United States

Department of

Agriculture

Economic 

Research 

Service

Risk, Government 

Programs, and the 

Environment Technical

Bulletin

Number 1908

Michael J. Roberts

Craig Osteen 

Meredith Soule
Abstract

Nearly all farm business ventures involve financial risk. In some instances, private and

public tools used to manage financial risks in agriculture may influence farmers’ production decisions. These decisions, in turn, can influence environmental quality. This bulletin

summarizes research and provides some perspective on private and public attempts to cope

with financial risks and their unintended environmental consequences. Specifically, it

examines the conceptual underpinnings of risk-related research, challenges involved with

measuring the consequences of risk for agricultural production decisions, government programs that influence the risk and return of farm businesses, and how production decisions

influence both the environment and the risk and average returns to farming.

Keywords: risk, agricultural production, government programs, environment.

About the Authors

Michael Roberts and Craig Osteen are economists at the U.S. Department of Agriculture,

Economic Research Service. Meredith Soule was formerly with the Economic Research

Service and now works for the U.S. Agency for International Development.

Acknowledgments

Several reviewers contributed valuable comments and suggestions on earlier drafts of this

report. The authors thank John Antle, Robert Dismukes, Barry Goodwin, Carol Jones,

Nigel Key, Utpal Vasavada, Keith Wiebe, Edwin Young, David Zilberman, and participants

of an internal USDA-ERS seminar for valuable comments and suggestions on earlier

drafts. We especially thank Margriet Caswell, who, in addition to providing her own valuable advice on content and presentation on two earlier drafts, carefully synthesized the

comments and suggestions of the other reviewers. Without the thoughtful suggestions of

these generous colleagues, this report would not have been possible. We also thank

Marcel Aillery and Wen Huang for contributing material for early chapters drafts and Dana

Rayl West for editing and Anne E. Pearl for the document design layout. The authors

remain solely responsible for any errors that remain.

United States

Department

of Agriculture

Risk, Government Programs,

and the Environment

Michael J. Roberts, Craig Osteen,

and Meredith Soule

March 2004

Technical

Bulletin

Number 1908

www.ers.usda.gov

Electronic Report from the Economic Research Service
ii  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iv

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Financial Risks in Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Chapter 2. Risk in Perfectly Functioning Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Risk Versus Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Perfect Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Risk Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Decisions under Uncertainty in Perfect Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

A Two-Period Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Nonlinear Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Dynamic Decisions under Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

For Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

Chapter 3. Risk in Imperfect Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Market Imperfections and Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Asymmetric Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Adverse Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

Moral Hazard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

Risk Aversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

No Risk Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Credit Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

Contracting Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Transaction Costs and Industrial Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

An Addendum on Psychology-Based Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

For Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28

Chapter 4. Government Farm Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29

Historical Government Payments to Farmers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29

Price-Contingent Payments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

Lump Sum or Decoupled Payments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32

Crop and Revenue Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32

Brief History of Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32

Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33

Disaster Assistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

Land Retirement Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35

For Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37
Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908 iii

Chapter 5. Risk Production Decisions and the Environment . . . . . . . . . . . . . . . . . . . . . . . . . .38

The Agro-Environmental System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38

Intensive-Margin Choices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38

Pest Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

Irrigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40

Fertilizers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

Extensive-Margin Choices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

Technology Adoption Choices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

Input Substitution and Environmental Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43

For Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .48

Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49

Complicated Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49

Some Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49

For Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .52

Figures

Figure 1. National income growth and farm income growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Figure 2. Irrigation affects yield risk in a nonlinear way . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Figure 3. Sequential decisions under uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

Figure 4. The effect of credit constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

Figure 5. Historical government farm payments, 1933-1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

Figure 6. Government farm payments, 1996-2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

Figure 7. Comparison of net farm income, government payments, and land values . . . . . . . . . . .31

Figure 8. Total disaster payments, 1991-2000, in year 2000 dollars . . . . . . . . . . . . . . . . . . . . . . . .34

Figure 9. Pesticide use on major crops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40

Figure 10. Comparing yields of irrigated and nonirrigated corn . . . . . . . . . . . . . . . . . . . . . . . . . . .41

Figure 11. Risk, government programs, welfare, and the environment . . . . . . . . . . . . . . . . . . . . .50

Roberts, Michael J.

Risk, government programs, and the environment.

(Technical bulletin (United States. Dept. of Agriculture);

no. 1908)

1. Agriculture and state--Environmental aspects-- 

United States.

2. Farm risks--Economic aspects--United States.

3. Farm management--Environmental aspects--

United States.

I. Osteen, Craig D. II. Soule, Meredith J. (Meredith Jean)

II. United States. Dept. of Agriculture. Economic Research

Service.

III. Title.

HD30.255

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National Agricultural Library Cataloging Record:
iv  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

Summary

Prices and yields of most agricultural commodities vary markedly across space and

time in unpredictable ways. Farmers must cope with these and other financial risks,

sometimes with the assistance of government programs designed, in part, to mitigate

these risks. How do these risks and the government programs designed to mitigate

them influence what crops farmers choose to produce, amounts they produce, and their

production practices? How do farmers’ production decisions, in turn, influence environmental quality? 

Volumes of basic and applied research address these questions. These volumes encompass many different views of risk and apply many different models. Multiple models

are used to address the same empirical phenomena and policy questions, sometimes

without the acknowledgement of alternative views. Even fewer articles describe

testable hypotheses that might distinguish the empirical relevance of one model as

compared to others, which makes it difficult to gain a perspective on all views.

Risk often influences farmers’ production incentives, even when the costs of risk-coping are small. In other instances, when risk-coping costs are large, risk affects production incentives in entirely different ways; it may even influence the structure and organization of whole agricultural sectors. This report introduces and reviews the concepts

and findings from the disparate literature on risk, government agricultural programs,

and their impact on the environment.

The report also provides a roadmap to the different ways risk influences production

decisions in agriculture. It describes the market mechanisms that might theoretically

exist to provide risk-coping tools for farmers, the reasons why such risk-coping tools

may not exist, and the extent to which government programs may alleviate the costs of

risk coping. It also describes applied research that examines the links between risk

and the production decisions farmers make, how different agricultural production practices influence the risk and returns of farming, and how different production practices

influence environmental quality.

After considering many views on the links between risk, government programs, and

their impact on the environment, as well as methodological challenges involved with

assessing these links, we draw six conclusions. These conclusions are meant to clarify

key difficulties involved with research in this area so that future research can work to

overcome them. Briefly, we find:

(1) Many studies emphasize farmers’ attitudes toward risk (or “risk aversion”) when 

analyzing farmers production decisions. However, risk can affect farm production decisions through many other channels. For example, greater uncertainty 

about rainfall may alter a farmer’s fertilizer applications, regardless of his or her 

attitude toward risk.

(2) In some cases, the effects of risk on production can appear similar regardless of 

the channel through which they arise. Because policy implications may be different depending on which channel gives rise to the effects observed, it is important to evaluate the relative importance of the different channels.
Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  v

(3) Risk is difficult to measure, and the effects of risk are easily confounded by the 

effects of factors that have nothing to do with risk. Empirical estimates regarding 

the effects of risk will be more precise if they carefully account for these factors. 

(4) Our understanding of the effects of risk would benefit from an improved understanding of how key environmental factors (such as location, climate, and soil) 

affect land use. These factors are among those most likely to confound the 

effects of risk in empirical studies, and are central to understanding the effects

of agricultural production on environmental quality. 

(5) Many economic models linking production to risk are static in nature. These

models overlook important longrun risks, which are more difficult for farmers to 

insure against than shortrun risks. Longrun risks are central to key production 

decisions, including capital investment, technology adoption, crop rotations, and 

tree plantings.

(6) Economic models of behavior contain many basic assumptions about the way 

people make decisions in the face of uncertainty, but experimental research deriving from psychology casts doubt on some of these basic assumptions. The practical relevance of these findings for farmers’ production decisions remains poorly 

understood and is an open area for future research. 

Financial Risks in Agriculture

Farming, like many entrepreneurial ventures, has many

financial risks. Prices for corn, soybeans, beef, pork,

and most other commodities, as well as the inputs used

to produce these commodities, can vary widely and in

unpredictable ways both during and between seasons.

Crop yields on non-irrigated land can be more variable

and uncertain than prices. 

Uncertain prices and yields are the principal financial

risks in agriculture, but there are other risks too. Other

important risks pertain to variability and uncertainty in

the quality of agricultural commodities, uncertainty

about the productivity of new seed varieties and technologies, and, increasingly, uncertainty about future

governmental actions, such as the size of future direct

payments or new environmental regulations.

How well do farmers cope with these risks? How do

these risks influence the crops and livestock that farmers choose to produce, the amount they produce, and

the inputs and agricultural production practices they

use? How do these input and production practice

choices, in turn, affect environmental quality? For

over 30 years, economists have studied these questions

from many different perspectives, both theoretically

and empirically. Yet, despite a plethora of research,

few sharp conclusions emanate from it. Indeed,

whether or not risk is of any practical importance at all

appears to remain a contentious issue (Just and Pope).

Despite the still-ambiguous consequences of risk,

concern about risk and farmers’ ability to efficiently

cope with it has served as an important backdrop for

government agricultural support programs. These

programs, a mainstay of U.S. agriculture since the

Great Depression, have mainly taken the form of

price supports and supply control programs. In

recent years, government programs that directly target risk have been expanded to include counter-cyclical payments and newly increased subsidies on yield

and revenue insurance. In addition, congressionally

approved ad hoc disaster assistance serves to compensate farmers for extreme local weather events that

damage or destroy crops. All of these programs

transfer wealth to certain farmers from the rest of the

economy and reduce specific risks associated with

some farm operations.

These policies give rise to questions about how well

private markets might function to provide risk-coping

tools to farmers in the absence of government policies

and to what extent government programs actually alleviate the costs of coping with risk.

New questions have emerged about the magnitude of

unintended environmental consequences that may stem

both from risk and from government programs.

Agricultural production inputs and byproducts, such as

pesticides, nutrients, and livestock waste, may cause

environmental problems if managed improperly. For

example, farmers may overapply pesticides and fertilizer inputs in order to smooth profit flows, which, in

turn, may contribute to water quality degradation of

streams and lakes. With such a scenario, government

programs that insure farm income may have the added

benefit of reducing excess applications of environmentally damaging inputs. On the other hand, subsidized

crop insurance may encourage farmers to plant crops

on fragile lands, which could harm the environment. 

Risk also lies at the heart of some theories that characterize the organization and size of farms, which, in

turn, may have new environmental consequences. For

example, recent trends in the livestock industry indicate growing numbers of farmers are signing contracts

with processors rather than producing their product

independently and selling via spot markets. This

increase in contracting has been associated with an

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  1

Risk, Government Programs,

and the Environment

Introduction
increase in the size and concentration of livestock

farms. As livestock farms become larger and more

geographically concentrated, local land areas are

unable to absorb all the nutrient waste, creating potential environmental problems. If risk management

underlies a driving force behind this structural change,

then a better understanding of it will help researchers

and policymakers understand the associated environmental problems. Thus, understanding the link

between risk and structure as well as the links between

structure and the environment can help to inform agricultural and environmental policy.

Objective

This report presents an overview of the interactions

between public and private risk management strategies

and environmental resources. The report first

describes how individual incentives are shaped both by

risk and by government programs that possess riskmitigating properties. Risk can influence production

decisions in many ways. Researchers have illustrated

these different influences using different modeling

approaches. Since different approaches often are used

to describe the same phenomena, this report provides a

description of all approaches in a single place to help

researchers gain perspective on the many ways risk

can influence production decisions.

In Chapter 2, we define risk as it is usually understood

within economic analysis, explain how markets can

trade and thereby share risks, and then review two

general ways that risk can influence choices in an idealized world of perfect markets. In Chapter 3, we

explain why markets are not perfect and review three

additional approaches for modeling risk in imperfect

markets. Each approach is presented using the simplest possible example—the goal is not to spell out

every implication of every approach, but to illustrate

the economic tradeoffs underlying each one, so the

reader can see how the diverse views of risk differ.

Chapter 3 concludes with a discussion about why risk

research appears to lack harmony.

Government programs can influence agricultural risk

and returns, and they may directly or indirectly influence the environment. We present a brief overview of

the relevant agricultural programs in Chapter 4. Then,

in Chapter 5, we characterize the different kinds of

production alternatives available to farmers, individually and collectively, and explain how these alternatives may influence risk, returns, and the environment.

In Chapter 6, we discuss the complicated interaction of

risk, government programs, and the environment, and

provide six conclusions that we feel future research

should bear in mind.

2  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
This chapter defines risk as it is usually modeled and

understood within the agricultural economics literature

and explains how perfect markets could, in theory,

take risk into account. These concepts, which provide

the building blocks researchers use to analyze risks,

are important for understanding how risk-management

policies influence farmers’ decisions. 

The primary financial risks farmers face involve variations in yields and prices. For example, on a typical

corn, wheat, or soybean field in the United States, the

coefficient of variation (for definition of terms in

bold, see glossary at end of each chapter) of year-toyear yields is about 0.3 (Makki and Somwaru). This

means that the average field will post a yield of more

than 30 percent above or below the historical trend

curve, approximately one-third of the time. The coefficient of variation of year-to-year price changes is

over 0.2 for most commodities. Because farmers’

profits, on average, are only a small share of their

total revenues, the large variability of both prices and

yields implies that, in any single year, a farmer’s profit may vary widely for reasons beyond his or her

immediate control. Holding all other factors the

same, these numbers imply that revenue for the average “lucky” field and year (those with above-average

yields in years with above-average prices) is well over

twice that of the average “unlucky” field and year

(those with below-average yields in years with belowaverage prices).

Risk Versus Uncertainty

Most economists do not draw a sharp distinction

between risk and uncertainty. Usually both terms refer

to situations in which economic variables vary randomly according to probability distribution functions

that are known to decisionmakers. Some draw the distinction that risk refers to instances in which probability distribution functions are known and uncertainty

refers to instances in which probability distribution

functions are unknown (Knight). This distinction

holds particular relevance in empirical analyses, in

which researchers almost never know the true probability distributions. In economic modeling of risk,

however, economists usually make the assumption that

economic agents hold beliefs that are consistent with a

known probability distribution function. This view is

generally referred to as the Bayesian or Subjectivist

view of probability, and it is the view adopted for most

of this report.

The Bayesian view of probability contrasts with the

Frequentist view of probability, which holds that probabilities equal the relative frequencies from repeated

experimental trials as the number of trials tends toward

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  3

Assumptions that Imply Perfect Markets

1. Symmetric information Economic agents (individuals, firms, community groups, government) 

have the same set of information about all aspects of production, exchange, and distribution activities, including market opportunities, available technologies, costs of production under alternative production arrangements, the quality of goods produced, and the intentions of other agents.

2. Large numbers of buyers and sellers A large number of buyers and sellers of all kinds of goods 

and services prevents individual buyers or sellers from influencing prices.

3. Free entry and exit Economic agents can freely enter and withdraw from markets. 

4. Profit maximization Economic agents are motivated purely by profit and/or utility maximization 

and are cognitively capable of making decisions perfectly to achieve these objectives, subject to budget constraints. 

5. No externalities Actions taken by one economic agent do not directly influence costs or utility of 

other agents (except via prices).

Chapter 2–Risk in Perfectly Functioning Markets
infinity. Because it is impossible to perform an infinite number of experimental trials, to a Frequentist,

the true probability distribution function is simply

unknowable, which corresponds to the notion of

uncertainty as distinct from risk. Except where explicitly stated otherwise, we assume in this report that

probabilities are known to the decisionmakers and we

use the words risk and uncertainty interchangeably.

Perfect Markets

Theory implies that, when markets function perfectly,

there is no particular reason why risks should be a

major policy concern in agriculture. Perfect markets

do not, however, imply perfect certainty. The assumptions that underlie the perfect-markets condition are

still strong and, interpreted literally, unrealistic.

Despite their lack of realism, the assumptions that

underlie this condition provide a useful starting point

from which to construct more realistic models. One

may then reconsider (or test empirically) each assumption and hopefully move on toward more realistic

models and assumptions, which should lead to a deeper understanding of agricultural production and the

implications of agricultural policy.

The perfect-market condition implies that competitive

prices and wages exist for all goods and services in all

possible contingencies (potential “states of the

world”). For example, taken literally, the perfect-markets condition implies that separate and competitive

markets exist for a bushel of corn delivered August 30,

2002, contingent on when rainfall in Cowley County,

Kansas, is both an inch above normal and an inch

below normal in a given month, holding all other conditions the same. Accordingly, many refer to a perfect

market as one with “complete contingent claims.” To

satisfy the perfect-market condition literally, one must

imagine an infinite number of separate contingent markets just for corn delivered on August 30, 2002. There

would also be an infinite number of markets for corn

delivered on August 31, and another infinite number

for each and every other good and service (or potential

good and service) that might be delivered at every

moment into the infinite future. 

Although a perfect-markets assumption is implausible,

only slightly less implausible is the observation that

tradable securities, insurance, and futures markets

effectively constitute contingent claims to deal with

the largest risks faced by individuals. Furthermore,

contingent claims might be combined in ways to track

other risks that are not explicitly traded. For example,

a farmer cannot actually sell a bushel of corn in June

for delivery in August contingent on a month’s rainfall

in Cowley County, Kansas; but he might use a futures

market to sell a bushel of corn in June for unconditional delivery in August and purchase yield insurance for

his crop. Taken together, these claims might well

approximate a contingent market for corn deliveries.

Risk Sharing

Trade in contingent claims effectively allows individuals to share all risks regarding wealth, health, and the

weather. If people are averse to risk, then sharing

risks reduces the risk that any individual must live

with, and thereby increases the welfare of all those

pooling their risks. In this way, the gains from risk

sharing are much like the economic reasoning that

more generally underlies gains from trade. In perfect

markets, when all risks are shared, individual risks are

averaged across everyone in the population—unlucky

outcomes are averaged with lucky outcomes, reducing

total risk. Therefore, weather in Cowley County,

Kansas, would not be of particular concern. What

matters is the aggregate outcome, the cumulative sum

of all risks stemming from the weather and otherwise.

Farmers would find it beneficial to pool their risks not

only with other farmers but also with individuals in

other sectors of the economy. Thus, despite the large

price and yield risks with which farmers must contend,

if these risks are uncorrelated with national income

then, in principle, they are completely insurable under

perfect markets.

To measure the potential insurability of agricultural

risks, one can examine the relationship between farm

income and national income. Figure 1 shows the

growth rate for real net farm income from 1950 to

2000 overlaid with the growth rate of real gross

domestic product (GDP) over the same period. As 

one might expect, farm income varies far more than

national income; however, the two series are positively

correlated. It is not readily apparent to the naked eye,

but these two series have a positive and (narrowly) statistically significant correlation equal to 0.302—they

tend to move up and down together, but just slightly.

Because national farm income averages away most of

the income risk faced by individual farmers, even

nationwide net farm income grossly underestimates the

risk faced by a typical farm operation. Under perfect

4  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
markets, when all income risks are pooled, farmers

and all other individuals in the economy would be

concerned only with the variability in GDP, which is a

small fraction of nationwide farm income and only the

tiniest fraction of the income variability experienced

by an uninsured (or non-risk-sharing) farm operation.

