United States 

Department of 

Agriculture

Economic

Research

Service

Erik J. O’Donoghue, Anne B. Effl and, 

Joseph C. Cooper, and Chengxia You

Identifying Overlap in 

the Farm Safety Net

Economic

Information 

Bulletin

Number 87

November 2011
Recommended citation format for this publication:

O’Donoghue, Erik J., Anne B. Effland, Joseph C. Cooper, and Chengxia You. 

Identifying Overlap in the Farm Safety Net. EIB-87. U.S. Dept. of Agriculture, 

Econ. Res. Serv. November 2011.

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United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

Due to the sheer number and complex combinations of farm safety net programs, policymakers are wary of overlap in producer support programs. This report clarifi es competing 

defi nitions of the farm safety net, offers a typology of potential duplication and overlap, and 

presents an analytical method for measuring overlap using a number of current risk management programs. This report focuses on the likelihood of overlap among ACRE, SURE, 

and crop revenue insurance using an ERS-developed analytical method that identifi es and 

measures overlap among programs by using a calculation of farm revenue that includes 

government program payments as a benchmark for intended levels of compensation. Results 

from recent ERS research suggest that budgetary savings could be achieved if ACRE and 

crop insurance were formally integrated, although fi ndings also suggest that altering the 

farm safety net may cause unintended production consequences. 

Keywords

Farm Service Agency (FSA), Risk Management Agency (RMA), farm safety net, overlap, 

income support, risk management.

Acknowledgments

The authors thank Suchada Langley, William Liefert, Daniel Pick, Sarahelen Thompson, 

and Ed Young of the Economic Research Service (ERS), Joy Harwood of USDA’s Farm 

Service Agency, and several anonymous referees for their helpful comments. We also thank 

Dale Simms for his help editing and preparing the report for publication and Curtia Taylor 

for the design and layout of the report.

Erik J. O’Donoghue

Anne B. Effl and

Joseph C. Cooper

Chengxia You

Identifying Overlap in the 

Farm Safety Net Economic

Information

Bulletin

Number 87

November 2011
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

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

 Defi ning the Farm Safety Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

 Patterns of Program Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Defi ning Overlap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 A Typology of Overlapping Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Applying the Typology To Identify Overlap . . . . . . . . . . . . . . . . . . . . . . . . 13

 Previous Research on the Potential for Overlap . . . . . . . . . . . . . . . . . . . 13

 2008 Farm Act Programs and Their Potential for Overlap. . . . . . . . . . . 13

 Calculation of Potential Overlap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

 Overlap Implications for ACRE, SURE, Crop Insurance, 

 and Disaster Payments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

 Future Research: Exploring the Potential for Type II Overlap . . . . . . . . 19

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Appendix I: Acronyms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Appendix II: Brief Overview of Farm Safety Net Programs . . . . . . . . . . . . 25

Appendix III. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Appendix IV. Changes to Federal crop insurance premiums 

per acre when integrated with a national, State-, and county-level 

ACRE payment (2009 crop year). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
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Summary

What Is the Issue?

Recent public discussion of Federal farm programs has suggested that 

the current array of programs, constructed over time through successive farm acts and other legislation, has created the potential for overlap 

among programs. While the Government Accountability Offi ce (GAO) has 

explored overlap with respect to fraud, it found little systematic evidence 

of such abuse. However, farmers often receive payments from multiple, 

similar programs, which suggests to some observers that overall payments 

from these multiple programs could exceed actual losses. Identifying and 

measuring overlap could help to trim Federal spending while maintaining 

a farm safety net capable of meeting the needs of farmers and the broader 

society. 

What Does the Study Propose?

Because many U.S. farm programs are designed to prevent actual duplication of coverage, assessing overlap in programs depends largely on how 

overlap is defi ned. This report offers a conceptual framework for identifying 

program overlap in the farm safety net and an exploration of how to measure 

that overlap. We propose a typology of overlap as the basis for identifying 

program overlap and, within that framework, explore the interactions 

between the new Average Crop Revenue Election (ACRE) and Supplemental 

Revenue Assistance Payments (SURE) programs and crop revenue insurance, as well as avenues for further research into other forms of overlap. 

• To identify the potential overlap of farm safety net programs, we classifi ed overlap as falling into two broad categories: 

o Type I—Multiple types of support that together provide coverage 

above levels intended by any of the individual programs, including 

cases of actual duplication where, for example, multiple payments 

compensate for the same loss; and

o Type II—Patterns of participation in multiple programs that may 

offer coverage of the same commodity and/or risk so that, even 

though coverage does not exceed intended levels for any individual 

program, total support still exceeds levels necessary to meet farm 

safety net goals of maintaining farm business viability.

• This report focuses on Type I overlap. To examine and measure 

Type I overlap, ERS researchers adopted the concept of considering 

Government program benefi ts as farm income, based on the model 

incorporated in the new SURE program. ERS researchers identifi ed 

specifi c overlap of risk coverage among the ACRE, SURE, and crop 

insurance programs. For example, if the ACRE payment was integrated 

into a revenue-based crop insurance program to eliminate overlap 

between the two programs, premiums could decrease between 20 and 

38 percent for a State-level ACRE program, depending on the farm/crop 

combination explored.
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• Many risk management programs are designed to preclude overt duplication. However, overlap among them may occur because different 

programs may protect producers from similar risks at different points 

in time or under several different risk scenarios. For example, a crop 

yield insurance policy covers potential production losses for an individual crop on a farm without reference to prices, covering a pre-harvest 

risk. Meanwhile, SURE covers post-harvest, whole-farm revenue losses 

that refl ect gains and losses for multiple commodities and for production- and/or price-based losses. The combination of risk management 

programs with income support programs may provide a more overt form 

of overlap, since precautions to prevent such duplication have not been 

incorporated into policy design.

How Was the Study Conducted?

The study is intended to provide a conceptual framework for considering 

the question of overlap in the U.S. farm safety net. The conceptual framework was developed from exploration of the economic literature and public 

debate on the U.S. farm safety net and potential overlap/duplication among 

programs, culminating in the proposed typology of farm program overlap to 

guide further research. The typology is explored through a review of recent 

and ongoing work that explores the operation of the new programs under 

the 2008 Farm Act—ACRE and SURE—and their interactions with Federal 

crop insurance and ad hoc disaster assistance programs. Additionally, we 

use USDA’s Farm Service Agency (FSA) program administrative data, 

county-level USDA’s Risk Management Agency (RMA) data, and individual 

payment data reported by FSA to the Internal Revenue Service (IRS) to 

illustrate the patterns of farmer participation in various programs. 
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Introduction

Federal policies have long been in place to provide U.S. farmers some form of 

economic safety net. The number and variety of these policies has increased 

over time as legislators address new economic contexts and technological 

change. Recent debates about the budget defi cit and early discussions of the 

2012 Farm Bill have cited the farm safety net as an essential component of 

U.S. agricultural policies. At the same time, debate participants contend that 

the sheer number and complex combinations of available farm programs must 

create some overlap in farmer support. Identifying and eliminating overlap 

could trim Federal spending on farm programs while maintaining a strong 

safety net for farmers in times of need.

Determining whether and how current farm safety net programs overlap is 

a complex task. We aim to lay the conceptual groundwork for making that 

assessment by identifying patterns of farm program participation that help 

to clarify the extent of overlap in support. We consider programs directly 

linked to the farm safety net, and patterns of program participation. We then 

present a typology of “farm safety net” programs that provides a framework for identifying overlap, specifi cally between two new 2008 Farm Act 

programs—Average Crop Revenue Election (ACRE) and Supplemental 

Revenue Assistance (SURE)—and crop revenue insurance and ad hoc

disaster programs. 

Defi ning the Farm Safety Net 

In most cases, the term “farm safety net” refers to the combination of 

price support, income support, and risk management programs designed 

to support farm businesses and, through them, farm households.1 A recent 

Congressional Research Service report (Shields et al., 2010) describes the 

farm safety net as composed of three types of programs—commodity 

programs, risk management programs, and supplemental disaster assistance—all of which focus on the viability of the farm business. In contrast, 

Gundersen et al. (2000) examined the potential effect on farm programs of 

considering the safety net in terms of farm household income or well-being. 

The farm household focus was motivated by observations that traditional 

farm program support was concentrated in certain regions and received by 

larger farm operations that often had wealth and incomes higher than the 

national average. Using several scenarios to examine potential changes in 

payment distribution, Gundersen et al. found that gearing the safety net to 

ensure a minimum income or earnings level for farm households would affect 

both the regional distribution of payments and their distribution by farm size.

Current discussion of farm safety net programs has not generally focused on 

defi ning minimum farm household well-being levels as the measure for need. 

Rather, current debate centers on the compilation of all currently operating 

programs linked to the farm business (see House Agriculture Committee 

hearings at http://agriculture.house.gov/farmbill/farmbill.aspx?GID=21).

Still, issues about the unequal distribution of payments by farm size, 

commodity, and region continue to surface. Some observers continue to question the value of a “safety net” that provides little assistance to many U.S. 

farms, yet offers seemingly multiple layers of assistance to other farms (see, 

1Conservation programs have sometimes been identifi ed as part of the farm 

safety net, since they can involve direct 

transfers to producers for adoption of 

environmentally friendly production 

practices or retirement of environmentally fragile land. We have not included 

them in this study because they are 

not designed to directly address farm 

business viability and, in general, cover 

only costs and/or income foregone in 

adopting particular practices. Increasing 

interest in designing “green payments” 

that can address both safety net and 

environmental objectives has led to 

research examining the degree to which 

producers may receive both safety 

net and conservation payments. ERS 

research (Classen and Morehart, 2006; 

Claassen et al., 2007) indicates that 

only 15 percent of farms that receive 

Government payments receive both 

types of payments. However, these 

farms receive 40 percent of conservation payments. Future research may 

need to examine the extent to which 

conservation payments should be 

considered part of a farm’s portfolio 

of safety net support, in addition to 

examining how these payments affect 

decisions about land use and production 

practices that interact with other farm 

programs.
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for example, Environmental Working Group press releases at http://www.

ewg.org/farming). 

