FOREWORD

The Agricultural Finance Review is an annual publication of the Economic Research Service, U.S. Department of Agriculture. Articles report on concepts and research in a broad range of agricultural finance issues that relate to farm and rural finance; financial management and firm growth strategies; insurance; income; farm supply, processing, and distribution industries; financial institutions; taxation; rural economic development; and the organization of agricultural production. The Review is a collection of contributed articles which are presented in a nonmathematical, nontechnical manner to make them accessible to the widest possible audience. The current volume was compiled by John B. Penson, Jr., David A. L.ins, and W. Fred Woods.

Manuscripts and editorial correspondence relating to the Review should be addressed to Philip T. Allen, Economic Research Service, U.S. Department of Agriculture, Washington, D.C., 20250, or to David A. L.ins, Economic Research Service, 305 Mumford Hall, University of Illinois, Urbana, Illinois 61801. Manuscripts should be submitted in duplicate and should not exceed 20 pages in length including tables, footnotes, and references. Manuscripts should be double spaced with tables and figures on separate pages. Number footnotes consecutively throughout the manuscript, and list them on a separate page after the text. Please state in a cover letter why your manuscript would be of interest to the readers of the Review, and indicate whether the material has been published elsewhere.

Publication of the conclusions and viewpoints expressed bytheauthorsdoesnot necessarily represent an endorsement by the U.S. Department of Agriculture. Articles may be quoted, reprinted, or abstracted without authorization; however, citation is appreciated. Comments from readers are welcomed.

A VARIABLE AMORTIZATION PLAN TO MANAGE FARM MORTGAGE RISKS

h:v C. B. Baker1

ABSTRACT

A plan is proposed to index farm mortgage payments with respect to prices and yields. The plan also includes a mandatory debt reserve and amortization insurance. Net cash flows after debt service are compared with those of a conventional farm mortgage. Variants of the plan and its application are discussed.

KEYWORDS: Amortization, debt reserve, risk management.

Farm prices have become more volatile and risk management is a much greater concern since 1971, when farm eredit legislation 14] increased the maximum Federal Land Bank loan commitment from 65 to 85 percent of appraised value. Prices received by farmers have fluctuated more than prices paid, and crop reserves have been depleted. Government programs were minimized by the Agriculture Act of 197:l, and the value of U.S. farm commodities sold in foreign markets soared. These changes tended to destabilize farm prices.

At the national level, farm income is much higher today than it was in 1971. But with volatile farm prices and much higher costs of production, the individual farmer's hazards have increased, detracting from his standing as a borrower of capital. This paper proposes a way in which agricultural lenders can offset the greater instability of a borrower's income by widening the lender role in agricultural risk management. This plan could improve the capacity of financial markets to absorb financial risks of farm borrowers 11 ].

THE ROLE OF FINANCIAL INTERMEDIARIES

Financial intermediaries play an essential role in allocating capital over space and time. Markets have evolved for exchanging financial assets. They act as financial intermediaries for their customers, much as do commodity markets. Financial markets provide the basis for transmitting information and for equating margin returns among individuals and firms that have differing uses for assets. Financial assets vary in terms of maturity, risk, and liquidity. Most savers need liquidity and risk levels that differ considerably from the needs of borrowers. Hence, savings require intermediation before they are compatible with properties of farm loans. Risks associated with farm loans exceed the low risk tolerances of most savers, so risks also require intermediation. Finally, long mat uri ties are required

'Professor of agricultural economics, University of llhnms at Urbana-Champaign.

1

for tolerable repayment schedules for borrowers. In short, farm loans have limited marketability. To widen their market, extensive financial intermediation is often needed in terms of liquidity, risk, and maturity.

As in other marketing processes, the intermediator expects sufficient returns on the committed resources. Returns are generated for the intermediating maturity, risk, and lfquidity differences between savers and borrowers. For example, in the Farm Credit System, the riskt; for debt instruments offered by borrowers are modified by pooling the financial assets and by lenders when they jointly assume the obligations to buyers of Federal Land Bank bonds. Maturities are also intermediated through this arrangement so that :30-year farm loans ean be made with money from the sale of Federal Land Bank bonds with maturities of 10 years and less. In practice, liquidity intermediation is less extensive, because institutions

buy and hold many bonds of large denominations. The management of intermediation is an essential

task of farm-related lenders. Lenders cannot assume risks comparable with those of the borrower because of margins earned in a competitive market. Lenders do not share in the equity gains and losses of borrowers. To meet their obligations to investors who furnish loan funds, lenders must be assured of a l->1.eady flow of repayments. If the safety of the principal or reliability of repayment shows any perceptible increase in risk, then interest paid to buyers of f]r;,meial assets in secondary markets would he quickly increased. In turn, higher interest rates would be charged to farm borrowers. In addition, an increase in margins certainly would he required for lenders to meet the higher costs of intermediation. Accelerated increases in land values, heightened variability of prices received by farmers, and increased loan commitments as a percentage of land values can change.~

A Variable Amortization Proposal

The Federal Land Hanks have long had a voluntary plan under which the farm borrower had the option of paying into a reserve any excess over his amortization commitment. The plan is seldom UHed because thl• farmer can realize higher returns by using the excess funds in hiH farm operations. We propose a plan that adds three new features: the plan would he mandatory, it would provide an index procedure for determining the amount of excess required or the amount of deficit permitted for the borrower, and it would include an amortization insurance element not previously ineluded.

Many farm lenders say that in fact, if not in contract, farmers now have a variable amortization commitment. If earningH fall because of conditionH beyond the farmer'H control, the amortization payment can be extended or otherwise temporarily modified. In yearH of surplus they can pay in multiples of their amortization commitment. Others say that such informal modifications subject the horrowl'r to an undetermined financial riHk and the h·nd!'r to addPd costs of intermediation. Multiple payment doeH not eliminate next year's obligation. The borrower must manage the same risk, and the lender may havP added capital requirements and loan management problems. A mdhod is needed to reduce the borrower's uncertainty and the intPrml'diation costs as well. The method propoHed hen· centPrs on thP use of a borrower's debt reHerve and on amortization insuran<·<• paid for by the

"<:enP Swaekhamt~r Haid that FPderal Land BankH t•xpeett>£1 to borrow $~1 billion in 1!17!"> to finance a loan volume that haH increaHed hy more than ~:,o perc<'nt in livl' years. On April~. 197!), the system paid 7.4f> p1•reent on bonds sold with a 1!177 maturity and H.IG JH'rCPnt on thoHP with a 19H~ maturity.

2

borrower. The plan is best described by a simplified example.

Consider an owner of 50 acres in 1966. At$1,015per acre his land was worth $50,750, and he also owned other assets worth $79,:3:30. He owed an intermediate term debt of $10,000. His net worth was $120,080. With :no acres which he rented, he netted about $12,000 per year after taxes and intermediate debt servicing. At age :30, his future expectations were reasonably good, and he wanted to buy more land. He had an opportunity to buy 120 acres in his neighborhood at the price of $1,015 per acre, or $121,HOO.

To evaluate the purchase, he examined his household requirements. These costs were about $H,OOO per year and showed little chance of easing with a family growing in size and wants. 'rhus, he could expect a residual of $4,000 from his current operation. The GO acres he inherited, his other assets of about the same value, and the residual makeup the substantial capital accumulation already reflected in his 1966 net worth. The added 120 acres would produce an estimated $G,OOO per year, after taxes and debt servic-ing on the $10,000 added intermediate debt for financ-ing a largerlineofmachinery. After buying the farm, his assets would increase to $261 ,HHO and debts to $141,HOO. He would pledge the 50 acres he ownH to the mortgage lender in lieu of a cash down payment. He would then owe the lender the full land purchase amount of $121,H0().'1

Hence, the lender would be asked for a loan equal to 70.6 percent of the market value of all land pledged. This amount exceeds the commitment allowable for Federal Land BankH in 1966, but it is well within the HG percent limit allowed after the Farm CreditActof 1!171. The problem is to eHtimate the effects on the borrower and lender of a heavy loan commitment. Given the hiHtory of the loan applicant, the loan would probably have been advanced if current lending ruleH had been in effect in 1966.

In 1966 the interest charged on farm mortgage loans waH about 6 percent per year. The annual amortization on the $121,HOO for a maturity of 20 years would be $10,619 (rounded to the nearest dollar); for 211 yearH, $9,G2H; for :30 years, $H,H4H; and for :lG years, $H,401. The borrower's expected income after houHehold requirements, taxes, and added debt service for machinery would be about $9,000. Hence, he would repay a loan with a maturity of :30 years with a maturity of:lO years, but a maturityof:3Gyears would provide a greater margin for flexibility. The

'If the loan had been from thl' Federal Land Bank. thl' borrower would need to buy stock equal to live pPrcent of th~· loan. lienee, the total would have been $12H.~JG, or 7~ .. 1 rwrcent of the value ofpl!'dged land. The stock purchasP was not induded in the example. This purchase would incrPaHI' th!' amortization commitment, thus increaHing thl' variation of any cash flow afterdebtserviceand thPrelatiVI' value of the variable amortization plan.

$H,40 1 per year is used to illustrate the operation of the variable amortization plan.

With this repayment plan the amortization commitment could absorb most of the borrower's disposable income after household expenses. If his expectations are realized, he could meet each payment when due and accumulate equity as the loan is retired. If his expectations are exceeded, he would have a surplus. If his expectations fail by more than $ti00 he could not meet the payment without borrowing or reducing household outlays.

II e should have bought the 120 acres in 1966, because land prices have since increased. How he would have met the amortization commitment is an important concern. Had he bought the farm in 1969 at a higher price, he would have faced the sharp decline in corn yields in 1970. Had he bought the farm in 1970 at a still higher price, he would have faced a sharp decline in corn prices. But had he bought it in 1972, he would have enjoyed a good crop year and increasing prict>s in the first year, perhaps sufficient to build a n·sPrvP for bridging later periods of distress. Clearly tiH' farmer'H welfare is heavily inf1uenced by eventH ovc>r which he has little control, eHpeeially in early v<'ars after a largt> investment. . l'rict>s and yields for corn and soybeans are summarized in table 1. Indexl's of prices paid for inputH also are Hhown. For simplicity the cropland is· dividt>d Pqually between corn and soybeanH. Hence, lh<'y are Pqually weighted in the adjuHting equation tlwt accountH for both priceH and yields. Variations in household and tax costs also are taken into a('('Ount. These too affect the incomP available for mortgagp debt Hervice.

An incomt> index (l) iH calculated on the baHis of pri<'<'H r<•ceivPd (Pi{), prices paid (PP), and yields (Q)

and iH givt>n in tablt> 1.

.5PRt (corn)+ .5PRt (soybPans)

.GPPt-l (E)+ .13PPt-l (T) + .27PPt-l (II)

(corn)+ .5Qt-l (soybeans) I

In the denominator, !<:is the price index for farm inputH, Tis tax, and His the price index for houHehold inputH. The numerical valueH are weights. Values in

the numerator of the price ratio equal 1.0, as do those in the denominator and in the quantity term. The weights reflect simple assumptions, respectively, that corn and soybeans contribute equally to income net of farm expenses; that 60 percent of the value of sales goes for farm inputs, I;~ percent for taxes, and '27 percent for household expenses; and that corn and soybeans are given equal shares of production resources. Clearly the weights arC' subject to refinement and to modification on the basis of empirical research. In an operational lending program the weights likely would bt> set at levels conHistent with farms typical of selected area, size, and type groups.

The adjustment index, I, is given for each year in the first column of table '2. In the second column, the

Table 2-lncome index, predicted income, indexed debt service requirement, and amount to dcht reserve for a

!'.ash yrain farm, 1966-73 (35-year loan).

1 ')(,I I 'l<>8 I !)( J ~)

l 'J I 0 I 'J I 1 I'll? l 'll J

Vcur

. T . . . . . I

1 lncorlll~ /\lllfltlnt requ1recl for

r lnc()tiH~ after tax ~

ttlCICX illlCI IJet>l

IHHJ~.c!Jold 1 I servicr! 1

H').{J 1 0?.')

()O.lJ

1 ??.? 8(>.?

1 tl'J . .J ?08.1

--- --··-·--· -i-.

1.8,0 I 0 <),?II I

B,IS 1 I O,'J'l8

I ,I '.JH I .J,tiJ I 1 B ,I fl.l

$1,471 H,fJ41l

I,(, 10

lO,;>(Jt,

1,?4 I

I !/J4? 11, 1->J/

I Uet>t r c:t..et vc L.

()

$24J ()

1 ,8(J 11

0 4,14 I 'J, 1 J I

1 lncotne Hldcx tnullli>''(~d llV ~f,(},O()() (r(HJIICI(•d tn !leilll."·,t

thlll;n ii1H1 diVtCI<Jcl tJy I 00. ·' IIIU)IIIf~ rncl1~>< rrntl\iplwd LJy $H,t10 1

anrJ dtvtdr•d bv ·100. 1 1~~JtJtlllr~d ''" rJf"'!Jt ...,,rvt<-l'IP.'i<-, ~.H,/101: 0.

index is applied to income after taxes and household expenHes ($9,000). In the third cclumn, it is applied to farm mortgage debt service requirementH ($H,40 I). The last column gives the amount required to be paid into a debt reHerve. If the requirement for debt Hervice iH $H,40 I or less, the amount to the debt reserve is zero. When the requirement exceeds $H,401, the amount in excess must be paid into thP debt reserve. The debt reserve balanee can be drawn upon in defieit years to complete the payment to the lender at the contract level of $H,401.

In table 2 available income in the first, third, and fifth years is less than $H,401. A conventional

Table 1 -Selected mdexes for cash grain farms in Illinois, 1966-73. 1 i f>tiC£'5 r(!( Cl VI!(] J

Year · -· 1 I

(..qrn I (>oylH'rll1~ ProdtH_t __ ,_otl ~ .... __ ' ,,.~~-~ ----·· !_l_t.'_'_''~~~!~~~l-I-~J __ J:_<_~_r!~~~--~~-~~~~~~-~-~-~ ! __ _ ~------ ·--- -- ·----· ____ j_ ________ --''---- ---L- --- - ----· ~ -----

I 'lUI . . . :. , . : .. : 100.0 100.0

l'JI,H 10/.8 98.0

im . •. • • • • • • • • •••• I 1ii1 :Hi ... ·········~ 2451 ?21.6

--,-------------- -----·- -- ··--·----·------------------- .. -------·~-------------Hasc year is 196/. ~Pticcs and yieldr, are haserl on Illinoi!i IJitnots Coopnrattve Crop Hcporlinq Service. 1 Prtce-s arc based on

-'1J!ricull111·a[ 8lalislicx, Annual Summary (Ccntrill ltlinots), ,\uriculfttra/8lalistic~·. U.S. Department of Aqriculltuc, l'J/4.

Input pr1ces p;tt<l 1

<)C) ');.> 9H 81 . .3 'J? .')

100 100 IIJO 100.0 100.0 10? 11 I 10'1 84.8 ')'J .I

JOG 1?4 10') 103.6 1 I I .4 110 13~ 1 14 7 ~). s 'J 1.4

I J!J 143 119 101.1 !Oil.8 12? !52 124 I 14.~ 1 I I .I !4C> 1~6 138 1 OR.? 1 0~J.1

3

amortization cornmitnwnt in thosp yPars would n·q u i rP t.lw bo tTOWl'r to rt>d uc!' h ousPhold outl nys or to ht~rrow Plst>wlwrP. But inconw in all yt>ars can mt>Pt thl' adjustPd rnortgagP dt>ht st>rvict> rPquirPrnPnt.s. In thl' fin.;t and third yPars, sine<• no dt>bt rPs<•rvP would havp a<'<·umulatPd, t.lw hal an<'<' oft.lwpaym<•nt would Ill' drawn from an amortization insurancp policy takPn by thP horrowPr as a condition oft.lw loan. By thP fifth yPar t.lw paynwnt halancP would conw from tlw dPht n•sprvP. TablP :1 shows a n•curring 7-year sPriPs.' ( ~olumn I is th<• amount of amortization paid

Table 3--Sources of amortization: $8,401 each year.

Ycat of <il!i>l ~~~~·::,:',~~', l. ~~~~~~e [_~,:~L:,·~I:~,~~= I Jul/ars /Jolla/'s I Jnllurs

1.~11 () <);Jtl ., B,ilO I 0 ()

I ,b.lb ?43 ~) / ;)

'i H,•IU I () (I

') . I ,?41 1,1 GO 0 ,, H,401 () ()

I H.~OI () ()

H I,IJ 7 I q;JI) ()

~) .. B,IJO I l) ()

10. I ,t, Jt1 I(,~) ()

II H,IJIJ I () 0

I'' I.?~ I 1,1!.0 0

13. H,40 I 0 ()

14 B,IJOI () ()

l ~) .

1 /\lll<>llrll lt0111 (Dittlllll J flf l<ihl(•? Ot ~.H,/10\, WIIH1tCVf'r IS

~II tit I let.

from current income of the borrowPr, column 2 is the amount from the debt reserv<•, column :lis thP amount from amortization insuranc<'.

Table 4 shows the annual transactions and balances for the debt reserve, and the borrower's return from the debt reHervP balance. The return from the debt reserve is based on an assumption that the borrower receives an interest rate equal to the interest rate of his farm mortgage loan. The figures are derived directly from table :l except for those in the last column which are obtained by multiplying by 6 percent the debt reserve balance of the preceding year. In table :1 the calculations do not go beyond the 14th year. After that year insurance iH no longer needed, becau,;e the size of reHerves is large enough to meet payment deficitH. The 7-yearpattern iH assumed to recur. Further re,;earch is needed to judge when the reHerve iH large enough to juHtify dropping the in,;urance. The reserve balance at the end ofthP 14th year is larger and the remaining loan balance is smaller. By this time thl' reserve balance equal,; 2fi percent of the remaining loan value, and it is more than three times the annual amortization commitment. ReHerve payments could be required again if the debt reHerve balance drops below a preaHHigned level.

'This is an arbitrary nwthod of' r('pn•s<•nting tiH•unstnhlP Pconomic <•nvironnwnt f~H·I'd by tlw horrowPr and tlw il'ndPr.

;>

l. ,, ~-(,.

I

!l ..

9. JO. II I'' l J. 14

I~ . ]().

]/

l H .

I'! ;>o.

?I

/?

?3 . ~4.

?~>

2(,. ?I .. ?B .

Table 4-Debt reserve: transactions, balance, and returns to borrower 1

( lebt F~et lit II'> to

fPScrvc bort owct ·

balanr r.·t .I

/)fJ/Iar,'> /Jullars })u/lars I )tJI/t~rs

(I 0 () ()

/•l.l 0 ?43 ()

() :·~3 () I'• 1 ,IH.~; () l ,Btl~) [)

() l,lbO /U~ IJ;> 4,141 () II,Bti<J 4;> 'l, I J I 0 I?, CJ II ?'ll

0 t)/1.1 J J,O~' J H J'l ?IJJ () 1 J,?l)b 1 H J

() I(>~ l ;) • ~) 3 1 I<)H J ,Hr.~ () I'~' .JlJ (J /',;;

0 l' l (>() I .l,?Jb Hbtl

4, I~ I 0 I I, .J II 1'!4 'l,l.ll () ?(,,~oH 1,04.1

() '1?1l ~ ~) '~>84 J '~J9 1 ;'II j (I ?~,H?I I ' I) ~ ~)

() I b~> ?~,()(,? J,•,•,o 1 ,fH,~, () ?hI<)? I 1,')()/]

() I, I bO 2~ I I() I J,(,J(,

~. 141 0 ?'J,<JOB I , ~ , ll l ,

'!, l J I () JtJ,OJ() l , I q ~J () ())II JH, J 1 ~~ :',Jil?

?43 () :.H1,J~)H /,?HI () /{)~ J/,~9.~ ? ,.30?

I ,HG~ () J'l,IJ~H ? .?~1)

0 1,1 (,0 JB,?'JH ?,JbH 4,141 () ''7 ,4 J<l :) ,:'<)B

'l,l J I () .. ~ 1 .~ 10 :-'.'.>·1l)

1 /\II illlHHlll\'l ill!' !Otllldcd to \IH' llbliC'".\ cltlllar :I 10111

coltJtlttl II of lilbl<: ;). 1 I torn< oluntn;? of I<IIJie !. 1 1 nd ()I year

'0.0<, x dPI>It!~<;,etve bdlt~nu~ 111 table I •·usr~d to rP.Illc lnt~rt. tllr

\itlle rcqtlltPd \o IC\IIl' IIIC loan LOIIIcl bP. ~h()t\cnr.cl If IIH'

"rctutn~ to I><Ht()wcr" wctc clCJcJcd to lt1e ctdJI rc~etve hcllarH~

mstead (I! b1~ttl(] J.Jilid out to tlw bot tower 111 < .1~11.

Table f> show,; the evolution of the amortized loan. The loan value at the beginning of each year iH ,;hown in the firHt column. The initial loan is $12l,HOO, and 70.<-i percent of land value was offered to secur<' the loan. The interest paid each year is 6 percent of the outHtanding balance. The total amortization payment is constant at $H,401 each year. The pnrt of the payment applied for interest declines each year, and the part paid on principal increases. Sine<' the debt reserve balance also increases, though erratically (see column :l of table 4), at som<' time it will equal the (diminishing) loan balance. In thiH example, the equality occurs in the 2Hth year of the loan. Hence the amortization table is terminated at thl' beginning of the 1 !-lth year. At thiH time the borrower iH debt free and has a surplus of$4,6H2 after u,;ing the debt reserve balancP to retire the remaining principal of his farm mortgage loan.

The re,;ults for the borrower are shown in tablP fi. The table ,;hows the net cash flow that remains aftl'l" debt service, and it accounts for any returns from the debt reserve balance and for the cost of amortization insurance in the first 14 years(Hl'e footnotl' 2 of table 6). The residual cash f1ow reveals the Htriking difference between the variable amortization plan

Table 5-Amortization table: remaining balance and interest paid, annual payment of $8,400.55

(35-year loan at 6 percent).

PaYment for Year of debt Beginning

I balance Interest Principal

Dolla1·s Dollars Do/lai"S

1. ........... 121,800 7,308 1,093 2 ............ 120,707 7,242 1,159 3 ............ 119,548 7,17 3 1,228 4 ............ 1'18,320 7,099 1,302 5 ............ 117,018 7,021 I ,380 6 ............ 115,639 6,938 I ,463 7 ............ 114,176 6,851 1,550 8 ............ 112,625 6,758 1,643 9 ............ 110,982 6,659 1,742

10 ............ 109,240 6,554 1,847 11 ............ 107,393 6,444 1,957 12 ............ 105,436 6,326 2,075 13 ............ 103,361 6,202 2,199 14 ............ 101,162 6,070 2,331 15 ............ 98,830 5,930 2,471 16 ............ 96,359 5,782 2,619 17 ............ 93,740 5,624 2,777 18 ............ 90,963 5,458 2,943 19 ............ 88,020 5,281 3,120 20 ............ 84,900 5,094 3,307 21 ............ 81,593 4,896 3,505 22 ............ 78,087 4,685 3,716 23 ............ 74,372 4,462 3,939 24 ............ 70,433 4,226 4,175 25 ............ 66,258 3,975 4,426 26 ............ 61,832 3,710 4,691 27 ............ 57,141 3,428 4,97 3 28 ............ 52,169 3,130 5,271 29 ............ I 46,898

1 Loan retired witil debt reserve balance at t11e end of year 28 (surplus to borrower is $4,672).

and the conventional plan. Also it reveals problems of the variable amortization plan in a "raw" state.

The advantage of the variable amortization plan is clear enough for the lender-it more nearly guarantees an even flow of repayment. The advantage to the borrower is not so evident in the first few years. Especially in years 4, 6, and 7 when income conditions are good, the amortization requirement moves upward and the low debt reserve balance is not sufficient to generate much of a return. Meanwhile the conventional borrower has access to large increases in disposable income. The variability of cash flow after debt service is reduced. The coefficient of variation drors from 151 percent to 68 percent during the 28 years of the loan.

The net cash flow gradually increases under the variable amortization plan, but its behavior is repeatedly erratic under the conventional plan. Finally, in the variable plan a debt reserve balance accumulates. At the end of the 28th year it is sufficient to retire the remaining loan balance. In the conventional plan, retiring the balance does not occur. Many conventional borrowers acquire sufficient surpluses to retire loans before the final maturity date.

5

Discussion

At this state of devPiopnwnt tlw plan has not lwt>n refined. Also not all of tlw advantag(•s for tlw borrower or lender are shown in tlw PxampiP. No gains are reported for tht> borrowpr from tlw increased financial stability undt•r tlw variahiP amortization plan. Presumably tlw incrt>ased financial stability would free him for boldPr and mon· profitable plans in marketing [21 and production. It would provide wider and more dt>pendahlt> access to short-term and intermediatl~term loans. 1-!t•nc(• liquidity management, including crPdit management, is cheaper. ThesP asppcts an• !wing investigated in current research I :lj. ThP Pcorwmic results may he notable and could givP the bot-rowt•r a better understanding of the economic advantagt>s of the plan.

The plan is subject to considt>rahlP modification. The most obvious modification is to scalP down thP upward indexation of amortization requir<'ment.

Table 6-Cash flow under a variable amortization plan and under a conventional amortization plan (all rounded

to nearest dollar). -------,----------·---c---- -------------

C(1~11 flow iJIICI cicl>l l{ettlt n lo .::,erv~;_e tllldCt ..

borrower 1 le~s

Year of debt cost of <lllHHlt- Varial"llc zat 1011 lllSlll .111( P .llll(>tlrlat iDn (:ntlVCtlt l(lllill

plo~n ' plan·t

/Jolla I'.'> /)uf/ors /Jol/w·s

-~>00 33 l'l I 2 -~00 ll/ 8[,()

3 ·48~ ()() .;>;>()

4 -SOO ?J? ? ,'..>()I

5 -388 J:' I -(>43

6 ·458 431 ~.OJb

-209 1,04? I 0,38?

8 334 812 ·J<J I 9 283 900 8<.0

10 298 843 -??0

11 ?52 <)84 2, :,~)I

12 3G4 881 -C,4J

13 294 1,189 ~.OJb

14 S43 I, 194 1 0,38?

15 I ,!:>91 2.124 ·391 16 l,!JJS ?, 152 860 17 I ,550 2,09~ -2:·o 18 1,!..104 2.? :J(> 2,597

19 1,61 G 2,133 .(>I]J

20 1 ,!:>4G ?,41] 1 ~,,OJU

21 1 ,79~ 3,04G 1 0,38?

22 2,342 2,81!.J J'll

23 2,287 2,904 HhO 24 2,302 2,84 I -??0

25 2,25<> 2,988 :? .~<)I

2G 2,368 2,88~ ·C•4 3 27 2,298 3,193 ~.o:J()

28 2,'..>46 3,19 I 1 O,JI:l?

1 CotunHl 4 ol tal)IC 4. 1 1 he 111Staancc cost ttlfottqll veat 1•1 ts

$500 (actuarial cost of $400 plus an allowanu· fot til'> til anu~

administration). Tl1cn the insu!'ance ts disconttnued, L)CLZilt~e tl•c debt reserve balance ts larger and t11c remaHllll<J loan balance a1Hl amortization commitment arc sn1allcr. 'Column 2 less t:olunlll J.

Table 2, plus column 1 of thts table. 4 Colutnn ? ()f table ? less

$8,401.

This would n•ducP tlw "ppnalty" bornP by the hotTOWPr undPr good income conditions compared to the position undPr a conventional amortization plan. Such a modification coulc1 ntain most of the favorahlP fpaturPs of thP varia hlP amortization plan and simply rPducP tlw ratt• of increase for his amortization commitmPnt. Less erratic behavior of twt cash flow, <·xhibit('(l h.v tlw conventional plan, still could IH' a(TomplishPd.

1\ SP('ond modification might lw a reduction in the intPrPst ratP chargPd thP borrowl'r who agrees to the plan. Tlw advantagP to tht> lend<•r is clear and should lw shared with the borrower. Two options are availabll': to chargP l!'ss on thl' farm mortgage or to pay morp on tht>debt n•serve balance. The first option se!'ms more feasible, sincl' it will he reflected in the borrower's net cash flows in early years, when the problems arl' most evidl'nt.

Whetlwr the lender gai nsand can share these gains with the borrower depends on the response of financial markets to the new fi naneial instrument. In the future, with increased loan commitments, increased variability of farm income, and the consequent threat of periodic delinquencies, a variable amortization plan may well restrain a rPiative increase in the rate paid on Federal Land Bank bonds. Any plan that holds this promise is well worth examining.

Another issue is the establishment of the base for indexing the amortization requirement. In the simple example presented here, the base is fixed at the residual income level existing at time of purchase. Clearly there are appealing alternatives. Income of the past five or even three years could be used as a base. Another base could be the projected income over the same period as was used in appraising the farm. Assumptions for the appraisal would be readily available for this added application. The indexes could be applied as already indicated. But they would be applied to a different dollar "commitment" to find the amortization requirement for each year.

In -the illustration, the amortization insurance was discontinued after the debt reserve had accumulated to three times the annual amortization commitment and to 2!) percent ofthe remaining loan balance. The actual discontinuance point clearly is subject to more careful examination. But with high loan

commitments and amortization cornmitm<'nts, the past few years indicate the importance of a "final" reserve to support the borrower's obligations in early years of the farm mortgage contract. .

The borrower in the illustration is required to pay into the debt reserve plan so long as hP r<>mains i;1

debt. As with amortization insurance, the need for further debt reserve payments is lessened as the c!Pbt reserve increases. For example, when thedt>bt reserve balance reaches some multiple of the amortization commitment, such as :l, perhaps the debt resPrvp payments could be discontinued. In the example, this occurs in the I 9th year (see column:~ of table 4). ThP termination could be accelerated if the "returns to borrower" from the debt reserve balance accumulates in the latter rather than being paid out to the borrower as described in the example.

Finally, judgment must be made of the class of loans to which the plan can be applied and of the conditions when amortization insurance is needed. Historically, loans at 6!) percent or less of appraised value have pr<~duced negligible default rates. But higher loan commitments and greater economic instability suggest a new lending environment. They also imply a greater relative importance of the annual expected cash flow. The most appealing suggestion is to set some maximum for the ratio of loan to appraised value, such as !JO percent, or of amortization payment to predicted residual cash income, such as HO percent. Above this maximum the variable amortization plan would be required in addition to the debt reserve and amortization insurance.

The formulation and use of the index are subject to further study, including such aspects as factors, weights, and fraction of I to use in making adjustments. The fraction has been discussed, with special reference to upward indexation. An important alternative to examine is shortening the maturity period to more nearly exhaust the expected residual cash income. Factors and weights that ref1eet those of typical farms should be chosen. These choices would lessen the risk of influencing the farm organization of a borrower by repayment terms of the loan. It is important to devise an adjustment process that is simple in concept and based on data that are easily available to both lender and borrower.

REFERENCES

Ill

121

Baker, C. B. "An Economic Alternative to Concessional Farm Interest Rates," Australian Journal of Awicultural Economics, R:a, Dec. 1974. Bolen, Kenneth R., Farmer Responses to Market Uncertainty, Ph.D. thesis, Univ. Ill. at Urbana­Champaign (In progress).

6

[31 Stone, Kenneth E., Effects of a Variable Amortization Plan on Organization and Income of illinois Farms, Ph.D. thesis, Univ. Ill. at

lJ rbana-Champaign (In progress). [41 lJ .S. Congress, Farm Credit Act of 1971, Rept. No.

92-679, 92d Congress, 1st Session, Nov. I 9, !971.

IMPACT OF OFF-FARM INCOME ON FARM FAMILY INCOME LEVELS

by Donald K. Larson 1

ABSTRACT

Net farm income alone does not depict the total income of farm operator families in general. Including off-farm income improves the situation. When classified by net cash income from only farm sources, 72 percent of the farm families were in the low­income group in 1970. But over half of these families moved to a higher group when off-farm sources of income were included.

Keywords: Net farm income, off-farm income, income, farm operator families, income status.

Growth in off-farm income ranks among the major developments affecting farm family income [ fJ, 6, 7, R, 9, 10, 14]. In light of this growth, net from only farm sources is not an adequate measure of total farm family welfare. Thus, understanding the total income situation of farm families is basic for effective decisionmaking on important income transfer questions. For example, information on who needs assistance and how much is needed can help ensure effective allocation programs.

In the past the farm business was assumed to be the main source of farm family income, and a family's economic welfare was tied closely to the level of net farm income. The opportunity for the farm operator and family members to pursue a nonfarm business or obtain off-farm employment was presumed to be limited. 2 Any income from off-farm sources had been considered to be of minor importance to the family.

The median income of farm families has historically been lower than that of nonfarm families. But in the 1960's, the income gap between farm and nonfarm families narrowed, partly because income from off-farm sources increased [1]. Realized net farm income increased from $11.7 billion in 1960 to $16.8 billion in 1970, or by 44 percent. For this same period, offfarm income rose 105 percent from $8.5 to

'Agricultural economist, Economic Research Service. "Opportunities for off-farm employment, however,

Probably were greater for farm families Jiving near urban Industrial concentrations [4].

7

$17.4 billion, three times faster than rwt farm earnings. Also, in 1960 off-farm incomp was 4~ percent of total net income per farm operator family. By 1970 it was fil percent Ill[.

The farm operator's income status examined in this study is based on net farm incomp and on total nt't income from both farm and nonfarm sources. 1 The relative importance of each ineome sou reP by inconw group also is discussed.

Data Sources and Definitions

The data used in this study were prepart>d by thP Bureau oftheCensus, U.S. DepartmentofCommercl', and based on the 1970 Financial Survey of Farm Operators and Landlords [ 1:3[. 4 The study examinl'S only the income status of farm operators. I )pfintions of terms are basically those found in the 19!19 Census of Agriculture [ 16].

Operator's net cash farm income equals I H70 gross farm sales minus the landlord's share of products

"Income is not an all-indusivP nwasun· of •·•·onorni(' welfare. Other studies sugg<•st anoth<·r nwthod for measuring economic wei fa rP by <li'I'Ounting, for<·xampl<·. for family wealth via net worth 1~. 171. ])ata s..ts that l'ontain income and wealth elt•mPnts in a Hingl<· ohsPr"Vation an· oflen not available. But incomP is a useful l'ritl'fion whl'n data on the family arp limit!•d to IPvPl and soun·<'H in a sin~d<· vear. · 'Data used also includl· unpuhli;;lwd tahulationH pn·parPd hy the Census Bureau from this finarH'ial Hlii'Vl'.V.

~old and any cash paid for rent, and minus cash opt>rating expenses paid by the farm operator and memlwrs of hi~ family in 1970.''

Operator's total net cash income represents the combint>d net earnings from farm and off-farm sources received by the operator and family members. Off-farm income sources are defined as: cash wages, salaries, commissions, and tips from all jobs; net profit from operation of nonfarm businesses or professional practices; government farm program payml'nts; custom work and rental of agricultural property; social security, pension, retirement, veteran's benefits, annuities, unemployment insurances, workmen's compensation, and old age assistance income; and rental of nonfarm property, interest, dividends, recreational service, and various other sources.

Income from farm programs generally is included in fa'rm income rather than treated as an off-farm source of income (as tabulated in the census data)." While the census tabulations were not structured to handle farm program payments as a farm income source, this does not alter the basic conclusions of the study.

Results

For 1970 the sample, after expansion, was made up of 2.4 million farm families. Their net cash income from farm and off-farm sources was $26.2 billion (table 1). Net cash income from farming, excluding Government payments, was $11.1 billion. Over four out of five farm families reported income from off­farm sources. These sources totaled $15.1 billion in 1970. Thus, off-farm income exceeded net cash farm income in the survey year.

Wages and salaries were most often reported by farm families and were the largest part of off-farm income (table 1 ). About 5a percent of all farm families received wage and salary earnings in 1970. These earnings accounted for three-fifths of the total off­farm income which was more than $8.8 billion. Earnings were about four times the total benefits reported received from Government farm programs. But Government farm program payments reported by farm families in 1970 still ranked second in terms of the number reporting and amount of off-farm mcome.

Custom work was performed by nearly one-fifth of the farm families in 1970, but the amount from this source was less than 6 percent of the total off-farm income (table 1). The relatively small amount may

··Deductions for depreciation of farm buildings and machinery plus changes in value of crops and livestock inventories were not included.

';!<'arm program benefits are considered as a transfer of income in this study. But it is recognized that these program payments consist of income transfers and payments for resources removed from production.

8

Table 1-Number and percentage of farm families reporting off-farm income and the amount

____ obtained from speci~~ ~~~-rc_es, __ 1:?_~~------ ___ _

I tern

All farm farn111es ... .

Farrn farrlllies report­ing off-fa.rrn 1ncon1e

Off-farm income by source: Wages and salaroes Nonfarm business F-arm progran1

payments ..... . Custom work, etc . . Social Securily, etc. Nonfarm proper-

ties, etc . ...... .

farm famal1es

Propor-Number tion 2

Thou- Percent :mnd

2,409 100.0

1,976 82.0

1,268 52.6 237 9.8

1,062 44.1 467 19.4 434 18.0

242 10.0

Off-farm 111con1e

Propor-Amount lion'

Million Petc<'nt dollars

15,064 100.0

15,064 100.0

8,840 58.7 1,654 11.0

2,432 16.1 855 5.7 758 5.0

524 3.5 . -.----------

1 Oata are based nn (131 and unpublisl>ed tabulations by U.S. Department of Commerce, "19/0 Agricultural F1nancc Survey of Farm Operators and Landlords," /9(HJ Cen.<us of Al.!riculture. 2 Sorne farm fan1ilics received incorne from more than one off-farm source, so the proportions of off-farm sources total more than 100 percent. -'Dollar proportions of off-farm sources are a percentage of total dollars and thus total 100 percent.

partly reflect custom work done as a favor to neighbors and not as an income source seriously pursued. Also, custom work is limited to specific times of the year and is more adaptable to certain types of enterprises, such as cash grain or row crops.

About 10 percent of the farm families reported a nonfarm business or professional practice in 1970 (table 1). Also, a similar proportion reported the rental of nonagricultural property and income from various other sources. Social security, pension, retirement, and other related sources were reported by 18 percent of the farm families, but these sources accounted for only 5 percent of total off-farm income.

Although off-farm earnings exceeded net cash farm income in 1970, it is important to know if off­farm income was equally important to all farm families.

Off-Farm Income Versus Farm Income

Off-farm income made up 57.5 percent of the total net income reported by farm families in 1970 (table 2). Nearly 22 percent of those families reported net farm losses of about $2.1 billion. Total off-farm income of those reporting losses was $4.3 billion-more than double the total net losses. 7 Without off-farm income,

7The proportion of those reporting net farm losses in 1970 who also had off-farm income was not available from the data tabulations. But most of those with farm losses probably had off-farm income.

many probably could not have sustained or even covered losses for an extended period.

Many farm family objectives have been formulated as hypotheses and the results f.,riven in table 2. For example, generally it is assumed that farmers desire increasingly large operating units which will return enough income so off-farm earnings are not needed. Table 2 supports the hypothesis that the relative importance of off-farm income declines as net farm income increases. But some farm families have established a role for farming in their lives, and they Pither do not want or cannot afford to expand their farming operation. Farm and off-farm pursuits may be permanent for some families and intermittent for others, depending on their annual needs and the availibility of off-farm jobs. Also, some may be changing occupations [ :3]. Because off-farm income is an important source for farm families in general, it influences their economic welfare-as measured by net cash income in this study.

