United States

Department

of Agriculture

Economic

Research

Service

Electronic

Report

Decoupled Payments

in a Changing Policy

Setting Agricultural

Economic 

Report 

No. 838

November 2004
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Economic Research Service/USDA

United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

November 2004

Agricultural

Economic 

Report 

No. 838

Decoupled Payments in a

Changing Policy Setting

Chapter Authors:

Mary Clare Ahearn, Robert N. Collender, Xinshen Diao,

David Harrington, Robert Hoppe, Penni Korb, 

Shiva S. Makki, Mitchell Morehart, Michael J. Roberts,

Terry Roe, Agapi Somwaru, Monte Vandeveer, 

Paul C. Westcott, and C. Edwin Young* 

Editors: Mary E. Burfisher and Jeffrey Hopkins

Abstract

The studies in this report analyze the effects of decoupled payments in the

Federal Agriculture Improvement and Reform (FAIR) Act on recipient households, and assess land, labor, risk management, and capital market conditions that

can lead to links between decoupled payments and production choices. Each

study contributes a different perspective to understanding the response of U.S.

farm households and production to decoupled income transfers. Some use new

microdata on farm households collected through USDA’s Agricultural Resource

Management Survey (ARMS), initiated in 1996, and its predecessor survey.

These data are used to compare household and producer behavior and outcomes

before and after the FAIR Act. Other studies use applied or conceptual models to

characterize the impact of introducing decoupled payments. Collectively, the

chapters represent an early stage in the empirical analysis of decoupled payments.

The studies address many aspects of the payments’ household impacts but

remaining issues call for additional analysis. As the analytical paradigm changes

with the evolution of farm programs, the development of appropriate data and

models will improve our understanding of farm program impacts on the behavior

and well-being of U.S. farm households, and the agricultural sector.

* Xinshen Diao is with the International Food Policy Research Institute. Shiva S. Makki is with

Ohio State University and the World Bank. Terry Roe is with the University of Minnesota.
Acknowledgments

We especially thank Mary Bohman and Carol Jones for their contributions

to the development of this report. The authors also gratefully acknowledge

the reviewers of the chapters in this report: Brian Buhr, Otto Doering,

Vernon R. Eidman, Barbara Fecso, Jill Findeis, Bruce Gardner, Joseph

Glauber, Carol Goodloe, Barry Goodwin, Munisamy Gopinath, Matt Holt,

Wally Huffman, Barrett Kirwan, David Orden, Daniel Osgood, Glen

Pederson, Timothy Phipps, Andrew Schmitz, Betsy Sellers, and Randy

Zeitner. We also thank Dale Simms for editing and production assistance

and Anne Pearl for text layout and graphic design contributions.

National Agricultural Library Cataloging Record

Decoupled payments in a changing policy setting.

(Agricultural economic report; no. 838)

1. Agriculture--Economic aspects--United States.

2. Agricultural subsidies--Economic aspects--United States.

3. Agricultural price supports--United States.

4. Transfer payments--United States.

5. Agriculture and state--United States.

I. Ahearn, Mary. II. Burfisher, Mary E. III. Hopkins, Jeffrey.

IV. United States. Dept. of Agriculture. Economic Research Service.

V. Title.

HC79.S9

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The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs

and activities on the basis of race, color, national origin, sex, religion, age, disability,

political beliefs, sexual orientation, or marital or family status. (Not all prohibited bases

apply to all programs.) Persons with disabilities who require alternative means for

communication of program information (Braille, large print, audiotape, etc.) should

contact USDA's TARGET Center at (202) 720-2600 (voice and TDD).

To file a complaint of discrimination, write USDA, Director, Office of Civil Rights,

Room 326-W, Whitten Building, 14th and Independence Ave., SW, Washington, DC

20250-9410, or call (202) 720-5964 (voice and TDD). USDA is an equal opportunity

provider and employer.
Contents

Introduction

Mary E. Burfisher and Jeffrey Hopkins . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Chapter 1: Farm Program Effects on Agricultural Production: 

Coupled and Decoupled Programs

Paul C. Westcott and C. Edwin Young . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Chapter 2: Decoupled Payments: A Dynamic, 

Economywide Perspective

Terry Roe, Agapi Somwaru, and Xinshen Diao . . . . . . . . . . . . . . . . . . . . .18

Chapter 3: Decoupled and Coupled Payments Alter 

Household Labor Allocation

Mary Clare Ahearn, David Harrington, Robert Hoppe, and Penni Korb . .25

Chapter 4: Decoupled Payments and Farmers’ Production 

Decisions Under Risk

Shiva S. Makki, Agapi Somwaru, and Monte Vandeveer . . . . . . . . . . . . . .33

Chapter 5: Decoupled Payments to Farmers, 

Capital Markets, and Supply Effects

Robert N. Collender and Mitch Morehart . . . . . . . . . . . . . . . . . . . . . . . . . .40

Chapter 6: Effects of Government Payments on Land Rents, 

Distribution of Payment Benefits, and Production

Michael J. Roberts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55

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Introduction

Mary E. Burfisher and Jeffrey Hopkins

For decades, economists have proposed decoupled farm subsidies in order to

support agricultural producers without distorting commodity production or

trade. Decoupled payments provide income transfers of a fixed amount to

producers while allowing them to make market-based decisions about

whether, or which commodities, to produce.1 The program design of decoupled payments breaks the link between a producer’s actions and eligibility

for or level of payment. In contrast, “coupled” farm support, which is based

on current prices and production of specific crops, distorts production incentives. This can lead to overproduction, lower market prices, higher program

costs, and an inefficient allocation of national resources, often with spillover

effects on world markets. 

In the 1996 Federal Agriculture Improvement and Reform (FAIR) Act, the

United States revamped its farm subsidies with the introduction of a decoupled payment program. “Production Flexibility Contracts” (PFC) provided

annual lump-sum cash payments to farm operators based on their historical

program crop production.2 PFCs were fixed payments announced in

advance for the duration of the FAIR Act (1996-2002) and transferred a

total of $36 billion to eligible producers, with an average payment per

eligible household in 2001 of about $9,000. The FAIR Act also provided

greater flexibility in planting decisions and terminated acreage reduction

programs. Decoupled farm payments—now called “direct payments” under

the 2002 Farm Security and Rural Investment (FSRI) Act—amount to about

$5 billion annually. 

Has the introduction of decoupled farm payments in the FAIR Act helped

improve the well-being of farm households—defined broadly to include

their income, wealth, and their work choices? Have they increased the

market orientation of U.S. agriculture, or do they distort production and

trade as do coupled farm programs? The challenge in studying decoupled

payments is that it calls for a broader analytical paradigm than that used for

more traditional U.S. farm programs. For coupled programs, the main

impacts can be observed in commodity markets. By changing the returns to

production of specific commodities or to input uses, coupled payments

create incentives that directly influence production decisions. Producer

response to coupled programs can be mostly captured in a commodity or

farm enterprise framework that focuses on these relative price changes. The

main impact of decoupled payments, in contrast, is their effect on the

income and wealth of recipient households. To assess the possible impacts

of decoupled payments on U.S. agriculture, we need to know about recipient

households’ spending, saving, and working decisions—in agricultural and

nonagricultural activities—and how these decisions may have changed with

increased income and wealth. In addition, we can consider indirect links

between decoupled payments and production choices that may exist due to

risk preferences and market conditions. 

2 For more information on farm and

commodity programs covered by the

1996 farm act as amended, see

http://www.ers.usda.gov/briefing/

FarmPolicy/19962001commodity.htm.

For information on farm policy terms,

see the glossary at www.ers.usda.gov/

features/farmbill/2002glossary.htm. 

1 “Decoupling” is sometimes used

to refer to the increased market orientation of farm programs, and the term

“decoupled" payment is sometimes

broadly used to refer to payments that

have minimal impacts on production.

Our study defines decoupled payments

as subsidies implemented as direct

income transfers to U.S. producers,

that are not dependent on current production, input use, or prices.

Implementation rules provide a context-neutral way to define a farm program as decoupled. This is because

the actual production impacts of

decoupled payments can vary across

countries and different time periods for

reasons that are not related to program

design but are instead related to local

market conditions, and household and

producer preferences. 
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Effects of Decoupled Payments 

on the Household

Decoupled payments’ effects on a household can be traced through the flow

of its income and expenditure (fig. 1) (USDA, 2003). Payments contribute

to total household income, along with other income sources that include

nonfarm wages, interest, and dividends. Households decide whether to allocate income to current consumption or to savings. Age, preferences, wealth,

and tax implications typically influence this decision. Income increases the

ability to currently consume. Market expectations about future decoupled

payments may be reflected in the household’s land asset values, and thus in

increased wealth. So both current and expected future payments affect the

household’s consumption decisions. Consumption of goods and leisure is

often overlooked when assessing decoupled payments, but a household’s

allocation of payments to current consumption is an important consideration

that competes with the use of the subsidy in the farm operation. Furthermore, a change in consumption — such as food/household supplies, rent,

mortgage, or leisure time – captures part of the subsidy’s effect on farm

household well-being.

Savings represent, in part, a plan to pay for consumption in the future. And

when a household’s income is variable year to year, precautionary savings

can help smooth short-term consumption, allowing the household to maintain some threshold consumption when income is low. Households typically

invest their savings across a portfolio. In general, households allocate investments based on a comparison of expected rates of return. Farm households

can be expected to increase onfarm investment until its expected returns are

no longer as great as those available from off-farm opportunities. Since

lump-sum decoupled payments do not directly affect either onfarm or offfarm rates of return, they theoretically would not affect onfarm investment

or production levels through capital market channels as long as these

markets are efficient.

Goods Leisure

Consumption

Farm assets Nonfarm assets

Savings Taxes

EXPENDITURE

Choices influenced by household characteristics

 and preferences, including risk attitudes, interest rates, tax consequences

INCOME

(Wages, interest and dividends,government transfers, gifts and bequests)

Figure 1

Flow of household income and expenditure
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Increases in income and wealth may also change a household’s tolerance for

risk. Much attention has been given to how risk affects agricultural production, with mixed findings.3 However, a household can also adjust its savings

and investment portfolio or its work choices, to suit its new risk tolerance,

perhaps in lieu of changes in farm production. 

Decoupled payments are more likely to influence production decisions when

“market failures” exist. These include inefficiencies, rigidities, or incomplete information in factor (labor, capital, and land), insurance, or

commodity markets in agriculture. Market failures may lead to links

between decoupled payments to the household and its farm production decisions. For example, credit constraints are a market failure that prevents

producers from making profitable investments in the farm. Decoupled

payments may alleviate this constraint and enable the household to allocate

the additional liquidity to the farm. Testing for the presence of market failures, such as credit constraints, is an indirect way to determine whether

changes in household income and wealth from payments could influence

production.

Analyzing PFC Payments, 1996-2001

The studies in this report analyze the direct effects of decoupled payments

in the FAIR Act on household behavior, and assess land, labor, risk management, and capital market conditions that can lead to links between decoupled payments and production choices. Each study contributes a different

perspective to understanding the response of U.S. farm households and

production to decoupled income transfers. Some use new microdata on

farm households collected through USDA’s Agricultural Resource Management Survey (ARMS), initiated in 1996, and its predecessor survey. These

data are used to compare household and producer behavior and outcomes

before and after the FAIR Act. In this approach, attributing causation to

program changes is difficult because it requires controlling for other factors

that may also have affected the outcomes. Other studies use applied or

conceptual models to characterize the impact of introducing decoupled

payments relative to no payment, based on a stylized set of assumptions

about economic behavior. 

Westcott and Young (chapter 1) provide a conceptual introduction for the

chapters that follow, describing and comparing the production incentives of

coupled and decoupled payments. They consider policy and market conditions that could lead to links between decoupled payments and production,

discussing four avenues through which effects could occur. Westcott and

Young conclude that effects of decoupled programs on planted acreage are

smaller than acreage effects of price- and production-linked coupled

programs, which are typically not very large relative to total acreage because

of the inelasticity of U.S. supply response. Thus, although no program

appears to be completely without potential effects on agricultural production,

they argue that effects of decoupled programs are likely to be small. 

Full planting flexibility under the FAIR Act allowed producers to grow any

crop or fallow land without affecting the size of or eligibility for decoupled

payments. Westcott and Young provide evidence pointing to the increased

3 See for example, Roberts, et al.

(2004).
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market orientation of U.S. agricultural production. They compare 2001

actual plantings with PFC acreage, finding that farmers exercised significant

flexibility in acreage allocations compared to their historically based PFC

acreage.

Roe, Somwaru, and Diao (chapter 2) use a stylized model to simulate the

longterm path of consumption, savings, and investment behavior of U.S.

farm households receiving decoupled payments. They consider two

scenarios regarding capital markets: one in which farm households are

unconstrained in their ability to allocate the saved portion of their payment

across an investment portfolio, and a second in which farm households are

assumed to invest only onfarm, either due to preferences, liquidity

constraints, or segmented capital markets. All other markets are assumed to

be efficient. Because the payments do not increase returns to farm assets,

the results for the first scenario shows no increase in onfarm investment or

production. The results for the second scenario show a shortrun increase in

agricultural capital stock of 0.3 percent. But in the long run, the payments

are found to have almost no effect on farm investment or production, even

with imperfect capital markets. This is because excess investment on the

farm is self-correcting: increased onfarm investment drives down the returns

to farm capital, reducing farm households’ incentive to save and increasing

their rate of consumption out of the payment until returns are equalized

across all investments. 

Households consume leisure as well as goods. Decoupled payments make

leisure, like other goods, more affordable. They increase the value of

leisure relative to the cost of leisure – the marginal value of additional wage

earnings, which in theory may lead to a reduction in hours worked both onand off-farm. Ahearn, Harrington, Hoppe, and Korb (chapter 3) estimate

the impacts of decoupled payments on participating households’ labor allocations on and off the farm. After controlling for various factors that may

influence labor allocation, they report that decoupled PFC payments

decreased the number of off-farm hours worked and on-farm work hours

rose modestly. These results imply that farm households respond the same

way to decoupled payments as they do to coupled payments. These findings

call for further study of farm labor markets, particularly of the ways they

may differ from nonfarm labor markets in, for example, their provision of

nonmonetary benefits.

Changes in a household’s income and wealth can also change its tolerance

for risk. Farmers’ risk aversion may affect production decisions because

wealth-induced changes in their risk tolerance due to decoupled payments

could influence production levels, input use, or crop mix. Makki,

Somwaru, and Vandeveer (chapter 4) review empirical studies of risk aversion of U.S. farmers. These studies have generally found evidence of risk

aversion for most U.S. farmers, but with a wide range of risk attitudes.

Thus, although farmers who receive PFC payments likely display varying

attitudes toward risk, it is certainly plausible that some such farmers are

willing to assume more risk. Yet, Makki et al. conclude that the resulting

effects on production are likely to be small for several reasons. Payments

are on average low (less than 3 percent) relative to the net worth of participants. Farm production is only one of many outlets farm households use to
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take on additional risk. Surveys find that producers use various tools—such

as insurance, hedging, and management strategies—to mitigate risks. And,

farm households can respond to changes in their risk attitudes with adjustments throughout their portfolio, such as off-farm employment and investing

in nonfarm real estate or financial assets. Finally, they review the small

empirical literature on risk-related production effects of decoupled

payments, which finds minimal production impacts.

Financial capital markets are characterized by imperfections that can induce

outside investors and creditors to ration capital or credit and impose other

costs that could cause onfarm investment to be linked to farm household

cash income. Collender and Morehart (chapter 5) examine empirical

evidence of the extent to which these imperfections may affect farm investment and production. Previous research indicates that farm investment

patterns do not rely on farm cash income except in relatively rare circumstances, both for the sector and for individual farms. In particular, during

severe farm recessions, capital market imperfections are associated with

inefficiently low investment, especially for farmers with limited credit histories or in weak financial positions. More recent data do not indicate patterns

of capital investment or credit use that would be consistent with the presence of significant capital or credit constraints among commodity program

participants. These observations, in turn, imply decoupled payments may

move farm sector investment to more efficient levels during severe recessions in the farm economy.