Consistent with some of these concerns, most current programs limit the 

amount of support producers can receive. Several programs restrict the level 

of receipts from individual programs, while other programs base eligibility 

for payments on household and farm income levels.

Since each producer support program is implemented differently, additional 

consideration of program details and participation is necessary for developing hypotheses regarding overlap and analytical methods to address the 

issue. Potentially signifi cant program differences include whether programs 

offer insurance or income support; idiosyncratic or systemic risk protection;2

price-, yield-, or revenue-based protection; crop-specifi c or whole-farm-based 

coverage; and whether or not a program is tied to production. 

Patterns of Program Participation

Patterns of program participation can also affect how we conceptualize 

and address the issue of overlap. Federal farm safety net programs provide 

several different types of support that, while not mutually exclusive, can be 

divided broadly into three main categories: price support, income support, 

and risk management. Price support programs, used only for dairy and sugar, 

maintain domestic market prices above world market prices and therefore do 

not involve government payments provided directly to producers. Although 

they may raise market receipts for producers who also receive other forms of 

income support, price support programs are not easily linked to individual 

farms and/or producers, so we do not include them in our overlap analysis. 

To aid the identifi cation of overlap, we group farm safety net programs into 

two main categories based on whether benefi ts are triggered by income 

or production losses; one group is triggered by a loss, and the other is not. 

Income support programs provide benefi ts without reference to individual 

(or group) documented income or production losses. Risk management 

programs, in contrast, provide benefi ts when an individual (or group) can 

document an income or production loss. We differentiate the various farm 

programs between these two categories based on the presence (or absence) 

of individual or group loss triggers to facilitate analysis of programs with the 

potential to overlap through compensation for the same loss or coverage of 

the same risk. Categorizing the programs in this fashion does not necessarily 

imply any intended purposes of these programs—since policymakers rarely 

include legislative text that explicitly conveys program intent—but simply 

focuses on how the program functions.

Income support programs include both programs tied to the production of 

specifi c crops and those that allow producers to make production decisions 

independent of their payment receipts. Risk management programs include 

both commodity-specifi c insurance—which can protect against price, yield, 

or revenue losses—and whole-farm revenue protection. Additionally, policymakers have introduced a wide variety of disaster programs tied to the effects 

of specifi c natural events such as drought, fl ood, fi re, and disease or pest 

outbreaks. We provide brief descriptions of these programs (see Appendix 

II).3

2Idiosyncratic risks, such as those 

related to a localized weather event or 

to farm management decisions, occur at 

the individual farm level. Systemic risk, 

such as a global fall in prices, occurs 

over a wide area or affects the entire 

market or market sector.

3Both current program details and 

provisions under the 2002 and 1996 

Farm Acts are available on the ERS 

Farm and Commodity Briefi ng Room 

at: http://preview.ers.usda.gov/Briefi ng/

FarmPolicy/ProgramProvisions.htm.
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Agricultural producers have a wide array of support programs available to 

them. Programs can be tied to specifi c commodities or, more generally, to 

whole-farm revenues. Farm programs can provide support when individual 

losses are incurred, or may provide benefi ts irrespective of current production 

decisions or market conditions. And program expenditures can vary across 

space and time, depending on such factors as market conditions, weather 

patterns, and pest infestations (table 1).4 Despite spanning three farm bills, 

data in table 1 show that the major drivers of changes in support came from 

payments directly linked to prices. In 2001, a year of low commodity prices, 

total Federal aid from income support and risk management programs came 

to $26.9 billion (in 2010 dollars). In the years 2007-2010, commodity prices 

were substantially higher, and support from these programs averaged $12.8 

billion over those years; during the years of higher prices, producers averaged roughly $14 billion less from both income support and risk management programs—programs that did not change substantially over time. In 

2001, producers received roughly $9.5 billion more from the marketing loan 

benefi ts program and approximately $6 billion more from the ad hoc market 

loss assistance program, a program similar to the countercyclical payments 

introduced in the 2002 Farm Act. 

Because program designs and purposes vary, producers can be expected to 

participate in multiple programs on the same farm, depending on factors like 

the number and types of commodities the farmer produces (or has historically produced) and current market/production conditions (fi gs. 1a-1d). For 

example, farmers in central California received high levels of direct and 

countercyclical payments (DCP) and crop insurance (RMA) support in 

2007, but lower levels of marketing loan benefi ts (MLB) and crop disaster 

payments. While cotton and rice dominated payments made to farmers in the 

center of the State, the bulk of those payments accrued to programs based on 

historical production through DCP base acres rather than to those based on 

current production of the crop such as MLBs or crop insurance. This suggests 

that farms with historical cotton and rice base may have taken advantage of 

planting fl exibility to plant other crops on their base. Similar combinations 

of programs can be seen for Iowa, Kansas, and Texas (fi gs. 1b-d). Producers 

on these farms can receive both DCP payments for historical crop base along 

with payments for current production that may provide price protection, 

short-term credit, and coverage for crop and/or livestock losses. 

Farm-level program data confi rm this pattern of multiple program participation (table 2). For example, 99 percent of farms growing cotton in 

California in 2007 received direct payments, 98 percent received countercyclical payments, 17 percent received marketing loan benefi ts, and 10 

percent received Milk Income Loss Contract payments and/or dairy disaster 

payments. The payments received by these cotton farms could be associated with any of the crops either currently or historically grown on the farm. 

Similar patterns of multiple payments hold for farms in the other States.

While program payment data show that farmers can, and often do, receive 

support from multiple sources, this in itself does not indicate the existence of 

overlapping payments within the farm safety net. Identifying overlap requires 

consensus defi nitions of overlap, accurate understanding of the interactions 

among programs, and a means to measure the level of any potential overlap. 

4Data from various sources are used 

in this overview, including national, 

county and farm-level (aggregated to 

the county level) FSA administrative 

data and national and county-level Risk 

Management Agency program data. 

Due to structural lags in the timing of 

payments for post-harvest disasters 

and other losses, obtaining complete 

data for a particular crop year requires 

collecting data from several calendar 

years within the administrative data. 

For example, to obtain complete data 

for crop year 2007, data from calendar 

years 2007-2009 are required. Because 

data from calendar year 2010 were not 

available to us at the time of writing 

this report, we restrict all our data 

examples to the year 2007 or earlier 

for consistency, with the exception of 

national data shown in table 1. Note 

that this means that 2008 Farm Act 

programs such as ACRE and SURE are 

not included in the county and farmlevel overview of program participation shown in fi gures 1a-d and table 

2. National-level preliminary and 

estimated data for these programs are 

included in table 1, and we address 

these programs in more detail later in 

the report.
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Table 1

Government programs, by program year, 2001 and 2007-2010

Program 2001 2007 2008 2009 2010 

$ million (2010)

Income Support Programs

DCP/PFC & MLA

Total expenditures 11,488 6,081 5,627 5,130 4,860

Market Loss Assistance payments (MLA) (ad hoc) 6,485 -- -- -- --

Production Flexibility Contract payments (PFC) 5,003 -- -- -- --

Direct Payments (DP) -- 5,341 4,433 4,907 4,860

Countercyclical Payments (CCP) -- 740 1,194 223 0

MLB

Total expenditures 9,838 71 978 132 115

Certifi cate Exchange Gains (CEGs) 2,308 64 843 4 --

Loan Defi ciency Payments (LDP) 6,828 7 135 125 113

Marketing Loan Gains (MLGs) 702 0 0 3 2

Milk Income Loss Contracts (MILC) -- 0 2,194 764 182

Risk Management Programs

Risk Management Agency (RMA)

Total expenditures1 2,165 3,980 5,799 5,478 4,710

Total premiums (participation) 3,616 6,832 10,039 9,035 7,592

Premium subsidies (expenditures) 2,165 3,980 5,799 5,478 4,710

ACRE -- -- -- 451 24

Crop Disaster

Total expenditures 2,801 831 2,234 777 61

Supplemental Revenue Assistance (SURE) -- -- 2,159 683 na

Non-insured Assistance Program (NAP) 219 71 70 91 58

2001 Crop Disaster Assistance (ad hoc) 982 -- -- -- --

2001/2002 Crop Disaster Assistance (ad hoc) 1,566 -- -- -- --

2005/2007 Crop Disaster Assistance (ad hoc) -- 760 -- -- --

Sugar diversion payment 33 -- -- -- --

Tree Assistance Program (TAP) 1 -- 5 3 3

Livestock Disaster

Total expenditures 602 136 208 176 57

2005/2007 Livestock Compensation Program (LCP) -- 117 -- -- --

2005/2007 Livestock Indemnity Program (LIP) -- 14 -- -- --

Dairy Disaster Program -- 5 0 0

--continued 
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Table 1

Government programs, by program year, 2001 and 2007-2010 (continued)

Program 2001 2007 2008 2009 2010 

$ million (2010)

Pasture Recovery Assistance 29 -- -- -- --

Flood Compensation Program 25 -- -- -- --

Lamb Meat Adjustment Program 26 -- -- -- --

Livestock Indemnity Program (LIP)2 0 -- 28 64 17

Livestock Forage Disaster Program (LFP) -- -- 169 100 33

Emergency Livestock Assistance Program (ELAP) -- -- 11 12 7

Total expenditures 26,894 11,099 17,040 12,908 10,009

Sources: DCP/PFC & MLA, MLB, and MILC program data from 2012 President’s Budget Commodity Credit Corporation (CCC) Commodity 

Estimates Book; crop insurance program data from USDA’s Risk Management Agency online summary of business; disaster program data 

from USDA Offi ce of Budget and Program Analysis (OBPA).

CCC data for 2009 and 2010 are estimates as of release of the 2012 President’s Budget; crop insurance data for 2009 and 2010 are preliminary as of Oct 17, 2011; OBPA data for 2008-2010 are preliminary as of Oct 4, 2011.

All values are expressed in 2010 dollars using a GDP (gross domestic product) chain-type price index where 2005 = 100.

1Total expenditures for crop insurance are premium subsidies, since a share of total premiums is paid by producers.

22008-2010 LIP payment data are for 2008 Farm Act authorized programs.
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1The category “All other crops” includes barley, mohair, payments for the Non-insured Assistance Program (NAP), oats, peanuts, safflower, 

sorghum, unshorn pelts, and wool.