Table 2--Distribution of farm families and net cash income from all sources by net income groups and

total otf-farm income as a percentage of total net income, 1970'

Net ttlcontc

~11 Oll p~

Net los~

~o to tjqCJ

~1.000 to $?,99<) tJ,OOO to ~~~.999 $o,OOO to $7,499 $/,'JOO to 1>9,999 $tO,OOO to $14,99'l $1o,OOO to $?4,999 $?!> ,000 01 more ..

/\11 qtoups . . .. --------·----

--~~-~= ==-r-.~,).1:,~:·~·~ ---r ()1"111 lllCOillC ftOill I)CfCCillaqe

fan11lte~ fill soutces of tot.11

net ttlCOI!t('' -------- - --- ~- ----

I)C'I"CCIII :Hill ion Pt•rc('lll

dollars

~ 1.~ ?,?4 l <'> ?:'.? J,4(>u 'lJ.~

19.4 3/J9? /().()

9.3 ?,O?tl ~/.0

7 .l ?,Q(,li 41J.l 5.3 J,h~> L 33.7 ~.0 ?,380 ?7.0 4.9 2,82') 20.fl 3.9 ~.Sil 1 13.9

100.0 ?b,2l6 s 7 .!} -----------------

1 Data are l>a~cd on 11 Jl arfd unpu!JitshccJ lCllHJiat1ons by U.S. Depattment of Cornrnctce, ''19/0 /\gricuttural r mancc ~urvcy of f-arm Ope~ators and I andlorcls," I~)()!) Cc·11sus o{ ,\f.!riculllll'<'. 2 flroportton LJased on all fottll fanllltes. 1 f'arnt inLomc wao; neqattve, but off-farttt mco1ne offset farm losses.

Farm Family Income Status

E. I. Reinsel developed a technique to appraise the economic status of people reporting farm earnings [6). Farm families were placed in five income groups according to their net cash income from farming and net cash income from combined farm and off-farm sources. Also, all losses were treated as an economic loss.x

"'I' he distribution in table :1 does not necessarily imply, for example, that families in low-income status during 1970 would remain in that status over time. Unfavorable w1eather, low prices, or income tax considerations may have Paced some affluent families in that group.

9

The relationship between total net cash income from all sources and incomP group of the family is considerably different than whpn nPt inconw from only the farm is considerrd (tabh• :n. When classified by net cash incomrfrom :mly farm sources, 72 percent of the farm fa mil irs were in tlw low-incomP group in HJ70. But when incomp from off-farm sources is taken into account, over halfofthe families in the low-income group moved to a higher income level. Net farm income alone is an imperfect indicator.

Table 3--Numbcr and percentage of farm faomhes with net cash income only and net cash oncome I rom all

sources, by income groups, 1970' ------------ 1--c la~<,tlt<.~tt 101 t IJv

---------------

I dl"tlt I ~lit Ill V

tllL<JillC ()I ()till'>:

Nel ca'>lt 1cllrtl

IIH.OIIll1 OIIIY 1

NtllllbCI

ot PtOIJOI·

1i'llllii1CS I Hill

------·--·-

i lhou I'C'I'C'c'nl

I .'>a lid

:f,;"l'>,OOO ot lllOt"C I ').l 1.'1 $1'),000 to ~;'tl,CJ9lJ I I I ~.a

""·""" '" ""·] 14? ~-C)

:1>',,000 to $9,'l'J'l Jl l l.l.tl I c>s lllilll $'>,1l00. I, _14 4 72.4

/\11 <.Jtuup, . ?,IIO<J I 00.0

Nl·t l <I'-ll IHl ()Ill~;

II(Jitt .111 •,tJtlrll'', 1

NtilltiH~I

'" l)lfliJtJI

lillllr IH:', I Hill

11/uu }',·lt'l'/1(

.... und

l'.ll I ')

il:' J:l.'J

'l?:s I/(, {I~);"' -:>! t H.\ I 1•1_',

;',•IOlJ 100.0 - ···--~------·--·--··- -·-

1 1)Jta t'l()'-.l-!(1 l)ll II ~I tltlcJ 11/lj.llii.Jit~lll~CI tilllttldlrllll'~ I>V lJ .,_

tlCPiJitllll~llt ol CurnrtlCICC, "10/() /\tjll(tilllltill r 111dllt(' '-,IJIVCV

()f f dtlll ()fJ81a(OIS <.Jilcl l ;JilC!itH(i\," /.IJ(i.1/ ('J'/1.'>11." uf ."\I.:I'll"Ufl/11'1'

1 Pt~l"tiltll'> to all fi:ltlll opt~tillOt), Ill< hidlll\..1 IIIO')t~ CIPIIIIC<I by

Ccn~u·, a-:, p;lr I -time JtHI part ·I c•ltt cnwttl l.n 111 11pcr;tl or'.. 1 L x ( ltJ des qovCII11llCrtl procp·cntl p~lYillt~rtt ..,_ 1 lllLitttlt~')

qOVCII\IliCIJl prcHJI"dlll IJd\illlCIIh (IIH1 c111 OllrCI Olf l.lltll IIIU)IIH'

~OIIr<..CS. 'lncllltl<''::. nperatot~ t<'llOIItrHJ llCI ll)'i'•l~S. \Itt~

loW·IIlC<>Illt! bcn1.1llltdtll I') c)CnP(iJIIy IJP.Iow ~~,,000 abcH1I $.~.400

for .1 IOrlTl fc:l!lliiV ol lour 111 lfllO 11;'1. Hut lllC L<~ll~u-:. Cfill.l

were not spcuftcally_ ~;tructut~tl to au nun! for pc!:-.oll'::. dt ot

below povcrl y tnCeltlle h~vcl<,.

The relative importance of each income source for the group is shown in tables 4 and 5. Except for social security and similar earning sources, off-farm income sources were reported less often by families with low income than by those at higher income levels. About one-third reported wage and salary earnings in 1970. Their earnings averaged $2,220, suggesting that some worked only part time or they had low-wage jobs. The low-income group had the highest proportion of families reporting income from social security, pensions, and similar earning sources. But the average amount of social security income was the lowest for the low-income group. Based on average earnings, the low-income group includes many who need help.

Wages and salaries and nonfarm business or professions contributed to the higher income level of some farm families. These off-farm income sources were reported more often by families above the low-

Table 4- Faron families reporting off-farm income from specific sources, by income groups, 1970' ---------- ·----- ----·-- -- -------------------- ------------·----- ----------------------------------

Families reporting off-fann IIICOine from--

r arn1 farntly NurtiiJt!t ol /'\vc:raqc Farm rnLottll: qrottp'. 1 ,}ll t I IIi~'; net cas11 Waqes a11cJ Nonfarrn progr arn Custom Socoal Nonfar111

fan11 tncortlC sa lane<; hu~iness payments work security properties -··- ------ L_ ________ -------

J'hoii:WIItf I )o/lars /'(•t-c•·n/ 1'4'1'('('111 [',•rcc•nl l'rtcf'nl PPrcent l'erc(•nt

\;'~.000 c>r IIIOrC I'll :J I ,(,flO ll<l_8 I 9.? 52.5 27.6 8.5 H\.4 1.1 ~.ooo IO '124,999 312 8,590 63.2 15.2 52.1 24.6 11.4 15.7 ~10,000 to , 14,'19'1 4?3 ll,280 b9.(, 11.:! 49.7 19.1 12.7 11.9 \~l,OOO lo $C) ,9t)f) b~)2 ?,Ill 0 t-)3.6 9.1 44.7 18.1 18.9 8.9 I. C'.,S than $~.000 8:JI ·I ,110 J I.<) !J.5 35.6 16.7 24.8 5.9

f\ll qrotJpS ~.40<1 4,630 52.6 9.8 44.1 19.4 18.0 10.0 ----~--~----·--···--

'I lata l>ased <>n II J 1 and unnublosl·>e<l tablulations by U.S. Department of Coonnoerce, "1970 Agriucltural Finance Survey of f- ar111 Operators and I andlord~," I ~Ui!J Ct'II:Hl.'i of :1J,!hculltu·e.

Table 5--Average net-cash income from all sources, and off-farm income of farm families by income groups, 1970' _l ___ 1= SOUICCS lll<OiltC SillaYIC'> hliSIIleSS

--------- ------- ----- ----- -----

Off-farm incornc fron1 4

--.----------.--------.----------Farn1

prograrn pay111Cnts

Custom work

Social security

Nonfarm properties

,,:c~;,1 ,t110f~~1<:~:~~ _j' a~:~:~~~~'.','cl orr1~~"';lwaqe~ an~TNonfarn1 }Ju/lars

1>;>~l,000 or lllOIC

$1 '>,0()() to $?'1,<199 ~10,000 to $11l,999 $~,000 In $9,999 L_es~ tllan ~~.000

All qroup~ ..

~ l, .l.!O 18,790 12,170

7 ,45() 640

I 0,880

1 0,()50 J !:>,700 I 0,200 1 0,2~0

7,890 B,?90 5,01.10 !..1,360 1,810 2,220

b,?:,o (),<170

21,890 8,300 6,160 3,080 7,130 7,090 2,780 2,150 2,620 2,300 4,710 1,830 1,350 2,050 1,310 3,290 l,JGO 1,200 1,910 970 2,020 1,240 830 1,2~0 780

6,980 ? ,290 1,830 1,750 2,170 ------------~--- ~----------- ---·- ··-----------

1 Uata basecl on 1131 <111d unpuhlislll~d tabulations by U.S. ncpart lllent of COII1111erc..e, 10 19/() J\qriudtural Fl!lallt:C Survey o1 f--t~rn) Operalor~ and Lcllldlord~," f.C}(i,t) Cen.;;u,..; o{.\J!riculfurc>.

income group (table 4). Also, Government farm program payments and custom work were more often reported by those families in the higher income groups, and more had nonfarm business or professional interests.

Implications

Farm income considered alone certainly understates the total income picture of farm operator families. Results show that including off-farm income greatly altered the income levels of farm families in 1970. Thus, the idea that farm family welfare is tied only to farming is tenuous. The number of families whose only income comes directly from food and fiber production is a relatively small proportion of all farm families. Off-farm income may provide the staying power for families that have small resource holdings. This may limit the opportunity for other~ to expand their farm operators

10

1 Con1bined total net incon1C from farm and off·farrn sources. 1 Average a1nount based on all farrn fa1n1lies. 4 Average amount l1ascd on those reportong. Table 4 shows percentage reporting.

by acqumng land. Available off-farm jobs may partly contribute to the observed stability in number of farm residents in recent years [15].

Because many farm operators have combined farming with nonfarm interests, their level of total income is closely linked to economic conditions in the nonfarm sector. Thus, not only is farm policy important to farm families but also public policies designed primarily for the nonfarm sector.

Income alone fails to account for the farm family's ability to sustain farm losses by means other than off­farm interests or employment. Financial reserves-either owned, borrowed, or as insurance-often are used to meet unexpected shortrun contingencies. Further refinement is needed in examining the impact of alternative policies affecting family incomes. Continued development of appropriate income concepts related to the farm family would improve the measurement of their total economic welfare.

REFERENCES

111 Carlin, Thomas A., "Farm Families Narrowed the Income Gap in the 1960's," Agricultural

. Finance Review, Vol. 3:3, July 1972. 121 and Edward I. Reinsel,

"Combining Income and Wealth: .An Analysis of Farm Family 'Well-Being'," American Journal of Awicultural Economics, Vol. 55, No. 1, Feb. 197:3.

1:31 Hathaway, Dale E., and Arley D. W a! do, Multiple r!obholding by Farm Operators, Mich. State Univ. Agr. Exp. Sta. Res. Bul. 5, East Lansing, 1964.

111 , J. Allen Beegle, and W. Keith Bryant, People of Rural America, A 1960 Census Monowaph, U.S. Dept. Commerce, 1968.

[51 Larson, Donald K., "Wage and Salary Income: A 'Big Crop' For People with Farm Earnings," Journal of the Northeastern Agricultural Economics Council, Vol. 3, No. 1, May 1974.

161 Reinsel, E. I., Farm and Off-Farm Income Reported on Federal Tax Returns, U.S. Dept. Agr., Econ. Res. Serv., ERS-a8:3, Aug. 1968.

171 , People with Farm Earnings ... Source and Distribution of Income, U.S. Dept. Agr., Econ. Res. Serv., ERS-498, Mar. 1972.

[8] Sharples, Jerry, and Allen Prindle, "Income Characteristics of Farm Families in the Corn Belt," Agricultural Finance Review, Vol. 34, July 197~).

11

[9]Smith, Edward T., "Economic Profile of Limited Resource Farmer/' Limited Resource Farmers, National Fertilizer Development Ctr. Bul. Y-44, Muscle Shoals, Ala., May 1972.

[10 [U.S. Department of Agriculture, "Contour~> of Change," The Yearbook of Agriculture 1970.

1111 , Farm Income Situation. Econ. Res. Serv., FIS-222, July 197:{.

[12[U .S. Department of Commerce, "Characteristic~> of Low Income Population 1970," Current Population Reports, Series P-60, No. H 1.

[1 :3[ , "Farm Finance," 1969 Census of Agriculture, Pt. 11, Vol. V, Spec. Rept., Aug. 1974.

[lrl[ , ''Farm Management, Farm Operators," 1969 Census of Agriculture, Ch. :l, Vol. 11, Gen. Rept., 197:3.

[15[ , "Farm Population of the United States: 1973," Current Population Reports, Series P-27, No. 45, Sept. 1974.

[16] "General Information; Procedures for Collection, Processing Classification," 1969 Census of Awiculture, Ch. 1, Vol. 11, Gen. Rept., 197:.!.

[l7]Weisbrod, Burton A., and W. Lee Hanson, "An Income-Net Worth Approach to Measuring Economic Welfare," American Economic Review, Vol. 6:3, No.5, Dec. 1968.

INFLATION AND FARM FINANCING: RECENT TRENDS AND PROJECTIONS

by WPslcy N. Musser and Fred C. White1

ABSTRACT

UecPnt inflationary patterns of farm outputs and inputs have substantially changed problemsoffarm finance. Sinceinputpricesareexpected torisefasterthan output prices over the next few years, new problems of financing farm expansion and survival are likely. This article examines the impact of recent and projected inflation on the dynamic procPss of financing farm firms. Rapidly rising output prices rl·cently made survival less difficult, but financing expansion has become a greater problem. This partly Pxplains the recent slackening in the decline of farm nwnbers. If tlw projected decelerating output price inflation and the continuing input price inflation materialize, survival problems may arise for many U.S. farms and more <"redi t will be needed.

KEYWORDS: Farm finance, finn growth and survival, inflation, simulation.

Inf1ation has become a prominent feature of the American economy in the seventies. The farm sector shared in this inflationary process when the annual average of the index of prices farmers received rose 64.G percent from HJ70 to November 1974. The index of prices famwrs paid also increased [i(j,[ percent( 101. This pattern of high inf1ation is unique for farmers. Before the seventies, farmers experienced increases in many farm input prices, but prices for most farm products were relatively stable. Compared with this historical one-sided inf1ation, the high inf1ation rate for farm inputs and outputs after 1970 can be termed the new inf1ation.

This article considers the impact of the new int1ation on the dynamic process of financing farm firms. Particular emphasis is placed on firm expansion and survival. Specific objectives are to(!) contrast the financing process under the new inf1ation with that under the historical inflation and (2) consider the implications of future inflationary patterns on farm expansion and survival. The farm financing process at the national level is discussed, and a simulation analysis of a representative farm in south-central Georgia illustrates the financing process at the individual finn level.

INFLATION, FIRM EXPANSION AND SURVIVAL

Firm survival and expansion are affected by the value of assets required for a farm and the level of equity available for financing these assets. When debt is used, the amount of equity needed is less than the value of the assets. In this typical situation, the equity needed to finance required assets can be analyzed rather than the value of required assets for

1 /\;;si slant profpsson; of agri<"ul tural Pl'onom ics, lJnivl'rHit.v of (;eorgia at Athens.

12

a particular farm. Through profit accumulation and asset value appreciation, actual equity is generally greather than the equity needed to finance the assets. The firm may hold current or other assets which are not necessary for the farming operations. More likely, actual debt is lower than the maximum debt the firm could assume, because previous debts may have been repaid and assets may have appreciated in value since the last financing episode. In this case debt could be higher and, therefore, equity needed to

financP req uirecl assets coulcllw lower than the actual equit~'·

Equity n eeclecl to finance requi rt>cl assetR is useful in anal vzing firm survival and expansion. Farm survival is possible so long as actual equity is greater thnn t!H• t•quity needed to finance required assets. Similarly, an incn~mental expanRion is possible if nctual pquity is greater than the <'quity needed to financ(• required assets on an expanding farm. The impact of different inflation pattemR on farm expansion and survival affects the value,of farm asspts and availability of debt financing. These in turn influt•nce thP equity needed to finance required assets and the process of equity accumulation. The impact of historical inflation on farm expansion and survival iH discussed first. It can be compared to the impact of nPW inflation and then to the impact of l'uturP inf1ation pntternH.

Historical Inflation and Farm Finance

Historical inf1ation has been charaderiwd as a cost-price Hqueeze 11. 71. Compared with stahl<> price I<'Vl'lH, thiH inflation is followPd by lower net farm incomt>s and therefore a slower rat<' of t•quity accumulation from profits. Hut farmland prices are aff(•cted by historical inf1Rtion, and equity ae(·umulates and increases because land values appreciate. ThuH, historical inflation has mixed effectH on the rate of accumulation of equity by farm ow11erH. In contrast, this inflation increaseH thevalue of required assets for a farm, lwcause the price of assets purchased from the nonfarm seetor and th(• valu<' of farm real estate increases with inflation.

The impact of historical inflation on value of required assets and on equity accumulation is sufficient to determine its effect on the financing problems of unlevered (debt free) firms. Because debt is not used by an unlevered firm in its financial strueture, the increased value of real eRtate after appreciation can not be used for financing non land WHourees or additional land. Expansion under historical inf1ation 'requires increased profits, because the increased value of nonland reRourees on the current farm and of all additional assets for PXpanHion must be financed from equity accumulated from profits. Compared to stable prices, the cost-price squeeze of historical inf1ation restrictR efforts of the dPht free farmer to expand. His problems of survival are more likely with historical inf1ation.

For a levered firm, the level of required equity must also be considered in order to determine the impact of inflation. The degree of leverage used in this article is measured by the ratio of debt to assets.~ For a firm that is willing to increase debt as land values increase, part of this inflationary increase in value can be financed with debt. In particular, part of the

"l~h<: ratio of deht to equity is another nwasure of levl'rage, hut t!. ts not used in this article.

13

equity incn•ase from land can IH' usPd to finaiH't' expansion, to financt• tlw incn·asr· in value of nonland n•sourt·t>s, or to pay for intl;~ting coHts of production and consumption. So long as profits exceed consumption costs, hiHtori\'al inflation of land values can help achi(•Vt' Hurvival and <>xpansi"n goals.

The financinl prohlemf-' of histmit'al inllation <ll't• directly related to tlw abilit.v of farmt·rH to t•xpand debt financing. Since hiHtoliml intlation was HSHociated with nggrt>gatl• t'l'onomit' t•xpansion, adPquatt> crPCiit W<lS availahl!' during this pl'riod. LowPil Hill id<>ntifil's credit <>xpansion as onl' of tht• bent'f'icial aspedsofinflation forfarnwrsj:lj. Data in tahll• 1 indicatt> that farnwrs l'Xpanded tlwir <'I'Pdit US(> during 1HG:l-71 and rnisPd tlwir d<>gn'<' of levt>rage.

Table 1 -Debt-to-Assnt ratios lor U11itBd Stales farm proprietors, 1963-1974'

J <)(, 3 . l ')(;" .. J<)(;!J ..

1 q()(j .. ' .

1 'JC1 I . , .

I 'l0U ... ]909. 1 ') 10 .. I 9li . 1')1? ' 19/:l .. 19/4

Ycdt

/'t 'rt'l 'I If

1 (). ~. 11.0 I .I . I l:' .. l

I ?.B I.>. 3

IJ.(,

1'1.1 14. J 1.\.'.J

J .I. I I ;•. I

I ,1rrt1 tlul1!1_'.ts

4') t.1 \ 1'

/'i"/'('('11/

:•1 . .1

:' J ~J

;-' ·~ '!J :) 1 1.0 ,'(I __ \

:··(_) .:l

~o .n SO.B It _q

,ll.ll :)1-1.0

The economic t-nvironmPnt of tlw sixtiPs was conducive to farm size ('Xpansion. Inflation plat·t>d survival pressurP on !Pss efficit-nt farmers by furtht•r squeezing their already low net in(·om<>s. L<>ndt'rs hesitatPd to advance additional credit. on incn•asing land values so tlwS(• farmers could f]rulnc(' tlwir consumption nnd produetion expenst>s. With an expanding economy providing opportunitiPH f'm· occupational mobility, HOme farnwrs wPre willing to sell their farms. MorP pfficit>nt farnwrs, how!'vPr, maintained positivt> profits and accumulatpd equity and therefore had acceHs tocredit. Efficit>nt farnwrs increased their leverage in order to expnnd during this period. Although oth('l' factors affectPd this dynamic adjustment process, data in t.able2 indica!.(• that the decreaHing numlwr of fanns and t.hP increasing average sizE' of farms Higni ficantly slowpd as historical inflation Pnded in 1970.

New Inflation and Farm Finance

under the new inllation that dPvdoped after I mo. survival became leHs of a problem than under historical inflation, but expansion lwcanw mon· of a problem. With the favorable tnmd in farm output

Table 2-Changes in number of farms and average size of farms, 1963-74'

farms Average si?c of farms

[Jeer ease 7 /\rea lncrca~c 2

'/'!Jousonds PcnTnl i\ CI'C'S /'('1"('l'ril

1963 .. 3,572 322 1964 .. 3,4~ 7 3.22 332 3.11 19()~ 3,356 2.9? 340 2.41 196(> 3,25/ 2.95 348 2.3~

1 ~)() 1 3,16? 2.9? 355 ?.0] 1968 3,071 2.88 363 2.2~

1 'lG') ..... 2,999 2.34 JG9 1.65 1910 ..... ?., ~)~4 !.50 373 1.08 1971 . . . 2,909 1.52 377 l.OJ 19/2 2,870 1.34 381 1.0b 1 ~)7 3 .. ?,844 .91 383 .53 1974 ..... 2,821 .81 385 .52 -~·-~--- -- -~-- ·-·------

1 Data based on 1111. 1 Change from preceding year.

prices, the cost-price squeeze disappeared, and inflation became beneficial for equity accumulation. Although the rate of change in value of assets on the current farm accelerated, this acceleration did not endanger survival except for firms that rented much of their land or had very limited access to debt capital. In addition, aggregate economic policies of controlling inflation reduced employment opportunities. These economic conditions reduced incentives for selling farms.

Expansion became more of a problem under the new inflation because of financial conditions external rather than internal to farm firms. The favorable price-income situation produced record net farm income available for equity accumulation. In addition, the rate of land value appreciation accelerated. Although the rate of increase in value of required assets for expanded farms increased, the real expansion problem occurred because the equity required to finance assets for expansion increased at an even faster rate. Because of higher interest rates associated with the national policy to control inflation, farmers relied more heavily on equity relative to debt in farm expansion. Thus, the equity to finance assets for expansion increased not only because the value of required assets increased but also because farmers were reluctant to finance expansion at previous degrees of leverage. As a result, expansion became less feasible.

Data in tables 1 and 2 are consistent with this viewpoint on the impact of the new inflation on farm expansion and survival. Higher farm incomes and higher interest charges dampened the use of debt.

14

The debt-to-asset ratio declined for rPal PstatP in 1 ~)(~ and. in subsequent years and for non-real ('Sta(p in I D7:l and 197 4. The con tin uancl' of long-run trends in farm numbers and size could rPf1ect adjustmPnts to optimum farm size rather than financing prohkms. Continued slowing in the relativP trPnds are apparPnt during this period. Possibly the amelioration of thp r-mrvival problem and difficulties in financing

.expansion havP contributed to a slower ratP of' stmctural change. With unrestrictt>d credit, thP record of farm profits would have provided incentiv<•s to increase expansion, and t>quity accumulation would have been available to finance such expansion .

Continuance of the New Inflation and Farm Finance

The future inflationary pattern facing agricultun· is subject to considerable uncertainty. Particularly vulnerable is the favorable output price trends. J\s Tweeten and Plaxico have suggested [fi[, thP intermediate projections for farm prices are clospr to historical trends than were prices in the early seventies. Favorable weather and thP supply response to higher prices can stimulate increasing supplies. In addition, a world-wide recpssion combined with favorable weather in other countrit>s can reduce domestic and foreign demand. The result would be a decrease in output price trends or even a negative trend. The behavior of beef prices in 1974 followed these trends.

Price trends of inputs purchased from the nonfarm sector would not be expected to duplicate the behavior of output prices. Fuel conservation policies can raist> the price of petroleum and petroleum-based inputs and generate additional cost-push inflation. In addition, cost-push inflation generated by previous inflation could continue. The result would he a return to historical inflationary patterns, and farm input price increases would be higher than during the previous period of historical inf1ation. Tweeten and ~uance's conclusion concerning historical inflation would be realized: " ... with high rates of inflation tlw farming industry is destined for hard times" [7, p. 914[. The expected decline in net incomes would result in declining and perhaps negative rates of equity accumulation. A decline in land value trends in response to unfavorable farm incomes could further intensify the decline in equity accumulation. This would produce survival problems for many U.S. farmers.

REPRESENTATIVE FARM SITUATION

The analysis of the impact of inflation on a individual farm firm was based on a representative farm in south-central Georgia.'1 Costs and returns used for the farm enterprises reflected above-average management, and the enterprises were at levels of optimum profits. Principal enterprises included tobacco, peanuts, cotton, corn, soybeans, and hogs. The fam1 size was 400 acres for the current farm, but it could be expanded to 600 acres. Total assets at the beginning of the analysis inc! uded land, machinery, and equipment necessary for the enterprise levels on the 400-acre farm. Table :~ shows the beginning financial position of the farm, and the level of debt ref1ects debt-to-asset ratios of ~10, 50, and 75 percent.

Table 3-Beginni ng financial position for a rapresentative 400-acre farm under

alternative leverage positions, January 1, 1971

Debt -tD-a~set 1 ot io of

I tern Jo pc~c~ --~~-~;;~~~~~rl~~~~r~~-;;-

nul/ars

r otal rHJrH.:astl assets lJ~,SOJ 1 J~.~OJ J J~.~OJ

Uc))t 1 40,()~] G I ,I '.J 'l I 01 ,l>!> I Equit v in nonc...a~/1

as~et s ... . . . . . 94,8!>2 6/' 1':>2 33,846 cash II ,Jl! ll ,3/l ]] ,37! l<.>tal equity ... 106,223 1'!, 123 4:>,21 7 Mmrrnurn equity to

I expand ... 1%,78') 147,031 86,082 -------- ----------- ---------

1 Debt was assumed to equal tile debt~usset ratio times total norH.:as/1 assets. 1hrs assumption reflects t/Jc aqricultural lina11crng requirement of tanq1blc assets for security fur loans.

Consumption withdrawals at the beginning of the planning horizon were assumed to equal $15,000 annually. Beginning prices of outputs and inputs were averages for 1970-72.

Methodology

The analysis used a simulator which was adapted from the General Agricultural Firm Simulator developed by Hutton and Hinman [ 4]. With the aid of computers, farming operations were simulated under

'This representative farm model has been described in detail in other writings [2, 5).

various situations. The simulator accounts for cash flows from annual operations and value of ass<t stocks for a specified period of years. In analyzing inf1ation it is particularly important to annually adjust the prices of inputs, outputs, and assl't valuPs. With these features, the value of required assets and equity levels for various time ppriodR and the inflation patterns can be readily calculated.

The planning horizon for the analysis was 1971-HO. Projections for Hl7f'r80 are of primary interest. Hy including 1971-74, the financ.ial position of the firm at the beginning of 1975 could be estimuted at levels that would be consistent with the previous new inflationary price levels.

Price Trends for Different Inflationary Patterns

The two sets of price trends which reflect historical and new inflation are presented in table 4. The

Table 4-Annual percentage changes in price associated with historical and new inflation

I tcm ~I i~lorical NL'W

l'erct'UI l)crcclll

Peanuts .............. . O.O(J~~ 0.1 O<)J4

Tobacco . .04RH .119!.> ~

Cotlon lint .......... . .O(ii? .I /318 Cotto11 ~eecl ..... . .06/2 .?4/89 Corn .......... . .06l>G .41031 Soybeans ....... . .0(;~5 .31330

Wh9t ................ . .OCJSS .32330 Hogs .................. . .0911 .25305 CoasLJI bcnnucla l1ay .06GG .04]48

Annual production exf..)cnditurcs .03 .17113 Far111 machmery ..... . .0354 .]049!:>

Livestod'\ cquiprncnt .03~4 .15495 Land .................... . .0 7 1 7 .126 3 7 Consumption expenditures .03 .103/2

historical trends were developed from U.S. Department of Agriculture projections for 1985 and are intended to reflect normal world-wide production circumstances 11]. The new inflation trends were calculated from price changes from 1971 through 1974. Prices for outputs ref1ect season averages received by Georgia farmers 110]. Land value trends also reflect Geor!{ia price trends [9]. Other input price trends were based on indexes of prices paid by U.S. farmers for various classes of production items 110).

OPPORTUNITIES FOR EXPANSION WITH INCREASE IN LEVERAGE

Two situations in which expansion was possible at the beginning of the planning horizon can be developed. First, to reach the firm's targeted debt-to­asset ratio, the beginning equity may be greater than the minimum equity needed to expand. The initial

15

levels of equity required for expansion and three debt­to-asset ratios are recorded in table ~~- Expansion is possible if the maximum acceptable debt-to-asset ratio was 30 percent and if the firm's current equity is greater than or equal to $195,789. Similarly,

expansion is possible with a SO percent rntio if equity is $147.o:n and with a 7S percent ratio if equity is $H6,0H2.

ThP second Hituation is a more interesting analytical problem. When equity is at the minimum for the firm\; dPsired debt-to-asset ratio, expansion is initi<dly possible only if the maximum allowable lPvPrage iH incrPnsed. If at least 70 percent of assets (that is, :l<l-pPrcent debt) are covered by ownereq uity, expansion is possible at the beginning of the period

by incn•asing the dPbt-to-as1-wt ratio to 7f> P<'r<'<'nt (table :1).

For a firm that has a dd>t-to-nscwt ratio of :HJ percent and that allows its ratio to incrPHH<' to on lv ;1()

percent, expansion n•quires morr Pquity from .twt farm income and land va]ut• apprPciation. Furth<'r equity accumulation is also nPc<•ssary for firmH that are not willing to increasp their lPveragl'. TlH• rpst of thiH paper concentratPs on analyzing the procpss of accumulating equity for expansion.

ANA.LYSIS OF INFLATION PATTERNS

To compare historical and new inflation, the farm operations were simulated for the !{)-year planning horizon using each of the 8ets of price trend relations. To project the impact of future inflationary patterns, the Himulation incorporated a combination of historical and new price trends for 197!) and for subsequent crop years. One simulation used historical output price trends, and another used both histmical output price and land value trendH. Output prin·H were increased from 1974 levels by using historical trends. Thu~:~, the analy~:~is of future trend~:~ did not consider the worst po~:~sible inflation pattern~:~. TheHe patterns could involve either negative trends in output prices after 1974 or a drop in 197!) priceH compared to Lhe long-run historical trend h•veL

J{esult~:~ of the simulation runs are summarized in table G. l~quiiy, minimum equity to expand, and minimum equity to survive are presented for 1971, 197 4, and 19HO under various inflationary situationH. In this table, the minimum equity needed to expand L'<jUals the equity needed to finance tlw required assetH for expandin,.; to a 600-acw farm under three debt-to-asset ratios. Similarly, minimum equity to survive Pquals the equity to finance required assets on tl1e 400-acre farm. For this analysis it was assum1HI that cn)ditors would not initiate foreclosure ;;o long as pquity wa;; grPater than I 0 percent of the valuP of land and nonbnd resources. An altt~rnate interpretation of thiH survival criterion is that additional financing, or refinancing, is pos~:~ible so long as equity is at. least 10 percent of asset.;;. Thus, potential foredo~'un~ becau;;e of liquidity problernH can be forestalled with additional debt. Thefirstsetof re;;ults reflect historical trends forth~: I 0-year period, and the second ref1ects nev• trends for 10 years. The third and fourth sets summarize projections with historical output price trend;; and new input price trend;; after 1974.

Historical Inflation

Using historical rates of inflation for prices of farm inputs, outputs, and land value, the representative 400-acre farm experienced positive equity accumulation over the 10-year planning horizon because of increased profitH and land value appreciation. Hesults are presented for three debt-to-

16

aHset ratios. They ref1ect the initial finan('iul situation for the repreHentativl' farm ns well as th!' maximum debt-t.o-aHsei ratio to be UH<'d in PxpanHion. The level of equity varied wi t.h tlw dPgr<'<' of levPrag1•. Equity grew from $12:\,000 in I m I t.o $2f)!i,OOO in I DHIJ with :!0 percent debt; it grew from $!'">9,000 to $1 :>:>,000 with 7G percent dd>t. (table G). This farm had no problem of survival. Equity for all ratioH of I<'V<'rag<• was greater than the minimum t>quity nePdPd to survive 1974 and 19HO. Without incn~asing h•vpragl', sufficient equity to t>xpand to the fiOO-acn• fnrm during the 1()-year planning JH'riod a<·<·um ul at Pel on lv with the highest leverage. If thP farm started with ;I debt of 7G percent, by 19HO it accumulatPd $1 f>fi,OOO in equity, but only $14H,OOO was required for Pxpan Hi on.

New Inflation

If thP inflationary trends bet.wpen 1971 and I fl711 continued through 19HO, the representative farm would experience a high rate of equity accumulation (table G). Although the value of assets requin•d for expansion would lw increaHing, l'avorahlt> earningH would make expan;;ion possiblP even if the t1rm hml low leverage. In 19HOequityisgrPnterthanminimum equity to expand filr all ratios of leverage. J:<~xpansion with 7G percent debt is al~:~o possible in 197G. The equity in 1971 is $149,000, but the mini mum Pq uity to expand equals $1:39,000. As noted earlier, tlw new inflation trends for output pricPs until 19HO iH not likely, so these results are not realistic for plannin~ purposes.

In table G the third set reflects a continuation of new inf1ationary trendH for input and land prices. It also shows historical trends for output prices beyond 1974. Recent rates of land value appreciation are not likely to continue through 1980 if output priceH follow historical trend~:~. The third set is a transition between the second and fourth sets. The second and fourth sets have the same land value appreciation. The equity is lower in 1!-lH0-$:36:3,000 compared with $H47,000 with :~0 percent debt, $:318,000 compared with $804,000 with GO percent debt, and $~60,000 compared with $7Gl,OOO with 75 percent debt. ThiH lower equity reflects the lower profits ofthecost-price squeeze after 197 4.

Table 5-Simulated r•:sults of operations for a representative 400-acre farm under three debt-to-asset ratio~ and two inflationary patterns'

Del> I lo il~~td ldliO of -----·---

30 pc1 c:cnt ~() PC I< ell I I'J pt!rc.c·nt Mnl!ll~tllt• Pattern of tnflallon

ancl dp!JIH.:atlon Year --------------- --------- -- ------------- cq11tly to

MirllrlHIIII Mllllrlllllll MIIJIIIIIIJIJ '>lit V1Vc11

Eqtllly-' eqtlllY to LqllliY' ('(jlllly to l {jtJily ' t·quity to nxpa11d 4 c!xpand 4 l'XP<liHI"

lll•.tiHH.al 1nflaiiOil applied lo output LC)/l pr~tcc;, mput prices, and 1,1nd value 19/4

1'!80

New lnllallon applied to output I<J/J IIIICC'>,tnpttt prices, and land valttu 19/4

New 111flation applied to input price~ cJiltl lilll(J valliC dtHJ IHstoncul nlfldliOil clf)pi1UCI to CHJiplll !)I ICC~

New tnflatiOil applied to tnput pncc:s <llld ht'>lortcal tnflc.ltton c~ppltccJ to outp11t p11cc~ and 1,1t1CI val11e

I 'J!lO

1<J80

I'JBO

1,00(1 c/o liars

1:>3 ](d

;>~(>

I J I ?.?3 fl4 7

Ju:J

210

1 t· or 1911, 1C)74, and 1980, equity acclllllttlalecl frnm lolfminq OSJCrations, as qcncratecl frotn t11c simulation analvsis, ts cmnp.;HccJ with min11num equity needed to expand and minimu111 equity ncccJcd to survive. ·'The reported debt-asset tJfio idcmltftes the initial financial pos.ition of the farn1 as well as 111c maximurn lcvcraqc to bn used in expansion. '1 Equity

In the fourth set of table G, land value and output prices were assumed to increase at historical rates, and input prices continued to rise at higher rates. Equity in 19HO was greater than minimum equity to survive and was less than minimum equity to expand for all ratios of leverage. But the lower rate of land value appreciation translated iuto lower equity levels in 19HO than in the third set. Under all of the debt­asset ratios, the level of equity increased only slightly from 1974 to 19HO. In fact, land values increased more than equity. A cost-price squeeze would result from the continuing input price inflation at new rates and from a slowing of output price inflation to historical levels. This could result in insufficient profits to cover interest payments and consumption in the last part of this decade. Even though equity stabilizes because of land value appreciation, a survival problem related to liquidity could develop. Refinancing of real estate would be necessary to cover current consumption and production costs.

Conclusions and Implications

The structural changes occuring in the sixties were apparently slowed by unusual inflationary patterns that American agriculture has experienced in the seventies. Favorable output price trends, combined with accelerated and value appreciation, have offset the cost-price squeeze that produced a survival problem for many farmers in the sixties. Despite the rapid increase in equity for efficient farmers, farm expansion has slowed in the seventies. The analysis

/,000 l,()()(j /,()()() /,II()() 1,000 I ,I WII cJollar.'i do/lw·.o.; dollars dollars dollar ... dollars

?10 :>JO C)'.J 1 ~J I 'J 1) 91 249 I.JO 186 fl') 10/ Ill ]~!, ?J;> ?ld l ~ ~) 148 :'b

?;)2 IOJ 16/ h/ <)8 I~

.!14 I 'JO ?JI \'l'l IJ<J ;->;! b32 804 41/ /~I ?B? '1?

b:.l? 318 4// ;~(·0 ;JB? tJ.;l

~34 40/ :'·1 / l.!

17

acculllUidtcrl fronl fartninq opea.1t1olls rncluUes net c.~1~h

accun1ulation Jlld asset value appreciation. '1 Mlllillllltll CflLlltY is the i.nnount needed to cxnancJ rrom a 400-acrc fattn to a 600~acrc filrtll. ·, Minimuan equity needed Lo survive~ repro· scnts l 0 percent of assets required to operate the 400-c:H.:H~

fartn.

of the representative farm shows that equity accumulation for an efficient farm was more rapid under this new inflation than under historical patterns. In addition, the analysis demonstrated that this inflation affected the potential for expansion. A highly levered firm could expand in 1971) under the new inflation but not until 19HO under historical inflation. Because of the expansion potentials and the reversal of increasing leverage in agriculture, farmers readed negatively to higher interest rates. This reaction contributed to the slowdown in farm size expansion.