Decoupled Payments, Land Values, 

and Land Rents

Land values and rents reflect expectations about future returns from both

agricultural production and government payments. PFC payments were

made on the basis of land enrolled in the program. PFC acreage was primarily land enrolled in supply management programs for wheat, rice, corn,

barley, oats, sorghum and cotton at least once during 1991-95. PFCs were

pre-determined lump-sum payments. In theory, the link between fixed and

foreseeable program benefits and PFC acreage would allow the payments to

be fully reflected in the market for PFC acreage (adjusting for tax considerations and the buyer’s subjective discount rate on future benefits). In the

case of land rental, the program-induced increase in profits-plus-payments

will tend to be passed through to the land owner. If land rental markets are

efficient, and if decoupled payments are completely nondistorting, one

would expect rents to rise dollar for dollar with those payments. Some

contend that higher land values and rents due to programs reduce the

competitiveness of U.S. producers. However, these higher land-related costs

simply reflect the capitalization of benefits into land values and the passthrough of benefits from tenants to landlords on leased acreage.

In a simulation of decoupled payments, Roe et al. (chapter 2) show a longrun increase in U.S. aggregate land values from PFC payments of 8 percent,

under a scenario that assumes all markets operate efficiently. Roberts

(chapter 6) examines the effects of government payments (excluding conservation) on 1997 cash-lease rental rates for base acres. He compares them to

payments’ effects on cash-lease rental rates prior to the FAIR Act, when
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payments were more closely tied to production levels. The effect of PFC

payments on land rents is important to consider because it reflects the division of payment benefits between tenants and landowners, and most acreage

enrolled in the PFC program was rented. Roberts finds that approximately

one-third of each payment dollar on leased acreage in 1997 was passed

through to landlords via higher land rents. Although this rate is somewhat

higher than the 22-percent pass-through rate prior to the FAIR act, it is far

less than the dollar-for-dollar increase of a full pass-through, indicating that

program benefits are shared between tenants and landlords. The incomplete

pass-through rate could indicate that decoupled payments distort production

activities and profits or that the land markets operate imperfectly, and adjust

slowly. Further study is needed to trace out the full implications of this

finding.

Farm Households: Changing Directions

for Policy and Analysis

U.S. farm programs are changing. Since the mid-1980s, U.S. farm

commodity policy has evolved from a program of price supports and

controlled supply to include multiple objectives (most notably to include

environmental protection) and the facilitation of freer markets in agriculture.

The introduction of PFC payments in 1996 further weakened the links

between commodity programs and production decisions by basing these

payments to farm households on historical criteria. 

The studies in this report explore aspects of the microeconomic behavior of

farm households as it relates to the impacts of income transfers. The studies

describe recipient households’ consumption, savings, and investment

behavior as their income and wealth increase. The studies include analyses

of market conditions, testing for the presence of inefficiencies or market

failures that would link changes in household income to production decisions. Collectively, the chapters represent an early stage in the empirical

analysis of decoupled payments. The studies address many aspects of the

payments’ household impacts but other issues call for additional analysis. As

the analytical paradigm changes with the evolution of farm programs, the

development of appropriate data and models will improve our understanding

of farm program impacts on the behavior and well-being of U.S. farm

households, and the agricultural sector.
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Chapter 1

Farm Program Effects on

Agricultural Production:

Coupled and

Decoupled Programs

Paul C. Westcott and C. Edwin Young

Direct government payments to the U.S. agricultural sector and other farm

program benefits boosted farm income in 1999-2001, particularly during

1999-2001 when direct government payments exceeded $20 billion annually

(fig. 1-1). More than a third of these direct payments were disbursed as

emergency assistance, which augmented direct government payments from

existing farm commodity programs, such as production flexibility contract

(PFC) payments and marketing loan benefits (loan deficiency payments and

marketing loan gains), as well as payments from conservation programs.

Besides these direct government payments, other support to the sector

included crop insurance premium subsidies and price supports for selected

commodities, such as dairy, sugar, and tobacco.

This chapter describes and compares how different types of farm programs

can create economic incentives that may affect production decisions. We

focus on production incentives and supply response because, in general,

additional outcomes (including prices, domestic use, and exports) reflect

changes in the market equilibrium following the change in production. 

Figure 1-1

Direct government payments

0

5

10

15

20

25 

1996 1997 1998 1999 2000 2001

Marketing loan benefits Emergency assistance

Conservation programs Production flexibility contracts

$ billion

Calendar year

Source: Economic Research Service, USDA.
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U.S. Farm Programs: Different Links to

Production and Prices

Different types of government payments and other farm program benefits

influence agricultural markets in different ways. A billion dollars in loan

deficiency payments, for example, will affect production decisions and

market outcomes differently than $1 billion in PFC payments. This variation in effects among different programs largely reflects how closely

program benefits are linked with farmers’ behavior and market outcomes.

Farm programs are coupled if there is a direct link between the determination of the program benefit and the farmer’s production and market conditions (such as prices). In turn, the benefits of coupled programs affect

per-unit net returns associated with specific production choices. That is,

coupled programs may increase farmers’ profit from growing crops such as

corn or soybeans. As a result, these programs have the greatest potential to

affect agricultural production and agricultural markets.

In contrast, decoupled payments are fixed income transfers that do not

depend on the farmer’s production choices, output levels, or market conditions. Decoupled program benefits do not subsidize production activities,

inputs, or practices. These income transfers do not change per-unit net

returns, so they have no direct effect on production decisions for specific

commodities.

However, because decoupled payments raise the overall income and

economic well-being of farm households, indirect influences on agricultural

production can occur through wealth and other effects. Overall, effects of

decoupled payments on production are likely to be small in aggregate

because of the many different uses of the payments. This is particularly true

when one considers the farm household, rather than only the farm operation,

as the decisionmaking entity. A household has a wide array of consumption, savings, nonagricultural and agricultural investment, and off-farm and

onfarm labor allocations that may adjust in response to decoupled payments.

U.S. agricultural commodity policy has been moving toward increasing

market orientation with the introduction of programs that have reduced the

degree of coupling of benefits to production (see box, “U.S. Agricultural

Policy Evolution Toward Greater Market Orientation”). This trend reflects,

in part, the related policy goals of reducing market distortions and fulfilling

commitments to international trade agreements. 
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U.S. Agricultural Policy Evolution Toward

Greater Market Orientation

Beginning in the mid-1980s and continuing through the 1990s, a series of important

changes in U.S. farm commodity programs moved agriculture from the highly

managed sector of the early 1980s toward one with greater market orientation,

particularly with regard to programs affecting farmers’ production decisions (Young

and Westcott; Orden, Paarlberg and Roe; Westcott, Young, and Price). 

Following the experience with high commodity loan rates of the early 1980s that

supported market prices and led to large stocks of grains, farm legislation in 1985

lowered commodity loan rates for wheat, feed grains, soybeans, upland cotton, and

rice. Additionally, the 1985 Act introduced marketing loans for rice and upland

cotton, which effectively moved the loan program for those crops from providing

price support to providing income support without supporting market prices (Westcott and Price). Target prices used for income-supporting deficiency payments

also were reduced and program yields for these payments were frozen. Still, plantings of program crops remained constrained for program participants by provisions

which combined to encourage farmers to plant the same program crops over time.

Farm legislation in 1990 furthered the move toward market orientation in supply

response by introducing planting flexibility on a part of farmers’ base acres.

Producers could respond to market signals in planting choices on “normal flex

acres,” which represented 15 percent of a farmer’s base acres. These acres were

not eligible for income support payments and planting alternative crops on this

land did not penalize the farmer through a loss of historical program base. Additional planting flexibility was permitted on “optional flex acres” (another 10

percent of base acres), although deficiency payments were forgone on any of this

land that was planted to another crop. A further market-oriented change under this

legislation was the extension of marketing loans to oilseeds in 1991 and to wheat

and feed grains in 1993, moving loan programs for these crops to ones providing

direct income support rather than price support.

The 1996 Farm Act fundamentally redesigned income support for major crops with

the termination of acreage reduction programs and target-price-based deficiency

payments and the introduction of decoupled production flexibility contract (PFC)

payments, with almost total planting flexibility. Base acres for program crops,

which had been a constraining aspect of annual supply management programs,

were eliminated and replaced with PFC acreage that was used as the basis for

making PFC payments. With only a few limitations, planting of most alternative

crops was permitted on a farmer’s entire acreage base. These policy changes

provided greater freedom for farmers to make production decisions based on

market signals.

During the latter years covered by the 1996 act, a series of supplemental emergency assistance packages provided market loss assistance (MLA) payments to

farmers. As for PFC payments, most MLA payments were distributed to farmers

based on enrolled PFC acreage and did not depend on current production.

The 2002 Farm Act extended many of the types of programs of the 1996 Farm Act

and the ad hoc emergency spending bills of 1998-2001. Marketing assistance loans

were continued, decoupled direct payments replaced PFC payments, and countercyclical payments were intended to institutionalize market loss assistance payments.

Importantly, the 2002 act also retained nearly full planting flexibility without base

acre constraints to allow farmers to continue to respond to market signals in their

production choices. The legislation also allowed farmers to update base acres used

for direct payments and counter-cyclical payments to reflect 1998-2001 plantings,

although only 39 percent of base acres were updated.
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Coupled Programs Affect Aggregate 

Land Use and Crop Mix

Coupled programs that are closely linked to the farmer’s production of

specific crops affect total land use and also distort the mix of crops planted.

Program benefits that are linked to production of specific crops increase

expected returns to those commodities. That is, an increase in production

receives additional program benefits, which provide incentives to expand

output. As a result, production decisions for those commodities are based

on expected returns from both the marketplace and government payments.

Cross-commodity effects may also occur because changes in expected

returns for one crop affect relative net returns among cropping alternatives.

Some farmers would likely respond to a coupled payment by increasing

total planted area and/or shifting the mix of crops toward those with higher

coupled payments.

Two economic studies analyzing coupled programs (crop insurance and

marketing loans) demonstrate how their benefits directly augment market

returns and thereby influence planting decisions. Crop insurance changes

the distribution of expected income when yields are low. U.S. subsidies for

crop insurance premiums are proportional to the premium. Since premiums

are higher for crops that are riskier to insure, premium subsidies are higher

for those crops, which encourages production of riskier crops and production in riskier regions. Young, Vandeveer, and Schnepf report that government crop insurance subsidies of about $1.5 billion a year would add about

960,000 acres (about 0.4 percent) to annual production of eight major field

crops, with plantings of wheat and cotton expanding the most.

Marketing loans provide another type of coupled benefit by raising farmers’

revenues for current production when market prices are low. When

commodity prices are below commodity loan rates, program benefits

augment market receipts and, thus, create an incentive to produce specific

crops. Annual effects of marketing loans reported by Westcott and Price

vary by year, depending on the absolute and relative magnitudes of the

expected crop-specific marketing loan benefits. With marketing loan benefits ranging from around $5 billion to over $8 billion in 1999-2001, total

acreage planted to eight major field crops was increased by an estimated 

2 to 4 million acres (less than 2 percent) annually in those years. Acreage

effects for individual crops reflect year-specific expected relative benefits

among cropping alternatives each year. In some situations, marketing loan

benefits can result in larger effects on individual crops than in aggregate.

The moderate effects of these coupled programs (less than 2 percent

increases in acres) partly reflect an inelastic acreage response in the farm

sector, where overall crop plantings change proportionally less than the

economic incentives provided by prices and net returns. Despite recent

increases in the responsiveness of plantings to price changes, facilitated by

nearly full planting flexibility (Lin et al.), overall supply responsiveness

remains inelastic.
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Decoupled Payments Raise Household 

Well-Being, Potentially Production

Benefits of decoupled programs do not depend on current production or

market prices. Production decisions for specific crops are not directly

affected by these transfer payments because net returns per unit of production are not changed. Nonetheless, decoupled payments may have indirect

effects on agricultural production and markets through:

 changes in producers’ wealth, leading to higher farm investment 

and changing risk attitudes, 

 effects on slowing or accelerating farm consolidation, 

 expectations about future program eligibility and payment basis 

that influence current production decisions, and 

 repeated ad hoc programs that change producer expectations 

over time.

The direct effect of decoupled payments is to raise the overall income and

economic well-being of farm households. A farm household can decide to

use these transfers in the farm operation or for nonfarm alternatives such as

consumption, savings, and nonagricultural investments. Household work

choices, both off-farm and onfarm, may also change. These resource allocation decisions of the household are important for determining the potential

indirect effects of decoupled payments on production decisions. 

Any indirect effects of decoupled payments on production would be more

general than the commodity-specific effects of coupled programs, affecting

total land use or overall productivity gains, for example. The choice to

grow more of any crop would reflect expected market returns across

competing uses rather than the decoupled payment (see box, “Production

Flexibility Contract Acreage and 2001 Plantings”). As with coupled

programs, lower prices resulting from any production increases can

moderate subsequent production effects and other market impacts.

Farm programs typically encompass many features that can affect the

market, and individual mechanisms can often overlap. Coupled programs

can influence production through these same mechanisms (in addition to

their more direct effects through raising net returns), and many farm

programs have both coupled and decoupled properties.

Wealth Effects on Investment 

and Risk Attitudes

Mechanisms by which decoupled payments may potentially affect production decisions include: (1) a wealth-facilitated investment effect, reflecting

reduced credit constraints and/or reduced costs of capital, and (2) a direct

wealth effect that changes risk aversion.

Decoupled payments can affect agricultural production by increasing the

wealth of farmers, typically through the capitalization of expected farm

program benefits into the value of farmland.
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Production Flexibility Contract Acreage

and 2001 Plantings

National, State, and county data show many significant differences in 2001

plantings compared to Production Flexibility Contract (PFC) acreage under

the 1996 Farm Act, largely due to shifts in acreage allocations as producers

used planting flexibility provided in that legislation. These results suggest a

lack of a strong link between program acreage and current planting decisions.

Instead, production choices reflect the ability of farmers to respond to

expected market returns among competing crops (augmented by expected

marketing loan benefits when prices are low), as well as to agronomic and

rotational considerations.

PFC payments under the 1996 Farm Act were allocated on a commodity

basis, but were linked to historically based contract acreage that reflected

past commodity program enrollment rather than being linked to current

production. PFC payments were made for seven program crops: wheat, corn,

sorghum, barley, oats, rice, and upland cotton. PFCs also gave producers

nearly complete planting flexibility in their production decisions without loss

of program acreage or program benefits. For example, land that had been

enrolled in the wheat deficiency payments program for any of the crop years

1991-95 could be entered into a 7-year production flexibility contract with

the government in 1996. PFC payments on this contract acreage were

considered to be for wheat. The land was required to remain in an agricultural use and there were restrictions on planting most fruits and vegetables,

but planting flexibility allowed producers to shift to other crops or leave the

land idle.

Nationally, 2001 plantings to the seven PFC program crops represented about

82 percent of total PFC acreage. On a crop-specific basis, shares ranged from

a low of 45 percent of barley PFC acreage planted to barley to a high of about

96 percent of upland cotton PFC acreage planted to upland cotton. U.S. wheat

plantings in 2001 represented about 76 percent of the level of wheat PFC

acreage, and corn plantings represented about 93 percent of corn PFC acreage.

Looking more closely at data for upland cotton, the crop with 2001 national

plantings relatively the closest to its crop-specific PFC acreage, significant

variation in this share is shown by state-level and county-level data (box fig.

1). At the state level, upland cotton plantings were more than 20 percent

below cotton PFC acreage in Arizona, New Mexico, California, and Oklahoma, but were more than 20 percent higher than cotton PFC acreage in

Florida, Georgia, North Carolina, South Carolina, Virginia, and Kansas.

Further disaggregation to the county level also indicates an absence of a

strong link between cotton PFC acreage and 2001 upland cotton plantings

(see cotton map). For example, cotton acres greatly expanded above historically based cotton PFC acreage in the Southeastern states of North Carolina,

South Carolina, and Georgia as farmers used planting flexibility provided

under the 1996 farm act, with no constraints related to PFC acreage or annual

acreage reduction programs.