In a nutshell, farm income risk is of no concern to

farmers, if markets are perfect.

Decisions under Uncertainty

in Perfect Markets

Price and yield uncertainty can influence agricultural

production decisions even when markets are perfect.

The perfect markets assumption implies that only the

best possible decisions will in fact be made and that

farmers’ individual tolerances for risk are of no relevance to those decisions. However, perfect markets do

not imply that decisions in the face of uncertain prices

and yields are equivalent to decisions that would be

made if prices and yields were certain values fixed at

the average level observed in the real world, or if they

were variable but known with certainty in advance.

The effects of uncertainty on production decisions

result from the physical irreversibility of some decisions and the fact that some decisions must be made

before the decisionmaker knows the outcome of a random event. In other words, these kinds of effects have

to do with how the flow or level of information interacts with the timing of decisions. 

A Two-Period Example

Many of the ways in which risk can influence decisions under perfect markets can be illustrated using a

simple two-period model of profit maximization. In

the first period, farmers make production decisions

given their beliefs about the possible outcomes of random events beyond their control (such as the weather).

To illustrate, let π(x,s) denote profits as a function of a

farmer’s production decisions (the vector x) and

exogenous random factors (the vector s). In perfect

markets, farmers are completely insured against uncertain variability in profit itself; so their rational objective is to maximize the expected value of π given their

beliefs about s, which are summarized by a probability

distribution function. The value of the farming operation (denoted V) is given by

The relevance of uncertainty (the randomness of s) to

decisions depends on the shape of the profit function.

Consider the neoclassical profit function for a competitive firm where x = [L (labor), K (capital)], s = [p

(output price), w (wage), r (rental price of capital)],

and the production function q(L,K) transforms labor

and capital inputs into a single output q:

Note that, in this example, all prices are uncertain and,

because the firm is competitive, prices enter the profit

function linearly. One can write the value of the firm

as

Decisions in this uncertain environment are, therefore,

equivalent to an environment wherein prices are fixed

at the expected values of the random prices. In general, if one can write the profit function as π(x,s) = sa +

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  5

-80

-60

-40

-20

0

20

40

60

80

1950 1960 1970 1980 1990 2000

Real net farm income growth Real GDP growth rate

Figure 1

National income growth and farm income growth

Percent change

V max , . ( ) x

1 = E[π x s ]

V = −− max ( ) L,KE q L,K E w L E r K . [ ] [ ] [] p

1This objective is not correct unless returns to farming are uncorrelated with returns of the aggregate economy. A slightly positive

correlation is evidenced between net farm income and GDP, so

farm income risk will have a small influence on production choices even in perfect markets. Asset pricing is concerned primarily

with the measures of aggregate risk and the covariance of tradable investments such as stocks and bonds (and agricultural production decisions) with the aggregate. This report focuses mainly

on the implications of individual farm risks, which are mostly

uncorrelated with the aggregate. (See Cochrane for modern

review of asset pricing.)

π(, ) ( ) . x u = −− pq L,K wL rK
sbg(x), where sa and sb together comprise s and g(x) is

an arbitrary production function (profit is a linear

function with respect to s), then decisions in certain

and uncertain worlds will be equivalent in this way.

This result follows from the fact that, in this special

case, E[π] =E[sa] + E[sb] g(x), so that optimal decisions about x depend only on the expectation of s.

This equivalence between choices in certain and

uncertain worlds is a special case that crucially

depends on three assumptions: (1) perfect markets (or

complete risk sharing), (2) all uncertainties enter the

objective linearly, and (3) a two-period world wherein

all decisions must be made in the first period.2 If any

one of these three assumptions is altered, this equivalence no longer holds. The rest of this chapter discusses relaxation of assumptions (2) and (3)—the instances

where risk matters to decisions even when markets are

perfect. The next chapter discusses the implications of

imperfect risk sharing.

Nonlinear Risks

Statistics suggest that yield uncertainty could be as

large as, or even larger than, price uncertainty. Unlike

price risks, farmers also might control the degree of

uncertainty associated with yields, often in nonlinear

ways. For example, irrigation might push up yields at

the bottom of the distribution more than those at the

top (see figure 2). A linear effect, by contrast, would

influence all possible yield levels by the same

amount, either additively or proportionately. This nonlinearity negates the above equivalence results for

uncertain prices.

The nonlinearity of yield uncertainty in response to

certain inputs follows from the nature of plant growth.

Justus von Liebig’s Law of the Minimum states that

yield is proportional to the amount of the most limiting

plant growth factor, whichever it may be (van der

Ploeg). Because the most limiting factor is often supplied by the weather (such as rainwater, sunlight, or an

absence of hail or pests), human provision of additional inputs results in higher yields only when those

inputs turn out to be the most limiting factor. Thus,

irrigation increases yields greatly during years with little rainfall but increases them much less in years with

heavy rainfall.

The role of risk in perfect markets also hinges on the

timing of decisions relative to the resolution of uncertainties. Returning to the irrigation example, if irrigation decisions can be made after observing the rainfall

and moisture content of the soil, then the optimal

amount can be applied in all states of the world. In this

context, more risk really implies more variability in the

marginal value of irrigation applications (and in the

amount applied). On the other hand, if the investment

decision or application amount must be made prior to

observing rainfall and moisture content of the soil, then

risk affects production in an entirely different way.

Another example is that farmers may sometimes apply

surplus nutrients, such as fertilizer and phosphorus, to

their crops (Babcock). When plant growth is limited

by a factor other than nutrient applications, the excess

can build up in the soil and ultimately increase nutrient

runoff into streams and lakes, a potentially serious

environmental problem.

Why would farmers apply costly surplus nutrient

applications if such applications do not result in higher

yields? A straightforward explanation involves the

way uncertainty about required nutrient applications

6  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

2One may substitute for the third assumption that all decisions are

completely and costlessly reversible.

Yield without irrigation

 Yield with irrigation

Probability

Probability

Figure 2

Irrigation affects yield risk in a nonlinear way
enters into the profit function nonlinearly. Pre-existing

nutrient levels in the ground are unknown to the

farmer and vary spatially across a field. Weather conditions may also preclude mid-season re-entry into the

field to adjust nutrient applications as needed. Thus, a

farmer must apply nutrients before knowing how much

will be needed to maximize plant growth via von

Liebig’s Law of the Minimum.

One can demonstrate the effects of these uncertainties

using the two-period model wherein x =[n (nutrient

applications)] and s = [n (the uncertain level of n that

maximizes plant growth)], and m is the price of nutrient applications. Profits are characterized by

where revenues,

The exogenous scalar m (not random)

denotes the price of nutrient applications. The associated perfect-markets value function is

V= max E[R] – mn

where

The function F(.) denotes the probability distribution

function (or cumulative density function) of

Inspection of this profit function makes clear that

uncertainty about n is central to the decision about n—

it is incorrect to substitute the expected value of n for

the random variable.

The first-order condition of the optimization problem

implies that the economically optimal level of nutrient

applications (n*) solves the equation

Without further assumptions about the distribution

function F(.) one cannot solve explicitly for n*. It is

possible, depending on the distribution function and

the ratio m/r, that n* is either greater than or less than

the expected level of n. Unless uncertainty about n is

arbitrarily small, however, an economically optimal

application of nutrients may often induce an agronomically excessive application level. This contrasts with

applications in a certain world in which no excess

nutrients would be applied. The expected level of

excess nutrients is equal to the probability that (n ( <

n*) multiplied by the expected value of (n* - n) given

that (n < n*). Mathematically, expected excess nutrients are equal to 

Thus, uncertainty about the level of nutrients required

for optimal plant growth influences the level of nutrient applications, the level of excess nutrients applied

on average, and indirectly, the environmental quality

of streams and lakes.3

In general, uncertainty that enters into the objective in

a nonlinear fashion will be an important determinant

of agricultural production decisions. Except for price

uncertainty faced by competitive firms, nonlinear

risks are likely to be the rule rather than the exception

in agriculture. And when firms are not competitive

(that is, they have market power or control over the

prices they receive) price uncertainty also may be

important.4 The existence of market power, however,

violates the perfect-markets condition, which is the

focus of this chapter.

Dynamic Decisions under Uncertainty

Even when markets are perfect and uncertainty enters

the profit function linearly, risks will often influence

decisions in dynamic environments. Unlike the twoperiod model presented above, many decisions in the

real world have implications over the long run.

Furthermore, people possess a certain amount of flexibility about the timing of these decisions. For farmers,

these longrun decisions include decisions about when

to plant a new orchard or replace an old one, whether

to alter a previously planned crop rotation in light of

changing commodity crop prices, and when to buy a

new tractor, combine, or irrigation system. 

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  7

π π (,) ( ) ( x s = − n, n R n, n) mn,

R n, n) = rn n < n ( if and equal

rn n > n. if =

EP P [] [ ] [ ] [ ] R n > n rn n < n r n n > n = + E

n .

F n nf n n f n n - f n n n <

m / r.

( )( 1+ ( )) + ( ) - ( ) E * * *** * * [ ]

= −1

F(n ) n n n < n ** * E[ - ].

3Note that if farmers pay nothing for damages associated with

excess nutrient applications, then those damages constitute a negative externality, which violates the assumptions that underlie

perfect markets.

4 When a producer holds some market power, it is not precisely

correct to refer to “price uncertainty,” as if it is exogenous. In

such a context, prices are jointly determined by the producer’s

decisions and by random events beyond his or her control (for

example, demand uncertainty).

=− + ( ( )) ( ) 1 F n rn F n r n n < n . E[ ]
Three elements, central to relevance of risk to these

kinds of decisions, cannot be embodied within the

two-period model above. These three key elements

are: (1) the irreversibility or partial irreversibility of

the decision, such that it entails longrun consequences,

(2) the decisionmaker’s ability to control both the timing and nature of the decision, and (3) when uncertainty becomes resolved over time, it holds the implications that persist into the future.

We can illustrate all of these features using a slightly

more complicated three-period example. In the first

period, a farmer may choose to buy a tractor or to wait

until period two to make the purchase decision. If he

buys the tractor in period one, it will increase his output in periods two and three. If he decides to buy the

tractor in period two, it will increase output only in

period three. Profits depend on whether or not the

farmer buys a tractor and the price of the crop, which

is uncertain. The price of the crop in periods two and

three will not be learned until period two (p2 = p3). So

the farmer can make a more informed decision if he

waits until period two to make it; however, by waiting

until period two, he will miss out on the second period’s increased output that would have been provided

had he purchased the tractor in period one. For this

example, suppose that it is worthwhile to buy the tractor if prices turn out to be high but not worthwhile to

buy the tractor if prices are low.

Should the farmer wait in the first period? The answer

depends on the odds of high versus low prices and

how much profit is affected by both the price level and

the tractor purchase decision. There are four possible

profit levels. Suppose that these are 0 and +1 for low

and high prices without a new tractor, and –1 and +2

with a new tractor. In this case it is optimal to wait if

the probability of a high price is less than 2/3. If the

farmer did not have the option of waiting until period

two—that is, the farmer were forced to make his decision in the first period—then he buys the tractor if the

probability of a high price is greater than 50 percent.

Note that this example, illustrated in figure 3, possesses each of the above three elements. First, the tractor

is assumed to be an irreversible decision. If, after the

second period, the farmer could return the tractor for a

full refund in the event that prices turned out low, then

risk would have no bearing in this model—the decision would be based on the probability of 1/2, not 2/3.

Second, the farmer has control over the timing of his

decision. If the farmer were for some reason forced to

make his purchase decision in the first period, the

same equivalence between the certain and uncertain

models described above for the two-period linear

model would occur. Third, whether prices turn out

high or low in the second period, the random outcome

persists into the third period. If the uncertainty were

transitory rather than persistent (it had no bearing on

the price in period three), then whatever the farmer

learns about the price in the second period, he learns

too late to make any use of it; once again, the choice

would be based on the probability of 1/2, not 2/3.

For this three-period example, the lower the probability of high prices, the more likely it is optimal for the

farmer to wait until period two to make his decision.

In more general problems, the greater the level of

uncertainty about the profitability of irreversible decisions, the more likely the farmer is to wait. The intuition is that the greater the level of uncertainty, the

greater the amount of information the farmer will

obtain by waiting to make his decision. Because

information is always valuable, a greater flow of it

over time is more likely to forestall decisionmaking,

all else the same.

Beyond this simple model, how relevant is dynamic

uncertainty to agricultural decisionmaking? Some

basic features of modern agriculture suggest that it is

very important for many decisions. In thinking about

the relevance of dynamic uncertainty to a particular

decision, one should pose three questions about each of

the three crucial elements of the above model: (1) How

long-lived or irreversible is the decision? (2) How

much latitude does the decisionmaker possess regarding the timing of his or her decision? (3) How much

information transpires over time, and for how long

does the relevance of information persist into the

future?

Many basic decisions in agriculture possess all three

elements. Decisions regarding capital investment and

technology choices are partially if not totally irreversible. Farmers may be able to sell purchased

machinery and equipment but are not likely to procure

the full price. There also are transaction costs associated with salvaging machinery. Other kinds of investments, such as drip irrigation systems that entail large

installation costs or educational expenses to learn how

to implement new technologies, may be totally irreversible (Marra and Carlson).

8  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
Because both the disposition of the land and the stock

of pests to which crops are susceptible depend on what

crops were planted in prior rotations, even year-to-year

planting decisions entail a certain amount of irreversibility. For example, switching from a corn-soybean rotation to a corn-soybean-soybean rotation

involves certain irreversibilities beyond the nominal

machinery and human capital investments that may be

required for such a change. To the authors’ knowledge, there has been no formal supply-response analysis of field-crop rotations in light of dynamic price

uncertainty and past irreversible planting decisions.

Such an analysis might help to explain why supply

elasticities for corn, soybeans, and wheat are seemingly so inelastic. “Waiting” in this context, implies a

tendency to forestall altering a crop rotation in light of

changing prices. Given large enough uncertainty

about future prices, it may take large price swings

before farmers are finally induced into switching from

previously planned crops. 

Irreversibility is clearly important for tree crop plantings and removals. Trees have a long production life,

removal is irreversible, and new plantings require

several years of growth before they reach full productive capacity. Replacing a current stand with a

new stand (and perhaps a new variety) therefore

entails large irreversible costs. 

Although farmers have full discretion with regard to

the timing of most decisions, the third element, the

persistence of new information, is more relevant for

some kinds of risks than for others. The stochastic

properties of commodity prices imply that they are

random walks or near random walks. (A pure random

walk implies that the best guess for next year’s price

is the current price.) In other words, shocks to commodity prices persist very long into the future. Quite

the opposite from prices, yield shocks are highly transitory in nature—yield in one season usually provides

little or no information about the level of the next season’s yield. Yield variation is due mainly to the

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  9

Buy tractor

or

Wait

Period

One

Period

Two

Period

Three

buy

wait

Low price

High output

High cost

Low price

Low output

Low cost

Buy tractor or

Don’t buy

High price

High-output

High cost

Buy tractor or

Don’t buy

High price

High output

High cost

Low price

High output

High cost

High price

High output

High cost

High price

Low output

Low cost

High price

High output

High cost

Low price

Low output

Low cost

Low price

High output

High cost

buy

buy

don’t buy

don’t buy

Bad luck

Bad luck

Good luck

Good luck

(–1, –1) (0, –1) (0,0) (0, +1) (+2, +2) (+1, +2)

Figure 3

Sequential decisions under uncertainty

Payoff =(Period Period)

 Two, Three
weather, and this year’s weather provides little information about next year’s weather. Thus, price risks

are typically important to dynamic decisions under

uncertainty, and yield risks are relatively unimportant,

so long as markets function perfectly.5

The next chapter examines how risk influences decisions in imperfect markets. In imperfect markets, transitory yield risk can influence decisions in ways different from perfect markets. This difference between

yield uncertainty and price uncertainty can be useful in

analyses that attempt to sort out the empirical relevance of the different ways risk affects agricultural

production. We return to this point in Chapter 3, which

includes a brief discussion of empirical strategies.

To analyze dynamic decisions under uncertainty

requires the use of dynamic programming. The intuition of dynamic programming, illustrated in the simple three-period example above, utilizes the logic of

backward induction. As the name suggests, these

problems must be solved in reverse chronology,

through all possible contingencies in all future periods.

For more realistic problems, the dynamic program

requires use of a computer to solve. These models

also can be econometrically calibrated with economic

data (Rust). For certain time-continuous problems one

can derive analytical solutions using stochastic calculus (Dixit and Pindyck). Review of these particular

methods is beyond the scope of this report.

Conclusion

When markets function perfectly, individuals can share

their individual risk so that the only risk individuals

intrinsically care about is aggregate risk. However,

even when markets are perfect, risk is still important

to many kinds of agricultural production decisions.

The relevance of risk in this context has to do with the

timing of information as compared to the timing of

decisions. Within a two-period framework, risks that

enter into the objective in a nonlinear fashion influence choices because they must be made without

knowledge of certain key variables, such as in the

nutrient example. Price uncertainty, however, does not

influence decisions in this two-period context because

uncertain prices enter into the objective function linearly—choices are equivalent to those that would be

made if prices were fixed at their expected level. Risk

is also important in dynamic environments when farmers have control over the timing of their decisions. In

dynamic environments, farmers must balance the cost

of waiting to make a decision against the potential

gains that stem from the new information that they

might collect.

10  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

5Note that in the beginning of Chapter 1 we quantified yield risk

using deviations from a trend curve and quantified price risk

using year-to-year changes. We quantified yield and price risks in

these ways because yield shocks are mostly transitory whereas

price shocks are mostly permanent.
For Further Reading

Antle, John M. “Sequential Decision Making in

Production Models.” American Journal of Agricultural

Economics, Vol. 65, No. 2 (May1983) pp. 282-290.

Babcock, Bruce. A. “The Effects of Uncertainty on

Optimal Nitrogen Applications,” Review of

Agricultural Economics, Vol. 14, No. 2 (1992) pp.

271-80.

Berck, Peter, Jacqueline Geoghegan, and Stephen

Stohs. “A Strong Test of the von Liebig Hypothesis.”

American Journal of Agricultural Economics Vol. 82,

No. 4 (November 200) pp. 948-55.

Cochrane, John H. Asset Pricing. Princeton

University Press (2001).

Dixit, Avinash K. and Robert S. Pindyck. Investment

Under Uncertainty. Princeton University Press (1994). 

Deaton, Angus. Understanding Consumption.

Clarendon Lectures in Economics. Oxford University

Press (1992).

Harwood, Joy, Richard Heifner, Keith Coble, Janet

Perry, and Agapi Somwaru. “Managing Risk in a

Farming, Concepts, Research and Analysis.”

Agricultural Economic Report Number 774, United

States Department of Agriculture, Economic Research

Service (March 1999).

Just, Richard E., and Rulon D. Pope (Ed.). A

Comprehensive Assessment of the Role of Risk in

Agriculture. Kluwer Academic Publishers, Boston,

MA (2002).

