1970). Operational efficiency relates to the cost-reducing alternatives and the technologies used for physically providing marketing services. Exchange efficiency focuses on the coordination of activities as the product flows through the marketing chain. Price is then discovered by the interplay of demand and supply forces. It is obviously imperative to market efficiency that full information be available to aide the market to allocate resources efficiently. Given ample information flows, competition will then enforce an efficient price performance (Kohl, 1967).

Cummings (1967) examines pricing efficiency for wheat prices in India. He examines these pnces in tens of three efficiency criteria, namely whether prices reflect supply and demand conditions; whether seasonal price differences consistently exceed storage costs and whether, on average, price differences exist amongst markets. He finds that on average spatial price differences tended to be less than transportation costs.

Kiser (1 993) suggests that the effect of blending of grain is ultimately to reduce the marketing costs involved - hence to improve the efficiency of the market in this regard.

Rausser et al. (1987) review the effects of incomplete or asymmetrically distributed infomation and suggest that when this happens, a first-best solution cannot be obtained. Tirole (1988) reviews much of the literature on quality and wntes that imperfect information is the basis for the quality issue in economics. In a second best situation, there are generally extemalities between economic agents that must be corrected. In a one-shot relationship, infomed customers (famiers) exert a positive extemality on uninformed ones. The govemment can then improve welfare by subsidizing the acquisition of infomation. Ultimately, transaction costs lead to incomplete contracts.

This study examines the effect of market structure on the actions of elevator managers. Market structure is an important variable because it specifies the bounds within which lie the firm's feasible cornpetitive strategies (Warrack, 1970). Shepherd (1990) defines a market as a group of buyers and sellers exchanging goods that are highly substitutable for each other. 49 Markets exist in two main dimensions: (1) product type and (2) geographic area. In the pure case, there is one distinct product that is sold in a distinct geographic area. In terrns of geographic dimensions mentioned by Shepherd, some areas of the Prairies, such as the primary south of Winnipeg, are serviced by many elevators of al1 sizes. On the other hand there are regions which have few elevators - such as The Pas, Manitoba where the nearest cornpetitive elevator is over two hundred kilometers away.

Economists have provided a number of criteria for evaluating market performance. The criteria that can be considered the most relevant for evaluating agricultural commodity markets and which are important to this study include:

Efficiency, or increasing the amount of output per unit of input;

Equity, or treating ail producers (and consumers) as having equal importance;

Market responsiveness - regulations and policies that allow and encourage producers and other decision makers to quickly respond to changes in economic incentives increase efficiency, equity and progressiveness in the industry (Hill and Bender, 199~)'~'.

In the United States, the wheat market is generally considered one of the best empirical examples of the pricing process under conditions approaching perfect competition and a high degree of pricing efficiency is assumed to prevail (Fams, 1964) In this paper, Farris attempts to analyze the pricing process for soft red at the primary elevator level in selected areas of Indiana and the results generally show substantial departures from perfect competition. The study is broken into two parts, the first one being a comparison and analysis of elevator quoted paying prices for #2 wheat (US) and the second phase represents a study of the differences between elevator grading and price discounting of samples of wheat and the grading of that same sample in laboratory settings. If the grain elevation industry is efficient, the expectation should be that the information flows will be somewhat complete so that, except for transportation, the prices charged by elevators should be equal. Yet variations in price for 1955 amongst elevators ranged from 8 cents a bushel to 2 cents

101 Other criteria for evaluating market performance are: Progressiveness; Stability of prices and income; Risk minimization and equity of income distribution. 50 a bushel with, in fact, considerable variations even taking place between elevators located in the same towns.

3.1.2 Conjectural Variations

The economic theory with regards to the two market extremes - perfect competition and monopoly - are well defined models and in their simplest foms these models have unique and well defined market equilibria (Varian, 1992). An however does not have a unique outcome in either the short terni on the long run and these problems are thus more difficult to examine.

The simplest type of oligopoly is a duopoly where only two fims CO-existin the same market, each being aware of their mutual interdependence. Thus, when only two firms are selling a homogeneous product there are a number of things that they can do. At one extreme they can be rivals - each pursuing their own self interest while taking into account the cornpetitor's actions. At the other extreme they can be perfect collaborators, sharing the market to their common advantage. This latter behaviour is described by a collusion or cartel model (Perrakis, 1990; Kreps, 1990).

The essence of oligopoly problems is that the profit that each firm makes depends not only on its own decisions, but also on those of its competitors. The analytical determination of equilibrium in the Cournot model is made easiest by means of the concept of the reaction function which give one firm's optimal output as a function of the output levels of al1 other firrn~'~*.A Nash equilibri~rn'~~results in which each firm optimizes its objective function given the strategy pursued by its rival. These fims are then at an equilibrium, where neither fimi wants to change what it is doing, given how it believes the other fims will react to any change (Q, = a,; j z i). In a Cournot oligopoly there is one such function for each oligopolist. In the Cournot model, the firm accepts other fims outputs as given but under conjectural variation, the finn accepts their reacfion as given (Cubbin, 1988).

102 The Coumot-Nash assumption involves setting a firm's conjectural variation to zero. 'O3 A Nash equilibrium is defined by Kreps (1990, at page 404) as: "A strategy profile in which each playets part is as good a response to what the others are meant to do as any other strategy available to that player." Many economists use a general conjecturai variations mode1 in their empirical research on market power (Carlton & Perloff, 1994) and as a result a considerable body of research has been organized around the concept of reaction functions (or best- response functions) and of conjectural variations. Conjectural variations are now widely used as a way of modeling the degree of collusion (and hence competition) that exist in a market (Tirole, 1989; Jacquemin, 1987; Varian, 1992; Shepherd, 1990).

An example of the use of conjectural variation in model building is given in Dixon (1 986). An investment model in which fims react with consistent conjectures (firms actually respond in accordance with their assurned conjecture) was developed. Here dx firm's conjectures are expressed as a scalar @i , firm i's belief about - which equats dx firm i's conjecture about firm j's proportional response to changes in xi. If Qi = O (i =

1,2) the Cournot model applies. If $i > O then a collusive model applies where fims tend to follow each other. If @i = -1 then the competition model results since fim i believes that any change in its own output will be exactly offset by a change in the other firm's output.

3.1.3 Principal-Agent and Strategic Behaviour

The reiationship between the CWB (the principal) and elevator companies (the agents) is govemed by an explicit and fomal contractual re~ationship'~~.

The analysis of principal and agent problems have also been well covered in the literature. Principal-agent problems anse when one party to a contract (e.g. the CWB) engages another party (e.g. an elevator company) to take actions on its behalf (e-g. purchase grain from famiers) in situations of asymmetric information (Strong and Waterson, 1987; Kreps, 1990). When solving these problems, both the principal and the agent are assurned to be rnotivated by self-interest. Generally the two problems which are examined are those of moral hazard and adverse selection. These problems are also often examined in the vertical integration literature (Clarke, 1985; Casson (1986); Barkema) in situations where transfers of goods take place between an upstream firm (e.g. an elevator company) and a downstream fim (e.g. the CWB).

104 The CWB enters into forma1 written contracts with the elevating companies. 52 Casson in particular examines situations where the upstream supplier obtains physical custody of the good first and thus has a better opportunity to examine it (and thus to effect changes to it) than has the downstream buyer. Because of the supenor information, the upstream supplier has the opportunity to "deceive" the downstrearn buyer - holding for example the best quality for their own uselo5. Arrow (1984) deals with information asymmetry between upstream and downstream producers and suggests that this may provide an incentive for vertical integration. Williamson (1 970, 1972, 1975) singles out opportunism (i.e. the tendency to take advantage of profitable opportunities with guile) and bounded rationality (Le. human inability to cope with large decision making problems) as reasons for fimis to integrate (Clarke, 1985). Williamson's concem is with transaction costs and the inability of parties to a contract to cover al1 contingencies. Hence long terni contracts have to be incomplete, thus creating room for opportunistic behaviour. Parkhe (1993) provides a good review of the literature on game theory and inter-fim cooperation with particular reference to what he calls "the shadow of the future" - the desire of fims to cooperate rather than cheat in long tenrelationshipslo6.

Associated with the principal agent problem is the question of strategic behaviour which comes into play as the elevators corne to consider whether to pay a higher price for grain of lower quality. These types of problems have often being considered in game theoretic models where an incumbent has to decide what to do about a potential rival's entry (Lyons, 1987). Choices available to a fim are generally whether to be passive or to fight back and a commitment is often introduced by having sunk costs. Once a decision is made, it is considered irreversible, with sunk costs being costs

105 In the case of elevators, it is clear from what is said in the first two chapters of this study that the elevator companies effect changes to the quality of the grain the purchase from faners on behalf of the CWB. The issue is whether the CWB &nd hence famiers) should automatically capture the benefits. One way for this to happen would be for the CWB to change the method of payment. Another would be for the CWB to integrate upwards and take over the functions of the elevator companies. The re ulatory environment could also be changed to facilitate cornpetition whilst a9 lowing the rents to be passed on. As mentioned earlier, the CWB and the Canadian Grain Commission (CGC) must provide an incentive to elevators to maintain the value of the grain. It is these incentives that yield rents and generate the opportunistic behaviour of the elevators.

'O6 Elevator companies are aware that their contracts with the CWB have to be renewed every year hence their actions must be controlled by this knowledge. Legislation governs the way the CWB must deal with elevators. which once spent can no longer be recovered. An example of sunk costs is the building of HTP elevators by the elevator companies. Kreps (1 990) probably provides the best review of games and game theory.

3.1.4 Rent S~king

Rent seeking by economic agents has been studied in a large number of settings. Buchanan (1968) defines rent as a payment made to the owner of resources over and above the price that that resource could command in an alternative use Le. it represents a receipt in excess of opportunity costs. Krueger (1974) examines and develops a model of rent seeking where restrictions on international trade have been introduced. Rent seeking, she suggests, is part of economic activity in some areas of the world and part of a fimi's resources are devoted to this activity. In al1 cases, there is a deadweight loss associated with rent seeking. Tirole (1988) submits that the bottom line is that rent seeking behaviour wastes some of the (monopoly) profits because of rent dissipation Le. the total expenditure by firms to obtain the rent will be equal to the amount of the rent. Posner (1975) adds that rents have no socially valuable by-produ~ts'07.Christopherson (1986) examines rent seeking activity in the Saskatchewan hog industry in ternis of the industry's demand for regulation. There is, he suggests, both a social and a private demand for regulation'". The social demand for regulation is related to an attempt to shift out the Production Possibilities Frontier. The private demand for regulation is an attempt by individuals to transfer income into their own hands. In other words, social demand is an attempt to construct a larger pie while private demand (the rent seeking activity) is an attempt to capture a larger slice of the pie. Buchanan (1968) distinguishes rsnt seeking from profit seeking in that rent seeking generates social waste rather than social surplus while profit seeking describes the behaviour of people where they are attempting to maximize their own

is not modeled here, it can be expected that the welfare of Canadian to the sum of producer i.e. farmers and elevator Company surplus) is increased by the blending of grain at country elevators since consumers are generally foreign individuals and greater quantities of the higher priced products are sold at an equivalent cost. From a farmer's point of view, the "worst case" scenario is that farrners gain nothing and elevators capture al1 rents. 108 See Gardner (1979) for an economic analysis of regulation in agriculture. opportunities and in the process they can benefit societylog. Tulock (1966) examines rent seeking activities and provides the example of a thief who invests in capital and effort (burglar's tools etc.) and the other members of society who invest in locks and other protection. Over time, the interaction between investments in locks and safes and the pay off on lock picks and dynamite simply come to an equilibrium. This equilibrium would however be very costly to society in terms of investment in theft and anti-theft devises.

3.1 .S. Market Power

To examine how performance varies with structure, certain measures of market structure are needed. Market power is the ability to price with discretionary difference from the price perfect competition would enforce (Wanack, 1967. 1970)~'~.Shepherd (1990) suggests that a firm's market share is a useful concept. It is the share of the industry's total sales revenue and it can range from virtually zero to one hundred percent. Market share, according to Shepherd, is the most important single indicator of a firm's degree of monopoly power in an ordinal sense i.e. compared to higher or lower shares in the same market. Higher market shares alrnost always provide higher monopoly power while low shares involve little or none. Stigler (1970, 1980) examines the situation where a unifom price is imposed upon an industry and poses the question whether non-price cornpetition will simply replace price c~mpetition~~'.

Cariton and Perloff (1 994) also suggest that industry concentration is the structural variable that is most reflective of structure. lndustry concentration is measured as a function of the market shares of some or al1 of the fims in a market. The most common variable used to measure structure in a market is the four-fim concentration ratio (CR4). Another measure is the Herfindahl-Hirshman Index (HHI) which equals the sum of the squared market shares of each fim in the industry (Carlton & Perloff (1994), page 344). Other measures that can be used to measure structure are buyer concentration, barriers to entry and degree of uriionization.

109 Described by Gordon Rausser as PERT (Political Economic Resource Transfer) and PEST (Politicai Economic Seeking Transfer). 110 In the current study, pricing differences are reflected as grading differences. 111 Price competition is eliminated by the fact that the CWB sets the pnces for grain.

55 A market power parameter used by Deprano and Nugent (1 969) is k = (where P = AC Pnce and AC is the average cost) with k > 1 representing a simple value of market power. The extent to which the value of k exceeds one provides a quantification of the degree of market power.

3.1.6 Grading and Quality Perspectives

It should be noted that there is, in fact, no one single generally acceptable definition of grades - nor has a tmly valid al1 encompassing economic theory for grading being devised. Zusman (1 967) examines a theoretical grading scheme but this is done from the point of view of the consumer only112. Hennessy (1 995) suggests that there are two perspectives on the motive for grading. One aspect relates to quality control, aIthough he suggests that quality controls may often only serve as a disguise for volume controls. The other perspective relates grades to quality identification for the purposes of transactions where participants cannot inspect the product. Thus a grading system "could be the formalization of a language - a public good - whereby ail would benefit from the resulting precision and honestyn.

There can indeed be a number of different view points on exactly how a grading system should be designated al1 depending on a person's position in the marketing chain. One example of this is that the definition given in 'The Grains and Oilseeds Handbook (1993)' there is no mention of end-use quality or econornic relevance to the miller - i.e. what the miller is really looking for in the grain purchased does not appear in the definition. It follows that if asked for an opinion, a miller might very well want to define grades differently. In fact milles prefer to value grain on its merits - the grain's millable value - rather than on its grade? It has also been suggested that grading is simply an effort to reduce variability in grain shipment (unifonnity within and between shipments being considered very important by irnporters and end-users) - or to increase information. Fergus Chisholm, the Head Miller at CSP Foods in Saskatoon, Sask. also described the biggest probiem facing miliers as being the non-uniformity of

-- - - - 112 For a merchants view of grading, see for example Shaw (1961 ). 113 Interview with Fergus Chisholm, Head Miller - CSP Foods, and Don Bumess, Head Miller - Robin Hood Mills, both of Saskatoon, Sask. (1 995). grades from year to year1I4- i-e. as the qualities of the crop changes from one year to the next, the grading values change so that shipments over one year will tend to be very unifom but there may have a large degree of variability across crop years notwithstanding the fact that the grade numbering does not change1? To indicate the divergence of grading factors from one country to the next, the U.S. is the only major exporter that measures dockage as a quality factor separate from foreign material. The limitations of the U.S. grading system, especially as far as dockage levels in grain is concerned, are set out in a report of the U.S. Federal Grain Inspection Service (1989)116 which recomrnends the introduction of dockage as a grading factor in that country.

Mehren (1961) suggests that grades and "quality" can be taken to be synonymous. The issue of minimum quality standards has been examined by a number of researchers. Bockstael (1 984) investigates the question of minimum quality standards and finds that minimum quality standards may in fact be prevalent because they generate rents for an industry in a way which is more politically acceptable than direct protection. Shapiro (1983) also develops a model that provides a theory of optimal minimum quality standards where the sellers choose the quality. The focus of this paper is on reputation i.e. the sellers are induced to maintain their reputation for high quality because of the premiums that they can capture. Matsumoto and French (1971) examine the brussels sprout market and they develop an empirically quantifiable model which is aimed at achieving the best - or a least a better - mix of qualities.

Some authors have reserved a large section of their text books on rnicroeconomic principles to the economic aspects of quality and product differentiation. This would

114 The problem was descn'bed as follows: "The big est problem with grading is that it is not unifom from year to year. ~fal1 the crop is 8ousy al1 of a sudden, what was a #1 wheat last year becomes a #3. That may be allowable in the market place - if the worldwide market says that that is the best that is available then it (a #1) can command a premium - but as far as a miller is concerned, if the grain is junk then it is junk, and the flour will never be there but you still have to pay a premium." 115 Thus the standards that make a grain a #2 wheat one year might make that same wheat a #1 or a #3 the following year. 116 Report on the Effects of lncluding Dockage and Foreign Material as a Grading Factor for Wheat (1989). indicate that the problems associated with quality are taking on added importance. These authors include for example Tirole (1 988) and Denzau (1992).

3.2 Blending Research

An interesting review of blending at the pnmary elevator level took place in the United States shortly after the first world war when the US Federal Trade Commission studied the issue in depth (The Grain Trade, IWO).

A great difficulty to be found in dealing with micro level data such as one would obtain from a study of individual elevators is the lack of available data with which to cany out empirical work. What happens inside an elevator when grain is purchased and then loaded in a rail car or truck is, for obvious reasons, considered confidential information by al1 elevator companies.

This probtem was, to a limited extent, resolved in the study done by the Federal Trade Commission when it commissioned the study of the Amencan grain trade. The report of the Commission represents a broad survey of the grain industry in the early part of the century. It would seem that the Commission was able to obtain the data used in the study simply because elevator companies were themselves ignorant of the activities of their own grain agents and they were now been given an opportunity to find out for themselves what went on inside their own line elevators.

Famers contended that pnmary elevators had been undergrading, overdocking, and undeweighing the grain purchased by them. The commission made an atternpt to ascertain the actual facts in regard to this question. The commission interviewed many elevator managers who generally maintained that they either graded, docked, and weighed their grain purchases with accuracy or with a tendency to favour the faner. In other interviews, this time with the head office management of the grain companies, the Commission was invariably informed that their companies suffered heavy losses through over-grading by their pnmary agents. Management laid particular stress upon the grading side of the controversy and when they were asked for details on the other features of the study i.e. weighing and docking, they maintained that the gains, if any, on these items were entirely offset by the loss on grades. As a result, and so as not to rely on the conflicting views of the elevator and corporate managers, the Commission decided to undertake a cornparison of the actual grades, dockage, and weights at the primary elevator with those of the same grain graded at a terminal market. The data for this study was obtained from the elevator companies themselves.

Four companies were selected: The Cargill Elevator Co, the Northwestem Elevator Co., the Andrews Grain Co., and the Ossborne-McMillan Elevator Co. At the time of the Commission's inquiry, these four companies were operating approximately 275 elevators, handling many million busheis of grain a year. It was decided to confine the study of grading, weighing, and docking to wheat because wheat was at the time the most important grain in the four north western States of the US., both in ternis of volume of production and amount marketed. Wheat was also the grain on which it was possible to obtain the most complete record of daily terminal market prices.

For al1 the elevators operated by the four line companies selected, the Commission obtained the grades, dockage, and weights as reported by the elevator manager of these companies in the country when the grain was bought and the grades, dockage, and weights applied by the grain inspection department to the same grain upon its arriva1 at the terminal market. The data was obtained from the books of the four companies for five crop years of the penod 1913 to 1917. It was, however, decided to use the results for only three years because of the large amount of labour and expense involved in compiling the data and 1913 to 1915 were then selected as being representative years.

Average terminal market prices were applied to the grades reported as purchased in the country and also to the grades retumed by the terminal market inspection department. The difference between the two in terms of loss or gain was then computed.

The results of the study showed that of the 25 million busheis of wheat purchased during three years, the four companies made a profit of about $87,000 on dockage (amounting to something over one-third cent per bushel). The companies also made a profit of about $60,000 on weighing (or around cent per bushel). The companies, however, made a loss of approximately $230,000 on grading. These results seem to indicate that the agents of these companies practiced a fom of cross- 59 subsidization by grading grain higher than it wananted but recouping the losses incurred through higher dockage and incorrect weighing117.

The relative consistency of the profits of the line elevator companies in weighing and docking and of losses on grades required an explanation which was suggested to be the following.

Generally speaking, it was on grading that the elevator manager was most likely to make mistakes. The grading of grain requires that a variety of issues be taken into consideration and individual judgrnent plays a very important part1". In the case of weighing and docking, greater accuracy was possible than in grading, for the reason that dockage and weights could be determined by a mechanical process. There was little or no opportunity or need for exercising individual judgment and there should, in consequence, be less error than in determining grades. The commission did however recognized that grading error on its own still could not constitute an explanation of the consistent losses on grading.

