INFORMATION TO USERS
This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer.
The quality of this reproduction is dependent upon the quality of the copy subm itted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.
In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion.
Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps.
Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9” black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order.
Bell & Howell Information and Learning 300 North Zeeb Road, Ann Artror, Ml 48106-1346 USA 800-521-0600 UMI
CAPITAL STRUCTURE. BUSINESS RISK. AND INVESTOR
RETURNS FOR AGRIBUSINESSES
DISSERTATION
Presented in partial Fulfillment o f the Requirements for
the Degree Doctor o f Philosophy in the Graduate
School o f The Ohio State University
B y
Wavne Saint Aubvn Henrv'. B.S.. M.B.A.. M.S.
*****
The Ohio State University 2000
Dissertation Committee; .Approved by
Professor D. Lvnn Forster. Adviser
Proessor Marvin T. Batte .Adviser Professor Claudio Gonzalez-Vega Department o f Agricultural. Environmental, and Development Economics UMI Number. 9994874
UMI
UMI Microform 9994874 Copyright 2001 by Bell & Howell Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code.
Bell & Howell Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, Ml 48106-1346 Copyright by Wayne Saint Aubvn Henrv
2000 ' ABSTRACT
The U.S. agribusiness industry is undergoing transition to a more competitive environment where efficiency is becoming increasingly important. This research examines the relationship between investor returns, capital structure, and business risk. It is an attempt to address the issue o f financial efficiency in the agribusiness industry and. while the issue is a large one. the study specifically deals with examining the effects o f debt, liquidity, and business risk on investor returns.
The hypothesis investigated is that publicly held agribusiness firm performance
(i.e. investor rate o f return as measured by stock price and dividend yield) is a function o f capital structure and business risk. A sample o f 50 U.S. agribusiness firms is selected and data collected on each firm for the years 1989 to 1998. Capital structure is measured by the firm's debt-to-asset ratio (capturing firm solvency), the debt structure index
(capturing firm liquidity), and the coefficient o f variation o f operating income (capturing firm business risk).
The analysis o f the study was conducted in three phases: a regression utilizing the lO-year averages o f each variable, a longitudinal study looking at the cross-sectional data over time, and a simultaneous equation approach, examining the simultaneous interaction among the variables included. The preponderance o f the evidence indicates that capital structure is relevant
to investor returns to agribusiness. As the t'lmi's debt-to-asset ratio increases, investor
returns first increase and then decrease at the point where the increased use o f debt capital causes the cost of the firm's capital to increase due to the increased risk o f default.
Firm liquidity is shown to be negatively related to investor returns, t he higher the liquidity o f the firm (the more conservative its liquidity management strategy), the lower the returns to agribusiness investors.
Business risk is indicated to be significant to investor returns in the mean regression. Also, the simultaneous equation approach suggests that business risk is directly relevant to liquidity and debt, which in turn directly impact investor returns.
Business risk seems to interrelate with capital structure to affect investor returns.
lit Dedicated to my parents. Artnel Samuel Henrv and Merle Elaine Henrv
IV ACKNOWLEDGMENTS
I wish to express my sincere gratitude to my adviser. Dr. D. Lynn Forster, for his intellectual support, encouragement, and enthusiasm during my research. His relentless patience and support have made this research possible.
I wish to express my sincere gratitude to Dr. Marvin T. Batte and Dr. Claudio
Gonzalez-Vega for their consistent attention and encouragement, and for their insight and timely advice.
I also wish to thank Dr. Cameron Thraen and Dr. Stan Thompson for lending their econometric expertise and for giving critical feedback at important times.
I am grateful to Dr. Warren F. Lee and Dr. .Allan E. Lines for their constant support and encouragement.
I wish to thank Dr. Richard Meyer for his support early in the research process.
Finally. I would like to express my sincere gratitude to my family for their tremendous love, consistent support, and 'behind the scenes' encouragement. Without them this research would not be possible. VITA
July 14. 1970 ...... Born - Kingston. Jamaica
1991...... B.S. Economics and Management. The University o f the West Indies. Kingston. Jamaica
1994...... M.B.A.. Howard University
1994 - 1995...... Lecturer in Finance. Howard University
199 6...... M.A. Economics. The Ohio State University
1997 - 2000 ...... Graduate Teaching and Research Associate. The Ohio State University
FIELDS OF STUDY
Major Field: Agricultural. Environmental, and Development Economics
Minor Field: Finance
VI TABLE OF CONTENTS
Pa»e
Abstract ...... ii
Dedication ...... iv
Acknowledgments ...... v
Vita ...... vi
List of Tables ...... ix
List of Figures ...... x
Chapters:
1. Introduction ...... 1
1.1 Rationale for the Study ...... 1 1.2 Objectives of the Study ...... 2 1.3 Organization o f the Study ...... 3
2. Conceptual Basis ...... 4
2.1 The U.S. Agribusiness Industry ...... 4 2.2 Capital Structure ...... 13 2.3 Business Risk ...... 19 2.4 Investor Returns ...... 23 2.5 Structural Model: The CAPM ...... 25
3. Data Requirements and Methodology ...... 28
4. Findings and Discussion ...... 32
4.1 Preliminary Analysis ...... 32 4.2 Data Analysis ...... 36
5. Summary and Conclusion ...... 49
vii Appendices ...... 72
Appendix A: Description o f Agribusinesses ...... 73 Appendix B: Rates o f Return: 1989 - 1998 ...... 86 Appendix C: Firms Used in Industry Comparison.... 87 Appendix D: Econometric .Analysis ...... 88
List o f References ...... 96
Vlll LIST OF TABLES
Table Paue
1 World Population Estimates and Projections to Year 2050 for Regions and Countries ...... 56
2 Agribusiness Firms Selected for Study ...... 57
3 Average Annual Stock. Price: 1089 - 1908 ...... 58
4 Measures o f Capital Structure and Business Risk 59
5 Investor Return. Capital Structure, and Business Risk... 60
6 Comparison o f Agribusiness to Selected Industries 61
7 Rates o f Return: 1989 - 1998 Mean ...... 62
8 Mean Regression: 5 Cases ...... 63
9 Panel Regression ...... 65
10 .Agribusiness Firms By Sector ...... 66
11 Comparison o f .Agribusiness Sectors ...... 67
12 Sector Regression ...... 68
13 Hausman Test ...... 69
14 Simultaneous Equation Approach ...... 70
15 Sensitivity Analysis of Variables ...... 71
IX LIST OF FIGURES
Fi mire Page
1...... Real Return/Debt-to-Asset Function ...... 64
2 Real Retiim/Biisiness Risk Function...... 64 CHAPTER 1
liMKODCCllUIS
1.1 Rationale for the Studv
The issues o f capital structure, business risk, and the search for an optimal deht- equity mix have received increased attention in the corporate finance and economic literature, but relatively little o f this has been specifically applied to the U.S. agribusiness industry.
The U.S. agribusiness industry consists o f a wide spectrum o f cooperatives and industrial companies (both public and private) engaged in the growing, processing, and merchandising o f raw agricultural commodities. Exports o f cash crops, livestock, and dairy products are essential to the agribusiness industry, with the value o f U.S. agricultural exports having risen three times faster than domestic sales in the past decade.' The industry is operating in an environment o f increased globalization, and consequently, product demand is more volatile due to increased sensitivity to national, regional, and global economic conditions and competition. Also, due to the global trend toward freer markets, by the Federal Agriculture Improvement and Reform .Act (F.AIR) o f 1996. U.S. agribusiness.will see a reduction in the government's influence on the
' Agribusiness Inilustrv Survey. Standard & Poor's Indusirv Surveys. January I99Q. industry through the year 2002." Finally, unlike many other industries, the agribusiness
industry is particularly susceptible to erratic weather conditions, and is facing a global trend o f worsening atmospheric conditions.^
While the agribusiness industry has had one o f the highest productivity rates among U.S. industries (Standard & Poor. 1999). these factors have presently served to mitigate against increased industry profitability. There is an increased awareness o f the importance o f efficiency in the agricultural sector as a whole, and of this, efficient financing o f agribusiness.
1.2 Objectives of the Studv
The objective of this research is to examine the effects of firm capital structure and business risk on investors’ rates o f return in U.S. agribusiness from 1989 to 1998.
The hypothesis to be investigated is that publicly held agribusiness firm performance (i.e. investor rate o f return as measured by stock price and dividend yield) is a function o f capital structure and business risk.
* .Among Other things the FAIR Act: iii eliminates provisions for price-sensitive Jeficieney payments, (li) eliminates dairy price
1 I.3 Organization of the Studv
The research w ill be organized as follows:
/. (ntrodiiction
Rationale for the Study
Objectives and Hypothesis
II. Concepfiuil Framework
The U.S. .-Vgribusiness Industry
Capital Structure
Business Risk
Investor Returns
Structural Model: The CAPM
III. .Meihodolog}'
Data
Model
IV. Fintlings ami Discussion
V Summarx' and Conclusion
support starting in the year 21)00. and ( iil) reduces funds for commercial agricultural export programs. C H A P TER 2
CONCEPTUAL BASIS
2.1 The U.S. Agribusiness Industry
Agribusiness can be defined to include the sum total o f all operations involved in the manufacture and distribution o f farm supplies; production operations on the farm; and the storage, processing, and distribution o f farm commodities and items made from them
(Davis and Goldberg. 1957). The agribusiness industry is composed of a complex series o f firms. These firms supply inputs to farms, produce farm products, process agricultural products, and market these commodities to the final consumer (Cramer. Jensen, and
Southgate. 1997).
Sectors within the Industry
The commodities that the agribusiness industry grows and processes are widespread. They include crops, meats, poultry, dairy products, fish, fruits, vegetables and beverages. They also include related products like chewing gum. vegetable oil. and animal fat. Generally, companies operate within one o f these major categories and focus on one or two commodities within that segment. For example. IBP focuses on the
■ According to the National Center for .Atmospheric Research in Boulder. Colorado (from S & P Indiistrv Survevs. January UWd). 4 production o f beef and pork, while Tyson Foods is a major processor o f chickens (see
Appendix A for a description o f these and other firms used in this study ).
Agribusiness firms are generally divided between those engaged in the early or
middle stages o f making a processed food product and those engaged in the later stages.
Some agribusiness firms (e.g. .A.rcher-Daniels-Midland and Universal Foods) engage in
such activities as growing, harvesting, milling, and/or processing raw agricultural
commodities. They process and merchandise these commodities into end products that
include oils, syrups, starches, and meals used in the food and feed industries, as well as
com sweeteners used in soft drinks. These end products generally are not sold to
consumers but rather to later stage processors and food packagers, who use these
ingredients in making finished consumer products.
Other agribusiness fimis (e.g. IBP. Con Agra, and Hormel Foods) slaughter
livestock and chickens for others to process further. Dairy processors, such as Dean
Foods, process raw milk into milk and related products such as cheese and butter.
Companies involved in the later stages o f producing consumer food products (e.g.
Kellogg. Fl.J. Heinz, and Hershey Foods) sell their finished goods to tbod wholesalers and retailers, which in turn sell the products to consumers. In recent years, these foods
products companies have capitalized on the growing trend toward away-from-home
eating by increasing their sales to restaurants, schools, and other institutions.^
' .Agribusiness Industry Surves. p. 13. Standard & Pcmr's industry Survevs. July 1999. 5 Food Processing. Wholesaling, and Retailing
Another approach to examining the agribusiness industry (one used later in this analysis) is to distinguish among those tirms in the industry involved in food processing, those involved in food wholesaling, and those in food retailing.
Food processors are the link between farmers and tbod wholesalers and retailers.
The processing firm adds form utility^ to the raw farm product, transforming these commodities into finished consumer food products (e.g. the transformation o f sugar beets into sugar and wheat into bread).
Wholesale firms assemble, regroup, and dispatch farm products to food processors or link food processors with retailers. They tend to be more effective distributors because of their intimate market knowledge and because they handle large product volumes (Padberg et al.. 1997). Wholesalers can specialize in one product group
(e.g. fruit or cheese), region (e.g. Midwestern states or Eastern Europe), or in one or more specific functions (e.g. cash and carry).'’
Food retailers sell directly to consumers and institutional outlets. Various organizations exist in food retailing. While there are still a large number o f small independents, many retail food outlets are members o f large chains. A retail food chain is a group o f eleven or more stores owned and operated by the same firm (Cramer et al..
1997). Chain stores emerged in the late nineteenth century and initially combined wholesaling with retailing, and to a lesser extent, processing. Consequently, they were able to lower costs, cut prices and force higher-priced independent wholesalers and retailers out o f business. In later years (post World War II). the chain stores began adopting the supermarket type o f operation, offering a wide assortment o f every day food products and durables in order to serve the general food consumer.
Because the independent food retailers and wholesalers lost business to the chains, they responded with their own vertically integrated organizations.’ In some cases, groups o f independent retailers created cooperative wholesale systems to supply them with merchandise (backward integration). In other cases, wholesalers developed special relationships with independent retailers, forming wholesaler-sponsored voluntary chain- type organizations (forward integration), adopting methods used by the chain stores.
Many food-processing firms have also become vertically integrated, selling finished products directly to wholesalers, retailers and consumers.
Competition is particularly keen in the food retail sector. In the 1990s the sector has been characterized by relatively lower profitability due to this strong competition, low domestic food price inflation, low population growth, and overcapacity (Standard &
Poor's Industry Surveys. June 1999).
Beyond intra-industry competition, agribusiness firms must deal with fluctuations in the availability and price of agricultural commodities. These are due to unpredictable factors such as weather and plantings, domestic and foreign farm policies and programs, changes in global demand caused by population growth and changes in standards o f living. Generally, throughout the identified segments within the industry (processing, wholesaling, and retailing), price changes in agricultural commodities can be passed
' flw t'orm utility ot a product can he defined as the consumer s preference for the product in a specific form (Padberg et a ! . 19971. Tills is to be distinguished from a product's time utility (added by storage) and place utility (added by transportation! " Cash and carry wholesalers specialize in keeping a wide assortment o f products, but avoid product and credit delivery through to the consumer via adjustments in markups and margins. Wholesalers and
retailers do not face as much volatility in the availability of food products due to the
storage technology made available by the processors. To reduce their exposure to the risk
o f market fluctuations, food processors use the commodities market to hedge part or all
o f their inventory and related purchase and sales contracts. As these firms become more
globally integrated, they also need to hedge against adverse movements in the currency
markets to protect operations in other countries.
Agribusiness and the International Economv
Demand within the industi-y is driven primarily by population growth and economic conditions, specifically the purchasing power of consumers. The U.S. agribusiness industry serves a domestic market that is relatively mature, with consistent but modest growth likely in the future.^ In emerging and developing countries, demand
for protein-rich foods is growing more rapidly than in the United States because o f higher population growth, rapid industrialization, rising disposable income and higher income elasticities. The world's multiplying population means more mouths to feed (see Table
1). Consequently, the U.S. agribusiness industry is strongly positioned to take advantage o f future increases in worldwide food demand.
In 1998. total agricultural exports was approximately $54 billion, and is expected to slip to approximately $49 billion in 1999. reflecting lower market prices for crops and
livestock, and reduced exports to Asia and Russia, two o f the bigger U.S. export markets.
' Vertical integration is the linkage o f firms in difi'erent stages o f production or marketing under the ownership o f a single firm (Cram er et a t. I9d7). ' Feeding a hungry world'. .Agribusiness Industry Survey, p. ft. Standard & Poor's Industry Survevs. July 1999. Alternatively, in 1998 the United States imported $37 billion in agricultural products and is expected to import approximately $38 billion in goods in 1999.
Knutson. Penn, and Flinchbaugh (1998) suggest that agribusiness firms tend to be free-trade oriented. They tend to be strong supporters the North .American Free Trade
Agreement (NAFTA) and the General Agreement on Tariffs and Trade (GATT)(novv the
World I rade Organization), and tend to oppose policies that would involve more government regulation. They suggest the following reasons for this:
(i.) Free-trade policies expand trade volume. Agribusiness profits from volume.
though this is more typical for agricultural commodities than for branded food
products. Competitiveness and profits are easier with lower price levels.
(ii.) Government regulations tend to reduce firms' fiexibility in decision-making or
impose additional costs. Free-trade policies reduce the market power of both
producers and consumers, giving agribusiness intermediaries relatively more
market power.
(iii.) Free-trade increases price variability, allowing market intermediaries to generate
higher returns from hedging activities.
Knutson et al. (1998) suggest that farmers tend to have similar attitudes toward deregulation, except that they prefer farm programs, as these specifically benefit them.
Transition in Farming
The industry's activities center around the farm. In 1998. there were 2.192 m illion farms in the United States, and 2.191 million in 1997.^ These greatly contrast with the 6.8
' Ibid.. p .13 million farms in operation in 1935. This decrease in number can be explained in part by the change in the size o f the average farm. In 1935 the average farm was 155 acres, with total farm acreage o f 1.054 million acres. In 1998 the average farni was 435 acres, and total farm acreage was 954 million acres. This decrease in acreage can be explained by higher crop yields resulting from technological advances.
In 1998. nine out often U.S. farms were classified as individual operations, having individual owners. Due to their large number, these operations also account for the largest share o f faimland (71 percent) and gross farm sales (74 percent).
