United States

Department of

Agriculture

Economic

Research

Service

Economic

Research

Report

Number 123

August 2011 Ronald Sands and Paul Westcott (coordinators) 

Impacts of Higher Energy 

Prices on Agriculture and 

Rural Economies
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Recommended citation format for this publication:

Sands, Ronald and Paul Westcott (coordinators), J. Michael Price, 

Jayson Beckman, Ephraim Leibtag, Gary Lucier, William McBride, 

David McGranahan, Mitch Morehart, Edward Roeger, Glenn 

Schaible, and Timothy R. Wojan. Impacts of Higher Energy Prices 

on Agriculture and Rural Economies, ERR-123, U.S. Dept. of 

Agriculture, Econ. Res. Serv. August 2011.

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Impacts of Higher Energy Prices on Agriculture and Rural Economies / ERR-123

Economic Research Service / USDA

United States 

Department 

of Agriculture 

www.ers.usda.gov 

A Report from the Economic Research Service

Economic

Research 

Report

Number 123

August 2011

Impacts of Higher Energy 

Prices on Agriculture and 

Rural Economies

Abstract

Agricultural production is sensitive to changes in energy prices, either through energy 

consumed directly or through energy-related inputs such as fertilizer. A number of 

factors can affect energy prices faced by U.S. farmers and ranchers, including developments in the oil and natural gas markets, and energy taxes or subsidies. Climate change 

policies could also affect energy prices as a result of taxes on emissions, regulated emission limits, or the institution of a market for emission reduction credits. Here we review 

the importance of energy in the agricultural sector and report the results of a case study 

on the economic implications for the farm sector of energy price increases that would 

arise from plausible, constructed greenhouse-gas-emission reduction scenarios. 

Higher energy-related production costs would generally lower agricultural output, raise 

prices of agricultural products, and reduce farm income, regardless of the reason for the 

energy price increase. Nonetheless, farm sector impacts were modest for the scenarios 

and time periods examined. We demonstrate the unique distribution of effects resulting 

from price (or cost) increases for different types of energy due to pricing their carbon 

content, as well as the relative use of energy in production of different agricultural 

commodities.

Ronald Sands (coordinator), rsands@ers.usda.gov

Paul Westcott (coordinator), westcott@ers.usda.gov

J. Michael Price, mprice@ers.usda.gov

Jayson Beckman, jbeckman@ers.usda.gov

Ephraim Leibtag, eleibtag@ers.usda.gov

Gary Lucier, glucier@ers.usda.gov

William McBride, wmcbride@ers.usda.gov

David McGranahan, dmcg@ers.usda.gov

Mitch Morehart, morehart@ers.usda.gov

Edward Roeger

Glenn Schaible, schaible@ers.usda.gov

Timothy R. Wojan, twojan@ers.usda.gov
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Our analysis focuses on relatively short-term adjustments to higher energyrelated costs and does not include potential financial benefits from sequestering carbon or reduced climate change. Finally, we find that agricultural 

sector impacts on farming-dependent counties would not be substantial but 

would be potentially largest where education and employment levels are 

relatively low, while effects on rural communities due strictly to energy 

production adjustments would be concentrated in the few U.S. counties with 

significant employment in energy extraction industries. 

Keywords: energy prices, costs of production, fuel, fertilizer, farm 

income, agriculture, greenhouse gas emissions, farming dependent counties, rural economy, Economic Research Service, ERS, U.S. Department of 

Agriculture, USDA

Acknowledgments

This report benefited from the many useful comments and suggestions 

provided by Bruce Babcock, Iowa State University; Patrick Westhoff, 

University of Missouri; Kay Smith, Dan Skelly, and Marie LaRiviere, Energy 

Information Administration, U.S. Department of Energy; Jared Creason, U.S. 

Environmental Protection Agency; Jan Lewandrowski, Office of the Chief 

Economist, U.S. Department of Agriculture; and Mary Bohman and John 

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

We also thank Dale Simms for editorial assistance and Susan DeGeorge for 

preparing the charts and for other design and production contributions.
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Contents

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

Chapter 1

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

Chapter 2

Energy and Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Chapter 3

Defining a Case Study—Linking Energy Prices to 

Greenhouse Gas Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Chapter 4

National Impacts of Higher Energy Prices . . . . . . . . . . . . . . . . . . . . . . . . . 12

Chapter 5

Impacts by Farm Production Specialty and Region . . . . . . . . . . . . . . . . . . 21

Chapter 6

Rural Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 

Chapter 7

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Appendix A

Greenhouse Gas Pricing Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Appendix B

From Greenhouse Gas Emission Prices to Energy Prices . . . . . . . . . . . . . 44

Appendix C

Energy Price Impacts on Retail Food Prices . . . . . . . . . . . . . . . . . . . . . . . . 46
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Summary

What is the Issue?

Agricultural production consumes large amounts of energy, either directly 

through combustion of fossil fuels, or indirectly through use of energy-intensive inputs, especially fertilizer. Over 2005-08, expenses from direct energy 

use averaged about 6.7 percent of total production expenses in the U.S. farm 

sector, while fertilizer expenses represented another 6.6 percent. However, 

these sector averages mask much greater energy intensities for major field 

crops. Agricultural production is therefore sensitive to changes in energy 

prices, whether the changes are caused by world oil markets, policies to 

achieve environmental goals, or policies to enhance energy security.

To illustrate the flow of energy prices through the agricultural system from 

farm to retail, we construct three scenarios: a reference scenario of agricultural production from 2012 through 2018, and two alternative scenarios 

over the same time period with energy price increases expected to result 

from pricing greenhouse gas emissions. Price increases for different energy 

sources in the alternative scenarios are based on their carbon content. Results 

are compared to the reference scenario to estimate economic implications. 

Higher energy-related production costs would generally lower agricultural 

output, raise prices of agricultural products, and reduce farm income in the 

short run.

What Did the Study Find?

•	Energy-related production expenses vary significantly for different crops. 

On a per-acre basis, corn and rice have the highest energy-related costs 

of the eight major crops (corn, sorghum, barley, oats, wheat, rice, upland 

cotton, and soybeans) examined in this report, while soybeans have the 

lowest. With higher energy-related expenses (fuel up an average of 2.6 

to 5.3 percent; fertilizer up 4 to 10 percent), total acreage for these eight 

crops would decline by an average of 0.2 percent (under the lower price 

change scenario) to 0.4 percent (higher price change scenario) over 2012-

18. Planted area would decline for seven of the eight crops, the exception 

being soybeans. 

•	Energy-related expenses also affect livestock producers. Although their 

direct energy costs are lower than for crop production, livestock producers 

would face higher feed costs under both the lower (0.2 to 0.6 percent 

higher annually, 2012-18 average) and higher (0.6-1.3 percent higher) 

energy price change scenarios. Poultry production would be less affected 

than beef and pork, since poultry is the most efficient feed-to-meat 

converter of the animal types.

•	The scenarios analyzed did not account for potential changes in technology (beyond those implicit in the reference scenario) in response 

to sustained increases in energy prices. Additionally, a decades-long 

declining trend in energy use per unit of output in the agricultural sector 

is likely to continue, which is only partly represented in the scenarios 

by increasing yields. For these reasons, reported impacts of higher 

energy prices on the agricultural sector may be somewhat overestimated. 
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Additionally, longer run impacts of further energy price increases would 

not be proportionately as large as the short-term impacts we report here.

•	Effects also vary regionally. The Mississippi Portal region is most affected 

by higher energy costs, due to the predominance of fertilizer-intensive 

crops like cotton. Farms in that region would see net cash income decline 

by 8 to 19 percent on average (in 2014) under the lower and higher energy 

price change scenarios, respectively.

•	Although increased agricultural commodity prices affect consumer 

food prices, retail food prices are more affected by energy costs in food 

processing, distribution, and marketing than in agricultural commodity 

production. For the scenarios and time period focused on in this report, 

the Consumer Price Index (CPI) for food—including food at home and 

food away from home—would be 0.6 to 0.9 percent higher than without 

the simulated energy-related cost increases for electricity, diesel fuel, and 

natural gas.

•	It does not appear that impacts through the agricultural sector of the higher 

energy prices scenarios studied in this report would have a substantial 

effect on farm county economies and populations. In general, farm counties tend to have relatively few people without high school degrees, very 

high proportions of adults employed, and low poverty rates compared with 

other nonmetro counties. Some farm-dependent counties in the Mississippi 

Portal region may be relatively more affected by energy-related farm 

income losses.

•	A decrease in fossil fuel production under an emissions tax or a capand-trade program would reduce overall employment in related energy 

extraction industries. Counties specializing in energy production are overwhelmingly rural. However, few nonmetro counties derive a substantial 

share of nonfarm employment from energy production, so overall rural 

impacts would be small, with the exception of some mining counties, principally located in eastern Kentucky and West Virginia.

How Was the Study Conducted?

Two key economic models at USDA’s Economic Research Service (ERS)—

the Food and Agricultural Policy Simulator (FAPSIM) and the Farm-Level 

Partial Budget Model—were used as the foundation of this analysis. We 

started with a range of prices for carbon dioxide emissions, taken or derived 

from studies by the U.S. Environmental Protection Agency and the U.S. 

Energy Information Administration. Both studies are based on the American 

Clean Energy and Security Act of 2009 (House Resolution 2454), which 

specified an increasingly stringent cap on U.S. greenhouse gas emissions 

from 2012 through 2050. Corresponding impacts on prices for electricity, 

natural gas, and petroleum products were also provided by these studies. We 

focus on the 2012-2018 timeframe, which corresponds to the timeframe of 

results provided by the FAPSIM model.

Implications of these energy-related price impacts for changes in agricultural production costs were used as input to FAPSIM to provide national 

agricultural sector effects. The Farm-Level Partial Budget Model was used 

to convert national impacts into changes in farm business net cash income 
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for nine resource regions in the United States. Econometric regression analysis provided a link from agricultural producer prices to retail food prices, 

including energy costs in food processing, distribution, and marketing channels from the farm to retail.

Results focus solely on effects of higher cash expenses associated with emissions pricing, and do not include potential financial benefits from sequestering carbon or reduced climate change.
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Chapter 1

Introduction

Agricultural production consumes large amounts of energy, either directly 

through combustion of fossil fuels, or indirectly through use of energyintensive inputs, especially fertilizer. Over 2005-08, expenses from direct 

energy use averaged about 6.7 percent of total production expenses in the 

U.S. farm sector, while fertilizer expenses represented another 6.6 percent. 

However, these averages mask much greater energy intensities for major field 

crops. Several factors can influence energy prices faced by U.S. agriculture: 

availability of natural gas, world oil prices, energy taxes, or a greenhouse gas 

policy designed to reduce carbon dioxide emissions.

To illustrate the flow of energy prices through the agricultural system from 

farm to retail, we construct three scenarios: a reference scenario of agricultural production from 2012 through 2018, and two “what-if” scenarios over 

the same time period with higher energy prices. For illustrative purposes, 

energy price increases in the alternative scenarios are driven by prices on 

greenhouse gas emissions. The analysis uses a suite of models maintained at 

USDA’s Economic Research Service (ERS). This case study is designed to 

(1) demonstrate the use of ERS models to simulate a change in energy prices 

through the U.S. agricultural system; (2) provide two scenarios of increased 

energy prices, with price changes for fossil fuels weighted by carbon content; 

and (3) provide an expanded discussion on methodology used to produce an 

earlier, related report (USDA, Office of the Chief Economist, 2009).

Different alternatives to limiting greenhouse gas emissions have been 

proposed over the past several years, and additional approaches are likely 

to be developed in the future. The assumptions for greenhouse gas emission 

prices, and resulting energy price impacts used in the two primary scenarios 

discussed in this report, are taken or derived from analyses by the U.S. 

Environmental Protection Agency (U.S. EPA, 2009) and the U.S. Energy 

Information Administration (U.S. EIA, 2009a). As such, the energy prices 

analyzed are in a range of recent climate change policy discussions and are 

meant to be illustrative rather than forecasts. EPA and EIA published separate analyses of the American Clean Energy and Security Act of 2009 (H.R. 

2454), which includes a cap-and-trade system. However, results discussed 

here are not dependent on the emissions limitations resulting from cap-andtrade—alternatively, an emissions tax could have been the underlying mechanism driving higher energy prices.1 The analysis is differentiated from those 

estimating effects of general energy price increases because of the relationship of price increases for different energy sources to their greenhouse gas 

emissions based on carbon content.

The focus in this report is on relatively short-term impacts of higher 

energy-related costs and does not include potential financial benefits from 

sequestering carbon or of reduced climate change. Recent studies that have 

examined potential benefits from agricultural practices to control climate 

change include analyses of methane digesters on livestock operations (Key 

and Sneeringer, 2011) and no-till farming (Horowitz et al., 2010).

1Greenhouse gas emissions can be 

reduced by other, non-price options. 