The fact that elevator companies seem to lose on blending was confirrned in a study by Giannakas, Gray and Lavoie (1996) who showed using aggregate data supplied by the CGC that blending has been going on at prirnary elevators but this has not generated rents for the elevator companyllg. Giannakas et al (1996) suggest that these grade losses occur because of measurement error that takes place in the

117 An example of this in the current day situation is the fact that elevator companies do not charge the full tariff that is allowed under CGC reguiation. This seems to indicate that cornpetition in the Prairies is still very active. There is probably some degree of cross-subsidization going on in country elevators operations with the companies keeping their tariffs low and recouping lost revenue from other sources such as blending and dockage. The CGC also controlled the tariff at the terminals and at these points, elevator companies charged higher fees than the authorized maximum, with the permission of the CGC, of course (source: discussion with CGC officials). 118 See Section 2.1.2.2 for a discussion of how and why grades are set. 119 This study uses aggregate estimates of the quality of rain at country elevators. The data for these estimates is in tum obtained by the 8GC from samples sent in by elevator managers. Discussions with CGC officiais indicated that the CGC had little or no control over how the samples were gathered b these managers. Hence they had some degree of skepticism over the accuracy OY the samples. The samples are now being obtained by the CGC directly from farmers as zn attempt to improve the process. process of blending where a manager is too busy to force customers to wait white each load is being tested1?

In a study of what was called the "hidden costsn of the grain industry, Pincemin (Pincemin, 1988; Rosaasen 1990) examined the Ioss to farmers because the grain companies bought grain at one grade but blended it up with another to seIl it at an other grade. Also examined were the "hidden costsn incurred by farmers when grain was discounted as tough or damp and then blended with other (straight) grain instead of artificially drying it. This study, which made use of aggregate data, adapted estimates made by the Saskatchewan Wheat Pool and the Saskatchewan Grain Reporter to estimate the total quantity of each grain that would have been delivered to elevators. This estimate was then compared to publicly available numbers on total unloads at terminal positions. The results generated by Pincemin for wheat deliveries indicated a wide disparity over different years with loses being made in some years and gains in others. The final result indicates that elevator companies generated less than one third of a cent a bushel over the ten year period 1976 - 1985 from blending different grades of wheat.

As was mentioned earlier, a number of options are available to an elevator manager Men a delivery is made by a farmer or when a rail car is loaded. An elevator manager is not obligated to mix wheat with wheat and he/she can, as an example, mix in other low value products to wheat to bring the foreign material content to the minimum contractual obligation or the maximum allowable foreign material level in a grade. As Allen (Allen, 1995) indicated, rye, a low value bulk commodity which if often priced lower than feed wheat, is added to feed wheat which has a high (10 percent) tolerance tevel of mixed grain to bring the foreign material level of the wheat to 10 percent. There are also other reported cases of buyers simply dropping dirt in a car lot to bnng up the level of foreign material to its acceptable grade level.

Johnson and Wilson (1 993) investigate wheat cleaning decisions at primary elevators and, using a mathematical prograrnming model, detemine the optimal decision for

120 This study examines the blending of high protein wheat and it determines that when measurernent error is incorporated into the model, the potential to generate rents from blending fall sharply. 61 elevators given that they can either clean dockage out of grain or blend itl". The net effects resulting from cleaning grain or blending are the sarne but the decision depends on the amount of dockage in the grain and the value of the screenings that are obtained through cleanings. Screenings may be very valuable. The costs involved in transporting uncleaned grain also play a part in the decision as to what to do with grain in the elevator. ln 1987, the value of screenings were such that elevators preferred to clean and recover the screenings rather than blend.

3.3 Spatial Theory and Models

3.3.1 Introduction

The purpose of this section is to review bnefly the model development that has taken place in the spatial literature. This review firstly examines some simple theoretical structures and continues on to examine some more complex mathematical models.

3.3.1.1 Use of Space to Describe Interaction of Firms.

The purpose of model building is to simplify the object of study in order to focus on the phenornena of one's interest (Perrakis, 1990). This is done in this study by using a theoretical model that is adapted from models which have been grouped into a wider category known as "spatial" models. Spatial models are included in the sphere of imperfect competition models since they attempt to represent the strategic interaction of economic agents. The characteristic feature of imperfect competition models is that the sellers in these markets have some power to discriminate on price'?

Discriminatory and non-discnminatory pricing was defined by Phlips (1983, 1984) as follows.

A pricing policy is non-discriminatory when two vaneties of a product are sold by the same seller to two different buyers at the same net price, the net price being the price paid by the buyer corrected for the cost associated with product

121 A third decision variable is simply to ship dirty (unclean) grain. 122 As will be modeled later, blending allows an elevator manager to discriminate amongst the farmers who have grain to deliver - discrimination takin place when the elevator manager grades one load of grain as a #1 (on which price 8 is paid) and then grading a same quality grain to another farmer as a #2 (on which price Pa is paid) . differentiation. It fol10 ws that price discrimination takes place when two va Be fies of a commodity are sold by the same seller at different net prices, the net price being the price corrected for the cost associated with the product differentia tion.

Under classical microeconomic theory, a firm may discriminate in price (Greenhut et al, 1975) provided that:

1. It controls the means by which its overall market is separated into a number of distinct submarket and

2. The elasticity of demand varies between various submarkets. As is pointed out by Greenhut (1975), even if al1 markets exhibit identical demand curves for a particular commodity, distance costs have the effect of generating varying demand elasticity's between spatially separated markets. In other words, in the absence of some institutional or legislative constraints, price discrimination will naturally characterize the spatial economy so that the profit-maximizing spatial competitor will price discriminate (Greenhut, 1975). Thus a feature intrinsic to spatial competition is that firms price discriminate and that they will face varying cornpetitive pressures from the other firrns in the market.

Transfer (or transportation) costs are the most important single variable determining spatial price relationships (Tomek and Robinson, 1972). Price/location models usually utilize several different pricing policies to indicate the degree of price discrimination that is being modeled, namely: miIl or fob pricing where a unifonn fob price is charged and al1 transportation costs are passed on to the buyer, a uniform delivered pricing formula where al1 consumers are charged the same delivered price and a more general spatial discrimination price where a buyer is charged a location-specific price so that differences across buyers may not reflect the difference in transportation costs. The price effect of spatial competition depends on how a firm anticipates its rival's reaction to a change in price, both in terms of:

the impact on prices of a change in the environment in which the fims operate and;

a change in the actual price level (Beckman (1968), Greenhut, (1970), Greenhut & Ohta, 1975). 63 A very large number of models that have been described in the literature make use of spatial theon'es (see for example; Ponsard (1983); Artle and Carnithers (1988); Devletogou (1 965); Economides (1 993); Sheppard et al. (1992)'". Knoblauch (1 991) generalizes location games to a graph and Multigan and Fik (1 994) examine price and location conjectures in medium and long nin spatial competition. Damania (1 994) extends the model by Eaton and Lipsey (described later in this section) in situations in which firms sustain long term collusive spatial equilibrium. This model makes extensive use of game theory to show that when market areas are srnall, collusion between fims tends to become difficult.

Spatial models are usually used to explain the effect of different pricing policies and the existence of market power in non-competitive markets (Fraysse and Grimaud, 1991). The idea behind these models is that fims (or in the case of this study, elevators) produce at particular locations and self their productfservice in a series of spatially separated markets'24. Transport costs and distance result in some loss (information loss, inconvenience etc.) which form an important aspect of their economic environrnent (Greenhut et al, 1987). These firms have to choose a production location, the markets in which they will attempt to seIl their products and the price that they will charge for their service. At the same time, these firms face a diversity of competitive conditions in their various markets. For example, in some markets a firm may face very severe competition while being a near-monopolist in some other direction of the market. Thus fims (elevators) and customers (farmers) can be described as existing and making their decisions in a spatial econornic en~ironment'~~.

- -- - - 123 Much of the research that uses spatial models can be found in the Journal of Industrial Economics and the Journal of Regional Science. 124 As will be noted in Chapter 4, the function of an elevator in the market can be modeled either on the supply side (the elevator sells a service being the elevation, cleaning and loading of grain) or on the demand side (the elevator buys grain for the CWB). 125 The objective of this study is to try to detemine to what extent cornpetition affects the ability of an elevator manager to retain rents generated in the blending process. To do this, the research methodology takes on a different approach to that used by most of the theoretical literature reviewed here in that the elevator purchases the grain so that the concern here is the farmers supply curve. 3.3.2 Spatial Models It is important to keep in mind that, in the models described below, what is called "space" represents the natural criterion for the separation of markets Le. "space", "distance" and "location" are merely labels used to represent a wide variety of important economic phenomena (Greenhut, 1970), such as, for example, product differentiation or in the case of this study, grading and blending.

3.3.3 Linear Models Probably the most notable paper written on the location issue was presented in Hotelling's 1929 "Stability in Competition" (Hotelling. 1929). In this seminal paper Hotelling described a model of two fims competing to seIl a homogeneous product to customers spread evenly along a linear market. In equilibrium, the fims locate at the center of the market rather than being in the locations that would minimize transportation costs. This effect represents what has been called the principle of minimum differentiation. This principle has been used to explain why, for example, one often finds an industry such as the auto industry concentrated in one area and could be used to explain why one would find High Throughput (HTP) elevators dustered together in one town (e.g. Davidson, ~askatchewan'~~).This paper was then followed by Smithies "Optimum Location in Spatial Competitionn (Smithies, 1941) which extended Hotelling's two-firm model to a third firm.

Eaton and Lipsey (Eaton and Lipsey; 1975) present a simple model that can very effectively be used to illustrate Hotelling's 1929 location mode1 so that this latter model will be used to describe the basic theory.

Let us assume that:

Customers are distributed unifomly throughout a linear market such as one shown in Figure 3.1 below (this in effect means that customers are evenly spread along the whole length of the line) ;

126 Davidson, Saskatchewan, a town half way between Saskatoon and Regina has three elevators; Two large HTP elevators owned by the Saskatchewan Wheat Pool and UGG situated at the north and south end of the town and a small Pioneer (standard size) elevator in the center of the town. Each customer purchases one unit of the productfservice per unit of time;

Transport costs are a function of distance;

Customers buy from the finn that quotes the lowest delivered price and al1 firms charge the same miIl price (i.e. prices are fob the firms location so that the customer located the farthest from the fim pays the highest cost;)

There are no costs to relocation so that any finn can costlessly change its location if it so desires;

All fims have either zero conjectural variation so that each finn assumes that ail other fims will leave their own location unaltered or they assume that whatever it does, other fims will change their own location so as to cause the maximum possible loss on the first finn;

All firms seek to maximize profits.

A simple one dimensional market model such as that described in Figure 3.1, shows a market of size 1 with n fims spread along the line. For example, firms 1 and 2 are located together at location y (i.e. the boundary between fims 1 and 2 is y), fim 3 is at location 3y and firms n and n-1 are situated at location 1-y. Firms 1 and 2 (and n and n-1) are described as being paired because the distance between them is equal to 6, an arbitrarily chosen "small" distance. When fims are paired i.e. when they are separated by distance 6, the principle of minimum differentiation applies. In the diagram, firms 1 and n are penpheral fims and al1 other finns are interior fims. As can be seen, a peripheral fim is one whose market boundary is an exterior boundary (point O or 1 on the line) while an interior firm is a firm whose entire market boundary is an interior boundary. Figure 3.1 : Eaton and Lipsey's Model

The equilibrium conditions for this market, where an equilibrium exists when no fim will prefer any other location but the one at which it is currently situated, can now be found. By examining the market, it can be observed that each fim's market can be divided into two sides (the half-markets), one on the left and one on the right of its location. Fim 1's market for example is y and fin2's market is half the distance from y to 3y (the location of fim 3.) The left hand side of fim 3's market is y (or 3y - 2y) while the right hand side is not detemined until firm 4 locates. It is clear that for an equilibrium to obtain, a firm's whole market cannot be smaller than any other firm's half-market (the fim will simply move and pair up with the other finn) and a peripheral fimi will always be paired with another firm (because an unpaired peripheral fim can always increase its market by painng up with its interior neighbour). Alternative scenarios for this mode1 are:

One firm captures the whole market and it can accordingly locate anywhere in the market space;

Two fims will both be peripheral firms and they must be paired because the peripheral fim can always increase its market share by pairing up with its neighbour. They must also be paired at the center of the market because of the condition that one fims' whole market cannot be srnaller than the other fims half market;

Equilibriurn conditions cannot be satisfied when three firms are present in this market (this was confirrned in a subsequent paper by Shaked (1982) which provided a mathematical reforrnulation of the problem);

Four firms will pair at the first and third quartile. An interesting perspective is introduced into the models by considering equilibrium conditions when the zero conjectural variation condition is removed. Assuming that one fim (firm j) will relocate as soon as the other finn (fim i) has picked its position, it will do so by pairing with i on the long side of its market. In this case, firm i might want to adopt a Minimax (minimize the maximum damage) strategy of choosing that location that will minirnize the damage that fim j can do it - Le. it will maximize the short side of its market by simply locating in the middle of its own market (at the midpoint between its two neighbours). In this case, fims will locate at the socially optimal configuration with each firm being spaced along the line so as to have equal market areas of lln and equal half markets of 112n. Transportation costs to the customer should therefore be minimized which is, of course, contrary to the position shown in the zero conjectural variation model. This indicates that the assumptions made to spatial models are indeed critical to the results and that a researcher should indeed be very careful about the process of building these models.

3.3.4 The Circular Models

The method utilized above to detemine the equilibrium condition can easily be extended to an unbounded space such as would be obtained by modeling the market as a circle of unitary cir~umference'~'. The length of the interval between any two firms is just the circumference of the circle divided by the number of firms. In the case of two firms, any configuration is an equilibrium one, since each firrn captures half the market irrespective of its location on the circumference. However, a third firm not locating in arc c c' on Figure 3.2 will cause one of two other firms to be in disequilibrium. Three fims locating along the line is very difficult to model because equilibrium depends on a number of variables. For example, a coalition could develop between two firms to try and eliminate the third firrn.

127 Probably the best known of the circle models is Salop's (1 979) model where: - firms are located around a circle and; - the model introduces a second or outside good so that there are two goods in this market, a undifferentiated and a differentiated good. Figure 3.2: A Circular Model with Firms on the Boundary.

Firms can also locate on the plane rather than on the line in which case the problem of in-betweeness becomes less important. In Figure 3.3 below, taken from Boulding (1966), a circular island is assumed with (as is usual in many of these models) a uniforrn distribution of buyers (Boulding, 1966 at page 485). The island scenario suggests that this market is indeed bounded. The best location for a single seller is in the middle, location A. Another fim coming into the market will locate somewhere near location B (giving A, the first fim, a slight advantage) and a third firrn might locate at C. Obviously, endless possibilities (and complications) exist.

Figure 3.3: Three Firms Locating in a Plane.

Other circular model issues (such as, for example, the demand function for a representative fim) can be determined by simply taking out a segment of the circumference and straightening it out (Lipsey et al. 1988). For example, a rnodel with three fims is shown in Figure 3.4 below. A representative fim is located at O with its two neighbouring fins each located at a distance L from it, with one fim at -L and one to the right at L. It is assumed that al1 fims except the representative firm charge a common price p. The price p charged by each of the two neighbouring firms and the pnce p, charged by the representative firm are measured verticafly in Figure 3.4.

Figure 3.4: Section of a Circumference

The diagonal lines marked a and b represent the total price, including transportation costs that the consumer must pay and so they can be regarded as being the delivered price schedule for each of the neighbouring fims. So the price of the product to a consumer who buys from the representative fin located at O and pays pr for a product and who takes it to point Fis p, + < If on the other hand the buyer buys from the fin - located at 1, then the final delivered pnce will be p + I - x. The point (and point x) are important because they represent the market boundary between representative firm and the fim located at -L (and at L). Customers to the left of cpurchase from the representative firm at O and those to the right purchase from the firm at 1. In other words, 2 satisfies the following equation: Thus, as the representative fim lowers its price, p, moves to the right. As the price increases, moves to the let Since the same principle applies to the market to the left of 0, the intervals between O and and O and x are the same length so that the representative firrn's market is simply 2 c. Each customer is assumed to buy 1 unit and, as before, customers are assumed to be evenly spread along the whole length of the market segment so that the quantity demanded from the representative firm is also 2 Thus, the representative fimi's demand function can be calculated as:

3.3.5 More Linear Models

The linear model shown above can be easily extended to determine the effect on the firms of unilateral or multilateral pn'ce changes (Boulding, page 472). In this modified model, shown in Figure 3.5 below, there are just two fims in the market vying for customers located evenly along a line.

Ap, represents the miIl pnce at point A (firm A) and Bpb represents the mil1 price of fim . Again, the slope of the price line away from the locations of fims A and B (lines p,mmW and pbmpan)n'se to take account of the transportation costs. The slope of the lines away from A and B is in fact equal to the cost of transportation. Line MNR represents firm A's marginal net revenue which is simply the marginal revenue obtained by including one extra unit of buyers less the marginal cost involved. At a point close to the fim where economies of scale are not advantageous, marginal costs are likely to be high so that net marginal revenues are low.

128 The two fims adopt here the "mil1 basensystem of pricing, with a price for firm A at its own location (or mill) equal to APa and a pnce for fim B equal to BPb. 71 -- A K K' B

Figure 3.5: Spatial Monopoly and Spatial Cornpetition.

These then increase as more customers are captured and then decrease as some capacity limit is reached. The transportation lines intersect at M and the pn'ce charged by both fims to a buyer at location M is the same. Point M is called a 'boundary of indifference' because at point M the buyer is indifferent between going to fin A or fim B. This boundary then divides the field into two market regions, one where fim A is dominant to the left of K and one where fim B is dominant to the right of K. Now if A were to reduce its miIl price, a new buyer line is fomed such that the new boundary will now move to KI. Fim A gains al1 the customers between K and K' and fim B in turn loses these. if fim 6 responds in kind, it will recapture lost customers and the new boundary of indifference will now be back at K but at a lower price.

Whether any of these actions take place depend on:

Whether a price cut by one fim will increase its net revenue provided the other fim does not retaliate (which will depend on the shape and location of the MNR line) and;

The probability of the second finn in fact retaliating (Boulding. page 473). If the Marginal Net Revenue is high at the boundary of indifference (at which point, a price reduction leads to an incremental change in the number of customers which finn A can capture), the gains from a one-sided cut are likely to be high - especially if the firm is operating in a region of under-capacity. For example, as firm A reduces its price, its Marginal Net Revenue falls (for example to MNRâ) and whether or not this results in a gain depend on the size of the loss in revenue area (area bounded acef) versus the gain in revenue area (area abcd).

Figure 3.5 above can be used to compare the effect of spatial monopoly and spatial competition with the essential difference being that the first has no constraint on its market area other than that imposed by the individual demand function and the other has a market area constrained by its neighbour. At mill price p,, a monopolist firm "a" will seIl to consumers at ni" whereas firm "an subject to competition from firm "bWhas a market constrained to m.

The models that will be described in the next section investigate imperfect competition spatial models under two distinct headings - spatial competition with non-discriminatory prices and spatial competition with discriminatory prices. The distinction is important since the assumptions of the models rely critically on this distinguishing feature (Greenhut et al, 1987). Many models look at spatial competition from the point of view of . Lofgren's (1 986) theoretical paper on the other hand has interesting implications because of its investigation of the properties of spatial monopsonies. The point is made that a renewable natural resource (e.g. agriculture or forestry) often has a large number of owners and is evenly distributed spatially. The combined effect of important transportation costs and spatially concentrated buyers result in a monopsonized market solution where the local industry gets a spatial monopsony due to transportation costs. Similar assumptions as before apply: A linear market with a single buyer; al1 sellers have identical supply curves; constant transport costs per unit distance and seflers dispersed over the space according to some density function. Three kinds of pncing policies are introduced - fob (mill) pricing; cif (uniform) pricing and (spatial) price discrimination.

In the Greenhut monopoly with non-discriminatory pncing model (1987), the seller can use different pricing rules but once a rule is chosen al1 customers are treated the same way (Greenhut et al, 1987). Although many conjectural variations in non- discriminatory cases can be imagined, three particular models are used:

The Loshian competitive model, under which the firm assumes that its rivals will react identically to its own pdce actions; A Loshian fim believes that price changes will be exactly matched by its rivals and it accordingly believes that the market area is fixed. It therefore pnces as does the spatial monopolist within its own market area;

The Hotelling-Srnithies (H - S) competitive model, under which the fim assumes that its rivals will not react to a proposed price change (the Bertrand - Nash assumption). This fim believes that its pricing policy will affect demand within its market area so that it believes its market area will change. Thus an H-S fim reacts to a more elastic demand curve than the actual one faced and the manager expects sales to rise following a price cut resulting from both the price change to customers in its area but also from the increase in its market radius;

The Greenhut-Ohta (G - O) competitive model, under which the firm assumes that its price at the boundary of the market will be constrained to some known fixed value. A fimi with G - O expectations believes that an even greater increase in its market radius will follow a reduction in its miIl price.

It should be noted that both the H - S and the G - O models are likely to provide the fins acting in this manner with incorrect beliefs regarding the other fins' actions so that their action is likely to deviate from the profit maximizing strategy.

The mathematical models for both the short run and the long run can be exarnined briefly:

1. The Short Run Mode1

For the short mn model individual demand is given by the following general functional form: and the price p paid by the buyer is the f.0.b. mil1 price plus the transportation cost to the location of the buyer i.e.

where m is the miIl price and tr the transportation costs per unit. Total production costs are

where F are the fixed costs, c is a (constant) marginal cost and Q is total output.