Similarly, variation in plantings relative to crop-specific PFC acreage is shown

for wheat at the state level and the county level (box fig. 2). Of the 42 States

with NASS-reported production data, 32 have wheat plantings more than 20

percent lower or 20 percent higher than the State-level wheat PFC acreage.

County-level wheat plantings in 2001 also show no strong link to wheat PFC

acreage (see wheat map), again reflecting the use of planting flexibility with no

supply management program constraints, such as acreage reduction programs.
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Greater cash flow provided by decoupled payments and higher wealth may

also facilitate more production through increases in agricultural investment

if farmers otherwise face credit constraints or limited liquidity. Some of the

payments are likely to go to consumption, savings, and nonagricultural

investments, with the largest share typically going to consumption. But,

agricultural investment could also rise. For credit-constrained farmers,

lenders may be more willing to make loans to farmers with higher guaranteed incomes, higher farm equity, and lower risk of default. Great loan

availability facilitates more production by allowing these farmers to more

easily invest in profitable opportunities on their farm. Although Collender

and Morehart (chapter 5) did not find evidence of significant credit

constraints among program participants in the aggregate, they did indicate

Percent

< 50

50 - 80

80 - 100

100 - 120

120 - 150

> 150

Source: ERS, based on 2001 county-level data from NASS and FSA. 

Data not available for all counties.

Cotton production flexibility contract acreage planted to 

cotton varied widely

Box figure 1

Percent

< 50

50 - 80

80 - 100

100 - 120

120 - 150

> 150

Source: ERS, based on 2001 county-level data from NASS and FSA. 

Data not available for all counties.

Wheat production flexibility contract acreage planted to

wheat varied widely

Box figure 2
4 Again, use of the payments in the

farm operation would be competing

with alternative uses by the farm

household, including consumption and

nonagricultural investments.

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that some farmers are likely credit constrained and would alter their production with decoupled payments.

For some farmers, increased liquidity provided by decoupled payments may

also reduce the need for loans to cover short-term operating costs or longer

term farm-related investments.4 While there are opportunity costs when

self-financing the farm operation, they would be lower than expenses for

commercial loans. In these situations, the lower cost of capital could

increase the size of the farm operation or raise investment in the farm, either

of which could increase farm output.

If changes in wealth due to decoupled payments influence producers’

perception of or attitudes toward risk, they may take on more risk in their

agricultural and nonagricultural portfolios. Such farmers may choose to

adjust their overall production and/or may switch to riskier crops with

higher average (but more variable) expected returns. Chavas and Holt

(1990) found evidence of declining absolute risk aversion with higher

wealth, implied by positive wealth effects on the plantings of corn and

soybeans.5 Makki, Somwaru, and Vandeveer (chapter 4) discuss in more

detail the potential for risk-related production impacts of PFC payments,

arguing that these effects are likely modest. 

Farm Consolidation Effects

Consolidation in the agricultural sector has been a long-term trend, partly

reflecting increased productivity. How decoupled payments may affect this

ongoing trend is uncertain, but important. 

Two competing arguments concern the potential effects of decoupled

payments on consolidation. On the one hand, decoupled payments could

slow sector consolidation if the payments allow marginally viable, smaller

farms to remain in business longer. Such farms may be able to cover shortterm variable expenses associated with the yearly decision to produce, but

may not be able to cover longrun economic costs, remaining in the sector

because of rising land values. Decoupled payments could help these farms

by relieving a credit constraint or by providing lower cost funds.6 In

general, these farms tend to be less efficient. So, keeping them in operation

would likely lower aggregate production if the land would have been used

by more efficient, larger producers with higher yields. 

In contrast, decoupled payments could accelerate sector consolidation if

larger operations use the payments to buy smaller operations or to rent more

acreage. This would occur especially if these large operations were previously credit constrained or if the lower opportunity cost of using these funds

(relative to the costs of commercial loans) were sufficient to motivate

expansion.7 Any resulting increase in consolidation would be expected to

raise aggregate production because larger operations typically are more efficient due to better management and other economies of size. Larger operations tend to more readily adopt new technology and use production

practices that raise yields (Caswell et al.). Additional effects may reflect

increased production incentives due to lower costs per unit of output.

Caswell et al. report that larger farms tend to have higher application rates

5 Chavas and Holt (1990) derived

wealth effect elasticities of 0.087 for

U.S. corn plantings and 0.27 for U.S.

soybean plantings. Because much

farmland is leased, many payments go

directly or indirectly to nonoperator

landlords rather than to farm operators; those payments would not be

expected to have production effects.

The Chavas and Holt model estimates

reflect the implicit portion of payments captured by operators. In 1954

to 1985 (the estimation period used by

Chavas and Holt), an average of about

37 percent of total farmland was

leased, based on ERS calculations

using Census data. This compares

with about 41 percent of total farmland being leased in 1997, based on

the same data source. Thus, any

upward bias in the Chavas and Holt

elasticities would be minimal because

the increase in the share of farmland

leased since their estimation period

has not been very large.

6 Credit constraints for small farms

may not be significant in the aggregate

because of off-farm income that is typical for these households.

7 As earlier, use of the payments in

the farm operation would be competing with alternative uses by the farm

household, including consumption and

nonagricultural investments.
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for a number of inputs, consistent with their lower production costs resulting

from higher efficiency. 

Net effects on consolidation from these competing arguments are uncertain,

but are not likely to be large (Yee et al., 2004). Relief of credit constraints

is probably not a major factor. Although there are likely to be some creditconstrained U.S. farms, a number of empirical tests show no evidence of

significant credit constraints in the sector in the aggregate (Collender and

Morehart), suggesting minimal effects from the marginal change in credit

constraints. Also, a large share of PFC acres is rented, about 60 percent in

1996 (USDA, 2003). As a result, a portion of program benefits are passed

through to nonoperator landlords, limiting their effects on consolidation (see

discussion of pass-through of PFC payments to landlords in Roberts,

chapter 6). 

Any effects on agricultural output that could result from a change in

consolidation trends would apply only to land that moved into a larger

operation through sale or rent or to land that was held back from sale on

smaller operations. Additionally, the payments may simply shift the timing

of land transfers with any resulting production effects being only temporary. Thus, any effects of decoupled payments on consolidation are likely

to alter trends already underway only marginally, with little effect on

aggregate yields and production. 

Benefit Eligibility and Payment Basis

Some farm programs provide benefits that are not linked to current production decisions, input use, or market prices but are weakly coupled to production decisions through land use constraints. For example, PFCs under the

1996 Farm Act required land to remain in agricultural uses as a condition of

eligibility for PFC payments.8 Although this requirement permitted the land

to be idled, such program provisions can affect overall crop production by

providing incentives to prevent some land from leaving the sector. Once the

decision is made to keep the land in agriculture and not to convert it to a

permanent nonagricultural use, the farmer then may decide to produce on

that land if expected revenues exceed production costs. Even if the land is

permitted to be idled, it is more readily available to return to agricultural

production if economic conditions warrant. Similarly, restrictions on the

plantings of most fruits and vegetables on base acres under planting flexibility provisions could influence production if the land would otherwise

have been planted to those crops.

The basis for the distribution of farm program benefits may also affect

producers’ expectations of how future benefits will be disbursed. Payments

linked to past production may lead to expectations that future benefits will

be linked to current production. Such expectations would affect expected

net returns for program crops and could thereby affect current production

decisions. For example, farmers may not fully use planting flexibility to

move away from historically planted and supported crops if they expect

future farm programs to permit an updating of their base acreage, which

forms the foundation for many payments. Instead, farmers would have

incentives to build and maintain a planting history for program crops,

8 This requirement was continued

under the 2002 Farm Act as a condition

for receipt of direct payments and

counter-cyclical payments.
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perhaps limiting their response to current market signals. Similarly, use of

nonland inputs that affect current yields may be influenced if farmers expect

that future farm legislation will permit an updating of payment yields. Such

updates may also reduce incentives to grow different varieties of program

crops that have marketable characteristics but lower yields.

Overall effects of such expectations are likely to be small due to the uncertainty that future farm legislation would permit updating base acreage and

program yields, the uncertainty of the provisions of any such legislation, as

well as the discounting of benefits payable in the future. Nonetheless, if

farmers expect to be able to update their farm-level program parameters, the

economic efficiency of production could be reduced if producers do not

fully respond to market signals. The importance and potential effects of

these policy expectations also depend on expected market prices, which

would affect the expected value of future program benefits. If expected

market prices are low, the value of future benefits would be relatively high,

so building or maintaining base acreage or program yields would be of

value. However, if expected market prices are higher, future program benefits would be lower and the associated value of base acreage and program

yields would be smaller.

Repeated Ad Hoc Programs Can 

Change Producer Expectations 

Programs whose payments are announced and distributed after production

decisions have been made (such as unanticipated ad hoc emergency assistance) can be argued to not distort production and thus may initially be

decoupled. However, continued use of these programs when prices or

production are low may change farmers’ perceptions of the programs’

design. These payments change producers’ realized revenues and repeated

payments may alter the distribution of future expected revenues by raising

expectations that such payments will recur in similar market situations.9 In

so doing, farmers may perceive such programs as less ad hoc and more

coupled to market conditions.10

As a consequence, these payments reduce potential downside revenue risks,

which may affect production decisions for risk-averse producers, as

expected payments become part of their risk management portfolio. The

revenue stabilization consideration for risk-averse producers would supplement the typical profit maximization incentive underlying planting decisions. Thus, if risk-averse producers have probabilistic expectations of

future assistance based on past government actions, particularly if there is a

connection (or perception of a connection) between the probability of such

payments and market conditions (low prices or production), then production

choices may be influenced. 

Programs that reduce the risk of low revenue outcomes when prices or

production fall to low levels can lead to production effects by raising the

lowest levels of expected revenues, thereby reducing financial risk associated with those market situations. The more these ad hoc benefits are

viewed as linked to specific production activities, the greater their potential

influence on production choices for those activities, as the expectation of 

10 See page 4 of USDA (2003) for

an illustration of how the degree of

coupling of farm programs changes,

reflecting the links of the benefits to

production and prices. 

9 For example, with emergency

assistance packages enacted in the

United States six times in 1998 to

2001, farmers may now expect this

type of government assistance to be

more likely when prices or production

are low.
11 Note that the likelihood of ad

hoc assistance is quite variable as it is

subject to political and budgetary concerns. Farm households are likely to

have better risk management instruments.

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benefits becomes part of the farm household’s risk management portfolio.11

Expected assistance that addresses crop-specific production problems, such

as droughts or floods, can be viewed as similar to fully subsidized crop

insurance, affecting planting decisions by reducing risk, and potentially

leading to increased production of those crops by risk-averse producers. In

contrast, expectations of less specific assistance would likely affect aggregate production through a more general reduction of overall revenue risks. 

Conclusions

Different types of farm programs influence agricultural markets in different

ways. Decoupled farm programs provide income transfers that raise the

overall income and economic well-being of farm households. Decoupled

payments do not have direct effects on production decisions or agricultural

output because they do not change returns to production. However, decoupled programs can have indirect effects on farm production decisions and

aggregate output. This contrasts with coupled farm programs, such as crop

insurance and marketing loans, which create direct incentives to expand

farm output of specific commodities by increasing expected returns per unit

of production, in addition to their potential indirect effects. 

Indirect influences of decoupled payments on production may result from

the effects of increased wealth on risk attitudes or investment, farm consolidation, and expectations about program eligibility and payment basis, and

repeated use of ad hoc programs. Despite a variety of potential indirect

effects of decoupled programs on farmers’ decisions, production effects are

likely smaller than direct effects of price- and production-linked coupled

programs such as marketing loans. This is particularly true when one

considers the payments within a household framework, and that consumption, savings, nonagricultural and agricultural investment, and off-farm and

on-farm labor allocations may adjust to changes in income and wealth.

Further, overall planting effects of coupled programs are typically not very

large compared with total acreage because of the inelasticity of supply

response in the U.S. farm sector. 

Thus, although no program appears to be completely without potential

effects on agricultural production,12 effects of decoupled programs are likely

to be small. Further research is needed, however, to measure such effects

empirically. A useful framework for such analysis is presented in OECD

(2001), which discusses effects on agricultural policies of programs with

different degrees of coupling to production decisions.

To the extent that agricultural production is affected by decoupled

payments, this supply response has additional market effects on prices,

domestic use, and exports. For example, any increase in production resulting

from programs would tend to lower market prices. These price declines,

along with planting flexibility provided by the 1996 and 2002 Farm Acts,

can moderate subsequent production effects.

12 A report by the Organization for

Economic Cooperation and Development discusses the theoretical effects

of decoupling and reaches a similar

conclusion that “it seems difficult to

contend that any policy measure can

be entirely production or trade neutral.”
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Chapter 2

Decoupled Payments:

A Dynamic, Economywide

Perspective 

Terry Roe, Agapi Somwaru, and Xinshen Diao

Decoupled payments, introduced in the 1996 Farm Act and renewed in

2002, are lump-sum income transfers to farm operators independent of

their current production, factor use, or commodity prices. This chapter

considers whether decoupled payments may alter producers’ resource allocation over time and lead to effects on production. We use a dynamic,

economywide applied general equilibrium (AGE) model to simulate the

effects of annual decoupled payments in U.S. agriculture over time. The

dynamic, intertemporal dimension is necessary because a stream of annual

payments can be expected to influence recipients’ decisions about how

much to consume versus save over a long-term time horizon. An economywide approach is important because the payments redistribute income from

urban to rural households, and may result in sectoral changes in resource

allocation within the economy. The main link between decoupled

payments and agricultural production in this framework is through recipient households’ decisions to invest in agricultural assets. 

Can Decoupled Payments Have Neutral 

Market Effects? 

Decoupled payments increase the income and wealth of recipient households. In response, over time, these households are likely to consume more

goods and to increase savings. However, whether these individual household

decisions affect resource allocation and aggregate levels of agricultural

production depends on the behavior of those that are taxed to provide the

transfer. Effects on recipients can exactly offset the consumption and

investment effects of those taxed such that, after the transfer, resource allocation and production at the market level are unaffected. Generally

speaking, this result occurs when recipient households have consumption

and savings patterns in proportion to their income that is identical to those

paying for the subsidy. Under these circumstances, the wealth effect of a

transfer on recipient behavior is offset by the negative wealth effect on those

taxed to provide the transfer.13 Of course, in real economies, identical

taxed/recipient preferences are unlikely.

A neutral outcome depends on other conditions as well, including the availability of financial capital markets that work perfectly to allocate savings to

investors in all sectors of the economy, the presence of opportunities to

insure against future risks, and the absence of fixed costs. These conditions,

too, are unlikely to prevail in real markets. For example, agricultural capital

markets differ from nonfarm capital markets. Unlike corporations, farmers

cannot issue securities or bonds to finance farm activities; instead, they must

rely on land and other assets for collateral. Thus, segmented capital markets

can lead to different capital effects on individuals outside agriculture, who

13 Also, individual preferences can

be identical but differ in the share of

disposable income spent on goods and

services at different income levels. In

this case, the behavior of recipients

can differ from that of taxed individuals, with the result that transfer payments can affect market allocations

over time.
14 See Roe et al. (2002) for details

of the analysis and the underlying

AGE model. 

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are taxed, than on recipients in agriculture. This effect might be greater if

farmers face liquidity constraints or if they prefer to invest in agriculture

that share of decoupled payments not allocated to consumption. The difference in these diverged capital markets does not imply that returns to capital

in agriculture departs from returns in other sectors of the economy, at least

in the long run, since farm households also invest in stocks, bonds, and

other financial instruments (USDA, 2003). However, in the short run, an

increase in agriculture’s capital stock should lead to production effects —

but to what extent? 

