GOVERNMENT PAYMENTS AND FARM

BUSINESS SURVIVAL

NIGEL KEY AND MICHAEL J. ROBERTS

Using farm-level panel data from recent U.S. Agricultural Censuses, this study examines how direct

government payments influence the survival of farm businesses, paying particular attention to the

differential effect of payments across farm-size categories. A Cox proportional hazards model is used to

estimate the effect of government payments on the instantaneous probability of a farm business failure,

controlling for farm and operator characteristics. Results indicate that an increase in government

payments has a small but statistically significant negative effect on the rate of business failure, and the

magnitude of this effect increases with farm size.

Key words: agricultural payments, duration analysis, farm business survival, hazard model.

Economists and policy makers have long beeninterested in how government payments in-fluence the growth and survival of farm busi-nesses (e.g., Leathers, 1992; Tweeten, 1993;Atwood, Watts, and Baquet, 1996; Huffmanand Evenson, 2001). With government pay-ments to farmers exceeding $20 billion in1999, 2000, 2001, and 2003, farm paymentshave received greater public scrutiny, withsome maintaining that payments unfairly ad-vantage large operations (e.g., Williams-Derryand Cook, 2000; Becker, 2001). These con-cerns spurred congressional efforts to tightenpayment caps on large-scale producers duringthe 2002 Farm Act debate (e.g., Nelson, 2002).The effect of payments on farm survival con-tinues to be an important issue in on-goinginternational trade negotiations, where agri-cultural support programs are a major sourceof contention.

This study uses a unique limited-access farm-level panel data set created from the 1987,1992, and 1997 Censuses of Agriculture toderive the first estimates of the effect of gov-ernment payments on the survival of individ-ual U.S. farm businesses. Specifically, we usea Cox proportional hazards model to esti-mate the effect of government payments onthe instantaneous probability (hazard rate) ofa farm business failure. We consider the sur-

Nigel Key and Michael J. Roberts are agricultural economists atthe Economic Research Service.

The views expressed are those of the authors and do not nec-essarily correspond to the views or policies of ERS, or the U.S.Department of Agriculture.

vival of individual businesses, controlling forthe size, location, and organizational structureof the operation, and the age, race, sex, andcareer specialization of the operator. Separateestimates of the effect of payments on busi-ness survival are obtained for four farm-sizecategories.

This study exploits an exogenous sourceof variation in government payments—diffe-rences in payments that result from differencesin “base acreage” in otherwise similar farms.Farmers that operate the same amount of land,located in the same county, producing the samecrop may receive different levels of govern-ment payments if they have different amountsof land enrolled as “base acres”—land enrolledin a particular commodity program based onpast plantings. Prior to 1996, acreage reduc-tion provisions and restrictions on what couldbe planted on base acreage discouraged somefarmers from fully participating in govern-ment programs—between 15% and 40% ofeligible cropland was not enrolled in a Fed-eral program (USDA-ERS). Due to histor-ical variation in participation, similar farmshad different base acres and received differentamounts of government payments.

We find that government payments have asmall but statistically significant negative ef-fect on the instantaneous farm business failurerate. We also find that government paymentsreduce the failure rate proportionally morefor larger farms. These results suggest thatpast agricultural support payments have con-tributed disproportionately to the survival oflarge operations.

Amer. J. Agr. Econ. 88(2) (May 2006): 382–392Copyright 2006 American Agricultural Economics Association

Key and Roberts Payments and Farm Survival 383

Related Literature

There is a substantial theoretical and empiricalliterature relating to firm size and firm survival.Jovanovic (1982), Ericson and Pakes (1992),and Pakes and Ericson (1998) present modelsin which firms (or entrepreneurs) are uncertainabout their own efficiencies at startup. In thesemodels, firms gradually learn about their abili-ties over time. The longer a firm operates in themarket, the more information is gained. Firmsthat revise their perceptions of their ability up-ward over time tend to expand, while those re-vising downward tend to contract or exit. Con-sequently, the longer a firm has existed, thebigger it will be and the less likely it will be tofail. Empirical studies generally confirm thesetheoretical predictions (Dunne, Roberts, andSamuelson, 1988; Baldwin and Gorecki, 1991;Audretsch, 1991; Audretsch and Mahmood,1995; among others).