Knight, F.H. Risk, Uncertainty and Profit. Silver

Lake Publishing Company, New York: Sentry Press

(1964).

Makki, Shiva S., and Agapi Somwaru. “Asymmetric

Information in the Market for Yield and Revenue

Insurance.” Technical Bulletin No. 1892, United States

Department of Agriculture, Economic Research

Service (April 2001).

Marra, Michele C., and Gerald A. Carlson.

“Agricultural Technology and Risk.” In Richard E.

Just and Rulon D. Pope (Ed.). A Comprehensive

Assessment of the Role of Risk in Agriculture. Kluwer

Academic Publishers, Boston, MA (2002).

Mas-Colell, Andreu, Michael D. Whinston, and Jerry

R. Green. Microeconomic Theory. Oxford University

Press, Inc., New York, (1995).

Paris, Quirino. “The von Liebig Hypothesis.”

American Journal of Agricultural Economics, Vol. 74,

No. 4 (November 1992) pp. 1019-28.

Rust, John. “Numerical Dynamic Programming in

Economics” Handbook of Computational Economics.

Vol. 1 pp. 619-729. Handbooks in Economics, Vol.

13. Amsterdam; New York and Oxford: Elsevier

Science, North Holland (1996).

van der Ploeg, R.R., W. Böhm, and M.B. Kirkham.

“On the Origin of the Theory of Mineral Nutrition of

Plants and the Law of the Minimum.” American

Journal of Soil Science. Vol. 63 (1999) pp. 1055-1062

. 

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  11
12  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

Glossary

backward induction A method for deriving optimal behavior by working sequentially backward in 

time through all possible contingencies.

coefficient of variation Measures the proportional variation of some data. It is approximately equal to the

average proportional difference from the average. Mathematically, it is equal to 

the standard deviation divided by the average.

perfect markets A condition that implies that competitive prices exist for all goods and services in 

all possible contingencies. The assumptions that underlie the perfect markets condition are summarized in the box on page 3. In the context of risky environments, perfect markets are sometimes referred to as complete market of contingent claims.

probability distribution A function usually denoted as F(x) that gives the probability that the realization of

function a random variable will take on a value less than or equal to x. F(x) is always positive, weakly increasing in x, tends to zero as x tends to negative infinity, and 

tends to 1 as x tends to positive infinity. It is sometimes referred to as the 

cumulative density function.

random walk A time-series stochastic process (yt

) in which the current level of the series 

includes the sum of all past random shocks—the best guess for next period’s level

is the current level. Mathematically, yt – yt-1 = et

, where et is a random variable 

independent of all ei for all i not equal to t.
Market Imperfections and Risk

Chapter 2 examined the ways in which risk influences agricultural production decisions when markets

are perfect. Among other assumptions, perfect markets imply that a complete (or “as if ” complete) set

of contingent claims exists to facilitate farmers’ sharing their risks with all others in the economy. In

such a world, the good or bad luck of each individual

is averaged with the good and bad luck of everyone

else, which leaves individual farmers to care only

about aggregate risk, not the risk of their particular

farm operation. In perfect markets, uncertainty faced

by an individual farm operation is relevant to farmlevel decisions only to the extent that it embodies a

certain lack of information.

This chapter focuses on different consequences stemming from risk. These consequences follow when

markets are imperfect, a complete market of contingent claims does not exist, and individuals cannot

share all their risks. Amid imperfect risk sharing,

farmers may in fact care about the income uncertainty

associated with their particular farm operation. For

example, in bad years they may be unable to consume

as much as they do in good years. The disutility associated with variable consumption, formally defined as

risk aversion, may cause farmers to alter their production decisions to make farm income less risky and

variable. Farmers may also diversify their incomeearning opportunities by working off the farm.

Imperfect markets can hinder farmers’ ability to obtain

funds to purchase or rent all the inputs required for

production. A farmer’s good luck may, therefore,

bring unexpected cash flow and, with it, new profitable farming opportunities. On the other hand, bad

luck can force a farmer into bankruptcy or a forced

sale even when the ongoing farm operation is expected

to be profitable. This result differs from perfect markets in which all individuals can exploit all profitable

opportunities.

Information-related effects of risk examined in

Chapter 2 remain relevant when markets are imperfect;

however, there has been little research to examine how

information-related effects interact with imperfect risk

sharing. Recent theoretical research by Athey is a

notable exception. This chapter focuses on the direct

effects of imperfect risk sharing.

Asymmetric Information

In reality, individuals and firms share many risks and

often finance production using contingent claims.

Individuals and firms often borrow, save, and fund

investments using financial markets. But these markets probably do not constitute complete contingent

claims—individuals still bear considerable risk that

would be insurable in a perfect world. What prevents

market participants from trading risks via contingent

claims? Why are not all risks shared? 

The foremost answer to this question put forth by

economists is asymmetric information. The problem

is not that inherent uncertainties exist, but rather that

some individuals are better informed than others.

Akerlof’s seminal (and Nobel prize-winning) paper,

“A Market for Lemons,” put down in formal mathematical language what almost anyone who has bought

a used car plainly understands: typically, one who sells

a used car knows more about its uncertain quality than

a potential buyer. This fact helps to explain why a car

is usually worth less than the new purchase price the

moment after a buyer drives it off the dealer’s lot.

The logic underlying lemon markets is as follows.

Those who buy used cars rationally expect that the car

they purchase will be of average quality. Those selling

cars of above-average quality are less willing to sell

because they can receive only an average-quality

price; so some potential sellers choose to keep the car.

Buyers rationally understand that those with high-quality used cars are less likely to sell, so the buyers are

willing to pay only a lower-than-average quality price.

As a result, sellers of even average-quality cars are

less willing to sell, because they cannot receive even

an average-quality price. And so on.... Thus, used car

markets are thinner than they would be if car quality

were plainly observable—that is, if one could costlessly spot “a lemon.”

The general observation that some individuals are

better informed than others helps us to understand

why some markets, including those for certain contingent claims, will not always exist. And when they

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  13

Chapter 3–Risk in Imperfect Markets
do exist, they will differ from those predicted in perfect markets.

Individuals and firms work to overcome inefficiencies

caused by asymmetric information by writing contracts, developing reputations for high-quality goods

and services, offering “money back guarantees,” and

so on. These mechanisms constitute costly signals that

markets can use to credibly communicate information

from one party to another. However, due to the costs

embedded in sending these signals, these mechanisms

will not work to share all risks.

The two main conceptual ideas used to better understand asymmetric information environments are

adverse selection and moral hazard.

6 The tension

underlying each of these ideas can be presented as an

optimal contract between informed and uninformed

parties, labeled respectively as the “agent” and the

“principal.” 

Adverse Selection

Adverse selection involves situations in which the

agents know something about their situations the principals do not know, e.g., the used-car sellers in

Akerlof’s market for lemons. From the perspective of

the principals, they would like to write different contracts for different types of agents. For example, some

agents may own riskier farms than others, and insurance companies (the principals) would like to charge

different premiums for different risk classes. They cannot do so because they cannot distinguish the riskier

classes from those that are less risky. In some situations, the problem of adverse selection can be partially

overcome by offering menus of contracts to agents

who reveal their types through the contracts that they

select. For example, insurance companies typically

will offer a menu of different premium-deductible

combinations to differentiate high-risk types from lowrisk types (Makki and Somwaru). Indeed, according

to the revelation principle, an optimal insurance

scheme will always induce agents to reveal whether

they are low risk or high risk. But, in some situations,

these markets simply will not exist—some risks are

uninsurable.

Moral Hazard

The problem of moral hazard involves situations in

which both parties know the probability distribution of

possible outcomes but in which the agents’ decisions

are not explicitly contractible. For example, the physical and mental effort exerted by a laborer may not be

measurable in a way that can be explicitly defined in a

compensation contract. In moral-hazard environments,

the principal and agent must write a second-best incentive scheme based on some observable and verifiable

outcome(s), such as yields, prices, and/or profits. The

observable outcome depends on the agent’s decision

(usually called “effort”) but also on luck. Because the

agent’s compensation depends on luck in addition to

his decisions or effort, his income is not completely

insured. Indeed, Robert Shiller regards moral hazard

as the chief reason why not all risks are shared: 

“Living standards are not fully insurable because of

moral-hazard problems: if people or organizations

knew that their income were guaranteed regardless

of the amount of effort that they put in, then there

would be a markedly reduced incentive to make

efforts to maintain income.” (Shiller, p. 1)

Thus, moral hazard embodies a natural tension that

arises between insurance (risk sharing) and incentive

provision.

Adverse selection and moral hazard give compelling

reasons for why risks are not insured. The focus of

this report, however, pertains to the consequences of

imperfect risk sharing for agricultural production

choices. Are agricultural production decisions different because farm income (or profit) risks are not fully

insured? If so, how are they different?

The answers to these questions depend on the model

used to answer them. Below we review three broad

departures from perfect markets that are often used to

shed light on how imperfect risk sharing might influence agricultural production. First, however, we

review economists’ standard definition of risk aversion,

because it is often the cornerstone of these departures.

Risk Aversion

Since publication of seminal papers by Arrow and

Pratt, the notion of “risk aversion” to economists has

been synonymous with “diminishing marginal utility

of consumption.” Because consumption is inextrica14  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

6A third type, called hidden information, is closely related to

adverse selection, so we do not review it here. See Chapter 14 in

Mas-colell, Whinston, and Green for an explanation of this kind

of asymmetric information as well as more formal presentations

of adverse selection and moral hazard.
bly tied to an individual’s wealth, under certain conditions, it is also synonymous with “diminishing marginal utility of wealth.” In other words, the richer one is,

the less one values an additional dollar. And the more

risk averse one is, the more rapidly one’s marginal

utility declines with one’s level of consumption or

wealth.

Mathematically, if utility is defined as u(c), where c

represents consumption, risk aversion implies that

u”(c) < 0 (’ and ” denote the first and second derivatives, respectively). If consumption is random with an

average value of c* then risk aversion (u”(c) < 0)

implies u(c*) > E[u(c)]: the utility of average consumption is greater than the expected utility of consumption. The two measures of risk aversion, pioneered by Arrow and Pratt, are the coefficient of

absolute risk aversion (-u”(c)/u’(c)) and the coefficient

of relative risk aversion (-c u”(c)/u’(c)). In accordance

with these two measures of risk aversion, there are two

commonly used utility functions in which these measures of risk aversion are the same, regardless of the

amount consumed. Given a measure of risk aversion

r, these two utility functions are 

Constant absolute risk aversion (CARA):

u(c) = A – e – rc

Constant relative risk aversion (CRRA):

u(c) = – Ac (1-r)/(1 – r) 

In both utility functions, larger r implies more risk

aversion—a larger difference between u(c*) and

E[u(c)] for the same probability distribution function

of c.

For dynamic models in which utility is additively separable over time, risk aversion also measures the elasticity of intertemporal substitution. Indeed, the concepts of intertemporal substitution and risk aversion

are very similar: the first measures willingness to substitute consumption between one period of time and

another; the second measures willingness to substitute

between one “state of nature” and another. In the first

case, the more inelastic the intertemporal substitution,

the more one prefers a smooth consumption profile

over one that grows or diminishes rapidly with time; in

the second case, the more risk averse one is, the less

one prefers more uncertain consumption over less

uncertain consumption. The two concepts are identical

if an individual’s overall utility (after the fact) through

a period of fluctuating consumption is the same,

whether or not the fluctuations in consumption were

anticipated before the fact.

Although diminishing marginal utility makes good

common sense, to some it may seem strangely disconnected from the notion of risk. Indeed, there is growing skepticism in psychology and some areas of economics about the fundamental (behavioral) equivalence of risk aversion and diminishing marginal utility

(Kahneman and Tversky; Rabin, 2000). This skepticism stems in part from observed levels of risk aversion, both experimentally and in market data, that are

so large as to defy rationalization via diminishing marginal utility (Mehra and Prescott; Rabin, 2000).

Furthermore, although the concepts of risk aversion

and intertemporal substitution are conceptually quite

similar, empirically they differ substantially (Weil).7

Aside from pushing economists to reconsider their

notion of risk aversion, these empirical findings imply

that even a modest amount of unshared agricultural

risk could be crucially important to many agricultural

production decisions.

No Risk Sharing

The most prevalent way to model the effect of imperfect risk sharing on agricultural production choices follows an influential paper on price uncertainty by

Sandmo that assumes no risk sharing at all. This

approach assumes that farmers choose inputs to maximize a static, concave (risk-averse) function of profits,

like the utility function described in the last section

except that current profits are substituted in place of

consumption. This objective appears similar to the

two-period objective presented in Chapter 2, except it

is the expected “utility” of profits that is maximized

rather than just expected profits.

Again denoting production decisions by the vector x

and exogenous random factors by the vector s, the

value of the farm operation is given by

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  15

7See the box, “The Equity Premium Puzzle” (p.17).

V Eu = max ( ) x[ ] π( ) x, s .
Because the utility function is itself a nonlinear function of profits, random factors will influence optimal

decisions even when they enter into the profit function

linearly. Thus, the equivalence of production decisions, whether or not prices are uncertain, no longer

holds as it did in Chapter 2. All kinds of uncertainty

now matter for production decisions.

This optimization problem, the solution to which characterizes the way risk influences production choices, is

somewhat more complicated than the two-period problem of Chapter 2. We can characterize an approximate

solution to the general problem by taking a secondorder Taylor expansion around the average (or expected) level of profits. Specifically, we can approximate

by

where VAR(π(x,s)) denotes the variance of profits.

Because risk aversion implies negative u”, production

decisions that increase profit variance, all else the

same, are underutilized in comparison to those in a

perfect-markets world; while those that decrease profit

variance, all else the same, are over-utilized in comparison to those in a perfect-markets world.

Production decisions that may influence the variance

of profits include the applications of specific inputs,

such as nitrogen and pesticides, as well as allocations

of acreage between crops. Because crop returns will

not be perfectly correlated, an incentive to mitigate

risk through crop diversification is built into this

objective (Freund).

Although the no-risk-sharing approach may seem as

extreme and unrealistic as perfect markets, the no-risksharing assumption is relatively simple analytically. It

allows one to see how risk may influence farmers’

decisions when they can avail themselves of very few

risk-coping tools. The problem with using such

extreme assumptions is that it attributes all nonlinearity

in the current-period objective to risk preferences. In

reality, farmers’ preferences may matter, but the nature

of the constraints that prevent them from sharing (or

insuring) all of their risks probably matters more.

One might, however, interpret the risk-aversion coefficients estimated within this paradigm loosely—not as

preferences per se, but as an amalgamation of preferences and constraints on risk sharing. This interpretation, however, could be misleading, in part because

constraints may be more binding in some states of the

world than in others. For example, a farmer with large

cash reserves and little debt may be better able to selfinsure against random income shocks than a farmer

with small cash reserves and large debts may. The static risk-averse objective cannot encapsulate these timevarying constraints.

More important, however, there is no reason to believe

that the nature of the constraints will be such that they

can be approximated by a smooth, concave utility

function. This modeling approach thus obscures

important features of these constraints. Indeed, it is

possible that a risk-neutral or risk-averse individual

will appear to be risk-loving, given the asymmetric

information environment in which the person resides

(Ghatak and Pandey). This point is explained in more

detail below in the discussion about contracting.

Despite these shortcomings of the no-risk-sharing

approach, its simplicity, as compared with other ways

of modeling the influence of risk, has inspired a large

literature that examines the effects of risk on agricultural production decisions. It has been applied to

interesting analyses of production scale, technology,

and crop allocation decisions (e.g., Just and Zilberman,

Just et al., Freund). 

Credit Constraints

In perfect markets, it is inconsequential whether capital investment is financed with retained earnings, equity, or debt, an important result first observed by

Modigliani and Miller. One implication of asymmetric

information, however, is that it may preclude financing

of some profitable farming operations (Stiglitz and

Weiss, Jaffee and Russell, Jensen and Meckling). If

asymmetric information prevents farmers from obtaining external funds to finance their operations, then

they may need to accumulate funds on their own via

savings or retained earnings, contrary to Modigliani

and Miller. 

Farm income risk holds new implications in this environment. Good luck, perhaps in the form of higherthan-expected prices and/or yields, may provide a

farmer with resources to finance an operation for

which the farmer could not previously obtain funds.

16  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

( ) ( )

( )

V u E u" E

 VAR

≈ + max

x [π π ] [])

π

() (

( ),

x, s x, s

x, s

1

2
Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  17

The Equity Premium Puzzle

Through the study of financial markets, researchers have learned that individual behavior toward risk is

complex and that it is probably different from that depicted in standard economic models. Perhaps most

noteworthy is the observation that over the last 100 years stocks have earned, on average, a premium of 6

to 8 percent greater than the returns of Treasury bills and other low-risk investments, a phenomenon normally explained by risk aversion. The degree of risk aversion implied by this premium is very large, a

well-known observation in finance called the “equity premium puzzle” (Mehra and Prescott; Hansen and

Jagannathan; Rabin, 2000). 

The size of the stock-market premium is puzzling because aggregate consumption growth is relatively

smooth. In other words, the fluctuations of the stock market have caused little variation in consumption,

so the only way the large risk premium and small consumption variability can be reconciled under the

standard notion of risk aversion is if people are extremely risk averse. In fact, researchers have shown that

(regardless of assumptions about individual preferences) the stock market premium implies that the marginal value of a dollar varies by an average of 30 to 50 percent per year or more (Hansen and

Jagannathan). 

If a small but non-negligible portion of the population held a more modest degree of risk aversion (such

that they were willing to tolerate somewhat larger consumption variability in exchange for much greater

consumption growth), then the premium would be driven down via competition. These individuals would

sell low-risk assets and buy stocks, ultimately pushing down the price of safe investments and bidding up

the price of stocks, thereby reducing the size of the premium. Therefore, the equity premium puzzle not

only implies that some people are very risk averse, but that most people are very risk averse.

The equity premium puzzle becomes even more confounding when the level of risk aversion implied by it

is checked against the historical patterns of interest rates and consumption growth. To understand this

alternative viewpoint, it is important to recognize that the concepts of risk aversion and intertemporal substitution are conceptually quite similar—the first measures willingness to substitute consumption between

one “state of nature” and another, and the second measures willingness to substitute consumption between

one period of time and another. In standard economic models of consumption, these concepts amount to

the same thing: variation in marginal utility caused by consumption variation. A high degree of risk aversion therefore implies highly inelastic intertemporal substitution—people are very unwilling to substitute

consumption over time.

If individuals are unwilling to substitute consumption between one period of time and another, and consumption is growing, then they would have a strong desire to shift future consumption toward the present.

The collective desire to shift future consumption to the present should drive interest rates up to levels

higher than actually observed. This phenomenon has been termed the “risk free rate puzzle” (Weil).

Furthermore, as consumption growth fluctuates over business cycles, interest rates also should fluctuate

far more than they actually do. 

To resolve these and other puzzles, economic researchers are increasingly turning toward psychological

models of behavior. (See the section “An Addendum on Psychology-Based Models, p. 21”.) In the future,

researchers may wish to investigate whether these empirical observations from finance are relevant to

farmers’ decisions in light of the financial risks they face.
Bad luck, on the other hand, may cause a farmer to

scale back previously planned (though still profitable)

operations due to a new shortage of funds. In an

extreme case, a farmer may be forced to sell or declare

bankruptcy, even when the ongoing farm operation is

expected to earn acceptable profits over the long run.