Restricting farm expansion in the early seventies may be helpful for many farm firms if a severe price­cost squeeze develops from continued input price inflation and declining output price trends. Even with continued land value appreciation, highly­levered efficient farms would not be able to cover expenses and reach desin>d levels of consumption. Less effident operators, part-owners, or renters would have serious survival problems.

The inflation projections show the severe financial vulnerability of agriculture to continued national inflation. Although the input price inflation of the seventies has been related to national inflation, the rapid output price inflation has been related to various weather problems. A series of years of favorable weather combined with continued input price inflation would probably result in financial disaster for agriculture. Providing adequate agricultural credit is essential to offset this possibility while input prices are stabilizing.

REFERENCES

Ill Brandow, C. K, The Distribution Amon!{ Auricultural Producers. Commodities. and Resources of (;ains and Losses (rom Inflation in the Nation's l~'('()nomy, Amer. ,J. Agr. Econ. G:l; ~Jl :l, I>Pc. 1971.

121 ('hang, Anne An-Ning, Analysis of the Impact of Sl'lected Variahles on Farm Growth. unpub. master's thl•His. llniv. Ga., 1974.

I :11 I! ill, Lowl'ii D., IJiscussion-lmpact of Inflation rm Auriculture, Amer . . ). Agr. Econ., 6:3: 914-91!\ I )Pc. 1971.

111 llutton, H. F.. and H. K Hinman, A General Auricultural Firm Simulator, AE I< J{S 7'2, Dept. Agr. Econ. and Hunt! Soc., Penn. State Univ., Ma.v 19!iH.

lSI MussPr. WPsiP.V N .. and Fred C. White, The Impact of Manauement on Farm /~'xpansion and Surt•iual. JHI!H'rpn•!·wntPdatSouthl'rn Agr. Econ. Assoc. Annual MPl'ting, New ()rf(•anH, La., Fl'i>. :2.:,, 1m:,.

lfil Twt't'II•Jl. i,utiH•r, and .Janws l'laxico, U.S.

lH

Policies (or Food and AuriculturP in an Un.stahle World. A m<'r. ,J. A g-r. Econ., fi6: :l64-:l71, May 1974.

171 TweetPn, Luther, and Leroy quance, ThP Impact on Net Farm Income of National Inflation, Amer. ,J. Agr. Econ., :,:l: 914, Dec. 1971.

I HI lJ .S. Departmcm t of Agriculture, Economic f{l'Hearch Service, Balaru·p Sheet of the Fann.inu Sector, 1974, Agr. Inf. Bul. :376, St>pt. 1974.

191 U.S. l>epartmPnt of Agriculture, Economic· f{esearch SPrvice, Ueoruia Farms Rented (or ('ash: Gross Rent Per Acre and Value of Rented /,and. J.9!i.9-74. unpub. data, I 974.

110 I lJ .S. Dt'partmPnt of Agriculture, StatiHticaJ f{pporting Sl'rvice, Auricultural Prices, 1971 Annual Summary, .Junl' 197'2, ,Junt• IB7!J, and Nov. I 97-1.

1111 lJ.S. Department of Agricultun·. Statislil'ai f{pporting Sprvil'P, Numher of Farms and /,and In Farms. ,Jan. 197-1.

ECONOMIC EFFECTS OF A GUARANTEED ANNUAL INCOME ON A REGIONAL ECONOMY

by

Jerome .J. Hammond and Ralph A. Loomis1

ABSTRACT

In recent years, many income main ten ace schemes have been proposed to help the poor achieve an adequate income. This artiele summarizes a study of the regional economic effects of bringing all the poor in theStateofWashington up to a minimum income level using a guaranteed annual income (GAl) program. Input-output analysis is used to determine the impact of a GAl on the regional economy of Washington. The analysis shows potential benefits to the State from a GAl program. The size of the total income multiplier for all four options tested substantially enhances arguments for use of an income maintenance program.

Keywords: Guaranteed annual income, input-output analysis, Washington State economy.

Since the 1960's, Americans have become increasingly concerned with persistent poverty in their own country. Not only are there "pockets of poverty" in such areas as Appalachia, but poverty is also found in most of the large cities and many of the wealthy agricultural regions. Many programs have been initiated over the years to help the poor, but they have not reached all those in need. The President's Commission on Income Maintenance Programs states:

The postwar period has. witnessed a remarkable improvement in the material welfare of most Americans. Even with the effect of inflation taken into account, median family income grew by 76 percent between 1947 and 1967. The proportion offamilies enjoying a total income of $10,000 or more increased from 22 to :H percent during the same period. And, in recent years, we have taken justifiable satisfication in reduction of poverty from 22 percent of the population in 19!i9 to 1:3 percent in 1968. But the fact remains that 25 million persons are still poor [H, p. t:lj.

--·---'Agricultural economist."!, l•:conomie lie:,warch Servic!',

statio11ed at Washington, IJ.C., and at Pullman, Wash., 1'1'HP<'dively. l~dwnrd I. !{(~insel and two anonymous 1'1'VI<'Wers provided helpful comments.

19

The Council of Economic Advisors, in the Economic Report of the President, /9(i.9, writing about combating poverty in a prosperous economy, states:

Americans are increasingly prm;perous. Median family income in the United Statt•s (in eons tan t I H67 prict>s) rose from $H, ~ 1 0 in I H!iH to $7,974 in 1967, a gain of ~H percent in H years. Yet many families are still not able to attain minimum living Htandards. A preliminary estimate indicates that in 1968 about~~ million people lived in households with incomes below the "poverty line." While this is far fewer than in the past-more than 40 million wPre similarly situated in I H60-too many Americans remain poor I H. p. If">! 1.

There has been much discussion about the cost of eliminating poverty in the United States. Luther Tweeten [ 6, p. 71 stateH: "It is now becoming dear that the cost as measured by reduced output of goods and services under a comprehemdve income maintenance program is small, probably less than I percent of national income." Writing on the same subject, Robert Heilbronerl4, p. 41 states:" .. .it would take a

rPdi~tl;hution of only a f<·w pen·Pnt of GNP to bring all poor fami liP~ a hovl'llw official dt>~ign atNI poverty IPvPI."

Tlw co~t oftaking carp of the poor. a~ pointed out by Tw(•etPn and llt>ilbronPr, would lw small compared with thP prohh•m ofpovPrty. If productivity continues to i ncrPa~l' in thl' lJ ni tt-d Statt>s, thPn the ability to rPducP povprty lwconws a relatively easier task. N(•vprtiw](·~s. thP proportion of the population below tlw povpriy linP rPmains a sprious problem. The l'n•sidPni'~ Commission on Ineomp Maintenance Programs IHI eondudt'H that eve~ if the existing wP!fan· and relatt>d programs were improved, they could not assun• that all AmericanH receive an adPquatl' incomP. ThuH, the Commission rt>commt'IHIH thP adoption of a new program of ineom<· supplenwntat.ion for all Americans in need, not just isolated groupH.

In reeent years, there have been many income maintPnanee schemes proposed that would help the poor to achiPve an adPquate im·ome. A guaranteed annual incomP (GAl) is one of these proposals. Various aspPds of GAl programs have been t'xaminPd and studies have emphasized the problems of idt>ntifying recipients and the effects of a GAl on work t>ffort and labor supply. On the other hand, few studi l'H h avp emphasized income and output effects of a GAl program on a national or regional economy.

Research Objectives and Method of Study

Input-output analysis is used here to determine the rt>gional economie impaet of a guaranteed annual in('om!' for the poor in Washington State. In an input­output framework, final demand provides the stimulus for change in the system. A GAl would chang!' final demand for the output of particular s(•ctors of the State economy. Therefore, the analysis indudps projeciionH of changes in output and income m·cessary to meet t.hP change in final demand that would result from a GAL

The basis for the GAl input-output model used in thiH Htudy is the gross flows table of the 1967 WaHhington input-output model. That model aggrpgatl'd eeonomic activity of the StatP into '27 induHt.rial st>dors (table I) Ill.

For purpoHes of this analysis, personal eonsurnption is dividNl into that by low-income <·onsurnPrs and that by high-in<~ome consumers.~ The model differs from tht• classical "open" input-output modl'l in that the J>Prsonal consumption sectors and value created by tnose sectors are estimated within th(• model. Consumption relationships are estimated for all sectors for both income groups.

'St•(•j:q for an Pxplil'il mntl~t•mnthi<'al d<'H<Tiption of tlw guaraniPl'd annual inl'onw input-output Jll()(l<'l.

20

Table 1-Ciassification of Washington industries for input-output analysis, 1967

lnput·oulpttl sec tot

2 J . . . •

B 9

I<J II 12 l:J Ill I~

](i

II 18 l'J ?!J

?I ?? ?3 ;>4

?.!> ?fi 21

Assumptions

l:lrief descrlpt,on

I' 1eld and seed crops Ltvcstocl< and products Vcq!!lablcs, fr·uits, and other agri. rorc~stry, fishinq, ancJ mimng Meat and cJairy products CCllltliny, preserving, and beverages nrain rntlls i11H1 other foods Textiles and apJJarel L •JrlliJer r111d wood Plywoorl rnills PaJJcr ancl allied products p, int1ny and publishing Cllcmtcals and petrolcun1 Stone, clay, and glass Iron and steel Nonferrous metals 1- al)nca ted n1ctals MaciHncry 1\crospacc other tran~porlalHHl equiprTwnl ()lllCI 111allllfilCll1(1119

C()nstruclion ft ansportatron service~ Conllllll nic11t ion services Trade (wholcsille a11d retail) F111anc.c, msurance, ilnd real estate Scrvtccs

Although the GAl input-output model has some important modifications, all basic assumptions of input-output apply. In addition, the model assumes that Washington's contribution to the GAl program is paid for by taxes of high-income consumers and that such income (GAl) goes directly to low-income people, thus increasing final demand. The Survey of Consumer Expenditures. 1960-61 [71 was used to account for differences in personal consumption expenditures between high-income and low-income consumers. Use of such data assumes that residents of Washington consume about the same goods and services as do other residents in the West. Also, the proportion of expenditures by consumers was assumed to be about the same in 1967 as in 1961. Finally, it was assumed that the Personal Consumption Expenditures Grid used in the Commerce Department's 196:3 U.S. input-output study [ G] effectively allocates expenditures by Washington consumers among the manufacturer, transportation services, and the wholesale and retail trade sectors.

Guaranteed Annual Income

In 1967, it would have cost an estimated $536 million to bring all Washington families and unrelated individuals, with an annual income of less than $:3,:350, up to that level (table 2).

The model is designed only to use a poverty figure of $:3,:3110 for 1967, since the 1960-61 consumption data

Table 2--Cost of $3,350 guaranteed annual income~ Washington State (in 1967 dollars)

lnCOlllC

C1.JSS 1

LeS'> t11r111 $ J, II G $1, II 7 to $2,231

$2,232 l•> $3,350

r I/ tal

Cst1n1ated F .:nTlllics and lt1C01llC to

unrelated brin9 every. indiviclu.11~, one up to

1%7 S.3,J50 I[)COinC

.\'ru11 hrr [)o/lal'.'>

lO?,lel $? ,1 'll 119,296 ] ,6/S

91,2/G :;ss

312,/59

CIIC:tra11leed i nu1rnc

per cia'\~

.\1 il/iuns

$28t> 200 ~]

~3G

1 rt-,c l \)()0 tnco11W ci.Jsses were ilCI_ilJSte(l to l9b I usiiHJ tla! . onStlllJer pnu: inclr.x. For CXiltnDit"!, the~ 1 C)()/ lncnrrn~ ClilSS 1)t

·1,),11 I tO 'f,,?,?]l Wnllld llAVC t"leCn $1,000 to 'f. 1.,')09 in 1.0h0.

uc;ed to divide total consumption into that consumed hv low-income and high-income consumers is s;•parated at the $:l,OOO level. The consumer price index adjusts the $:l,OOO figure upwards to $:l,:lfi0 for I~Hi7.

Results of Analysis

J<;c;timation of regional economic effects of a GAl f'or Wnshington residents was done under four c;eperatP options:

(I) GAl financed jointly by the Federal Covernment and Washington State with the major hurdm on the Federal Government

(L!l GAI financed jointly by the Federal Uovernment and Washington State with the major hurd en on Washi n~-,rton State.

t:ll GAl financed by Washington State. (4) GAl financed by the Federal Government. The multipliers derived from the input,output

model provide a measure of the impact on output, ('Onsumption, and income in each sector and income in t.h<' State economy that could be expected from the guaranteed annual income.' 1 Therefore, the multipliers indicate, for the four options considered, which sectors of the economy are likely to be affected and the significance of the impact. For example, a positive multiplier for output in the wholesale and retail trade sector suggests that output will rise; a negative multiplier suggests that output will decline. In addition, the total income multiplier indicates the significance of the impact on the State economy. The larger the multiplier, thegreatertheexpeeted impact.

Option 1: GAl F.inanced Jointly, Major Burden on Federal Government

)

The GAI under this option is based on the ratio of taxes paid by Washington residents to total Federal

'Since the model is closed with respect to personal <'<msumption, the multipliers include direct, indirect, and Induced income effects.

21

taxes. The State's contribution to its own program would amount to over $7 million, and the net transfer of funds from the Federal Government would be nearly $fi29 million (table :J). It is assumed that Federal tax rates do not change.

The output and consumption multipliers for low­income and high-income consumers, as well as the earned income multipliers, are largest in the wholesale and retail trade, finance, insurance and real estate, and services sectors. These sectors show the largest increase in output, consumption, and earned income.·1 No structural changes in the Washington economy are implied.

Under option 1, the total change in output would be over $fi89 million; the total change in consumption for low-income consumers, about $:{69 million: 'and the total change in consumption for high-income consumers, $99 million. The change in earned income for all sectors, including earned income through personal consumption expenditures for low-income consumers, totals nearly $444 million (table :l).

The most noteworthy effect of option 1 is that the total income in the Washington economy increases by $1.H 1 for every dollar ofGAI added to the economy.

Table 3·-Sumtnary findings of a guaranteed annual incorne in Washington State, 1967, under alternative optiuns

IICill <li>IIOn \ llpl'~.L>pi>o>l I I<~~~_','~'_~ \J i/liu11 t!ullo1·s

Cll.:l 1",1! I 11~(-~{_j dllllllilf

i IICOfl H~ I •,j(, ~ ) ) { I ~' "' ~J -H)

r cdcrJI I fdllS I t;rc,

(IH!l) ~) ?. ~) .'I p, () 'l]fl

I ot ul clliinqe ill

otrlput ~ H~J 4 (r;l ] I l ~) () ."'

l'olal 1: lla nqc~ "' IOW-irl(OillO

COilSlltllptioll JG'l Jb() j(Ji~ JGq

Tut<ll CI1<JIHfl' '" hiqll·lllCOtllC'

C011Siltllplion <)9 O.J 0.69 10? rota I chan~JC "' ea1ned rnconw <'lt.l4 ~ j 3 :-> ~) ~) tir.l(l

I olal increa~c "' inc orne ' 'l I .J !J J 1 ;J ~ ~) (JH?

lnLrl liH.Oill(~

llHtl! ipi!CfS 1 l.l\l l.1.U 0,<18 1 .H ~

1 [[liS iS ltH~ i·lllliJlltll IICU~".~>diV \11 dl-lii('VC d 'h ~.J~() Ill( l)llH' fDI

nverv la1n11Y and ttntci,.Jtcd ltHilv'l<.ll!dl 111 Wi1~11tll 1 Jion St •. Jtc. 2 Total Cl1anye in CillllCd IIICI)IIH: 1', <11:11\'f~(! llY dCIC111HJ !I1C Chilll(jl~ i;l eatnccl i1H:nnw by t11e ?I ">l~ctnr~. lll·'·,t.~lC' ,·mel 1111: clldl-,qc '"

earnect lnt.cJJnc tl1rouql1 pl't'>lltl,JI Ulll'lltltliJ1lHill c~.xpetldJtu,e<> bv

loW-illt:omc ancl lli<Jil-incotnc cl'lllSttlllers. IIH· tnlitl 1/ll_'r"C;l'>t' 111

lllCOJTIC is cJer ivnd by ;KidiJHJ ttl~ F eclcr.-11 t1 diiSfr.r of funds to tile

totcJI ctlan~-~e in e.1rncd 111corllC. Tl1t:' t!lt.tl 1111 tllllf~ HllJitqJire''' i"HL'

dCrtV(:!d by ciJVIdltllJ lllC lnt,tl ill<'fCdSe Ill IIIU>IllC hV till' (~AI

lt(ltJrP. of $~1.J(1 mill1or1.

1Tlw t~·rm "earned incomp" means ineomp t·arned hy the sectors in-StatP as a result of a GAl and is usPd in thi;; study to differentiate from autonomou;; inconw ((ii\[), which originates from sources out.sid<' lht> State.

Option 2: GAl Financed Jointly, Major Burden on State

Tht• (;AI under this option is based on the ratio of taxt'H paid by Washington State residents to total Feclt•ral t>XpPncli tureH in the State. Under this option, tlw State would n•cei ve over $21 H million as a transfer of funds from tht• Federal Government (table:~).

Sim·p the State'H contribution to the GAl program is almost $:l!H of the $fi:l6 million, the output multipliers. conHumption multipliers for high-income ("(Jl1sumers, and earned income multipliers are Homt•what smaller than for option 1. Consumption multipliers for low-income consumers differ little for opt.ionH 1 and 2, because the GAl is designed to benl'f"it low-income commmers.

The most noticeable change in output between option 1 and option 2 occurs for wholesale and retail tradt', which decreases nearly $44 million under option 2. Most of the consumption multipliers for high-income consumers were zero or near zero, indicating that consumption by high-income consumers would change little as a result of the GAI program. If the State's contribution were any higher for option 2, the consumption multipliers for the high­income group would be negative.

The earned income multipliers are much lower than under option 1. This is especially true for the communication services, wholesale and retail trade, finance, insurance and real estate, and services sectors. The total increase in output is $462 million. Consumption increased by nearly $:-366 million for low-income consumers and declined by $0.:~ million for high-income consumers. All of the sectors, including personal consumption expenditures for all consumers, earned over $:-3:{;) million more than they would have without the program. The total income multiplier for option 2 is l.o:3, indicating that total income in the Washington economy increases by $1.o:3 for every dollar of GAI added to the economy (table :3).

Option 3: GAl Financed Entirely by the State

Under this option, the State's contribution and the GAl figure are identical, because the $536 million is a direct transfer of funds from high-income to low­income consumers within the State (table 3). Therefore, the consumption multipliers for low­income consumers remain nearly the same as under options 1 and 2.

Under option :J, nearly all of the output multipliers, consumption multipliers for high-income consumers, and earned income multipliers are considerably lower than for options 1 and 2. All output multipliers remain positive, except for the textiles and apparel sector, which was only slightly negative.

All consumption multipliers for high-income consumers were negative except for the iron and steel

22

sector, which was zero because of rounding. For every dollar of GAI going to low-income consumers consumption by high-income consumers would diminish by 1:1 cents.

The total change in output for option :J is almost $:37:3 million. Consumption increased by over $:l!i4 million for low-income consumers and declined by $69 million for high-income consumers. The total increase in earned income, inc! uding income earned through personal consumption expenditures amounted to more than $255 million (table :3). '

The total income multiplier for option :3 shows how much income varies with a GAl funded by redistribution of income from high-income to low. income consumers in the State's economy. The model indicates that income would increase by $0.48 for every dollar of GAl redistributed, substantially less than estimated for options 1 and 2.

Option 4: GAl Federally Financed

Under this option, the GAl amounts to a transfer of $Ga6 million from the Federal Government to low­income consumers in Washington (table :!). Therefore, the results of options 1 and 4 are much the same, although the State's contribution under option 1 amounts to $7 million.

The output multipliers, consumption multipliers for low-income and high-income consumers, and earned income multipliers are all nearly the same as under option 1. However, because the transfer of funds from the Federal Government would be greater under option 4, estimates under this option are always higher.

The total change in output under option 4 is $592 million, the total change in consumption for low· income consumers is $369 million, and the change in earned income amounts to $384 million. When earned income as a result of personal consumption expenditures is added, earned income amounted to $446 million (table 3).

The total income multiplier for option 4 shows how much the State of Washington income varies under a Federal GAl program. Income would increase by $1.83 for every dollar of GAL Therefore, the total income multiplier for option 4 would be the largest of all four options.

Conclusions and Umitations

Results of this study show significant potential benefits to the State of Washington from a guaranteed annual income program. The size of the total income multiplier for all four options tested substantially enhances arguments for use of an income maintenance program. This study, along with other analyses of income maintenance programs, provides added justification for the

adoption of such a program. Presumably, similar effects could be expected in many other regions.

Guaranteed annual income programs are not designed specifically to foster economic growth: Hence, the relative growth stimulus of public funds invested in the guaranteed annual income program is secondary to the goal of assisting low-income families and individuals to improve their income position. A GAl program would deal with low­earning capacity, not its causes.

The gross domestic product (G DP) for Washington increased under all four options of the GAl program. Without the GAl, the GDP amounted to $12,7:l~l million. The GDP for the options in this study ranged from $12,988 million under option a to nearly $1a, 71() million under option 4.

As expected, the greater the transfer of funds from the Federal Government, the larger the impact of the GAl program on the State's economy. Option 4 has the largest net transfer-the full $5:36 million-and thus the greatest impact. For example, the total change in output is $592 million, compared with $:l7:l million for option :~. which has a transfer of funds amounting to "zero."

Because the GAl under option :3 is funded by the high-income consumers of the State of W ashin~-,rton, the total income multiplier-0.48-is the smallest of the four options. For every dollar redistributed from high-income to low-income consumers in Washington, aggregrate income expands by $0.48. This confirms that low-income consumers spend

relatively mon• of thl'i r Pxtra inconw on <'()11 sutn pt ion than do high-inconw <·onsumPrs.

WhPn appli~·d to tlH• P<'onotll_\· of t lw St:11 <' "' Washington, th<' {;A I in put-output !ll()d<·l pr!l\· id<·s useful in formation, hut it doPs h a V<' sonH· li 111 i I nl i" n s. One limitation is that UH• consumption d:ll;1 in tiH· modt>l nllow a povt>rty lin(• figun• of only ~:\,:\:-,(1 for J9(-i7, the year to which tlw analysis appli<·s. Thus, whi-le tlw model itself is fl<•xihk th<· hnsil' consumption data an• not. lkcausp tlw transactions table is a pictun~ of thl' <'<·onomy in a giv<·n tinH· period, usually for a yPar, tlw modPI is static.

ThP assumptions of input-output analysis ar<' som(~what limiting. For Pxampk, constant l<'l'hnil'al coefficients limit input-output analysis to th<· shod run. It can be argued that tPchnologil'al advan<·ps and relative price changps bring substitution of inputs, and causP technical copfflci(•nts to dwng<' in the long run. In reality, on<' unit of output dot•s not always require tlw samp proportion of inputs as thl' model assumPs. NeithPr cloPs a s(•clor producing two different types of goods always produc<• tlH'm in the same ratio. Further, economists nn· awarP thnt external economies and clisl'eonomil's <·xist. but t.hPy an~ not incorporated into input-output analysis.

This study analyzes the guarantPPd annual itwonw only in the State of Washington and SJH'l'ific inferences about other States ('annot. ])(' mad(• from the analysis. However, this study do(•s provid(~ a method of res(~areh which could lw adapt.Pd for usl' in other regions and States.

REFERENCES

ill Beyers, William E., and others, Input-Output Tables for the Washington Economy, 1967, Seattle, Wash., Graduate School of Business Administration, Univ. Wash., Dec. 1970.

121 Bourque, Philip J., and others, The Washington Economy: An Input-Output Study, Seattle, Wash., Graduate School of Business Administration, Univ. Wash.andWash.(State) Dept. of Commerce and Economic Development, 1967.

131 Hammond, Jerome J., The Economic Effects of a Guaranteed Annual Income: An Input-Output Analysis of the State of Washington, Ph.D. thesis, Wash. State Univ., 1974.

14] Heilbroner, Robert, "The Economic Problem," Newsletter, Vol. IV, No.1, Englewood Cliffs, N. J., Prentice-Hall, Inc., fall 1972.

23

151 National Economies Division Sl.afl. "Input­Output Structure of thP U.S. Economy: I !H1:l," Survey of Current BusineHs, tJH: 16-17, Nov. I H69.

161 Tweeten, Luther, "Emerging Issues for Sparsdy Populated Areas and Re~-,rions under a National Growth Policy," Paper preHented to ,Joint American and Canadian Agr. Econ. Assn., Edmonton, Alberta, Canada, Aug. I 97:!.

171 U.S. Bureau of Labor Statistics, Survey of Consumer Expenditures, If)(i0-(i I, Supp. :1, l't. A, BLS Rept. 2:\7-92, 1966.

18] U.S. President, Commission on ltwom<' Main­tenance Programs, Pouerty Amid Plenty, 1!)(19.

19] Economic Report of the President, I!)()!!, ,Jan. 1969.

HOW COMMERCIAL BANKS FINANCE SMALL FARMERS THROUGH AGRICULTURAL CREDIT COOPERATIVES

IN INDIA

by Glenn C. W. Ames 1

ABSTRACT

Major objectives cJf the recent nationalization of 14 commercial hanks in India included opening of rural banking services and expanding the availability ufloans for small farmers. Commercial banks have had mixed results in using agricultural credit cooperatives to help finance agricultural production. The cooperatives have the facilities and experience that are needed. If managerial costs are subsidized and cooperatives are revitalized, improved working relations between the banks and cooperatives could lower administrative costs, encourage farmers to seek loans, and decrease repayment problems. This paper describes the systems approach to financing small farmers.

KEYWORDS: India, rural, credit, bank.

In the past 21i years, governments of many developing countries have started small farmer loan programs. The most common type has been the special loan program in which a single agency is responsible for agricultural lending. Examples of spec'ial programs include supervised credit in Latin America, cooperatives and farmers' associations in Africa and Asia, and total input package programs. The results of the special program strategy are widely available in literature [2].

A second common strategy wa~ to persuade such institutions as private, commercial, and state banks to finance the needs of cultivators of small landholdings [ 6j. Several authorities call this the systems approach. 2 It is a program of government­sponsored programs of indirect financing through private lenders jl j. The systems approach encourages financial institutions to allocate a larger proportion of their loans to small farmers. This approach has

'Assi;;tant profes!;or, Department of Agricultural Economics, University of GeorJ.,ria. ThP United States Agt>ncy for International Development providPd financial support for research conducted in India.

'Th!• term "systems approach" has been usPd to describe produdion loans provided by such legally recognized entities as commercial banks and farmer cooperatives.

24

been used in India, Pakistan, Bangladesh, and several Latin American countries. Little information, however, is available on the effectiveness of the program or how it works when it is combined with the special program strategy [ 6].

Major differences between the special loan program and the systems approach often involve the degree of government financing and participation. Special loan programs include direct government financing and closely supervised distribution of agricultural input kits containing seeds, chemical fertilizers, plant protection chemicals, and extension information for selected groups of farmers. On the other hand, objectives of the systems approach are to induce lenders to service the borrowing needs of farmers that have small landholdings and to assist them in using ,larger production loans. Generally lenders supply production loans and allow borrowers to purchase inputs from available suppliers. In initial stages, systems lenders lack adequate criteria for identifying the progressive farmers and the infrastructure limitations that often lead to production credit repayment problems [5].

The purpose of this article is to examine systems financing. The example shows how nationalized commercial banks in India finance small farmers. Specific objectives are (1) to describe how commercial

banks finance farmers through cooperatives, (2) to dPscribe how nationalized banks meet the needs of small farmers in Karnataka (formerly Mysore) State

in India; and (;{) to outline some adjustments that may he needed by commercial banks to serve the requirements of small farmers.

BANK FINANCING OF FARMERS THROUGH COOPERATIVES

Indian cooperatives arP a fundamental part of the National Five Y t•ar Development Plan. State govPrnmt>nts can stimulate agricultural development hv using cooperativ<>s. The Reserve Bank of India fi·nanct•s and subsidizes the agricultural cooperatives through a three-tiPr organization consisting of the staLl' cooperative through a three-tier organization consisting of the state cooperative Apex banks, district cooperative central banks, and primary agricultural credit cooperative societies at the village lt>vPI. The H.eserve Hank of India grants loans to the state cooperative Apex bank. The Apex bank finances the district cooperative central banks. They, in turn, make loans to the village cooperatives. The primary cooperativt>s then finance the farmers' seasonal agricultural operations through the crop loan system [ 14 [. The organization of the Indian eoopt>rative system is illustratPd in figure 1.

U nti I 1969 commercial hanks operated only in <'ilit>s and towns with large commercial and industrial centers. Hut in July 1969, 14 major commercial banks in India were nationalized. This opened up new sources of finance for agricultural eredit cooperatives. As a result of public policy since nationalization, commercial banks increased the capital available to farmers on a selective basis. Supplying crop production loans to farmers and revitalizing the administration of the cooperative soeieties have been two important tasks facing the eommercial banks.

Even before nationalization, the Reserve Hank of India used its licensing policy to encourage a branch Pxpansion in rural areas. Since nationalization, over fi4 percent of the G,:158 new banks opened in rural areas IH]. During 197:3-74, commercial banks opened l,G9:~ new offices in India. OfthPse, 909 new offices, or f>7 percent, were opened by 14 nationalized hanks, and 19 percent were opened by the State Bank of India and its subsidiaries. Other commerical banks orwned the rest [ 1:![.

How Commercial Banks Finance Cooperatives in Karnataka State

In 1970 commercial banks introduced a program of finandng primary agricultural credit cooperatives in SPven districts of Karnataka State. The district eooperati ve central banks were administratively and financially weak. They were ill-equipped to meet Hlo{ricultural borrowing needs of all farmers [ 18]. The commercial banks encountered many of the same problems that hampered the district cooperative central banks, such as high overdue payments and

25

defaulting borrowers. The district cooperative hanks usually assigned to the commercial banks those societies with the poorest financial resources and operational efficiency." Many cooperatives assigned to commerdal banks could not borrow from the district cooperative banks, because their large loans were overdue and had not been repaid. Under these circumstances, commerdal banks could not advance 1ww loans until thP cooperatives recovered the old dPhtH. Several banks ddt•ted numerous coopt>ratives assig1wd to them lweause of these debts. ConsPquently, many commercial banks havt' had limitt>d sueePsH in expanding agricultural credit to <"Uitivators through the cooperativt>s.

The program of financing primary agricultural credit cooperatives by nationalized commercial banks started with 12 participating banks during the 1970 Kharif (fall) production season. Banks WE're assigned ()41) cooperatives or about 6 percent of all agricultural credit cooperatives in Karnataka State in I 970-71 [ I8[. About 76 percent ofthese groups were actually financed by commercial banks during the first year. At the end of September 1974, the eommerdal banks had been assigned 991 cooperatives, and they had financed 841. Commercial bank financing of cooperatives in Karnataka State is shown in table 1.

The Government of India and the Reserve Hank now tmcouragt• commercial banks to finance cooperatives t~ven in districts where the district cooperative central banks are financially sound. The l{eserve Bank of India also revised upward the rate of interest charged cooperatives. Farmers were previously charged about 2 peretmtage points above the rate charged cooperatives for short-term crop production loans [ 12[. 1

Cooperatives are an important source of crop production loans for farmers. Loans consist of cash and of kind components, usually fertilizer. If a farmer in Bangalore District, for example, applies for a loan

1[n J{Prll'ral. distril"l coor)('rativt• t•t•ntral hanks havt• not had thP finandal rt•soureps to mt•Pt tlw llel'<ls of all coorH•rntivt• sodl'tiPs. l•:vPn so, the numht•r of coopt•rative~o~ f'inant·l'd h:v tlw Bangalon• llistrict. Coopl'raiivt- Cmtral Bank has inen•ast•d from Hii in I Blm· 70 to 14H in I ~ln-7:l. In addition, tlw numlwr of cooJwrnt.ive mPmlwrs rPCl'iving short. and nH•dium-term t"rPdit rost• from I .~lf1H in I BH~l-70 t.o 2 H(iB in I H72· 7:!. Tlw average arnounl of t"r!'dit pPr horrowl•r r:IS(' from ns. H~fl toRs. 1.:3~0 during tht• 1.4llllll' pt•riod.

1Tht• Hesl'fvt• Bank revised upward tlw rat!' of inten•st charged eooperativt'l:< twiet• during I H74. 'l'lw rnte mnvPd from 7 to I I pereent on loans of less than Hs. I ;,o.ooo and 12 pcrct.•nt ahovt• that amount.

Life Insurance Corporation of India

'

Indian Cooperative Credit System

Reserve Bank of India that includes­

Agricultural Credit Department Agricultural Refinance Corporation

I I I I t

State Government

Food Corporation of India Cotton Corporation of India

I

Revenue Department

Ag ro-1 nd ustries Corporation

Cooperative Department

Agricultural Department

State Land Development Bank

Primary Land Development Banks

State Cooperative Apex Bank

District Cooperative Central Bank

Large farm Medium farm

---Services ----Finance Figun· 1

26

Commercial · Banks

Small Farmers Development Agency

Farmers Service Society

Table 1 -Commercial l,;ml< loans to cooperatives ill Karnatal<a State, lndi" 197 3-74'

Cattara nan!, .. <-,tJle Hcud' ol MVS11re

~)VtHiiUll,~ j),lllh

lrrdr,trr t Jvu SC:ilS Htllll< ..... .

Ur11kd c . .rtHrlL'I'Ci,ll Bani<

c,\a\(• Han\.: ol 1-IVdi.":r.:Jb,HI

UrlHllt Bani.:. t>l ltt<lra

Vy,Yil H,1111, I t.l.

Ct:ttltiil H.1r1l~ ol lndtJ

l'tltlJ,dJ Ndtt,lll.ll C<Hpor<J·

trott f{.trtk I ld.

lulat

I "II I 'i 1.1

Ct••JIIt·t.t

lttlc]llf'f!<l

(>/()

/\JlHHttlt

()! IIJ,lll'l

.\/ilfio11 /'II}JC'f','\ 1,

8.9:'8 I 7 .?!o I ~),C) j !J

.41? J.~ 7 3 2. /4~ .J~b

1 .0?8 .1 ()()

.'..>CJ?

.lb\

Wrn\t~r

Cu<lJH-:1,1·

I r vt~ ._.

ft tlancecl

Ntunh,·r

jl

(,8

13

!>

1 ?

?

1.!9

1 •)otrru~ of tldl<~ ,., 11?1. -'lndnr... rn.Jd(' •lttrttt~JIIw ~<.lld!'il {fall)

c_ 1o1q>tnq sc~dsnn r'nrql'lly .June: tllluttqll ~'>··plclnl>,~r. '1 Orlll~ llhHk

d~tr 111q Hal>1 (wtnler) Ltl)pptrlq Sf~<JSon tt'dl.llly r )clolwt llnotl•ilt

.L\11\l.ll y. 1 C.l1Cllltll1~rl 011 \lie i)ar,l~ of 10,111'> (/ll WI liCit dll

to finance an acre of high-yielding variety of rice, the local cooperative would approve a loan consisting of lis. 170 in cash, H.s. ~HO for fertilizer, and H.s. fi() for ppsticides II Oj_,-, Accordingly, loans for other crops are adjusted to estimated cash production expenses. Credit limits per crop per acre are set during the annual field workers conference of bank representatives, extension officials, and prominent farmers.

Many of the commercial banks adopted the scales of finance for short-term crop production loans used by the district cooperative central banks. In some <"asPs different scales of finance were used. Conference committees resolved the differences lwtween the commercia! banks' and district cooperative banks' loan limits for specific crops in their respective districts. In addition to crop production loans, the commercial banks provided intermediat{! term financing to farmers through <"OOI><'ratives. IntPnnediatP krm loans were approved for dairy, poultry, swine, sheep, and sl'riculture enterprises as well as for deepening old wells, sinking new wells, installing electric pump sPts, and purchasing power tillers. Commercial hanks issued Rs. :~:31,000 in intermediate term loans to 11 cooperatives by the end of the Rabi (winter) 1971-n. By the end of September 1974, commercial hanks loaned Rs. 6,514,000 in intermediate term loans to ~:32 societies, an increase of :300 percent in 4 Yl'ars!I~l-

Since the beginning of the program, commercial banks increased the average amount of the loan per cooperative society from Rs. 27,000 during the fall of

--------·Onp rupel' l'quals $0.1:! U.S. dollar.

'

27

l'JIJ/1·1

1\llHH I rl\.,

of lnJn:.

~Hill ion l'llfJC'es

h

1 '1 'J.) J.b~ 1

.:J j~

,] ~()

.2b<J

.051

.OG3

.004

.08'J

.OI'l

~)./~?

CllOIJCiol

ltVI:~

ltrlfliiU~cl

lJB ?hh I~

~.

J~

')

II 1 j

/\rnt 11111 t nf lu.11l~

1/ il/in11 l'llfll'c>s"

C),/() I

20.1 J? 1.3~()

.3~ 1 I. 12b

.2(,()

.t. I~

.81 1

.180

.07!.1

4:'' 144

Lncl ot Vt',H

CoopCI.l

I IV(''} /\tiltH! II\

l1n.1nt. t:(l of 1\),)t\')

Sutlll)('r t1lillioll l'ti/H'I'S

117 1 3.0~2 ](,4 2b. I<);'

81 B.GO I 12 .~92

~lO 3.420 48 3.03~

~9 .<J~R

?9 .'J I 4 10 '1 13 1 ~ l.?tl(,

8 '!/()

B~ I t~:)JJf)J

~utsld11d1nq hal.ltll(· w.v, oW(]rJ ~1~, of <;cptemiJcr JO, JC)/,1.

< dl< 111.._1tvrt 011 1111: !Ja~t~ of loans ltllltally made per uoppltHJ

~t!r:lSOtl. ''(>nc l'tJper~ equals 1,().}J U.S. dollat.

1970 to Rs. 7fi,OOO in 1974. The average loan per cooperative in 1974 was Rs. 70,000, up 7 percent from 197:3. FarmerB' cash expenses have increased dramatically, straining resources of commercial banks. In Bangalore District the price of ammonium sulphate rose from Rs. fi40 per ton in 1970 toRs. 9~5 in ,June 1 H74.n

Commereial banks also worked with the Small Farmers' Development Agency 7 (SFDA) I fi, 9, 11] in Kamataka State to help farmers who owned 5 acres or less obtain credit and agricultural inputs from cooperative societies. In addition, commereial banks sponsored Farmers' Service Societies in cooperation with the State Government and SFDA for small marginal farmers who are receptive to modern innovations. Programs involving commereial banks ind ude the State's crash sericulture program started in 197:3. Commercial hanks were directed to finance and expand the credit program for sericulturists, silk reelers, and others in the industry in Mysore, Kolar, and Bangalore districts.

"This unpublished data i;; from M. B. Nnnjnppn, .Joint I Jin•dor, Bun•au of Economies and Statistics, BangalorP, India, Decem her 1 :!, HJ74. '

'In 1970 the <iovernment of India !'stabli;;lwd the Small Farmers' IJeveloprnent A!{erH:i<'H to lwlp small landholders obtain financial and technical assistanc<' for modern a!{ricultural production. ThP ng<>ndPs identify small landholders who have potentially viahlt• operations. Thl~ agPnei<>K provide the landholden~ with share capital requirements, risk funds, and other sulll;idies so tlwy can bt•t·omP active mPmlwrs of coopPrntivPs. ThP farnwrs can then qualify for crop production and int.errnedintt>-lerm loans. The a!{encies work dosl'ly with district coopPrativP central banks and commercial banks t.o eoordinatP tlw flow of funds from cooperatiVPH to small farnwrs.