These include energy-efficiency standards for buildings and motor vehicles 

and renewable energy standards for 

electricity generation.
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Chapter 2

Energy and Agriculture

Agricultural production consumes significant amounts of energy, especially in production of field crops. Consequently, energy prices affect costs 

of production in the agricultural sector. Production costs are important to 

farmers’ net returns (profitability), defined as receipts for selling their output 

minus costs of its production, and net returns influence farmers’ production 

decisions. Net returns affect what crops are produced by affecting the allocation of acres each season. Net returns affect farmers’ livestock production 

choices as well, subject to biological constraints.

Energy consumption in the sector can be either direct—as with gasoline, 

diesel, petroleum, natural gas, electricity, and energy use for operating irrigation equipment (see box, “Irrigation and Energy”)—or indirect, as with fertilizer (see box, “U.S. Supply of Nitrogen-Based Fertilizer Coming More From 

Abroad”). Over 2005-08, expenses from direct energy use averaged about 6.7 

percent of total production expenses in the sector, while fertilizer expenses 

represented another 6.6 percent. This time period saw an increase in energy 

costs, and the combined share of these inputs reached nearly 15 percent 

in 2008. Additionally, feed costs for livestock production include indirect 

energy costs due to the influence on crop prices, such as for corn and soybean 

meal.

 The importance of energy and the effects of energy price changes are not 

uniform across commodities or regions, as energy intensity in production 

varies considerably. Figure 2.1 shows the share of total operating costs for 

selected crops represented by the two largest energy-related input categories 

(fuel, lube, and electricity; and fertilizer) in 2007-08. Operating costs are 

out-of-pocket cash expenses paid for production inputs for each commodity. 

Operating expenses reflect the quantities and prices of production inputs and 

thus depend on production practices used by farmers. Costs cover inputs such 

as seed, fertilizer, fuel, lube, electricity, feed, chemicals, and repairs.

Figure 2.1

Energy-related inputs relative to total operating expenses, 2007-08 average

Source: USDA, Economic Research Service, Cost of Production Estimates.

Percent

Fertilizer

Fuel, lube, and electricity

0

10

20

30

40

50

60

70

Sorghum Oats Wheat Corn Barley Rice Soybeans Cotton
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Sorghum has the highest share of energy-related inputs while cotton has the 

lowest. For sorghum, oats, wheat, corn, and barley, energy-input categories 

are more than 50 percent of operating expenses.2

The distribution of energy-related input costs for these crops is different 

in absolute terms. Per-acre operating costs are important for determining 

producer net returns, which influence farmers’ cropping choices. Rice, corn, 

and cotton have the highest per-acre expenses for energy-related inputs (fig. 

2.2). While rice and cotton have the highest per-acre costs for fuel, lube, and 

electricity, corn has the highest costs for fertilizer. Again, energy-related 

costs for soybean production are relatively low.

Direct energy costs account for smaller shares of operating costs for livestock 

operations, representing less than 5 percent of operating costs for hogs and 

cow-calf operations and about 10 percent for milk production in 2007-08 

(table 2.1). Livestock operations also see indirect effects of energy costs 

through higher feed costs. In 2007-08, feed costs accounted for about 11 

percent of total operating costs for cow-calf operations, 58 percent for hog 

production, and 76 percent for milk production.

2Energy shares in figure 2.1 are much 

greater than the overall sector average 

of 15 percent reported earlier in this 

chapter, which is the energy-related 

share in all production costs, both variable and fixed. Figure 2.1 displays the 

share relative to operating costs only, 

and fertilizer is a large share of operating costs for field crops. Also, the agricultural sector average includes other 

activities such as livestock production, 

which is less energy intensive.

U.S. Supply of Nitrogen-Based Fertilizer 

Coming More From Abroad

Fertilizer is an important component of production costs for crops, especially 

corn. Nitrogen fertilizer production is energy-intensive as natural gas makes up 

70 to 80 percent of production costs. U.S. nitrogen fertilizer supplies, while 

historically domestic, have been increasingly imported over the past decade. 

Shares of U.S. nitrogen fertilizer are now nearly equal between domestic and 

foreign suppliers. 

U.S. nitrogen supply from domestic production and net imports, 1999-2010

Million nutrient tons

Production

Fertilizer year1

Net imports

20

16

12

8

4

0

1999 2000 01 02 03 04 05 06 07 08 09 10

1

The fertilizer year runs from July of the preceding year to June of the year indicated in 

the chart. 

Source: Huang (2009), USDA, Economic Research Service. Updated using data from 

the U.S. Department of Commerce for nitrogen production and from ERS for net imports.
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Regional Differences in Costs of Energy Inputs

Energy-related input costs also vary by region, due primarily to crop composition and reliance on irrigation. Figure 2.3 illustrates this variation for wheat 

and soybeans, two sectors at opposite ends of the energy-input share spectrum. For wheat, the regions with the largest shares of costs from energyrelated inputs are the Fruitful Rim, the Heartland, and the Prairie Gateway. 

For soybeans, the regions with the largest share of costs from energy-related 

inputs are the Southern Seaboard and the Eastern Uplands. The Northern 

Great Plains has the lowest share of energy-related inputs for both wheat and 

soybeans, well below the national averages of 60 percent and 31 percent.

Wheat production costs in the Northern Great Plains and the Prairie Gateway, 

where the majority of the crop is grown, present an interesting contrast in 

operating expenses. While the two regions have a similar share of production costs attributable to fertilizer expense (37 percent), the shares of costs 

accounted for by fuel, lubrication, and electricity are much different (28 

percent for the Prairie Gateway, versus 12 percent for the Northern Great 

Plains). This is largely due to the high level of irrigation used in the Prairie 

Gateway.

Figure 2.2

Energy-related expenses, selected crops, 2007-08 average

Source: USDA, Economic Research Service, Cost of Production Estimates.

$ per acre

Fertilizer

Fuel, lube, and electricity

0

50

100

150

200

250

Sorghum Oats Wheat Corn Barley Rice Soybeans Cotton

Table 2.1

Energy-related inputs relative to total operating expenses, 

livestock, 2007-08 average

Fuel, lube, 

and electricity Feed1

Percent

Cow-calf, per bred cow 10 11

Hogs, per hundredweight gain 4 58

Milk, per hundredweight sold 5 76

1Feed for cow-calf costs includes supplemental feed, concentrates, and 

other feed; feed for milk costs excludes grazed feed.

Source: USDA, Economic Research Service, Cost of Production Estimates.
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Figure 2.3

Energy input costs as a share of operating costs for soybeans and wheat by ERS Resource Region, 

2007-08 average

Basin and Range

ERS Region

Eastern Uplands

Fruitful Rim

Heartland

Mississippi Portal

Northern Crescent

Northern Great Plain

Prairie Gateway

Southern Seaboard

na = Not available.

Source: USDA, Agricultural Reserve Management Survey.

Soybeans na

Wheat 57

Soybeans na

Wheat 67

Soybeans 22

Wheat 49

Soybeans 35

Wheat 65

Soybeans 31

Wheat 65

Soybeans 39

Wheat na

Soybeans 37

Wheat na

Soybeans 47

Wheat na

Soybeans 36

Wheat 61
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Irrigation and Energy

Irrigation makes a significant contribution to U.S. agricultural production. For 2007, the market value of agricultural products sold for all farms was $297.2 billion, with irrigated farms (a farm irrigating any land) accounting 

for nearly 40 percent of this total. The average farm value of products sold for an irrigated farm ($393,700) was 

more than 4 times the average value for a non-irrigated farm ($93,900). Irrigation makes an obvious contribution to the value of crop products sold, but it also contributes to the farm value of livestock and poultry products 

via the use of irrigated crop production used as feed.

0

Acres of irrigated land, 2007 

1 dot = 10,000 acres

Source: USDA/National Agricultural Statistics Service.
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In 2007, 56.6 million acres in the United States were irrigated (51.5 million harvested cropland 

acres and 5.1 million pastureland and other cropland acres), accounting for about 7.5 percent of 

total cropland and pastureland acres. About 16.6 percent of U.S. harvested cropland acres nationwide were irrigated, while only about 1.2 percent of total U.S. pastureland acres were irrigated. 

The map shows 2007 irrigated acres by State, with each dot representing 10,000 acres. Most irrigated agriculture occurs in the 17 Western States.

Energy use in irrigated agriculture is determined primarily by the amount of land devoted to irrigation, the quantity of applied irrigation water, and the status of irrigation efficiency—more conserving irrigation systems and water-management practices generally mean lower per-acre energy 

costs.

In 2008, energy costs for irrigation pumping for U.S. agriculture were over $2.6 billion, again 

mostly in the 17 Western States (see table). This regional distribution of energy-related irrigation 

costs influences the regional farm-business impacts derived from the Farm-Level Partial Budget 

Model, discussed in chapter 5.

Energy expenses for irrigation pumping are the dominant cost for irrigated agriculture. However, 

for the 17 Western States, total irrigation costs for 2008 also include $671.5 million for purchased 

water costs, $659.6 million for irrigation maintenance and repair costs, and $870.0 million for 

hired and contract irrigation labor.

Expenditures for groundwater (well) and surface-water pumps on irrigated 

farms, 2008

Expenditure by energy source

Region

Total 

expenditure Electricity Natural gas

Liquid petroleum 

gas, propane, 

butane Diesel Gasoline

$ million

Western States 2,223.2 1,411.9 412.3 27.1 368.9 3.0

Leading Eastern States1 409.6 118.8 3.9 8.7 276.7 1.5

Source: Farm and Ranch Irrigation Survey (2008), National Agricultural Statistics Service, USDA.

1The 11 Leading Eastern States are Arkansas, Florida, Georgia, Illinois, Indiana, Louisiana, Michigan, Minnesota, 

Mississippi, Missouri, and Wisconsin.
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Chapter 3

Defining a Case Study—Linking Energy 

Prices to Greenhouse Gas Emissions

For illustrative purposes, we analyze the potential impacts of higher energy 

prices resulting from pricing greenhouse gas emissions. The agricultural 

sector accounts for approximately 8 percent of U.S. greenhouse gas emissions, mostly as carbon dioxide, methane, and nitrous oxide (U.S. EPA, 

2010). Carbon dioxide is emitted when fossil fuels are combusted, methane is 

emitted through enteric fermentation by livestock and manure management, 

and nitrous oxide is emitted through the use of nitrogen fertilizer.

Various alternatives have been proposed to limit future U.S. greenhouse gas 

emissions. A common feature is the establishment of a cap-and-trade system 

for emission allowances, with an emissions cap covering the majority of U.S. 

greenhouse gas emissions. The resulting price of greenhouse gas emissions 

is that which clears the market for emission allowances. In the cap-andtrade proposals, agriculture is not a covered sector: agricultural emissions 

of methane and nitrous oxide are outside the cap. Nonetheless, a cap-andtrade system affects agriculture indirectly through an increase in fossil fuel 

prices, based on the carbon content of each fuel and the price of greenhouse 

gas emissions. A similar increase in fossil fuel prices would result from 

taxing greenhouse gas emissions directly.3 Appendix A provides a graphical 

supply-demand framework for analyzing how the pricing of greenhouse gas 

emissions affects energy markets, and compares a tax on emissions with a 

cap-and-trade system.

Agriculture consumes significant amounts of energy: refined petroleum for 

farm machinery, electricity for irrigation and other equipment, natural gas 

indirectly through nitrogen fertilizer, and coal indirectly through electricity. 

A price on greenhouse gas emissions affects fuels differently, depending on 

the carbon content per unit of energy. Among the major fossil fuels—coal, 

refined petroleum, and natural gas—coal has the greatest carbon emissions 

per unit of energy and natural gas the least. Therefore, a price on carbon 

dioxide not only increases the price of energy, but also affects relative prices 

between fossil fuels. Natural gas becomes less expensive relative to coal and 

refined petroleum when greenhouse gas emissions are priced.

A common unit of measurement is needed to compare the impact of different 

greenhouse gases on the climate. Emissions of greenhouse gases other than 

carbon dioxide are first converted to their carbon dioxide equivalent so that 

total emissions—in metric tons of carbon dioxide equivalent (t CO2-eq)4—

can be summed across different greenhouse gases.5 Working in terms of 

carbon dioxide equivalent provides a way to price emissions across the 

various greenhouse gases in common units, dollars per metric ton of carbon 

dioxide equivalent. Appendix B provides a detailed description of converting 

carbon dioxide prices to an increment in the price of various fossil fuels.

3See Climate Change 101: Understanding and Responding to Global Climate 

Change: Cap and Trade, by the Pew 

Center on Global Climate Change, for 

an introduction to cap and trade, along 

with a glossary of key terms.

4Until recently, greenhouse gas emissions were frequently reported in units 

of carbon equivalent instead of carbon 

dioxide equivalent. The two approaches 

differ by the molecular weights of carbon (12) and carbon dioxide (44).