The fundamental pricing equation in non-discriminatory models can now be derived as follows:

Aggregate spatial demand at the mil1 price m over a market of radius R and uniform density D is

Profits for the firm are n(R,m) = (m - C) Q(R,~)- F (3.9) The First Order Condition for the profit-maximizing choice of mil1 price is then

The elasticity of aggregate demand with respect to miIl pnce is

The equation can be solved to find the pricing equation for any aggregate demand as follows: This equation suggests that the Iess (more) elastic is the aggregate demand Q (R,m), the higher (lower) will be the f.0.b. miIl price m* that satisfies equation 3.12 above. The elasticity of individual demand with respect to miIl price

m df - fV(m+tr) ~(r,m) = --- - -rn f dm f (m + tr) so that elasticity of individual demand with respect to mill phce is a function of distance (and thus transport costs) from the producer. The aggregate demand elasticity can be calculated to be;

where w is a weighting function f(m + tr) w(r) = R f(m + tr)dr

aR and v, = - - aM

The weights w applied to the individual demand elasticity of consurners distance r from the seller represent a proportion of total demand taken by these consumers. Because f (m + tr) is monotonically decreasing with respect to distance, w is also rnonotonic decreasing with respect to the radius.

Thus the aggregate demand elasticity, and thus the ultimate f.0.b. price, is detemined in a large part by the behaviour of the individual demand elasticity &@,m.} The price that a fim will charge depends, of course, also on the belief that it holds conceming its nvals' reaction to a price change, that is, it is dependent on the conjectural variations the spatial competitors attribute to each other. Conjectural variations represents the increase in market radius that a spatial competitor expects following a reduction in mill a. price. Given that VI = --m'

vi = O for Loshian competition

VI = 9'2 t for H - S competition vi = l/t for G - O competition Thus:

R e,, Km)= I~(r.rn)w(r)dr+ mw(R) O 2t

In a market of known radius R the following pncing strategies will be optimal:

so that the Loshian competitor adopts the highest price followed by the H - S competitor and then the G - O competitor.

2. The Lona Run Model

In the long fun, no fim will desire to enter or exit the market so that al1 existing fims will be making just nomal profits. Thus a spatial fim is considered to be making normal profits if the market radius and miIl prices are such that the profit function (equation 3.9 above) is equal to zero i.e.

.- (rn - C) 1f (m + tr)dr + ]f(m + tr)dr

The numerator here is always positive so that the miIl pricing strategy of firms can be determined by evaluating the denominator of this equation.

The ranking of long-nin equilibrium f.0.b. miIl prices are, frorn highest to lowest the Loshian price. the H - S price and finally the G - O pnce which is similar to the results obtained in the short fun. The situation changes however when one considers models which incorporate spatial pnce discrimination of delivered or miIl prices, where prices Vary across markets.

Thus discrimination can take place in favour of more distant consumers so that delivered price, for example, do not increase to the full extent of transport cost (the miIl price is lower for these consumers) or discrimination may be in favour of more proximate customers.

The assumptions of this model are that the cost function is the same for al1 fins so that production and pricing decisions with respect to consumer ri are independent of production and pricing decisions with respect to consumer ri, with the proviso that no retrading is allowed between consumers. This model implies that the firms' market area is determined by way of a market point by market point solution so that conjectural variations are inapplicable in the case of discriminatory pricing models. Discriminatory pricing requires emphasis on the demand and cornpetitive conditions in -each market of the fims entire market space.

Maximization of profits is thus achieved by identifying the pricelquantity decision at each location that are such that marginal costs equal marginal revenue. This gives rise to a simple spatial pricing rule which can be depicted as in Figure 3.6 for two customers located at ri and r2.

Greenhut and Greenhut (1975) provide a variant of the discriminatory price model presented above by introducing market overlap so that fims now share locations. In this model, fims hold Coumot-type conjectures so that they assume that the other firms will not react to any action taken. Up to this point. it was assumed that the fims simply located in a definite place and that there was no ovedap. Q2 QI ' MR Figure 3.6; A Spatial Pricing Rule.

For example, in Figure 3.7 below, firms in an unbounded market simply locate (price) where they please (let us say at A and B). The bel1 shaped curves above points A and 6 indicate the marginal net revenue curves for both fims. As indicated earlier, the MNR curves increase as the fim expands its markets to the left and to the nght then decrease as the fim reaches capacity at a fixed pnce. If fim 6 (the second finn) focates outside space qr, then it will maximize its own MNR and both firms' market will not overlap. If fim 6 were to locate at B' so that the MNR curve crosses r, it will then reduce the total net revenue of both fims. There will also be a boundary of indifference created where the buyer will be indifferent between purchasing from either fim. Figure 3.7: Net Marginal Revenue Curves for Firm A and Firm B

In the following model by Greenhut and Greenhut (1 975), the market is assumed to be of length L with n fimis located at ni (location 0) and na (location R) on Figure 3.8 below. All n fims try to supply location r.

O

Figure 3.8: Location of Firms

Letting the supply of firm j to location r be qi (j = 1,...., n) then total supply to market r will be:

and the pnce will be

Aggregate profits for any partïcular fim is again maximized if the finn maximizes profits at each and every market location r. where ri is the distance of firm i from location r. The profit maximizing equilibrium price equation for each firm in the market selling to location r is:

where E is the elasticity of demand function at location r with respect to delivered price p which is defined to be

and

Thus each fim equates marginal revenue to the combined marginal costs and average transport costs to market r of the n supplying firms. Using this formulation, the authors attempt to determine the effects of price and location choices in three overiapping markets: Local competition; distant competition with complete market overiap and distant competition with partial market overlap. From equation 3.28 it can be seen that the average transport costs of the n suppliers is determined by the location of these suppliers. Thus the delivered pnce to any market point r will be determined by the locations of the firms supplying that market point (as ri increases, p(r) increases). The authors also show that increase in local competition results in decreases in both price and in the degree of price discrimination and as the number of firms approach infinity, the price approaches marginal cost. Each firms delivered price becomes c + tr and the standard outcome of nondiscriminatory pRcing result, with each fim pricing at marginaf cost. lncreased competition in distant markets that overiap lead to fimis pricing at marginal production costs plus average transportation costs to that location. The results indicated above show that the pBcing behaviour of competitive fims is significantly affected by the location of other firms in the markets in which they are cornpeting. Thus if competitive fims are highly concentrated, pn'ces in distant markets can be expected to be relatively high. If however cornpetitors are more widely spread, then each fim, in trying to serve a particular market, will have to take into account the strength of cornpetition from local producers.

This effect can be seen by analyzing pficing behaviour in the Cournot Nash assumption in the case of 2 fims and then by extending the assumption to the case of n fims.

3.4 Summary

Economic literature has developed a wide diversity of models on which to base research on economic phenornena. In the last twenty many researchers have applied the basic thoughts originally put to use by Hotelling (1 927) to explain the interaction to two sellers on a beach, to many models. A similar approach will be used in Chapters 4 that follows and in Chapter 5 to show how the behaviour of elevator managers and famers can be modeled as actors interacting in a spatial economic world. Shepherd's (1990) market share mode1 of monopoly power will be used in the tests that follow and the relationships described by Greenhut et al (1987) will be extensively relied upon. Chapter 4

Model Specifications

This chapter reviews the theoretical foundations of the spatial model which is presented in Chapter 5.

The Chapter is divided into two sections:

1. Spatial Competition - Implications of Competition on Elevator Actions;

2. Conclusion - Effects of Overgrading and Undergrading;

4.1 Spatial Competition - Implications on Elevator Actions

Before the conceptual model can be fomalized in Chapter 5, some aspects that have important implications for the model need to be examined.

4.1.1 Shipping "On-the-Line"

Table 4.1 below firstly provides a list of the notation that is used in the exposition that follows.

Table 4.1 Notation Used in the Models

1 = Location of famer along the Market Spatial Line (the horizontal axis) - equivafent to elevator market share with a range O -1;

t = Per unit transportation costs of famer,

x = Total quantity of grain in market area (range O - 1);

x, = Quantity of grain delivered to elevator 'a'; = Quantity of grain delivered to elevator 'b';

A, = Conjecture of elevator 'a' as to the response of elevator 'b' to its own actions;

Ar> = Conjecture of elevator 'b' as to the response of elevator 'a' to its own actions; ma = Marginal cost of elevator 'as; mb = Marginal cost of elevator 'b';

Pa = Elevation tariff charged by elevator 'a';

Pb = Elevation tariff charged by elevator 'bJ;

#1 = Higher quality grain;

#2 = Lower quality grain;

Pl = Price of higher quality grain;

PP= Price of lower quality grain; y = Grade break officially established by the CGC; ya = Grade break used by elevator 'a'; ya = Grade break used by elevator 'b';

$ = Point on the Grade Spatial Line (the vertical axis) up to which a #2 grain can be upgraded with certainty;

Note: The rents that are generated through the blending process motivate elevators to internally alter the location of y. Hence the location of y, and yt, in the spatial worid developed in this study will not necessarily be the same as that of y. As this study will show the different locations of y, and yb will depend on the spatial structure around the elevators.

The assumption of a unifom distribution allows for an explanation of the problem without getting sidetracked by the mathematics which can very quickly become intractable when a more general specification is used. The exposition is also limited to one dimension (Le. one characteristic). Adding more dimensions to the model only adds complications without improving on the explanatory value.

Shipping on-the-line can be represented graphically as in Figure 4.1.

Superior Quality

O a 13 1 = inferior quality Y zu zh

Figure 4.1 : Shipping on the Line with a Uniform Distribution

Figure 4.1 can be explained as follows.

The location of a and f3 represent the upper and lower bounds of a distribution of all the possible values of a single characteristic of a crop. With this distribution of quality values, the average quality of that characteristic will be z, as shown on Figure 4.1 above. In this model, quality is an exogenously detemined random variable which can take any value in any particular year within the range of O to 1 (or superior to inferior grain) depending on weather conditions and other factors. The range and values of a and p change from year to year so that the value of z, changes from year to year.

In Figure 4.1, there exists so many quality possibilities between a and f3 that without a grading system it would be impossible for anyone to properly segregate any number of shipments from faners into their separate or individual quality'? When a grade cut-

129 We can intuitively accept the idea that the costs of segregation into individual bins (involving inspection, storage etc.) would indeed become prohibitive. off is fixed which provides for a minimum value of a grade, such as point y, a grade window is established - with quality #1 being al1 grain with quality values to the left of y and the lower quality grade being al1 grain with quality values to the right of y (exclusive of y). As long as grain quality for the two grades lie along a possible range of values, the seller can establish the quality within that range. As will be explained in the example that follows, it economically rational for the quality to go to the legal minimum i-e. the quality standard becomes simply the minimum of the grade and the quality of grain that is delivered will become:

r z, if a grading system did not existt3';

y for the better quality grade when a two-grade system exists and;

r p for the lower quality grain13'.

To incluce a higher quality within a grade, the buyer will be required to pay a premium or shipments will simply tend to the minimum.

4.1 -1.l An Example

The issue can be explained by using the following simple two-grade model which uses the uniform (or rectangular) distribution to simplify the exposition (Figure 4.2 is added for clarity of the exposition)13*. t is assumed that a fanner is delivering 30 tonnes of grain to an elevator (denoted as elevator 'a') which, if graded correctly by the manager, would be graded a #2. In this two-grade, one characteristic scenario, the CGC grades as #1 al1 grain that is delivered to a terminal which has a quality level between O and y (where O represents the best quality and y is inclusive). On grain graded #1, the CW8 pays the elevator price Pl. Al1 other grain (i.e. along the quality segment to the right of y) is graded #2

130 The quality would be at the mean of the distribution since there would be no incentive to blend to the line. 131 With the uniform distribution, the quality of the grain is likely to be at the average of the lower grade, i.e. at point zh. The incentive lies in the #1 region only. 132 The uniform distribution has the property that the distance along the horizontal axis (the quality axis in this case) will also denote the quantity of grain. This aspect of the uniform distribution is important in developing the example that follows. Although the example simplifies reality to a great extent, it is sufficient to explain the effects of blending. on which a price of Ppis paid. In this example, the grain that is delivered has a quality distribution in a range to the nght of y and it is accordingly graded a #2 by the elevator manager. The elevator Company pays the producer a price of Pp on the 30 tonnes of grain as graded by the elevator manager (this is done by giving the farmer an elevator receipt negotiable at any bank in Canada), but the manager is then able, given the distribution of quality, to upgrade grain along line segment (y, - y) to quality level y by blending it with some other grain in the elevator which has a quality at or to the left of y. This results in the lowering of the quality value of the #1 grain. For example, let us assume that the elevator manager has 10 tonnes of a #1 grain in a bin. Let us assume further that this grain has a unifom distribution of quality along line segment y - p. When the 10 tonnes of #1 and the 10 tonnes of #2 are blended together, the resultant average quality level of the new blend will be y. Since the stylized model assumes that the distribution of the new grain and the #1 grain is unifom, segment y, - y is equal in length to segment y - f3 and the twenty tonnes of grain (of which ten tonnes will originally have had a quality value between y - P and 10 tonnes will onginally have had a quality value between ya - y) will now have a mean value of y on which a price of Pi is paid. ln his seminal paper on the market for cars described as "lemons", Akerlof (Akerlof, 1970) has the value of cars in the second-hand market falling because buyers are unable to differentiate good frorn poor cars, and in the extreme the second hand market for cars simply disappears. In the case of elevators, it is the seller's market for grain above the grade break that disappears'?

- - 133 Giannakas, Gray and Lavoie (1996) provide a different graphical view of blending on the line in the case where an elevator blends the protein content of grain. Their description of this effect is provided at the end of the chapter in Addendum 4.1 (Figure 4-9). PZ Supenor quality

A r A I

O 6 T Ya CY Quality (grade) Pdf 1 Distribution of al1 the grain before blending / Distribution of #1 after blending

'.* - 'a .'a .'0 Possible Distribution of #2 after blending

Ya P Y

Figure 4.2: Blending with a TwoCrade Scenario

Legend:

y = Grade established by the CGC as the cut-off point between a #1 and a #2.

ye = Point to which elevator 'a' is willing to pay Pl for #2 grain. Equivalent to total quantity of #2 grain that is blendable. y - fl=Quantity of #1 used to upgrade #2 Distance y, - y = distance y - $ The mathematical model that flows from the example above (given that the elevator manager grades the grain correctly when the producer delivers) is as follows:

where E represents the expected value.

Given the elevator agent's objective in this case of maximizing blending rents, the result is that?

or:

Receipts from the CWB minus payment to the producer minus the cost of operation > 0,

(with x, being the quantity of grain delivered by an individual farmer to elevator 'a' and Pl > P2).

The rents shown as area "abcd" on Figure 4.2 represent the difference between receipts from the CWB and payments to farrners which is positive to the elevator in this case. In this model, the rents can therefore be defined as being a payment received by the elevator that is in excess of the minimum payment that would have been necessary to have the elevator provide the elevation service (the elevation tanff). The elevator manager would have been willing to ship the grain to the CWB and receive Pi on segment y and Pz on segment 1 - y, an amount which is equal tu the payment to the famer, given of course that the famer will have paid the elevator the

134 This model provides for discrete changes. 89 elevation tariff plus other charges for cleaning and dockage which are excluded here for the sake of simplicity.

If the grain in the elevator was actually of a much better #1 quality (lets say grain with quality level unifomly distributed between B and O), the elevator manager would obviously have been able to upgrade a larger quantity of #2 than was achieved in the above example and the rents that would have been generated in the process would have been far largerl? Hence delivering a grain whose quality is not located at y but to the left of y results in a loss of blending potential to the elevator Company.

Three points can be made with regard to this simple example:

r Given a two-grade rnode~'~~,and a minimum contractual obligation to ship grain with quality level y, the profit maximizing option is to ship grain with this quality level by blending #1 and #2 to level y. This results from the fact that when the CWB calls for a delivery of a #1 from a primary elevator, it is not concemed whether the quality of that #1 is at O or y'".

It is clear that individually, some farrners do not get the real value of their crop. For example, a famer with wheat at quality O gets no more than a farmer at quality y which is of course discriminatory against the famer who produces grain close to O. The faner who delivers grain which is used to upgrade the #2 grain to #1 (Le. grain with quality value between O and y) receives, subject to what is said later, no benefit in this system.

Given the simplifying assumptions of a unifom distribution, the producer's costs of production are not affected by the location of the grade. But if the location of y (controlled by the CGC) were to be moved closer to O, changes in

1% In this case, the elevator manager would have blended 10 tonnes of #1 with 20 tonnes of #2 grain. 136 Two grades would be the minimum requirement in a gradin system. The other extreme would be a sample market where gain would be soY d stnctly on its own merit. 137 The example used here relates only to shipments from a primary elevator. For reasons given in Chapter 2, shipments using export grades are usually guaranteed to be at the averaae of the grade. production would occur (depending on whether the premium was greater than the cost).

4.1.2 Cornpetition When the elevator faces competition from other elevators, the above scenario rnay change because the situation rnay well be reversed - the elevator manager rnay need to offer famers a better grade than the quality of the grain rnay justifyl" so as to provide a throughput level that will minimize the average cost of the elevator operation.

In the case involving what one rnay cal1 pure competition, when a large number of elevators are competing for the famer's business, the mathematical model then needs to be changed as follows:

or:

Receipts from the CWB minus payment to the producer minus the cost of operation = O This means that the elevator manager, who still blends the grain as was done before, passes the rents to farmers. Thus the payments increase by PI - P2 to those producerç who deliver grain with quality over the line segment [Ta - y] and the blending profit to the elevator fall by an equal amount.

As indicated above, the elevator manager still needs to blend the grain, either to generate revenue for the Company or to recoup an overgrade given a farmer.

4.1.3 Modeling Elevator Actions

4.1.3.1 Supply or Demand?

There are several ways of looking at the function of an elevator in the market chain:

-- 138 Even though in such cases the elevator rnay very well atternpt to make up the overgrading by taking excess dockage or use some other way to recoup the overgrade. The elevator buys the grain for and on behalf of the CWB in which case farmers are sellers and elevators are buyers of grain. In this case, blending rents passed on to fanners could be modeled as being an increase in the price of the wheat.

or the issue could be flipped around and;

a The elevator is a supplier of a service, namely the elevation, storage and cleaning of grain in which case blending revenues passed on to farrners could be modeled as a reduction in the level of the tariff charged for this service (in this case, farrners actions would be modeled on the demand side of the equation).

Thus the effects of blending can be modeled as either a supply or demand side effect.

4.1.4 Use of Spatial Models in a Supply Setting As will be seen later, the theoretical model that is used to denve the testable hypotheses about the possible actions of farmers and elevators lies along two axes, the horizontal axis representing the actions of famers and elevators and the vertical axis representing the grading (blending) effectsl".

4.1 -5 The Horizontal ~xis"

The elevator manager can be modeled as facing an identifiable supply curve in that:

Each unit (truck load) of grain can be identified in terrns of its blending quality and ;

139 The discussion assumes that: - The elevator manager has perfect knowledge of the grade standards and of the quality of the grade of the sample being tested and; - that blending will take place if the grain is blendable. 140 The horizontal axis in the spatial model that follows represents the actions of a famer who responds to the actions of elevator managers as to the grade that is offered on #2 The vertical axis in tum represents the actions of elevator each other for the famers grain. Grading is Each unit (truck load) of grain can be identified in terms of any number of variables such as 'its distance of origin from the elevator' or 'size of fam',

The manager should therefore be able to break supply (CQ; j = 1.... n deliveries by farmers to the elevator) into various "submarkets" for which hekhe may be willing to pay different prices or off er different subsidies.

In Figure 4.3 on the following page, homogeneous farmers are equally distributed along a (closed) market of length 1. An elevator is located at each end of the market (i.e. at points a and b). Famers at point I would get Pl for their #1 grain (and Pq for their #2 grain etc.) if one of the elevators happens to be situated at the fan. The further the distance between the elevator and the fan, the greater the cost of transporting a unit load of grain to the elevator. If the elevator is located at a, the net price received by the farmer is [Pl - t],where ta = per unit distance transportation cost. In this graph, famers at I are indifferent between delivering a unit truck load of grain to elevator 'a' or elevator 'b', since the net price received for a load of grain (which is assigned the same code) is the same.

The distance (dl - 1) represents the market area captured by elevator 'a' when it pays Pi for #2 grain (assuming, of course, that elevator 'b' does not respond in kind - i.e. elevator 'a' holds Hotelling-Smithies conjectures). Famers at dl now become indifferent between delivering a #2 grain to elevator 'a' or elevator 'b' i.e. when: 4 I dl Elevator 'a's capture area Elevator 'a's- extended capture area

Figure 4.3: Farmer Delivery lndifference Points.

where ti = the reduction in the net price (due to the transportation costs) received by farrners located at point I when they deliver to an elevator located at point a.

Each famer's supply function is identical to that of any other seller - each famer simply responds to the price stimuli and deliverç to the elevator that offers himlher the highest price where the term net price can be defined as [P - (t - f)] with f representing any trucking subsidy paid by the elevatorl'".

The aggregate supply curve represents a simple summation of the individual supply cuwes subject to some distribution function (of farmers or grain).

Using the assumption above, the farrneh supply schedule can be graphed as in Figure 4.4 on the following page.