For small businesses, the personal charac-teristics of the owner, such as educational at-tainment, can be important for small businesssurvival (Bates, 1990; Taylor, 1999). The op-erator’s age may be another important deter-minant of firm size and survival. Age may becorrelated to knowledge about the firm’s com-petitive abilities—with older owners able toacquire more information (Jovanovic, 1982).Alternatively, the operator’s age may be re-lated to financial liquidity. In the presence ofliquidity constraints, it may take many yearsfor business owners to accumulate sufficientnet worth to obtain a certain scale of produc-tion (Evans and Jovanovic, 1989; Holtz-Eakin,Joulfaian, and Rosen, 1994).

Government payments could influence farmbusiness survival through a variety of mech-anisms. Farms receiving high payments peracre could bid up prices of fixed resources—especially land—causing low payment-per-acre farms to shrink or exit. Payments couldalso influence farm survival through capitalmarket mechanisms. Government paymentseffectively raise a farm’s net worth. This couldmake it less costly for the farm to obtain financ-ing when liquidity constraints cause a farm’scost of capital to depend on its net worth(Hubbard, 1998). If large farms are liquid-ity constrained and small farms are not, thenan increase in payments per acre can causelarge farms to expand and increase in num-ber, which bids up land prices causing smallfarms to shrink and decline in number (Keyand Roberts, 2005). Higher payments may also

make agriculture more profitable relative to al-ternative occupations, which could reduce theincentive to exit farming.

Although a limited number of economet-ric studies have attempted to explain changesin the size and survival of individual farmsbased on characteristics of the farm opera-tor or farm (Sumner and Leiby, 1987; Hallam,1993; Zepeda, 1995; Weiss, 1999; Kimhi andBollman, 1999), none have considered the roleof government payments. A few studies haveexamined the relationship over time betweengovernment payments and aggregate measuresof farm structure, including the national agri-cultural bankruptcy rate (Shepard and Collins,1982), the total number of farms (Tweeten,1993), and average farm size (Huffman andEvenson, 2001). More recently, Dixon et al.(2004) used state-level panel data to examinehow government payments and other factorsinfluence Chapter 12 bankruptcy filing rates.To our knowledge, this is the first study to ex-amine the effect of government payments onthe survival of individual farms.

Data

The data used in this study are from the Censusof Agriculture files maintained by the NationalAgricultural Statistics Service.1 The Census isconducted every four or five years and includesall U.S. farms. While offering a very large pop-ulation, the Census does lack some informa-tion that would be useful for this study, suchas operator educational attainment, and de-tailed financial information, such as return onassets or debt-coverage ratio. Since we are in-terested in the effect of government paymentson farm survival, and to reduce sample hetero-geneity, we restrict our analysis to “programcrop farms”—those operations with StandardIndustrial Classification (SIC) codes indicat-ing specialization in wheat, corn, soybean, rice,cotton, or “cash grains.”2 Farms with these sixSIC codes receive the largest shares of govern-ment farm payments.

Data from the 1978–97 Censuses can beused to examine farm survival rates by SIC

1 More information about the Census of Agriculture can befound at: http://www.nass.usda.gov/census/.

2 SIC codes for wheat, corn, soybean, or rice are assigned if anyone of these crops account for at least 50% of sales. An operationis classified as a “cash grain” farm if a combination of these crops,or another cash grain not elsewhere classified totals at least 50%of sales.

384 May 2006 Amer. J. Agr. Econ.

Table 1. New Program Crop Farm (1982) Survival Rates over Time byFarm Type

Farm Category 1982 1987 1992 1997

All program crop farmsNumber surviving 140,876 70,478 45,122 31,630Survival rate (50.0) (32.0) (22.5)

Wheat (SIC = 111)Number surviving 20,592 10,534 6,678 4,697Survival rate (51.2) (32.4) (22.8)

Rice (SIC = 112)Number surviving 1,750 864 525 330Survival rate (49.4) (30.0) (18.9)

Corn (SIC = 115)Number surviving 46,150 23,091 14,876 10,363Survival rate (50.0) (32.2) (22.5)

Soybean (SIC = 116)Number surviving 34,875 15,398 9,311 6,392Survival rate (44.2) (26.7) (18.3)

