AGRICULTURAL FINANCE REVIEW · 14/05/1996 · AGRICULTURAL FINANCE REVIEW Department of Agricultural, Resource, and Managerial Economics, Cornell University Volume 56 Preface Agricultural

AGRICULTURAL FINANCE REVIEW

Department of Agricultural, Resource, and Managerial Economics, Cornell University Volume 56

Preface

Agricultural Finance Review (AFR) provides a forum for discussion of research, extension, and teaching issues in agricultural finance. This annual publication contains articles contributed by scholars in the field and refereed by peers.

Volume 43 was the first to be published at Cornell University. The previous 42 volumes were published by the United States Department of Agriculture. AFR was begun in 1938 by Norman J. Wall and Fred L. Garlock, whose professional careers helped shape early agricultural finance research. Professional interest in agricultural finance has continued to grow over the years, involving more people and a greater diversity in research topics, methods of analysis, and degree of sophistication. We are pleased to be part of that continuing development. We invite your suggestions for improvement.

The effectiveness of this publication depends on its support by agricultural finance professionals. Your support has grown each year in terms of submissions and reviews of manuscripts. We especially express thanks to those reviewers listed below. Grateful appreciation is also expressed to the W.l. Myers endowment for partial financial support. Thanks are also due to Nancy Brown for receiving, acknowledging, and monitoring manuscripts, Sandra Allen for desktop publishing, and Judith Harrison for technical editing.

Bruce Ahrendsen Freddie Barnard Peter Barry David Bessler Larry Bitney J. Roy Black Hoy Carman Robert Callender Robert Collins Eric DeVuyst Bruce Dixon Craig Dobbins Richard DuVick Paul Ellinger Allen Featherstone Gregory Hanson Steve Hanson W.E. Hardy, Jr. Bruce Jones

VOLUME 56 REVIEWERS

David Leatham David Lins Randall Little Don Lybecker Bob Martin Emanuel Melichar William Meyers Charles Moss Don Paarlberg George Patrick Glenn Pederson Gary Schnitkey Bruce Sherrick Loren Tauer Richard Trimball Calum Turvey Alfons Weersink Fred White

Manuscripts will be accepted at any time, but the deadline for the 1997 issue is February 3, 1997.

John R. Brake, Coeditor Eddy L. LaDue, Coeditor

Restructuring the Farm Credit System: tLBERT R. MAI'lN

A Progress Repor LIBRARY

Warren F. Lee and George D. Irwin t1pR 3 1997

Abstract lTHACA, NY 14853 The Farm Credit System (FCS) is a

The Cooperative Farm Credit System (FCS) was formed over the pertod 1916 to 1933. Its basic structure remained essentially unchanged until the mid-1980s when severe financial stress among FCS borrowers led Congress to pass legislation that provided for mandatory and voluntary restructurtng. Much of this restructurtng has taken place. The number of banks has been reduced from 37 to eight, and there has been a corresponding decline of nearly 40% in the number of lending associations. To date, evidence of improved financial performance due to restructurtng is inconclusive. A number of other issues related to the system's structure remain unresolved. Future research needs to be guided by a better understanding of how the roles of FCS institutions have been changed by restructurtng.

Key words: Farm Credit System. structural change, financial performance, histortcal development.

Warren F. Lee Is a professor of agricultural economics at The Ohio State University. and George D. Irwin Is a senior economist with the Farm Credit Administration. McLean, VIrginia. The authors are grateful to Carl Zulauf, an anonymous reviewer, and the journal editors for many helpful comments and suggestions. This Is manuscript no. 62-96 of the Ohio Agricultural Research and Development Center. The opinions and conclusions expressed here are those of the authors and do not necessarily reflect the views of the Farm Credit Administration.

nationwide network of cooperatively owned financial institutions that lend to agrtcultural producers. rural homeowners. farm-related agrtbusinesses. farmer-owned cooperatives. and rural utilities. As of September 30. 1995. there was approximately $57 billion in FCS loans outstanding, including $15 billion in loans to cooperatives. FCS loans to farmers represent about one-quarter of total agrtcultural credit. Most of the loan funds are obtained from the sale of systemwide securtties in the national financial markets through the Federal Farm Credit Banks Funding Corporation. FCS securtties have "government-sponsored enterprtse" (GSE) status. Although they are not explicitly guaranteed by the federal government. their regulatory exemptions and preferences allow the FCS to market large volumes of bonds and discount notes at favorable interest rates. generally only 10 to 30 basis points above U.S. Treasury securtties.

The lending institutions consist of seven regional Farm Credit Banks (FCBs), CoBank Agrtcultural Credit Bank (CoBank ACB). the St. Paul Bank for Cooperatives (St. Paul BC). and 228 local lending associations. The geographic makeup of the system as of early 1996 is shown in Figure 1. Other entities include Farm Credit Leasing Services Corporation, Federal Agrtcultural Mortgage Corporation. and the Farm Credit Council. System institutions are regulated by the Farm Credit Administration and their liabilities are insured by the Farm Credit System Insurance Corporation. These and other entities are shown in Figure 2. A glossary of terms descrtbing the vartous components of the FCS is presented in the Appendix.

Figure 1. Farm Credit System Structure, January 1, 1996

---~~~ The Eastern Idaho

ACA is funded by the

Western FCB

0 Q,=

•'\::>

{)

The Albuquerque, Eastern New Mexico,

and Southern New Mexico PCAs are

funded by the FCB of Texas.

• Headquarters, Farm Credit Bank A Headquarters, St. Paul BC

• Headquarters, CoBank ACB

~' .• 17PCAs v 48FLBAs

FCB of Texas

The FLBAs in Alabama, Louisiana, and

Mississippi generate and service loans for

the FCB of Texas. The Northwest Louisiana

PCA is funded by the FCB of Texas.

Source: Farm Credit Administration, FCA Quarterly Report.

CoBank Regional Banking Center

D

Ag Credit ACA (Ohio), Central Kentucky ACA

(Kentucky), Chattanooga ACA (Tennessee), and

Jackson Purchase ACA (Kentucky) are funded

by the AgFirst FCB.

Note: Total institutions in the 1996 FCS structure = 236 (66 PC As, 70 FLBAs, 60 A CAs, 32 FLCAs, 6 FCBs, 1 ACB, and St. Paul BC).

* CoBank ACB serves five ACAs in the former Springfield district.

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Agricultural Finance Review, Vol. 56, 1996 Lee and Irwin 3

Figure 2. Organizational Structure of the Farm Credit System

Major Components of the Farm Credit System

National Organizations

Farm Credit Leasing Services

Corporation (Minneapolis, MN)

Federal Farm Credit Banks

Funding Corporation

(Jersey City, NJ)

Federal Agricultural Mortgage

Corporation "Farmer Mac"

(Washington, DC)

Trade Associations

The Farm Credit

Council (Washington, DC)

Affiliated Regional

Farm Credit Councils

Federal Regulation

and Insurance

Farm Credit Administration (Mclean, VA)

Farm Credit System

Insurance Corporation

(Mclean, VA)

System Banks, Associations, and Borrowers

' Federal Land Bank

Associations (FLBAs)

I

Farm Credit Banks and Associations

Farm Credit Banks

I

' ' Production Federal Credit land Credit

Associations Associations (PCAs) (FLCAs)

I , Farmer, Rancher, and

Rural Homeowner Stockholders/

Borrowers

' Agricultural Credit

Associations (ACAs)

I

..... -

Banks for Cooperatives with National Charters

CoBank St. Paul

ACB Bank for

(Denver, CO) Cooperatives (St. Paul, MN)

I I

' 5 Former Cooperative

Springfield Stockholders/

District Borrowers A CAs

I

Source: Barry, Ellinger, Hopkin, and Baker (1995, p. 483), updated to reflect recent mergers and other changes.

4 Restructuring the Farm Credit System: A Progress Report

Structural change and, at times, controversy have characterized the Cooperative Farm Credit System since it began with the passage of the Federal Farm Loan Act on July 17, 1916. The major impetus for recent and ongoing restructuring was the Agricultural Credit Act of 1987, which was signed into law in January 1988. At the beginning of 1988, the FCS consisted of 377 local lending associations and 37 banks, considerably more than at present (see Figure 3). The objective of this article is to review and evaluate changes in the institutional structure of the Farm Credit System over the last decade. The term "structure" as used here refers to the number, size, and types of institutions that make up the system.

Historical Perspective

Both Hoag and Murray have provided insightful analyses of the early years of the Farm Credit System. The FCS was developed in piecemeal fashion over the period 1916 to 1933. The lack of reliable and affordable sources of mortgage loans for farmers was a growing problem throughout the latter part of the nineteenth century. With completion of settlement, the era of "free" land ended and it was given economic value, requiring financing for purchases. Commercial lenders were unreliable and cyclical in making mortgage loans in rural areas. The repayment periods on mortgages that were available were too short, and a combination of high interest rates and renewal fees made loans expensive. By the tum of the century, what Murray called the "Farmers' Campaign for Credit" finally received political attention.

A cooperative farm credit system, along with rural free delivery of mail, an extension service, and farmer cooperatives, were key recommendations of the Country Life Commission that was appointed by President Theodore Roosevelt in 1908. In 1913, two commission delegations traveled to Europe to study farm finance

institutions, especially the very successful Landschajt system in Germany. Recommendations from these and followup commissions led to the passage of the 1916 Farm Loan Act which created the Federal Land Banks (FLBs). The 1916 act also established the Federal Farm Loan Board, a bureau within the Treasury Department that was the administrative head of the new Land Bank System.

Hoag identifies two shortcomings of the 1916 act. One was its failure to address short-term credit needs. There was also a conflict between those wanting a mechanism to assist commercial lenders and those wanting a cooperative system. A compromise solution was legislation that established cooperatively owned National Farm Loan Associations, later called Federal Land Bank Associations (FLBAs), and investor-owned Joint Stock Land Banks. In other words, there were both wholesale and retail intermediaries. This conflict between proponents of commercial versus cooperative, and wholesale versus retail systems has always been an issue in most legislative activity with respect to the FCS.

The first attempt to address short-term credit needs was the passage of amendments to the Federal Farm Loan Act in 1923 to create the Federal Intermediate Credit Banks (FICBs). The FICBs were designed to be wholesalers of funds for a variety of direct lenders, including commercial banks, livestock loan companies, farmer cooperatives, and privately capitalized agricultural credit corporations. As noted by both Hoag and Murray, the anticipated high volume of lending by the FICBs to commercial banks and farmer cooperatives never materialized. Some agricultural credit corporations and livestock loan companies made substantial use of FICB loans, but activity was confined mostly to the western states. In their independent works, Murray and Hoag cite restrictive pricing, heavy-handed regulation, and the FICBs' inflexible lending policies and terms as

Figure 3. Farm Credit System Structure, January 1, 1988

_.SA SACRAME

0

• Farm Credit Banks Federal Land Bank Federal Intermediate Credit Bank Bank for Cooperatives

• Central Bank for Cooperatives

Source: Farm Credit Administration, FCA Quarterly Report.

~-0

Note: Total institutions in the 1988 FCS structure= 414 (232 FLBAs, 145 PCAs, 12 FLBs, 12 FICBs, 12 District BCs, and Central BC).

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major reasons for this limited lending volume. Weak loan demand in both the FLB and FICB systems was also caused by weak farm income from about 1919 to well into the 1930s. From a political perspective, credit is sometimes viewed as being a substitute for income when, in fact, prospects for good farm income are required to justifY the extension of credit.

In March 1933, the Farm Credit Administration (FCA) was established as an independent federal government agency. As Murray noted, the FCA "brought together into one administrative agency almost all federally sponsored farm credit agencies and activities existing at the time" (p. 191). These included the seed loan agency from the U.S. Department of Agriculture (USDA), the FLBs and FICBs from the Federal Farm Loan Board (which had been under the Treasury Department), and regional agricultural credit corporations from the Reconstruction Finance Corporation. The Farm Board, an independent agency that had price stabilization functions, was replaced by the FCA. During its first term as an independent agency, the FCA also had responsibility for the Cooperative Research Service (which later became part of the USDA) and, initially. for establishing federal credit unions. The Farm Credit Act, signed on June 16, 1933, rounded out the FCS by establishing Production Credit Associations (PCAs) and the 13 Banks for Cooperatives (BCs).

The FCA, by Executive Order, was placed under the Department of Agriculture in 1939. It remained there until 1953, when it became an independent agency within the Executive Branch. The contrast between the 1939-53 period when the FCA was under the Department of Agriculture versus the 1933-39 and 1953-present periods as an independent agency highlights an Important issue. Were the programs purely commercial credit, or were credit programs also available as vehicles to complement the administration's general farm policies?

The FCS structure (in the sense of types of institutions) that was developed in 1933 remained essentially unchanged for over half a century. But there were several organizational changes between 1933 and 1988. As noted earlier, the status of the FCA as an independent agency changed in 1939, and again in 1953. Equally Important, the FCS was modernized by gradual conversion from total government ownership, to joint ownership, to total private ownership-all culminating in 1968. As long as there was any government capital, the system was subject to the provisions of the Government Corporation Control Act. Intervention in a bond sale by the Johnson Administration in 1968 was the final stimulus to achieve total private ownership.

A Commission on Agricultural Credit was formed in 1969 to chart the future of the FCS. Its recommendations led to passage of the Farm Credit Act of 1971, which was a major overhaul of the 1933 legislation. The process of converting the FCA from a head office and spokesperson began with passage of the 1971 legislation. The 1971 act converted the FCS from a mixed government corporation to a private, cooperatively owned system of institutions. The 1971 act also moved significant authority from management to boards of directors. Over the period from 1971 to 1985, a series of changes in relationships and structure of decision making occurred, including differential supervision of banks and associations. consolidated securities, and the creation of service and trade organizations such as FarmBank Services and the Farm Credit Council.

The 1971 legislation also broadened the range of services the FCS could offer to include rural home mortgages, leasing, financing international marketing, and lending to rural utilities. The FLB maximum loan limit was raised from 65% of "normal agricultural value" to 85% of the market value of real estate collateral. Prior to the change, FLB loans could

Agricultural Finance Review, Vol. 56, 1996

seldom exceed 50% of market value. In general. the 1971 act positioned the FCS to take advantage of the unprecedented growth in U.S. farm lending that began in the early 1970s. FCS farm loan volume (market share) rose from less than $12 billion (23%) in 1970 to a peak of nearly $65 billion (33%) in 1984. Over the same period, the FCS share of the farm mortgage market increased from 27% to 44%. Although the maximum FLB loan was 85% of market value, the average loan-to-value ratio ranged between 55% and 60% after 1971.

Current Restructuring

As noted earlier, the number and types of FCS institutions have changed considerably since the mid-1980s. As was the case early in the century, the impetus for change was severe financial stress in the farm sector and in FCS institutions, and Congress played a major role in the restructuring. Legislation passed in 1985, 1986, and 1987 set the direction and parameters for institutional restructuring of the FCS. These legislative initiatives were designed to deal with the deteriorating financial condition of many system institutions, to restore investor confidence in FCS securities, and to protect borrowers from frozen stock investments and from what some perceived to be heavy-handed loan servicing actions. Peoples, Freshwater, Hanson, Prentice, Thor, and Melichar provide a comprehensive review of the environment in which the legislation was developed.

Beginning in 1981, there was a growing debate between proponents of forbearance for delinquent borrowers and the need to protect the financial health of FCS institutions for all borrowers. Underlying this debate were differing assumptions about the probable duration of the farm recession. Proponents of forbearance assumed that the farm economy would soon return to the prosperous levels of the 1970s, and that delinquent borrowers

Lee and Irwin 7

needed only a little more time to ride out the recession. The FCS was divided and uncertain about how to proceed. The distressed loans were confined in certain geographic regions. and approaches to dealing with them were varied. As the recession persisted and land values declined. it became more and more apparent that forbearance was leaving the FCS with fewer options for its own survival. In a June 1985 statement. the Federal Farm Credit Board urged the FCS to move aggressively while there was still time to solve its own problems.

In September 1985, the Governor of the FCA announced publicly for the first time that the FCS was in serious financial difficulty and would require government assistance. Wall Street reacted swiftly. The spread between FCS and Treasury securities widened from the normal 10-30 basis points to 125 basis points and more within a few days. A major objective of the 1985 Farm Credit Amendments Act was to calm the financial markets. The 1985 act strengthened the FCA's examination, regulatory, and enforcement authorities. For the first time, the FCA was put on equal footing with other regulators in terms of enforcement authorities, for example, to issue cease-and-desist orders, to levy civil money penalties, and to remove officers and directors. The makeup of the FCA board was changed from a 13-member board representing the 12 districts and the Secretary of Agriculture, to a three-member. full-time board appointed by the President.

The 1985 legislation also created the Farm Credit System Capital Corporation. The Capital Corporation was created because the 'joint and several liability" of the banks for FCS securities had been tested and found wanting. There was considerable dissension among the banks and associations over joint and several liability. Moreover, capital located at the association level was not directly accessible for joint and several liability, even though the associations owned the


banks. The Capital Corporation had two major roles-to coordinate the transfer of capital from strong to weak FCS institutions and to serve as a warehouse for troubled assets. The motive for creation of the Capital Corporation was to provide a mechanism for the FCS to use its intemal reserves and avoid the need for federal assistance. However, a backstop line of federal credit was included in the 1985 legislation to reassure investors in FCS securities.

The financial condition of the farm sector, and hence the FCS, continued to deteriorate through 1986. A fundamental problem was the continuing drop in land values. A growing number of FCS borrowers had dual problems-inadequate cash flows and deteriorating collateral positions. Under then-existing accounting rules, FCS institutions could be quickly shifted from "going concem" to "liquidation" accounting status, under which the costs of acquiring, managing, and liquidating acquired property were added to their liabilities. Moreover. the self-help provisions of the 1985 legislation were not working. Strong associations and banks objected strenuously to transferring their capital to weak ones. Several associations failed and, in some cases, their borrower stock was frozen.

Fears over frozen stock and uncompetitive FCS interest rates prompted thousands of financially healthy FCS borrowers to go elsewhere for their loans. This borrower flight added to the FCS's funding problems. The reduced need for new funds from bond sales negated any opportunity to "blend down" the average cost of funds. At the same time, delinquent borrowers felt that the FCS was overly aggressive in loan foreclosure and liquidation actions. Again in 1986, Congress had to consider legislation to deal with a system that seemed likely to fail, and that was perceived as being unfair to some of its borrowers. Its solution, the Farm Credit Amendments Act of 1986, was only a stopgap measure. The 1986

act allowed financially troubled FCS institutions to use regulatory accounting procedures to amortize high-cost debt and loan losses over periods ranging up to 20 years.

This second attempt to buy time in hopes that the FCS would recover without federal assistance also failed. By early 1987, it was apparent that federal assistance would be needed. Peoples et al. point out that Congress struggled with the issue of drafting a narrow FCS bill or addressing a broader range of agricultural credit issues. By now, Congress more clearly understood that it was not dealing with a single, unified entity. The FCS was a loosely knit organization made up of hundreds of federated cooperatives-banks, associations, and their related service organizations. In the end, Congress focused its efforts on trying to help farm borrower constituents rather than the FCS per se. Symbolically, Title I of the 1987 act is "Assistance to Farm Credit System Borrowers." The 1987 act also spelled out borrowers' rights. not only for the FCS, but also for Farmers Home Administration (FmHA) borrowers. As was the case with the 1916 legislation. the "commercial versus cooperative" compromise was incorporated into the 1987 act. In retum for providing up to $4 billion in government assistance to the FCS, Congress attempted to placate commercial lenders by creating the Federal Agricultural Mortgage Corporation (FAMC, also known as Farmer Mac), a mechanism for a secondary market for farm mortgages.

This legislation also contained a number of provisions affecting the structural makeup of the FCS; specifically, the 1987 act did the following:

1. It created a Farm Credit System Financial Assistance Board to administer up to $4 billion in federal financial assistance to troubled system institutions. This board replaced the Capital Corporation. The Financial

Agricultural Finance Review, Vol. 56. 1996

Assistance Board's charter expired on December 31, 1992, as specified in the act.

2. It created the Farm Credit System Insurance Corporation (FCSIC) to protect holders of systemwide debt obligations. The insurance fund is an added layer of reserves that would come into play before the banks' joint and several liability for systemwide securities.

3. It mandated changes in the system's organizational and operating structure and expanded the authority for voluntary mergers and reorganization:

• Within six months of enactment, the FLB and FICB in each district were required to merge into a Farm Credit Bank (FCB).

• Within six months after the district bank merger, directors of each PCA and FLBA that served substantially the same territory were required to draft a plan for merging the two associations. and to submit the plan to a stockholder vote. If the merger was approved, the resulting association would become a direct lender. termed an Agricultural Credit Association (ACA). A few of these "Section 4.11" mergers resulted in overchartering, where two associations compete with each other.

• The act established an 18-month process for developing a plan to merge the 12 Farm Credit districts into no fewer than six districts. Ultimately. an FCS committee recommended that proposals for interdistrict mergers be deferred.

• The law also established a process to develop a proposal for voluntary mergers of the BCs, effectively giving members a choice on alternative forms of BC mergers. Depending on

Lee and Irwin

the outcome of this vote, the BCs could have had competitive or exclusive territories.

9

• The act contained other reorganization provisions. including mergers of unlike institutions. reconsideration of pre-1988 mergers by shareholders. affiliations with adjoining districts, and voting procedures. All mergers required approval by the FCA, but Financial Assistance Board approval was also required on mergers of institutions receiving federal financial assistance. A few associations ultimately chose to affiliate with adjoining districts.

The restructuring provisions of the 1987 act were designed to correct many of the structural inconsistencies in the system that resulted from its piecemeal evolution over the 1916-33 period. The act permitted, for the first time, the merger of unlike associations (PCAs and FLBAs) to create ACAs, thereby consolidating the real estate and non-real estate lending functions. Establishing the insurance fund and positioning the FCA as an armslength regulator put the system more on a par with other financial Institutions. As is often the case. increases in authority for federal regulators never seem to emerge until after serious problems arise. Prior to 1985. the FCA had delegated the authority to supervise and examine the various associations to the FICBs and FLBs. as permitted by law. The Farm Credit Council now carries out many of the advocacy roles once performed by the FCA. although the statute continues to provide that the FCA board has authority to recommend directly to Congress any needed changes in the system.

Restructuring Progress

Although the 1987 act spelled out procedures for mandatory and voluntary mergers, there was considerable merger activity before 1988. Like associations


have merged since the 1920s, and unlike associations in the Springfield, Baltimore, and Columbia districts were jointly managed. some for as long as 40 years. In October 1982, the Federal Farm Credit Board issued a policy statement which announced that structure was the prerogative of district boards, but that the FCA would expect more coordination of services to borrowers than had existed in the past.

Table 1 shows trends in the numbers of associations over the 1983-96 period. Between 1983 and 1988, the number of FLBAs and PCAs declined by 49% and 64%, respectively. This attrition in association numbers was due primarily to mergers of like associations. In many instances, these pre-1988 mergers were motivated by financial problems brought on by growing loan delinquencies in the early 1980s. PCAs were the first institutions to experience problems, and several failed in 1982-83. Even though the number of system banks remained unchanged until 1988, several were jointly managed FLB-FICB and/or BC combinations. In the Louisville district,

for example. the three banks were placed under common management In January 1986. Before 1988, each district had a single board which served Its group of banks, rather than each bank having its own independent board.

Following passage of the 1987 act, the total number of associations continued to decline, and the number of direct lending associations (DLAs) increased. Prior to 1988, PCAs were the only type of DLA. FLBAs acted as servicing entities for FLBs, but the loans were actually made by the banks. Two new types of DLAs-Agricultural Credit Associations (ACAs) and Federal Land Credit Associations (FLCAs)-came into the picture beginning in 1988, when 33 ACAs were formed from mergers of PCAs with FLBAs. The flurry of merger activity in 1988 was due to a provision of the 1987 act that required a merger vote if a PCA and an FLBA served "substantially" the same territory. "Substantially" was defined by the FCA to be 90% or more overlapping territory. Some of these "Section 4.11" mergers created overlapping territories. Those affected by Section 4.11

Table 1. Trends in the Numbers of Farm Credit System Associations (1983-96)

January 1 FLBAs PC As A CAs FLCAs Total

1983 474 421 0 0 895 1984 462 399 0 0 861 1985 436 362 0 0 798 1986 306 216 0 0 522 1987 232 155 0 0 387 1988 232 145 0 0 377 1989 154 94 33 0 281 1990 146 84 40 2 272 1991 120 111 44 18 293 1992 85 72 70 23 250 1993 77 70 69 27 243 1994 73 69 66 30 238 1995 71 69 60 32 232 1996 70 66 60 32 228

Source: Farm Credit Administration, FCA Quarterly Report (various issues).


mergers were given the opportunity to create their own ACA. but few chose to do so. As of January 1, 1996, 158 (69%) of the system's 228 associations were DLAs. The 70 remaining FLBAs were located in districts served by the FCB of Wichita (22) and the FCB ofTexas (48). These FLBAs are still not direct lenders-they originate and service loans that are made by FCBs. FCBs in other districts may still hold or service some mortgage loans because not all of the portfolios were necessarily downloaded to ACAs and FLCAs when they were created. (Tables 2 and 3 show active Farm Credit institutions as of January 1, 1988 and January 1. 1996, respectively.)

In 11 of the 12 districts, mergers of the FLBs and FICBs occurred pretty much as scheduled in the 1987 act. Eleven FLBFICB mergers were consummated by July 1, 1988. The Jackson FLB was placed in receivership, leaving the Jackson FICB without a merger partner. The real estate lending functions in the former Jackson district went to Texas under a bidding process. The FCA initially ruled that the Jackson FICB should also merge with Texas, but after a lengthy battle in the courts and in Congress, most of the Jackson FICB went to Columbia.

In 1988, shareholders of 10 of the 12 district BCs voted to merge with the Central BC to form the National Bank for Cooperatives, or CoBank. The St. Paul and Springfield BCs voted to remain independent. As a consequence of the particular merger option chosen by the shareholders, these three remaining BCs could compete nationally. The Springfield BC later merged with CoBank.

Mergers between district FCBs proceeded more slowly. While the 1987 act provided for a committee to study mergers of districts, the committee completed its work in 1989 without proposing any mergers. Apparently the timing was inappropriate for interdistrict mergers, given all the financial problems to be addressed. Later, a number of interdistrict mergers did

Lee and Irwin 11

occur, though not as a part of any master plan.

The first interdistrict FCB merger occurred in May 1992, when the St. Louis and St. Paul FCBs merged to form AgriBank FCB which is headquartered in St. Paul. The Jackson FICB merged with the Columbia FCB on October 1. 1993. Six newly created FLBAs in the former Jackson district originate farm mortgage loans for the Texas FCB. which was the winning bidder for most of the loan portfolio from the Jackson FLB receivership. On January 1, 1994. the Louisville FCB merged with AgriBank. The Spokane and Omaha FCBs merged to form AgAmerica FCB on March 3 I, 1994. On April 1, 1995, the Baltimore and Columbia FCBs merged to form AgFirst FCB. The system's first Agricultural Credit Bank (ACB) was formed on January 1. 1995, when the Springfield FCB and BC merged with CoBank to form CoBank ACB.

In addition to mergers. some FCS institutions are experimenting with joint ventures and strategic alliances to reduce costs and increase the variety of services they can offer. Two banks, CoBank ACB and AgAmerica FCB, have formed AgCO Service Corporation to provide data processing and management information services for the two banks and for AgAmerica's associations. AgriBank has entered into a strategic alliance with IDS Financial Services to provide members with financial planning services. Another service corporation, Farm Credit System Financial Partners. Inc .. was formed to provide support services to the five remaining ACAs in the former Springfield district. Western FCB and Farmer Mac have formed an alliance to expand their secondary market programs. AgFirst FCB is using Farmer Mac to become a nationwide pooler of rural home mortgages for the Federal National Mortgage Association (Callender 1992, 1995; FCA; USDA 1996).

Structural changes among banks and associations can be visualized by


Table 2. Active Farm Credit Institutions as of January l, 1988

Total Banks Region FLBAs PC As Associations FLBA/FICB/BC Total

Springfield 18 18 36 3 39 Baltimore 26 26 52 3 55 Columbia 20 1 21 3 24 Louisville 9 6 15 3 18 Jackson 1 2 3 3 6 St. Louis 21 4 25 3 28 St. Paul 26 23 49 3 52 Omaha 31 32 3 35 Wichita 15 18 33 3 36 Texas 44 23 67 3 70 Sacramento 20 21 41 3 44 Spokane 1 2 3 3 6

Total 232 145 377 37* 414


*Banks total includes the Central Bank for Cooperatives.

Table 3. Active Farm Credit Institutions as of January 1, 1996

Bank PC As FLBAs A CAs FLCAs A CBs FCBs BCs Total

CoBankACBa 0 0 5 0 1 0 0 6 AgFirst FCB 1 0 39 0 0 1 0 41 AgriBank FCB 19 0 11 19 0 1 0 50 FCB of Wichita 17 22 0 0 0 0 40 FCB ofTexas 17 48 0 0 0 0 66 Western FCB 11 0 4 12 0 1 0 28 AgAmerica FCB 1 0 1 1 0 1 0 4 St. Paul BCb 0 0 0 0 0 0 1 1

Total 66 70 60 32 6 236


Note: Table does not include service corporations, nor does it include institutions in liquidation.

a CoBank ACB has authority to serve cooperatives nationwide and ACAs in the former Springfield district.

b The St. Paul Bank for Cooperatives may lend to eligible borrowers located within any territory served by Farm Credit System institutions under the Farm Credit Act of 1971.


comparing Figures 1 and 3. In addition to banks and associations, there are a number of other system entities, as illustrated in Figure 2. There are several other dimensions of FCS restructuring that cannot be captured in tables, maps. or organization charts. Geographic location does not always accurately describe association-bank affiliations. The recent affiliation of ACAs in the former Springfield district with CoBank ACB is one example of this change. With the breakup of the Jackson district. Alabama, Mississippi, and most of Louisiana are affiliated with AgFirst FCB for non-real estate credit and with FCB of Texas for real estate mortgage loans. Associations adjoining another district were allowed to affiliate with the neighboring district under Section 4.33 of the 1987 act. As a result, Eastern Idaho ACA is with the Western FCB. three New Mexico PCAs and the Northwest Louisiana PCA are with the FCB of Texas, and four A CAs from the former Louisville district are with AgFirst FCB. For the most part, however, geographic patterns still predominate because most mergers have combined the old districts relatively intact.

Efforts to implement the secondary mortgage market provisions of the 1987 act began with the chartering of the Federal Agricultural Mortgage Corporation (Farmer Mac) in 1988. Farmer Mac is an FCS institution that was originally capitalized with stock sold to FCS institutions. commercial banks. insurance companies, and other financial institutions. Underwriting standards were developed and certified poolers were identified by 1990. To date, Farmer Mac has not been a significant factor in the farm mortgage market. Major reasons cited are weak demand for fixed rate mortgage loans and ample lender liquidity that has allowed them to hold loans in their own portfolios. Before 1996, originating lenders were required to hold cash reserves or subordinated participations equal to 10% of the amount of loans pooled. This requirement caused

Lee and Irwin

interest rates on loans pooled to be less competitive than they otherwise might have been.

Preliminary Evaluation of Restructuring

13

Many of the restructuring provisions of the Agricultural Credit Act of 1987 have been accomplished. There are now six FCBs. one ACB. and one BC. The number of associations is considerably smaller and most are now DLAs. Less than $1.3 billion of the authorized $4 billion in financial assistance was actually used: furthermore, much of it has been repaid early as part of the bank merger process. and provisions have been made to repay all of it on schedule. Most important. the system has returned to sound financial health as measured by operating profits and capital adequacy. The two major potential benefits of restructuring are improved operating efficiency due to less administrative overhead and less credit risk due to geographic diversity. Have these improvements been realized?

Geis and Callender analyzed FCS efficiency following passage of the 198 7 act. They found that the BCs had experienced some success in reducing costs after their merger in 1989. But this improvement occurred in all three banks, including the two that did not merge. In CoBank, reductions in some expenses (such as salaries and occupancy expenses) were offset by increases in other operating expenses.

Callender ( 1991 b) examined the pre- and post-merger performance of the FCBs. He concluded that the mergers had negligible effects on system financial performance. He also noted that individual institutions reduced risk through internalization of diversification possibilities, not through better overall risk management. Callender provided the following plausible explanation for these results:

14 Restructuring the Fann Credit System: A Progress Report

There are good reasons why economies of scale and scope may fail to be realized after a merger, especially in the short run. For one, differences in the sizes of preand post-merger institutions may not be sufficient for these economies to be evident. In addition, they may already have been realized through pre-merger structure or cooperation, especially in a system with the level of shared components and shared management as the FCS. There may also be short-run obstacles to the realization of cost savings including incompatibility of management philosophy and operating principles, existing investment in incompatible management information system technologies, and expenses for severance benefits related to reductions in work force (p. 39).

Collender (1991a) used a more extensive Dupont analysis to examine changes in the financial performance of district banks and their related associations over the period 1986-89. He found that financial performance had improved overall, but there were wide variations among districts. Collender attributed the improved performance to increases in interest and non-interest income, but the system had not achieved lower operating costs as a percentage of assets.

Wang used a translog cost function and call report data to examine potential economies of scale and scope in DLAs over the period 1988-92. He found that overall system financial performance was improving during the period of major restructuring. However, performance improvements could not be attributed to restructuring per se. Wang found evidence of economies of scale and scope only among DLAs with less than $25 million in total assets; i.e., small associations could achieve lower long-run average costs by expanding through growth or merger to more than $25 million in total assets. There was some evidence of mild overall diseconomies of scale. The lowest average total cost was observed in DLAs with total assets of $100-200 million. Wang's findings were similar to results of many

studies of scale and scope in the financial services industry. There is general consensus that economies of scale and scope exist only among relatively small institutions. Small institutions can generally achieve economies of scale by increasing in size to more than $100 million in total assets (see, for example, Humphrey).

Chien, Leatham, and Ellinger examined cost efficiency using the call reports and a single-equation stochastic cost function frontier approach. They found that all size groups of FLCAs and all but the largest PCAs and ACAs exhibit economies of scale and increasing returns to scale. These results, and the current rash of merger activity in the financial services industry, would seem to indicate that there apparently are no diseconomies of scale beyond $100 million in total assets. O~jectives such as more control, more total returns, and more market power can be achieved as long as there are no significant diseconomies.

It is likely that interdistrict mergers have produced some reduction in credit risk due to geographic diversification. Jeschke, Schnitkey, and Lee used a portfolio model to analyze potential risk reduction from FCS district bank mergers. Based on a strategy of merging districts with low or negative correlations in net farm income, they recommended the following pattern for interdistrict mergers: Spokane/Omaha, St. Paul/Springfield, Wichita/St. Louis, and Louisville/Baltimore/Columbia. Sacramento and Texas would not merge. (These merger references were as of 1988-89, as shown in Figure 3. Several have since merged and most have new names.) In some respects, the mergers that have occurred resemble the realignment recommended by Jeschke et al. The system's credit quality has improved substantially since 1988, but it would be difficult to determine empirically whether this improvement can be attributed to restructuring. Clearly, improved net farm income, lower interest


rates, and a recovery in land values all played a role in the system's recovery.

The evidence of performance improvements due to system restructuring is mixed and inconclusive. Perhaps it is too early to draw conclusions. Call reports for FCS institutions in electronic form first became widely available to researchers in 1988. Results of empirical analyses such as those cited above were probably overwhelmed by the turmoil in the system-mergers, consolidations, liquidations, and serious loan quality problems. Moreover, FCS loan volume was much lower in the period during which the mergers took place. so it is difficult to sort out the effects of downsizing from the effects of mergers. It is possible that some parts of the FCS were scaled on the assumption that loan volume would recover to the $80 billion level of the mid-1980s. As it turned out, the system's loan volume remained flat at only $50 billion over the period 1985-95, and all growth in market share went to commercial banks.

We really do not know what the system's performance might have been without restructuring. Perhaps it would have continued to deteriorate! Hence, some improvement may have been associated with restructuring, but the experience cannot be validated empirically. Research efforts of this type should continue now that the system's structure has stabilized somewhat. Broadly speaking, research to date seems to have focused on technique, with little understanding of the enterprises being modeled. For example, most analyses ignore the major shift in functions between banks and associations. The wholesale bank concept in particular would seem to distort the functions of the institutions to such a degree that the only meaningful comparison would be combined bank and association costs before and after the restructuring. The FCS now offers researchers a population with greater variance in sizes and structures, so that cross-sectional as well as time-series analyses are now possible.

Lee and Irwin

Future Changes and Policy Issues

15

Barry, Brake, and Banner used principalagent concepts to suggest a framework for evaluating the changing role of the banks. They identify the broad principal-agent relationships in the FCS as investors in FCS securities on the one hand and the interests of taxpayers and Congress on the other. A second set of principal-agent relationships represents the interests of taxpayers/Congress in the safety and soundness of the FCS. The important premise is that the role of FCS banks has been substantially reduced by restructuring. This analysis leads the authors to pose some important questions regarding future structural change:

What, then, are the appropriate structural units within the Farm Credit System? Is further restructuring a legitimate policy issue, or should the market. managerial, and technological forces now at work be left to run their course? ... Are the banks considered an integral part of the FCS intermediation process, [or] more a supplier of services? ... In either the service or intermediation role, are 11 district banks needed, or would four. one, or none be adequate? (Barry et al. 1993, p. 244).

The question about the appropriate number of banks is an important one. There are now eight banks, and the recent change of CoBank from a Bank for Cooperatives to an Agricultural Credit Bank raises the question of whether fewer FCBs or ACBs could provide all of the wholesale intermediation and other services for the entire system. Hopkin, Sporleder, Padberg, and Knutson examined the cost savings that would occur with complete versus partial consolidation of the 13 BCs following passage of the Agricultural Credit Act of 1987. Economies of size were measured by estimating a long-run average cost function using 1981 and 1986 annual, cross-section data. Cost savings were estimated to be 25 basis points with total


consolidation, 9 basis points with fiveregion consolidation, and 4.6 basis points with four-region consolidation-i.e., the more consolidation, the better.