Direct payments are targeted to land planted to program crops in the base

period, and so lead to an increase in land asset values. A change in the price

of land affects wealth. Consequently, payments can affect the investment and

consumption behavior of those who own land, since landowners likely try to

equate (risk and tax-adjusted) returns across all assets in their portfolio,

including land. In addition, since land is used as collateral, payments might

increase access to capital for those farmers who face credit constraints. 

An Intertemporal, Economywide Model

Analysis: Bracketing Two Outcomes 

We use two versions of an intertemporal, economywide model of the U.S.

economy to simulate decoupled payments in U.S. agriculture.14 One version

presumes that ideal conditions hold in capital markets: the markets in agriculture and the rest of the economy are perfectly integrated so that any differences in shortrun rates of return to capital and land are instantly arbitraged to

zero. In the second version, we assume recipients’ investment alternatives

are strictly limited to agricultural assets. Credit constraints, investment preferences, or restricted investment opportunities could contribute to such

segmentation. In fact, U.S. farm households hold diversified investment

portfolios – evidence that agricultural capital markets are not fully segmented

(USDA, 2003). The two scenarios we describe should be considered as

bracketing the possible outcomes of the decoupled payments. 

The models otherwise are identical in their specifications. Households are

presumed to hold identical preferences at all income levels for consumption

of goods and services. Household consumption and savings decisions

respond to changes in prices and returns. Assets are aggregated into three

broad categories—capital in agriculture, capital not in agriculture, and land.

The model is calibrated to represent 1997, while rates of growth in total

factor productivity, growth in the U.S. labor force, and selected other parameters are taken from other research for the baseline run. The model reproduces key outcomes observed for the actual economy in 1997-2001. 

We assume that decoupled payments, equal to $6.112 billion in 1997, are

made to farmers each year from 1997 on. Thus, our results suggest the

directional effects of direct payments rather than the exact magnitude. All

results are compared with the base, or the path of the economy without

direct or other payments to farmers.
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The Case of Integrated Capital Markets

This analysis presumes that investors allocate savings so as to arbitrage

away any differences in returns to the three assets (land, agricultural capital,

and capital in the manufacturing and service sectors). Effectively, the rate

of return to agricultural capital is maintained at the same rate as returns to

capital in the rest of the U.S. economy. Since household preferences are

assumed to be identical, consumption and investment behavior of the recipients of decoupled payments are exactly counterbalanced. As a result, when

the payments are not tied to production or prices, they have no effect on

production levels, even over time. 

However, since payments are linked to “program” acres, land values are

affected. The $6.1-billion annual payment, in the short run, causes land

values to exceed their base-level values by almost 9 percent (fig. 2-1). Land

values then taper off to about 8.3 percent above their longrun base value.

Many studies have documented that decoupled payments, even though they

aim to benefit farm households, have an important side effect—to raise land

values. For example, Goodwin et al. (2003a, 2003b) found that decoupled

payments have had small effects on land values, ranging from 2 to 6 percent

in the Northern Great Plains and Corn Belt regions. Bernard et al. (2001)

found larger effects in a study that included both coupled and decoupled

payments. Our analysis also finds higher land values.

These land value effects are due solely to decoupled payments. Competition

for land (and a right to the payment) causes renters to pay higher rates to

owners. If the land is sold, the buyer is willing to pay more if the payment

remains tied to the land. Of course, decoupled payments and the rise in

land values change recipients’ consumption patterns and level of assets. In

the short run, asset values of recipient households rise by about 2 percent

above their base values, due mostly to the rise in land values. Most of the

payments are spent on consumption; this proportion rises over time while

the proportion saved falls. Total consumption expenditures are about 0.8

Figure 2-1

Decoupled payments’ effects on land values are similar with 

segmented, nonsegmented capital markets

7.0

7.5

8.0

8.5

9.0

9.5

10.0

10.5

1 5 10 15 20 25 30 35 40 45 50

Years

Percent increase in cropland values relative to base 

Nonsegmented market 

Segmented market
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percent higher than expenditures in the absence of transfers (fig. 2-2). The

rise in the value of assets held by recipient households should also increase

their access to credit. If liquidity constraints are binding, then PFC

payments may not be decoupled in that sense. 

The Case of Segmented Capital Markets

In the second version of the model, we assume that capital markets in agriculture are segmented from those in the rest of the economy – farmers are

assumed able to invest payments only in agricultural assets. Within agriculture, and within the rest of the economy, rates of return are equalized, but

returns to agricultural assets are no longer perfectly arbitraged with nonagricultural assets (although they are in the long run.) 

With segmented markets, within the first 10 years of payments, the rate of

return to agricultural capital declines by a modest 0.1 percent (fig. 2-3) below

the capital rental rate observed in the base scenario. The effect on the returns

to capital outside agriculture and on the price index of goods is almost imperceptible. And, even though direct payments continue in equal amounts

throughout the period, agriculture’s return to capital slowly converges with

that of the rest of the economy. In other words, in spite of the presumed

differences between agriculture and the rest of the economy, in the long run,

direct payments do not distort the rate of return to capital in agriculture. 

Figure 2-4 shows why direct payments cause returns to agricultural capital

to decline. In early periods, farmers tend to allocate more of their payments

to investment in agricultural assets than in later periods. In the short run, the

amount of capital invested in agriculture rises to about 0.25 percent more of

the capital stock than would otherwise be accumulated (relative to the base).

As additional capital investments lead to diminishing returns to capital

stock, farmers save less and spend more of their decoupled payments on

final goods. In the long run, the amount of capital employed in agriculture is

equal to the amount that would be employed without transfer payments; in

Figure 2-2

Decoupled payments increase assets and consumption expenditures:

nonsegmented capital market

0

0.5

1.0

1.5

2.0

2.5

1 5 10 15 20 25 30 35 40 50

Years

Assets

Expenditures

45

Percent change relative to base
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other words, payments do not affect the longrun level of capital stock in the

sector. Nevertheless, the half-life of the adjustment is about 25 years

because the depreciation rate for buildings and structures is minor. The

effect on capital stocks in the rest of the economy is almost imperceptible. 

As farmers increase their levels of capital stock, more labor hours, relative

to the base, are also allocated to production (fig. 2-5). These hours come

from a combination of reduced leisure time and more hired labor.15 Decoupled payments encourage the employment of capital relative to labor (fig. 

2-6). This is because the assumed preference for investing in agriculture

causes the rate of return to capital to fall slightly relative to the change in

wages. The change in the wage-rental ratio encourages more substitution of

capital for labor relative to the base. In the long run, the wage-rental ratio

converges to the level expected in the absence of payments. 

Figure 2- 3

Decoupled payments lower on returns to capital in agriculture: 

the segmented capital markets case

-0.12

-0.10 

-0.08 

-0.06 

-0.04 

-0.02 

0

0.02 

1

 

5 10 15 20 25 30 35 40 45 50

Years

Price index

Wages

Returns to non agricultural capital

 Returns to agricultural capital

Percent change relative to base

Percent change in sector capital stock relative to base

Figure 2-4

Decoupled payments have small effects on the stock of capital

in agriculture: the segmented capital markets case

0.30

0.25

0.20

0.15

0.10

0.05

0

-0.05

1 5 10 15 20 25 30 35 40 45 50

Years

Manufacturing

Agriculture

Services

15 Since leisure is typically found to

be a normal good, although not modeled explicitly in this report, the combination of wealth and price effects

would likely leave the average level of

leisure consumed by farmers to be virtually unchanged. The slight increase

in agricultural labor in the segmented

market case relative to the base year,

comes from the hired labor market.

Nevertheless, in absolute terms, in all

of the analysis, there is an outmigration of labor from agriculture.
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The decline in agriculture’s rate of return to its capital stock also affects the

price of land. As agriculture becomes more capital-intensive and returns to

agricultural capital fall (shown in fig. 2-4), the returns to land rise. Land

prices in the first 5 years of the simulation rise to a greater extent than in the

case where capital markets are assumed to be nonsegmented, by roughly 1

percentage point in the short run (fig. 2-1). After 5 years, land values

become similar to those of a nonsegmented capital market. Effectively, with

decoupled payments, the segmented capital market speeds up agriculture’s

capital accumulation and convergence to its longrun equilibrium. 

Finally, do the resource allocation effects of decoupled payments affect

aggregate agricultural production? U.S. agricultural production rises by an

estimated 0.17 percent of its base value in the short run. In the long run,

output returns to approximately the levels that would prevail in the absence

Figure 2-5

Decoupled payments cause a small increase in agricultural

employment: the segmented capital markets case 

Percent change in sector labor demand relative to base

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

1 5 10 15 20 25 30 35 40 45 50

Years

Manufacturing

Agriculture

Services

Figure 2-6 

Decoupled payments cause small shortrun increase in agricultural

production and in use of capital relative to labor: the segmented 

capital markets case 

Percent change relative to base

0

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

1 5 10 15 20 25 30 35 40 45 50

Years

Ratio of capital to labor

Agricultural production
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of payments (fig. 2-7). The effect that prevails in the long run is the

elevated price of land (8.4 percent). 

Conclusions

Are decoupled payments to farmers likely to affect resource allocation in

agriculture? If not, decoupled payments can be thought of as an efficient

policy instrument to transfer resources from one segment of the population

to another, with minimal distortion of production or trade. Since the real

economy is obviously complicated and encumbered with imperfect markets,

this is a complex question. Our contribution lies in showing the circumstances under which payments have minimal market distortions. We also

consider the most stringent capital market imperfections – when recipients’

investment opportunities are restricted to agriculture – and show just how

distorting these payments might be.

Our economywide analysis finds that if agricultural capital markets are

perfectly integrated with capital markets in the rest of the economy and if

the taxed and recipients hold identical preferences for goods and services,

then the key effects of payments over time are to increase the value of land

by about 8 percent and, of course, to increase the wealth of program recipients and their expenditures on final goods. 

If we presume that farmers invest in agriculture that portion of decoupled

payments not spent on consumption, payments seem to affect resource allocation and production. Over the long run, recipient households respond to

declining rates of return to agricultural capital by increasing their consumption and lowering their savings rate until rates of return between agricultural

and nonagricultural assets are re-equilibrated. As a result, the small production increases in the short run, less than 0.2 percent, become negligible in the

long run. The only long-term effect of payments is to increase land values. 

Figure 2-7

Decoupled payments have small and declining effects on output but 

lasting effects on land values and land rental rates: the segmented case

7.6

7.8

8.0

8.2

8.4

8.6

8.8

9.0

9.2

9.4

9.6

1 5 10 15 20 25 30 35 40 45 50

Years

Percent change from base Percent change from base 

0

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

Land rent (left scale)

Agricultural production (right scale)
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Chapter 3

Decoupled and Coupled

Payments Alter Household

Labor Allocation

Mary Clare Ahearn, David Harrington, Robert Hoppe, and Penni Korb

U.S. agriculture is one of the most productive industries in the U.S.

economy and one of the most productive agricultural systems worldwide.

Labor used in U.S. agriculture, both hired and family labor, has declined in

absolute terms since 1948 (Ahearn et al., 1988). The long-term trend of less

farm labor (in favor of other production inputs) has enabled farm operators

and other household members to allocate more of their time to off-farm

jobs. Off-farm income has dominated cash earnings of most farm families

for over three decades and for those farm families, the pursuit of an off-farm

career may supersede the effect of farm policy (including decoupled

payments) on farm household decisions. As a result, off-farm labor conditions, as well as onfarm earnings and government payments, influence

households’ labor supply decisions. 

In this chapter, we examine the relationship between farm household labor

allocations and the decoupled and coupled commodity payments received

by households. We first describe a theoretical model of household labor

allocation that incorporates both on- and off-farm labor markets. We use the

model to derive predictions about the response of household labor supply to

coupled and decoupled payments. We then present descriptive statistics as

well as statistical results—that control for factors other than government

programs—to analyze farm household labor response to payments.

Theoretical Model of Household Labor Choices 

In the standard household labor allocation model, households are assumed

to maximize “utility” – the consumption of goods and leisure. Consumption

is limited by a household budget constraint based on labor and other sources

of income and a fixed amount of time for labor and leisure16 (see Singh et

al., 1986; Huffman, 1991). A key factor linking labor, leisure, and household income is the marginal return from working an additional hour, the

market wage rate. The wage rate is also the “opportunity cost” of allocating

an hour to leisure rather than to working. 

From this model, we can derive the demand for farm household labor in

farming, the demand for household leisure time, and the off-farm labor

supply of the farm household. The derivations depend on assumptions

about the decision process of the household and about the “completeness”

of labor markets. Complete labor markets imply that off-farm labor opportunities are available to farm households and that nonfamily farmworkers

are available for hire locally.

16 Individuals must allocate some

of their time to maintaining their

households. This time is considered

as part of leisure, the residual time

category. 
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The model also distinguishes between two types of income: earned labor

income and nonlabor income, such as an income transfer or dividends. The

household is predicted to respond to them differently. Earned labor income

can have two effects on the labor allocation decision. A wealth effect

occurs if an increase in wealth or income causes the household to want to

work less and enjoy more leisure. A substitution effect occurs if the household increases work hours in response to the higher marginal return to labor

and reduces leisure in response to its higher opportunity cost. Nonlabor

income has only a wealth effect, but no substitution effect, because it does

not change returns to labor. 

To make this model more useful for applied analysis, it can be extended to

incorporate household resource allocations among farm work, off-farm

work, and leisure time. And, whereas the household budget is assumed to

be fixed in the standard model, a farm household’s budget can be viewed as

dependent on farm production decisions, i.e., the budget is endogenous.

Taylor and Adelman (2003) call this a “farm profit” effect. 

Decoupled/Coupled Payments

and Total Work Hours

A decoupled payment is an income transfer; its amount does not vary with

changes in hours worked onfarm. A coupled payment is labor income that

varies with the amount of output, and hence the amount of labor input. So,

how would decoupled and coupled payments be expected to alter the allocation of farm households’ labor? And if farm households choose more

leisure time, would it be at the expense of farm or off-farm work?

Following are some model predictions:

 Workers will tend to decrease total hours worked in response to 

decoupled payments. If a farm household receives decoupled 

payments, the impact on labor hours worked is certain because 

there is no change in the hourly return from work. Household 

members will prefer to work less and enjoy more leisure as a 

result of the wealth effect. Conversely, if decoupled payments 

are removed, hours worked are likely to increase to compensate 

for reduced wealth. 

 The effect of coupled payments on total hours worked is ambiguous. Increased wage rates can cause a household member to want 

to work more and consume less leisure because each hour of work 

now brings a greater return (substitution effect). But it can also 

cause a household member to want to work less and consume more 

leisure if it has more income than before (wealth effect). The net 

effect will be determined by a household’s individual preferences 

and the magnitude of payments. Only by observing the behavior of 

households can we determine the impact of coupled payments on 

the allocation of time and labor.

 When more leisure time is demanded, farm households will tend 

to reduce their labor hours in the job with lower marginal returns.

If a farm household is not involved in off-farm work, fewer hours 

would be devoted to farming if more leisure time is demanded by 

the household as a result of increased income. For multiple-job holders
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who prefer more leisure time, theory predicts that a household will

decrease its work hours at the job with the lower marginal returns. So,

if farm work has a higher marginal return, a household desiring more

leisure would allocate fewer hours to off-farm work. However, a definitive prediction in such a case depends on assumptions about the household decision process and whether labor markets are complete.

Often, economists assume that households first make optimal farm production decisions and then decisions about consumption. Where increased

nonlabor income results in increased leisure, the assumption of making farm

decisions first dictates that farm households will work less at their off-farm

job in lieu of reducing farm work. However, it can alternatively be assumed

that households first allocate their labor to off-farm work and subsequently

allocate their remaining time to farm production (or make decisions simultaneously). Hence, the differing assumptions can result in differing theoretical predictions about which labor hours are reduced – farm, off-farm, or

both. Because the effect of government payments on leisure time is uncertain, the use of data to study actual decisions made by recipients is essential. 