Consequently, approximately 94 percent o f U.S. farms are considered small farms, with annual sales under $250.000.
In the 1990s farmers faced increased economic risk. Lower commodity prices and the prospect o f reduced government price supports (by the 1996 F.A.IR .A.ct) meant that farmers had to operate more efficiently to maintain profitability. Additionally, new trade agreements signed through the World Trade Organization (WTO) increased the competition faced by the industry.'" Partly due to this, some agribusiness firms became more vertically integrated, and their increased market power threatened to reduce farmers' income.
In response to this, farming cooperatives" became increasingly popular, as an increasing number of farmers joined them as a means of reducing costs. The cooperatives allowed farmers to gain the benefit o f processing, wholesaling, or retailing a food item.
These agreements include a call for members to reduce existing taritTs by an average o f 24 percent over ten years, and a reduction m export subsidies by 21 percent in volume and 36 percent in value by the year 2000. ' ' A cooperative is an association formed by tarmer-owners to help them market and distribute their crops and livestock (Agribusiness Industry Surveys. Standard & Poor's Industry Survevs. Julv 1999), 10 Agribusiness finns such as Archer-Daniels-Midland purchase a significant portion of
their raw materials from cooperatives.
Evaluating the Agribusiness Firm
According to the Agribusiness Industry Survey.'" an analyst must take the
following steps when evaluating an agribusiness company:
( I.)Consider external factors such as economic and market conditions.
(2.) Assess the nature o f the company's product lines and any factors that may be unique
to the company.
(3.)Evaluate the income statement, the balance sheet and the cash flow statement.
Economic conditions are vital to the industry's health. The level o f demand for a
firm's products is determined partly by economic conditions domestically and abroad,
worldwide population growth, and agricultural product market conditions. .As global
economic conditions improve, living standards rise resulting in growing demand for
grains, meats and dairy products. Over the years, many major U.S. food and beverage companies have sustained steady growth by expanding into international markets. ’ .A
number o f the firms in this study have sizable international presence.
In focusing on a specific firm, the analyst must determine the nature o f the agricultural product the firm makes. Commodity agricultural products (e.g. grains, wheat,
soybeans) are generally produced with little product differentiation. Firms that have
significant fixed expenses must therefore produce in large quantities to reduce per-unit costs. I f a firm's production volume is increasing, it can spread its fixed costs over more
'■ Agribusiness Industrv .Surveys, o 21. Standard & Poor's Industry Survevs. July 19W.
I I units o f production, resulting in lower per-unit costs and higher profits. Conversely, if a firm's production volume is declining, unit costs w ill rise and profits w ill fall. For these fimis. maintaining adequate capacity utilization rates is critical in sustaining profit margins.
Value-added products, such as genetically enhanced seeds, are produced in smaller quantities than unaltered seeds, and are designed for specific applications or customers. While firms that produce commodity products have little control over selling prices during times o f shortages or excess supply, those that produce value-added products have greater control in its pricing policies.
It is also necessary to consider characteristics that may be specific to the particular firm. As demand for agricultural products is global, the firm's geographic presence is a major factor in this regard. .A broad geographical mix among the firm's customers reduces its exposure to an economic downturn in any one country or region, as this helps smooth out the firm's sales and earnings trends.
It should also be noted that conditions that might be good for one segment in the industry might be detrimental to another. For example, while farmers may benefit from higher selling prices for grain and wheat, food processors buying these products w ill be hurt by increased raw material input costs.
The approach to the analysis o f the agribusiness firm's financial statements is generally similar to that o f other consumer-related product firms. Yet. because the agribusiness firm's operating environment and financial position can be affected unpredictably by erratic weather patterns, raw material cycles, changing government
'' Foods & Nonalcoholic Beverages Industry Survey. Standard & Poor's Industry Surveys. June 1999 regulations, trade agreements, and commodity prices, its balance sheet must be as strong as possible.'■* Higher liquidity is needed to offset any changes in the commodity markets, which are subject to raw material cycles of either limited or overabundant supplies. High liquidity is also needed to compensate for the highly competitive nature o f the indiistiy. where competition usually takes the form of low prices, high volumes, and tight margins.
fhe agribusiness firms in this study include food processors, food wholesalers and food retailers. The description o f the industry given here is cursor)' at best, and firms will differ one from the other in any o f a number o f ways. But the concepts o f capital structure and business risk are applicable to all firms within the industiy. and these concepts are discussed next.
2.2 Capital Structure
The capital structure o f a firm can be defined as its mix o f debt and equity instruments used to finance its assets. More clearly it refers to the mix ot'long-term or permanent sources of funds used by the firm (debt, preferred stock, and common equity).''
In a seminal article published in 1958. Professors Franco Modigliani and Merton
Miller proved under very restrictive assumptions that a firm's value was independent o f its capital structure. Assuming no corporate or personal taxes or bankruptcy costs, the increase in return to stockholders in a firm using leverage is exactly offset by the risk associated with this debt financing. However, subsequent research relaxing these limiting assumptions showed that capital structure was not irrelevant, but indeed affected firm
Agribusiness Industr>- surveys, p 23. Standard & Poor's Industry Surveys. July 1999 value. The M odigliani-M iller study proved very important though, as it helped to reveal what was required for capital structure to be relevant and hence affect a firm 's value.
Capital structure becomes important as we recognize the following:
(l.)Each source o f financing has a different cost, and so capital structure affects the
firm's cost o f capital, and ultimately, the return on investment to the firm's
shareholders.
(2.)As we w ill examine, more use o f debt by the firm may mean higher returns to
shareholders, but also higher risk due to interest payments and the increased risk o f
default.
(3.) There is an optimal capital structure of the firm, one that minimizes the firm's cost of
capital and maximizes the firm's value.
Factors to consider in determining capital structure include:
- Corporate and personal taxes
- Bankruptcy costs
- Signaling Effects
- Managerial Preference - Pecking Order Theory
- Agency Costs
- Business Risk
Remaining factors include managerial risk aversion, lender and bond-rater requirements, industry standards, and retention o f control. However, for the purposes o f this study, only those listed above w ill be discussed.
" (Kcown. Petty. Scott, and M artin. 1998). .As the tlrm tnianccs a portion o f Its assets w ith securities bearing a tlxed rate o f return (debt and preferred stock) it is said to be employing_/ino«c;a/ leverage. 14 Corporate and Personal Taxes
Due to the tax deductibility o f the interest expense on debt, firms enjoy a tax shield with the use o f debt financing. However as Miller ( 1977) points out. the owners o f debt w ill be subject to personal tax and thus w ill "gross up" the level o f interest payments required by the differential in the taxes they w ill have to pay. This mitigates against the corporate tax advantage to the firm in the use o f debt. Nevertheless, the net effect o f taxation policy to the firm is generally assumed to be a positive tax incentive for debt financing, though smaller than that if no personal taxes were assumed (Wijst, 1989 ).
Bankruptcy Costs
As the firm increases its use o f debt financing it increases the probability o f default on its interest obligations. Should the firm actually fall into financial distress and the assets o f the firm were sold for less than their market values, both bondholders and stockholders would suffer losses. Other costs associated with the actual bankruptcy o f the firm include legal and professional fees for lawyers and accountants in undertaking bankruptcy proceedings, management time in preparing associated reports, and so on.
Even where the firm does not come to the point o f actually declaring bankaiptcy, increasing use o f debt financing can weaken the financial condition o f the firm. As the risk o f bankruptcy increases, creditors may be less willing to lend to the firm, suppliers may be unwilling to continue to offer goods on credit, and so on. Thus, bankruptcy costs, including the costs associated with the threat o f bankruptcy, discourage the use o f debt financing by the firm.
15 Signaling Effects
In a world characterized by asymmetric information - where tlrm managers
(insiders) have better information about the firm's financial prospects than do investors and the public as a whole (outsiders) - there are various signals conveyed to the public in a firm's issue o f debt or equity.
In raising funds to undertake project proposals, the firm issuing debt signals more favorable prospects as this more risky financing method is associated with management anticipating higher returns from the proposed project(s).'^ The potential returns from a project funded by a debt issue are shared among fewer investors than would those o f a project funded by an equity issue. The firm issuing equity is therefore sometimes perceived as not being sufficiently creditworthy to obtain debt financing. Empirically, debt issues have been associated with positive stock price effects, or in some cases, no effect at all.
Signaling theory also holds that investors believe that firm managers are more likely to issue equity when they believe that the stock is overpriced (Cooley and Roden.
1991). Thus the announcement of an equity issue is seen as an admission by the managers that the stock is indeed overpriced. Empirically, stock issues have been associated with negative price effects.
Signaling effects would therefore encourage the use o f debt financing as opposed to equity financing by the firm when it is performing well.
16 Managerial Preference - Pecking Order Theory
The Pecking Order Theory (Myers. 1984) describes managerial preference for internal financing (retained earnings) over external financing (debt and equity issues ) because o f the Dotation costs'’ and signaling effects associated with external financing. If external financing is required, managers prefer to issue low-risk bonds first, proceeding to common stock last. The pecking-order theory also implies that firms w ill use less than their debt capacity”* in order to reserve credit to finance future investments.
The Pecking Order Theory therefore says that firms w ill favor debt financing to equity financing in their capital structure.
Agencv Costs
An agency relationship is defined as "a contract under which one or more persons
(the principal(s)) engage another person (the agent) to perform some service on their behalf which involve delegating some decision making authority to the agent" (Jensen and Meckling. 1976). The problem is that the agent w ill not always act in the best interest o f the principal but may pursue his/her own interests. To lim it divergence from their interests, the principal can monitor the agent and create appropriate incentives to induce the agent to act in a manner consistent with the interests o f the principals. .Agency costs include therefore the monitoring expenditures by the principal, as well as costs associated with creating incentives for the agent to act in a manner compatible with the interests o f the principal.
Underlying this is the assumed nsk aversion ol'investors - additional risk w ill not be undertaken unless compensated by additional return. " Flotation costs are those costs incurred by the firm in issuing securities. These include investment hanking tees, pnnting and engraving tees, and accounting and legal fees.
17 In the context o f the firm, the agency problem results from the separation o f the management (agent) and security holders (both bond and stockholders )( principal) o f the tlrm.' ' Because the managers may not act in the best interest o f the security holders, the latter w ill incur costs in creating incentives for the managers to do so and in monitoring their behavior. Incentives usually take the form o f executive compensation plans
(including stock options) and perquisites (country club memberships, corporate airplanes, and so on). Monitoring usually takes the form o f bonding the managers, auditing financial statements, reviewing executive compensation and perquisites, and restrictive covenants on debt (prohibiting additional debt being incurred, dividends being paid, assets being sold, and so on).
Managers can act in the interest o f stockholders at the expense o f bondholders
(e.g. by investing in extremely risky projects). To minimize this, the restrictive covenants on debt can be extensive. Thus at high debt-to-equity ratios, the agency cost o f debt financing can be quite high, increasing the overall cost of capital to the firm. Yet if there is very little debt financing, managers can misuse "free cash” at the expense o f stockholders (Jensen. 1986). The use o f leverage thus introduces the threat o f failure and induces management to be more efficient. There is therefore a trade-off in the use o f debt and equity financing and the associated agency costs.
As we w ill see. agency costs w ill be determined in part by a firm's business risk.
If a firm has a high level of risk inherent in its operations (independent o f how it is financed), it would not be prudent for that firm to take on much debt financing. In the
" Debt capacily is the maximum proportion o f debt the firm can include in its capital structure and still maintain its lowest composite cost of capital (Keown et al. 199S). This will be the only one discussed, though there are other agency relationships associated with the firm (e.g. between bondholders and stockholders, firm management and the customers o f the tlrm . the firm and its employees, etc. ). 13 event that it has to do so, the protective covenants associated with this financing may be
substantially high, driving up the agency cost o f debt to the owners o f the firm. I f the firm
has high business risk, even without employing leverage the agency cost o f equity to the
stockholders w ill be high due to the need for increased monitoring o f management decisions in such an environment.
In light o f these factors therefore, a firm's optimal capital structure is the mix of debt, preferred stock and common equity that minimizes the weighted cost to the firm of
its employed capital, which in turn helps in maximizing the firm's value.
2.3 Business Risk
Business risk refers to the relative dispersion (variability) in the firm's expected earnings before interest and taxes {EBIT)(Keown et al. 1998). Stated another way. business risk can be defined as the uncertainty inherent in projections o f future return on assets or return on equity if the firm uses no debt (Weston and Brigham. 1993). Business risk is to be differentiated 'ixom fhumcial risk which refers to "the added variability in earnings available to a firm's shareholders, and the added chance o f insolvency caused by the use of securities bearing a limited rate o f return in the firm's financial structure"
(Keown et al. 1998. p.342). Financial risk therefore is the added variability in the firm's earnings due to the use o f debt financing.
Business risk is affected by:
- sales volume variability
- cost variability
- competition
19 product demand
product diversification
operating leverage
Sales Volume and Cost Variability’
The more variable the volume o f sales o f the tlrm and the cost of its inputs used in the production process, the more uncertain w ill be its revenue stream and hence its net earnings or earnings before interest and taxes.
Competition and Product demand
The more competitive the environment in which a tlrm operates, the more uncertain will be its sales (in both volume and price) and consequently its EBIT. This is due to potential changes in market share due to changes in prices, costs and strategies o f competitors.
Increased competition w ill also increase variability in product demand and hence EBIT.
Product diversification
In the study o f risk, it is shown that some risk can be diversified away by a well- structured portfolio (Keown et al. 1998). As one element in the portfolio performs poorly, this can be offset by another element in the portfolio doing well. Product diversification refers to the firm developing entirely new products for new markets ( Boone and Kurtz.
1999). The more the firm can diversify its product, the lower its business risk w ill be.
20 Operating Leverage
Operating leverage refers to the presence o f fixed operating costs (as opposed to variable operating costs) in the firm's cost structure. By employing tlxed operating costs, a small change in sales revenue is magnified into a larger change in operating income (EBIT).
The degree o f operating leverage is measured by the ratio:
percentage change in EBIT / percentage change in sales.
High operating leverage w ill therefore give rise to more variability in operating income and higher business risk.
Agricultural Risk Management
When combined, business and financial risk magnify the potential losses to the agribusiness tlrm. Risk management involves designing and implementing strategies and techniques to mitigate against the business and financial risk o f the firm and. in so doing, minimize its potential losses. Because lower risk is typically associated with a reduction in the expected returns to the firm, risk management strategies usually take into account this risk-retum tradeoff.
As Barry et al. (1995) point out. the risk management process can be divided into three phases:
(i.) Designing strategies to deal with risk.
(These are generally longterm plans utilizing various risk responses to a range o f uncertain events).
(ii.) Implementing and utilizing specific risk responses when uncertain event occurs.
21 (iii.) Restoring the ability o f the firm to implement and utilize the risk response should the event recur.
Some risk management techniques may focus on reducing the likelihood o f the adverse event occurring, while others may focus on enabling the firm to better deal with the event once it occurs.
Examples o f risk management techniques that reduce the likelihood of the adverse event occurring include diversification and hedging through forward or futures contracts.
The firm can diversify in different ways, including across products, assets, customers, or suppliers. The key to effective diversification is to include elements in a portfolio that are negatively correlated with each other, so that an event affecting one element in the portfolio one way is offset by that event affecting another element in the portfolio in the opposite way."'^ Consider a simplified example. Firm .A, is a meat processing firm specializing in beef only, while Firm B is a processor o f both beef and poultry. Suppose that consumer tastes and demand shifts from red meat to white meat, resulting in consumers purchasing less beef and more chicken. Firm A would suffer losses due to the shift in demand. While Firm B's sales in beef would also decline, it would experience a corresponding increase in the sale o f chicken, and thus the positive effect would offset the negative, leaving Firm B in the position as if there was no change in market conditions.
Examples o f risk management techniques designed to increase the ability o f the firm to deal with adverse events once they occur include liquidity management and the
■" This is the extreme case, as it is unlikely to find elements in a portfolio that arc perfectly negatively correlated with each other. 1") purchase o f insurance. Both enable the firm to have reserves on hand to deal with the
adversity, while the latter transfers the risk outside the firm (to the insurance company).
Other types o f risk management strategies adopted by agribusiness firms include
vertical integration and participation in government programs. These are specifically
designed to reduce production and marketing risks.
2.4 Investor Returns
While the term "investors” may include both holders o f debt and equity
instruments (bonds, preferred stock, and common stock), for the purposes o f this study it
refers specifically to the holders o f common stock in the agribusiness firm.
Common stock represents an ownership interest in a corporation. The owner of common stock is entitled to dividends paid on the stock, but the actual payment o f common stock dividends is at the discretion o f the management o f the firm, as it could opt to retain and reinvest the earnings rather than pay it out as dividends. The stock could also be sold at some future date at either a price greater than the purchase price, in which case the investor receives a capital gain, or at a price less than the purchase price
(whereby the investor receives a capital lo.ss).