5The IPCC Working Group I Technical 

Summary (IPCC, 2007a) provides background on the weighting of greenhouse 

gases relative to carbon dioxide.
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Greenhouse Gas Price Scenarios

In addition to the direct effects that pricing greenhouse gas emissions has on 

different energy prices related to the different carbon content of each fuel, 

further indirect impacts can occur due to adjustments and interactions of 

the individual fuel markets for coal, natural gas, electricity, and petroleum. 

To represent these direct and indirect effects, we draw on analyses by EPA 

and EIA to provide a range of emission prices and energy market impacts to 

derive price changes for petroleum products, natural gas, and electricity.

Two scenarios of emission prices from these studies are displayed in 

figure 3.1. The first scenario is based on the “core policy scenario” (U.S. 

Environmental Protection Agency, 2009) of the American Clean Energy and 

Security Act of 2009 (H.R. 2454); the second, and higher, price scenario is 

derived from the “basic case” analysis of the same legislation by the U.S. 

Energy Information Administration (2009a). Thus, these scenarios provide a 

range of energy prices consistent with recent climate change discussions.

An important assumption in both the EPA and EIA analyses is that greenhouse gas emission prices increase smoothly over time at a constant annual 

percentage rate. This follows from provisions in H.R. 2454 for banking 

emission allowances. EPA’s emission prices increase 5 percent per year, 

while EIA’s rise at a 7.4-percent annual rate. Alternatively, these emission 

prices could result from specifying corresponding emission taxes with fixed 

annual rates of increase.

The EPA study provided greenhouse gas emission prices at 5-year intervals through 2050; prices for other years in this analysis were calculated by 

interpolating between the 5-year results and extrapolating back from 2015 to 

2012. The EIA study reported emission prices for each year through 2030. 

Although this report primarily focuses on effects of the pricing of emissions 

on agriculture over the next decade, figure 3.1 shows the full availability of 

emission prices from those studies to provide a longer term perspective on 

Figure 3.1

Greenhouse gas emission prices under two scenarios, 2012-2050

Source: USDA, Economic Research Service calculations based on EPA (2009) and 

EIA (2009a).

2005 $/ton, CO2 equivalent

2010 15 20 25 30 35 40 45 50

0

10

20

30

40

50

60

70

80

EPA, 

core policy scenario

EIA,

basic case
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potential energy cost impacts. (See related box, “Longer Term Adjustments 

to Higher Energy Prices,” p. 19.)

Implications of greenhouse gas emission prices for different fuels (petroleum, 

natural gas, and electricity) reflect two factors. First is the direct increase in 

the price for each fuel that reflects the emissions price applied to the carbon 

content of the fuel. Second is the economic effect that relates to adjustments and interactions of the markets for different fuels and the potential 

for substitution among alternative fuels in response to changes in relative 

prices. For example, the composition of fuels to generate electricity shifts 

away from greenhouse-gas-intensive fuels as the emission price increases. 

Since coal results in more greenhouse gas emissions per unit of energy than 

natural gas, coal prices would be more affected by an emissions price than 

would natural gas prices. Over time, some substitution between these fuels in 

Benefits of a Greenhouse Gas Mitigation Policy 

for Agriculture

In this study, we focus on costs to U.S. agriculture of increases in energy prices 

associated with two greenhouse gas price scenarios. We do not analyze potential 

financial benefits from such a policy. However, two types of benefits could be 

important to agriculture: payments to agricultural and forest land managers for 

sequestering carbon, and reduced climate change.

Agriculture may benefit by generating and selling emission offsets. The term 

offset describes a reduction in emissions or increase in sequestration of greenhouse gases produced by one entity that can be used by another entity to offset 

its own emissions. Agricultural land can sequester carbon through changes in 

tillage practices, forest management (such as a longer rotation age), and converting land to forests (afforestation). However, developing a market to pay for 

emissions reductions or enhanced carbon sequestration is challenging.

The amount of carbon stored in soils or trees varies across location, and measurement of the total stock of carbon can be expensive and uncertain, especially 

for carbon in soils. Nonetheless, one analysis of such a program estimated annual 

gross revenues to agriculture from offsets could exceed $2 billion (in real 2005 

dollars) by 2020 and could reach almost $30 billion by 2050 (USDA, Office 

of the Chief Economist, 2009). These calculations were based on an analysis 

underlying the EPA 2009 study.

The state of the science on anticipated climate impacts is summarized by activity and by world region in a 2007 assessment by the Intergovernmental Panel 

on Climate Change (IPCC, 2007b). For increases in global average temperature 

up to 2 degrees Celsius, the general trend is for increased yields in colder environments and decreased yields in warmer environments. This may be accompanied by increased insect outbreaks and heavy precipitation events. Plant health 

declines for larger increases in temperature. 

Deriving monetary estimates of climate damages or benefits to U.S. agriculture involves at least three major areas of uncertainty: variation across climate 

models, effect of carbon dioxide fertilization, and climate impacts outside the 

United States. Economic adaptation can reduce the consequences of climate 

change, especially through international trade in agricultural products.
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electricity production and other uses would occur in response to the changes 

in relative prices, shifting more toward natural gas. The net result would be 

for the price of natural gas, due to heightened demand, to rise more than its 

technical emissions factor would suggest, while coal’s price would increase 

less. Overall, this suggests an additional impact on the price of fertilizer, the 

production and price of which are dependent on natural gas.

Table 3.1 displays the percent increase in energy prices relative to those in 

the reference scenario for three fuels under the two scenarios of greenhouse 

gas emission prices. These energy price impacts are taken or derived from 

results in EPA (2009) and EIA (2009a) and reflect both the direct effects of 

emission prices and the indirect effects of fuel market interactions, as represented in the models underlying those studies.6 The focus in this study is on 

short-term impacts, corresponding to the 2012-18 period shown in table 3.1. 

6Electricity prices reported by EIA 

include rebates to consumers through 

2025 (as provided for in H.R. 2454) 

and therefore do not reflect the incremental impact of emission prices. Thus, 

for consistency between the two price 

scenarios used here, electricity prices in 

our higher price impact scenario were 

constructed from the EIA results by 

adding emissions price increments to 

the prices from the reference scenario. 

See page 58 of EIA (2009a) for EIA 

treatment of electricity rebates.

Table 3.1

Change in fuel prices relative to the reference scenario, 2012-18

Average, 2012-2018

Petroleum 

price

Natural gas 

price

Electricity 

price

Percent increase

Lower price change scenario 3.2 5.4 10.7

Higher price change scenario: 13.4 14.0

 Industrial distillate fuel oil 7.8

 Transportation diesel fuel 6.6

Source: EPA (2009) and USDA-ERS calculations based on EIA (2009a) 

Note: For the higher price change scenario, industrial distillate fuel oil was used to

derive impacts on prices for fertilizer and transportation diesel fuel (distillate fuel oil)

was used to derive impacts on prices for direct fuel use.
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Chapter 4

National Farm-Level Impacts of 

Higher Energy Prices

To assess effects on agriculture, assumptions for energy price changes 

summarized in table 3.1 were used to derive changes in prices paid by 

farmers for the two largest energy-related agricultural production inputs: fuel 

and fertilizer. Fuel price impacts in each scenario are based on 80 percent 

of the percentage change in petroleum prices from the reference scenario. 

Fertilizer price impacts are based on the percentage changes in natural gas 

prices (times 0.65) to reflect nitrogen-based fertilizer production costs and 

the percentage changes in petroleum prices (times 0.15) to reflect phosphate 

and potash production costs.

Table 4.1 shows the average percentage increase in prices paid by farmers 

for fuel and fertilizer for the two scenarios. The effect on producer input 

prices is about twice as large for the higher price impact scenario as for the 

lower price impact scenario. These price impacts are moderate in comparison to historical variability in these input prices, particularly over the past 

decade. Prices paid by farmers for fuel and for fertilizer each changed (some 

increases and some decreases) at double-digit rates in 9 of the 10 years from 

2001 to 2010, including an 81-percent price spike for fertilizer in 2008. 

Nonetheless, despite the price volatility, the 2010 index for fuel had risen 110 

percent from 2000 and the fertilizer index was up 123 percent, representing 

average compound annual rates of 7.7 and 8.3 percent, respectively.

To assess the impacts on major field crops and livestock, changes in agricultural production costs arising from higher energy prices are used as inputs 

to FAPSIM (see box, “The Food and Agricultural Policy Simulator”). This 

model calculates the impacts of changes in production costs on supply, 

demand, and prices in major agricultural commodity markets. At the aggregate level, FAPSIM also computes associated changes in sectorwide production expenses and net farm income. Model simulations for the different 

scenarios and time periods assume no changes in technology beyond those 

implicit in the reference scenario’s trends. However, some endogenous 

adjustments in input use are represented in the model through yield responses 

to fertilizer prices for some crops.

Higher prices for energy-related agricultural inputs (fertilizer and fuel) raise 

the cost of production for all crops (table 4.2). For the lower price impact 

Table 4.1

Prices paid by farmers for energy-related agricultural inputs, 

2012-18 average

Lower price change 

scenario

Higher price change 

scenario

Percent increase

Fuel 2.6 5.3

Fertilizer 4.1 10.0
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FAPSIM is an annual, dynamic econometric model of the 

U.S. agricultural sector. The model was originally developed 

at the U.S. Department of Agriculture during the early 1980s 

(Salathe et al., 1982). Since that time, FAPSIM has been 

continually re-specified and re-estimated to reflect changes 

in the structure of the U.S. food and agricultural sector. The 

model includes over 800 equations.

FAPSIM contains four broad types of relationships: definitional, institutional, behavioral, and temporal. Definitional 

equations include identities that reflect mathematical relationships that must hold among the data in the model. For 

example, total demand must equal total supply for a commodity at any point in time. The model constrains solutions 

to satisfy all identities of this type.

Institutional equations involve relationships between variables that reflect certain institutional arrangements in the 

sector. Countercyclical payment rate calculations are an example of this type of relationship.

Definitional and institutional equations reflect known relationships that necessarily hold among the variables in the 

model. Behavioral equations are quite different because the 

exact relationship is not known and must be estimated. Economic theory is used to determine the types of variables to 

include in behavioral equations, but theory does not indicate 

precisely how the variables should be related to each other. 

Examples of behavioral relationships in FAPSIM are the 

acreage equations for different field crops. Economic theory 

indicates that production should be positively related to the 

price received for the commodity and negatively related to 

prices of inputs required in the production process. Producer 

net returns are used in the FAPSIM acreage equations to 

capture these economic effects. Additionally, net returns for 

other crops that compete with each other for land use are 

included in the acreage equations. While the model covers 

the U.S. agricultural sector, trade for each commodity is included through econometrically based export equations.

For the most part, FAPSIM uses a linear relationship to approximate the general functional form for each behavioral 

relationship. Generally, the parameters in the linear behavioral relationships were estimated by single-equation regression methods. The large size of the model precludes the use 

of econometric methods designed for systems of equations. 

Ordinary least squares was used to estimate the majority 

of the equations. If statistical tests indicated the presence 

of either autocorrelation or heteroscedasticity in the error 

structure of an equation, maximum-likelihood methods or 

weighted least squares were used.

Temporal relationships are empirical equations that describe 

the inter-relationships between variables measured using 

different units of time. For example, not all of the variables 

in FAPSIM are measured using the same concept of a year. 

Commodity data are reported on a marketing-year basis, 

budgetary data are reported on a fiscal-year basis, and farm 

income data are reported on a calendar-year basis. As a result, empirical equations are sometimes needed to establish 

relationships among variables in these different temporal 

categories. For example, cash receipts for crops are reported 

on a calendar-year basis, but production and price information for crops are on a marketing-year basis. Equations are 

used in FAPSIM to estimate cash receipts using information 

from both marketing years that overlap the calendar year.

Commodities included in FAPSIM are corn, sorghum, 

barley, oats, wheat, rice, soybeans (including soybean meal 

and soybean oil), upland cotton, cattle, hogs, broilers, turkeys, eggs, and dairy. The dairy model contains submodels 

for fluid milk, evaporated and condensed milk, frozen dairy 

products, cheese, butter, and nonfat dry milk. Each commodity submodel contains equations to estimate production, 

prices, and different demand components. FAPSIM also includes submodels to estimate the value of exports, net farm 

income, government outlays on farm programs, retail food 

prices, and consumer expenditures on food. All of the submodels are linked together through the variables they share 

in common.

FAPSIM is primarily designed to evaluate short-term impacts. Therefore, the model does not endogenously account 

for changes in technology that would likely result from sustained higher energy costs. In the scenarios examined in this 

analysis, we did not introduce any exogenous changes to 

technology beyond those implicit in the reference scenario’s 

assumptions. Additionally, a decades-long declining trend 

in energy use per unit of output in the agricultural sector is 

likely to continue, only partly captured in the scenarios by 

increases in yields. For these reasons, the model likely overestimates the impact of higher energy prices on the sector.