Although the theoretical model predicts an adjustment away from labor hours

with a lower marginal return, data on farm and off-farm labor returns, relative to hours worked, do not support the expectation that farm households

closely align their labor allocation with their farm returns. All U.S. farm

households receiving payments allocated, on average, 60 percent of their

work hours to farming but derived only 20 percent of their income from the

farm (table 3-1). Only on very large farms (which represent 5 percent of all

farms) does the share of work hours on the farm correspond closely to the

share of earned income from farm sources. Obviously, considerations in

addition to net farm income enter into the time allocation decisions of farm

households. The allocation of family labor varies considerably by the lifecycle of the family and farm type. Other factors include capital gains

returns, tax management, farm succession planning, psychological rewards,

Table 3-1—Share of hours worked in farming (by operator and spouse

combined) and share of earned household income from farming for participating farms, by farm type, 2000

Participating farms' Farming share of 

share of: the household's:

Farm type Operator Value Government Earned Work

households of pro- payments income1 hours

duction

Percent

Limited-resource 2 --a --a --b 61

Retirement 8 1 1 --b 76

Residential 31 6 9 --b 29

Farm occupation-low sales 28 10 14 --b 70

Farm occupation-high sales 18 21 27 38 77

Large 8 23 26 64 80

Very large 5 40 22 82 83

All family farms 100 100 100 20 59

a = Less than 1 percent. b Negative farm income and positive total income (from both farm and earned off-farm sources).

Source: Agricultural Resource Management Survey, 2000.
17 Total payments in 1991 were

about the same level as in 1996 ($8.2

billion). In 1996, the $5.9 billion in

PFC payments were comparable to

$5.9 billion in coupled payments in

1991, and conservation payments were

relatively constant across the years.

By 1999 and 2000, PFC payments and

conservation payments were about the

same as in 1996, but loan deficiency

payments and emergency assistance

resulted in total payments of just

above $20 billion. 

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and expectations about future program eligibility (see box, “Factors Other

Than Current Income Affecting Farm Labor Choices”). Similarly in considering off-farm work, health insurance benefits are often a factor in a worker’s

off-farm employment choices (Jensen and Salant, 1986).

Do Farm Households Adjust Labor in 

Response to Government Payments?

Having reviewed some commonly held theories regarding income and labor,

we now examine farm household responses to coupled and decoupled

payments. USDA’s Agricultural Resource Management Survey tracked

labor supply responses to coupled payments as well as PFC payments

during the 1990s. These data can be used to demonstrate the impact of

government payments on labor hours on and off the farm during periods

(1991, 1996, and 2000) when different types of policies were in place (see

Westcott and Young, chapter 1).17 This descriptive analysis is supplemented

by three separate statistical analyses that isolate the effects of coupled and

PFC payments on labor allocations. 

 Households’ onfarm hours changed little during the 1990s. The 

level of payments, as well as how they were distributed, varied in 

many ways across the time period, but farm operators and spouses

receiving commodity payments maintained consistent farm work schedules during 1991-2000. ARMS data show very little difference for either

operators or spouses in farm time allocations between 1991 and 2000

(fig. 3-1). 

Descriptive analysis is a first step in determining the impacts of payments

on labor allocations, but more advanced statistical analysis can control for

other variables that may affect these decisions. Using 2001 data, El-Osta et

al. (2004) analyzed the separate effects of three payments (PFC, disaster,

and coupled loan deficiency payments) on farm, off-farm, and total hours

worked for a sample of operators who received all three types of

0

10

20

30

40

50

1991 1996 2000

Operator Spouse

Figure 3-1

Average farm hours worked per week by program commodity

participants 

Source: 1991 Farm Costs and Returns Survey; 1996 and 2000 Agricultural Resource

Management Survey.

Note: Differences across years are not statistically significant

Hours
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Factors Other Than Current Income Affecting

Farm Labor Choices

Capital gains. Some farmers may own and operate their farms in anticipation

of capital gains from increasing land values. Other farmers may be largely

retired but maintain the farm to minimize capital gains taxes from selling or

transferring the farm prior to death (Harrington, 1983; Davenport et al., 1982). 

Current income tax management. Farm losses can be used to offset income

tax liability on nonfarm income. This tax advantage could outweigh the

incentive to leave farming for farms that have net income losses (Davenport et

al., 1982).

Farm succession. Farmers intending to pass the farm onto future generations

may place a value on this option in addition to current-year returns. Farmers

surveyed in 1988 were twice as likely to state they intend to bequeath their

farm as to sell it (Whittaker and Ahearn, 1991). 

Psychological rewards. Farmers likely get satisfaction from farming beyond

monetary returns. This presumed psychological dividend is often offered as

an explanation for why farmers choose to stay on the farm despite low, and

even negative, profits. 

Expectations that current farm operation may affect future program eligibility.

Farmland owners may expect that future rules of eligibility to receive payments

may be conditional on how they operate their farms in the current period. Some

farmers may perceive that producing traditional program commodities, even if

the payments are decoupled from current year production decisions, may maintain or increase their eligibility for future payments. A 2002 ARMS survey of

farmers at the close of the FAIR Act found a great deal of diversity in their

expectations about government support under a future farm bill. 

Farmer expectations at the end of the FAIR Act about prices

and government support under a future farm bill.

Survey question Share responding1

Do you expect to receive about the same level of 

government support for this operation during Percent

the next 4 years?

Yes 41

Expect more 9

Expect less 25

Unsure 25

Do you expect government support regardless

of price developments during the next 4 years?

Yes 65

No 35

During the next 4 years, do you expect the general 

trend in the prices of the commodities you produce to be—

About the same 65

Decline 16

Increase 19

1 Excludes respondents who refused. Refusals varied from 2-6 percent of respondents.
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payments.18 Moreover, they provided two treatments for payments, one that

treated the decision to participate in programs as endogenous and the other

as exogenous. All government payments combined had a positive impact on

the hours that operators worked on the farm. When payments were modeled

separately, PFC payments still had a weakly significant and small positive

impact on operator’s farm hours worked. For the average recipient who

received just over $9,000 per year in 2001, the estimates suggest they might

increase total work hours by about 1 workweek per year. 

In another study using pooled 1998-2000 data, Dewbre and Mishra (2002)

found that PFC payments did not have a statistically significant impact on

farm hours worked for those payment recipients who allocated hours to both

farm and off-farm work. Their analysis is not directly comparable to ElOsta et al. in research design. Dewbre and Mishra rexcluded retirement and

residential/lifestyle farms and controlled only for farm size and receipt of

other nonlabor income. (Dewbre and Mishra did not report an analysis of

the impact of PFC payments on farm labor for the group that did not work

off the farm.)

The modest labor response to decoupled payments reported by Dewbre and

Mishra is consistent with similar findings in nonfarm labor markets. For

example, the labor allocation model has been applied to labor supply decisions of lottery winners. Imbens et al. (2001) found that lottery winners

who won an average of $80,000 per year (for 20 consecutive years) reduced

their labor supply between 4.1 and 9.3 hours per week (from a base of 37.5

hours per week). However, small lottery winners, receiving annual

payments of $15,000 or less, did not significantly alter their supply of labor.

While the conditions in which PFC payments are given differ in important

ways from lottery winnings, the example illustrates the relatively minor

impacts on labor decisions to be expected from small, unconditional income

transfers. Since lottery winners reduce work and increase leisure, while

PFC recipients increase work on the farm, there are likely benefits of farm

work or aspects of farm labor markets that differ from nonfarm.

 Work off the farm increased during the 1990s; still, coupled and

decoupled payments helped reduce off-farm work hours. ARMS data

show a pronounced increase in off-farm work in the latter half of the

1990s (fig. 3-2). To augment this simple comparison of hours worked in

different years, we describe estimates of a labor supply function that

controls for dynamic variables that also may affect off-farm work hours,

such as local labor conditions.

El-Osta et al. (2004), with an off-farm labor supply model using 2001 data,

found that all payments combined—and PFC payments individually—had a

negative impact on off-farm work. Ahearn et al. (2002) reached a similar

conclusion using a model with operators who participated in government

programs during 3 years in the 1990s. Ahearn et al. found no difference in

the effect of payments on off-farm labor supply between 1991 and 1996

when coupled payments declined and PFC payments were introduced.

Payments had a weaker negative effect on off-farm labor supply in 1999,

when payments were a mixture of coupled and PFC payments. The smaller

impact in 1999 was due to the significantly greater transfers in that year,

rather than a difference in the impact of the different payment types. These

18 Specifying the sample to be only

those farms that received all three

types of payments provided the most

stringent statistical test. When the

analysis was performed with a larger

sample for farms that received any of

the three categories of payments, the

statistical significance of the relationships between payments and hours was

even stronger.
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analyses reinforce previous studies showing that coupled government

payments decrease the likelihood of farm operators working off the farm

(El-Osta and Ahearn, 1996; Mishra and Goodwin, 1997).

Given the consistency across studies, we conclude that the statistically

significant increases in off-farm work of farm operators from 1996 to 2000

may have been even greater had payments not been so high. This conclusion holds for both coupled and decoupled payments.19 

 Operators of large farms providing the bulk of output continue to work

more than 40 hours per week on their farm. Of those farms that

receive payments, more than 80 percent of production comes from the

largest 30 percent of farms. Almost all of the operators of these farms

are only employed on their farms. For example, in 2000, operators of

the largest farms worked about 60 hours per week on their farms, and

very little, on average, off their farms. This is consistent with the labor

allocations of large farms in years prior to the 1996 Act. 

Conclusions

The impact of decoupled payments—versus coupled payments—on farm

labor and agricultural supply, compared to a scenario with no program

payments, is much more complex than can be portrayed by the simple,

shortrun models of labor allocation presented here. Still, we found that both

decoupled and coupled payments help to decrease off-farm work hours. We

also found that the introduction of lump-sum payments after the 1996 Farm

Act seems to have encouraged farm households to devote slightly more

hours to farm work. 

The labor allocations of farm families are intertwined with their goals and

decisions about managing farming operations now and into the future. If

farm families adjust their work little or not at all in response to decoupled

payments, it may be due to considerations such as expectations about future

0

10

20

30

40

50

1991 1996 2000

Operator Spouse

Figure 3-2

Average off-farm hours worked per week by program participants 

Source: 1991 Farm Costs and Returns Survey; 1996 and 2000 Agricultural Resource

Management Surveys. 

Note: For the operator and spouse, the 1991-96 change was not statistically significant;

the 1996-2000 change was statistically significant.

Hours

19 This is consistent with Dewbre

and Mishra (2002) who found, for

households that worked on and off

farm, PFC payments did not affect

their farm hours but did reduce the

total hours they worked. That is, PFC

payments likely induced farm households to work less off their farm in

the late 1990s than they would have

otherwise.
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program eligibility. In addition, farm families may be limited in how they

apportion their labor: farm work is highly seasonal and off-farm jobs are

often inflexible in their time requirements. Life-cycle considerations influence labor choices, too. Perhaps most important, large commercial farms

provide the bulk of U.S. output. The introduction of decoupled payments did

little to alter the allocation of labor on these farms, where operators typically

devote 60 hours per week (full-time) to farm work. 

What is the impact on overall farm production? The potential effects of

labor shifts on supply depend in part on the amount of labor used in production, and this varies across farm sectors (see box, “Family and Hired Labor

on Farms Receiving Government Payments”). Also, if decoupled payments

led to changes in hours worked by farm households, hired labor could adjust

to maintain production levels—if rural labor markets are functioning well.

Other material inputs may also substitute for labor, offsetting any impacts on

production levels from changes in household work hours. Production effects,

therefore, can be expected to be proportionately smaller than any changes in

labor inputs in response to coupled or decoupled payments. 

Family and Hired Labor on Farms Receiving

Government Payments

Farm operators and their families supply, in aggregate, about two-thirds of the

labor hours worked on U.S. farms, but labor shares vary considerably by

participation in government programs and commodity specialization. 

Family and hired labor FTE by specialization and program

participation, 2000 

0

100

200

300

400

500

600

Family FTE Hired FTE

1,000 FTE (2,000 hours per year)

P NP P NP P NP P NP P NP P NP P NP P NP 

P = Payments NP = No Payments

 Program Other High- Beef Hogs Dairy Poultry Other

commodities field value livestock

 crops crops

Note: Farm specialization = commodity accounting for at least 50 percent of the farm’s 

value of production.
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Chapter 4

Decoupled Payments

and Farmers’ Production

Decisions Under Risk

Shiva S. Makki, Agapi Somwaru, and Monte Vandeveer

When decoupled payments were enacted in 1996, some identified ways that

decoupled payments might indirectly influence production decisions

(Hennessy, 1998; Tielu and Roberts, 1998; and Antón, 2000). Such indirect

links include easing farmers’ credit constraints, raising farmers’ wealth and

thus their tolerance for risk, instilling expectations of future payments, and

affecting farmers’ labor-leisure decisions. In this chapter, we are concerned

primarily with farmers’ wealth, risk attitudes, and production decisions.

Decoupled payments increase producers’ wealth, and this enhanced wealth,

in turn, could increase their tolerance to risk. This willingness to assume

more risk could lead to shifts toward more acreage for riskier crops or

changes in use of risk-reducing (or increasing) inputs, leading to changes in

total plantings. If such decisions at the farm level are significant, aggregate

U.S. production could change, which could affect world markets.

Decoupled Payments and U. S. Farmer 

Income and Wealth

The size of decoupled payments since 1996 raises the question of whether

they might still induce additional production indirectly. Specifically, do

farmers’ income and wealth increase to the point where they become willing

to assume more risk and alter their production decisions?20

Like other government payments, decoupled payments represent an immediate supplement to farm household income and can be used for current

needs, including paying for farm expenses and meeting family living costs.

Decoupled payments also affect farm household wealth – the value of assets

less liabilities – when they are used for purposes such as reducing debt,

investing in the farm, or nonfarm investments. For landowners, decoupled

payments also influence wealth by increasing farmland values. The value of

agricultural land should largely reflect its current and future earnings potential. Because PFCs become a part of returns to farmland, they become capitalized into its price, changing the wealth of land-owners. Using a dynamic

model, Roe et al. (chapter 2) estimated that PFC payments increased aggregate land values by about 8 percent. Goodwin et al. (2003a, 2003b) found

that decoupled payments have increased land values, ranging anywhere

from 2 to 6 percent in the Northern Great Plains and Corn Belt regions.

Barnard et al. (2001) found that the gap between aggregate land values with

and without government payments was about 13 percent during 1990-97,

increasing to about 25 percent during 1998-2001 when payments included

MLA and marketing loan benefits in addition to PFC payments. Note that

farmers who buy land after the payments have already been capitalized into

20 The remaining discussion will

focus on payments having characteristics similar to the PFC payments and

not the Market Loss Assistance emergency payments made in 1998 to 2001.

Also, we do not address the issue of

updating and expectations related to

the direct payments created under the

2002 FSRI Act.
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the land price do not benefit, since the capitalized value of the payments

was, in effect, paid to the previous owner.

However, not all PFC payments benefit farm operators since not all farmland is owned by operators. Tenant farmers may pass these payments

through to landlords through higher farmland rental payments and as a

result do not benefit from the entire direct payment or its capitalization into

land values. An ERS report indicates that about 60 percent of the acreage

enrolled in the PFC program was rented in 1996 (USDA, ERS, 2003).

Roberts (chapter 6) reports a one-third pass-through rate from farmers to

landowners on cash-rented land in 1997. These values suggest nonfarm

landlords received at least 20 percent of the payments (60 percent of land

times 33 percent pass-through rate). After adjusting for payment passthrough, Roberts estimates that the total (coupled plus decoupled) payments

in 1999 represented an average of 3 percent of overall farm household

wealth for households receiving payments. Decoupled payments in 1999

accounted for 24 percent of total direct payments to these farm households. 

Risk and Risk Management in Farming

Does greater wealth make producers more willing to accept risk in their

production decisions? Risk is a fundamental component of the farm business and farm household, and it influences production choices and farm

management decisions. The many sources of risk in agriculture range from

price and yield risk to income/financial risk to personal injury/health risk

(Harwood et al., 1999). Farmers who have borrowed money are at risk of

default if income falls short. Because prices, yield, and other outcomes are

contingent on markets and weather, the consequences of production decisions are not known until long after those decisions are made. 