Cash grain (SIC = 119)Number surviving 32,643 18,330 12,396 8,927Survival rate (56.2) (38.0) (27.3)

Cotton (SIC = 131)Number surviving 4,866 2,261 1,336 921Survival rate (46.5) (27.5) (18.9)

Note: The survival rate (in parentheses) is defined as the number farms surviving in a given period, as a percentage of the

total number of new program crop farms established in 1982.

code.3 Table 1 presents the survival rates bySIC code for program crop farms that werefirst observed in the 1982 Census (these farmsinitiated production sometime between 1978and 1982, as 1978 was the year of the previ-ous Census). About 50% of new farms failedwithin the first five years. After ten years, about32% of farms remained in business, and af-ter fifteen years 22.5% remained in business.These survival rates are comparable to whathas been reported for non-agricultural firms(e.g., Audretsch, 1991; Mata, Portugal, andGuimaraes, 1995; Disney, Haskel, and Heden,2003). Consistent with past studies, the proba-bility of survival generally increases with theage of the firm (Evans, 1987a,b; Audretsch,1991). Cotton and rice farms had somewhatlower than average and cash grain farms some-what higher than average fifteen-year survivalrates.

Because of the way information in theCensus of Agriculture is collected, this studyfocuses on the duration of a farm business con-tinuously operated by the same individual. TheCensus collects information as to when the cur-

3 Individual Census data are not available prior to 1978.

rent operator began to operate the farm, butnot about how long the farm has been oper-ating. Hence, there is no way to estimate thelife of a farm business unless the same oper-ator manages it.4 Consequently, we define asurviving farm as one remaining in businessand having the same operator; farms remain-ing in business with a different operator wereremoved from the sample.5

This study examines the survival of programcrop farms operating in 1987—the first yearthe Census of Agriculture began collecting in-formation on government payments. Our sam-ple includes the 200,187 program crop farmsthat had at least 10 acres of land and $10,000in sales in 1987 and for which information on

4 The Census tracks operations over time using a Census FileNumber (CFN). The Census defines a farm as out of business ifthere is no response to the Census questionnaire or the question-naire is returned stating that the farm is no longer operating. How-ever, if a farm changes operators through a business transaction orinheritance, the CFN may change even though the business is stilloperating. Hence, it is not possible to estimate the duration of afarm business based on how long the CFN appears in the Census.

5 We consider a farm to have the same operator if the age ofthe operator differs by five years between consecutive Censuses.About 8% of continuing farms had operators whose age differedby more or less than five years, and were therefore eliminated fromthe sample.

Key and Roberts Payments and Farm Survival 385

Table 2. The Average Farm Business Lifespan by Sales and Government Payments as a Shareof Sales

Government Payments as a Share of Sales (�) − Quartiles

Q1 Q2 Q3 Q4Sales Quartiles (0 < � < 0.12) (0.12 ≤ � < 0.22) (0.20 ≤ � < 0.36) (� ≥ 0.36) Q4 – Q1

Q1 ($10,000 ≤ sales < $23,991)Years 25.37 25.22 26.24 27.87 2.50∗∗∗

(SE) (0.119) (0.163) (0.157) (0.124) (0.172)Obs. 17,031 8,615 9,253 15,145

Q2 ($23,991 ≤ sales < $50,600)Years 25.94 26.60 28.00 28.48 2.54∗∗∗

(SE) (0.137) (0.136) (0.129) (0.120) (0.182)Obs. 12,130 11,153 12,320 14,441

Q3 ($50,600 ≤ sales < $104,390)Years 26.04 27.45 28.66 28.28 2.24∗∗∗

(SE) (0.138) (0.137) (0.114) (0.117) (0.181)Obs. 9,952 13,343 13,642 13,114

Q4 (sales ≥ $104,390)Years 26.16 27.80 28.03 28.29 2.13∗∗∗

(SE) (0.156) (0.077) (0.083) (0.118) (0.195)Obs. 6,141 24,039 21,994 11,696

Note: Three asterisks (∗∗∗) indicate that the null hypothesis of equal mean lifespan for the first and fourth payments-as-a-share-of-sales quartiles is rejected at

the 0.001 significance level.

all variables was available.6 The Census allowsus to identify whether a farm business ceasedoperating between 1987 and 1992, or between1992 and 1997, or whether it was still operat-ing in 1997. In addition, the Census records theyear in which the current operator began man-aging the operation. Therefore, the observedlifespan of the farm business is defined as 1987minus the year the operator initiated farmingon the operation plus 2.5, 7.5, or 10, depend-ing on whether the operation failed by 1992,failed by 1997, or remained in business in 1997,respectively. If the operation remained in busi-ness in 1997, the lifespan is right censored.