Financing Difficulties in Bangalore District

Thl' Bangalore District in Karnataka State had many of the typical problems encountered hy commercial hanks in financing cooperatives. These prohlPms indudt>d large amounts of overdue credit from coopl'r ati Vl'S, poor admi ni strati on of the vi !!age soci l'ti t's, and tht> apprehensions of offici a] s who wert> afraid that commercial banks would weaken the farmpr's faith in the cooperative movement. During 1970· 71, :1 commercial hanks were assigned 7'2 coo1wratives. They actually financed GH societies with Rs. '2.G4 million, an average of Rs.• 4:{,000 per

sodety. During 197'2-7:3, commercial hanks were assigned another 144 societies, an increase of 100 percent since the program started. But only fi hanks actually made loans to H 1 cooperatives in 197'2-7:1. The other :l banks were not able to d(•velop a finanrp program for the cooperatives assigned to them during the year IH]. In addition, many banks werp apparently expanding their programs to rnppt

development targets. The targets WPrP thP number of societies served rather than meeting the credit nePds of a majority of farmers. Commerc-ial bank financing of cooperative societies in Bangalore District is shown in table '2.

Table 2 -Commercial and District Cooperative Central Bank financinu of cooperative societi"s "' Banyalore District for 1970-71, 1971-72, anti 1972-73

()Ullt Lt' dtHI Vt:c1t

CooJJl'tattvc'-> d">'>I!JtH'i.l

I ') I IJ I I I 9/J .; ?

191?-1 J

Cuopctdllvc·~ ltiJJtl{ 1:1.1

I ')I 0 I I I') I I 12

I 'J I? I .J

/\tll< tl Ill! r 'I ,I: 1 • r 1 . \ · •

' . r 1 ~ , J ' 1 ' · , 1 ' 1 1 , I

I 'J I U I I

I 'J I I ·I?

1 'l I? ·I J

Shorl·lt.!llfl Crt'dtt tr.~pdltl

I') l 0 I I

I 'l /I· I :•

I 'J I? I J

Udtl<j.JitHC'

{J.<:.t:. Hattl<

{,j()

{)_]{)

~)HI\

HI)

I 1 I 14u

:>.I I

.!.8:l

.J. I 'J

I'J '3? J I

J()

I 0

~

8

(J. 12

.l/

J I

.II

.!4 38 39

34 ?<) ?<)

J . ~)? 1.?0

t>O ~ J 1.\

1 '-•IHJILI': II/I. In 19/?/l.~.stxconHncrctalbank~loatH:cJHJ cool)crativv. 1{<:., J.H tllllltnrl, lnforr11ation 011 llw alllOUill of

credtt fro1t1 llldtvtclu<.ll IJ.:Itll"'• was ttot available~ in sub~cquent

CaltiHit

lie] nl<

--. Nunzber ...

()

4J j~)

()

.J 14

~ • - 111illion rupe('s ·

()

.31!

J.??

· - . l'etcenl · ·

% ~JO

I 0 JO

8

~.

:: 8

I.?H

31 j()

.JI

II 10 I J 10 3? 10

') ~

II 4

:>a .!.

O.J 1 0.08 .J? .O'J

years. IIH! Can~Ha Hdlil-. cl.:lllncd 1l ftnanccd one mot(; cooperattve soclt:ty lltdtl lite [.)C!JHlty neq15lrar's report indtcatcd .•

FARMERS SERVICE SOCIETIES: COOPERATIVES FOR SMALL AND MARGINAL FARMERS

The National Commision on Agriculture of India recommended that commercial banks sponsor Farmers' Service Societies. These societies would provide, from one source, integrated loans and technical services for small and marginal farmers who are receptive to modern agricultural technology ]9, p. ii]." Generally the SFDA or Marginal Farmers and Agricultural Labourers Agency (MFAL) organizes the Farmers' Service Societies at the block

'H. Y. Halagopal was interviewed at the State Hank of Mysore, Hangalore, on ,June 16, 1972.

28

development level, serving a population of about 10,000. A Union of Farmers' Service Sodeties at the district level coordinates the policies.

Fanners' Service Societies are registered in the State as primary cooperative societies. According to the National Commission on Agriculture, Farmers' Service Societies should supply all of the development needs of small and marginal fanners, agricultural laborers, and village artisans either directly or by special arrangements with other agencies. The Lead Bank in each district-nationalized bank, State bank, or cooperative-has the overall responsibility of

integrating farm produdion loans with the supply of inputs and services for small landholders [15[. Initially Farmers' Service Societies were organized in districts that had functioning SFI>A's. Bangalon• and Mysore Districts are the primary ones in Kamataka State. Although these societies follow tht> cooperative practices, they are too new to Pvaluate adequately.

Operational Problems in Bangalore District

The officers of participating commercial banks reported several operational problems at the bank and village levels. First, many commercial banks lacked experience in financing small farmers. Their main problems were the leadership of village cooperatives, collecting past debts, and restoring farmer confidence in cooperatives. Second, crop failures because of climatic conditions and insect damage prevented many farmers from repaying their loans [ 4, p. 29-31]. Third, the new credit program was delayed because the commercial banks and the district cooperative central (D.C.C.) banks continued to differ in the selection and financing of cooperatives [16[Y Finally, cooperative officials and some farmers feared that commercial banks would dampen the cultivators' enthusiasm for cooperative credit and weaken their faith in cooperation [7]. Bureaucratic disputes over credit policy between the commercial banks and D.C. C. Banks was certainly a problem in the State.

According to one bank, the problems of coopera­tives were poor management, lack of supervision,

"Since inception of the program in Hangalore llistrid, commercial banks have charged that cooperatives were not allotted to them from the D.C.C. Hank as promised and hence they could not finance them. However, commercial hanks have not financed all of the cooperatives allotted to them and these were retransferred to the D.C.C. Hank. Confusion over sources of finance often means that farmerH do not receive credit on time, if at all.

reluctant attitudt> of th(' borrmvt·rs toward rPpay· m(mt, and int'f'ficiPnt rP<'OVPr.v pro<·Pdun•s-'" Th<·st• problems ac<·ount<>d for tlw lnrgP amount of ovPrdu<• credit at the time the cooJJPrativ<•s W<'r<' tak<•n ov<•r h.v the bank.

The Government of India's National ( 'ommission of AgriculturP cotH'ludPd that sUJH'rvision of cooperative socit>ti ps was tlw crucial k <':V to s u<'<'<•ssf'ul institutional crPdit programs IDI. In th<· past. supervision of COO(H'rHtivPs has not hPPn a two-wav flow of in formation. I nstPad, tlw l ).( '.<'. hank~' recovery offic<>rs gPrwrall:v hav<> dogrnatil'ally dominated th<' <'OOJH'rativ<' sol'iPtiPs. TIJ('y W<'r<' inten~sted only in thP r<>cov<'r:V JH'l'<'('ntag<• from a society rathPr than in thP undPrlying ('a usps of th<· cultivator's repaymPnt prohlPms. If til<· <·omrn<·t'l'ial hanks' agricultural PXtPn si on ofli ens a r<• s<>n sit i \'<'to farmers' needs, tlwy can makP substantial progr<•ss in improving crop production r<•pa:vm<>nt through enlightened supervision of coopPrativ<' socit>li<'s.

Commercial banks in Bangalorl' District had a wide range of ratPs for short-tPrm loan n•payrnl'nts. The State Bank of MysorP atlri huted its initial t•n•clit repayment SUccess nm JWI'Cent) to thP SUJ)l'rior training of the paid secretary at tlw villag<' l<>v<>l and to the bank's technical officier who show<•d tlw fanners how to use production cn·diL

Other agricultural loan off'icprs blanwd poor leadership at the local coopt>rative l<•vd for thPir repayment problems. These officPrs also n•cognizl'd that the uncertain rains in tlw d r:vfarming rpgion s of Bangalore District created rt>paymt>nt difficultiPs for farmer;; because of low n<>l returns p<'r atT<'. In general, the major r<~asons for crop produetion (T<'dit repayment were the low returns from dryland farming, poor cooperativ<' leadt>rship and administration, and di;;trict politics (factionalism).

"'This is h:u-wd on a l<'lt<'l' f'rom K. S. Kamalh. St•nior SurwrintPndPnt of lh<' ( :anara Hank, 1\angalon·. India. on F<'hruary I :r, I D71.

POLICY CONSIDERATIONS

Nationalized commercial banks developed credit programs for previously unfinanced groups. The banks designed several liberalized credit programs under the Lead Bank scheme for such neglected sectors as agriculture, small-scale industries, small businesses, and self-employed persons in their assigned districts. A few banks developed pilot projects to provide productive credit, at concessional interest rates, to special categories of borrowers who would not normally qualify for loans [3]. In addition, commercial banks are sponsoring Farmers' Service Societies for small and marginal farmers. But these liberalized credit programs could face the same repayment problems as those of the distrid cooperative central banks.

29

Agricultural finance officer;; must not plac<' grmt<'r financial burdens upon small farm<'rs just to nwd development targets. Prolitahle r<'lurns on agricultural investment an• rwcessary. If PXCPssivP development targets an• impostPd on commPrcial bank;; to encourage thPi r financing programs and if a solid base for agricultural production is not built, then the previous pattPrn of ovPrdul' loans and failing cooperative sodetieH will be r<'p<>atPd.

Agricultural loans must I)(' bas<>d on a r<>alistic asHessment of farnwrs' requirPrnPnts. Also, th<> commercial bank;;' scalPs of li nanc<' are bas<'d on th<> psti mated production costs and prod udi on pot<·nt i al of tlw borrower. ThPs<' scal<'s lll'<'CI JH•riodic rPvisi<>ll to rdlect changes in agricultural t<·chnology :l!ld

pmdu-:"tion co!'!s. Tlw comnwrcial hanks' program of providing tt·chni('al :1ssis!.mH'P along with loans through Fann<•r:-:' S<•rvict•SociPtiPs ha:-: po!.Pn!.ial. But :-:onw of tlw farm loan ofllcPrs, or PX!.Pnsion agents, rnav IH' young agricultural coiiPgt• graduatPs with lit lit· pr:ll'!.it·:d PXpPrit>IH'P and litt.lt> u:-:ahiP tPchnical :Hh·in· lo off'Pr. Tlwv will find that building the L 1 mwr' s trust is d i !lieu] t i fthPi r rPcomnwnda ti ons do Jlllt improvl' tlw f:1rnwr's ability to rppa.v loans.

Conclusions

( :omnwrcial banks havt> had mixed succesH in providing financing through cooperativeH for farnwrH with Hmall landholdingH. The changPH brought b.v the r·;ppcial systems approach have not in<TeaHt>d loanable funds for small farmers in Ka rnataka State. Commercial banks did not finance all of their aRHigned cooperatives nor all of their fa mwr mPm lwrH. For exampl<', in Kamataka State commercial bankH financed about :~9 percent of the mPmbers of their cooperativPs in 1974.

SevPral reaHons account for the failure of the lt>nding inHtitutionH to provide loans for most small landholdPrs. FirHt, commercial bankH were very c<mservative in their approach to financing farmers through cooperatives. They had little previous experience with cooperatives and agricultural

11 FarmPrs do not borrow from their coopPratives for such rl'asons as defaults on past loans, failing to provide cornpll'l<' land rPcords, and acquiring funds to provide share capital requirempnts of nbout. I 0 pPrcent of thP value of the loan. ( 'onst><.JUPntly, they rPiy on traditional sources of loans-monf•ylenders, relatives, anci friends.

finance. Commercial banks felt that if their staffs and IPncling activities were overextended, they might losf• direct superviHion of cooperati ves. 11 Third, a lack of basic resources prevented small farmers from, obtaining adequate financing. Finally, lending inHti tutions concentrated their loans in the hands of a few cooperatives and farmers to improve the rate of loan repayments.

Several policy alternatives could make crop production loans to small landholders through cooperatives more attractive to commercial banks. If commercial banks were given subsidies for managerial costs of paid secretaries at the cooperative societies, overhead costs of making and collecting small agricultural loans could be reduced. The Small Farmers' Development AgencieH now update small farm land records in many districts. Also, administrative confusion occurs between commercial bankH and district cooperative banks over responsibility of financing cooperatives in districts where both banks operate. This confusion leads to delays, raises administrative cobts, discourages farmers from seeking loans, and contributes to repayment problems. Improved centralized administrative planning at the distriet level could reduce costs and make agricultural loans more attractive to commercial banks.

Agricultural lending patterns of commercial banks will change slowly. Political and economic problems continue to slow the systems approach in meeting the financial needs of small producers. But if commercial banks succeed in providing loans, inputs, and technical assistance through revitalized cooperatives, the banks can improve loan repayments and make a significant contribution to agricultural development.

REFERENCES

111 Adams, Dale W., and Joseph L. Tommy, "Financing Small Farms: The Brazilian Experience 196G-69," Awicultural Finance Heuiew, :~R: a6-41, Oct. 1974.

121 Agency for International Development, Spring Heuiew of Small Farmer Credit, 20 vol., Feb. to :June 197:{.

I a J Agricultural Finance Corporation Limited, A~-tricultural Development Schemes for Financin~-t in North Kana.ra District (My sore State) (Under the Lead Bank Scheme), Bombay.

l4 j AmeH, Cilenn C. W., and David W. Brown, Cooperative Credit for Farm Production in Mysore State, India, Univ. Tenn. Agr. Exp. Sta. Bul. G20, Oct. 197:t

151 Heliraya, K. V., The Symbolic Relationship Between SFIJA/ MFALS and Commercial Banks, Paper presented to The National Seminar on SFDA & MFAL Programmes, Vigyan

30

Hhavan, New Delhi, April 11-13, 1972, (Mimeographed).

[ 6] Ghosal, S. N., "Farm Financing by Commercial Banks: A Strategy," Prajnan, 2: 499-506, Oet.­Dec. 1973.

[7] Hedge, R. R. "The .l<~ntry of Commercial Banks in the Field of Agricultural Credit." Co-operative Training College: Special Issue, 7: 101-103, Mar. 1974.

[8] National Commission on Agriculture, Government of India, Interim Report on Credit Services for Small and Marginal Farmers and Agricultural Labourers, New Delhi, Dec. 1972.

[9] Rao, D.C. Guruuaja, Scale of Finance (as revised) to be Enforced with Effect from May, 1972. Bangalore: Manager, Bangalore District Cooperative Central Bank, 1972, (Mimeographed).

1101 Re{!ional Workshop of Small Farmers IJeuelopment A{!encies of the Southern States, ,June 10-12, 1972. Administrative Training Institute, Mysore, 1971.

j1ll Registrar of Cooperative Societies. Financinu of Primary! At?rirultural Credit Societies hy Commercial Banks-State Level StandinR Committee Meetinf! to he held on 18, I. 1975 at :J.·OO P.M. Agenda. Note. Vidhana Souda, Bangalore, Karnataka State, India, Jan. 1H, I97ii, (Mimeographed).

[121 l{eserve Bank oflndia, Annual Report and Trend and Progress of BankinR in India 1973-74. Bombay, Aug. 1974.

j13l [{eserve Bank of India, Report of All India Rural Credit Review Committee. B. Venkatappiah. Chairman. Bombay, 1969.

1111 Hl's<'rve Bank of India, Report of the Expert (; mup on State Hnact ment s Hauing a He a ring on Commrorcial Hanks /,ending to Auriculture. Bomha.v. 1971.

31

115] Sangameswar, G. K., Brief Note on Finan cia! Assistance Prouided by the Banlv; and the Ban galore IJist rict Central Cooperatilw Hank. Ltd. IJep. RegistrarofCoop. SociPtie8, Ban gal on•, ,June G, 1972, (Mimeographed).

[ 161 Sagameswar, G. K., A Brief Note on Short Term and Medium Term CrPdit channeliud throuf.!h the Awicultural Credit Coopcratiue Socil'fies in Banf?alorc District hy the Banf?alore District Cooperative Central Bank Ltd .. Ban{.!alore and the Commercial Hanks for the years 1970-71. 1971-72 and I 972-73 and also the pro{.! ram m1• for the year 1973-74." Dep. He~-,ristrar of Coop. Societies, BangalorP, 197:!.

[] 71 Syndicate Bank, Proceedings ol thP Seminar on Financing of the Primary At?ricultural Credit Societies by Commercial Banks held at Man.ipal on 24th Nouember. HJ71, Manipal, India, 1971.

FINANCING FARM BUSINESSES IN THE UNITED STATES

by

John B. Penson, Jr. and Dmwin L. Williams1

ABSTRACT

Results from a recent survey of farm businesses provide a unique insight into how farm operators and landlords combine their own funds with borrowed funds in finanl'ing farm operating expenses and capital purchases. Farm businesses are much more reliant on borrowed funds in financing capital purchases than suggested in previous studies. Substantial differences are noted in the financing of farm business expenditures when examined by regions and economic class of farms.

Keywords: Borrowed funds, owned funds, operating expenses, capital purchases, sources of funds, uses of funds.

The application of borrowed funds to financing farm business expenditures has long been of interest to agricultural economists. Previous studies by Tostlebe [6[, Johnson [ 1], Melichar [2, :nand Penson [4] have examined the financing of selected farm cash uses of funds. Each of these studies has been limited to residually determining the application of internal funds to financing annual farm capital flows by subtracting net increases in total debt owed by farm units from total cash flows of farm capital. Inherent in this accounting convention is the assumption that all net increases in total debtduringtheyearareused solely to finance annual farm capital flows.

There are at least two approaches one can take to remedy this accounting impasse: (1) broaden the accounting of uses of funds to include other cash uses of funds potentially financed by net increases in total debt, or (2) include only those net increases in debt used to finance annual farm capital flows. Penson and Baker I 5] have proposed a sources-and-uses-of­funds (SAUF) statement for the farm sector which takes thP first approach. Rather than attempting to measure the percentage of annual capital flows financed by net increases in total debt apart from other uses of funds by farm units, they include all other cash uses of funds such as personal consumption and cash withdrawals. The study

'i\gri!'ul tural t•<·onomists, l•:<"onomi!' J{csearch Service, stution<'d at !'urdu<' University, WPst LufayPth•, Ind.

32

reported in this article takes the second approach. Recently released results from a special sample survey of farm businesses taken by the U.S. Department of Commerce in 1971 are used in the first two sections of this study to examine a complete range of farm expenditures by farm operators and landlords and determine how these cash uses offunds were financed. These survey results show more clearly how farm business operating expenses and capital purchases are financed for specific inputs than previously possible. A SAUF statement for the farm business sector based on these results is assembled in section three of this article to residually determine the remaining uses of funds by farm businesses, thereby giving a morecompletepictureof the sources of funds available to the farm business sector and the uses to which these funds are applied.2

The percentage of total farm capital flows financed by internal versus borrowed funds is compared for the farm business sector SAUF statement developed by Melichar [2, ::3] and the survey results. The fourth section examines differences noted in financing

"Tlw farm business sector sources-and-uses-of-funds statement is distinct from th<' farm sectorSAUF statement in that it includes only those flows on farm business a<"count. It docs not includt• such flows on farm household a<"<"ount as purchases of household furnishings and Pquipment. The farm sector SAUF statement, on the other hand, is normally defined to include both flows on farm business and farm household accounts.

operating expenses and capital purchases among specific subgroups of farm operators. The final section presents a summary of the study and a proposal for future surveys.

Farm Operating Expenses and Their Financing

Cash operating expenses incurred by U.S. farm businesses in 1970 totaled $32. ?billion, of which farm operators accounted for 91.9 percent, landlords for :3.6 percent, and contractors for 4.5 percent." Of the categories studied, "Feed, seed, fertilizer, pesticides, and fuel" was the largest single operating' expense item incurred by farm businesses (table 1).

Farm operators financed 72.5 percent of their total farm operating expenses with their own funds and

1/\]1 data usPd in this study are bast>d on published sarnpli ng estimates provided by tiH• lfl70 Survey of /\gricultural Finance takt>n in t>arly 1971 by the Bureau of Uw Ct>nsus, U.S. Departmt•nt of Commerce. Summary data and sampling information for the survey are availablt• in 1101.

the remammg 27.fi percent with borrowed fund:-; repayable in less than 12 month:-; (2:3.9 pprcPnt) or 12 months or more (:3.6 percent). They financed H.'> percent of expenses on upkeep of farm building:-;, fences, drains, and irrigation systt>ms with th('irown funds. However, they financed less than halfoftht>ir purchases of livestock (other than breeding stock and dairy cows) and poultry with their own funds.

Landlords financed approximately 92 percPnt of their operating expenses with their own funds and the remainder with borrowed funds. Thus, farm operators relied more heavily than landlords on borrowed funds to finance their share of total farm operating expenses.

Farm Capital Purchases and Their Financing

Farm capital purchases, including purchases of land and existing improvements, totaled $10.2 billion in 1970. Of this amount, farm operators accounted for 94 percent and landlords the remaining 6 percent (table 2). Purchases of tractors and machinery (both new and used) represented the largest single outlay

Table 1-Expenditures for farm operating inputs and method of financing, by item, farm operators and landlords and expenses paid or provided by contractors, 1970'

Operating expense 1tem r olal cost

Upkeep of farm bu1ld1ngs, fences, drains, and irngation systen1s ....... .

Purcl1ases of livestock and poultry, oll1er ll1an breeding stocks and datry cows ...................... .

Feed, seed, fertilizer, pesticides, and fuel ........................ .

All olller agricultural operating expenditures• ................... .

Subtolal·operalors .............. .

. Upkeep of farm buildings, fences,

drains, and irrigation syste1ns ....... . Purchases of livestock and poultry,

o111er lhan breeding stocks and dalfy cows ...................... .

Feed, seed, fertilizer, pesticides, and fuel ........................ .

All other agncullural operating expenditures 4 •.•••.•..•.••......

Subtotal-landlords .............. .

Subtotal-contractors ............. .

Total ...•......•..............

Mil. <lot.

1,003.0

!:>,9~6.1

12,494.~

10,62~.1

30,078.'1

156.2

89.3

543.2

374.7 1,163.4

1,472.9

32,715.0

Figures may not add to totals due to rounding. N.A. = not applicable or not available.

l'erccnl

3.1

18.2

38.2

32.5 91.9

.5

.3

1.7

1.1 3.6

4.0.

100.0

1 Based on sampling estimates for 49 Stales, excluding Alaska, provided by the 1970 Survey of Agricultural Finance, U.s. Dept. of Commerce, Bureau of tl1e Census. 1 Includes pay· 'nents in cash with own funds at time of purchase or within I month of purchase. 'Excludes credit obtained on charge

33

Financed by:

Own T funds' I

J>c•/-ccnl

Borrowed runds"\

Jlei'CC!II f

All operators

8~.0

49.7

72.0

84 .l 72 -~

15.0

50.3

28.0

15.3 27.5

All lancllords

97.4

70.0

92.2

9!>.2 92.2

2.6

30.0

7.8

4.8 7.8

Contractors"

N.A. N.A.

• 73.2 a. 26.8

·1 cnn of loan

Un<.le1 1 J2monlh5 1 I

l'ercc•nl

12 -~

4 3.2

?4.5

l3.G 23.9

1.4

?4.4

7.1

4 .I G.7

N.A.

• ?3.3

12 months or more

2.tl

7 .I

3.5

1.7 3.6

1.2

~.6

.l

.7 1.1

N.A.

• 3.5

accounts for 1 month or less. ·'Include~ cxpcn~es fur ldbcn, machine lure, custon1 wtHk, etc .. , ft1osc who l"laci production tontracts with farm operators and p.1icl for or f)lovidccl cc1·tain opcratinq expense 1tems sucl1 as feed, seed, fcrtllitcr, ct11ck~. and feeder livestod<. "F 01 operators ()11(1 landlords combined, cxclud­inq contractors.

Table 2-Capital purchases and their financing by ite'll for all farm operators and landlords, 1970 1

Capital item purchased Total cost

Mil. clol. l'ercen/

Land, including buildings ............ . 2,059.1 276.3

1,229.6

20.2 Irrigation Improvements ..........•... 2.7 Other land improvements ............ . 12.1 Movable irrigation equipment:

New ........................... . Used ...........•.........•.....

112.9 31.8

1.1 .3

Tractors and machinery: New ........................... . Used .......................... .

1,867.1 757.3

18.3 7.4

Trucks and autos at net cost: New ...............•...•........ Used .......................... .

Breeding llvestuck 4 •••••••••••••••••

All other capital items .......•.......

938.0 368.0

1,513.6 424.2

9,577.9

9.2 3.6

14.9 4.2

Subtotal-operators .............. . 94.0

Land, Including buildings ............ . Irrigation improvements ............. . Other land improvements ............ .

228.4 72.4

112.9

2.2 .7

1.1 Movable Irrigation equipment:

New ........................... . .1 Used .......................... .

10.8 1.9 {')

Tractors and machinery: New ........................•... Used ..................•........

Trucks and autos at net cost: New ................•........... Used .......•...................

Breeding livestock 4 ••••••••••••••.••

All other capital Items .............. .

32.3 19.7

55.7 19.4 40.0 17.7

.3

.2

.5

.2

.4

.2 Subtotal-landlords .............. . 611.3 6.0

Total: Operators and landlords ..... . 10,189.2 100.0

Figures may not add to totals due to rounding. 1 Based on sampling estimates provided by the 1970 Survey

of Agricultural Finance, U.S. Dept. of Commerce, Bureau of the Census. For 49 States, excluding Alaska. 2 1ncludes payments in

for farm operators. Purchases of land including existing improvements represented the largest single capital purchase for landlords.

.Farm operators financed 51.2 percent of their total capital purchases with their own funds and borrowed the remaining 48.8 percent (table 2). Of the borrowed funds, 12.7 percent was to be repaid in less than 12 months while 36.1 percent was to be repaid over a period of 12 months or more. A wide array of financing behavior is seen when these totals are broken down into separate parts . .Farm operators, for example, financed 73.5 percent of their purchases of land including existing improvements with borrowed funds, 68 percent of which was to be repaid over a period of 12 months or more. Approximately two­thirds of the cost of operator purchases of nonreal · estate items, with the exception of tractors and machinery, was financed by applications of own funds. Only 48.3 percentofoperatorpurchasesofnew

34

Financed by:

Own funds 2

Percent

I Borrowed

funds 3

Percent

Operators

26.5 69.0 55.4

56.1 69.7

48.3 55.7

63.8 67.3 63.3 63.7 51.2

35.1 88.9 83.4

78.2 1' _,

62.0 69.9

81.3 80.4 85.6 89.0 64.4

52.0

Landlords

73.5 31.0 44.6

43.9 30.3

51.7 44.3

36.2 32.7 36.7 36.3 48.8

64.9 11.1 16.6

21.8 l 3 -7

38.0 30.1

18.7 19.6 14.4 11.0 35.6

48.0

Term of loan

Under 112 months 12 months 3 or more

Perc·enl

5.5 11.4 12.4

14.0 14.6

16.8 15.9

I 1.1 14.0 16.4 12.7 12.7

~.9

1.0 6.4

7.6 ... 22.7 14.3

4.3 9.3 1.1 6.6 6.b

12.3

68.0 19.6 32.2

29.9 15.7

34.9 28.4

24.~

18.7 20.3 23.6 36.1

59.0 10.1 10.2

14.2 I 9 ..1

15.3 15.8

14.4 10.3

7.3 4.4

29.0

35.7

cash will1 own funds at time of purchase or within 1 month ol purchase. 3 Excludes credit, such as charge accounts, obtained for 1 month or less. 4 1ncludes dairy cows and heifers.

tractors and machinery was financed with own funds.

Landlords financed approximately 65 percent of their purchases of land including existing improvements with borrowed funds. Overall, they financed ::!5.6 percent of their capital purchases with borrowed funds, of which 29 percent was to be repaid over a period of 12 months or more.

Farm Business Sector SAUF Account

In addition to providing information on farm expenditures and how they are financed, the sample survey also included information on other sources and uses of funds. A.nong the sources of funds measured were the value of gross farm sales and the amount of income earned from off-farm employment, Government payments, pensions, annuities,

unemployment insurance, retirement pay, veteran's payments, workmen's compensation, and old age assistance. Uses of funds, besides farm business operating expenses and capital purchases, included farm property taxes and unallocated borrowed funds. Given this additional information, we can assemble a SAUF statement for the farm business sector which will enable us to residually determine the remaining uses of funds by farm businesses.

Table 3 presents a SAUF statement for the farm business sector which details the sources of funds to and theusesoffunds by farm businesses in 1970. This statement reflects the "product concept" approach to soda! accounting suggested by Carlin and Smith Ill]. That is, it includes only those sources and uses of funds related to the production of farm products. Approximately 68 percent of the sources of funds to the farm business sector came from sales of farm products in 1970. Theothermajorcategory, increases in borrowed funds, accounted for almost :l5 percent of total sources of funds. Those sources of income reported by farm operators not directly related to the production offarm products totaled $11,777 million in 1970.4 Total farm operating expenses accounted for 47.1 percent of total uses of funds according to the sample survey, while total capital purchases accounted for almost 15 percent.

If we subtract farm business expenditures, farm property taxes, and unallocated borrowed funds from the total sources of funds related to farm production, we can attribute the remaining uses of funds to principal repayment, net increases in financial assets on farm business account, and cash withdrawals to the farm household and nonfarm sectors. This residual equaled $20.6 billion or almost :~o percent of the total uses of funds in 1970 (table :3). The residual can be refined more by subtracting ( 1) the increases in debt to be repaid in less than 12 months, some $11.3 billion according to the sample survey results; and (2) net increases in financial assets, which totaled only $0.8 billion on both business and household account in 1970 [7).'• The remaining $8 to $9 billion, therefore, can be attributed to cash withdrawals to the farm household and nonfarm sectors.

A comparison of the capital flows examined in previous sector SAUF statements 12,3,4,5) which are based on published USDA data series, and the capital

.''\'his total was comprised of: (1) (wages and salaries from off-farm employment ($H,R40 million), (~) operation of ll!~n:arm businesses or professional practices ($1 ,lifi4 ~llhon), (a) rental of nonfarm property, dividt•nds, and Interest ($R55 million), and (4) other income such aH soeial seeurity payments, pensions, annuities, rrtin•ment pay, and S!•vera_l categories of transfer payments ($11~4 million).

'While all short term borrowed funds ($11.:\ billion) undoubtedly will not be repaid by the end oftheyear, som1•of lh(' longer term borrowed funds will he. Thus, two-thirds of ~otaf fa_rms borrowings ($17.1 billion) or all short term farm >orrowmgs are assumed to be repaid within the year.

purchases outlined in tnhll• :1 rt>vPal st•vt>ral npparPnt statistical discn•pa ndPH. For pu rl'h as1•s of land including t'XiHting improvPmPnts, tiH' pn·,·iousst>dor accounts havr shown $:1.H billion for tlw nl'l us1• of funds to pure hasP rPal estnt<· from dis!'ont in ui ng proprietors. ThP SA lJF sta tPnwnt outlin<•d in tab! I','!,

on the other hand, shows only $L!.~HH billion in purchases of land and existing improvl'm<·nts from both continuing and discontinuing propril'tors as a use of funds. In addition, rPnl pstatP irnprovPnwnts, according to prPvious SP<'tor Hl'counts, Wl'r<' $1.:1 billion in 1H70 whilt' thl' survt•y n•sults pn•st•ntt>d in table :3 show $l.H4H billion. A final dis<TPpanl'y associated with farm !'apital f1ows involv1•s machinery pun· hasPs Iwt of t.rad1•i n valu(•s. I 'n·vious sector accounts report purchasl's of IWW mnl'hint•r:-.• and motor vehicles of $4.!) billion in I H70 wh ill' tlw survey results show $4.0!i7 billion in purdws<•s of new and used machinery and motor v<'hid(•s. ThPsP discrepancies should be rPconl'ilPd, giv<•n thP magnitude of the differPnCl'S lwtw<.•Pn previously published sector accounts and thl' survt'y n•sults.

In examining the financing of farm capital flows, previous sector SAUF statt•nwnts have focused on net increases in debt because gross flow data havt' not been available for all lender groupH. These seetor accounts suggested that in 1!-!70, for examplP, approximately :16 pern•nt of total farm capitall1ows were financed by net incn•ases in dPbt. Tlw survl'y results, on the other hand, suggest that 4H pPr<:Pnt of total farm capital purchases were financed h:v J,!ross

increases in debt. I:<~ven if we foC'us on the financing of new capital purchases in the sector (for examplP, exclude purchases of land and existing improvements as well as purchases of used machinery and motor vehicles), 44.~ pPrcPnt of this capital flow was financed by gross inereasPs in debt. A comparison of these ppreentagPs (4H percent versus :36 percent) suggests that vt>ry littlt• of tlw incrt'ase in debt during 1970 was repaid. HowevPr, net increasPs in debt owed nonreporting IPndPrs in 1H70 may be substantially less than originally pstimated.'; If this alternative estimate is used, the ratio of net increaseR in debt to total farm capital flows would have been only about 20 percent. SuC'h a ratio 1-mggests that farm businesses did repay a substantial portion of thl'i r capital borrowing during 1 H70.

The much greater USP of borrowed funds to financl' investment in farm plant and equipml•nt suggl•st.l•d by J.!ross as oppos<~d to net incn•a!:Ws in cll•ht ( 4H percent versus :lO pPrcpnt) can hP uspful to policy makers. For example, thP finding may suggPst more clearly the potential impact that a changp in monetary policy could have on thP h•vl'l of farm capital purchases and, ultimately, on the growth of the sector's productive capacity. J{pgardlt>ss of which

';A suh:-;tantial downward !'hangl' may lw warmnll'd for tlw "individuals and ot.lwr" IPrHIPr gmups in tlw lllliii'Pal l'statP 1h•hi spries 171-

Table 3--Sources and uses of funds statement for the farm business sector, 1970'

SOUfCCS of funds: ValuC' of cpos:, fann sales ..

Covcrnnwnl nilVl'llt.'lll~ ..•...........•....• , ...•..........•......••...••

Cltstnm worl<: ancJ rental of farr11 propel tv ................ , ................ . Ill( rf'<l~Cs 111 l~orrowc<l ltiiHJs to fmance: 2

fotal 1a1 rn operat1nq expenses

I otdl capital purcl1ases ..... lJtl::.pec1f1ed u~es ...........................•............•........•.•.

~ale ol real csl.1lc by c.ont11H1i11q p1 oprielors·1 ................................ . f tiiiCJS or value ol Input~ provided by contractors ............................. .

Total sour ccs of tuncls

uses of funds: M.Jirllcnance of 1Caf estate assets ......... , .............................. , .. Purct-lascs of l•vestock ott1er tllan breeding stocl' or dairy cows .................. . Feed, seed, fertiiiLer, and fuel ...... , ............... , ..................... . Expcns(.'~ pa1d or inputs provided by contractors ....•................... , .... . All other farrn operating expenses

Total far 111 ()pcrat1ng expenses

Purchases of land Including existing improvements ............................ . Real estate capital i111proven1enls 4 •••••••••••••••••••••••••••••••••••••••••

Purc!1ases of new and used tractors and mach1nerys ........................... . Purc11ases of new and used trucks and autos s ................................ . Purchases of breeding stock . _ ...... _ ...................... _ ............. . All ot11er cap1tal items .................... _ ............. _ .. _ ........... .

Total capital purc11ascs ................................. _ ............ .

Unallocated borrowed funds .................... _ . _ ..........•............ Farrn prc>perty taxes .. , ........................................ , ....... . Decreases,,, borrowed funds, net i1icreases in financial assets, and cash withdrawals

by continuing proprietors to t11e farm house11old and nonfarm soctors .......... --

Total uses of funds ........ _ .......... _ ........... _ ................. .

:Hil. <iol. l'e/'(_'('11 I

47,234 68.0 2,432 3.5

855 1.2

8,381 12.1 4,892 7.0 3,832 5.5

.383 .6 1,473 2.1

69,482 100_0

1,159 1.7 6,045 8.7

13,038 18.8 1,473 2.1

10,999 15.8

32,714 47.1

2,288 3.3 1,849 2.1 2,676 3.9 1,381 2.0 1,554 2.2

442 .6

10,190 14.7

3,832 5.5 2,169 3.1

20,577 29.6

69,482 100.0

'Based on sampling estimates provided by tl'lC 1970 Survey of Ayl'lcultural Finance, U.S. Dept. or Commerce, Bureau of tile Census. ~Includes only funds borrowed to finance farm business expenditures. Borrowed funds on household account would be included in t11e SAUF 'tatement for t11e farm household sector. 'The pruceeds fron1 tile sale of real estate earned by continuing proprietors (PSF-<E) is not based solely on the sample survey results. The proceeds fron1 t11e sale ot real estate assets by continuing proprietors is comprised of two components: (1) proceeds from the sale of roal estate to continuing proprietors (PSREC). and (2) proceeds from the sale of real estate to the nonfarm sector (PSREN). Tl1ese components are given by:

where: PCON = purchases by continuing proprietors ($2,288, see table 2)

PSREt= PSREC 1 +PSRENt

PSRECt = PCONt * RMN

PSRENt = LILDFMt * VLACt * RMN

concept is used, any conclusions regarding the financial position of the sector should be presented in a broader context which includes analysis of cash withdrawals of continuing proprietors and potential income transfers from other sectors. For example, farm operators received $11.8 billion in income from sources other than their farm businesses in 1970 (see footnote 4). If the assumption made above regarding the level of principle repayment is at all reasonable, farm operators and landlords in general can conceivably divert a substantial arpount of funds after meeting income tax and household requirements to financing farm business

36

RMN percent of sellers remaining In farming (9.5%, see [8])

LILDFM = reduction of land in farms (4.92 mil· I ion acres, see [ 9] )

VLAL average value of acres transferred to non-agricultural plnfores ($354, see [8])

4 This transaction includes purc11ases of movable irrigation equipment. 5 Total cost minus trade-In values thus explaining why trade-in values Is not listed as a source of funds.

expenditures. Further study is necessary to determine to what degree farm operators and landlords do substitute between these alternative uses of funds, given changes, for example, in the interest rate charged on farm borrowings.

Farm Business Subsector Comparisons

In an effort to provide further insight into the financing of farm business expenditures, selected financial outcomes are broken down into specific subgroups of farm operators. Table 4 presents regional survey data for the farm operators' share of

;:., -J

Table 4-Percentage d1stribut1ons and average amounts of selected farm financial items for farm operators,by farm production regions, 19701

I tern

Ope( a tor casll fcl•cn ope, atrng expense~: Percent of U.S. totdl

Average opc1attng expen<>es for ~~~o,t•nq operahJrs Percent financed by llot rowed ft~nds

Operator cap• tal pt~fct1ases.:

Pe• cent uf lJ .S. totdl

/\vetdge cap• tal purchase 1or tepw t1ng oper.Jtor"i

Pe1cent frndnced by bo• rowed f•rnds

Operator borrowed funds:·l

Perc:ent t)f U.S. t~)tai

Average 5/){Ht-terlll funds for •epor!tng operato•~ Average 1onger-tern1 funds for repot trng operatt)f5

Total number of farn'"ls rota! value of farrn prnduct sales Total far n1 expcnd•tures'"'

U111t

Pet. Dol. Pet.

~ >( t .

Dol

~ct.