The stock value or stock price is determined as the present value o f its expected stream o f cash flows (Brigham. Gapenski. and Ehrhardt. 1999). These expected cash
flows consist o f ( 1) the dividends expected each year and ( 2 ) the price investors expect to receive when thev sell the stock. Thus, the price o f a share o f common stock is:"'
Po = Do(l+g)/ks-g = D |/k s -g
Where:
Po = actual market price o f the stock today
Do = firm ’s last dividend paid
g = expected growth rate in dividends
ks = expected rate o f return on the stock
D| = first dividend expected
Rearranging for k^. the expected rate o f return on the common stock, we get:
ks D| / Po + g
Expected rate o f return = Expected dividend yield + Expected growth
rate or capital gains yield
This model is expeciationciL in that it derives the expected rate o f return on common stock and utilizes the expected growth rate in dividends. The analysis o f this study utilizes ex post data, data o f the past. Thus for any year, ks w ill be the return on the common stock for that year, calculated using the dividends received over that year. The capital gains yield w ill simply be the change in the stock price between the two consecutive vears.
■' ;\ssuming normal or constant growth in dividends. 24 2.5 Structural Model: The CAPM
The Capital Asset Pricing Model (CAPM) originated by Professor William F.
Sharpe (Sharpe. 1964) is used to explain investors' expected return in the presence o f
risk. Generally, risk associated with a firm's stock can be defined as the potential
variability associated with revenue or income streams. The total risk to the firm is made
up u f(i.) Jiversiuabic (unsystematic or company-specific) risk, and (li.) nondiversitiable
(systematic or market) risk (Brigham et al. 1999).
Diversifiable or company-specific risk is a result o f factors that are unique to the
particular firm (e.g. success fill or unsuccessful marketing programs, employee strikes,
contract negotiations, etc.). As the name suggests, the diversifiable risk o f a stock can be
eliminated by the investor holding that stock in a diversified portfolio o f stocks. Thus,
bad events in one firm w ill be offset by good events in another.
Nondiversitiable or systematic risk stems from tactors that systematically affect
most firms (e.g. war. inflation, recessions, high interest rates, etc.). Since most stocks
tend to be negatively affected by these factors, systematic risk cannot be eliminated by diversification.
The C.APM holds that the expected rate o f return on security i. k,. is determined by:
k, = krf + pi (km - krf)
Where:
krf = risk-free rate o f return
km = required return for the market portfolio
Pi = the systematic risk o f security i (beta)
25 The systematic risk o f security i is defined as:
P, = Covariance (kj. km) / variance km = pi-m cr, / am
where:
pj.m = correlation coefficient between security i and the market,
a = standard deviation.
If Pi = 0. then security i has no systematic risk.
If Pi= 1. then the security has systematic or market risk equal to the typical stock
in the market.
If pi > 1. then the security has more market risk than the typical stock in the market.
The CAPM says that because we can remove company-specific or unsystematic risk, there is no reason to believe that the market w ill reward the investor with additional returns for assuming risk that could be avoided by simply diversifying. Empirically, it has been shown that the market rewards diversification (Brinson. Singer, and Beebower.
1991). The investor can (i.) lower risk without lowering expected returns and/or (ii.) increase expected returns without assuming more risk.
In light o f this discussion and our study, the question arises: If business risk is idiosyncratic or company-specific and can therefore be diversified away, why is it being considered as a potential determinant o f agribusiness investor returns?
The CAPM is based on some fairly unrealistic assumptions including (i.) perfect markets (including no taxes or transaction costs) and (ii.) market efficiency (the price o f the security reflects all available information, and all investors agree on the interpretation and significance o f information). As the discussion characterizing this study has 26 indicated, we have relaxed some o f these assumptions o f the CAPM to allow for taxes, bankruptcy (insolvency) costs, and asymmetric information (as with Signaling Theory).
With the relaxing o f these assumptions it is therefore possible for the study to yield findings contrary to those predicted by the CAPM. The CAPM is at best only a rough approximation to the relationship between risk and return (Keown et al. 1998). and it is therefore plausible to believe that a security's returns are not solely determined by its beta.”
This study is not an empirical test of the CAPM. The CAPM simply seri es as a behavioral model for the study as there is no superior or suitable alternative. In relaxing some o f the assumptions on which the CAPM is built, we can find the coefficient on business risk in our study to be highly significant as a determinant o f agribusiness investor returns.
- Fama and French studv 11992' supports this assertion, though its fmdines have been disputed. '27 C H A P T E R 3
DATA REQUIREMENTS AND METHODOLOGY
To achieve the stated objectives, a sample o f some 50 U.S. agribusiness firms are selected (see Table 2). The data on these firms mainly come From:
1. The Disclosure Global Access Database.
2. The Bloomberg Financial Services Database.
3. The Securities and Exchange Commission (SEC) Database.
For each firm, measures of investor returns, capital structure, and business risk are calculated for the ten years 1989 to 1998. Investor returns are then regressed against capital structure and business risk for these years to examine the relationship, if any. between the variables. The following Ordinary Least Squares regression model is proposed:
R.R, = a + PiDEBT, + P^LIQj + P3BRISKj + s
Where:
RR = Real rate of return expected by investors for firm i.
DEBT = firm solvency (Debt to Asset ratio)
LIQ = firm liquidity (Debt Structure index)
BRISK = business risk (variability in EBIT)
28 Investor Real Rate o f Return
The firm rate o f return is the lO-year (1989 - 1998) mean o f the change in average stock
price between consecutive years plus dividends. Thus the investor return for 1995 =
(Piw5 - Piw) / Piw4 + (Dign 5 / P|qi)4 ). This is the llrm 's Mom/zK// rate o f return. The
firm's real rate o f return is calculated as:
Real Rate of Return - ((1 4- Nominal Rate o f Return) / ( 1 + Inflation Rate)) - 1.
The inflation rate"^ used here is calculated from the Consumer Price Index for A ll Urban
Consumers (Ibbotson Associates. 1999). Table 3 lists the average annual stock prices for each firm over the ten years, while Table 5 includes mean nominal and real rates o f return. .Appendix B shows the inflation rates over the ten years.
Capital structure
The proxies for the firm's capital structure are
(i.) its debt to asset ratio (Total Debt /Total Assets), and
(ii.) its debt structure index i.e. (LT debt/Total debt)/(Fixed assets/Total assets)
(See Table 4).
The debt to asset ratio captures firm solvency. A firm is solvent when its assets exceed its liabilities. The ratio for a firm can range from 0 (no debt financing) to 1 (assets financed entirely with debt). Traditional capital structure theory postulates the relationship between the firm's average cost o f capital and the debt to asset ratio to be U-
Intlation is defined as the rate o l change o f consumer goods prices I Ibbotson /Associates. I9dd). 29 shaped (Keown et al. 1998). For a while the firm can borrow funds at a relatively low after-tax cost o f debt. Even though the cost o f equity is rising due to increasing debt
financing (because the firm is assuming greater risk), it does not rise at a fast enough rate to offset the use o f the less expensive debt financing. During this range o f financing, the average cost o f capital declines and the stock price rises. Eventually, the increasing use of debt financing increases the risk o f firm insolvency (inability to meet debt obligations).
This causes the cost o f debt, and consequently, the firm's average cost o f capital to rise, hence the U-shaped cost curve.
Therefore, the higher the debt to asset ratio, the higher the poieniial profitability of the firm due to financial leverage, but also the greater the firm's risk o f insolvency.
The debt structure index (DSI) captures firm liquidity (\hQ ability to meet maturing débl obligations). It describes the balance between longterm debt and fixed assets, and, when compared to common financial ratio analysis, more sufficiently addresses the timing o f liability maturities and rates at which assets are converted to cash
(Mazzocco. 1989). The DSI encapsulates the matching principle of financial management, which advises management to match the maturity o f capital sources with the maturity o f their uses (Cooley and Roden. 1991). The DSI can range from zero to infinity. The higher the index, the more conservative the firm's liquidity management strategy. Longer term debt instruments typically carry a higher rate o f interest than short term debt instruments as debt holders are compensated for holding the security (and not cash) for a longer period o f time. This would be relatively more costly to the firm, but also relatively less risky as the firm reduces its refinancing risk as well as the risk from
30 rising interest rates (Brigham. Gapenski. and Ehrhardt. 1999). Therefore, as the DSI increases, it is expected that the firm's profits and risk will decrease.
Business Risk
The proxy for each firm's business risk is the variability o f its earnings before interest and taxes. This is measured as the coefficient o f variation in Operating Income (i.e. the standard deviation o f Operating Income tor the 1989 - 1998 period divided by the mean
Operating Income tor the period)(See Tables 4 and 5). The Operating Income o f the tlrm captures its earnings net o f or free t'rom the effects o f debt financing. The higher the coefficient o f variation in Operating Income for a tlrm. the greater is its business risk. C H A P TE R 4
FINDINGS AND DISCUSSION
4.1 Preliminary Analysis
Table 4 shows the average asset size o f the 50 agribusiness tlrms selected to be
S4.2 billion. Total assets range from $56 million (Golden Enterprises) to $37 billion
(Unilever NV). On average, a little more than half these assets (59 percent) are financed with debt. The majority of the assets arc long term in nature (59 percent), while on average. 41 percent of the total debt is long term debt. The debt structure index for the sample ranges from almost no debt (Weis Markets) to 1.6 (Chock full O' Nuts). The average debt structure index is approximately 0.7. The measure o f business risk ranges from O.l (Weis Markets. Fleming), to 1.1 (Great Atlantic & Pacific). The average coefficient o f variation is 0.34.
By way o f comparison. Table 6 shows the 50 agribusiness firms compared to four industries; banking, computer software and services, newspaper, and petroleum. These were selected from the industries listed in the Value Line Investment Survey. For each industry. 10 firms were selected (see Appendix C) and the measures o f the 10 firms were averaged to represent the mean o f that industry""*. The financial measures being compared are: the debt-to-asset ratio, the current ratio, the coefficient o f variation o f the operating
margin, the average beta o f the industry, and the capital gain returns.
O f the five industries shown, the agribusiness industry has the median level of debt-to-assets (59 percent). The banking industry has approximately 92 percent o f its assets in debt. This relatively high proportion is due to the fact that the banking industry in this context is comprised mainly of depository institutions (commercial banks), and customer deposits in the bank represent its debt. The petroleum industry, on the other hand, has the lowest level o f debt at 55 percent.
For the purpose o f comparison among the industries, liquidity is measured by the current ratio (current assets / current liabilities). Generally, the liquidity levels o f the industries seem to correspond to the average debt burden o f each industry. An industry with a relatively higher level o f debt carries a corresponding high level o f liquidity to hedge against the relatively higher default risk due to the level o f debt. The lone exception to this is the banking industry. In this case, default risk management is regulated through reserve requirements imposed by the federal government. O f these five industries, the agribusiness industry is among the more liquid, second to the computer software and services industry. As highlighted earlier, liquidity management is a major risk management tool o f the agribusiness industry.
The coefficient o f variation o f the operating margin measures the business risk o f each industry. O f the five industries, the banking industry is most risky. As we have seen, banking is a highly leveraged industry and is extremely sensitive to economic conditions
As ihe exception, the debt/asset and current ratios were taken from the .Almanac of Business and Indtisirial Financial Ratios, with a in its day-to-day operations. This is borne out by the prevalence of regulation in the industry.
The agribusiness industry bas the second highest level o f business risk. There are sources o f business risk specific to the agribusiness industry. Chief among these is the influence o f weather conditions. Production volatility is exacerbated by the sensitivity of cultivation and harvesting to climatic conditions. Competition within the industry is also keen, as agribusinesses operate within a domestic market that is mature, while facing increasing competition internationally.
The computer services and software industry has the highest level o f business risk behind the banking and agribusiness industries. O f the five industries highlighted, the newspaper industry has the least business risk.
Continuing the discussion on risk, beta is a measure o f the industry's market (or systematic) risk, that is. its sensitivity to factors systematically affecting all industries
(e.g. inflation or recession). An industry with a beta o f 1 has relatively the same systematic risk as the average industry in the market (where the market represents all industries in the economy).
The banking industry has the highest level o f systematic risk, followed by the computer software and services industry, the newspaper industry and the petroleum industry. O f the five industries, the agribusiness industry has the lowest o f level o f market risk. This may be mainly due to the fact that food products in the U.S. (and moreso in developing economies around the world) are relatively income inelastic. People need food to surv ive, and thus, even as economic and market conditions restrict choice and
singe figure representing the industry. 34 reduce disposable income, people w ill still demand food. Thus, while the agribusiness
industry has a relatively high level o f business risk, it has a relatively low level o f market
risk.
Finally, the capital gain returns measures the change in the annual stock prices
from one year to the next, not considering dividend yields to the investor. O f the five
industnes. the computer software and services industry posted the highest average capital gain over the 10 year period (1989 - 1998). Over this period o f time, technology stocks boomed, with the upward trend still continuing into the twenty-first century, though maybe not as dramatic. Banking has the next highest capital gains returns, followed by the newspaper industry. The agribusiness industry has the lowest returns above the petroleum industry, which was characterized by low oil prices in the early nineties.
The relatively low returns by the agribusiness industr>' highlights the importance of examining issues concerning investor returns to agribusiness and the factors that possibly affect it.
Table 7 shows the average rates o f return over the 10-year period. For the 50 firms, the average capital gain return is 12.4 percent, while the average nominal return is
16.2 percent. The average real return o f 13 percent compares with the Standard & Poor's
500 (large stock) return o f 16 percent, the small stock return of 11 percent, and the long term corporate and government bond returns o f 8 and 9 percent respectively. The risk free rate for that period was 2 percent. The standard deviations o f the returns are shown in parentheses. These reflect that, while the stock returns o f the agribusiness sector are more volatile than the long term bond market returns, they are not as volatile as the returns o f the S&P 500. which in turn are not as volatile as small stock returns.
35 4.2 Data Analysis
In analyzing the data, the regression was run using the original model proposed.
The result o f this regression is shown as Case 1 in Table 8 . Here the coefficients on the
debt-to-asset (debt) and liquidity variables are significant at the 5 percent level o f
significance. The sign o f the coefficient on the debt variable is positive, which says that
as we increase the use o f debt in the firm, investor returns w ill increase. However, as we
have earlier outlined, financial theory postulates that investor returns increase to a point
(due to the tax incentive and the lower cost o f debt capital), but then decreases due to
increased risk o f insolvency. We w ill therefore need to modify the specification o f the
regression equation in an attempt to account for this.
The coefficient for firm liquidity (the debt structure index) is negative, in keeping
with theory. This says that as the liquidity o f the firm increases (the more conservative its
liquidity management strategy) the profits o f the firm w ill decrease. The business risk
coefficient is positive, in keeping with the theory, but not significantly different from
zero. Overall, the model explains some 12 percent of the variations in investor real
returns.
The original model was then modified to include a linear dependent - logarithmic
independent variable specification (Case 2). This specification examines absolute changes in investor real returns against relative changes in the debt, liquidity, and
business risk variables. Here the model explains a little over 10 percent o f the variations
in investor real returns. The signs on each coefficient are in keeping with what we would expect from financial theory but. with the exception o f the constant term, none o f the
36 coefficients are significant at the 5 percent level. The debt and liquidity coefficients are significant at the 10 percent level o f significance.
In Case 3. the original model was modified to include nonlinear relationships between investor returns and debt, and investor returns and business risk. This specification explains some 34 percent o f the variation in investor returns. The sign on the coefficient on the debt-to-asset ratio is positive, and negative on the coefficient on the square o f that variable. This says that as we increase the use o f debt in the tlrm. investor returns first increase then decrease. However, while this is in keeping with what we would expect from financial theory, the coefficients on the debt variables are not significant at the 95 percent confidence level. The sign o f the coefficient on liquidity is what we would expect given financial theory, and the coefficient itself is significant at the 5 percent level o f significance. Regarding the business risk coefficients, as the firm assumes more and more business risk, investor returns w ill increase to some point, but then decrease as the increased risk increases the cost o f capital o f the firm. This would lead to a decline in the profitability o f the firm and thus in the return to investors. These coefficients are also significant at the 95 percent confidence level.
The model is modified in Case 4 to include a cubic term on the debt-to-asset variable. This exacts a curvilinear relationship between debt and investor returns. The signs on the coefficients say that investor returns first increase with the use o f debt by the firm, and then decreases at a decreasing rate. Figure 1 shows firm real return as a function o f debt. It indicates that firm returns again increase with increasing use o f debt. This may be explained as follows: i f after a period o f declining profitability due to the increased use of debt financing, the firm chooses to issue more debt, such a firm may be perceived
37 by investors as embarking upon significantly profitable projects, and hence able to
sustain the increased debt burden. This regression model specification explains some 46
percent o f the variation in investor returns, and all the coefficients are significant at the 5
percent level, with the signs on the coefficients all in keeping with financial theory. The standard error of the estimate is some 9 percent, the relatively lower variation indicating a better goodness-ot-tit.
Case 5 entails a model with a dependent variable o f investor real returns net o f dividend yield (capital gains only). The results are similar to that o f Case 4 with respect to the signs on the coefficients, but this model explains a slightly higher proportion o f the variation in investor returns (48 percent). An explanation for this can be that dividends have a stabilizing effect on stock return, as they tend to be fairly constant over time.