Source: USDA, Economic Research Service, based on Farm Service Agency administrative records.

Dollars

Figure 1a

Program expenditures by program and crop, 2007: CA

DCP

MLB

RMA

Crop 

disaster

Livestock 

disaster

Corn

Soybeans

Wheat

Cotton

Rice

All other

crops1

$0 $1-$500,000 $500,001-$5M >$5M

Program expenditures Payments made directly to crop Payments made to base acres
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1The category “All other crops” includes barley, crambe seed, flax, mohair, payments for the Non-insured Assistance Program (NAP), 

oats, sorghum, sunflower, and unshorn pelts, and wool.

Source: USDA, Economic Research Service, based on Farm Service Agency administrative records.

Dollars

Figure 1b

Program expenditures by program and crop, 2007: IA

Program expenditures Payments made directly to crop Payments made to base acres

DCP/PFC 

& MLA

MLB

RMA

Crop 

disaster

Livestock 

disaster

Corn

Soybeans

Wheat

All other

crops1

$0 $1-$500,000 $500,001-$5M >$5M
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1The category “All other crops” includes barley, canola, flax, mustard, payments for the Non-insured Assistance Program (NAP), 

oats, peanuts, sesame, sorghum, sunflower, and unshorn pelts, and wool.

Source: USDA, Economic Research Service, based on Farm Service Agency administrative records.

Dollars

Figure 1c

Program expenditures by program and crop, 2007: KS

Program expenditures Payments made directly to crop Payments made to base acres

DCP/PFC 

& MLA

MLB

RMA

Crop 

disaster

Livestock 

disaster

Corn

Soybeans

Wheat

Cotton

All other

crops1

$0 $1-$500,000 $500,001-$5M >$5M
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1The category “All other crops” includes barley, mohair, payments for the Non-insured Assistance Program (NAP), oats, peanuts, 

sesame, sorghum, sunflower, and unshorn pelts, and wool.

Source: USDA, Economic Research Service, based on Farm Service Agency administrative records.

Dollars

Figure 1d

Program expenditures by program and crop, 2007: TX

DCP

MLB

RMA

Crop 

disaster

Livestock 

disaster

Corn

Soybeans

Wheat

Cotton

Rice

All other

crops1

Program expenditures Payments made directly to crop Payments made to base acres

$0 $1-$500,000 $500,001-$5M >$5M
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Table 2

Program stacking in California, Iowa, Kansas, and Texas farms, 2001 and 2007

Crop grown on Farm Service Agency (FSA) farm:

Corn Cotton Rice Soybeans Wheat

Program1 2001 2007 2001 2007 2001 2007 2001 2007 2001 2007

Percent of FSA farms growing (crop) and receiving program payments in:

California

PFC/DP 98 100 99 99 > 99 100 -- -- 99 100

MLA/CCP 97 < 1 98 98 > 99 < 1 -- -- 98 < 1

MLB 66 14 49 17 71 13 -- -- 48 14

Dairy 2 8 2 10 < 1 2 -- -- 2 8

Crop Disaster 4 3 < 1 < 1 < 1 < 1 -- -- 4 3

Livestock Disaster 1 < 1 < 1 0 < 1 0 -- -- 1 < 1

Iowa

PFC/DP > 99 100 -- -- -- -- 79 100 > 99 100

MLA/CCP > 99 0 -- -- -- -- 97 0 99 0

MLB 91 6 -- -- -- -- 100 6 88 7

Dairy 2 3 -- -- -- -- 0 2 1 2

Crop Disaster < 1 1 -- -- -- -- 0 1 < 1 3

Livestock Disaster 1 < 1 -- -- -- -- 0 < 1 1 < 1

Kansas

PFC/DP 97 100 -- -- -- -- 95 100 > 99 100

MLA/CCP 93 < 1 -- -- -- -- 95 < 1 > 99 < 1

MLB 91 3 -- -- -- -- 100 3 73 3

Dairy < 1 2 -- -- -- -- 0 1 1 1

Crop Disaster < 1 12 -- -- -- -- 0 13 3 14

Livestock Disaster < 1 < 1 -- -- -- -- 0 < 1 < 1 < 1

Texas

PFC/DP > 99 100 > 99 > 99 > 99 100 -- -- > 99 100

MLA/CCP 99 < 1 > 99 99 > 99 < 1 -- -- > 99 < 1

MLB 56 7 46 13 59 2 -- -- 39 8

Dairy < 1 1 < 1 < 1 0 0 -- -- < 1 1

Crop Disaster 3 14 11 23 < 1 1 -- -- 15 14

Livestock Disaster 2 3 1 2 < 1 1 -- -- 5 4

1 This table includes the following programs: Production Flexibility Contract (PFC); Direct payments (DP); Marketing Loss Assistance (MLA); 

Countercyclical Program (CCP); and Marketing Loan Benefi ts (MLB), along with Dairy, Crop Disaster, and Livestock Disaster programs.

Source: USDA, Farm Service Agency, Administrative Data, Program years 2001, 2007
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Defi ning Overlap

Much of the debate on overlap in current farm programs suggests that 

commodity programs often provide payments beyond the level needed 

to sustain farm businesses, or possibly even pay twice for the same loss. 

Defi ning overlap is a fundamental fi rst step in identifying how these shortcomings may occur among farm safety net programs. 

If several programs operate in a similar fashion—protecting farm income 

or covering production risks, for example—quite different programs may 

create overlap if producers participate in a number of programs for a range 

of commodities without reference to their cumulative effect on producers’ 

overall farm revenue. In other cases, farm programs may operate in different 

manners, one providing income support and another protecting against 

very specifi c market or weather-related losses, for example. Together, these 

programs could combine to provide what is perceived to be more than safety 

net support, even if neither provides support that exceeds the intended levels 

of the individual programs themselves. 

One context for defi ning overlap is to consider the objectives of Federal agricultural support. For the most part, these objectives are not formally laid out 

in the text of agricultural legislation. Therefore, one can presume only the 

most general objective, which is to minimize total program costs subject to 

achieving a wide variety of goals (e.g., goals relating to farm and consumer 

safety nets, the environment, and equity issues). In this context, overlap then 

means any increases in budgetary costs over those needed to strictly satisfy a 

broad range of program goals. To be of value in the policy process, then, any 

overlap identifi ed must be measurable. 

A Typology of Overlapping Support

Because overlap may occur in various forms, and programs can vary dramatically, it may be most useful to consider more than one defi nition of overlap/

duplication and analyze program combinations for evidence of: 

(a) Actual duplication, such as compensation by more than one program 

for the same losses; 

(b) Overlap, such as multiple types of insurance payments that together 

provide compensation above levels intended by the individual 

programs;

(c) Overlap, such as systematic participation in multiple programs 

(stacking) that may (or may not) offer similar coverage so that, together, 

programs may provide support that exceeds what is necessary to 

maintain farm business viability, even if producers never receive 

compensation above intended levels for each individual program; and/or

(d) Cumulative support to individual producers from multiple programs 

that may exceed some defi ned minimum well-being goals.
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While these four types (a-d) present a conceptual overview of the kinds of 

overlap that may exist among farm safety net programs, measuring overlap 

presents a serious obstacle. For example, although types (a) and (b) can theoretically remain separate, in practice the distinction is less clear. Because 

different programs do not explicitly target the exact same event in exactly 

the same fashion, unless fraud takes place—which the GAO (2009) has 

found to be relatively rare— actual duplication appears unlikely. Moreover, 

even in cases where actual duplication may be taking place, risk coverage is 

complex and such duplication is diffi cult or impossible to prove. It is easier to 

show that together two programs are providing compensation above intended 

levels, assuming those levels can be specifi ed. Therefore, although the fi rst 

two classifi cations in the typology may help to make distinctions in conceptualizing overlap, when examining programs for overlap potential, we combine 

the two classifi cations (a and b) for measurement purposes. While it is arguable how best to measure the intended level of support, ERS researchers have 

developed a method to measure this concept based on the program design of 

the Supplemental Revenue Assistance Payments (SURE) program.

Type (c), or overlap through stacking, requires creating a benchmark level of 

revenues required to maintain farm business viability (across all sizes and 

types). While work in this area is preliminary, we follow similar lines of 

logic to determine an intended level of support as used for overlap types (a) 

and (b).

Finally, defi ning well-being—necessary for measuring type (d)—also presents a formidable challenge. Although payment eligibility and individual 

program payment limits suggest some consensus regarding limitations on 

support, there is no precedent or defi ned well-being goal that could be used 

to identify and measure type (d) overlap. Hence, we restrict our examination 

of overlap among various programs to those areas where we can quantify 

an intended level of support (e.g., programs like crop insurance, ACRE, and 

SURE stipulate guaranteed levels of support), rather than attempting to identify a measure of well-being that is a subjective concept.

Thus, we collapse our original typology into two classifi cations as the basis 

for quantitative analyses. Type I refers to the case where multiple types of 

payments together provide compensation above levels intended by the individual programs, including cases of actual duplication where, for example, 

multiple payments compensate for the same loss. Type II refers to the patterns 

of participation in multiple programs (stacking) that may offer coverage for 

the same commodity and/or risk so that program benefi ts exceed the levels 

necessary to maintain farm business viability, even though producers may not 

receive compensation above the intended levels for any particular program.

In this report, we focus on Type I overlap, exploring how multiple programs 

together might provide compensation above levels intended by any of the 

individual programs. In particular, we focus on the new programs introduced 

in the 2008 Farm Act—specifi cally the Average Crop Revenue Election 

Program (ACRE) and the Supplemental Revenue Assistance Payments 

Program (SURE)—and how these programs interact with crop revenue 

insurance and each other. While our focus remains on Type I overlap, we do 

consider the relevance of Type II overlap and how we might examine and 

measure it in future research on the impacts of farm safety net policies.
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Applying the Typology To Identify Overlap

If certain conditions (e.g., market, weather, etc.) result in producers systematically receiving support from multiple programs, those programs can be 

considered correlated with each other. For example, if a farmer is enrolled in 

both a crop insurance program that insures against declines in revenue and in 

ACRE, the farmer may receive payments from both programs provided the 

revenue losses are substantial enough to trigger both programs. A correlation 

between the factors that trigger program payments suggests the potential for 

overlapping payments. 