PeL On1. l)OJ

Pet. P(':t.

JJr.t.

All figures may not add to totals due to round1ng. Credit obtained or debt owed on 1-nlonth cha1 ge ac­counts was excluded fron1 the survey, and therefore is not reflected in the amounts of borrowed funds or debt outstanding listed above.

1 Based on sampling estrmates for 49 States, excluding Alaska, prov1ded by the 1970 Survey of Aqricultural Finance, U.S. Dept. of ConlHlerce, Bureau of the Census.

f'JortJ,.

east

1.~

l4,3S3 1C..3

7.3

/,249 45.8

~.3

12,081 L0,87~

6.:> ·I.':J

7 .'l

8 .. i :..:. :oo

2ll.u

!0.0 ~.81>5

5U./

R.B g, 791 g,flO I

I lJ./

8.0

a."

Cc,,:,

H~!t

1 l ,-i~>B ?0.b

:2 . .>.2 ~. '~) -1 ~

S\1.1

?2.8 j_ 2,:::>01 J l ,s 1 7

22.9 ?2. I

2l.ll

l2.H

lll .·l 1~ 2 3\).2

13.~

8.20H ~2.3

1::>.9 19,027 l2.G2~

9.7 12.4 12.1

C.J

~.l! J

21.2

8.2 4,705

-15.0

~.8

6,o44 8,519

15.4 7.4 7.2

(_)_/

~ 0,984 23.5

5.3 ~.840

46.3

5.5 13,1 17

8,911

l.G ~.~. 7 b.7

:i.3

8,828 27.8

4.~

S,G45 50.b

4.5

11,8?9 10,•101

6.1 4.8 <1.

10.3 11,794

JC.5

I !.I

I ,3•!·' 40.9

10./ 21,582 10,/1.1

lfJ.'l

0.:-1fJ.J

Moun­t;Hn

f).5

25,997 28.!

8.1

10,4 72 51.4

().4

25,608 17,358

4.6 8.2 8.9

bJ !3.?

29,589 25.0

9.4 12,Y29

~ 1.3

11.3 37 ,56t> 24,183

5.6 I 1.9 11.9

United

States

100.0 12,484

27.5

100.0 7,0 J 7 48.8

100.0 14,981 ll,860

100.0 100.0 100.0

~Excludes fann operatmg expenses paid or provided by contractors and landlords. Of the total cost of national farrn operating expenses for 1970, operators paid about 92 percent; contracto•s, 4.5 percent; and landlords, 3.6 percent, based on the san-.ple survey estimates. 1 1 ncludes pu1chases of land. 'Includes only "allocated" borrowed funds fused for spectffc operating expense or capital item). Excludes "unallocated" borrowed funds (for un-

specified farn1 uses such as replenishment of working capital or deposit in bank accounts). U.S. farm operators' total borrowings 10 1970 amounted to an estimated 516,685 million, of wh1ch $12,958 million was "allo· cated" and 53,727 rnillion was "unallocated". "Includes both landlord and operator shares.' Includes landlord and operator operating, expenses and capttal purchases.

fann husint•ss t'XIH'IHliturPs and borrowed funds. Tnhlt• :1 pn•st•nts tlwst' sanH' surv!'y rPsults by t't'OIHllllic or gross salPs dasst•s.

Regional Comparisons

( · omp;1risons among tlw l () farm production n•gions st udit•d indicn(ps that tlw Corn B<;li ;t<'t'ounlt•d for about oJw-lifth of tlw total amount 111

1·ach of tlw financial outt·onw categorit>s studied. The av<·rag<' amounts IH'I' operator for operating <'XJH'Ils!'s, eapital purclws!'s, and farm bonowings, howpver, W!'l'<' higlwst in tlw Mountain and Pacific regio11s. This is probably dut•, nt lmst in part, to the n•lativPI.V lnrgpr op1•rations in the two n~gions.

Farm opPrators in tlw NorthPrn Plains finnnced tht• higlwst JH•rc!.'ntag<' of their operating expenses with borrowPd funds (:l() pt•rcent), followt>d by those in llw Soutlwrn Plains and Corn Belt. 01wrntors in the Nortlwast l{<'gion rPiit•d tlw least on borrowed funds to linann• operating l'XJWIHWS. Operators in the Northern Plains were mort- reliant on borrowed funds to ti nam't' capi t.al purchases (!i2 percent) than operators in tlw ot.lwr n•hriom;; studiPd, while oJwrators in the Sou tlwrn PI ai n H were tlw least rt'liant.

Tht• rPgional di ffNenct•s noted in t.h e percentages of operatinl-{ t•xpenses and capital purchases financed bv loan fundH suggest. t.lw rwed for an in vestigntion of ft.Htors hypothPsized to caust• ret-.riona.l differences in horrowi ng and in vpstnwnt behavior. Operators in the Northeast l{egion, for example, were much less reliant on bormwed funds than operators in the other n•gions wlwn both categories of expenditures were considerod. Is this du(' to regional diffPrtmcHs in i nt.Pn·st rn tPs, ton onp rice l'X tern a! rationing of credit, or to intemnl rationing of credit use by more conservativP horrowt>rs? These and other questions relat(•d to regional flows of borrowed funds cannot be answ!'red hv data now available. The presentHtudy's role, t.lwref;>re, is limited to pointing out that these diffPrl'IH'l's do exi:-;t. and that further research on the factors inf1 ut•nl'ing tlwsP outcomes is warranted.

Ot.fwr intt•n•sting regional relationships are noted in t.abh• 4. For Pxmnple, t.hP Com Belt had 2:l percent of the farms, produced 2:1 perct•nt of total farm prod ud sah•s, and n•eei vt•d 2:1 percent of total horrowt•d funds in ilw United States. Thus, farm busi nPsHes in the Corn Belt appearPd to have received an amount of bmTowed fundH equal to their contribution to. grosH salt•s of farm products. The Appalachian n~gion, on the otlwr hand, had lfi. pt•rcent of th<· farms hut produel•d only 7yercent o~ U.S. groHH saiPs of farm product.s and received only h perc<'nt of the total borrowed funds. Like the NortlwaHt l{egion, Uw Appalachian Region rec<~ived a snwller percentage of total borrowed funds than it eon tri hut<•d to national sales of farm produc.:ts. The opposite situation occurrt•d in othern!gions, however .. Thl' Mountain Hegion, for t•xumple, had !i percent of

the farms, produced R percent of the total sales of farm products, and received 9 percent of the total borrowed funds. Do these regional differences in the ratio of borrowed funds to total gross farm sales reflect differing attitudes toward risk on the part of lenders, differences in risk aversion on the part of borrowers or differences in the cost of capital as hypothesized earlier?

Economic Class Comparisons

The financing of farm business expenditures is further examined by noting the differences which existed for specific economic classes of farm businesses in 1970. Operating expenses for those operaton:l of farm businesses with gross sales. of $100,000 or more in uno averaged $1H7,000, of Which :l2 percent was estimated to have been financed with borrowed funds (table 5). Operators of farm businesses having lower gross sales not only reported lower average operating expenses but, in general, financed a much lower percentage of their operating expenses with borrowed funds.

A somewhat different pattem existed in the financing of capital purchases. The relative importance of borrowed funds to finance capital purchases did not decline much by econo.mic class PXct•pt for operators of noncommercial farm busint•sses.~· While the average amount of capital purchases was substantially lower for smaller commercial operations, the spread between the percentage of capital purchases financed with borrowed funds by operators of the largest versus the smallest economic class of commercial farm businesses was only :1.2 percent.

Approximately H4 percent of the borrow~d funds allocated to financing farm business expenditures by operators in 1970 went to operators associated with business having gross sales of$20,000 or more. These operators accounted for only 25 percent of the tot.al number of farm businesses in the United States m IB70 and almost RO percent of farm product saJes. Only I. 7 percent of borrowed funds allocated to financing expenditures by farm operators went to operators of noncommercial farm businesses. Operators of noncommercial farm businesses accounted for :-w percent of all farm businesses but only 1.{) percent of total farm product sales.

Summary

Figure 1 summarizes farm expenditures by far~n operators and landlords and methods of financing Ill W70. For farm operators, operating expenses accounted for approximately three-fourths of farr.n Pxpenditures. They financed 72 percent of thell'

--···-·~-----~-

. Noncomnwrcial farm bu:-~i nesstos are defined herPin ns t.hmw units with IPss thun $2,!i00 in gross far~1 :;nlt•s 01

o(wmted on a part-time or n part-retin•ment hns1s.

Distribution of Farm Costs for

Maintenance of Real Estate

Operators and Landlords and Percentage Financed by Own Funds, 1970*

FARM OPERATORS

Labor, Machine Hire, Custom Work, and Other 35.3% (84.7%)

Feed. Seed, Fertilizer, and Fuel 41.5% (72.0%)

Operating Expenses* • $30.1 Bil.

72.5% Financed by Own Funds

Livestock Other Than Breeding 7.7% '(70.0%)

Labor, Machine Hire, Custom Work, and Other 32.2% (95.2%)

Feed, Seed, Fertilizer, and Fuel 46.7% (92.2%)

Other 4.4% (63.7%)

L------+---:

LANDLORDS

Other 2.9% (89.0%)

Breeding Stock

Land, Including Existing Improvements 21.5% (26.5%)

RcC!I Esl<11e lrn proverncr1ts 17 .2''o (57 .9''io)

Tractor·s and

Capital Purchases $9.6 Bil.

51.2% Financed by Own Funds

Land, Including Existing Improvements 37.4% (35.1 %)

Real Estate I mprovernents 32.4% (85.1%)

Operating Expenses** $1.2 Bil.

6.5% (85.6%) Capital Purchases $0.6 Bil.

64.4% Financed by Own Funds

Tractors and Farm

Machinery 6.5%

(65.0%) 92.2% Financed by Own Funds

'Nun1bers in parentheses financed by own funds. ·'Operating expenses paid or provided by contractors are not included.

l'i~lll'l' 1

*"' 0

Table 5-Percentage distributions and average amounts of selected farm financial items for farm operators, by economic class of farm, 1970 1

Item

Operator cash farm operating expenses: 2

Percent of U.S. total ..••..•...• Average operating expenses for reporting operators Percent financed by borrowed funds .......•

Operator capital purchases: 3

Percent of U.S. total .... Average capital purchase for reporting operators Percent financed bY borrowed funds ....•.

Operator borrowed funds: 4

Percent of u.s. total ••. Average short-term funds for reporting operators Average longer-term funds for reporting operators

Total number of farms •.•... Total value of farm product sales Total farm expenditures•

All figures may not add to totals due to rounding. Credit obtained or debt owed on 1-month charge ac­counts was excluded from the survey, and therefore is not reflected in the amounts of borrowed funds or debt outstanding listed above.

1 Based on sampling estimates for 49 States, excluding Alaska, provided by the 1970 Survey of Agricultural Finance, U.S. Dept. of Commerce, Bureau of the Census.

Economic class of farms

Unit I $100,000 or more

Pet. 38.5 23.1 17.3 10.6 Dol. 186,881 34,261 15,458 8,410 Pet. 32.3 34.8 25.0 17.0

Pet. 20.2 23.5 22.6 14.8 Dol. 37,949 13,738 8,486 5,661 Pet. 48.1 52.2 52.8 49.5

Pet. 35.9 27.8 20.0 9.6 Dol. 119,413 23,690 10,643 6,046 Dol. 51,020 18,826 11,481 8,394

Pet.

I 2.6 8.4 13.9 15.7

Pet. 34.1 25.5 19.6 11.3 Pet. 34.6 23.6 18.3 11.3

2 Excludes farm operating expenses paid or provided by contractors and landlords. Of the total cost of national farm operating expenses for 1970, operators paid about 92 percent; contractors, 4.5 percent; and landlords, 3.6 percent, based on the sample survey estimates. 3 1ncludes purchases of land. 4 1ncludes only "allocated" borrowed funds fused for specific operating expense or capital item). Excludes "unallocated" borrowed funds (for un-

Part· retirement

5.1 2.8 0.7 1.4 0.4 4,302 2,424 1,007 1,151 957

11.0 8.9 5.5 6.0 3.0

7.9 5.1 1.5 3.8 .6 3,964 2,875 2,052 2,024 1,616

41.4 44.9 22.4 40.3 21.9

3.7 2.3 .3 1.3 .1 3,226 2,146 1,101 1,358 811 5,850 4,465 3,148 3,108 4,153

14.9 14.5 9.1 15.4 5.5 5.3 2.6 .5 .8 .3 5.7 3.3 .9 1.9 .4

specified farm uses such as replenishment of working capital or deposit in bank accounts). U.S. farm operators' total borrowings in 1970 amounted to an estimated $16,685 million, of which $12,958 million was "allo­cated" and $3,727 million was "unallocated". 5 1ncludes both landlord and operator shares. 6 Includes landlord and operator operating, expenses and capital purchases.

operating expenses and 51 percent of their capital purchases with their own funds. For landlords, operating expenses were only two-thirds of farm expenditures. They financed 92 percent of their operating expenses and 64 percent of their capital purchases with their own funds. Feed, seed, fertilizer, and fuel expenses accounted for the largest share of operating expenses for both operators and landlords. Operators focused the largest share of their capital purchases on tractors and farm machinery while landlords spent almost 70 percent of their capital purchases on real estate assets.

A farm business sector SA UF statement based, for the most part, on the survey results shows that farm expenditures accounted for 62 percent of total uses of funds by farm businesses. This SA UF statement also indicates that gross increases in debt in the farm business sector

• Represented almost 25 percent of those sources of funds to farm operators and landlords related to

the production of farm products • Financed 48 percent of total farm capital

purchases by continuing proprietors • Financed 44 percent of gross capital formation

in the farm business sector in 1970. Substantial differences were noted among regional

and economic class comparisons of the financing of farm business expenditures by farm operators. Further research is needed to determine the factors causing these differences.

Additional sample surveys are also needed to support the development and publication of an ongoing SA UF statement series forth e farm business sector which, like that discussed in this report, details the importance of borrowed funds to financing specific farm expenditures. The development of such a data series would not only improve the informational system but also provide the basis for more meaningful empirical studies of investment and borrowing behavior of farm businesses.

REFERENCES

[1] Carlin, Thomas A., and Allen G. Smith, "A New Approach in Accounting for Our Nation's Farm Income," Agricultural Finance Review, Vol. 34, July 1973, pp. 1-6

[2] Johnson, D. Gale," Agricultural Credit, Capital and Credit Policy in the United States," Federal Credit Programs, Commission on Money and Credit, Prentice-Hall, Englewood Cliffs, N.J., 1963, pp. 355-423.

[3] Melichar, Emanuel, "Aggregate Farm Capital and Credit Flows Since 1950 and Projections to 1980," Agricultural Finance Review, Vol. 33, July 1972, pp. 1-7.

[4] Melichar, Emanuel, "TheFarmBusinessSector in the National Flow of Funds Accounts, 1970 Proceedings of the American Statistical Association meeting, Detroit, Mich., pp. 571-576.

[5] Penson, John B. Jr., and C. B. Baker, An Aggregate Income and Wealth Simulator for the Farm Sector: Its Description and

41

Application to Policy Analysis, U.S. Dept. Agr. Tech. Bull. [In press]

[6] Penson, John B. Jr., An Aggregative Income and Wealth Simulator for the U.S. Farm Sector: Its Description and Application to Policy Analysis, unpublished Ph.D. thesis, Univ. Ill., 1973.

[7] Tostlebe, Alvin S., Capital in Agriculture: Its Formation and Financing Since 1870, Princeton Univ. Press, Princeton, N.J., 1957.

[8] U.S. Bureau of the Census, 1969 Census of Agriculture, Vol. V, Special Reports, Pt. II, Farm Finance, 1974.

[9] U.S. Department of Agriculture, ERS, Balance Sheet for the Farming Sector, Agr. Info. Bull. 376, Sept., 1974.

[10] U.S. Department of Agriculture, Farm Real Estate Market Developments, CD-79, July 197 4.

[11] U.S. Department of Agriculture, Number of Farms and Land in Farms, SpSy3, Jan. 1974.

CAPITAL FINANCE ACCOUNTING IN THE FARMING SECTOR

by Richard Simunek and Carson Evans1

ABSTRACT

Farm capital accumulation totaled over $120 billion from 1960 to 1974, or 211 percent of net farm borrowing. Internally generated funds from capital consumption allowances, net real estate transferred, and saving contributed 80 percent of total capital finance. Internal financing is so important that future farm financial research needs to consider depreciation analysis, investment allowances, and saving behavior in addition to interest rates, credit availability, and other external elements that have been the primary focus of past farm financial research.

Keywords: Balance sheet, farm income, economic accounts, capital flows, capital finance, depreciation, asset transfers, capital borrowing, saving.

Farm capital accumulation is the sum of gross capital expenditures, farm inventory change, and financial asset change. It totaled $120.5 billion from 1960 to 1974, or 211 percent of net farm borrowing (table 1).< Yet little is known about farm capital accumulation or its method of financing, because no existing capital accounting framework divides current farm production into consumption and saving (net capital formation).

Adopting a capital finance account based on national economic accounting concepts would permit measurement of farm saving. National economic accounting proce,.dures are designed mainly to measure production, because producing goods and services triggers consumption and saving [1, 7].

This article develops a farm capital finance account for 1960-1974 based on national economic accounting concepts to accomplish two objectives. The first is to identify farm saving and assess its relative importance in comparison to other capital financing sources. The second is to demonstrate the effectiveness of the capital finance account in monitoring farm capital accumulation and its

1 Agricultural economists. Economic Research Service. "Estimates in this paper illustrate an accounting

procedure and are not official Department of Agriculture estimates.

42

internal and external financing. Opening and closing farm balance sheets are linked by asset revaluations and the entries in the farm capital flows account [ 4]. The capital flows account delineates the forms in which capital is accumulated, and the capital finance account shows the ways it is internally and externally financed.

Two financial accounting statements are the flow· of-funds statement by the Federal Reserve System and the sources and uses of cash funds statement for the farm sector [3,5,8]. But they do not measure saving because of conceptual differences. Flow-of­funds accounting by the Federal Reserve System contains gross capital expenditures, farm inventory change, and financial asset change as farm capital accumulation. It also includes capital consumption allowances at replacement cost and total farm debt increase as capital finance. This procedure has resulted in continual net cash disinvestment in the farming sector since 1955. The sources and uses of cash funds statement for the farming sector cannot measure saving, because sources and uses are not limited specifically to capital accumulation and finance.

An account used for capital formation and finance is not new. The capital finance accountforthe United States based on national economic accounting concepts has been published by the U.S. Department

Table 1-Farm capital finance account, United States, 1960-74

Item Total

Million dollars

Capital Finance Account

Gross capital expenditure ...... 4,488 4,614 5,022 5,411 5,688 6,105 6,688 7,446 6,696 6,865 7,285 7,357 7,947 10,412 1U!95 103,319 Inventory change ............ 397 336 620 629 -817 1,042 -83 657 124 99 6 1,397 873 4,014 2,403 11,697 Financial asset change ........ -484 -70 313 -136 331 265 191 446 549 395 391 805 1,188 859 426 5,469

Accumulated capital ........ 4,401 4,880 5,955 5,904 5,202 7,412 6,796 8,549 7,369 7,359 7,682 9,559 10,008 15,285 14,124 120,485

Capital consumption allowances I at book value ............. 3,119 3,141 3,235 3,344 3,448 3,582 3,758 4,020 4,286 4,510 4,783 5,243 5,484 6,076 6,838 64,867 Capital consumption allow-

~ ances at replacement value .. I 4,337 4,388 4,530 4,696 4,903 5,111 5,384 5,781 6,200 6,574 6,760 7,350 7,881 8,906 10,640 93,441 w Less: Depreciation valuation

adjustment .............. 1,218 1,247 1,295 1,352 1,455 1,529 1,626 1,761 1,914 2,064 1,977 2,107 1,397 2,830 3,8Q2 28.574

Real estate transferred ........ 1,494 1,588 1,562 1,148 1,452 1,830 2,139 2,599 2,429 1,700 1,936 1,559 1,234 1,169 1,113 24,952 Capital borrowing ........... 815 889 992 1,253 738 1,390 1,461 1,647 1,350 1,050 247 2,256 2,647 3,328 4,021 24,084 Saving .................... -1,027 -738 166 159 -436 610 -562 283 -696 99 716 501 643 4,712 2,152 6,582

Capital finance ............ 4,401 4,880 I

5,955 5,904 5,202 7,412 6,796 8,549 7,369 7,359 7,682 9,559 10,008 15,285 14,124 120,485 I

Cash Flows j

Externally generated funds 2 ••• i 1,404 2,287 2,921 3,000 2,416 3,854 3,371 3,368 3,059 2,572 1,457 4,630 6,231 8,793 7,713 57,076 Internally generated funds 3 .•• ·I 3,586 3,991 4,936. 4,651 4,464 6,022 5,335 6,902 6,019 6,309 7,435 7,303 7,361 11,957 10,103 96,401 Farming income accruing to !

14,477 14,737 14,641 13,4 73 16,144 17,382 15,577 15,776 17,853 17,592 18,096 23,322 farm proprietors 4 ••••.••••• 1 13,802 43,224 37,909 293,990

1 Preliminary. 2 The net increase in total farm debt book value capital consumption allowances, real estate operators derived on book value capital consumption including capital boroowing and CCC loans. 3 Includes transferred, and saving. 4 Total net farm income of farm allowances plus net rent paid to nonoperator landlords.

of Commerce since 1929. A summary set of the national economic accounts is given in table 2, including the capital finance account which is referred to as the gross saving and investment account. Tostlebe published a capital account for the

farming sector in 1957, and his procedures are illustrated for more current years in table 3. In estimating capital finance, Tostlebe did not separate capital borrowing from total farm borrowing, probably because information about loan purposes

Table 2-Summary of National Income and Product Accounts, 1973 1

Line Item Item Billion dollars

I. National Income and Product Account

Con1pensation of en1ployees ................ . 786.0 2 Wages and salaries ....................... . 691.6 3 Disbursements (2-7) ....•.......•......•. 691.7 4 Wage accruals less disbursements (3·7+5-4) .. . -.1 5 Supplements to wages and salaries .......... . 94.4 6 Employer contributions for social

1nsurance (3·15) ..................... . 48.4 7 Other labor income (2·8) ................ . 46.0

8 Proprietors' income (2.9) ................... . 96.1

9 Rental income of persons (2-10) ............. . 26.1

10 Corporate profits and inventory valuation adjustment ............................ . 105.1

11 Profits before tax ....................... . 122.7 12 Profits tax liability (3·12) ............... . 49.8 13 Profits after tax ..................... . 72.9 14 Dividends (2-11) ................... . 29.6 15 Undistributed profits (5·5) ........... . 43.3 !6 Inventory valuation adjustment (5-6) ........ . -17.6

17 Net interest (2-13) ........................ . 52.3

18 NATIONAL INCOME ...................... 1,065.6

19 Business transfer payments (2-17) ............ . 20 Indirect business tax and nontax liability (3-13) .. 21 Less: Subsidies less current surplus of

government enterprises (3-6) .............. . 22 Capital consumption allowances (5-7) ......... . 23 Statistical discrepancy (5·10) ................ .

CHARGES AGAINST GROSS

4.9 119.2

.6 110.8

-5.0

NATIONAL PRODUCT . . . . . . . . . . . . . . . . . . . . 1,294.9

24 25 26 27

28 29 30 31 32 33 34

35 36 37

38 39 40 41 42

Personal consumption expenditures (2-3) 805.2 Durable goods .......... . . . . . ...... 130.3 Nondurable goods .. . ... . . . . . . . . . . . . . . - 338.0 Services .. . . . .. . . . . . . . . . . . . . . . . . . . 336.9

Gross private domestic Investment (5-1) . ...... 209.4 Fixed investment . . . . . . ... . . . . . .. . .... 194.0

Nonresidential .. . . . . . . . . . . . . . . . . . 136.8 Structures . . . . . . . . . . . . . . . . 47.0 Producers' durable equipment ..... . . . . . 89.8

Residential structures - .. . . . . . .......... 57.2 Change In business Inventories .. . .... 15.4

Net exports of goods and services . ... . . . . . . 3.9 Exports (4-1) . . . . . . . . . . . . . . . . . . . . . . 100.4 Imports (4-3). . . . . . . . . . .. . . . ...... 96.4

Government purchases of goods and services (3-1) 276.4 Federal . . . .. . . . . . . . . . . .. . . 106.6

National defense . ............. . . . . . . .. 74.4 Other . . . . . .. . . . .. . . . . . . . . . . 32.2

State and local . . . . . . . . . . . . . . 169.8

GROSS NATIONAL PRODUCT ............. 1,294.9

II. Personal Income and Outlay Account

Personal tax and nontax payments (3-ll) ...... .

2 Personal outlays .......................... . 3 4 5

Personal consumption expenditures (1-24) .... . Interest paid by consumers (2-15) .......... . Personal transfer payments to foreigners

(net) (4-5) ........................... .

6 Personal saving (5-3) ...................... .

PERSONAL TAXES, OUTLAYS,

151.3

829.4 805.2

22.9

1.3

74.4

AND SAVINGS ........................... 1,055.0

See footnote at end of table.

44

7 Wage and salary disbursements (l-3)

8 Other labor Income (1-7) .................. .

9 Proprietors' income (1-8) .................. .

10 Rental Income of persons (1-9) ............. .

11 Dividends (1-14) ........................ .

12 Personal Interest income .................. . 13 Net interest (1-17) .................... .. 14 Net interest paid by government (3-5) ...... . 15 Interest paid by consumers (2-4) .......... .

16 Transfer payments to persons .............. . 17 From business (1-19) ................... . 18 From government (3-3) ................. .

19 Less: Personal contributions for social

601.7

46.0

96.1

26.1

29.6

90.6 52.3

... 15.4 22.9

117.8 ll.9

113.0

insurance (3-16) . . . . . . . . . . . . . . . . . . . . . . . . 42.8

PERSONAL INCOME ................•.... 1,055.0

Table 2-Summary of National Income and Product Accounts, 19731 -Continued

I Billion

I Billion

une Item dollars Line Item dollars

Ill. Government Receipts and Expenditures Account

1 Purchases of goods and services (1·38) .......... 276.4 11 Personal tax and nontax payments (2-1) . . . . . . . i51.3

2 Transfer payments ......................... 115.6 12 Corporate profits tax liability (1·12) . ......... 49.8 To persons (2-18) ........................ 113.0 To foreigners (net) (4-4) ................... 2.6 13 I ndlrect business tax and nontax liability (1-20) 119.2

14 Contributions for social insurance ............ 91.2 5 Net Interest paid (2-14) ..................... 15.4

15 Employer (1-6) ......................... 48.4 6 Subsidies less current surplus of govern-

ment enterprises (1·21) .................... .6 16 Personal (2-19) ......................... 42.8 7 Less: Wage accruals less disbursements (1-4) ..... .0

8 surplus of deficit (-),national Income and product accounts (5·8) •••••••••• 0 ••••••••• 3.5

9 Federal ................................ -5.6

10 State and local .......................... 9.2

GOVERNMENT EXPENDITURES AND SURPLUS ........................... 411.5 GOVERNMENT RECEIPTS ................ 411.5

IV. Foreign Transactions Account

1 Exports of goods and services (l-36) 100.4

2 Capital grants received by the United States (net) (5·9) ....................... . . 0

RECEIPTS FROM FOREIGNERS 100.4

3 Imports of goods and services (1-37)

4 Transfer payments from U.S. Government to foreigners (net) (3-4) .................. .

5 Personal transfer payments to foreigners (net) (2-5) ............................ .

6 Net foreign Investment (5-2) ............... .

PAYMENTS TO FOREIGNERS

96.4

2.6

1.3 .1

100.4

V. Gross Saving and Investment Account

Gross p~ivate domestic invest- 3 Personal saving (2-6) 74.4 ment (1-28) ........................... . 209.4

4 Wage accruals less disbursements (1-4)......... .0 2 Net foreign Investment (4-6) . . ..• . . • . . . . . . . . . . .1

5 Undistributed corporate profits (1-15) . . . . . . . . 43.3

6 Corporate Inventory valuation adjustment ( 1-16) -17.6

7 Capital consumption allowances (1-22) . . . . . . . . 110.8

8 Government surplus or deficit (-), national Income and product accounts (3-8) . . . . . . . . . 3.5

9 Capital grants received by the United States (net) (4-2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .0

10 Statistical discrepancy . • . . . . . • . . . . . . . . . . . . . -5.0

GROSS INVESTMENT . . . . . . . . . . . . . . . . . . . . . 209.4 GROSS SAVING & STATISTICAL DISCREPANCY 209.4

1 Numbers In parentheses Indicate accounts and Items of counter-entry In the accounts.

was lacking. He also did not include net real estate transfers. Total cash flow was divided between cash funds from capital consumption allowances valued at replacement cost and from cash funds retained from income. Development of an account used for farm capital formation and its financing along national economic accounting procedures was not pursued. Later, researchers added sources and uses of noncapital funds to financial accounting statements that culminated in the sources and uses of cash funds statements (table 4). This path of development occurred probably because financial analysts desired

45

an account to explain and project total farm cash and debt.

Capital Finance Account

The conceptual basis and method of estimating each economic and financial aggregate-in the capital finance account shown in table 1 can be examined before identifying the sources of farm capital financing and appraising their relative importance. iwporlanclil.

Gross capital expenditures consist of expenditures for tractors, trucks, automobiles, other machinery

Table 3-Uses and sources of farm capital funds for selected years

Item 1960

Million dollars

Uses: Gross capital cxpen-

ditures .............. 4,488 6,105 7,285 I nvcntory change ....... 397 1,042 6 Financial asset change ... -484 265 391

Total uses . . . . . . . . . . . 4,401 7,412 7,682

So• trees: Increase 111 toto1t debt .... 1,404 3,854 1,457 Cash flow from:

Capital consumption allowances at replace-mcnt value .. 4,337 5,111 6,760 Net income .. . . . . . . . . -1,340 -1,553 -535

Total sources ......... 4,401 7,412 7,682

1974

11,295 -2,403

426

14,124

7,713

10,640 -4,229

14,124

Table 4-Amounts of cash sources and uses of funds for the United States farm sector for selected years'

lten1 1960 1974

/Jillion dollar.•

rotal casll uses of funds ....... 23.5 32.8 39.3 73.3 Purc11ascs 0f nwctlincry and

n•otor vehicles ........... 2.8 4.2 4.9 7.8 Capital improvenwnts to real

e~tate Jssets ............. 1.7 1.9 2.4 3.5 Other capital purchases ...... -.3 1.0 1.4 1.2 Purchases of real estate frorn

discontinuing proprietors ... 3.0 4.3 3.8 9.5 Personal cons11mption and

other usc:) .............. 16.3 21.4 26.8 51.3

Totul c.ash solaces of funds . . . . 23.5 32.8 39.3 73.3 Cash.income from farm and

off-farm sources .......... 22.4 28.8 37.1 65.2 Net dow of real estate loans -- .7 2.3 1.1 5.0 Net flow of nonreal

estate loans . . . . . . . . . . . . . .4 1.7 1.1 3.1

1 Source: U.S. Department of Agriculture, Economic Re­search Service, i\p,ricultura/ Finance Oullooh, AF0-16, Nov. 1975.

and equipment, land improvements, service buildings, and dwellings as published in the Farm Income Situation !10]. Sales and purchases of land between continuing farm proprietors within the farming sector are only an exchange of capital and not a change in the magnitude of capital available for producing livestock and crops. Thus, it is inappropriate to include purchase of land as gross capital expenditures in the capital finance account basettl on national economic accounting concepts, except to the extent that there is a positive net acquisition of land from nonfarm sectors. Purchases of breeding livestock, dairy animals, and animals for wool, and expenses for orchard development are charged off as a current expense in estimating total net farm income. For this reason, they are not included as capital expenditures in the capital finance acoount.

46

Gross capital expenditures includes only cash expenditures and excludes capital formation attributable to such own-account activity (nonpurchased capital) as a fence built with homegrown materials and the fanner's own labor. Other examples include buildings, orchards, and machinery which the fanner builds or develops himself. Including own-account capital formation in the capital finance account increases saving by a like amount, but no measurement of own-account capital formation is possible with existing farm data .

Farm inventory change for total net farm income and the capital finance account measures the change in physical quantities of.livestock, poultry, and crops on farms valued at average prices during the year. Farm inventory change includes own-account breeding and dairy stock and crops, but their measurement as a flow is understated compared with their measurement as a net stock change. Breeding stock, draft animals, dairy cattle, and animals raised for wool are not inventory items under national economic accounting procedures, but they are classified as fixed capital assets. Capital consumption allowances are not estimated for livestock assets under present total net farm income concepts; expenditures for these assets are charged to current expense. Including these livestock in the capital finance account as fixed assets transfers appropriate current farm production expenses into gross capital expenditures and increases capital oonsumption allowances. But the net effect on investment and income is unknown. De~eloping a capital finance account with this definitional change is beyond the scope of this article .

Financial asset change equals financial assets on hand at the end of the year less financial assets on hand at the beginning of the year[9]. This change is a key item in the construction and concept of the capital finance account. For example, book value capital consumption allowances are an expense permitted by the Internal Revenue Service for tax purposes. They provide a major source of internally generated funds. If the funds from this source are not spent, financial asset change increases by the amount of book value capital consumption allowances. If all the funds from book value farm consumption allowances are not used for farm capital formation but are transferred out of the farming sector, the effect on farm financial asset change is zero since outflow of capital consumption allowances equals its inflow. This example can also be applied to assets transferred and capital borrowing.

Saving in the capital finance account does not equal retained earnings nor financial asset change. It is the amount of current production not consumed; dissaving is the amount of capital stock consumed. Capital finance must equal capital accumulation in the capital finance account. Saving is derived as a residual by subtracting capital consumption

allowances, real estate transferred, and capit!tl burrowing from accumulated capital. Since saving is derived as a residual, the concepts and estimates of capital consumption allowances, real estate trans­ferred, and capital borrowing are examined in detail.

Normally assets transferred consist of trade-ins, salvage value of scrapped assets, and capital transfers. But assets transferred in table 1 include only net real estate transferred, because farm gross capital expenditures on machinery and motor vehicles are already estimated on a net purchase basis in the farm expense accounts; that is, net of trade-in value. The net decline in acres in farms reported in Number of Farms and Land in Farms is valued by using the average value per acre transferred to nonagricultural uses reported in Farm Real Estate Market Developments to estimate cash funds internally generated by net real estate transferred. Estimates of land in farms represent places defined as farms as in the census of agriculture.

Capital borrowing is the net increase in real and nonreal estate debt used to acquire motor vehicles and equipment, construct buildings, and make major repairs and improvements to land and buildings. It is estimated from published reports tempered with judgment. Federal land banks (FLB' s), life insurance mmpanies, and Farmers Home Administration (FmHA) are the most important long-term institutional farm lenders. Benchmark data for new money loaned by FLB's for repairs and improvements to farmland and buildings exist for 1!-l!iO and 1965; annual data are available for 1968-73. Annual data on similar loans are available for life insurance companies for 196:3-73. Annual data for FmHA are available for 1963-73 but they are not so complete as information for FIB's and life insurance companies. Sellers of farmland are important in financing land sales but are probably not loan sources for other capital purposes. Commercial bank loans fit the capital borrowing definition, but most likely nearly all such loans are classified by the banks as nonreal estate secured loans.

Information on nonreal estate loans for capital borrowing purposes is not so complete as it is for real estate secured loans. Benchmarks on the percentage of production credit association (PCA) loans advanced to purchase machinery and equipment, construct buildings, and improve farm! and and buildings are available from Farm Credit Administration surveys made in 1956, 1962, 1966, and 1971. The 1966 Federal Reserve Bank survey is the only reliable benchmark available for banks, so bank capital lending is estimated by using that benchmark and applying the trend experienced by PC A's for other years. Capital lending by FmHA and miscellaneous lenders is based primarily on the 1970 ~:en sus Farm Finance Survey and the trend of PCA's 18 again applied.

47

Capital consumption allowances equal depreciation plus accidential capital damage. They are the replacement cost capital consumption allowances reported in the Farm Income Situation converted to book value. The value is based on the 1969 book value farm balance sheet published in The Balance Sheet of the Farminf? Sector, 1970 and on the depreciation rates used for estimating total net farm income. Replacement cost valuations of balance sheet assets and capital consumption allowances are needed for interindustry comparisons and for calculating rates of return. But book value capital consumption allowances are the charges permitted by the Internal Revenue Service, and thustheyr~flect internally generated funds of farm proprietors.

Valuation of depreciation at its market value in the capital finance account has not been examined before, probably for two reasons. First, financial analysts derive cash flow from income as cash receipts less cash expenses. Although adequate for financial purposes, this procedure sidesteps depreciation valuation and the estimate of saving. Second, capital stocks and flows are valued at current replacement prices only for the farm sector. Applying national economic accounting concepts to the farm sector has not been pursued by either agricultural or nonagricultural analysts until recently. Capital consumption allowances for nonfarm sectors in the saving and investment account in table 2 are included at book value. National income technicians, while desiring replacement cost capital consumption allowances in the capital flows account, have probably not realized the full ramifications in the capital finance account.

In the first place, incentive to increase capital formation is affected by changes in the investment and depreciation tax laws by reducing income tax obligations through book valuedepreciation. Second, planning and projecting capital budgeting and financing and relating capital formation with the variation in direction and value of financial transactions depend on book value depreciation. For example, including the replacement value depreciation of $10,640 million in the 1974 farm capital finance account instead of the book value of $6,H:3B million results in lessening external capital financing requirements by $:3,H02 million. This result is not consistent with the fact that farm proprietors borrowed $4,021 million of capital funds.

Note that the depreciation valuation adjustment of $28,574 million from 1960 to 1974 is 119 percent of capital borrowing. This adjustment supports the claim that book value depreciation charges reflect internally generated funds. Or from another perspective, capital consumption allowances at replacement cost plus real estate transferred equals 98 percent of capital financing. This relegates the contribution of capital borrowing to 2 percent, again assuming zero saving. Capital borrowing, under

these circumstances, must equal 4 percent of the increase in total farm debt for 1960 through 1974, or $2.092 million, which data collected concerning borrowing purposes do not substantiate.

Finally, capital gains or losses are not ignored in the capital finance account but are properly r~corded in the assets transferred series as trade-ins, scrapped assets, and assets transferred. For instance, acres of land transferred to nonfarm sectors are valued at current market value and not book value. Capital gains also affect capital finance indirectly by enhancing capital borrowing that is secured by existing assets. But capital consumption allowances, inflated for price increases, do not in any way generate internal funds.

Internal Capital Financing

As previously stated, farm capital accumulation totaled $120.5 billion from 1960 through 1974, or 211 percent of net farm borrowing. The existence of internal capital financing explains how farm proprietors were able to carry such a heavy financial burden. Internally generated funds dominated farm capital financing from 1960 through 1974 and accounted for 80 percent of total capital finance (table 1). Heavy support of capital accumulation by internal financing sources is expected in the farming sector, since heavy internal capital financing is a characteristic of industrialized economies [6]. Internal capital financing is important not only because of its magnitude, but also because it is a fund source not immediately subject to disruptions in the national money markets.

Relati"Ve Importance of Capital Financing Sources

Internal capital financing is the major share of total capital financing, and capital consumption allowances are the major part of internal capital financing. From 1960 to 1974, funds generated from capital consumption allowances permitted by the Internal Revenue Service are 54 percent of total capital finance and 114 percent of externally generated funds.