Dividends are paid on common stock at the discretion o f the firm's management, and thus management policy (capital structure decisions) and firm business risk do not affect the payment o f dividends as much as they do changes in the stock price. This model, being net o f dividends, would therefore better account for the variation in investor returns.
The approach o f using the means o f the variables for each firm has been used in the literature before. In his study examining capital structure, business risk and investor returns to agribusiness, Forster (1996) took the means o f these variables over the period
1984 to 1993. In his study, as with this, the results seem to be quite credible and concise
(that is, with significant estimates and signs largely in keeping with financial theory). But utilizing this approach gives rise to a concern o f the efficient use o f the data collected. By
38 using the averages o f the variables over the ten years, we are not able to fully capture specific or significant differences, if any, between the companies and between the years being studied.
One method o f dealing with possible differences between tlrms is to use risk- adjusted rates o f return as the dependent variable. This approach, utilized by Forster
( 1VV6 ), standardizes the rates ot return by dividing the mean return o f each company by the standard deviation o f those returns. This accounts for differences in the variability of returns across firms. However, it still utilizes the ten-year averages o f the variables involved, and does not address possible macro effects to the firms in any specific year(s).
Longitudinal Study
.■\ better way o f dealing with these is to adopt a panel or longitudinal approach to the data. In so doing, we combine both cross-sectional and time-series analyses of the data. Instead o f using the 10-year mean for each variable (return, capital structure, risk), every observation o f each variable for each company is included in the regression model, moving the number o f observations from 50 to 500.
A two-way Fixed Effects model*' is used with dummy variables utilized to account for differences between firms, and to test if any macro effect in any year affected all firms simultaneously. Possible macro effects affecting all firms include factors such as war, recession, and interest rates. The differences among firms may be due to any number o f characteristics unique to each firm (e.g. exceptionally competent management, highly motivated staff, etc.). Accurately identifying specific reasons for these differences would
39 therefore be difficult to do. The dummy variable coefficients in the Fixed Effects model are inserted merely for the purpose o f measuring shifts in the regression line arising from unknown variables. While we suggest what some o f these variables might be, the purposes o f our study do not necessitate their specification.
A dummy variable is used for each firm, representing a different constant term for each firm (50 firm dummies in all). The dummy variable for Firm I (Albertson's) is omitted so that it becomes the benchmark to which the other firms are compared. The dummy variable coefficient for each firm measures the extent to which the expected real rate o f returns for that firm differs from that o f Albertson's. A / test is used to determine if the returns to a firm are significantly different from the benchmark.
Similarly, a dummy variable is used for each year, starting with year 2 ( 1990).
The first year ( 1989) serves as the base year, and a t test is again used to determine if any year is significantly different from this base year.
The original regression model is therefore modified as follows:
RRj = ai + ctiD] + ...... + a + piDEBT,- + PjLIQ, + P4BRISK, + P5BR.ISK,- 4. s Where: ai = Constant (i.e. expected real return o f benchmark firm in base year). D] = Dummy variable for Fimi 2 (Alexander & Baldwin). D t2 - Dummy variable for Time 2 (1990). D tio = Dummy variable for Time 10 (1998). and so on. ~ .A Random Effects model could alternatively be used, but It assumes that the random error associated with each cross-section unit 40 The results o f this regression are shown in Table 9. The returns o f two tlrms (Fleming and Safeway) were significantly different from the benchmark firm at the 5 percent level of significance. Six years were significantly different from the base year (1990 to 1994. and 1998). It is difficult to identify specific causes for the differences in return between Albertson's. Fleming, and Safeway. Fleming may have significantly lower returns than Albertson's because it operates in the food wholesale sector, characterized by relatively less bargaining power and market share in the industry (Fadberg et al.. 1997). Safeway may have significantly higher returns than Albertson's because o f the unusually high level o f debt it carries (89 percent). Appendix B bears out that stock and bond returns for the years 1990 to 1994 and 1998 were generally significantly different from the average returns for that 10-year period. However, as said earlier, it is difficult to identify specific reasons for the differences among firms. The inclusion o f the 58 dummy variables also clouds the effect o f the capital structure and business risk variables on investor returns. The result is that the debt, liquidity, and business risk variables are insignificant at the 5 percent level. (agribusiness firm ) is uncorrelated w ith the other regressors, an assumption tiiat is unlikely in our case. 41 Sector Regression To better account for differences among tlrms. the tlrms were grouped according to sectors within the agribusiness industry: Food Processing. Food Retail (or Grocery Store), and Food Wholesale (see Table 10). Table 11 shows a comparison between the sectors. The food retail sector has the highest returns, followed by the processing sector. This may be explained by the fact that the retail industry has relatively the highest market share and bargaining power within the industry (Fadberg et al.. 1997). Processors also may have relatively substantial returns because o f considerable pricing power through adding value and utility to the food product. While there is no definite pattern in capital structure among the sectors, food wholesalers carry the highest levels o f debt and liquidity. Regarding business risk, the food processing sector has the highest level o f business risk. Processors receive most o f their input from farmers, thus production and harvesting risks experienced by farmers primarily affect processors, who largely utilize forward and futures markets to hedge against this. In running the regression, a dummy variable is used for each sector, representing a different constant term for each sector. The dummy variable for Sector 1 (Food Processing) is dropped, and the sector serves as a benchmark to which the other sectors are compared. Additionally, instead o f using dummy variables for time, the Standard & Poor's 500 Stock returns is used to capture the general market effect on all firms simultaneously. 42 The regression model capturing this is as follows: RRi = a i + a iD i + otgD] + PiSP 500i + piDEBT, + P3DEBT,- -f P4LIQ, P5BRISK, -PftBRISKj- + e Where: tt| = expected return for Sector 1 (Food Processing) in base year (1989). D] = Dummy variable for Sector 2 (Food Retail). D3 = Dummy variable for Sector 3 (Food Wholesale). SP500 = Returns calculated from S&P 500 Stock Composite Index (to capture market effect), and so on. A [ test is used to test if the sectors are significantly different from each other. .As Table 12 shows, the sector regression explains some 13 percent of the variation in investor real returns. The expected real returns for the Food Wholesale sector are significantly lower than those o f the Food Processing sector at the 10 percent level o f significance. This may be partly explained by the fact that the food processors have more flexibility in pricing than the food wholesalers and are more involved in the use o f futures contracts for hedging in commodity markets. Similarly, because o f the large volume o f product held at any one time, wholesalers may typically maintain higher levels o f liquidity. The coefficients on the debt and liquidity variables are significant at the 5 percent level, with signs in keeping with what we would expect from financial theory. The coefficients on the business risk variables are not significant at the 5 or 10 percent levels. 43 Causality At this point another issue arises in our analysis. One may ask: Does capital structure affect firm performance (stock price), or does the stock price affect capital structure? Indeed, one may argue that the ability of the firm to raise funds in the capital market (and hence the firm 's capital structure) is determined by the firm's performance in the market as gauged by its stock price. Yet we maintain that it is the decisions o f the firm's management regarding capital stmcture that lead to stock price reactions. The managers enact specific policies governing the operation o f the firm and the stock market reacts to these policies. Managers can then adjust policy based on the reaction o f the market, but the causal variable in this analysis is the capital structure management by the firm. Simultaneous Equation .Approach Nevertheless, in determining the rigidity o f our model, we must test for possible endogeneity between the return and capital structure variables. Investor real returns may be partly determined by the firm's capital structure (the subject o f our analysis), while capital structure may simultaneously be determined in part by firm investor returns. Furthermore, investor returns may be determined by the simultaneous interaction o f the debt and liquidity variables in our analysis. Table 13 shows the result o f a Hausman test testing for a possible endogenous relationship between investor returns and capital structure. In Step 1. the debt variable is regressed against the liquidity and business risk variables. In Step 2. investor real returns are regressed against the debt variables as well as the residual o f the regression o f Step 1. As the results show, the coefficient on Step I's residual ('ehat') is significantly different 44 from zero at the 5 percent level o f significance. The conclusion is that we can reject the null hypothesis that the coefficient on 'ehat' is equal to zero (i.e. we can reject the hypothesis that there is no simultaneous relationship between the real return and debt variables). In so doing, we stand, at most, a 5 percent chance of committing a Type 1 error (rejecting the null hypothesis when it is in fact true). We are therefore 95 percent confident in this analysis. Having made this determination, we conduct a Three-Stage Least Squares procedure, doing simultaneous regressions involving the following three equations: (1.) RRi = a, ^ a:D: ^ a^D] + pi DEBT, + p.LIQ, P 3BRISK, (2.) DEBTj = ttj - oiDi + ajD3 + PiSPSOOj + P^RRi P^LIQj (3.) L I Q i = ai ^ aiD] + asD] ^ piSPôOOj + p:BRISK, ^ P3BRISK,- + P4BRISK," Results are shown in Table 14. The first equation specifies our original model, with real returns being specified as a function o f debt, liquidity, and business risk. The second equation specifies debt as a function o f real returns and liquidity, while the third equation specifies liquidity as a function o f business risk (the cubic temi on business risk in Equation 3 is mainly for identification purposes). The exogenous variables are assumed to be the sector dummy variables, the S&P 500. and business risk. The results in Table 14 show debt to be a significant determinant o f real returns (Equation I). The results o f this equation also indicate the Food Wholesale sector having significantly lower returns than the Food Processing sector, a finding already discussed in 45 our sector regression shown in Table 12. At the 5 percent level o f significance, debt is shown to be a significant determinant o f investor real returns. Liquidity and business risk are not shown to be significant variables in determining real returns. Equation 2 shows liquidity to be a significant determinant o f debt. The positive coefficient suggests that as liquidity increases the firm can afford to assume more debt, as higher liquidity reduces default risk. Real returns, as indicated, is not a significant determinant o f debt. The results o f Equation 3 show the Food Retail sector to have significantly higher levels o f liquidity than the Food Processing sector, while the Food Wholesale sector has significantly lower levels than the processing sector. The findings regarding the wholesale sector help to explain what we observed in Equation 1. It suggests that maintaining higher levels o f liquidity in the sector reduce that sector's profitability and returns relative to the processing sector. The retail sector is shown to have significantly lower levels o f liquidity but not a corresponding higher rate o f return. This may be explained by the fact that the retail sector is characterized by relatively low profitability, as was highlighted earlier in the study. The results o f Equation 3 also show the coefficient on the S&P 500 to be a significant determinant o f liquidity at the 5 percent level o f significance. It suggests that as there are positive shocks to the market, the agribusiness firms w ill maintain higher levels o f liquidity, possibly to minimize default risk as they seek to increase borrowings to take advantage o f opportunities. The results also indicate that business risk is significant to liquidity at the 5 percent level. The signs on the coefficients suggest that as the firm's business risk increases, the firm w ill initially increase its liquidity to mitigate 46 against this increased business risk. However, as business risk continues to increase, the firm w ill begin to reduce its financial risk (debt levels) and consequently reduce its liquidity levels. Combining the results o f the simultaneous equations and the sensitivity analysis seem to indicate that debt, liquidity, and business risk all relate to investor real returns either directly or indirectly. Debt seems to relate most directly to investor real returns, while business risk to liquidity, and liquidity to debt. Table 15 shows a sensitivity analysis o f the variables. The first portion o f the analysis is conducted using the system o f equations shown in Table 14. Holding each variable at its mean, the equations predict a real return rate o f 2 0 percent, a debt-to-asset ratio o f 0.6, and a liquidity level o f 0.76. These compare to the actual means o f 13 percent. 0.59 and 0.67 respectively. If there is a positive shock to the macroeconomy o f 10 percent, or if a market effect causes returns on the overall market to increase by 10 percent, real returns to the agribusiness industry w ill increase to 22 percent. The debt and liquidity levels w ill increase to 0.61 and 0.77 respectively. If there is a 10 percent increase in the average business risk o f the industry, the average liquidity level o f the industry w ill be most affected, increasing to 0.78. while the debt and real return levels w ill increase to 0.61 and 20.3 percent respectively. The second portion o f the sensitivity analysis o f Table 15 is conducted using the sector regression equation of Table 12. This looks specifically at the sensitivity o f real returns to changes in the other variables. At the mean o f the other variables, real returns is predicted to be close to 20 percent. The response o f real returns to changes in the SP500 47 and the business risk variables is similar to tliat predicted from the system o f equations. In response to a negative shock to the macroeconomy o f 10 percent, real returns fall to 19 percent. As the average business risk o f the industry decreases by 10 percent, the average real returns falls 1.5 percent to 19.5 percent. Real returns increase with an increase in the level o f debt, and decrease as the debt level is decreased. As liquidity increases real returns decrease, and vice versa. The findings o f these sensitivity analyses are what we would expect, given the finance literature and the findings o f our research. Overall, the research, progressing with increasing complexity o f analysis, has had fairly concise and consistent findings. A conclusion o f this study on capital structure, business risk and investor returns must include an overview o f all the analyses conducted herein. 