141 The introduction of trucking subsidies allows for continuous changes rather than discrete changes as would be the case for simple upgrades. 94 Qar Elevator- 'a's capture area

Figure 4.4: Aggregate Supply Curve for Wheat

with the subscripts [al] attached to the quantity and price axes being the price that elevator 'a' has to pay to a famer situated at location I and dl and the quantity of grain delivered and n being the number of grades. The transportation subsidy obviously reduces the fannets cost of transportation (the marginal cost of transportation) and it accordingly would have the effect of shifting the supply curve downwards. The transportation subsidy may not necessarily apply across the CWB so that the #1 supply line may not necessarily be parallel to the Qn supply line. The elevator manager may also apply discriminatory pricing to this area of the graph by offering farmers further away from the elevator a higher subsidy than those close to point 1. In this case, the slope of the (t - b) line would not be Iinear as drawn since ts increases as Q increases from point 1.

The distance [O I] represents the quantity of grain that an elevator captures by simply being located closer to a famer than any other elevator (given the initial assumption that the other elevator is not offering a higher grade for lower quality grain). The elevator does not need to offer a higher price, or any transportation subsidy, for this grain since the elevator's optimizing solution also represents the solution to the famer's own maximization function to deliver to the elevator that pays the highest net price14*. Also, the willingness of the elevator manager to pay a price higher than P, must derive from the maximization of the difference between the benefits (the grain's blending value and the tanff) and the costs (MC or handling costs). From point I onwards, the elevator must however be willing to pay a higher price (or offer a transport subsidy) to increase grain throughput.

On the supply curve, the elevator is unable to capture more market share at Pl (a #1 grain cannot be upgraded any further) through any price (grade) premiums and is now Iimited to paying the transportation subsidy.

4.1.5.1 Sharing the Rents

In the mode1 that follows, elevators have three methods of generating purchases of grain:

They capture the market area which minimizes famiers' transportation costs (the elevatots market area) and;

They can reduce some (or all) other famiers transportation costs by offering transportation subsidies (the elevatots extended market area) or;

They can offer some (or all) other famers a higher grade for grain of given quality (except #1).

Let us assume that a load of grain has a quality standard equal to #2 for which a farmer would be willing to accept price Given the assumptions of the model, elevator 'a' (or 'b') will not offer any farmer situated on their half of the line a to b (midpoint 1) on Figure 4.3 any other price than Pa. This means that the elevator manager can price discriminate to maximize profits. A proportion alU of the grain along segment [a + Il (on which the elevator will pay Pq) will be upgraded to #1 and on this quantity the elevator manager will capture the rents (equal to Pl - Pz) and the balance will simply be delivered to the CWB as a #2 for which price P2 will be paid.

142 The elevator mana er might want to offer transportation subsidies to entice a farmer to deliver ea4 ier than he/she would otherwise do. This possibility has to be excluded f rom the model. 143 The famer's options are somewhat limited. 144 As modeled in the section titled ''The vertical axisn. Thus, in the linear segment [a Il of Figure 4.3:

a The elevator manager can be initially rnodeled as acting as a monopolist given the famiers optimizing decision to deliver to the elevator that "paysn the highest net price. The terni "acting as a monopolist" simply means here that the elevator manager grades any load of grain correctiy at the required minimum pnce (and no trucking premia are ~ffered'~~).

Elevator manager 'a's decision is also entirely nsk free since the price that he/she pays is a direct (one to one) function of the quality of the grain but the pnce received from the CWB will include rents equal to a(P1- P2). Risk only anses in segment (dl - 1) in that some grain of proportion (1 - a) cannot be upgraded and will sel1 (to the CWB), for Pz whatever its purchase price.

The net benefits generated along line segment [dl - Il for grain of #2 quality will in turn be;

To the elevator:

(dl - I)(V - MC) - a(d1 - I)(PI - P2) (4.7a) with V representing the elevation revenue obtained by the elevator on every tonne of grain of throughput. Equation 4.7a divides the revenue of the elevator in two, one being the revenue from throughput and the other the revenue from blending. In theory, one would expect the marginal revenue from handling to be positive (or equal to zero) or the elevator manager would simply not purchase the grain. The marginal revenue from blending Le. a (dl - I)(Pl - P2) should at Ieast equal O (theoretically) or the elevator manager offers P2 and in accordance with the farmer's optimizing rule, the farrner delivers to elevator 'b'. As will be noted later, the evidence seems to suggest that elevator managers are often willing to lose on the blending (capture negative blending rents) and this is done presumably to increase throughput (increase V).

To farmers at (dl - 1)

145 An elevator manager who grades a #2 as a #2 is acting correctiy (i.e. no cheating is but the fact that the load is not graded a #1 (assuming it is within suggests that some form of economic power is at play. 97 a(dl - [)(Pi - Pz) - t > O (or O if Pz is paid (on portion [(l- a)(dl - I)])

If P2 is paid, the famer delivers to elevator 'b'.

To famers at dl: O. (4.7~)

This section has been examining elevator 'a's actions and simply assumed that elevator 'b' would not respond. The apportionment of the benefit to elevator 'a' and to farmers is obviously critically dependent on the reaction of elevator 'b' to elevator 'a's actions. In modeling the actions of elevators, one would establish different degrees of conjectures (conjectural variation) and examine the results as will be done when the spatial model is developed. This point will be discussed further in the next section when a circular market is examined briefly.

The situation then can be summarized as follows:

Segment a + 1, elevator manager 'a' can act as a rnonopo~ist'~~if both elevators grade correctly;

Segment 1 + dl, conjectural variation apply (for elevator 'a');

Segment dl -, b, elevator manager 'b' can act as a monopolist if both elevators grade correctly.

4.1.5.2 A Circular Model

The market modeled in the previous section can afso be examined from above as follows:

146 As defined earlier. Figure 4.5: Elevator's Market Area - A Circular View

Given the assumption that both elevators grade correctly, the elevators automatically capture the whole market area bounded by the circurnference of their respective circles (v,) because the farmer's profit maximizing cnteria is met. To extend its market area. elevator 'a' must pay a higher price Le. grade #1 loads of grain that are of quality #2 to famers in the shaded area above (part of elevator 'b's market when elevator 'a' treats vl as its market area).

When elevator 'a' decides to extend its market area, a new circle with circumference v2 is created. It is relatively easy to calculate the distance SI to SZ. With trucking premia, the length of line SI to sz will be continuous with incremental increases in elevator 'a's market increasing for every srnall increase in the premium Le.:

For grade increases alone, distance sr to s2 represents discrete jumps which can be calculated as the solution to:

SI - s2 > O as long as (PI - P2) - (fa - b) # O (4-9)

What happens when elevator 'a' decides to pay a higher price for lower quality grain depends on the conjectural variation assumed. If the elevator manager expects elevator 'b' to respond in kind (- al, = l)lg, elevator 'a' will have to assume that its &la

- 147 Loshian cornpetition. market area will simply reverts to area vi in Figure 4.5 . With Cournot assumptions

(- b = O)'" elevator 'a' will expect its market area to expand to v2. &la

To respond to any market share increasing action by elevator 'a', elevator 'b' has only one option and that is to pay Pl on #2 grain. Again, whether elevator 'bJis willing to do this will depend on its ability to blend the grain to #1 status, a fact which will depend on the distribution of grain quality within its capture area and on the portfolio of grain quality already in the bins.

Thus what the elevator manager does is critically dependent on the ability of the grain to be blended. It is difficult to imagine situations where an elevator manager would be willing to pay a higher price for low quality grain in situations where the manager knows that no amount of blending will upgrade the grain14g. Hence it is necessary to examine the parameters of what is called in this study "the vertical axisn.

4.1.6 The Vertical Axis In terms of the model developed above, elevator 'a' captures al1 the grain iocated between a and 1 for which it will pay the famer the price for that quality grain as is being delivered. A proportion a of this grain is upgraded and the elevator manager captures the rents so generated. This leaves market area (dl - 1) and the specific question: Where should dl (or equivalently a) be located to rnaximize profits?

For line segment (dl - 1) along which the elevator manager will only purchase a portion of proportion a at the higher price, it may be useful to look at this area not only as location of farmers in a linear market but also as the "locationn of the quality of grain along a linear market, since this determines the elevator manager's willingness to pay a higher price.

148 Hotelling - Smithies cornpetition. 149 As will be discussed later, this actually appears to be the case. Reasons for this will be modeled in a later section. It will, it is suggested, take place in situations where the tariff is higher than the rents lost so that positive revenues are still generated. a X quality of grain Figure 4.6: Manager's Willingness to Pay Higher Price.

Figure 4.6 provides a theoretical view of the manager's willingness to pay a higher price for grain (upgrade grain), where the symbol q represents an elevator manager's willingness to pay a higher (discrete) price. The negative slope of the line simply indicates that as the quality of the grain falls, there exists a diminishing willingness on the part of the elevator manager to pay the famer a higher price. The elevator manager faces a tougher decision as the quality of a #2 truck load of grain moves away to the nght of p. By paying a higher price (PI), the elevator manager rnay be able to increase throughput, but he/she faces the risk of being unable to blend the grain to #1 quality if he/she is unable to capture enough volume to offset these grading losses. This will not only entail a threat to the very existence of the elevator if

(with the subscripts k and j representing delivenes of grades to terminals and primary elevators respectively) but it may also reduce the elevators access to rail cas and it may also result in calls for an audit of the elevator manager's operations.

Thus, the nearer the quality of the grain to a grade break point (e.g. points starting at or to the immediate nght of point as. for example point pl), the higher the elevator manager's willingness to pay the higher price for the grain (Le. Pl for #2 grain). A grain of quality fi1 which is marginally a #2 grain and where, possibly, only one grade factor may fall short of its maximum/minimum limit, has a high blending value both for an upgrade to #1 quality, but also as an upgrader of #3 grain.

The blending marginal value of one extra unit of grain depends on a number of variables - thus some grain may have a high (positive) marginal value in blending wtiereas some other grain may have a low marginal value in blending. By using a simple model, an elevator manager can theoretically attempt to determine the optimum decision for region (dl - I)? For example, assuming a uniform distribution of the quality of the grain (the grain has an equal probability of being a f3, grain as it has of being a x quality grain) and a diminishing marginal blending value of grain within a grade break, the elevator manager might decide that grain with quality level PI has a probability of 1 of being able to be used in a blend (either upgraded to #1 or for upgrading #3 quality grain) and grain at B (the poorest #1 grain) has a probability of 0.1 of being used in a blend. An efevator manager buying grain with quality fi will still be able to upgrade it but he/she would need ten times more of the high quality grain for each unit of the low quality grain. At fil the ratio of the blends is 1/1 and at the ratio is 10/1. As the quality of the grain within a grade falls, the probability of not being able to upgrade it increases - Le. the risk of being stuck with unwanted grain increases. The elevator manager can then easily caiculate the blending worth of a delivery depending on its "locationn along the horizontal line of Figure 4.6.

4.1.7 Negative Blending Rents

Provided Equation 4.10 (or 4.10a) is satisfied, there are situations where an elevator manager would be willing to generate negative blending rents on grain purchases.

150 See for example Giannakas et al (1996).

102 - -- ... - Negative Blending

Quantity Capture / Area

Figure 4.7: Negative Blending Rents

Figure 4. 7 above represents a hypothetical situation that is intended to illustrate a situation where negative blending rents improve an elevators economic retums.

The following are depicted in Figure 4.7:

A linear schedule T which represents the (constant) elevation revenue charged by the elevator:

A fixed cost line FC;

r A straight line FC + VC (fixed costs + variable costs) which represents a theoretical costs structure.

A dotted line which represents hypothetical blending revenues of the elevator. In region [O - 11, the blending revenues increase since the elevator manager, it is assumed, does not need to upgrade grain15'. The "capture arean is, as modeled before, a geographical area around the elevator which is automatically captured by the elevator by virtue of the fact that farmers maximize their objective function of delivering to an elevator that pays the higher price, when transportation costs are taken into account. In this 'space' the elevator receives both the elevation tanff T as well as the blending rents BR. At point 1, the elevator manager has to upgrade grain to generate more throughput but to point 2 this is only done on grain that can be upgraded - hence blending revenues are zero and famers capture al1 rents. The revenues of the elevator are limited to T. From point 3 onwards. the manager cannot upgrade the grain Le. a #2 grain remains a #2 grain notwithstanding the fact that it is bought for a #1 price. The manager will be willing to do this if:

T 2 BR - MC'^^ (4.1 Oa)

0 A dark line which represents the tariff revenue plus the blending rents. This line is the summation of line T and line BR. At point 2, curved line v2 shows the decline in blending revenues with the double headed arrow showing the loss on blending. Total elevator revenues are still positive since Equation 4.1 0a is satisfied.

A basic principle of economic theory is that profits are maxirnized where marginal revenues equal marginal costs (represented on the graph as the slopes of the T + BR (or slope lines SI and s2) and FC + VC lines (or slope line s,). At point 2, the slopes of lines s3 is not equal to either sr or s2. They are equal at point 3 in a region in which the elevator is incurnng negative blending rents (i.e. sa II si at point 3).

4.1 -7.1 Elasticity Considerations

The retums from blending, the retums from the elevation tariff and the elasticity of supply faced by the elevator can be modeled as follows. In this case it is assumed, as

151 Unless another elevator were to upgrade grain. This would simply reduce revenues to T.

'52 i.e. If the tariff is $8.00 and the price of a #1 is $1 03.00 and the price of a #2 is $100.00, the manager will generate $5.00 in revenues. Profits will be positive provided marginal costs are below $5.00. in Equation 4.7, that the elevator rnaximizes profits by charging an elevation tariff on each tonne of grain of throughput plus some blending rents that are retained. The elevator manager must equilibrate the revenue from each at their maximum level with the knowledge that the higher the grade premium offered, the higher the tariff revenue but the lower the blending rents that will be retained. The issue is how these rents will be shared.

The eIevator's objective function is:

MaxiI=(P+R-rn-G)Q(G) G where:

P = The tan'ff charged by the elevator;

Q = Quantity of throughput;

G = Grade premium offered famiers; rn = Marginal cost of elevator;

R = Blending rents generated per tonne of grain.

It is assumed that Q1(G)> 0153

Taking First Order Condition:

where Q1(G)= -aQ aG

153 As mentioned throughout this stud , the throughput of the elevator is assumed to be function of the grade prernium OX ered fanners.

105 To introduce the elasticity of supply in the equation, we can divide both sides of the equation by G and substitute in equation 4.1 3 for Qt(G):

aQ G where -- - E G the elasticity of supply to the elevator. aG Q

Multiplying both sides of the equation by G:

G (R-G) =-(P-m)+- EG

Ci mus as EG increases, - becomes smaller and (R - G), which represents the amount EG of the rents retained by the elevator, will likewise become smaller i.e. the more elastic the supply to the elevator the more rents has to passed on to famiers?

The objective is to equate marginal revenue from throughput with the marginal revenue frorn blending (less cost of operation) to maximize profits Le. as long as:

[(R - G)+ Tl > m (4.1 8) the elevator manager should be overgrading.

Graphically, this can be shown as follows:

154 The elevator blends the grain. This generates rents but some (or all) of the rents are passed on to famers as described earfier. To generate more throughput, the elevator has to reduce the retained rents. 1 O6 Marginal /-' Outlay Grade Premium

Competitive: All rents to farmers

1 \ b QI QM QC Quantity -Monopoly: All rents to elevator Figure 4.8: Elasticity of Supply and Rents In Figure 4.8, the elevator is assumed to face an upward sloping supply curve in which the greater the grade premium offered, the greater the supply to the elevator. At QI the elevator receives a quantity of grain (equivalent to capture area of Figure 4.3). The line indicated as MVP represents the value to the elevator of the rents that will be eamed (i.e. [(R - G) + T - m]

In standard economic theory, the competitive fim prices at the point where MVP = m (the supply line) whereas the monopsonist prices along the marginal outlay curve'".

In the case descnbed by Figure 4.8, the competitive firm buys QC quantity of grain and it pays out Gc in grade premium (effectively al1 rents go to the faner) and in the monopoly (monopsony) situation, the elevator pays out GM in grade premium (or zero grade premium - ail rents to the elevator) but it only takes in QM quantity of grain Le. the elevator manager will forsake some tariff revenue for blending rents.

155 The monopsonist wouid, in this case, be an elevator facing no cornpetition.

1O7 4.2 Conclusion - Effects of Cornpetition

Grain of any quality other than #1 can be used in two ways:

Unit(s) of this grain can be upgraded or;

Unit(s) of this grain can be used to upgrade units of a iower quality.

Units of #1 grain can only be used to upgrade lower quality grain.

The basic assumption that was used in developing the ideas set out above is that farmers strÎve to grow a crop that will be given the highest grade by the elevator manager when it is delivered to an elevator. Now, whether the fanner has, in fact, produced a good quality crop or not, he/she is likely to believe that the grain in the grain truck parked on scale at an elevator should be given the highest grade possible. To achieve this end, a famer will be willing to argue with the elevator manager and the farmer may very well indicate that he/she will take the grain to a cornpetitor unless a higher grade is given.

When an elevator manager blends grain, either an overage or a shortage in the total quantity of one or more grades should appear in the books of the elevator. If grain is overgraded in the country, this overgrading should appear as a shortage in one or more of the grades given at the terminal as compared with the primary grades and an overage in one or more of the lower grades. Similariy, if grain is undergraded, the result should be reversed and an overage will then appear in one or more of the higher grades given by the CGC graders and a shortage in one or more of the lower grades, as compared with the primary grades.

Thus, so far as profits and losses are concemed, the elevaton profit and the faners lose on undergrading in the country1". Elevators, on the other hand may lose, and the farmers profit, from the reverse i. e. overgrading if upgrades of grain is not possible ex

1 56 Undergrading takes place when an elevator manager grades a higher quality grain with a lower grade. Theoretically, no undergrading should take place since a farmer has the option of taking a sample to the CGC whose decision is final (the CGC does charge a fee for this service so that some famers may be deterred from doing so). If an elevator bu s a #1 grain as delivered to a terminal for a #2 price, the elevator will benefit. SucK undergrading should only take place by mistake unless the elevator manager is fraudulent. post its purchase from the famerlm. Otherwise. the elevator simply recovers the rents passed on to fanners by blending the grain prior to delivery to a terminal. In al1 cases the profits or losses on grades can be represented by the differences between the prices for net tonnes bought in the country and for net tonnes sold at the terminal market.

It should also be recognized that market pressures will often force an elevator to blend grains. If for example an elevator is buying a disproportionate quantity of #3 grain and the CWB is marketing #1 grain, the elevator manager must attempt to upgrade as much as possible of the #3 or helshe will suffer loses (loss of rail cars etc.). Of course, blending also takes place for the competitive reasons set out above - either to capture rents for the Company or to recoup rents passed on to famers.

Chapter 5 that follows uses the assumptions outlined above to establish the foundations of the spatial model used in this study.

157 The nature and amount of loses will depend on the spatial competition. 1 O9 Addendum 4.1

Blending- on the Line with Grain Protein Before Blending Probability Density FI incticin After Blending

Grade

I Cumulative 1 O O Density I I Function 8 I I 1 I I I rn Blending I

O I 1 a ---

Figure 4.9: Blending on the Line with drain Protein

Adapted frorn Giannakas et al (1996) Chapter 5

The Spatial Model

This chapter develops the conceptual spatial model that is used to generate hypotheses on the distribution of blending rents between farmers and elevators.

The Chapter is divided into three sections:

1. The Spatial Model;

2. Identification of Test Variables;

3. Conclusion.

5.1 The Model

5.1.1 Introduction

The model that will be used to formulate the hypotheses regarding the effects of competition on the distribution of blending rents uses aspects of spatial theory as was described in Chapter 3. It is original in that it combines a location model along two axis but it also looks at the problem from a supply side rather than a demand side. The theoretical model develops reaction functions which, given certain conjectural variation assumptions, detemine the actions of elevator managers. The meaning of the two ternis, reaction function and conjectural variations, were explained in Chapter 3. 5.1.2 Assumptions The market is made up of faners who own equal units of grain uniforrnly distributed along a linear market of size 1.

The market is made up of grain which can be sorted into two distinct grades: A high quality grain graded #1 and a low quality grain graded #2.

The elevator maximizes profits from throughput and blending. The elevators can compete for the units of wheat by offen'ng farrners a higher grade for those units other than #1 grain than the quality of the grain actually justifies.

a To increase blending opportunities, the elevator has to increase throughput by attempting to draw from a larger area. It is assumed that the larger the area drawn from. the greater the vanability of the grain - hence the greater the blending opportunities.

It is assumed that blending has some costs (time, use of bins etc.) and hence that there exists some trade-off between increasing throughput and blending.

Famers deliver to the elevator that pays the highest net price (net of trans portation costs).

In theory, elevators should only pay a higher price than the quality of the grain warrants if the grain purchased can be upgraded to the quality of the higher price paid to the farmer. This depends on the blending value of the grain.

5.1.3 The Objective Functions Initially, there are two choice variables namely:

The extent (or location) of I in a market of size n where 1 = the distance to the elevator with t = per unit transportation costs and'";

158 With the uniform distribution used in this model, the location of 1 is equivalent to a quantity of grain. 112 0 The "locationn of ya (and yb) the maximum point along the linear market to which the managers of elevator 'a' and elevator 'b' are willing to upgrade the deiivery;

The model uses parameters whose location is indicated in Figure 5.1 below. The subscripts a and b are used to indicate whether the paymeot for a delivery is being made by elevator 'a' or elevator 'b' respectively.