There are several potential advantages of a single-bank FCS. Administrative overhead could be spread over a larger loan volume. Risk could be reduced by greater geographic, commodity, and type of loan diversity. Customer service could be enhanced through greater specialization of loan personnel skills by size and type of loan. But there are disadvantages. While some unit costs might decrease. others (travel costs, for example) might tend to increase when a single bank serves a broader geographic area. Moreover, a single bank might not have the flexibility and diversity needed to serve unique regional markets.

Considerable opposition to the trend to fewer and larger banks and associations comes from borrower I shareholders who fear a loss of local control. As originally created, the FCS was a grassroots organization with a strong local community orientation. The Farm Loan Act of 1916 authorized any 10 farmers who could qualify for a total of $20,000 in Land Bank loans to qualify for a National Farm Loan Association charter. There was also no attempt to prevent overlapping charters. This extreme degree of local control and widespread overchartering led to serious problems during the 1920s and early 1930s.

The effect of restructuring on borrower I director versus management control is difficult to assess. When drafting the 1987 legislation, Congress carefully spelled out that all bank and association mergers had to be approved by a majority of the stockholders. Anecdotal evidence indicates that efforts to persuade stockholders to vote in favor of consolidation plans emphasize potential benefits such as the greater dependability and efficiency of the new (and larger) institution. Most proposals also

emphasize that credit decisions would continue to be handled through local offices and that at-risk borrower stock would be exposed to less risk. Postmerger quarterly and annual reports to stockholders typically emphasize the lower interest rates and new services made possible by the mergers.

The effects on borrower loyalty with respect to control versus greater efficiency have important implications for market share. Perhaps the borrower loyalty issue could be better addressed by adopting more consistent policies on unallocated surplus and patronage refunds. Callender ( 1995) reports that the FCBs all have amounts of capital that exceed regulatory minimum amounts. He suggests that "the ability to repatriate capital to borrower I stockholders should improve FCS's competitive position because it effectively lowers the cost of funds to borrowers" (p. 22). Clearly, capital levels in FCS institutions must exceed regulatory minimums, and optimal amounts will vary depending on loan portfolio risk and anticipated growth in loan volume. Overall, FCS at-risk capital as a percent of total loans has increased from about 11 o/o in 1989 to nearly 20% currently. During the same period, the percentage of loans in nonaccrual status or 90 days past due declined from 5.5% to 1. 7o/o (USDA, February 1996). Research is needed to help FCS institutions determine how earnings should be divided between unallocated surplus and patronage refunds.

Brake and Lins suggest that the FCS needs to address its own internal competitive structure. From 1933 to 1988, the FCS followed a policy of exclusive lending territories for associations. As noted earlier, there are now a few instances of overchartering where associations compete with each other for similar loans. The overchartering issue is controversial. The original justification for creating the Farm Credit System was to encourage competition with


commercial lenders and assure farmers of a dependable supply of funds. Some argue that a second cooperative competitor is costly and inefficient. Others believe that competition between cooperatives improves market performance as long as predatory behavior is constrained. Research is needed to determine which of these views is correct. In other words. how do exclusive and overchartered markets compare in terms of efficiency and quality of services offered?

Brake and Lins raise other system structural issues. They point out that FCA regulatory costs and structure affect system efficiency and competitiveness. They also stress that the roles of the FCSIC and the FCA differ. The FCA. the FCSIC, the FCBs. and the Funding Corporation all have vested interests in the safety and soundness of the system. Given its small size, the system needs to avoid a duplicative regulatory apparatus such as the one in commercial banking where the Federal Reserve System. the Federal Deposit Insurance Corporation, the Comptroller of the Currency. and state superintendents or commissioners of banking have overlapping regulatory functions.

Some of the questions and concerns about regulatory structure were at least partially answered when Congress passed the FCS Regulatory Relief Bill in January 1996. This legislation repealed a provision in 1992 legislation that would have required the FCA and FCSIC to have separate boards of directors effective January 1. 1996. Thus, both agencies will continue to have the same board. This and other provisions of the 1996 legislation should result in minimal duplication in the functions of the FCA and FCSIC. FCS banks are increasingly becoming wholesalers of funds with fewer direct supervisory functions. To some extent, supervision by banks has been replaced by the Contractual Interbank Performance Agreement (CIPA) and the Market Access Agreement (MAA). CIPA and MAA are voluntary mechanisms adopted by the

Lee and Irwin 17

banks to monitor each other in terms of safety and soundness. According to one estimate, when the Farm Credit System Insurance Fund (FCSIF) reaches a secure base amount. the FCS regulatory cost will amount to some 6-7 basis points of the borrower's dollar, a small cost compared to the assurance of continued GSE status which offers double-digit savings in interest costs.

In a comprehensive review. Collender and Erickson suggest that the safety and soundness of the FCS need continuous attention. Although the authors note that the FCS is considerably safer and sounder than before the mid-1980s, they cite the following potential problem areas: (a) potential conflicts of interest in the system's management structure; (b) the need to deter banks from taking imprudent. unnecessary risks now that they have insurance; (c) the high concentration of insured liabilities and at-risk capital in a small number of banks; and (d) the separation of ownership and managerial control.

Concluding Observations

Restructuring in the Farm Credit System generally has proceeded along the path spelled out in the Agricultural Credit Act of 1987. The number of banks has been reduced from 37 to eight. and the number of associations has declined by nearly 40%. In about half of the districts. the farm real estate and non-real estate lending activities. for the most part. have been combined in ACAs. which are onestop lenders. Financial stability, as measured by earnings and capital. has been restored.

While there are far fewer institutions. the FCS organizational structure has become much more complex. There are now four types of associations and three types of banks. along with several national and regional service entities and trade associations. Is this complexity needed for a relatively small financial institution? With $55 billion in total assets. the FCS


would not rank among the 10 largest commercial banks in the U.S.

As further restructuring occurs, the FCS needs to address several unresolved problems. The system still has not regained a significant amount of the market share that was lost to other lenders during the 1980s. It is questionable whether a financial institution with lots of "brick and mortar" and high levels of service can be the competitive prototype in the rapidly changing financial services industry. The FCS will continue to face stiff competition from commercial banks which themselves are being transformed by mergers, nationwide branching, and technological change. The market share held by vendors and other nontraditional lenders is also increasing. The FCS will also be challenged by the industrialization of the industry it serves.

The FCS continues to seek expanded lending authority to extend beyond its traditional agricultural market to a broader rural development mission (Erickson and Callender). At the same time, the system's authority as the only major agricultural lender with "agency status" for its securities continues to be challenged. Commercial banks have proposed legislation that would permit them to borrow from, become stockholders in, and serve on boards of directors of FCBs. In other words, commercial banks would have the same powers and roles that FCS associations currently have, including access to the agency market. This proposal represents a return to 1916, when both cooperative National Farm Loan Associations and commercial Joint Stock Land Banks had access to Federal Land Bank funds.

Congress passed legislative changes in an effort to rescue Farmer Mac in early 1996. The Farm Credit System Reform Act gave the FAMC authority to become a pooler in its own right. This act also eliminated the 10% subordination requirement, so

Farmer Mac should now be able to offer lower, more competitive interest rates. Many believed that these and other finetuning provisions were essential to Farmer Mac's survival. It remains to be seen whether these statutory changes will be sufficient to make Farmer Mac a significant factor in the farm mortgage markets. Farmer Mac is essentially a wholesale GSE, much like the Joint Stock Land Banks and FICBs before 1933. Could it be that Farmer Mac will be the third wholesale GSE to fail in agriculture? Perhaps a GSE as a residual supplier cannot work in an agricultural setting.

Hopefully, this article will help researchers to better understand the institutional diversity that exists in the FCS. The structure of the system in terms of the number, size, and types of institutions will continue to change, though probably at a slower pace than in the last 10 years. Most research efforts will likely attempt to identifY an optimal structure in terms of efficiency and financial performance. Results of empirical analyses should be interpreted in light of the large changes in the roles of some institutions-for example, the reduced role of the banks. At the same time, some new institutions may not be very different from the ones they replaced-for example, ACAs that replaced jointly managed PCA/FLBA units. It also needs to be recognized that there are lags between the time when structural change takes place and when performance improves. Of course, the underlying objective of these research efforts should always be to measure the effects of structural change on the quality of financial services available to agriculture.

Basic research is needed on the costs incurred from merging two or more financial institutions that may have incompatible computer systems or different credit cultures and management philosophies. This research should go beyond empirical studies that analyze financial data from FCA call reports. To fully understand the implications of


structural change, it is necessary to collect primary data directly from those who had the most at stake-management, loan officers, and perhaps most important, the borrower/shareholders and board members who voted for or against the mergers and other changes.

References

Barry, P.J .. J.R. Brake, and D.K. Banner. "Agency Relationships in the Farm Credit System: The Role of the Farm Credit Banks." Agribus.: An Intemat. J. 9,3(1993):233-45.

Barry. P.J., P.N. Ellinger. J.A. Hopkin, and C.B. Baker. Financial Management in Agriculture, 5th ed. Danville. IL: The Interstate Printers and Publishers, Inc .. 1995.

Brake, J.R., and D.A. Lins. "Farm and Rural Credit Issues." In Agricultural Policy Issues and Choices for 1995, pp. 363-79. Boulder, CO: Westview Press, 1994.

Chien, M.-C., D.J. Leatham, and P.N. Ellinger. "An Analysis of the Scale Economies and Cost Efficiencies in the Farm Credit System." In Regulatory, E.ffk:iency, and Management Issues Affecting Rural Financial Markets. pp. 172-83. Proceedings of the 1994 NC-207 Regional Research Committee Meeting. Dept. of Agr. Econ .. University of Illinois. Urbana, April 1995.

Callender, R.N. "An Analysis of Financial Performance of Federal Land Banks, Federal Intermediate Credit Banks, Farm Credit Banks, and Related Associations, 1986-89." Staff Rep. No. AGES-9117. USDA/ERS, Washington, DC, April 1991a.

--. "Changes in Farm Credit System Structure." In Agricultural Income and Finance: Situation and Outlook Report, pp. 37-44. Pub. No. AF0-44.

Lee and Irwin 19

USDA/ERS, Washington, DC, February 1992.

--. "Farm Credit Institutions Attain High Capital Levels." In Agricultural Income and Finance: Situation and Outlook Report, pp. 22-23. Pub. No. AF0-56. USDA/ERS. Washington. DC. February 1995.

--. "Have Mergers Improved the Financial Performance of Farm Credit Banks?" In Agricultural Income and Finance: Situation and Outlook Report. pp. 35-40. Pub. No. AF0-40. USDA/ERS. Washington. DC, February 1991b.

Callender, R.N., and A. Erickson. "Farm Credit System Safety and Soundness." Pub. No. AIB-722. USDA/ERS, Washington. DC. January 1996.

Erickson, A., and R.N. Callender. "Farm Credit System Seeks Expanded Powers." In Agricultural Income and Finance: Situation and Outlook Report. pp. 33-37. Pub. No. AF0-52. USDA/ERS. Washington, DC, February 1994.

Farm Credit Administration. FCA Quarterly Report. FCA. Risk Analysis Div., McLean, VA. Various issues through third quarter 1995.

Federal Farm Credit Banks Funding Corp. Farm Credit System: Annual Information Statement. Jersey City. NJ. Various issues.

Geis. L.M., and R.N. Callender. "Has Consolidation Improved the Operating Efficiency of the Banks for Cooperatives?" In Agricultural Income and Finance: Situation and Outlook Report. pp. 41-45. Pub. No. AF0-48. USDA/ ERS, Washington, DC. February 1993.

Hoag, W.C. TI1e Farm Credit System: A History of Financial Self-Help. Danville. IL: The Interstate Printers and Publishers, Inc., 1976.

20 J<estructuring l.he Farm Credit System: A Progress Report

Hopkin, J.A., T.L. Sporleder, D.I. Padberg, and R.D. Knutson. "Evaluation of Restructuring Alternatives for the Banks for Cooperatives." J. Agr. Cooperation 3(1988):72-82.

Humphrey. D.B. "Cost Dispersion and the Measurement of Economies in Banking." Federal Reserve Bank of Richmond, Econ. Rev. (May/June 1987):24-38.

Jeschke, D., G.D. Schnitkey, and W.F. Lee. "Geographical Lending Diversification: An Analysis of Regional Agricultural Asset Returns and Risk." Agr. Fin. Rev. 49(1989):64-73.

Murray, W.G. Agricultural Finance: Principles and Practice of Farm Credit. Ames, lA: The Iowa State College Press, 1941.

Peoples. K.L., D. Freshwater, G.D. Hanson. P.T. Prentice, E.P. Thor, and E. Melichar. Anatomy of an American Agricultural Credit Crisis: Farm Debt in the 1980's. Lanham, MD: Rowman & Littlefield Publishers. Inc., 1992.

U.S. Department of Agriculture. Agricultural Income and Finance: Situation and Outlook Report. Annual Lender Issue. USDA/ERS, Washington. DC. Various issues through February 1996.

Wang, C.-R. "An Empirical Analysis of Economies of Scale and Scope in the Farm Credit System." Unpub. Ph.D. dissertation. The Ohio State University, 1994.

Appendix: Glossary of Terms

The Nationwide System

• Farm Credit System (FCS). A nationwide financial services cooperative with lending institutions and other entities

chartered under authorities in the Farm Credit Act of 1971, as amended.

Lending Institutions

• Farm Credit Banks (FCBs). Make direct long-term agricultural loans secured by farm real estate through Federal Land Bank Associations. Provide wholesale loan funds to direct lending associations: Production Credit Associations, Federal Land Credit Associations, Agricultural Credit Associations, and other financing institutions (OFis).

• CoBank Agricultural Credit Bank (CoBank ACB). Finances agricultural cooperatives and rural utility cooperatives nationwide. Finances U.S. agricultural exports and provides international banking services to farmerowned cooperatives. Provides wholesale loan funds to Agricultural Credit Associations (ACAs) in the former Springfield Farm Credit District.

• St. Paul Bank for Cooperatives (St. Paul BCJ. Finances agricultural cooperatives and rural utility cooperatives nationwide.

• Federal Land Bank Associations (FLBAs). Take applications for and service longterm real estate loans for FCBs.

• Agricultural Credit Associations (A CAs). Have direct lending authority to make short-, intermediate-. and long-term loans to retail customers with funds obtained from FCBs or CoBank ACB.

• Federal Land Credit Associations (FLCAs). Have direct lending authority to make long-term real estate loans to retail customers with funds obtained from FCBs.

• Production Credit Associations (PCAs). Have direct lending authority to make short- and Intermediate-term loans to retail customers with funds obtained from FCBs.


• Federal Agricultural Mortgage Corporation (FAMC). Also known as Farmer Mac, is an independent entity within the FCS created by the Agricultural Credit Act of 1987 to coordinate a secondary market for agricultural mortgage loans.

Service Entities

• Farm Credit Leasing Services Corporation (FCLSC). Owned by system banks and provides financial leasing services to agricultural producers, agricultural cooperatives, rural utility cooperatives. and FCS institutions through nine regional sales offices.

• Federal Farm Credit Banks Funding Corporation (FFCBFC). Manages and coordinates the sale of systemwide bonds and notes in the national financial markets. These securities are the joint and several liability of all system banks.

Lee and Irwin 21

• Trade Associations. The Farm Credit Council, based in Washington, DC, and federated District Farm Credit Councils represent the FCS institutions before Congress, the Executive Branch, and the public. These councils also provide support services such as training, marketing, insurance, and purchasing on a fee basis.

Regulation and Insurance Entities

• Farm Credit Administration (FCA). The independent federal regulator responsible for examining and insuring the safety and soundness of all system institutions.

• Farm Credit System Insurance Corporation (FCSIC). Established in 1988 to insure timely payment of principal and interest on systemwide debt securities.

A Time-Series Analysis of the Relationship Between Inflation and Productivity Growth in the U.S. Agricultural Sector Peter .J. Saunders

Abstract

This study investigates the relationship between inf1ation and productivity growth in the U.S. agricultural sector. Productivity growth is measured by the total factor productivity and lnf1ation is approximated by use of the consumer price index and the GDP implicit price def1ator. Data are initially subjected to ADF tests to determine the order of integration, with test results indicating that a VAR model in the second differences is an appropriate tool for a further econometric analysis.

The Granger causality framework is employed to analyze the existence of causal relationships among the test variables. Applying the minimum FPE causality testing method, a bi-directional causal f1ow between lnf1ation and productivity is established. Findings show that inf1ation has a negative impact on the overall factor productivity in the U.S. agricultural sector, and that productivity growth in the sector has a statistically significant negative impact on inf1atlon. Thus, agricultural productivity growth has benefitted the entire U.S. economy.

Key words: productivity, inf1atlon, Granger causality.

The author Is Chair of the Department of Economics. Central Washington University.

The escalation of lnf1ation in the latter part of the 1970s and the early 1980s has led to a revived Interest in examining the relationship between inf1ation and productivity. Two opposing theoretical views exist on this issue. The conventional view maintains that productivity growth is exogenous In the inflation/productivity relationship. According to this theory, It is the initial growth in productivity that leads to a subsequent Increase in output, thereby shifting the economy's aggregate supply curve to the right. This Increase in the aggregate supply then results in lower overall prices. Consequently, under this hypothesis, productivity growth has a negative causal impact on the economy's prices.

However, It Is also possible to postulate that lnf1ation may be the exogenous force In the above-described relationship. Jarrett and Selody describe several ways in which lnf1ation negatively impacts productivity growth. Inf1ation can lead to an Inefficient mix of factor inputs, which in tum may adversely affect labor productivity. Inf1ation also distorts the signals that prices supply to producers by Introducing more uncertainty. Under inf1atlonary conditions, producers are more likely to make wrong and costly investment decisions. These errors may lead to a decrease in overall factor productivity. Furthermore, according to Jarrett and Selody, lnf1atlon can negatively affect optimal contract length and planning horizons. lnf1ation can also be

Agricultural Finance Review. Vol. 56, 1996

responsible for an increased tax burden on businesses and individuals due to the existing nonneutral tax laws.

Several recent studies have investigated the linkages between inflation and the U.S. agricultural sector. Just and Miranowskt analyzed the factors which affect farmland prices. Their structural model of land prices examines the effects of inflation on savings-return erosion, capital erosion, and reduction in real debt. With respect to land prices, the authors found that "the large price swings are largely explained by inflation rates and changes in real returns on alternative uses of capital" (p. 168). Bordo and Schwartz investigated the effects of money supply changes and price changes of basic commodities on price movements in the U.S. Their analysis is confined to the 1870 to 1913 time period. They report empirical evidence suggesting that prices of basic commodities. such as wheat prices, do not play an important role in the general price level determination: the general price level is primarily determined by changes in the money supply. Bessler provides additional empirical evidence on this issue within the confines of the Granger causality testing framework. The results of Bessler's study suggest that although wheat prices play an important role In explaining agricultural prices (a more significant role than monetary changes), they do not causally explain Industrial prices. Industrial prices are better explained by movements In the money supply.

The studies noted above provide Important empirical evidence on how some factors, such as wheat prices, determine prices In the U.S. economy. However, other factors, such as productivity changes. may also have an Important Impact on Inflation. As previously explained, productivity growth may well have a moderating Influence on Inflation due to an overall output growth, and thus may play an Important causal role In the general price level determination.

Saunders 23

Conversely, inflation can also negatively impact productivity via its effect on uncertainty. The impact of inflation on uncertainty in the long run was investigated by Ball and Cecchetu. They found that inflation raises long-run uncertainty. Consequently, according to the authors, inflation has substantial costs, including unstable output resulting from monetary policy swings. Essentially. monetary policy swings lead to economic recessions which. in tum, cause a downturn In the output. An Increase in uncertainty may also lead to fewer longterm contracts. including loans designed to finance productive investment. Thus. it might be argued that there exists a longrun relationship between inflation and productivity.

In addition to all of the above outlined ways in which inflation may negatively impact productivity. there Is one channel of particular importance in the case of the U.S. agricultural sector. A large quantity of the U.S. agricultural product is exported. Domestic Inflation has an important impact on the competitiveness of the U.S. agricultural sector. In particular. increased domestic inflation negatively affects relative prices (i.e .. U.S. prices relative to prices abroad). Hence. domestic inflation negatively Impacts the U.S. agricultural sector's competitiveness. thereby reducing its exports. The reduction In our agricultural exports. coupled with a reduction in farm income. may adversely impact investment decisions In the farm sector. thereby reducing the productivity in this sector. Under this scenario, the emphasis is on the demand factors which influence the Inflation/ productivity relationship as opposed to the supply factors. Consequently, viewed from the demand side, Inflation is the exogenous force in this relationship.

Inflation can also Impact productivity in the U.S. agricultural sector through its effect on real income of farmers. Inflation

24 A Time-Series Analysis ...

can actually increase real income of farmers. This is due to the fact that farmers' money incomes are flexible, and as such they adjust to the rate of inflation. In general, unanticipated inflation leads to a redistribution of income from fixedincome earners to flexible-income earners. As a result, farmers are net real income gainers in times of unanticipated inflation. Higher real incomes can be used to purchase new capital investment, thereby increasing the productivity in the farm sector. Starleaf, Meyers, and Womack investigated the impact of inflation on the real income of U.S. farmers, and found empirical evidence showing that U.S. farmers have benefitted in times of unanticipated inflation. This result implies that unanticipated inflation may well lead to an increase in productivity in the farm sector.

It could also be argued that inflation may have strong adverse effects on agricultural productivity and output. Johnson outlines three different ways in which inflation negatively impacts agricultural productivity. First, an incorrectly anticipated inflation will lead to resource misallocation. This effect is due to the additional uncertainty introduced by variable inflation rates in the farm sector. Second, an unanticipated increase in inflation can lead to adverse changes in relative prices. The distortion in relative prices can have a negative Impact on output in the U.S. agricultural sector. According to ,Johnson, this effect is particularly important for those types of domestic farm products sold on international markets. In the long run, an increased rate of domestic inflation relative to inflation rates abroad will lead to a U.S. exchange rate depreciation and, in tum, to increased absolute prices of U.S. farm products. The third factor responsible for an adverse impact of inflation on U.S. agricultural sector productivity is concerned with government policies. In particular, drastic actions taken to bring a runaway inflation under control, such as a sizable decrease in the money supply, can

have a dramatic negative impact on the output in the U.S. agricultural sector. Johnson concludes by suggesting that inflation

... has not had a measurable impact upon agrtcultural production or productivity. This statement should not be assumed to mean that Inflation has had no effect. but simply that our measures are not sufficiently refined to pick up what effects there may have been (p. 923).

Clearly. a strong case can be made for developing statistical methods which are sufficiently refined to provide further empirical evidence on the relationship between inflation and productivity in the U.S. agricultural sector. The review of economic literature presented above leaves the issue of the inflation/ productivity relationship unresolved. Research results reported to date for both the entire U.S. economy and the U.S. agricultural sector do not provide conclusive evidence on this relationship, pointing to a need for further empirical research on this topic.

The purpose of this study is to provide such evidence. The empirical analysis is confined to the U.S. agricultural sector. Because the income and subsequent investment decisions in this sector are especially sensitive to inflation, examination of this sector of the U.S .. economy is of particular interest. As explained above, inflation may have a negative impact on farm income and investment due to the demand fluctuations associated with changes in the export volume. It can also negatively affect productivity because of its impact on resource allocation and the distortion of relative prices. However, inflation may also have a positive influence on the agricultural sector's productivity as it increases farmers' real income relative to other fixed-income earners in the economy. Empirical examination of the impact of price changes on agricultural


productivity may indicate to what extent. if any, productivity in this sector is influenced by exogenous factors, such as price changes. As such, it may provide further empirical evidence on the linkages between the macro sector of the U.S. economy and its agricultural subsector. 1

The study is divided into four sections. In the first section. the methodological issues concerning the time-series data analysis are outlined. Next, the data are subjected to unit root tests. The unit root test results determine the next step in the data analysis, which involves estimating the model within the Granger causality testing framework. The results of the Granger causality tests are described in the third section, followed by a summary of the overall conclusions in the final section of this article.

Methodology in the TimeSeries Analysis

The empirical analysis is confined to the U.S. agricultural sector. The focus of the study is to investigate what empirical relationship. if any. exists between inflation and the overall factor productivity in this sector. Productivity is measured by the farm output per unit of total factor input (PROD), while inflation is approximated by two different price indexes: the consumer price index for all urban consumers (CPIU). and the gross domestic product implicit price deflator (GDPD). Since the main objective of this study is to investigate the relationship between inflation and productivity in the U.S. agricultural sector, it is important to use variables which would accurately measure inflation. Both the CPIU and the

1 This Investigation can Indicate what effects, If any. a monetary policy has on the U.S. agricultural sector. Theoretically, this policy determines the rate of Inflation. Inflation may have a negative Impact on agricultural productivity. Hence, the effects of macroeconomic policies can be traced to the U.S. agricultural sector.

Saunders 25

GDPD are widely accepted measures of aggregate prices, and therefore of inflation in the U.S. economy. The GDPD accurately captures price movements across the entire U.S. economy, while the CPIU measures the changes in the typical basket of goods and services purchased by urban consumers. All of the data are yearly, ranging from 1948 through 1991.2

Specific data by year and by variable (PROD, GDPD. and CPIU) are provided in the Appendix.

Using any time-series data in an econometric analysis necessitates undertaking several preliminary statistical steps. Initially it is necessary to determine whether the data to be used for any subsequent analysis represent a trend stationary (TS) process or a difference stationary (DS) process. 3 The traditional detrending procedure consists of separating the trend from the cyclical component. This procedure is only appropriate forTS type time-series variables. Nelson and Plosser assert that its use is inappropriate and undesirable when the time series are of the DS type. In general, DS type variables are nonstationary and they contain unit roots. 4 Consequently, it becomes necessary to differentiate any such time series prior to subsequent econometric

2 The choice of yearly data Is most appropriate for the task at hand since the bulk of agricultural production occurs once a year. The time-series data were obtained from various annual Issues of the Economic Report of the President (Congress of the U.S.).

3 The main difference between these two processes Is that TS type variables return to the deterministic trend function, while there Is no such tendency with DS type variables. See Nelson and Plosser or McCallum for a more detailed explanation of this point.

4 A time series Is defined as being weakly stationary If Its mean. variance. and covartances are finite. and If all of these are Independent of time. Conversely, If the variance Increases over time. then the series becomes explosive. Given this result. such time series should not be used for hypothesis testing. For a detailed explanation of the stationarity of time-series data and Its application to econometric hypothesis testing. see Stock and Watson.


analysis. Applying the OLS regression technique to an undifferentiated DS time series would result in the serial correlation of the error terms, thereby rendering any subsequent hypothesis test results unreliable.

According to Nelson and Plosser, an OLS estimation technique can be used to identifY the time-series data type. The test is based upon estimating the two following equations:

Y1 = u + pt + U1 (1)

and

(2)

Equation (1) tests the TS model. In this specification, tis the time trend. The DS type variables are tested by using equation (2). In both of these models, there exists a linear trend which needs to be eliminated. However, the appropriate method for its elimination differs, depending on the DS versus TS determination. As previously noted, TS type variables can be adjusted by separating the trend from the cyclical component; DS type variables are nonstationary. and thus should be subjected to a differentiation procedure. 5

The actual determination of the DS type variables is based upon the results of the Dickey-Fuller test. 6 The main purpose of this test is to determine whether each individual time series contains unit roots. The presence ofunit roots identifies such a variable as the DS type.

Determination of the presence of unit roots in time-series variables indicates that

5 The differentiation procedure may Involve taking the first or the second differences of the levels. or using the log forms. The exact specification Is determined by the ADF test results.

6 Detailed explanations of these tests and their Implications can be found In Fuller; Dickey and Fuller; Dickey, ,Jansen, and Thornton; and Holden and Thompson.

there may exist a long-run relationship among such variables. In particular, if the time series are found to be integrated of order one 1(1). then there may exist a longrun relationship among them. To investigate this possibility, it becomes necessary to carry out a cointegration analysis of the data. Establishing the existence of cointegration implies that a stable long-run relationship holds among the test variables. The researcher may then be interested in investigating the short-run dynamics of the model by employing the error correction model estimation technique. If, on the other hand, there is no evidence of cointegration, then the VAR estimation is appropriate for any further analysis of the data. The same conclusion applies for the time-series data which are not 1(1).

Unit Root Test Results

The unit root tests provide information on the degree of integration of each individual time-series variable. In the present case, the augmented Dickey-Fuller (ADF) test was used to accomplish this task. 7 This test necessitates estimating the following equation:

I

+ L Y1!J.Xt-1 + ut. 1-1

(3)

The hypothesis tests are based upon the comparison of calculated statistics with the critical MacKinnon statistics. Results

7 The ADF test Is used Instead of the original DickeyFuller test since the tlme-serles error terms often are not white noises. A detailed analysis of the differences between these two tests can be found In Alse and Bahmani-Oskooee, among others. Lags ranging from one to four years were examined. Theory indicates that Inflation and productivity changes may impact an economy with a substantial time lag. Consequently, it Is appropriate to examine several lags In the ADF testing procedure.

Agricultural Finance Review, Vol. 56. 1996 Saunders 27

Table 1. Augmented Dickey-Fuller (ADF) Test Results for PROD, CPW, and GDPD (1948-91)

Variable I Lag 2 Lags 3Lags 4 Lags

PROD a --1.800 -0.621 0.024 0.507 PRODb -7.604 -6.116 -4.939 -2.688 PROD" -9.225 -7.746 -8.898 -7.430 CP/Ua -0.876 -0.641 -1.284 -0.869 CPIUb -3.645 -2.836 -2.575 -2.047 CPIUC -5.667 -4.173 -4.520 -2.535 GDPDa -1.108 -1.008 -1.514 -1.086 GDPDb -2.699 -2.345 -2.176 -2.283 GDPDC -5.135 -3.858 -3.316 -3.668

Note: The critical MacKinnon statistic at the 5% level of significance is -3.52.

a ADF test results for 1(0). b ADF test results for 1(1). c ADF test results for 1(2).

of these tests are summarized in Table 1. The tabulated statistics indicate the order of integration for each test variable. These results indicate that all three test variables are nonstationary. and therefore are OS type variables. It is further evident that while PROD has only one unit root. and thus is 1(1). both CPIU and GDPD variables contain two unit roots. Consequently. CPIU and GDPD are 1(2). This implies that the second differences of their levels are stationary. The cointegration testing technique may not be appropriate in the present analysis, as both the CPIU and the GDPD are I(2). 8 These results also indicate that a V AR model in the second differences is an appropriate tool for any subsequent econometric data analysis. Therefore, this modeling method was used in all Granger causality testing.

Granger Causality Tests

The Granger causality testing framework can be used as a V AR model specification.

8 The colntegration testing framework can be applied In cases where all test variables are I( I). For a detailed discussion of colntegration and Its application to timeseries modeling, see, e.g., Maddala; Enders.

This method is particularly useful in a bivariate model testing framework such as in the present case. It can give an indication of causal flows between the two test variables. As such, it can provide information on which of the two test variables is exogenous. In the present case, this method can shed some light on the two opposing views on the relationship between productivity growth and inflation. This technique was successfully used by numerous authors in analyzing relationships in the U.S. agricultural sector (e.g., Barnett. Bessler, and Thompson; Bessler; Saunders 1988. 1991).

Numerous procedures exist for testing the direction of Granger causality in bivariate contexts (e.g., Guilkey and Salemi; Thornton and Batten; Hsiao 1979, 1981). In all of these procedures, the issue of the lag selection must initially be resolved. The lag selection can be determined in two ways. First, it can be an arbitrary selection based on theoretical and previously established knowledge. Second, this selection can be based upon a statistical criterion. Empirical evidence suggests that an arbitrary lag selection


can adversely determine the outcome of causality tests (Hsiao 1979, 1981: Thornton and Batten: Saunders 1988). Consequently, a strong case can be made for using a statistical criterion as a guiding tool in the causality test lag selection. In the present case, Hsiao's minimum final prediction error (FPE) criterion is employed in selecting the appropriate lag structure. 9

Hsiao's minimum FPE causality testing technique involves undertaking several statistical steps. 1° First, the FPEs of onedimensional autoregressive processes for all three test variables are calculated by varying lags from one to eight. The minimum FPE specifications and the number of lags associated with these specifications are reported as equations (4), (6). and (9) in Table 2. The second step treats these variables as the only output of the system. When inflation is measured by the CPIU variable, CPIU is

9 The minimum FPE causality testing procedure has several advantages over the conventional F-test. First, It avoids choosing between the two conventional levels of slgnlflcance in causallty testing. Second. it allows examination of all lags in a predetermined lag range. Additionally, this procedure allows entering test variables with different lag lengths into the test equations. Hsiao ( 1979) suggests that conventional F-tests may rapidly exhaust the degrees of freedom because both test variables must be entered with the same lag structure. This point is particularly important in cases where the Initial sample size is relatively small, such as in the present case. Consequently. the minimum FPE lag selection procedure Is appropriate for the task at hand. However. using this method may lead to overlltting the model. A detailed analysis of problems (such as overflt tlng) associated with several methods for selection of linear regression models Is undertaken by Geweke and Meese.

10 Hsiao's causality testing technique Involves computing final prediction errors (FPEs) for all test variables within a predetermined lag range. The FPEs are computed for both the univariate and the bivariate test specitlcatlons. The speclflcatlons yielding the minimum FPEs (along with their lag structures) are chosen for the purpose of ldentil'ylng the causal flows between the pairs of test variables. The FPE Is computed as (SEE)2 · (T + K)/T. In this specification, SEE Is the standard error of the regression, T Indicates the number of observations. and K Is the. number of parameters.

assumed to be the independent variable which controls the outcome of the PROD variable. The minimum FPE criterion is then used to determine the optimum lag order of CPIU while holding constant the order of PROD determined in the first step of this procedure. The results of these estimations are reported in Table 2 as equation (5).

Equation (7) in Table 2 shows the results of the bivariate specification when the roles of PROD and CPIU are reversed. The causality implications are made by comparing the minimum FPEs of the first and second steps of estimation. If the minimum FPE of the second step is less than that of the first step [as reported in equation (5)], then CPIU (applying the Granger causality framework) causes PROD. To obtain overall causality implications, the above procedure must be repeated while using CPIU as the initial test variable and PROD as the independent variable. The results of this step are evident from estimating equation (7). In this case, too, the minimum FPE of the second step is less than that of the first step. Consequently, the combined results of estimating equations (4)-(7) indicate the existence of a bi-directional causal flow between PROD and CPIU. The same testing procedure is then undertaken using the GDPD variable as the measure of inflation.

Causality implications for both measures of Inflation are reported In the last column of Table 2. They clearly indicate the existence of a bi-directional causal flow between CPIU and PROD as well as between GDPD and PROD. This Is an Important finding. It indicates that in the case of the U.S. agricultural sector, inflation and productivity causally impact one another. Consequently, both abovedescribed theoretical views on the relationship between inflation and productivity find some empirical support when the U.S. agricultural sector is examined. It is also important to note that the same results are obtained for both

Agricultural Finance Review, Vol. 56. 1996 Saunders 29

Table 2. Causality Testing by Computing Minimal Final Prediction Errors (FPEs) for PROD, CPID, and GDPD (1948-91)

Dependent Independent Minimum Causality Equation Variable Variable FPE Implications

(4) PROD (5) 20.742 20.742 > 13.129 (5) PROD (5) CPIU (7) 13.129 CPIU=> PROD (6) CPIU (4) 1.298 1.298 > 0.892 (7) CPIU (4) PROD (2) 0.892 PROD=> CPIU (8) PROD (5) GDPD (8) 11.962 20.742 > 11.962

GDPD=> PROD (9) GDPD (2) 0.534 0.534 > 0.436 (10) GDPD (2) PROD (3) 0.436 PROD=> GDPD

Note: Numbers In parentheses in columns 2 and 3 are lags resulting from the minimum FPE causality testing procedures.

measures of Inflation. The theoretical underpinnings of the above-estimated model can be further strengthened by examining the signs and sizes of the coefficients in equations (5). (7). (8). and (10). These results are reported in Table 3. Economic theory dictates that inflation should have a negative impact on productivity. The same assertion is made with respect to the impact of productivity on inflation.

Equations (5) and (8) show the impact of price changes (as measured by CPIU and GDPD) on productivity. A broad Indication of this impact is given by the sign and the size of the lagged coefficients of the inflation variables. In both cases. inflation Initially has a negative impact on productivity. The sums of the lagged Inflation coefficients for all relevant lags determined under the minimum FPE testing procedure In equations (5) and (8) are -1.609 and -2.897. respectively. Therefore, It would be fair to conclude that Inflation adversely affects productivity In the U.S. agricultural sector. This outcome supports some of the views outlined In the introduction section of this article (Jarrett and Selody; Ball and Cecchetti). The results contradict Johnson's conclusions. By using a sufficiently refined causality testing method, such as the minimum FPE

technique. findings suggest that inflation does indeed causally Impact productivity In the U.S. agricultural sector. Consequently. reducing inflationary pressures in the overall U.S. economy would benefit the agricultural sector.

An analysis of the results of the estimation of equations (7) and ( 1 0) (see Table 3) provides Important Information on the impact of agricultural productivity on the overall level of Inflation In the U.S. economy. These results are startling. They indicate that productivity changes in the U.S. agricultural sector have a very strong. statistically significant moderating impact on inflation In the U.S. In both cases. the lagged PROD variables have negative and statistically significant coefficients. It also appears that the productivity changes have a relatively fast impact on inflation. as indicated by the lag structures of two and three years. respectively. resulting from the minimum FPE causality testing procedure. These results give empirical support to the conventional view concerning the productivity /inflation relationship; i.e .. they Indicate that productivity changes do have a causal impact on Inflation. Given these results. it would be reasonable to conclude that overall productivity growth in the U.S. agricultural sector has benefitted the entire U.S. economy.