48 CHAPTERS SUMMARY AND CONCLUSION The agribusiness industry is undergoing transition to a more competitive environment where efficiency is becoming increasingly important. Improving efficiency in the agribusiness industry would mean examining and improving upon techniques and strategies in many areas, including cultivation, production, transportation, storage, finance, and marketing. This research has examined the relationship between investor returns, capital structure, and business risk. It is an attempt to address the issue of financial efficiency in the agribusiness industry and. while the issue is a large one. the study specifically deals with examining the effects o f debt, liquidity, and business risk on investor returns. The analysis o f the study was conducted in three phases: a regression utilizing the 10-year averages o f each variable, a longitudinal study looking at the cross-sectional data over time, and a simultaneous equation approach, examining the simultaneous interaction among the variables included. The shortcomings o f the regression utilizing the 10-year averages are twofold. Primarily, averaging the data over the ten years does not efficiently utilize the information contained in the data. The data may contain significantly high measures in 49 particular years and significantly low measures in other years, but this would not necessarily be highlighted as the data is averaged. There is thus a smoothing effect as the data is averaged. Secondly, i f there is simultaneous interaction between the variables o f the model, this is not dealt with by this approach. The longitudinal study more fully captures the Informational content o f the data, looking at each measure for every year. While this is a better approach than the mean regression, it also fails to account for the simultaneity issue. The simultaneous equation approach therefore represents a superior econometric technique than the mean regression and longitudinal study as it both utilizes all the data and accounts for simultaneous interaction present in the data. The preponderance o f the evidence indicates that capital structure is relevant to investor returns to agribusiness. As the firm's debt-to-asset ratio increases, investor returns first increase and then decrease at the point where the increased use o f debt capital causes the cost o f the firm's capital to increase due to the increased risk o f default. This is in keeping with traditional finance theory, which postulates a U-shaped average cost o f capital curve as the debt-to-asset ratio increases. Firm liquidity is shown to be negatively related to investor returns. The higher the liquidity o f the firm (the more conservative its liquidity management strategy), the lower the returns to agribusiness investors. This is due to the fact that liquid assets are generally less risky (having lower interest rate risk, lower probability o f default, etc.) than illiquid assets, and therefore offer lower rates of return. As the business risk o f the firm increases, it w ill assume relatively higher levels o f liquidity to mitigate against this increasing risk. As the financial risk o f the firm increases with the increased use o f debt financing, the 5 0 firm w ill again respond with relatively higher levels o f liquidity. The increased risk to the firm is typically associated with increased rates o f return (at least initially), while increasing liquidity lowers returns. Thus, while liquidity management is an important firm response to its business and financial risk, the firm must take into account this risk- retum tradeoff as it designs and implements its risk management strategies. While business risk is indicated to be significant to investor returns in the mean regression, it is not found to be significant in the longitudinal study. The reason for this seemed to lie in the simultaneous interaction o f the return, debt and liquidity variables. The simultaneous equation approach suggests that business risk is directly relevant to liquidity and debt, which in turn directly impact investor returns. If the business risk o f a firm is deemed to be relatively high, that firm would probably assume relatively little debt (financial risk) and. conversely, if the business risk o f the firm is not perceived by that firm's management to be substantial, then it would be more willing to assume higher levels o f debt. .As we have discussed, these levels o f debt w ill have an impact on the investor’s return. As its business risk increases, the firm tends to assume higher levels o f liquidity, which tend to reduce investor returns. If the firm has relatively low business risk, it can maintain lower levels o f liquidity, which allow for higher rates o f return to investors. Business risk therefore seems to interrelate with capital structure to affect investor returns, seemingly at odds with Capital Asset Pricing Model theory, which says the only relevant risk to investors is market risk. However, a closer inspection w ill reveal that the CAPM was formulated to analyze risk in a portfolio context, wherein the investor is assumed to hold a portfolio o f well-diversified stocks. Our analysis says that business risk w ill affect the returns to the firm 's common stock. The fact that the investor can hold that stock in a well-diversified portfolio o f stocks is secondary. Having said this, we can again look at agency theory and the potential implications these results w ill have. Well-diversified investors (stockholders) can better tolerate financial distress than firm managers, as losses on one stock can be offset by gains on another stock in the same or another industry. Managers of the firm tend to not be as diversified as investors, and thus will prefer stable returns to the firm to better ensure the continuity o f their jobs. Firm managers may therefore be more conservative in their use o f leverage than the average investor would prefer, and moreso as the business risk o f the firm increases. In such cases, managers can be motivated to act in the interests of the investors through the structure o f the incentive programs, as well as the threat o f takeover by other firms. Similarly, we can examine the agency problem between stockholders and bondholders. Because increasing the use of debt increases the returns to firm stockholders (at least up to a point), while higher liquidity levels tend to reduce profitability, the stockholders, through the management of the firm, can undertake actions detrimental to the bondholders o f the firm. Stockholders may attempt to increase the business risk o f the firm as well as assume higher levels o f debt in the hope o f achieving higher returns. Bondholders, who have affixed claim on the assets o f the firm, will not share in theses higher returns. Yet. if the firm fails, the bondholders w ill have to share in the losses. Thus agency costs w ill include costs borne by the stockholders arising from restrictive covenants on debt, which serve to protect the bondholders. S') Based on the findings herein, there is no reason to believe that the U.S. agribusiness industry is significantly different from the typical U.S. industry. The findings o f the relevance o f capital structure and business risk to investor returns are consistent with the postulations o f financial theory and with what we would expect in the average industry. Though the agribusiness industry is characterized by particular types o f risk not relevant to many other industries (e.g. weather and atmospheric conditions), the fundamental nature o f risk and its effect on investor returns are the same. The returns to the agribusiness industry are neither abnormally high nor low. suggesting that the risk-retum tradeoff in this industry is dealt with in a very similar manner to that within other industries, and that the market recognizes this. Usefulness of Findings This research helps to determine whether the firm's mixture o f debt and equity instruments has any impact on the returns to agribusiness investors. The study does not attempt to arrive at the optimal debt-equity mix for agribusiness firms, but mainly determines a correlation between firm performance and capital structure. The research also incorporates the issue o f business risk in examining investor returns. It has shown that business risk seems to affect investor returns as it interrelates with capital structure. While it is not shown to directly affect investor returns, it is relevant as it determines the levels o f debt and liquidity that the firm can prudently assume, which in turn directly affects investor returns. D3 Implications for Further Research (i.) Future research can attempt to identity the optimal financing mix o f the agribusiness firms, that is. the levels o f debt and liquidity assumed by the tlrm that yield the highest rates of return to the investor. (ii.) Further research can be done comparing the performance o f the U.S. agribusiness industry with the traditional farming sector in the U.S. One can examine the effects o f capital structure on performance between these industries. (iii.) The performance o f the U.S. agribusiness industry can be analyzed through the year 2005 to examine the impact that the deregulatory policies of the World Trade Organization (WTO) has had on the industry. (iv.) A study can be done comparing the U.S. agribusiness industry with the agribusiness industry of one or more countries. To the agribusiness investor, the practitioner, the academician, and the researcher, the hope is that this study has helped to shed light on the agribusiness industry, answer relevant questions, as well as provide a backdrop for future research to be conducted in this and related areas. 54 TABLES AND FIGURES Area 1975 1935 2000 2053 (Millions, except % change) ° /n r.hg % r.hn Çhq World 4085 4850 18.73 6241 28.68 10713 71.66 North America 239 264 10.46 305 15.53 419 37.38 United States 216 238 10.19 275 15.55 383 39.27 Canada 23 25 8.70 30 20.00 36 20.00 Europe 474 492 3.80 490 •0.41 486 -0.82 Western Europe 361 371 2.77 390 5.12 358 -8.21 Central Europe 113 121 7.08 127 4.96 128 0.79 USSR (Former) 255 279 9.41 312 11.83 386 23.72 Oceania 21 25 19.05 31 24.00 44 41.94 Australia/New Zealan 17 19 11.76 23 21.05 28 21.74 Middle East 133 177 33.08 280 58.19 821 193.21 Asia 2232 2670 19.62 3424 28.24 5165 50.85 Japan 112 121 8.04 128 5.79 109 -14.84 China 918 1050 14.38 1303 24.10 1655 27.01 India 623 770 23.60 1018 32.21 1623 59.43 Indonesia 137 172 25.55 221 28.49 341 54.30 Latin America 323 403 24.77 531 31.76 848 59.70 Mexico 61 77 26.23 103 33.77 170 65.05 Brazil 109 138 26.61 181 31.16 281 55.25 Africa 408 541 32.60 842 55.64 2543 202.02 Source: Economie Research Service, U.S. Department of Agriculture, April 1993. TABLEl: WORLD POPULATION ESTIMATES AND PROJECTIONS TO YEAR 2050 FOR REGIONS AND COUNTRIES 56 1 Albertson's, inc. 26 Lance Inc. 2 Alexander & Baldwin 27 McCormick & Co. 3 American Stores 28 Michael Foods 4 Archer Daniels Midland 29 Monsanto 5 Bestfoods 30 Nash Finch Co. 6 Campbell Soup 31 PepsiCo 7 Casey's General Stores 32 Pilgrim's Pride 8 Chiqulta Brands 33 Pioneer Hi-Bred 9 Chock Full O' Nuts 34 Quaker Oats 10 Coca Cola Company 35 Ralston Purina 11 ConAgra 36 Richfood Holdings 12 Dean Foods 37 Ruddick Corp. 13 Dole Food 38 Safeway 14 Fleming Companies 39 Sara Lee 15 Flowers Industries 40 Smithfield Foods 16 Food Lion 41 Supervalu Inc. 17 General Mills 42 Sysco Corp. 18 Golden Enterprises 43 Tootsie Roll 19 Great Atlantic & Pacific 44 Tyson Foods 20 H J Heinz 45 Unilever NV 21 Hershey Foods 46 Universal Foods 22 Hormel Foods 47 Weis Markets 23 IBP Inc. 48 Whitman Corp. 24 International Multlfoods 49 Winn Dixie Stores 25 Kellogg 50 Wrigley Jr. Co. Source: The Value Line Investment Survey. TABLE 2: AGRIBUSINESS FIRMS SELECTED FOR STUDY 57 ncke laaa laaz laae 1395 1994 1993 1992 1991 1999 1999 Albertson’s, Inc. ABS 6 3 .6 8 7 4 7 2 5 3 5.525 3 2.875 29 25.75 2 5.2 5 19.525 1 8 2 5 13.875 Alexander & Baldwin ALEX 2 3.2 5 27.31 25 23 22.25 25.75 2 4.75 28.25 22.25 37 5 American Stores ASC 3 6.9 4 2 0.56 20.44 13.38 13.44 10.75 10.94 8 .4 7 6 56 7 0 6 Archer Daniels Midland ADM 17 125 20.556 19.953 15.547 15.958 11 89 13.187 15687 10 265 9 9 3 7 Bestfoods BFO 5 3.25 50.25 35.062 3 1.9 37 24.781 22.155 23.552 2 1.045 19.375 17.156 Campbell Soup CPB 55 55.625 38.406 28.718 21.062 19.525 2 0 .1 5 5 2 0.3 43 14 14,031 Casey's General Stores CASY 13.031 12.687 9.375 10.937 7.5 5.125 4 187 3 .406 1.281 2.781 Chiqulta Brands COB 9 .5 6 2 15.312 12.75 13 75 13.525 11.5 17 25 40 32 15.525 Chock Full O' Nuts C H F 5 .1 2 5 7 5 5 .2 5 5.578 7 546 7 89 7 4 3 7 5 062 5 546 Coca Cola Co. KO 57 5 6.5 87 5 2.525 3 7.1 25 25.75 22.437 20 937 20.052 11 525 9.555 C onAgra CAG 31 5 33.125 24.875 2 0.525 15.525 13.187 15.562 17 75 1 2 3 2 8 9 5 D ean Foods DF 4 0 .8 1 2 59.75 3 2 2 5 2 7 5 29 32.625 2 8.1 25 3 0.5 25 75 22.171 Dole Food DOL 30 45.75 33.875 35 19.859 2 3.122 2 7 75 31 093 2 5 3 7 5 3 0 0 1 5 Fleming Companies FLM 1 0.3 75 13.437 17 25 2 0.525 23.25 24.75 31 5 3 4.375 35 25 30.125 Flowers Industries FLO 23 937 20.552 14328 8 .078 8.052 8.5 8 .8 2 8 7 2 1 8 6.109 8 4 3 7 Food Lion FDLN 1 0.052 8.25 10.125 5.587 5.125 5.525 8 .1 2 5 18.171 9 078 7 5 G eneral Mills GIS 77 75 71.525 53.525 5 7.75 47 49.984 5 5.3 59 6 0.552 4 0 3 1 2 2 9 755 Golden Enterpnses GLDC 5 .3 7 5 5.75 7 75 8 .125 5.875 7 8 7 5 7 5 6.875 7 2 5 5 875 G rea t AUanbc & PaciHc GAP 2 9 .5 2 5 2 9.5 87 3 1.875 23 18.125 27 2 3.8 75 28.375 44 125 5 8 8 7 5 H J Heinz HNZ 5 6 .6 2 5 5 0.812 35.75 3 3.125 24.5 23.921 29421 25.921 2 3 2 5 2 3 3 2 8 Hershey Foods HSY 6 2 .1 8 7 61.937 43.75 32.5 24 187 24.5 2 3 5 2 2.187 18.75 17 937 Hormel Foods HRL 32.75 32.75 25.875 2 4.5 25 24.75 22.125 2 3 5 21.375 19.125 15 875 10 437 IBP Inc. IBP 2 9 125 2 0.9 37 2 4 2 5 25.25 15.125 12.937 10.052 7 312 7 687 International Mulbfoods IMC 2 5 .8 1 2 2 8.312 18.125 2 0.125 18.25 19.25 2 7 375 28.875 24 328 19 328 Kellogg K 3 4 .1 2 5 49.525 32.8125 3 8.625 29.052 28.375 3 3 5 32.687 18 958 16.906 Lance Inc. LNCE 19.9 37 2 6.3 12 18 15.375 18 22.75 2 3.75 21 75 21 24 McCormick & Co. MKC 3 3 .8 1 2 28 2 3.552 24 125 18.25 24 625 3 0.25 26.375 1 2875 1 2 8 7 5 Michael Foods MIKL 30 2 4.375 12.75 1 1 5 2 5 9.875 8 10.125 14 875 15 9 0 7 8 M onsanto MTC 4 7 .5 42 38.875 2 4 5 14.093 14.671 1 1 5 31 13.578 9 5 5 6 11 531 Nash Finch Co. NAFC 14 2 5 19 21 25 18.25 15.5 17.75 18.5 17 17 25 24 875 PepsiCo P EP 4 0 8 7 5 3 6.25 2 6.89 25.671 15.556 18.781 19.078 15.552 11 953 9 .796 Pilgrim's Pride CHX 19 937 15.562 8.625 6 5 7 5 9.75 5.875 5 .125 6 5 5 5 5 6 7 5 Pioneer HFBred PHB 2 5.5 35.75 23.328 18.545 11.5 13 8 9 2 1 8 .2 5 4 0 7 8 4 9 6 8 Q uaker Oats O AT 5 9.5 52.75 38.125 34.5 30.75 35.5 3 2.525 37.437 25.421 2 7 765 Ralston Purina RAL 3 2 .1 2 5 29 953 23.556 2 0.1 09 14.39 1 1 3 2 8 13.578 1 5 0 7 8 14 609 11 828 Richfood Holdings RFH 2 0.7 5 28.25 24.