Several surveys have asked farmers to rank the risks they confront. The

1996 ARMS data indicate that producers of field crops—such as wheat,

corn, soybean, tobacco, and cotton—were concerned more about yield and

price variability than about other categories of risk, while producers of

vegetables, greenhouse crops, cattle, and poultry were most concerned about

changes in government laws and regulations. Across all farms, ARMS data

clearly indicate that producers are most concerned about changes in government laws and regulations (institutional risk), variability in crop yields or

livestock output (production risk), and uncertainty in commodity prices

(market risk) (Harwood et al., 1999). Concerns about risk also vary across

types of producers and by farm type and size groupings (Musser and

Patrick, 2002).

Farmers generally use a combination of risk management tools to mitigate

risk. Risk management strategies include diversification, production

contracting, maintaining liquid assets, and crop insurance. In crop production, farmers may reduce risk by using more drought-tolerant varieties,

varying tillage practices, or irrigating if possible. To transfer the risk of

falling crop prices, farmers may use forward contracting, futures, or options.

Government payments also provide support during periods of low

commodity prices and in the event of natural disasters.
21 Basis risk is the risk that the producer’s local cash price does not track

perfectly with the price of the hedging

instrument, such as a futures contract.

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The farming household has additional means for coping with risk. Off-farm

employment is a significant source of income for many farm households,

and this source is usually more stable than farm income. Most farmers have

some life insurance, health insurance, and insurance on major property such

as their home, automobiles, and farm equipment. Investments can be

shifted between farming and nonfarming uses. Household consumption

may be tightened if income drops.

Even so, all of these strategies do not completely mitigate the risk of low

incomes. For example, farmers with crop insurance must still absorb the

deductibles, and price-hedging strategies often maintain some basis risk.21

Government emergency payments usually do not cover all yield shortfalls.

Even off-farm employment may be uncertain. So farming decisions will

always be made in the face of at least some risk. Consequently, understanding farmers’ attitudes toward risk is critical to ascertain how they may

make use of decoupled payments.

Farmers’ Attitudes Toward Risk

Farmers allocate their assets and engage in production activities to maximize the utility of their income or wealth, rather than to simply maximize

expected profits (see box, “Measures of Risk Aversion”). This implies that

farmers use decision rules that account for not only expected profits but also

the risks associated with their production and management decisions. When

a decisionmaker prefers a particular amount of income generated with

certainty from an economic activity or adoption of technology over an alternative that, on average, provides the same expected return but also has

uncertainty in its outcome, the individual is said to be risk averse. A riskaverse person still prefers more income to less, but would be willing to give

up some income in exchange for a more stable stream, while a risk-neutral

person is interested only in expected or average profits and would not be

dissuaded by any uncertainties in prices or output. The trade-off may

depend on the level of wealth.

An individual’s preference now and into the future for a certain outcome

over an uncertain one with equal expected value can be measured by the

risk aversion coefficient. Empirical studies have generally found evidence

of risk aversion for most U.S. farmers, but with a wide range of risk attitudes (table 4-1). These studies date back 30 years and have examined the

risk attitudes of many groups of farmers by using a variety of approaches to

measure these attitudes.22

Some studies have examined whether the agricultural sector as a whole

exhibits risk-averse behavior. In studies of the U.S. corn and soybean

sectors, Chavas and Holt (1990 and 1996) found evidence of risk aversion

as well as decreasing aversion to risk as wealth increases. Lence (2000)

found mild risk aversion for the U.S. agricultural sector but also found that

relative risk aversion among U.S. farmers appeared to decrease over time

since the mid-1930s. 

Just and Pope (2002) summarized research on risk preferences as generally

supporting the “stylized fact” that utility (see box, “Measures of Risk Aversion”) is increasing at a decreasing rate with wealth and profit; that is,

22 Young (1979) categorizes these

methods as direct elicitation of utility

functions (DEU), experimental methods (EM), and observed economic

behavior (OEB). The DEU process

consists of interviewing farmers to

determine their preferences among

risky alternatives for hypothetical gains

and losses. The EM process consists

of presenting farmers with real risky

prospects (that is, significant monetary

payoffs instead of simply hypothetical

choices) and observing their decisions.

The OEB method consists of estimating risk attitude parameters reflected in

observed farming decisions, such as

input levels and crop acreage mix.
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Table 4-1--Risk attitudes of U.S. farmers: Results of empirical studies

Source Description of Measurement method1 Sample size Risk attitudes Effect of wealth

producers

Bard and Barry Illinois farmers DEU-interval method 81 farmers >50 percent averse Not evaluated

Brink and McCarl Midwest grain OEB-compared profit 38 farmers 66 percent averse, Not evaluated

farmers max. vs. utility max. in 34 percent neutral,

QP model 0 percent loving

Chavas and U.S. corn and OEB-model based on Aggregate-used Averse Decreases

Holt, 1990 soybean sectors acreage allocations national data aversion

Chavas and U.S. corn and OEB - model based on Aggregate-used Averse Decreases

Holt, 1996 soybean sectors acreage allocation national data aversion

Collins, Musser, Oregon grass DEU-estimated utility 37 farmers 16-32 percent averse Not evaluated

and Mason seed growers functions 38-52 percent 

neutral, 30-32 

percent loving

Halter and Oregon grass DEU-estimated utility 44 farmers About equal Not evaluated

Mason seed growers functions across averse, 

neutral, loving

Hildreth and Minnesota cattle DEU-estimated 13 farmers 85 percent to 8 Generally

Knowles producers various utility functions percent averse, decreases

varies by aversion

functional form;

King and Eastern Colorado DEU - interval 10 farmers 30 percent No clear

Oamek wheat farmers approach averse, relationship

70 percent mixed

Lence U.S. agricultural OEB-model based Aggregate-used Averse Not evaluated

sector on asset allocations national data

Lin, Dean, California crop OEB-compared utility 6 farmers 50 percent Not evaluated

and Moore farmers max and profit max averse,

33 percent neutral,

17 percent mixed

Love and Iowa corn and OEB-estimated using 264 farmers in 3 Averse for all No change

Buccola soybean farmers FOC for input choices counties-data 3 counties (imposed by

in utility max model aggregated by functional form)

county

Ramaratnam, Texas grain DEU-estimated various 26 farmers 100 percent to Varies by funcRister, Bessler, sorghum farmers utility functions 73 percent averse, tional form

and Novak varies by functional

form;

Saha, Shumway, Kansas wheat OEB-estimated using 15 farmers Averse Decreases

and Talpaz farmers FOC for input choices (observations aversion

in utility max model aggregated)

Schurle and Kansas crop and DEU-interval method 90 farmers 80 percent Not evaluated

Tierney livestock farmers averse,

2 percent neutral,

18 percent loving 

Tauer New York dairy DEU - interval method 72 farmers 34 percent averse, Group test:

farmers 39 percent neutral, decreases

26 percent loving aversion

Thomas Kansas crop and DEU - interval method 30 farmers 20 percent averse, Generally 

livestock farmers 13 percent loving, decreases

67 percent mixed aversion

Wilson and Minnesota swine DEU - interval method 45 farmers 42 percent averse, 33 percent decreases, 

Eidman producers 36 percent neutral, 21 percent constant,

22 percent loving 18 percent increases,

28 percent mixed

1DEU = direct elicitation of utility, OEB = observed economic behavior, FOC = first order conditions, and QP = quadratic programming.
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Measures of Risk Aversion

Economists have theorized that individuals use decision rules that account for not only expected profits but also the risks

associated with their management decisions. That is, individuals allocate their assets and engage in production activities

that maximize the “utility” of their income or wealth, rather than simply maximize expected profits. “Utility” in this sense

accounts not only for the mean or average level of income or wealth but also for its variability or riskiness.

A decisionmaker is said to be “risk averse” when he or she prefers a particular amount of income received with certainty

over an alternative that, on average, provides the same expected return but also has uncertainty in its outcomes. Put

another way, a risk-averse person still prefers more income to less, but prefers less variability over greater variability. A

more risk-averse person is willing to accept a smaller income with certainty, relative to the expected value of the risky

prospect. A person who cares only about expected profit and is indifferent to its variability is said to be “risk neutral,”

while a person who prefers more variability for a given level of expected profits is said to be “risk loving.”

Risk aversion is indicated by a utility function that shows decreasing marginal utility as the level of income or wealth (w) is

increased. Indifference to risk is represented by a linear utility function. More formally, risk attitude is defined by the second

derivative of the utility function: U′′(w) < 0 implies risk aversion, U′′(w) = 0 implies risk indifference or neutrality, and

U′′(w) > 0 implies risk preference.

An individual’s preference for a certain outcome over an uncertain outcome with equal expected value is measured by the

risk aversion coefficient, and this measure is suitable for comparisons across individuals or comparisons across income or

wealth levels for a single decisionmaker.

The degree of risk aversion is measured by coefficient of absolute risk aversion, coefficient of relative risk aversion, and

coefficient of partial (relative) risk aversion. The “coefficient of absolute risk aversion” is defined as: ρa = - U′′(w)/

U′′(w); while the “coefficient of relative risk aversion” is defined as: ρr = - w U′′(w)/U′′(w). A third measure of risk aversion is the “coefficient of partial (relative) risk aversion,” defined as: ρp = -x U′′(x)/U′′(x), where x is gain or loss or operating income. Partial risk aversion is the same as relative risk aversion, except that it is defined in terms of loss or gain,

rather than wealth (Robison and Barry, 1987; Newberry and Stiglitz, 1981).

Constant Absolute Risk Aversion (CARA) implies that the preferred option in a risky choice situation is unaffected by

the addition or subtraction of a constant amount to all payoffs. In other words, a person whose aversion to a particular

level of risk is not affected by their level of wealth is said to display CARA. A negative exponential utility function such

as U = 1-exp(-cW) exhibits CARA property. ρa = c for this utility function. CARA is not a desirable property because it

fails to represent rational decisionmaking. Most empirical studies in agricultural economics reject the assumption of

CARA (Pope and Just, 1991; Chavas and Holt, 1996).

Decreasing Absolute Risk Aversion (DARA) implies that an individual becomes more willing to accept a particular risk

as his or her wealth increases. A log utility function such as U = ln(w) exhibits DARA property. ρa = 1/w, implying that

an individual becomes less risk averse as his or her wealth increases. Chavas and Holt, 1996 and Saha et al. 1994, found

agricultural decision maker preferences to be consistent with DARA.

Economists have theorized that most people probably become less averse to a particular level of risk as their wealth

increases and, in this case, are said to display DARA. In a sense, a person with a million dollars would be less averse to a

gamble with some probability of losing $100 than if this person had only $2,000 to start with.

Increasing Absolute Risk Aversion (IARA) implies that an individual is less willing to accept a particular level of risk as

his or her wealth increases. A quadratic utility function such as: U = w - bw2 exhibits IARA property. ρa = 2b/(1-2bw)2

suggests that an individual becomes more risk averse as his or her wealth increases. Since IARA implies rarely observed

response to risk, quadratic utility function is not generally assumed in the literature.

Constant Relative Risk Aversion (CRRA) implies that the preferred option among a set of risky alternatives would not

be changed if all payoffs were multiplied by a constant amount. That is, an individual will have constant aversion to a

proportional loss of wealth even though the absolute loss increases. A special form of the power utility function such as U

= {1/(1-r)}W(1-r) exhibits CRRA property. ρr = r for this utility function. CRRA is implicit in many risk analyses in

which calculations are on a per-acre basis. CRRA is inappropriate for risk analysis in agriculture because farmers with

different farm sizes are known to react differently to risky alternatives.

In sum, with CRRA, a person feels the same about losing 10 percent of $100 and losing 10 percent of $1,000. With

increasing RRA (IRRA), a person is more averse to losing 10 percent of $1,000 than to losing 10 percent of $100, while

with decreasing RRA (DRRA) a person is more averse to losing 10 percent of $100 than to losing 10 percent of $1,000.
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producers are generally risk averse. But they also found decreasing absolute

risk aversion to be common among producers, implying that as producers

become wealthier over time they have more tolerance for risk. Although

farmers who receive PFC payments likely display varying attitudes toward

risk, it is certainly plausible that some such farmers are willing to assume

more risk.

Decoupled Payments and Production Decisions

Decoupled payments may influence farmers’ production decisions if

changes in wealth alter their attitude toward risk. A farmer’s level of risk

aversion may affect production decisions and management choices in

several ways. The most obvious is the mix of farm outputs. Like an

investor trying to balance risk and returns in a securities portfolio, a

producer may adjust the acreage mix of crops to reflect some tradeoff

between risk and returns. Compared with a more risk-averse farmer, a less

risk-averse farmer would plant more land to a riskier crop or plant on

marginal land if it enabled greater returns (Hardaker et al.).

Aversion to risk may also affect total output and input use. A more riskaverse producer, who dislikes income variability, may prefer slightly lower

output and expected returns if variability of returns also declines (Sandmo,

1971). So if decoupled payments raise farmers’ wealth and their tolerance

for risk, they may take on more risk in their production choices in pursuit of

higher returns. Risk aversion could also affect input decisions to the extent

that the level of input use affects output variability. Other things equal, a

risk-averse producer would prefer to use less of an input (such as fertilizers

for corn production) that increases output variability, compared to input use

for expected profit maximization (MacMinn and Holtman, 1983).

As mentioned, some have argued that decoupled payments encourage

producers to increase their production and alter cropping patterns, with

unintended aggregate effects. Many theoretical studies have described these

possible links. For example, Tielu and Roberts (1998) examined how

decoupled payments may boost production by increasing farm investment

(increasing wealth and lowering risk), reducing farm exits (by raising land

values), and increasing output in the long run (by creating expectations of

future payments). They argued that the wealth and risk effects of decoupled

payments on production are likely minimal. 

Only a few empirical studies have examined the actual magnitude of such

effects on crop production; these are simulations of the payments that

suggest that decoupled payments have little effect on U.S. acreage allocation

and production. Young and Westcott (2000) applied the acreage elasticities

with respect to wealth from Chavas and Holt (1990) to U.S. crops receiving

PFC payments. Because PFC payments are small (3 percent of total farm

wealth) and because the impact of wealth on acreage is also small, Young

and Westcott conclude that acreage shifts from PFC payments would be

minimal.23 Assuming that farmers receive the full value of PFC payments,

the estimated acreage shift for the seven crops covered by PFC payments

would range from 180,000 to 570,000 acres annually, on a base of about

23 This elasticity shows the percentage change in crop acreage in response

to a 1-percent change in farm wealth.
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180 million acres. Accounting for the pass-through of PFC payments to

nonfarming landlords would reduce this effect even further.

Burfisher et al. (2000) used a computable general equilibrium model of the

United States, Canada, and Mexico to show the effects of decoupled

payments on agricultural production, prices, and trade. They incorporated

risk premiums reflecting the variability of net returns for four major crops

into their model. Here, risk premiums work like a tax or added cost,

resulting in lower overall production. By reducing risk premiums, decoupled payments lead to higher production. Their model suggests that a 50-

percent increase in decoupled payments would boost U.S. crop production

slightly, ranging from 0.5 percent for wheat to 1.1 percent for oilseeds.

Burfisher et al. used a relative risk aversion coefficient of 2.0 for the U.S.;

using Lence’s (2000) value of 1.13 would reduce the risk premiums by

roughly half, resulting in an even smaller acreage shift.

Conclusions

The effects of decoupled payments have become prominent as governments

consider how to fulfill their WTO obligations to limit payments that influence production, prices, and trade. The notion that decoupled payments

might influence production through “risk effects” presumes that such

payments increase farmers’ income and wealth such that they become less

risk averse. This change in attitude could then be manifested through

changes in input use, a new output mix, and changes in overall production. 