Because of the way we define the age ofthe business, all lifespans are left truncated.We do not begin to observe businesses until1987—a known time after they began oper-ating, and the risk set does not include busi-nesses that failed prior to 1987. For example,of all businesses initiated in 1980, we only ob-serve those businesses in 1987 that survivedat least seven years. We do not observe farmsthat failed before 1987. Hence, for businessesthat began in 1980 the lifespan is left truncatedat seven years. The observed lifespan is there-

6 Deleting farms with less than $10,000 in sales (which repre-sent about a fifth of the observations) focuses the analysis on farmhouseholds where farm business income is a larger share of to-tal income and where government payments are likely to play animportant role in the decision to continue farming.

fore conditional on the period of truncationbeing exceeded.7

Methods and Results

To examine the relationship between govern-ment payments and farm business survival wefirst compare the average observed lifespan forfarm businesses of different sizes and differentshares of government payments in total sales.Table 2 shows that, with few exceptions, withineach sales quartile, a larger share of govern-ment payments in sales corresponds to a longeraverage lifespan.8 For example, for farms withmore than $104,390 in sales, those farms wherepayments comprise less than 12% of sales havean average lifespan of 26.16 years compared to28.29 years for those farms where paymentscomprise more than 36% of sales. The last

7 Left truncation is accounted for in the estimated likelihoodfunction associated with the Cox proportional hazard model andthe product-limit survival function estimates discussed in the nextsection (see SAS 9.1 PHREG Procedure, p. 2998, for details).

8 The average observed lifespans reported in table 2 do not ac-count for left truncation or right censoring, meaning these averagesare not unbiased estimates of the true lifespans. The standard error(SE) for the difference between any two average lifespans can becalculated by taking the square root of the sum of the individual

squared standard errors (

√SE2

1 + SE22). For example, the differ-

ence in average lifespans for the first sales quartile and second andthird payments quartiles is 26.24 − 25.22 = 1.02, and the SE for

this difference is√

1.572 + 1.622 = 0.226, which gives a t-statisticof 4.5.

386 May 2006 Amer. J. Agr. Econ.

Figure 1. Kaplan–Meier estimated survival functions for farms in the upper and lowergovernment-payments-as-a-share-of-sales quartiles

column in the table shows the difference inthe average lifespan for farms in the first andfourth payments-as-a-share-of-sales quartiles.A t-test reveals that we can reject the null thatthe mean difference in lifespans is zero for allthe sales quartiles.

Next, we compare Kaplan–Meier nonpara-metric survivor function estimates for farmbusinesses with high and low government pay-ments as a share of sales in 1987 (the firstyear of the study). Figure 1 illustrates thatfarms in the bottom government-payments-as-a-share-of-sales quartile (govpaycat = 25)are less likely to survive than are those in thetop quartile (govpaycat = 75). The Kaplan–Meier estimation does not account for the lefttruncation of the lifespans mentioned above,so the estimated survival probabilities are bi-ased.9 However, a comparison of the survival

9 Survival probability estimates are biased upward as short-livedbusinesses are disproportionately excluded from the sample.

functions illustrates a clear difference betweenthe groups. After 30 years, only about 53% offarms in the bottom payments-share quartilesurvived compared to about 64% of farms inthe top quartile. Farms in the bottom payment-share quartile have an estimated mean lifespanof 30.5 years, compared to 35.9 years for farmsin the top quartile. Statistical tests reveal thatit is very unlikely that the survivor functionsare identical across the government paymentstrata. Both the Savage (log-rank) test and theWilcoxon test indicate a significant differencein the survival rates of farms that did not re-ceive government payments in 1987 and thosethat did receive payments.10

A statistically significant difference betweenthe estimated survival functions is not strongevidence that government payments influence

10 The logrank test has a chi-square statistic of 1,090.1 with anassociated p-value less than 0.0001; the Wilcoxon test has a chi-square of 1,180.7 with an associated p-value less than 0.0001.