In short, the existence of credit constraints implies that

it sometimes takes wealth to make wealth.

The anatomy of credit-constrained capital investment

is depicted in figure 4 (also see Hubbard). In perfect

markets, a firm chooses inputs to maximize expected

profits, which implies that the marginal value product

(MVP) of an input is set equal to the price of the input.

Thus, in perfect markets, the marginal value product of

capital (MVPK) should equal the interest rate (assuming no capital depreciation). This unconstrained level

of investment is depicted as I** in figure 4. However,

if asymmetric information constrains farms from borrowing investment funds or otherwise obtaining them

externally, then investment will be lower than in perfect markets. One might imagine that the cost of capital to a farmer equals the real rate of interest so long as

the level of investment is less than the current net

worth (NW). To borrow funds beyond NW becomes

more costly, as depicted by the upward sloping “constrained capital supply” curve. Given a sufficiently

high level of investment demand or sufficiently low

NW, the constrained level of capital investment (I*)

will be less than the unconstrained level.

Because income or profit shocks influence NW, these

shocks also influence the constrained supply of investment funds and the level of investment. The effect

from profit risk is quite different from those previously

discussed. In some circumstances, the effect may be

difficult to sort out empirically from other kinds of

effects. For example, a shock to the price of an agricultural commodity is likely to persist into the future

(see Chapter 2). The price shock, therefore, shifts out

the supply of capital by increasing NW, but also shifts

out the demand for capital. Any attempt to measure

the effect of the credit constraint requires careful control of investment demand. Due to the effects of

dynamic uncertainty discussed in Chapter 2, controlling for investment demand can be difficult.

Sometimes credit constraints can restrain capital

investment (or other inputs) even without a shortfall in

wealth, if wealth is illiquid or otherwise costly to

quickly leverage in order to obtain funds precisely

when needed. In this context, credit constraints are

sometimes called liquidity constraints.

Most of the research on credit constraints involves

empirical tests regarding their mere existence—

whether or not capital investment is sometimes forestalled due to a shortfall of cash. These findings have

been used to assess whether and how much businesscycle fluctuations in the macro economy stem from

investment cycles that derive from liquidity constraints

(Hubbard). Much of this research finds evidence of

liquidity constraints in manufacturing and other sectors

of the economy. Additional research reports a certain

amount of skepticism regarding these empirical findings, in part because of possible confounding of

investment demand and supply shocks noted above

(Gomes). A lot of research also examines the role of

credit constraints to agricultural production, consumption, and poverty in developing countries (Bardhan

and Udry).

Several papers have tested for credit constraints in

U.S. agriculture (Bierlen and Featherstone, Hubbard

and Kashyap, Barry et al.); all of these papers report

evidence that these constraints are important. Farmers

also tend to save more than people with other kinds of

(presumably less risky) occupations and tend to hold a

disproportionate share of their assets in farmland and

farm capital (Carroll). The Economic Research

Service (ERS) reports that U.S. farm households have

a higher average income than nonfarm households

18  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

Investment

demand

(MVPK)

Cost of 

capital

r

real interest rate

Investment NW I**

Constrained capital 

supply 

External funds

I*

profit shocks

Figure 4

The effect of credit constraints

Notes: NW is the internal net worth of the firm or individual; I* 

is the optimal (credit-constrained) investment; I** is unconstrained

investment; r is the real interest rate; MVP is the marginal value

product of capital.

K 
($61,947 vs. $56,313 in 2000) and higher average net

worth than nonfarm households ($492,195 vs.

$272,083 in 1998), yet they consume less on average

than nonfarm households ($27,981 vs. $35,250 in

1998) and, therefore, save more. ERS reports that, in

2001, 63 percent of farm households held half or more

of their wealth in farm business assets. Farmers also

report that they use cash reserves as their primary

method of risk coping (Harwood et al.). These facts

suggest that the role of self-finance, and therefore risk

in light of credit constraints, appears to be a key feature in agriculture. Unlike the no-risk-sharing model,

the importance of risk is not driven by individual

tastes, such as risk aversion or diminishing marginal

utility of wealth.

The main focus of this report, however, is whether risk

(and in this case, whether risk in light of credit constraints) influences the nature of agricultural production decisions in the United States. We are especially

interested in decisions that pertain to environmental

quality. To our knowledge there has been little

research that explicitly examines the role of credit constraints to decisions besides capital investment, nor

any empirical research that attempts to identify the

environmental consequences of these constraints (at

least outside of developing economies).8 These effects

could be more complicated than our simple presentation above. Credit constraints may lead farmers not

only to reduce the quantity demanded of certain inputs

such as capital, but also to substitute other, less expensive (and less efficient) inputs, and to manage alreadyowned resources less efficiently than if they were not

credit constrained. For example, credit-constrained

farmers might plant more high-value, but land-degrading, crops; or they might delay adoption of conservation practices in the short run in the hope of building

up financial resources to afford more efficient longrun

decisions in the future. They might also be less

inclined or less able to adopt expensive irrigation or

precision technologies, which may influence environmental quality.

Contracting Models

Because asymmetric information provides the theoretical underpinnings of both the no-risk-sharing and credit-constrained approaches to modeling imperfect markets, perhaps the most theoretically pleasing approach

is to build the asymmetric-information environment

explicitly into the model. This approach is the one

taken by the contracting literature (Hueth and

Hennessy).

This section illustrates the contracting approach and

describes the nature of the theoretically optimal contract in moral hazard environments. Recall that moral

hazard environments involve situations in which specific actions taken by an individual (such as a farmer

supplying labor and other inputs) cannot be contracted

explicitly by an outside interest (such as a landlord,

bank, or other financier). Instead, an incentive contract is written whereby the individual is compensated

based on some observable and verifiable outcome that

depends on both the individual’s supplied inputs and

random events outside the individual’s control—both

inputs and luck matter for output. If the individual is

risk averse, then such an incentive contract is always

second best, because uncertainty in the individual’s

compensation causes “excessive” risk bearing which

must be shouldered in order to provide incentive for

the individual.9 If the contract paid a fixed wage, and

thereby protected the contractee from risk, then the

contractee would have no incentive to apply costly

inputs. If the inputs themselves were contractible,

then the optimal action could be undertaken while providing full insurance to the individual (if the outside

party is risk-neutral). The problem involves a tension

between providing the right incentives versus providing income insurance.

The standard analytical setup typically is as follows.

A business venture earns stochastic profit π(x,s),

which depends upon a vector of inputs (x) applied and

the outcome of some random variables s. For example, x could index the on-farm costs for inputs and

labor and s the random events related to weather.

Suppose that another individual owns the business

venture and cannot explicitly contract the laborer’s

choice for x, and therefore must choose a compensation scheme for the laborer based on realized profits,

π(x,s). The contract is defined by the function w(π)—

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  19

8An exception by Roberts and Key examines the effect of credit

constraints on planting decisions.

9The word “excessive” is in quotes because some might disagree

with its use in this context—because in a world with asymmetric

information, the information-constrained optimum is the best

that one can do. The term is used here only to highlight the

tradeoffs that must be made in the moral hazard environment

that are not present in a perfect-markets world with fully contractible inputs.
given realized profits π, the laborer gets w(π) and the

owner gets π – w(π).

The value of the contract to the principal (such as a

bank or financier) is defined by

subject to: E[ulab(w(π(x,s)))]≥ u (IR constraint)

and

(IC constraint)

The IR (individual rationality) constraint ensures that

the laborer’s expected utility under the contract is

greater than or equal to the laborer’s opportunity cost

(u, a lower bound for the laborer’s utility). The IC

(incentive compatibility) constraint ensures that the

laborer’s effort is at an optimal level given the contract w(π).

The solution to this contracting problem can be complicated, even when both the vectors x and s contain

only one variable. Even in this simple case, one cannot be assured that the contract w(π) is monotonically

increasing or even continuous—in general it can be

highly nonlinear.

The ambiguous shape of the contract curve implies

that production decisions in asymmetric environments

can be influenced by risk in complicated ways. In the

first model presented in Chapter 2, the farmer chose x

simply to maximize expected profits, E[π]; in the norisk-sharing model, this was generalized to maximization of E[u(π)]; under the optimal contract, the objective is generalized further to maximization of

E[w(u(π))].

This report shows that it is nonlinearity of one form

or another that makes risk important to decisions.

Within a static (two-period) environment, there are

now three layers of nonlinearity that must be assessed

in order to trace out the influence of risk. The first

layer entails nonlinear influence of uncertainty on

profits; the second layer entails individual preferences

(curvature of the utility function); and the third layer

entails nonlinearity in the incentive scheme (w()).

This third layer is by far the most complicated. The

nature of the incentive scheme (its nonlinearity) is

itself determined by the nature of the uncertainty and

how decisions made by the farmer influence those

risks. That is, the contract and risk are jointly dependent on each other. In the first two layers, at least the

objective was exogenous. 

Furthermore, the nature of nonlinearity in the contract

scheme is more ambiguous than in the other two layers. Nonlinearity of the first layer is governed by features of the production function about which natural

science may provide some insight, such as von

Liebig’s law of the minimum. Nonlinearity in the second layer is governed by reasonable assumptions about

individual preferences—that is, diminishing marginal

utility of consumption. In the third layer, however,

small differences in the environment may lead to very

different contract shapes, which may be discontinuous

as well as nonlinear. This means that risk could have

almost any conceivable influence on production choices, depending on subtle features of the profit function

and the probability density function for s.

Finally, this problem differs from reality by its static

nature. Compensation schemes will be complicated further when they take into account the sequential flows of

information, input decisions, and profits (Antle).

The complexity of even the simplest asymmetric information problems creates large empirical challenges.

Although the tensions posed by the moral hazard problem are representative of those in many production,

marketing, tenure, and debt contracts observed in agriculture, and though it seems clear that asymmetric

information in conjunction with risk will influence

agricultural production decisions, developing testable

hypotheses with regard to these effects is difficult.

Theory alone cannot predict whether imperfect markets increase or decrease input use of any particular

kind of input, even in the simplest representations of

the problem.

Transaction Costs and

Industrial Organization

Ronald Coase’s 1937 article, “The Nature of the Firm”

raised and answered a fundamental question: Why do

firms exist in a market economy? In a world of perfect markets, all factors in the economy, including

material inputs, labor, and technologies, are priced and

traded in competitive exchange—all production decisions are decentralized. A firm, by contrast, is a cen20  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

x u

lab

 = arg max [ ( )))] xE w( π(x, s .

VE w( ) x,w()= max ) [ ] π() ( x, s − π x, s
tralized institution of decisionmaking in which many

intermediate factors are produced and freely provided

within it. What determines the size and nature of

firms, these centralized decision-making units? 

Coase’s answer to these questions is transaction costs.

Transaction costs between individuals or firms are

those associated with finding goods and services and

negotiating prices for them. Transaction costs within

firms include costs of monitoring workers or otherwise

providing incentives that induce them to make choices

and exert efforts as instructed by their managers.

Coase hypothesized that the size and nature of firms is

determined by an optimization problem: the industry

structure we observe is the one that minimizes transaction costs. Since Coase, transaction costs have been

used to explain the structure and organization of all

kinds of social and political institutions (Williamson). 

One modern interpretation of transaction costs

involves the loss of efficiency embedded in the second-best incentive schemes of asymmetric-information

environments. These transaction costs are sometimes

called agency costs. As a crude model, we might

imagine that, within a firm, workers earn fixed wages;

but the firm must employ costly tiers of managers to

monitor their work. In other words, workers’ actions

(elements of the vector x in our modeling approaches)

are explicitly contracted, but at a cost. Between firms,

prices serve as a second-best incentive scheme, as in

the moral hazard contract. Firm size, industry size,

and organization evolve so as to minimize the sum of

these within-firm and between-firm transaction costs,

which include agency costs. The moral hazard model

of the last section showed that an important kind of

agency cost depends on the amount of risk—the optimal contract involves balancing a natural tension

between incentive provision and insurance. Thus, the

greater the level of risk, the greater the agency costs,

all else the same.

The point is that because risk is central to agency costs,

an important kind of transaction cost, risk may also be

important for understanding the size and organizational

structure of farms. And the structure of agriculture

may entail important environmental consequences.

Historically, farms have been among the most decentralized production units in our economy. Even today,

sole proprietorships and partnerships account for a

majority of U.S. farms and total agricultural production value.10 For some commodities, however, this

structure is beginning to change. Producers are

increasingly signing production and marketing contracts with processors who often provide many of the

material inputs to production. The institutional distinction between the farm and the processor is becoming less clear. 

Furthermore, in some industries, the prevalence of

contracting has been associated with larger farm operations and a greater geographic concentration. The

hog and broiler industries are striking examples of

these trends. A related environmental challenge

involves the management of nutrient waste originating

from the increasingly concentrated livestock industry.

Local cropland and pastureland cannot physically

assimilate the high concentration of this waste

(Gollehon et al.).

Theoretically, risk, structure, and environmental quality are related. Empirically, structural change and environmental quality are geographically correlated for

certain livestock industries. However, because there

are many kinds of transaction costs besides risk-related

agency costs, the causal link between risk, structure,

and the environment remains ambiguous.

An Addendum on 

Psychology-Based Models

Most of the research on agricultural risk is based on

expected utility theory (EUT). Expected utility theory

also provides the basis to substantiate economists’

notion that risk aversion is synonymous with diminishing marginal utility of consumption. Formally, it says

that preferences are linear in probabilities: the value V

an individual places on the probabilities P(s) over possible states of the world (s) that yield utility u(s) is

equal to V(P(s), u(s)) = ΣP(s)u(s).

The key assumption underlying EUT—the one with

the least obvious implications and the one that has

received the greatest amount of skepticism—is the

independence axiom. The independence axiom is a

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  21

10In 1997, sole proprietorships made up 86 percent of U.S. farm

households and produced 53 percent of U.S. farm product sales.

See the USDA-ERS briefing room on farm structure,

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

Closeup.htm
close cousin of the transitivity assumption in standard,

certain-world utility theory. It says that a linear transformation of a set of lotteries should not alter an individual’s ranking of lotteries. That is, if an individual

prefers lottery A to lottery B, then after probabilistically combining these two lotteries with a third lottery

(say, C), the individual should prefer the augmented

lottery A over the augmented lottery B. For example,

the independence axiom implies that if A is preferred

to B, then (50 percent chance of A + 50 percent chance

of C) must be preferred to (50 percent chance of B +

50 percent chance of C).

To make the above example concrete, suppose lottery

A is $3,000 for sure, lottery B is an 80-percent chance

of $4,000 and a 20-percent chance of $0, and lottery C

is $0 for certain. If one prefers lottery A to lottery B

(as most people do in experimental trials), then the

independence axiom implies that (25 percent chance of

A + 75 percent chance of C) should be preferred to (25

percent chance of B + 75 percent chance of C). The

second two lotteries amount to (a 25-percent chance of

$3,000 plus a 75-percent chance of $0) and (a 20-percent chance of $4,000 plus an 80-percent chance at

$0). Contrary to the independence axiom, in experiments most people prefer augmented lottery B over

augmented lottery A despite their preference of unaugmented lottery A to unaugmented lottery B (Kahneman

and Tversky).

This and other experimental anomalies have cast

broader doubt on all economists’ standard risk-modeling approaches. Other notable anomalies include a

tendency by individuals to overweight small-probability events, to be risk averse with respect to losses and

risk loving with respect to gains, regardless of wealth,

and to choose differently between substantively identical alternatives depending on how the alternatives are

“framed.” Although these anomalies have been well

known for many years, and many generalizations of

EUT have been developed in attempts to accommodate

experimental findings, little research applies these generalizations to observed agricultural production decisions. Applications in mainstream economics are just

beginning to gain wider acceptance and application.

Application of these experimental phenomena to

observed economic behavior remains a new frontier in

economic research. Delayed application of non-expected utility theories has been slow to arise due to basic

questions that remain about how to interpret the experimental findings for application to real economic behavior. Consider, for example, an essential difference

between experimental risk behavior and real-world

behavior under uncertainty: in an experimental setting,

probabilities are objective values provided by the

experimenter; in the real world, probabilities are subjective beliefs or “feelings” an individual holds about

possible outcomes. It is not clear that individuals hold

the cognitive ability to connect their beliefs (or feelings) to the rational concept of probability. But, it is

possible that individuals’ behavior is in accordance

with expected utility outside of an experimental setting,

even if individuals cannot articulate those beliefs in

objective terms, such as probabilities. For example, it

seems unlikely that a randomly chosen individual can

accurately report the probabilistic odds of experiencing

a car accident on two alternative driving routes, but that

person may still be capable to choose between them,

perhaps in accordance with EUT. If an individual can

choose without articulating probabilities, then one

might want to reconsider the practical relevance of

decisions made in an artificial experimental context,

wherein objective probabilities exist, but wherein the

individual has not experienced the probabilities so as to

internalize exactly what they portend.

Another reason that few researchers have applied nonEUT theories (so-called “generalized expected utility

theories”) is that many of these alternative theories

also fail to match all experimental evidence (Camerer). 

Experimental evidence aside, some economic data

suggest a psychological basis for some decisions under

risk. Despite enormous research efforts, the so-called

“equity premium puzzle” (see the box on page 17)

remains deeply puzzling to financial economists. And

leading theories that try to reconcile the large premium

that stocks earn over bonds are increasingly looking

toward psychological motives with roots in habit formation, herd behavior, and generalized expected utility

models (Cambell and Cochrane, Shiller, Epstein and

Zin). Psychology-based models have also been used

to explain certain features of life-cycle consumption

behavior (O’Donoghue and Rabin).

Future research in agriculture may benefit from further application of these psychology-based models to

the empirical study of microeconomic production

decisions.

22  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
Conclusion

On the back cover of their recent book, A

Comprehensive Assessment of the Role of Risk in 

U.S. Agriculture, Just and Pope write: “After all the

research on agricultural risk to date, the treatment of

risk in agricultural research is far from harmonious.”

Partly in an attempt to put some perspective on this

disharmony, we have systematically reviewed the different ways that risk can be important for agricultural

production decisions

To tease out the influences of risk, researchers have

been forced to make extreme and often unrealistic

assumptions so as to draw sharp conclusions. Chapter

2 describes how risk influences decisions, even when

markets are perfect and all risks are insured. In this

chapter, we have explained why all risks are not actually insured (because of market incompleteness caused

by asymmetric information) and reviewed the general

approaches used to approximate the effects of risk in

imperfect markets. Although researchers have obtained

relatively precise conclusions regarding the effect of

risk using the no-risk-sharing and credit-constraints

approaches, in general, contract theory suggests that

there are no precise conclusions with regard to the

effect of risk on production choices. Thus, it is not surprising that research on agricultural risk lacks harmony.