-,25 1 7 8 2 8 10.571 11.871 7 0 45 4 6 7 1 3.125 2.525 Ruddick Corp. RDK 23 17.437 14 11.5 9.552 115 10.052 7 625 5 5 5 2 7 2 1 8 S afew ay SWY 6 0 .9 3 7 31.625 21 375 12.875 7.968 5.312 3.25 4 4 3 7 3 062 7.937 S ara Lee SLE 2 8 .1 8 7 28.155 18.625 15 12.525 12.5 15 1 4 4 5 8 8 375 Smithfield Foods SFDS 3 3 .8 7 5 33 19 15.875 15 9.25 9 5 8.375 4 937 3 .052 S upervalu Inc. SVU 28 20.937 14.187 15.75 12.187 18.125 15.552 13.625 11 875 14.5 Sysco Corp. SYY 2 7 .4 3 7 22.781 15.312 16.25 12.875 14.625 13.157 11556 8.406 7 906 Tootsie Roll TR 37.984 29.453 18.125 1 7.509 13.255 14.859 15.75 14.484 7 812 6 .796 Tyson Foods TSN 2 1 .2 5 2 0.5 2 2.828 17.421 14.171 15 15.171 13.328 10.328 8 .295 Unilever NV UN 8 7 .0 4 6 5 4.0 93 43.234 33.578 26.751 25.5 2 3.343 2 3.9 06 19.5 17.89 Universal Foods UFC 2 7 .4 3 7 21.125 17.525 2 0.0 52 13.75 16.052 15.875 19.937 15.052 11.937 Weis Markets WMK 3 8 .8 7 5 35 3 1.8 75 2 8.25 24.125 27 2 5.125 2 5.375 30 25 30 Whitman Corp. WH 2 5 .3 7 5 15.375 14.375 1 4.609 10.843 10.218 9 .255 8 .4 0 6 4.655 7 4 2 1 W inn Dixie Stores W IN 4 4 .8 7 5 43.587 31.625 3 6.875 25.587 25.812 3 8.3 12 18.75 15.1875 15.25 Wrigley Jr. Co. WWY 89.562 79.562 56.25 5 2.5 49.375 44.125 32.125 25.921 17.078 1 7 8 7 5 TABLE 3: AVERAGE ANNUAL STOCK PRICE: 1989 -1998 58 Total Assets Fixed/Total Long-Term/ Debt Structure C.V. of Ooec. Firm (Mllllonajl Debt/Assets Assets Total Debt Index Income 1 Albertson's, Inc. 3625.7 0.519 067 2 0.428 0.633 0 3 7 2 Alexander & Baldwin 1650 0.603 0.859 0.553 0.644 0.242 3 American Stores 7498.5 0.744 0.709 0.582 0.821 0.124 4 Archer Daniels Midland 8650.9 0.413 0.581 0.469 0.007 0.237 5 Bestfoods 5593.5 0.695 0.626 0.469 0.75 0.196 6 Campbell Soup 5121.6 0 63 0.685 0 43 5 0.635 0.335 7 Casey's General Stores 342.2 0.517 0.856 0.464 0.543 0 43 5 8 Chiqulta Brands 2527 0.72 0.608 0.688 1 129 0.343 9 Chock Full O' Nuts 197 0.702 0.492 0.794 1.613 0.402 to Coca Cola Co. 13192.3 0.602 0.616 0.217 0 353 0 349 11 ConAgra 9438.1 0.744 0.482 0.37 0.768 0 303 12 Dean Foods 1017.3 0 5 2 0.564 0.361 0.64 0 167 13 Dole Food 2741.1 0.698 0.602 0 46 2 0.768 0.156 14 Fleming Companies 3501 0.723 0 57 9 0.561 0.969 0 094 15 Flowers Industries 855 0.565 0.705 0.348 0.494 0 85 7 16 Food Lion 2566.9 0.599 0.549 0 4 3 7 0.796 0 27 17 General Mills 3825.5 0.805 0.739 0.426 0.576 0.146 18 Golden Enterprises 55.8 0.17 0.465 0.051 0.111 0,21 19 Great Atlantic & Pacific 3053.3 0.678 0.604 0.425 0.702 1 063 20 H J Heinz 6589.1 0.638 0.616 0.407 0661 0 23 5 21 Hershey Foods 2736.4 0.536 0.677 0 397 0 586 0 23 6 22 Hormel Foods 1123.9 0.403 0.434 0.402 0.926 0.192 23 IBP Inc. 1928 0.583 0.548 0 4 5 0 821 0 576 24 International Multifoods 5878 0 42 8 0.652 0.472 0 715 061 25 Kellogg 43178 0.617 0.7 0.22 0.315 0.147 26 Lance Inc. 278.6 0.219 0.569 0.181 0.318 0.26 27 McConnick & Co. 1231.7 0.65 0.579 0 40 3 0.695 0.174 28 Michael Foods 2832 0 584 071 0 51 0 718 0 904 29 Monsanto 10302.9 0.635 0.597 0.54 0 904 0 198 30 Nash Finch Co 599.1 0.652 0.43 0.382 0.889 0,44 31 PepsiCo 21084 7 0.714 0.773 0.566 0.732 0.178 32 Pilgrim's Pride 460.9 0.695 0.602 0.529 0 879 0 6 7 33 Pioneer Hl-Bred 1273 0 308 0.431 0.098 0 22 7 0.333 34 Quaker Oats 3433.4 0.784 0.635 0.464 0.73 0 18 35 Ralston Purina 4789.8 0.827 0.638 0.562 0.88 0.131 36 Richfood Holdings 500.1 0.65 0.445 0.31 0.696 0.721 37 Ruddick Corp. 651.6 0.561 0.589 0.385 0 653 0246 38 Safeway 6039.3 0.894 0.713 0.591 0.829 0.543 39 Sara Lee 10377.2 0.634 0.607 0.342 056 3 0281 40 Smithfield Foods 513.4 0.66 0.507 0.439 0.665 0.641 41 Supervalu Inc. 3655.2 0.675 0.605 0.483 0.798 0 247 42 Sysco Corp. 2732.2 0.583 0.442 0.383 0.867 0.307 43 Tootsie Roll 298.6 0.207 0.53 0.23 0.434 0.378 44 Tyson Foods 3591.3 0.669 0.693 0.544 0.784 021 9 45 Unilever 14V 36834 0.723 0.429 0.351 0.618 0.442 46 Unlveisal Foods 734.3 0.566 0 58 4 0.479 0.82 0.119 47 Weis Markets 847.2 0.131 0.325 0 0 0 096 48 Whitman Corp. 2314.8 0.659 0.702 0.593 0.844 0.193 49 Winn Obde Stores 2243.3 0.519 0.373 0.174 0.466 0 29 50 Wrigley Jr. Co. 938 9 0.28 0.386 0.225 0.582 0.327 MEAN 4174.9 0.587 0.590 0.413 0.695 0.340 TABLE 4: MEASURES OF CAPITAL STRUCTURE AND BUSINESS RISK: 1989 • 1998 MEAN 59 A v r a o e Average Average Debt/Assets Debt/Aasets*2 Debt Structure C.lL^LQfiac Caoltat Gain Nom inal Baal (Capital (Capital Index D.S.I.*2 Income c . v ^ H rm Return Return Return S to ic tu n l Structural fUauldltvl (Liquidity) iBus.Rfskl (Bus.RiSl Albertson’s. Inc. 0.152 0.175 0.141 0-519 0.269 0.633 0.401 0.370 0.137 Alexander & Baldwin -0.032 0.003 -0.025 0.603 0.364 0.644 0.415 0.242 0.059 Amencan Stores 0.197 0.264 0.229 0.744 0.554 0.821 0.674 0.124 0 0 1 5 Archer Daniels Midland 0.065 0.075 0.043 0.413 0.171 0.807 0.651 0.237 0 0 5 6 Bestfoods 0.119 0.180 0.148 0.695 0.483 0.750 0 563 0.196 0.038 Campbell Soup 0.147 0.193 0.159 0.630 0.397 0.635 0.403 0 335 0.112 Casey’s General Stores 0.288 0.311 0.276 0.517 0.267 0.543 0.295 0.435 0.189 Chlqutta Brands 0.032 0.050 0.017 0.720 0.518 1.129 1.275 0.343 0.118 Chock Full O' Nuts 0.101 0.101 0.070 0.702 0.493 1,613 2.602 0.402 0.162 Coca Cola Co. 0.225 0.262 0.225 0.602 0.362 0.353 0.125 0 349 0.122 ConAgra 0.131 0.174 0.139 0.744 0.554 0 7 6 8 0-590 0.303 0 092 Dean Foods 0.099 0.123 0.090 0.520 0.270 0 6 4 0 0.410 0.167 0 0 2 8 Dole Food 0 0 3 7 0.050 0.020 0.698 0.487 0.768 0.590 0.156 0 0 2 4 Fleming Companies -0.082 -0.054 •0.083 0.723 0.523 0.969 0 9 3 9 0 094 0.009 Rowers industries 0.132 0.203 0.170 0-565 0.319 0.494 0 2 4 4 0.857 0.734 Food Lion 0.125 0.140 0.107 0.599 0.359 0.796 0 6 3 4 0.270 0 0 7 3 General Mills 0.119 0.156 0.122 0.805 0.648 0576 0332 0.146 0 021 Golden Enterpnses -0.072 -0 0 1 4 -0.042 0.170 0.029 0.111 0 0 1 2 0 210 0.044 Great Atlantic & Pacific -0.024 -0 006 -0.034 0 678 0.460 0.702 0,493 1 063 1 130 H J Heinz 0.094 0.133 0.101 0.638 0.407 0.661 0 437 0.235 0.055 Hershey Foods 0.138 0.178 0.144 0.536 0.287 0 5 8 6 0 343 0 2 3 6 0.056 Hormel Foods 0.094 0.115 0.082 0 4 0 3 0.162 0 9 2 6 0 857 0.192 0.037 IBP Inc 0.159 0.185 0.150 0.583 0.340 0.821 0674 0.576 0.332 International Multifoods 0 0 40 0.079 0.048 0.428 0.183 0.715 0.511 0.810 0 656 Kellogg 0.109 0.162 0-128 0 6 1 7 0.381 0 3 1 5 0,099 0.147 0.022 Lance In c -0.024 0.021 -0 007 0 2 1 9 0.048 0.318 0.101 0 260 0 068 McComSck & Co. 0.138 0.163 0.130 0.650 0.423 0.695 0 4 8 3 0.174 0.030 Michael Foods 0.155 0.172 0.138 0 584 0.341 0.718 0.516 0.904 0.817 Monsanto 0.170 0.290 0.253 0.635 0.403 0.904 0.817 0.198 0.039 Nash Rnch Co. -0.072 -0.035 -0.062 0.652 0.425 0.889 0.790 0 4 4 0 0.194 PepsiCo 0.163 0.197 0.163 0.714 0.510 0.732 0.536 0.178 0.032 0.449 Pilgrim's Pride 0.204 0.215 0.182 0.695 0.463 0 879 0.773 0 670 Pioneer HFBred 0.268 0.382 0 342 0.308 0.095 0.227 0.052 0.333 0.111 Quaker Oats 0.107 0.149 0.117 0.784 0.615 0.730 0 5 3 3 0.180 0 032 Ralston Purina 0.114 0.185 0.151 0.827 0.684 0 8 8 0 0.774 0.131 0017 Richfood Holdings 0.254 0.265 0.228 0.650 0.423 0.696 0.484 0.721 0.520 Ruddick Corp. 0.129 0.172 0.139 0.561 0.315 0.653 0.426 0.246 006 1 Safeway 0.423 0.423 0.388 0.894 0.799 0.829 0.687 0.543 0.295 Sara Lee 0.140 0.200 0.166 0.634 0.402 0.563 0.317 0.281 0.079 Smithfield Foods 0 3 0 9 0.309 0.270 0.660 0.436 0.865 0.748 0.641 0.411 Supervalu Inc 0.088 0.141 0.109 0.675 0.456 0.798 0.637 0.247 0.061 Sysco Corp. 0.152 0.179 0.146 0.583 0.340 0.867 0.752 0.307 0.094 Tootsie Roll 0.222 0.243 0.208 0.207 0.043 0.434 0.188 Û.37& 0.143 Tyson Foods 0.116 0.121 0.087 0.669 0.448 0.784 0.615 0.219 0.048 Unilever NV 0.162 0.265 0.229 0.723 0.523 0.818 0.669 0.442 0.195 Universal Foods 0.065 0.134 0.101 0.566 0.320 0.820 0.672 0.119 0,014 Weis Markets 0.032 0.059 0.029 0.131 0.017 0.000 0.000 0.096 0.009 Whitman Corp. 0.143 0.191 0.158 0.659 0.434 0.844 0.712 0.193 0.037 Winn Dixie Stores 0.138 0.190 0.156 0.519 0.269 0.466 0 2 1 7 0 290 0.084 Wrigley Jr. Co. 0.169 0.224 0.189 0.280 0.078 0.582 0.339 0.327 0.107 TABLE 5: INVESTOR RETURN. CAPITAL STRUCTURE. AND BUSINESS RISK: 1989 • 19M MEAN 60 C.V. of Capital Debt/ Current Operating Gain INDUSTRY Assets Rgüo Margin Beta Return Agribusiness 0.587 1.42 0.34 0.74 0.12 Banking 0.922 1 0.422 1.23 0.22 Computer Soft. & Serv. 0.612 1.58 0.259 1.18 0.41 Newspaper 0.561 1.31 0.168 0.93 0.14 Petroleum 0.546 1.16 0.186 0.82 0.08 Notes: a. With the exception of the agribusiness industry, the industry measures were calculated using the mean measures of 10 firms in each industry (see Appendix C for a listing of these firms). For the agribusiness industry, the mean measures of the 50 firms were used. b. With the exception of the coefficient of variation figures, measures for the industries are calculated using 1989 - 1998 means. For the coefficient of variation, 1990 - 1998 means were used. c. Current ratio used as measure of liquidity for all industries (including agribusiness). d. The coefficients of variation of the operating margins for the industries are compared to the coefficient of variation of operating income for agribusiness, and the coefficient of variation of net interest income for banking. TABLE 6: COMPARISON OF AGRIBUSINESS TO SELECTED INDUSTRIES 61 50 Agribusiness Firms: Capital Gain 0.1243 Nominal Return 0.1618 Real Return 0.1286 (.1279) S&P 500 Real Return 0.1647 (.1491) Small Stock Real Return 0.1145 (.1944) Longterm Corporate Bonds 0.0785 (.0924) Longterm Government Bonds 0.0881 (.1125) U.S. T-Bill Real Return 0.021 (.0122) Notes: a. S&P 500 = Standard & Poor’s 500 Stock Composite Index. b. Small Company Stock = A portfolio of stocks represented by the fifth capitalization quintile of stocks on the NYSE. c. Longterm Corporate Bonds = Salomon Brothers longterm, high-grade corporate bond total return index. d. Longterm Government Bonds = A one-bond portfolio with a maturity near 20 years. e. Treasury Bills = A one-bond portfolio contaning, at the beginning of each month, the bill having the shortest maturity not less than one month. TABLE 7: RATES OF RETURN: 1989 -1998 MEAN 62 Dependent Variable Coefficient t-value R-sooared R-squared Adjusted C A SE 1 rret = debt liq brisk 0.1164 0.0538 Debt 0 2536 2.186 Liquidity -0.1697 -2.208 Bus. Risk 0.0335 0.488 Constant 0.1168 1.955 CASE 2 (Lin-Log Model) rret = Indebt + inliq InOnsk 0.1026 0 0441 Debt 0.079 1 77 Liquidity -0.0702 -I 655 Bus. Risk 0 026 0.991 Constant 0.2104 5.032 CASES rret = debt + debt2 + liq * bnsk + bnsk2 0 3413 0 2664 Debt 0.3634 0.881 Debt2 -0.0357 -0.0921 Liquidity -0.2272 -3 214 Bus. Risk 0 9589 3.867 Bus. Risk2 -0.9126 -3.865 Constant -0.0581 -0.5746 C A S E 4 rret = debt * debt2 + debl3 liq * bnsk * brtsk2 0.4578 0 3821 Debt 4.3922 3.187 Debt2 -3 6204 -3.029 Debt3 5.4787 3.04 Liquidity -0.2353 -3.625 Bus. Risk 0.7854 3.348 Bus. Risk2 -0.7614 -3.425 Constant -0.5359 -2.936 C A S E 5 egret = debt -i- debl2 debts + liq + brisk + brisk2 0.4785 0 4057 Debt 4.234 3.291 Debt2 -8.4765 -3.191 DebtO 5 45 3.24 Liquidity -0.1905 -3.144 Bus. Risk 0.8316 3.797 Bus. Risk2 -0.7746 -3.733 Constant -0.5423 •3.183 wfiere: rret = stock pnce real returns egret = stock pnce real returns net of dividends (capital gams only) TABLE 8: MEAN REGRESSION: 5 CASES 63 REAL RETURN/DEBT-TO-ASSET FUNCTION 0.6 0) 0.4 z /■ a: 0.2 3 -B- H 0 LU - 0.2 _l < -0.4 lU - 0.6 - 0.8 DEBT/ASSET RATIO FIGURE 1 REAL RETURN/BUSINESS RISK FUNCTION 0.2 0.4 3 0) •0.6 QL Business Risk FIGURE 2 64 rrel = d2-d50 12-110 * debt +- debt2 liq » bnsk brisk2 Denendent Vanable l-yglue R-Sduared R-squared Adjusted 0 3102 0.2105 D2 -0.2119 -1 798 03 0.0747 0.6015 04 -0.0652 •0.5337 05 0.0448 0.3694 06 -0.015 -0.1266 07 0.0286 0.2454 08 -0.1403 -1 11 DA -n 152R .1 071 010 0.1092 0 8983 038 0.3076 2.14 039 0.0737 0.6174 040 0 0 5 9 9 0 4984 T2 -0 192 •3,685 T3 0 1299 2.473 T4 -0 1902 -3 6 3 4 T5 -0.2121 -4 028 T5 ■0.2142 -4 071 T7 0.0174 0 3328 T3 -0 0901 -1 713 T9 0.0709 1 346 no -0 1536 •2.877 Oebt 0 7035 0,9361 Oebl2 -0 7425 ■1 282 Liquidity •0 0577 •0 6603 Bus. Risk 0.1195 1 268 Bus. Risk2 -0.0339 ■0 937 Constant 0,1489 0 5956 TABLE 9: PANEL REGRESSION 65 Food Processing Alexander & Baldwin Kellogg Archer Daniels Midland Lance Inc. Bestfoods McCormick & Co. Campbell Soup Michael Foods Chiquita Brands Monsanto Chock Full O' Nuts PepsiCo Coca Cola Co. Pilgrim's Pride ConAgra Pioneer Hi-Bred Dean Foods Quaker Oats Dole Food Raiston Purina Flowers Industries Sara Lee General Mills Smithfield Foods Golden Enterprises Tootsie Roll H J Heinz Tyson Foods Hershey Foods Unilever NV Hormel Foods Universal Foods IBP Inc. Whitman Corp. International Multifoods Wrigley Jr. Co. Food Rfetall Albertson's Inc. Ruddick Corp. American Stores Safeway Casey's General Stores Weis Markets Food Lion Winn Dixie Stores Great Atlantic & Pacific Food Wholesale Fleming Companies Supervalu Inc. Nash Finch Co. Sysco Corp. Richfood Holdings TABLE 10; A g RlgUSJMES.S-FIRMS BY SECTOR 66 FOOD FOOD FOOD PR00ES.6JN0 BETAIL WHOLESALE Capital Gain 0.152 0.175 0.088 Nominal Return 0.198 0.209 0.121 Real Return 0.162 0.174 0.088 Debt/Assets 0.58 0.573 0.656 Liquidity (DSI) 0.698 0.607 0.854 C.V. of Oper. Income 0.231 0.186 0.205 Nota; All figures are the 1989 - 1998 means. TABLE 11 : COMPARISON OF AGRIBUSINESS SECTORS 67 rret = ü2-d3 * spSOO + debt + debt2 + liq + bnsk -r brisk2 Dependent Vanable t-vaiue R-SQuared R-squared Adjusted 0,133 0.1189 02 0-0098 0 2895 0 3 ■0.0727 -1.724 SPSOO 0.6766 7 747 Debt 0.7869 2.505 üeou -u.o 164 -4.166 Liquidity -0.1421 -2.643 Sus. Risk 0.0681 0.8569 Bus. Risk2 -0.0478 -1 39 Constant -0 0878 -1.108 TABLE 12: SECTOR REGRESSION 68 S T E P O N E debt = d2-d3 • sp5Q0 ♦ liq * bnsk I restd = ebat DependeoLVanable C oeffiaent lvalue S_-SQuared R-squared Adjusted 0 3377 0 331 02 0 025 1 434 0 3 0 0 2 0 9 0 933 2 SPSOO 0 007 3 0 1583 LIQ 0 3537 15 27 BRISK -0 1189 -0 534 8 CONSTANT 0 3347 17 38 STEP_TWQ rret = debt • ehat C oeffiaent t-value RiSduared R-squared Adiuated 0 0095 0 0055 OEBT -0 2024 -1 616 EHAT 0 .3332 2 .165 c o n s t a n t 0 2757 3 696 TABLE 13: HAUSMAN TEST 69 Exogenous Variables: d2 d3 spSOO brisk brisk2 briskS Equation 1: rret = d2-d3 + debt + liq + brisk Equation 2: debt = d2-d3 + spSOO + rret + liq Equation 3: liq = d2-d3 spSOO + brisk + brisk2 + brisk3 Variable Coefficient t-value 02 -0.0092 -0.1168 03 -0.30S8 -2.S9S7 OEBT 3.8S02 2.1744 LIQ -0.4326 -0.6143 BRISK -0.1737 -1.6113 02 0.019S 0.976S 03 0.0239 0.6S32 SPSOO 0.2327 1.1171 RRET -0.1407 -0.4393 LIQ 0.2691 2.8637 02 -0.0713 -2.13SS 03 0.1573 3.7222 SPSOO 0.2723 3.6132 BRISK 0.4379 4.296 BRISK2 -0.18 -1.7113 BRISK3 0.0187 0.7237 (System R-Square = -0.9079) TABLE 14: SIMULTANEOUS EQUATION APPROACH 70 (System of Equations) At +10% -10% +10% -10% VARIABLE Means SPSOO SPSOO BRISK BRISK Rret 0.200 0.217 0.183 0.203 0.197 Debt 0.601 0.607 0.597 0.605 0.597 Liquidity 0.761 0.765 0.757 0.776 0.746 (Sector Regression) At +10% -10% +10% -10% +10% -10% +10% -10% VARIABLE Means SPSOO SPSOO BRISK BRISK Debt Debt LIQ LIQ Rret 0.198 0.209 0.186 0.200 0.195 0.199 0.192 0.188 0.207 TABLE 35; SENSITIVITY ANALYSIS OF VARIABLES APPENDICES APPENDIX A Description of Agribusinesses Used in Analysis (listed Alphabetically) Albertson’s, Inc. is one o f the largest retail food-drug chains in the United States. It operates 983 stores in 25 Western. Midwestern and Southern states. Retail operations are supponed by 11 Company-owned distribution centers. The Company employs 100.000 people. .Alexander & Baldwin, Inc. has two major subsidiaries: ( 1 ) .A&B Hawaii, which includes property development and management and agricultural operations (Hawaii's largest cane sugar plantation and coffee) and (2) Matson Navigation, the dominant carrier o f containerized cargo and autos between the U.S. West Coast and Hawaii. The firm has approximately 2.330 employees. American Stores Company is one o f the nation's leading food and drug retailers operating supermarkets, stand-alone drug stores and combination food/drug store units. The Company's stores operate under the names .Acme Markets. Jewel Food Stores. Lucky Stores. Osco Drug and Sav-On. At year-end 1997. the Company operated 1.557 stores in 26 states and employed approximately 121.000 associates. Archer Daniels Midland is in the business o f procuring, transporting, storing, processing, and merchandising agricultural commodities and products. It is one o f the 7 3 world's largest processors o f oilseeds, com. and wheat. Foreign sales account for approximately 35 percent o f total sales. The Company has about 23.603 employees. Bestfoods, formerly CPC International, is one o f the largest U.S. food companies. It operates in 62 countries throughout North .A.merica. Europe. Latin .America and Asia. Brands in the U.S. include ICnorr soups, sauces and bouillons. Hellmann's and Best Foods mayonnaise and salad dressings. Skippy peanut butter. Mazola com oil. .Alueller's pasta. Thomas' and Boboli breads and Entenmann's cakes. The Company has about 42.000 employees. Campbell Soup Company is a leading manufacturer o f canned soups, spaghetti and Mexican sauces, fruit and vegetable juices, and bakery