The effects of payments on risk attitudes and production are likely small for

several reasons. While many farmers are likely to alter their response to

risk as they become wealthier, decoupled payments are small compared with

participating farmers’ net wealth—after adjusting for pass-through to

nonfarming landowners. More important, while empirical studies of

farmers’ risk attitudes indicate some evidence of risk aversion, producers do

not respond to risk solely through adjustments to production or inputs.

Surveys find that producers already use various tools—such as insurance,

hedging, and management strategies—to mitigate risks. And, farm households can respond to changes in their risk attitudes with adjustments

throughout their portfolio, such as off-farm employment and investing in

nonfarm real estate or financial assets. 
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Chapter 5

Decoupled Payments to

Farmers, Capital Markets, 

and Supply Effects

Robert N. Collender and Mitchell Morehart

Are there distortions in farm capital markets that prevent U.S. farmers from

making profitable investments? In other words, is U.S. farm production

constrained by imperfect access to capital? If so, decoupled payments could

mitigate these capital market imperfections both by increasing internal funds

and by expanding access to market credit. This, in turn, could induce more

onfarm investment and production. On the other hand, if farmers are not

forgoing profitable capital investments, then decoupled payments will not

substantially change production through this mechanism.

Capital Markets’ Role in Agricultural

Production

Understanding the linkages between capital markets and agricultural

production is essential to understanding whether decoupled payments are

likely to induce increases in farm investment and production. However,

before considering the relationship between capital markets and production,

it is useful to distinguish between real and financial capital. Real farm

capital consists of physical assets such as buildings, machinery/equipment,

and breeding stock. The term “real” capital is usually reserved for assets

whose usefulness extends over several production cycles. (Although land—

see chapter 6—is a physical asset used over multiple production cycles, by

convention it is excluded from real capital.) Financial capital represents the

means by which ownership or control of real assets is acquired, and ownership can be financed in two ways. Equity capital represents a direct ownership claim financed through the assets of the owner, while debt capital

represents an ownership claim financed by a lender. Leases are another

important source of financial capital in U.S. agriculture. Leases and

contract production arrangements do not represent an ownership claim but a

right to use assets under specified conditions. 

The importance and composition of physical assets vary substantially

across farm types and regions, as shown by the 2000 ARMS data (table 5-

1). Physical assets make up 80 percent of the value of total assets on

commercial farms, versus 95 percent for rural residence farms.24 Land is

usually the dominant physical asset, except for poultry where farm buildings account for 41 percent of total assets. Farm equipment is a strong

contributor to total assets for cash grain and hog operations. The composition of assets also varies greatly by region. Land ranges from 40 percent

of total assets in the Northern Crescent to 60 percent in the Basin and

Range region.25

24 The farm typology groups farms

into three groups: commercial farms

(all farms with greater than $250,000

in sales), intermediate farms (operator’s primary occupation is farming,

with sales less than $250,000 per year)

and rural residence farms (operator is

retired, their main occupation is not

farming, or has limited economic

resources).

25 Definitions of ERS regions are

described at: www.ers.usda.gov/

publications/aib760.
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Farmers’ aggregate access to real capital can influence the supply of agricultural products to the market. Special-purpose buildings, breeding stock, and

machinery and equipment are all costly and critical to agricultural production. Such physical assets may enable farmers to adopt new production

technologies that further enhance productivity. 

Farmers’ access to financial capital can directly affect their access to real

capital. Without efficient financial capital markets, farmers might delay

adopting more efficient technologies as they become available. Agricultural

capital markets also allow farmers to pursue profitable investment opportunities without having to save the necessary funds or sacrifice their own

current standard of living. As such, financial markets enable the movement

of purchasing power and productive assets to those who can use them most

profitably. This accelerates efficiency gains in agricultural production and

farm management, and thus improves overall agricultural productivity.

Table 5-1—Importance and composition of physical assets in total farm business assets by farm type and

region

Share in total physical assets

Total

Item Land Operator's Other Farm Breeding physical

dwelling buildings equipment animals assets

Percent

All farms 48 13 13 11 4 89

Commodity program participants 49 9 10 15 5 88

Nonparticipants 47 17 15 8 4 91

Farm typology (collapsed):

Commercial farms 43 3 16 13 5 80

Intermediate farms 49 13 11 12 4 89

Rural residence farms 50 22 11 9 3 95

Production specialty:

Cash grain 50 9 7 18 1 85

General field crops 53 15 10 11 1 90

Fruits, vegetables, and nursery crops 39 7 26 8 0 80

Beef cattle 54 15 9 8 6 92

Hogs 34 11 22 14 3 84

Poultry 25 13 41 12 2 93

Dairy 35 6 17 13 18 89

General livestock 42 27 12 8 4 93

Resource region:

Heartland 48 12 10 14 2 86

Northern Crescent 40 18 17 12 5 92

Northern Great Plains 50 6 7 138 84

Prairie Gateway 51 12 9 12 5 89

Eastern Uplands 48 18 12 11 5 94

Southern Seaboard 50 15 13 10 3 91

Fruitful Rim 44 12 18 7 3 84

Basin and Range 60 9 9 7 5 90

Mississippi Portal 50 15 7 18 3 93

Source: 2000 Agricultural Resource Management Survey, USDA.
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Decoupled Payments in an

Imperfect Capital Market 

Most farmers do not have access to outside equity investors, and so must

rely on their own financial resources or on leases, contract arrangements, or

borrowed funds for liquidity and capital investment. This reliance is essential to any potential link between decoupled payments and increased production through imperfect capital markets. 

If farmers are operating to maximize profits and face efficient capital

markets, farm investment decisions are based on a comparison of the

expected rates of return from onfarm and off-farm investments. Farm

investment occurs until expected returns on additional investments are no

longer as great as those available from off-farm opportunities. Lump-sum

decoupled payments do not directly affect either onfarm or off-farm rates of

return. Instead, they provide farm households with increased purchasing

power to allocate among a variety of uses, including both investment and

consumption. In this case, decoupled payments would not affect onfarm

investment or production levels through capital market channels.

However, financial capital markets are generally characterized by imperfections including asymmetric information and adverse incentives. (Transactions costs and imperfect competition can also cause capital market

imperfections, but they are outside the scope of this chapter.) Asymmetric

information is when outside sources of capital (lenders, partners, and shareholders) have less information than farm managers about how the capital

will be used. In turn, asymmetric information enables farm managers to

understate the riskiness and overstate the expected profitability of their

investment opportunities to outside investors in order to reduce financing

costs and increase profits. This combination of incomplete, asymmetric

information and adverse incentives can induce outside investors and creditors to ration capital or credit and impose monitoring costs on a farm. 

Credit rationing occurs when lenders refuse to fully fund loans to borrowers

who meet their credit standards, even if borrowers offer to pay higher

interest rates. Credit rationing occurs because lenders know that the higher

the interest rate, the more likely that willing borrowers will be to undertake

riskier investments and to understate investment risks, increasing the probability of default and lowering the expected return on the loan. That is,

increasing interest rates are associated with greater adverse selection problems for lenders. Redlining is an extreme form of credit rationing that

entails refusing to lend in certain areas or to certain types of businesses, and

may affect farmers during periods of low income, falling land values, or

restrictive monetary policy. Restrictive monetary policy may lead to credit

crunches, wherein lenders have insufficient reserves to expand or even

maintain their credit portfolios. 

As a result of such lender responses, some agricultural producers may face

credit constraints that prevent them from maximizing their profits from

farming in a given production period. In other words, a credit constraint

exists when the farm qualifies for credit under conventional underwriting

standards but is unable to find a willing lender.
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Capital market imperfections may result in a variety of constraints on farm

production. A liquidity constraint exists when a farm is unable to pursue its

most profitable production plan because it lacks the shortrun ability to pay

for inputs such as seed, fertilizer, or animal feed. A capital constraint exists

when a farm is unable to pursue the most profitable production plan because

it lacks access to sufficient physical capital. By this definition, a farm facing

a capital constraint could pay the competitive market price for additional

capital with the returns it would earn from additional capital investment. 

If farmers are unable to maximize profits because of capital market imperfections, decoupled payments could mitigate this problem and lead to

increases in investment and production. Because the initial state was

distorted by market imperfections, these increases will tend to move the

sector toward greater economic efficiency.

Beyond the immediate addition to purchasing power provided by decoupled

payments, two other payment-related effects can increase farm creditworthiness and reduce the likelihood of lenders rationing credit to farmers. First,

decoupled payments improve access to credit by increasing the value of

farmers’ most important source of collateral, land (Barnard, 2001). Since

the 1996 FAIR Act, decoupled payments have been linked to ownership of

specific cropland and do not require current production. The direct link

between base acres and known program benefits allows sellers, purchasers,

and lenders to calculate payments’ value through capitalization—the same

process used to calculate the value of a bond, mortgage, or any other known

payment stream over time. The capitalization of decoupled payments adds

to the value of land. Second, to the extent that the payment stream can be

anticipated, lenders may allow farmers to pledge them as a source of repayment capacity. Both of these effects are important only with credit market

imperfections; if markets were perfect, lenders would readily fund all profitable farm investments.

Farm Household Objectives Can Alter

the Effect of Decoupled Payments 

In addition to improving creditworthiness, decoupled payments may allow

marginally viable farm operations to remain in production and even to

increase the capital invested in their farms. Farmers operating unprofitable

farms may have better off-farm investment opportunities with higher financial returns, but may accrue more personal rewards (both financial and

nonfinancial) by devoting the funds to farming. 

To the extent that decoupled payments allow these farmers to cover their

costs of production and family living expenses, they may reduce the aggregate efficiency of production in the farm sector. However, the effect on

overall farm production is ambiguous because some of the resources

controlled by these farmers would remain in the agricultural sector and be

used more efficiently (if such farms exited), while other resources would

exit the sector. If farmland values increase because of decoupled payments,

farmers may be less likely to consider alternative uses of the land. The

magnitude of any effect on production depends on the willingness of

marginally viable farmers to increase their investment in farming, how
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responsive farm production is to new investment, and how fast farmers

invest in new real capital as their liquidity improves. Aggregate effects for

each of these channels will also depend on the distribution of production

and productive assets across farmers with varying costs of capital, tax liabilities, risk attitudes, and profit constraints.

Decoupled Payments and Capital Markets: 

The Literature 

The potential linkages from farm program payments through capital markets

to farm production are indirect and complex, making them difficult to

measure empirically. Analyzing the relationships in a computable general

equilibrium model of the U.S. economy, Roe et al. (chapter 2), find that in

the case of segmented or inefficient capital markets, direct payments have

limited shortrun and almost no longrun impacts on farm production relative

to a baseline case with no program payments. An increase in land values

and rental rates were the main longrun impacts found. 

Other economic studies shed light on parts of this payments-capital-production linkage, but were not designed to illuminate the entire chain of causation or to address the equilibrium impact of capital market imperfections on

the level of U.S. farm production or trade. In general, each study illuminates one of the following issues: how capital market imperfections affect

farm investment (Hubbard and Kashyap, 1992; Bierlen and Featherstone,

1998; Barry et al., 2000), the responsiveness of agricultural production to

new investment (Saha et al., 1994), or the speed with which farmers adjust

their stock of real productive capital as risk and other business conditions

change (Vasavada and Chambers, 1986; Halvorsen, 1991). These studies

demonstrate how farm investment behavior has been dependent on farm

cash flows, but do not address decoupled payments since they predate the

program. USDA’s ARMS data indicate little potential for decoupled

payments to affect agricultural production through increased investment

levels on farms that receive payments but are not currently cost-efficient

producers of program commodities. 

Studies linking capital market imperfections to farm investment

Studies have investigated the relationship between capital market imperfections and farm investment both for the sector in aggregate (Hubbard and

Kashyap) and for farms in particular States (Bierlen and Featherstone for

Kansas farms; Barry et al. for Illinois farms). Using farm sector data from

1914 through 1987, Hubbard and Kashyap find that the rate of farm investment can be explained by rates of return during periods of high net worth.

But the level of internal reserves held by farmers determines the rate of

investment agriculture when the sector experiences declining net worth.

Their empirical tests indicate that a change in the value of collateral (primarily farmland) is an important determinant of investment spending. Thus,

the impact of capital market imperfections on aggregate investment patterns

in the U.S. agricultural sector has been statistically significant only during

periods of negative shocks to farm sector net worth, namely the sector recessions of 1921-33 and 1981-86. 
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Similarly, Bierlen and Featherstone tested for financial constraints in farm

machinery investment among commercial farms in Kansas from 1976 to

1992. They find no evidence of financial constraints during the boom

period of the late 1970s, but some evidence of constraints during the 1980s

recession and the recovery of the 1990s. The effect of cash flow on investment of those farmers most likely to be credit constrained – those with high

debt-to-asset ratios and younger operators – varied particularly with the

stage of the business cycle. Thus, during the agricultural recession of the

1980s, the investment behavior of these farmers depended heavily on their

ability to generate internal funds. Barry et al. also find that younger farmers

and those with lower credit scores in Illinois (1987-94) were more likely to

be affected by capital market imperfections. That is, they relied more

heavily on current cash flows to fund investment.

So, during some farm recession periods, capital market imperfections are

associated with inefficiently low investment for some farms and for the

sector as a whole. On the other hand, these studies do not find evidence that

investment is inefficiently high for farms with strong cash flow or for the

sector as a whole during periods of strong cash flow or high asset values.

These observations, in turn, imply decoupled payments may move farm

sector investment to more efficient levels in recession periods.

Studies of capital stock adjustment rate, and links between 

investment and production. 

Other studies address the linkage between investment and production. Saha

et al. found that production increases by 0.2 to 0.25 percent for each 1-

percent increase in investment, depending on whether risk attitudes are

taken into account in modeling. That is, production changes at one-fifth to

one-fourth the percentage rate that investment changes. In addition, empirical studies suggest that the rate of adjustment in capital stocks to economic

shocks is quite slow. Halvorsen finds that capital inputs adjust to new equilibrium values at rates ranging from a little over 1 year for durables to about

3 years for structures. In contrast, Vasavada and Chambers find that aggregate farm capital stock takes about 10 years to adjust. Thus, investment

would have to increase by 4 to 5 percent to have a 1-percent impact on

sector output, and perhaps only if conditions prevailed for several years.

However, financial constraints are unlikely to have long-lasting effects on

sectoral investment and production since competitive pressures will, over

time, force financially constrained farms to sell assets to those who can

achieve higher returns. 

Evidence from 2000 ARMS data

If capital market imperfections exist, decoupled payments could affect the

investment decisions of participants. Imperfections, such as a binding credit

constraint, would force farmers to deviate from their optimal financial structure by considering internal sources of funds or choosing to lease instead of

financing capital purchases with debt. Additions to income from government programs provide liquidity that can relax constraints associated with

imperfect capital markets.
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Data correlating program participation, capital investment decisions, and

farm efficiency can illustrate how much capital market imperfections

impair agricultural investment. Whittaker and Morehart (1991) found that

one in five Midwestern cash grain farms was unable to operate at minimum

cost during the 1980s due to debt and/or collateral constraints. Nasr et al.

(1998) showed that more efficient Illinois grain farms were more highly

leveraged. The cause is ambiguous. Lenders may expect grain farms with

greater leverage to “work harder” to meet debt repayment obligations, or

more efficient farmers may be viewed as more creditworthy and find

leverage more profitable.

If farmers faced substantial capital or credit constraints, one would expect

that higher cost farmers would be forced to invest less in machinery and

equipment over time. However, recent ARMS survey data for commodity

program participants fail to support the existence of a capital constraint

related to cost structure. While high-cost soybean producers carry, on

average, a significantly lower debt-to-assets ratio than do other soybean

producers, this is not true for high-cost corn, cotton, or wheat producers

(fig. 5-1).26 Relative to low-cost producers, high-cost producers of

soybeans, corn, cotton, and wheat have, on average, equal or greater farm

assets, land, or buildings per acre farmed and investments in

machinery/equipment per acre farmed (fig. 5-2). In addition, capital expenditures per acre on high-cost farms, on average, equal or exceed those in

low cost farms (fig. 5-3). Thus, there is little evidence that inefficient

producers of program commodities are inefficient because they lack physical capital. This observation, in light of Saha et al., suggests that

increasing the capital investment of inefficient producers would not significantly increase their production of program commodities. Moreover, highcost producers account for much less production than their one-third

population share—ranging from 10 percent of total production for high-cost

soybean producers to 21 percent for high-cost wheat farms—so the modest

potential effect of decoupled payments on capital-induced production

becomes even more so.