There are several dimensions to the disharmony. The

first dimension involves the contrast between risk’s

effects in perfect markets versus risk’s effects in

imperfect markets. In perfect markets, risk affects outcomes mainly through the interaction of decision timing and the timing of resolving uncertainties—in other

words, effects that pertain to information flows. In

imperfect markets, the effect of risk affects outcomes

mainly through nonlinear objectives caused by imperfect risk-sharing or imperfect insurance. These two

kinds of risk effects are fundamentally different.

Research, however, tends to focus on one kind of

effect to the exclusion of the other. In some instances,

research has explained the same empirical phenomenon using both approaches. One example concerns

excess nutrient applications in agriculture. These can

be explained using a model in which applications must

be made before knowing the nutrient requirements of

crops. If crop growth is a nonlinear function of fertilizer applications, then excess nutrients may often be

applied. Excess applications have also been explained

as an attempt by farmers to reduce profit variability.

Another example, also prevalent outside agriculture

(especially macroeconomics), concerns the “lumpiness” of capital expenditure over time. This phenomenon can be explained using the model of dynamic

uncertainty in Chapter 2 or via the credit-constraints

approach of Chapter 3. There has been relatively little

research to date that attempts to reconcile the relative

importance of these two effects by considering them

simultaneously, or by carefully controlling for one

effect, while accurately measuring the other.

A second dimension involves the multitude of ways

risk can influence choices in the real world of imperfect markets. The no-risk-sharing and credit-constraints approaches to approximating imperfect markets are necessarily crude approximations of the real

world. Some of the disharmony may therefore arise

because the crude approximation used in one situation

may be less applicable to another. Indeed, the crude

approximations may obscure the true effects of risk

altogether.

A third dimension of disharmony pertains to the challenges of empirical identification. The challenge of

empirical identification is well understood in economics

generally: social scientists do not have laboratories in

which they can experimentally control for all factors,

and then carefully vary one (such as risk) so as to

uncover its effect. This problem, however, is particularly acute when attempting to measure the effects of risk.

To appreciate this difficulty, consider the challenges of

identifying the effects of risk on agricultural production versus the effect of education on wages, a classic

topic in labor economics. There is a positive correlation between education and wages, and it may seem

reasonable that the higher wages of the more educated

are caused by the greater amount of time they spent in

school. But maintaining the causal link is difficult.

Ability is likely correlated with both education and

wages; however, intrinsic ability is usually unobservable. The empirical challenge to measuring the returns

to education is to sort out the effects of education separate from intrinsic ability, which is hard to do.

Like labor economists who estimate the returns to education, risk researchers also must control for the effect

of convoluting factors statistically associated with risk

but not caused by it (like, ability in the educationwage example). Two farms facing different risks are

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  23
probably different in other respects also. How can

one be sure which differences are attributable to the

difference in risk as opposed to other differences,

especially when many of the other differences are

unobservable? With risk, the identification problem is

even more difficult because there are so many ways

that risk could influence choices. The researcher

needs to control not only for factors besides risk, but

also for all the different ways that risk is important, so

as not to confound one type of risk effect for another.

Perhaps the most challenging task with empirically

identifying the effects of risk involves measuring risk

in the first place. That is, a researcher needs measures

of risk and production decisions that are different

under different circumstances, like the different education levels and wages of different individuals that are

used to measure returns from education. Researchers

cannot ask farmers about objective measures of risk

and production decisions as easily as they can ask people about their levels of education and wages. How

does one quantify the level of risk on a farm? And

how much does it actually vary from one farm to

another or from one year to another? Even if

researchers’ notion of risk can be clearly communicated to farmers, it seems unlikely that farmers will be

able to communicate risk measures to researchers in a

consistent and objective way.

The data most readily available to agricultural risk

researchers are data on prices and yields. Prices and

yields vary from year to year and from region to

region and may serve as a source for identifying some

kinds of risk effects. To measure most kinds of risk

effects, however, the variance in yields must vary

from region to region and year to year. Changes in

variance over time and space are more difficult to

objectively quantify, and are almost surely associated

with other factors, such as climate and soil types. The

difference in variances across time and space also may

be too small to provide enough statistical power to

measure a risk effect of interest. In other words, measuring the importance of risk to production decisions

may be like trying to measure the returns from education on earnings in a world where nearly everyone has

spent the same amount of time in school—the returns

may be large or small but the data cannot reveal which

is true. Thus, it could be that some of the disharmony

in risk research has stemmed from inherent difficulties

involved with empirically identifying which of the risk

effects we have reviewed are most important for

understanding observed behavior.

24  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
For Further Reading

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Arrow, Kenneth J. Aspects of the Theory of Risk

Bearing. Yrjo Jahnssonin Saatio, Helsinki (1965).

Athey, Susan. “Investment and Information Value for

a Risk Averse Firm.” Massachusetts Institute of

Technology, Department of Economics Working

Paper: 00/30 (2000).

Bardhan, Pranab, and Christopher Udry. Development

Microeconomics. Oxford University Press (1999).

Barry, Peter J., Ralph W. Bierlen, and Narda L.

Sotomayor. “Financial Structure of Farm Business

under Imperfect Capital Markets” American Journal

of Agricultural Economics Vol. 82, No. 4 (November

2000) pp. 920-33.

Bierlen, R., and A.M. Featherstone. “Fundamental Q,

Cash Flow, and Investment: Evidence from Farm

Panel Data.” Review of Economics and Statistics, Vol.

80 (August 1998) pp. 427-35.

Buschena, David. “Non-Expected Utility: What Do

the Anomalies Mean for Risk in Agriculture.” In Just

and Pope (eds.) A Comprehensive Assessment of the

Role of Risk in U.S. Agriculture. Dordrecht and

London: Kluwer Academic Press (2002).

Cambell, John Y., and John H. Cochrane. “By Force

of Habit: A Consumption-Based Explanation of

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Political Economy Vol. 107, No. 2 (April 1999) pp.

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Camerer, Colin F. “Recent Tests of Generalizations of

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Kluwer Academic (1992).

Carroll, Christopher D. “Buffer-Stock Saving and the

Life-Cycle/Permanent Income Hypothesis.” Quarterly

Journal of Economics, Vol. 112 (1) pp. 1-56.

Coase, Ronald. “The Nature of the Firm.” Economica,

(November 1937) pp. 386-495.

Deaton, Angus. Understanding Consumption.

Clarendon Lectures in Economics. Oxford University

Press (1992).

Epstein, Larry G., and Stanley E. Zin. “Substitution,

Risk Aversion, and the Temporal Behavior of

Consumption and Asset Returns II: An Empirical

Analysis.” Journal of Political Economy, Vol. 99, No.

2 (April 1991).

ERS briefing room on farm income and 

costs: farm income forecasts. See

http://www.ers.usda.gov/Briefing/FarmIncome/

August 2002_outlook.pdf.

Freund, Rudolf J., “The Introduction of Risk into a

Programming Model.” Econometrica Vol. 24, No. 3

(July 1956) pp. 253-256.

Gollehon, Noel, Margriet Caswell, Marc Ribaudo,

Robert Kellogg, Charles Lander, and David Letson.

Confined Animal Production and Manure Nutrients.

U.S. Department of Agriculture, Economic Research

Service, Agriculture Information Bulletin No. 771,

June 2001.

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pp. 616-20.

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Harwood, Joy, Richard Heifner, Keith Coble, Janet

Perry, and Agapi Somwaru. Managing Risk in

Farming: Concepts, Research and Analysis.

Agricultural Economics Report No. 774. U.S.

Department of Agriculture, Economic Research

Service (March 1999). 

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Hubbard, R. Glenn. “Capital-Market Imperfections

and Investment.” Journal of Economic Literature, 

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An Application to U.S. Agriculture.” Journal of

Political Economy Vol. 100, No. 3 (June 1992) pp. 

506-534.

Hueth, Brent, and David A. Hennessy. “Contracts and

Risk in Agriculture: Conceptual and Empirical

Foundations.” In Just and Pope (eds.) A

Comprehensive Assessment of the Role of Risk

in U.S. Agriculture. Dordrecht and London: Kluwer

Academic Press, 2002.

Jaffee, Dwight M., and Thomas Russell. “Imperfect

Information and Credit Rationing.” Quarterly Journal

of Economics, Vol. 90, No. 4 (November 1976) pp. 

651-666.

Jensen, Michael C., and William H. Meckling.

“Theory of the Firm: Managerial Behavior, Agency

Costs, and Ownership Structure,” Journal of Financial

Economics Vol. 3(4) (October 1976) pp. 305-360.

Just, David. “Information, Processing Capacity, and

Judgment Bias in Risk Assessment.” In Just and Pope

(Eds.) A Comprehensive Assessment of the Role of Risk

in U.S. Agriculture. Dordrecht and London: Kluwer

Academic Press, 2002.

Just, Richard E., and Rulon D. Pope (eds.). A

Comprehensive Assessment of the Role of Risk in U.S.

Agriculture. Dordrecht and London: Kluwer Academic

Publishers (2002).

Just, Richard E., and David Zilberman. “Stochastic

Structure, Farm Size, and Technology Adoption in

Developing Agriculture,” Oxford Economic Papers,

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Just, Richard E., David Zilberman, Eithan Hochman,

and Ziv Bar-shira. “Input Allocation in Multicrop

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Economics, Vol. 72, No. 1 (February, 1990) pp. 200-

209.

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Econometrica Vol. 47 (1979) pp. 263-292.

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757-64.

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of Damage Control: Why Specification Matters”

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Premium: A Puzzle.” Journal of Monetary Economics

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26  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
Shiller, Robert. Irrational Exuberance. Princeton

University Press (2000).

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Rationing in Markets with Imperfect Information”

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Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  27
28  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

Glossary

agency costs Costs in monitoring or lost efficiency associated with providing appropriate incentives in asymmetric information or “principal-agent” environments.

expected utility theory A theory for economic behavior that assumes preferences are linear in probabilities. 

That is, the value of a probability density function of possible states of the world (s), 

in which u(s) equals the utility should state s arise, is equal to Σs P(s)u(s), where P(s) 

denotes the probability that state s will occur.

risk aversion Usually a measure of the degree of curvature of a cardinal utility function of wealth. 

In general, a measure of preference of more certainty as compared to less certainty, 

all other factors remaining constant. 

moral hazard A situation in which an individual exploits an incentive scheme for personal gain 

against a greater social cost. An extreme example might be insurance fraud (setting 

one’s house on fire to collect insurance). More subtle examples include leaving the 

door unlocked to a car that is theft insured, or surfing the internet while at work 

(shirking). In the economic literature, moral hazard is synonymous with hidden 

action information environments. 

adverse selection An asymmetric information environment in which one party cannot visibly distinguish different types or qualities. Without the proper incentive scheme (which is cost

ly) “bad” types will claim to be “good” types.

hidden information Like adverse selection except the “types” do not learn which type they are until after 

they have committed themselves to a contract (but before they make an unobservable 

decision, such as how much effort to exert).

independence axiom A key underlying assumption (and implication) of expected utility theory. If the vectors {p, q, and r} denote probabilities over a vector of possible outcomes (i.e., lotteries), and p is preferred to q, then the independence axiom implies that αp + (1-α)r is 

preferred to αq + (1-α)r.

the revelation principle If an optimal incentive scheme in an asymmetric information environment exists, it 

will be one that causes all agents to truthfully reveal their types.

transaction costs The costs associated with exchanging goods and services (as opposed to physically 

producing them). These costs may include physical costs such as search time and 

transportation costs as well as agency costs.
Direct payments from the Federal Government to

farmers exceeded $20 billion per year in the years

1999-2001. Nearly all of these payments were associated with government farm programs that targeted producers of major field crops: corn, soybeans, wheat,

rice, and cotton.11 By comparison, total cash receipts

for these crops were less than $40 billion in 2000.12

Given the magnitude of the payments paid in conjunction with government programs for these crops relative

to receipts, they may strongly influence agricultural

production. Some of these programs include risk-mitigating features such as price-contingent payments and

subsidized crop insurance, and therefore may interact

with the risk effects described in Chapters 2 and 3.

This chapter briefly reviews the largest agricultural

programs with risk-mitigating features and discusses

possible implications for production. To date, there

has been little research that examines how government

programs influence production via their risk-mitigating

features—most farm-program analyses use a “firstorder” approach that ignores risk effects discussed in

Chapters 2 and 3. Although the report does not formally analyze the effects of these subsidies from the

vantage of each of the risk-modeling approaches discussed in Chapters 2 and 3, it does suggest some qualitative predictions of these approaches. Four of the

largest programs currently in place are reviewed: marketing loans, lump-sum transfers (sometimes called

decoupled payments), disaster relief, and land retirement programs. Government programs may also

induce certain unintended consequences separate from

or contrary to program goals. Some of these possible

consequences are discussed.

Historical Government 

Payments to Farmers

Figure 5 shows total government farm payments from

1933 to 1995 (in constant 1995 dollars). Government

programs for some crops (such as sugar, peanuts, and

tobacco) have been realized via trade restrictions and

other mechanisms that do not necessarily bring about

direct government payments to farmers.

Figure 6 shows total direct government farm payments

from 1996 to 2001.13 In this period, there were some

notable changes in the structure of agricultural payments. The 1996 farm act curtailed price-contingent

income supports for major commodities and replaced

them with Production Flexibility Contracts (PFCs).

The sizes of these lump-sum payments were determined by the amount of land enrolled in farm programs prior to 1996, and were scheduled to decline

after 1997. In subsequent years, however, as prices

declined, PFC payments were augmented with

Marketing Loss Assistance (MLA) payments, which

scaled up the size of already-scheduled PFC payments.

MLA payments make up the majority of payments

classified as “Emergency Assistance” in figure 6.

Emergency assistance payments also include ad hoc

disaster relief. This period also experienced rapid

growth in subsidized crop and revenue insurance and,

in the most recent years, growth in certain loan rates

(price-contingent payments, as explained below), most

notably, for soybeans.

One notable feature about government farm payments

is that they tend to be counter-cyclical with the economic performance of the agricultural sector, especially over the last three decades. Note that payments

reached record levels during the 1980’s farm crises,

declined as commodity prices increased in the early to

mid-1990s, and increased to new record levels during

recent years when commodity prices fell.

The counter-cyclical nature of the payments is especially evident at the regional level, as seen in figure 7.

The counter-cyclical nature of the payments is evident

in all the regions, but especially so in the Heartland

and Northern Great Plains, regions where government

payments are highest relative to net farm income and

net farm income is most variable.14 It appears as

though government payments insure (partially) risks to

farm income. 

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  29

Chapter 4–Government Farm Programs

11See Wescott and Young for a breakdown of payments by type

and crop in the year 2000.

12Total cash receipts for agriculture as a whole were about $194.5

billion in 2001.

13The figure excludes insurance indemnity payments, for which

the associated premiums were heavily subsidized after 1994.

14See Heimlich for details on how these farm resource regions

are defined.
Relative to both government payments and net farm

income, agricultural land values (the dotted line in figure 7) are relatively stable. The relative stability of

land values may also suggest a role of insurance

played by government payments. The land value data,

however, are interpolated between Census years (1978,

1982, 1987, and 1997), so the apparent stability may

be a spurious feature of data construction.

Price-Contingent Payments

At present, USDA uses the Marketing Loan Program

to support (in effect) prices farmers receive for certain

commodities. Marketing loans allow farmers to borrow the posted “loan rate” for each unit of a commodity produced and stored as collateral. They may later

sell the commodity on the open market and repay the

loan (if prices are above the loan rate), or default on

the loan without recourse (if prices are below the loan

rate). Alternatively, farmers can elect to receive a loan

deficiency payment for the difference between the loan

rate and market-derived price, should the loan rate turn

out higher. Thus, the loan rate effectively serves as a

floor on prices farmers receive.15

A price floor affects both the expected return and the

level of risk that farmers face. First, by truncatingthe

bottom of the distribution function of anticipated

prices, the price floor increases the average or 

expected price farmers receive. Second, it reduces

the amount of downside price risk. In perfect markets, the first aspect is most relevant: the greater average return induces greater production. This effect

ultimately entails a certain loss in economic efficiency. Excess production must be stored, destroyed, or

30  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

Figure 6

Government farm payments, 1996-2001

Source: Economic Research Service, USDA.

0

5

10

15

20

25

1996 97 98 99 2000 01

Miscellaneous Emergency Assistance Conservation

Marketing Loan Program Production Flexibility Contracts

Billions of 1995 dollars

15More details on the current marketing loan program and how it

has changed over time can be found in Westcott and Price.

Figure 5

Historical government farm payments, 1933-1995

0

5

10

15

20

25

Year

Feed grains Wheat Rice Cotton

Wool Conservation Miscellaneous

1933 38 43 48 53 58 63 68 73 78 83 88 93

Source: Economic Research Service, USDA.

Billions of 1995 dollars
sold on the world market at a price that is lower than

its economic cost.

In the simple two-period approach of Chapter 2, the

second effect is zero (recall the equivalence result for

price uncertainty). In the dynamic uncertainty approach

reviewed in Chapter 2 and in all the approaches of

Chapter 3, both effects matter: reduced downside risk

and increased expected returns both induce greater production. Unlike the first effect, the second one may

increase efficiency rather than reduce it.

There also are more subtle general equilibrium effects

to consider. As domestic farmers collectively produce

more in response to the greater expected returns, the

average world price falls. Thus, the greater the loan

rate, the lower the world price. If farmers also produce more because of the risk-mitigating features of

price supports, then world prices could be pushed even

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  31

1980 1985 1990 1995

Northern Crescent

Year

Net Farm Income

Land Value

Government Payments

6000

4000

2000

0

2100

1400

700

NFI and G, $ per acre LV $ per acre

1980 1985 1990 1995

Heartland

6000

4000

2000

0

2100

1400

700

1980 1985 1990 1995

Northern Great Plains

-200

0

200

400

600

800

1000

100

200

300

1980 1985 1990 1995

Prairie Gateway

3500

2500

1500

500

300

600

900

1980 1985 1990 1995

Mississippi Portal

6000

4000

2000

0

2100

1400

700

1980 1985 1990 1995

Fruitful Rim

6000

4000

2000

0

2100

1400

700

1980 1985 1990 1995

Basin and Range

2500

2000

1500

1000

500

0

750

500

250

1980 1985 1990 1995

Eastern Uplands

6000

2000

0

10000

800

1600

2400

1980 1985 1990 1995

Southern Seaboard

10000

5000

15000

0

1500

3000

4500

Notes: The solid line indicates net farm income (NFI), less government payments; the dashed line indicates direct government payments (G); the dotted 

line indicates agricultural land values (LV). Land values (the right-side axis) are on a scale equal to exactly 3 times that of government payments and net

farm income (the left-side axis), so relative values can be visually compared across regions. All values are in 1996 dollars. Net income and government 

payments data were interpolated from county-level census data on land and buildings with the value of buildings removed. See Heimlich for a more thorough

description of the nine farm regions.

Figure 7

Comparison of net farm income, government payments, and land values

Source: Economic Research Service, USDA.
lower. These general equilibrium effects are largest for

commodities for which domestic production comprises

a large share of the world market, such as soybeans

and especially corn.