Previous Research on the Potential for Overlap

Past studies of previous farm bills explored the possibility for this kind of 

overlap between countercyclical payments (CCPs) and crop insurance (e.g., 

Hauser et al., 2004) and potential duplication by CCPs and marketing assistance loans (e.g., Hart and Babcock, 2005). The former found little overlap 

between the risk protection provided by CCPs and crop insurance, since 

CCPs cover inter-year risk while crop insurance covers intra-year risk. The 

latter study found the potential for price protection duplication between the 

loan program and CCPs due to the way the production incentives inherent 

in loan defi ciency program payments (LDPs) interact with the combination 

of loan rate and target price in the CCP payment calculation. Exploring a 

broader range of farm income support safety net programs, Gray et al. (2004) 

found that these programs helped support farmers’ incomes in a way that 

diminished the value of crop revenue insurance coverage, which may have 

prompted the large crop insurance subsidies required to induce farmers to 

adopt crop insurance.

2008 Farm Act Programs and Their 

Potential for Overlap 

Two new programs—ACRE and SURE—became law under the 2008 Farm 

Act. ACRE provides payments to a farmer if a covered commodity’s Stateand farm-level revenues both fall below guarantees that are determined using 

recent prices and yields. SURE provides supplemental revenue insurance at 

the whole-farm level.5 Because both of these programs insure revenues, the 

potential exists for them to interact with each other and with crop (revenue) 

insurance products to generate overlapping support within the farm safety 

net. As a result, some research has examined potential duplication and 

overlap in these programs.

At a House Agriculture Committee hearing in May 2010, Bruce Babcock, 

Director of the Center for Agriculture and Rural Development and Professor 

of Agricultural Economics at Iowa State University, testifi ed that the 

ACRE program “duplicates coverage that is available from the crop insurance program” and questioned why taxpayers should be asked to fund both 

programs (Babcock, 2010a, b). Babcock made several suggestions about 

how to make ACRE a more effective and widely used program and proposed 

a simplifi ed model of how ACRE might operate using county-level rather 

than State-level guarantees. He used historical yields and prices to estimate 

the average annual ACRE payments per acre for various crops and levels 

5See http://preview.ers.usda.

gov/Briefi ng/FarmPolicy/

ProgramProvisions.htm for current 

program details and provisions under 

the 2002 and 1996 Farm Acts.
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of coverage— and their associated yearly costs—if the county-level ACRE 

program had been in place from 1980 through 2008. To eliminate overlap 

in his modifi ed ACRE program, Babcock suggested that, “[a]t a minimum, 

it would make sense for existing crop insurance policies to be modifi ed to 

account for county ACRE payments.” He estimated that reducing overlap 

with crop insurance could easily result in annual savings in excess of $4 

billion (Babcock, 2010b).

While Babcock suggested that ACRE and crop insurance overlap to a large 

degree, others have countered that these programs cover different parts of 

producer price and revenue risks with minimal overlap. Zulauf et al. (2010) 

argue that ACRE, crop insurance, and SURE overlap minimally due to how 

each program focuses on a different aspect of risk: crop insurance covers 

idiosyncratic risk (individual farm-level crop risks); SURE supplements crop 

insurance but works at the whole-farm level; and ACRE focuses on systemic 

risk, the more widespread risk found at the State level. Additionally, coverage 

levels differ for the various programs. Together, this suggests that the 

different programs may be covering different parts of the revenue risk distribution and that payments do not result in substantial overlap. 

For example, Zulauf et al. (2010) argue that because the ACRE State trigger 

operates at 90 percent of recent State revenues, the farmer should be insured 

for up to 90 percent of his losses. Furthermore, since ACRE payments are 

limited to 25 percent of the cap (the 90 percent), ACRE only protects the 

individual farmer from revenue losses between 67.5 (90 - 25 percent of 90) 

and 90 percent of the farm guaranteed revenue. In other words, ACRE does 

not cover any losses of revenues that fall below 67.5 percent of expected 

revenues. To extend this argument, suppose a producer purchases a crop 

insurance policy guaranteeing at least 70 percent of expected revenues. If 

that farmer experiences a bad crop year and incurs substantial losses, realizing only 50 percent of expected revenues from the market, the farmer’s 

crop insurance will boost his revenues to 70 percent while ACRE (provided 

the State trigger is met) would pay for losses between 67.5 and 90 percent 

of expected revenues. This means that actual overlap would only be that 2.5 

percent (between 67.5 and 70 percent) for which both programs pay. 

Examining Kansas and Iowa farms using a counterfactual model of program 

support, Zulauf et al. (2010) found that, relative to crop receipts, adding 

ACRE increased farm revenue by 5 to 10 percent (depending on location) 

for farms with crop insurance and that overlap existed (i.e., ACRE and crop 

insurance paid out for the same portion of loss for at least part of the total 

losses). This overlap was minimal, however, amounting to less than 5 percent 

of ACRE payments. Zulauf et al. note that the overlap increases as insurance coverage increases. For instance, if the farmer adopts a crop insurance 

policy that insures up to 85 percent (versus up to 70 percent as in the previous 

example) of expected revenues, overlap is substantially larger, running from 

67.5 up through 85 percent of expected revenues.

Clearly, overlap can mean different things to different people, as evidenced by 

the disparate conclusions drawn by Babcock (2010b) and Zulauf et al. (2010), 

leaving the existence of overlap among the programs an open question. In 

part, this is due to differences in program design. For instance, Federal crop 

insurance based on farm-specifi c losses addresses risk at the farm level, while 
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the scale of the revenue risk in the ACRE payment is calculated at the State 

level. Hence, crop insurance and ACRE address the same revenue risk only 

to the extent that farm and State revenue are correlated. Whether payments 

made under the two programs cover the same risk or are simply coincidental 

depends on the defi nition of “same risk.”

Calculation of Potential Overlap

Rather than approaching overlap as duplicate coverage of the same percentages of the risk distribution as in Babcock (2010b) and Zulauf et al. (2010), 

our typology focuses on whether total support from multiple programs 

exceeds intended levels. While defi ning the “intended level” of support is a 

major hurdle to this approach, the SURE program rules provide a legislative 

precedent. Although the legislative text authorizing SURE does not explicitly 

address the intended level of support, it does identify what programs enter 

into the determination of farm income for the SURE payment calculation. 

In calculating actual whole-farm income to compare against the market 

revenue guarantee, the SURE program considers the broad suite of farm 

program support (ACRE revenue payments, MLBs, CCPs, a portion of direct 

payments, other Federal disaster payments that cover the same loss, and net 

insurance indemnities) to be part of actual farm income.

Based on the principles of the SURE payment calculation, total revenues 

(including those from a variety of farm programs) can then be compared to the 

guaranteed level of expected market revenue of the farm (or crop, depending 

on the program being examined) for any number of program scenarios (see 

box, “Calculating Potential Overlap”). If a farmer receives support from more 

than one program and total revenue (where total revenue is gross revenue plus 

government payments) exceeds the percentage coverage level of expected 

revenue, then overlap has occurred. Moreover, this approach allows us to 

determine the extent of overlap and which programs caused the overlap. 

Figure 2 illustrates how overlapping support can occur between two 

programs. To simplify, we simulate the interaction using crop revenue insurance and an ad hoc loss compensation program. The crop revenue insurance 

prevents the farmer’s revenue from dropping below 65 percent of expected 

revenue, while the ad hoc payment provides support to the farmer when the 

county revenue falls below 70 percent of expected county revenue. Since the 

programs together work to ensure that the farmer receives at most 70 percent 

of expected revenues, measuring the overlap would consist of summing up 

all revenues, including the crop insurance net indemnities and the ad hoc 

payments. If total revenues exceed 70 percent of expected revenues (the 

program guaranteed level of revenue), overlap has occurred. In the fi gure, the 

purple area represents the market revenues the farmer generated. The gray 

area represents the revenue coming from the crop insurance program while 

the light tan area shows the additional payments that the farmer receives from 

the ad hoc program. Since the farmer incurred a loss below 70 percent of 

expected revenues but, through the interaction of the two programs, ended up 

with revenues exceeding the guaranteed level, overlap occurred.
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Overlap Implications for ACRE, SURE, Crop Insurance, 

and Disaster Payments

Using this approach to calculating overlap, a recent study by Cooper (2010) 

simulates the interaction between the current ACRE program design and 

revenue-based insurance for corn, soybean, and wheat farms in Illinois, 

Kansas, and North Dakota. The overlap calculations center around expected 

prices and yields for the 2009 crop year using yield data from the National 

Agricultural Statistics Service (NASS) from 1975 through 2008, futures 

Calculating Potential Overlap: An Illustration 

for an Individual Producer

Overlap can occur when multiple programs together provide compensation 

above levels intended by the individual programs, defi ned here as the level of 

expected market revenue that the individual programs guarantee the producer 

will receive, regardless of market outcome. 

For example, if a producer takes out a 65-percent crop revenue insurance policy 

and has expected market revenue (based on historical yields and prices) of $100, 

the policy guarantees that the producer will receive at least $65 in revenues. If 

the producer generates revenues of $65 or more, the crop policy will not provide 

any benefi ts; if revenues drop below $65 (say to $55), the policy ensures the 

producer receives $65 by providing $10 in benefi ts.

Now suppose that the farmer also benefi ts from an ad hoc loss compensation 

program that provides benefi ts when the average county revenue drops below 

70 percent of expected county market revenue and ensures that the individual 

farmer receives at least 70 percent of expected market revenue. For expositional 

purposes, neither the ad hoc loss compensation program nor the crop insurance 

take into account the benefi ts the farmer receives from the other program.

Under this scenario, if the producer earned $55 in market revenues, he would 

receive $10 from crop insurance and $15 from the ad hoc loss compensation 

program. All together, the producer would then earn $55 + $10 + $15 = $80. 

Given that the producer was guaranteed to receive $70 at most (70 percent of 

expected market revenue), the two programs interacted to produce an overlap 

of $10 (see fi g. 2).