Table 3. Results of Estimating Equations (5), (7), (8), and (10)

- Equation (5) - - Equation (7) -

Variable Coefficient Variable Coefficient Statistics (Lag) (t-Statis.) Statistics (Lag) (t-Statis.)

I??= 0.923 PROD (-1) -1.760 I??= 0.569 CPIU (-1) 0.384

(13.528) (2.615)

SE=3.10 PROD (-2) -2.311 SE = 0.868 CPIU (-2) -0.452 (-9.662) (-2.908)

F= 23.607 PROD (-3) -2.373 F= 6.808 CPIU (-3) 0.113 (-8.119) (0.787)

PROD (-4) -1.671 CPIU (-4) -0.365

(-6.121) (-2.699)

PROD (-5) -0.715 PROD (-1) -0.096 (-4.346) (-4.333)

CPIU (-1) -1.300 PROD (-2) -0.074 (-1.806) (-3.157)

CPIU (-2) -0.983

(-1.120)

CPIU (-3) -0.471

(-0.493)

CPIU (-4) -1.063

(-1.374)

CPIU (-5) 2.150

(2.622)

CPIU (-6) -2.140

(-3.025)

CPIU (-7) 2.198

(3.063)

- Equation (8) -

Variable Coefficient Statistics (Lag) (t-Statis.)

R?- = 0.941 PROD (-1) -2.052

(-14.852)

SE = 2.943 PROD (-2) -2.668

(-11.051)

F= 26.522 PROD (-3) -2.755

(-9.613)

PROD (-4) -2.294

(-8.447)

PROD (-5) -0.913

(-5.114)

GDPD (-1) -2.560

(-2.413)

GDPD (-2) -1.756

(-1.489)

GDPD (-3) -3.367

(-2.905)

GDPD (-4) 2.142

(2.148)

GDPD (-5) 0.286

(0.295)

GDPD (-6) -1.108

(-1.224)

GDPD (-7) 2.918

(3.375)

GDPD (-8) 0.548

(0.599)

-Equation (10) -

Variable Coefficient Statistics (Lag) (t-Statis.)

R?- = 0.337 GDPD (-l) 0.172 (1.046)

SE = 0.615 GDPD (-2) -0.109

(-0.702)

F= 3.360 PROD (-1) -0.063 (-3.552)

PROD (-2) -0.060

(-2.331)

PROD (-3) -0.024 (-1.238)

c.> 0

~

~ en (1)

~· en ~ ;:J p E" en fjj•


Concluding Comments

This study has investigated the existence and strength of causal flows between productivity and prices in the U.S. agricultural sector, using yearly data on PROD, CPIU, and GDPD. The initial focus of the analysis was to identifY the appropriate method of investigation within the time-series data testing framework. This task was accomplished by subjecting the time-series data to unit root testing. The results of unit root tests indicate that all data series are nonstationary. Furthermore, the CPIU and the GDPD data are I(2). Based on this result. a VAR model in second differences was used for further testing of the data. This model was then used in the Granger causality testing framework.

Hsiao's minimum FPE causality testing method was employed for the purpose of identifYing the causal flows among the test variables. The results indicate the existence of a bi-directional causal flow between CPIU and PROD as well as between GDPD and PROD. This empirical finding is important because it suggests that productivity growth in the U.S. agricultural sector is affected by exogenous factors, such as inflation. It also indicates that productivity growth in this sector has had a moderating influence on inflation in the entire U.S. economy.

Further examination of the appropriate test equations indicates that inflation has a strong adverse impact on productivity in the U.S. agricultural sector. Consequently, the results analyzed above give no empirical support to the hypothesis of a positive impact of inflation on productivity in the U.S. agricultural sector. Instead, the findings indicate that inflation may have some serious negative effects on productivity in this sector. This may be due to the manner in which inflation reduces the competitiveness of the U.S. agricultural sector, thereby decreasing the volume of exports. The

Saunders 31

reduction in the volume of agricultural exports negatively impacts income in the agricultural sector. Lower farm income may well adversely impact investment decisions and, subsequently, the productivity in this sector. Additionally, inflation may adversely affect productivity and output in the U.S. agricultural sector because of its misallocation-of-resources effect. and because it distorts relative prices. Empirical evidence presented in this study supports these conClusions.

References

Alse, J., and M. Bahmani-Oskooee. "Are the Twin Deficits Really Related?" Contemp. Policy Issues 10(1992): 108-ll.

Ball, L., and S.G. Cecchetti. "Inflation and Uncertainty at Short and Long Horizons." Brookings Papers on Econ. Activity l ( 1990): 215-54.

Barnett, R.C., D.A. Bessler. and R.L. Thompson. ''The Money Supply and Nominal Agricultural Prices." Amer. J. Agr. Econ. 65(1983):301-07.

Bessler, D.A. "Additional Evidence on Money and Prices: U.S. Data 1870-1913." Explorations in Econ. History 21(1984): 125-32.

Bordo, M., and A.J. Schwartz. "Money and Prices in the 19th Century: Was Thomas Tooke Right?" Explorations in Econ. History 18(1981):97-127.

Congress of the U.S., Council of Economic Advisors. Economic Report of the President. Washington. DC: U.S. Govemment Printing Office. Various annual issues, 1948-91.

Dickey, D.A., and W.A. Fuller. "Distribution of the Estimators for Autoregressive Time Series with a Unit Root." J. Amer. Statis. Assoc. 74(1979): 427-31.


Dickey, D.A., D.W. Jansen, and D.L. Thornton. "A Primer on Cointegration with an Application to Money and Income." Federal Reserve Bank of St. Louis. Review 73(1991):58-78.

Enders, W. Applied Econometric Time Series. New York: John Wiley and Sons, 1995.

Fuller. W.A. Introduction to Statistical Time Series. New York: John Wiley and Sons. 1976.

Geweke, J .. and R. Meese. "Estimating Regressions of Finite but Unknown Order." Jntemat. Econ. Rev. 22(1981): 55-70.

Granger, C.W.J. "Investigating Causal Relationships by Econometric Models and Cross-Spectral Methods." Econometrica 37( 1969) :424-38.

Guilkey, O.K.. and M.K. Salemi. "Small Properties of Three Tests for GrangerCausal Ordering in a Bivariate Stochastic System." Rev. Econ. and Statis. 64(1982):668-80.

Holden, K., and J. Thompson. "CoIntegration: An Introductory Survey." British Rev. Econ. Issues 14(1992): 1-55.

Hsiao, C. "Autoregressive Modeling of Canadian Money and Income Data." J. Amer. Statis. Assoc. 74(1979):553-60.

---. "Autoregressive Modeling and Money-Income Causality Detection." J. Monetary Econ. 7(1981):85-106.

Jarrett, P.J., and J.G. Selody. "The Productivity-Inflation Nexus in Canada." Rev. Econ. and Statis. 3(1982):361-67.

Johnson, D.G. "Inflation, Agricultural Output, and Productivity." Amer. J. Agr. Econ. 62(December 1980): 915-23.

Just, R.E., and J.A. Miranowski. "Understanding Farmland Price Changes." Amer. J. Agr. Econ. 75(1993): 156-68.

MacKinnon. J.G. "Critical Values for Co-Integrating Tests." In Long-Run Economic Relations, edited by R.F. Engle and C.W.J. Granger. London: Oxford University Press, 1991.

Maddala, G.S. Introduction to Econometrics. New York: MacMillan Publishing Co., 1992.

McCallum, B.T. "Unit Roots in Macroeconomic Time Series: Some Critical Issues." Federal Reserve Bank of Richmond. Econ. Quarterly 79(1993): 13-43.

Nelson, C.R.. and C. Plosser. "Trends in Random Walks in Macroeconomic Time Series." J. Monetary Econ. 10(1982): 139-62.

Saunders, P.J. "Causality of U.S. Agricultural Prices and the Money Supply: Further Empirical Evidence." Amer. J. Agr. Econ. 70(1988):588-96.

---. "An Empirical Investigation of Causal Relationships Between the Money Supply, Prices, and Wages in the U.S. Agricultural Sector." Agr. Fin. Rev. 51(1991):35-42.

Starleaf. D.R., W.H. Meyers, and A.W. Womack. "The Impact of Inflation on the Real Income of U.S. Farmers." Amer. J. Agr. Econ. 67(May 1985): 384-89.

Stock, J.H., and M.W. Watson. "Variable Trends in Economic Time Series." J. Econ. Perspectives 2(1988): 147-74.

Thornton, D.L., and D.S. Batten. "LagLength Selection and Tests of Granger Causality Between Money and Income." J. Money, Credit. and Banking 17(1985): 164-78.

Agricultural Finance Review, Vol. 56, 1996 Saunders 33

Appendix

Table AI. Statistical Data for Variables by Year (1948-91)

Variables

Year PROD GDPD CPW

1948 51.0 20.0 24.1 1949 50.0 19.9 23.8 1950 49.0 20.2 24.1 1951 51.0 21.3 26.0 1952 53.0 21.5 26.5 1953 53.0 22.0 26.7 1954 55.0 22.2 26.9 1955 55.0 22.9 26.8 1956 56.0 23.6 27.2 1957 55.0 24.4 28.1 1958 60.0 24.9 28.9 1959 60.0 25.6 29.1 1960 62.0 26.0 29.6 1961 64.0 26.3 29.9 1962 66.0 26.9 30.2 1963 68.0 27.2 30.6 1964 69.0 27.7 31.0 1965 71.0 28.4 31.5 1966 72.0 29.4 32.4 1967 75.0 30.3 33.4 1968 77.0 31.8 34.8 1969 77.0 33.4 36.7 1970 78.0 35.2 38.8 1971 83.0 37.1 40.5 1972 84.0 38.8 41.8 1973 88.0 41.3 44.4 1974 79.0 44.9 49.3 1975 85.0 49.2 53.8 1976 85.0 52.3 56.9 1977 93.0 55.9 60.6 1978 87.0 60.3 65.2 1979 90.0 65.5 72.6 1980 85.0 71.7 82.4 1981 97.0 78.9 90.9 1982 100.0 83.8 96.5 1983 88.0 87.2 99.6 1984 103.0 91.0 103.9 1985 111.0 94.4 107.6 1986 111.0 96.9 109.6 1987 117.0 100.0 113.6 1988 112.0 103.9 118.3 1989 124.0 108.5 124.0 1990 127.0 113.3 130.7 1991 126.0 117.6 136.2

A Comparison of Tax Burdens of U.S. Dairy Producers Gregory M. Perry, Clair J. Nixon, and Mary C. Stoff

Abstract

In this study, we estimate the federal, state, and local tax burdens on dairy farmers in 48 states. The study utilized case farm scenarios in New York, Wisconsin, and California to demonstrate how tax incidence varied for milk producers among states. For the states analyzed. Artzona, Oregon, New York, and Washington consistently had the highest overall tax burdens, while Delaware. North Dakota. and Alabama had the lowest. The results for the three case farms were highly correlated, suggesting that the relative rankings are robust to changes in farm size and structure.

Key words: tax incidence, dairy producers.

Gregory M. Perry Is an associate professor In the Department of Agricultural and Resource Economics, Oregon State University; Clair J. Nixon Is the Coopers and Lybrand Professor of Accounting. Texas A&M University; and Mary C. Stoff Is a graduate research assistant In the Department of Agricultural and Resource Economics, Oregon State University. The authors wish to thank Stuart Smith of Cornell University for his help In providing and Interpreting the data. ·

A rich body of literature exists in the area of agricultural taxation. Yet, much of this literature focuses on particular types of taxes. such as the federal income tax (Lins, Offutt, and Richardson: Nixon and Richardson; Long). local property taxes (Conrad and DeBoer). or sales and excise taxes (Perry, Nixon. and Stoff). Relatively few studies have simultaneously examined several taxes: instead, they have focused on issues in either a particular state (Little and Fettig: Hardin) or the United States as a whole (Boyd and Newman).

Although a number of studies have been conducted to estimate all taxes paid in all 50 states by households in general, no known investigation has been conducted to estimate or measure different types of tax payments by farmers in these states. In 1991, for example, a state-by-state comparison of taxes for nine Income levels suggested state and local taxes were regressive In most states (Mcintyre, Kelly, Ellinger, and Fray). A major problem is encountered when attempting to apply these results to agriculture, however. Income for most U.S. households is obtained from salaries and wages. In contrast, farm families generate a significant portion of their Income from the farm business, thereby being subject to both household and business taxes.

Identification of state and local taxes for agricultural production sector participants is important for three reasons. First, the net returns to farmers in each state are directly affected by their state and local tax burdens. A tax disadvantage in one state, as well as increased costs from environmental legislation, for example, may create incentives for farmers to sell their businesses and relocate to a lower


tax state or one with fewer regulatory restrictions. 1

The second reason for conducting a stateby-state comparison of taxes focuses on fairness and equity. In the recent past, some states and local governments have considered raising tax rates, eliminating current tax exemptions, or levying new taxes in order to balance budgets. In a scramble for additional funds, legislators need information concerning the differential impact of the proposed taxes on different production sectors within their jurisdiction as compared to other states. Often, policymakers consider only the global effects of proposed changes (i.e., all households, or households versus businesses).

Finally, an analysis of tax burdens in agriculture is important as a matter of balance. Federal. state, and local governments receive most revenue from income, sales, or property taxes. Heavy reliance on one or two of these taxes or imposing high taxes on a particular segment in the economy (e.g .. high property taxes on business) can be more harmful to society than seeking an overall balance among all taxes.

The objective of this study is to compare estimated federal. state, and local tax burdens for the contiguous 48 states as a measure of relative impact of taxation on each state's dairy farms. Several factors motivated a focus on dairy enterprises. First, income on dairy farms is received shortly after production, greatly reducing the ability to move income from one tax year to another. Second, because dairy farms exist in all of the 48 contiguous states, the results have some relevance to researchers and policymakers across the

1 As an example. a number of dairy farmers In California have sold their farms and relocated to Idaho. clUng a more favorable business climate. In particular. less environmental regulation and the perception that taxes are lower motivated the move to Idaho.

Perry, Nixon, and Stoff 35

country. Finally, dairy hired labor costs as a percentage of total costs falls roughly in the middle of similar percentages for all major crop commodities (see U.S. Department of Agriculture, Economic Indicators of the Farm Sector: Costs of Production, 1991). Taxes associated with hired labor should be in line with those for other crops and livestock products.

Methodology, Data, and Assumptions

A commonly used approach when seeking to measure relative tax burdens is to access primary data and summarize the results. Such an approach reveals the quantity of each tax paid by farmers. Using a ceteris paribus approach to the non tax costs of production allows an examination of the ranking of tax burdens across states. Once a ceteris paribus ranking of taxes is identified, the issue of robustness of results becomes relevant. Specifically, what effect will changing the farm cost structure have on the relative ranking of total tax burdens for dairy farmers? Rankings that are robust to changes in farm structure suggest that a particular state's ranking will exhibit little change when the cost structure changes. If the rankings are sensitive to farm structure, then the ranking of most interest will be for that farm having a cost structure most like that typical for the state of interest.

Identifying cost data that are sufficiently detailed to permit this type of analysis was difficult. Data were obtained for farming operations in Wisconsin, California, and New York.2 These states rank as the top three dairy states in the United States and are located in different geographical regions of the country. The farms also reflect substantial differences in size, productivity. and capital structure.

2 Specific details concerning data sources used to develop the data sets for the three dairy farm scenarios are available from the senior author on request.

36 A Comparison of Tax Burdens of U.S. Dairy Producers

The Wisconsin data set was based on a 1992 survey of 86 dairy farms throughout Wisconsin. A general summary of the data set is given in Table 1 for this and the other farms considered in the study. The average herd size (64 cows) was somewhat larger than the state average (53 cows). and milk production per cow was also above the state average (Frank). The farm size and value of farm assets for this 64-cow farm were estimated by consulting with local appraisers in Marathon County, the top dairy county in Wisconsin.

No equivalent data set could be located in California. The Milk Stabilization Branch of California's Department of Food and Agriculture annually surveys dairy farmers throughout the state in order to estimate costs of production, but the subsequent reports do not offer sufficient detail to be useful in this study. Based on that agency's report for 1991, the average size of dairy farms in Southern California (where much of California's dairy industry is centered) was about 800 cows. An accounting firm specializing in agricultural clientele was asked to provide tax returns and balance sheet information for an 800-cow dairy considered average in terms of profitability and structure. These data

formed the basis for the California case study farm. Unlike the other two farms, the California farm (located in San Bernardino County) had only enough land for buildings and corrals. Consequently. no costs associated with feed production were incurred by the farm.

Cornell University's 1991 "Dairy Farm Management: Business Summary, New York State" (Smith, Knoblauch, and Putnam) was used as the principal data set for the New York farm. In 1991, 407 firms participated in the study. The data are grouped into nine different farm size categories based on number of cows. The middle (or fifth) category was used as a basis for our study. Although the data set also includes information for corporations and partnerships, each farm analysis was made assuming a sole-proprietorship business organizational form. The data set did not contain detailed estimates on the average asset mix for New York. To deal with this situation, a tax assessor, farm credit officer. and extension economist at Cornell provided estimates of the value of farm buildings and farm home(s) for each farm size. The New York averages for asset structure and productivity were somewhat similar to the Wisconsin data set.

Table 1. General Descriptive Information for the Three Case Study Dairy Farms

Wisconsin California New York

Number of Cows 64 800 91 Total Farm Receipts ($) 174,748 1,623,070 240,941 Pre-Tax Variable Farm Expenses($) 138,969 1.407,944 186,879 Off-Farm Income($) 9,377 0 6,836 Number of Family Members 4 2 4 Farm Acreage:

Owned Cropland 157 0 174 Owned Pasture and Other Land 50 23 148 Rented Land 32 0 187

Estimated Value ($) of: Buildings 60,000 446,000 70,000 Equipment 90,000 236,000 141,000 Farm Home 30,000 120,000 40,000


The analysis was deterministic and utilized a spreadsheet approach to calculate revenues, expenses, and additional adjustments needed to determine taxable income. For each state, a spreadsheet was constructed that differed from spreadsheets for other states only in tax calculations and rates. Hawaii and Alaska were excluded from the analysis because both states have relatively small agricultural sectors and dairy industries whose structures are quite different from those in the other 48 states.

Taxes not only vary substantially from state to state, but often vary a great deal from county to county within a state. Unfortunately. few states calculate and provide current. data on local taxes for farm enterprises. To address this issue. a county was selected from each state to be representative of the state as a whole. In general, the county with the largest dairy industry (as determined from U.S. Department of Commerce agricultural census data for 1987) in each state was selected. If a particular tax required narrowing the geographic area of research to a specific town, the county seat was chosen.

Sales of milk, calves, crops, and miscellaneous Items were treated as ordinary income for tax purposes. Sales of cull cows, other adult livestock, 3

equipment, and land sales were treated as capital gains or depreciation recapture income. Other variable costs were treated as Schedule F/Fonn 1040 expenses. All relevant taxes in Wisconsin, California, and New York were backed out of the initial data sets to obtain "clean" numbers for use in other states.

Calculating property taxes was difficult. owing to the wide variability in how property is taxed in each state. Market-

3 Smaller dairy operations were assumed to be engaged In some nondairy livestock production. Consequently, 50% of other livestock sales were treated as ordinary Income.

Perry, Nixon, and Sto.Jf 37

value estimates for the buildings. farm home, equipment, and other personal assets in Wisconsin, California. and New York were assumed to remain the same in all states. If these assets were assessed at something less than market value. an official from the County Assessor's or County Treasurer's office was asked to estimate the average percentage difference between market and assessed value. Local assessments were used in each state for the house lot, crop, pasture. and waste acreage, recognizing that these values do differ substantially from state to state.

Property tax millage rates were also obtained from the office of the County Assessor or County Treasurer in each state. 4 An official in each office was asked to explain how the property tax bill was calculated for a fanner in that county. including any percentage reductions in the rate or assessed value. as well as special exemptions. Further. the official was asked how the office arrives at taxable value for farm assets. how the final tax is calculated, and how different assets are treated under property tax law.

Typically, individuals renting land to dairy fanners would pass on a portion of these taxes in the form of higher land rent. For the purposes of this study, 50o/o of the property taxes estimated for rented land were assumed passed on and included in total property tax payments. Personal property taxes on equipment. livestock. farm inventory, and licensed vehicles are also imposed in a number of states. Balance sheet information from the data sets was used to determine taxable values.

Payroll taxes for hired workers consisted of the employer's portion of social security (7.65o/o), state and federal unemployment insurance. and worker's compensation insurance. State unemployment insurance tax rates (which depend largely on the experience rate and may vary from

4 Millage rates represent the dollar amount of taxes due per thousand dollars of assessed value.

38 A Comparison ofTax Burdens of U.S. Dairy Producers

year to year) were solicited by phone from each state's unemployment insurance office. Given that each farm size was treated as a sole proprietorship, the owner, spouse, and minor children were exempted from unemployment and worker's compensation in most states. In addition, most states exempt employers from paying unemployment insurance if they have fewer than 10 employees or less than $20,000 in payroll per calendar quarter. Consequently, unemployment insurance was only of importance for the California farm.

Worker's compensation insurance rates were also obtained through telephone contact, with either the appropriate state office or the regional office of the National Council on Compensation Insurance (NCCI). A lack of historical payroll information generally limited the analysis to use of the new employer's rate. Although worker's compensation insurance was a general requirement of employers in all 48 states, 19 states provided farmers with a general exemption from purchasing this insurance. In addition, 13 states provided a partial exemption for farmers. In most cases, these partial exemptions applied to all three farm sizes considered in this analysis. Communications with accountants for farmers in two states with general exemptions (Idaho and Texas) revealed that none of their farm clients purchased worker's compensation insurance. Consequently, even though disparities exist, the assumption made was that worker's compensation insurance was not included as a payroll tax unless required by the state. 5

Although sales and excise tax data came from several sources, the primary source

5 According to these accountants, farmers avoid potential liability problems by providing generous health and other benefits to their workers and letting them take time off (with pay) when they are hurt. The extra cost of these generous benefits was substantially less than purchasing worker's compensation insurance.

was Commerce Clearing House's (CCH's) tax guides that provide a summary of state and local taxes. The CCH summary was supplemented by information specific to agriculture requested from the tax offices of all 48 states. Telephone calls were also made to all 48 states to elicit clarifications about ambiguous points in their sales and excise tax laws. The CCH data also contained information on excise taxes for tobacco, alcohol, and gasoline; gross receipts taxes on business transactions; and vehicle registration costs for each state. Federal excise tax information was supplied by the Internal Revenue Service and the compliance office of the Alcohol, Tobacco, and Firearms Agency. Even though four states impose no sales tax, there are excise taxes levied on dairy farmers in all 50 states. For example, all states levy some excise taxes on gasoline used in highway vehicles. Further, most states impose a tax on utilities.

Annual family withdrawals reported in the data were divided into three categories: (a) tax payments, (b) savings and investments, and (c) living expenses. Tax payments were based on estimated income and self-employment taxes paid in 1991. Savings and investments were estimated based on a Minnesota survey of farm family expenditures and national expenditure survey data. Living expenses were subdivided into the following · categories: food at home, food away from home. utilities, clothing, furniture, vehicle, auto fuel, other auto expenses (insurance, etc.), recreation, medicine, insurance, personal care items, tobacco, alcohol, household supplies, charitable contributions, reading material, education, and miscellaneous expenses. To subdivide overall living expenses by category, equations were estimated based on unpublished 1990 consumer expenditure survey data obtained from the U.S. Department of Labor. The category of medicine includes both prescription and nonprescription drugs, as well as medical supplies. The following cost percentages

Agricultural Finance Review, Vol. 56. 1996

were attributed to the individual medicine categories: prescription drugs. 60%; nonprescription drugs, 30%; and medical supplies (ace bandages, gauze, ointments, etc.), 10o/o. The categorization of medical expenditures was important because many states do not levy a sales tax on prescription drugs. but do on nonprescription drugs and supplies.

Off-farm Income was assumed to be salary income earned by the spouse and subject to a 7.65% social security tax. Adjusted gross income was the sum of net farm income (ordinary income), capital gains. depreciation recapture. and off-farm income, less one-half of self-employment tax liabilities. Taxable income was determined as adjusted gross income less personal exemptions and itemized deductions. State and federal income taxes were calculated using standard 1991 married/filing jointly federal and state income tax rates. A few states had property tax rebates that were beneficial to farmers. These benefits were generally subtracted from state income tax values following the standard procedure used in each state.

In New York and Wisconsin. four personal exemptions were claimed, assuming a twoparent, two-minor-child family. Only two exemptions were claimed for the California farm. Each state's Income tax was programmed separately, with federal Income and self-employment ta.'{ formulas held constant across all states. Changes in federal income and self-employment taxes were the result of different property, sales, excise, and state Income tax levels.

Results and Analysis

The New York Case

Table 2 details the results for the 91-cow New York dairy. Although this dairy size is above average, dairy farms of this size are relatively common in most states.

Perry. Nixon. and Stoff 39

State and local taxes were highest in Arizona, New York, New Mexico, Washington, and California. Property taxes represented. on average, 56% of the total state and local taxes, so it was not surprising that three of these states (Arizona, New York, and New Mexico) were in the top five for property taxes. Washington had the second highest estimated business sales tax and aboveaverage property taxes. New Mexico was also well above average in property and business sales taxes. Arizona, New Mexico, and California property taxes were high because of moderate to high property tax rates. combined with comprehensive taxes on real and personal property.

Four western states (Washington. California, Nevada. and New Mexico) were in the top five highest business sales tax states. All four states tax a broad range of farm inputs. including equipment, building supplies. medicine, and fuel. Sales taxes were higher than property taxes in seven states (Nevada. Tennessee, Alabama. North Dakota, Louisiana, Mississippi, and Missouri), and are comparable in size for several other states.

The five states with the lowest state and local tax burdens were Delaware. North Dakota, Texas. Alabama, and West Virginia. Four of these states (Delaware, North Dakota, Alabama. and West Virginia) were among the six lowest states for property taxes. Texas had no state income tax and very low business sales taxes.

Payroll taxes were highest in Oregon, Minnesota, Louisiana, New Hampshire. and Connecticut. In Oregon, Minnesota, Louisiana. and Connecticut, payroll taxes represented about one-third of total tax obligations. TWenty-nine states did not require unemployment or worker's compensation insurance be paid for this size farm, leaving social security as the only payroll tax ($1.542). Overall, payroll taxes represented 18o/o of all taxes. Federal income and self-employment taxes accounted for 25o/o of all taxes.


Table 2. Local, State, and Federal Taxes for 1991 by State for 91-Cow New York Dairy($)

State

AL AZ

AR CA

co CT

DE FL GA

ID

!L

IN

!A

KS

KY LA ME

MD MA

Ml

MN

MS MO MT NE NV

NH

NJ NM

NY

NC

NO

OH OK

OR PA RJ

sc so TN

TX

UT

vr VA WA

wv WI

WY

Avg.

Property

1,!44

9.7!3

3.515

5.253 5,171

4.685

907

6.835

4.421 5,762

4.810 6,095

6,199

5,216

2,649

542

6.407 3,687

4,017

4,156

3.700 1,821

1,679

7,139

5,266

3,041

7,304

5,895

7,325

8.406 2,417

865

4,093

3,077

8.865

2.147 6,531

3.497 5,948

2,393

2,661

2,517

5,909

3,923

5,322

1.784

5,621

3,238

4.449

- Sales and Excise -

HouseBusiness

1.914 2,693

2,392

4.634

1.780

532

733

2,818

1,671

1.360 1.744

1.529

902

865 1,300

3,162

1.517

360

851

904

1.450 2,729

1.702

533

1.484 3,580

464 1,194

2,833

2,341

1.253 2,112

965 1,389

564

1,512

1.030

632 2,277

2,502

869

1,256

843

780

4,603

1.371 1,369

1,988

1.652

hold

1.159

1.328 1,300

1.253

854

1,265

722 1,226

1,225

1,208 1,289

1,034

1.305

1.130 1,!51

1,360

1.187 1,069

978

931

1,022

1,382

1,227

572 1.153 1,332

664

1.121

1.252

1.419 1,264

1,225

1,224

1,624

618

964 1,232

1,121

1,141

1.707

1.440

1.413

1,079

1,043

1,452

1,361

1,205

992

1,17.1

Income

942

479

1.076

25

570

13

1.284

0 888

473

736

1.085

1,118

578

1.423

351 408

978

2,158

886

732

522

1,050

648

596

0

1,034

339

309

270

1.072

340

508

991

960

756

576

649

0 0 0

1,000

509

1,005

0 796

1,125

0

651

Total State and

Local

5.159

14.213

8.283

1l,J65

8,375

6.495

3.646 10,879

8,205

8,803

8.579 9,743

9,524

7,789

6,523

5,415

9,519

6,094

8,004

6,877

6,904

6.454 5,658

8,892 8,499

7.953

9.466 8,549

11.719 12,436

6,006

4,542

6,790

7.081 11,007

5,379

9,369

5,899

9,366

6,602

4,970

6,186

8,340

6,751

11.377 5,312

9,320

6,218

7,924

Federal Income

2.424

839

2.060

1,226

1.698

1,979

2.631

1.602

2,075

1,977

2.134

1.955

1.865 2,179

2,377

1.849 1,917

2,290

2,071

2,440

1,831

2,256

2,377

1,508

2,095

1.991 1,679

1.943

1.661

1.399

2.375

2.427 2,071

2,264

1,254

2,143

2,096

2,426

1.913

2,223

2.435

2,212

1.817

2.292 1,325

2.414 2,071

2,232

2.007

Soc. Sec.

1.797

523

1.429

523

1.062

1,346

2,007

964

1,444

1.344

1.503 1.322

1.231 1,549

1,750

1.214 1,283

1,661

1.439

1.371

1.196 1,627

1,750

869 1,463

1,359

1,042

1,310 1,024

758

1.747 1,800

1.440 1,635

611

1.513

1.465

1,799

1,279

1,594

1,809

1,582

1,182

1,664

684 1,787

1.439

1,602

1,371

Payroll

1,542

4,198

1,542

4.374

3,916

4,446

1.542 1,542

1,542

1.542 1,542

1,542

3,157

1,542

1,542

4,646

1.542

2,796

3,463

1.542

5,028

1,542

1.542

4,086

1.542

1,542

4,573

3,164

1,542

3,644

1.542

1,542

3.311 1,542

5,887

3,222

1.542

1.542

1,542

1,542

1,542

2,847

3,568

1,542

3,607

1,542

1,542

1,542

2,472

Total All

Taxes

10,922

19.773

13,314

17,288

15,051

14,266

9,826

14,987

13,266

13,666

13,758

14,562

15,777

13,059

12.192

13,124 14,261

12,841

14,977

12,230

14,959

11,879

11,327

15,355

13,599

12,845

16,760 14,966

15.946

18,237 11,670

10,311

13,612

12.522 18,759

12,257 14.472.

11,666

14,100

11,961

10,756

12,827

14,907

12,249

16,993

11,055

14,372

11,594

13,773


The combination of all taxes placed Arizona, Oregon, New York, California, and Washington as the five highest tax states. Total taxes were lowest in Delaware, North Dakota, Texas, Alabama, and West Virginia. Total taxes in Arizona were twice those in Delaware.

Another way of looking at these results is on a per hundredweight of milk sold. Average annual milk production for this farm size category was about 1.6 million pounds. Hence, the average amount of taxes paid per hundredweight of milk was $0.86, or about 7% of the 1991 milk price. The per hundredweight value for taxes ranged from $0.61/cwt for Delaware to $1.24/cwt in Arizona.

A number of additional questions arose when examining these results. For example. how do these rankings compare with rankings for the average household in each state? To address this question, 1990 estimates of income, state sales and excise, motor vehicle, and property taxes by state were divided by number of households to determine average tax burden per household in each state. Using this approach. Arizona ranked 16th, Oregon 18th, New York 1st, California 7th, and Washington 21st in taxes per household. On the low end, Delaware ranked 39th, North Dakota ranked 41st, Texas 26th, Alabama 47th, and West Virginia 43rd in household taxes. The Spearman rank correlation coefficient for these two rankings was .505, suggesting that modest positive correlation exists. Consequently, average tax payments for households in general do provide some indication of tax obligations incurred by this type of dairy farm household, but substantial diffe(ences exist for some states.

The Wisconsin Case

Table 3 provides a summary of estimated taxes paid for the 64-cow Wisconsin dairy when subjected to tax laws in the other 4 7 states. This farm size most closely

Perry, Nixon, and Stoff 41

corresponds to the 1987 average farm size for all U.S. dairies (61 cows), although dairies of this size are uncommon in states such as California and Texas (see 1987 Census qf Agriculture, U.S. Department of Commerce). The top five states for the Wisconsin farm were the same as in the New York scenario, although the order was different. The five lowest tax states were also the same for both scenarios.

Property taxes represented a smaller proportion of total state and local taxes for the Wisconsin farm, with household sales and excise taxes increasing in their share of the total. Although the farm generated enough income to incur federal income taxes in most states. social security taxable income was negative in all 48 states. In essence. farm profits were generated by sales of cull cows and bulls. sales that are not subject to selfemployment taxes. Federal income and social security taxes represented 18% of all taxes. with payroll taxes accounting for 22%. The Spearman rank correlation coefficient for these two rankings was . 981, suggesting a high degree of correlation in rankings among the states.

The correlation coefficient between total taxes paid under the New York and Wisconsin results was .986. further evidence that the two results are very similar. This correlation in results was not surprising. given the amount of similarity in dairy production between the two areas. One might expect that the New York-Wisconsin results also provide a reasonable estimate of relative tax burdens for dairy farms of this size in the Northeastern and Lake States.

The California Case

The final scenario examined an 800-cow California dairy. The results are summarized in Table 4. At this level. the mix of taxes changed markedly. Payroll taxes became the largest single tax category. representing approximately 40% of total tax obligations. Farmer federal

42 A Comparison of Tax Burdens of U.S. Dairy Producers

Table 3. Local, State, and Federal Taxes for 1991 by State for 64-Cow Wisconsin Dairy($)

State

AL AZ

AR

CA co CT DE FL GA

10 IL

IN lA

KS

KY LA ME MD MA MI MN

MS MO MT NE NV NH

NJ NM

NY NC NO OH OK OR PA Rl

sc so TN

TX

UT

vr VA

WA wv WI

WY

Avg.

Property

736

5,866

2,198

2,936

3,775

3,208

761

4,500

2,661 3,938

3,654

3,807

4,085

3,868

1.725

Ill 4,337

2,623

2,882

2,597

2.284

1.297

1.053 4,775

3,661

1.983

5,428

4,512

4,810

5,913

1.662

551

3,122

2,123

5,849

1,326

4,543

2,526

4,158

1,580

1,962

1.921

3,823

2,576

3,634

1,205

3,981

2,205

3,149

- Sales and Excise -

Business

1,435

2,263

1.814

3.547

1.381

511

548 2,123

1,379

1,173

1.500

1,215

817

783

1,083

2,370

1,255

316

765

775

1,201

2,199

1,476

421 1,222

2,802

365 1,017

2,380

1.959 1,010

1,648

881

1.078

445

1.510

746

508

1,778

1,857

757

1,173

751

734

3,505

1,009

1,128

1,697

1.340

Household

1.248 1,441

1,403

1,363

918

1,365

771

1,324

1.321

1.307

1.391

1' 121 1,388

1,219

1,250

1,477

1,285

1,155 1,058

1,004

1,104

1,493

1.325

612 1,242

1,447

706

1,211

1.350 1,530

1,363

1,320

1,327

1.738

658 1,046

1.331

1,209

1,228

1,851

1,561

1,528

1,169

1' 121 1,571

1,471

1,302

1,071

1,264

Income

707

349

751 (51)

296

0

859

0

601

247

567

872

783

369

992

185

182

711

1,983

646

399 266

716

425

401

0

360

218

157

61

697

222

273

599

613

591

368

324

0 0

0

575

265

666

0

578

745

0

429

Total State and

Local

4,126

9,919

6,166

7,795

6,370

5,084

2,939

7,947

5,962

6,665

7,112

7,015

7,073

6,239

5,050

4,143

7,059

4,805

6,688

5,022

4,988

5,255

4,570

6,233

6,526

6,232

6,859

6,958

8,697

9,463

4,732

3,741

5,603

5,538

7,565

4,473

6,988

4,567

7,164

5,288

4,280

5,197

6,008

5,097

8,710

4,263

7,156

4,973

6,048

Federal Income

1,594

(246)

1,345

123

693

1,208

1,752

834

1.357 1,222

1,290

1,267

1,050

1,323

1,535

1,047

1,196

1,425

1,234

1,548

968

1,425

1.544

617 1,287

1,233

608

1.104

911

143 1,548

1,584

1,224

1,466

(76)

1,265

1,337

1,580

1,150

1,436

1,575

1,325

1.032 1,472

241 1,597

1,258

1.415

1,147

Soc. Sec.