Pre-1996 farm payments, which mostly stemmed from

the target-price-deficiency-payment program, were

contingent on certain features that offset the excess

supply they might have induced. The target-pricedeficiency-payment did not entail government accumulation of commodities via defaulted loans—it simply paid farmers the difference between a posted target

price and the market price on enrolled acreage (if the

target price was higher than the market price). Unlike

the loan-rate program, the payments were allotted

according to base acres and historically based yields,

not actual production. Base acres were allocated via a

formula of farmers’ historical plantings, a formula that

has changed over time. Thus, if target prices were

especially high in one year then farmers could not

quickly increase their acreage to take immediate

advantage of it; instead, they had to slowly accumulate

their base acreage over time. Pre-1996 programs also

offset supply response by requiring that farmers “set

aside” (leave fallow) a certain percentage of their

acreage in order to participate in the program and

receive the subsidies. Although these restrictions are

not part of the current loan rate program, base acres

still dictate the size of lump-sum decoupled and

counter-cyclical payments.

Since the 1985 Food Security Act, eligibility for the

loan-rate program and other subsidies does require

cross-compliance with certain provisions to conserve

soil and wetlands (notably the so-called sodbuster and

swampbuster provisions).

Lump Sum or Decoupled Payments

The structure of farm program payments changed

markedly in 1996, a time when market prices stood

well above target prices and program payments were

historically low. Unlike the pre-1996 programs, the

amounts of these decoupled Production Flexibility

Contracts (PFC) and Market Loss Assistance (MLA)

payments do not depend on the acreage farmers’

plant, nor the crops they plant, nor other current production choices. Economists have long advocated

decoupled payments of this kind over subsidies such

as price supports because they provide income transfers to farmers without distorting commodity production. If markets function perfectly (as described in

Chapter 2), then PFC and MLA payments should not

influence production.

If, on the other hand, markets are imperfect (as

described in Chapter 3), then lump-sum payments hold

ambiguous consequences for production. The payments

may provide farmers with more resources to finance

production. Alternatively, the payments may cause

farmers to feel wealthier and more inclined to spend

their time in leisure rather than in work, so they may

produce less. However, because farmers receiving payments represent a small share of the aggregate labor

force, this small shift in labor supply should have negligible consequences in general equilibrium. Compared

with the farm programs that these payments replaced,

the lump-sum payments may also provide farmers with

less insurance against unexpected price declines.

Crop and Revenue Insurance

Although non-subsidized private multi-peril crop insurance is unavailable in agriculture, it is ambiguous

whether this “missing market” is a result of asymmetric

information (like Akerlof’s market for lemons

described in Chapter 3), because insurance companies

do not have the financial resources to insure widespread disasters, or because government insurance programs have crowded out private insurance markets.

Because government agricultural programs have been

around for so long, it is difficult to predict what private

insurance markets would look like in their absence.

Brief History of Insurance

This section briefly reviews the history of crop insurance programs. (See Coble and Knight and Glauber

and Collins for more detailed reviews.)

Congress first authorized Federal crop insurance with

establishment of the Federal Crop Insurance

Corporation (FCIC) in 1938. The program was initiated as an experiment, limited mostly to major crops in

the main producing areas, and participation rates were

low. Crop insurance remained experimental until passage of the Federal Crop Insurance Act of 1980. 

Since 1980, the crop insurance program has steadily

increased the number of crops insured and the level of

premium subsidies to induce greater participation. The

goal of the subsidies has been to obviate the need for

32  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
ad hoc disaster payments. In 1980, the subsidy was 30

percent of the premium. By 1994, the premiums for

participating farmers were entirely subsidized for catastrophic risk protection (coverage for 50 percent of

average yield at 60 percent of expected price). If

farmers elected a coverage level of at least 65 percent

of yield at 100 percent of expected price, then the peracre subsidy was 25 percent larger than the catastrophic premium rate. Program participation also required

farmers to pay a $50 sign-up fee for each crop insured

per county.

Beginning in 1996, farmers who elected not to enroll

in the crop insurance program with the minimum catastrophic coverage were required to waive their eligibility for Federal disaster assistance that might be made

available later in the crop year. Although these provisions are still in effect, disaster relief was provided to

both insured and uninsured farmers subsequent to the

1996 provision. In low-yield years, some insured

farmers, through a combination of insurance indemnities plus disaster relief, obtained well over 100 percent

of their expected returns. 

Participation increased markedly with the 1994 subsidies, with about two-thirds of field crop acreage and

over 80 percent of eligible acreage insured under the

1994 program. Insured acreage, however, included

less than half the eligible farms; and more than half

the insured acreage included only catastrophic coverage. Prior to the large 1994 increase in subsidies, participating acres included less than 40 percent of those

eligible. Since 1994, the program has expanded coverage to more crops and, under the Agricultural Risk

Protection Act of 2000, subsidies for coverage above

the catastrophic level have increased.

Implications

Like price supports, federally subsidized crop insurance changes both the average returns and risk from

farming. By paying indemnities when yield or revenue is less than a specified value, crop insurance

modifies the net-revenue distribution function by truncating the lower end, while the premiums reduce net

revenue for all states of nature. If insurance were

actuarially fair, then the premium would equal the

expected value of the indemnity payment, and average

returns would remain unchanged. For most kinds of

insurance available in private markets (such as automobile, life, and homeowners insurance), premiums

are larger than indemnities on average—that is how

insurance companies cover their expenses and earn

profits. With federally subsidized crop insurance,

however, the unsubsidized share of the premium is less

than the indemnity payment on average—the government loses money on average. Participating farmers

therefore enjoy both higher average profits and lower

risk from participation.

Because crop insurance is both profitable and risk

reducing, one might expect nearly full participation by

growers. It is therefore puzzling that such large premium subsidies were required to induce participation.

There are at least three explanations for this anomaly.

First, because land is heterogeneous, some fields have

different yield distribution functions than others, differences that cannot be accurately measured. Farmers,

having better information about yield potential than

insurers do, may choose to insure only the fields that

are profitable to insure. In other words, there is a

problem of adverse selection.

Second, farmers may expect to receive disaster relief

(discussed in the next section) even if they choose not

to participate, in effect receiving free insurance.

Although nonparticipating farmers are required to

waive their eligibility for disaster relief, in practice

both nonparticipating and participating farmers have

received disaster compensation in recent years.

A third explanation could be that in the current asymmetric information environment, farmers actually

behave as if they are “risk loving.” If farmers are

credit constrained (as described in Chapter 3), it is

unclear exactly how risk choices are augmented by the

constraint. It is possible that farmers face limited liability on the downside (they may declare bankruptcy

for a limited cost), but obtain new investment opportunities on the upside, causing them to seek risk, all

other factors remaining constant. Indeed, theoretical

research using the contracting approach (described in

Chapter 3) shows that risk-loving choices result from

optimal contracts in limited liability environments

(Ghatak and Pandey).

Some argue that farmers who receive crop insurance

reduce input use because indemnities compensate for

under-applications, a form of moral hazard (Quiggen;

Horowitz and Lichtenberg). In general, predictions

about input use depend on whether inputs are riskincreasing or risk-decreasing, among other assumpEconomic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  33
tions. All else the same, farmers insured from downside risk will have an incentive to choose inputs so as

to increase yield risk. If an input is risk-increasing,

more will be applied per acre; if it is risk-decreasing,

then less will be applied per acre. To the extent that

subsidized insurance increases overall returns, there

also could be an expansion of acreage farmed (which

increases input use (Chambers and Quiggen)). 

Coble and Knight provide a recent review of the

research that examines crop insurance participation

incentives. Research by Young and Vanderveer and

Goodwin and Vanderveer examine the effect of crop

insurance subsidies on planted acreage.

Disaster Assistance

Congress has regularly provided ad hoc emergency

assistance to farmers to partially or totally offset

financial losses due to severe weather and other natural disasters (e.g., drought or flood) or stressful economic conditions. Although these programs insure

farm income somewhat, the ad hoc nature of the

assistance makes this program less amenable to systematic analysis.

In recent years, the overall level of disaster assistance

has increased markedly. After accounting for inflation,

disaster payments in 2000 were greater than in 1994,

the previous record set for these payments due to the

1993 Midwestern floods. Disaster assistance payments from 1991 to 2001, adjusted for inflation, are

plotted in figure 8. These payments do not include the

Market Loss Assistance payments described above,

which provided compensation for generally low prices,

not for specific disasters.

Land Retirement Programs

Since the 1950s, land retirement programs have served

as the largest kind of agricultural conservation program. These programs pay farmers to remove land

from cropland production to provide environmental

benefits and limit crop supply. At present, land retirement takes place mainly through the Conservation

Reserve Program (CRP).

The CRP, USDA’s largest conservation program, pays

an annual rent to participating farmers to remove highly erodible or otherwise environmentally sensitive land

from agricultural production and shares the cost of

establishing land cover, usually in the form of trees or

grass. The rental contracts last for 10 to 15 years and

are allocated on a competitive basis, with bids weighted according to an index that includes environmental

characteristics of the land and the rental payment

requested by the farmer. Lands eligible for enrollment

must have recently been used in crop production and

have a minimum degree of environmental sensitivity.

The primary goal of the program is to reduce soil erosion, improve water quality, foster wildlife habitat, and

provide other environmental benefits.

34  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

Figure 8

Total disaster payments to farmers, 1991-2000, in year 2000 dollars

Source: Economic Research Service, USDA.

0

0.5

1

1.5

2

2.5

3

 1991 92 93 94 95 96 97 98 99 2000 01

Billion dollars
Because CRP enrollment substitutes a fixed payment for

an uncertain crop return, it affects the income risk associated with participating farm operations and, depending

on the size of the rental payment, also may affect the

average returns. It is therefore possible that the risk

effects described in Chapter 3 will influence production

choices on lands not enrolled in CRP. For example, in

the no-risk-sharing approach, farmers may be willing to

plant new cropland and/or take on new risks since a

portion of their income is assured under CRP.

This possible risk effect underlies one rationale for

slippage observed in land retirement programs.

Slippage is a term associated with the phenomenon that

total commodity production decreases proportionately

less than the proportion of acres retired. The main reason slippage occurs is because the retired land tends to

be of lower-than-average quality and therefore have

lower-than-average yields. Another source of slippage

could be that farmers simply plant new cropland to

substitute for land idled under the program (Wu). Yet

another way slippage might occur is through a pricefeedback effect: because CRP shifts supply inward,

prices increase, which may induce farmers to convert

noncropland to cropland. However, because the supply

shift is small relative to the world market, there should

be only a small price response. The risk effect provides another rationale for this kind of slippage.

Before enrolling land in the CRP, farmers may have

been unwilling to expand production (and increase risk)

due to risk aversion. With reduced risk from CRP,

enrolled farmers may be willing to take on additional

risk through expanded production. Counter to this supposition, recent research suggests that slippage in the

form of noncropland to cropland substitutions is, in

fact, small (Roberts and Bucholtz).

Conclusion

Among the risk-modeling approaches reviewed in

Chapters 2 and 3, most analyses of agricultural production decisions have utilized either the simple twoperiod model of Chapter 2 or the no-risk-sharing

approach of Chapter 3. Furthermore, except for some

of the research on crop insurance, analysis of government programs has mainly used the two-period model

of Chapter 2. That is, most analyses ignore risk-coping costs by assuming markets are perfect—so yield

and price risks are unimportant—and then, approximate effects of government programs using equivalent

per-unit subsidies, in effective, these amount to supply

and demand analyses. One might refer to this

approach as a “first-order” analysis, because it examines only incentives that relate to average profits. In

addition to expected values, risk effects may also be

deemed important. In that case, assessment of these

risks, estimates of how farmers and market intermediaries respond to them, and appraisals of how government agricultural programs interact with these responses requires a modeling approach that somehow characterizes the market imperfections causing risk to be

important in the first place (like the approaches

reviewed in Chapter 3). One might refer to this

approach as a “second-order” analysis, because profit

variance will play an important role.

Although second-order risk effects may be unimportant for some kinds of questions, they may be important for others. On the one hand, if the analyst wishes

to focus on a specific consequence of a government

program, such as the acreage response to a higher

crop-insurance subsidy, then risk effects might be safely ignored. Because even first-order acreage responses

are likely to be small (cropland supply elasticities are

highly inelastic), there seems to be little need of a second-order analysis. On the other hand, if the analyst

wishes to conduct a cost-benefit analysis of a crop

insurance program, then a second-order analysis may

be necessary. Because the deadweight loss—a measure of economic inefficiency induced by first-order

distortions—is likely to be small, it is possible that the

second-order gains from a government program will

offset the deadweight loss associated with first-order

distortions, even if second-order effects are small in

size. The potential second-order benefits provided by

these programs might include increased farm productivity (the credit constraints approach of Chapter 3)

and consumption smoothing of farm households (all

Chapter 3 approaches), both of which could be associated with the farm-income stability they promote.

These features, among others, are ignored when the

second-order effects associated with risk response are

not incorporated into government program analyses.

The practical problem with incorporating risk effects

into analysis of government programs is the inadequacy of the current understanding of these effects.

Before applying imperfect-markets approaches to

analysis of government programs, researchers may

first want to try reconciling some of the disharmony

among approaches to modeling imperfect markets.

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  35
For Further Reading

Chambers, R.G., and Quiggen, J. “ Decomposing Input

Adjustments Under Price and Production Uncertainty,”

American Journal of Agricultural Economics, Vol. 83,

No. 1 (2001) pp. 20-34.

Coble, Keith H., and Thomas O. Knight. 

“Risk Management and the Role of the Federal

Government.” In Just and Pope (eds.) A

Comprehensive Assessment of the Role of Risk 

in U.S. Agriculture. Dordrecht and London: Kluwer

Academic Press (2002).

Ghatak, Maitreesh, and Priyanka Pandey. “Contract

Choice in Agriculture with Joint Moral Hazard in

Effort and Risk.” Journal of Development Economics

Vol. 63, No. 2 (December 2000) pp. 303-326.

Glauber, Joseph W., and Keith J. Collins. 

“Risk Management and the Role of the Federal

Government.” In Just and Pope (eds.) A

Comprehensive Assessment of the Role of Risk 

in U.S. Agriculture. Dordrecht and London: Kluwer

Academic Press (2002).

Goodwin, B.K., and Monte Vanderveer. “An

Empirical Analysis of Acreage Distortions and

Participation in the Federal Crop Insurance Program.”

Paper presented at the workshop, “Crop Insurance,

Land Use, and the Environment,” U.S. Department of

Agriculture, Economic Research Service, September

20-22, 2000.

Heimlich, Ralph. Farm Resource Regions. Agriculture

Information Bulletin Number 760. U.S. Department

of Agriculture, Economic Research Service

(September 2000).

Horowitz, J., and E. Lichtenberg. “Insurance, Moral

Hazard, and Chemical Use in Agriculture.” American

Journal of Agricultural Economics. Vol. 75(1993) pp.

926-935.

Quiggin, J. “Some Observations on Insurance,

Bankruptcy and Input Demand.” J. of Econ. Behavior

and Organization. Vol. 18(1992) pp. 101-110.

Roberts, Michael J., and Shawn Bucholtz. “Slippage or

Spurious Correlation: An Analysis of the Conservation

Reserve Program.” A paper presented at the Annual

Meetings of the American Agricultural Economics

Association, Long Beach, CA, August 2002.

Westcott, Paul C., and Michael Price. Analysis of the

U.S. Commodity Loan Program with Marketing Loan

Provisions. U.S. Department of Agriculture,

Economic Research Service, Agricultural Economic

Report No. 801. 26 pp, April 2001.

Westcott, Paul C., and C. Edwin Young. “U.S. Farm

Program Benefits: Links to Planting Decisions &

Agricultural Markets.” Agricultural Outlook (October

2000) pp. 10-14.

Wu, Junjie. “Slippage Effects of the Conservation

Reserve Program.” American Journal of Agricultural

Economics, Vol. 85 (4) (November 2000).

Young, Edwin, and Monte Vanderveer. “The Effects

of the Federal Crop Insurance Program on Wheat

Acreage.” In Wheat Yearbook, WHS-2001, U.S.

Department of Agriculture, Economic Research

Service (March 2001).

36  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
Glossary 

base acres The number of acres to which many agricultural subsidies are tied. In recent years, 

Production Flexibility Payments and Market Loss Assistance Payments were determined

according to a farm’s 1996 base acres. Base acres are determined by a formula of historical plantings, a formula that has changed over the years.

decoupled Agricultural subsidies that are not tied to the current level of production or planting 

government decisions.

payments

general equilibrium As opposed to a partial equilibrium, in which prices usually are fixed, a general 

equilibrium takes into account price feedback from other factors, such as demand. 

slippage The difference between the proportional reduction in land and the proportional 

reduction in production. The term is usually used in reference to land retirement 

programs. Within these programs, the total amount produced usually declines less

that proportionately with the number of acres idled, resulting in slippage.

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  37
Production choices are resource allocation decisions

that determine how much of what crops to produce

and how to produce them. These jointly determined

decisions include: intensive-margin choices, such as

per-acre applications of pesticides, fertilizer, or irrigation water; extensive-margin choices, such the

amount of land used for crops and livestock, the allocation of acreage among different crops, and crop rotations; and technology adoption choices, which

include machinery, irrigation type, tillage practice, use

of precision technologies, and seed type. Technology

choice may influence extensive-margin choices, and

both technology and extensive-margin choices affect

intensive-margin choices.

Chapters 2 and 3 focused on simple examples to illustrate the different ways risk might influence production choices. Some of the examples showed how risk,

through its influence on production, may indirectly

influence the environment. Chapter 4 explained how

government programs such as crop insurance and price

supports influence both the expected returns and variability of returns, and therefore may influence production and (indirectly) environmental problems.

Chapters 2 and 3 referred to these choices abstractly

using the vector x. This chapter reviews more comprehensively the production alternatives available to

farmers and how these alternatives collectively influence productivity, risk, and the environment. This

chapter focuses mainly on the basic tradeoffs available

to growers and policymakers, rather than the motives

that underlie the production decisions actually made.

The Agro-Environmental System

Agricultural production choices affect both production

efficiency and the environment by changing the topography and plant and animal life on land and in waterways; by influencing aesthetics, water flows and quality, soil quality and erosion; and by introducing synthetic chemicals into the environmental system.

Environmental conditions, such as weather, soil type

and fertility, soil moisture, and the stochastic variability of these conditions, in turn, influence grower production decisions. Ultimately, the environmental

effects of production choices depend on the resulting

mix of crops grown and the production practices used.

Many interest groups are concerned about the effects

of production and input choices on worker safety, consumer health, fish and wildlife, ground and surface

water quality, air quality, and soil erosion.

Intensive-Margin Choices

Intensive-margin choices usually pertain to per-acre

use of variable inputs on agricultural land. The intensive-margin inputs mainly include labor, fertilizers,

irrigation water, and pesticides. Holding land quality,

crop choice, capital, and technology fixed, these inputs

influence profits mainly by their effect on yields, product quality, and cost. The use of these inputs has been

associated with various adverse health and environmental effects (USDA, ERS, 2002). Due to these

effects, the agricultural economics literature probably

has studied these inputs more than the others. 