However, the model does allow for changes in input use 

within the context of the reference scenario’s technologies. 

Changes in the mix of crops planted reflect acreage shifting 

to less energy-intensive crops. Yields decline due to the 

lower fertilizer use caused by higher energy prices.

Although energy prices explicitly affect crop production, the 

only linkage to energy prices in the livestock sector in the 

model is through its effects on feed costs. Thus, the model 

understates the effects of higher energy prices on the livestock sector.

The Food and Agricultural Policy Simulator (FAPSIM)
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case, the largest absolute changes in variable production costs are for crops 

that use more energy-related inputs, most notably rice, corn, and cotton. 

However, compared with total crop-specific variable production costs, highcost rice and cotton are relatively less affected by the energy input changes 

(up 1.6 and 0.9 percent, respectively) than corn (up 2.3 percent). This is 

due to the lower energy-input share relative to production costs for rice and 

cotton producers (fig. 2.1). Soybean production costs—both absolute and 

relative—are less affected than those of most other crops.

For the higher price impact scenario, production cost impacts for all crops are 

generally 2-2.5 times as large as those under the lower price impact scenario, 

largely reflecting the different magnitudes in the underlying assumptions for 

greenhouse gas emission prices. The relative impacts among the crops are 

similar across the scenarios.

Higher Production Costs Lower Producer Net Returns 

and Reduce Overall Plantings

Changes in production costs affect producer net returns, which can cause 

farmers to adjust planted acreage.7 With higher production costs for crops, 

net returns fall and farmers respond by reducing plantings. The resulting 

decline in overall production raises crop prices, which (with inelastic 

demand) provides some partially offsetting gains in revenues from crop sales. 

Nonetheless, overall producer returns are still reduced. Thus, total acreage 

planted to major field crops decreases by more than 450,000 acres per year 

(average over 2012-18) in the lower price change scenario, and over 1 

million acres (0.4 percent) in the higher price change scenario (table 4.3).8

Individual crop results are mixed, however. Relative changes in net returns 

among cropping alternatives, along with differences in producer responses to 

changes in economic incentives, result in varying impacts for each crop.

For the lower price change scenario, wheat acreage is down the most at 

265,000 acres (0.4 percent) and corn acreage drops about 200,000 acres (0.2 

percent). However, cross-commodity relationships among net returns for 

alternative cropping choices result in soybean acreage increasing by about 

93,000 acres—producer returns for soybeans decline less than for corn, 

providing economic incentives to shift some acreage from corn to soybeans.

7Net returns equal the production value 

of a crop minus its cost of production.

8See USDA (2009) for a discussion of 

the reference scenario.

Table 4.2

Change in variable cost of production, 2012-18 average

Average annual impact

Lower price change scenario Higher price change scenario

$/acre Percent $/acre Percent

Corn 7.37 2.3 17.59 5.4

Sorghum 3.44 2.0 7.87 4.5

Barley 3.09 2.0 7.27 4.8

Oats 2.55 2.2 6.02 5.2

Wheat 3.05 2.3 7.19 5.3

Rice 8.57 1.6 19.63 3.7

Upland cotton 4.93 0.9 11.45 2.1

Soybeans 1.57 1.1 3.65 2.6
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For the higher price change scenario, larger acreage adjustments occur 

although relative changes among crops are similar. Wheat and corn still 

experience the largest acreage reductions (making up more than 84 percent of 

the total acreage decline). Again, there is a net switch in acreage to soybeans 

(up more than 200,000 acres) as their net returns are affected least among 

crops.

Lower Crop Production Raises Prices for Most Crops

With the exception of soybeans, crop production is down, leading to higher 

prices under each scenario (table 4.4). Price increases for the lower price 

change scenario are moderate, averaging 0.6 percent or less for each crop. 

The prices for soybeans and soybean products drop slightly as acres shift 

to soybeans from other crops. Price increases under the higher price change 

scenario range from 0.5 to 1.4 percent for most crops, while prices for 

soybeans and soybean products again fall.

Table 4.3

Change in planted acres, 2012-18 average

Average annual impact

Lower price change scenario Higher price change scenario

1,000 acres Percent 1,000 acres Percent

Corn -201 -0.2 -467 -0.5

Sorghum -17 -0.2 -31 -0.4

Barley -8 -0.2 -19 -0.5

Oats -34 -1.0 -79 -2.3

Wheat -265 -0.4 -614 -1.0

Rice -8 -0.3 -17 -0.6

Upland cotton -23 -0.2 -55 -0.5

Soybeans 93 0.1 208 0.3

Total -464 -0.2 -1,073 -0.4

Table 4.4

Change in price, 2012-18 average

Average annual impact on price

Lower price change scenario Higher price change scenario

$/bushel Percent $/bushel Percent

Corn 0.02 0.6 0.05 1.4

Sorghum 0.02 0.6 0.05 1.4

Barley 0.02 0.5 0.04 1.1

Oats 0.01 0.6 0.03 1.3

Wheat 0.02 0.4 0.05 0.9

Rice 0.03 0.3 0.07 0.6

Upland cotton ($/cwt) 0.14 0.2 0.34 0.5

Soybeans -0.01 -0.1 -0.02 -0.2

Soybean meal ($/ton) -0.17 -0.1 -0.37 -0.1

Soybean oil (cents/lb) -0.02 -0.1 -0.05 -0.1
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Implications for Fruits and Vegetables 

A separate model was used for the fruit and vegetable sector since horticultural products are not included in FAPSIM. Data from USDA’s Agricultural 

Resources Management Survey (ARMS) were used to estimate the effects 

of energy price changes on costs of production in the fruit and vegetable 

sector. Average per farm effects on variable costs of production were estimated based on the increased input prices for fuels, electricity, and fertilizer described earlier. Implications for market adjustments for fruits and 

vegetables are discussed in the accompanying box on page 17 by comparing 

changes to those that occurred in 2008. 

Higher Energy Prices and Higher Feed Costs Lead 

to Lower Livestock Production and Higher Livestock 

Product Prices

Under both scenarios, higher corn prices and only moderately lower soybean 

meal prices lead to an increase in feed costs for the livestock sector (table 

4.5). In addition, higher energy prices raise other production costs for livestock.9 As a result, livestock production declines slightly. Although the 

impacts are not large, they vary across livestock species, mostly reflecting 

the relative shares of corn and soybean meal in typical feed rations. 

Consequently, pork and beef are affected more than poultry. Relative results 

are similar across the two scenarios, with impacts roughly proportional 

across the different livestock categories.

Aggregate Production Expenses for the 

Agricultural Sector Rise

Total production expenses in the agricultural sector rise by an average of 

$1.73 billion per year over 2012-18 (table 4.6) under the lower price impact 

scenario. The largest changes in production expenses are for fertilizer and 

lime and for fuel, oil, and electricity due to the changes in the energy-related 

input prices. Most other categories of expenses decline slightly due to lower 

production. For the higher price change scenario, increases in production 

expenses for energy-related and total inputs are 2-3 times larger, in line with 

the relative magnitudes of underlying energy prices.

9Energy-related production costs for 

livestock are not explicitly included 

in FAPSIM. To include the effects of 

higher energy-related costs, FAPSIM 

results for cattle, hogs, and chickens 

were augmented by results from a separate meat-sector model that includes 

detailed production costs (Weimar and 

Stillman, 1990). Dairy sector FAPSIM 

results were similarly augmented by estimates of the effects of higher energyrelated production costs expressed in 

corn-equivalent costs in dairy producer 

net returns using FAPSIM structure. 

In each case, overall adjustments are 

dominated by the feed cost effects, 

reflecting their relative importance in 

the cost structure of livestock production (table 2.1).

Table 4.5

Change in feed prices and livestock production, 2012-18 average

Lower price change scenario Higher price change scenario

Feed cost

Livestock 

production Feed cost

Livestock 

production

Average annual percent impact

Beef 0.6 -0.1 1.3 -0.2

Pork 0.4 -0.1 0.9 -0.2

Young chickens 0.2 0.0 0.6 0.0

Milk 0.4 0.0 1.0 -0.1
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Energy Price Changes and the Fruit and Vegetable Sector

Labor is the single largest variable cost for vegetable, melon, fruit, and tree nut farms. However, the 

second largest expense component is fertilizer and agricultural chemicals. During 2004-06, fertilizer and 

agrichemicals accounted for about 18 percent of variable cash expenses for the production of vegetables 

and melons, and 13 percent for specialized fruit and tree nut operations.1 Motor fuels and oil used to run 

tractors, generators, and irrigation pumps accounted for 5 percent of vegetable cash costs and 4 percent 

of cash costs for fruits and tree nuts. For this analysis, fertilizer application rates per acre were assumed 

to remain constant. Over the longer run, however, growers would likely adjust application methods, 

amounts, timing, or the mix of crops produced in response to fertilizer price increases.

In addition, electricity is required by these farms to operate irrigation pumps, ice makers, lights, and 

sorting and packing equipment. Electricity is the largest component of the public utility expense (which 

also includes telephone, water, and Internet access). According to Agricultural Resources Management 

Survey data, electricity accounts for 2-3 percent of cash costs on fruit and vegetable farms.

Impacts of higher fertilizer, fuel, and electricity prices on variable costs within the fruit and vegetable 

sector are generally small (2 percent or less) across the two price scenarios (see table). 

By contrast, overall input prices for vegetables and melons jumped an estimated 19 percent in a single 

year (2008) due largely to rapidly rising fuel and fertilizer prices. Input prices were up 13 percent that 

year for specialized fruit and tree nut farms. Fruit and vegetable production in 2008 showed little effect 

from the sharply higher fuel and fertilizer prices. Fruit and tree nuts are largely produced from fixed acreages; as long as adequate labor is available and a viable market exists, fruit will be harvested. Processing 

vegetables (and an increasing share of fresh vegetables, particularly fresh-cut and packaged products) are 

almost entirely produced under contract, and growers negotiated higher contract prices for 2008 to offset 

some of the increased production costs. Because consumer demand for fruits and vegetables is relatively 

price inelastic (K. Huang, 1993; You et al., 1998) and does not change too much with price changes, 

much of the 2008 price increases were passed up the marketing chain. The result was the largest increase 

(6.2 percent) in overall consumer prices for fruits and vegetables in over 10 years. 

A similar response with limited production impact is likely under the energy price change scenarios addressed here, particularly since the magnitudes of the cost impacts are much smaller than seen in 2008. 

Further, to the extent that consumer demand for fruits and vegetables may be more responsive to sustained price changes in the longer term, overall retail price impacts for fruits and vegetables would be 

smaller than in 2008. 

1Specialized farms are those that derive at least 50 percent of their revenue from a given commodity or commodity 

group, such as vegetables and melons. 

Impact of greenhouse gas emission pricing on variable cash 

production expenses of fruits and vegetables, 2012-18 

Commodity

Lower price change 

scenario

Higher price change 

scenario

Average annual percent impact

Vegetables and melons 1.1 2.2

Fruits and tree nuts 0.9 1.6

Total fruits, tree nuts, and 

vegetables 1.1 2.0
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Net Farm Income Reduced

Net farm income declines an average of $1.5-3.4 billion (1.8-4.1 percent) 

per year over the 2012-18 period (table 4.7) across the two price impact 

scenarios. This change is due primarily to higher production expenses, which 

are partly offset by higher cash receipts due to reduced production and higher 

prices.

Impact on Retail Food Prices

Changes in consumer food prices come from two major sources: (1) energyrelated impacts on commodity prices and (2) higher energy costs in the 

food marketing system. FAPSIM accounts for the former effects but not the 

latter. Thus, a separate analysis was conducted to derive impacts in the food 

marketing system based on historical energy pass-through rates between food 

processing stages.10 These results were combined with the FAPSIM results 

to give the overall retail food price impacts.

Overall, the average 2012-18 effects are annual increases of 0.98 and 1.33 

percent in the Consumer Price Index (CPI) for food at home and 0.16 and 

0.23 percent for the food-away-from-home CPI (table 4.8). These changes 

combine for average annual increases of 0.63 and 0.85 percent in the total 

food CPI over 2012-18 for the lower price and higher price change scenarios, 

respectively. For each scenario, most of the retail food price impact is from 

10See Appendix C for the methodology 

of estimating historical pass-through 

rates of energy prices in the food 

marketing system and a description of 

the analytical process underlying the 

results.