Key and Roberts Payments and Farm Survival 387

survival because other factors may be corre-lated with both payments and survival. Forexample, high-payment farms are larger onaverage, are more concentrated in certaintypes of farms and in certain regions, and aremore likely to grow certain crops. If these fac-tors are correlated with both government pay-ments and duration of farm survival, we mayobserve a relationship between payments andsurvival that is not causal. To control for thesefactors we use the more general Cox propor-tional hazard model (Cox, 1972).

The Cox model assumes a parametric formfor the effect of the explanatory variables onsurvival, but allows the form of the underly-ing survivor function to be unspecified. Cox’ssemiparametric model has been widely used toexplain firm survival (e.g., Mata, Portugal, andGuimaraes, 1995; Audretsch and Mahmood,1995; Disney, Haskel, and Heden, 2003). Thesurvival time of each member of the popula-tion is assumed to follow a hazard functiongiven by

hit = h0(t) exp(x′i �)(1)

where h0(t) is the baseline hazard function,xi is a vector of explanatory variables, and� is a vector of parameters. To estimate �,Cox (1972, 1975) proposed a partial likelihoodfunction, which eliminates the unknown base-line hazard function and accounts for the factthat survival times are censored.

Table 3 presents model estimates under fouralternative specifications. In all four columns,the explanatory variables include characteris-tics of the farm business and farm operator inthe initial period, 1987. Firm characteristics in-clude business size (logarithm of total agricul-tural sales), indicator variables for the SIC ofthe farm, the organizational structure of thefarm (family-owned or otherwise), and totalsales category (quartiles). In terms of operatorcharacteristics, we use indicators for ten op-erator age categories, for the operator’s race(white or otherwise), and the operator’s mainoccupation (farming or otherwise).11 To ex-amine how the effect of payments varies byfarm size, we interact the natural logarithmof government payments with the sales quar-tile indicators. The logarithm of sales is usedbecause the distribution of government pay-ments (like sales) is highly skewed and because

11 The ten age categories allow for a flexible nonlinear relation-ship between age and survival. This specification produced a betterfit than a model using age and age-squared.

this transformation facilitates interpretation ofthe coefficient.12

In columns 2–4, we introduce 38 state fixedeffects and the 24 sales–SIC interaction effects(four sales quartiles times six SIC crop cate-gories). Column 3 introduces a control for theyear in which the farm initiated production,and column 4 introduces a measure of thefarm’s debt-to-asset ratio. After discussingthe results of these four regressions we fo-cus on interpreting the government paymentscoefficient.

Across all model specifications, we find thatlarger enterprises are less likely to fail thansmaller ones, which is consistent with studiesof nonfarm businesses. We also find that hazardrates are significantly lower on farms that arefamily-owned, or have an operator who is maleor white. The hazard rate is not significantlyassociated with the operator having farmingas a primary occupation.

As many farm businesses fail when the op-erator retires, it is not surprising that beingyounger than seventy years old (the missingcategory is seventy years or older) reduces theexit hazard, and that the magnitude of this re-duction in the hazard is greatest for farmers be-low fifty-five years. However, comparing farmsoperated by farmers below the normal retire-ment age (fifty-five years) reveals that youngerfarmers have a lower instantaneous probabil-ity of failure than older farmers: holding allelse constant, the hazard is smallest for opera-tors 30–34 years old and it increases graduallywith age until farmers are 50–54 years old. Thisresult does not support the hypothesis that ageis positively related to financial liquidity or tothe acquisition of information in a way that en-hances the likelihood of survival.

In column 3 we introduce a control for theyear in which the farm initiated productionin order to control for policy changes thathave occurred over time. Rules governingprogram participation and payments changedsubstantially over the period in which we ob-serve farm survival (1987–97). Among otherchanges, planting restrictions were lifted andpayments largely decoupled from productiondecisions with the 1996 FAIR Act. Becauseprogram rules changed, it is possible that the

12 The natural logarithm of government payments is set to zerowhen payments equal zero. As an alternative specification, we triedusing government payments as a share of receipts (sales plus pay-ments) which has the advantage of being bounded between zeroand one. The main results obtained using this specification did notdiffer substantially from the results obtained using the logarithmof payments.