717

717

717

717

717

717

717

717

717 717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

717

Payroll

1,421

3,891

1.421 4,056

3,618

4, Ill

1.421 1,421

1.421 1,421

1,421

1,421

2,921

1,421

1,421

4,330

1,421

2,585

3,205

1,421

4,643

1,421

I ,421

3,770

1,421

1.421 4,225

2,923

1,421

3,371 1,421

1,421

3,053

1.421 5,439

2,971

1,421

1,421

1,421

1,421

1,421

2,625

3,300

1,421

3,356 1,421

1.421

1.421

2,283

Total All

Taxes

7,858

14,281

9,649

12,691 11,398

11,120

6,829

10,919

9,457

10,025

10,540

10,420

11,761

9,700

8,723

10,237

10,393

9,532 II ,844

8,708

II ,316

8,818

8,252

11.337

9.951 9,603

12,409

11.702

11.746

13,694

8,418

7,463

10,597

9,142

13,645

9,426

10,463

8,285

10,452

8,862

7,993

9,864

11,057

8,707

13,024

7,998

10,552

8,526

10,196

Agricultural Finance Review, Vol. 56, 1996 Perry, Nixon, and Stoff 43

Table 4. Local, State, and Federal Taxes for 1991 by State for 800-Cow California Dairy($)

State

AL

AZ

AR CA

co CT DE FL GA

ID

IL IN lA

KS KY LA ME MD MA

MI MN

MS MO MT

NE NV

NH

NJ NM

NY NC

ND

OH OK

OR PA RI sc SD TN

TX UT

vr VA WA

wv WI

WY

Avg.

Property

2,152

25.309 14,301 8,682

II. 784 13,267 4,244

15,432

14.953 12.354 15.011 14.812 7,348

13,717

4.268 484

13,444

3,525

9,893

6.296

9.164 6,734

5,749

19,526

11.278 6,539

14.338 13.453 20,333 18.327

5.391 70

14.396 11.155 12,587

995 17,035 12,155 14,409

4.376 9,105

7,245 9,564

5,980

12.333 6,157

13.416 7,886

10.437

- Sales and Excise

HouseBusiness

6.725 13.455 8,794 9,561 3.737

3.450 1.751 9,242 7,984

5.285 8,692

9.103 4.838 4.835 7,716

10.137

6.593 1.650 4,708

4.203

8.436 7,436

7,655

356 4.416 8.455

654 8,158

9.973 8.727 3.489 7.128 5,360

5.264

713 11,351

4.975 3.440 6.880 9,809

5.107 6.494 4.872 4,992

15.379 8.617

7,256

6.373

6,546

hold

1,800 2,134

2,043 2.044 1,298

1.960 1.043 1,925

1.902 1.910 2,013

1.652

1.898 1,754

1.856 2.196 1.878 1,679

1,537

1.449

1.609 2.172

1.927 842

1.779 2.151

940 1.761 1.943 2.200 1.969 1.905 1.947 2.443

883 1,545 1,926

1.742 1.752 2,722

2,310

2.221 1,729

1.592 2,314

2.146 1,892

1.551

1,831

Income

2.016 241

1,859

562 1,296

253 3,519

0

1.901 1.663 1.489 2.288 2,280 1,215

2.794 839

2.275

2.411

7.128 1.276

1.056 1.721

2.631

1.143 1.703

0

2.939 789

968 912

3.325 1.256 1.242 1.333 1,115

1.283 1.412 1.462

0

0

0

2,277

1.257 2,273

0 2.443 2,992

0

1.559

Total State and

Local

12.693 41.139 26.997 20.849 18.115

18.930 10.557 26.599 26,740

21.212 27.205 27.855 16,364

21.521

16.634 13.656 24.190

9.265 23.266

13.224 20.265 18.063

17.962

21.867 19.176 17.145 18.871 24.161 33,217 30.166 14.174 10,359

22.945 20.195 15.298 15.174 25,348

18.799 23.041 16.907 16,522

18.237 17.422

14.837 30,026

19.363

25.556 15,810

20.373

Federal Income

8,874

305 4.878 3,540 3,877

3,679

9.312 4.963 4.921 6,209

4.811 4,735 5,309

6.222 7.754

4.435 5,644

8.212

3.722 5,368 2,481

7,516

7.659

2.595

6.094 7.406 3.221 3.543

3.823 2.477 8,375 8,968 3,714 3,976

2.188 5,026

5.135 4,285 5,950

7.981 7,865 5,055

4.382 6,650

2.683 7,075

5.087

7.791

5.329

Soc. Sec.

588 0

0

0

0

0

1.099 0

0 0

0

0

0

0

242 0

0

486

0 0

0

0

73

0 0

0

0

0

0

0

613 473

0

0 0

0

0

0

0

0

0

0 0

0 0 0

0

141

77

Payroll

10.879 25,477

11.074 26,667

25.392 26.532 10,691

10.744 10.946 11.689 11.374

10.744 19.554 11.179

10.999 26.577 10.639 16.552

21.884

21.562 32.284 10.744

10.079

26.104 14.154

11.869 28.598 21.990

11.378 23.069 11.351 12.985 22.346 23.906 38.327 21.368

11.332 22.780 10.534 10,744

11.014 18.949 23,421

16.298 21.815

10.879

11.195 11.384

17,334

Total All

Taxes

33.034 66,921

42.949 51.056 47.384

49.141

31.659 42.306 42,607

39.110

43.390 43.334 41.227 38.922 35.629 44.668 40,473

34.515

48.872 40.154

55.030 36.323

35.773

50.566 39,424

36.420 50.690 49.694

48.418 55.712 34.513 32.785

49.005 48.077 55.813

41.568 41.815 45,864 39.525 35.632

35.401 42.241

45.225 37.785

54.524 37.317 41,838

35,126

43.114


income and social security taxes were only 13% of the total. The share represented by state and local taxes in the overall total was 4 7%, but the mix was somewhat different. In particular, business sales and excise taxes represented 32% of the total state and local taxes, whereas for the New York and Wisconsin farm scenarios the percentages were substantially lower (21% and 22%, respectively).

Four of the top five high-tax states (Arizona, Oregon, New York, and Washington) under the New York and Wisconsin case situations were in the top five for the California farm. Minnesota was the lone addition to this list, a result of very high worker's compensation costs combined with state and local taxes that were at the national average. The three lowest tax states (Delaware, North Dakota, and Alabama) did not change from the previous two scenarios, with North Carolina and Maryland placing fourth and fifth in this category.

The Spearman rank correlation coefficient generated when comparing the New YorkCalifornia case studies was .830. Similarly, the correlation coefficient between Wisconsin and California was .848. These high levels of correlation were surprising, given the sizable differences in farm size and structure between California and the other two states. Furthermore, the correlation coefficient between total taxes paid under the New York and California case studies was .866, with a correlation of .876 resulting from the Wisconsin-California comparison. These comparisons between states cause us to believe that rankings would be much the same regardless of the type of U.S. dairy farm examined. Further, the relationship across the results for these three states is much stronger than a comparison with general household tax burdens in each state.

Conclusions and Implications

Federal, state, and local tax burdens were measured for three different dairy farm

cases in the 48 contiguous United States. The analysis was conducted for the 1991 tax year. The results suggest a substantial difference in tax burdens across states, with significant disparities in how this tax burden is shared by dairy farms of different sizes.

Total taxes were consistently high for dairies in Arizona, Oregon, New York, and Washington. On the other end of the spectrum, dairy farmers in Delaware, North Dakota, and Alabama enjoyed tax burdens that were consistently half that of the high-tax states. The greatest tax imbalances occurred in Louisiana and North Dakota because property taxes were so low for farms in both states.

The results highlight a noteworthy problem inherent to many cost-ofproduction studies. Property taxes are usually explicitly included as a cost-ofproduction item in Economic Research Service (ERS) and extension budgets, with sales and excise taxes on inputs also included implicitly as part of input costs. Yet, states differ in their reliance on these taxes as well as the income tax. By failing to include the state income tax (and personal consumption taxes) in budgeting exercises, economists may arrive at misleading results as to the true tax burden for the budgeted commodity. For example, in a number of states, the state income tax alone was estimated to represent over 15% of all state and local dairy farm tax liabilities. A suggested alternative is to report property and business sales tax obligations in a "taxes paid" category separate from other farm expenses. Property taxes on home and buildings, consumption taxes, and income taxes could be prorated based on the commodity's share of total income and reported under this "taxes paid" category. This separation of taxes from other costs would better inform policymakers of the impact of taxes on dairy producer after-tax wealth.

Farmers have a number of options available to lower their taxes that were not included in this study. These options


include development of a favorable work history to reduce worker's compensation, incorporation of the farm business, strategies for minimizing employee payroll tax liabilities, and possibly paying rent to a spouse for land owned by the spouse.

Although this study focused on the tax burdens of U.S. dairy producers, other major farm types warrant some discussion. It would be expected that grain farmers would generate more ordinary income (rather than capital gains income), and so their social security taxes would be higher. However, there would also be greater flexibility to move income around, which might result in a lower overall income tax. In addition, the grain farmer would likely incur smaller sales and excise taxes because of fewer improvements to real estate, but these same farmers would presumably have greater fuel taxes. Worker's compensation taxes would be lower. Property taxes also would likely be lower because land is taxed at less than market value, while improvements to land are taxed closer to fair market value.

The tax burden of cattle ranchers Is apt to be similar to that of grain farmers because of less facilities investment. An important difference between dairy farmers and cattle ranchers is that a significant part of the income from cattle sales is capital gains. Thus, the cattle rancher would have lower social security taxes and, for the highincome rancher, a lower income tax rate than a comparable dairy farmer.

Row crop farmers, representing the other major farming enterprise group, are typically more labor intensive, and so their worker's compensation costs are closer to those of dairy farmers than to grain or cattle operations. Row crop farmers are less able to move income around, but are generally more profitable than grain farmers. The income and social security tax obligations of row crop farmers are likely more closely aligned with those of dairy farmers than with grain

Perry, Nixon, and Siolf 45

or cattle producers. Because land has a high value, higher property taxes would be expected for the row crop farmer than for a grain farmer. However, row crop farmers and grain farmers would incur similar sales and excise taxes.

The results provided in this study represent only the relative amount of taxes that dairy farmers in each state would pay. Differences in costs of production, from the case study farms both within and across states, will change the farm operator's tax burden. There is also no attempt to determine the value. in the form of government services, that the dairy producer receives as a result of tax payments. Still. these results do provide a baseline of relative tax burdens of dairy farmers in each state. Historically. the problem of insufficient data has been a major deterrent to economists attempting to estimate total taxes paid by farmers in each state. Because this analysis was based on actual tax rates and procedures for each state, the results constitute reasonable estimates of taxes paid for the 1991 tax year by dairy farms of the types analyzed here. Nonetheless. the accuracy of these estimates largely depends on how well the cost and asset structures of the three farm scenarios considered reflect those of dairies in other states.

References

Boyd, R., and D.H. Newman. "Tax Reform and Land-Using Sectors in the U.S. Economy: A General Equilibrium Analysis." A mer. J. Agr. Econ. 73(1991):398-409.

Commerce Clearing House, Inc. Multistate Sales Tax Guide, Vols. 1-9. Chicago: Commerce Clearing House, Inc., 1993 with updates.

Conrad, J., and L. DeBoer. "Rural Property Tax Delinquency and Recession in Agriculture." A mer. J. Agr. Econ. 70(1988):553-59.


Frank, G. "Estimated Cost of Milk Production on Selected Wisconsin Dairy Farms-1992." In Managing the Farm, Vol. 27, No. 3. Dept. of Agr. Econ .. University of Wisconsin, 3 May 1994.

Hardin, M.L. "Estimating 1990 Federal and State Income Taxes." OSU Current Report. Coop. Ext. Ser., Oklahoma State University, Stillwater, November 1990.

Lins, D.A., S.E. Offutt. and J.W. Richardson. "Distributional Impacts of the 1986 Tax Reform Act." A mer. J. Agr. Econ. 69(1987):1021-26.

Little, R.D., and L.P. Fettig. "Replacement of Taxes on Farm and Residential Property by Increasing State Income Tax in Illinois: A Simulation." N. Cent. J. Agr. Econ. 11(1989):83-94.

Long, J.E. "Farming the Tax Code: The Impact of High Marginal Tax Rates on Agricultural Tax Shelters." Amer. J. Agr. Econ. 72(1990):1-12.

Mcintyre, R.S., D.P. Kelly, M.P. Ellinger, and E.A. Fray. "A Far Cry from Fair: CTJ's Guide to State Tax Reform." Pamphlet. Citizens for Tax Justice, Washington, DC, April 1991.

Nixon, C.J., and J.W. Richardson. ''Tax Signal to Commercial Farmers-Get Larger or Get Out." Choices (2nd Quarter 1987): 12-14.

Perry, G.M .. C.J. Nixon, and M.C. Stoff. "Sales and Excise Taxes: Differential State Subsidies to Production Agriculture." Agr. Fin. Rev. 54(1994): 80-93.

Smith, S.F., W.A. Knoblauch, and L.D. Putnam. "Dairy Farm Management: Business Summary, New York State 1991." Dept. of Agr. Econ., Cornell University. Ithaca, NY, September 1992.

U.S. Department of Agriculture, Economic Research Service, Agricultural and Rural Economy Division. Economic Indicators of the Farm Sector: Costs of Production, 1991-Major Field Crops and Livestock and Dairy. Pub. No. ECIFS 11-3. Washington, DC: U.S. Government Printing Office, February 1994.

U.S. Department of Commerce, Bureau of the Census. 1987 Census of Agriculture. Economic and Statistics Administration, Pub. No. AC87-A-51. Washington, DC: U.S. Government Printing Office, 1989.

U.S. Department of Labor, Bureau of Labor Statistics. Unpublished consumer expenditure survey data for 1990. Washington, DC.

Agricultural Prices, the Gold-Exchange Standard, and the Great Depression David A. Bessler

Abstract

Historical decompositions are used to investigate the role of the abandonment of the fixed gold standard in the reflation of prices of four agricultural commodities over the year 1933 and the first month of 1934. The increase in the price of gold over the study period had a substantial positive influence on the four agricultural prices.

Key words: gold standard; historical decomposition; gold. cotton, com, hog, and lard prices.

David A. Bessler Is a professor of agricultural economics at Texas A&M University. John P. Nichols provided helpful discussion and literature on the topic studied In this article. Comments by M!lton Friedman and Michael Bordo helped focus the study on historical decompositions. More recent comments by Em!l Mel!char were helpful In drawing !mpl!cat!ons with respect to International trade and cotton prices. Mel!char also offered helpful suggestions on the nontechnical Interpretation of the results. An unknown referee made substantive comments that Improved the final version of the paper. Finally. Derya Guven helped with the graphical presentations of the historical decompositions.

This study focuses on the gold-exchange standard and its role in the determination of four agricultural commodity prices during one year of the Great Depression. We explore the thesis, which apparently was first argued by George F. Warren. that reflation of prices in the United States could be achieved through the abandonment of the fixed gold standard. The thesis argues that this reflation would occur (primarily) through trading in agricultural commodity markets.

The article is presented in three sections. First. we review Warren's writings with respect to the Great Depression. Next, we present empirical results from analysis of prices of four agricultural commodities from the year 1933-prior to and just after abandonment of the fixed gold-exchange standard in the U.S. Finally, the empirical findings of this study are discussed.

The Warren Hypothesis

In his 1935 book, Gold and Prices. coauthored with F.A. Pearson. Warren attributes the initial cause of the Great Depression to the reestablishment of the gold standard by numerous western European countries (Warren and Pearson, pp. 106-07). Warren viewed the return of Germany (1924). the United Kingdom (1925). France ( 1928). and other European countries to the gold-exchange standard as a (perhaps the) major cause of the Great Depression. I That is, by tying

1 In 1921. stx nations-Cuba, Nicaragua, Panama, the Philippines, Salvador, and the United States-were on the gold standard. By 1928, only China. Honduras, Japan. Peru, Portugal, Romania, Spain, Turkey, the U.S.S.R .. and Yugoslavia, out of 54 nations [Including all major Industrial countries). were not on the gold standard (see E!chengreen. pp. 188-90).

48 Agricultural Prices, the Gold-Exchange Standard, and the Great Depression

currencies in these countries to gold, the demand for gold worldwide increased relative to other goods and services. Since the redemption rate in the U.S. (i.e., dollars for gold) was fixed, the price of other goods and services had to fall to accommodate the increased demand for gold. Warren suggested that by going off the fixed redemption rate, an economy could reflate prices, that is. bring flexiblepriced goods back into line with inflexiblepriced goods. Warren and Pearson write:

The sudden and drasllc collapse in prices which began in 1929 required a greater amount of deflation than could be carried out. therefore lhe metal content of the gold currencies had to be reduced to restore the flexible prices to the level of the inflexible ones (p. 192).

Much later, Pearson, Myers, and Gans describe Warren's views on the possible solutions:

There were only two solutions to the great Maelstrom of the early thirties: either confiscate, say, fifty percent of the bank deposits; halve the face value of all life insurance policies; lower debts. taxes, wages. salaries, freight rates, telephone charges. and so on. down to the level of the deflated farm and wholesale prices; or raise farm and other basic commodity prices to meet the slowly declining wages. salaries, debts, taxes. and cost of living. Warren believed that the reflation of farm prices was preferable to complellng the process of deflation (p. 5600).

Once the U.S. decided to go off the fixed gold price standard (March 1933), agricultural prices (as well as other primary products) were at the center of the adjustment process. Agricultural products were traded in auction-type markets in which prices moved quickly to reflect changes in supply or demand. Other prices In the economy were contracted over longer time periods, and thus were not capable of adjusting quickly. Warren and Pearson describe the adjustment process after March 1933:

... there was no control over the price of gold. It fluctuated with the waves of opinion based on inflationary and deflationary propaganda and was influenced by speculators and actions of other governments. Whenever the price of gold rose rapidly. there was anticipation that it would rise very high and commodity prices rose more than the price of gold. Whenever the price of gold fell, commodity prices fell. Even small changes were reflected In commodity prices. This theory thus distinguishes between flexible-priced goods (agricultural and other basic commodities) and Inflexible-priced goods. The Initial Impact of the adjustment thus Is felt In speculative markets and Is not independent of traders· expectations (pp. 192-93).

Thus, Warren and Pearson offered an explanation for two major points of interest surrounding the Great Depression: its initial cause and its prolonged depth. Of course, a great deal has been written about these two topics. Much of this writing dismisses the gold standard as the major cause of the malady (see, for example, Kindleberger); however, there is widespread agreement that the gold standard was important in its international transmission.

In summarizing an interesting paper on this topic, Brooks reaches the conclusion that the "bizarre Warren theory was unsound" (p. 126). His interpretation of the "gold experiment of 1933" seems to support the 1939 Federal Reserve Board statement (as given in Brooks, p. 126): "(E]xperience has shown ... that prices cannot be controlled by changes in the amount and cost of money .... " Others, of course, believe the Warren theory (the reflation part) to be essentially correct. Friedman and Schwartz state:

The aim of the gold policy was to raise the price level of commodities, particularly farm products and raw materials, which sustained the greatest relative decline during the preceding years of deflation ... the decline In the


foreign exchange value of the dollar meant a roughly proportional rise In the dollar price of such commodities, which Is, of course, what did happen (pp. 465-66).

More recently. Eichengreen writes:

According to the conventional wisdom, the currency depreciation made possible by abandoning the gold standard failed to ameliorate conditions in countries that left gold and exacerbated the Depression in those that remained. Nothing could be more contrary to the evidence. Depreciation was the key to economic growth. Almost everywhere it was tried. currency depreciation stimulated economic recovery. Prices were stabilized In countries that went off gold (p. 21).

Eichengreen's analysis of the period differs a bit from that of Warren. as the former argues that abandoning the gold standard was a necessary but not sufficient condition for reflation. Eichengreen comments:

It was not only the gold standard as a set of Institutions that posed an obstacle to economic recovery, however, but also the gold standard as an ethos. Though abandoning gold convertibility was necessary for adopting reflationary policies, it was not sufficient.... [Oinly when the principles of orthodox finance were also rejected did recovery follow (p. 21).

Warren argued instead that the markets would adjust immediately to abandonment of gold convertibility. Of course. Eichengreen's argument is global-holding for all gold standard countries, while Warren's was made for the specific case of the United States.

In this analysis, we look at four individual commodities (cotton, hogs. corn, and lard) and how their prices varied with that of gold over the period April 15, 1933 to February 1, 1934. In addition, we wish to shed light on the role of the Reconstruction Finance Corporation (RFC)

Bessler 49

and its success at driving up commodity prices through direct buying of gold in European markets (buying from October 22, 1933 through December 7, 1933). The RFC's gold buying is a candidate institution for unorthodox finance which is perhaps critical to Eichengreen's "footnote" to the Warren thesis. Further. it is evident from Warren's work (as quoted above) that trading in commodities would lead the price reflation, once a country abandoned a fixed gold price. An interesting question is whether this required substantial degrees of international trade. That is to say. would commodities produced and consumed domestically exhibit the same degree of price reflation as commodities that were traded in international markets?

In our set of four agricultural commodities. cotton was a heavily traded international commodity. lard a moderately traded international commodity. corn a lightly traded international commodity, and hogs were not traded internationally. For example, in the year 1932. cotton exports (about 8.5 million bales) accounted for over 60o/o of U.S. production (about 13 million bales). lard exports (about 550 million pounds) accounted for about 25o/o of U.S. lard production (about 2.4 billion pounds). and corn exports (about 8.8 million bushels) less than 1 o/o of U.S. com production (2.5 billion bushels) (U.S. Department of Agriculture). 2 It is interesting to observe whether responses of cotton and lard prices to shocks in gold prices are relatively greater than responses in com and hog prices. Warren argued that all commodities (not just those traded internationally) would respond to changes in the price of gold (refer to Warren and Pearson, p. 194).

Below. we consider the statistical relationship between daily prices of gold in the U.S. and each of the four commodities--cotton. hogs. com. and lard.

2 Exports and production are taken from Agricultural Statistics. 1936 (Tables 37. 98. and 329) and 1939 (Table 500).

50 Agricultural Prices, the Gold-Exchange Standard. and the Great Depression

Given modern innovations in time-series analysis. we can now offer more precise statements on the exact lead/lag relationships. if any. between gold price and commodity prices. We examine individual commodity prices rather than an index of prices. so as not to bury responses in aggregation errors. Here we focus on nominal prices; we do not look at real prices explicitly. However, following Warren's views (as discussed above). prices of agricultural commodities adjusted quickly (they were flexible-priced goods), while other prices in the economy were fixed. Any short-run response in flexible-priced goods implied a response in real prices.

Data

Daily cotton, com, hog, and lard prices are investigated over the period January 1933 through February 1934. This period was marked by the following occurrences: bank holidays (March 6-9), the embargo on gold and prohibition of trading in foreign exchange (March 9). the reestablishment of trading in foreign currency (April 20), the London International Monetary Conference (June-July), the gold purchase activity by the Reconstruction Finance Corporation (October-December), and finally. the reestablishment of a fixed gold price in the U.S. (February 1. 1934).

We use as the price of gold the daily price quote in London adjusted by the daily dollar/pound exchange rate in New York. These prices are reported in Warren and Pearson (p. 169). Daily spot (cash) hog, com, and lard prices are taken from the 1934 Annual Report of the Chicago Board of Trade. Data on daily cash cotton prices are from the New Orleans Cotton Exchange as reported in daily issues of the Wall Street Journal over the 1933-34 period. Any missing values due to holidays were replaced with the most recent historical data point (assuming the data are generated as a random walk). The normal trading week of Monday, March 6 through Saturday, March 11 is

omitted in its entirety. as it was the period covering the bank holiday and nontrading on the various commodity exchanges. We chose to eliminate the entire week of partial data so as not to disturb any weekly lags or patterns which might be present in the data. (The data are given in the Appendix.)

Analysis

Tests for unit roots are given in Table 1. Here we offer Dickey-Fuller tests, augmented Dickey-Fuller tests, and Durbin-Watson tests on levels and first differences. The tests provide consistent results that the levels are nonstationary: the Dickey-Fuller and augmented DickeyFuller tests show pseudo t-statistics greater than -2.9 in all cases, and the Durbin-Watson statistic is less than .30 in all cases. Tests on first differences yield consistent evidence that the differenced series are generated as stationary processes.

While the levels appear to be nonstationary. linear combinations of the levels (in contemporaneous time) may be stationary. Tests of cointegration are reported in Table 2. Here. we apply the eigenvalue tests outlined in Johansen. The procedure is based on the errorcorrection representation:

t.x(t) = J.l + r(1)t.x(t- 1) + ...

+ r(k- 1)t.x(t- k + 1)

+ nx(t- k) + E(t).

(1)

where r(I) =-[I- n(l)- ... - n(I)) for I= 1, ... , k- 1; and n = -[1- n(1)- ... - n(k)). Here, the n(I) are (p x p) matrices of autoregressive parameters from a vector autoregression (VAR) in levels of x(t) of lag order k, J.l is a constant, and e(t) is a white noise innovation term. Label the model given by equation (1) as H1•

Equation (1) resembles a VAR model in first differences, except for the presence of the lagged level of x(t- k). The matrix n Is

Agricultural Finance Review, Vol. 56, 1996 Bessler 51

Table 1. Dickey-Fuller, Augmented Dickey-Fuller, and Durbin-Watson Tests on the Nonstationarity of Individual Series

Series DFa ADFb DWC

Gold Prices -1.51 -.55 (3) .03 Cotton Prices -.98 -.98 (0) .01 Com Prices -2.04 -2.15 (1) .02 Hog Prices -2.28 -2.28 (0) .05 Lard Prices -1.92 -1.92 (0) .04 !'!.. Gold Prices -28.42 -12.72 (2) 2.82 !'!.. Cotton Prices -18.65 -18.65 (0) 2.03 !'!..Com Prices -15.99 -15.99 (0) 1.73 !'!..Hog Prices -19.42 -19.42 (0) 2.11 !'!.. Lard Prices -18.17 -18.17 (0) 1.98

a DF tests are t-statistics associated with ~ 1 estimates from the following prototype OLS regression:

An approximate 5% critical value is -2.9 (reject the null of nonstationarity for t-tests less than the critical).

b ADF tests are t-statistics associated with ~ 1 estimates from the following prototype OLS regression: K•

l'!..Xt = ~o + ~~x~-~ + L a.kt!..XL-k' k=I

Numbers in parentheses denote the number of lags K* used in each regression, as determined by the Schwarz loss criterion (see Doan, pp. 5-18). An approximate 5% critical value is -2.9 (reject the null of nonstationarity for t-tests less than the critical).

c DW tests are Durbin-Watson statistics from an OLS regression of the series in the leftmost column on a constant. A critical value of .26, as given in Sargan and Bhargava (Table 1). is used.

the error-correction term, which contains information about the long-run (cointegrating) relationship between the variables in x. There are three possibilities of interest: (a) if 1t is of full rank, then x(t) is stationary in levels and a V AR in levels is an appropriate model; (b) if 1t has zero rank, then it contains no long-run information, and the appropriate model is a VARin differences; and (c) if the rank of 1t is a positive number rand is less than p (in our case, p = 5), there exist matrices a

and ~. with dimensions p x r. such that 1t = a.W, In the latter case, Wx(t) is stationary, even though x(t) is not. The hypothesis that there are, at most, r cointegrating vectors is labeled H2(r); that is, 1t is of reduced rank r < p.

The treatment of the constant is particularly interesting, as under possibility (c) the constant term can be decomposed into two parts-that in the intercept of the cointegrating relation (P'x(t)). and that representing a linear trend (see Johansen for details). These alternatives lead to sequential hypothesis testing with respect to the rank (r) of Jt.

If there is a linear trend in the model. label this hypothesis H2(r). which is unrestricted. If there is no linear trend in the model. label the hypothesis H2(r)*, which is restricted. Johansen provides the rationale for sequential hypothesis testing to decide jointly for the rank of the cointegrating vector (r) and whether there is a linear trend in the model. He suggests

52 Agricultural Prices, the Gold-Exchange Standard, and the Great DepressfJJn

Table 2. Trace Statistics and 5% Critical Values With and Without Time Trend

Lag T* c (5%)* T C(5%)

1 55.64 75.33 51.27 68.91 2 45.96 75.33 40.52 68.91 3 49.63 75.33 40.58 68.91 4 49.05 75.33 39.97 68.91 5 47.58 75.33 39.74 68.91 6 47.72 75.33 40.17 68.91

Notes: Tests are trace-type as given in Johansen and Juselius. At each lag, the null hypothesis is that the number of cointegrating vectors equals zero; reject for T (T*) greater than C (5%) (C (5%)*). The T*-tests are associated with no time trend. while the T-tests are associated with a time trend. These tests are carried out using the ordered eigenvalues A.1

or A. i, where i = 1, ... , p (an asterisk is used to indicate that the eigenvalues have been calculated without a linear trend in the model). The trace test considers the hypothesis that the rank of n is less than or equal to r. The trace test with the time trend is given by:

p

-2ln(Q; H2 IH 1) = -T L ln(l- A. 1).

i=r+ I

A similar test is defined for no time trend specifications, with A.i substituted for A.1• The results in this table are all relative to r = 0. Critical values are taken from Johansen and ,Juselius [Appendix Table A3 (for C*) and Appendix Table AI (for C)).

testing the hypotheses in the following sequence: H2 (0)*, H2 (0), H2 (1)*, H2(1), ... , stop testing the first time we do not reject.

Trace statistics for r = 0, with and without a linear trend, are given in Table 2. We report the test results for lags 1-6, as we are uncertain of the number of lags (k) which generate this multivariate series. In all cases, we fail to reject the null hypothesis that the rank of n is zero. This suggests that there are no long-run (cointegrating) relationships among these four series. If they are related, it will be as a differenced VAR [an appropriate model for these data is equation (1), with then matrix equal to zero).

In Table 3, we give marginal significance levels associated with likelihood ratio tests on successive V ARs on first differenceslags 0-6. The null hypothesis in each case is that the coefficient matrix on the higher order lag is equal to zero. The null is rejected at the 1 o/o level of significance for lag 2 and at the 5% level for lag 6.

Lutkepohl recommends the use of the 1% significance level for these tests. Accordingly, in the remainder of the article, we analyze a VARin firstdifferenced data of lag order 2.

Marginal significance levels on lagged variables in each equation of the V AR are given in Table 4. These are derived from standard F-tests associated with the hypothesis that lags of each variable in each equation have coefficient values equal to zero. Notice that gold p1ices appear to be exogenous at usual significance levels (.05 or lower). Changes in lagged gold prices appear to (most significantly) influence changes in hog prices, but only at a 14% marginal significance level. In general, the results presented in Table 4 suggest no strong lagged relationship between these commodity prices and gold prices. These tests, of course, ignore contemporaneous relationships.

In matrix equation (2), below, we give the diagonal and upper triangular portion of


Table 3. Significance Levels Associated with Likelihood Ratio Tests (L(k)) on Autoregressions of Order k Fit to First Differences

kvs. k+ 1 L(k)

0 VS. 1 .00 1 VS. 2 .00 2 vs. 3 .11 3 vs. 4 .15 4 VS. 5 .73 5 vs. 6 .05

Notes: The null hypothesis on successive tests, that the autoregressive matrix at lag k + 1 has all elements equal to zero. is rejected at the significance level given in the table. The test statistic, as described in Doan (pp. 7-8), is given as:

L(k) = (T- c) (log IIkl -log IIk+ll ).

where Tis the number of observations. cis a small sample correction which is equal to the number of variables in each unrestricted equation. and Ik and Ik+l are restricted and unrestricted error covariance matrices.

Table 4. Significance Levels from F-Tests on Exogeneity of Each Series in Each Equation of the V AR Model

Equation

Lagged 6. Gold 6. Cotton 6. Corn 6. Hog 6. Lard Variables Price Price Price Price Price

6. Gold Price .00 .56 .86 .14 .52 6. Cotton Price .22 .58 .00 .94 .12 6. Corn Price .85 .20 .02 .69 .72 6. Hog Price .91 .89 .50 .01 .27 6. Lard Price .06 .01 .07 .00 .18

Notes: Tests are from F-tests on grouped coefficients from lagged variables in each equation of the VAR. The null hypothesis in each test is that the coefficients associated with each lagged variable are as a group equal to zero (reject for low marginal significance. e.g., less than .05).

the symmetric correlation matrix of contemporaneous innovations. The correlations are listed in order: gold, cotton. corn, hogs, and lard. Notice that for gold prices (first row). the strongest correlation in innovations is that for cotton prices (.412), followed by lard prices (.254). corn prices (.187). and hog prices (.007). The relative size of the correlations agrees

ordinally with the relative importance of international trade for each commodity (recall the discussion given above). Notice. too, that hog price innovations are not strongly correlated with any of the other four commodities' innovations, while cotton, corn. and lard innovations show relatively large (in the neighborhood of .4) correlations among themselves.


II .412 .187 .007 .254

.394 -.021 .412

-.022 .441 (2)

l .174

Additional detail can be provided on the information flow between gold price innovations and commodity price innovations by looking at the historical data on a day-to-day basis, giving special emphasis to particular events. Following Doan, we can partition the historical value of each series at each date into a base projection plus the accumulated effects of past and current shocks in each series. Such partitions are conditional on the way contemporaneous correlation is allocated between series. That is, the covariances behind matrix (2) can be assigned to some assumed causal flow among the five variables in contemporaneous time. The most common way to deal with this problem has been to use a Choleski factorization [of the covariances behind matrix (2)). which is equivalent to specifying a recursive, within-period, causal flow between each series (series 1 causes series 2, series 1 and 2 cause series 3, etc.). An alternative procedure is to allow for possible nonrecursive flows (following what is now called a "Bemanke ordering").

The system of contemporaneous innovations is modeled, in general, as equation (3):

1 rt12 nl3 nl4 nl5 El(t) vl(t)

1tzl 1 1tz3 1tz4 1tz5 Ez(t) vz(tl

1t31 1t32 1 1t34 1t35 E3(t) v3(t)

1t41 1t42 1t43 1 1t45 E4(t) V 4(t)

where E1(t) is the observed (nonorthogonalized) innovation in period t in gold prices, E2(t) is the observed innovation in cotton prices, E3 (t) is the

(3)

observed innovation in com prices, E4(t) is the observed innovation in hog prices, and E5(t) is the observed innovation in lard prices: the u 1(t) notations, where i = l to 5, are orthogonal shocks: and the n1J notations, where i,j = 1 to 5 and i * j, are parameters (to be estimated or a priori set equal to zero) which connect contemporaneous innovations. It is assumed that the diagonal elements are one. Clearly, we cannot identity a n matrix with all elements, nlJ * 0, i ot. j, with observational data. The matrix given by equation (3) is underidentified.

In the problem under study, we have fairly strong reason to believe that movements in gold prices would be near exogenous (in contemporaneous time), as the London gold market opened several hours before the Chicago and New Orleans commodity markets. Additional flows of contemporaneous causality are more difficult to justifY a priori. We considered several possible combinations of zero restrictions on the n1J which result in an overidentified n matrix. These can be tested for by using the likelihood ratio test suggested by Sims.3

We argue that gold price innovations are not dependent on shocks (in contemporaneous time) in the other commodities, so that n 1J = 0, where)= 2, 3, 4, 5. We have no strong a priori reason to expect contemporaneous shocks in the

3 The likelihood ratio test on the overldentifying restrictions on the parameters relating observed innovations (e1) to orthogonal innovations (v1) derives from the following: Write the vector of orthogonal innovations (v) as a linear function (A) of nonorthogonallnnovations (c): v = &. The test statistic is given as: (2log (det(Q)) - log (det (:!:))IT, where n is the variance-covariance matrix derived from the A matrix restrictions, :!: is the variance-covariance matrix derived from the observed nonorthogonal innovations, TIs the number of observations used to estimate the model, log is the logarithmic transformation, and det is the determinant operator. The test statistic is distributed chi-squared with (n(n- 1)/21 degrees of freedom (n is the number of series in the VAR). The null hypothesis is that the overldentifying restrictions are "true."


com price/hog price/lard price "subsector" to cause contemporaneous shocks in cotton prices, so we set n21 = 0, where j = 3, 4, 5. We imposed one zero element in row three (n31 = 0). As com was not a large export commodity (see the discussion given above), we do not allow contemporaneous gold innovations to affect com prices. Similarly. we do not allow gold price innovations in contemporaneous time to affect hog prices, because hogs are not traded internationally (n41 = 0). Further, we do not allow cotton innovations or com innovations in contemporaneous time to affect hog prices. Both of these latter zero restrictions relate to the perishability (nonstorability) of hogs. Finally. we do not allow com and hog price innovations in contemporaneous time to affect lard prices. We do not offer these zero restrictions as being held with strong prior beliefs. A likelihood ratio test (refer to footnote 3 for details) of these restrictions is rejected at a marginal significance level of .40, indicating rather strongly that the restrictions are not unreasonable.

Equation (4) summarizes our modeling of contemporaneous innovations:

0 0

-.22 0

(.03)

0

0

-1.88

(.44)

0 0

-.05 -.34 0

(.03) (.06)

0

0

.96

(.66)

0

0

0

-2.93

(.49)

-.12

(.04)

(4)

Here, E1(t) and u1(t), where i = 1 to 5, are defined as in equation (3). The numbers in parentheses are standard errors. Other orderings were considered and tested. One such ordering that appears plausible allows gold price innovations to

Bessler 55

directly affect cotton price only, in contemporaneous time; i.e., n51 = 0 in equation (4). This ordering was rejected at a marginal significance level of .22. Further testings of zero restriction on n21 •

n32, and n52 are rejected at very low levels of significance (.01 or lower). Below, we use results from the ordering summarized by equation (4) as ordering (i), and the additional restriction that n51 = 0 as ordering (ii).

Historical decompositions under ordering (i) on changes in gold. cotton. com, hog, and lard prices are presented in Figures 1, 2, 3, 4, and 5, respectively. Each figure presents the accumulated partition of the change in each series at each date into that due to gold price and that due to all other causes. (We combine the base projection component and that due to each non-gold series into the "all other" category). The partitions begin at the 79th trading day of 1933 (as recorded in Warren and Pearson, Table 9), which was April 15-two trading days prior to the resumption of trading in foreign exchange by U.S. citizens. The decompositions are run out for the next 250 trading daysthrough February 1, 1934. the date Congress passed the Gold Reserve Act which reestablished the price of gold (at $35 per ounce). We have used the ordering of contemporaneous innovation correlations according to equation (4). ordering (i). Partitions based on ordering (ii) look similar to those presented in the figures, and thus are not included here.