Table 4.6

Change in farm sector production expenses, selected categories, 

2012-18 average

Average annual impact

Lower price change scenario Higher price change scenario

$ billion Percent $ billion Percent

Fertilizer and lime 1.21 4.6 2.95 11.2

Fuel, oil, and electricity 0.88 6.2 1.79 12.8

Total production 

expenses 1.73 0.6 3.91 1.3

Table 4.7

Change in farm income, selected categories, 2012-18 average

Average annual impact

Lower price change

scenario

Higher price change

scenario

$ billion Percent $ billion Percent

Cash receipts

 Crops 0.13 0.1 0.30 0.2

 Livestock and products 0.15 0.1 0.39 0.3

Total cash receipts 0.28 0.1 0.69 0.2

Total production expenses 1.73 0.6 3.91 1.3

Net farm income -1.52 -1.8 -3.35 -4.0
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the effect of higher energy prices in the food marketing system rather than 

from agricultural commodity price impacts. This partly reflects the low 

farm-value share (20 percent) of retail prices for domestically produced farm 

foods.

Longer Term Adjustments to Higher Energy Prices

If energy prices were to continue to rise in years beyond the short-term focus 

in this report reflecting, for example, further increases in prices for greenhouse 

gas emissions such as depicted in figure 3.1, additional long-term adjustments 

would occur in the agricultural sector. 

Continued shifts in the mix of crop produced would reflect the shift away from 

energy-intensive crops to those whose production costs are less reliant on energy inputs. As illustrated in the short-term impacts, this would include, for example, shifts in planted acreage away from corn to soybeans. In addition, other 

structural adjustments in agricultural production practices would take place as 

farmers seek to improve energy-use efficiency, such as modifying their use of 

energy-related inputs in the production of specific crops. This could include 

using less fertilizer, perhaps by targeting its use more effectively, or using production practices that do not require as many trips across fields with tractors 

and other farm equipment (USDA-ERS). Further, energy use per unit of output 

in the agricultural sector would be likely to continue its decades-long decline 

(USDA-OCE and ERS, 2009; Ball, 2010). Some of these effects may be facilitated by the development of new technologies and may be accelerated if higher 

energy prices spur technological investment and development.

Nonetheless, longer run adjustments to higher energy prices within the agricultural sector would follow the same dynamics as seen in the short-term results. In response to higher production costs, total plantings would be reduced 

and acreage would shift among crops, leading to changes in commodity prices. 

Higher overall feed costs would lead to adjustments in the livestock sector. In 

the farm income accounts, total cash receipts would be higher due to higher 

crop and livestock prices. But production expenses would rise more, resulting 

in lower net farm income from the continued rise in energy prices. Retail food 

prices would rise further, with increases reflecting energy cost-related increases 

in the food marketing chain more than higher farm-level commodity prices.

Table 4.8

Change in retail food prices, 2012-18 average

Average annual impact

CPI category Lower price change scenario Higher price change scenario

Percent

Food at home 0.98 1.33

Food away from home 0.16 0.23

Total food 0.63 0.85
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The Role of Industry Rebates

One source of energy price changes is the introduction of costs for greenhouse gas emissions, 

through either a tax on emissions or through a cap-and-trade system. For example, in many 

cap-and-trade proposals, sectors of the economy are classified as either covered or uncovered. 

Covered sectors are required to hold allowances equal to the quantity of their greenhouse gas 

emissions. Typically, agriculture has not been a covered sector with respect to methane and 

nitrous oxide emissions. Nonetheless, agriculture faces increased prices for fertilizer, natural 

gas, electricity, and diesel fuel.

However, such proposals also typically provide transition-period rebates to designated industries, which can lessen the effects on agriculture. An example is the treatment of energy-intensive trade-exposed (EITE) industries, such as the nitrogen fertilizer production industry (see 

box, p. 3). For such industries, foreign competitors would not see the increases in energy prices 

that occur in the United States, so rebates of emissions allowances to those domestic industries 

could offset any competitive advantage foreign producers would otherwise gain.

Two additional scenarios were analyzed to assess the effects of such rebates on reducing the 

impacts of energy price changes on agriculture. Implications of energy price changes for production costs were derived for 2012-2018, years that would be covered in the transition period 

for the rebates, based on previous legislative proposals. Here, however, impacts for nitrogenbased fertilizer prices were reduced to reflect the rebates that offset higher natural gas prices, 

resulting in smaller increases in costs of production in the agricultural sector (see table).

With overall production cost impacts reduced, effects throughout the sector are lower as well. 

For example, aggregate reductions in planting are 133,000 and 354,000 acres in these two 

scenarios, compared with 464,000 and 1.073 million acres without rebates. Impacts on farm 

income are about half those that result without rebates (see table 4.7).

Agricultural changes with higher energy prices, but with natural gas-related 

rebates to nitrogen-based fertilizer industry, 2012-18 average

Lower price change scenario Higher price change scenario

Cost-of-production changes: $/acre Percent $/acre Percent

Corn 1.44 0.4 4.72 1.5

Sorghum 1.52 0.9 3.71 2.2

Barley 0.85 0.6 2.41 1.6

Oats 0.69 0.6 1.97 1.7

Wheat 0.80 0.6 2.31 1.7

Rice 3.74 0.7 9.14 1.7

Upland cotton 1.76 0.3 4.56 0.8

Soybeans 0.55 0.4 1.43 1.0

Farm income changes: $ billion Percent $ billion Percent

Cash receipts

 Crops 0.02 0.0 0.08 0.0

 Livestock and products 0.03 0.0 0.12 0.1

Total cash receipts 0.05 0.0 0.20 0.1

Total production expenses 0.80 0.3 1.91 0.6

Net farm income -0.76 -0.9 -1.72 -2.1
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Chapter 5

Impacts by Farm Production Specialty 

and Region

Impacts for farm businesses by farm production specialty and by region 

are based on results from the Farm-Level Partial Budget Model (see box). 

The model uses scenario results from the FAPSIM simulations as inputs to 

derive the impacts presented in this chapter. Results from the model can be 

summarized across various groupings of farms such as by resource region, 

commodity specialization, or farm size. However, since farm business performance varies within any of these groupings, results indicate average impacts 

within a group rather than impacts on individual farms.

Impacts by Farm Production Specialty

A simulation of how higher energy prices will affect farms with different 

farm production specialties reveals that some segments of agriculture would 

be more affected than others. The analysis here focuses on results for 2014.

Net cash income for all farm businesses is estimated to average 5 percent 

and 10 percent lower in the two price impact scenarios (fig. 5.1), relative 

to the 2014 reference case, primarily because of higher input expenses. Of 

the major commodity types, wheat, cotton, and rice farm businesses are 

estimated to have the most significant bottom-line impacts, with incomes 

9-10 percent below the reference scenario in the lower energy price impact 

scenario and 22 percent lower in the higher impact scenario. Other crop 

businesses show smaller decreases in net income. Meanwhile, livestock 

producers (beef cattle, dairy, poultry, and hogs) are less affected (net cash 

income down about 3 percent in the lower price impact scenario and down 

4-7 percent in the higher scenario) as most of their energy-related cost 

increases are smaller and indirect, coming through higher feed expenses.

Figure 5.1

Reduction in farm business net cash income, by farm production specialty, 2014

Note: Reductions reflect the difference in farm business net cash income in 2014 (with greenhouse gas emission prices) 

relative to a reference case (without greenhouse gas emission prices).

Percent reduction

Wheat Corn Beef Hogs Poultry

cattle

Specialty

crops

Cotton

and

rice

Soybeans

and

peanuts

Dairy All farms

Lower price impact scenario

Higher price impact scenario

0

5

10

15

20

25
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Regional Impacts

Variation in impacts across farm production specialty explains how higher 

energy prices affect some regions more than others. The region most affected 

by higher energy costs in these scenarios is the lower Mississippi Delta 

(Mississippi Portal) region (figs. 5.2 and 5.3). Fertilizer-intensive crops like 

cotton dominate in this region, with net cash income for the region estimated 

to decline by 8 and 19 percent in the two price scenarios. The Basin and 

Range, Southern Seaboard, Northern Crescent, and Northern Great Plains 

regions are also estimated to see declines in net cash income, from 6 to 7 

percent in the lower price impact scenario and from 12 to 14 percent in the 

higher price scenario. The balance of the country is estimated to experience 

a drop in net cash income of 4 percent in the lower price scenario and 9 

percent in the higher price scenario.

Farm-Level Partial Budget Model

The Farm-Level Partial Budget Model provides disaggregated regional and farm production specialty information for 

farm businesses based on the national impacts reported earlier. U.S. farm businesses include more than 800,000 commercial and intermediate family and nonfamily farms where farming is the primary activity of the operator. The model 

operates on individual farm data from USDA’s Agricultural Resource Management Survey to provide estimated impacts 

on per-farm averages of components of the income statement and balance sheet. 

Traditional whole-farm budgeting is done on the basis of fixed-point estimates of production, prices, and financial variables to predict point estimates of financial results. Ideally, a whole-farm budget includes fixed and variable production 

costs and other non-cash components necessary to measure accrual income. However, the majority of applications are 

targeted to specific changes in costs or returns, without allowances for changes in depreciation, other noncash charges, 

inventory changes, and other accrual adjustments. Therefore, it is more appropriate to characterize the modeling approach as partial budgeting analysis.

The model incorporates elements of income and expenses to project cashflow. Totals for assets and debt are used to forecast major elements of the balance sheet and debt repayment ability. The forecast changes are applied to each farm in the 

survey and then summarized to evaluate changes from the base year. Results reported here focus on per-farm averages 

of net cash income. 

Model Limitations

The model reflects historical production patterns and farm structure. Potential behavioral or production responses by 

farms are not included in the model. The effects of weather, structural changes, and behavioral response are reflected in 

the forecast error along with any error associated with the input parameters. 

Overall impacts here can be greater than those in the national farm income accounts due to a number of factors. This 

reflects, in part, the model’s focus on “farm businesses” whose concentration of expenses is higher than for all farms. 

Further, part of the difference relates to the treatment of rent in production expenses. Rental expenses for farm businesses 

at the individual farm level in the Farm-Level Partial Budget Model are assumed to come directly out of net cash income, 

regardless of whether the rental payments go to another farmer or go outside the agricultural sector. In contrast, the national farm income accounts use net rental costs to the sector (net rent to non-operator landlords), counting only rental 

costs that go outside the agricultural sector.
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3% to 6%

More than 6%

Reduction in net cash income

Figure 5.2

Reduction in farm business net cash income by resource region, lower price impact scenario, 2014

Source: USDA, Economic Research Service. 

Less than 10%

10% to 15%

More than 15%

Reduction in net cash income

Figure 5.3

Reduction in farm business net cash income by resource region, higher price impact scenario, 2014

Source: USDA, Economic Research Service. 
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Chapter 6

Rural Impacts 

Earlier chapters of this report focused on how agricultural production, farm 

income, and food prices would adjust in response to energy price changes 

resulting from the pricing of greenhouse gas emissions. This chapter 

considers how those changes in energy prices and related changes in energy 

production are likely to affect rural communities. We consider three types of 

industries: farming, energy extraction (coal, petroleum and natural gas), and 

energy-intensive mining and manufacturing, and identify counties relatively 

specialized in these industries as likely to be the most affected.

The focus in this chapter is on how resilient affected communities and 

their populations are likely to be. We consider several county attributes. 

First, local adjustments are likely more difficult where affected counties 

are bunched together. Residents have much less chance of commuting to a 

neighboring county with a healthier economy. Second, low education levels 

impede adjustment by making it less likely for residents to find jobs elsewhere or for local economies to attract or generate new businesses. Low 

employment rates and high poverty suggest that the local economy is weak 

even without any loss of energy-related jobs. 

In general, we find that impacts will be small and focused on a small number 

of nonmetro counties. Few rural counties remain specialized in farming or 

energy extraction. In some of these counties, those with populations having 

low educational attainment and high poverty and unemployment rates, 

energy sector adjustments seem likely to compound existing socioeconomic 

problems. Other farming or energy counties, largely outside the South, have 

relatively prosperous populations, but high out-migration. 

Farming dependent counties

While land in the United States remains extensively farmed, the rural 

economy has become diversified and relatively few counties are still highly 

dependent on agriculture. These counties tend to be thinly settled, remotely 

located, and lacking in natural amenities. ERS defined 403 nonmetropolitan 

counties as “farming dependent” in 2004, based on their having at least 15 

percent of income from farming or 15 percent of employment in farming. 

Most are in the Great Plains, although they are also found scattered across 

the West and South (Fig. 6.1). These are generally the counties likely to be 

most affected by changes in farm income. About two-thirds of nonmetro 

farm income is generated outside of these farming counties, where effects are 

likely to be small due to the small role of agriculture in the local economy. 

We turn later to the farm county distinction between counties with under 

40 percent of their land in crops and counties with higher proportions of 

cropland.

In the regional results for the higher price impact scenario from chapter 5, 

over half of the farming dependent counties are in resource regions where 

anticipated effects of pricing emissions are expected to be minimal (Table 

6.1). Most of the rest of the counties are in regions with anticipated modest 

effects of 10-15 percent. Twelve farming dependent counties are in the 
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Mississippi Portal resource region, where farm business net cash incomes are 

estimated to be reduced by over 15 percent. 