388 May 2006 Amer. J. Agr. Econ.

Tabl

e3.

Cox

Pro

port

iona

lHaz

ard

Mod

elE

stim

ates

ofFa

rmB

usin

ess

Dur

atio

nun

der

Var

ious

Spec

ifica

tion

s

(1)

(2)

(3)

(4)

Va

ria

ble

Co

eff

.S

EC

oe

ff.

SE

Co

eff

.S

EC

oe

ff.

SE

Lo

gS

ale

s−0

.04

30

.01

0−0

.06

10

.01

0−0

.05

90

.01

0−0

.05

40

.01

4S

IC1

11

(wh

ea

t)−0

.28

70

.01

51

.84

21

.41

81

.70

11

.42

2−1

4.6

20

53

.36

8S

IC1

12

(ric

e)

0.1

44

0.0

23

1.6

83

1.3

27

1.5

55

1.3

30

−14

.42

75

3.3

65

SIC

11

5(c

orn

)−0

.15

70

.01

20

.83

81

.22

90

.96

31

.23

3−1

4.6

94

53

.36

0S

IC1

16

(so

yb

ea

n)

−0.1

90

0.0

13

1.2

62

0.8

51

1.3

32

0.8

51

−6.8

42

43

.47

8S

IC1

19

(ca

shg

rain

)−0

.34

40

.01

21

.06

70

.62

11

.13

60

.62

10

.67

51

.09

7O

pe

rato

r’s

ag

e<

30

−0.7

41

0.0

17

−0.7

30

0.0

18

−0.8

50

0.0

18

−0.8

24

0.0

34

Op

era

tor’

sa

ge

30

–3

4−0

.77

70

.01

7−0

.76

80

.01

7−0

.86

30

.01

7−0

.86

80

.03

1O

pe

rato

r’s

ag

e3

5–

39

−0.7

52

0.0

17

−0.7

44

0.0

17

−0.8

40

0.0

17

−0.8

76

0.0

30

Op

era

tor’

sa

ge

40

–4

4−0

.71

60

.01

6−0

.71

00

.01

7−0

.80

80

.01

7−0

.84

80

.02

9O

pe

rato

r’s

ag

e4

5–

49

−0.6

96

0.0

16

−0.6

91

0.0

16

−0.7

91

0.0

16

−0.7

98

0.0

29

Op

era

tor’

sa

ge

50

–5

4−0

.62

60

.01

5−0

.62

40

.01

5−0

.72

20

.01

6−0

.70

40

.02

8O

pe

rato

r’s

ag

e5

5–

59

−0.3

53

0.0

14

−0.3

52

0.0

14

−0.4

35

0.0

14

−0.4

09

0.0

26

Op

era

tor’

sa

ge

60

–6

4−0

.11

50

.01

3−0

.11

20

.01

3− 0

.16

00

.01

3−0

.12

50

.02

4O

pe

rato

r’s

ag

e6

5–

69

−0.1

70

0.0

14

−0.1

69

0.0

14

−0.2

03

0.0

14

−0.1

97

0.0

26

Se

x=

Ma

le−0

.30

60

.01

8−0

.30

70

.01

8−0

.30

60

.01

8−0

.29

40

.03

3R

ace

=W

hit

e−0

.16

00

.04

0−0

.10

30

.04

1−0

.10

40

.04

1−0

.12

60

.07

0O

rga

niz

.=

Fa

mil

y-o

wn

ed

−0.4

12

0.0

08

−0.4

04

0.0

08

−0.4

09

0.0

08

−0.4

34

0.0

12

Ma

ino

ccu

pa

tio

n=

Fa

rme

r−0

.02

80

.00

8−0

.03

60

.00

8−0

.03

10

.00

8−0

.07

80

.01

6S

ale

sq

ua

rtil

ea

Ye

sY

es

Ye

sY

es

Sta

tea

–Y

es

Ye

sY

es

Sa

les

qu

art

ile

∗SIC

cate

go

rya

–Y

es

Ye

sY

es

Init

iati

on

ye

ara

––

Ye

sY

es

De

bt–

ass

et

rati

o–

––

0.0

57

0.0

20

Lo

gG

ov.