This time frame included numerous political (economic) events which, from an ex post perspective, Warren and Pearson characterize as important. These events are identified by date in Figures 1-5 as letters A-K. and are detailed in the footnote to Figure 1. Dates from April through mid-October 1933 represent passive or facilitating actions by the Roosevelt administration. Here, the administration was relying on market forces to bring about the reflation of prices. The period between October 22


and December 7, 1933 is characterized by the administration's activist efforts. Actual purchases by the U.S. government in the European gold markets were made in order to stimulate commodity prices.

Figure 1 illustrates gold price partitions. Notice that the near exogeneity of gold prices is reflected by the rather minuscule movement in gold price attributed to the four agricultural commodities. The partitions due to gold price innovations for cotton, corn, and lard prices (Figures 2, 3, and 5, respectively) are quite similar to the gold price partitions. In contrast, the partitions for hog prices (Figure 4) appear qualitatively different from the other commodities (visually, gold price innovations appear to have a much less pronounced influence on hog prices).

A few persistent patterns appear in the graphs. First, across all four agricultural commodities, the major increases in prices due to "all other" sources in Figures 1-5 appear to occur within the first 100 trading days following April 15, 1933. This period represents the early days of much of the activist agricultural legislation of the Roosevelt administration. The Agricultural Adjustment Act was passed on May 12, and is represented as point C in each of the figures. And, while the act included provisions related to gold pricing, it also contained major policies related to the supply of agricultural products. For all four agricultural commodities, we see a rather large and persistent increase in prices immediately before and just after passage of the act. The Agricultural Adjustment Act incorporated direct provisions for three of our four commodities-cotton, corn, and hogs. Further, it had obvious indirect influence on lard prices.

Cotton had substantial acreage plowed under as a result of the act in the summer of 1933, hog and pig numbers were reduced through governmental purchase programs in the summer of 1933, and corn was stored under seal in return for

nonrecourse loans in the fall of 1933. These programs had the intended effect on price (see discussion of each in Benedict and Stine), although some unintended results were noted as well (such as plowing under of marginal land, and more Intensive harvest of reduced acreage resulted in higher cotton yields). In our Figures 1-5, the effects of these programs would be revealed through time In our "all other" category, and while we do not break out each commodity-specific effect, we do note that the period of time just before and after passage of the act appears to show a more pronounced positive impact on commodity prices relative to the period of time later on in the year.

It is interesting as well to note that the activist buying of gold on the European markets (which began on October 22, 1933, and continued for approximately 30 days) did result in increased gold prices and similar increases in agricultural prices (points H through I). While we use Brooks' date of December 7, 1933 for the date at which this activist buying ceased, it appears from Figures 1-5 that the buying may have stopped in late November, rather than on December 7. (Notice that in all five figures, the increase in prices due to gold innovations appears to level off about 12 trading days before point I, which is December 7.)

Starting with April 17, gold price innovations account for positive innovations in prices of all four agricultural commodities. For the next 18 trading days, gold price innovations resulted in higher agricultural prices. The next large increase due to gold innovations begins in all four series at or just prior to June 5. This second burst lasted until 10 days following the London International Monetary Conference (July 3, point F).

From mid-July onward, gold price shocks are responsible for an approximate threeweek downturn (or slowdown in increase) in agricultural prices. This time span roughly corresponds to the period labeled


$I oz

16

14

1 2

10

8

6

4

2

0

Figure 1. Decomposition of Gold Prices

y

' ---;--

~-----------------------------------H ________________ J __ ~~ 1 51 201 251

trading days since April 15. 1933

Notes: Intervention dates A-K (identified by Warren and Pearson as important) are defined as follows:

A =April 19, 1933: Provisions to keep the dollar at par in foreign exchange are discontinued. B = May 1, 1933: Gold is refused to holders abroad of United States securities. C =May 12, 1933: The Agricultural Adjustment Act is approved. D =June 5, 1933: The gold clause in all contracts is cancelled. E =June 19, 1933: President Roosevelt opposes any agreement of international currency

stabilization. F =July 3, 1933: Secretary of State Cordell Hull (at the London International Monetary

Conference) issues a statement reiterating that the U.S. will not seek international agreements on exchange rates.

G =August 29, 1933: Newly mined gold is permitted to be exported and sold at the world market price.

H =October 22, 1933: President Roosevelt authorizes the Reconstruction Finance Corporation (RFC) to buy gold newly mined in the United States.

=December 7, 1933: Gold purchases by the RFC cease. (Source Note: This date was derived from Brooks.)

J =January 15, 1934: President Roosevelt introduces the Gold Reserve Act of 1934 to Congress.

K = February 1, 1934: The Gold Reserve Act is approved by Congress, fixing the price of gold at $35 per ounce.


$I cwt

4

3

2

0

-1

Figure 2. Decomposition of Cotton Prices

"'' ,..~ \. .... \ " "'" ,,. .... ,. ~ ..... "'' ,~ '···""- 1- .-:l,J. -

I \ rr ,,., .... ,/ \• ... / _, -...

+ +- -i-A B C

-1-- + D E F

+ G

+ H J K

-2 51 1 01 151 201 251

Refer to Figure 1 footnotes. trading days since April 15, 1933

Cents I bu

25

20

15

1 0

5

0

-5

-10

-1 5 1

LEGEND: due to gold prices due to all other

+ intervention dates

Figure 3. Decomposition of Com Prices

+-+- . +·+-·-\-. + B C D E F G H

51 101 151 201

+ -l-J K

251



$I cwt Figure 4. Decomposition of Hog Prices

1.5

1

0.5 --"'"""""'--.., ..... ___ ,.- -· .......,.-:, . . .

0

-0.5

-1

-1 .5

-2 ++-+· .... + +· + + A B c D E F G H I J K

-2.5 1 51 101 1 51 201 251


$I cwt

3

2

1

0

-1

-3 1

A B C

LEGEND: due to gold prices due to all other

+ intervention dates

Figure 5. Decomposition of Lard Prices

+- -i-+· + + D E F G H I

51 101 1 51 201

+ -t J K

251



the "Hazy Hue" by Pearson et al. In our Figures 1-5. the "Hazy Hue" appears to end about one week following point G, at which time newly mined gold was permitted to be exported and sold at world market prices. Actually, the first purchases under this policy were made on September 8 (Warren and Pearson). Here, the Roosevelt administration was relying on the market to drive up the price of gold through gold traders. Warren and Pearson describe the effect of this policy as "psychological ... to indicate that the dollar was no longer 23.22 grains of gold" (p. 163). From our figures, it appears to have had the intended effect for about two or three weeks. This period signaled the end of the passive elements of Warren's plan. Brooks writes of this period:

Roosevelt's resolve to tinker with the dollar stiffened. The free market could not be trusted to continue depressing the gold value of the dollar. It would be necessary to put the second, activist, part of Warren's theory into practice; it would be necessary, that is, for the United States Treasury to stage a bear raid on the dollar (p. 126).

The non passive portion of the Warren theory involved implementation of methods to allow the federal government to purchase gold-thereby driving up the price of gold and (hopefully) agricultural commodity prices. This "activist" component of the thesis included two parts. Initially, the U.S. government purchased newly mined gold in the U.S. These purchases were made from late October (point H in the figures) until about early November 1933. From November 2 until early December 1933 (point I in the figures), gold purchases by the RFC were made in the world markets (Paris and London). Notice from Figures 1-5 that increases in commodity prices attributable to gold price innovations ceased to occur (in any sustained pattern) about two weeks prior to the December 7 date, calling into question the exact date at which effective gold buying ceased.

Summed responses over this period are presented in Table 5. We partition the change in price from April 15, 1933 to February 1. 1934 (ilP) as that due to changes in gold prices and that due to all other sources. We offer two different partitions: (i) for an ordering of contemporaneous correlation following the "Bemanke" ordering summarized in equation (4), and (ii) for the alternative ordering which is the same as that given in ordering (i), except n51 = 0. Notice that responses are similar under both orderings; accordingly, we restrict our discussion to ordering (i).

The price of gold changed from $21. 14 per ounce on April 15, 1933 to $33.74 per ounce on February 1, 1934. This $12.60 increase in price was achieved through gold price innovations of +$12.63 per ounce and "all other" innovations of -$.03 per ounce (reflecting the apparent exogeneity of gold price in our five-variable VAR). More to the question of our study, cotton price increased by $4.70 I cwt over the entire period. The majority of this increase ($3.38/cwt) was due to gold price innovations, and the remainder ($1.32/cwt) was due to "all other" sources.

Com price increased 17.75¢ /bushel over the period-with nearly all of this accounted for by gold price innovations (17.54¢). Hog prices increased by 10¢ /cwt, with +70.3¢ being attributable to gold price innovations and -60.3¢ due to "all other" innovations. Finally, the summed partitions for lard prices show that the $1.375/cwt increase was distributed as +$2.356/cwt due to gold price and -$.981/cwt due to "all other" sources.

It is interesting to consider whether the passive (free market) period of the Warren thesis accounted for more movement in these prices than did the activist period. In Table 6, we break the partitions of Table 5 into two time frames: the passive period (April 15-0ctober 21, 1933) and the


Table 5. Changes in Prices and Associated Partitions (April 15, 1933-February 1, 1934) Under Two Alternative Bemanke Orderings of Contemporaneous Error Correlation

Partition

Due to: Series 6.P ($) Ordering Gold($) All Other($)

Gold +12.60/oz. +12.63 -.03 ii +12.54 +.06

Cotton +4.70/cwt +3.38 +1.32 ii +3.28 +1.42

Corn +.1775/bu. +.1754 +.0021 ii +.1542 +.0233

Hogs +.10/cwt +.7030 -.6030 ii +.5723 -.4723

Lard +1.375/cwt +2.356 -.981 ii +1.884 -.509

Notes: Partitions are from the moving average representation of the differences in VAR of gold prices. cotton prices. com prices. hog prices. and lard prices. The general form of the decomposition derives from the following representation:

j-1

6XT•j = L AsUT•j-s + L AsUT•j-s' s=O s=j

Here, A 5 is the moving average parameter matrix (based on orthogonalized innovations) found from inverting the two-lag VAR fit to first differences of the five-variable process (6X1). U1 is a five-element innovation term. The second summation on the right-hand side of the above equation represents the base forecast at period T; the first term represents the partitioned forecast uncertainty due to each of the five components of the VAR. We have "lumped" that part of the first term which is due to non-gold prices together with the second term (base forecast) to form the "all other" category. The ordering of contemporaneous correlation is based on: (i) a Bemanke ordering behind text equation (3), which has gold price innovations "causal" in contemporaneous time for cotton and lard. and cotton price as causal in contemporaneous time for lard and com (but not for hogs). and (ii) a Bemanke ordering identical to (i) except that gold is causal for cotton only in contemporaneous time. The changes are aggregated over the entire period of April 15. 1933 to February 1. 1934; 6P is the change in price over the entire period. This change is then equal to that due to gold innovations and that due to all other innovations. as given in the two right-hand columns of the table.

activist period (October 22. 1933-December 7, 1933). Note under both orderings that the bulk of the movement in gold attributed to gold price shocks occurred in the passive period (AprilOctober); similarly, the largest portion of the agricultural price movements

attributed to gold price shocks occurred during the passive period.

If we look at relative responses. by considering the percentage changes in agricultural prices divided by the percentage change in gold prices, we get

62 Agricultural Prices. the Gold-Exchange Standard. and the Great Depression

Table 6. Partitions April 15, 1933-0ctober 21, 1933 versus October 22, 1933-December 7, 1933, Under Two Alternative Bemanke Orderings of Contemporaneous Error Correlation

April 15-0ctober 21 October 22-December 7

Due to: Due to:

Series Ordering t1P ($) Gold($)

Gold i +7.92/lb. +7.99 ii +7.93

Cotton j +2.43/cwt +2.15 ii +2.09

Com +.0800/bu. +.1120 ii +.0984

Hogs +.70/cwt +.48 ii +.39

Lard +.625/cwt + 1.491 ii +1.194

See notes to Table 5.

similar responses for cotton, corn, and lard prices. To illustrate, for cotton (April 15, 1933-February 1, 1934). we have an elasticity of +.84: i.e., (the response in cotton due to gold innovations divided by the price of cotton on April 15, 1933) divided by (the change in gold price divided by the price of gold on April 15, 1933). or (3.38/6.72)/(12.60/21.14) = .84. Similar elasticities are found for corn (+.84) and lard (+.88). Hog price responses are much less elastic (+.30).

Discussion

In this study, we empirically investigated the dynamic relationship between daily prices of four agricultural commodities and the price of gold. We found evidence that is consistent with the latter part of the thesis advanced by George F. Warren-that is, agricultural prices did respond quickly to gold price innovations (shocks). The pattern of adjustment is similar across three commodities-cotton, corn, and lard prices; hog prices appear to be less responsive to changes in gold prices. The findings support the earlier

All All Other($) t1P ($) Gold($) Other($)

-.07 +3.26/lb. +3.43 -.17 -.01 +3.41 -.15 +.28 +.73/cwt +.93 -.20 +.34 +.90 -.17

-.0320 +.0675/bu. +.0486 +.0189 -.0184 +.0429 +.0246

+.22 -1.05/cwt +.19 -1.24 +.31 +.16 -1.21

-.866 -.100/cwt +.649 -.749 -.569 +.520 -.620

work of Friedman and Schwartz and call into question interpretations which dismiss the reflation portion ofWarren's theory on gold and prices. We offer evidence that prices of two commodities which were traded internationally (cotton and lard) did not respond more to gold price innovations than did prices of a commodity which was not traded internationally in large quantities (corn). Hog prices did not respond as much to gold price innovations as the other three commodities studied. This suggests that · the extent of price response to gold price innovations was less a function of international trade and more a function of supply elasticity (hogs trading as hogs were less storable and more price inelastic than the other three commodities studied).

In 1933, farm prices rebounded from their depression lows. Several major U.S. policy changes, including agricultural supply controls and gold policy, occurred during that year. Because these events were not observed in a controlled experiment, the impact of gold policy alone was not obvious. Its impact on prices has been


subject to different interpretations. We have conducted an analysis of daily data to separate (as is possible with observational data) the impact of the price of gold from other influences on prices of four agricultural commodities after April 19, 1933. In the second quarter of the year, gold price increases combined with other factors pushed farm prices upward. Our analysis indicates that gold contributed substantially. In the third quarter, farm prices languished, even as the price of gold rose further, calling the efficacy of the gold effect into question. Our analysis suggests that gold served to moderate substantial downward pressure from other factors. Observers at the time saw only the disappointing total result. In the fourth quarter, the government drove the price of gold further upward through purchases of gold. The apparent mixed response in farm prices is shown in our analysis to be due to differing directions and fluctuations in the influence of other factors, which undoubtedly muddied the analytical waters for observers at the time and led to differing interpretations of the success (or lack thereof) of the gold purchase policy.

Our investigation found that the sharp rise in the price of gold during the purchase period was a substantial positive influence on farm commodity prices.

References

Benedict, M.R., and O.C. Stine. The Agricultural Commodity Programs: Two Decades of Experience. New York: The Twentieth Century Fund, 1956.

Brooks, J. "Gold Standard on the Booze." The New Yorker 58(13 September 1969):126.

Doan, T. RATS: User's Manual, Version 4.0. Evanston, IL: Estima, 1992.

Eichengreen, B. Golden Fetters: The Gold Standard and the Great Depression, 1919-1939. New York: Oxford University Press, 1992.

Bessler 63

Friedman, M., and A. Schwartz. A Monetary History of the United States. 1867-1960. Princeton, NJ: Princeton University Press, 1963.

Johansen, S. "Determination of Cointegration Rank in the Presence of a Linear Trend." OJiford Bull. Econ. and Statis. 54(1992):383-97.

Johansen, S., and K. Juselius. "Maximum Likelihood and Inference on Cointegration-With Applications to the Demand for Money." Oxford Bull. Econ. and Statis. 52(1990):169-210.

Kindleberger, C. The World Depression, 1929-1939. Berkeley: University of California Press, 1973.

Lutkepohl, H. "Comparison of Criteria for Estimating the Order of a Vector Autoregression Process." J. Time Series Anal. 6(1985):35.

Pearson, F.A., W.I. Myers, and A.R. Gans. "Warren as Presidential Advisor." Farm Econ., no. 211(December 1957): 5598-5676.

Sargan, J.D., and A. Bhargava. "Testing Residuals from Least Squares Regression for Being Generated by the Gaussian Random Walk." Econometrica 51 (1983): 153-7 4.

Sims, C. "Are Forecasting Models Useful for Policy Analysis?" Federal Reserve Bank of Minneapolis, Quarterly Rev. (Winter 1986):2-16.

U.S. Department of Agriculture. National Agricultural Statistics Service (USDA/NASS). Agricultural Statistics. Washington, DC: U.S. Government Printing Office, 1936 and 1939.

Warren, G.F .. and F.A. Pearson. Gold and Prices. New York: John Wiley. 1935.


Appendix

Table Al. Data on Gold Price, Cotton Price, Corn Price, Hog Price, and Lard Price (January 2, 1933-February 28, 1934)

Date Gold Cotton Corn Hogs Lard

,Jan. 1933 2 20.63 3 20.63 4 20.59 5 20.59 6 7 8 9 10 II 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Feb. I 2 3 4 5 6 7 8 9 10 II 12 13 14 15 16 17 18 19 20 21 22 23

20.56 20.57

20.62 20.59 20.58 20.58 20.65 20.57

20.58 20.55 20.53 20.57 20.60 20.56

20.63 20.65 20.55 20.55 20.49 20.54

20.60 20.58

20.53 20.59 20.59 20.57

20.56 20.59 20.58 20.59 20.59 20.60

20.60 20.59 20.55 20.63 20.79 20.72

20.74 20.63 20.63 20.83

5.95 5.95 6.20 6.13 6.13 6.1.3

6.13 6.33 6.18 6.18 6.18 6.18

5.99 6.19 6.08 6.12 6.15 6.08

6.15 6.15 6.15 6.07 6.11 6.11

6.00 5.88

5.80 5.77 5.85 5.76

5.89 5.95 5.95 6.00 6.03 6.00

6.00 5.89 5.93 5.93 5.93 5.99

5.99 5.99 5.99 5.89

25.00 25.00 25.00 25.50 25.50 26.25

26.00 25.50 26.50 27.00 26.50 26.00

25.50 25.00 25.00 25.25 26.00 25.50

25.50 26.00 26.00 26.00 26.00 25.75

25.75 25.75

25.50 25.75 25.50 25.00

25.25 25.50 25.50 26.00 26.00 25.75

25.75 25.50 25.50 25.25 25.25 25.50

25.50 25.25 25.25 25.00

3.20 3.20 3.30 3.25 3.40 3.30

3.25 3.20 3.30 3.35 3.25 3.25

3.40 3.40 3.30 3.40 3.50 3.40

3.50 3.50 3.35 3.50 3.65 3.40

3.40 3.40

3.60 3.50 3.40 3.35

3.65 4.00 4.15 3.90 3.90 3.75

3.75 3.85 3.75 3.70 3.70 3.60

3.70 3.60 3.60 3.65

4.30 4.30 4.50 4.70 4.70 4.70

4.60 4.60 4.50 4.30 4.15 4.05

4.025 4.00 3.975 4.00 3.975 3.975

3.975 3.975 3.975 3.875 3.875 3.875

3.80 3.80

3.80 3.80 3.85 3.85

3.875 3.925 3.975 3.975 3.925 3.925

3.925 3.85 3.825 3.80 3.80 3.80

3.775 3.775 3.775 3.775

Date Gold Cotton Corn Hogs

24 25 26 27 28

Mar. I 2 3 4 5

20.76 20.77

20.79 20.73

20.78 21.06 20.85 20.85

5.99 5.82

5.82 5.82

5.95 5.95 5.95 5.95

6-11 (trading week omitted) 12 13 20.53 5.95 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Apr. I 2 3 4 5 6 7 8 9 10 II 12 13 14 15 16 17 18 19 20 21

20.74 20.78 20.83 20.83 20.66

20.66 20.61 20.64 20.69 20.69 20.66

20.66 20.62 20.67 20.67 20.57

20.64

20.62 20.60 20.71 20.74 20.69 20.70

20.67 20.71 20.77 20.96 20.96 21.14

21.14 21.26 22.32 23.20 22.68

5.95 5.95 6.80 6.40 6.45

6.45 6.18 6.24 6.32 6.37 6.37

6.19 6.32 6.26 6.26 6.26

6.33

6.26 6.33 6.38 6.47 6.47 6.47

6.51 6.60 6.57 6.72 6.72 6.72

6.58 6.72 7.12 7.31 7.28

24.75 24.50

24.50 24.00

23.75 24.00 24.50 24.50

26.50 26.50 26.50 27.75 28.25 27.25

29.00 28.75 29.50 29.50 30.00 30.50

30.75 31.25 32.50 31.50 31.25

31.25

32.50 32.50 36.50 36.75 35.00 34.50

34.75 34.25 34.25 34.00 34.00 35.00

35.50 34.50 36.25 37.75 38.75

3.65 3.60

3.60 3.60

3.65 3.75 3.90 4.00

4.10 4.15 4.10 4.15 4.20 4.15

4.30 4.35 4.15 4.20 4.25 4.05

4.00 4.00 4.05 4.00 4.10

4.00

4.00 3.90 4.00 4.00 3.90 3.80

3.80 3.90 3.95 3.90 3.90 3.90

3.90 3.90 3.75 3.80 3.90

Lard

3.775 3.775

3.775 3.775

3.775 3.80 4.00 4.00

4.875 4.725 4.725 4.725 4.650 4.575

4.50 4.35 4.15 4.325 4.325 4.375

4.325 4.275 4.50 4.15 4.15

4.15

4.175 4.175 4.175 4.25 4.175 4.175

4.175 4.375 4.325 4.375 4.375 4.475

4.45 4.45 4.75 5.175 5.075

Agricultural Finance Review

Table Al. Continued.


Apr. 1933 (cont'd.) 22 22.57 7.38 37.50 23 24 25 26 27 28 29 30

May I 2 3 4 5 6 7 8 9 10 II 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

June I 2 3 4 5 6 7 8 9 10 II 12 13 14 15 16

22.92 22.97 22.99 22.57 23.29 23.71

24.07 24.28 24.07 24.40 24.84 25.03

24.35 24.30 24.36 24.65 24.48 24.55

24.39 24.08 24.37 24.03 23.74 23.73

23.89 23.98 24.10 23.99 24.13 24.40

24.59 24.59 24.72

24.53 24.50 24.53

24.53 24.78 24.92 25.05 25.19 25.46

25.54 25.04 25.06 24.56 24.87

7.41 39.25 7.41 38.00 7.44 38.50 7.38 37.00 7.34 36.00 7.71 38.00

8.00 38.50 8.07 39.00 8.07 40.75 8.14 41.00 8.36 43.50 8.56 44.75

8.36 45.50 8.31 44.50 8.60 45.50 8.91 47.25 8.91 48.00 8.76 47.00

8.62 47.00 8.62 46.00 8.69 48.75 8.55 47.50 8.46 45.25 8.18 44.25

8.27 44.75 8.60 46.00 8.60 46.50 8.50 44.75 8.85 44.50 9.00 46.50

9.10 47.00 9.10 47.00 9.17 46.25

9.10 45.00 9.15 45.75 8.96 45.00

9.17 45.00 9.04 44.75 9.08 44.25 9.00 44.50 9.13 45.50 9.17 45.50

9.32 45.50 9.23 45.75 9.23 44.75 8.85 45.25 9.07 45.00

3.90

4.15 4.10 4.05 4.05 4.00 3.80

4.00 4.05 4.05 4.05 4.05 4.00

4.20 4.30 4.30 4.55 4.80 5.00

5.05 5.15 5.35 5.45 5.23 5.20

5.15 5.09 5.00 5.00 5.10 5.15

5.10 5.10 5.00

4.95 5.00 5.05

4.85 4.90 4.80 4.65 4.65 4.60

4.80 4.85 4.70 4.60 4.75

5.2fS

5.425 5.375 5.40 5.35 5.20 5.55

5.525 5.575 5.675 5.65 5.75 5.80

6.075 6.00 6.15 6.40 6.675 6.80

6.775 6.60 6.70 6.625 6.475 6.275

6.15 6.40 6.50 6.425 6.475 6.725

6.65 6.65 6.65

6.45 6.625 6.55

6.45 6.35 6.35 6.225 6.35 6.375

6.325 6.30 6.175 6.10 5.95

Bessler 65


17 18 19 20 21 22 23 24 25 26 27 28 29 30

July I 2 3 4 5 6 7 8 9 10 II 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Aug. I 2 3 4 5 6 7 8 9 10 11

24.89

25.34 25.41 25.53 25.78 25.79 25.83

25.95 26.23 26.76 26.06 26.29

26.56

27.54 27.54 27.79 28.54 28.98 29.29

29.83 29.02 29.61 29.78 29.68 29.70

29.82 30.18 29.96 28.88 29.07 28.68

28.88 28.78 28.64 28.07 28.11 27.93

27.81

27.48 28.16 28.22 28.15 28.05

28.05 27.92 27.99 28.02 28.01

8.92 44.75 4.65

9.18 46.75 4.60 9.09 47.00 4.70 9.24 46.75 4.60 9.19 47.75 4.60 9.35 47.50 4.60 9.41 49.00 4.50

10.29 51.75 4.50 10.29 54.50 4.55 10.09 54.25 4.75 I 0.09 50.25 4.65 10.03 52.25 4.65

I 0.10 54.25 4.40

10.38 56.00 4.70 10.38 56.00 4.70 10.17 59.00 4.65 10.39 61.00 4.65 10.18 62.00 4.75 I 0. 15 60.00 4.60

10.63 62.50 4.60 10.63 64.75 4.70 11.46 64.50 4.90 11.40 64.25 4.85 11.55 63.75 4.70 11.40 65.25 4.60

11.55 66.00 4. 75 11.68 65.50 5.00 11.24 64.50 4.85 10.54 60.50 4.75

9.94 51.00 4.90 10.13 50.00 4.70

10.53 49.50 4.80 10.43 50.50 4.55 10.63 53.50 4.70 10.87 58.00 4.80 10.47 57.25 4.70 10.50 53.50 4.55

9.98

10.36 10.40 10.29 10.14 10.06

9.86 9.54 9.82 9.65 9.25

47.25 4.70

52.50 4.70 54.50 4.75 54.50 4.70 55.00 4.70 55.00 4.50

53.50 4.50 56.00 4.50 57.25 4.60 58.00 4.75 57.75 4.65

5.95

6.225 6.025 6.05 6.125 6.05 6.10

6.35 6.50 6.50 6.425 6.40

6.375

6.60 6.60 6.625 6.75 6.975 7.125

7.50 7.825 7.65 7.525 7.55 7.65

7.85 7.90 7.45 6.40 6.40 6.40

5.80 6.55 6.80 6.75 6.30 6.50

5.875

6.40 6.325 6.375 6.10 6.10

6.025 5.85 6.10 6.05 5.95


Table AI. Continued.

Date Gold Cotton Com Hogs Lard

August 1933 (cont'd.) 12 27.94 9.16 51.50 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Sept. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Oct. 1 2 3 4 5

27.68 27.56 27.58 28.10 28.05 28.09

28.23 28.17 28.46 28.61 29.37 29.96

29.09 29.50 29.11 29.33

29.62 29.81

29.81 29.75 29.71 29.77 29.72 29.22

29.28 29.15 29.64 30.04 30.45 30.78

31.44 31.64 32.07 31.81 32.03 31.90

31.51 31.58 31.35 31.33 31.63 31.69

31.94 32.24 31.93 31.80

8.95 51.00 8.68 50.50 8.55 47.00 9.23 49.25 9.15 55.00 9.28 55.25

9.24 55.25 9.37 56.00 9.22 56.00 9.26 55.00 9.52 54.50 9.52 54.50

9.46 54.00 9.46 53.50 9.32 51.75 9.24 53.50

9.20 52.00 9.20 52.00

9.20 52.00 8.77 52.00 9.02 50.25 8.87 51.00 8.68 49.50 8.51 49.00

8.76 48.75 8.76 49.50 9.16 49.50 9.28 51.25 9.33 51.00 9.47 52.75

9.86 53.25 10.25 51.50 9.84 51.50 9.40 50.00 9.60 47.50 9.91 49.25

9.85 48.50 9.81 49.00 9.81 47.75 9.70 47.25 9.64 47.50 9.72 47.00

9.61 46.50 9.56 45.75 9.69 45.25 9.47 43.50

4.50

4.65 4.65 4.60 4.50 4.55 4.55

4.65 4.60 4.60 4.50 4.60 4.45

4.50 4.45 4.40 4.40

4.50 4.50

4.50 4.50 4.60 4.60 4.60 4.45

4.55 4.65 4.75 4.75 5.00 5.00

5.00 5.10 5.25 5.40 5.45 5.30

5.25 5.25 5.25 5.15 5.10 4.80

5.15 5.30 5.45 5.25

5.75

5.75 5.95 5.70 5.425 5.10 5.35

5.475 5.62 5.675 5.60 5.80 5.80

5.675 5.40 5.45 5.40

5.50 5.50

5.50 5.425 5.45 5.50 5.50 5.425

5.525 5.525 5.60 5.775 5.825 5.90

6.125 6.10 6.00 5.875 5.775 5.825

5.70 5.70 5.60 5.55 5.45 5.30

5.40 5.575 5.75 5.55


6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Nov. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

31.68 31.32

31.22 31.38 31.11 31.11 30.16 30.02

29.20 30.09 29.74 29.52 29.13 29.06

29.85 30.63 30.83 31.01 30.84 30.62

31.23 31.17

31.64 32.28 32.25 32.18

32.27 32.27 32.81 33.55 33.13 33.12

33.32 33.85 34.91 33.94 33.39 33.58

33.84 34.51 34.08 33.21 32.70 32.89

32.00 32.60 32.46 32.46

9.19 9.22

9.37 9.26 9.22 9.22 8.98 9.00

8.58 9.04 8.96 9.06 9.15 9.15

9.22 9.45 9.59 9.52 9.57 9.46

9.46 9.42

9.45 9.48 9.52 9.45

9.28 9.28 9.59 9.75 9.75 9.78

9.82 9.91 9.94

10.08 9.94 9.94

10.01 10.01 9.96 9.77 9.90 9.83

9.63 9.70 9.77 9.77

41.50 38.50

40.50 40.50 40.00 40.00 38.50 36.25

37.00 39.50 41.25 40.75 42.00 43.00

46.00 45.50 47.50 48.00 47.00 46.75

47.00 44.5

42.00 42.00 43.00 45.00

45.75 45.00 47.00 49.00 48.50 48.50

49.25 50.00 50.00 49.50 49.50 48.00

48.75 49.25 48.50 46.75 46.75 46.75

46.50 46.75 46.25 46.25

5.40 5.35

5.55 5.25 5.10 5.10 5.20 5.10

5.00 4.60 4.70 4.55 4.70 4.60

4.65 4.50 4.45 4.60 4.60 4.45

4.35 4.25

4.35 4.25 4.30 4.25

4.40 4.55 4.55 4.40 4.50 4.50

4.55 4.55 4.60 4.60 4.50 4.35

4.25 4.20 3.90 4.00 4.05 3.95

3.85 3.85 3.90 3.90

5.35 5.40

5.475 5.35 5.25 5.25 5.25 4.725

4.65 5.15 5.05 4.95 4.95 5.10

5.20 5.20 5.35 5.35 5.30 5.30

5.30 5.20

5.075 5.075 5.10 5.175

5.90 5.90 5.95 6.125 6.10 6.10

6.15 6.20 6.15 6.15 5.80 5.90

5.775 5.45 5.25 5.075 5.25 5.05

4.95 4.65 4.85 4.85

Agricultural Finance Review



Dec. I 2 3 4 5 6 7 8 9 10 II I2 13 I4 I5 I6 17 I8 19 20 21 22 23 24 25 26 27 28 29

32.54 32.40

32.04 32.47 32.46 32.32 32.67 32.70

32.34 31.96 31.77 32.28 32.36 32.34

32.64 32.57 32.29 32.02 32.21 32.29

32.29 32.29 32.24 32.05 32.02

30 32.61 31

Jan. 1934 I 32.61 2 32.71 3 32.64 4 5 6 7 8 9 10 II 12 13 14

32.88 32.56 32.51

32.24 32.20 32.42 32.3I 32.33 32.56

9.85 47.00 9.85 46.00

9.75 46.25 9.88 48.50 9.88 49.00 9.88 49.75 9.84 49.75 9.93 51.50

9.93 52.00 9.89 51.50 9.89 50.50 9.93 50.25 9.93 49.25 9.98 48.00

9.93 47.75 9.98 47.75 9.93 46.50 9.89 45.50

10.06 48.00 10.06 48.00

10.06 48.00 10.00 48.00 10.10 49.00 10.14 50.50 10.14 48.50 10.29 48.50

10.29 48.50 10.29 50.25 10.34 51.50 10.50 49.00 10.40 50.25 10.50 50.75

I0.63 51.00 10.67 51.50 10.81 51.50 I0.84 51.75 10.78 52.00 10.98 52.00

3.75 3.55

3.55 3.50 3.50 3.55 3.60 3.50

3.40 3.30 3.40 3.45 3.30 3.40

3.40 3.25 3.25 3.35 3.40 3.40

3.40 3.75 3.50 3.40 3.40 3.35

3.35 3.50 3.65 3.55 3.75 3.75

3.65 3.70 3.65 3.50 3.65 3.50

4.80 4.75

4.80 4.95 4.95 5.00 5.05 5.05

5.05 4.85 4.75 4.80 4.80 4.80

4.525 4.525 4.525 4.45 4.525 4.525

4.525 4.775 4.90 4.85 4.80 4.85

4.85 5.40 5.45 5.40 5.40 5.375

5.35 5.40 5.425 5.40 5.375 5.375

Bessler


15 I6 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Feb. I 2 3 4 5 6 7 8 9 10 II 12 13 14 15 16 17 18 I9 20 21 22 23 24 25 26 27 28

32.99 33.47 33.14 32.93 33.38 33.19

33.25 33.I9 33.07 32.77 32.92 32.81

33.31 33.21 33.50

33.74 34.04 34.11

34.55 34.64 34.06 34.31 34.45 34.48

34.48 34.47 34.60 34.77 34.70 34.64

34.60 34.28 34.72 34.72 34.63 43.67

34.68 34.61 34.72

II.40 11.26 11.19 11.12 11.29 11.18

II. II 11.14 11.09 11.09 10.98 11.12

11.35 11.32 11.42

11.42 II. 56 11.73

11.76 11.93 12.00 12.24 12.30 12.42

12.42 12.42 12.20 12.20 12.35 12.35

I2.I8 12.08 12.17 12.17 12.17 12.14

11.88 11.97 11.97

52.75 3.60 52.00 3.55 52.75 3.55 51.75 3.65 52.00 3.60 52.25 3.50

51.25 3.60 51.125 3.70 51.75 3.60 52.00 3.50 51.50 3.75 52.00 3.75

51.75 53.00

3.85 4.00

51.125 3.95

52.75 51.75 52.25

52.00 52.00 51.00 50.50 51.25 51.00

51.00 50.75 49.50 51.50 50.50 50.50

50.00 50.00 49.25 49.25 50.25 51.00

50.50 49.50 50.00

4.00 4.50 4.50

4.35 4.65 4.80 4.85 4.80 4.50

4.50 4.85 4.75 4.75 4.75 4.55

4.65 4.70 4.60 4.60 4.65 4.75

4.95 4.80 4.65

5.60 5.625 5.60 5.50 5.55 5.55

5.525 5.525 5.425 5.30 5.40 5.575

5.65 5.725 5.725

5.85 5.80 5.80

5.90 6.025 5.95 6.075 6.15 6.40

6.40 6.425 6.45 6.55 6.50 6.425

6.40 6.275 6.30 6.30 6.55 6.50

6.50 6.50 6.50

67

Farm Credit System Insurance Risk Simulation Model

Peter J. Barry. Bruce J. Sherrick, David A. Lins, Delmar K. Banner. Bruce L. Dixon. and John R. Brake

Abstract

A stochastic simulation model is developed for use by the Farm Credit System Insurance Corporation to facilitate the evaluation of the long-term adequacy of the insurance fund. and to serve as a tool for reevaluating fund adequacy as the risks and capital positions of the FCS banks change. The model explicitly accounts for the effects of credit risk, interest rate risk. and part of liquidity risk through a combination of probability distributions, accounting specifications, and estimated relationships among key variables affecting FCS bank performance. Applications of the model are illustrated under alternative risk conditions.

Key words: simulation, insurance, risk, Farm Credit System.

Peter J. Barry. Bruce .J. Sherrick, David A. Llns, Delmar K. Banner, and Bruce L. Dixon are with the Center for Farm and Rural Finance at the University of Illinois, Urbana-Champaign and the University of Arkansas. John R. Brake Is the W.l. Myers Professor of Agricultural Finance at Cornell University.

The Farm Credit System Insurance Corporation (FCSIC) was established by the Agricultural Credit Act of 1987 to provide a safety reserve for investors in farm credit debt securities in the event of default by one or more banks of the Farm Credit System (FCS). The FCSIC insurance fund stands before joint and several liability by the FCS banks to protect the investors against default. Together with other risk management practices of the FCS institutions and the safety and soundness regulations of the Farm Credit Administration, the insurance fund is intended to forestall the need for future government assistance if financial adversities like those of the 1980s reoccur in the future (Harl; Peoples, Freshwater, Hanson, Prentice, Thor, and Melichar). A detailed description and assessment of safety and soundness of the FCS, including the role of the FCSIC, is found in the 1996 U.S. Department of Agriculture (USDA) publication by Callender and Erickson.