Although there has been considerable variation over time and from one agricultural area to another, farm incomes overall have risen considerably over 

the past decade, explaining in part why farming counties have been largely 

immune from the recent recession. County average total nonfarm earnings 

rose in farming dependent counties as well in 2001-2008, more than in other 

nonmetro counties. Some of this rise may reflect the relatively high educaTable 6.1 

Change in farm and nonfarm income and population for farming dependent counties 

Change 2001-2008

County and region type

Number of 

counties

Aggregate 

realized net 

farm income1

Average 

nonfarm 

earnings

Population 

change 

2001-2010

---------------------------Percent---------------------------

Farming dependent counties2 403 43.9 15.4 -2.8

 Resource region loss in farm business net cash income3

 Less than 10% 216 35.8 15.5 -2.4

 10% to 15% 175 64.2 16.1 -2.7

 Greater than 15% 12 31.0 5.2 -12.9

Other nonmetropolitan counties 1,620 70.8 12.5 3.0

1Expressed as aggregate as some counties had negative income in base year. Source: BEA REIS data files, adjusted for inflation using the Index 

of Consumption Expenditures.

2See text.

3See Figure 6.1.

Figure 6.1

Farming counties and regional reduction in net cash income, higher price impact scenario, 2014

Source: USDA, Economic Research Service. 

< 10%

10-15%

>15%

Regional net cash 

income reduction 

Cropland < 40%

Cropland > 40% of total land

Farming dependent counties, 2000
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tion levels in most farming counties (see below), as more highly educated 

members of the labor force have had an increasing advantage in the labor 

market. Despite the rise in farm and nonfarm earnings, however, farming 

dependent counties continued to lose population.

The 12 farming dependent counties with estimated farm business net cash 

income losses more than15 percent, all in the Mississippi Portal resource 

region, stand out from most of the remaining farming counties. While these 

12 counties had substantial gains in net farm income, their average gain 

in nonfarm earnings was only 5 percent, only a third as large as the other 

farming counties, and their average rate of population loss was 13 percent 

over 2000-2010, much higher than in other farming counties. 

These differences reflect socioeconomic characteristics. While farming 

counties in general tend to have relatively few people without high school 

degrees, very high proportions of adults employed, and low poverty rates 

compared with other nonmetro counties, the Mississippi Portal region counties have markedly lower education levels than other counties, only 60 

percent of their working age adults employed (12 percentage points less than 

the average among other nonmetro counties), and nearly twice the poverty 

rates of other farming counties (table 6.2). Even with substantial growth 

in farm net cash income in 2001-2008, these counties showed little sign of 

growth and development. 

Changes in farm income over the past decade have depended sharply on 

whether the farm specializes in crops or in livestock. While the prices of 

many crops have risen, livestock producers have been hampered, in part, by 

the rise in feed grain prices, most notably corn. Consequently, we also looked 

at farming dependent counties separated by crop and livestock specialization. 

As an approximation of local specialization, we divided farming counties 

into those with less than 40 percent of their land in crops, to reflect livestock 

specialization, and those with 40 percent or more of their land in crops, to 

reflect field crop specialization and examined past trends and current socioeconomic situations.

Table 6.2 

County average statistics reflecting vulnerability for farming dependent and other nonmetro counties, 

by resource region impact on farm income

County type and resource region expected 

loss in net farm income

No HS diploma, 

ages 25-641

Employed, 

ages 22-591 Poverty rate1 No. of counties

Percent

Farming dependent counties3 13.8 77.4 15.7 403

 Resource region loss in farm business net cash income

 Less than 10% 15.5 77.7 14.9 216

 10% to 15% 10.8 78.2 15.6 175

 Greater than 15% 26.5 60.4 30.7 12

Other nonmetropolitan counties 15.1 72.5 17.0 1,620

1Source: American Community Survey data files, 2005-2009 averages. 

2See: Nonmetropolitan Outmigration Counties: Some Are Poor, Many Are Prosperous, Economic Research Report No. 107. 3Farming dependent counties had either 15 percent of income (1998-2000) or employment (2000) from farming. See Measuring Rurality Briefing 

Room: http://www.ers.usda.gov/Briefing/Rurality/Typology/
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The distinction according to extent of county cropland yields two remarkably different pictures. “Livestock counties,” farming counties with under 40 

percent cropland, saw a loss in net farm income of 14 percent between 2001 

and 2008, whereas “crop counties,” with at least 40 percent of their land in 

crops, had an overall gain in net farm income of 86 percent.

Differences are less pronounced in other measures, however. The average 

gain in nonfarm earnings was 19 percent in the livestock counties, which 

tended to maintain their population sizes over the decade. Nonfarm earnings in crop counties rose by about 13 percent, comparable to the rise in 

the nonmetro counties not dependent on farming, but lower than might be 

expected given their rise in farm income. Moreover, these counties lost 

an average of more than 5 percent of their population over 2000-2010, 

a greater rate of loss than in 1990-2000. A major part of the gains in net 

farm incomes in crop counties might be lost to higher land prices, regional 

market centers, and the broader world, rather than staying in the community. 

At the same time, it cannot be said that population loss in these counties 

reflects economic hardship. The crop counties have highly schooled populations, with an average of 88 percent of the population ages 25-64 having a 

high school diploma, over 80 percent of the population ages 22-59 actively 

employed, and an average poverty rate of only 14 percent (for a discussion of 

prosperous outmigration counties, see McGranahan, Cromartie and Wojan, 

2010).

All told, it does not appear that higher energy prices in the scenarios studied 

in this report would have a substantial effect on farming county economies 

and populations. Farm dependent counties with relatively high farm income 

losses that also have lower education levels, higher unemployment, and high 

poverty would likely be most affected. Livestock counties showed some 

resilience in 2000-2010, with substantial gains in nonfarm earnings, despite 

the decline in farm earnings. Some of these gains may reflect growth in 

value-added activities such as meat packing. Some may reflect the relative 

Table 6.3

Comparison of farming dependent counties based on extent of cropland

Cropland as a percent 

of county land, 19972

County characteristics1 Under 40% 40% or more

Change in previous decade Percent

Aggregate change in realized farm net income, 2001-2008 -13.5 85.8

Average gain in nonfarm earnings 18.9 12.5

Population change, 2000-2010 0.3 -5.5

Socioeconomic characteristics, 2005-09 average

Lacking HS diploma, ages 25-64 15.9 12.1

Employment rate, ages 22-59 73.8 80.4

Poverty rate 17.9 13.8

Number of counties 184 219

1For sources, see Tables 6.1 and 6.2.

2Source: ERS, based on 1997 Census of Agriculture data files.
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attractiveness of these counties to tourists, retirees, and others seeking attractive rural settings. 

Energy production counties

Energy production activities in the United States are concentrated in a 

limited number of nonmetropolitan counties. While effects of changes in 

energy production related to the pricing of emissions would be small for 

rural America overall, impacts in energy-producing counties would be more 

pronounced. Nonetheless, the impact on nonmetro areas would be larger than 

in metro areas, mainly because local economies specialized in energy production are overwhelmingly rural. Differential regional effects would come from 

the geographic distribution of energy sectors.

Geographic Concentration of Coal, Crude Oil, 

and Natural Gas Extraction

The number of places in the United States where coal, crude oil, and natural 

gas can be economically extracted is limited, explaining why these industries 

are among the most spatially concentrated in the economy. For this analysis, 

we identify those counties deriving 5 percent or more of their nonfarm 

employment from coal, crude oil, or natural gas. At this threshold, employment losses from coal, crude oil, and natural gas extraction of 20 percent 

imply county-level job losses of 1 percent.

More than 61 percent of coal industry employment is located in counties 

meeting this 5-percent threshold. The oil and gas industry is less spatially 

concentrated, with counties meeting the 5-percent employment threshold 

comprising 38 percent of industry employment. By comparison, textiles 

is the most spatially concentrated manufacturing industry, with about 60 

percent of industry employment located in counties meeting the 5-percent 

threshold.

The coal industry is most concentrated in eastern Kentucky and West 

Virginia, where several counties deriving more than 15 percent of nonfarm 

employment from the industry are adjacent to each other (fig. 6.2). However, 

counties highly dependent on coal are also found in Pennsylvania, Indiana, 

Illinois, North Dakota, Utah, and Wyoming. States with counties deriving 5 

to 15 percent of nonfarm employment from coal include Montana, Colorado, 

Texas, Louisiana, Mississippi, and Alabama.

Counties highly dependent on crude oil and natural gas production (over 15 

percent of nonfarm employment) are more numerous, and most prevalent 

in Texas and Oklahoma (fig. 6.3). Contiguous collections of highly dependent counties are found in Texas and Oklahoma as well as on the borders 

of Colorado and Utah, and North Dakota and Montana.11 Alaska and West 

Virginia are two other States with counties highly dependent on crude oil or 

natural gas extraction, joined by counties in Kansas, Louisiana, Mississippi, 

and Illinois deriving 5 to 15 percent of nonfarm employment from the 

industry.

Figure 6.4 demonstrates the spatial concentration of energy sector jobs in 

total, including the extraction of coal and crude oil/natural gas, oil refining, 

11Wyoming is also likely to have a 

number of counties that are highly 

dependent on crude oil production, but 

detailed industry-level employment 

data from this State have not been 

made available to ERS.
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Figure 6.2

Counties with significant employment in coal mining, 2008

Coal extraction share 

of nonfarm jobs 

<5%

5-15%

>15%

Source: ERS, based on Bureau of Labor Statistics QCEW (Quarterly Census of Employment and Wages) data files and Bureau 

of the Census County Business Patterns data files. Statistics for some counties are estimates as exact employment numbers are 

suppressed to maintain confidentiality. 

Source: ERS, based on Bureau of Labor Statistics QCEW (Quarterly Census of Employment and Wages) data files and Bureau 

of the Census County Business Patterns data files. Statistics for some counties are estimates as exact employment numbers are 

suppressed to maintain confidentiality. 

Energy share 

of nonfarm jobs 

<5%

5-15%

>15%

Figure 6.3

Counties with significant employment in oil and gas extraction, 2008
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and electricity and natural gas utilities. The number of States containing 

nonmetro counties deriving 5 percent or more of nonfarm employment from 

the energy sector overall increases somewhat, but the real jump is in the 

number of counties identified as highly dependent counties (over 15 percent 

of nonfarm employment from energy sector jobs). This is mainly because 

large electricity generation plants often locate in coal counties (much less 

often in oil and natural gas counties).

Characteristics of Counties Specialized in Coal 

and Crude Oil/Natural Gas Extraction

Having identified those counties likely to be most affected by reductions in 

conventional energy production, the follow-on issue is the ability of these 

counties to adjust to job loss. Coal-mining counties in Appalachia characterized by high volatility in the local economy along with a strong attachment 

to place by lifelong residents are of special concern, because the implied 

low-elasticity of migration with respect to unemployment suggests that the 

response to permanent job loss may be slow, particularly if the job losses are 

perceived as cyclical. 

Only 57.6 percent of residents age 22 to 59 in Southern coal counties were 

employed in 2005-09 (table 6.4), an employment rate 15 percentage points 

lower than the national nonmetro average. These same counties also have 

very low high school completion rates, with over 22 percent of those age 25 

to 64 lacking a high school diploma. The low employment rate and educaSource: ERS, based on Bureau of Labor Statistics QCEW (Quarterly Census of Employment and Wages) data files and Bureau 

of the Census County Business Patterns data files. Statistics for some counties are estimates as exact employment numbers are 

suppressed to maintain confidentiality. 

Energy share 

of nonfarm jobs 

<5%

5-15%

>15%

Figure 6.4

Counties with significant employment in energy sectors, 2006
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tional achievement of the population helps explain chronically high poverty 

rates. Adjustment to permanent job loss in the coal industry in these counties 

is likely to be protracted.

Counties dependent on crude oil and natural gas extraction in the South, 

mainly in Texas and Oklahoma, do not appear to be as hindered as the coal 

counties by structural impediments. Higher high school completion rates and 

much higher employment rates than in Southern coal counties suggest these 

local economies may be more resilient to permanent job loss in the energy 

sector. Non-South counties dependent on coal or crude oil/natural gas extraction should be even more resilient based on very high employment rates, very 

high rates of high school completion, and poverty rates substantially lower 

than the national nonmetro average.

Geographic Concentration of 

Energy-Intensive Industries

Industries in the economy with production particularly dependent on 

energy are also vulnerable to an increase in energy prices. We consider an 

industry to be energy intensive if expenditures on energy are greater than 

5 percent of total sales.12 The energy-intensive manufacturing industries 

are found predominantly in Chemicals (325), Nonmetallic Mineral Product 

Manufacturing (327), Primary Metal Manufacturing (331), Metal Ore Mining 

(2122), and Nonmetallic Mineral Mining (2123).13

Nonmetro areas contain a higher share (8.19 percent) of counties with 5 

percent or more of their employment in energy-intensive industries (table 

6.5), but this is not substantially higher than the metro share (6.97 percent). 