We use artifi cial programs (though similar to actual programs) in this example 

to demonstrate how overlap is determined and what programs contributed to 

the overlap. This approach enables us to explore how Federal spending might be 

reduced while maintaining a viable safety net for agricultural producers. 

For instance, program administrators might design the ad hoc program to take 

into account the $10 in benefi ts the farmer already received from the crop insurance program. This would limit the producer’s payment to $5, thereby trimming 

Federal spending. In fact, recent Farm Service Agency ad hoc programs have 

taken into account receipts from other risk management programs, such as crop 

insurance, thus limiting this potential type of overlap. However, this method of 

calculating overlap allows for examination of more complex interactions among 

multiple types of programs than provided in this example.
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price data at planting and harvest over the same period, and farm-level yield 

variability information derived from Risk Management Agency (RMA) crop 

insurance premiums. These data feed into the estimation of density functions 

for both prices and yields. Random, correlated draws of prices and yields 

are then made to generate simulated data sets for the representative farms 

used in the analysis. In turn, the simulated data sets provide the information 

necessary to calculate market revenues and support coming from both ACRE 

payments and crop insurance indemnities, assuming the producer selects a 70 

percent revenue coverage rate. 

Cooper fi nds that the ACRE program covers a signifi cant portion of the farmlevel revenue risk. Moreover, the cost of providing crop insurance could 

be lowered if the ACRE payment is included as part of the farmer’s actual 

revenue when calculating the farmer’s crop insurance indemnity payment, an 

approach consistent with the way the SURE payment is calculated.

Including the ACRE revenue payment as part of the farmer’s crop revenue 

will lower the variability of the farmer’s revenue from year to year, thus 

lowering the farmer’s crop insurance premium. The level of impact will 

depend on the extent to which the farmer’s revenue is correlated with the 

Source: Economic Research Service, USDA.

Figure 2

Overlap between hypothetical crop revenue insurance program 

and ad hoc county-level revenue program for an individual 

Percent of expected revenue

100

80

60

40

20

0

Ad hoc program payment

Crop insurance indemnity

Overlap

70% of 

expected 

revenue

Market revenue

}
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State revenue. Cooper fi nds that integrating the ACRE payment into a 

revenue-based crop insurance program could lead to decreases of 10 to 41 

percent in crop insurance premiums, depending on the farm/crop combination examined. However, while integrating the ACRE program into crop 

insurance would lower premiums and thus the premium subsidies paid by the 

government, practical issues in jointly administering such a program to make 

payments in a timely fashion would still need to be addressed.

Following Cooper’s (2010) simulation methods, this report (and Cooper and 

O’Donoghue, 2011) analyze Illinois, Minnesota, and South Dakota corn, 

soybean, and wheat farms and examine how overlap changes when altering 

ACRE from a State-level program to either a national-level or a county-level 

program. We also explore whether receiving benefi ts from multiple programs 

affects farmers’ business decisions. Like Cooper, we identify overlap between 

ACRE and a revenue-based crop insurance policy. For the set of locations 

and crops examined, if ACRE was formally integrated with crop insurance to 

account for the overlap between the two programs, we found that insurance 

premiums would drop between 6 and 29 percent for a national-level ACRE 

program, between 20 and 38 percent for a State-level ACRE program, and 

between 29 and 45 percent for a county-level ACRE program (for county- and 

crop-specifi c results, see the table in Appendix III). 

In this report (and in Cooper and O’Donoghue, 2011), we also found that 

higher levels of overlap led to larger potential farm management responses 

(see Appendix IV for technical details of the model used for this report). 

For example, depending on the extent to which crop insurance premiums 

depart from actuarial fairness,6 producers may alter their chosen levels of 

crop insurance coverage and their levels of planted acreage with changes in 

the availability of a crop revenue insurance program, SURE, and an ad hoc 

disaster program.7 If the farmer started with crop insurance coverage that 

was actuarially fair net of the premium subsidy (that is, the premium the 

farmer actually paid, after the subsidy was applied, was actuarially fair), 

and was not allowed to alter land choice, adding ad hoc assistance lowered 

a farmer’s demand for insurance coverage from 67 to 65 percent.8 This is 

due to the fact that ad hoc disaster assistance, which is free to the producer, 

can be viewed as a partial substitute for crop insurance in covering farm 

risk. However, if the crop insurance premium the farmer sees was super-fair 

(that is, the premium is initially constructed to be actuarially fair but, due to 

government subsidies, results in an expected benefi t to the farmer when the 

policy is purchased), then the crop insurance coverage level was unaffected 

when adding ad hoc assistance, remaining at 80 percent.

When the insurance premium was actuarially fair, adding SURE induced the 

farmer to select a higher level of coverage (from 67 to 75 percent coverage). 

In general, SURE should be expected to increase the demand for crop insurance given that by design the SURE payment increases with crop insurance coverage rates (in a nonlinear fashion); that is, buying more insurance 

induces larger SURE payments. However, if the insurance was actuarially 

super-fair, then adding SURE did not induce an increase in the already high 

insurance coverage rate of 80 percent. In addition, the insensitivity of insurance coverage rates to program mix is partly because insurance premium 

subsidy rates decrease as the farmer chooses higher insurance coverage 

rates. However, the relationship between the two varies at different levels of 

6An insurance scheme is actuarially fair when the premium is equal to 

the expected value of the indemnity 

payments. If the premium is lower, then 

the insurance product is actuarially 

super-fair, resulting in net benefi ts to 

the producer.

7Because of the complexity in 

analyzing farmer decisions in the presence of these programs, the simulation 

of the farm decisionmaking was limited 

to one crop and a farmer in a location 

with high yield risk (a representative 

wheat farmer in Hyde County, South 

Dakota), which magnifi es the impacts 

of the availability of disaster programs.

8In this analysis, ad hoc assistance 

is assumed to be available every year. 

Historically, although ad hoc disaster 

assistance for crop losses has been 

provided in most recent years, coverage 

has varied. In some cases, only specifi c 

crops or particular regions or disaster 

events have been included, and 

frequently producers have been required 

to accept coverage for losses in only 

one of a number of covered years.
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coverage. Hence, chosen insurance coverage rates can be somewhat sticky 

around certain coverage rates. 

The impacts of program selection on insurance coverage and acreage in the 

case where acreage was allowed to vary was conducted under the assumption that the crop insurance premium paid by the farmer (that is, net of the 

subsidy) was actuarially fair. When acreage was allowed to vary, the insurance coverage rate always stayed at 80 percent. Most likely, with the premium 

subsidy rate inducing some stickiness in the coverage rate, the farm decisions were manifested by more fl exibility in setting acreage. In this scenario, 

making crop insurance available raised planted acreage while the availability 

of ad hoc disaster assistance further increased acreage and combining crop 

insurance with SURE led to the highest levels of planted acres. However, 

adding ad hoc disaster assistance on top of insurance and SURE caused a 

decrease in planted acreage (but still higher than under crop insurance alone). 

The likely reason is that because SURE payments decrease under the presence of ad hoc assistance—given that the latter are explicitly integrated into 

the SURE payment calculation—the farmer’s incentive to plant more acres 

can fall under the presumption of ad hoc assistance. 

For the farmer with a normal level of risk aversion whose only choice variable is planted acreage, the standard economic model suggests that planted 

acreage in a crop increases as revenue for the crop becomes less variable, 

whether due to the purchase of crop insurance (e.g., Goodwin et al., 2004; 

Glauber, 2004; Moschini and Hennessy, 2001) or the availability of ad hoc 

assistance (Glauber, 2004). Our results are consistent with this behavioral 

model with respect to these specifi c programs of interest. 

However, these fi ndings also suggest that interactions among programs are 

suffi ciently complex that relative production impacts are not always evident 

beforehand. In particular, the relationships among the policy parameters, 

yield distributions, price distributions, input costs, and the specifi cation of the 

farmer’s decision model are suffi ciently complicated that it is hard to say in 

advance what exactly will happen to planted acreage under a mix of government programs. Altering programs, such as integrating them to deal with 

potential overlap, may have unintended consequences. 

Future Research: Exploring the Potential 

for Type II Overlap

In contrast to Type I overlap, where overlap produces benefi ts beyond 

intended levels of individual programs, Type II overlap leads to benefi ts 

within intended levels, but which exceed levels necessary to meet farm 

safety net goals in terms of farm business viability. This type of overlap is at 

the core of the public farm-safety-net debate, which is focused on the sheer 

number of programs available to farmers. But identifying and measuring 

overlap is a complex task, and this may be particularly so for examining 

Type II overlap wherein farm business viability may be a moving target. 

Some studies of previous farm bills have explored the potential impacts of 

participation in multiple programs. Gray et al. (2004), in particular, found 

that the income support from a range of farm safety net programs—including 
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the historically based Production Flexibility Contract (PFC) and Market Loss 

Assistance (MLA) payments and marketing loan program benefi ts—may 

have prompted the offer of larger subsidies to entice participation in crop 

revenue insurance programs. Hauser et al. (2004) examined interactions 

between CCPs and MLBs, while Hart and Babcock (2005) examined interactions between CCPs and crop insurance, both focusing on the overlap in risk 

coverage between these two types of programs. All three studies focus on the 

risk management aspects of price-linked program designs in what we have 

categorized as income support, refl ecting both the diffi culty of categorizing 

programs and the multiple ways in which programs may interact. 

Future research into Type II overlap may shed additional light on how these 

price-linked income support and risk management programs are combined 

and may overlap. These programs differ importantly in their design and 

implementation, and especially in their incorporation of rules precluding 

duplication of support from other programs. Because many risk management 

programs are designed to preclude overt duplication, overlap among them 

may occur because programs may protect producers from similar risks at 

different points in time or under different risk scenarios. 

The combination of risk management programs with income support 

programs may provide a more overt form of overlap, since precautions to 

prevent such duplication have not been incorporated into policy design. 

Overlap of this kind, including income support not linked to prices (DPs), 

might occur within the context of a whole-farm revenue defi nition of farm 

business viability, where adding all program payments together—including 

payments based on historical production parameters—would be part of the 

analysis.