Pa

y∗S

ale

sQ

1−0

.03

30

.00

2−0

.03

40

.00

2−0

.03

50

.00

2−0

.03

10

.00

3L

og

Go

v.P

ay

∗Sa

les

Q2

−0.0

48

0.0

02

−0.0

48

0.0

02

−0.0

50

0.0

02

−0.0

46

0.0

04

Lo

gG

ov.

Pa

y∗S

ale

sQ

3−0

.07

00

.00

2−0

.07

10

.00

2−0

.07

40

.00

2−0

.08

10

.00

4L

og

Go

v.P

ay

∗Sa

les

Q4

−0.0

87

0.0

03

−0.0

87

0.0

03

−0.0

90

0.0

03

−0.0

92

0.0

03

Lo

g-l

ike

lih

oo

d−1

,15

5,8

88

−1,1

55

,28

4−1

,15

4,3

50

−38

3,7

24

LR

chi-

squ

are

(p-v

alu

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Key and Roberts Payments and Farm Survival 389

effect of payments on survival was differentin the 1987–95 period (call it regime 1) and inthe 1996–97 period (regime 2). Similar farmshave a different likelihood of spending moretime in regime 2 relative to regime 1 depend-ing on when they initiated production. In oursample, farms that initiated production at anearlier point in time would be less likely to ex-perience regime 2 than farms that began later,ceteris paribus. The estimates in column 2 donot control for this policy change, so our esti-mates of the effect of 1987 payments on sur-vival could be biased depending on whetherthe effect of payments on survival increasedor decreased after 1995.

We address this potential problem by includ-ing the regressions fixed effects for the year inwhich the farm initiated production (there areseventy-seven different starting years). Differ-ences between farms that were caused by theyear in which the farm began farming (andhence associated with the likelihood of beingin regime 1 or 2) are captured by the fixed ef-fects. By removing these fixed effects we are ef-fectively estimating the average effect of 1987payments on the survival of farms. Using aWald test we can reject the joint hypothesisthat all initial-year coefficients are zero at the0.001 significance level. However, comparingcolumn 3 with column 2 reveals that the pa-rameter coefficients on the variables of interestdid not change much. This result is not surpris-ing because the first regime accounts for eightof the ten years over which we observe farmssurviving or exiting.

In column 4 we introduce a proxy for thefarm’s debt-to-asset ratio. The census col-lects information on the total value of landand buildings on the farm, which serves asa proxy for assets, and information on inter-est expenditures, which can be used to esti-mate farm debt.13 Information on the valueof land and buildings and interest expendi-tures is only available on the “long form” ofthe survey, which is sent to about a third ofall operations—reducing our sample to 77,594.The results in column 4 indicate that a higherdebt-to-asset ratio raises the hazard rate. Thecoefficient associated with debt asset ratio issignificant at the 5% level.

13 To convert interest expenditures to debt, we use the annualaverage thirty-year fixed rate mortgage rate (http://www.mbaa.org/marketdata/data/02/fm30yr rates.htm), and assume a thirty-yearloan in the fifth year of repayment. Using standard amortizationmethods, this implies a debt to interest expenditure ratio of 9.75for 1987.

The coefficients associated with the loga-rithm of government payments interacted withsales quartiles are statistically significant andconsistent across the four model specifications.To interpret these coefficients, we can rewrite(1) as

ln hit = ln h0(t) + x′i �.(2)

Let �g be the coefficient associated with thenatural logarithm of government payments(ln gi), an element of xi. It follows that

�g = (dhit/dgi )(gi/hit ).(3)

That is, �g is the responsiveness of the condi-tional probability of farm business failure toa change in government payments, expressedas an elasticity. Hence, the estimate from col-umn 3 indicates that a 10% increase in govern-ment payments reduces the instantaneous rateof business failure by 0.35%, 0.50%, 0.74%,and 0.90%, for a representative farm in firstthrough fourth sales quartiles, respectively.14

For example, for a farm in the fourth quartilewith a hazard rate of 0.500, a 10% increasein government payments would decrease thehazard rate to 0.495.