Most analyses of input response to risk on the intensive margin use models akin to the nutrient example

in Chapter 2, the no-risk-sharing approach reviewed

in Chapter 3, or some combination of the two. Pope

and Kramer (1979) discussed a model that underlies

many arguments that risk-reducing or informationincreasing programs, such as crop insurance or new

precision technologies, may encourage increased efficiency and/or decreased use of risk-reducing inputs,

such as pesticides. 

It remains controversial, however, whether pesticides,

irrigation, and fertilizer applications increase,

decrease, or have no effect on yield and profit risks

(Leathers and Quiggen, 1991 and Horowitz and

Lichtenberg, 1993). The conventional wisdom is that

pesticide use and irrigation reduce profit risk while

fertilizer use increases profit risk, as measured by variance (Leathers and Quiggen, 1991). Horowitz and

Lichtenberg (1993) argued, based on an empirical

analysis, that pesticides can increase risk in some circumstances. Other authors, such as Babcock and

Hennessey (1996) and Smith and Goodwin (1996),

questioned their argument and result, suggesting that

model specification was a problem. Other research

indicates irrigation may increase risk and that fertilizer

use may reduce risk under some circumstances. Thus,

even within the confines of the no-risk-sharing

38  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

Chapter 5–Production Decisions and the Environment
approach, it remains ambiguous how risk influences

the use of these inputs and vice versa. 

Whether or not intensive-margin applications increase

or decrease yield variance depends in part on the information available to the farmer at the time of application. Recall the nutrient and irrigation examples in

Chapter 2. The more accurate a farmer’s assessment

of nutrient and water needs, the more precise will be

the applications of these inputs, which could lead to

more or less applied on average. As a result, technologies that reduce uncertainties about input need may

change the use of intensive-margin inputs.

Greater application precision, however, may lead to

either increased or decreased yield variance. On the

one hand, greater precision may push all yields up

toward the maximum growth potential, increasing the

average and reducing the variance. On the other hand,

other stochastic factors that influence growth (such as

sunlight and rainfall) may drive input need (the

amount that maximizes yield, all other factors being

constant). High input needs therefore may occur when

yields are either especially low or especially high. For

example, low sunlight may reduce both yields and irrigation needs, while steady rainfall increases yields and

reduces irrigation needs.

Following the notation from the nutrient example in

Chapter 2, define agronomic need as n’ and the

amount applied as n*. Agronomic need is variable and

may be uncertain at the time of application. If need is

known at application time and the input is costless,

then n* = n’. Thus, the relationship between application rates, average return, and return variance is

ambiguous and depends on three factors: 

1) The information available with respect to agronomic need of the input (uncertainty about n’).

2) The stochastic relationship between marginal 

needs and yield (covariance of yield and n’).

3) The relationship between agronomic need and 

other factors, such as input costs and output price.

Both the information available and application timing

(the level and uncertainty about n’) are endogenous—

they are determined in part by other input decisions.

The following subsections discuss specific kinds of

intensive margin inputs, possible environmental consequences of their use, and how risk can influence

their application.

Pest Controls

Farmers use pesticides, biological methods, and cultural techniques, to prevent yield and quality losses from

insects, nematodes, diseases, and weeds. Post-harvest

practices are also used to control pests during storage

and shipment. Pests can reduce not only the amount of

product harvested, but also the product quality and the

price received. In addition, counterproductive effects

of pesticide use, such as increased pest resistance and

the mortality of pests’ natural predators, can reduce the

efficacy of future pesticide treatments.

Depending on the active ingredient, inappropriate use

of agricultural pesticides may create adverse human

health effects by contaminating food and drinking

water or exposing farm workers, as well as adversely

affect the health of desirable wild animals and plants.

Some health effects, especially those stemming from

chronic exposure to pesticides, are difficult to measure.

Pest infestations are probabilistic events. Some pests

may be chronic problems that cause damages year

after year, while others may pose infrequent threats of

large damage. Pest infestations and the susceptibility

of crops to damage can vary within a season and

between seasons depending on a variety of factors,

such as weather and previous cropping practices. 

Some authors characterize pesticides as damage control agents that prevent yield loss when an infestation

occurs but have no effect otherwise (Leathers and

Quiggin, 1991 and Lichtenberg and Zilberman, 1986).

By reducing damages when pest infestations occur,

pesticide use will increase average yields and reduce

yield variance (similar to figure 2 concerning irrigation

in Chapter 2), as long as pest infestations are the only

variable factor in the crop-pest system and there are no

auxiliary effects. Auxiliary effects include increased

pest resistance and eradication of pest predators, pesticide-induced damages to plants (if the pesticide is phytotoxic), and ways pest infestations relate to other stochastic factors that affect profits, such as yield and

output price. Horowitz and Lichtenberg (1993) argued

that if pest infestations interact with other stochastic

factors affecting yields or profits, increased pesticide

use can increase risk.

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  39
If the benefits outweigh the costs, farmers may use

scouting or monitoring to reduce uncertainties regarding pest infestations and damages, and thereby apply

pest controls more judiciously. Some research suggests

that poor information about the degree of infestation

leads to higher application rates (Feder, 1979), but this

result is not generally true (as in the nutrient model in

Chapter 2). 

Some research suggests that producers overestimate

average insect and disease damages and consequently

over-apply pesticides (Pingali and Carlson, 1985).

Research also suggests that younger farmers who have

more schooling and who use scouting or Extension

Service information make less use of pesticides and

more use of more labor-intensive control practices.

Pesticide use increased markedly during the 1960s and

1970s as the percentage of acreage treated with these

materials increased, peaking in the early 1980s when

total cropland also was historically high. Since the

early 1980s, aggregate pesticide use has remained relatively stable (figure 9).

Irrigation 

In the most arid regions of the West, where precipitation can be the limiting factor for crop growth, irrigation is essential for production of certain crops, including some high-value vegetable and tree crops. In other

areas of the United States, where precipitation can be

sufficient to support crop production in most years,

irrigation augments soil moisture during drier periods,

prevents associated yield losses, and therefore increases average yields and reduces yield variance.

Irrigation often is the basis for more intensive crop

production systems that increase the use of other

inputs, such as fertilizers and pesticides. However, irrigation can reduce water available from groundwater or

reservoir supplies for other uses. It can also affect

water quality through increased nutrient leaching,

increased nutrient and salt concentrations in runoff to

surface water, or increased salinity in poorly drained

soils. Diversion of water for irrigation in the West has

reduced or eliminated flows in many streams, which

has reduced or destroyed habitat for many species,

such as salmon (National Research Council, 1996). 

Irrigation may help producers capture peak prices

attributable to drought-induced production shortfalls,

particularly for price-sensitive local markets such as

forage hay, fresh fruit, and vegetables. In many areas

of the country, irrigation contributes to higher quality

and higher valued fruit and vegetable production,

through control of freeze damage and crop cooling to

delay early flowering or maturation. Fertilizers, pesticides, and other chemicals may be applied within the

irrigation system, providing more controlled application and increased production potential. Irrigation may

also be used for manure spreading, thereby reducing

40  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

Figure 9

Pesticide use on major crops

Source: Economic Research Service, USDA.

0

100

200

300

400

500

600

700

Million pounds active ingredient

Other Fungicides Insecticides Herbicides

1964 66 71 76 82 90 91 92 93 94 95 96 97 98 99 2000 
disposal costs and lowering chemical nutrient applications for combined livestock and forage operations.

Some researchers argue that irrigation is an important

risk-reducing input (Boggess et al., 1983). The primary source of risk reduction involves reduced yield variability and crop loss resulting from inadequate soil

moisture in the crop root zone. When irrigation supplements rainfall, a probabilistic event, it has the characteristics of a damage control agent, such as a pesticide, and could simultaneously increase expected yield

and reduce variance—if managed so that no crop damage occurs. However, the timing and quantity of water

applications could influence whether or not irrigation

increases or decreases yield risk.

For example, figure 10 plots corn yields in Boone

County, Nebraska, a county that has a long history of

large plantings of both irrigated and nonirrigated

acreage. Although the data suggest that irrigation applications increase average yields and reduce yield variance, some of the difference may occur because irrigated and nonirrigated lands are different in other respects.

Incomplete information about soil moisture and weather has implications for decisions about quantities of

irrigated water applied, timing of applications, and

crops irrigated (Bosch and Eidman, 1987).

Information value stems from more efficient water

application with respect to its marginal return, which

varies depending on other stochastic conditions (See

the nutrient example in Chapter 2). 

Fertilizers

Farmers use chemical fertilizers, manure and other

organic materials, and/or crop rotations to replace

nutrients withdrawn from the soil during production of

certain crops (such as corn). Without augmenting the

soil with nutrients, crop yields or quality would

decline in many cases. Primary nutrients for crop

growth and development include nitrogen, phosphorus,

and potassium, but other micro-nutrients are also

important. If improperly applied, fertilizers can leach

into groundwater or drain into surface water. Nutrients

in surface water can cause eutrophication, oxygen

depletion, fish kills, and reduction in recreation opportunities. High nitrate levels in drinking water also

have adverse human health effects.

Fertilizers, especially nitrogen, have been identified as

risk-increasing inputs based on the results of field

studies. A risk-averse farmer, therefore, would apply

fertilizers less frequently than a risk-neutral one

(Leathers and Quiggen, 1991). Fertilizer use may

increase the mean and variance of yields by increasing

the probability of high yields under some conditions,

such as adequate rainfall, and of low yields under

other conditions, such as inadequate rainfall when

chemical burning can occur. However, some research

indicates that fertilizer use reduces the probabilities of

lower yields (Babcock and Hennessy, 1996). Some

farmers may perceive nitrogen fertilizer as a riskreducing input, based on their experience

(SriRamaratnam and others, 1987). 

As with pesticides and irrigation, incomplete information about nutrient availability is an important source of

uncertainty. Information and decision criteria, such as

those obtained from biophysical simulation models, can

influence fertilizer application decisions. Soil and tissue

tests provide estimates of nutrient deficits, which can

result in more accurate application rates. Precision

farming techniques with variable rate technologies can

help to minimize the yield variation in a field. To the

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  41

Figure 10

Comparing yields of irrigated and nonirrigated corn

Source: National Agricultural Statistics Service, USDA.

1950 1960 1970 1980 1990 2000

 Boone County, Nebraska, corn yields 

 Year 

Yield (bushels/acre)

0

50

100

150

Notes: Red indicates irrigated yields; blue indicates nonirrigated

yields. The standard deviations of the residuals (differences between 

actual yields and the fitted trend line) for irrigated and nonirrigated

acreage are 11.8 and 17.8 respectively. The coefficents of variation 

for irrigated and nonirrigated are 0.11 and 0.30, respectively.
extent that uncertainty leads to “over-application,” soil

or tissue tests might reduce application rates

(SriRamaratnam and others, 1987). However, some

studies show that testing might have no effect on application rates or perhaps increase them (Musser and others, 1995). Extreme wet or dry weather can reduce the

value of soil tests, to the extent that benefits do not

cover costs (Huang, Lu, and Uri, 1999).

Fertilizer loss after application, which is affected by

weather, is a stochastic event that creates uncertainty

about the amount of the nutrient in the soil available for

the crop’s uptake. As a result, farmers may increase the

amount of fertilizer applied to ensure that enough of the

nutrient is available for the crop (Babcock, 1992). 

Extensive-Margin Choices

For crop production, extensive-margin choices entail

how many acres of what crops should be grown.

Decisions about what crops to grow reflect expectations of returns, including expectations of prices,

yields, and costs. 

Land and associated natural factors, such as climate,

are important factors in crop production. Natural factors such as climate, soil type, fertility, and soil moisture affect yield. Production costs and response to production inputs also affect yield, while the distance

from markets or transportation terminals can affect the

net returns to crops and alternative uses. Variability of

such factors as temperature, rainfall, and distance from

markets contributes to regional differences in crops

grown and production practices. Variability of natural

factors such as soil type, slope, moisture, and fertility

within individual operations or fields are sources of

spatial yield variability, influencing the choices of

crops and production practices.

Natural factors also influence the environmental

effects of crop choices. The combinations of crop and

management practices, including crop rotations and

tillage, and natural factors, such as soil type and slope,

affect water runoff into streams and percolation into

groundwater, which, in turn, affect soil erosion, and

the movement of pesticides and nutrients into aquatic

systems. Farm production choices also influence populations of desirable and undesirable wildlife, insect,

disease, and plant species by modifying their habitats.

Row crops, such as corn, cotton, or soybeans, generally encourage more surface runoff and erosion than

close-grown crops, such as small grains or alfalfa.

Continuous production of row crops can result in

greater infestations of some pests than some crop rotations (USDA, ERS, 2002).

The choice of crops affects the average level and variability of net returns. In a static, deterministic framework, growers choose crops that maximize net returns;

but for a variety of reasons, yields, prices, and costs

are uncertain. In particular, yields of some crops may

be more susceptible to drought, pests, or other losses

than other crops. Some growers may choose crops

with potentially high returns that are susceptible to

yield losses and protect them with pesticides, fertilizers, and irrigation. Other growers may choose crops

with potentially lower returns but with less need for

additional inputs. For example, some growers in

drought-prone areas might choose to grow irrigated

corn or cotton, while others choose to grow nonirrigated sorghum. Some crop rotations may have pest control or fertility benefits, but, in the absence of crop

insurance, may also help growers to diversify against

revenue losses from price variability and yield losses

(Freund, 1956).

Economic incentives, whether created by high output

or low input prices or by government subsidies, can

encourage changes in crop choices that increase the

acreage of some crops and reduce the acreage of others. These changes can have environmental implications. Increases in the acreage of crops with high

input use can increase soil erosion and deposit more

pesticides and fertilizers into the environment. In

some cases, more erosion-encouraging crops could be

substituted for soil-conserving crops on less productive

(in terms of fertility, soil moisture, or other natural factors that contribute to higher net returns) and more

erodible land. For example, high corn prices or program subsidies and low alfalfa prices could encourage

growers to increase corn and reduce alfalfa acreage.

This crop change would tend to increase erosion on

acreage previously growing alfalfa, and to the extent

that alfalfa was planted on more erodible acreage, the

erosion would be even greater than on the average

corn acre.

Technology Adoption Choices

By adopting new technology in the form of new production practices and equipment, growers may be able

to produce crops where they could not be previously

42  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
grown, increase yields, improve crop quality, or reduce

production costs. These decisions affect the level and

variability of net returns. Some changes can be minor

modifications of current production systems to

improve efficiency, such as using a new pesticide or

seed variety. In more extreme cases, growers may

make major changes in crops grown and production

practices that require major investments in equipment

and management skills. For example, a grower might

change from growing a dryland crop, such as sorghum

or hay, to more input-intensive irrigated corn or cotton

or, perhaps, high-value fruit or vegetable crops.

These adoption decisions can affect what crops are

grown, where they are grown, what inputs are used,

and, thus, have environmental implications. An important trend throughout the 20th century was the

increased use of agricultural chemicals, machinery,

new seed hybrids and varieties, as well as irrigation

equipment and reductions in per-acre labor use, all of

which contributed to increased productivity. For

example, synthetic organic herbicides, first developed

during World War II, were used on more than 90 percent of corn acreage by 1980 (Osteen and Szmedra,

1989). These changes contributed to public policy

concerns about the health and environmental effects of

pesticides, fertilizers, and irrigation. However, some

new technologies may improve production efficiency

and reduce adverse environmental effects. Precision

agriculture methods can apply variable rates of pesticides or fertilizers within a field based on sub-field

level information about pest infestations or nutrient

needs. Some genetically modified crops can reduce

the application of some synthetic organic pesticides.

Growers face a number of risks when adopting new

practices or equipment, with incomplete information

being a major source of risk. New products might not

be accepted in the marketplace, causing product prices

to be lower than anticipated. New practices, pesticides, or equipment may not perform as anticipated.

Growers may not have the management skills or

knowledge to effectively use new equipment and attain

the desired results from new practices. The greater the

change in production practices and equipment, the

greater these uncertainties might be.

Some adoption decisions require investments that create financial risk. Growers might have to purchase

specialized irrigation, tillage, or pesticide application

equipment. Replanting an orchard to a new crop or

variety may require destroying the old orchard, planting new trees, and waiting for trees to mature to bearing age. When debt is incurred, the grower needs to

maintain a cash flow for loan payments. Price or yield

variability can be important concerns for investment

decisions, because the grower might not have sufficient revenue for loan payments in years when prices

or yields are low. To justify taking such a risk, the

grower may plant crops that promise a higher return,

but might also choose inputs, production practices, or

insurance to reduce risks associated with price and

yield variability. For example, irrigation may contribute to higher valued crops, higher yields, and

reduced yield variability, but the grower incurs debt

and higher annual production costs (Boggess and

Amerling, 1983). However, growers might diversify

crop choices and use pesticides, fertilizers, and irrigation practices to prevent yield or revenue losses

(Vandeveer and others, 1989). 

Institutional factors, sometimes unanticipated, such as

regulations or other policies, can prohibit or discourage

the use of some practices or equipment. For example,

the U.S. Environmental Protection Agency’s (USEPA)

pesticide registration process requires testing and

reviews to ensure a safe product, but may slow the

introduction of new materials. Alternatively, regulations on or policies to discourage currently used practices may encourage the adoption of new ones. For

example, USEPA registers safer pesticides while

restricting or banning other materials with undesirable

environmental effects. In addition, State University

Research and Extension programs can provide information that helps growers change pest control practices. 

Input Substitution and

Environmental Implications

While risk management policies may have no direct

health or environmental effects, they can influence the

use of inputs and practices that do. Key elements that

influence input use are output prices, input prices, and

marginal productivities of production factors, as well as

the yield or revenue risk effects of inputs. Many production practices can generate environmental benefits:

a) reductions in pesticide, fertilizer, or irrigation use;

b) changes in cropping patterns to reduce soil erosion

and surface runoff; c) planting more-highly erodible

acres to soil-conserving crops; and d) increases in the

use of information and precision technologies.

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  43
However, some practices that could reduce use of pesticides or fertilizers, such as some crop rotations or

cultivation practices, can have environmental costs

such as increased soil erosion. Programs, policies, or

market factors that encourage the use of one or more

inputs could discourage the use of others and affect the

acreage and output of different commodities. No clear

consensus has emerged from empirical economic

research about the effects of risk management policies

(such as subsidized crop insurance), on crops grown,

input used, or other production practices.

Much of the economic literature implies that increasing the use of one risk-reducing input discourages the

use of other risk-reducing inputs and encourages the

use of risk-increasing ones. The implications are that

risk-reducing inputs are substitutes for each other, as

are risk-increasing inputs, but that risk-reducing and

risk-increasing ones are complements for each other.

Some controversy remains about the risk effects of

using various inputs and how their use would respond

to risk management policies. While much of the literature says that pesticide use and irrigation decrease

risk, and fertilizer use increases risk, some economic

research supports the opposite conclusions (Babcock

and Hennessy, 1996; Horowitz and Lichtenberg, 1993;

Leathers and Quiggen, 1991; Smith and Goodwin,

1996). However, several of these authors argue that

pesticide and fertilizer use reduce the probabilities of

low yields and indemnities, even if variance might

increase (Babcock and Hennessy, 1996; Smith and

Goodwin, 1996).