Analysis of Type II overlap within a whole-farm revenue context can even 

include examining the interaction of separate benefi ts from the same program 

for multiple commodities. One way for a farmer to manage risk is to diversify 

his portfolio by planting several different crops in a season or even adding 

other sources of income such as livestock operations. The goal is to reduce 

revenue volatility by engaging in multiple income-producing activities that 

do not have perfectly correlated revenue streams. Program support based 

on single crops runs counter to the farmer’s traditional risk management 

practice of diversifying across several outputs, and it ignores the likelihood 

that losses in one output may be offset by gains, or at least smaller losses, in 

another. Hart et al. (2006) found that actuarially fair insurance premiums 

for hypothetical whole-farm revenue insurance on a well-diversifi ed farm 

were signifi cantly lower than the actuarially fair premiums for the individual 

outputs. Lower premiums may result in lower Government costs to subsidize the insurance. In principle, applying this result to commodity program 

support means that if a whole-farm-based revenue payment replaced current 

crop-specifi c revenue support, the Government could trim spending while 

still providing farmers with downside risk protection.
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Conclusions

In this report, we have focused on the likelihood of overlap among ACRE, 

SURE, and crop revenue insurance using an ERS-developed analytical 

method that identifi es and measures overlap among programs by using a 

calculation of farm revenue that includes Government program payments 

as a benchmark for intended levels of compensation. We have also considered a possible path for future research analyzing the potential for multiple 

programs to generate benefi ts that exceed farm safety net goals, even while 

each program, taken individually, does not provide support above intended 

levels. 

Our purpose in this report has been to build a conceptual foundation for 

considering the problem of potential overlap and duplication among programs 

of the Federal farm safety net and to offer some initial analysis of potential 

overlap among these programs in the context of public debate surrounding 

the Federal defi cit and the upcoming 2012 Farm Bill. We have clarifi ed 

competing defi nitions of the farm safety net and offered a typology of potential duplication and overlap. Analytically, we have presented a method for 

measuring overlap and an application using a number of current risk management programs. 

Occasionally there appears to be evidence of (1) overlap in support that actually duplicates coverage for the same losses and (2) support from multiple 

programs that provides compensation beyond that intended in individual 

programs (what we have termed Type I ). Additionally, the availability 

of various programs can alter the incentives producers face. Production 

outcomes may be driven, at least in part, from the selection of farm safety 

net programs provided to support farmers in times of need. Indeed, in some 

cases, due to the complexity of both the programs and the interactions 

between the programs, perverse incentives may arise that have the potential 

to lead to unintended consequences. For example, in simulations where ad 

hoc disaster assistance was added to a crop revenue insurance policy and 

SURE, farmers decreased the number of acres they planted, likely due to the 

fact that SURE integrates the ad hoc payments into its revenue calculation 

and SURE payments decrease under the presence of ad hoc assistance.

In the current context of fi scal austerity, better understanding of the interactions among different programs should help policymakers anticipate and 

address the potential outcomes—both in budget savings and in unintended 

production consequences—from any changes in the farm safety net. 
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References

Babcock, Bruce A. 2010a. Testimony. Hearing to Review Agricultural Policy 

in Advance of the 2012 Farm Bill, Committee on Agriculture, U.S. House 

of Representatives, 111th Cong., 2d sess., May 13.

Babcock, Bruce A. 2010b. “Costs and Benefi ts of Moving to a County ACRE 

Program.” Center for Agricultural and Rural Development (CARD) 

Policy Briefs. Iowa State University CARD Policy Brief 10-PB 2.

Claassen, Roger, and Mitch Morehart. 2006. Conservation Policy Design: 

Greening Income Support and Supporting Green. Economic Research 

Service, U.S. Department of Agriculture. Economic Brief No. 1. March. 

Claassen, Roger, Marcel Aillery, and Cynthia Nickerson. 2007. Integrating 

Commodity and Conservation Programs: Design Options and Outcomes. 

Economic Research Service, U.S. Department of Agriculture. Economic 

Research Report No. 44. Oct.

Cooper, J. 2010. “Average Crop Revenue Election: A Revenue-Based 

Alternative to Price-Based Commodity Payment Programs,” American 

Journal of Agricultural Economics 92(4): 1214-1228.

Cooper, J. and Erik O’Donoghue. “Identifying and Reducing Overlap in Farm 

Program Support,” Selected Paper, 2011 AAEA & NAREA Joint Annual 

Meeting, Pittsburgh, Pennsylvania, July 24-26, 2011. 

Glauber, J.W. 2004. “Crop Insurance Reconsidered,” American Journal of 

Agricultural Economics 86(5): 1179–1195.

Goodwin, B.K., M.L. Vandeveer, and J.L. Deal. 2004. “An Empirical 

Analysis of Acreage Effects of Participation in the Federal Crop Insurance 

Program,” American Journal of Agricultural Economics 86(4): 1058-1077.

Gray, Allan W., Michael D. Boehlje, Brent A. Gloy, and Stephen P. Slinksy, 

2004. “How U.S. Farm Programs and Crop Revenue Insurance Affect 

Returns to Farm Land,” Review of Agricultural Economics 26(2): 238-53.

Gundersen, Craig, Mitchell Morehart, Leslie Whitener, Linda Ghelfi , James 

Johnson, Kathleen Kassel, Betsey Kuhn, Ashok Mishra, Susan Offutt, and 

Laura Tiehen. 2000. A Safety Net for Farm Households. USDA, Economic 

Research Service Agricultural Economic Report No. 788.

Hart, Chad E., and Bruce A. Babcock. 2005. Loan Defi ciency Payments 

Versus Countercyclical Payments: Do We Need Both for a Price 

Safety Net? Center for Agricultural and Rural Development, Iowa State 

University. Briefi ng Paper 05-BP 44. Feb.

Hart, C.E., D.J. Hayes, and Bruce A. Babcock. 2006. “Insuring eggs in 

baskets: Should the government insure individual risks?” Canadian 

Journal of Agricultural Economics/Revue canadienne d’agroeconomie

54(1): 121-137.
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Hauser, Robert J., Bruce J. Sherrick, and Gary D. Schnitkey. 2004. 

“Relationships among government payments, crop insurance payments, 

and crop revenue,” European Review of Agricultural Economics 31(3): 

353-368.

Moschini, G., and D.A. Hennessy. 2001. “Uncertainty, risk aversion, and risk 

management for agricultural producers,” in Handbook of Agricultural 

Economics (B. Gardner and G. Rausser, eds.), Vol. 1, Elsevier Science.

Shields, Dennis A., Jim Monke, and Randy Schnepf. 2010. Farm Safety Net 

Programs: Issues for the Next Farm Bill. Congressional Research Service 

Report 7-5700. Sept. 10. 

U.S. Government Accountability Offi ce. 2009. U.S. Department of 

Agriculture: Improved Management Controls Can Enhance Effectiveness 

of Key Conservation Programs. Testimony of Lisa Shames, GAO, 

Natural Resources and Environment Director, before the Subcommittee 

on Conservation, Credit, Energy, and Research, House Committee on 

Agriculture. GAO-09-528T. March 25.

Zulauf, Carl, Gary Schnitkey, and Michael Langemeier. 2010. “Average Crop 

Revenue Election, Crop Insurance, and Supplemental Revenue Assistance: 

Interactions and Overlap for Illinois and Kansas Farm Program Crops,” 

Journal of Agricultural and Applied Economics 42(3): 501-515.
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Appendix I: Acronyms

Acronym Full Name

ACRE Average Crop Revenue Election Program

AGI Adjusted gross income

CAT Catastrophic loss coverage insurance policy

CEG Certifi cate Exchange Gains

CCP Countercyclical Payments

DP Direct Payments

ELAP Emergency Livestock Assistance Program

ERS Economic Research Service

FSA Farm Service Agency

IRS Internal Revenue Service

LDP Loan Defi ciency Program

LFP Livestock Forage Program

LIP Livestock Indemnity Program

MILC Milk Income Loss Contract

MLA Market Loss Assistance

MLB Marketing Loan Benefi t

MLG Marketing Loan Gain

NAP Noninsured Assistance Program

PFC Production Flexibility Contract

RMA Risk Management Agency

SURE Supplemental Revenue Assistance Payments Program

TAP Tree Assistance Program for Orchardists and Nursery Tree 

Growers
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Appendix II: Brief Overview of Farm Safety 

Net Programs

Income Support Programs

Although there are some elements of both income support and risk management in a number of programs, our approach places four of the major 

commodity programs in the income support category: (1) Direct Payments 

(DP), which succeeded Production Flexibility Contract (PFC) payments; (2) 

Countercyclical Payments (CCP), which replaced Market Loss Assistance 

(MLA) payments;9 (3) Marketing Loan Benefi ts (MLB), which include 

Marketing Loan Gains (MLG), Certifi cate Exchange Gains (CEG), and Loan 

Defi ciency Payments (LDP); and (4) the dairy Milk Income Loss Contract 

(MILC) program. All of these programs are administered by USDA’s Farm 

Service Agency (FSA).

Policymakers introduced PFC payments in the 1996 Farm Act as non-marketdistorting replacements for traditional target prices and defi ciency payments. 

Unlike earlier programs, producers could grow any commodity (with some 

restrictions on fruits, vegetables, and wild rice),10 or even leave their land 

fallow, and still receive payments that were based on the historical production 

on their land. PFC payments were supplemented in 1999-2001 with annually 

authorized ad hoc MLAs that distributed additional payments to holders of 

PFCs as a compensation for low commodity prices. 

In the 2002 Farm Act, policymakers replaced the PFC payments with DPs, 

and CCPs formalized the MLAs with a statutory system of target prices that 

triggered supplemental payments to producers eligible for DPs when prices 

for their historically produced commodities dropped below targeted levels. 

Both programs continue under the 2008 Farm Act. 

CCPs have been limited in recent years by high prices for most commodities, while DPs continue to be paid irrespective of market conditions. DPs 

thus make up a considerable share of the total payments currently made to 

producers each year (roughly $5 billion). 

Farm operators can also obtain MLBs through the Marketing Assistance 

Loan Program, offering both price protection and short-term, low-interest 

non-recourse loans with benefi ts tied to actual production. Producers pledge 

harvested production as collateral for short-term loans to allow them to delay 

marketing and avoid selling into a fl ooded market immediately upon harvest. 