Price

Quality (grade) O 1

ya and y, should be located along segment [$ - y] Figure 5.1: Location of ya and yb, the Elevator's Optimal Grade Break Points

where y is fixed exogenously (by an extemal agency, the CGC) as the point break between a #1 and a #2 grade and we are interested in finding the optimal location of points y.and yt,. This problem can be solved as follows:

A farmer located anywhere in the market area is indifferent between delivering to elevator 'a' or elevator 'b' when: where as used above 1 represents the location of an elevator (one is located at 1 and the other at (1 - 1)) and t is a per unit transportation cost which is the same for both elevators. This equation can be solved for I as follows:

The total quantity of grain to be delivered in the market can be denoted as x. The quantity of grain delivered to elevator 'a' (denoted &) is lx and to elevator 'b' (denoted xb) is (1 - I)x- The value of I from equation 5.5 can now be substituted as follows:

Equations 5.6 and 5.7 represent the indifference points of famiers. The point at which elevators are willing to upgrade grain in the market needs to be ascertained. This is done by solving the elevator's profit maximization equation as follows:

An elevator objective functions (solved here for elevator 'a') is:

= [(y - ya)(P1 - P2]& Taking FOC with respect to ya (with the symmetric result for yb) we obtain:

Thus both elevators locate their endogenously determined grade break as a function of the "officiain grade break y, the premium of Pi over P2 and the transportation cost from farm to elevator.

The model needs now to be expanded by assuming that the grade is itself uniformly distributed and by incorporating into the model conjectural variation which would provide some idea of the reaction of one elevator to the existencelbehaviour of another elevator depending on the competitive situation existing in the market. Again a duopoly situation will be modeled in a static environment. Although elevators in the real world may face cornpetition from a large number of elevators, a duopoly situation is made sufficient by assuming that the degree of cornpetition is sirnply a function of the distance between the elevators. When the two elevators are close to each other (as for example two elevators in a single town) competition is strong and if they are modeled as far apart, competition behveen them is weaker.

The aim of the formulation below is to obtain reaction functions for elevator 'a' and elevator 'b'. A reaction function will provide the optimal choices for the cut off point for each grade made by each elevator depending on their beliefs about the actions of the other elevator - i.e. where elevator 'a' will locate y=, given its assumptions as to where

elevator 'b' wiil locate yb.

5.1.4 The Complete Mode1

In the complete model which incorporates the actions of the farmer and of the elevators and their conjectures, the horizontal axis of Figure 5-1 is shifted vertically with the best quality grain (#1) at the top of the line and the lower quality grain (#2)at the bottom of the line. Graphically the model can be represented in a two-coordinate system as shown in Figure 5.2.

Official grade break point. lity Axis

Blendable area. ya or y, should be in this area unless the elevator Elevators can blend #t2 into #, to this point. -blending.

O ' Market Axis elevator 'a' elevator 'b' location of fariner

Figure 5.2: The Vertical and Horizontal Axes

It is always assumed, as was explained earlier, that the elevator manager will attempt to grade to the grade break (Le. on the line). Hence, the mean of the grade (Le. the #1 and the #2 as blended together to make the "new" quantity of #1) should equal y so that blending always takes place to the grade break. The Greek letter is the profit maximizing grade that should be given the famer by the elevators which in the set of equations below is equivalent to either ya or y,. Since the meaning of is clear in the equations (Le. whether applies to elevator 'a' or elevator 'b'), the superscript *is eliminated in Equation 5.1 7 onwards.

The mean of a grade which is distributed unifomly over a Iine O - 1 is:

where @ is the cut off point for blending given the location of y, the grade break point established by, let us Say, the CGC.

The solution to the location of is:

4=2y-1

Two conditions apply:

Y = y (the elevator manager cannclt grade lower than the actual quality of the grain)

The farmers are indifferent between delivering to two separate elevators when:

Pl (1 - y,) + P2 ya - Pa - t I = Pi (1 -%) + PZ% - Pb - t (1 - 1) (5.17)

The solution to I is similar to that given in equations 5.6 and 5.7. Solving equation 5.17:

G = V'Z - 11 2f [(Pa - Pb) +(ya - )((PI- Pz)] (5.1 8)

& = + 1 2f [(Pa Pb) +(~a- )((Pl - Pz)] (5.1 9)

Elevator 'a's objective function is: na= XI [(Pa- ma) + PI [(1 - 0) - (1 -y,)] + P2 [O - ya)] (5.20) na= x 1 [(Pa - ma) - Pi [( - ya) + Pz (0 - ya)] (5.21 ) na = x 1 [(Pa - ma) - (PI - Pz) (O - ya)] (5 -22) where:

Pa = the elevation tariff charged by elevator 'a'; ma = marginal cost of elevator a; x = the total quantity of grain with I being the share the elevator gets;

Solving for the total differential dn to introduce al1 the effects:

-=-dt 1 [(pl - pz )(A. - 91 au, 2t with A, = -a', *a an an Solving in the same fashion for -and - and setting an to O: ai av. a an = [(Pa- ma) -(PI-Pz) ((2y- I)-ya)- a,+l (PI - P2)=O

an =1/2t [(Pa - ma) - (PI - PP)((2y - 1) - ya)] (A - 1)+ % - 1/ 2t [(Pa - Pb) +

an =M- 1/2t [ya-w+((2~-1) - ya)](h- 1)] (Pi - Pz) + Pa- (A- 1)Pa- Pb+ma(h-1) =O Solving for w (ya) -(pi - p2 ) t 1 Yb [[2y- 1) (h.- 1) + (1 -(A- 1)yd (Pi - P2) + 2t =~-z (Pa - Pb) - (Pa - ma)(A, - 1 ) 1 Yb = [(Pa - Pb) - [(Pi - P2) (2y - ) -(Pa - ma)] (1 - h) - t] + (2 - A) ya PI - pz

This equation provides us with the first reaction curve. The same fomulation to solve for elevator 'b' can be used:

di --mb - [(Pb - mb) - (Pi - P2) ((2~-1) -Y,)- au, + I (Pl - Pz) = O

where d, = -a, ab

1 Yb= [(Pa - Pb) + [(Pl - P2)(2y - 1) - (Pb - mb)] (1 - b)+ t] +

The values assigned to ha and A+, represent the conjectural variation assumption that can be applied to a particular situation where:

h. = 1 is a monopoly situation

h = O is a Cournot situation where both elevators assume that the other elevator will not respond to any action of the first elevator, and h = -1 represents the competitive situation.

Equations 5.32 and 5.38 provide two reaction curves which will both be equations of the reaction of elevator 'b' as a function of elevator 'a's action taken from the point of view of elevator 'a's and elevator 'b's profit functions - the maximum point at which elevator 'a' is willing to upgrade a #2 grain to a #1 grade. Graphically, these reaction functions take the fom shown in Figure 5.3.

Figure 5.3: Graph of Reaction Functions.

5.1.5 Reaction Functions

Various scenarios were examined by assuming various prices and values of h. The values of the base scenario that was used in the Excet spreadsheet is set out in Table 5.1. The initial value of (I is 0.4 which is the solution to the model when y is equal to 0.7. The prices and h values in Table 5.1 represent the initial numbers used to solve for the reaction functions. The meaning of the variables used in Table 5.1 was set out in Table 4.1 in Chapter 4'?

Table 5.1: Table of Values for Base Scenario

Source: Simulation Model

Using the values set out in Table 5.1, equation 5.32 and equation 5.38 were solved simultaneously which produced the following solution^'^^:

1. For h = O (Cournot)

For ya (G) = -0.4 + 2 ya

For ya (~b)= .2 + 2

with

15' 15' See Page 84 160 The equations were solved using the solver utility in Excel 7. All graph were generated by the Mathematica computer program. yb = 0.4

These two reaction functions were graphed as follows:

O 0.2 0.4 O. 6 O. 8 1

Figure 5.4: Reaction Functions: h = O and base solution

This solution is rather interesting because the values of the cross over point at 0.4 on each axes indicates that under a Cournot scenario, the elevator manager will sirnply pass al1 blending rents to the faner in an attempt to capture the elevation tariff. Thus each elevator makes $2.50 per tonne of grain (The elevation tariff less the marginal cost) and profits are positive. Famers gain $20.00 on each tonne of #2 delivered whose quality is situated between y and 4. The grain is upgraded (at no risk to the elevator manager) and shipped to the terminais.

2. For h = -1 (The com~etitivesituation):

The equations were then solved again this time with a value of y of -1 and the following results were obtained:

For ya = -0.675 + 3 ya (5.43) Y, For yb = .225 + - 3 with: ya = .3375 yb = -3375 which provided the following reaction graphs:

O 0.2 0.4 0.6 0.8 1

Figure 5.5: Reaction Functions: h = -1 and base solution

In the competitive situation, the elevator manager is willing to grade below the maximum point ($) which has the effect that negative blending rents are generated on a portion of the grain delivered. In each case, the elevators still charges a tanff fee of $8.00 per tonne and the marginal cost of elevation is $5.50. Hence each elevator generates $2.50 per tonne of grain. On #2 grain whose quality lies between y and @, the elevator will lose the blending rents which will be captured by al1 famers who deliver #2 grainT6'. The problem here is that the degree of competition forces the manager to grade grain whose quality lies below $. On this level of quality of grain, the elevator manager may not be able to upgrade it, hence losses on grading may be incurred. In ternis of standard competitive economic theory, the elevator manager will

161 As explained earlier, farmers who deliver #1 grain wiil not benefit unless they can arrange some side deal on seeds, fertilizer etc. 123 be willing to do this to the point where profits are zero. In either case, famers gain fi nancially'".

3. For A = 1 (the mono~olvsituation).

When elevator managers are faced with a monopoly situation, it is clear that they are not concemed by the actions of other elevators (which are assumed not to exist). Hence, when graphed, the reaction cuwes are simply parallel to each other and an equilibrium situation does not exist. In the case of monopoly, a value of h = 0.75 was used which resulted in the following solution:

For ya = -0.1 5 + 1.25~~ (5 -45)

For yb = 0.12 + .8ya (5-46) with:

162 The losses in the base case could be considerable since the premiurn of #1 over #2 is quite large. This kind of activity ma tead to large losses by the elevator which may ultimately lead to the closure of the eY evator. ln the ton run. farmers may lose as a result of the closure of elevators. As shown in section f 1.5.1, had the premium been less than the tanff charge. the elevator manager could have bought the grain and still corne out ahead. Records of the CWB show that grave problems are encountered by elevator companies when the initial payment is changed by the CWB midway through a crop year. All calculations done by elevator managers about blending values then become incorrect which may then cause them large losses. Reference to overgradin losses is made by the CE0 of the Saskatchewan wheat Pool (Dialogue with the CL! O - Satellite uplink, 1996 - see page 32). - -- O 0.2 0.4 0.6 0.8 1

Figure 5.6: Reaction Functions: Â. = 1 and base solution.

In a monopoly situation, the values of ya and for both elevators shift to 0.7'~~.In this case, no breaks are given to the farmers. All grain that has quality value up to y on Figure 5.2 is graded a #2 by the elevator manager. The grain whose quality value lies between 9 and y is blended with grain whose quality value lies between y and 1 and shipped as a #1. The elevator manager generates $2.50 in elevation tariff for each tonne of grain delivered as well as $20.00 for each tonne of grain bought whose value lies between 9 and y.

5.1.6 Other Scenarios.

The base scenario is one where the transportation costs are not being subsidized (i.e. the elevators do not provide an allowance to the farmers as an inducement to bring grain to the elevator). The marginal cost of $5.50 was assumed to represent a reasonable cost of operation of an elevator and the price of grain is a normal average pricel". The initial tariff of $8.00 is approximately the tariff charged by e~evators'~~.

163 The solution actually lies above 0.7. Since this results in an elevator manager effectively cheating (paying the famer a #2 price for #1 grain) the solution has been constrained to lie at or below 0.7. 164 $5.50 is probably on the high side. A number of different scenarios were fun in Excel using various prices, tariffs and costs. For example, one scenario allowed the elevators to progressively increase their tariffs. The first result with a tariff of $7.00 placed the elevators well outside the modelJs limits of a gamma range of 0.4 to 0.7 so that elevators are willing to buy a lower quality grain at a higher price even though they may not be able to upgrade it. The difference between PIand Pz is $5.00 which represents the potential loss which the elevator can suffer with the gain in tariff revenue being ($7.00 - $3.50 = $3.50) As the tariff increases to $8.00 and $10.00, the elevator's willingness to upgrade grain increases to the point where the elevator is simply willing to buy al1 the grain offered at the higher price to generate the tariff profit of $6.50'~.

5.2 Identification of Test Variables

5.2.1 Introduction

Chapter 4 and the first section of Chapter 5 developed firstly the foundations of the theoretical rnodel and then the theoretical model itself. The model is one of a simple two grade, one characteristic world in which a famier cornpetes with two elevators for the rents that come into being when grain is blended in an elevator.

The model was developed for one purpose; to generate testable hypotheses as to what is reasonable to expect regarding both the creation of these rents and their distribution amongst the parties. Two issues needed to be resolved, namely whether elevators do generate rents by blending different quality grain and whether the distribution of the rents changes as the degree of spatial competition around the elevator changes.

The movement of the reaction function as the values of h change, and hence as the assumptions of the market change, clearly indicate that as the market becomes less competitive, the elevator should retain more of the rents generated through blending. Hence the assumptions that will be tested using the actual data from the input and

165 Tariff schedules are available from each elevator operating Company. 166 The effects on the distribution of blending rents obtained under various scenarios are effectively the sarne. Hence only the results from the initial "normaln scenario are provided here. output of elevators (the grain delivered to elevators by famers and the grain sent to terrninals by the same elevators) are the following ones:

5.2.2 Elevators Ability to Generate Rents The first issue that is examined relates to the ability of an elevator to generate rents. For this purpose, the variability of grain deliveries can be taken to be the most meaningful measure of an elevator manager% ability to generate these rents'". When the quality of deliveries frorn famers do not Vary much, it becomes difficult for an elevator manager to effect any useful blending. The elevator manager's aim is to blend one grade with another to achieve pnce differentials since blending grain within a grade simply retains the same value of the grain. If al1 the grain delivered to an elevator is a #2 grade, the best the elevator manager can do is blend it to #2 grade. with no resulting gain. But a wide variability gives the elevator manager a great amount of choice in ternis of blending - for exarnple shipments of #1 and #3 grades give the elevator manager a good probability of blending the #3 to a #2, hence achieving a profitable grade gain.

The ability to generate rents will thus be assumed in this study to depend specifically on the elevator's ability to draw grain which has a greater variation of quality (grade or protein content) so that mix and match opportunities are available to the elevator manager.

167 Other measures could be used as well - for example size of the elevator. or number of bins or turnover. Adding variables in this fashion would have rendered the statistical tests invalid by rendering the separation of effects between al1 variables impossible to measure. A method had therefore to be found to generate a number that would reflect the variability of the grain in an elevatots catchment area. Since it is not possible to get a suitable variance of delivery number by using grade deliveries only (a grade is simply a number used by the CGC) the following number was calculated for each elevator and year'?

2 IN;

where P is the price per grade and IN represents the aggregate delivery of each grade to elevator i. The square root of equation 5.58 was then calculated and used as the measure of variability of wheat deliveries to the elevators. The variable is denoted SD in the econometric mode1 that follows.

Little variance in grain deliveries makes blending very difficuit and it is therefore assumed that the larger the standard deviation in grain deliveries, the larger the ability of the manager to blend grain.

Hence:

The rent generating value of the standard deviation of deliveries can also be considered to be quadratic in nature. Although a larger elevator'" will have more bins in which to effect the blending, more importantly is the fact that a busy elevator manager will not have the time or the available space in which to blend effectivelyl". For example, a 2,000 tonne elevator handling 20,000 tonnes of grain (a tum-over ratio of 10) can be considered to be much busier than a 10,000 tonne elevator handling the same tonnage (tum-over ratio of 2). The wider the standard deviation, the more bins

168 As will be noted in Chapter 6, each year's total values were aggregated into a single number. 169 Not being an HTP elevator. 170 Le. al1 the bins will be filled with many different types of grain of one kind or another. have to be utilized to store the vanous qualities and the more difficult it becomes for an elevator manager to find space in the elevator. The variable representing the square of the standard deviation number will therefore be introduced into the test equation as a method of obsenring the change in rent generation as the standard deviation of deliveries increasest7'.

It is also assumed that:

With this assumption, the standard deviation function can be graphed as follows:

Rents

Figure 5.7: The Standard Deviation Function

5.2.3 Elevator's Ability to Retain Rents The second issue that ffows from the results of the base scenario relates to the ability of the elevator manager to retain the rents that can generated. This factor was detennined in the model to depend on the degree of cornpetition around the elevator. Hence, the greater the degree of cornpetition, the less the manager can retain of the

171 Table 6.4 in Chapter 6 provides a description of the mean SD for the three years covered in the study with the total number of tonnes of wheat delivered to al1 elevators. rents (or the more need to be passed on to farmer) that are generated in the elevator. In the test that follow in Chapter 6, this variable can be assurned to have a negative sign which cmbe graphed as follows:

Rents

1 Capacity of cornpetition (tonnes)

Figure 5.8: Ability of Manager to Retain Rents

5.2.4 Aggressiveness of Manager to Seek Business An issue that is also considered important relates to the aggressiveness of the elevator manager to seek business. The higher the pnce the manager is willing to pay, the more grain purchases will be made'". It can be assumed that two variables play an important part in the results achieved in this regard by the elevator manager, namely the degree of competition faced by the elevator relative to the market share of the elevator. Market share is, for obvious reasons, an important number. When this number is weighted by the capacity (size) of the elevator, it eliminates from the finai equation the relevance associated with the turnover of the elevator. By using market share (as weighted by the share of capacity that the elevator has in relation to the capacity of al1 elevators in the competitive region) one can effectively ascertain whether an elevator manager is doing a more aggressive job at drawing customers to the e~evator"~.Hence a variable:

172 The manager is effectively changing the size of the circle in Figure 4.5. Price here is equivalent to grade. 173 The competitive region will be described fully in Chapter 6. 130 Market S hare of Wheat AGGR. = (5.51) Capacity Share of Elevator S torage

will be introduced into the tests equation. Again, the assumption that is being made here is that the more aggressive the manager is at getting business (i.e. increasing throughput and thus raising the revenue from operations) the less will be retained of the rents generated from blending operations.

5.3 Summary of Economic Relationship

The basic issue that this research attempts to ascertain is whether spatial cornpetition amongst elevators on the Prairies matter. The results obtained from the spatial model indicate that spatial competition should indeed affect the actions of an elevator manager. It is thus conjectured that the economic relationship that exists in the spatial world of etevators and farmers is the following one:

Blending Rents = f (SD, TCOMP, AGGR) (5.52)

The final issue that needs to be dealt with is to effectively test these presumptions.

Chapter 6 describes the fom of the data that was assembied to test the hypotheses that competition matters, describes the nature of the econometric tests that were effected and their results. Chapter 6

Data Collection, Data Analysis and the Econometric Model

This Chapter reviews the methods utilized to collect and process the data. The chapter also sets out the intuition behind the econometric model and it reviews the results of the econometric tests.

One elevator company provided some data on the years of experience of their elevator managers. A separate regression was run to test the hypothesis that the experience of the manager has a positive effect on the elevators rent-retaining capability.

This chapter is divided into three sections:

1. data description and collection;

2. the econometric model, the hypothesis and the methodology;

3. summary.

6.1 Data Description and Collection

6.1.1 Introduction

To be able to test hypotheses regarding blending rents generated inside primary elevators, the necessary microeconomic data has to be obtained. To do an effective test, this data needed to be in a format which would enable a cornparison of the grades of deliveries to and from individual elevators. The data that is required to generate these cornparisons can be considered to be highly confidential and to be the personal property of elevating companies. It may be possible to obtain such data from an individual company but any test would then have to be constrained by the fact that the results from only one company are examined. As mentioned in Chapter 2, the Canadian Wheat Board (CWB) obtains, through the course of its daily operation, data on al1 deliveries to and from elevators. This information it obtains in essentially two ways.

1. The CWB is the single buyer al1 the CWRS Board wheat produced in the d rai ries"^. It is thus infomed on a daily basis of al1 shipments to and from an elevator. The CWB also manages the movement of al1 rail cars from elevators to tenninals and it is infomed by either the elevator company (for deliveries to primaiy elevators) or the Canadian Grain Commission (CGC) for deliveries to terminal elevators of the quantity and grade of al1 grain in tran~it"~.

2. The elevator companies pay famiers for al1 grain delivered to an elevator and at some point in time the elevator companies are reimbursed by the CWB. The CWB becomes aware of the quantity and quality of a shipment of grain to a primary elevator when it is delivered by the faner and it likewise becomes aware of the delivery by the company to a terminal when it is called upon to effect a refund to the company for the payment made to the farrner.

Thus the CWB has in its possession ail of the information necessary to be able to wn a test on the blending activity inside an elevator. As discussed in Chapter 4, if grain is overgraded in the country. one would expect this overgrading to appear as a shortage in one or more of the grades given at the terminal as compared with the primary grades and an overage in one or more of the lower grades. Similarly, if grain is undergraded, the result should be reversed and an overage will then appear in one or more of the higher grades given by the CGC graders and a shortage in one or more of the lower grades, as compared with the primary grades.