Moreover, the highest share of counties meeting this threshold is found in 

nonmetro areas adjacent to a metro county (10.7 percent), suggesting that 

these energy-intensive industries overall do not tend to concentrate in remote 

locations.

12The U.S. Environmental Protection 

Agency has a list of industries and 

measures of energy intensity and trade 

intensity on its website. This list was 

used to identify industries with an 

energy intensity greater than 5 percent. 

This energy-intensive definition is 

similar to, but less restrictive than, the 

definition of an energy-intensive, tradeexposed industry in H.R. 2454.

13 The NAICS (North American Industry Classification System) code for each 

industry is shown in parentheses.

Table 6.4. 

Nonmetropolitan county average 2005-09 statistics reflecting 

economic vulnerability: energy extraction counties 

States with energy 

extraction counties

No High School 

diploma, 

ages 25-64

Employed, 

ages 22-59 Poverty rate

Number of 

counties

-------------------Percent--------------------

South

Oil extraction counties* 21.9 69.4 18.7 75

Coal extraction counties* 22.6 57.6 23.3 30

Other counties 19.2 68.4 19.6 527

Non-South

Oil extraction counties* 10.1 79.1 12.1 43

Coal extraction counties* 9.1 76.7 14.3 15

Other counties 10.6 77.3 14.3 436

Other States 13.7 73.1 16.0 922

Total 14.8 72.6 16.7 2,048

*Extraction jobs at least 5 percent of nonfarm jobs, 2006.

Sources: American Community Survey data files, 2005-2009 averages 
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Mining operations in Mountain West States such as Nevada, Wyoming, 

Montana, and Arizona (fig. 6.5) tend to locate in more remote areas and, 

in the case of Nevada, are highly clustered. Other notable clusters that 

are manufacturing based include the groups of counties in southwestern 

Alabama, western Maine, and the northern tier of Pennsylvania. Overall, 

energy-intensive industries are distributed much more evenly than either coal 

or oil/gas extraction.

Source: ERS, based on Bureau of Labor Statistics QCEW (Quarterly Census of Employment and Wages) data files and Bureau 

of the Census County Business Patterns data files. Statistics for some counties are estimates as exact employment numbers are 

suppressed to maintain confidentiality. 

Energy-intensive

industry share of 

nonfarm jobs 

<5%

5-15%

>15%

Figure 6.5

Counties with significant employment in energy intensive industries, 2008

Table 6.5

Distribution of counties by employment in energy-intensive industries, 

by county type, 2006 

County type

No energyintensive 

employment

Energy-intensive employment

< 5% 5 to 15% > 15%

Metropolitan counties (number)

 Share of metro counties (percent)

131

(12.02)

883

(81.01)

65

(5.96)

11

(1.01)

Nonmetro counties (number)

 Share of nonmetro counties (percent)

619

(30.20)

1,263

(61.61)

137

(6.68)

31

(1.51)

Counties adjacent to metro (number)

 Share of subcategory (percent)

228

(21.49)

719

(67.77)

97

(9.14)

17

(1.60)

Counties nonadjacent to metro (number)

 Share of subcategory (percent)

391

(39.53)

544

(55.01)

40

(4.04)

14

(1.42)

Source: ERS, based on Bureau of Labor Statistics QCEW (Quarterly Census of Employment and Wages) data files 

and Census Bureau County Business Patterns. 
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Chapter 7

Conclusions

Energy inputs are important to agriculture, with direct and indirect energyrelated expenses representing an average of more than 13 percent of total 

farm production expenses in 2005-08. To illustrate the role of energy in the 

agricultural sector, we analyze impacts and adjustments of higher energy 

prices that other studies suggest would result from the pricing of greenhouse 

gas emissions, a potential result of climate change policy.

Such a policy could be implemented through a tax on emissions or through 

a regulatory cap on emissions as part of a nationwide cap-and-trade system. 

The pricing of emissions would lead to higher energy prices economywide 

and would result in adjustments throughout the agricultural sector to direct 

and indirect energy-related increases in costs of production. 

In the crops sector, overall planted acreage would decline as higher energy 

costs lower producer net returns. Additionally, the mix of crops produced 

would adjust to relative changes in net returns—while plantings of most 

crops would fall, acreage planted to soybeans rose in our results as their 

production costs are less affected by higher energy costs than most other 

crops. As a consequence of these acreage and production changes, prices 

for most crops would increase, the exceptions being prices for soybeans and 

soybean products. 

With higher corn prices and only moderately lower soybean meal prices, 

feed costs in the livestock sector would rise in addition to increases in 

energy-related production costs for livestock. As a result, livestock production would fall and prices would increase. Although impacts in our scenarios 

are not large, pork and beef are affected more than poultry, mostly due to 

relative shares of corn and soybean meal in typical feed rations.

While these adjustments in agricultural commodity markets raise cash 

receipts, increases in production expenses are greater, leading to overall 

reductions in net farm income.

Effects are not distributed equally across all sectors within agriculture or 

across all regions of the country. Relative impacts reflect the importance 

of energy inputs in production costs of different commodities, the relative importance of different commodities in different regions, and other 

market characteristics. Nonetheless, farm sector impacts were modest for the 

scenarios examined.

Retail food prices also increase in response to higher energy prices, by less 

than 1 percent in the scenarios analyzed. Not only are farm commodity prices 

higher, but energy-related processing, distribution, and marketing costs 

are higher throughout the food marketing chain from the farmgate through 

wholesale and retail levels. Most of the retail food price impact is from the 

effect of higher energy prices in the food marketing system rather than from 

agricultural commodity price impacts.
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Agricultural sector impacts of the higher energy prices in the scenarios 

studied in this report are not likely to have large effects overall on farm 

county economies and populations because their education and employment 

levels are generally high compared with other nonmetro counties. Some 

counties in the Mississippi Portal region, where estimated energy-related 

farm income losses are largest, would likely be relatively more affected.

Few nonmetro counties derive a substantial share of nonfarm employment 

from energy production, so rural impacts from energy production adjustments due to emissions pricing would be small. Nonetheless, employment 

impacts will be larger in nonmetro counties mainly because counties that 

specialize in energy production are overwhelmingly rural and some counties 

would be particularly affected. Coal mining counties in eastern Kentucky and 

West Virginia may have particular difficulty adjusting to significant job loss 

because of lower education and economywide employment levels in these 

areas.

Longer run increases in energy prices would lead to further adjustments in 

the agricultural sector. Importantly, the development and adoption of new 

technologies would facilitate changes in production practices and energy 

use in agriculture, potentially mitigating economic impacts of higher energy 

prices. Agricultural production practices would be modified to improve 

energy-use efficiency. Energy use per unit of output in the agricultural sector 

would be likely to continue its decades-long decline, also reducing energy 

price impacts. 
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References

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Department of Agriculture, Economic Research Service Data Sets, table 1, 

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Representation, Estimation, and Testing,” Econometrica 55: 251-276.

Harris, J. Michael, Kenneth Erickson, James Johnson, Mitch Morehart, 

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Bulletin 431, The Georgia Agricultural Experiment Stations, University of 

Georgia.
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Appendix A

Greenhouse Gas Pricing Mechanisms

Two primary mechanisms can be used that would result in the introduction 

of prices for greenhouse gas emissions. First, a system of taxes on emissions 

could be used, which would be directly added to the prices of associated 

fossil fuels. Second, an emissions cap-and-trade system could be implemented, where quantity limits on emissions are established and costs for 

the required emission permits would indirectly add to prices of fossil fuels. 

While in many ways these two alternatives are similar in their effects on 

energy markets, there are also significant differences between them administratively, operationally, and analytically.

Pricing of Emissions Through Taxes

A system of taxes on greenhouse gas emissions would affect prices for 

fossil fuels. This approach can be illustrated in the accompanying figures. 

Figure A.1 starts with a simplified depiction of the U.S. energy market, as an 

aggregate, with no system for pricing greenhouse gas emissions. The market 

equilibrium is determined by the intersection of the supply curve (S) and the 

demand curve (D) and is at point e. The equilibrium energy quantity is qe

with pe the equilibrium price. 

Figure A.2 adds a tax on greenhouse gas emissions. The amount of the tax 

would be equal to the vertical shift in the supply curve, the distance from 

point e to point a. The new equilibrium would be at point e’ with a price of pe’

and an equilibrium quantity of qe’. Note that the increase in the equilibrium 

price (pe’ - pe) is less than the amount of the tax.

The situation depicted in figure A.2 is for the aggregate market for energy, 

but implications would vary among different fuels. For individual fuel 

markets, the upward shift of the supply curve would reflect the tax rate set for 

D S

e

qe

pe

Energy 

price

Energy 

quantity

Figure A.1

U.S. energy market, no emissions pricing
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emissions as well as the amount of emissions associated with each specific 

energy source. For fuels with higher emissions, this vertical shift would be 

greater. If demands across the different fuels are similar, the greater shift in 

the supply curve for larger emitting fuels would imply larger reductions in 

equilibrium quantities for those fuels. However, at least theoretically, this 

might not occur if, for example, demand for the fuel with higher greenhouse 

gas emissions was significantly more inelastic (less price responsive, or 

steeper).

Emissions Pricing Through a Cap-and-Trade System 

A cap-and-trade system puts a cap on greenhouse gas emissions and allows 

trading of emission permits among different fuels. For example, a cap may 

reduce emissions by some specified amount (say 10 percent) from a base 

level. Trading of permits may result in more than a 10-percent reduction in 

emissions from one energy source and less than 10 percent from another 

source.

The requirement for holding emission permits associated with an emissions 

cap can be placed at various stages in the production processes within an 

economy, ranging from the original energy source to the industries that use 

energy. Here, it is assumed that the emission permits are held at the original 

energy source. 

Analytically, it is also useful to look at the parts of a cap-and-trade system 

separately, first the cap component and then the permit trading aspects. As 

with the emissions tax framework, the aggregate energy market is discussed 

first. 

D S

a

S’

qe

e

e’

qe’

pe

pe’

Energy 

price

Energy 

quantity

Figure A.2

U.S. energy market with emissions tax
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Framework for a Cap Under a Cap-and-Trade System

For a particular technology, emissions are related to production. Thus, a cap 

on emissions places a constraint on production of energy proportional to the 

required emissions reduction.

This mechanism is depicted in figure A.3. Starting with the same initial 

supply and demand framework shown in figure A.1, the reduction in output 

corresponding to the emissions cap is shown in figure A.3 by moving along 

the supply curve from point e to point b, lowering permitted production to 

the level needed to achieve the specified emissions reduction. 

At point b, the market is not in equilibrium since the price at point b on 

the supply curve is below the price on the demand curve at this quantity. 

However, under a cap-and-trade system, suppliers must hold permits for 

emissions and would be willing to pay as much as the initial price gap for 

those permits. This cost for the emission permits is represented by raising the 

supply curve to S’, resulting in a new equilibrium point at e’. The equilibrium quantity (qe’) is the energy use level associated with the required emissions reduction (the cap), and the new equilibrium price is pe’.

Energy Market Differences Between an Emissions Tax 

and an Emissions Cap

At this stage of developing the analytical framework, an emissions tax and 

an emissions cap (as part of a cap-and-trade system) appear to be analytically 

identical or closely similar, despite the operational differences in the underlying policy mechanism. However, there are differences between the two 

systems beyond the operational and administrative aspects of the approaches 

because the tax works through the pricing of emissions while the cap operates by setting the permitted quantity of emissions. 

D S

b

S’

qe

e

e’

qe’

pe

pe’

Energy 

price

Energy 

quantity

Figure A.3

U.S. energy market with emissions cap
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We now discuss implications of these differences and illustrate them by 

building on the analytical frameworks of figures A.2 and A.3. Both an emissions tax and an emissions cap result in the pricing of greenhouse gas emissions, either through the tax or the cost of emission permits under the cap. In 

both cases, the emission pricing system raises the price of energy from fossil 

fuels and reduces the overall use of energy. 

However, important differences result from the different mechanism used 

in each approach. A tax specifies the price of emissions, but the equilibrium 

quantity (and the associated emissions reduction) is unknown in advance and 

will be determined in the marketplace. In contrast, a cap-and-trade system 

specifies the emissions reduction (and implicitly associated equilibrium quantity), but the equilibrium emissions price is unknown in advance. 

This difference can be illustrated by augmenting the analytical frameworks 

of figures A.2 and A.3 with an alternative demand curve. For the emissions tax, figure A.4 adds a more inelastic (less price responsive, or steeper) 

demand curve, D’. While the vertical shift of the supply function associated 

with the specified tax remains the same, the new, more inelastic demand 

function intersects the shifted supply function at a new equilibrium point, e’’. 