If prices fall below the rate at which the loan is taken, the producer can repay 

the loan at the market price, netting a benefi t called an MLG (or CEG, which 

is an alternate method for repaying loans used frequently by cooperatives 

handling repayment for multiple members’ loans).11 Congress also provided 

an alternative means to benefi t from this program for producers who did not 

wish to take out loans—they can receive LDPs, which are payments equivalent to the value of the MLG they would have received for their production 

had it been placed in the loan program. Recently, high commodity prices 

have resulted in few payments through this program, but the value of the lowinterest loans keeps participation relatively high. 

9We include programs under the 1996 

Farm Act that are no longer operating 

because they are included in some of 

the analysis described in this report. 

10Planting for harvest of fruits, 

vegetables (other than lentils, mung 

beans, large and small chickpeas, 

and dry peas), and wild rice was 

prohibited on base acres under 

the PFC program and continues 

under the DP program, except 

in the following situations: (1) 

Harvesting double-cropped fruits, 

vegetables, and wild rice on base 

acres is permitted, without loss 

of payments, in any region that 

has a history of double-cropping 

covered commodities with the 

otherwise prohibited crops. An 

individual farm need not have 

a double-cropping history, only 

the region. (2) Harvesting of any 

fruits, vegetables, or wild rice on 

base acres is permitted, with an 

acre-for-acre loss of direct and 

countercyclical payments for each 

base acre planted to the otherwise 

prohibited crop, if the Secretary 

determines that there is a history of 

planting those crops on the farm. 

(3) Harvesting a specifi c fruit, 

vegetable, or wild rice on base acres 

is permitted, with an acre-for-acre 

loss of direct and countercyclical 

payments for each base acre planted 

to the specifi c crop, if the Secretary 

determines that the producer has an 

established planting history of the 

specifi c crop. In such a case, the 

quantity harvested cannot exceed 

the producer’s average annual 

planting history of the crop during 

the 1991-95 or 1998-2001 crop 

years, excluding any crop year with 

no acres planted to that crop.

11Certifi cate exchange gains were 

ended in 2009 under provisions of the 

2008 Farm Act.
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Dairy producers may also participate in a standing income support program, 

the Milk Income Loss Contract (MILC) program, which provides support on 

a limited share of production during periods of low prices. 

Overall, income support programs have few, if any, limitations imposed upon 

them. The restrictions that do exist are either broad (e.g., income-based eligibility limits) or idiosyncratic (e.g., specifi c payment limits for peanuts) and 

generally do not take into account benefi ts received from other programs. The 

most common limitations for income support come in the form of adjusted 

gross income (AGI) limits—farmers with average AGI above a certain level 

are not eligible for benefi ts12—and payment limitations on the total amount 

of payments an individual may receive from particular individual programs. 

For example, under the 2002 Farm Act, eligibility for DPs requires that a 

producer’s income fall below the maximum AGI limit ($2.5 million) and 

that an individual not collect more than $40,000 per crop year per base 

commodity, excluding peanuts, with an additional $40,000 limit for peanuts 

(if applicable).13

Risk Management Programs

Included in our defi nition of risk management programs are (1) crop insurance, including yield and revenue policies; (2) all forms of disaster assistance; 

and (3) the new Average Crop Revenue Election (ACRE) program (which 

includes elements of both income support and risk management).14

USDA’s Risk Management Agency (RMA) administers the crop insurance 

program and offers policies for a wide array of individual crops, as well as 

for some livestock forage operations. Producers may also elect to insure their 

whole-farm revenue with an Adjusted Gross Revenue (AGR) policy. The 

crop insurance program operates through a partnership with private crop 

insurance companies, whose agents work directly with individual producers. 

The Federal Government subsidizes policy premiums at different levels, 

depending on coverage and type of policy elected. Producers can choose 

only minimum, or catastrophic (CAT), loss coverage (frequently required 

for participation in other commodity loss programs) or coverage up to 85 

percent of yield or revenue losses. Yield insurance policies compensate for 

losses due to below-average yields while revenue policies compensate for 

losses due to below-average revenues, based on either reduced yields or low 

prices. An FSA-administered program—the Noninsured Assistance Program 

(NAP)—provides catastrophic-level loss coverage for crops not covered by 

crop insurance.

Disaster programs prior to the 2008 Farm Act have been primarily ad hoc—

authorized in response to ongoing or recent weather and other extreme conditions. Programs were authorized in most years since the 1996 Farm Act and 

the eligible areas, covered commodities, and compensation levels have varied 

over time. Most programs have been designed to compensate for crop loss or 

damage, livestock feed loss or damage, livestock casualty losses, and tree/

nursery loss or damage. In some cases, loss programs have been authorized 

for individual commodities like sugar and dairy, among others. 

Under the 2008 Farm Act, farmers may participate in a number of new 

standing disaster assistance programs. These programs follow a pattern 

12Payments in a given year do not 

affect AGI eligibility for that year.

13Both current program details 

and provisions under the 2002 and 

1996 Farm Acts are available at: 

http://preview.ers.usda.gov/Briefi ng/

FarmPolicy/ProgramProvisions.htm.

14ACRE relies on a trigger mechanism whereby if a farmer can document 

an individual loss, he may be eligible 

for ACRE benefi ts—depending on 

whether the State trigger was also met. 

Due to the presence of the revenue loss 

triggers, we classify ACRE as a risk 

management program. However, ACRE 

also has income support characteristics. 

ACRE has a weak individual farm loss 

trigger, due to the farm benchmark 

revenue being valued at a 100-percent 

coverage rate, thereby allowing even 

a 1-cent-per-acre loss in revenue to be 

suffi cient for triggering a farm revenue 

loss. Furthermore, the benchmark 

revenue includes the farmer’s crop 

insurance premium (if any). Hence, it 

is possible for the program to trigger 

a farm income loss even when actual 

gross revenue exceeds expected gross 

revenue. Finally, even though the 

program only covers 85 percent of base 

acres, the program has no provisions 

to prevent the ACRE revenue payment 

from exceeding, or being less than, the 

farmer’s actual loss in gross revenue.
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of implementation similar to previous ad hoc programs, addressing crop 

losses; livestock casualties and feed losses; and tree, vineyard, and nursery 

losses, as well as some new coverage for losses from disease outbreaks. 

The programs were authorized in the 2008 Farm Act under Supplemental 

Agricultural Disaster Assistance and include the Supplemental Revenue 

Assistance program (SURE), the Livestock Forage Disaster Program (LFP), 

the Livestock Indemnity Program (LIP), the Emergency Livestock Assistance 

Program (ELAP), and the Tree Assistance Program for Orchardists and 

Nursery Tree Growers (TAP).

Since 2009, producers have been offered an alternative revenue-based 

program, Average Crop Revenue Election (ACRE), which replaces CCPs and 

reduces DPs and potential MLBs. The ACRE program, new under the 2008 

Farm Act, offers revenue guarantees based on State and farm-level revenue 

triggers tied to rolling average yields and prices, with limits on how much the 

guarantee can rise or fall from one year to the next. 

In contrast to income support programs, risk management programs have 

more detailed limitations designed to preclude overt forms of overlap or 

duplication. These limitations often include requirements to enroll in, or 

account for, payments from other risk management programs. For example, 

to be eligible for the Noninsured Assistance Program (NAP), a farmer may 

not be enrolled in the crop insurance program for the same crop. Similarly, 

the Livestock Assistance Program benefi ts are reduced if the producer also 

received benefi ts from previous Livestock Compensation Programs and/

or the Cattle Feed Program. AGI limitations also apply to many of these 

programs.15 15See http://preview.ers.

usda.gov/Briefi ng/Farm Policy/

ProgramProvisions.htm for current 

program details and provisions under 

the 2002 and 1996 Farm Acts.
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Appendix III

Appendix table 1

Changes to Federal insurance premiums when integrated with a national, State-, and county-level ACRE 

payment (2009 crop year)

Location Crop

APH 

yield

Full 

insurance 

premium

(base)1

Full ins. 

premium 

integrated 

with national 

ACRE

Percent 

decrease 

relative to 

base

Full ins.

premium 

integrated 

with State 

ACRE

Percent 

decrease 

relative to 

base

Full ins. 

premium 

integrated 

with county 

ACRE

Percent 

decrease 

relative to 

base

McLean, IL Corn 183 23.69 20.86 12% 14.89 37% 13.14 45%

Soybeans 54 14.84 10.60 29% 9.95 33% 8.22 45%

Hamlin, SD Corn 131 39.31 36.88 6% 29.22 26% 27.52 30%

Soybeans 38 23.89 20.25 15% 19.01 20% 15.61 35%

S. Wheat 52 21.48 17.64 18% 13.52 37% 13.12 39%

McLeod, MN Corn 162 29.55 26.79 9% 21.96 26% 20.97 29%

Soybeans 44 17.03 12.96 24% 10.88 36% 9.99 41%

S. Wheat 50 20.56 16.83 18% 12.79 38% 12.12 41%

ACRE = Average Crop Revenue Election.

APH = Actual production history.

1Revenue assurance with base price option, 70% coverage, for basic units (source, RMA/USDA). These are the full per acre premiums 

unsubsidized by the Federal Government, i.e., they are (1-0.41)*(farmer paid premium). 

Source: USDA, Economic Research Service.
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Appendix IV: Technical

The empirical analysis of the Average Crop Revenue Election (ACRE) and 

insurance interactions and of Supplemental Revenue Assistance (SURE) 

follow the procedures in Cooper (2010). The calculations center stochastically 

around expected prices and yields for the 2009 crop year. Data used for the 

analysis includes yield data from the National Agricultural Statistics Service 

(NASS) over 1975 to 2008, futures price data at planting and harvest over 

the same period, and farm-level yield variability information derived from 

USDA’s Risk Management Agency (RMA) crop insurance premiums. These 

data feed into estimation of density functions for prices and yields. Random 

but correlated draws of prices and yields are made from these density functions to generate simulated data sets. Gross revenue, ACRE payments, crop 

insurance indemnities, and SURE payments are calculated for the simulated 

prices and yields.