Two situations can occur with regards to a grain delivery. The elevator manager either grades the grain correctly (the 'monopolyn situation) or a higher grade is given to the

'" This study uses data from CWRS wheat deliveries only. j7' The information received by the CWB includes numbers on grade, moisture content, protein content, dockage level, rail car nurnber etc. grain (the hompetitive" situation1"). In terms of contrasting deliveries at a terminal with those at a primary point, there is no possible way of finding out if a grain graded #1 by an elevator manager was actually a #2'". In the books of the CWB, the grain is recorded as a #1 delivery to the primary elevator and the same thing applies at the terminal. However, by making the basic (and reasonable) assumption that the elevator manager will always attemptl" to upgrade a grain coded #2 (Le. which appears as a #2 in the books of the CWB), then the discrepancy referred to in the paragraph above will appear - the total tonnage of #2 grain delivered by farmers to the elevator will not equal the total tonnage of 62 grain delivered by that elevator to a Hence companng the input of a primary elevator in each grade category with the output of that elevator (its deliveries to a terminal) should indicate whether any positive or negative value in blending rents have been retained by the elevator manager'".

6.1.2 The Sample

As discussed in Chapter 2, there are a large number of elevator points on the Prairies and it would have been an impossible task to collect data from al1 of these elevators. Hence some initial choices had to be made regarding the sample that would be used: The decision variables included:

The number of elevators or elevator points that should make up the sample. It was decided to use data from 87 delivery points (130 elevators) since this would represent approximately 10 percent of the total number of elevators in

- - 176 The assumptions of the model are that a #1 cannot be graded a #2 (or else cheating occurs) but a #2 can be a #l. Paying a #1 price for a #2 grain is equivalent to paying a higher pnce. 177 This section refers to #1 and #2 grades for easy reference. The data that was collected covered al1 grades and protein breaks. Hence upgrading takes place along the whole length of the grade (and protein) spectnrm, not only for the top two grades. 178 It is not possible to know if the elevator manager always blends, but we can assume that hekhe will at least attempt to blend when the occasion anses. 179 i.e. there should be more #1 available for delivery and less #2. 180 It is, as was stated above, impossible to ascertain how much "rents" will have been passed back to farmers. ~askatchewan'~'. This number was considered a sufficiently large enough number to validate the statistical tests.

Which elevator points to choose. The sample could have been chosen randomly or specific points chosen. It was decided to make a rough choice of elevator points over the three prairie provinces- These elevators comprise groups with a wide selection of size and degree of cornpetiti~eness'~~.Some elevators were chosen because of their rernoteness while others were chosen because they were situated in highly competitive locations (for example towns with four elevators with one or more High Throughput (HTP) elevators close by). Some elevators were chosen because of the closure of a cornpetitor during the sample period. Fifteen elevators were chosen because they were considered to be HTP elevators. Thus an attempt was made to obtain a selection of elevators which would have provided the sample with as wide a range of competitive situations as possible.

The penod of the sample. The CWB retains the data from al1 elevator delivenes for a three year period before the data is consigned to magnetic tape. Hence it was initially decided to obtain data for the crop years 1992 to 1995, with the data for 1995 being for the half year August 1995 to December 1995.

Some problems in the use of the 1995/1996 crop year data may result in that this data may affect the value of the statistical tests. Since the full set of data for the complete crop year 1995/1996 was not available, an average number would have had to be calculated to bring the 1995 data in line with the data

from the three previous years la3.Hence, although the data was processed, it was decided to exclude it from the econometric tests.

181 These numbers represent the average number of points over the 3% years of the data that was obtained from the CWB. 182 In terrns of a confidentiality agreement with the CWB, no elevator or elevator Company can be named. 183 For example, by taking the average from the deliveries for the three previous years and dividing this number by two. The distribution of elevators amongst the nine companies that make up the sample is set out in Table 6.1 .a and the distribution of their capacity is set out in Table 6.1 .b.

Table 6.1 a: Listing of Elevator Ownership

Elevators in Sam~le 18 51 26 15 5 6 2 6 1

Source: Data supplied by the CWB

The data was collected for use in this study by personnel at the CWB'". Essentially the data represented the daily operations of each elevator, with a listing of the grade given by the elevator manager to each truck load from a fam and an equivalent listing of the railcar movement from that elevatorla6. The beginning and closing inventories were not obtained but it is believed that because of the particular nature of the grain gathering process used by the CWB for CWRS grain, it was reasonable to expect that opening and closing inventories would be close to being ~irnilar'~~.Also, for the

184 To facilitate the processin of the data, each company was assigned a number. Due to the confidentiality O9 the data, the number and not the name of the company is reproduced here. 185 The Executive Cornmittee of the CWB, which authorized the procurement of the data for this study, was presumably interested in the results of the study which should show whether cornpetition on the Prairies affects gross returns to famiers. 186 Over the three and half years covered by the sample, there have been hundreds of thousand of deliveries by fanners and railcar arrivais at terrninals. Hence the body of data obtained was huge, compnsing thousands of observations on deliveries and grades. 187 A similar effect would be achieved by assuming that the elevator was empv at the beginning and end of each crop ear. The effect of limited data on the opening and closing inventories for the 1995f Y996 crop year would also be difficult to judge which added one more reason to exclude this data from the tests. As will be described later, the lack of data on inventories may affect the econometric results. 1993/94 and 1994/95 crop years the closing inventory of one year would be the opening inventory of the next year. Finally, the CWB attempts in a general way to equilibrate deliveries from al1 the elevators in the system so that it is reasonable to assume that opening and closing inventories would be proportionally similar across elevaton on the rair ries". Hence no attempt was made to obtain this data.

Table 6.1 b: Capacity Distribution of Elevators in the Sample

-- . Ca~acity Number (2000 tonnes 4 2001 to 3000 tonnes 13 3001 to 4000 tonnes 34 4001 to 5000 tonnes 23 5001 to 6000 tonnes 21 6001 to 7000 tonnes 10 7001 to 8000 tonnes 8 8001 to 9000 tonnes 2 9001 to 10000 tonnes 6 1 0001 to 1 1 000 tonnes O 1 1 00 1 to 1 2000 tonnes 2 12001 to 13000 tonnes 2 13001 to 14000 tonnes O 14001 to 15000 tonnes 1 15001 to 16000 tonnes 1 1 6001 to 17000 tonnes O 17001 to 18000 tonnes 1 18001 to 19000 tonnes O 19001 to 20000 tonnes 1 > 20,000 1

Source: Grain Elevators in Western Canada. 1992 - 1995

Of these elevators, 37 were situated in Manitoba, 84 in Saskatchewan and 9 in AI berta.

The rent (left hand side) variable is the variable that is mostly affected by the inventories. If these inventories are proportionall equal across the system, the effects of inventories can be eliminated from the C values. 1 37 6.1.3 The Data Manipulation Process

6.1 -3.1 The Blending Rents

The computer program Excel 7 was deerned sufficient for the manipulation of the data from the elevators comprising the sample. The deliveries to and from each elevator for each grade were first sorted and aggregated. For each year, the aggregated grades were multiplied by the initial payment made to farmers (which would then have been the subject of a refund from the CWB) and the aggregate payment made by the elevator to famiers and the aggregate payment made by the CWB to that elevator was then calculated. The difference between what was paid by the elevator (the value of the grain to the elevator) and what was paid by the CWB (the value of the grain to the CWB) was then detemined. Each of these numbers was then divided by the respective quantities of grain to and from each elevator. This number represents a blended value for each delivery. The difference between the value of the deliveries is called in the econometric model that follows "the blending rentsn which will be represented by the letter Y.

This number is therefore calculated to be:

where:

- subscripts k and j represents deliveries to terminal (per individual grade) and primary elevators respectively;

- the subscript i represents the grade of the deliveries;

- variable Q is the quantity of the grade.

- variable P is the initial payment when the delivery is to a primary elevator (QI)and the payment to the elevator Company when delivery is to a terminal elevator (Qk). 6.1.3.2 The Cornpetition

Two separate sets of data were collected to estimate the degree of spatial competition surrounding the elevators of the sample (denoted below as the choice elevators). These were 'market share' and 'elevator capacity' within a 40 kilometer radius of that elevator (denoted below as the competitive area)'? The calculation of competition was done visually with the use of an elevator location map obtained from the CWB. A circle with a radius equivalent to forty kilometers around the elevator was drawn and the capacity of each elevator within that radius was then retrieved.

Market share was calculated as follows: The data for al1 wheat deliveries to al1 the elevators within the competitive area was obtained from publicly available data (Grain Deliveries at Prairie Points 1992 - 1995). The elevators market share was then calculated to be:

Market Share = -Xi

where i represents the choice elevator and X are the deliveries to al1 elevators j (inclusive of elevator i).

The capacity of an elevator is a number that indicates how much grain an elevator is theoretically able to store in a single period. This number was considered important since it provides an idea of the competition faced by the choice elevator in relation to al1 other elevators in the competitive area. Since it can reasonably be assumed that the further away the cornpetitor's location is, the less the degree of competition that elevator will face, the capacity numbers for each elevator was linearly weighted by its distance from the choice e~evator'~~.The data on elevator capacity was obtained from publicly available data (Grain Elevators in Western Canada, 1992 - 1995).

189 Famers, CWB officials and elevator managers who were infomally consulted considered the forty kilometer radius to be a suitable distance to use for this calculation. 190 An elevator in the same town as the choice elevator was given a weighting of one. An elevator fo kilometers away from the choice elevator was weighted by a value of 1.4. Althou it is recognized that this represents a very arbitrary method, it was considered to the best method available. From the sets of data, the following information was also obtained:

The number of elevators in the competitive area;

The number and location of HTP elevators in the competitive area;

The name of the Company that owns the elevators;

The total deliveries of grain and oilseeds to each elevator. This number was deemed important and useful in detennining the share of wheat in relation to the total amount of business that the elevator did each year;

Whether the delivery was to a , private, public or farmer-owned elevator;

O The province in which the elevators were situated;

The number of CWB permit books filed by famers at each of these elevators;

Since total wheat deliveries were known, some reasonable estimates could be made with regards to the total elevation tariff that would have been received as also some estimate of the total dockage charges that would have been receiptedlgl;

Whether any elevator was during the period covered by the sample (1992 - 1995) closed.

From the numbers that were generated above a series of average numbers could also easily be obtained: These include:

- The average rent per tonne, calculated as:

B 1endingRents Average Rent = ~heatDelivenes

- The average cornpetition faced by the choice elevator, calculated as:

191 This number is not considered important except as an indication of total possible revenue that could have been generated on CWB grain. No calculations as to costs could be compiled hence profits numbers could only be guessed at. cornpetition Capacity Average degree cornpetit ion Elevators

(and a similar number can be calculated for total (wheat and oilseed) delivenes to the elevator).

Table 6.2 below provides a list of the average cornpetition faced by the elevators in the sample.

Table 6.2: Average Cornpetition Faced by Elevators

58217 tonnes 58304 tonnes 1994 57807 tonnes

Source: Calculated from data supplied by the CWB

Other practical statistics calculated were:

The activity (concentration) ratio, calculated as:

TO~4 deliveries Activity Ratio = Deliveries

The turnover ratio, calculated as:

2 Deliveries Turnover Ratio = Elevator capacity

Some elevators tumed over their grain stocks 10 times or more whereas some elevators tumed over less than t~vice'~*.

For al1 these statistics, a percentage number could also be obtained in tems of either CWRS wheat, total wheat or total grain and oilseeds Le.

192 These statistics are for CWB wheat only. The elevators whose turnover was very small would have generated business from some other source (e.g. oilseeds). 141 Capacity of the Elevator (6-7) Total wheat deliveries

Table 6.3 provides a Iist of summary data relating to the elevators in the survey.

Table 6.3 Summary Data on Deliveries to Elevators

Cro~Year Total Whea f amers Permit Too 4 (tonnes) Books eli ive ries^^^ 1.92 261 1747 25747 0.899 1993 '-1 1921 281 25952 0.896 1994 1546269 26724 0.892

Source: Grain Deliveries at Prairie Points 1992 - 1995

3. The Standard Deviation of Grades

The calculation for the standard deviation number used in this study was given in Section 5.2.2 of Chapter 5. Table 6.4 below summarizes the standard deviation data over the crop years 1 992193 to 1994/95.

Table 6.4: Summary of Standard Deviation of Grades Hiah Low Mean 9

Source: As Calculated from th1 é Data.

- 193 This number represents the percentage of deliveries (as an average number) that are made up by the top four grades (including protein levels) delivered to elevators in the sample. It is equivalent to the Activity Ratio. 142 A summary statistical report of the data used to test the hypotheses is set out in Table 6.5.

Table 6.5. Statistical Review of Data Used in Regression

Rents SD AGGR TCOMP CAP MS TOTCAP CS TOTE WHEAT Mean -0.92 8.41 0.76 41812 5825 0.14 47636 0.16 11 46128 Median -1 -1O 8.49 0.71 40322 4575 0.09 46064 0.1 1 12 35949 Maximum 12.96 13.61 3.12 228179 34920 1.00 237679 1.00 25 634687 Minimum -1 155 4.10 0.21 O 1630 0.02 2400 0.03 O 2865 Std. Dev. 2.80 1 -33 0.40 24993 41 46 0.17 25841 0.17 5 59605

Source: As calcufated from data provided by the CWB.

Where:

Rents = Blending rents - Mean per tonne of throughput in $ (equation 6.1)

SD = Variability of deliveries (the square root of equation 5.48)

AGGR = Market share of elevator / Capacity Share (Equation 5.51)

TCOMP = Cornpetition faced by elevator within 40 km radius (calculated as Ccapacities of competitive elevators within 40 km radius)

CAP = Capacity of choice elevators (in tonnes)

MS = Market share of choice elevators (equation 6.2)

TOTCAP = Total capacity of elevators in competitive area (tonnes)

CS = Capacity share (equation 6.4)

TOTE = Total number of elevators in competitive area

WH EAT = Total wheat deliveries to choice elevators. 6.2 The Hypothesis, the Econometric Model and Review of the Test Results

6.2.1 - The Hypotheses. The following two hypotheses were tested:

Hl, = Elevator managers are unable to generate rents through blending as described in this study:

Hl, = Elevator managers generate rents by blending, and

HZ0 = Elevators keep the rents generated irrespective of the degree of corn petitîon.

H2a = The elevators pass rents back to famers depending on the degree of spatial cornpetition around the elevator.

6.2.2 The Econometric Model

The econometric relationship that is hypothesized to exist in this study is as follows:

i = 1 ,...., N = 130 (cross-sectional data) t = 1,... ,T = 3 (time-series data)

Where:

Yk = a vector of blending rents generated as described earlier, weighted by the total wheat deliveries to that elevator (equation 6.1 ).

pl = A vector of intercept ternis (in matrix algebra, a vector of ones);

X2 = Standard deviations numbers calculated as described earlier (SD); >G = Standard deviations numbers squared (SW2);

Xq = Competitive elevator capacities calculated as described earlier (TCOMP);

Xs = Competitive elevator capacities multiplied by the number of elevators in the forty kilometer radius (TCOMP*TOTE);

Xs = A variable representing the uaggressivenessnof the elevator manager. As Market share rnentioned in Chapter 5, this variable wilI be calculated as Capacity Share

q = An error tem.

The response parameters to fi6 describe how average blending rents change when one variable changes, holding the other variables constant. Here, the important attributes that are of interest are the sign of the parameter and the magnitude of the parameter.

Variable Xs is an interactive term which is introduced to examine the marginal effect of the change in the number of elevators on the competitive effect of elevator capacity on rents. When the first derivative of the equation is taken, the parameter will measure this effect Le.:

d Re n ts = p, + &Elevator Numbers a Cornpetition

In matrix notation, the mode1 becomes:

The fotlowing assumptions are made about the error terms:

E[e] = O

cov (e) = E(ee') = 621~ The sample used is sufficiently large that the assumption of a normal distribution can be made. Therefore:

e - N(O,& ) and y - N(XB, ~IT) (6.1 3) The data is such that several other relationships that are believed to be significant can be tested by using dummy variables which have a value of 1 when a desired situation occurs and a vaiue of zero othenivise. These are;

A provincial dummy for elevators situated in Saskatchewan, Manitoba or Alberta. (Alberta is excluded from the regression dummies). These dummies are denoted DIP and D2P in the econometric results;

A year dummy for each of the three years 1992 to 1994 (1 994 is excluded). These dummies should be denoted D1Y and D2Y in the econometric resultsl*;

A company dummy to separate individual company effects (five dummies are included for the five largest companies - al1 others are excluded). These dummies are denoted DlC, D2C1 D3C, D4C and D5C in the econometnc results;

Dummy variables for HTP elevators. One dummy variable will have the value of 1 when the elevator is itself an HTP elevator and O otherwise. The other dummy variable will have a value of 1 when the competitive elevators include an HTP elevator and O othenivise. These dummies are denoted Dl6 and 026 in the econometric results'";

The dummy variables were considered to have their effect on the intercept terni and hence that the effects are comrnon across al1 elevators. With the dummy variables included, the statistical model becomes:

Some hypothesis needs to be made a priori about the sign of these variables. For these, reference needs to be made to both standard economic theory and, as mentioned earlier, to the results obtained when various prices and conjectural variations hypothesis were made in the theoretical model of Chapter 5.

194 As will be discussed later, it was decided to ag regate the data for 3 years into one single set of data. Hence the year dummy varia% le became irrelevant. 195 The letter D stands for dummy variable; the lettes 8,Cl Y and P stand for Big, Company, Year and Province respectively. Thus the following conjectures are made:

a The greater the variability of grain deliveries (by grade), the greater the ability of the elevator manager to generate rents. Hence the sign of parameter B2 is expected to be positive and, as equation 5.50 indicates, the sign of parameter

93 is expected to be negative indicating that the positive effects of variability of grain deliveries diminish the larger the size of the effect.

The greater the degree of competition, the lower the rents the elevator manager can retain. Hence the sign of parameter pq is expected to be

negative and the sign of parameter B5 is likewise expected to be negative.

With regard to the signs of the durnmy variables, it is believed that an HTP elevator may reduce the ability of the manager to blend. In the case of HTP elevators, there are competing aspects that need to be taken into account: Although they need to draw grain from much larger areas (hence the SD measure could be expected to be greater), the design of the elevator is directed at speed and at moving as much grain as possible within a short period of time. Hence these elevators have larger but fewer bins than conventional elevators and the speed with which grain cars need to be loaded makes it difficult for the manager to attempt to mix at the car-loading time. The different effects between standard elevators and HTP elevators will be captured by the dummy variable- A test of the hypothesis that there is no difference between conventional elevators and HTP elevators can be carned out by testing the nuIl hypothesis:

Ho: The HTP dummy variable parameter = O vs:

Ha: The HTP dummy variable parameter # O

The provincial, Company and year dummy variables will al1 have indeterminate signs since these effects cannot be judged a priori.

196 With the variable names as mentioned above. 147 6.2.3 Choice of Test Method

The question that requires careful analysis is the choice of the econometric method to be used and the choice of a rule to detennine the method that is most likely to provide econometncally sound results of the independent variables.

In its more general form, the data, which is made up of both cross-sectional and time series data, allows for several methods to be utilized to test for the existence of relationships between the variables.

Three methods of running the regression are available:

Each observation could be treated irrespective of the year and al1 five hundred and thirty sets of observations are then stacked one on top of another and a regression run on that set of data as if it were in essence a cross-sectional unit (the subscript t is eliminated from the equation). In this forrn, the Generalized Least Square estimator reduces to pooled ordinary least squares or;

The data set can be arranged in tha form of a longitudinal or panel set and a regression run on this data with the cross-section specific variables used with the time series cross-section estimation procedures provided by econometric programs (for example the Ume Senes-Cross Section estimation procedure in Eviews). In this form, each submatrix or subvector has T observations as follows:

The problem that is encountered when using this method is that the time series is very short (three years) but the cross-sectional data is very wide. This problem can be dealt with by creating a spreadsheet of stacked panel data which can be tested in a econometric program such as Eviews, but essentially the short time series data does make its value somewhat suspect. In this format, the data can be used as a simple stacked regression as follows:

Observation 1

Observation 130

The assumption can be made that the parameter vector B is the same for al1 observations so that the var-cov matrix V will have the following form:

For this model, the Generalized Least Square Estimator simply reduces, as mentioned, to pooled ordinary least squares. As usual, the model needs to be tested for cross-sectional heteroscedasticity and cross-sectional correlation and appropriate measures taken should these present a problernlg7.

197 The computer program Eviews can be pro rammed to run White's heteroscedasticity test which compensates 9or the presence of Heteroskedasticity in the data. Econometric computer programs such as Eviews allow for easy testing of the assumption that there is in fact no parameter variation across fims (across the sectional units) i.e. that the regression is in fact not one of the following fom.

This test takes the fom of a test of a structural change over al1 the observations.

The third format that could be used is to combine al1 data for each of the three

3 crop years in a single consolidated format (Xyi) by adding al1 values for the

three crop years togethe?. This would then produce one set of data made up of 130 observations, with one single observation for each elevator. A certain amount of information is lost by using this format, but the problem of the absence of opening and closing inventories is reduced. It is of course difficult to know to what extent the inventories have an effect on the results of the tests and much depends on how large the inventories were and the level of difference between the opening and closing numbers.