The 1987 act states that the level of the insurance fund shall be 2% of insured system obligations, or some other amount as determined appropriate by the FCSIC. The insurance fund is being funded through premium payments by the FCS banks, who in tum have passed these costs on to lending associations and ultimately to agricultural borrowers. A general goal of the FCSIC is to minimize the borrowers' costs of funding the reserve while providing appropriate safety for investors in FCS securities. At year-end 1995, the insurance fund totaled $902 million, or 1.65% of adjusted insured


obligations of $54.6 billion. 1 It is anticipated that the 2% target will be reached sometime during 1998.

The FCSIC has sought to build a quantitative model to assist in determining the adequacy of the 2o/o target level for the insurance fund, and to serve as a tool for periodically reevaluating fund adequacy as the risks and capital positions of the insured institutions change. This article presents the basic structure and operating properties of the insurance risk simulation model and illustrates its use under alternative risk scenarios.

FCSIC Risk Environment

The major and measurable sources of risk affecting the Farm Credit System and the FCSIC are credit risk. interest rate risk, and liquidity risk (Barry, Banner, Lins, Sherrick, Brake, and Dixon). Credit risk is the potential delinquency or default by borrowers and insufficiency of collateral pledged as security to cover loan balances. Interest rate risk is created by the effects of unanticipated variability of interest rates on a bank's cost of funds and the market values of its assets. liabilities. and equity capital. Liquidity risk arises from lack of access to funding sources to meet financial obligations. Other sources of risk to the FCSIC include possible changes In the agency status of FCS securities. competitive conditions in the agricultural credit market. political risks, loss-sharing arrangements among the FCS institutions, and risks arising from Institutional consolidations, new lending authorities, and excessive concentration in loan portfolios.

Some of these risks interact with others, especially in terms of potential changes in credit risks. The total risk experienced by

1 Adjusted Insured obligations reflect a deduction for 90% of federally-guaranteed loans and 80% of stateguaranteed loans.

Barry, Sherrick, Lins. Banner. Dixon. Brake 69

each institution also may reflect the risk attitudes and operating philosophies of institutional management. both of which may change over time. In addition, total risk will reflect the relative effectiveness with which the FCS Institutions manage these risks (for example. through asset diversification, adjustments in lending terms, allowances for loan losses, capital positions. assetliability management, and institutional monitoring).

Finally, the risks passed through to the insurance fund will also reflect how system institutions respond to the safety and soundness regulations of the Farm Credit Administration, risk monitoring by the FCSIC, and the collective. self-initiated actions of the insured banks. The latter Include the Contractual Interbank Performance Agreement (CIPA). established in 1991, and the Market Access Agreement (MAA). established in 1994 (Callender and Erickson). Under CIPA. the insured banks have reached a contractual agreement to establish accounting and financial standards (in addition to those of the Farm Credit Administration) for banks and to impose additional financial discipline on the banks. MAA provides a mechanism for limiting a bank's participation in the sale of systemwide, consolidated securities if its performance deteriorates significantly.

In aggregate. the structure of the FCS banks poses significant challenges for formulating an actuarially sound insurance program. Actuarial costs are most easily calculated when risks are distributed independently over a large number of homogeneous. insured units (Doherty). Risk within the insurance portfolio (measured by the standard deviation of aggregate loss) then decreases as the number of insured units increases-and the distribution of average loss per period approaches a normal distribution as the number of insured units increases.

70 Farm Credit System Insurance Risk Simulation Model

The charactertstics of the insured banks and their operating environment do not exhibit these insurance-preferred attributes. The number of insured banks is low (eight at year-end 1995), with differences in loan volume ranging from $2.3 billion for the St. Paul BC to $14.6 billion for CoBank. Losses across banks have not been independently distrtbuted, as illustrated by the widespread yet unevenly distrtbuted farm financial stress and loss repayment problems of the 1980s. Furthermore. incidences of catastrophic loss that would trigger a demand for insurance funds likely are concentrated in short periods of time. For example. the 1980s were the first major loss experiences on a broad scale throughout the FCS since the 1930s, indicating a loss concentration covering a four- to five-year period out of approximately 50 years. These conditions combine to yield asymmetric, heterogeneous, correlated loss distrtbutions. Without independence and large numbers, nondiversifiable risk is much greater and nonnormal claims distributions are more prevalent.

Conceptually, the set of bank-levelloss distributions could be analytically combined to yield an aggregate loss distribution. However, given the various functional forms for bank-level loss distributions that were estimated from data, that problem quickly becomes intractable. Instead, numeric methods are most commonly used with procedures for relating individual loss distributions that are intended to preserve as many of the moments of the joint loss distribution as practical (Paulson and Dixit).

Model Development

The modeling approach followed in this study is based on stochastic simulation of a portfolio of FCS banks operating on an annual basis using @RISK modeling software in conjunction with LOTUS 1-2-3.

The simulation begins with the spreadsheet specifications of a bank's accounting framework in which the operations of an FCS bank are portrayed by a system of coordinated pro forma financial statements: beginning and ending balance sheets. the income statement. the statement of cash flows, and the statement of equity change. The balance sheet and cost structures of individual banks are first constructed using data from bank call reports and related studies. Key variables representing credit risk. interest rate risk. and liquidity risk are modeled as probability distributions based on historic data. The simulation model uses Monte Carlo procedures to repeatedly sample from these distributions, and then works through the accounting structure of the bank. linkages with other banks, and key specifications on asset-liability management (e.g., variable rate loan prices and the degree of balance between selected categories of rate-sensitive assets and rate-sensitive liabilities) to generate a probability distribution of simulated outcomes that are evaluated in terms of loss probability criterta specified by the FCSIC.

The individual bank models are cast In a multi-bank framework representing a portfolio of insured units (Doherty). Each bank has an expected level and variability of performance along with empirtcally measured interbank correlations of loan losses. The multi-bank framework thereby yields a distrtbution of aggregate claims on the insurance fund. A single-pertod model is utilized in order to allow more detail on each bank's specific characteristics, within the portfolio of banks, and because the determination of policies for the level and timing of premiums for building and replenishing the Insurance· fund was beyond the scope of the project, as defined by the FCSIC. The model also can be employed in a multi-period analysis through recursive runs in which bank performance in one pertod provides the


beginning conditions for the following period. With these specifications. the model accounts for the major. measurable sources of risk. Elements requiring consideration outside the model are the less easily measured sources of risk, including political risks, personnel changes, human errors, moral hazard behavior, loan concentration, and major structural changes in the FCS. However. the user can reflect the effects of his or her subjective expectations about these sources of risk by scaling coefficients on loss distributions and by modification of other inputs.

Bank Accounting Specifications

Figure 1 presents an abbreviated flowchart of the basic accounting specifications of the single-period bank model. The model is based on accrual accounting, although cash flows together with loss accounting procedures are emphasized in determining potential claims on the insurance fund. The beginning balance sheet is linked to the ending balance sheet by the respective sources and uses of cash experienced by the bank during the accounting period, and by any appropriate revaluations of assets and/or liabilities. The magnitude and composition of beginning assets and liabilities are specified by the model user based on current call report data for the respective banks. Included in the call reports. and thus in the model. are a detailed accounting of various asset, liability. and capital items, as well as revenues, interest payments. operating costs, and other items affecting a bank's net income during an accounting period.

The holdings of assets and obligations to debt and equity holders give rise to sources and uses of cash during the accounting period. Sources of cash include interest payments and principal payments received on loans, interest and principal payments on maturing and continuing investments, and other sources

Barry, Sherrick, Lins, Banner, Dixon, Brake 71

of revenue. Uses of cash include interest and principal payments to investors in debt securities; dividends or other forms of payment to equity holders, if applicable; and payments of operating expenses. income taxes. loan losses. and other cash outflows. These cash flows were also estimated from information provided in the FCS call reports, and FCA data supplied by the FCSIC.

Liquidations of assets provide sources of funds if required cash outflows exceed cash inflows, until equity capital is exhausted. Altematively, accumulations of cash, new investments, or other uses of funds will occur if cash inflows exceed cash outflows. Any shortfall or excess not otherwise described results in a direct charge or credit to equity so that the model's assets always balance with liabilities and equity. Net income is calculated for the period using data from the cash flow summaries, the balance sheets, applicable revaluations, and other sources.

Loss Probability Criteria

An important component of the model is a clear portrayal of the conditions characterizing the FCS banks that would lead to claims on the insurance fund. The potential incidence of claims also reflects the FCSIC's ability to intervene with problem banks. prior to actual bank failure. The FCSIC can utilize a set of response options based on the criteria of minimizing the cost of dealing with problem situations. Thus, flexibility in the specification of conditions leading to claims is a preferred model characteristic.

The set of conditions leading to claims is based on loss probability criteria specified in 1994 by the FCSIC to use for recognizing in its own financial statements any probable loss that can be reasonably estimated. The loss criteria are based on FCS bank performance that would adversely affect three key bank capital