Cooper (2010) estimates the insurance premium integrated with ACRE for 

the case of ACRE payments calculated at the State level of yield aggregation 

with this report and Cooper and O’Donoghue (2011), including estimates of 

the insurance premium integrated with ACRE when ACRE payments are 

calculated at the county and national levels of yield aggregation. The revenue 

insurance premiums assume the farmer chooses a 70-percent coverage rate.

This report and Cooper and O’Donoghue (2011) then expand on Cooper’s 

2010 analysis to examine how overlap in coverage between SURE and ad 

hoc can affect both insurance demand and planted acres. Provided that a 

farmer purchases crop insurance (or Noninsured Crop Disaster Assistance 

Program (NAP) for noninsured crops), the producer becomes eligible for 

SURE, a whole-farm revenue program. A farmer may only receive SURE 

payments if the farm operation is located in a county where a disaster has 

been declared, in a county contiguous to a disaster county, or if the farmer 

personally suffered production losses amounting to 50 percent or more of 

normal production levels. Additionally, a producer must suffer a 10-percent 

production loss to at least one crop of economic signifi cance on the farm. For 

an individual, the SURE payment is: 

(A.1) max(0.60( ),0) T

t t tt SURE D G R =∗ − ,

where Gt is the SURE guarantee and Rt 

T

 is total farm revenue, and where Dt 

equals 1 if a farmer is eligible for SURE payments based on the county and 

farm loss criteria, and 0 otherwise. 

The SURE guarantee (Gt

) depends on the level of crop insurance coverage 

selected by the producer, expected prices, and the producer’s actual production history (APH) yield, but is limited to no more than 90 percent of typical 

or expected revenue:

(A.2) min 1.2 ( ), 0.90 max( , ) b bC

t jt jt jt jt jt jt jt

j j

G a py ap y y θ ⎛ ⎞ = ⎜ ⎟ ⎝ ⎠ ∑ ∑

where ajt is planted acreage of crop j (or acreage where planting was 

prevented) in period t, pb

jt is the Revenue Assurance (RA) base price for the 

insured commodity, jt y is the farmer’s expected yield, C

jt y is the producer’s 
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counter-cyclical payment program yield or an “adjusted yield,” and θ represents the farmer’s chosen insurance coverage rate (a subscript denoting the 

farmer is dropped for clarity). Total farm revenue explicitly includes market 

revenue, commodity program payments, Federal disaster payments, and net 

crop insurance indemnities:

(A.3)

( )

max 0, ( , ) ( , , )

0.15

T N b

t jt jt jt jt jt jt jt t

j j

t t tt

R a p y a I y PREM y p MLB

DP CCP or ACRE AH

θ θ ⎛ ⎞⎛ ⎞

= +∗ − + ⎡ ⎤ ⎜ ⎟⎜ ⎟ ⎣ ⎦ ⎝ ⎠⎝ ⎠

++ +

∑ ∑

where (, )jt I y θ denotes the indemnity paid to the producer (equal to zero if 

losses do not exceed the deductible), (, , ) b PREM y p θ jt jt is the producer paid 

insurance premium per acre, MLBt

 is the producer’s (farm-level) total

marketing loan benefi ts summed across all eligible crops produced by the 

farmer, DPt

 is the producer’s total direct payment, CCPt is the producer’s 

total counter-cyclical payment, ACREt

 is the farmer’s total revenue payments 

under the Average Crop Revenue Election program summed across all the 

farmer’s eligible planted acres in all eligible crops, where CCPt

 and ACREt

are mutually exclusive, and AHt

 are other Federal disaster payments covering 

the same disaster.16 The price pN

jt is the “National Average Market Price” as 

determined by USDA’s Deputy Administrator.

We assume that ad hoc disaster assistance comes from USDA Secretarial 

declarations, although it can come in a variety of forms (hence the ad hoc), 

including Congressional legislation written to cover specifi c disaster events. 

USDA Secretarial disaster declarations require a 30-percent or greater yield 

loss due to natural disaster in at least one crop in a county, and require that 

the State Governor make a request to the USDA for disaster assistance (e.g., 

FSA, 2009). 

In the simple quantitative model of the political economy of the ad hoc 

process we assume that the ad hoc disaster program is available every year 

and that the State Governor makes the request with 100 percent probability 

whenever the yield loss criterion is met. We specify the farmer’s ad hoc 

disaster payment rate that is tied to county losses and payable to the farmer’s 

planted acreage in that crop as * max 0, * { } ( ( )) bC C AH p ry y a j jj j j = − φ , 

where r is the disaster trigger rate (set at 0.70, per current USDA rules for

Secretarial disaster assistance), and φ is the probability that the 

State Governor makes a request for assistance. We assume φ = 1 if 

max 0, ( ) ( ) bC C

jj j p ry y − > 0. To show the potential for payment overlap when ad 

hoc and the standing SURE program are not integrated, we consider a hypothetical SURE program (denoted as HSURE) that does not include ad hoc 

payments in the SURE payment calculation.

We use the generated price and yield date in the simulation of expected 

utility (EU) maximizing behavior by a representative farmer in a county 

where yield risk is relatively high (Hyde County, South Dakota) compared to 

the cornbelt. We further assume that the farmer has constant absolute risk 

aversion (CARA) and chooses acreage and insurance coverage to maximize 

16If the eligible farmer chooses to 

be in enrolled in the Average Crop 

Revenue Election program (ACRE) 

rather than in the traditional commodity 

program, then the CCP payment in t is 

replaced by an ACRE revenue payment, 

DPs are reduced by 20% and the loan 

rate in the MLB by 30%.
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the expected value of a negative exponential utility function over G·S = 

1,000,000 simulated price and yield, and insurance combinations as 

(A.4) ( ) 1 2

1 , ,

1 1 k S G w

k a a

Max EU w e

S G

λ

θ

⋅ −

= = − ⎡ ⎤ ⎣ ⎦ ⋅ ∑ ,

where λ is the absolute risk aversion coeffi cient and w is wealth in this 

concave von Neumann Morgenstern utility function. Wealth wk is wo

plus net returns under six risk reduction program alternatives: (a) no 

insurance coverage; (b) insurance coverage; (c) insurance coverage and 

ad hoc payments; (d) insurance coverage and HSURE payments (where 

SURE is the same as HSURE in the absence of ad hoc payments); (e) 

insurance coverage, HSURE, and ad hoc payments; and (f) insurance 

coverage, SURE, and ad hoc payments. Wealth wk

 under each scenario 

includes direct payments for corn, soybeans, and wheat, with the share 

of payments for each crop based on the number of base acres in each 

crop in the county, valued at the base yield rates for that county, with 

the total value of these payments being DP = $6.86 per acre for the 

farmer.

17

Wealth wk for each price-yield realization k is defi ned (in multicrop format) 

as:

(A.5) ( ) N

o j jk jk j k j jk

j jj

w w DP a p y C D a I k θ ⎛⎞ ⎛⎞ =+ + − ++ − ⎜⎟ ⎜⎟ ⎝⎠ ⎝⎠ ∑ ∑∑

( ) j jk j

j j

a PREM AH θ ⎛ ⎞ + ⎜ ⎟ ⎝ ⎠ ∑ ∑ ,

where Cj

 is the production cost for each crop j, Dk is the total HSURE 

payment (if applicable to the scenario), I

jk(θ) is the per acre insurance 

indemnity, PREMjk(θ) is the insurance premium, and AHj

 are ad hoc disaster 

payments.18 

We then assume the farmer has a moderate risk aversion premium of 20 

percent (e.g., Hurley, Mitchell, and Rice, 2004; Mitchell, Gray, Steffey, 2004) 

to explore how production decisions with respect to land use might change 

with alternative program availability. The associated absolute risk aversion 

coeffi cient λ (as in Equation A.5) is scaled to the standard deviation of net 

revenue for a farm normalized to 1 acre in size at the end of the previous crop 

year using the approach in Babcock, Choi, and Feinerman (1993).19 For the 

sake of transparency in the results, initial wealth is set high enough so that 

the farmer’s budget constraint is never binding, and as such, relationships 

between marginal benefi ts and costs determine the activity levels. 

References

Babcock, B. E. Choi, and E. Feinerman. “Risk and Probability Premiums for 

CARA Utility Functions”, J. Agri. and Res. Econ, Vol 18(1):17-24. 1993.

Cooper, J. 2010. “Average Crop Revenue Election: A Revenue-Based 

Alternative to Price-Based Commodity Payment Programs,” American 

Journal of Agricultural Economics, Vol. 92, No. 4 (July 2010): 1214-1228.

17Note that these annual fi xed 

payments do not require production 

of the crops; we therefore include the 

soybean and corn direct payments 

regardless of whether our farmer has 

decided to grow only spring wheat.

18Note that under current expected 

prices, the probability of marketing loan 

benefi ts and counter-cyclical payments 

being issued are zero for the crops in 

question, and as such, are not included 

in wk

.

19For the representative farmer, the 

baseline standard deviation is $107.19 

evaluated over our SxG simulated price 

and yield combinations, where θ=0.7, 

cost functions are quadratic, and no 

SURE or ad hoc payments. This base 

scenario yields a λ equal to 0.003835. 
32

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Cooper, J. and Erik O’Donoghue. “Identifying and Reducing Overlap in Farm 

Program Support,” Selected Paper, 2011 AAEA & NAREA Joint Annual 

Meeting, Pittsburgh, Pennsylvania, July 24-26, 2011. 

FSA. “Program Fact Sheets: Emergency Disaster Designation and 

Declaration Process,” Farm Service Agency, USDA, Washington, DC, 

2009.

Hurley, T., P. Mitchell, and M. Rice. (2004). “Risk and the Value of Bt 

Corn,” American Journal of Agricultural Economic, Vol. 86, No. 2 (May 

2004):345-358.

Mitchell, P. , M. Gray, and K. Steffey. “A Composed-Error Model for 

Estimating Pest-Damage Functions and the Impact of the Western Corn 

Rootworm Soybean Variant in Illinois,” American Journal of Agricultural 

Economics, Vol. 86, No. 2 (2004): 332-344.