To reduce any problern that may anse from an incorrect interpretation of the econometric results resulting from the possible use of incomplete data, it was decided to use the last method above in testing the model. AI1 data was therefore consolidated as mentioned.

198 This excludes the Yi year of the 1995 - 1996 crop year. 150 6.2.4 Test of Structural Change

One common application of F tests is to test for structural change. When specifying a regression model such as the one used here, it was assumed that the assumptions applied to al1 the observations and it is not possible under these circumstances to test for constancy of the parameters. This assumption can be tested by splitting the data into two groups, one group Ti being used for estimation and the remaining group Tz (where T2 = 1 - Tt) being used for testinglg9.

The literature provides no particular set of rules regarding the separation of the data. Since the data has a time series component, the easiest and most compeiling procedure is simply to take the first two years of data (Ti observations) and leave the last year (T2 observations) for testing.

A number of tests are available. A test that is suitable is Chow's Forecast Test. Using this test, the equation is first estimated with the Tl observations (the first 90 observations). The results are then used to predict values of the dependent variable in the remaining T2 data points (the remaining 30 observations). A vector of discrepancies are then created between the predicted and the actual values.

If the discrepancies are found to be small, there is little doubt that the estimated equation is correct. This test contrasts the size of the prediction enors with the variance which can be expected if the nuIl hypothesis is true (i.e. that the fi values are constant) and that the predicted observations come from the same model as that underlying the estimated equation. The output of this test comes in the fom of an F statistic and a Likelihood ratio with associated probability values. The F statistic is computed as:

T-K where SSER is the residual sum of squares when the regression is fitted to al1 T observations, SSEu is the residual sum of squares when the regression is fitted to Ti

lg9Eviews User's Guide (1995). observations, and K is the number of coefficients in the estimated regression. J is the number of observations in the restricted sample.

Essentially the same test can be nin on the dummy variables by introducing restrictions on the regression where, as above, SSE, and SSE" are the sums of squared errors from the restricted and unrestricted least squares estimation respectively. Under the nul1 hypothesis that al1 J restrictions are true, the F-statistic in the equation has an FI~,cr-mldistribution and the nuIl hypothesis is rejected if the computed F statistic exceeds an appropriate critical value from the distribution. If the value of F is less than some F, value, the conclusion can be made that the restrictions are compatible with the sample of data and that there exists no sample evidence to suggest that the restrictions are false.

6.2.5 Redundant Variables Test Economic theory usually provides a basis for choosing the variables to include in the design matrix of a statistical modalm. However, economic theory only provides a general guideline: In many cases there exists some uncertainty about the variables to include or exclude.

The following problem arises when misspecifying the matrix of explanatory variables:

If the design matrix (the matrix of independent variables) omits relevant variables, then the OLS estimators will be biased but will have smaller sampling variability than the unbiased rule.

If the design matrix includes irrelevant variables, the least squares estimator will be unbiased, but the sarnpling variances will not be as small as would exist if the correct model is specified.

The redundant variable test is a test which enables a researcher to test whether a subset of variables in an equation have zero coefficients and should therefore be deleted from the equation. The output from the test is an F-statistic and a likelihood ratio (LR) statistic, with associated probabilities. The F-statistic for this test is based on

200 Judge et al (1985). the difference between residual sums of squares in the restricted (some variables are excluded) and unrestricted regressions (al1 variables are included) regressions. The LR statistic is based on the log of the ratio of the restricted maximized likelihood to the unrestricted maximized likelihood. and under general conditions has an asymptotic XZ distribution with degrees of freedom equal to the number of deleted variables.

The redundant variable test could be a method used to determine whether any of the variables should be dropped from the regression equation if no pnor economic theory exist as to whether the variable should remain in the econometric equation. In this study, economic theory (as developed in the spatial model) has indicated the variables of interest to be used (except for the dummy variables whose sign is indeteminate) and hence they will be retained and only the dummy variables dropped from the equation if statistically insignificant.

6.2.6 Testing the Equation Two tests are available:

Ramsey's RESET test can be used as a general test for model specification error. The test is used to determine the case where an incorrect functional form is being used. In such a case, the OLS estimators will be biased and inconsistent thus rendering conventional inference procedures invalid. For the model:

The nul1 and alternative hypothesis are:

The test is based on an augmented regression:

The test of specification error is that a = 0.

In the case of an incorrect functional fom, the omitted portion of the regression may then be some function of the regressors included in X. Z should then bel for example: -7 -3 A4 z = [y-;y ;y etc.] where 9 is the vector of fitted y values from the regression of y on X. The superscript represents the powers to which the predictions are raised. In this case, the specification of the model could be limited to the squared and cubed values of the regressorç. Output fmm this test consists of the F and the LR X2 statistic for testing the hypothesis that the coefficients on the forecast vectors are ail zero.

Another method that can be used to test for model misspecification is through the Box- Cox transformation. The Box-Cox transformation generalizes a linear model as follows:

For a given value of h, the model:

is a Iinear regression that can be estimated by least squares. The value of h is usually assurned to be the sarne for al1 the variables in the rnodel. Typically, a value of h is found by scanning the range -2 to + 2 in increments of 0.1. The lowest sums of squared deviations for various values of h provide the optimal value of h with which to estimate the regression coefficient^^^'. Values of h = 1 and A. = 2 are often used to transform both the independent and the dependent variables in a log-log:

and linear-log models:

In the case of this study, a number of the dependent variables were negative which rendered the use of a logarithm impossiblep2. One solution would have been to

201 For h = 1, the model simply reduces to the linear model used in the basic model above.

202 See Table 6.8. 1 54 transfomi al1 left hand side variables by adding to each value an amount equaI to the largest negative number so that al1 numbers would have been positive (and in one case zero). This solution was rejected as being impractical since it sirnply reduced to a non-meaningful manipulation of numbers.

Hence, the procedure that was followed in the tests was to run the full regression with al1 variables inciuded and test the results by running a further regression eliminating those dummy variables whose statistical value indicate they should be dropped from the regression equation. Some sensitivity analysis was camed out on the remaining variables to examine whether the removal of any variable significantly affects the results.

6.2.7 A System of Equations One way to increase sampling precision is to effectively use al1 the sampie and any non-sample information that is available (Griffiths, 1993). As it stands, Equation 6.14 has as one of its independent variable the standard deviation (SD) of deliveries but no effort has as yet been made to test the validity of the SD equation itself (Equation 5.48).

To test this equation, a regression equation of the following form was estimated:

where W = Total wheat deliveiies to elevator i.

Since it can be expected that there exists a contemporaneous correlation between the error terni of regression Equation 6.1 4 and regression Equation 6.2 t , use of the Zellnets Seemingly Unrelated Regression procedure (SUR) provides a consistent estimation rule (Griffiths, 1993). Hence Equation 6.1 4 was incorporated into a system of equation with Equation 6.21 as follows: The econometric program Eviews as already been programmed to nin SUR regressions and the results appear below.

6.2.8 The Test Results

The results of the tests were as fo~lows~~~:

6.2.8.1 Statistical Test No. 1

Coefficient Std. Error Prob. Elasticity

Constant 0.61 976 5.54 SD 0.41 632 1.19 SD"2 -0.03588 0.07 TCOMP -0.00001 11 0.00 TCOM P*TOTE 0.00000 o. O0 AGGR. -0.47749 0.70 HTP (own) 1.37315 0.78 HTP (comp) 0.01 543 0.54 CO1 -0.53865 1.O3 CO2 O. 34572 0.93 CO3 -0.21 060 0.93 CO4 -1 -42137 1.10 CO5 -1.66338 2.41 Provl -1 .O6666 2.03 Prov2 -1 -92709 2.01 Constant 8.361 00 0.1 5 Wheat 0.00000 0.00

First Eauation R-squared 0.09434 Durbin-Watson 1-98084

Second E~uation 0.00241 1.59343

The elasticity numbers were calculated using the standard formula:

M3 A table of the values used in the regression is given in Addendum 6.1.

156 where both Y, TCOMP, SD and AGGR are taken at their mean values. Since the mean of Y is negative (see table 6.5) the sign on the elasticity coefficient is not the same as that on the variable coefficient.

The marginal value of parameter SD needs to be calculated (to equate it with the values given in the sensitivity tests below) because of the presence of the quadratic terrn as follows:

where SD is again taken at its mean value. lnserting the calculated values of the parameters;

SD = 0.41 632+(2*-0.03588*8.41) = - 0.1 8718 (6-24) As suggested in Section 6.2.5, the regression equation was then rerun by excluding from the regression al1 dummy variables with probability values higher than 0.10 (Le. equivalent to testing the regression at the 0.90 level). Thus al1 dummy variables except HTP Dummy (where the elevator is itself an HTP elevator) were dropped and the regression rerun in this forrn.

6.2.8.2 Statistical Test No. 2

Coefficient Std. Error t-Statistic Prob. Elasticity

Constant SD SD"2 TCOMP TCOM P*TOTE AGGR. HTP (Own) Constant Wheat First Eauation R-squared Du rbin-Watson Second Eauation R-squared Durbin-Watson with the marginal value for SD as described in equation 6.24 changing as follows:

157 6.2.8.3 Sensitivity Tests

To examine the sensitivity of the regression results to changes in the right-hand side variables, the following additional regressions were also run: Sensitivity Test 1 - Run the regression on three choice variables (SD, TCOMP and AGGR) only and the HTP dummy (Table 6.6~)

Sensitivity Test 2 - Run the regression on two choice variables (SD and TCOMP) only and the HTP dummy (Table 6.6d)

6.2.8.4 Sensitivity Test 1

Coefficient Std. Error t-Statistic Prob. Elasticity

Constant 1.93747 1.60228 1.20920 0.22770 SD -0.2651 3 O. 1 8470 -1.43547 O. 15240 2.431 TCOMP -0.00001 0.00001 -1 .O2844 0.30470 0.5059 AGGR. -0.40961 0.67672 -0.60530 0.54550 0.338 HTP (Own) 1.34664 0.76520 1.75984 0.07960 Constant 8.361 50 O. 14723 56.79048 0.00000 Wheat 0.00000 0.00000 0.56254 0.57420 First Eauation R-squared 0.04773 Du rbin-Watson 1.98656 Second Eauation R-squared 0.00241 Du rbin-Watson 1.59347 6.2.8.5 Sensitivity Test 2 Coefficient Std. Error t-Statistic -Prob. Elasticity Constant 0.26760 SD O. 1 5340 2.4209 TCOMP O. 1 4390 0.454 HTP 0.071 90 Constant 0.00000 Wheat 0.57420 First Eauation R-squared Durbin-Watson Second Eauation R-squared Du rbin-Watson 6.3 Summary of Results

6.3.1 Introduction The regression equation represents the econometric equivalent of the economic model set out in Chapter 5, namely:

Blending Rents (Y) = f (SDl TCOMP, AGGR) (6.25)

To the econometric model were added a number of dummy variables and two further variables, a quadratic term on the variable SD and a multiplicative tem on variable TCOMP. Reasons for the inclusion of these two variables were mentioned in Chapter 5. The three variables of immediate concem are, however, the values of and the sign of parameters SD, TCOMP and AGGR and these will be dealt with in more detail below.

It should be explained at the outset that since al1 the variables in the full regression model (Statistical tests 1 and 2 which include secondary tests on SD~.TOTE and the dummy variables) have very high probability values (with the possible exception of dumrny variable HTP (own) which will be discussed later), there is in fact insufficient statistical evidence to reject the nuIl hypothesis that the B's have a zero value. This would then indicate insufficient statistical evidence to reject the nuIl hypothesis that the right-hand side variables have no effect on the generation and distribution of blending rents (Le. hypotheses H 1 ,, and H2,, cannot be rejected).

When the secondary effects are excluded from the regression (i.e. in the two sensitivity tests), the probability values are such that the evidence exists that the nuII hypothesis Ha: f3 = O can be rejected in favour of acceptance of Ha: B # O for both hypotheses. It should be noted that the values of al1 the parameters remain very much constant through the changes - it is only their statistical relevance that changes.

Test 1 and 2 have the effect of separating out the effects of the SD variable and as expected, the value of SD is positive and SD~is negative. The presence of the very small value of parameter B4 makes the effect of the number of elevators somewhat negligible. The value of the variable SD taken on its own is negative which was not expected. The sign of the variable AGGR is as expected. The more competitive the 159 elevator manager is, the less blending rents are retained. The fact that the elevator is an HTP elevator has some positive effects on the rents that the elevator retains. This goes against the predicted sign of the parameter. In regards to these rents, HTP elevators may well have a willingness to pay a trucking premium to draw high grain to the elevator - much of the blending rents are then passed on to farmers via reduced freight charges.

The spatial mode1 developed in Chapters 4 and 5 centers around the sign and value of the TCOMP variable. The negative sign is as expected and the value indicates that as competition changes by 100,000 tonnes, rents retained by the elevator change invenely by $1.00 per tonne of throughput. The average competition faced by elevators is 58.000 tonnes and their average throughput is 46,000 (over 3 yean). Elevators on average can be expected to transfer to fanners (since the sign of the coefficient is negative) rents equivalent to approximately $0.40 per tonne of throughput or approximately $20,000 over a 3 year period?

6.2.9 Effects of Manager's Experience Data relating to elevator manager's years of experience was obtained from one elevator Company. Since it was not possible to ascertain whether these managers had been in office for the particular reference elevator over the whole of the four year sample period, a regression was run over the year 1992 only.

The following regression equation was tested with the result given in Table 6.7 .

XI^ = Years of experience of elevator manager for station 1; x21 = Standard Deviation (SD)

X31= SD Squared ai=Capacity of Competition (TCOMP) xsl= TCOMP * total number of elevators

-- 204 $0.40 per tonne represents approximately 5 percent of the elevation revenue. Taken against the value of a tonne of grain, the number is not significant but it does take on some importance when the revenue of the elevator is taken into account. %I= The measure of aggressiveness of the manager as discussed earlier The results of this test are given in Table 6.7.

Table 6.7: The Manager's Experience

Variable Coefficient Std. Error t-Statistic Prob.

MAN 0.18773 0.359314 0.522468 0.6046 SD 9.8341 81 6.473378 1.51 91 73 0.1 377 SD"2 -0.565916 0.41 3486 -1.368647 0.1798 TCOMP 0.000397 0.000444 0.893681 0.3776 TCOM P'TOTE -3.26E-05 2.75E-05 -1 -185087 0.244 MSICS -5.80001 4 6.978489 -0.831 127 0.41 15 C -46.81 786 25.23575 -1.85521 9 0.072

R-squared O. 160827 Adjusted R-squared 0.01 6969 F-statistic 1.1 17959 Durbin-Watson stat 2.409605 Prob(F-statistic) 0.37202 Addendum 6.1 Regression Data obs COMP CAP MS 1OTCAP TOTE WHEAT 1 37048 5660 0.08 42708 11 28891.158 2 1 9320 1740 0.06 21 O60 7 20465.1 64 3 65246 4560 0.05 69806 19 14297.282 4 651 89 4620 0.04 69809 19 1 1541 -678 5 39825 4400 0.07 44225 9 38281.364 6 57624 3240 0.02 60864 14 6267.764 7 54978 5880 0.07 60858 14 12934.989 8 36372 4210 0.04 40582 14 1 6835.345 9 41 91 6 3850 0.06 45766 12 2891 2.096 10 48932 2800 0.07 51 732 13 25603.45 11 12108 5770 0.37 17878 4 5381 6.775 12 12520 5360 0.1 9 17880 4 27022.664 13 21 204 5270 0.1 9 26474 6 1 0747.472 14 22824 3650 0.07 26474 6 25470.299 15 21 468 5010 0.17 26478 6 28571.81 9 16 43806 2950 0.05 46756 14 3681 7.182 17 47509 6900 0.18 54409 11 43388.904 18 46695 7710 0.13 54405 11 1 9640.61 9 19 391 71 15230 0.08 54401 11 28392.91 9 20 34570 3530 0.1 8 381 O0 10 48383.458 21 521 85 3600 0.06 55785 15 23203.1 8 22 25688 5910 0.29 31 598 8 541 19.289 23 28656 4000 0.1 3 32656 9 38058.662 24 81 664 3950 0.03 8561 4 22 23547.845 25 47760 15460 0.18 63220 10 90001 -562 26 4641 0 16810 0.15 63220 10 1 O262 1 -934 27 56670 6550 0.1 2 63220 10 122538.088 28 O 3800 1 3800 O 21 795.253 29 35 1 45 6440 0.08 41 585 11 41 738.886 30 35849 5740 0.1 2 41 589 11 60831 -693 31 48984 9500 0.08 58484 13 39455.939 32 43667 4220 0.08 47887 13 4682 1 -182 33 83886 3610 0.05 87496 22 121 391.63 34 68420 19080 0.27 87500 22 1 71 04.7 35 17436 6910 0.17 24346 6 45688.14 36 28956 3660 0.06 3261 6 12 24889.483 37 74298 9550 0.1 3 83848 21 67670.54 38 69095 2970 0.02 72065 13 31 338.067 39 68302 4230 0.03 72532 13 39226.407 40 70252 2270 0.02 72522 13 24978.1 57 41 13356 3420 0.09 16776 4 2864.848 42 34944 41 20 0.23 39064 12 89353.179 43 26960 3640 0.1 9 30600 8 64599.107 44 27360 3240 0.14 30600 8 481 98.359 45 26848 3890 0.09 30738 8 27572.588 46 60359 4930 0.03 65289 13 41 873.271 1 62

Chapter 7

Review of Results and Conclusion

This Chapter reviews the overall objectives of the thesis as described in Chapter 1. For each objective, associated results are summarized. The Chapter also sets out conclusions and recommendations for further study.

The Chapter is divided into 4 sections:

Section 7.1 reviews the aspects of the problem that motivated the research. In particular, the effects that the regulatory environment on the Prairies has on the actions of elevators managers are reviewed.

Section 7.2 examines the results obtained in this study. These results are compared with those of previous efforts undertaken on this subject and some conclusions are reached.

Section 7.3 sets out the limitations of the study and their possible effects on the mode1 are examined and;

Section 7.4 sets out recornmendations for further research.

7.1 Review of the Problem

7.1.1 Introduction

The economic effect of blending were firstly traced in Chapter 1 and these are reviewed briefly here.

It is frequently the important function of an economic study to determine whether any one market participant is acting non-competitively. The reason for this is that when a fim is in a position to exert some market power, it is able to influence market conditions by, for example, selling its product at a price that is higher than its marginal cost of production.

The maximum prices that elevator companies can charge for their services have for many years being regulated and these have also been unifomly set across al1 companies. Hence it is not appropriate to undertake a study of competitiveness of grain elevator companies by simply examining their tariff charges. One aspect of the trade that is, however, largely unregulated (at the primary elevator level at least) is grain blending. Revenues that corne into being when different qualities of grain are blended represent the consequence of a non-price competitive action by the elevator manager when these are passed on to a farmer. Blending can therefore provide a platforni for grain companies to act in a competitive manner in an otherwise regulated market. Under certain circumstances, the opposite can also, of course, apply.

7.1 -2 References to Grain Blending

There are numerous references in the grain trade literature to the mixing of grain by grain merchants. References to grain blending include, for example, the following ones:

Wilson (1978): ".. this, because of the fact that the wheat of the same grades both from north and south (Saskatchewan) are graded almost regardless of their protein content, and mixed together at terminals, thus reducing, to the detriment of the southem farmer, the value of his average wheat, and to the benefit of the northern farmer for his poorer wheat."

David Henderson, Technical Buying Director for Warburton's Ltd., a milling company in Bolton, United Kingdom, who speaking at a conference in Winnipeg in 1993, explained why his company buys wheat from canadaa5.

"Canada is the only country in the world that actively specifies the varieties of wheat that can be grown. The Canadian grading system ensures that variations in wheat are minimized. ln Canada, wheat is blended approximately six times as the grain moves from the Prairies and into teminals at ocean ports. In most countries the majority of wheat goes directly to the miIl or directly

205 Quoted from the Canada - Alberta Farm Business Management Initiative. 166 to an export terminal. In the UK up to 90% of wheat is transported directly from farms to flour mills. In Europe (in 1993) there are no grading systems operating nor are there facilities to cany out blending to iron out inconsistencies. In the UK, Warburtons tend to buy only single vaneties and even then enormous variation in that variety is evident."

Hill (1990): Virgil McNamee, chief grain inspecter for the Toledo Board of Trade, responded that it is true, blending is a part of the elevatots operation. Elaborating on this a few minutes later during the 1974 hearings, McNamee explained that the poor quality of the 1974 crop would have been a disaster for Iowa and Illinois farmers had it not been for the elevators "mixing it and blending it and drying it and doing everything possible to upgrade it."

Notwithstanding these indications that the mixing of grain takes place, and the certain knowledge that famers have that their grain is subjected to some sort of blending after it is unloaded (famers know of course that their grain is going to be loaded into a bin that already contains other grain), very little economic research has been camed out on this issue. In fact, to date, only a few attempts have been made at developing any fomal economic models of the effects that blending has on competition and on the performance of the market.