An important issue is how crop insurance might affect

the use of information and of pest, fertilizer, and irrigation management practices. There are a number of

arguments that information and crop insurance can

reduce fertilizer, pesticide, or irrigation water use

(Feder, 1979; Horowitz and Lichtenberg, 1993).

However, to the extent that information and management practices substitute for crop insurance as riskreducing agents, encouraging crop insurance could discourage their use (Norgaard, 1976). Conversely, if economic incentives changed, increased use of crop rotation, management practices, or information could

reduce purchases of crop insurance. None of the

empirical research reviewed indicates whether crop

insurance would necessarily have a greater or lesser

effect on per-acre use of pesticides than other pest

management practices. Similarly, crop insurance

could discourage the use of management practices or

information that helps to reduce fertilizer or irrigation

water use.

However, Loehman and Nelson (1992) showed that an

increase in the use of one risk-reducing input does not

necessarily mean a decline in the use of another riskreducing input. They showed that two risk-reducing

inputs, two risk-increasing inputs, or a risk-reducing

and risk-increasing input could be risk substitutes or

complements. Inputs that are production substitutes

could potentially be risk substitutes, while production

complements could be risk complements, but marginal

profits and prices are also factors. Based on their discussion, crop insurance purchases could encourage use

of pest monitoring or other information, crop rotations,

or other pesticide-free practices as complementary

components of systems to reduce pest losses more

risk-efficiently than scheduled, prophylactic pesticide

use. Similarly, the results of Wu and Babcock (1998)

indicate that insurance and nitrogen testing could be

complementary in reducing risk, but it is unclear

whether soil testing would increase or decrease fertilizer application rates.

Pesticides, fertilizers, or irrigation could also be substitutes or complements in their risk effects. Irrigation or

fertilizers can increase the value of the crop to be protected or create conditions for pest losses in some situations and, thus, increase the value of pesticide use as

a damage- and risk-reducing agent. Alternatively, irrigation or fertilizers can reduce the potential for pest

losses and the value of pesticide use by improving

plant vigor. Pesticide use might improve yield

response to fertilizer or irrigation. 

Risk-management policies or the use of risk-reducing

inputs could encourage other extensive margin decisions that increase risk. Crop insurance, especially if

subsidized, and disaster payments may have encouraged increased planting of higher value crops that

increase the use of pesticides and fertilizer and may be

more prone to soil erosion. Those programs may have

also encouraged planting of such crops on less productive or more erodible acres (Gardner and Kramer,

1986; Wu, 1999; and Keeton, Skees, and Long, 1999). 

Some growers have decreased the use of crop rotations

that reduce pest populations, continuously planted

higher-value crops, and increased pesticide use to prevent pest damage. For example, preplant methyl bromide use encourages continuous double-cropping of

44  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
high-value vegetable crops in Florida, continuous

strawberry cropping in California, and replanting of

orchards in California without several years of fallow.

Soil insecticides allow farmers to grow continuous

corn rather than rotating corn with soybeans or other

crops to control soil insects. 

Conceivably, the use of some inputs, crop diversification, or crop insurance might encourage investment by

reducing yield or revenue risk, especially on high-value

crops, and increasing the risk-adjusted return on investment. Therefore, subsidized crop insurance or disaster

payments could encourage investments, such as irrigation equipment, to produce higher value crops that ultimately rely on greater intensive margin input use.

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  45
For Further Reading

Babcock, B.A. “The Effects of Uncertainty on Optimal

Nitrogen Applications,” Review of Agricultural

Economics, Vol. 14, No. 2 (1992) pp. 271-80.

Babcock, B.A., and D. Hennessy. “Input Demand

Under Yield and Revenue Insurance,” American

Journal of Agricultural Economics, Vol. 78, No. 2,

(1996) pp. 416-27.

Boggess, W.G., and C.B. Amerling. “A Bioeconomic

Simulation Analysis of Irrigation Investments,”

Southern Journal of Agricultural Economics, Vol. 15,

No. 2 (1983) pp. 85-98.

Boggess, W.G., G.D. Lynne, J.W. Jones, and D.P.

Swaney. “Risk-Return Assessment of Irrigation

Decisions in Humid Regions,” Southern Journal of

Agricultural Economics, Vol. 15, No. 1 (1983) pp.

135-43.

Bosch, D.J., and V. Eidman. “Valuing Information

When Risk Preferences Are Nonneutral: An

Application to Irrigation Scheduling,” American

Journal of Agricultural Economics, Vol. 69, No. 3,

(1987) pp. 658-68.

Caswell, Margriet, Keith Fuglie, Cassandra Ingram,

Sharon Jans, and Catherine Kascak. Adoption of

Agricultural Production Practices: Lessons Learned

from the U.S. Department of Agriculture Area Studies

Project. Agricultural Economic Report No. 792, U.S.

Department of Agriculture, Economic Research

Service, January 2001.

Epperson, J.E., H.E. Hook, and Y.R. Mustafa.

“Stochastic Dominance Analysis for More Profitable

and Less Risky Irrigation of Corn,” Journal of

Production Agriculture, Vol. 5 (1992) pp. 243-47.

Freund, Rudolf J. “The Introduction of Risk into a

Programming Model.” Econometrica Vol. 24, No. 3

(July 1956): 253-256.

Feder, G. “Pesticides, Information, and Pest

Management Under Uncertainty,” American Journal 

of Agricultural Economics, Vol. 61, No. 1, (1979) pp.

97-103.

Gardner, B.L., and R.A. Kramer. “Experience with

Crop Insurance Programs in the United States.” Crop

Insurance for Agricultural Development: Issues and

Experience. P. Hazell, C. Pomareda, and A. Valdes,

eds. Baltimore: The Johns Hopkins University

Press, 1986.

Goodwin, B.K., and M. Vandeveer. “An Empirical

Analysis of Acreage Distortions and Participation in

the Federal Crop Insurance Program.” Presented at the

Workshop on Crop Insurance, Land Use, and the

Environment, U.S. Department of Agriculture,

Economic Research Service, Washington, DC,

September 20-21, 2000.

Horowitz, J., and E. Lichtenberg. “Insurance, Moral

Hazard, and Chemical Use in Agriculture.” Amer. J.

Agr. Econ. 4, 75 (1993) pp. 926-35.

Huang, W-y., Y.C. Lu, N.D. Uri. “An Evaluation of

Soil Nitrogen Testing Considering the Carry-Over

Effect,” Journal of Sustainable Agriculture, Vol. 15,

No. 3 (1999) pp. 5-34.

Keeton, K., J. Skees, and J. Long. “The Potential

Influence of Risk Management Programs on Cropping

Decisions.” Selected paper presented at the annual

meeting of the Amer. Agri. Econ. Assn., August 8-11,

Nashville, TN, 1999.

Leathers, H., and J. Quiggin. “Interactions between

Agricultural and Resource Policy: The Importance of

Attitudes toward Risk.” Amer. J. Agr. Econ. Vol. 73,

No. 3 (1991) pp. 757-64.

Loehman, E., and C. Nelson. “Optimal Risk

Management, Risk Aversion, and Production Function

Properties.” J. of Agr. and Res. Econ. Vol. 17, No. 2

(1992) pp. 219-231.

Lichtenberg, E., and D. Zilberman. “The Welfare

Economics of Price Supports in U.S. Agriculture.”

Amer. Econ. Rev. Vol. 76, No. 5 (December 1986) pp.

1135-1141.

Musser, W.N., J.S. Shortle, K. Kreahling, B. Roach, WC Huang, D.B. Beegle, and R.H. Fox. “An Economic

Analysis of Pre-Sidedress Nitrogen Test for

Pennsylvania Corn Production,” Review of Agricultural

Economics, Vol. 17, No. 1 (1995) pp. 25-35.

National Research Council. A New Era for

Irrigation, National Academy Press: Washington,

DC, 1996, pp. 203.

46  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
Norgaard, R.B. “Integrating Economics and Pest

Management,” In Integrated Pest Management. J.L.

Apple and R.F. Smith (eds.), Plenum Press, New

York, 1976, p. 200.

Osteen, Craig D., and Philip I. Szmedra. Agricultural

Pesticide Use Trends and Policy Issues, U.S.

Department of Agriculture, Economic Research

Service, AER-622, 1989, 17 pp.

Pingali, P.C., and G.A. Carlson. “Human Capital,

Adjustments in Subjective Probabilities, and the

Demand for Pest Controls,” American Journal of

Agricultural Economics, Vol. 67, No. 4, (1985)

pp. 853-61.

Pope, Rulon D., and Randall A. Kramer. “Production

Uncertainty and Factor Demands for the Competitive

Firm,” Southern Economic Journal, Vol. 46, No. 2

(1979), pp. 489-501.

Smith, V.H., and B.K. Goodwin, “Crop Insurance,

Moral Hazard, and Agricultural Chemical Use,”

American Journal of Agricultural Economics, Vol. 78,

No. 2 (1996) pp. 428-38.

SriRamaratnam, S., D.A. Bessler, M.E. Rister, J.E.

Matocha, J. Novak. “Fertilization Under Uncertainty:

An Analysis of Producer Yield Expectations,”

American Journal of Agricultural Economics, Vol. 69,

No. 2, (1987) pp. 347-57.

U.S. Department of Agriculture, Economic Research

Service Agricultural Resource and Environmental

Indicators, 2000, 2002

<www.ers.usda.gov/Emphases/Harmony/issues/

arei2000/> .

VanDeveer, L.R., K.W. Paxton, and D.R. Lavergne,

“Irrigation and Potential Diversification Benefits in

Humid Climates,” Southern Journal of Agricultural

Economics, Vol. 21, No. 2 (1989) pp. 167-74.

Wu, J. “Crop Insurance, Acreage Decisions, and

Nonpoint-Source Pollution.” American Journal of

Agricultural Economics, Vol. 81(May 1999) pp. 305-

320.

Wu, J., and B.A. Babcock, “The Choice of Tillage,

Rotation and Soil Testing Practices: Economic and

Environmental Implications,” American Journal of

Agricultural Economics, Vol. 80, (1998) pp. 494-511.

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  47
Glossary

intensive-margin choices In the context of crop production, these choices concern the amount of a variable 

input used per acre. In the traditional static framework, the optimal amount occurs

where marginal productivity equals input price.

extensive-margin choices In the traditional framework, land should be allocated to the use that generates the 

greatest return. Land on the extensive margin is at the break-even point for production, because it generates no rent when all variable inputs are optimally allocated. If output prices increase, land rents increase, so that more acreage can be allocated to crop production.

technology adoption Production takes place within the context of technology that defines what can be 

produced and how inputs are used in the production process. Technology adoption

concerns choices of new outputs, production practices, and equipment. Choices 

can vary from minor adjustments in the production process, such as a different pesticide or seed variety, to major changes in final outputs produced and production 

process and large investments in new equipment.

48  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA
Complicated Interactions

In this report, we have reviewed the agricultural economics literature that examines the interactions of risk,

government programs, and the environment. Because

this literature is so large, reviewing all of it is a daunting task; and because it is so divergent, no sweeping

paradigm exists to efficiently characterize it and put all

of it into perspective. 

Rather than review all research papers piecemeal, or

attempt to fashion a sweeping generalization, this

report breaks the overview into its constituent components. First, the report reviews the basic perfect-markets and imperfect-markets approaches to modeling

risk, illustrating each approach using a simple example. Next, it provides an overview of the government

programs that affect incentives via changes in both the

risk and return to production activities. Finally, it

examines the nature of agricultural production decisions, how these decisions affect risk and return, and

how they may lead to environmental damages. 

This report summarizes the basic modeling approaches

and the basic issues—the building blocks that make up

a more comprehensive view. But it provides little in

the way of comprehensive analysis of all the components together—risk, government programs, and the

environment. These interactions are complex, and can

be woven together in many different ways. 

Figure 11 illustrates these complicated interactions

between risk, government programs, production decisions, and the environment. It shows how exogenous

factors (those determined outside the agricultural production system), such as the weather, climate, population, and so on, feed into endogenous choices and factors, such as the economic environment (perfect versus

imperfect markets), market mechanisms (private contracts versus spot markets), and production choices.

The nature of production, in turn, determines environmental consequences. And the culmination of all these

factors collectively determines more traditional welfare measures, such the amount produced, the cost of

production, the distribution of wealth, the smoothness

of consumption, and so on. In order to draw conclusions, every model must make simplifying assumptions about the structure of these components and their

interactions.

Some Conclusions

This review of risk, government programs, the environment, and their interactions provides six main conclusions:

1) Risk effects are not synonymous with risk aversion. A large share of agricultural risk research 

uses the no-risk-sharing approach described in 

Chapter 3, an approach that assumes that all consumption smoothing must take place via smooth

ing of income or profits. Within this approach, 

risk effects are ultimately tied to farmers’ degree 

of risk aversion, usually characterized by a 

smoothly concave utility function. In reality, markets do work to share risks, but do so imperfectly.

These imperfections imply that risk usually will

matter in ways it does not in perfect markets, but

not necessarily in a way that can be characterized

by a smooth, concave utility function. From the

vantage of the contracting approach of Chapter 3,

it becomes clear that the way risk influences production decisions depends mainly on the constraints imposed by asymmetric information, not

by individual preferences. In the future,

researchers may want to apply the no-risk-sharing

approach sparingly and interpret results from this

approach judiciously (not as preferences, but as a

complicated amalgamation of preferences and

constraints on risk sharing).

2) The production consequences that stem from

imperfect risk sharing can be observationally

equivalent to information-related risk effects. In

some instances, the predictions stemming from

information-related risk effects reviewed in

Chapter 2 will be observationally equivalent to

those of the imperfect risk-sharing approaches

reviewed in Chapter 3. Our understanding about

the importance of risk would benefit from empirical work that carefully disentangles one kind of

effect from the other. One way of doing this is to

focus on the stochastic properties of the risks in

question. The end of Chapter 2 describes how the

transitory nature of yield shocks implies that they

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  49

Perspectives
50  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA

Figure 11

Risk, government programs, welfare, and the environment

GOVERNMENT PROGRAMS

(Sometimes Exogenous)

Price Supports

Disaster Relief

CRP

Insurance Subsidies

Trade Restrictions

MARKET IMPERFECTIONS

Asymmetric Information

Transaction Costs

Limited Commitment

Limited Liability

FARM PRODUCTION

DECISIONS

Acreage Farmed

Acreage Allocations

Input Use

Technology Choice

Industry Structure

ENDOGENOUS

INTERACTION

MARKET MECHANISMS

Prices

Private Contracts

Borrowing and Saving 

Insurance

WELFARE EFFECTS

Government Expenditures

Consumption Smoothing

Productivity

Prices

Income Distribution

EXOGENOUS FACTORS

Weather and Climate

Land Characteristics

Available Technologies

Population Growth

Globalization and Trade

Preferences

ENVIRONMENTAL EFFECTS

Erosion

Pesticide Residues

Nitrogen Leaching

Wildland and Habitat Preservation
should have no effect in a perfect-markets world

but do matter when markets are imperfect, and

may therefore serve as an effective device to differentiate these two kinds of effects. The problem of observational equivalence is more acute

with price shocks, which persist over long time

horizons.

3) Static models overlook important longrun risks.

Longrun risks are the most difficult to insure and

may be important for understanding longrun decisions, such as capital investment, technology

adoption, crop rotations, and tree plantings.

Because futures markets typically extend only

about 2 years, and because farmers cannot know

for certain what crops they will grow in the

future, no explicit longrun contingent claims

exist. These longrun risks also appear to be

insured in part by the countercyclical nature of

government program payments. Moreover, these

longrun decisions tend to be large-scale and ultimately drive intensive-margin input use, which

entail perhaps the most studied of environmental

consequences. Although longrun risks could be

the most interesting and salient of the risks that

interact with government programs and the environment, they also will be the most challenging to

assess empirically. Among other reasons, this

empirical task will be challenging because 

longrun risks are important via both the dynamic

uncertainty approach of Chapter 2 and the creditconstraints approach of Chapter 3, two views of

risk that hold very different policy implications. 

4) Risk effects are difficult to identify and easy to

misidentify. To identify the effect of risk, one

needs data from different environments with different risks. The problem is that risk is difficult

to measure objectively; and it is even more difficult to measure differences in risk over space and

time. Furthermore, differences in risk, when they

can be identified, usually are closely associated

with other kinds of differences. For example,

yields on irrigated acreage are less variable than

yields on nonirrigated acreage; but irrigation technology also may allow production of crops that

are incapable of being produced on nonirrigated

land; and irrigated lands may have systematically

different soils than nonirrigated land. In general,

spatial differences in risks will be heavily confounded by climate, soil type, and other kinds of

physical heterogeneity. And different individuals

(i.e., those with more or less risk aversion) may

be drawn to more or less risky farming regions.

Time-series variation in risk can be more difficult

to quantify than spatial differences. Research on

agricultural risk could therefore benefit from a

more careful search for natural experiments—

exogenous sources of identification that are not

correlated with other uncontrolled factors. One

compelling natural experiment, for example,

might be the precipitous increase in crop insurance participation that occurred with the 1994

subsidy increases. The construction and use of

improved panel data sets would allow researchers

to better control for many types of heterogeneity

that confound many empirical analyses.

5) Future research would benefit from a better

understanding and accounting of agro-environmental links. Geographic heterogeneity is intrinsically confounded with risk, as noted in conclusion

4: different farms face different risks, in part

because different farms are located in areas with

different weather, climatic conditions, and soil

types. The environmental consequences of agricultural production decisions also depend on

weather, climatic conditions, and soil types.

Thus, to understand how risk influences the environment via its effect on production decisions,

researchers need a greater understanding as to

how different agricultural production decisions

affect environmental quality in different locations. 

6) Future research would benefit from exploring the

practical relevance of experimental violations of

the Expected Utility Theory (EUT) for agricultural production decisions. The penultimate section

of Chapter 3 discussed certain psychological phenomena, observed both in experiments and in

aggregate economic data, that contradict economists’ standard notion of risk aversion.

Economists’ standard notion of risk aversion

hinges on EUT and diminishing marginal utility

of consumption. The evidence, however, suggests

that individuals display more risk aversion than

diminishing marginal utility can plausibly imply,

hold preferences for risk that are inconsistent with

their preferences for intertemporal substitution,

and, in economic experiments, violate the independence axiom of EUT, among many other

anomalies. There has been little application of

these findings to agricultural production, for

which risk remains a central feature. Future work

Economic Research Service/USDA Risk, Government Programs, and the Environment/TB-1908  51
may benefit from further investigations into the

relevance of these psychological phenomena for

the microeconomic behavior of farm production

decisions under risk.

For Further Reading

Just, Richard E., and Rulon D. Pope. “Past Progress

and Future Opportunities for Agricultural Risk

Research” in Just and Pope (eds.) A Comprehensive

Assessment of the Role of Risk in U.S. Agriculture.

Dordrecht and London: Kluwer Academic Press, 2002.

52  Risk, Government Programs, and the Environment/TB-1908 Economic Research Service/USDA