Real estate transferred accounted for 21 percent of total capital finance from 1960 through 1974. Real estate transferred is not directly subject to manipulation by public policymakers through investment credit or depreciation tax law changes. For these reasons, it would seem that real estate transferred should not rank so high in importance in future farm financial research as depreciation analysis and saving behavior.

On the other hand, real estate transferred may be an important reserve for financing capital formation. The farm saving rate from 1960 through 1974 equals 2.2 percent. Thus, although the value of real estate transferred is relatively small, this type of asset transfer, probably unique to the farming sector, may help to explain the low farm saving rate. If no real

48

~tate were transferred and capital consumption allowances and capital borrowing remained constant, then the saving rate would have had to increase from 2.2 percent to 10.7 percent.

Other factors contributing to the low saving rate are the current definitions of gross capital expenditures and fixed capital assets. As stated previously, including own-account capital formation in the capital finance account increases saving by a like amount. But no measurement of own-account capital formation is possible with existing farm data. Redefining outlays for feeding livestock, dmry animals, and orchard developments from current account to the fixed capital account would also increase farm saving.

Saving may play a critical role in farm capital accumulation. A small change in the saving rate, the part of farm proprietors' income used for capital formation, leads to a large change in internally generated funds. For example, a saving rate of 3.8 percent in 1965 caused funds generated from saving to equal 8.2 percent of capital finance and 15.8 percent of externally generated funds. The results for 1973 are more striking. A saving rate of 10.9 percent in 1973 caused funds generated from saving to equal 30.8 percent of capital finance and 53.6 percent of externally generated funds even though total net farm borrowing was the second largest on record.

Capital borrowing in the farm capital finance account is an equal saving by nonfarm sectors, and it is recorded as savings in nonfarm capital finance accounts. Net real estate transferred in the farm capital finance account is recorded as gross capital formation in nonfarm capital finance accounts. Financial asset change, net real estate transferred, and capital borrowing equal zero for the capital finance account if all sectors of the economy are included. Saving for the economy can be derived by adding saving in all sector capital finance accounts.

Empirical results in this article should be viewed and used with discretion. The capital finance account was quantified to the extent possible using available data, and judgement WllS necessary in many instances. However, empirical estimation of the capital finance account permits easier illustration of national economic accounting concepts. And just as important, issues are addressed that are probably not so directly confronted as in the estimated capital finance account. Future efforts will be devoted to improvement of data collection and estimation procedures.

Implications

Because of the importance of internal financing, future farm financial research should consider depreciation analysis, investment allowances, and saving and consumption behavior in conjunction with interest rates, credit availability, and other aspects of external farm financial research. In

addition, the role of own-account capital formation as affected by changes in income or capital borrowing needs exploration. For example, does an increase in income or credit availability cause an increase in total capital formation or a shift from own-account capital formation to purchased capital? Saving, the amount of current production not consumed, is required to increase productive capacity. The amount of saving must be determined before macro-economic theory as embodied in Keynesian economic theory can be applied to the farming sector. For example, the estimate of saving permits identification of consumption behavior of the farm sector via the consumption function. And, within the farming sector, the capital finance account is only one account in a set of economic accounts required for monitoring economic performance (1 and table 2). Establishing other national farm economic accounts and concepts based on national farm economic accounting procedures will further increase the usefulness of the capital finance account. In addition, integration of

economic theory concerning relationships between the farm and nonfarm sectors can be more readily achieved through the national income and product accounts. Thus, the capital finance account based on national economic accounting concepts is essential for understanding the farm capital accumulation process.

Summary

The capital finance account based on national economic accounting concepts identifies three important financial processes. First, internal capital financing comprised the major share of total capital financing from 1960 to 1974 and exceeded total net farm borrowing. Second, internal capital financing is a dependable fund source not subject to immediate disruption by conditions in the national money markets. Third, saving is important because small changes in the saving rate lead to large changes in internally generated funds.

REFERENCES

[1] Carlin, Thomas A., and Charles R. Handy, Concepts of the Agricultural Economy and Economic Accounting, Amer. Agr. Econ. 56: 964-975, Dec. 1974.

[2] Evans, Carson D., and Forest G. Warren, Farm Credit and Tight Money in 1966-67, Agr. Finance Rev. 28:1-13, Nov. 1967.

[3] Penson, John B., David A. Lins, and George D. Irwin, Flow-of-Funds Social Accounts for the Farm Sector, Amer. Jour. Agr. Econ. 53:1-7, Feb. 1971.

[4] Simunek, Richard W., Capital Accounting in the Farming Sector Using National Economic Accounting Concepts, contributed paper, 1974 AAEA annual meeting, College Station, Texas, Aug. 1974.

[5] Smith, Allen G., Is There Disinvestment in the

49

Farming Sector? Amer. Jour. Agr. Econ. 54:275-278, May 1972.

[6] Shapiro, Eli, Discussion of Long-term Trends in Capital Formation and Financing, Jour. Finance, Vol. 10, May 1955.

[7] United Nations, A System of National Accounts, - Ser. F, No.2, Rev. 3, New York, 1968.

[8] U.S. Department of Agriculture, Economic Research Service, Agricultural Finance Outlook. AF0-16, Nov. 1975.

[9] U.S. Department of Agriculture, Economic Research Service, Balance Sheet of the Farming Sector 1974, AIB-376, Sept. 1974.

[10] U.S. Department of Agriculture, Economic Research Service, Farm Income Situation, FIS-222, July 1974.

HOW DIFFERENCES IN STATE UNEMPLOYMENT INSURANCE PROVISIONS WOULD AFFECT BENEFIT PAYMENTS

TO AGRICULTURAL WORKERS

by Joachim G. Elterich and Richard F. Bieker1

ABSTRACT

Different State statutes on unemployment insurance (UI) would result in significantly different benefit payments to hired farmworkers, were they given permanent UI coverage. For a uniform work force under 1971 UI provisions, coverage of farm workers would range from 66 to 87 percent among the States. The proportion of insured workers that would be beneficiaries would range from 29 to 35 percent; those beneficiaries who would become benefit exhaustees would range from 4 to 27 percent; total benefit payments to workers during periods of involuntary unemployment would range from $266 to $486; and program costs to employers would vary significantly.

KEYWORDS: Unemployment insurance, hired farmworkers, farm income.

Historically agricultural workers have been excluded from most social legislation in the United States, including unemployment insurance (UI).~ Unemployment insurance originated as part of the Social Security Act of 1935.:1 Its major objectives were employment stabilization of firms, aggregate income maintenance in the economy in general, and insurance against personal loss of earnings for individual workers[2]. By 1938, all States had opted for inclusion in the cooperative Federal-State

1 Associate professor, Department of Agricultural and Food J<~conomics, University of Delaware at Newark, and associate professor, Department of Economics and Business, Delaware State College at Dover, respectively.

"The research reported herein started with contract UIS-72-!1 between the Manpower Administration, U.S. Department of Labor and the University of Delaware. Subsequent work was performed under the regional research project NE-58 entitled Economic and Sociological Study of Agricultural Labor in the Northeast States. This article will also he published, with approval of the Director of the Delaware Agricultural l<~xperiment Station, as Miscellaneous Paper 687, University of Delaware, Newark.

"The program is alternately referred to as "unemployment compensation" and "unemployment insurance." For a discussion of the issues involved, see [ lj.

50

Unemployment Insurance Program, but until December 1974, agricultural workers were exempted from coverage for two major reasons. First, it was argued that the structure of agricultural employment-large numbers of farms with small numbers of employees per farm-made the program administratively unworkable for farmworkers. Second, it was argued that the seasonality of agricultural employment would result in large benefit payments which would threaten the solvency of the insurance system. These concerns continue to dominate discussions in extending, on a permanent basis, unemployment insurance to agricultural workers, whose exclusion is increasingly challenged on equity grounds [3]. Currently, agricultural workers in all States are temporarily covered under the special Unemployment Assistance Act, enacted by Congress on Decem her 19, 197 4. Three States and Puerto Rico have mandatory coverage of specified agricultural employment, and in other States employers may elect voluntary coverage.

The U.S. Department of Labor recently initiated a series of studies to estimate the cost of extending unemployment insurance to hired agricultural workers [ 4). The cost rate (expressed as benefits paid

Eligibility for Unemployment Insurance and Benefit Determination

Worker is a Benefit Exhaustee

Yes

Examine Worker Data

Worker is Covered

Workeris1nsured

Determine Potential Benefit Amount

Worker Becomes a Beneficiary

Determine Actual Benefit Amount

Figure 1

51

No

No

No

Worker is not Covered

Worker is not Eligible for any Benefits

Worker is disqualified for benefits

Worker Is not a Benefit Exhaustee

out to insured workers as a percentage of their taxable payroll) varied considerably for the 17 survey States, ranging from 0. 76 to 6. 7 percent [5).4 Apparently, a substantial proportion of this variation can be explained by differences in unemployment insurance statutes among States [7].

Should unemployment insurance be permanently extended to agricultural workers, these differences could have implications for costs and locations of agricultural production, the mix of inputs used in production, and the relative costs of hired versus family labor. In addition, these differences in costs imply differences in benefits to workers during periods of involuntary unemployment. Differences in benefit paymente could affect the labor supply decisions of workers and could influence the interindustry mobility of workers.

Finally, significantly different benefit payments indicate that the countercyclical effect of unemployment insurance varies among States. In view of the potential implications of significantly different State statutes on agricultural production and employment, this article examines the differences m costs and benefits of State unemployment insurance provisions, given permanent coverage of agricultural workers.

Procedure

The different States' unemployment insurance statutes can be evaluated by analyzing how a standardized set of agricultural workers would fare if they filed for benefits alternately under the UI provisions of the 48 contiguous States. More specifically, we seek to determine if the values for the UI variables are significantly different when a standardized work force files for benefits under the different State statutes. The work force used in the analysis is a sample of 632 hired farmworkers in Delaware and West Virginia. These workers constitute the original survey workers for these two survey States[6]. A detailed 52-week work history for fiscal year 1970 was obtained for each survey worker. Consistent with the original estimating procedure, this data year was used for both the base year and the benefit year. 5 The UI system as of July, 1971. was simulated for each of the 48 contiguous States [7] and provided estimates for each of the important UI variables for each State (fig. 1). Specific values were obtained for the following:

Insured worker-A worker who has sufficient wage

4The survey States are Connecticut, Delaware, Florida, Maine, Maryland, Massachusetts, Minnesota, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Texas, Vermont, Washington, and West Virginia.

'•The base year is usually some period of 4 quarters or 52 weeks preceding the filing of a UI claim. The benefit year usually starts with the filing of a UI claim and extends for 1 year.

52

credits (earnings) in covered employment6 and/or employment during his base year to qualify for benefits during his benefit year.

Beneficiary-An insured worker who has 1 or more weeks of compensable unemployment during his benefit year.

Benefit exhaustee-A beneficiary who has suffered sufficient unemployment to exhaust his benefit rights.

Total benefit amount-The total amount of benefits which a worker receives during his benefit year. This depends upon his base period earnings and/or employment and the ·magnitude of his compensated unemployment during his benefit year.

Weekly benefit amount-The benefit amount which a worker receives for a week of compensable unemployment.

Cost rate-Ratio of benefits paid to taxable wages. The taxable wage base refers to the covered wages, up to $4,200, paid to each worker by a covered employer during a calendar year.

Findings

Differences in State unemployment insurance statutes are examined below with respect to (a) qualifying requirements, (b) compensation levels, and (c) resulting cost rates. The basic findings generated by the computer simulation are shown in tables 1 through 4.

Qualifying Requirements

Variation in stringency of qualifying requirements is indicated by the variation in the proportion of the standardized set of workers who qualify as insured workers under each State's statutes. Qualifying requirements are designed to );leparate individuals with a history of strong attachment to the labor force from those with weak attachment. Tests based on specified weeks of work and/ or levels of earnings determine whether an individual may receive benefits when unemployed. The more stringent a State's qualifying requirements, the lower the' proportion of the standardized set of workers who will qualify as insured workers, and vice versa.

Findings from the computer simulation indicate that the proportion of the standardized set of workers who qualify as insured workers ranges from 66 percent under Wisconsin's statutes to 87 percent under the statutes of Maine, Iowa, and Delaware. The median proportion of insured workers is 84 percent. Thus, the data display a rather skewed distribution

6Virtually all employment is covered employment. Exceptions are domestic help in private homes, agriculturft labor, services for the immediate family, non~r? 1

organizations, services for Federal instrumentalities, services for State and local governments, maritime workers, and self-employment.

about the upper values of the range. In only five States did less than 80 percent of the workers qualify for benefits (table 1).

When the States are divided into quartiles with respect to the incidence of insured workers, the difference is greatest between the first and second quartiles. No significant differences are found between the second and third quartiles and between the third and fourth (app. table A).

Beneficiaries are a subset of insured workers (in table 1 they are expressed as a proportion of insured workers). Specifically, they are workers who meet the

earnings and/or employment criteria for qualifying as insured workers, and in addition have one or more weeks of compensable unemployment.

The incidence of beneficiaries ranges from 29 percent in Washington and Massachusetts to :3fl percent in Connecticut, DelaWare, Wisconsin, and New York. Most of the values cluster around :w to :32 percent, with a median of31 percent. When the States were divided into quartiles with respect to the incidence of beneficiaries, all quartiles except the second and third were found to be significantly different at the 5-percent level (app. table A).

Table 1-Proportion of survey farm labor force that would qualify as insured workers, beneficiaries, and exhaustees, under the July 1971 State Ul provisions'

State Insured workers 2 BeneficiariesJ Ext1austees"'

Percent Han/' Percent llanh Pen•cnt Han/,

Alabama 84.1

Arizona 84.0 Arkansas .. 86.3 California . . .. 85.5

Colorado .. 84.6 Connecticut 86.3 Delaware -. .. 86.6 Florida .. 82.6 Georgia .. 84.9 Idaho .. 74.5 Illinois . . . . .. 83.9 Indiana . . .. 86.1 Iowa .. 87.0 Kansas .. 86.4 Kentucky 84.0 Louisiana 85.5 Maine .. 87.2 Maryland . . .. 86.0 Massachusetts .. . . 81.6 Michigan .. 81.6 Minnesota .. 78.6 Mississippi 85.8 Missouri .. 83.4 Montana .. 82.8 Nebraska .. . . 84.2 Nevada .. . . 85.1 New Hampshire 86.0 New Jersey .. 83.4 New Mexico 83.9 New York .. 80.0 Nort11 Carol ina .. 84.2 North Dakota .. . . . . 83.8 Ohio ... . . . . . . 81.8 Oklahoma 84.1 Oregon .. . . 81.7 Pennsylvania .. 84.5 Rhode Island 82.8 South Carolina 84.6 South Dakota 83.6 Tennessee . . . . .. 81.8 Texas .. . . 84.8 Utah . . .. 81.9 Vermont 77.0 Virginia .. 83.2 Washington .. . . . . . . 77.4 West Virginia .. 85.8 Wisconsin .. . . 65.5 Wyoming . . .. 82.6

1 Significance tests between quart lies of statistics in the table are reported in appendix table A. 2 As a percentage of all workers With farm and/or nonfarm wage credits. 3 As a percentage of In-

27 30.6 9 19.4 26 24 30.6 14 19.4 27 43 31.7 37 22.9 43 36 31.1 27 16.7 19 32 30.7 17 21.7 38 44 35.1 48 10.7 1:' 46 34.5 45 9.7 11 14 30.8 20 22.7 42 34 30.8 21 26.5 47

2 32.4 40 15.7 17 22 30.6 11 20.0 31 42 30.9 22 25.1 44 47 31.5 34 26.1 46 45 31.6 35 22.4 44 25 33.5 42 1 7.5 20 37 30.9 23 21.1 34 48 31.3 31 25.7 45 40 34.3 43 8.2 8

7 29.2 2 14.3 16 8 31.8 39 11.7 14

5 29.8 3 12.4 15 38 31.1 28 21.2 35

18 31.3 29 21.0 33

15 31.6 36 19.2 24 29 31.0 25 21.6 37 35 34.4 44 19.4 28 41 31.5 33 9.5 10

19 31.3 30 10.9 13

23 31.8 38 7.4 6

6 34.8 46 5. 7 3

28 30.5 8 5.1 ?

21 30.2 5 18.0 21

11 30.8 19 6.7 5

26 30.4 7 19.5 29

9 30.7 18 16.3 18

30 30.6 13 3.7

16 30.9 24 19.0 23

31 30.6 10 19.3 25

20 30.3 6 22.3 40

10 31.3 32 18.8 22

33 30.6 12 26.9 48

12 30.7 16 20.2 32

3 32.8 41 6.3 4

17 30.1 4 21.5 3l>

4 28.9 9.4 9

39 31.1 26 8.0 34.9 47 22.2 39

13 30.6 15 19.9 30

sured workers with farm and/or nonfarm wage credits. ·• As a percentage of beneficiaries witll farm and/or nonfarm wage

erect Its.

53

Benefit exhaustees are beneficiaries who have exhausted their benefit entitlements. The proportion of beneficiaries who were benefit exhaustees ranged from 3. 7 percent in Pennsylvania to 27 percent in Texas and Georgia, 7 with a median value of 19 percent. Eleven States had an exhaustee rate of less than 10 percent, while 17 States had a rate higher than 20 percent. Two types of duration occur among the States.8 States which provide for uniform duration tend to show a lower proportion of beneficiaries exhausting their benefits than do States with a variable duration. This would almost have to follow, since a worker with less than 26 weeks of compensated unemployment and thesamenumberof weeks of potential duration would exhaust his benefits in a variable-duration State whereas he might not in a uniform-duration State, where all beneficiaries have a potential duration of 26 weeks.

When the States were divided into quartiles with respect to exhaustees, all groups were significantly different at the 5-percent level (app. table A).

The proportion of all farmworkers with sufficient wage credits to qualify for benefits (insured workers) reflects the stringency of the statutes with respect to qualifying requirements. The proportion of workers who are insured may be different because of different qualifying requirements-that is, annual and/or high-quarter covered earnings and/or weeks of covered employment with specified levels of weekly wages. Because of differences in these requirements, the proportion of workers qualifying for benefits varies greatly because of a different population of workers considered for beneficiary status in each State. Hence, it is not too surprising to find State rankings on populations of insured workers to be significantly different from their rankings on the proportion of beneficiaries.

Since the simulation ensures constant economic conditions for the sample, the number of benefit exhaustees is restricted by the proportion of insured workers and beneficiaries and by differences in the duration of each State's benefit payments. The ranking of a State's proportion of beneficiaries does not correspond to the ranking of the ratio of exhaustees to beneficiaries because of differences in duration of benefits.

The extremes with respect to beneficiaries and exhaustees can be classified into four groups of States (table 1):

(a) States with liberal qualifying requirements and long duration of benefits (for example, in

'Benefit exhaustees may be underestimated in some cases since the base year and the benefit year are identical in our analysis.

"Uniform·duration States pay varying weekly benefit amounts for a specified constant period depending upon the beneficiaries' potential benefit entitlements. Variable­duration States pay benefits for a generally shorter period to beneficiaries with smaller entitlements while paying for a longer period beneficiaries with larger entitlements.

54

Connecticut, beneficiaries rank 48 and exhaustees rank 12).

(b) States with liberal qualifying requirements and short duration of benefits (in Arkansas, beneficiaries rank 37 and exhaustees rank 43).

(c) States with restrictive qualifying requirements and long duration of benefits (in North Carolina beneficiaries rank 8 and exhaustees rank 2). '

(d) States with restrictive qualifying requirements and short duration of benefits (in Virginia beneficiaries rank 4 and exhaustees rank 36). '

A high proportion of exhaustees may result in more unemployed filing for other social welfare programs and probably will intensify their search for available jobs.

Amount of Compensation

Another important difference in State unemployment insurance statutes is the variation in weekly amount and duration of benefit payments.

In evaluating the benefits that would be paid out to the standardized set of workers under the statutes of the different State programs, two measures of benefits are considered: (a) average weekly benefits, and (b) average total benefits during the benefit year. The first is the relevant measure when considering benefits paid to workers during short spells of unemployment. However, when considering the benefits that workers would receive over longer periods of unemployment, the latter measure is more important since it takes into account both the weekly benefit amount and the duration of compensable unemployment under the different State statutesY

The weekly and total benefit amounts and the duration of compensable unemployment are shown in table 2. The average weekly benefit amount ranges from $23.16 under West Virginia's provisions to $42.33 under New Jersey's provisions. The median weekly benefit amount is $36.06. When the States are grouped into quartiles with respect to average weekly benefit amounts, all group means were found to be significantly different at the 5-percent level (app. table A). The average duration for benefits of our population ranged from 8.6 weeks under Wisconsin's statutes to 11.8 weeks under New Hampshire's statutes, with a median value of 10.2 weeks. When distributed by quartiles, all of the averages of the number of weeks of compensated unemployment are significantly different at the 5-percent level.

The average total benefit amount ranges from $266 in West Virginia to $486 in New Jersey. The median average total benefit amount is $362. As is the ca~e with weekly benefits, the average total benefit amounts were found to be significantly different between each of the quartile groups at the 5-percent level (app. table A).

9Restricting the work history to 52 weeks would tend td understate benefits because of the concurrence of base an benefit years.

Table 2-Average total and weekly benefit amounts, and average duration of benefit payments for lleneficiaries, under the July 1971 State Ul provisions'

Average total Average weeks of Average w0ekly State benefit amounts compensated unen1ployrnent benefit amounts

Dollars Rank Number Ran/1 /Jollurs Uunh

Alabama . . . . .. . . . . . . . . . . . . . 341 10 10.3 31 33.00 8 Arizona . . . . ... . . . . . . .... . . 363 25 10.3 27 35.41 17 Arkansas . . . . ... . .... . .. 355 16 10.2 24 34.95 14 California . . . . . . . .... ... 403 41 10.5 35 38.55 38 Colorado .. . . . . . . . . . 403 40 9.6 11 41.85 46 Connecticut ..... . . . . . . . . 421 46 11.4 43 36.90 31 Delaware . . . . . .. . . . .. . ... 381 35 10.8 38 35.32 16 Florida . . . .. . . . . 305 2 9.5 8 32.47 4 Georgia . . . . . . ... . . 329 6 9.2 5 35.80 23 Idaho . . . . . . . . . . . . . . .. . . . 358 22 9.0 3 39.65 44 Illinois . . . . . . ..... . . . . 358 20 9.7 14 36.78 29 Indiana . . ..... • • • 0 ... . . 307 3 8.6 2 35.77 22 Iowa . . . . . . . . . . . . .. . . 415 44 10.2 25 40.86 45 Kansas . . . . ....... . . 361 24 10.0 18 36.21 27 Kentucky . . . . . . . . .. . . 358 21 10.0 17 36.00 24 Louisiana . . . . . . . . ... . . 416 45 10.8 37 38.68 39 Maine . . . . . . . . . .. . . 412 43 10.5 34 39.23 43 Maryland . . . . . . . . . . .. 423 47 11.3 42 37.56 36 Massachusetts .. . . . . . . . . . . . . 365 27 9.3 6 39.15 41 Michigan . . . .. . . . . . . . . 380 34 10.7 36 35.42 18 Minnesota . . . . . . . . . . . .. . . . . 347 12 9.6 10 36.17 26 Mississippi . . . . ........ . . . . 339 8 10.3 28 33.08 9 Missouri . . . • • • • • 0 .. .. . . 370 31 10.1 22 36.52 28 Montana .. . . . .. . . . . . . . . . 341 9 10.4 33 32.74 6 Nebraska ... . . . . . . ... 378 33 9.7 13 39.00 40 Nevada . . .. . . . .... . .. 366 28 9.7 15 37.51 35 New Hampshire .. . . . . . . . . . 352 14 11.8 48 29.78 2 New Jersey ... . . . . . . . . . . . . . 486 48 11.5 46 42.33 48 New Mexico . . . ........... . . . 389 38 11.4 44 34.02 11 New York ...... . . . ...... . . 370 30 11.0 40 33.69 10 North Carolina . . ....... . ... 367 29 11.2 41 32.69 5 North Dakota .... . . . . . . .. . . . . 364 26 10.4 32 35.04 15 Ohio . . ... . . . . . . . . . . . . . . . . 358 19 10.9 39 32.83 7 Oklahoma ... . . . . . . ..... . . 357 18 10.1 20 35.43 19 Oregon ... . . . . . . . . . . . . . . . . . 326 5 10.3 30 31.76 3 Pennsylvania . . . . . . . . . ...... 409 42 11.5 47 35.44 20 Rhode Island .. . . . . . ....... 374 32 10.0 19 37.29 33 South Carol ina ... • 0 ••• . . . . . . 351 13 10.3 29 34.18 12 South Dakota .. . . . . . . .... . . . 338 7 9.5 9 35.46 21 Tennessee .. . . . . . . . . . .... . . . . 353 15 10.2 26 34.67 13 Texas .... . .......... . . . . . . 384 36 10.1 21 37.98 37 Utah .... . . . . . . . . . . . ....... 357 17 9.9 16 36.13 25 Vermont ..................... 397 39 10.1 23 39.19 42 Virginia .. . . . . . . . . . . . . . . . . . . 346 11 9.4 7 36.91 32 Washington .. . . . . . . . . .... 388 37 9.2 4 42.26 47 West Virginia ........ . . . ..... 266 11.5 45 23.16 Wisconsin ... . . . . . . . . . . . . . . . . 316 4 8.6 36.89 30 Wyoming ... . . . ....... . .... 361 23 9.6 12 37.36 34

1 Significant tests between quartlles of statistics In the table are reported In appendix table A.

Variation in the cost of living among States is an important factor to consider when comparing benefits that would be paid to the standardized set of workers. Accordingly, the weekly and total benefits are adjusted by a regional Consumer Price Index for nonmetropolitan areas (table 3) (see app. B for derivation ofCPI's). The range in the adjusted weekly and total benefit amounts is less than the range in the unadjusted weekly benefit amounts. The unadjusted weekly and total benefit amounts for the lowest :anking State are 55 percent as large as the benefits In the highest State, while adjusted benefits for the lowest ranking State are 60 percent as large as the adjusted benefits in the highest ranking State. When the States are grouped into quartiles with respect to

55

adjusted weekly benefits, these average weekly benefits were found to be significantly different

·between all except two groups at the 5-percent level (app. table A). Adjusted total benefits were found to differ significantly between all groups.

The most significant effect of adjusting benefits is the shifting in ranks among States. After adjusting for differences in purchasing power, the ran kings of Southern States with respect to both weekly and annual benefits increase appreciably. On the other hand, the rankings of some Western and Northern States decline.

The foregoing results clearly indicate that there is no uniformity with which the different States insure the loss of personal earnings of workers during

Table 3-Average total and weekly benefit amounts for beneficiaries, deflated by a regional Consumer Price Index, by State, 1971 1

Average total benefits Average weekly_ benefit State in real terms amounts in real terms

Dollars Rank Dollars Rank

Alabama ............................ . 391 30 37.79 22 Arizona ............................. . 365 15 35.65 7 Arkansas ............................ . 406 37 40.02 38 California ........................... . 406 36 38.61 31 Colorado ............................ . 408 38 42.41 44 Connecticut ......................... . 428 42 37.53 19 Delaware ............................ . 388 27 35.92 9 Florida ............................. . 340 5 35.99 10 Georgia ............................. . 367 16 39.92 10 Idaho .............................. . 363 13 40.18 40 Illinois .............................. . 362 12 37.28 18 Indiana ............................. . 311 2 36.25 13 Iowa ............................... . 430 43 42.27 43 Kansas .............................. . 378 22 37.85 23 Kentucky ........................... . 399 34 40.15 39 Louisiana ........................... . 477 47 44.29 48 Maine .............................. . 414 40 39.49 35 Maryland ............................ . 430 44 38.20 29 Massachusetts ........................ . 371 18 39.81 36 Michigan ............................ . 385 24 35.90 8 Minnesota ........................... . 360 9 37.55 20 Mississippi ........................... . 389 28 37.88 25 Missouri ............................ . 387 26 38.17 28 Montana ............................ . 345 6 33.18 4 Nebraska ............................ . 391 31 40.34 41 Nevada ............................. . 368 17 37.76 21 New Hampshire ....................... . 354 8 29.98 2 New Jersey .......................... . 494 48 43.05 46 New Mexico ......................... . 449 46 39.25 33 New York ........................... . 376 20 34.26 6 North Carolina ....................... . 409 39 36.46 14 North Dakota ........................ . 377 21 36.25 12 Ohio ............................... . 362 11 33.27 5 Oklahoma ........................... . 373 19 37.03 17 Oregon ............................. . 329 4 31.97 3 Pennsylvania ......................... . 416 41 36.04 11 Rhode Island ......................... . 381 23 37.92 26 South Carolina ....................... . 391 32 38.12 27 South Dakota ........................ . 349 7 36.68 16 Tennessee ........................... . 393 33 38.66 32 Texas .............................. . 443 45 43.82 47 Utah ............................... . 361 10 36.62 15 Vermont ............................ . 400 35 39.45 34 Virginia ............................. . 386 25 41.16 42 Washington .......................... . 391 29 42.55 45 West Virginia ......................... . 296 1 25.83 1 Wisconsin ........................... . 328 3 38.30 30 Wyoming ............................ . 365 14 37.86 24

1 Eleven subarea consumer price indices were derived from 4 area Indices for nonmetropolitan areas (with populations of 2,500 to 50,000) as modified by 11 cities in subareas in establishing the subarea indices. The 4 area indices carried a weight of two-thirds, while the 11 city indices carried the remainder. Significance tests between quartiles of statistics in the table are reported In appendix table A. See appendix B for derivations of CPi 's.

periods of involuntary unemployment. Differences in average weekly benefit amounts result primarily from differences in benefit payment schedules and wage credits required for given benefit levels.

In West Virginia, for example, $700 in base period wages was required to qualify for the minimum weekly benefit amount of$12, while only $255 in base period wages was required to qualify for the minimum weekly benefit amount of $10 in New Jersey. At the upper end of the benefit schedules, $9,050 in base period wages are required to qualify for the maximum weekly benefit amount of $71 under

56

West Virginia's statutes. In contrast, only $1,811 in base period wages are required to qualify for $72 in weekly benefits inN ew Jersey. Variation in average annual benefits is a result of both variation in average weekly benefits and the allowable duration of compensable unemployment under the different State statutes.

Cost Rate

An important aspect of any social program is its cost. The unemployment insurance system in all

States is financed primarily by a payroll tax paid by covered employers. 10 The cost of extending unemployment insurance to the standardized set of workers is measured by the total actual benefits as a percentage of the taxable wage base. Obviously, the more generous the insurance provisions, the higher the cost of the program. Table 4 shows that the cost rates for the standardized set of workers ranges from 2.81 percent in West Virginia to 5.04 percent in Connecticut, with the median cost rate as 3. 70. When the States are grouped into quartiles with respect to the cost rate, the means were found to be significantly different among all four groups at the 5-percent level (app. table A).

The costs to an employer are illustrated in the following example. We assume an employer employs a worker for 2,000 hours per year at the hourly wage rate of $2.10. In West Virginia, the employer has a cost rate of2.81 percent, and in Connecticut, it is 5.04 percent. In addition, there is a half-of-1-percent Federal share for all States. The added costsofUI to a West Virginia and a Connecticut employer would be $.069 and $.116 per hour, respectively (.0281 + .005 x

$2.1 0, and .0504 + .005 x $2.1 0.)

'"Alabama, Alaska, and New Jersey levy a tax on the worker as well as on the employer. Since minimum and maximum rates that employers pay under experience ratings ~re constrained by statutes, these rates are not necessanly those ~hat the employers would pay at any point m time. It Is_ possib!e. at any given time that a given set of e~pl_oyers Will subsidize or be subsidized by other employers w1thm the ~tate. But employers as a group generally must bear the entire cost of the program. The simulated cost rates therefore, do reflect the different program cost rates amon~ States.

Conclusions

Because of differences in State statutes u?-employment permanent insurance coverage fo; hued farmworkers would result in (a) different proportions of a uniform work force that would be insured (from 66 to 87 percent), (b) significantly different proportions of the insured workers that would be beneficiaries (29 to 35 percent), (c) significantly different proportions of beneficiaries becoming benefit exhaustees (4 to 27 percent) (d) significantly different a:verage total benefit payments to workers (ranging from $266 to $486) during periods of involuntary unemployment, and, (e) significantly different program costs to employers.

The significantly different proportions of beneficiaries and differences in benefit payments (even after adjusting for differences in the cost of living) indicate that there is no uniform or standardized commitment among the States to the objective of insuring against personal earnings loss for individual workers. ,

The differences in benefit payouts among the States also indicate that States vary in theiruseofthe unemployment insurance program to stabilize aggregate economic activity. The countercyclical objective of UI with respect to the business cycle is served better by those States that show a higher proportion of insured workers and beneficiaries. This has direct implications for the income security of families and communities.

There is a significant difference in the employer cost of involuntary unemployment among the States as measured by the cost rate, which ranges from 2.8 to

Table 4-Cost rates for insuring the survey workers under July 1971 Ul provisions'

State

Alabama

~i~f~~:· ..•••••••••••••••• Connectic~; ...... · · · · · · · · · ·

~~f:J~' •••••••••••••••••• Illinois ................... . Indiana

~~!~::••• ••••• ••••.••.•• Massachus~t·t~ . · · · · · · · · · · · · · · Mtchigan · · · · · · · · · · · · · ·

~i~:~ii~~~. : : : : : : : : : : : : : : : : : ~ontana . : : : : : : : : : : : : : : : : : :

Percent I 3.47 3.69 3.85 4.24 4.14 5.04 4.50 3.07 3.40 3.43 3.63 3.23 4.50 3.90 3.99 4.35 4.44 4.93 3.43 3.91 3.19 3.59 3.82 3.53

Rank State

13 Nebraska ................ . 24 Nevada .................. . 29 New Hampshire ........... . 41 New Jersey .............. . 38 New Mexico .............. . 48 New York ............... . 45 North Carolina ............ .

3 North Dakota ............ . 8 Ohio ................... . 9 Oklahoma ............... .

22 Oregon .................. . 5 Pennsylvania ............. .

44 Rhode Island ............. . 30 South Carolina ............ . 35 South Dakota ............ . 42 Tennessee ............... . 43 Texas ................... . 46 Utah ................... . 11 Vermont ................ . 32 VIrginia ................. .

4 Washington .............. . 18 West Virginia ............. . 28 Wisconsin ................ . 14 Wyoming ................ .

's, .. gn1f1cance tests between quartiles of cost rates are reported in appendix table A.

57

Percent

3.91 4.23 3.77 5.01 4.10 4.08 3.72 3.64 3.56 3.61 3.25 4.18 3.80 3.59 3.39 3.57 3.94 3.55 3.97 3.43 3.43 2.81 2.86 3.61

I Rank

31 40 26 47 37 36 25 23 16 21

6 39 27 19

7 17 33 15 34 10 12

1 20

5.0 percent. If employers view the variable contribution rate (experience rating) as an incentive for stabilizing employment, then clearly the incentive for stabilization varies among States. In addition, if the cost of the unemployment insurance program is viewed by employers as a eost of production, variation in such costs could affect the location of economic activity.

The findings of this study show that there is considerable variation in the UI statutes among the different states. These differences could influence social welfare programs and economic efficiency. This stJ!dY has estimated the magnitudes of the differences in the provisions, but further research is needed to evaluate the implications of these differences.

Appendix table A-Results of the F-Test of significance of group means (analysis of variance) and Duncan's multiple range test of variables of tables 1·4 for groups of 121

Means

Average Average Average duration Average Average weekly

Group number Insured Bene· Ex· total of compen· weekly total benefit Cost workers flclar ies haustees benefit sated benefit benefits amounts rates

amounts unemploy· amounts In real In real ment terms terms

Percent Percent Percent Dollars Weeks Dollars Dollars Dollars Percent

1 78.6 30.2 7.5 325 9.3 31.93 341 33.69 3.24 2 83.3 30.8 15.9 357 10.0 35.39 372 37.22 3.59 3 84.6 31.3 20.2 372 10.4 36.85 393 38.66 3.89 4 86.2 33.5 23.8 414 11.3 39.88 434 41.68 4.47

Computed F 2 21.6 47.7 132.5 51.0 107.7 44.5 53.7 39.5 72.1 Standard error .704 .211 .609 5.147 .080 .495 5.277 .527 .061

1 Means joined by a line are not significantly different at the 5-percent level. Tests for each variable were performed separately. 2 F _05=2.82 and F _01 =4.26 with 3 and 44 degrees of freedom.

Appendix B-Derivation of Consumer Price Indexes for Rural Areas by Region

Approximate Consumer Price Indexes (CPI) were derived for each region from data supplied by the U.S. Department of Labor, Bureau of Labor Statistics.11

The idea was to obtain a rural CPI which was based

1 1The data available to the authors at the time and used for this study were for autumn 1972 (See Jean Brackett, "Urban Family Budget Updated to Autumn 1972," Monthly Labor Review, Aug. 1973, pp. 70-76.). For the same data for spring 1970 and spring 1969, see pages 13 and 29, respectively, of 3 Budgets for an Urban Family of Four Persons, 1969-1970, U.S. Dept. of Labor, Bur. Labor Statistics, Suppl. to Bul. 1570-5, 1972.

58

on the nonmetropolitan regional CPI's (fortheregion in which a particular State lay) and which was weighted by the CPI of a small (if possible) city within that region. The nonmetropolitan CPI's were weighted at two-thirds and the small city CPI's at one-third to arrive at the regional CPI's. The States were separated as closely as possible following the farm production regions of the Economic Research Service. Because data for only autumn 1972, was available (whereas July 1971 was desired), the indexes were based on the "lower" budget for a four· person family.

11]

[2]

[3]

[4]

[5]

REFERENCES

Malisoff, Harry, The Insurance Character of Unemployment Insurance, Institute for Employment Research, Kalamazoo, Mich., 1961; Arthur Altmeyer, Formative Years of Social Security, Univ. of Wis. Press, Madison, Wis., 1966; Eveline Burns, The American Social Security System, Houghton Mifflin Co., Boston, 1949; Valdemar Carlson, Economic Security in the U.S., McGraw-Hill Book Co., 1962; Paul Douglas, Standards of Unemployment Insurance, The Univ. of Chicago Press, Chicago, 1932; Charles Warden ur., "Unemployment Compensation, The Massachusetts Experience," in Otto Eckstein, ed., Studies in the Economics of Income Maintenance, 1967, pp. 73-96. Eckstein, Otto, (ed.), Studies in the Economics of Income Maintenance, Brookings Institution, Wash. D.C., 1967, pp. 14-16. Administration proposal contained in H.R. 8600, introduced on June 12, 1973, 93d Congress. Bauder, W. W., and others, Impact of Extension of Unemployment Insurance to Agriculture, rept. prepared with Regional Research Project NE-58 of Northeast Agr. Exp. Sta., submitted to the U.S. Dept. Labor, Oct. 31, 1972; Penn. State Univ., University Park, Pa. 1972. Elterich, Joachim G., and Richard F. Bieker, Analysis of the Variation of the Industry Benefit-Cost Ratio for Farm Workers Between

59

15 Survey States: Research Report I, Univ. Del., Newark, Del., 1973, p. 13; J. S. Holt, et. al., Economic and Social Considerations in Extending Unemployment Insurance to Agricultural Workers: Regional Report Two, rept. prepared with Regional Project NE-58 of Northeast Agr. Ex pt. Sta., submitted to the U.S. Dept. Labor, Oct. 1973; The Agricultural Unemployment Insurance Conference, A Compendium of Papers presented at the Agricultural Unemployment Insurance Conference, The Ohio State University, April 25-26, 1973, rept. sponsored by NE-58 Farm Labor Tech. Committee (USDL), Ohio State Univ., and Ohio Agr. Res. and Development Ctr.