For a more intuitive interpretation of theresults, we can use the estimated parametersfrom the Cox partial likelihood function toestimate the survival function. For the Coxmodel the product-limit estimate of the sur-vival function is

Si (t) = [S0(t)]exp(x′i �)(4)

where S0(t) is the estimated baseline sur-vivor function (see Kalbfleisch and Prentice,1980, for details). Figure 2 displays the es-timated survival functions for a representa-tive farm with average government paymentsand with a 50% reduction in government pay-ments.15 After ten years, farms receiving theaverage level of government payments havea chance of survival of about 35%, com-pared to only 25% for farms receiving half

14 In theory, farmers could respond to realized or expected gov-ernment payments. Realized payments provide a noisy estimateof expected payments because a large component of realized pay-ments is transitory. Consequently, if farmers respond to expectedpayments, our estimated coefficient likely underestimates the ef-fect of a change in expected payments.

15 Government payments have fluctuated by 50% or more in con-secutive years. For example, total direct payments rose from $9.2billion in 1992 to $13.4 billion in 1993 and then fell to $7.9 bil-lion in 1994. More recently, payments fell from $20.7 billion in2001 to $10.9 billion in 2002 before rising to $17.4 billion in 2003(http://www.ers.usda.gov/Data/FarmIncome/finfidmu.htm).

390 May 2006 Amer. J. Agr. Econ.

0

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0.5

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0.9

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50% Reduction in Payments

Base Level of Payments

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the average level of payments. Because theCox model accounts for left truncation, theunbiased product-limit estimates of the sur-vival probabilities are smaller than the biasedKaplan–Meier estimates shown in figure 1.

A large reduction in government paymentscould have substantially different implicationsfor farms of different sizes. Table 4 illustratesthe effect of a 50% reduction in direct gov-ernment payments on expected lifespans. Theeffect of the payment reduction is shown sepa-rately for payment recipients and for all farms.Larger operations experience a greater reduc-

Table 4. The Effect of a 50% Reduction in Government Payments on the Duration of FarmBusinesses

Estimated Life of Farm Business (Years)

Farms Receiving Payments All Farms

Sales Quartiles Base 50% of Base % Change Base 50% of Base % Change

Q1 9.44 (0.021) 9.24 (0.020) −2.06 8.83 (0.020) 8.68 (0.019) −1.71Q2 10.93 (0.024) 10.58 (0.023) −3.22 10.38 (0.022) 10.08 (0.022) −2.89Q3 12.91 (0.027) 12.32 (0.026) −4.59 12.43 (0.026) 11.88 (0.025) −4.38Q4 14.67 (0.031) 13.86 (0.029) −5.53 14.25 (0.030) 13.48 (0.029) −5.41

tion in life duration for two reasons. First, themarginal effect of a reduction in payments isgreater for larger operations. Second, a greaterpercentage of large farms receive governmentpayments (97.0% for the largest quartile, com-pared to 78.6% for the smallest quartile). Thetable shows that a 50% drop in direct govern-ment payments shortens the expected life ofthe largest farms by 5.4% from 14.25 to 13.48years, and shortens the expected life of thesmallest farms by 1.7% from 8.83 to 8.68 years.The positive relationship between scale and ef-fect size is expected as farm income represents

Key and Roberts Payments and Farm Survival 391

a larger share of total farm household incomefor larger farms (Mishra et al., 2002).

Conclusions

Government payments have a small but statis-tically significant positive effect on farm busi-ness survival. This finding could be explainedby several factors. Farms receiving relativelyhigh payments may be able to bid up the priceof land and other fixed resources—causingfarms receiving lower payments to exit. Gov-ernment payments may also relieve liquidityconstraints allowing farms receiving more pay-ments to achieve a more efficient scale and re-main in business longer. Additionally, higherpayments may make farming more profitablerelative to alternative occupations, thereby re-ducing incentives to exit agriculture.

The study also found that governmentpayments increase business survival ratesproportionally more for larger farms. This re-sult is probably attributable to the fact thatgovernment payments’ share of farm house-hold income increases with total sales. Whilepayments appear to disproportionately benefitlarger operations, the long run consequencesof an increase in payments for agriculturalstructure are ambiguous. Because the studydid not account for the size of farms enteringproduction, it is not possible to conclude thatlower failure rates for larger farms necessar-ily increase the concentration of production.Further work would be needed to understandhow government payments influence the sizedistribution of farm businesses.

[Received January 2005;accepted June 2005.]

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