7.1.3 The Blending Issues

A study of the blending of grain as is practiced on the Prairies requires a number of issues to be addressed. For example, the evidence obtained during the course of this research indicates that blending on a quasi institutionalized basis is not practiced in other grain producing countries on a scale comparable to that done on the Prairies. What sets Western Canada apart? Do organizations/institutions such as the Canadian Grain Commission (CGC) and the Canadian Wheat Board (CWB) and the many regulations in place play a part in grain blending at the primary eievator level? Is there then a need for more or less or possibly improved regulation? Is large-scale blending simply a consequence of the unequal relative size of the export market to the intemal market or possibly a product of the long distance that the grain needs to move to export market? How does blending of grain affect the cornpetitive nature of the relationship between famers and elevator companies? Is there a fom of market failure at work that enables elevator companies to act in a non-cornpetitive manne0 What will be the effect of the changing market structure on famer's revenues, with fewer but larger elevators being constnrcted by the elevator companies?

The list of subsidiary issues that need to be examined is also long: Is there some forrn of cross subsidization going on where elevator companies replace higher tariff fees by revenues obtained in blending? Should this then be replaced by a transparent set of rules? What would then be the effect on the market? How does the existence of alongside pnvately owned elevators affect the distribution of rents? If the marketing of grain is moving to "Identity Preserve" (see footnote 69) what consequence will this have on the incentive that exists for blending grain?

7.1.4 The Blending of Grain in an Economic Framework The passing of blending rents to farmers in effect represents the substitution of non- price competition in to price competition. It is, on the whole, difficult to place this effect in a pure economic theory framework without looking at the whole regulatory environment. One problem is that when a manager actually grades incorrectly (which is one way of passing rents back to the fanner), he/she is effectively breaking the ile es^^^. When the manager grades correctly (follows the rules) he/she is actually acting non-competitively. The rules are set by the CGC and the CWB and both these institutions enforce very strict penalties for breaking the ni~es~~'.In short, acting competitively requires the violation of the niles set by the regulating authorities.

Ultimately, the problem is one of asymmetry of information between elevator managers and farmers. This issue was discussed in some detail in Chapter 1. As much as the farmer rnay know that grain is being mixed, the famer is unable to ascertain the tnre benefits that flow from grain blending and he/she is therefore unable to negotiate effectively. The nature of the institution that is the CWB (see Chapter 2) forces price competition to be shifted to other forms of competition and this provides a veil behind

- - 206 This also a plies of course if the manager grades incorrectly, but by rnistake. It is assumed, OP course, that the erroneous grade is in favour of the famer. 207 It is possible that if either the CWB or the CGC were to audit unannounced the contents of an elevator for quantity and grade they would hand out large fines to the elevator Company concemed. which the manager can hide2? The manager can simply tell the famer that helshe is grading and pricing this way because grade and prices are set by the Commission and the CWB, and the elevator agent has Iittle or no say. 60th these organizations are so highly engrained in the Western Canadian farmer's psyche that the manager's negotiating task is made easy.

However, as was discussed many times throughout this study. the only thing that keeps the famer from simply losing al1 of the blending rents to the elevator is the competition that that elevator manager faces. As long as the manager faces some degree of competition, the asymmetric information set is less important because the famer has the option of delivering grain somewhere else. The question is then "how rnuch competition is needed before competition matters?"

7.2 Review of Results

This study could have been aimed at modeling any of the important issues that flow from the rents generated from blending. The specific objectives of the study were however Iimited to a study of competition and market structure because of the extent of the work that would have been required to broaden the research to other issues.

The study was therefore specifically aimed at examining the cornpetitive effect of blending only. As such, the study represents an attempt to ascertain the extent to which managers use as a marketing tool the "hidden revenuesmtg that blending generate.

The conjecture upon which the model is stnictured is that if there is a large degree of competition, then managers are simply compelled to pass the blending revenues to their customers (the famiers) to generate grain throughput and famiers gain. Othennrise, elevators benefit (Hypothesis H20: Elevaton keep the rents generated inespective of the degree of competition).

Before there can be a distribution of rents, rents have to be obtained. The study also developed a hypothesis relating to the generation of these rents - a hypothesis that

M8 Aside from grade improvements, managers can negotiate trucking premia or lower prices on chemical, fertilizer and equipment. Negotiations on the grain price are out of the question. M9 See Rosaasen, 1990. simply established the availability of rents as a function of the variability of the grain delivered to an elevator. This may well be viewed as a simplistic view of an otherwise very complex issue but given the availability of data, this hypothesis can be considered reasonable (hypothesis Hlo: Elevator managers are unable to generate rents through blending as descnbed in this study).

7.2.1 The Assumptions and the tiypotheses There are a number of potential explanations for the Iirnited amount of research that has been undertaken on the problems relating to gradinglblending and some of these are the same difficulties that were faced in developing this study.

It is very difficult to obtain acceptable data because such data is often considered confidential by the elevator companies;

The researcher often has to deal with issues that are not quantifiable, such as, for example, the degree of experience or the social standing in the community of an elevator manager;

It is difficult to generalize situations - what applies in one situation can change completely when a different set of circumstances apply.

There exist a large number of cross effects as for example, those enumerated in the following paragraph) which may be impossible to separate out.

This study required a number of simplifying assumptions to be made and these have to be viewed within the broad framework of the study.

There are innumerable reasons why an elevator manager can generate rents and other managers may not. Many of these cannot be accounted for. Location within the town, the friendliness of the manager, the attitude of local famers to the cooperative movement and the general lay out of the elevator will al1 have some effect on the quantity of grain delivered to an elevator. These "variablesnare simply not quantifiable and have to be excluded from a purely economic study.

There is, however, a set of data which is available and which can be used. This data relates to grain variability. As the models developed in this study have shown, the more variable the quality of the grain, the greater the blending opportunities. There

170 are, however, limits to this progression. At some stage the elevator manager will simply nin out of bin space and if the elevator is busy accepting many deliveries at one point in time, time also becomes a limiting factor. 80th these factors are recognized as having important effects on the range of actions available to a manager as they are likely to cause the manager to make an erroneous examination of the grain sample.

All these factors could possibly have been incorporated into the model, but then the nature of the study would have changed. Hence it was decided to limit this aspect of the study to the following:

The ability to generate blending rents was specifically assumed to depend on the elevator's ability to draw grain which has a greater variation of quality (grade or protein content) so that mix and match opportunities are available to the elevator manager;

Little variance in grain deliveries was assumed to make blending difficult and it was therefore concluded that the larger the variability of grain deliveries, the larger the ability of the manager to blend grain; The benefits that flow from grain variability exhibit, however, diminishing retums since the manager will at some time nin out of bin space in which to store the grain.

A large percentage of the grain deliveries to an elevator take place over a short period of time. This may well place a constraint on the elevator manager to keep grain trucks moving rapidly and it rnay limit the manager's ability to grade correctly (or incorrectly, but on purpose). This eventuality, where the elevator manager simply runs out of time with which to blend effectively, was excluded from this research;

The possibility that the elevator manager grades incorrectly because of inexperience was also excluded.

Although the ability of the elevator manager to retain the rents may be due to factors outside of competition, it can be considered that competition is probably the most significant single factoPlO;

21 0 This is accepted standard economic theory.

1 il Thus in the study:

Rent retention was modeled as depending on the degree of competition around the elevator. It was hypothesized that the greater the degree of competition, the less the manager can retain (or the more need to be passed on to farmer) of the rents that are generated in the elevator.

7.2.2 The Methodology The study required first a review of the grain market as it applies in Western Canada. Some aspects of this market are unique in that it is highly regulated and a special relationship also exists between famiers, the elevator companies and the CWBKGC. This interdependence needed to be reviewed carefully and this was done in Chapter 2.

The substantive research was undertaken by establishing in Chapter 3 the theoretical foundations for the use of a spatial model in the context of grain deliveries to an elevator. The literature on spatial markets established the fact that elevators and fatmers couid be modeled in a spatial economic environment - an environment in which space and distance are introduced as classifications for quantities of grain delivenes and grading. Chapter 3 examined, in particular, models in which the conjectural variation beliefs that spatial competitors hold for each other were examined. The interpretation that can be placed on these models is that the behaviour of cornpetitive fims is significantly affected by the "location" of other fims in markets in which they competez1'.

In Chapter 5 a model was then developed which allowed:

f he famer to rnaximize hislher objective function, and;

An elevator manager to rnaximize his/her objective function which depended on the retums obtained from blending as balanced by the elevation revenue generated when a farmer delivers grain to the elevator. Figure 5.2 established the movements of the famier along the horizontal axis of a spatial environment and the decisions of the manager to pass on blending rents was modeled

- -- 211 In this context, location means the pricing and grading of grain in an economic spatial environment. along the vertical axis of this environment. In this spatial world, decisions are assumed to be based on the quality of the grain in relation to some assumed grade break point established by the CGC.

The model was then tested:

By obtaining and processing data from the CWB. The nature of the data and the methods used to process this data are set out in Chapter 6;

By regressing the data in ternis of an econometric model which is developed in Chapter 6.

7.2.3 The Results

Three sets of results are available that can be used in reaching a conclusion on the economic effects of grain blending.

O The summary statistical review of the data which is reproduced here as Table 7.1 ;

The results of the only other known tests of blending rents, narnely that by Giannakas et al (1996), the US. Federal Trade Commission (The Grain Trade, 1920) and by Pincemin (1988);

The results of the econometric model nin in this study.

7.2.3.1 Statistical Review

Table 7.1 reviews the statistical summary of the data on which the regressions were run.

Rents SD AGGR TCOMP CAP -MS TOTCAP CS TOTE WHEAT Mean -0.92 8.41 0.76 41812 5825 0.14 47636 0.16 11 461 28 Median -1.1 0 8.49 0.71 40322 4575 0.09 46064 0.1 1 12 35949 Maximum 12.96 13.61 3.12 228179 34920 1.O0 237679 1.O0 25 634687 Minimum -1 1.55 4.1 0 0.21 O 1630 0.02 2400 0.03 O 2865 Std. Dev. 2.80 1.33 0.40 24993 41 46 0.17 25841 0.1 7 5 59605

Table 7.1. Statistical Review of Data Used in Regression Source: calculated from CWB data Some important sets of numbers stand out from Table 7.1. Over the three years for which the rents were aggregated (crop years 1992 - 1994):

a 80th the mean blending rents and the median of these rents are negative numbers. On average when the data is aggregated over these 3 crop years, elevators lose money on blending (or overgrading). This would suggest that, on average, elevator manages pass these rents back to farmers in the form of a higher grade than the quality of the delivery warrants. In effect, this is the same conclusion reached when the mode1 scenario of a competitive solution where negative blending rents were realized.

+ The range of the rents is quite considerable which would indicate that some elevators do very well from blending but that large losses can also occur.

The largest rents are produced in a High Throughput (HTP) elevator (this was determined from an examination of the data.)

7.2.3.2 Previous Studies

The Federal Trade Commission found that blending rents were negative, and that elevator managers generally recouped their losses from charging excessive dockage and other charges2'*;

r The Giannakas (1 996) study concluded, using aggregate data, that blending rents at elevator points were negative; This study assigned this to measurement error where an elevator manager is, for example, simply too busy to do a proper job of grading,

The Pincemin study was inconclusive showing a wide range of retums over different crop years. On the whole, the results indicated that blending rents were generally close to zero.

7.2.3.3 The Current Study - The value of the parameter TCOMP (the competitive variable) in the regression equation had the correct sign and it indicates an inverse relationship

212 These studies were reviewed in Section 3.2. 174 between competition and blending rents. The relationship between blending rents and the variable TCOMP can be summarized as follows:

Average Rents (per tonne of throughput) = -.O0001 * TCOMP (7-1)

When TCOMP is taken at its average value, the results are:

Rents (per tonne of throughput) = -.O0001 * 41812 (tonnes of competitive capacity) (7.2)

Average ~ents*'~= - $0.42 per tonne of throughput (7-3)

This result indicates that notwithstanding the regulated structure under which elevator companies have had to price their services, competition on the Prairies has a significant consequence on retums to farmer~*'~.This effect is most likely due to the fact that grain blending is used as a tool by elevator managers to attract the level of business which is needed to generate throughput. As the degree of competition surraunding the elevator increases, the more the benefits of grain blending flow to farmers.

The sign and values of the dummy variable parameters were generally not significant, except for the HTP (Own) variable, the results of which indicate an ability by an HTP elevator to retain rents. The effect of HTP elevators was rnodeled as a shifter of the intercept term. The HTP dummy variable shifted the intercept terni upwards by an amount of $1.38 which can be considered significant.

This is an important conclusion since the competitive structure of the market is changing dramaticaliy. Fewer elevators (and thus presumably lower competitive capacity around an elevator) will lead to a transfer of blending surplus from farmers to elevator companies (or a reduction of the amount now being yielded to farmers). This effect will be compounded by the fact that the Prairie grain gathering environment in the next century will be made up, to a large extent, of HTP elevators.

21 3 These rents were averaged out over three years. 214 The results indicate that rents retained by elevaton fall by approximately $1 .O0 per tonne of throughput for each 100,000 tonnes of competition capacity. Conversely, as the degree of competition fail, rents increase. For the purpose of testing the potential of elevators to generate rents, a variance of quality of grain deliveries was calculated (the square root of Equation 5.48). As mentioned earlier, this number can considered suitable as a measure of the variability of the grain deliveries.

The grain variability component of the statistical model was allocated between two variables: SD and its squared value SD~.It was hypothesized that the first derivative of the SD variable (equation 7.3) would be positive and the second derivative (equation 7.5) would be negative. This was not the case.

d Rents = Pz-2P,SD a SD

and:

These values of the SD variable indicate that the greater the variability of grain deliveries, the lower the ability of the manager to generate rents. There may be several factors which cause the sign of this variable to go against the hypothesized relationship2?

- A large percentage of the grain deliveries to an elevator take place over a short period of time. This may well place a constraint on the elevator manager to keep grain trucks moving rapidly and it may limit the manager's ability to grade correctly (or incorrectly, but on purpose).

- The manager may just be inexpenenced or rnay be inadvertently making grading mistakes. It is measurement errors of this nature that are hypothesized by Giannakas et al (1 996) as being the cause of the negative rents detected in their study.

215 The variability of the grain flowing into an elevator could be measured. The consequences on grading that flow from the other possible causes for grade differences are not measurable. It is difficult to estimate what weight each effect has on the ability of an elevator manager to generate rents The spatial model developed in Chapter 5 indicated that under cornpetitive conditions, elevator managers may be willing to grade grain below point $ on the vertical axis of Figure 5.2. Below this point, the possibility exists that a #2 grain graded #1 (in the model) cannot be upgraded. It may well be that what elevator managers would prefer to do is simply grade grain to a maximum level of @ (i.e. at worse come out even). This intent, given the pressures of time and space, is not possible to achieve. Hence blending rents generated by the elevator should be close to zero when the amounts passed on to famers are accounted for, but this is an objective which is not achievable.

The parameter of the variable AGGR which designated the degree of aggressiveness of the manager had the correct sign. This variable incorporated the relationship between market share and capacity share and it was hypothesized that the parameter would be negative. The more the manager buys in relation to its market share, the more rents need to be passed on to farmers.

The probability values for the complete model (statistical test #1 and #2) are very high as compared to the probability values for the reduced regression. This suggests that some caution needs to be applied when interpreting the results for these statistical models.

The probability values are however adequate (at the a = 15% significance level) when the reduced model in which only the 3 independent variables SD, TCOMP and HTP are included in the regression equation. This point is dealt with in greater detail in Section 6.3.1.

7.2.4 Conclusions. This study represents an attempt to develop an appropriate methodology in analyzing the effects of cornpetition on the monetary gain that accrue from the blending of various qualities or grades of grain inside a primary elevator.

As was indicated in this study, every elevator Company which is engaged in buying and selling grain makes it a practice to clean, rnix and condition grain in order to secure the screenings, improve the quality and to take advantage of the latitude within the requirernents of each standard grade by mixing large quantities of grain to the bottom level of such requirement.

Aside from anecdotal information that can be obtained from a discussion with an elevator manager as to the blending operation, it is generafly difficult to get definite numbers on the value to an elevator company of blending. Everyone knows that it takes place. but aside from elevator company officiais who generally keep this information pnvate, no one really knows how much is blended, the total monetary value of the blending and who ultimately benefits, the fanner or the elevator company.

The model developed in this study should provide an understanding of what happens in the interaction between an elevator manager and a faner. The data that was obtained is probably the best that is available given that the CWB is the recipient of al1 data on grain deliveries to and from each Prairie elevator.

Generally speaking several inferences can be drawn from the results of the econornetnc mode1216:

This study used a high level of dissaggregated micro economic data to test the hypothesis that competition forces an elevator manager to pass blending rents to farmers. The analysis of the data found that blending rents are negative, a finding which substantiates the results generated in previous studies in which aggregated data was used.

No evidence was therefore found of non-cornpetitive behaviour by elevator companies. This conclusion has obvious implications on the net retums to farmers as more elevators are being closed down to be replaced by fewer but larger elevators. It is therefore a consideration that should be taken into account by policy makers when considering changes to the regulatory environment. The end result of rail line closure and the rationalization undertaken by the Prairie elevator companies, and their effect on the number and distribution of elevators on the Prairies, will also be seen on the distribution of blending rents and thus on farmers' income;

21 6 These inferences are made from the results of sensitivity test 2 where the main variables of the modei (SD and TCOMP) plus HTP (which was statistically significant at the 0.1 0 percent level) were regressed on Y (Blending rents). 178 No inferences can be drawn on whether or not elevators have an added ability to generate rents depending on the vanability of the quality of the grain delivered to the elevator as too many unquantifiable variables play a par? in this process;

A strong inference can be drawn that High Throughput (HTP) elevators, in relation to other conventional elevators, have an effect on blending rent retention. This finding has obvious important implications for the future, since much of the grain elevation needs of Prairie farmers in the future will be met by HTP elevators. However, the fact that HTP elevators often provide farmers with altemate benefits such as trucking fees also needs to be taken into account. Any positive rents generated in these elevators must therefore be counter weighed by the other benefits that may flow to farmers.

The following dummy variables had significant effects on the intercept term (the outcomes are not statistically significant and care should therefore be taken in the interpretation of the results).

All the company dummies, with company 2 showing a positive shift and the 4 others a negative shift; The 2 provincial dummies shifted the intercept term downwards. The effect in Saskatchewan was much more pronounced than that in Manitoba. The province in which the elevator is situated and the company to which it belongs may thus affect its ability to retain blending rents.

In terms of the questions set out in the problem statement of Section 1.1.4, the response would, in short, then be:

The relationship between elevating firms and farmers is on the whole a cornpetitive one;

Prairie farmers generally capture the blending rents;

The changing structure of grain merchandising fimis will have an effect on the net income of farmers through the changes in the amount of blending rents retained/passed on to famiers. 7.3 Limitations of the Study

Some limitations to this study were mentioned in Chapter 1 and include the following:

The data that is used is comprised of grain delivered to the CWB (Board wheat) only. Large quantities of many different types of grain, oilseeds and other specialty crops are delivered to an elevator and it was impossible to obtain complete data on al1 the movements inside the elevator.

Other forms of non-price competition (e.g. service, trucking premium etc.) were not explicitly considered. These rnay be important, especially when deliveries to HTP elevators are taken into account.

The risk attitudes of farmers and elevator managers was not explored. Negative blending rents imply that an elevator manager rnay be passing on to famers more in grades than they rnay be able to recover from the CWB. This rnay be a risky undertaking on the part of the elevator manager which was not taken into account.

Ex post, it rnay have been more appropriate to limit the study to a few elevators since the body of the data that was processed was too large to be able to effectively keep track of individual movements. Much of the data had to be aggregated at some point so that some of its disaggregated value was lost.

A study with data covenng a longer period would probably have provided more statistically efficient numbers. This data set was, however, not available.

Measurement and blending errors by elevator managers committed through inexpenence or genuine mistake were not explicitly modeled. As mentioned earlier, the time criteria rnay play an important part in an elevator manager's behaviour. If a manager is very busy, as rnay happen in the fall after harvest or in the spring when fertilizer, seed and chemical sales are at their highest level, it rnay be difficult for the manager to grade correctly - hence blending rents rnay be the result of carelessness rather than a planned action by the manager. This proposition was not incorporated in the model. 7.4 Recommendation for Further Research

With a view to extending the methodology used in this study, the suggested areas for further research are:

The model used in this research assumed that deliveries of grain were more or less spread out evenly over a crop year. It would be useful to incorporate into any future rnodel the effects on blending of the constraints of space and time which an elevator manager faces and which were mentioned above.

Since data for individual deliveries to single elevators are available, it may be possible to model elevator operations against other similar/unlike elevators on an individual basis;

Elevator managers can be considered to be generally risk averse individuals. Hence introducing a risk component into the model (both with regards to the risk attitudes of farmers and elevator managers) may provide for more realistic results;

The use of the uniform distribution in the spatial model does impose some limitations. It should be possible to detemine a more realistic distribution of quality to introduce into the model.

An examination of differences between the prices paid for non-CWB grain and oilseeds in relation to the profit margin and those received for Board grain. These differences result in an ernphasis in maximizing revenues from handling CWB grain - a circumstance which has not been the subject of any studies to date. Bibliography

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