Figure 1. Abbreviated Accounting Specification of Single-Period Model

Beginning Balance Sheet ~arket R<ValUatioiis ) Liab1hhes --J ~

~~~ ~ / Cash Flows

- -·· Cash and

nvestments Bonds, Notes, Other Debt

Loans

Equity

·Sources

I. Interest and principal on existing loans

-loss rate dist. -interest rate dist.

2. Interest and principal on new loans

-loss rate dist. -interest rate dist.

3. Newbonds

4. Investment inflows -rate dist.

5. Other income

Uses

I. Interest and principal on existing securities -rate dist.

2. Interest and principal on new sec uri ties

·rate dist.

3. Other debt servicing -rate dist.

4. New loans

5. Operating expenses

6. Loan losses, other

Income Statement

Ending Balance Sheet Liabilities

As ""

Cash and nvestments Bonds,

Notes, Other Debt

Loans

Equity r-

Performance Checks:

-Capital Ratios -Liquidity -Solvency -Trends and Changes -----Implicit Fund Effects Corrective Actions

Adequate

Claims Amount

Inadequate

Secure Due

~ ~

~ ~ C1 ~ ~ ..... ~ (/)

~ ~ 1: a 2 ::0 !ii" i'i en

~ 6-;:::1

~ ![


ratios. 2 The levels for the three capital ratios determine how the FCSIC classifies the probability of a loss claim. Three loss claim categories are used: (a) remote. (b) possible, and (c) probable. The ratios and adverse performance classes are identified and defined by the FCSIC as follows:

1. Tier 1 capital ratio (the sum of surplus and paid-in capital. plus allowances for Joss on loans, plus other property owned, divided by the sum of gross nonaccrual loans and gross other property owned):

• Remote =greater than 150%, • Possible = 50-150%, and • Probable = Jess than 50%.

2. Leverage ratio (total surplus, as a percent of total assets):

• Remote= greater than 3.5%, • Possible= 1-3.5%, and • Probable = less than 1 o/o.

3. Bank collateral ratio (ratio of assets available as collateral to debt obligations requiring collateralization):

• Remote = greater than l 02%, • Possible = 10 1-1 02o/o, and • Probable = less than 10 1 o/o.

FCSIC personnel may employ additional judgment and discretion along with financial data in determining the corporation's loss allowance. Thus, the model contains a design flexibility that allows FCSIC personnel to specifY, for any particular simulation, the payable proportion of a claim needed to bring the value of the respective ratios to the more favorable boundary for that particular class (i.e .. remote, possible, probable). For example, specifYing the set of coefficients 0.05, 0.25, and 0.50 for the tier 1 capital ratio test implies that the FCSIC would

2 Additional criteria considered In the loss probability evaluations Include a bank's profitablllty, liquidity, concentration of lending associations. and other performance Indicators.

Barry, Sherrick. Lins, Banner. Dixon, Brake 73

pay 5% of a claim needed to bring a tier 1 capital ratio in the remote class to the top of the remote range (to an assumed boundary of 200%); the FCSIC would pay 25% of a claim needed to bring a tier 1 capital ratio in the possible class to the top of its range (to 150%); and the FCSIC would pay 50% of a claim needed to bring a tier 1 capital ratio in the probable class to the top of its range (to 50%). A 0.0, 0.0. and 1.0 set of coefficients would imply that the FCSIC only meets claims when the tier 1 ratio falls in the probable class, and then pays 100o/o of the claim needed to bring the ratio back to 50%. A 1.0, 1.0. and 1.0 set of coefficients would imply full payment of all claims that bring the tier 1 capital ratio to the top of the category into which the ratio fell. Similar sets of coefficients can be specified for the surplus and collateral ratios. Another model constraint assures that payments are made for additional shortfalls on bond obligations under claims payment conditions that fall below the boundaries of the probable category for each ratio.

An important element of these loss criteria is their linkages to changes in capital ratios. which are directly related in the model to the incidences of credit risk. interest rate risk, and liquidity risk. The occurrence of these risks ultimately deteriorates a bank's capital position and leads to claims on the insurance fund. The resulting levels of claims together with their incidence of occurrence over multiple iterations then determine the probability distribution of the banks' aggregate claims on the insurance fund. The results reported below are based on coefficients of 0.05, 0.25. and 0.50 for the tier 1 and leverage ratios, and 0.05, 0.25, and 1.00 for the collateral ratio.

Credit Risks

Credit risk is represented by each bank's probability distribution for loan losses estimated from annual loan Joss data over the 1960-93 period (using the software program BestFit to determine the


appropriate forms of the distributions and their parameters). Candidate distributions were restricted to those with zero support (can only take on positive values) or for which the domain could be restricted to correspond to the observed data region with a known coverage rate. For a complete description of the approaches used to fit probability distributions to data, see Hogg and Klugman.

A fundamental principle is to utilize a loan loss measure that best indicates the possibility of claims on the insurance fund. The FCSIC loss probability criteria discussed above focus, in varying ways, on deterioration of a bank's capital position as the primary indicator of insurance claims. Thus, the loss measure should reflect adversities affecting a bank's capital position, where these adversities may occur in one or more periods before a net charge-off and possible recoveries actually occur.

The allowance for loan losses on the balance sheet (and its annual adjustments through provisions and charge-offs) is intended to represent probable and estimable loss in the loan portfolio. By setting aside a portion of the loan portfolio to reflect probable losses, a bank's capital position is directly reduced, and an additional safety reserve is thus created in the balance sheet. Greater lending adversity is reflected by increases in the allowance for loan losses (through greater provisions for losses taken in the income statement), and thus greater reductions in the bank's capital position. Early in the 1960-93 data period, allowances were maintained at low levels by the FCS banks, reflecting the minimal loss experiences of FCS institutions and, for a time, a regulatory requirement for Federal Land Banks that the allowance be at least 1% of outstanding loan balances. Later in the data period, the allowances are considered to more fully reflect probable and estimable loss (Holland).

Even then, however, the choice between using historic time series of the ratios of allowances or provisions to total loans is complicated by the length of interval between when a provision is taken and when a charge-off occurs. The important point is that holding the allowance directly reduces the loan balance, thereby reducing the bank's capital position. Use of the allowance series results in higher potential claims, on average, against the insurance fund than would occur using either the provision or net charge-off series. However, the difference resulting from the choice of loss series is reduced as the length of time between the anticipation and realization of a loss becomes shorter. At the extreme, instantaneous anticipation and realization of net charge-offs would make the time series of net charge-offs appropriate to use.

In terms of the FCS, it is likely that the historic time intervals between recognition and realization of loan losses had the potential to be longer rather than shorter, especially in light of the low incidences of loss experiences prior to the 1980s. During that time, however, evidence from prior studies (Barry, DeVuyst, Lins, Miller, and Sherrick; Miller, Barry, DeVuyst, Lins, and Sherrick) and discussions with FCA personnel indicate that some FCS banks were prompt in their resolution of problem loan situations, while others employed more lengthy work-out and other assistance methods on problem loans. For the future, FCA personnel suggest, in accordance with generally accepted accounting principles (GAAP), that FCS institutions will resolve anticipated loan losses in a more timely fashion than in the past. Nevertheless, the extent to which timely resolution actually will occur remains to be seen.

For the analyses reported here, loan losses are represented by the ratio of the allowance for loan losses to total loans, based on the premise that the loss resolution experiences of the past may hold for the future as well. Under the


allowance approach, a bank's balance sheet taken from the current call report is entered into the model spreadsheet with net loans increased by the amount of the allowance for loan losses (leaving a zero allowance). and equity capital is increased by the amount of the bank's present loan loss allowance. The allowance values are then drawn from the probability distribution for loan losses based on the historic allowance series.3

Data for the loss ratios are compiled from annual call reports over the 1960-93 period for each current system bank, with merging of historic data for banks and districts that had consolidated prior to mid-1995. Means, standard deviations, coefficients of variation, and correlation coefficients among banks for the allowance ratio over the 1960-93 period are shown in Table 1. The low correlations in some cases and high correlations in others show the variation in farm financial stress experienced in the 1980s. Results from BestFit indicated that the predominant distributional form was the logistic, although Weibull and lognormal were found in some cases.

The use of historic data to generate loss distributions introduces potential structural changes in underlying probability positions over time. During the 1960 through 1993 period, virtually all of the loan losses occurred In the decade of the 1980s. A host of restructuring. regulatory, institutional, and managerial steps were then taken to strengthen the system's risk-carrying capacity (Callender and Erickson). Thus, the nature of future adversities may differ from those of the past. Past observations, however, were also generated through periods of substantial change, and consequently may

3 Model results obtained using the histone provision selies did not differ qualitatively from those found using the allowance selies. The results based on provisions and descliptlons of the appropliately respecified model are available from the authors upon request.

Barry, Sherrick. Lins, Banner, Dixon. Brake 75

be instructive in setting benchmarks for the frequency, magnitude, and duration of losses that FCS banks may experience in the future. Competitive pressures in agricultural finance could lead to future events yielding consequences similar to those of the 1980s, but for different reasons. Even though new safety mechanisms are in place, fundamental forces not currently identifiable could lead to recurrence of the losses of the 1980s. In this sense, the general characteristics of structural change in the past may reoccur in the future, thus giving value to the use of historic data.

Interest Rate Risk and Loan Pricing

Interest rate risk refers to unanticipated variation in market interest rates that, in tum, causes unanticipated variation in a bank's net interest margin and in the market value of a bank's equity capital through the effects of rate variation on the values of assets and liabilities. Both of these potential effects are monitored by the FCS banks and the FCA. The model is designed to introduce interest rate risk through draws from the probability distribution for the bank's cost of funds. The model then accounts for interest rate risk through a combination of estimated loan pricing relationships and the relative durations of assets and liabilities.

In each iteration of the model. an observation is drawn from a base interest rate distribution estimated from a 1960-93 time series of rates on U.S. Treasury notes. Cost of funds and loan pricing relationships for each bank are estimated from two sets of regression results. One set was generated by regressing a time series of each bank's ratio of interest paid on interest-bearing liabilities to the balance of interest-bearing liabilities for each year of the time period against the historic Treasury note rate series. These regressions are used to determine each bank's own cost of funds given the draw from the Treasury note distribution. Results from the other set of

Table 1. Risk Measures for the Allowance for Loan Losses by Farm Credit System Bank, 1960-93

FCS Bank

Measure Wichita Texas Western AgriBank AgAmerica AgFirst Co Bank

Mean (o/o) 2.12 0.76 0.88 1.25 1.80 0.84 1.13 Standard Deviation (o/o) 2.72 0.74 0.53 1.58 2.17 0.71 0.22 Coefficient of Variation 1.28 0.97 0.60 1.26 1.21 0.84 0.19

Correlation Matrix: Wichita 1.00 0.71 0.89 0.99 0.98 0.86 0.64 Texas 1.00 0.65 0.70 0.70 0.56 0.31 Westem 1.00 0.87 0.91 0.73 0.55 AgriBank 1.00 0.97 0.90 0.65 AgAmerica 1.00 0.82 0.63 AgFirst 1.00 0.59 Co Bank 1.00 St. Paul BC

St. Paul BC

1.37 0.65 0.39

0.44 0.10 0.40 0.45 0.44 0.47 0.86 1.00

-..J Q)

~ ~ (j

8. ::;:

~ (/)

~ ;3

~ I: i3 2 ::0 fii' ;>;"

C/)

l 6-;:l

I


regressions of historical interest earnings rates on loans against estimated interest expense rates for each bank were used to develop the loan pricing relationships in the model.

In the simulation, the process for a given iteration is as follows. First, an observation is drawn from the Treasury note distribution. For each bank, the cost of funds is then based on the predicted value from the historic regressions using interest rate draws as the independent variable in each bank's regression relationship, plus a drawing from the regression error distribution. From this cost of funds, the predicted interest rate charged on loan assets is similarly calculated using the second regression relationship and the estimated cost of funds as the independent variable.

The regression results representing the relationships between the cost of funds and the resulting loan rates thus indicate the effectiveness of administered loan rates as a part of the asset/liability management response to interest rate risk. For example, a perfectly positive correlation of 1.0 between the cost of funds and earnings rates on loans would signify the complete transfer on an annual basis of interest rate risk to borrowers: a correlation of 0.50 would represent a moderate level of management effectiveness in responding to interest rate risk through loan pricing: and a zero correlation would reflect no relationship between loan pricing and interest rate risk (Barnard and Barry). In general. loan pricing is an effective response to interest rate risk, as evidenced by correlation coefficients for each bank falling in a range of 0.83 to 0.97.

If asset/liability practices were successful in completely immunizing a bank against interest rate risk, then the results of interest rate shock tests would indicate no effects on the market value of a bank's equity capital. [A shock test, as in the Contractual Interbank Performance Agreement, measures the effects on

Barry. Sherrick, Lins, Banner, Dixon, Brake 77

market values of assets, liabilities, and equity of a specified change (e.g., 200 basis points) in the level of market interest rates.] In practice, however. complete immunization is impossible to achieve because of loan pricing limitations. differing time patterns of cash flows for assets and liabilities, holdings of fixed-rate investment securities. the related imbalances between the durations of assets and liabilities, and varying equity positions supporting the assets. As a result, the model is designed to enter scaling coefficients for each bank that represent the relative degree of mismatch between durations of assets and liabilities.

The model uses the duration specifications to calculate a gain or loss in the bank's market value of equity based on the difference between the mean value of the underlying interest rate (Treasury note) distribution and the interest rate drawn on a particular run as the change in interest rate (M). The magnitude (L'>.E) of the gain or loss of equity is calculated as the product of the relative rate change (L'>.i/ 1 + i) times the difference between the levels of assets (A) and liabilities (L), each weighted by their adjusted durations (Da. D 1):

The gain or loss in market value of equity in tum affects the capital portion of the three loss probability criteria (tier 1 capital ratio, leverage ratio. collateral ratio). and thus affects the frequency of claims to the FCSIC and the probability of insurance fund exhaustion. Initial values of liability durations are taken from various "Bond Facts Summary Schedules" from the Federal Farm Credit Banks Funding Corporation.

Other Data Relationships

Results from bank-level regressions of the historic earnings rates on marketable


securities relative to the series of Treasury note rates were used to specify the relationship between the interest rate drawn In each Iteration and the earnings rate on marketable investments. The relationship between loan volume changes and interest rates Is estimated from historic data provided from FCA call report data. The regression of system annual total volume changes over the 1980-93 period against weighted average loan rates provided an estimated interest rate elasticity coefficient of -.1163, which was used in each bank model. Besides a volume elasticity with respect to interest rate. the user can also enter information to affect loan volume independently from interest rate changes.

The historic operating costs of individual banks. including income tax payments, were confounded by changing bank structures over time and by changing degrees of direct lending. In response to this difficulty, an average base rate of .008 was specified for each dollar of assets, based on results of a study by the FCA of bank-level operating costs (Irwin). To reflect economies of size in lending, a base rate elasticity relative to loan volume of -.05 was specified to reflect the approximate rate of dilution of fixed expenses at the mean loan volume levels.

Base Model Results

The base model, including the measurable sources of risk, was run for 5,000 iterations with data inputs representing currently available (first quarter 1995) call report information. Most of the FCS banks exhibited strong financial positions In the mid -1990s, as characterized by capital ratios ranging from 6.4% to 15.2o/o with a weighted average of 8.8%, and loan/total asset ratios ranging from 78.3% to 84.1% with a weighted average of 81.7% as of March 1995.

As shown in Table 2, the distribution of the percentages of claims relative to

insured obligations by FCSIC has a mean value of .1 79% and a standard deviation of .512%. The cumulative probabilities and their associated claim levels also are shown In Table 2. The base case results in claims that would be .055% (.00055) or less of the insured obligations approximately 10% of the time, .077o/o or less 20% of the time, and so on to a level of .260% or less 90% of the time.

Table 2. Claims Distribution Information

Percentile Claims as a Percent Breaks of Insured Obligation

10% .055 20% .077 30% .094 40% .Ill 50% .129 60% .150 70% .174 80% .205 90% .260

Statistics: Mean .179 Std. Dev. .512

Sensitivity Analyses

The model was also subjected to extensive sensitivity analyses to gauge the responsiveness of fund adequacy probabilities to changes In parameter values reflecting alternative risk scenarios in the FCSIC operating environment and to aid in model validation (Barry et al. 1995). The changes reflect adjustments in the levels of key input variables, In the estimated coefficients for probability distributions, and in other estimated relationships used in the model. The changes were implemented on a one-at-atime basis, while holding other parameters constant, and then as simultaneous adjustments of multiple parameter values.

The effects on the probabilities of fund adequacy of changes in individual


parameters for all banks are shown in Table 3. Each result is based on 5,000 iterations of the model. The rows indicate the parameter changes relative to the base case for the allowance version of the loan loss distribution. The changes are grouped into four categories:

1. Credit risk, reflecting historic loan loss distribution scaled up and down by 300%, 250%, 200%, 150%, 75%, and 50% relative to the base case.

2. Interest rate risk, reflected by scaling up or down the first two moments of the interest rate distribution for cost of funds used in the base case, and by changes in the balance of durations for assets and liabilities (neutral durations for assets and liabilities result in zero vulnerability of market values of equity to interest rate risk). The details of the rate distributions are provided in a footnote to the table.

3. Financial structure, reflected by adjustments in the capital ratio and loans-to-assets ratio.

4. Operating characteristics reflected by adjustments to operating costs.

The left panel of columns in Table 3 indicates the probabilities of adequacy of selected secure base amounts of the insurance fund expressed as a percentage of aggregate insured obligations. The entry of .9912 in the "base case" row under the 1% column heading indicates that an insurance fund of 1 o/o has a 99.12% chance of adequacy. Entries in the right panel of columns indicate the percentages of insured obligations that are exceeded at the quartile breaks of the claims distribution-claims that are not expected to be exceeded 25%, 50%, and 75% of the time. The .0854 entry in the "base case" row under the 25th percentile column heading represents .0854% of insured obligations. Thus, the left and right panels differ only by whether selected secure base levels or selected probabilities

Barry, Sherrick, Lins, Banner, Dixon, Brake 79

of adequacy are predetermined. Together. the two panels give a more complete sense of the location and shape of the distributions than either panel alone.

Most of the credit risk scenarios in Table 3, as well as the financial structure and operating characteristic adjustments, indicate relatively low sensitivity of adequacy probabilities to these one-at-atime adjustments, reflecting the strong financial positions of the FCS banks in the mid-1990s. In addition to the results reported in the tables, loss distributions from the periods of the 1960s, 1970s, and 1980s were individually examined. As expected, the more volatile 1980s conditions led to higher relative losses than either the 1960s or 1970s, but the adequacy probabilities did not seriously test secure base amounts in the l% to 3% range, again due to the banks' relatively strong capital positions in 1994.

The results of the interest rate scenarios show relatively little effect on adequacy probabilities for the 1 o/o to 3% range of the secure base amount when neutral durations of assets and liabilities are specified. However. greater responsiveness of the adequacy probabilities occurs in the case of unbalanced durations on assets and liabilities. Non-neutral durations tend to lever the effects of losses that flow through to the insurance fund in an asymmetric fashion because cases in which mismatches generate gains for the individual banks do not result in additional positive flows to the insurance fund. These effects are shown by the bunching of losses in higher loss regions in the right panel of Table 3. The pattems among the relative probabilities and percentages of insured obligations are similar in both the asset -sensitive and liability-sensitive cases.

The effects on the probabilities of fund adequacy of multiple changes in parameter values are shown in Table 4. Four scenarios are based on altemative combinations of credit risk, interest rate


Table 3. Probabilities of Insurance Fund Adequacy Under Alternative Scenarios for Credit Risk, Interest Rate Risk, Bank Financial Structure, and Operating Characteristics

Percentage Insured Secure Base Amount as a Percent of Aggregate Obligations at

Outstanding Insured Obligations Quartile Breaks

Scenario 1% 1.5% 1.75% 2% 2.25% 2.5% 3% 25th 50th 75th

--------------------------- (probabilities) --------------------------- ------------ (%) ------------

Base Case A .9912 .9938 .9944 .9952 .9956 .9962 .9968 .0854 .1286 .1891

Credit Risk Loss Magnifier: 300% .8802 .9239 .9397 .9478 .9550 .9608 .9703 .2235 .3342 .5781 250% .9339 .9593 .9667 .9721 .9767 .9791 .9837 .1912 .2753 .4266 200% .9535 .9733 .9775 .9805 .9825 .9849 .9875 .1599 .2328 .3467 150% .9781 .9863 .9887 .9904 .9923 .9930 .9940 .1210 .1799 .2573 75% .9938 .9958 .9962 .9968 .9972 .9974 .9978 .0712 .1098 .1582 50% .9952 .9962 .9967 .9975 .9980 .9981 .9982 .0583 .0928 .1323

Durations & Interest Rates: a

D0 =neutral Rate distribution 1 .9918 .9946 .9950 .9954 .9958 .9962 .9972 .0847 .1336 .1972 Rate distribution 2 .9914 .9945 .9951 .9953 .9959 .9960 .9976 .0924 .1402 .2033

D0 = 3 x neutral Rate distribution 1 .7585 .7858 .7958 .8062 .8150 .8236 .8390 .0000 .0187 .9038 Rate distribution 2 .9086 .9426 .9536 .9624 .9680 .9728 .9818 .0321 .1174 .3195 Historic rates .7982 .8512 .8698 .8832 .8934 .9040 .9236 .0054 .0962 .6503

Da = 2 x neutral Rate distribution 1 .8422 .8738 .8868 .8982 .9070 .9128 .9280 .0090 .0541 .3818 Rate distribution 2 .9746 .9886 .9906 .9919 .9933 .9954 .9976 .0702 .1325 .2322 Historic rates .9290 .9642 .9730 .9798 .9838 .9891 .9920 .0312 .1123 .3010

D0 = 1/2 x neutral Rate distribution 1 .9696 .9910 .9930 .9938 .9946 .9950 .9956 .0499 .1982 .3887 Rate distribution 2 .9900 .9941 .9946 .9950 .9956 .9963 .9964 .0833 .1489 .2258 Historic rates .9938 .9940 .9952 .9954 .9960 .9968 .9974 .0574 .1383 .2519

Da = 1/3 x neutral Rate distribution 1 .9286 .9830 .9894 .9922 .9938 .9952 .9964 .0404 .2230 .5296 Rate distribution 2 .9904 .9934 .9942 .9948 .9954 .9958 .9970 .0754 .1520 .2427 Historic rates .9856 .9940 .9950 .9958 .9964 .9970 .9976 .0449 .1479 .2868

Financial Structure: Capital ratio = 2 .9952 .9954 .9960 .9962 .9968 .9973 .9992 .0355 .0667 .1165 Capital ratio = .05 .9900 .9938 .9951 .9958 .9959 .9966 .9971 .1151 .1646 .2294 Loans/assets= .95 b .9930 .9954 .9962 .9964 .9965 .9968 .9972 .0695 .1138 .1718 Loans I assets = . 70 b .9974 .9984 .9986 .9990 .9992 .9994 .9996 .0445 .0776 .1180

Operating Cost: Rate= .012 .9898 .9936 .9944 .9946 .9948 .9954 .9962 .1171 .1720 .2377 Rate= .004 .9929 .9949 .9952 .9956 .9958 .9966 .9969 .0598 .0954 .1422

a Interest Rate Distributions: 1 = lognormal with mean set to historic values and standard deviations set to twice historic values; 2 = lognormal with means and standard deviations set to half historic values; historic = gamma distributions estimated from historic data.

b Securities and loan levels adjusted; original allowance, allowance rate distributions, and ROF rates unchanged.

Agricultural Finance Review, Vol. 56, 1996 Bany, Sherrick. Lins. Banner, Dixon, Brake 81

Table 4. Probabilities of Insurance Fund Adequacy Under Alternative Combinations of Bank Risk and Financial Structure

Percentage Insured Secure Base Amount as a Percent of Aggregate Obligations at

Outstanding Insured Obligations Quartile Breaks

Scenario" 1% 1.5% 1.75% 2% 2.25% 2.5% 3% 25th 50th 75th

--------------------------- (probabilities) --------------------------- ----------- (%) ------------

Strongly Unfavorable: .5218 .6536 .7056 Capital ratio = .05 Loan/asset ratio= .95 Loan loss magnif. = 200% Interest rate distrib. = 1 Da = .5 x neutral

Moderately Unfavorable: .9808 .9894 .9920 Capital ratio= .10 Loan/asset ratio= .90 Loan loss magnif. = 150% Interest rate distrib. = 1 D0 = .9 x neutral

Moderately Favorable: .9952 .9958 .9986 Capital ratio = . 15 Loan/asset ratio= .85 Loan loss magnif. = 75% Historic rate distrib. D0 =neutral

Strongly Favorable: .9968 .9980 .9985 Capital ratio = .20 Loan/asset ratio= .80 Loan loss magnif. = 50% Interest rate distrib. = 2 D0 =neutral

"Allowance distributions used for loan losses.

risk, and financial structure. A "strongly unfavorable" scenario is represented by a very low capital ratio (.05), a high loan/total asset ratio (.95). high levels of expected loan loss (200% of the base case). high levels and uncertainty in the cost of funds distribution, and a substantial imbalance in the durations of assets and liabilities. A "strongly favorable" scenario is represented by the opposite effects-a high capital ratio (.20). a low loan-to-asset ratio (.80), low levels of expected loan losses and costs of funds (50% of the base case values for each), neutral durations of

.7454

.9932

.9988

.9986

.7812 .8052 .8412 .3461 .9443 2.0275

.9940 .9946 .9954 .0788 .1644 .2699

.9988 .9988 .9988 .0296 .0553 .0975

.9989 1.00 1.00 .0011 .0060 .0161

assets and liabilities. and favorable but stable interest rate distributions. Moderately favorable and unfavorable scenarios are represented by intermediate levels of these conditions.

In general, the adequacy probabilities respond markedly to the most unfavorable scenarios for the multiple parameter changes. For example, a 2% secure base amount was adequate in only 74.54% of the cases for the strongly unfavorable scenario. By contrast. adequacy probabilities were 100% for the higher


secure base amounts of 2.5% and 3% under the strongly favorable scenario. Within the scenarios, the adequacy probabilities ranged from 52.18% to 84.12% for the 1% to 3% secure base amounts in the strongly unfavorable scenario, and from 99.52% to 99.88% in the moderately favorable scenario. Clearly, the beginning characteristics of the banks and their risk environment significantly affect the adequacy probabilities for the secure base amount.

In interpreting the sensitivity analysis, it is important to consider that the particular changes in parameter values were not selected because they were believed to occur with any particular likelihood. In Table 4. for example, it is hardly surprising to find that unfavorable times cause weak performance and higher risks to the insurance fund. These values may approximate the conditions of the more adversely affected FCS banks at the worst of the stress times of the 1980s. However, unfavorable times may actually occur relatively infrequently. Rather, the focus of the sensitivity analysis is on gaining Insight and developing expectations about the responses of the model's results to changes In key parameter values, where these parameters reflect the banks' own financial conditions and operating characteristics as well as the effects of credit risk, interest rate risk, and other sources of risk in the banks' operating environment. Such insight and sensitivity information provide greater confidence in the applicability of the model over a wide range of environmental and bank performance conditions.

Trend Analysis: Multi-Period Effects

Although not the focus of its development and ongoing use, the model can be employed in a multi-period fashion by making a sequence of simulation runs in which one or more model parameters are adjusted in each run according to a known

or anticipated pattern of change. Suppose, for example, the banks are believed to be entering a period of adversity that may last for five years, with continued deterioration in key measures of bank performance. This deterioration could be reflected by a reduction in a bank's capital position, depletion of a bank's liquid reserves, mounting loan losses, increasing interest rates on consolidated farm credit securities, and a diminishing growth rate for loans.

The model user can observe the effects of these trends on the likelihood of depletion of the FCSIC Insurance fund through a sequence of separate runs In which the input variables for each year in the sequence are set at levels that reflect the anticipated pattern of deterioration. Such a pattern is illustrated by the set of anticipated values for these input variables In Table 5.

As shown in Table 5, the use of increasingly risky input variables, reflecting adverse trends, results in an increased likelihood of exhausting the insurance fund. The loss rates at the quartile breaks of the claims distribution Increase correspondingly. This particular specification of the multi-period analysis assumes that any depletion of the insurance fund is immediately replenished, although any other pattern of fund replacement could be similarly evaluated. Anticipation of multi-period improvement in bank performance can also be represented by favorable trends in the values of these input variables, yielding an increasing probability of insurance fund adequacy over time. The model user must specify the sequential changes in input values; the model itself does not make these projections.

Concluding Comments

The insurance risk simulation model reported in this article is being used by the FCSIC to facilitate the evaluation of the long-term adequacy of the insurance fund

Agricultural Finance Review, Vol. 56, 1996 Barry, Sherrick. Lins, Banner, Dixon, Brake 83

Table 5. Model's Use in Multi-Year Analysis

Year

Variables 1 2 3 4 5

Input Variables: Capital ratio (E/ A) .15 .12 .09 .06 .03 Loan/asset ratio .85 .88 .91 .94 .97 Loss magnifier 1.00 1.25 1.50 1.75 2.00 Interest rate distribution a stable historic variable high, high.

Output Variables: variable variable

Probability of adequacy of 2% secure base amount 1.00 1.00 .9876 .8000 .4908

Percentage of Insured Obligations ------------------------------------ (%) -----------------------------------

(by loss distribution percentile): 25th percentile .0045 .0381 .0598 .6100 1.3226 50th percentile .0066 .0563 .1168 1.0833 2.2028 75th percentile .0093 .0901 .2115 1.7979 3.1018

a Interest rate distributions are defined as follows: stable = means and standard deviations set to one-half historic levels, historic = rates estimated from historic data, variable = standard deviations set to double historic levels. and high = means set to double historic levels.

in light of potential claims on the fund made by the FCS banks. The model explicitly accounts for the effects of credit risk, interest rate risk, and part of liquidity risk through a combination of probability distributions, accounting specifications, and estimated relationships among key variables affecting performance of the FCS banks. The development of the model was based on use of appropriate concepts from finance theory. insurance principles, and statistics, and on the judgment. advice, and expertise of personnel of the Farm Credit System Insurance Corporation, the Farm Credit Administration, and FCS institutions. The model is also adapted to the absence of insurance-preferred characteristics by the FCS banks-in particular, the loss distributions facing the FCSIC insurance fund are not independent across insured banks, and the number of insured units is relatively small.

The model is designed for straightforward accommodation of updated data, revised parameter estimates, changed risk scenarios, future bank consolidations, new

measures of risk, and adjustments of the model's structural specifications. In general. the model serves as a useful tool. along with professional judgments. experience, policy decisions. and other sources of information. in evaluating the long-term adequacy of the insurance fund. It is essential to note that some important sources of risk to the insurance fund cannot be easily modeled and measured-examples include political risks, personnel changes. differences in loan concentrations among FCS banks, new lending authorities, human errors, and major structural changes in the Farm Credit System.

Future extensions of this line of work could focus on the dynamics of risks facing the insurance fund and on riskbased insurance premium strategies for building and replenishing the fund before and after claims occur. The design of early warning systems related to the effects of various external variables on bank performance could also be linked to the model's probability-based results.

84 Fann Credit System Insurance Risk Simulation Model

References

@RISK, Risk Analysis and Simulation Add-In Software, Version 3.0. Newfield, NY: Palisade Corporation, 1994.

Barnard, F.L., and P.J. Barry. "Interest Rate Deregulation and Agricultural Banking: A Risk Programming Analysis." Agr. Fin. Rev. 45(1985): 100-12.

Barry, P.J .. D.K. Banner, D.A. Llns, B.J. Sherrick, J.R. Brake, and B.L. Dixon. "Risk Profile. Research Plan. Analysis. and Formulation Report." Report prepared for Farm Credit System Insurance Corporation. McLean, VA. 1995.

Barry, P.J., C. DeVuysi, D.A. Lins, L.H. Miller, and B.J. Sherrick. "Stress Study of Agricultural Real Estate Loans." Report prepared for Office of Secondary Market Oversight, Farm Credit Administration, McLean, VA, 1994.

BestFit, Distribution Fitting Software for Windows, Release 1.02. Newfield, NY: Palisade Corporation, 1994.

Callender, R.N., and A. Erickson. "Farm Credit System Safety and Soundness." Pub. No. AIB-722. USDA/Economic Research Service, Washington, DC, January 1996.

Doherty, N.A. Corporate Risk Management. New York: McGraw-Hill, 1985.

Federal Farm Credit Banks Funding Corp. "Bond Facts Summary Schedules." Information Services Dept., Jersey City, NJ. Various monthly issues, 1988-95.

Harl, N.E. The Fann Debt Crisis. Ames, lA: Iowa State University Press, 1993.

Hogg, R.V., and S.A. Klugman. Loss Distributions. New York: John Wiley and Sons, Inc., 1984.

Holland, T. Farm Credit Administration, McLean, VA. Personal correspondence, 1994.

Irwin, G.D. "Variations in Operating Costs of Farm Credit System Institutions by Asset Size. Type of Institution, and Year." Internal Document. Farm Credit Administration, McLean. VA. 1991.

Miller, L.H., P.J. Barry, C. DeVuyst, D.A. Lins, and B.J. Sherrick. "Farmer Mac Credit Risk and Capital Adequacy." Agr. Fin. Rev. 54(1994):66-79.

Paulson, A.S., and R. Dixit. "Some General Approaches to Computing Total Loss Distributions and the Probability of Ruin." In Financial Models of Insurance Solvency, edited by J.D. Cumming and R.A. Derrig, Chap. 5. Boston: Kluwer Publishers, 1989.

Peoples, K.L., D. Freshwater, G.D. Hanson, P.T. Prentice, E.P. Thor, and E. Melichar. Anatomy of an American Agricultural Credit Crisis: Fann Debt in the 1980's. Lanham, MD: Rowman & Littlefield Publishers, Inc., 1992.

Optimal Leverage with Risk Aversion: Empirical Evidence Farrell E. Jensen and Lany N. Langemeier

Abstract

This study empirically tests the unconstrained utility maximization model often used in agricultural finance research. Using panel data, we compare the comparative statics from the theoretical model with estimated coefficients from the empirical model. A reasonable degree of consistency is found between the two models. Leverage is found to be affected theoretically and empirically by tax policy, risk, growth rate in the value of assets, and farm profitability.

Key words: optimal leverage, capital structure.

Farrell E. Jensen Is a professor of economics In the Department of Economics. Brigham Young University. Larry N. Langemeler Is a professor of agricultural economics In the Department of Agrtcultural Economics, Kansas State University. The helpful comments of two anonymous reviewers are gratefully acknowledged.

Decisions about financial leverage can have a major impact on the long-run survival of agricultural firms. During periods of financial stress, such as the early 1980s, firms with excessively high debt-to-asset ratios faced a higher probability of failure due to factors that negatively affected profitability and cash flows. Since firm survival can depend on capital structure choices, understanding the factors that affect leverage is important. Although much research on leverage has been conducted in the agricultural and general financial literature, there are still many disparities in results of theoretical and empirical research. Recognizing that many questions remain unanswered, a 1991 study published in the Joumal of Finance defined the need for more research on financial leverage to help resolve the differences in results between theoretical and empirical research (Harris and Raviv). More recently, a 1994 Agricultural Finance Review article called for further research to address capital structure issues in agricultural finance (Ahrendsen, Callender, and Dixon).

In the present study, we use firm-level data to empirically test the unconstrained expected utility maximization model that has often been used in agricultural capital structure studies (e.g., Collins; Barry, Baker, and Sanint). Various researchers have modified this model with assorted "bells and whistles" to analyze tax policy and other governmental policies like price supports and credit subsidies (e.g., Featherstone, Moss, Baker, and Preckel; Moss, Ford, and Boggess; Moss, Shonkwiler, and Ford). In their modifications to the basic model,

86 Optimal Leverage with Risk Aversion: Empirical Evidence

Ahrendsen et al. included investment tax credits and depreciation allowances.

Agriculture is comprised primarily of small, privately held firms, the majority of which are sole proprietorships. For these firms, optimal leverage will be influenced by owner risk preferences. Firms face two types of risk: business and financial. Business risk can be defined as variance of returns to assets and is caused by variability in factors such as operating income, output prices, input prices, output levels, etc. Given the level of business risk, the owner will choose a capital structure or a level of financial leverage which will maximize expected utility of returns to equity, subject to personal risk preferences. Thus, financial risk is determined by capital structure decisions. Risk-balancing issues as influenced by leverage are well documented in previous studies (see, for example, Gabriel and Baker; Barry and Baker; Collins).

In this analysis, we examine factors which affect leverage in the context of a model where expected utility of returns to equity is maximized. Our study reflects a reasonable degree of consistency between the comparative static results from the theoretical model and the estimated coefficients in our empirical model.

Definition of the Model

Owners of the firm receive payoffs depending upon which state of nature occurs. The balance sheet of the firm consists of land and real assets (such as buildings and equipment), and other short-term and intermediate-term assets. Each firm is either a privately owned proprietorship, partnership, or corporation.

The utility function is of the general form U = j(RE, cr~). where RE is the expected rate of return on equity and cr~ is the variance of rate of return on equity. The utility function is assumed to be concave,

so the owner of the firm shows risk-averse behavior. Marginal utility of expected returns to equity is positive and the marginal utility of variance is negative.

Rate of return on assets, a random variable, is determined by the state of nature which occurs, is independent of leverage and capital structure, and is defined as

- ii; RA = --·

L + fJ (1)

where RA is a random variable representing rate of return on assets; ii: is a random variable for operating income before depreciation, interest, and taxes; L is average land value for each year; and f)

is the average value of other assets for each year, not including land, which are subject to accounting depreciation.

In a theoretical model without taxes, Collins showed how leverage defines the relationship between RA andRe. Specifically, RE is determined by RA and o as

- -j ii; )f 1 ) RE - -- - ro l--- ' L + fJ ( 1 - 8)

(2)

where RE is a random variable to represent rate of return on equity, 8 is leverage denoting the debt-to-asset ratio for the firm, and r is the interest rate on borrowed funds. Since the rate of return on assets is stochastic, so is the rate of return on equity.

Now we include state and federal income taxes, social security taxes, long-term capital gains, depreciation tax shields, and returns to the operator's unpaid labor and management. Our model is an extension of previous optimal leverage models since it includes returns to operator's unpaid management and labor, and assets are divided into land and other assets. With these changes, rate of return on equity is defined after interest and tax considerations as


__ In(l-T)- F+ f9(L+8)(1-I.J'11+yr+MT RE-1------------~L--+~8------------

1 J 1 1 - [r8JO-nJ·j-J· (1 - 8)

(3)

where Tis the marginal income tax rate for the firm. y is assets purchased during the period which are subject to accounting depreciation. t. is total accounting depreciation as a percentage of e. r is Investment tax credits as a percentage of y, F is returns to owner's management and labor, f9 is the percentage growth rate in real value of assets of the firm over the period, and I.J' is the proportion of capital gains subject to ordinary income taxes. This shows rate of return on equity after removing returns for the farmer's management and labor. In the long run, management and labor would have to be compensated at a level equal to employment opportunities off the farm. The term y r represents investment tax credits that would accrue to the firm, and t.8T is the depreciation tax shield.

Taking the expectations of equation (3) and simplifYing gives

- lii:(l-T)-F+r(L+8)(1-I.J'T)+yr+t.8T R = g E

L+8

- [r8](1-T)).I_1 ). 1 (1 - 8)

where ii: is expected returns to operating Income before depreciation, Interest. and taxes, and r9 is expected percentage change in real value of assets over the period.

From equation (3). the variance of rate of return on equity is defined as

(4)

Jensen and Langemeier 87

2 J 1 ) crE = 1 (l-8)2(L+8)2

·{(l-T)2cr; + 2(1-T)(L+8)(1-I.J'T)crnru

+ (l-I.J'T)2 (L+8)2 cr;}, (5) 'I

where cr; is the variance of operating income, cr~9 is the variance in the growth rate of real assets, and crnr is the covariance between ii: and f9 . 9

If a negative exponential utility function is used, and if RA- N(RA. dl. then expected utility Is maximized by optimizing the following function (Collins):

(6)

where p is the risk aversion parameter.

By substituting our definitions for RE and cr ~, we find that

lii:(l- n- F+ r (L+8)(1-I.J'T) + yr + MT

V{8) = g

L+e

- [cOJII -n)

. L I J -.Sp L -O),'[L•OJ')

· {o-n2 cr; + 2(1-T)(L+8)(1-I.J'T)crnr"

+ (l-I.J'n2 (L+8)2 cr;,}. (7)

Expected utility is maximized as leverage changes by differentiating V{8) with respect to 8 and solving for 8.. The solution for 8. gives the amount of leverage that will maximize the expected value of utility from rate of return on equity. Second-order


conditions are satisfied since a concave utility function is assumed. The value of 8' is given in equation (8):

risk aversion, the theoretical model shows that farmers reduce leverage in more risky environments. Several variables have

(l - T)2 cr2 + 2(1 - T) (L + 8) (l - 'f'T) a + (l - tpT)2 (L + 8)2 cr2 . ·~ ~ 8' 1-p ----~----------------------------------------~ (L+8J{n(l-T) - F + rq(L+8)(l-'f'T) + yr + t.8T + r(T-l)(L+8l}

(8)

From equation (8) we can calculate the comparative statics that provide an indication of the theoretical signs of the variables to be tested with an empirical model. In our model, 8' is a target to which farmers continually adjust their capital structure by increasing retained earnings, acquiring or selling assets, increasing or decreasing debt, or increasing or decreasing equity investment. Equation (9) shows the variables we use for the comparative statics analysis:

8' = j(Ti, r9, L, 8, p, cr;, a~o' crnr;

T, yr, 'f', t., F, r]. (9)

Comparative statics results are shown in Appendix B. In all cases, we assume that O:o;T::; l, O::;tp::; l, L'?.O, 8?:.0, O::;r::; l. In addition, we assume that p has a positive value for each comparative static result in Appendix B. We do not have data on the risk aversion levels of the firms, so p was not included in the empirical analysis. Assuming a positive value for p is consistent with values of the risk aversion parameter used in other studies (e.g., Featherstone, Baker, and Preckel; Collins).

Comparative statics results show that 8' is increasing in firm profitability (it), growth rate in the value of other assets (r9 ),

investment tax credits (yr), and accounting depreciation (!'.). Conversely, 8' is decreasing in the three risk terms (a;, a; , and a.r ) as well as returns to

g g operator's management and labor (F), interest rate (r), and the risk aversion parameter (p). As would be expected with

signs that are not uniquely determined in comparative statics analysis, including land value (L), value of other assets (8), marginal income tax rates (T). and proportion of long-term capital gains subject to ordinary income taxes ('f'). Signs of these variables will be determined in the empirical analysis. Our comparative statics results are consistent with those of Ahrendsen et a!. for all variables included in their model.

Optimal leverage is not a static concept, but is more likely to be dynamic in nature since it can be expected to adjust through time as the economic environment changes. Collins and Karp show the dynamic nature of leverage in a model where financial leverage is also related to age, wealth, and the opportunity cost of farming. Furthermore, it is realistic to assume that farmers cannot quickly adjust the leverage of their farms in response to changes in economic factors that affect leverage. Recognizing that farmers are likely moving toward an optimum 8', we use a partial adjustment model (as was done in Ahrendsen et a!.) to show the adjustment from the observed leverage to the unobservable optimal level.

From equation (9), 8' can be defined as follows, where t refers to year and j to individual firms:

8;1 = ~o + ~~ Ti11 + ~2r:gJ1 + ~3~1 + ~48J1 2 2

+ ~5°nt + ~6°r + ~7CJnr + ~8Tjt gl gf

+ ~gYrJt + ~w'f'J1 + ~~~"'11

+ ~12FJt +~!3rt+ e11· (10)


The partial adjustment model can be defined as

(11)

where 81 and 81_ 1 are observed values of the current and lagged debt-to-asset ratio. Substituting for 8; 1 , we obtain the empirical model:

8Jt = bo + bi1iJt + bz":gJt + b3~t + b48Jl

2 2 + b5 crn1 + b6crr + b7cr r + b8 TJL

gl 1t gl

+ bgyfjl + biO'I'jl + biiLljl

+ bi2Fjt + bi3rt + bi48Jt-I + eJL" (12)

Coefficients in equation (12) are the shortrun response coefficients. Equations (13) and (14) show the long-run coefficients:

(13)


Description of Data

Data for this study were collected from farms, all of which were enrolled in the Kansas Farm Management Association. We used a sample consisting of 552 farms that were members of the association from 1973-88. The data are arranged in panel format with 16 years of data for each farm. Means and standard deviations for all variables are shown in Table 1. The sample is representative of farms in the association (although probably not representative of all farms, because it is the better managed farms that usually join farm management associations). The data set consists of 8, 796 observations. Where appropriate, data in Table 1 are shown in real terms. with 1982 = 100.

Empirical Tests of the Model

A complete description of all empirical variables in equation (12) is included in Appendix A. Estimation of the model was

Table 1. Descriptive Statistics of Variables (1982 = 100)

1973--81 1982-88 Pooled

Variable Mean S.D. Mean S.D. Mean S.D .

8 .294 .24 . 345 .33 .316 .28

it $74,857 $70,642 $59,789 $54,789 $68.353 $64.709

rg 15.49% 65.59% -4.64% 63.29% 6.07% 65.29%

L $321.101 $275,181 $319.952 $275.483 $320.605 $275.296

e $271.120 $196.774 $233,812 $194,819 $255.017 $196,291 (J2

n 4.25 X 109 1.85 X 109 2.94 X 109 6.49 X 108 3.68 X 109 1.60 X 109

(J2 rg 4,255 3.775 3,875 2.058 4,079 3,108

(Jnrg 122.785 179,047 57.691 288.614 92,718 238,268

T .33 .23 .27 .20 .31 .22

yr $1.981 $2.297 $934 $1.860 $1.529 $2.181

'¥ .40 0 .66 .30 .51 .24

Ll .08 .04 .10 .06 .09 .05

F $15.307 $3.140 $16.835 $3.129 $15.967 $3,225

r .02 .03 .07 .01 .04 .03

81-1 .298 .25 .339 .31 .316 .28


done using Tobit regression. The decision to use Tobit regression was based on the fact that 513 out of 8,834 observations had debt-to-asset ratios equal to zero (representing 5.8% of the total observations). In running the Tobit regression, the data were left censored.

The results of the Tobit regressions are shown in Table 2. In 1982, there was a major change in the agricultural environment when falling prices led to a period of financial duress for many farmers. To determine if there was a change in the financial structure after 1981, a Chow test was calculated with an F-value of 21.15 (the 1% level critical value is 2.10). As a result of this change in structure. regression results in Table 2 are shown separately for 1973-81 and 1982-88, along with the pooled results. For the 1982-88 period. the coefficient on the lagged dependent variable (81_ 1) was slightly higher than one. so that u [as defined in equation (13)] was slightly less than zero. In the partial adjustment model, a should be in the following range: 0 < a s 1. By including firm dummy variables during this period, we were able to bring the coefficient for the lagged dependent variable within the acceptable range.

For the 1973-81 period, all variables that were significant at the 10% and lower levels had signs that were consistent with the theoretical model except for variance in the growth rate of real assets (cr; ) and interest rates (r). Considering th~ 1982-88 period, the only significant variable that was not consistent with the theory was accounting depreciation (t.). In the pooled model, the only inconsistent variable was the interest rate (r). The covariance term, crnr , was dropped from

g each model due to problems with multicollinearity. When estimating the model for 1973, 'I' was dropped out because the value was .40 for each year, creating a linear relationship with the intercept term.

Our empirical results show that for significant estimates, o' is increasing in firm profitability (n) and in the growth rate in the real value of assets (r9 ), as predicted by the theory. Plumley and Hornbaker found that profits and leverage are positively related, as the most successful farms in their study had higher levels of leverage and were more profitable than less successful farms. Variance of returns to operating income (cr;) was significant with the correct negative sign in all three models. Thus, o' is decreasing in cr;. Variance in the growth rate of real assets (cr; ) had a correct sign for the 1982-88 period, but was incorrect for the 1973-81 period. Farmers appear to increase leverage as profits increase, but decrease leverage when the variance of returns to operating income increases.

These results are consistent with previous work showing that increased riskiness leads to lower levels of leverage (Collins and Karp). Likewise, Ahrendsen et al. also found that lower levels of risk lead to increases in financial risk through adjustments in the capital structure. Featherstone et al. ( 1993) also found the same negative relationships between risk and the level of leverage.

The investment tax credit variable (y r) has positive signs when significant, and the other two tax variables, marginal income tax rate (T) and proportion of capital gains subject to ordinary income taxes ('!'), show significant negative signs in all models. In the comparative statics analysis, the signs for T and 'I' were indeterminate. Therefore, increases in taxes reduce the optimal leverage. The accounting depreciation variable (C.) was not significant in two of the models, but was significant with the incorrect sign in the 1982-88 period. Signs for land (L) and other assets (6) were not defined in the theory, and the empirical model had three significant negative coefficients and one significant positive coefficient. The value for the lagged dependent variable for the

Agricultural Finance Review, Vol. 56, 1996 Jensen and Langemeier 91

Table 2. Tobit Regression Estimates of Real Factors Affecting Leverage for Different Models

Variables Description of Variables 1973-81 1982-88 Pooled Model

Intercept .07 .33 .05

- Operating Income before 2.34 X J0-7 -2.64 x w-8 1.97 X 10-7 n depreciation, Interest, and taxes (4.82)*** (-.36) (5.32)***

ry Percentage increase In value of 3.84 X 10" 4.22 x w-5 2.38 x 10-5

real assets (1.79)* (1.98)** (1.4 7)

L Real owned land value -5.79 X 10-8 -1.67 X 10-7 -3.18 x w-8

(-10.03)*** (-10.14)*** (-7.31)***

e Real value of other assets 2.95 X 10-8 4.37 X 10-S -9.41 X 10-9

(2.25)** (1.48) (-1.04)

a2 n Variance of n -2.64 x w- 12 -1.43xi0-11 -5.78 x w- 12

(-2.31)** (-4.14)*** (-5.48)***

a2 Variance of f9 1.59 X 10-6 -4.05 X 10-6 4.71 x w-9 rg

(3.46) (-4.22)*** (.01)

an r9 Covariance of ii: and f9

a a a

T Marginal federal and state income -.09 -.03 -.09 tax and social security (-8.06)*** (-3.19)*** (-10.88)***

yr Investment tax credits 4.27 X 10-6 -1.01 X 10-6 2.32 X 10-6 (5.61)*** (-1.15) (3.97)***

'I' Proportion of capital gains subject b -.03 -.03 to ordinary Income taxes (-3.75)*** (-7.66)***

!::. Depreciation -.03 -.08 .01 (-.54) (-2.93)*** (.61)

F Returns to unpaid farm labor -2.07 X 10-6 2.67 X 10-6 8.22 x w-7

and management (-2.11)** (1.16) (1.28)

r Real interest rate .19 .12 .13 (2.98)*** (.55) (2.99)***

81-1 Lagged dependent variable .91 .65 .98 (119.29)*** (40.42)*** (201.74)***

------------------------------------------------------------------------------------Noncensored values 3.786 3,217 7,003 Left censored values 160 258 418

Notes: We assume a normal distribution of the error term for the Tobit model. Numbers in parentheses are t-values. Single, double, and triple asterisks (*) denote significance at the 10%, 5%, and 1% levels, respectively.

a Due to multico111nearity, this variable was dropped from the empirical model.

bThis variable was dropped because the value was .40 for all years from 1973-81. This created a linear combination with the intercept term.


1973-81 period was .90, for the pooled data it was .98, and for the 1982-88 period it was .65. Including dummy variables reduced the size of the coefficients for the 1982-88 period. The size of these coefficients indicates the leverage adjustment process is lengthy [equation (13)]. Two models had only significant positive signs for r, showing that leverage increased with an increase in interest rates-which was not consistent with the theory.

Robustness Checks

Because our data were in a panel format, we tested sensitivity to the firm and time effects in two ways. Time differences were handled by running separate regressions based on the results of a Chow test. as explained previously. For the 1982-88 period, firm differences were handled by the inclusion of firm dummy variables for the reason mentioned previously. When specification of the model was changed, the coefficients were relatively stable. Thus, we have a reasonable degree of confidence in the estimates.

Estimates of Elasticity

Elasticity estimates provide an indication of the responsiveness of leverage to the different variables in the model. Table 3 presents the long-run elasticities that were calculated from the regression coefficients from the pooled model. In all cases, estimates were calculated using mean values of the variables and the Tobit regression estimates. For the long-run elasticity estimates, a value of .98 was used for bw which was the coefficient from the pooled model.

Variables with the highest elasticities were marginal income tax rates (T), variance of operating income (cr;), and proportion of long-term capital gains subject to ordinary income taxes ('¥). These were followed in importance by operating income (it) and land value (L).

Table 3. Long-Run Elasticity Estimates for Variables from Tobit Pooled Model

Long-Run Variable Estimates

-1t 2.13

rg .02

L -1.62

e a

(J2 n -3.37

(J2 a rg

b <Jnrg

T -3.86

yf .56

'I' -2.99

t::. a

F a

r .78

Notes: Elasticities are calculated at the means. A coefficient of .98 was used for calculating long-run elasticities. This value was obtained from the pooled model.

a Elasticities were not calculated if coefficients were not significant at the lOo/o level.

b Not estimated (see footnote a, Table 2).

Inelastic estimates are for investment tax credits (yf), the growth rate in the value of real assets (r9 ), and the interest rate (r). Because none of the other variables had significant coefficients at the 10% level, elasticities were not calculated. The elasticities of the tax variables indicate the importance of tax policy on farm leverage decisions. Also, the size of the elasticities for n and (J; indicate the effect of farm profitability and risk on leverage.

Conclusions

In this study, we found our empirical models to be highly consistent with economic theory that explains the optimal level of leverage for agricultural firms. The theoretical model is based on a negative


exponential utility function in which the objective is to maximize expected utility of rate of return on equity. All significant variables in the three different empirical models, with three exceptions, had signs consistent with the theoretical model. We found a significant difference in the structure between the 1973-81 period and the 1982-88 period when many farms experienced severe financial distress. Our results show the importance of operating profits. tax policy, and risk in influencing leverage and hence capital structure. We conclude that our model explains many of the relationships which affect the optimal level of leverage.

References

Ahrendsen, B.L., R.N. Callender, and B.L. Dixon. "An Empirical Analysis of Optimal Farm Capital Structure Decisions." Agr. Fin. Rev. 54(1994): 108-19.

Barry, P.J., and C.B. Baker. "Financial Responses to Risk in Agriculture." In Risk Management in Agriculture. edited by P.J. Barry. Ames, lA: Iowa State University Press, 1984.

Barry, P.J., C.B. Baker, and L.R. Sanint. "Farmers' Credit Risks and Liquidity Management." A mer. J. Agr. Econ. 63(May 1981):216--27.

Collins, R.A. "Expected Utility, DebtEquity Structure, and Risk Balancing." Amer. J. Agr. Econ. 67(August 1985): 627-29.

Collins, R.A., and L.S. Karp. "Lifetime Leverage Choice for Proprietary Farmers in a Dynamic Stochastic Environment." J. Agr. and Resour. Econ. 18(December 1993):225-38.

Featherstone. A.M., T.G. Baker, and P.V. Preckel. "Modeling Dynamics and Risk Using Discrete Stochastic Programming: A Farm Capital Structure Application."


In Applications of Dynamic Programming to Agricultural Decisions. edited by C.R. Taylor. Boulder. CO: Westview Press. 1993.

Featherstone. A.M .. C.B. Moss. T.G. Baker. and P.V. Preckel. "The Theoretical Effects of Farm Policies on Optimal Leverage and the Probability of Equity Losses." Amer. J. Agr. Econ. 70(August 1988):572-79.

Gabriel, S.C., and C.B. Baker. "Concepts of Business and Financial Risk." Amer. J. Agr. Econ. 62(August 1980):560-64.

Harris, M., and A. Raviv. "The Theory of Capital Structure." J. Finance 46(March 1991):297-355.

Moss, C.B., S.A. Ford. and W.G. Boggess. "Capital Gains, Optimal Leverage, and the Probability of Equity Loss: A Theoretical Model." Agr. Fin. Rev. 49(1989): 127-34.

Moss, C.B., J.S. Shonkwiler, and S.A. Ford. "A Risk Endogenous Model of Aggregate Agricultural Debt." Agr. Fin. Rev. 50(1990):73-79.

Plumley, G.O., and R.H. Hornbaker. "Financial Management Characteristics of Successful Farm Firms." Agr. Fin. Rev. 51(1991):9-20.

U.S. Department of Agriculture. "Farm Real Estate: Historical Series Data. 1950-92." Statis. Bull. No. 855. USDA/Economic Research Service. Washington. DC. May 1993.

Appendix A: Description of Empirical Variables

Table A1 provides a listing of the empirical variables (from equation (12)] along with definitions and further explanatory information. Variables are created in real form by dividing by the GNP implicit price deflator, with 1982 = 100.


Table AI. Description of Empirical Variables [from equation (12) I