[6] Elterich, Joachim G., and Richard F. Bieker, The Impact of Extending Unemployment Insurance to Agriculture in Delaware: Part I, Del. Agr. Expt. Sta. Bul. 392, Univ. Del., June, 1972. See also Richard F. Bieker, Joachim G. Elterich, and Steven F. Haley, The Impact of Extending Unemployment Insurance to Agriculture in West Virginia: Part I, Del. Agr. Expt. Sta. Bul. 393, June, 1972.

(7] U.S. Department of Labor, Manpower Admin. Comparison of State Unemployment Insurance Laws, Bur. Empl. Security, UI Serv., Jan. and Aug. 1971, tables BT-2, BT-4, and BT-8; USDL, Handbook for Interstate Claims Taking, data effective as of July 4, 1971.

DISCRIMINANT ANALYSIS OF LOANS FOR CASH-GRAIN FARMS

by Daniel J. Dunn and Thomas L. Frey1

ABSTRACT

Previous research on agricultural loan evaluation is reviewed. The development and application of evaluation techniques, stressing the use of discriminant analysis, is also reported. Using the discriminant analysis technique, a credit scoring model is developed and used to analyze PCA loan applications in the cash grain area of central Illinois.

Keywords: Discriminant analysis, financial ratios, credit scoring model, non-ratio characteristics.

Agricultural lenders must simultaneously evaluate new loan applications and judge the performance of loans already made. Their goal is to make new loan applications that will be successful over time and reject those that will become problems. Following the first loan, continual analysis is necessary as a basis for extending additional loan funds and for determining the amount and kind ofloan supervision needed.

Loan analyses have historically been made by personal examination of individual credit applications and files, combined with personal knowledge of an applicant and his operation. But the size of individual agricultural loans has increased rapidly in recent years. For example, the research department at the Federal Intermediate Credit Bank of St. Louis reports that the average size loan for the district, per member served, reached $35,525 in 1974, up $10,000 from 1972. The average size loan may reach $6G,OOO by 1980. Since larger loans involve greater risk, lenders are motivated to seek a better loan analysis system for new applications and for continuing loans.

Determining loan characteristics that indicate potential success or failure is an important concern. The relationships needed for success, the techniques

'Former graduate research assistant and associate prof!,ssor of agricultural finance, respectively, Department of Agricultural Economics, Univerr;ity ofillinois at Urbana­Champaign.

60

that can generate solutions, and relating the system to agricul turallending operations are also important. The objectives of this article are to highlight relevant studies of financial analysis for agricultural and nonagricultural firms and to report a recent study of central Illinois cash-grain farms. The study was designed to select and study loan characteristics available from the original loan application. The goal of the study was to determine what characteristics could be used to distinguish between loans that became problems and those that remained acceptable, several years after the original loan.

Previous Loan Evaluation Studies

The literature on financial analysis is so vast that a comprehensive view is not possible. This review will highlight two relevant consumer and commercial finance studies. Also, agricultural credit studies of predicting successful short-term agricultural loans will be reviewed.

Studies of consumer and commercial credit. Durand developed one of the first reported credit scoring models in 1941 [ 4]. He wanted to find a method which would enable used car firms to rank and group car buyers in one of two groups as determined by their credit risks. Durand found that with four variables he could determine a credit score that would significantly aid in distinguishing good and bad consumer installment car loans. Durand's study was an important contribution to financial

analysis because it was the first study to use discriminant analysis2 for credit scoring.

A recent study using financial ratios and discriminant analysis was conducted by Chesser[2]. The purpose of the study was to find a way to predict whether a commercial loan would fail in the next year. He used stepwise discriminant analysis to find a set of ratios that could be used for predictions. The ratios Chesser found important were (1) the cash plus marketable securities to total assets, (2) net sales and to cash plus marketable sec uri ties, (3) earnings before interest and taxes to total assets, (4) total debt to total assets, (5} fixed assets to net worth, and (6) net working capital to net sales.

Other studies not reported in this article suggest that sophisticated financial analysis has been developed in nonagricultural areas. Although credit scoring has existed since 1941, and has achieved long and widespread use in consumer credit, it has attracted little interest among agricultural lenders until recently.

Studies of agricultural credit. Four previous studies have focused on identifying problem loans in agriculture. Two used data from new loan applications to predict the ultimate success of loans. The others used data from existing loans to identify successful loans. A fifth study dealt with the relationship of personality characteristics and successful use of credit. Many characteristics are used to discriminate between successful and problem loans, including both financial ratios and non­financial characteristics.

In the early 1960's, Reinsel evaluated the use of data from new dairy farm loan applications as a means of predicting the outcomes of loans [11 ]. Data were collected from applications made of the early 1950's that were judged to be successful or unsuccessful in 1962 by the Production Credit Association (PCA) and the Farmers Home Administration (FHA). Discriminant analysis was used to determine the contribution of several variables in distinguishing between successful and unsuccessful loans. By using the discriminant model he separated the characteristics of successful and unsuccessful loan applicants. Most of the characteristics studied were non-ratio. Because application forms varied, characteristics of PCA borrowers differed from those of FHA borrowers. Significant characteristics for successful PCA and FHA borrowers are shown in table 1.

Predicting whether an applicant would be a poor or good risk for a PCA loan was studied by Bauer and Jordan [1). They analyzed 84\oans from two eastern Tennessee PCA's. No distinction was made between types of farms. The information consisted of good

. "Discriminant analysis is a statistical technique used to differentiate two or more classes of persons or objects based on common variables which are thought to be relevant.

61

loans and problem loans made during lHfiR-6!), and the amounts were adjusted to 196R-6H dollars for analysis. Stepwise regression analysis was UHed to find the significant characteristics. Multiple discriminant analysis was used to estimate coefficients for these characteristics. A weakness of this technique is that some of the significant characteristics may be highly correlated with each other, and they may be less significant in the discriminant model than characteristics that were dropped from the model with the stepwise regression. The significant characteristics found by Bauer and Jordan are also shown in table 1. Their study indicates that 85 percent of the new loan applicants could be correctly classified with the estimated function.

Johnson completed a study in 1970on the financial position and progress of PCA borrowers [6]. It provides a method of empirically identifying characteristics of current borr(Jwers to indicate whether they were making satisfactory progress or needed extra supervision. He collected data for the same operating year on ~J89 borrowers who had been members ofthree Missouri PCA's for at least 2 years. The study included 68 problem loans and covered seven farming types. The type of farming was not considered in building the discriminant models, but the application of the models was adjusted to include farm types. Using discriminant analysis, Johnson also compared loans in which collateral was pledged to those loans without collateral. The significant characteristics in Johnson's study are also listed in table 1. As part of his work, Johnson developed a model that is still used to evaluate loans discounted through the Federal Intermediate Credit Bank of St. Louis [7]. This model used three variables: a repayment index, the current ratio, and the debt-to­asset ratio.

In 1.971 Evans reported on successful and unsuccessful farm loans in South Dakota [51. He concentrated on existing farm operating loans and tried to identify borrower characteristics that showed developing unsatisfactory loan situations. He studied the differences between successful and unsuccessful loans of 100 PCA members and 100 FHA borrowers. All the farmer!'! were creditworthy at the time of their original application between 19fi5 and 1964. But by 1964-65, half of the loans were unsatisfactory in terms of repayment. The study was concerned with developments after the first loan was made. It was not concerned with the correct evaluation of available data by the lender at the time of the first loan application. The loans had to be successful or unsuccessful for at least 2 years before the study date. No distinctions were made among farm types which differed from 5,000.acre ranches to 80.acre crop farms. The data used for analysis came from original loan applications and from the last year's loan applications. Evans used discriminant

Table 1.-Significant characteristics for discriminating between successful and unsuccessful agricultural loans, as reported in major studies'

Ct1aracteristic

l. Number of acres in farm ................. . 2. Years of farming experience .............. . 3. Annual Increase in net worth after

age 20 and before first PCA loan .......... . 4. Ratio of nonreal estate debt to

total debts ........................... . 5. Nurnber of creditors .................... . 6. Farm ownersl1ip ....................... . 7. Ownership of life insurance .............. . 8. Health insurance .......................• 9. Annual average increase in net

worth after age 20 and before first FHA loan ........................ .

10. Size of family .......................•.. 11. Ratio of combined inter~st and

12. 13.

principal payments to net income Level of family living expenses ............ . Family living expenses as percent of total farm and family expenses ......... .

14. Debt to asset ratio ...............•...... 15. Farm value ........................... . 16. Magnitude of total liabilities .............. . 17. Marital status ........................ .. 18. Current asset to current

liability ratio ......................... . 19. Repayment Index ...................... . 20. Debt to net worth ratio ................. . 21. Gross income to current debts ratio ........ . 22. Net worth to PCA commitment ratio ....... . 23. Costs of operation . . . . . . . . . . . ......... . 24. Poor production records •................. 25. Ratio of nonreal estate debt

to value of nonreal estate assets •........... 26. Ratio of net worth to total

assets owned .......................... . 27. Expected income as a percentage of

the previous year's Income ............... .

Reinsel PCA study

X

X

X

X

X

X

X

1 Several items should be considered when using tl1is table. First, no attempt is made to show the direction of Influence of the cl1aracterlstics sl1own, as reported in the original research by the aut110rs. Second, this table combines two separate types of studies. The characteristics from a loan application are used to discriminate successful from unsuccessful loans as measured several years later. And characteristics from a current balance sheet for an existing borrower are used to classify tl1e loans at the present time; this approach parallels what credit examiners

analysis to test 15 characteristics from the last year's loan applications and 23 characteristics from the first year's applications. He found that the 23 characteristics of the first year's loan applications were not significant. But he did find significant differences in the characteristics of the last year's loan applications. The five most significant characteristics of unsuccessful PCA loans were the high ratio of debt to assets owned, high cost of operation, poor production record, high ratio of debt to net worth, and the large size of the borrower's household. The five most significant characteristics of unsuccessful FHA loans were the poor production record, high cost of operation, high ratio of nonreal estate debt to total debt, high ratio of non real estate debt to value ofnonrPal estate assets, and a low ratio

62

Reinsel FHA study

X

X

X

X

X

Bauer and

Jordan study

X

X

X

X

X

X

X

X

Johnson study

X

X

X

X

X

X

Evans PCA study

X

X

X

X

X

Evans FHA study

X

X

X

X

X

do manually. Third, in some cases several similar characteristics are kept separate to conform with the way authors reported their findings. Ratios of debt to assets, net worth to debt, and net worth to total assets are different ways to fl nd the total asset and liability structure. Fourth, data available have been inconsistent for studies reported. Obviously this has direct Implications for characteristics used and for those that were significant.

of net worth to total assets owned. This study was concerned mainly with the loan characteristics that determine the deterioration of a loan.

Krause and Williams added a new dimension by studying the relationship of personality characteristics to financial success [8]. Of 72 PCA and FHA borrowers and their wives interviewed, 52 increased in net worth from 1960 through 1964, and 20 decreased. From the interviews, personality variables were quantified for each borrower. A regression equation with 13 variables was used. Change in net worth was used as the independent variable. The results suggested "that testing instruments can be developed to provide guidance for present and prospective farm operators, entrepreneurs, and farm credit agencies .. [8, p. 623].

Present Study.

Developing better loan evaluations or credit scoring models for new agricultural loan applications is needed. Some good statistical techniques have been identified and relevant characteristics have been noted. The present study was thus designed to examine loans to grain farmers. It concentrated on predicting successful loans from data available on the original application [3].

Data. Study data were from loans made to PCA cash-grain farmers in central Illinois. These farmers obtained their first PCA loans during 1964-68 and were still members in 1971. The credit examination by the PC A's examiner determined whether the 1971 Joan was an acceptable loan or problem loan. Acceptable loans are defined by PCA's as loans that are highest in quality ranging down to and including loans that have significant credit weaknesses. Problem loans are defined by PCA as those that have serious credit weaknesses and need more than normal supervision, but they are believed to be collectible in full. Loans classified in 1971 as "vulnerable" or"loss" were not included in the study.

Of the 99 sample loans, 60 were classified "acceptable" and 39 were classified "problem" in 1971. Data were collected from the original loan application, since it is the principal source of information available to a lender before he makes his loan decision. These date were used to derive characteristics which were studied to determine the best predictors of acceptable and problem loans from cash-grain farms.

Characteristics studied were limited to information given in the first loan applications. This information shows 16 financial ratio characteristics and 6 non­ratio characteristics that are potentially significant measures for classifying cceptable" and "problem" loans.

The following financial ratios were considered: Current assets to current liabilities Total liabilities to net worth Current liabilities to total liabilities Total assets to net worth Current liabilities to net worth Total liabilities to total assets Net cash farm incbme to total assets Net cash farm income to current liabilities Spendable income to current liabilities Net cash farm income to total liabilities Spendable income to total liabilities Amount of note to net cash farm income Amount of loan payments during year to gross farm cash income Cash farm operating expenses to gross cash farm income Net cash farm income to net worth · Gross cash farm income to total assets

The following non-ratio characteristics of the applicant were considered:

63

Number of dependents Age Number of acres owned Number of acres rented Amount of life insurance Amount of credit life insurance

The ratios were used because they provide a relative measure of comparison between two observations. They are also a common media in financial analysis. The non-ratio characteristics were used because they provide pertinent information about the borrower that is not obtainable through ratios.

Methodology. Multiple discriminant analysis was used to determine which groups of ratios best discriminated between loans that became acceptable and those that became problems. This technique was used to find a linear combination of independent characteristics for projecting data on a line to maximize the distance between sampled population. This gives a linearorderingofthedata. Observations from the different populations are grouped in clusters, and the clusters are as distinct from each other as possible.

Figure 1 illustrates a hypothetical discriminant function through a population of loans. The discriminant function is drawn so that the maximum distance between the populations is shown. In figure 1, two observations are misclassified. An acceptable loan observation was <:<lassified as an acceptable loan. A desired discriminant function minimizes these classification errors.

The desired estimated discriminant function is of the form

where a 1, ... , an are the weighing coefficients to be applied to the n original values for each individual characteristic x 1, ... , x11• The weight given each of the original characteristics must be determined so that the resulting composite score Y will have maximum usefulness for classifying the two loan populations.

Stepwise discriminant analysis was first applied to the original22 characteristics. The goal was to find a subset of characteristics that could do as well as all22 items in classifying the two loan populations. Two specific advantages could result from a smaller subset of characteristics. First, with several of the original characteristics highly correlated, uncorrelated characteristics could be selected. Second, fewer characteristiCs would be more efficient from a lender's viewpoint.

A statistical significance level of95 percent was set for retaining characteristics aa a subset to be included in the final discriminant function.

A division point must be calculated to separate potential problem loans and potential acceptable

Typical Discriminant Function Through a Population of Loans*

y .-.--·-- discriminant function a

a a p p

a p p

a Region A Region B

a p

X

*Region A is a cluster of acceptable loans (a), and region B is a cluster of problem loans (p).

Figure 1

loans. This is done with the following formula:

where Y c is the calculated division point andY a and Y p are the composite means for the discriminant functions of the acceptable and problem loan groups, respectively. The standard for Y a and Y p are the composite means for the discriminant functions of the acceptable and problem loan groups, respectively. The standard deviations for Y a and Yp are ba and bp, respectively.

Results. Four characteristics met the 95 percent significance level for being included in the discriminant function. These characteristics were (1) the ratio of total liabilities to total assets, (2) the amount of credit life insurance on the applicant, (3) the amount of note (original PCA loan) as a proportion of net cash farm income, and (4) the number of acres owned. The significance levels of these characteristics are shown in table 2. The joint significance level exceeded 99 percent.

The correlation matrix for the four characteristics in table 3 shows ~low level of correlation among the characteristics. Each characteristic of a discriminant function adds additional information to separate populations. Thus, the low correlations reflect the

64

Table 2-Significance levels of characteristics

Degrees of freedom Percent

Characterlst lcs Numer· I Oenom· F-value signi· ator lnator flcance

Total liabilities to total assets ..... 1 94 16.99 99

Amount of credit life Insurance ... 1 94 5.02 99

Amount of note to cash farm Income 1 94 3.14 95

Acres owned •••• 0 1 94 2.80 95

Total ........ 4 94 8.63 99

Table 3-Correlation matrix'

Char acterlst ics x,

x' . . ..... . . . . . 1.000 x2 ... . . . . . .167 1.000 x3 ... . . . . . . . . . ·.128 .036 1.000 x4 . . . . . . . .... . . ·.230 .115 .013 1.000

1 The symbol x 1 is total liabilities to total assets, X2 is the amount of applicant's credit life insurance, x 3 is the amount of note to net cash farm Income, and x 4 is the number of acres owned.

additional discriminatory information added to the function by each of the characteristics.

The divisiori point (Yc) calculated from the composite group means and standard deviations from table 4 is 0.484.

Table 4-Composite group means and standard deviations for discriminant functions

Loan group

Acceptable .... · . · Problem .. · · · · · · ·

Composite group mean

0.351 .654

Standard deviation

1.767 2.269

A discriminant score (Y) calculated by substituting an applicant's characteristics into an equation using the coefficients from table 5 in the form:

Y = (0.9998) (x1) + (0.00001) (x2)- (0.01999) (x3)

Y can be compared with the Y c to determine if the applicant is a potentially acceptable borrower or problem borrower.

Table 5-Coefficients and means of characteristics for 99 loans

Means characteristics 1 Coefficient

Acceptable I Problem

XI . . . . . . . . . . . . 0.99980 0.31.4 0.4924 x, ............ .00001 5462.466 7 13747.3590 x, ............ .01999 2.1838 .4313 x, ............ .00070 55.7833 94.7436

1 The symbol X 1 is total liabilities to total assets, x 2 is t11e amount of applicant's credit life insurance, x 3 is the amount of note to cash farm income, and x 4 is the number of acres owned.

For this study, a loan applicant can be judged potentially successful if his Y is less than 0.484. A new applicant with a Y of more than 0.484 has the characteristics of a potential problem loan applicant.

A classification test using the Mahalanobis D~ statistic was constructed to evaluate this model. Twenty acceptable loan observations and 20 problem loan observations were selected at random from the ~~ observations used in estimating the model (data were not available to allow this test on observations outside those used for constructing the model). In the test the model correctly classified 75 percent of the tested loans. Lenders without the model correctly classified 50 percent of the test loans.

Analysis of Results. The ratio of total liabilities to total assets was by far the most important of the four characteristics in determining whether a loan would become acceptable or a problem. The results are based on data from the original applications, and the study relates those characteristics to the success of the loan several years later. Results indicate that the higher the ratio, the more likely the loan is to become a problem. Note the magnitude of the coefficient W.9998) in relation to Y c the dividing point of 0.484. Any single applicant with a ratio value of more than 0.5 would be classified in the problem loan category.

65

The importance of this ratio was discovered earlier by others ll, 5, 6].

The X2 variable, the amount of credit life insurance, represents the amount taken by the applicant. The results show that the greater the amount of credit life insurance, the greater the chance that the loan will become a problem. The coefficient for this characteristic is such that the value of Y n would move upward 0.1 for every $10,000 of credit life insurance. This relationship can be expected because of the direct correlation with larger debts and risks. It may well reflect an attitude of borrowers. Those who plan to lever heavily in the future and who are sensitive to the greater risks of higher leverage may take the most credit _life insurance.

The x3 variable, a ratio of amount of the note to net cash farm income, varies inversely. The larger the ratio, the more likely the loan will become acceptable in later years. But the impact of this value on Y is quite minor. Even if the ratio were fi: 1, it would only change Y by approximately 0.1. Short-term and intermediate-term credit were included in the original loans some borrowers obtained, which in total made up the "amount of note." The higher the ratio, the more overcommitted the borrower may be, and the more likely the loan will become a problem loan. On the other hand, the higher ratio may reflect the ability of some farmers to earn more than the interest cost of borrowed capital. This speeds their growth and makes them more likely to have an acceptable loan in the future.

The last significant characteristic is the number of acres owned. The more acres owned the more likely the loan became a problem loan. In moHt loan observations for this study, owners of land had large real estate debts and large financial commitments associated with those debts. Changing the discriminate score Y by 0.1 required 14:~ acres of owned land. These observations occurred in 1964-6H, when the average size of an Illinois farm was ~00 to ~~f> acres. Lof,>ically it is not simply the number of acres owned that is associated with problem loaml, but it is more likely the larger loan payments for larger real estate debts.

Summary and Conclusions

The evaluation of consumer and commercial loans has been greatly researched during the past :~5 years. Far less research has been done on agricultural loans. Yet the need for studies related to agricultural loans continues to increase as loans become larger and more risky.

This article attempts to put eva! nation of agricultural loans in perspective with previous research. The development and application of techniques is reported, especially the usp of discriminant analysis. Attention is directed to those

characteristics that can be used for evaluating loans. A credit scoring model is reported. It analyzes new

PCA loan applications in the cash-grain area of central Illinois. Stepwise discriminate analysis was used to select 4 statistically significant characteristics from among 22 characteristics. Total liabilities to total assets was by far the most important.

Further work is needed to build a theoretical relationship between definable characteristics and

ultimate success or failure of loans. Farmers and lenders are searching for more tangible guidelines on successful use of credit. The specific influence of debt commitments needs study. Short-, intermediate-, and long-term debts need separate evaluation. CrediJ; scoring models should also be studied in relation to the need for separate models for varied enterprise operations and for long-term and short-term loan commitments. Finally, further work is needed in evaluating new loan applications and existing loans.

REFERENCES

lll Bauer, Larry L., and John P. Jordon, A Statistical Technique for Classifying Loan Applications, Univ. Tenn. Agr. Expt. Sta. Bul. 476, March 1971.

[2] Chesser, Delton L., Improving the Evaluation Process of Commercial Loan Applications through the Utilization of Scoring Models and Financial Ratio Analysis, Ph.D. dissertation, Univ. Ark., 1972.

[ 3] Dunn, Daniel J., Evaluating Potential Loan Outcomes Based on New Loan Applications for Illinois Cash-Grain Farms, unpub. M.S. thesis, Univ. Ill., 1974.

[ 4] Durand, David., Risk Elements in Consumer Installment Financing, Nat. Bur. Econ. Res., New York, 1941.

[ 5) Evans, Carson D., An Analysis of Successful and Unsuccessful Farm Loans in South Dakota, Econ. Res. Serv., U.S. Dep. Agr., Feb. 1971.

[ 6] Johnson, Russel Bruce, Agricultural Loan Evaluation with Discriminant Analysis, Ph.D. dissertation, Univ. Mo., 1970.

66

(7] Johnson, Russel Bruce, and Albert R. Hagan, "Agricultural Loan Evaluation with Discriminant Analysis," Southern Journal of Agricultural Economics, Dec., 1973.

[8] Krause, Kenneth R., and Paul L. Williams, "Personality Characteristics and Successful Use of Credit by Farm Families," American Journal of Agricultural Economics, Vol. 53, No. 4, Nov. 1971. •

[9) Morrison, Donald G., "On the Interpretation of Discriminant Analysis," Journal of Marketing Research VI, May 1969.

[10] Overall, John E., and C. James Klett, Applied Multivariate Analysis, McGraw-Hill, Inc., New York, 1972.

[11] Reinsel, Edward Ignatius, Discrimination of Agricultural Credit Risks from Loan Application Data, Ph.D. dissertation, Mich. State Univ., 1963.

[12] Tinter, Gerhard, "Some Applications of Multivariate Analysis to Economic Data," Journal of the American Statistical Association 41, Dec. 1946.

DEBT STATUS OF U.S. FARM OPERATORS AND LANDLORDS BY ECONOMIC CLASS, 1960, 1966, 1970

by J. Bruce Hotel, Robert D. Reinsel, and William D. Crowley1

ABSTRACT

Census surveys indicated that larger farms made greater use of debt, and repayment ability tended to explain the close relation of debt to economic size. Debt also appeared important in explaining farm growth. The ratio of debt to equity was higher on larger units, and rented land was a greater component of assets utilized. Also, economic efficiency seemed greater as farms became larger which, provided substantial incentive to increasing size and consequently greater debt utilization.

Income from off-farm sources was an additional factor important in debt carrying capacity especially on smaller units. However, the extent to which off-farm income can be used to service debt on smaller farms is likely mitigated by mcome requirements for family living.

Keywords: Debt, economic class, farm

Although debt is a growing and essential source of funds in the farm production sector, only slightly more than half of all farm operators were indebted when surveyed at year-end during the last census in 1970. 2 Does this mean that debt capital is not as important as normally assumed? Or does the lower than normally assumed proportion of operators indebted simply reflect the unique characteristics of certain farmers in the sector? Do the higher debt levels for some farm sizes reflect less favorable economic situations or imply differences in the earnings and investment potential of various economic classes of farms?

In 1960 and subsequently in 1966 and 1970, special surveys were taken of both farm operator's and landlords' financial status (1, 7, 8, 9,).a Data from the 1970 survey, only recently available, now make it possible to examine the distribu~ion of debt and to

1 Agricultural economists, Economic Research Service, National Economic Analysis Division.

'From 1960 to 1970, farm debt outstanding at the end of the year doubled and the percentage of total cash flows financed by loan sources during the year increased from 17 lo:l? percent [3, 4].

:'Estimates of debt from census surveys are not identical With published reports by the U.S. Department of ~gnculture. The bases for these differences are briefly r escribed and evaluated in references 7 and 9.

67

compare changes over time. Although the data presented here are highly aggregated, it is possible to examine changes in who holds the debt and the magnitude of debt held by different farm sizes, the basis on which debt is supported and, the economic indicators that reflect the ability of the sector to meet debt commitments.

Who Holds Debt?

Debt funds associated with the farm sector are held either by farm operators or by landlords. Farm operators hold most of the debt associated with agriculture. They accounted for 84 percent of all real estate debt and 98 percent of all nonreal estate~bt in 1970 (table 1). For previous survey years, the proportion of debt held by operators was much the same. Although landlords owned an estimated .39 percent of the value of farmland and buildings, their total liabilities were relatively small. Thus, current real esta.te debt obligations fall primarily on farm operators who own justover60 percent of all land and building assets.

Concentration of Operator Debt

The proportion of operators having some debt at the time of the enumerations was greatest among

Table 1-Debt of farm operators and landlords, 1960, 1966, and 19701

Item

Number of--Farm operators ... Farm operators

with debt ...... All landlords ..... Landlords with

debt ..........

Farm debt held ·by-· Farm operators ... Landlords 2 ......

Both groups ....

Pro port ion of rea I estate value that

farm operators··· Owned ..... . Rented ..... .

1960

3,247

1,897 1,494

428

16.8 3.1

19.9

61 39

Thou.•ands

3,435

2,041 2,244

NA3

Billion dollars

29.1 6.8

35.9

Percent

61 39

1970

2,409

1,288 1,855

297

35.4 4.4

39.8

61 39

1 Estimates are as of December 31, 1960, mid-May 1966, and December 31, 1970. 2 1n 1970, an estimated one-third of all landlord debt was held by landlords who both rented land to others and farmed units of their own. Two-thirds was held by landlords not involved In farming operations. 3 Not available.

larger economic classes of farms (table 2). In 1970, for instance, only 53 percent of all operators had debt. But this percentage by economic class ranged from nearly 80 percent for the largest group of farms down to less than 40 percent for the smallest size units. For all farm operators, those with debt when surveyed declined from 58 percent in 1960 to 53 percent in 1970. Although the reason for this decrease is unknown, the percentage of farmers with debt was smaller for all economic classes except for the largest Class I farms, where the percentage with debt increased slightly from 76 percent in 1960to 79percent in 1970.4

·•The grouping of farms by sales class is described in footnote 1 of table 2.

Even with fewer farm operators and a smaller percentage of all operators with debt, total operator debt increased from $16.8 billion in 1960 to $35.4 billion in 1970, or 111 percent. The average level of operator indebtedness increased substantially for each economic class (table 2). On large class I farms with debt, the average debt was $48,814 in 1960 and $82,322 in 1970. On the smaller class VI and noncommercial farms the average level of debt jumped from $3,156 to $7,063 between the 1960 and 1970 surveys.

The proportion of total debt held by farm operators was higher among larger economic class farms and the proportion of debt held by the larger farms was substantially greater in 1970 than in 1960.5 Some important implications of these trends are that l'arger farms make greater use of debt and that debt may be an important factor in explaining farm growth. In 1970, class I, II, and III farms combined comprised approximately 41 percent of all operators, accounted for 29 percent of all operators in debt and held 83 percent of all debt (table 3). In 1960, these same three classes accounted for only a fourth of all operators, and less than two-thirds of all debt. Class I operators held a greater proportion of both real estate and nonreal estate debt outstanding in 1970-44 percent and 55 percent-compared with 21 and 26 percent, respectively in 1960.

Basis of Debt Support

Three factors are conjectured as being closely associated with the substantial increase in the

'•This increased concentration of debt reflects the movement of farms into the larger economic units where a greater proportion of operators were carrying relatively large debts. Since the gross value of farm sales reflects changes in farm commodity prices and farm output, the proportion of farms in each size class necessarily reflects changes in. these factors. From 1960 to 1970, farm commodity prices increased by 17 percent and total output increased 14percent. Thus, each factor made about the same contribution to the relative upward shift to larger economic sizes [2, 5].

Table 2-Farm operators in debt and their average debt, 1960, 1966, and 1970

Percent of farm operators In debt Average debt of indebted operators Farms classified according to

I I I I annual sales 1 1960 1966 1970 1960 1966 1970

Percent Dollars

All farms ••••••••• 0 •••• 0 ••••••••• 0 58 59 53 8,853 14,255 27,512

Class I, $40,000 or more ... - ......... 76 80 79 48,814 68,043 82,322

$40,000 to $99,999 ........... NA 1 NA 81 NA NA 168,241

$100,000 or more ............ NA NA 78 NA NA 54,941

Class II, $20,000 to $39,999 .......... 75 77 72 17,744 25,670 30,638

Class Ill, $10,000 to $19,999 .......... 73 73 62 10,936 16,627 2!,266

Classes IV and V, $2,500 to $9,999 ..... 61 62 47 5,828 9,324 11,976

Class VI, $50 to $2,4992 ............. 46 42 37 3,156 6,211 7,063 -1 Not available. 2 This class of farms also Includes noncommercial farms that are part-time and part-retirement enterprises.

68

Table 3-Farm operators with debt, 1960-70, and distribution of debt held by economiC

class of farm in 19701

Farm operators Operator debt Farm classed according to Non-annual sales Total With Total Real real

debt estate estate

Thousands Billion dollars

All farms In-1960 .......... 3,247 1,897 16.8 10.0 6.8 1966 .......... 3,435 2,041 29.1 18.9 10.2 1970 .......... 2,409 1,288 35.4 22.0 13.4

Percent

Pro port ion of all farms In 1970 that

are grouped In·-All farms .... 100 53 100 100 100 Class 12 " .... 11 9 48 44 55 Class II ..... 14 10 21 21 21 Class Ill .... 16 10 14 15 13

Total of I to II I .... 41 29 83 80 89

Classes IV and V .. 29 13 11 13 8

Classes VI-NC .... 30 11 6 7 3

1 Estimates are as of December 31, 1960, mid-May 1966, and December 31, 1970. 2 Table 2 shows the deut amount of annual sales for each class.

volume of debt as well as to changes in the proportion of debt held by different economic size categories of farms. They are: (1) changes in the value ofland and buildings owned by farm operators, (2) changes in net cash farm income, and (3) changes in income earnings from off-farm sources. Although the published data do not allow for statistical tests of significance, the trends identified tend to support the theoretical basis of a positive relationship between these variables and the volume and distribution of debt.

land and Building Values

The value of land and buildings owned by farm operators was$79.2billion in 1960. The value climbed 69 percent to $133.5 billion in 1970. Indebted operators owned 64 percent of this value in 1970. The increase in real estate debt in relation to that of the value of land owned during 1960-70 was much the same among economic classes. However, percentage increases in the level of real estate debt were relatively greater than increases in the value of real estate. This resulted in a general upward trend in the ratio of real estate debt to the value of land and buildings owned by indebted operators for each economic class. Class I operators, for instance, had real estate debt equal to only 18 percent of the value of land and buildings owned in 1960 but 28 percent in 1970. For class II operators the ratios increased from 21 to 27 percent. For class III and smaller economic

69

sizes, the amount of debt in relation to real e,>tate value was nearly 2:3 percent, also higher in 1970 than in 1960, but the relative increase was less than for the larger farms. Thus, more real estate debt was associated with each dollar of owiwd real estnte in 1970 than in 1960, especially for operators of the larger farm businesses. A possible factor is the potentially higher repayment capacity associated with the larger units.

Total Farm and Off-Farm Income of Farm Operators

Total net cash farm incomewas$8.2 billion in 1960. This advanced to $13.6 billion in 1970-a 66 percent increase.6 Concurrently, total net income from both farm and off-farm sources was $14.6 billion in 1960 and $26.2 billion in 1970-an increase of 79 percent.

Class I and II indebted operatorH colleetivdy held the largest share of total net cash farm income and total net income. Similarly, these two clasHeH of indebted operators held the largest share of total debt. But even more important in evaluating the financial base on which debt is Huppurted iH the extent to which the ratio of income to debt changed with different farm sizes. From ·1960 to 1970, it decreased for all farms (table 4). This would appear to indicate a less favorable position in terms of the ability to repay debt from farm income if loan conditions are assumed the same for the three survey periods.7 However, not all economic size categories of farms were affected the same. Class I operators showed an increase in their income in relation to their debt. Class II operators showed only a slight decrease. Class III and smaller size operators showed a substantial decline. This indicates that class III and smaller categories, with an estimated :n percent of all debt, experienced a decline in their ability to repay debt from farm income alone. ClasH I and II categories, with 69 percent of total debt, were either in an improved or relatively stable position in this respect.

However, the basis of debt support in farming is also influenced by receipt of income from off-farm sources. For all operators, 48 percent of total farm family income in 1970 was from off-farm work or other sources. Fifteen percent of total income for class I and 25 percent for class II farms was from off-farm work or other off-farm sources. The smaller the economic size of the farm, the greater the impottance

noperator's net cash fann income iH calculatPd by subtracting the farm operator's cm~h operating I'XpenHPH

and cash rent paid from theoperator'Hsharpofth!•valut>of farm products sold.

7The length of Joan changed littiP from l!loO to I~J70 but ' higher interest rates have likl>ly madP dPh1. sprvicing mon•

expensive per dollar of debt. Interest carrying l'hargeH would also be related to the mix hetwPen 1<hort and long('l' term debt.

Table 4-Ra~io of net cash farm income to operator debt, by economic class of farm'

Farm operators, 1970 Net cash farm income to total debt 2

Item 1 l Net cash 1 l Total Total debt farm income 3 1960 1966 1970

Thousands Billion Billion Percent Percent Percent dollars dollars

All farms ....................•.... 2,409 35.4 13.6 48 35 38

Percent

Operators with debt in-All farm classes . . .............. . . 53 100 67 29 25 25

Class 14 .... • • • • • • • • • • 0 ........ 9 48 41 28 25 31 Class II 0 ••• . . . . .. . . . . . . . . . . . . 10 21 16 32 31 30 Class Ill . .. . . . . . . . . . . ........ 10 14 7 35 30 21 Classes IV and v • 0 •••••• . . . . . . . 13 11 3 32 23 12 Class VI-N.C. ....... . . . . . . . . . . 11 6 0 8 7 NA 5

Operators without debt in-All farm classes . . . 0. ..... . . . . . . . . . 47 0 33 NA NA NA

Class I . . • • 0 •• ... • • • • 0 0 •••• . . . 2 0 13 Class II . . . . . . ....... . . . . . . . . . 4 0 8 Class Ill . . . • • • • 0. 0 • . . . ......... 6 0 6 Class IV and v ...... . . . . . . . . . . . . 16 0 6 Class VI-N.C . . . . . . . . . . . . . . . . . . . 19 0 1

1 Estimates are as of December 31, 1960, mid-May 1966, and December 31, 1970. 2 includes Government payments. 3 See

footnote 5 for definition of net cash farm income. 4 Table 2 shows the amount of annual sales for each class. 5 Not applicable.

Table 5-Ratio of net income (farm and off-farm) to operator debt, by economic class of farm'

Farm operators, 1970 Net Income to debt in-

Total Income Item Total (Net cash

No. debt farm and 1960 1966 1970 off-farm)

Thousands Billion Billion Percent Percent Percent dollars dollars

All farms 0 o 0 o 0 0 0 ro 0 o o o o o 0 o 0 o o o o o o 2,409 35.4 26.2 87 83 74

Percent

Operator with debt All farms ••• 0 •••••••••••••••••••• 53 100 64 55 55 47

Class 12 ••••••••••••••• 0 •• 0. 0 ••• 9 48 24 33 32 36

Class II 0 •• 0 •••••• 0 .............. 10 21 12 44 42 41 Class Ill ••••••••••• 0 ••• 0 ••••••• 10 14 8 47 46 44 Classes IV and V ..•.•..•..•.•...• 13 11 11 71 64 72 Class VI-N.C. ••• 0 •••••••• 0 •••••• 11 6 9 109 111 115

Operators without debt All farms •••••• 0 ••••••••••••••••• 47 0 37 NA3 NA NA

Class I .•.....•..•.•.••.••.•..•. 2 0 7 NA NA NA Class II •••••••••••••• 0 ••••••••• 4 0 5 NA NA NA Class Ill ••••• 0 ••••••••••••••••• 6 0 5 NA NA NA Classes IV and V •.••.•••••••••... 16 0 9 NA NA NA Class VI-NC .................... 19 0 11 NA NA NA

1 Estimates are as of December 31, 1960, mid-May 1966, and December 31, 1970. 2 Table 2 shows the amount of annual sales for each class of farm. 3 Not applicable.

70

of off-farm income from work or other sources-only 45 percent of total family income for class III but 96 percent for the smallest units.

These additional sources of income would appear to have a major impact on the capacity of the farm production sector to handle additional levels of debt, especially for smaller size units. Class IV and smaller farms held 17 percent of all operator debt in 1970 and, with the addition of off-farm income, the total income-to-debt ratio was substantially higher, and this ratio improved from 1960 to 1970 (table 5). This is in contrast to the less favorable situation when only farm income was considered. Although the income­to-debt ratio for class II and III operators was also higher when off-farm income was added to farm income, the ratio decreased three percentage points from 1960 to 1970. This indicates that the 20 percent of all operators in classes II and III with 35percentof operator debt were in a slightly less favorable repayment position relative to other farm sizes. Farms of these sizes are generally too large to allow the operator to engage extensively in off-farm work but not large enough to benefit from the economies of size attributed to class I farms. Resource adjustment

in class II and III farms will likely be more evident in future years as firm consolidation and efforts to increase income levels continue.

Average Resource and Income Relationships

The average valutl of assests, debts, and income flows for indebted operators of the various economic classes are in table 6. As the size of farm increases, the absolute value of assets utilized increases substantially, indicating the relatively large quantity of capital resources required to generate larger gross sales. The relative mix between sources utilized and other financial components for larger farms differs from that on smaller farms. Larger operators use debt more extensively in relation to their equity in owned assets. Consequently, the debt­to-equity ratio is greater on larger farms-29 percent on smaller farms but 39 percent on class I units.