products. Brand names include Campbell's. Franco-.American. V8. Pepperidge Farm. Prego. Godiva. Casera. Healthy Request, and Amott's. Foreign operations in 1998 accounted for 28 percent o f sales and 12 percent o f earnings. The Company has 24.250 employees. Casey's General Stores operates convenience stores in nine Midwestern states, primarily in Iowa. Missouri, and Illinois. Stores sell gasoline and a brand selection o f food (including freshly prepared pizza, donuts, hamburgers, etc.). beverages, and nonfood items. As o f 04/99 Casey's had 1.022 company-owned and 161 franchised locations. The company has 4.372 full time employees. Chiquita Brands International, Inc. is a leading international marketer, producer and distributor o f bananas and other quality fresh and processed food products sold under the Chiquita and other brand names. In addition to bananas, these products include other tropical thiit, such as mangoes, kiwi, and citrus, and a wide variety o f other fresh produce. The Company's operations also include fruit and vegetable juices and 7 4 beverages: processed bananas and other processed fruits and vegetables: fresh cut and ready-to-eat salads: and edible oil-based consumer products. Chiquita has approximately 37.000 employees. Chock Full O ’Nuts roasts, packs, and markets regular, instant, decaffeinated, and specialty coffees, and teas, for sale to retail, foodservice. and private label customers. It operates and franchises Quikava drive-through coftee shops ( 17 units, as o f 7/31/07). Its coffee business is strongest in New York. New England, and Florida. The Company has 1.430 employees. Coca-Cola Company is the world's largest soft drink company. It distributes major brands (Coca-Cola. Sprite. Fanta. Tab. etc.) through bottlers throughout the world. Business outside North .America accounted for 63 percent o f net sales and 73 percent o f profits in 1098. The Company is also the world's largest distributor o f juice products (Minute Maid. Five .Alive. Hi-C. etc.). Coca-Cola Enterprises is its 45 percent owned soft drink bottler. The Company has approximately 29.500 employees. ConAgra is the nation's second largest food processor. It operates in three segments: Packaged Foods (including shell-stable foods, frozen foods, cheese products, tablespreads and potato products): Refrigerated Foods (packaged meats, beef, pork and poultry products): Agricultural Products (crop protection chemicals and fertilizer, grain and bean processing and merchandising and specialty food ingredients). The Company has some 82.000 employees. Dean Foods Company manufactures and distributes fluid milk and otlier dairy products (75% o f 1998 sales, and 56% o f profits). It processes specialty foods: non-daiiy coffee creamers, cheeses, salad dressings, dips, and sauces (12% o f sales. 18% o f profits), and 7 5 pickles ( 13% o f sales. 26%of profits). The Company has approximately 13.000 employees. Dole Food Company, Inc. is one o f the largest companies engaged in the worldwide sourcing, processing, distributing, and marketing o f high-quality branded food products, primarily fresh fruit and vegetables. The Company has about 44.000 employees. Fleming Companies Inc. is one o f the nation's largest wholesale food distributors, acting as the principal source of supply for approximately 3.000 supermarkets and other retail outlets in 41 states. It also operates 260 corporate-owned stores. The Company has 36.000 employees. Flowers Industries Inc. is a growing branded foods company. It was founded in 1019 as a single family-owned bakery in Thomasville. Georgia. Today. Flowers serves national and regional markets with a variety o f quality, branded fresh and frozen foods. The Company operates 49 production, distribution, and sales companies in 16 states and employs more than 7.000 people. Food Lion is one o f America's largest supermarket chains. As o f January 1999. the Company operated 1.207 supermarkets and eight distribution centers in 11 Southeastern and Mid-Atlantic states. Food Lion and its subsidiary Kash n' Karry serve more than 100 million customers a week with fresh, quality products and services. With more than 90.000 employees. Food Lion is proud to be the largest private employer in both North Carolina and Virginia. General Mills Inc. is engaged in the manufacture and marketing o f consumer foods products. Well known brands include: Cheerios. Wheaties. Total. Chex. 7 6 Betty Crocker. Bisquick. Hamburger Helper, and Colombo. The Company has some 10.600 employees. Golden Enterprises, Inc. is a holding company which owns all the outstanding shares o f Golden Flake Snack Foods. Inc. Golden Flake manufactures and distributes a full line of salted snack items, such as potato chips, tortilla chips, corn chips, pretzels, fried pork skins, baked and fried cheese curls, peanut butter crackers, cheese crackers, onion rings and buttered popcorn. Golden Flake also sells a line o f cakes and cookie items, canned dips, dried meat products, and nuts packaged by other manufacturers using the Golden Flake label. No single product or product line accounts for more than 50 percent of Golden flake's sales, which affords some protection against loss of volume due to a crop failure o f agricultural raw materials. Great .Atlantic & Pacific Tea Co., Inc. operates 749 stores in 16 states, the District o f Columbia, and Ontario. Canada. The average store size is 38.000 sq. ft. Stores operate under A&P. Sav-.A-Center. Super Fresh. Kohl's. Dominion Stores. Food Emporium. Waldbaum's. Farmer Jack. Super Foodmart. and Dominion banners. The Company also serves 62 Food Basics franchise stores. It has about 80.000 employees. H.J. Heinz Company produces soups, ketchup, pickles, baby foods, baked beans. Star- Kist tuna. 9 Lives cat food. Ore-Ida frozen potatoes. Weight Watchers frozen items. Foreign sales accounted for 48 percent o f the total 1998 sales. The Company has about 38.600 employees. Hershey Foods Corporation is the largest U.S. producer o f chocolate and nonchocolate confectionery products. Major brands include Hershey's. Reese's. Cadbury. K it Kat. Sweet Escapes. TasteTations. Jolly Rancher. Good 'n' Plenty, and MilkDuds. The Corporation has 14.700 employees. Hormel Foods Corporation is a leading meat processor, specializing in pork products. It operates 12 plants tor slaughter and/or processing. Products are sold fresh, frozen, cured, smoked, cooked, and canned. Major brands include Hormel. SP.AM. Dinty Moore. .Maiy Kitchen. Little Sizzlers. Top Shelf. Chi-Chi's. Kid's Kitchen. The Corporation distributes products to supemiarkets and independent food stores in 50 states; products are also sold abroad. Hormel has approximately 11.200 employees. IBP, Inc. is the world's largest processor o f fresh beef and fresh pork. Principal products are boxed beef, pork and variety meats, mainly sold in the United States to grocery chains, meat distributors, wholesalers, restaurant and hotel chains, and food processors. Edible by-products include variety meats, beef tallow, pork lard, and sausage ingredients. Inedible by-products are used to make products such as leather, animal feed, and pharmaceuticals. IBP has about 40.000 employees. International Multifoods Corporation is a leading processor and distributor o f food products to foodservice. industrial, and retail customers in the United States and Canada. VSA distributes to vending machine operators: Multifoods Specialty Distribution to pizza restaurants and sandwich shops. The Company has about 4.400 employees. Kellogg Company, the world's largest manufacturer o f ready-to-eat cereals (37 percent of U.S. market. 52 percent of non-U.S. market), also produces convenience foods, including frozen waffles, toaster pastries, and snack bars. Brand names include: Kellogg's. Frosted Flakes. Rice Krispies. Frosted Mini-Wheats. Special K. Froot Loops. Nutri-Grain. Apple Jacks. All-Bran. Pop-Tarts. Eggo. Foreign 78 operations accounted tor 43 percent o f sales in 1998. 31 percent o f profit. The Company has 14.500 employees. Lance, Inc. manufactures and purchases snack foods and bakery products which are sold and distributed through its own sales organization, primarily in the southeastern and Midwestern United States to convenience stores, supermarkets, mass merchandisers, restaurants, club stores, drug stores, and wholesale grocery distributors. Products are also sold in schools, office buildings, and manufacturing plants through company-owned vending machines (approximately 60.Ü00). The Company has about 4.600 employees. McCormick & Company, Inc. is a leading manufacturer o f spices, seasonings, and other specialty food products for the consumer, industrial, and foodservice markets. It .A.lso makes plastic packaging products including bottles and tubes. Foreign operations accounted for 23 percent of sales in 1998. The Company has 7.600 employees. Michael Foods, Inc. has four divisions; egg products (M.G. Waldbaum and Papetti's: 60 percent o f 1998 sales. 79 percent of operating profits); refrigerated distribution (Crystal Farms; 22% and 9% respectively); dairy mixes (Kohler M ix Specialties; 13% and 8%); potato products (Northern Star; 5% and 4%). Employees were approximately 4.160 at 12/31/98. Monsanto Company operates in three business segments; Agricultural Products (47 percent o f 1998 sales). Pharmaceuticals (33%). and Food Ingredients (20%). Trade names include roundup (herbicide). Ambien. Arthotec. Celebrex, and Daypro (pharmaceuticals), and NutraSvveet. Canderel. and Equal (tabletop sweeteners). The Company has 31.800 employees. 70 Nash Finch Company is one o f the largest food wholesalers in the United States. It distributes food and related items to approximately 2.000 independent supermarkets and institutional accounts, including schools, hospitals, and military base commissaries (82 percent o f sales). Nash finch owns and operates about 93 supermarkets ( 18 percent of sales), and operates 19 wholesale distribution centers. Markets are in the Midwest. West, and Southeast. The Company has about 11.750 employees. PepsiCo, Inc. operates two major businesses: Soft Drinks. 48 percent o f 1998 sales and 35 percent o f operating profits: and Snack Foods. 49% and 64% respectively. It sold its majority interest in Pepsi Bottling Group in 04/99. Its major soft drink products include Pepsi-Cola. Diet Pepsi, and Mountain Dew. Specialty snack foods include Frito-Lay (major product offerings include Doritos. Rufiles and Lay's). Walker Crisps, and Smith Crisps. The Company has about 142.000 employees. Pilgrim's Pride Corporation is one o f the largest producers o f prepared and fresh chicken in North America, ranking fourth in the United States and second in Mexico. It is the twenty-eigth largest producer o f eggs in the U.S.. marketing fresh eggs primarily in Texas. Customers include Wendy's. Jack-in-the-Box. Stouffer's. Taco Bell, and Kentucky Fried Chicken. Mexico sales accounted for 21 percent of total sales in 1998. Pilgrim's Pride has about 13.000 employees. Pioneer Hi-Bred International, Inc. is a leading supplier of proprietary hybrid seed, with operations in seed com. soybean, sorghum, alfalfa, and wheat. It has an estimated 42 percent share o f the hybrid seed com market in North America. Hybrid seed com and soybean seed provided about 90 percent o f sales and virtually all o f operating profits in the past 5 years. The Company has about 5.000 employees. 80 Quaker Oats Company produces and markets food and beverages, including Quaker Oats. Life. Cap'n Crunch cereals; rice cakes: Gatorade beverages: and Rice-A-Roni. As o f 12/31/98 the Company had 14.123 employees. Ralston Purina Group is the world's largest producer of dog and cat foods and dry-cell batteries (45 percent o f Company’s annual sales). Brand names are Purina Dog and Cat Chow, and Eveready and Energizer. Foreign sales are approximately 31 percent o f total sales. It has about 30.000 employees. Richfood Holdings is the largest wholesale food distributor in its Mid-.Atlantic region. Richfood supplies food and related items to over 1.400 retail grocery stores, and provides ancillary services such as store site selection, accounting, financing, and computer services and advertising programs. It also operates 100 grocery stores (METRO. BASICS. Farm Fresh. Shoppers). The Company has about 9.500 employees. Ruddick Corporation is a holding company with two wholly-owned subsidiaries: Harris Teeter. Inc. (87% o f fiscal year 1999 sales. 53% o f operating profits), a Southeastern regional supermarket chain: and .American & Efird. Inc. ( 13° o and 47%. respectively), a manufacturer and distributor o f industrial sewing thread, with manufacturing operations in North Carolina and eight foreign countries. Ruddick has about 20.700 employees. Safeway, Inc. operates about 1.650 supermarkets in the California. Pacific Northwest. Rocky Mountain. Southwest. MidAtlantic. Illinois, and Western Canada regions. It operates 43 manufacturing and processing facilities and has 170.000 employees. Sara Lee Corporation is a diversified international manufacturer and marketer of branded consumer products with operations in coffee, specialty meats, baked goods. 81 foodservice distribution, household/personal care, and apparel. The food group includes Douwe Egberts. Hillshire Farms. Jimmy Dean. Ball Park. Kahn's. Mr. Turkey, and Sara Lee. The consumer products group includes Hanes. L'eggs. Kiwi. Bali. Champion. Plaviex. Coach, and Dim. Foreign operations accounted for 39 percent o f 1998 sales and 39 percent o f pretax income. The Company has about 138.000 employees. Smithfield Foods, Inc. is both the world's largest pork processor and hog producer. Its ten subsidiaries produce fresh and processed meat products, including fresh pork. hams, sausages, franks, and deli items. Brand names include Smithfield Lean Generation. John Morrell. Patrick Cudahy. Gwaltney. Valleydale. Esskay. Lykes. Krakus. and Schneiders. The Company has 33.000 employees. SUPERVALU INC. is the nation's largest food wholesaler, supplying over 4.930 retail food stores in 48 states and one o f the largest food retailers, with 460 stores. The revenue and operating earnings contribution in 1998 were as follows: Distribution: 70.8% and 70.2% respectively: Retail: 29.2% and 29.8%. The Company has 50.000 employees. Sysco Corporation is the leading U.S. distributor o f food and related products to the foodservice industry. It has approximately 325.000 customers in the U.S. and Canada, and serves restaurants, educational institutions, hospitals, nursing homes, hotels, and motels. It has 97 distribution facilities and selfservice centers in the U.S. and three in Canada. Sysco has 35.100 employees. Tootsie Roll Industries, Inc. produces candy. Products include Tootsie Roll. Tootsie Pop. Tootsie Bubble Pop. Tootsie Pop Drops, and Mason Dots. It has four plants in the 82 U.S.. and one in Mexico. International operations (Mexico and Canada) accounted for 8 percent o f 1998 sales. The Company has approximately 1.750 employees. Tyson Foods, Inc. produces, markets, and distributes value-enhanced, fresh and frozen poultry products, flour, com tortillas, and chips. It also has live swine, animal feed, and pet-food operations. Major customers are grocery chains, wholesale clubs, food distributors, fast-food franchises, military commissaries, and schools. Brands include Holly Farms. Tyson, and Weaver. The Company acquired Hudson Foods in 1998. It has about 59.400 employees. Unilever N.V. The Unilever Group is one o f the world's largest producers and marketers o f branded and packaged consumer goods. It also produces medical-testing products and operates tropical tea estates in the Far East. It does business in 90 countries through 500 operating companies around the world. Major food brands and products include Lipton teas and soups: Wish Bone salad dressings: Lawry's seasonings and sauces: Imperial. Country Crock. Promise. 