26 We define high- (low-) cost farms

as the third of each farm type with the

highest (lowest) unit costs.
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Low-cost Middle High-cost

0.20

0.15

0.10

0.05

0

Corn Wheat Cotton Soybeans

All

Figure 5-1

Except for soybean farms, high-cost farms, on average, have

debt/asset ratios that equal or exceed those of other farms

Debt/asset ratios

Low-cost Middle High-cost All

0

50

100

150

200

250

300

350

Corn Wheat Cotton Soybeans

Figure 5-2

On average, high-cost farms invest at least as much as low-cost 

farms in machinery per acre farmed

Dollars of machinery/acre

Low-cost Middle High-cost All

Figure 5-3

Capital expenditures per acre of high-cost farms exceed that 

of all farms, on average

0

10

20

30

40

50

Corn Wheat Cotton Soybeans

Capital expenditures per acre
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Conclusions 

The Federal Government has been experimenting with payments to farmers

that are decoupled from current farm production in an attempt to reduce the

distortions in trade and resource allocation associated with coupled

payments. This intended effect may be offset, however, if imperfections in

capital markets bind decoupled payments to farm production decisions.

Financial capital markets in agriculture are characterized by imperfections

relative to “efficient” markets, because lenders and borrowers have asymmetric information. 

Though imperfections exist, they do not appear to influence aggregate

investment. Data do not indicate differences in capital investment or credit

use that would be likely if significant capital or credit constraints existed

among commodity program participants. Investment per acre farmed is no

less for high-cost program participants than for low-cost participants and,

except for soybean growers, high-cost participants carry no less debt relative

to their assets.

The data are, however, limited. Farms and farmers vary considerably by

region, farm type, resource base, productivity, and the goals of their owners.

Some farmers may well face sufficient credit constraints (for liquidity or for

capital) so that receipt of decoupled payments allows them to continue or

expand production. However, empirical studies indicate that any increased

investment enabled under these circumstances would move the sector

toward greater rather than less efficiency. In addition, in a capital-rich

economy where few farmers are likely to be capital constrained, any impact

of decoupled payments would be transitory. Farmers unable to afford efficient levels of investment in productive capital would soon be induced by

competitive forces to relinquish control of their assets to unconstrained farm

owners or managers.
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Chapter 6

Effects of Government

Payments on Land Rents,

Distribution of Payment

Benefits, and Production

Michael J. Roberts

Economic reasoning and some empirical evidence suggest that farmland

rental payments increase with more direct government payments to farmers

(Barnard et al., 1997; Floyd, 1965; Gardner, 1992; Kuchler and Tegene,

1993; Goodwin et al., 2003; Kirwan, 2003; Lence and Mishra, 2003;

Roberts et al., 2003). The roles of farm operator and farm landowner

diverge on the 60 percent of U.S. cropland that operators rent from owners

(USDA, 2003).27 As a result, the degree to which farmland rents increase

with government payments strongly influences the distribution of payment

benefits between landlords and renters. By examining the degree to which

land rents increase with increasing payments, we also obtain indirect

evidence on the potential production impacts associated with domestic agricultural programs. This evidence is useful because a direct empirical

assessment may be difficult or impossible, especially when program

payments are decoupled. Understanding how agricultural payments could

affect production is central to tracing the full range of market effects of agricultural payment programs, including impacts on world commodity prices

as a result of national supply responses. 

Effect of Payments on Land Rents Depends

on Production Distortions

If agricultural land markets are competitive, land rents will vary according

to the profits tenant farmers expect to earn from farming. High-quality agricultural land, capable of producing higher yields or higher value crops, will

command a higher rent per acre. Similarly, agricultural lands eligible for

government payment programs also will tend to command higher rent per

acre. How much higher the rent may be depends on the features of government programs, including the flexibility granted (whether they may grow

crops, which crops they may grow, the production practices they may use).

Many agricultural payments—called coupled payments—are connected to

the amounts and/or prices of certain crops. To maximize profits plus

payments, farmers may use land differently than they would without the

payments—for example, by producing greater amounts of more heavily

subsidized crops. Altering types or quantities of crop production to boost

payments may generate additional costs and so lower net revenues, which

will offset some of the payments farmers receive. Moreover, if farmers

collectively produce more output in response to payments, commodity

prices will fall. When market revenues fall as a result of the program, peracre rents are expected to rise proportionately less than the per-acre

27 This figure is derived from table

74 of the Agricultural Economics and

Land Ownership Survey (2001).
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payments farmers expect to receive. If land markets are competitive, the

difference will reflect the amount that profits are reduced by the production

distortion. In other words, the greater the distortion, the less rents will

increase with increasing payments.

In contrast, lump-sum, or decoupled, agricultural payments are allocated

irrespective of land use, current production, prices, or input use. In the

absence of market imperfections, these payments provide farmers with no

incentive to manage their operation any differently than they would without

them, and rents will tend to increase dollar for dollar with level of payments

received. As an example, consider two parcels of land, identical in all

attributes except that the decoupled payments are linked to the second

parcel. If payments are decoupled, production activities and profits will be

identical on both parcels, and the increase in rent can be expected to equal

the level of payments. 

No farm program appears to be completely without potential effects on

production. Production Flexibility Contract (PFC) payments, established in

the 1996 Federal Agriculture Improvement and Reform Act (and called

“direct payments” in the 2002 FSRI Act), are perhaps the least coupled of

all U.S. Government payments to farm operators. These payments are

based on historical plantings and program participation and place few

restrictions on farmers’ production activities. The restrictions prohibit new

fruit or vegetable plantings or conversion of payment-receiving land to a

non-agricultural use. Thus, if these restrictions are not binding, these

payments would not be expected to reduce profits. Accordingly, farmers

would not be expected to discount the value of current payments when

determining rent. Alternatively, if the FAIR Act programs were to induce an

increase in production, which increases costs or lowers commodity prices

and thereby reduces profits, then the increase in rents would be expected to

increase less than the full amount of the payments. 

If the land rental market operates efficiently, the program-induced increase

in profits plus payments will tend to be passed through to the land owner –

either the full face value of payments or a smaller amount. If efficient land

markets are assumed, the amount by which payments are not passed through

to land owners therefore signals the degree to which payments could be

distorting production activities and profits. If, however, land rental markets

adjust slowly and/or incompletely, then the value of the payments may not

be passed through to land owners in the first year or two after the program

is implemented. Thus, one cannot determine to what extent incomplete

pass-through of payments in land rents is attributable to imperfect and/or

slowly adjusting land rental markets rather than production distortions

attributable to the payments themselves.

Evidence on the Links Between Payments

and Land Rents

A first step toward understanding wealth and production effects stemming

from coupled and decoupled payments is to determine how they affect land

rents. Land rents would be expected to increase more (relative to a context

with no payments) the smaller the production distortion induced by the
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payments. Land rents should rise commensurately with payments if payments

have no effect on production and if land markets operate efficiently.

Income or wealth effects, which have often been used to explain possible

production distortions from PFC and Market Loss Assistance (MLA)

payments, depend in part on the share of payments passed on to landlords

via higher rents. After accounting for rent pass-through, one can then

examine the relationship between payment benefits received and the wealth

of farm households that receive them. The magnitude of changes in wealth

may indicate the potential for the different kinds of production distortions

stemming from market imperfections (described in Chapter 3 on labor,

Chapter 4 on risk, and Chapter 5 on capital).28 Information about who ultimately receives payment benefits may also be interesting in its own right.

Roberts et al. (2003), by analyzing over 60,000 records of the Agricultural

Census, estimated the amount by which total government payments

(excluding conservation programs) increased land rents in both 1992 and

1997, on either side of the watershed 1996 FAIR Act. The estimates were

based on a statistical comparison of farm-specific per-acre rental costs with

county average rental costs and how much this comparison depended on the

amount of payments received per acre farmed. Many variables were used to

control for other factors affecting land rents, and statistical techniques

accounted for differences between actual payments received and payments a

farmer could have expected to receive at the beginning of the season, when

rental agreements are typically negotiated.29 For 1992, the study found that

on land rented via cash leases, 21 cents of each dollar in government

payments received (plus or minus 4 cents) was passed through to landlords

via higher rents. The estimate for the same farms in 1997 was 33 cents per

dollar of government payments (again, plus or minus 4 cents).30

These findings suggest that PFC payments have approximately 50 percent

greater effect on land rents than pre-FAIR coupled payments. A large share

of the benefits, for both coupled and decoupled payments, seem not to be

passed through to landlords. This is true even in 1997, a year in which

nearly all payments were from the PFC program.31 Because nonoperator

landlords own approximately 60 percent of cropland, they receive an estimated 20 percent of the total payment benefits via higher rent (33 cents of

the 60 percent of program dollars paid to tenant operators).

Of course, 1997 was the first year in which rental contracts were negotiated

after the FAIR Act, and 1992 was the second year after implementation of

the prior farm bill. If cash rents adjust slowly to the new program benefits,

we may be understating the benefits of the PFC payments to landlords –

both relative to the prior program and in absolute terms. Also, lands

receiving higher per-acre government payments likely differ in many ways

from lands receiving lower per-acre payments, even within counties, and our

analysis may be reflecting these unmeasured differences. Although great

care was taken to control for confounding factors, some unobservable

factors affecting rents may be correlated with government payments and

cause the estimates to be biased.

28 It makes sense that payment benefits retained by tenants would be relevant when assessing potential wealth

effects. It could be, however, that

wealth effects on landlords could also

play a role. Landlords sometimes

make decisions that affect production,

as they often supply equipment, irrigation water, or other inputs to production. In any case, tracing out the flow

of wealth is important for understanding where wealth effects are likely to

be largest.

29 A statistical technique called

“instrumental variables” was used to

account for expectation error.

30 The analysis was based on cash

rent leases only. In share-contract

leases, the landlord receives a share of

the proceeds from crops grown, where

the share is established at the beginning of the growing season.

31 The year 1997 was unique in that

nearly all government payments

(excluding conservation payments)

emanated from PFC payments. Of the

approximately $8 billion paid to farmers in direct government payments in

1997, $6.1 billion were PFC payments,

$1.7 billion were toward conservation

(not included in this analysis), and

$257.3 million came from other

sources. Unfortunately, the census data

do not discern between PFC payments

and other kinds of nonconservation

payments. 
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Distribution of Payment Benefits

Most payments are tied to current or historical production of certain field

crops. As a result, large farmers in Midwestern States generally receive

more government payments than other farmers. Large farmers also rent a

greater share of land from nonoperating landlords, and may pass more

payment benefits on to landlords. An account of payment pass-through is

therefore needed to understand the relationship between payments and

household wealth. 

Using 1999 ARMS data, Roberts and Key (2003) examined the relationship

between total (coupled, decoupled, and conservation) payments received and

the wealth of farm households. In 1999, payments totaled $21.1 billion, of

which 23.8 percent were PFC payments, 35 percent MLA payments, and

32.2 percent loan deficiency payments. Loan deficiency payments have

much stronger ties to production than PFC and MLA payments. Figure 6-1

shows the relationship between payments and wealth, both with and without

adjustments for payment pass-through via higher rents. Each point represents 1 percent of the sample of farms sorted according to household net

worth. The study adjusted total payments by reducing them (by a factor) for

land rented in by the farm operator from another land owner and increasing

them for land rented out by the operator to another farmer. The factor used

for all payments was the 1997 estimate of 33 cents per decoupled payment

dollar passed through to landlords.32 With or without the pass-through

adjustments, wealthy farmers receive far more payment benefits than less

wealthy farmers.

Table 6-1 presents data on government payments (coupled and decoupled)

to farm households in 1999, adjusted to take into account their tenancy

arrangements. More than 58 percent of farm households received no

government payments in 1999, mainly because they did not produce

program crops and did not participate in other programs. In contrast, 1.2

percent of farm households received slightly more than 25 percent of total

adjusted government payments, and about 0.2 percent of farm households

received almost 9 percent of all adjusted payments. Households in the

highest payment category (more than $150,000 of adjusted government

payments) averaged more than $2.1 million in net worth and $236,663 in

(coupled and decoupled) government payments. Across all farm households, adjusted total government payments in 1999 were $5,860, about 1

percent of average net worth. Across PFC recipients, adjusted (coupled and

decoupled) government payments in 1999 averaged $20,381, compared to

an average net worth of $562,567. In 1999, PFCs accounted for 24 percent

of total payments to producers.

By these tenancy-adjusted measures of well-being, a large share of government payment benefits went to the wealthiest farmers in 1999. Although

adjusted total payment levels are substantial for the higher payment categories, other researchers have shown that wealth transfers on this scale have

a relatively small effect on labor supply (see chapter 3).

32 Note that payments in 1999 were

substantially higher and of a different

composition than in 1997 so it is not

clear whether the pass-through rate

affected payments by the same factors

as in 1997. The calculations also

assume the factor is the same for share

leases as it is for cash-rent leases.
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Table 6-1—Coupled and decoupled payments to farm households, adjusted for land tenancy characteristics

Adjusted government Average unadjusted Average Share of farm Share of all Average farm

payments government adjusted households government household net

category payments government payments worth

payments

$ per household Percent $

0 0 058.1 0.00 507,263

$1 - 10,000 3,373 3,019 27.4 14.1 514,431

$10,000 - 25,000 19,312 16,476 7.8 21.9 719,726

$25,000 - 50,000 42,020 34,978 4.1 24.3 992,557

$50,000 - 75,000 76,234 60,494 1.4 14.2 1,210,949

$75,000 - 150,000 126,331 100,643 1.0 16.6 1,461,119

> $150,000 278,817 236,663 0.2 8.9 2,146,703

All farm households 6,966 5,860 100.0 100.0 562,657

All PFC participants 24,882 20,381 22.0 64.2 660,031

Source: Roberts and Key (2002). Data are from 1999 USDA Agricultural Resource Management Survey. All averages are weighted to account

for sample design. Payments are adjusted for payment pass-through on operators’ rented-out and rented-in acreage. See Roberts and Key for

details on how payments were adjusted for land tenancy arrangements.

Figure 6-1 

The Relationship between government payments and household

net worth, 1999

Note: Blue indicates payments received and red indicates adjusted payments, which 

account for higher rents paid and received.

5,000

10,000

15,000

20,000

25,000

30,000

Household net worth percentile

Government Payments ($)

0 20 40 60 80 100

Source: Roberts and Key (2002).
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Conclusions

Recent evidence suggests that government payments to farmers do induce

higher land rents. The estimated effect is much larger for PFC payments

than for pre-1996 payments, which had stronger ties to current production.

Although cash rents vary a great deal across farmland, the rents seem not to

vary dollar for dollar with PFC payments: only an estimated 33 cents of

each decoupled payment dollar is reflected in higher cash rents. Many factors may contribute to this observation. Some rural land rental markets may

not be competitive. It may take time for land owners to adjust rental agreements to changing government payment terms and levels, and they may not

seek to extract all farming benefits from these tenants due to familial or personal relationships. It may be that production is affected by PFC payments

via the wealth effect in combination with one of more of the market imperfections described in Chapter 1, by the remaining land restrictions, or perhaps via other channels not yet explored. Finally, it could be that these estimates are biased by variables not included in our model. At present, the

finding that renters pass through only a third of a lump sum payment

received may be viewed as puzzling.

The finding that wealthy farmers receive a large share of payment

benefits—coupled and decoupled—is less ambiguous. This relationship is

robust to different assumptions about the effect of payments on land rents

and provides some insight into the distribution of payment benefits.

Wealthy farm households receive sizable payments and produce most farm

output.
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