Empirical Variable

8

-1l

L

e

Description

This leverage variable was calculated as the ratio of total debt over total assets. with rented land excluded. Total assets include land, bu!ldings, equipment, and inventories. These are the average of the beginning- and end-of-year values for each firm. Total debt is the average of beginning- and end-of-year farm debt for each firm.

Included in this variable are real operating profits before depreciation, taxes, and Interest. It represents a return to operator's unpaid labor, management, and net worth.

This variable denotes the percentage rate of growth in real value of assets (L + 8). The calculation is based on USDA estimates of value of land and buildings for Kansas. The data were obtained from historical farm real estate statistics published by the USDA. The value was multiplied by the annual number of acres of cropland by each firm. This was necessary because land values were only updated every five years in our data.

This variable represents the real value of all land owned by the firm each year.

This variable Includes the real value each year of all assets owned by the firm other than land. Specifically, it includes buildings. equipment, and inventories.

This variable is calculated as the variance in realn. It was calculated as the crosssection variance for all firms by year. Thus there was a variance term for each year which was the same for each firm, since the data for all firms were used to calculate each variance.

This variable represents the variance of the rate of growth in real value of assets (f9 ).

It was calculated as the cross-section variance for all firms each year. Thus there was a variance term for each year which was the same for each firm, since the data for all firms were used to calculate each variance.

This term denotes the covariance between nand f9 • It was calculated as the crosssection variance for all firms each year. Thus there was a variance term for each year which was the same for each firm. since the data for all firms were used to calculate each variance.

T This variable is the marginal tax rate, calculated specifically for each firm by year. It Is the percentage rate that each farm would have had to pay on one additional dollar of income each year. All information was taken from federal and State of Kansas tax codes and applied to each farm by year. Also included are the social security taxes for each farm by year. Because data were available on personal exemptions and standard deductions, It was possible to calculate a specific annual marginal tax rate for each farm. For corporations, appropriate tax rates from the federal and state tax codes were used.

y r This variable was calculated by applying the investment tax credit percentages (when applicable) to new Investment each year by the individual firms. The Investment tax credit varied for different classes of assets. The correct percentage was applied according to the type of asset involved. The Investment totals were converted to real values.



Empirical Variable


Description

This term represents the proportion of capital gains subject to ordinary income taxes. Capital gains are represented by increases in the value of L + 8. This variable is based on actual tax codes for treatment of long-term capital gains.

This variable was calculated as the ratio of depreciation allowances by year for each .firm over the average value of depreciable assets owned by the firm.

F This variable denotes returns to operator's management and labor. For calculating returns to management, 2o/o of gross farm sales was used. Returns to unpaid farm labor were based on a fixed amount per year.

r In the data set used for this study, it was not possible to calculate the interest rate that firms were charged on borrowed funds. Therefore. the prime rate was used as a measure of interest rate. Likely, most of the firms did not pay the prime rate, but it should serve as a reasonable proxy for the interest rate paid by the firms. It is expressed in real terms.

Appendix B: Comparative Statics Results

In this appendix, the comparative statics results from taking the partial derivatives [equation (8)) are shown. To simplify the equations, we define:

A ii:(l - 1l - F + r (L + 8) (l - 'l'T) g

+ yf + .6.8T + r(T-1)(L+8). (A1)

The value of A will depend upon the profitability of the firm. For the comparative statics analysis, we assume that the value of A is positive. This assumption is supported by the empirical values shown in text Table 1.

In a further attempt to simplify the equations for the comparative statics analysis, the following is defined:

B ( 1 - 112 cr; + 2( 1 - 1l (L + 8)

· (l - 'l'T)cr + ( 1 - 'l'T)2 rrrg

As defined, B is really cr~.

(A2)

The sign of the derivative in several of the comparative statics results depends upon the sign of B. Observation of B indicates that the sign depends upon the sign of the covariance term. As shown in text Table 1, the value of the covariance term is positive. Therefore, we make the assumption that B will normally be positive.

Comparative statics are obtained by differentiating E/ with respect to each of the variables, as shown by equations (A3)-(Al6) presented in Table A2.


Table A2. Comparative Statics Results [from equation (8) I

a8' p(l- T)B > O

an (L+e)A 2 (A3)

a~· = lp(l- '¥71B) > 0 ar A 2

g

(A4)

a8' ln(l-11- F + 2r(L+6)(l-'¥71 + yr + Ll6T + 2r(T-l)(L+6)) - = pB g

aL (L + 6)2A 2

l(l-T)(l-'¥71crnr + (l-'¥n2 (L+6)cr;) .

- 2p " • (sign not defined) (L + 6)A

(A5)

a8' jno-n- F + 2r(L+e)(I-'¥n + yr + 2LleT + 2r(T-l)(L+eJ) - = pB g

ae (L + e)2A 2

1 (l - 11 (l - '¥71 cr + ( l - '¥712 (L + 6) cr; ) - 2p nrg g (sign not defined)

(L + 6)A

(A6)

a8' 1 B )

- < 0 ap (L + 6)A

(A7)

a8' jp!J -n') < 0 -2 acrn (L + 6)A

(AS)

a8* +I -'I';'(L • 9)) < O -2 acrr

g

(A9)


Table A2. Continued.

88'

ar _)Bin+(r9 'f'-r)(L+8) -ll81) l L + 8)A 2


(AlO)

(All) l2crnr [-L- 8 - 'f'(L + 8) (l - 2T)] - 2(1 - T) cr; - 2'¥(1 - 'f'T) (L + 8)2{) - p g

(L + 8)A

88'

ayr pB > 0

(L + 8)A 2

88' ~2(1- T)crnr + 2(L+ 8)(1- 'I'71cr;l 1 r Bl - = pT " 9 - pT _ 9 _

8'¥ A A 2

88'

(!ll

88'

aF

88'

ar

p8TB >0

(L + 8)A 2

1 pB l 0 - (L+8)A 2 <

pB(l- T) < O A2

(sign not defined)

(A12)

(sign not defined) (A13)

(Al4)

(Al5)

(Al6)

Developing a Farm Business Planning Extension Program: The North Dakota Experience

David M. Saxowsky, Cole R. Gustafson, and Laurence M. Crane

Abstract

This article reviews a planning process that was developed to assist farmers create a long-term business plan for their farm operation. A multi-step planning process that integrates business management with family goals is described. Participant evaluations of subsequent extension educational programs were favorable.

Key words: business plan, farm, planning process, farm management, goals. strategic plan.

David M. Saxowsky is an associate professor and Cole R. Gustafson is interim chairperson, Department of Agricultural Economics. North Dakota State University. Fargo. Laurence M. Crane. formerly an assistant professor, is with the National Crop Insurance Service, Overland Park. Kansas. The authors benefitted from the constructive comments provided by two anonymous reviewers.

Production agriculture continues to grow more sophisticated. not only in terms of production techniques and marketing strategies, but also in terms of business organizations. Likewise, the expanding use of specialized assets (e.g., facilities for concentrated livestock production. or capital-intensive pollution control equipment) leads to decisions that are more difficult to reverse and that have long-term implications for the business owners.

These changes increasingly require that farmers effectively communicate the status and direction of their business to lenders, landlords, regulators, investors, and contracting parties. Equally important is the farmer's ability to communicate business goals and needs to family members and partners. Consequently, long-term business planning for farms is receiving increased attention. The Agricultural Management Advancement Program being used in Michigan and Wisconsin is just one example of such training efforts (Harsh and Shaltry).

A group of Minnesota and North Dakota farmers, adult farm management instructors, and agriculture lenders agree that business planning is necessary if farmers are to reach their goals as efficiently as possible (Gustafson, Saxowsky, Crane, and Samson). Although long-term strategic planning has been discussed in business texts for several decades (e.g., DuBrin and Ireland), the discussions often do not provide the guidance farmers need with respect to interpersonal communication, enterprise


and risk management in a competitive environment, and farm financial analysis. The missing component is an explanation of a practical approach for farmers to consider in developing a long-term plan for their farm business.

Several Farm Credit Services associations of North Dakota and Minnesota approached North Dakota State University to develop materials and to cooperate in offering training on long-term business planning to farm owners. A research/ extension project was developed using grant funds from Farm Credit Services associations in North Dakota and Minnesota. including AgriBank.

The sponsors' requirement was that the educational program help farmers acquire a better understanding of their business by organizing their thoughts, actions. and decisions. This would include preparing a document that summarizes the farmer's assessment of the current financial and resource situation, goals for the operation and owners, and a plan for implementation.

Reaction Panels

To ensure that the final product would meet this objective, the researchers relied on two reaction panels to provide input and feedback at each stage of the project's development. One panel consisted of farm producers; the other was comprised of lenders and adult farm management instructors.

The group of eight North Dakota and Minnesota farmers selected by the sponsor to serve as a reaction panel brought a wide range of experiences and situations to the deliberations. For example, one farmer had a teenage son who could become involved in the business; however, the son's involvement would require a substantial capital outlay. For this reason, the farmer wanted to carefully plan five to 10 years into the future. Another farmer was in partnership with an

Saxowsky, Gustafson, and Crane 99

unrelated neighbor; this relationship was different than the more typical "familyowned" farm.

A third farmer co-owned the business with his brother, and they were looking to raise different commodities. But. this farmer had recently been divorced and was rethinking/reflecting on the impact the successful business had on him and his immediate family. A fourth farmer was a partner in a rapidly expanding farm operation that extended over several hundred miles. The coordination, communication. and need to maintain momentum were key issues for that business. A fifth reactor was a young farmer in a perilous financial situation.

Despite their varying experiences and situations, the members of the farmerreaction panel agreed that long-term planning was necessary.

The members of the second reaction panel brought a broader range of experience to the discussion because they each worked with a number of customers/clients (farm operators). They contributed ideas on how an outsider (such as a lender) might react to a farmer's long-term plan.

The researchers met separately with the panels several times to gather the members' observations and ideas. As materials were developed, copies were sent to panel members for their review. Although written comments were welcomed, most of the farmers' responses were presented orally at the next panel meeting. The lenders were more inclined to provide written suggestions.

Members of the reaction panels generally observed that they would expect farmers to be reluctant to engage in a formal planning process. They were concerned that farmers might be intimidated by the magnitude of the task. and, if the future does not tum out as they envisioned, farmers might view their effort as a failure. Despite these concerns, the panel members agreed that farmers engage in

100 Developing a Farm Business Planning Extension Program: ...

long-term planning-they are always looking to the future. even if they do not share or document their thoughts.

Business Planning Defined

The research emphasized how farmers can engage in long-term planning. Initial attempts to define a process were made and tested. errors and shortcomings were discovered, and changes were implemented.

One early change was to think about planning rather than a plan; therefore, the materials describe an ongoing thought process. rather than offer a specific outline or a sample plan. Similarly. it was soon recognized that the benefit of planning was not the document at the end of the process, but the thoughts that are developed and evaluated during the planning process. Thus, farmers are discouraged from thinking about planning as something that can be accomplished by hiring a consultant, because such an approach would not motivate farmers to think about their own situation. Consequently, the stated purpose of longterm planning is to help farmers better manage their business, rather than to provide a document for others (such as lenders) to use.

From the project's inception, the researchers strove to describe planning as a multi-step process. The number of steps varied as the materials were being developed. Similarly, the order of the steps was not clear, and various approaches were tried. Finally, it was agreed that farmers should be encouraged to commence the planning process by choosing the topic with which they are most likely to be comfortable-describing their current farm operation. The remaining steps then were ordered to reflect a thought pattem that business owners are likely to follow. Each step of the order Is described in a subsequent section.

Several observations were made early in the effort. First, many of the common planning tools (such as budgeting, financial analysis. and risk management) would be used in the long-term planning process. Second. some topics would need additional explanation (such as goal setting and assessing the future operating environment). Third. long-term business planning can serve as a format for organizing other planning efforts-such as relating the farmer's production plan to the marketing plan, to the finance plan, or to the retirement and estate plan.

A description of a 1 0-step planning process was developed and beta tested. To do so, the description was (1) shared with the reaction panels, (2) taught on campus to seniors in agriculture intending to farm, (3) presented to farm families attending off-campus wmkshops, and (4) used by a farm couple with the cooperation of the researchers. The responses gathered from these experiences revealed that some of the suggested steps needed refinement; they were difficult (if not impossible) to apply. Subsequently, Steps 5, 6, and 7 of the process were extensively revised.

Steps in Farm Business Planning

Farm business planning is an ongoing process that does not have a clear starting or stopping point. For this reason, longterm planning can be illustrated as a circle with multiple steps to address key questions (see Figure 1). Illustrating the planning process as a circle also emphasizes the following:

• A step does not need to be fully completed before moving on to the next one. There will always be another opportunity to revisit the topic during a subsequent planning cycle. (This is especially critical if efforts to finalize a step stall the overall planning process.)

Figure 1. Steps in the Ongoing Process of Long-Term Farm Business Planning

Self-Assessment of Skills and Interests

Business Inventory

Documenting, Sharing & Revising

Projected Operating Environment

Transition Plans

~ Alternatives

6---

Current Farm

Decision Criteria: • Profitability • Cash Flow/Feasibility • Equity Growth • Interests and Skills

8 / • Goal Fulfillment

~ Constraints

Monitoring & Control

~ ~ !:

2 a :31 ~ ~ ~ c:: Fi)"

~

~ CJl ?l ..... en en Ol

Cll

~ t: (/)

~ Q

~ ~ (/) 0 ?

~ ()

i3 ~

1-' 0 1-'


• Business owners should be encouraged to make changes in their plans as their situation evolves.

Assessing the Current Situation

(I) Current status of the farm business

The business planning process is an opportunity for farm owners to review their operation. A complete set of financial statements. although an integral part of describing a business. is far from adequate. Business owners also are interested in knowing what can be produced with the assets they control. Thus, a critical component of the business inventory is to describe the productive capacity of all assets that are accessible to the business (Gustafson et al.). In addition, a farm has intangible assets that are not listed on the balance sheet. A summary of labor resources, the business's credit reserve, and the business's capacity to assume risk are examples of important inputs that a manager needs to understand.

Other activities involved in developing a complete business inventory include:

• summarizing each enterprise in the business-what inputs are used. what commodity is produced, and how it is marketed;

• identifying the business owners, the farm assets each owns, and the method of compensating them for allowing the business to use their assets;

• delineating management strategies for financing, capital expenditures, labor, owner retirement, and income and related taxes (additional junctional plans);

• comparing availability of inputs to what is needed, in terms of quantity and timing:

• developing up-to-date financial statements (cash flow statement, income statement. and balance sheet);

• comparing the current situation with past financial situations and the financial situations of peer firms: and

• assessing the farm's capacity to assume risk.

Farmers should not expect to collect and document all of this information the first time they attempt this step; this is too imposing a task. Instead, farmers initially should expect to expand the documentation they already have, such as financial statements and a depreciation schedule. Then each year, they can enhance their description of the business. After several cumulative efforts, farmers will have constructed a complete description of their farm. Adult farm management instructors indicate that it is not uncommon to take three years to develop a thorough description and understanding of a farm business.

(2) Owners' interests and skills

The purpose of this step is for the business owners to get to know themselves, and other people critical to the success of their business. Key questions for each person to answer include:

• What am I currently doing?

• What am I qualified to do?

• What am I interested in doing?

Opportunities to modify or add enterprises could emerge as a result of completing this step. Similarly, this is a point in the planning process to begin evaluating nonfarm opportunities. In addition, owners should assess their individual willingness to assume risk.


(3) OWners' expectations about the future

The third step in the business planning process is a chance for owners to consider external factors that influence their farm. These factors include prices, costs, environmental legislation, government policies, technological advances, and consumer preferences. Likewise, the owners and their families could address more local factors that impact the quality of life, such as living costs and community amenities.

Determining Where the Farmer Wants to Be

(4) What the owners want to accomplish

This step urges fanners to establish and prioritize personal and business goals to guide them in their decisions. A meaningful goal is specific, measurable, challenging but realistic, time specific, and reflective of key result areas. Co-owners of a farm business rely on careful thought and forthright communication to collectively establish and prioritize their business goals. Likewise, fanners can reconcile their personal goals with their business goals. For many fanners, a primary goal for the business is to generate all or a portion of the income they need to meet their living expenses. This step can result in specific goals and a mission statement for the business.

Identifying, Selecting, and Testing Alternatives

(5) Projecting future performance of the current farm

The fifth step of the process involves projecting whether the current farm, without change, will meet the needs and fulfill the goals of the farm owners in the future. The analysis is accomplished by using the description developed in Step 1 and the long-term expectations specified in Step 3 to project future performance.

Saxowsky, Gustafson. and Crane 103

Responses to the following questions help determine whether the current operation will be acceptable in the future:

• Would the current business, with no changes. be satisfactory five years from now?

• Would the business be profitable?

• Would the business generate the necessary or desired cash flow?

• Would the owner equity be changing as desired?

• Would the business fulfill the owners' personal and business goals (Step 4)?

• Would the business permit the owners to pursue activities at which they are skilled and in which they are interested (Step 2)?

• Would the risk exposure align with the business's capacity to assume risk (Step 1) and the owners' willingness to assume risk (Step 2)?

If the answers to these questions are yes. the owner is ready to implement the plan (the outer circle illustrated in Figure 1). At this point in the process, the owners will want to review their functional plans (e.g., production. marketing, financing, labor management, and others) to assure that they are up to date and adequate.

If the answer to any of the questions is no. the owners should consider "taking a spin around the inner circle" to identifY and test alternatives. The owners may also want to describe their understanding of how the current farm is inadequate: i.e., what is the problem that they hope to resolve by adopting an alternative?

(6) Identifying and testing alternatives

This step involves identifYing, describing, and testing alternatives for farm managers who determine that the current farm


needs to be changed. The owners likely have a wide range of alternatives to consider. Alternative commodities, marketing strategies, financing arrangements, labor strategies, equipment ownership, and a career change are only a few of the types of options and opportunities a farmer may have. The criteria for testing an alternative are the same as for Step 5, and additionally ask:

• Does the alternative have an acceptable impact on the owners' level of risk exposure?

• Does the firm have the resources necessary to implement the alternative?

• Are the legal ramifications of adopting the alternative acceptable?

• Does the alternative address the problem that led to the search for the alternative?

(7) Developing transitional plans

In this step, the owners consider how they will transform their current business into their desired business. Such a transition will likely require a series of steps that identifY which aspects of the business need to be changed. Also as part of this step, the owners will establish a time frame for accomplishing the changes, and an understanding of necessary financial resources.

Steps 6 (identifYing and testing alternatives) and 7 (developing transitional plans) are drawn on the inner circle of Figure 1 to indicate that they may need to be repeated several times during a planning cycle. Each time the owners reach the point where the inner circle touches the outer circle, the decision criteria need to be applied. When all the answers are favorable, the owner is ready to proceed to implement the plan (the outer circle).

Some alternatives may require that the owners return to several other steps from the outer circle before proceeding to the development of an alternative to be tested on the inner circle. For example, they may want to revisit the operating environment (Step 3) to learn more about, or reconsider, factors that could impact the viability of producing an alternative crop.

(8) Constraints that may prevent implementation and contingency plans

This step addresses risk management; that is, the business owners identifY and prepare for events that significantly interfere with executing the plan. A constraint can be negative, such as adverse weather, or positive, such as an unanticipated $20,000 revenue. This step provides an opportunity for the farm owners to consider likely constraints and a method to manage them.

With this step, farm owners identify the resources, assumptions, and management expertise that impede the farm business from fulfilling the owners' goals. The cost of eliminating the constraint will be compared to the benefit of eliminating the constraint. An aspect of identifYing constraints also is to devise contingency plans.

Implementing the Plan

(9) Monitoring progress

Monitoring and control is a process of measuring performance and taking action to assure that the business is on track to meet its goals. In looking to fulfill longterm goals, owners realize that there are more Immediate objectives that need to be met. The purpose of this step is to develop a control process to take advantage of opportunities to improve.


(10) Documenting, sharing, and revising the plan

Documenting. Although farmers may be reluctant to write down their thoughts in the long-term planning process, developing a document helps. Writing clariftes thinking, identifies issues that otherwise may be overlooked, and prevents a difficult or detailed decision from being postponed.

Writing also reduces "selective recall" which can cause problems when family members or partners have different recollections of a discussion. Further. written documentation provides a record of the farmer's actions and thoughts-a record that can guide others if the farmer is not present when a decision needs to be made.

Sharing. The information resulting from the planning effort is private: it belongs to the business owners and their families. They choose how to organize it and with whom to share the plan. They do not need to share their ideas with anyone. However, the added confidence that the owners feel as a result of accumulating the information and completing the planning process often may convince them to share portions of the plan with others (such as a lender or landlord).

Revising. Business planning is not an exercise to be completed once and then forgotten. It is a process that is repeated-repeated at regular intervals, or more often if the need arises. Various farmers describe the process as "ongoing" and "never intern1pted." In the words of one farmer, "At times you focus on planning and take the time to record your thoughts and computations, but the rest of the time while operating the farm, you are almost always thinking about the longterm future and planning for it."

Summary of the Process

Business planning is a repetitive process. The starting and stopping points are not


always clear. After the initial run through the planning process. farmers can select any point at which to "hop on" and proceed to "go around" the planning circle.

The Farm Business Planning Manual

The educational materials are published as a manual in a three-ring binder: new information or documents can be added, obsolete materials can be discarded or replaced. and farmers can include other documents or references they use in their planning process. The manual is divided into several sections. each one addressing one step of the planning process. Each section contains an introductory leaflet that explains the purpose of the step. provides information and analytical tools that can be useful in completing the step. and specifies what is accomplished by completing the step.

Each section also includes a worksheet to guide users by posing questions or indicating the type of information that is necessary. However. many farmers will modifY the worksheets to meet their individual needs or situation. Most sections include one or more appendices to provide additional information about questions that may arise in completing the step or relevant analytical procedures.

Developing the manual raised several questions. One question concerned the appropriate content length of the leaflets. Panel members urged that the leaflets be relatively concise, focusing on basic ideas. Their reasoning was that lengthy documents tend to discourage readers. and still do not address all the questions that alise in the "real world." A second question was the level of detail that should be presented in the worksheets. Again, an initial theme was to keep them short. However, the reaction of workshop participants and college seniors was that additional detail was helpful. Based on these results, later versions of the


worksheets (especially the business inventory step) are more extensive.

A third question was how to provide an overview of this extensive process. Three introductory pieces were developed. First. a glossy pamphlet was developed to introduce the topic. It briefly identifies the need for and benefits of long-term planning, summarizes the 1 0-step process. and answers a few common questions about the process. This pamphlet is used to promote the manual and the training workshops. Second, a one-page overview was developed and is included as an introduction in the manual. Third, an executive summary of each step is printed on the tab divider sheets in the manual.

Points Emphasized During the Educational Programs

The following points are emphasized during the educational programs to help farmers feel more comfortable about longterm planning:

• Business planning focuses on the overall purpose and long-term goals of the farm operation.

• The business planning process helps farmers understand the relationship between the farm and the family that depends on income from the operation to meet all or part of its living expenses.

• Business planning does not replace, but helps organize, other planning efforts; business planning can be thought of as a framework for organizing and directing shorter-term planning efforts.

• The process of business planning facilitates discussions among the farmers, their families, and others; effective communication within the business and family is a necessity.

• Business planning cannot be accomplished if a spouse, partner, or

anyone else whose desires, decisions, and actions directly impact the farm operation has been excluded from the process.

• An important aspect of business planning is to link the progress of the farm to the activities and goals of the family.

• Business planning can help farmers prepare to work with lenders, landowners, or other business associates.

• The educational materials suggest how farmers and their families might organize their thought processes. It does not hand them a business plan, nor does this effort develop one business plan for all farmers to use--each farm business requires its own unique plan.

• A computer is not necessary for business planning, but farmers may find that a computer can help in organizing (and reorganizing) their thoughts and completing numerous computations.

• Writing is vital to the planning process for farm owners because it fosters critical thinking. A written record of thoughts needs to be developed despite concerns about writing down a vision for a future filled with uncertainty.

• Planning may leave the farmer and family feeling frustrated because they do not know all the answers, have all the necessary information, or understand how to use the information they possess. Farmers are urged to not despair, and are reminded that repeated efforts almost always pay dividends.

• At times, farmers and their families may not like what they discover about themselves or their business through this planning process, but it is better to identify as early as possible what may


not work in the future and take appropriate action now.

• Planning is hard work, but it can be enjoyable because farmers and their families are working together to create a better tomorrow.

Dissemination of Planning Materials

The developed material was taught as the curriculum in a senior-level class on campus, beta tested with a small group of individual farmers, and taught in a series of pilot workshops. The execution of the educational effort included promotional news releases and bulletins to the popular press. summaries on the Data Transmission Network (DTN). introductory sessions over the North Dakota Interactive Video Network (IVN). and in-service training sessions for lenders, county agents. and adult farm management instructors. These activities were augmented with radio and television interviews. and feature articles in regional and national newspapers and magazines. Similarly, the materials were the subject of


a symposium and poster session at the 1994 AAEA meetings. Consequently, the Farm Business Planning Manual is being used by individual producers in more than 35 states.

The producer workshops are conducted as two one-day sessions at least a week apart. An important component of the workshops is the use of breakout sessions into small groups. where the participants reinforce their understanding by discussing the concepts and exchanging ideas. The written evaluations of these workshops reveal the extent to which the objectives of this extension program are being met (Tables 1 and 2).

Initially. one goal was to have the farmers develop a business plan during the workshops. For example, several periods of time were set aside for participants to write their goals, future expectations. or a self-assessment. But these times were not productive; participants were not ready for such activities. Instead. they seemed more interested in discussing the planning process. The participants' reluctance to write may have been due to one or more of the following factors:

Table 1. Mean Response to Evaluation Questions (based upon 155 completed written evaluations)

Evaluation Questions

The objectives of this session were (7 = clearly evident. l = vague) The stated objectives were met (7 = strongly agree, 1 = disagree)

The structure/format of the session was (7 =excellent. 1 =poor) The meeting facilities were (7 = excellent, 1 = poor)

The work of the presenters was (7 = excellent, 1 = poor)

The workbook material was (7 = excellent, 1 = poor)

The in-class exercises were (7 = excellent, l =poor) My attendance at this session should prove (7 = very beneficial,

1 = of no value)

Overall, I consider this learning exercise (7 = excellent. 1 = poor)

Mean Response

6.05 5.89

5.88

6.09

6.35

5.68

6.05

6.06

6.09


Table 2. Selected Comments from Workshop Participants (based upon 155 completed written evaluations)

Give an example of something you gainedfrom this session that you can use on your farm:

• It provided an outlined, step-by-step, organized process of actually how to assess our farm business. set some goals, choose some alternatives to some of our operations, and monitor the entire process.

• Using enterprising methods to help make decisions.

• It will help me set a framework to develop a better business plan.

• I have gained an appreciation for the need to communicate. plan, and set out goals for the future.

List what you consider the major strengths of this session:

• Structured planning process-workbook to help achieve planning goals.

• The format, focusing on process! The group interaction, not getting into individual farm business financial matters. Privacy respected.

• Making you think and plan, the interaction with other farmers from a fairly wide area.

• The whys-to really think of why.

List what you consider the major weaknesses of this session:

• Need more specific examples.

• Could be spread out over another day-a lot of material and information to absorb.

• Would like to see overheads in the notebook (but then I'd write less and remember less-right?).

• I believe we will need some refresher courses to keep active and up to date.

Additional comments:

• Put this information on computer.

• I would like to see more examples.

• How do I communicate sensitive topics, and what do I do if the response seems indifferent?

• It would be helpful to have a follow-up session within the year.


• an unwillingness or discomfort with the level of effort and skill necessary to write their thoughts;

• limited room or table space at the workshops;

• not having previously thought about some topics (such as expectations for the future or self-assessment). and not having enough time during the workshops to develop their ideas fully;

• not having brought their business records to the workshop;

• their sense that it was a better use of time to talk with others than to work individually (i.e., they could work individually in other settings, but this was an opportunity to talk about planning with others); or

• their concern that not much could be accomplished in that setting because not everyone from their business or family was in attendance at the workshop.

Based on these early experiences, there is no longer an expectation that much writing will occur during the workshops. Instead, the workshops focus on more general discussion of the process and issues that may arise during planning. Instructors work to facilitate discussions with and among participants, rather than rely on lectures and writing exercises.

Discussion was hard to stimulate at one workshop because participants felt uncomfortable (and they stated so) that their neighbors/competitors were in attendance. Thereafter, instructors paid close attention to registration so they would be aware if "too many" attendees were from one community. The instructors also assured participants that they could meaningfully contribute to the discussion without revealing private information. However, a few participants


volunteered some candid comments about themselves and their business.

Due to the time constraint, the workshops were able to emphasize only the first several steps of the process-business inventory, self-assessment, future expectations, and goals. A major theme of the discussions was enhancing communication within the family and business. Participants also were interested in risk management and identifYing altematives, although time did not permit these topics to be fully developed and discussed during the workshops.

Despite the emphasis on discussing the planning process. participants were assigned the task of working on the first four steps between the two workshop sessions. There was wide variation in the effort made by participants, ranging from one farmer who said. "I've worked with my sons every day for 20 years; I know them, I do not need to do this"-to another farmer who stated, "I talked with my sons this past week and leamed some very interesting things that I never knew before."

The response of participants was encouraging. For example, several individuals brought to the second day of the workshop their spouse or business partner who had not attended the previous session. This seems to indicate that progress was made in helping farmers with their planning process.

A number of workshop alumni inquired about having a follow-up or "graduate" level program to help them move their planning activities to a higher plane. There also have been a number of requests for seminars on specific planning topics that were introduced in the workshops, such as labor management or transferring the farm assets/business. No follow-up workshops have yet been developed, but they would provide an opportunity to assess the extent to which participants are


using the planning process. No other follow-up has been conducted at this time.

Due to the nature of planning and the number of farm businesses, workshops cannot be the only (and perhaps not even the primary) delivery mechanism. More individualized attention should be considered, such as that provided to adult farm management instructors. Therefore, an important part of the educational effort has been and likely will continue to be "training the trainers," who then can explain the process to farmers and assist them in their planning efforts.

Despite requests, there are no plans at this time to computerize the planning materials. The materials are intended to be thought-provoking, and they are focused on posing questions to stimulate long-term visions. The computer's contribution to that end is not critical; it is only a tool that helps users organize and reorganize their thoughts. Similarly, the materials do not present a unique method of mathematical analysis; instead, farmers are urged to continue using and enhancing their current records and methods of analysis as one part of the overall planning effort.

Currently, a cooperative effort among the sponsors (Farm Credit Services and AgriBank) and extension faculty from 11 states is underway to develop video tapes to assist in delivering ideas about longterm business planning. Again, the goal is to present a process that farmers can use in conducting long-term planning. The tapes will introduce a planning process, supplement existing written materials, and support workshop instruction. They will not provide farmers with a plan or substitute for more complete training programs.

Conclusion

The need for information about long-term business planning has been recognized by

many throughout the agriculture industry. By focusing on the benefits of the thought process (rather than just on the document). agricultural instructors are able to help owners of farm businesses prepare for the future.

Business planning is a thought process wherein farmers assess their current situation. specify their goals, identify and implement alternatives for reaching their goals, and monitor their progress. The emphasis is on the long-term goals farmers and their families set for themselves and their business.

Farm business planning is a process that could become a routine procedure for helping farmers think about the long-run implications of immediate actions. The process also assists farmers in formalizing their long-term vision and provides a structure for organizing their ideas. Planning often facilitates communication, reduces the temptation to minimize or delay detailed analysis, and helps to answer the question "what should I do now?" Business planning is the process of mapping the future to assure that the destination is reached-whether the farmer is just starting, wants to maintain an existing business. or is retiring.

References

DuBrin. A.J., and R.D. Ireland. Management and Organization, 2nd ed. Cincinnati: South-Western Publishing Co., 1993.

Gustafson, C.R., D.M. Saxowsky, L.M. Crane, and J. Samson. "Farm Business Planning." Dept. of Agr. Econ., North Dakota State University, Fargo, 1995.

Harsh, S., and J. Shaltry. "Agricultural Management Advancement Program (AMAP)." Dept. of Agr. Econ., Michigan State University, East Lansing, 1994.

Ex:it Interview with John R. Brake* AFR Staff

Explanation

Effective with this 1996 issue of the Agricultural Finance Review, John Brake steps down as editor I co-editor after 14 years. Concluding his professional career of 37 -plus years in agricultural finance, John is retiring from Cornell at the end of this year. We thought it would be of interest to readers of this publication to get John's responses concerning his work with AFR and his years in agricultural finance.

-Eddy L. LaDue AFR Co-editor

• John R. Brake Is the W.l. Myers Professor of Agricultural Finance at Cornell University and has served as editor or co-editor of the Agricultural Finance Review since 1983.

AFR: You've been editor or co-editor of the Agricultural Finance Review since 1983. Tell us how this relationship came about.

JRB: I had moved from Michigan State to Cornell University at the start of 1981 to accept the W.l. Myers Professorship of Agricultural Finance. While attending a regional research committee meeting in 1982. Dave Lins (who was then with the USDA) told me that the USDA was undergoing reductions in funding-and in response, they were reducing the number of their publications. AFR was on their list to be discontinued. Given that Cornell had an endowed chair in agricultural finance that provided some degree of permanence to agricultural finance as an area of emphasis, Dave asked if we would be willing to take over the responsibility for, and publication of, AFR. Doing so seemed to me to be a reasonable undertaking for my position as well as a service to agricultural finance professionals.

One concern. of course. was how to cover the costs of publication. To encourage Cornell to take on the publication and to help with its costs. the USDA agreed to provide $3,000 per year for an unspecified (but since expired) period of time. As a second component of covering costs, we implemented page charges. Another consideration was that the William I. Myers estate had provided for an endowment to Cornell University for the purpose of furthering agricultural finance "collections." I expected to be able to draw upon those endowment earnings, if necessary, to help support the publication of AFR.

The USDA had published AFR for over 40 years prior to Cornell assuming responsibility for its publication in

112 Exit Interview with John R. Brake

1983. While AFR was housed at the USDA, articles were primarily "invited" manuscripts. We decided instead to make the publication a refereed journal. Since its relocation to Cornell, it has been refereed except for an invited feature article here and there in recent years, but even those were subjected to external review. And of course, in 1987, a special AFR issue-Financial Stress in Agriculture: Issues and Implications-was published containing all invited contributions.

AFR: You indicated that AFR had been published for over 40 years at the USDA. Could you give us a bit more history on that?

JRB: The publication was started by Norman Wall and Fred Garlock in 1938. Wall and Garlock were long-term USDA employees whose primary focus throughout the years had been agricultural finance. Back in the 1960s, I had met them both. At that time, I was considering a position in the USDA and had visited with Wall, then Chief of the Agricultural Finance Branch. Garlock was the USDA's representative on a USDA/Michigan State University cooperative research agreement in which I participated during the 60s.

I should add that a couple of years after AFR was transferred to Cornell University, Fred Garlock told me that he had learned of the transfer from USDA to Cornell and asked why we had been willing to take it on. I told him I just couldn't let it die. He seemed quite pleased at that.

AFR: You were one of the early members qf the second generation of agricultural finance economists. What was it like in those early days?

JRB: While Wall and Garlock were active in agricultural finance in the USDA, there was relatively little emphasis on agricultural finance at universities. I don't mean to say that there weren't faculty with interests and emphasis in

agricultural finance. Those faculty, however, were up in years and few students were attracted to the area. The "hot" areas in the 1950s were marketing, production economics, and agricultural policy. And of course, agricultural development exploded on the scene in the early 1960s.

I was hired by Michigan State in 1959. In those days, job descriptions, the recruitment process, and the hiring of personnel were much different than today. Search committees were rare to nonexistent. The department head made contacts with various prospects, often keeping track of those of interest throughout their graduate studies. The typical approach was to search for a promising prospect with interest in a given broad area of the department rather than looking for someone to fit a detailed job description. Since I had been an undergraduate student at MSU, I knew several of the faculty from having had them as instructors in classes. One of those was Larry Boger, who was then Head of the Department of Agricultural Economics at MSU. (They weren't called "chairs" in those days-and for good reason.) He kept in touch with me throughout my graduate work.

The day I began my career at MSU in 1959, Larry Boger showed me my desk, told me to finish anything remaining to be done on my thesis, draft a publication or two from the thesis, and think about what I wanted to work on at MSU. In short, I was not hired into agricultural finance-but I knew I'd like to teach. The first retirement I could see coming along in the department was that of E. B. Hill, who was teaching the agricultural finance and farm real estate appraisal classes. So, even though I was hired in a production economics slot vacated by Dean McKee, I chose to work in agricultural finance. Within the next year or two, whenever I saw some of my friends from graduate school days, they questioned why I had chosen agricultural finance. I think one of

Agricultural Finance Review, VoL 56, 1996

them even said, "It's a dead area." As it turned out, of course, in the 70s and 80s, agricultural finance was about as interesting as it gets. But in the late 50s, agricultural finance was indeed pretty quiet. By the 1960s, however, Chet Baker at Illinois was attracting a number of students to the area, and MSU was getting its share of students as well.

AFR: What made the 1970s and 80s so interesting?

JRB: Agricultural finance issues came to the forefront in the 70s. Many of us became concerned about cash flow, debt levels, and the repayment capacity of agriculture. While we wondered how the situation could continue, few of us expected the degree of financial problems that hit the sector in the 1980s. We'd studied about the events of the 30s. but we just hadn't personally experienced anything of that magnitude.

What an interesting time it was to be an agricultural finance economist. though. There were opportunities to serve on task forces, almost daily calls from news media. and many of us saw our names quoted in national newspapers. There were almost more invitations than we could handle to speak to farmer and other groups to provide analysis and assistance in developing an understanding of farm sector finances. The 1980s provided opportunities to assist in analysis and design of new programs and policies. New financial institutions were developed, and major changes evolved in the structure of existing institutions and in lending practices.

For example, there was some discussion in the early 70s concerning possible establishment of a secondary market. Some of this began within the American Bankers Association, and it was also addressed in the Board of Governors· 1975 publication on "Improved Fund Availability at Rural Banks." Ray Doll discussed the idea, too, in the 1980

AFR Staff 113

publication, "Future Sources of Loanable Funds for Agricultural Banks." Still, I hadn't expected to see establishment of a "Farmer Mac" during my career. Nor would we have dreamed we'd see the massive changes that have taken place in the Cooperative Farm Credit System. And. while many of us had read of foreclosure moratoria in the 30s, we hadn't anticipated we would see a repeat of such times. The last 20 years have been very interesting and very exciting times.

AFR: Looking back over these last 37 years. what changes do you consider most striking?

JRB: Certainly. as I've already noted. a major change has taken place in hiring practices, job descriptions. etc .. with respect to Ph.D.-level prospects. Probably one of the most discouraging changes I've seen has been the loss of support for higher education and the downsizing of educational institutions now taking place in our country. At a time when the need for information. analytical ability. and communication are at an all-time high. we are downsizing the very institutions providing those goods. We will come to regret the downsizing and trashing of our educational system, in my opinion.

Another change that concerns me is the reduced support the USDA receives for maintaining our national database on the farm sector. Continuing reductions in real funding are undercutting the quality and extent of coverage of sector data. One of the lessons we should have learned in the 80s crisis is that it's too late to begin collecting data after they're needed to answer policy questions. We'll repeat that lesson at some point in the future.

For an agricultural finance economist. one of the high points of the past 37 years is to have seen four of our numbers nominated for president of the American Agricultural Economics Association, and to have seen two of the four elected to that office. Chet Baker and Peter Barry were fine presidents


and brought credit to all of us. On the other hand, John Hopkin and I share membership in an even smaller, exclusive club made up of nominated-but-notelected presidential candidates.

AFR: What do you see as some of the highlights or contributions of your career?

JRB: Let's face it. My contributions were modest, and I'm not headed for the Hall of Fame. Still, I won some battles and made some contributions. One early high point came from publication of "The Interest Rate Calculator." co-authored with Mike Wirth. It went through several printings of over 10,000 copies each. Later came an MSU publication (Agricultural Economics Research Report No. 13) dealing with interest rate calculation. It attempted to get agricultural finance economists into the mathematics of finance, and it grew out of a rejected AJAE submission. (One reviewer's main argument against publication of the manuscript was that it "wasn't the way credit unions and lenders calculated interest." If I could have the last word-It IS today!) That was a popular report, and it was still being requested 15 years after its publication.

Certainly the joint authorship with Emil Melichar of the MEA-commissioned literature review of agricultural finance has to be a highlight. And, like many others, I served on some AAEA committees including the Finance Committee and the Foundation Board. The nomination for AAEA president was an honor regardless of the outcome. Appointment as the W. L Myers Professor of Agricultural Finance at Cornell was another professional honor and, of course, that led directly to my becoming editor I co-editor of the Agricultural Finance Review.

Two efforts in the 1980s were particularly satisfying. One was my participation In the establishment of the New York FarmNet Program. A year or so later, I

helped establish the Farming Alternatives Project at CornelL The FarmNet program is described in detail in an article I authored In the 1995 AFR. Incidentally, the first two reviews on the piece were initially against publication. and it began to look like "my" journal would refuse to publish the manuscript. However, because the two evaluations were quality reviews with constructive suggestions on how to Improve the article, I reworked and resubmitted it. The second-round reviews came back substantially more positive.

My most unusual and interesting assignment was a short-term appointment as Special Advisor to Harold Steele, CEO of the Farm Credit Administration, in late 1989 and early 1990. That experience grew directly out of taking part In a national commission on agricultural finance.

Probably one of my major contributions is represented by the large number of my former graduate students scattered around our profession. They have made substantial and sustained contributions of their own. Also, the interaction with other agricultural finance professionals at regional committee meetings. working together on task forces or other collaborative efforts, and sharing authorship on various manuscripts have led to professional contributions as well as to substantial personal satisfaction.

AFR: Have there been any major disappointments in your career?

JRB: It may sound strange, but my major disappointment is not a personal one. On two different occasions, I contributed my efforts to the process of putting together materials nominating Emil Melichar as a Fellow of the AAEA In my estimation, his contributions in agricultural finance stood out. He not only published many articles In various journals, but he provided input to the Board of Governors of the Federal Reserve System in a way and with a


quality that was truly unique. I was disappointed that the committees charged with selection of new Fellows could not look beyond their personal differences with him to acknowledge his unique accomplishments and contributions. sincerely believe that Emil deserved selection as a Fellow.

AFR: Were there any frustrations or notable satisfactions in serving as editor ofAFR?

JRB: A source of much satisfaction has been the strong support AFR has received from agricultural finance economists all over the country and in all organizations. This strong support came both in the form of submitted articles and through a willingness by most colleagues to provide us quality reviews on manuscripts. Occasionally there was a nice comment by someone concerning the makeup of the publication, and those were much appreciated.

There have been some minor frustrations related to quality of reviews. or even unwillingness to provide reviews. by some colleagues from time to time. While a review request does take time and everyone is busy. it's an important professional contribution. I know I have benefitted greatly from reviews I've received over the years, so I've tried to give something back. One of my first professional lessons at MSU came from being asked to review a manuscript by a senior colleague whom I respected so much that I couldn't find anything to criticize. He never asked me to review anything again. A good, critical but constructive review is priceless.

AFR: What do you see in the future forAFR?

JRB: First. I expect Ed LaDue will assume full editorship, and the publication will remain at Cornell. Probably, in the near future, AFR will be made available through

AFR Staff 115

electronic publication. Whether It will continue to be put out In "hard copy" isn't clear-but even if it is, the number of copies will be minimal.

I'd like to see more articles on teaching and extension programs in agricultural finance. There seem to be two problems, however. One is that teachers and extension personnel submit few articles for consideration. The other problem is that most of us have difficulty applying nonresearch criteria when reviewing such submissions.

I hope, of course, that AFR has a long future. Financial problems of farm operators will certainly continue to be important, as will credit/financial policies of government and institutions. Agriculture is consolidating at a rapid pace, and financing arrangements surrounding those changes and associated policy issues should be relevant topics. The importance of the issues, the number of professionals in agricultural finance, and their willingness/ability to write for peers will ultimately determine the fate of the publication. however.

AFR: What do you see in the future for the agriculturalfinance profession?

JRB: Probably the number of people in the agricultural finance profession will decrease in coming years. not from irrelevance, but as a part of the general downsizing of government and academia. Economics, however. tends to be characterized by cycles. Agricultural finance issues will again come to the forefront at some point in the future. As far as key issues needing future attention, I'd prefer to leave that assessment up to others at this point.

AFR: What is the plan for refilling the W. I. Myers Professorship?

JRB: We don't know yet what will happen with the W.l. Myers Professorship.


I hope the department will undertake a national search for an appropriate person. But, in these times of downsizing of higher education in New York, the replacement strategy and process are not clear. If a new W.l. Myers Professor is hired, I'd expect he or she would become associated with AFR, probably as co-editor.

AFR: So, what are your plans now as you leave AFR and retire from Cornell University?

JRB: As for me personally, I'm looking forward to doing more traveling, spending substantially more time with my daughters and grandchildren, doing some nonprofessional writing, finding some worthwhile volunteer opportunities, and becoming reacquainted with my music library.

AFR: Any parting words?

JRB: At this juncture of my career, I'm struck with our profession's inclination to put great emphasis on models and modeling-especially in evaluating research for publication. That's certainly appropriate. Nevertheless, I'd remind our readers that a focus on real problems and good timing in working on such problems will, in the long run, be what keeps our profession useful and productive.

I'm pleased and proud to have been a part of the agricultural finance profession these past 37 years. The quality of people, the collegiality, the respect for each other, and the contributions of this group have been outstanding. Had I the chance to start over, I'd do it again.

I thank you all for your friendship and good will.

Guidelines for Submitting Manuscripts

We invite submission of manuscripts in agricultural finance, including methodological, empirical, historical, policy, or review-oriented works. Manuscripts may be submitted for the research, teaching, or extension sections. Papers must be original in content. Submissions will be reviewed by agricultural finance professionals. The final decision of publication will be made by the editors.

State in a cover letter to the editors why the manuscript would interest readers of AFR and indicate whether the material has been published elsewhere. Also, indicate whether the manuscript is being submitted as a research, teaching, or extension article. Please prepare manuscripts to conform to the following outline.

Title. Short and to the point, preferably not more than seven or eight words.

Abstract. No more than 100 words.

Key Words. Indicate main topics of the article.

Author's Mfiliation. Institutional affiliation appears as a footnote at the bottom of the first page of the article.

Specifications. Manuscript should not exceed 25 pages of typewritten, doublespaced material, including tables, footnotes, and references. Put tables and figures on separate pages. Provide camera-ready art for figures. Number footnotes consecutively throughout the manuscript and type them on a separate sheet. Margins should be a minimum of one inch on all sides. Please number pages.

References. List alphabetically by the author's last name. Include only sources that are referred to in the article. Within the body of the article, references to these sources should state the author's last name (year of publication only if two or more publications are cited by the same author) and be placed in parentheses.

Procedure. Submit three typewritten copies to the editor. After the manuscript has been reviewed, the editor will return review copies to the author with a letter stating whether the article is accepted, rejected, or needs additional revision.

Published articles will be subject to a page charge of $50 per printed page. If an author has no financial support from an employer or agency, an exemption from the page charge may be petitioned.

If accepted, we will require the manuscript on a disk.

Submit manuscript, complete with cover letter, to:

Agricultural Finance Review 357 Warren Hall Cornell University Ithaca, NY 14853-7801

Documents

AGRICULTURAL FINANCE REVIEW · 14/05/1996 · AGRICULTURAL FINANCE REVIEW Department of Agricultural, Resource, and Managerial Economics, Cornell University Volume 56 Preface Agricultural