1 Can't Believe Its Not Butter. Brummel. and Brown spreads and sprays: Ragu and Five Brothers pizza and pasta sauces: Klondike and Breyer's ice cream: Gorton's frozen seafood. Unilever has about 267.000 employees. Universal Foods Corporation is an international manufacturer and marketer o f value- added flavor, nutrition, and color ingredients and dehydrated products for food processing and baking markets. The sales breakdown for 1998 was as follows: flavor 39%. color 22%. yeast 18%. dehydrated products 16%. .Asia Pacific 5%. The Company has 4.196 employees. Weis Markets, Inc. operates 158 retail food markets (at year end 1998). including both superstores and conventional stores, located in Pennsylvania (131 stores). Maryland (19). Virginia (2). New York (3). West Virginia ( 1 ). and New Jersey (2). The Company sells national brand merchandise plus 2.Ü00 items under its own trademarks. It also owns and operates Weis Food Service and Shamrock Wholesale Distributors: and owns Super Petz. a 36-store pet supply chain. The Company has 19.500 employees. Whitman Corporation distributes PepsiCo products through Pepsi-Cola General Bottlers, the largest independent Pepsi distributor in the world, accounting for about 12 percent o f all Pepsi-Cola products sold in the U.S. General Bottlers also has exclusive franchise agreements with PepsiCo for regions in Eastern Europe. It has about 6.525 employees. Winn-Dixie Stores, Inc. is one o f the nation's largest supermarket chains. It operates in Florida. North Carolina. .Alabama. Georgia. Louisiana. Kentucky. Mississippi. Virginia. South Carolina. Tennessee. Indiana. Texas. Oklahoma, Ohio, and the Bahamas. It operates 1.184 supermarkets, primarily under Winn-Dixie. Winn-Dixie Marketplace, and Thriftway banners. The Company also has 18 distribution centers. 22 manufacturing plants, and a truck fleet. It has about 132.000 employees. Wm. Wrigley Jr. Company is the world's largest manufacturer and seller o f chewing gum, specialty gums, and gum base. The principal brands are Doublemint. Spearmint. Juicy Fruit. Big Red. WinterFresh. Extra. Orbit, and Freedent chewing gums. The Amural Products subsidiary makes novelty gums, including BubbleTape. and Big League Chew: and markets Hubba Bubba bubble sum. 84 Foreign sales accounted for 59 percent o f the 1998 total, and 59 percent o f the preta.x profits. The Company has 9.200 employees. 85 APPENDIX B RATES OF RETURN: 1989 • 1998 (Momina!) Longterm Longterm IJ S Large Small Corporate Government Treasury Inflation YEAR Stock Stock Bonds Bonds Bills Rate 1989 0.3149 0.1018 0.1623 0.1812 0.0837 0.0464 1990 -0.0317 -0.2156 0.0678 0.0618 0.0782 0.0611 1991 0.3055 0.4463 0.1989 0.193 0.0559 0.0307 1992 0.0767 0.2335 0.0939 0.0805 0.0351 0.029 1993 0.0999 0.2098 0.1319 0.1824 0.029 0.0274 1994 0.0131 0.0311 -0.0576 -0.0777 0.0391 0.0268 1995 0.3743 0.3446 0.272 0.3167 0.0584 0.0254 1996 0.2307 0.1762 0.014 -0.0093 0.0496 0.0332 1997 0.3336 0.2278 0.1295 0.1585 0.0526 0.0171 1998 0.2858 -0.0731 0.1076 0.1306 0.0486 0.0161 Mean 0.2003 0.1482 0.112 0.1218 0.053 0.0313 Std. Dev. 0,1471 0.1952 0.0925 0.1125 0.0174 0.0135 (Inflation-adjusted) 50 Longterm Longterm U.S. Agri- Large Small Corporate Government Treasury Business YEAR Stock Stock Bonds Bonds Bills Firms 1989 0.2565 0.0529 0.1105 0.1283 0.0354 0.2404 1990 -0.0874 -0.2608 0.0066 0.0008 0.016 0.0533 1991 0.2667 0.4033 0.1631 0.1575 0.0243 0.374 1992 0.0474 0.1987 0.063 0.0501 0.0063 0.0572 1993 0.0694 0.1774 0.1015 0.1509 0.0014 0.0317 1994 -0.0133 0.0042 -0.0821 -0.1018 0.0118 0.0266 1995 0.3403 0.3113 0.2406 0.2842 0.0302 0.2564 1996 0.1912 0.1384 -0.0186 -0.0412 0.018 0.1493 1997 0.3113 0.2072 0.1106 0.139 0.0353 0.3084 1998 0.2654 -0.0878 0.09 0.1129 0.0316 0.0728 Mean 0.1647 0.1145 0.0785 0.0881 0.021 0.15701 Std. Dev. 0.1491 0.1944 0.0924 0.1125 0.0122 0.127982 Source; Ibbotson Associates. Stocks, Bonds, Bills and Inflation, 1999 Yearbook, (except agribusiness firm returns). 86 APPENDIX C FIRMS USED IN INDUSTRY COMPARISON (Table 6) Banking industry Computer Software & Services AmSouth Bancorp Adobe Systems Bank of America Corp. Automatic Data Processing, Inc. First Union Corp. Computer Associates KeyCorp Compuware Corp. Morgan (J.P.) & Co. Corel Corp. PNC Financial Electronic Data Systems SouthTrust Corp. Intuit Inc. Summit Bancorp Microsoft Corp. Wells Fargo & Co. Oracle Corp. Wilmington Trust Co. Symantec Corp. Newspaper Industry Petroleum Industry Dow Jones & Co. Ashland, Inc. Gannett Co. BP Amoco ADC Hollinger International, Inc. Chevron Corp. Knight Ridder, Inc. Conoco Media General, Inc. Exxon Mobil Corp. New York Times Co. Occidental Petroleum News Corp. Sunoco Inc. Thomson Corp. Texaco Inc. Tribune Co. Ultramar Diamond Shamrock Corp. Washington Post Co. Unocal Corp. 87 A P P E N D IX D Econometric Analysis A number o f tests were conducted to determine the robustness o f the data. Variance-intlating factors (VIFs) and measures o f tolerance were calculated to test for multicollinearity. the White test was used to test for heteroscedasticity. and the Runs test and Durbin-Watson d test were used to test for autocorrelation. In conducting these tests, the OLS regression model was used; RR, = a + piDEBT, + p^LIQ, + pjBRJSK, - e Where: RR = Real rate o f return expected by investors for tlrm i. DEBT = firm solvency (Debt to .Asset ratio) LIQ = firm liquidity (Debt Structure index) BRISK = business risk (variability in EBIT) The sample consists o f the 50 firms over the 10 years (500 observations). Multicollinearitv I f multicollinearity is present in the data, it means that there is a linear relationship among some or all o f the independent variables o f the regression model. If 88 this is the case, though the OLS estimators w ill remain best linear unbiased estimators (BLUE), they w ill have large variances and covariances, causing the t ratio of one or more coefficients to be statistically insignificant. This would make precise estimation difficult. Variance-lnflatinu Factor (VIF) To detect multicollinearity among the explanatory variables o f the regression model, variance-intlating factors (VIFs) were calculated as follows: VIF„ = 1/(1- r-,j) where r,j is the correlation coefficient between independent variables i and j. The VIF shows how the variance o f an estimator is "infiated" by the presence o f multicollinearity (Gujarati. 1995). As r",j approaches I. the VIF approaches infinity. If there is no collinearity between i and], the VIF w ill be equal to I. If the VIF is greater than 10, then the variables are deemed to be highly collinear. The correlation coefficients for our DEBT. LIQ. and BRISK variables were as follows: r:3 = -0.574 (DEBT/LIQ) r24 = 0.029 (DEBT/BRISK) r34 = -0.164 (LIQ/BRISK) From these, the VIFs were calculated: VIF 23 = 1 /(1-0.329) = 1.492 89 VIF24 = 1 /(1 - 0.0008) = 1.001 VIF34 = 1/(1 - 0.027) = 1.028 The VIFs shown above denote that some collinearity is present among the independent variables, but the level is not significant. Tolerance Another method used to detect multicollinearity is the measure o f tolerance. Tolerance is calculated as follows: TOLij = 1 / VIF,I I f variables i and j are perfectly related with each other then TOL,, will be zero. If variables i and j are not correlated then TOL,, w ill be equal to 1. The measures o f tolerance for the DEBT. LIQ. and BRISK variables were calculated as: TOL 23 = 1/1.492 - 0.67 TOL 24 = 1/1.001 = 0.99 TOL 34 = 1 /1.028 = 0.97 The results show that the highest level o f collinearity is between the DEBT and LIQ variables, but this is closer to one than to zero, and not deemed significantly high. In conclusion, while we detect the presence o f some multicollinearity in the data, it is not deemed to be sufficiently high to adversely alTect our estimates or warrant taking any remedial action. 9 0 Heteroscedasticity One o f the assumptions o f the classical linear regression model is that the variance of each disturbance term ei is some constant number equal to g~. Heteroscedasticity refers to the situation where the variances o f the disturbance terms are not constant, that is. Eler) = o r . In this case, while OLS estimators remain unbiased and consistent, they w ill not be minimum variance or efficient. As such, estimation would not be accurate. White's Heteroscedasticity Test To test for heteroscedasticity we use the White test: 1. We first run the regression: RR, = a + piDEBT, ^ p.LIQ, + P3BRISR, -r c and obtain the residuals '''Cj. 2. We regress the square o f these residuals on the following variables: V = DEBT + LIQ + BRISK ^ DEBT" 4- LIQ' 4- BRISK* + (DEBT*LIQ) + (DEBT* BRISK) + (LIQ*BRISK) (coefficients omitted) and obtain the coefficient o f determination R* from this regression. 3. Under the null hypothesis that there is no heteroscedasticity. the sample size (n) times the R" from the second regression follows the chi-square distribution with degrees of freedom equal to the number o f regressors: n * R- = ru r 4. If the calculated chi-square value exceeds the critical chi-square value (at 5 percent level o f significance), we reject the null hypothesis of no heterscedasticity and conclude that heteroscedasticity is present. In our case, the R" from the second regression was 0.0161. Therefore: 500 * 0.0161 = 8.05 while the critical chi-square value X'vji =16.919 (5 percent level o f significance). Since 8.05 < 16.919 we do not reject the null hypothesis and conclude that there is no heteroscedasticitv in the data. 9 2 Autocorrelation Autocorrelation refers to the situation where the disturbance term of any observation within the model is related to the disturbance term o f any other observation within that model. That is. E (eje,) # 0. In this case, the OLS estimators remain unbiased and consistent, but they are no longer minimum variance. Runs Test In the runs test, we calculate a confidence interval for k, the number o f runs. .At the 95 percent confidence level, if k falls within the interval; [ E ( k ) ± 1.96cTkJ we do not reject the null hypothesis that there is no autocorrelation in the data. E(k) = [2nin2 / (ni + n^j ] 1 Ok' = [2 nin 2( 2 nin 2 -n i-n ;)] / [(ni + n 2 )'{ni + n: - 1)| In our case, the SHAZAM computer software package gives us the following: k = 253 runs n, - 213 n2 = 287 This gives: E(k) - 245.524 Ok = 10.924 9 3 The 95 percent contldence interval is: 245.524 ± 1.96(10.924) Therefore, since 224.13 < 253 < 266.94 vve do not reject the null hypothesis that the observed sequence o f residuals is random. Durbin-Watson c/Test With the Durbin-vvatson d statistic. itT/ is found to be 2. there is no autocorrelation, either positive or negative. If c/ is equal or close to zero, there is positive autocorrelation, and vve reject the null hypothesis that there is no auto coiTelation. If t/ is equal or close to 4. there is negative autocorrelation, and vve reject the null hypothesis that there is no autocorrelation. From tables vve can find critical d|. and du values at the 5 percent level of significance.If 0 < t/ < du or 4-du < d < 4 vve reject the null hypothesis that there is no autocorrelation. If du < f/ < 4 - du vve do not reject the null hypothesis. 9 4 In oiir case, the SHAZAM software calculated the durbin-vvatson d statistic to be 2.1850. At the 5 percent level o f significance, the critical di. and du values are 1.74 and 1.8 respectively. Since 1.74 < 2.185 < 2.2 we do not reject the null hypothesis that there is no autocorrelation. 95 LIST OF REFERENCES Almanac ot Business and Industrial Financial Ratios. 28"' Annual hdition (1^97). 3T‘ Annual Edition (2000). Prentice Hall. Barr)'. Peter J.. Paul N. Ellinger. John A. Hopkin. and C.B. Baker. Financial Manauement in Agriculture. Fifth Edition. Interstate Publishers. Inc.. 1995. Baydas. Mayada M. (1994). "Capital Structure and Asset Portfolio Choice .Among Micro. Small and Medium Scale Manufacturing Enterprises in the.Gambia." Columbus. Ohio: Department o f .Agricultural Economics and Rural Sociology. Ph.D Dissertation. The Ohio State University. Bloomber". Inc.. The BLOOMBERG PROFESSIONAL^ Service Database. Boone. Louis E. and David L. Kurtz. Contemporary Marketing. The Drvden Press. 1999. Brigham, Eugene P.. Louis C. Gapenskl. and Michael C. Ehrhardt. Financial Management: Theory and Practice. Ninth Edition, The Dryden Press. 1999. Brinson. Gary P.. Ronald F. Singer, and Gilbert L. Beebovver. "Determinants of Portfolio Performance." Financial Analysts Journal. May - June 1991. Cooley. Philip L. and Peyton Foster Roden. Business Financial Management. Second Edition. The Drvden Press. 1991. 9 6 Cramer, Gail L.. Clarence W. Jensen, and Douglas D. Southgate. Jr.. Agricultural Economics and Agribusiness. Seventh Edition. John Wiley & Sons. Inc.. 1997. Crawford. I.M.. Agricultural and Food Marketing Management. Food and Agriculture Organization o f the United Nations. Rome. 1997. Davis. John FI. and Ray A. Goldberg. A Concept o f Agribusiness. Boston. .Mass.; Research Division. Harvard Business School. 1957. Disclosure "^ Global Access. Dunn's Analytical Services. Industry Norms & Ratios: One Year. Dunn & Bradstreet. Inc.. 1991. 1998. 1999. Fama. Eugene and Kenneth French. "The Cross-Section o f Expected Stock Returns. Journal of Finance. Vol. 47. No. 2. 1992. Forster. D. Lynn. "Capital Structure. Business Risk, and Investor returns for Agribusinesses." Agribusiness. Vol. 12. No. 5. 1996. Gujarati. Damodar N.. Basic Econometrics. Third Edition. McGraw-Hill. 1995. Haley, Charles W. and Lawrence D. Schall. The Theory o f Financial Decisions. Second Edition, McGraw-Hill. 1979. Ibbotson Associates. Stocks. Bonds. Bills and Inflation. 1999 Yearbook. International Monetary Fund. International Financial Statistics. February 2000. 9 7 Jensen, Michael C.. "Agency Costs of Free Cash Flow. Corporate Finance, and Takeovers." American Economic Review. Vol. 76. 1986. Jensen. Michael C. and William H. Meckling. "Theory o f the Firm; Managerial Behavior. Agency Costs and Ownership Structure." Journal o f Financial Economics. Vol. 3. 1976. ' Kennedy. Peter. A Guide To Econometrics. Fourth Edition. The M IT Press. 1998. Keown. Arthur J.. J. William Petty. David F. Scott. Jr.. John D. Martin. Foundations o f Finance: The Logic and Practice of Financial Management. Second Edition. Prentice-Hall. 1998. Knutson. Ronald □.. J.B. Penn, and Barry L. Flinchbaugh. Agricultural and Food Policy. Fourth Edition. Prentice-Hall. Inc.. 1998. Marsden. Keith, and Maurizio Garzia. Agro-Industrial Policy Reviews: Methodological Guidelines. Food and Agriculture Organization o f the United Nations. 1998. Mazzocco. M.A.. "Tlie Debt Stmcture Index: An Approach to Evaluating Financial Structure." Agricultural Finance Review. 49, 105 (1989). Miller. Merton H.. "Debt and Taxes." Joumal o f Finance. Vol. 32. 1977. Modigliani, Franco, and Merton H. Miller. "The Cost o f Capital. Corporate Finance and The Theory o f Investment." American Economic Review. Vol. 48. 1958. Moyer. R. Charles. James R. McGuigan. and William J. Kretlow. Contemporary Financial Management. Seventh Edition. International Thompson Publishing. 1998. Myers. S.. "The Capital Structure Puzzle." Joumal o f Finance. Vol. 39. 1984. 9 8 Padberg, D.I.. C. Ritson. and L.M. Albisu. Agro-Food Marketing. Centre for Agriculture and Biosciences International. 1997. Rumelt. Richard P.. "How Much Does Industry Matter?'’ Strategic Management Joumal. Vol. 12. 1991. Sharpe. William P.. "Capital .Asset Prices: .A Theory o f Market Equilibrium under Conditions o f Risk." Joumal o f Finance. Vol. 19. 1964 Standard & Poor’s Quarterly Dividend Record. Annual Issue. December 1989 - 1998. September 1999. Standard & Poor's Industry Surveys. January- 1999. June 1999. Julv 1999. Stiglitz. Joseph E. and .Andrew Weiss. "Credit Rationing in Markets with Imperfect Information." American Economic Review. Vol. 71. 1981. United States Department o f Agriculture. Agricultural Outlook Fomm '98. U.S. Government Printing Office. 1998. Value Line Publishing. Inc., The VALUE LINE Investment Survey ". Weston, J. F. and E. F. Brigham, Essentials o f Managerial Finance. Harcourt. Brace. Jovanovich. Orlando. FL. 1993. Wijst. D. van der. "Financial Structure in Small Business: Theory. Tests and Applications, ’ Lecture Notes in Economics and Mathematical Systems, Springer- Verlag. 1989. 9 9