Rented land also becomes a greater component of total assets utilized on larger farms. This results in the ratio of value of total resources utilized to equity capital being greater on larger farms-changing from 1.50 on the smaller farms to 2.07 on class I units. As farm size increases, the ratio of total resources

Table 6-Average resource and income relationships of indebted operators, by economic class of farm, 1970

Item

Value of assets used far­Land and buildings:

Owned .............••............ Rented •.......................•..

Other assets owned 2 .•.•••••••••••••••

Total asset value .................. .

Liability of operators: Real estate debt ..................... . Non-real estate debt .......•.........•.

Total debt ..•.........•..........

Equity in owned resources ............... . Net cash income:

Net cash farm income .............••. Government farm payments .......... . Off-farm ......•.....•.....••......

Total .............•.............

Ratio of-Value of rented land to assets utilized Operator debt to equity •••.....•...•...

Total value of asset utilized to equity •.... Total value of asset utilized to farm income 3

165,870 144,217 127,920

438,007

46,692 35,630

82,322

211,468

21,798 3,485 4,707

29,990

33 39

2.07 17.32

I II

71,070 61,300 50,215

182,585

19,177 11,461

30,638

90,647

7,683 1,535 3,461

12,679

33 34

2.01 19.80

Economic class of farm 1 -

I Ill

Dollars per farm

56,515 36,670 31,927

125,112

14,056 7,210

21,266

67,176

3,494 940

4,948

9,382

Percent

29 32

Dollars

1.86 28.22

I IV and V I

38,018 15,645 14,958

68,621

8,513 3,464

11,976

41,000

1,003 382

7,230

8,615

23 29

1.67 49.34

VI-N.C.

26,126 5,171 5,309

36,606

5,658 1,405

7,063

24,372

-263 197

8,207

8,141

14 29

1.50 NA

2 1 Table 2 shows the amount of annual sales for each class. estimated primarily from data In the 1969 Census of Agriculture.

Estimates of the value of other assets such as machinery and 3 Farm Income equals net cash farm Income plus payments from motor vehicles, livestock and poultry, and stored crops and government farm programs. 4 Not applicable. suppiles were not determined In the 1970 survey, but were

71

utilized per dollar of farm income (net cash farm income plus Government payments) declines, indicating greater economic productivity of capital with increasing farm size. Cliiss I operators, for example, utilized about $17 of total resources per dollar of farm income, compared with $28 or more for class III and smaller farm sizes. The sub~tantial difference between the ratio of resources utilized and the level of farm income indicates that income from other sources is likely to be important in determining the smaller farm operators' debt repayment capacity. Their apparent dependence on off-farm sources of income is reflected by t,he averages shown in table 6.

Summary and Conclusions

About 90 percent of the debt in the farm sectDr is held by farm operators who own only 61 percent of the value of land and buildings. Census survey estimates in December 1970 indicated only 53 percent of farm operators had debt outstanding. Although the proportion of all operators with debt outstanding on Dec2mber 31 underestimates the proportion of farm operators who used borrowed funds during 1970, a high correlation exists within different economic classes bPtween year-end debt outstanding and funds borrowed during the year (9, p. 59). Large:< farms made more extensive use of debt, ranging from about 80 percent of operators on clc.ss I farms to 40 percent on the smallest uri to. The concentration of debt on larger farms also increased from 1960 to 1970. Similarly, larger farms increased their share off arm income earned and land and buildings owned during this period.

The repayment ability on larger farms in comparison to the smaller ones appears favorable when based on farm income alone in relation to current debt levels, and thus may explain why most debt is so closely relat.ed to size of farm. On smaller farms, when nonfarm income is included, repayment capacity would appear to improve substantially. But possibly a larger part of their total income is required for family living needs, thus limiting the apparent increase in their debt repayment capacity. Additional

research is needed to explore the relationships between farm capital requirements, repayment capacity, and farm and off-farm income flows especially in view of the increasing importance of part-time fanning in recent years.

The smaller the farm, the greater the importance of off-farm income in rellltion to total income. The proportion now from only 15 percent of total income in 1970 for class I, but 96 percent for the smallest units.

The use of debt also appears to be a primary factor in explaining growth in farm size, but the causes and efiect relationship between debt and growth can only be hypothesized. Under this condition, debt is more of a factor determining size than it is a result of size.

As the size of farm increases, the relative mix between sources utilized and other financial components differs substantially from that for smaller farms. Debt in relation to equity in owned assets is higher on larger •1nits, and external capital in the form of rented b.nd is a greater component of total assets utilized. Although the use of aggregate estimates can be misleading, there appears to be eviclence of greater economic efficiency associated with larger farm sizes where most of the debt occurs. Class I farms, for instance, used only about $17 of total assets per dollar of farm income compared with $20 for claos II and over $28 on class III and smaller size units. Thus, a substantial incentive exi~ts for increasing size and, consequently, greater debt utilization. The factors relating to productivity, debt levels, and resource utilization are areas that need additional exploration.

Class II and III farms appear to be generally too large to allow the operator to engage extensively in off-farm work but not large enough to benefit fully from the greater returns evident on larger units. Resource adjustments among operators in these two groups will likely be more evident in future years as consolidations and efforts to increase income levels continue. Further research needs to be undertaken with respect to factors related to the adjustment process offarms in this category and the implications for the structure of agricultural production units.

REFERENCES

Ill

121

13]

[4]

(5 J

Melichar, Emil and others, Farm Debt Data from the 1960 Sample Survey of Agriculture, Bd. Gov. Fed. Res. System, 164. U.S. Department of Agriculture, Agricultural Prices, Annual Summary. Statis. Rptg. Serv.

Agricultural Finance Outlook, AF0-15 Econ. Res. Serv., Dec. 1974.

Balance Sheet of the Farming Sector,Econ. Res. Serv., AlB 376, Sept. 1973 and previous issueR

Crop Production, Annual Summary, Statis. Rptg. Serv.

72

[6]------Farm Mortgage Characteristics, Econ. Res. • Serv., ERS-527, Aug. 1973.

[7] U.S. Department of Commerce, Bureau of the Census, 1960 Sample Survey of Agriculture, Vol. V, Part 5, Special Reports, 1962.

[8]

[9]

1966 Farm Debt, Vol. III, Part 4, Special Reports, 1968.

Farm Finance, 1969 Census of Agriculture, Part II, Vol. V, Special Report, Aug. 1974.

EVALUATING DEBT-TO-PURCHASE PRICE RATIO IN FINANCING FARM REAL ESTATE TRANSFERS

by William McD. Herr1

ABSTRACT Several trends combined in recent years to increase the ratio of debt to purchase

price of credit-financed farmland. This increased ratio reflects changes in the exposure to risk for lenders and in equity needed for those who purchase farmland, but it is not an adequate measure of risk or equity changes. To more accurately measure these changes, the total value of security offered should be included. The measure should also be limited to those who are acquiring their first tract of land.

KEYWORDS: Assets, credit, equity, financing, lending, real estate tranfers, risk, security.

The ratio of debt to purchase price of credit­financed farmland tranfers rose from about 55 percent in the early 1950's to nearly 75 percent in

'Professor, Department of Agricultural Industries, Southern Illinois University at Carbondale.

recent years (fig. 1). The increased ratio reflects, in part, "a lengthening of the repayment period, the liberalization of lending limits, the increased use of contract financing, and the continuing uptrend in values" [4].

The time series showing the ratio of debt to purchase price is "useful to those interested in the

FARM REAl ESTATE DEBT AS A PERCENTAGE OF MARKET VAlUE ON TRANSFERS WITH DEBT

PERCENT

50r---~----~-----+-----r----~--~

1944 1949 1954 1959 1964 1969 1974

US. DEPARTMENT OF AGRICULTURE NEG. ERS G04· 74 (6) ECONOMIC RESEARCH SERVICE.

Figure 1

73

financial position of farmland owners and farm operators" [2]. But the ratio has been interpreted at various times as an indicator of changes in the equity contributions of buyers [3] and the risk exposure of lenders [5].

For example, the ratio declined betw,flen 1973 and 197 4. "Apparently many purchasers were willing and able to assume a larger equity position in land purchased as a result of realizing the high net incomes in calendar year 1973" [3]. In 1971 the ratio declined because lenders wanted to reduce their risk exposure. "As expected in a time of tight credit-lenders apparently rationed credit by allocating it to low risk customers and by requiring higher downpayments" f5].

This article shows that lenders' risk exposure may not have increased by nearly 50 percent over this period as might be implied by the series, nor did the average equity contribution of land buyers neces­sarily decline from 45 to 25 percent. In fact, these conclusions can only be inferred when the trans­action involves just two elements-a cash down payment and a loan, or when the value of additional security remains constant. Because these situations are special cases, the ratio can be misleading when it is used to indicate the risk exposure of the lender, the equity contribution of the buyer and, consequently, the financial position of farmland owners.

Debt Ratios in Financing Farm Real Estate

If the real estate transaction is segregated from the rest of the buyer's farm business, the lender's risk and the buyer's equity in such a transaction can be shown as follows in example 1.

Purchase price of land (value included in security) ..... _ ........... $10,000

Cash paid to seller ....................... 4,500 Loan .................................... 5,500 Ratio:

Debt-to-purchase price ................. 55% Debt-to-security value ................. 55% Buyer's equity to security value ....... 45%

The debt-to-security value of 55 percent is one measure of the lenders' risk exposure. The ratio of buyers' downpayment to security value of 45 percent represents the buyer's equity contribution to this transaction. A purchase that has these two elements for a recent year might be as follows in example 2.

Purchase price of land (value included in security) ................. $10,000

Cash paid to seller ....................... 2,500 Loan .................................... 7.500 Ratios:

Debt-to-purchase price ................. 75% Debt-to-security value ................. 75% Buyer's equity to security value ....... 25%

A transaction may include more than a loan and cash downpayment. It may involve additional

74

security which is generally the value of other land owned by the buyer. Hence, the elements of a more typical transaction would probably be as follows in example 3.

Purchase price of land (value · included in security) ................. $10,000 Cash paid to seller ....................... 2,500 Loan .................................... 7,500 Value of additional security .............. 5,000 Ratios:

Debt-to-purchase price ratio ........... 75% Debt-to-security value ................. 500'o Buyer's equity to security value ....... 500'o

If examples 3 and 1 represent typical transactions today and in the early 1950's, respectively, the debt­to-purchase price ratio would certainly have increased from 55 to 75 percent. But, the lender's risk­exposure as measured by the ratio of debt-to-security value would have declined, despite the rise in the ratio of debt-to-purchase price. Moreover, the buyer's equity in this separate transaction would have increased instead of declining from $4,500 (45(Jif,) to $2,500 (25(Vc,), because additional security was used. Although the contrast in loan situations in examples 1 and 3 may overstate what has generally occurred, it does show that misleading inferences can be made about the lender's risk and buyer's equity requirements from the debt-to-purchase price ratio.

Another way to view this is to assume that loans could not exceed 50 percent of the present market value of a security because of lending standards (example 3). If so, the buyer would need a $5,000 down payment in lieu of providing additional security. Thus, when the transaction is separated from the rest of the buyer's financial affairs, the lender has financed 50 percent of the value of assets included in the security, and the buyer holds 50 percent equity in the security.

Although no data are available to show the changing importance of additional security through the years, some data for a recent period show how various ratios can lead to different conclusions concerning risk-exposure and equity contributions of buyers.

Risk-Exposure

As indicated earlier, the risk-exposure of farm mortgage lenders can be measured by changes in the ratio of debt-to-purchase price. A low ratio is considered less of a risk to the lender, because a given decline in farm real estate values had less chance of depleting the value of the purchase tract when taken in security than when the ratio is high.

The debt-to-purchase price ratio as presently compiled may not measure the lender's risk, because the value of a security frequently exceeds thevalueof the purchased tract. The lender's risk is especially difficult to measure in the current farmland market, because much of the land is purchased as add-on

units rather than as complete farms. Data concerning farm sales financed by the Federal Land Bank in two midwestern areas show the importance of taking added security. 2

The ratio of debt-to-purchase price for these transfers financed by the Federal Land Bank averaged 77 percent in the Iowa area and 82 percent in the South Dakota area (table 1).3 The average transaction showed that a high proportion of the purchased price was financed, but the average land bank loan was much smallerproportionofthemarket valueofthe security, 64and55percent, repectively, in these areas.

The major lender had considerably less risk exposure than indicated by the percentage of the purchase price financed, because additional acreage owned by the buyer was included in the security. In Iowa this amounted to 10 percent more land. But in the South Dakota area it was about 100 percent, or an amount almost equal in size to that of the tract purchased.

Related data show why the ratios of acres taken for security to acres purchased differ substantially between the two areas. Assets and net incomes of Iowa buyers were about double the amounts reported for South Dakota buyers. Apparently Iowa buyers were able to provide more cash when purchasing land and also had stronger repayment capacities. In addition, returns in the Iowa area were probably more stable than in South Dakota. Therefore, less security (acreage) was required in Iowa than in South Dakota where income variability may be greater.

In both areas a number of transfers were reportedly

2Data obtained from the loan and farm sales register of the Federal Land Bank Management Information Service. Transfers were limited to those financed by the Federal Land Bank late in 1971, 1972, and early in 1973.

3Theseratios are nearly the same as those reported by the USDA for the Com Belt and Northern Plains regions [2].

100 percent financed (table 1). For these sales the data clearly show that 100 percent loans are obtained by expanding the security offered to include the purchase tract as well as additional land. The result for this group of sales is that the ratio of loan to market "Value of security was 65 percent in Iowa and 54 percent in South Dakota. The loan-to-security value ratios did not vary markedly from tracts where the percent financed was lower.

These data may not be typical of the entire country. But the loan-to-purchase price ratio of credit financed transfers can be misleading if the ratio is used to indicate the lender's risk exposure.

Equity Requirements for Buying Farmland

A second interpretation of the debt-to-purchase price ratio is that higher ratios make it easier to purchase land and for new firms to enter farming, because the implied cash downpayment is lower. If this is true, then the historical series of debt-to-value of the purchased tract indicates that since the early 1950's lenders have made it easier to acquire land and to finance entry into agriculture. This record has not substantially contributed to increasing the entry rate into farming, but it can make the entry easier. In 1970 the Commission on Agricultural Credit expressed concern about the financial requirements of young farmers, and in 1974 young farmers still rated adequatefinancingasoneoftheirmainproblems[1].

Data from the preceding section suggest one reason why higher debt-to-value ratios may not always help in land acquisition and entry into farming. An average down payment of about 25 percent implied by the ratio of debt-to-value of purchased land does not portray the situation accurately, because additional land is frequently required for security. The young farmer or the buyer who is purchasing land for the first time probably will not have other land to use as added security.

Table 1-Selected characteristics of Federal Land Bank Loans used to finance farm purchases, by percentage of purchase price financed in two areas of the Eight Farm Credit District

Characteristics of F LB loan

Percentage of purchase Percentage of price financed Number of Average loan value to Ratio of acres

observatIons percentage present market Acres purchased In security to financed value security acres purchased

In north-central Iowa: Under 60 .•...••.•..•••...•• 16 45 52 124 1.0 60·79 ••.•...••.....••..•••. 68 69 67 127 1.0 80·99 ••...•.•..•••••.••..•. 54 87 61 126 1.1 100 ....................... 19 100 65 123 1.5

Total or average ............ 157 77 64 126 1.1

In east-central South Dakota: Under 60 ••..••••••••.•••••• 16 45 55 167 1.8 60-79 •..••••.•..•.•••.••••• 12 68 60 238 1.3 80·99 ••..••.•.••••.•••••.•• 14 91 56 201 2.2 100 ....................... 37 100 54 231 2.4

Total or average ............ 79 82 55 214 2.1

75

Even though many transactions did require additional land as security, there were a number of loans in both areas where the loan to market value of security was 70 percent or more (table 2). Purchases of land in these instances imply relatively small equity contributions by buyers. But data from these areas do not clearly show the effect these high loan­to-security ratios have on entry into farming. In the Iowa area these loans generally were extended to buyers with above average asset and income levels and to those who were somwhat older and perhaps had greater farm experience. In South Dakota relatively fewer high loan-to-security ratio land transfers were made. These borrowers generally had about the same amount of assets and income as other borrowers who bought land, but their average age was substantially below that of the others.

These relationships underscore the variety of conditions that surround each loan. Focusing on a single factor, such as liberalizing the loan-to-security ratio, can have favorable implications for some individuals. But it can also result in substituting such other credit factors as capital position and farm

experience for tangible security. These qualifications may be as difficult for beginning farmers to meet as are cash down payments or additional security.

Summary and Conclusions

The time series that reports the ratio of debt-to­purchase price of farm real estate has been used to show changes in lender's risk exposure and in equity requirements for purchasing farmland. The ratio does not provide adequate information for either of these objectives, because previously owned land is frequently included in the security in addition. to the purchase tract. Moreover, when the loan is a high percentage of the value of the tangible security, the lender may have selected buyers who had a stronger record of assets, net income, and experience.

To measure the changing risk exposure oflenders, the series should be modified to include the total value of security offered. Also, to measure the relative ease of purchasing farmland, the series should be limited to those who are acquiring their first tract of land.

Table 2-Selected characteristics of Federal Land Bank borrowers obtaining loans, by ratio of loan to present market value of security, two areas of the Eighth Farm Credit District

Characteristic of borrower ,------------

Percentage of loan value to present Number of Net worth as market value of security observations Assets a percentage Net income Age of

of asset borrower

Dollars Dollars Years

In north-central Iowa: Under 50 percent •••••••• 0 •••••••••••• 31 202,000 56 22,100 40 50 to 59 percent ..................... 15 207,000 53 27,000 35 60 to 69 percent ••••• 0 ••••••••••• 0 ••• 70 370,000 56 33,900 47 70 percent and over ••••• 0. 0 •••••••••• 0 41 408,000 56 40,600 46

Total or average ••• 0 •••••••••••••••• 157 331,000 56 32,400 44

In east-central South Dakota: Under 50 percent •••••••••••••••••••• 0 19 155,000 57 16,200 45 50 to 59 percent ••••••••••• 0 ••••••••• 40 190,000 64 18,100 46 60 to 69 percent •• 0 •• 0 0 •••••••••••••• 8 147,000 55 22,700 41 70 percent and over •••••• 0 •••••• 0 0 •••• 12 176,000 59 18,800 37

Total or average ••••• 0 0 •••• 0. 0 •••• 0. 79 175,000 61 18,400 44

REFERENCES

[1] Financing Young Farmers, Report of the Conference on Financial Needs of Young Farmers, Indianapolis, Ind, Feb. 1974, pp. i and 2-4.

[2] U.S. Department of Agriculture, "Major Statistical Series of the U.S. Department of Agriculture, How They are Constructed and Used," Land Values and Farm Finance, Vol. 6, U.S. Dept. Agr. Hand b. No. 365, April1971, p. 15.

76

[ 3] U.S. Department of Agriculture, ERS, Farm Real Estate Market Developments, CD79, July 1974, pp. 9 and 35.

[ 4] U.S. Department of Agriculture, ERS, Farm Real Estate Market Developments, CD78, July 1973, p. 9.

[ 5] U.S. Department of Agriculture, ERS, Farm Real Estate Market Develpments, CD76, Aug. 1971, p. 4.

MEASURING FARM LEVEL CREDIT USE: A BRAZILIAN EXAMPLE

by Gerald I. N ehman and Dale W. Adams1

ABSTRACT

Many studies of agricultural credit in less developed countries are based on poorly specified measures of farm-level credit use. This makes it difficult to understand the implications of the results and to make cross-cultural comparisons. Six measures of credit use are discussed and evaluated in this paper. Data from 86 farm households in Brazil are used to illustrate the strengths and weaknesses of these six measures.

KEYWORDS: Agricultural credit research, Brazil.

Agricultural credit activities in less developed countries have become important economic research subjects.2 Unfortunately, few studies have clearly specified the measure of credit use employed or the time period covered by specific loans. Thus, the

'Economist with Battelle-Columbus Laboratories and professor of agricultural economies at The Ohio State University, respectively. This research was initially supported by the Agency for International Development. The views expressed herein may not reflect those of the funding agency.

'The very extensive review of small farmer credit programs carried out by the Agency for International Development during 1972 and 1973 is an example of this interest.

usefulness of the research is limited, because it does not allow cross-study comparisons of credit use and does not show how credit flows match farm and household needs during the production cycle.

This article evaluates the utility of the various methods of research into credit use in less developed countries. Data for Brazil illustrate the following measurement problems. First, much of the available data on farm-level credit use do not document the timing of farmer's loans during the production cycle. Second, methods of measuring borrowing behavior do not give results that are directly comparable. Third, conclusions about the use of credit in less developed countries differ according to the credit measure selected.

ANALYSIS OF SELECTED CREDIT MEASURES

Farm-level credit studies should use those measures of credit which best serve the research objectives. The advantages and disadvantages of various measures are discussed below, based on farm­level data collected in southern Brazil in 1971. Table 1 summarizes the evaluation.

Temporal Measures

The beginning and ending balance of loans in force measures the loans outstanding at particularly

77

critical times during the production year. Such temporal measures provide a picture of the debt situation when farmers are planting their crop and when they have completed the harvest, but they exclude any debt which has been repaid prior to the end of the period. If the information were available monthly, it would provide important information on short-term cash surpluses and shortages that affect farmers and decisionmaking. Unfortunately, monthly data are not generally available. Thus, the beginning and ending period balances are useful to

Table 1-Measures of loans outstanding during the 1970 to 1971 agricultural year, by source of loan, sample of 86 borrowers in Sao Paulo, Brazil'

Loans outstanding during production year

Source of Number New loans loan 2 of Contractual Sum of loans Beginning Ending received Time

farmers value 3 in force 4 balance balance during adjusted 9/30/70 6/30/71 1970/71 credit use 5

e------··

Cr$' Percent Cr$ Percent Cr$ Percent Cr$ Percent Cr$ Percent Cr$ Percent

Formal .... 43 454,500 83 445,700 83 212,700 89 277,800 80 37 3,600 82 367,700 90 Informal ... 52 91,600 17 91,400 17 26,500 11 69,400 20 79,700 18 42,700 10 Total ...... '86 546,100 100 537,100 100 239,200 100 347,200 100 453,300 100 410,400 100

··----------"--1 Based on farm interview data reported in Gerald I. Nehman Small Farmer Credit Use in a Depressed Community of Sao Paulo

Brazil, unpublished Ph.D. dissertation, The Ohio State University, 1973. 2 Formal loans are from banks and cooperatives. Informal loans are from all other sources. 3 1ncludes loan contracts in force during July 1, 1970, to June 30 1971. 4 Excludes portion of loans liquidated prior to Sept. 1970. 5 (Sum of loans in force) x (Months during year loan was In force) (1/12). 'one Cr$ (Cruzeiro) was equal to $0.20 (U.S.) in 1971. 'Does not sum to 86 because 9 farmers had both formal and informal loans.

show two points in time but not the extent to which money was actually used during the year. Often, the farmer is in a strong cash position at the end of the year and liquidates his production loans. On the other hand, his cash position may give him the leverage to negotiate a purchase of machinery, which would increase his debt load disproportionately. In both cases, the data are highly dependent on the timing of the interview, whether just before the harvest, during the fallow period, or just prior to planting.

The farmers interviewed in Brazil received Cr$453,300 in loans during the year. 4 Only 45 percent of these loans were in force at the start of the year. This shows that short-term loans predominate in the loan portfolios of the sampled farms. Short-term loans are encouraged by the high and uncertain rate of inflation in Brazil. Most of the loans were negotiated after the start of the year and are included in the loans received but not in the beginning balance. The ending balance for all farms was around Cr$347,200, or65 percentofthetotal valueof loans received during the year. This indicates that a majority of the short-term loans associated with the 5 to 7 month-long crop production process had been repaid by the end of June. Thus, both of these measures, though temporally pure (that is, representing points in time during the year) substantially understated the borrowed capital used by farmers during the year.

These are generally the only specific points in time for which data are available. If we use these measures, however, we are understating by around 50 percent the loans received during the year.

Measures of Credit Volume

The contractual value of credit volume is the original face value of loans on which an outstanding

•One Cr$ (Cruzeiro) was equal to U.S.$0.20 in 1971.

78

balance is owed during a period. The sum of loans in force, on the other hand, is the debt outstanding during a year. It includes the outstanding balance on loans received prior to the period plus all loans received during the period. The contractual value and the sum of loans in force are both measures based on the face value of the loan contract. These are the most commonly used measures of lending volume. Their major disadvantage is that they are aggregate measures that do not consider differences in repayment periods. For this reason, measures such as contractual value and sum of loans in force have limited application in economic analysis. The following example illustrates the problem. A borrower with 12 consecutive 1-month loans of $100 each has a total contractual loan value of$1,200fora 12-month period. A borrower with 1loan for$100 with a term of 1 year has a total contractual loan value of only $100. The economic impact of either of the loans would be essentially the same, however.

For the sample of farms, there is little difference between the contractual value and thesumofloans in force (Cr$546,100 and Cr$537,100, respectively). This is because only 2 percent of the loans carried over from previous years were paid by the start oftheyear.

Time-Adjusted Measure of Credit Use

A time-adjusted measure of credit use 'may be calculated by weighting the debt outstanding during the study period by the proportion of the year in which the debt was outstanding. We are suggesting that this time-adjusted measure be used when data are available on the months the loans were in force. Using this information, we reduced thevalueofloans if they were only used forpartoftheyear. Thus, a loan used for 6 months was assumed to be half as usable and half as valuable as one of the same face value that was used for the entire year.

For the Brazilian data, the time-adjusted method puts the credit use figure at Cr$410,400. This is 90 percent of the value ofloans receivedduringtheyear.

This calculation shows that the farmers had 10 percent less use from their borrowed funds than if all terms were for the entire year. While this me"asure does not specifically show the pattern of borrowing needs and the use of borrowed funds during the pro­duction cycle, we believe it to be the most realistic measure of credit use. It is superior to the time-specific measure (that is, beginning and ending balance) because it includes short-term loans received and liquidated during the year. It also treats equally the farmer who receives and pays off several short-term loans during the year and the farmer who keeps one loan for the entire year.

Selecting a Measure

By examining the source of Brazilian loans, we see that 83 percent of the contractual valueofloans came from formal credit sources (that is, banks). Loans from informal or nonbank sources accounted for the remaining 17 percent of the contractual value. This pattern was also observed for the sum of loans in force and the year-end balance measures. However, if the beginning-balance measure were used, one would conclude that only 11 percent ofthevalueofthe loans came from informal sources.

An analysis of individual farms shows an even greater diversity in results obtained by these measures. Some of the farms, for example, had zero outstanding balances on September 30, 1970, and on June 30, 1971, yet the owners used and repaid significant amounts of short-term credit in the intervening period. Other farmers had a few, old, long-term loans which had been largely paid off prior to July 1, 1970, but had few if any new loans during the 1970171 period. Their loans, therefore, reflected substantial original value, but negligible credit-use values as indicated by the other measures. Still other farmers had several significant loans for very short terms during 1970171. As a result, their loans were quite large in terms of contractual value and new­loans-received value. At the same time, the two outstanding balance figures and the time weighted credit availability figures were much smaller or zero.

The results given by these credit-use measures will

79

vary substantially, depending on the conditions being studied. Application of these six measures to a sample crop farm in the U.S. Com Belt would most likely give sharply different results than are reported in table 1. Not only must the researcher clearly specify his objective before selecting the appropriate measures, but he must also know a good deal about the farm households he is going to study and their patterns of credit use. He must know something about lending practices in rural areas and the production and consumption credit use patterns of farm households. There will likely be a number of situations where one or several of the commonly used measures can provide sufficient information to answer important research questions. However, it should be noted that these commonly used measures will perform best where relatively homogeneous farm households are being studied and where there is a relatively steady flow of credit to these economic units.

Conclusions

Over the past decade, there have been huge increases in formal agricultural credit extended in many of the less developed countries. These increases have been stimulated by large loans from theW orld Bank, the Agency for International Development, and several other regional banks. Policymakers are asking researchers for information on the results of these credit programs. Major reviews of credit activities were made by the Agency for International Development in 1972 and 1973, and by the World Bank and the Food and Agricultural Organization in 1973 through 1975. In many cases, data are not readily available on programs which will allow researchers to answer priority policy questions, especially about the economies of credit use at the farm level. Too many farm-level surveys conducted in less developed countries have collected credit-use information which is almost useless when cause­effect questions are at issue. More careful selection and specification of credit-use measures would greatly improve this kind of research, and also allow investigators to do a better job of answering policy questions.

BOOK REVIEWS

RURAL INDUSTRIALIZATION: PROBLEMS AND POTENTIALS

Edited by Larry R. Whiting

Twelve papers presented at a symposium on rural industrialization held at Purdue University in July, 1972, are now offered in book form. The papers present the separate views of agricultural leaders, educators, businessmen, farmers, government representatives, and a sociologist. The authors have different backgrounds and perspectives, but they effectively explore many facets of rural industrialization.

All parts of American society are affected by rural problems. Many of our current social ills a rein urban areas, but some urban problems of confrontation and mobility stem from overcrowding. Rural problems are much the opposite-people migrating to the cities. Rural America needs better employment opportunities, higher family income, and a sense of vitality to permanently check the loss of population that has cr~ated problems in rural and urban areas. Rural industrialization may be a way to accomplish these goals. The purpose of the conference and of this collection of papers is to organize, interpret, and communicate several views of industrialization in rural America.

Each article presents several perspectives on major aspects of the "rural dilemma." They discuss why the population became distributed as it is today. The concepts of location theory and industry migration were developed to show the underlying forces of previous population movements. The chances of a major redistribution, other than the current pattern, without substantial government assistance are evaluated. They consider the need for integrating an industry with the adjoining community, and they explore the opportunity of a smaller community to make this relationship more direct and mutually advantageous. "Community involvement" can lead to better labor-management relations and to higher productivity. Finally, the role of planners is outlined. This book is a valuable aid in weighing alternative programs to improve economic conditions in rural America.

WILLIAM H. GODFREY, JR. Department of Marketing

Boise State University

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FARM ACCOUNTING AND BUSINESS ANALYSIS

By Sydney G. James and Everett Stoneberg, The Iowa State University Press at Ames, 269 pp., 1974

This book introduces the fundamental elements of accounting and financial analysis, presents techniques for analysing individual enterprises and the total farm business, illustrates basic economic principles, budgeting, and linear programming, and explains some analytical tools of management. Tax management considerations are integrated throughout the text. A workbook of text questions and laboratory assignments for use in teaching can be purchased separately.

This book differs from other farm accounting texts in its organization. Chapters treating purchases and sales of capital, depreciation methods, and credit accounts precede a chapter about the net worth statement. Chapter 1 stresses the importance of records and accounts and briefly defines the functions of management, recordkeeping objectives, and the components of a farm's records and accounting systems. It also summarizes some accounting principles. However, more complete definitions of some terms would be useful for the beginning student. Chapter 2 relates capital investment accounts and depreciation accounts. Depreciation is discussed mainly in terms of IRS guidelines. Chapter 3 defines the basic terms and illustrates the basic tools need to understand credit accounts.

Chapter 4 examines inventory valuation and analysis of the net worth statement. Chapter 5 treats receipt and expense accounts and income statements under alternative accounting systems and procedures. It emphasizes single entry accounting and IRS restrictions.

Chapter 6 discusses the usage and methods of keeping crop and livestock production records for productivity and efficiency analysis. Chapter 7 analyzes the total farm business through income return measures, financial ratios, and efficiency measures. Chapter 8 presents methods of analyzing individual farm enterprises.

Chapter 9 outlines electronic data processing procedures, their use in farm accounting and provides guidelines for selecting a useful system. Chapter 10 provides a good basis for understanding tax consequences of various farm business transactions. It gives a framework for making decisions to minimize income tax and maximir.e

after-tax income. But one problem for any tax discussion is the constant need for revision as IRS rules change. Chapter 11 shows how records can be used as a management tool to help solve business problems and how they relate to the analysis phase of decisionmaking. This chapter emphasizes the use of records in farm planning. By using record information the management tools and techniques of basic economic principles, budgeting, and linear programming can be applied.

The final two chapters tie the farm household with the farm business. Chapter 12 discusses the need for family living expenditure records as part of the financial planning of the farm family. Chapter 13 suggests a farm business office and filing system.

These authors present a practical and useful guide for organizing, recording, and analyzing farm data. Their book emphasizes the use of records and aecounts as a commercial farm management tool. Although written as an introductory college text, the book would be well suited for technical agricultural programs offered by community colleges. The book contains several typographical errors, but these should not be major problems for the reader.

DWAINE K UMBI<~RGI<~R Economic Research Service

APPLIED, ECONOMICS: RESOURCE ALLOCATION IN RURAL AMERICA

Rueben C. Buse and Daniel W. Bromley, the Iowa State University Press at Ames, 614 pp .• 1976, $19.50

Beginning students in agriculture and natural resources economics should take heart. More and more of the new texts in the field are beginning to recognize the problems of bridging the gap from economic theory to economic policy and applied economics. These texts were long in coming, especially in the area of price analysis, where the transistion is particularly important. Mter all, textbooks should illuminate not shield. Perhaps authors of micro texts never experience difficulties in conceptuali~ing, or perhaps explaining the association between theory and policy is too difficult.

This book attempts to relate issues and policy to economic theory. It is oriented to micro analysis in ~he field of agriculture and may be a superior Introductory text for beginning students in a?ricultural economics. It begins with an elementary ~Iscussion of comparative economic systems, and InVestigates how the various alternative economic systems (the three "ism's", as well as our own "free market economy") influence society's answer to the

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fundamental economic problem of what, how, and for whom goods should be produced. After familiarizing the reader with the broad issues that serve as a backdrop for any rigorous study of economics, the authors launch into a detailed study of the micro determinates that provide the flesh and bones of any market system. Even here, the general concepts are introduced and integrated with the theoretical development to give perspective and meaning to specific topics that a professional economist thinks of as his tool kit. The concepts of specialization, comparative advantage, profit maximization, and consumer choice serve as springboards for discussing production, costs, economics of scale, market supply and market demand, elasticities, and market equilibrium. After presenting these principles, the authors construct the basic model of perfect competition that is the basis for welfare comparisons. As an extension of the basic analysis, some of the assumptions necessary for the neat capstone of the classical theory are relaxed, and the topics of dynamics and market structure are briefly examined.

Unfortunately this book makes no mention of oligopoly, although market imperfections and market structure are discussed. However, any book that attempts to encompass so much theory as this one must necessarily restrict the topics it considers. Certainly some differences of opinion about the importance of different topics can arise.

And in the larger sense, thesemattersarerelatively unimportant. This book is designed to relate economics to agriculture. Anyone who wants to see a self-contained development of economic theory can consult the most recent edition of one of the major texts. It will serve that need very well. But this text hopes to serve another objective. The authors use the theory they have introduced to treat topics and issues of the farm sector such as farm income, parity, and risk. They discuss the larger topics of agribusiness, human resources, and problems of the rural community. One topic that has mushroomed in importance for all Americans is natural resources, and they have covered the host of issues this entails-conservation, pollution, and resource valuation. The authors attempt to be inclusive in their treatment of natural resources, and they have succeeded.

This book is well written and should be comprehendable to the beginning undergraduate student of agriculture economics. With a few exceptions, it is well organized and presents the major aspects of theory necessary to any understanding of basic economic issues, as well as a discussion of those issues. It is a good introduction to resource allocation in rural America.

RALPH D. MAY Department of Agricultural I<:('onomi('H

University of Arkansa~:~

THE DEVELOPMENT OF RURAL AMERICA

Edited by George Brinkman, The University Press of Kansas, 140 pp., 1974, $8.50

This book is a collection of seven papers that cover the general areas of "The Nature of Rural Development," "The Social and Economic Conditions of Rural America," and "Rural Development AI ternatives." The authors are J. Carroll Bottum, Richard Hausler, Calvin Beale, George Brinkman, Luther Tweeten, Emery Castle, and Niles M. Hansen. They summarized material presented in their respective seminars on rural community development held at Kansas State University in 1971.

A President once said thatduringthe 1970's we can develop our land as we would like by creating a "new rural environment" and by halting undesirable migration patterns to urban centers. This book suggests that the impact on the urban problems of high density, increasing pollution, crime, lack of privacy, as well as housing and transportation problems can be altered by solving the rural problems of insufficient employment opportunities and the resulting outmigration from many rural areas. This is perhaps an overstatement, and the reader may become confused as he attempts to correlate the evidence offered to support it.

In the first part of the book Bottum gives a definitive view of the nature of rural development. The approach is basically "grass roots." That is, citizen committees can be more than catalytic in developing core strategy. They can be the driving force with close ties to the decisionmakers. The committee criteria suggested is somewhat utopian but, nontheless, valuable to the lay reader.

The Hausler paper completes the first section with a descriptive and somewhat historical approach to changes in rural development. But nothing is said about the future direction of planning.

A second section describes the social and economic situations that are indigenous to rural American

populations and markets. Beale presents convincing thoughts on rural population trends. Brinkman covers rural problems of poverty, education, health and housing. Rural problem development is covered along with an appraisal of problem intensities in 1971.

In the third section, Tweeten says that rural development programs are overlapping, inadequate fragmented, and rather inefficient. At this point th~ book become somewhat academic and technical. But he says that the traditional planner administrator needs to move from " ... program planning to plan. programming."

Castle provides some classical cost-benefit analysis concerning resources and rural development. The approach is also academic and traditional. The apparent sacredness of the cost. benefit tool is exploited as· communities make decisions affecting natural and human resources. There is a startling underlying theme for those who feel that costs and benefits cannot be measured in dollar terms. Very little solid futuristic thinking is provided, and the approach reflects the old syndrome of "more is better." This approach will probably not work in the future, and the lack of imagination here should challenge students to create a better methodology.

In the final section, Hansen deals directly with investments in growth centers as a means of solving rural poverty. The question posed in this section area fitting climax to the book and should be related directly to the work of Bottum and Tweeten.

The book provides good bibliographies. Butsomeof the descriptive historical contributions are outdated. The editor's philosophy is that we must continue to wage war against the problems of rural America. How we perceive and strategically address these problems is still an open question, but we ought to improve our response to rural problems.

WILLARD H. GODFREY, JR. Department of Marketing

Boise State University

OTHER PUBLICATIONS RECEIVED

Committee on Rural Banking Problems, Improved Fund Availability at Rural Banks (Report and Study Papers), Board of Governors of the Federal Reserve System, Washington, D.C., 1975, 133 pp., $1.00.

Lapedes, Daniel N., Editr>r-in-Chief, and the staff of the McGraw-Hill Encyclopedia of Science and Technology, Encyclopedia of Environmental

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Science, McGraw-Hill Book Company, New York, 1974, 754 pp., $24.50.

Oppenheimer, Harold L., and Stephen K. Weber, Cowboy Securities (Going Public in Agriculture), The Interstate Printers and Publishers, Inc., Danville, Ill., 1975, 548 pp., $14.95.

UNITED STATES DEPARTMENT OF AGRICULTURE WASHINGTON, D.C. 20250

OFFICIAL BUSINESS PENALTY FOR PRIVATE USE, $300

POSTAGE AND FEES PAID U.S. DEPARTMENT OF

AGRICULTURE AGR 101

FIRST CLASS