At this new equilibrium, the tax is the same, but the price and quantity are 

higher than at point e’. This implies that while the tax rate can be specified, 

the corresponding decrease in quantity and the associated reduction in emissions is not known ahead of time and will be determined in the marketplace, 

depending critically on the properties of the demand and supply functions.

In contrast, Figure A.5 adds a more inelastic demand curve to the initial 

framework figure A.3 for cap and trade. As before, the point b on the supply 

curve (S) that reflects the implementation of the cap is not in equilibrium. 

With more inelastic demand, the price gap from point b to the new demand 

function is larger. Under a cap-and-trade system, suppliers would now be 

willing to pay more for the required emissions. As represented in figure A.6, 

D S

a

S’

qe

e

e’

qe’

pe

pe’

D’

e’’

Energy 

price

Energy 

quantity

Figure A.4

U.S. energy market with emissions tax, alternative demand
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this higher cost for permits is represented by an additional upward shift of the 

supply function to S’’, resulting in a new equilibrium point at e’’.

In contrast with the emissions tax case, here the equilibrium quantity is 

unchanged with the alternative demand, and the associated level of emissions 

remains at the cap. Only the price for emissions permits and the associated 

equilibrium price for energy change.

D S

b

S’

qe

pe e

pe’

D’

e’

qe’

Energy 

price

Energy 

quantity

Figure A.5

U.S. energy market with emissions cap, alternative demand

D S

b

S’

qe

pe e

pe’

D’

e’’

e’

S’’

qe’

Energy 

price

Energy 

quantity

Figure A.6

U.S. energy market with emissions cap, alternative demand

and resulting supply shift
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Adding Emissions Permit Trading to the 

Cap-and-Trade System 

To add trading of emissions permits to the analytical framework of figure 

A.3, the aggregate energy sector is disaggregated into two (or more) energy 

sources, as depicted in figures A.7 and A.8. In each figure, qb represents the 

reduced level of energy output corresponding to the emissions cap, if each 

sector had the same percentage reduction in output.

However, an emissions cap does not affect all energy sources equally since 

each fuel has different carbon emissions per unit of energy. Consequently, 

the number of permits needed and associated costs vary across energy 

sources to reflect their different emissions. Thus, for a relatively high-emissions energy source, more permits would be needed, raising the supply curve 

more than for a low-emissions energy source.

Thus, two cases can occur. The first situation, represented in figure A.7, is 

when the cost of permits at the output level of qb raises the supply curve 

above the demand curve at that output level. This means the costs of the 

permits raises the supply price above what the market demand will pay. This 

is depicted by the permit cost, d - b, being larger than the increase in the 

market demand value of the energy source at a quantity of qb, c – b. At this 

output level, the demand price of pc is less than the price, pd, that suppliers 

would need to cover their added cost of permits. In this situation, production would be lowered, moving downward along the supply curve, S’, from 

point d to the intersection of the demand curve at point e’. At this point, the 

increase in the market value for the quantity qe’ of the energy source matches 

the cost of the emission permits, so the market is in equilibrium, with a 

market clearing price of pe’. This reduction in output, shown as moving along 

the supply curve from point d to point e’, is facilitated by the trading of emisD S

b

S’

qe

e

e’

qe’

d

c

qb

pb

pe’

pd

pe

pc

Energy 

price

Energy 

quantity

Higher emissions energy source 

requires more emission permits 

(allowances), shifting the supply 

curve up more

Figure A.7

U.S. energy market with cap and trade, two energy sources:

market for higher emissions energy source
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sion permits. Permits held by these suppliers are either sold to other markets 

or fewer permits are initially acquired. 

For the second situation, shown in figure A.8, the cost of permits at the 

output level of qb does not increase the supply curve as high as the demand 

curve. This is again depicted by comparing the permit cost, d - b, with the 

increase in the market demand value of the energy source at qb, c - b. Now 

the former is less than the latter. In this situation, production would increase 

by moving upward along the S’ supply curve from point d to the intersection 

of the demand curve at point e’. The market is in equilibrium at this point 

since the increase in the market value of the quantity qe’ of the energy source 

equals the cost of the emission permits. This change in output from point d 

to point e’ is again facilitated by the trading of emission permits, with additional permits bought by these suppliers.

The different situations under a cap-and-trade system as depicted here illustrate the economic incentives for greater reductions in use of higher emission 

energy sources and smaller reductions for the lower emission energy sources 

compared with the overall reduction in emissions set by the cap. While 

this would tend to be the case, price impacts also depend on the underlying 

supply and demand elasticities, so other situations can occur.

D S

b

S’

qe

e

e’

qe’

d

c

qb

Energy 

price

Energy 

quantity

Lower emissions energy source 

requires fewer emission permits 

(allowances), shifting the supply 

curve up less

Figure A.8

U.S. energy market with cap and trade, two energy sources:

market for lower emissions energy source
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Appendix B

From Greenhouse Gas Emission Prices to 

Energy Prices

The relationship between a price for greenhouse gas emissions and prices of 

fossil fuels depends on the carbon content of the fuel. Prices for emissions 

are typically reported in prices per metric ton of carbon dioxide (CO2), or 

carbon dioxide equivalent (CO2-eq) for other greenhouse gases. The impact 

of a CO2 price on the price of a fuel is additive:

(B1) 2

1 0 P P P k fuel fuel CO fuel = + × where

0 Pfuel is the original fuel price;

1 Pfuel is the fuel price adjusted for a price on carbon dioxide;

CO2 P is the emissions price in units of U.S. dollars per metric ton of carbon 

dioxide; and

fuel k is the carbon emissions factor, which varies by fuel.

Carbon emissions factors for different fuels are available from the U.S. 

Energy Information Administration (EIA) website as a spreadsheet. 

Emissions factors are in units of million metric tons of CO2 per quadrillion 

Btu, or Mt-CO2 per quadrillion Btu. This unit is the same as kilograms CO2

per million Btu (kg-CO2 per MBtu).

For converting Btu to units consumers are more familiar with, the following 

thermal conversion factors are needed:1

Crude oil: MBtu per barrel (bbl)

Natural gas: Btu per cubic foot

Coal: MBtu per short ton

Electricity: Btu per kWh (heat rate).

A useful physical conversion factor is:

1 barrel = 42 U.S. gallons (exactly).2

Next, we provide sample calculations for motor gasoline, fuel oil, natural 

gas, and electricity. The change in electricity price depends on the generating 

technology and fuel. This methodology can be applied to any given CO2

price.

1 Thermal conversion factors are found 

in Appendix A of Annual Energy Review 2007, available on the U.S. Energy 

Information Administration website.

2 This and other physical conversion 

factors are found in Appendix B of the 

Annual Energy Review 2007, available 

on the U.S. Energy Information Administration website.
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Sample Calculation for Motor Gasoline (Gallons)

Assume the price of motor gasoline is measured as dollars per U.S. gallon. 

We can calculate the additive portion due to a CO2 price as:

CO fuel 2 P k × .

This seems simple, but we need to convert the carbon emissions factor for 

motor gasoline into units of metric tons CO2 per U.S. gallon. Conventional 

motor gasoline has a carbon emissions factor of 70.88 kg-CO2 per MBtu, and 

a thermal conversion factor of 5.253 MBtu per bbl.

(B2) 2 2 70.88 5.253 0.00887

1,000 42 fuel

t kgCO MBtu bbl tCO k

kg MBtu bbl gallons gallon = × × × =

A price of $100 per t-CO2, translates to an additional 88.7 cents per gallon of 

motor gasoline.

Distillate Fuel Oil (Gallons)

Distillate fuel oil, which includes diesel fuel, has a carbon emissions factor 

of 73.15 kg-CO2 per MBtu, and a thermal conversion factor of 5.825 MBtu 

per bbl.

(B3) 2 2 73.15 5.825 0.01015

1, 000 42 fuel

t kgCO MBtu bbl tCO k

kg MBtu bbl gallons gallon

= × × × =

A price of $100 per t-CO2 translates to an additional $1.02 per gallon of 

distillate fuel oil.

Natural Gas (1,000 Cubic Feet)

(B4) 2 2

6 3

53.06 1, 028 0.05455

1, 000 . . (10) (10) . . fuel

t kgCO Btu MBtu tCO k

kg MBtu cu ft Btu cu ft

= × × × =

A price of $100 per t-CO2 translates to an additional $5.46 per 1,000 cubic 

feet of natural gas.

Electricity Generated from Coal, Steam Turbine 

(Megawatt-Hours)3

(B5)

3

2 2

6

973 (10) 0.973

(10) fuel

t gCO kWh tCO k

g kWh MWh MWh

= × × =

A price of $100 per t-CO2 translates to an additional $97.30 per megawatthour, or 9.7 cents per kilowatt-hour, for electricity generated from coal.

3 Source: IPCC, 2007c. “Energy 

Supply,” in Climate Change 

2007: Mitigation of Climate 

Change. Contribution of Working Group III to the Fourth 

Assessment Report of the Intergovernmental Panel on Climate 

Change, Cambridge University 

Press, p. 295.
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Appendix C

Energy Price Impacts on Retail Food Prices

In this analysis, we investigate the effect of predicted changes in energy 

prices working through the food marketing system to retail food prices. 

These projected impacts are conditional on a number of modeling and 

marketing system assumptions and are based on the estimation of historical 

energy pass-through rates between food processing stages. A brief description of the analysis process, the data considered, and detailed results follow.

To model the historical pass-through relationship for energy, we generally 

used an Error Correction Model that is inclusive of any co-integrating relationships between food prices within the data. Variations of this setup have 

been used widely in examining pass-through relationships among agricultural 

production levels of particular commodities.1 The model employed in this 

analysis follows a basic two-stage setup as originally proposed by Engle and 

Granger (1987) in which the long term or co-integrating relationship is first 

estimated:

(C1) P P u O t, 0 1 , = + + β β I t t .

then the complete Error Correction Model is estimated, incorporating the 

residuals of equation C1:

(C2) , 0 1, , 2, , 1

1 1

Q R

O t i O t i i I t i t t

i i

∆ φ φ ∆ φ ∆ γ ν P P P u − − −

= =

= +∑ ∑+ + +

The variables PO,t and PI,t in the above equations represent the output and 

input price levels, respectively. If the necessary conditions are not met for 

such a relationship (the individual series are not integrated of order 1 or 

the test for co-integration fails at 10 percent) then a basic Autoregressive 

Distributed Lag (ARDL) Model is used to estimate the pass-through relationship. In equations C1 and C2, the output price is considered to be the downstream food price series and the input price is the upstream food price series. 

Energy price changes are considered only as short-term variables in equation 

C2 due to the unlikelihood of a direct and consistent long-term relationship 

with the dependent variable.

The data used for the pass-through estimation were non-seasonally adjusted, 

monthly Bureau of Labor Statistics CPI and Producer Price Index (PPI) 

data. All price series were converted to their natural logs before use. The 

time period considered in this part of the analysis was 1983 to 2009, which 

was the longest time period available with consistent data. Aggregate price 

measures were used to represent commodity, processed, and retail food 

prices, while energy prices for electric power, diesel fuel, and natural gas 

were represented by their respective wholesale price series. Models including 

variables for energy price changes were estimated for the farm-to-wholesale, 

wholesale-to-FAH (food at home), and wholesale-to-FAFH (food away from 

home) marketing stages.2 To derive retail price implications of the farm-towholesale energy pass-through, results from those models were multiplied by 

the estimated pass-through relationships of wholesale food prices to the two 

different retail price categories, FAH and FAFH.

1See, for example, Meyer and 

von Cramon-Taubadel (2004) for a 

survey of studies using Error Correction Models.

2 All of the energy types were used in 

each pass-through model except in the 

case of processed food to FAH, where 

the effect of a change in the price of 

natural gas was not considered.
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Thus, the total estimated impacts of the predicted changes working through 

the food marketing chain to the FAH CPI and FAFH CPI can be described by 

the following equations:

(C3) , , , ( _ ) ( ( )) ( ( )) ˆ ˆ ˆ E CPI FAH e c E P a E P ∆ j FAH j = × × + × ∆ I j FAH j ∆ I j

(C4) , , , ( _ ) ( ( )) ( ( )) ˆ ˆ ˆ E CPI FAFH e c E P a E P ∆ j FAFH j = × × + × ∆ I j FAFH j ∆ I j

In the above equations, E(ΔCPI_FAHj) and E(ΔCPI_FAFHj) represent the 

estimated changes in the FAH CPI and FAFH CPI, respectively, from a 

change in energy input j; ê represents the estimated historical pass-through 

relationship between processed food and retail food prices; ĉj

 represents 

the estimated historical pass-through rate for energy input j at the farm-toprocessed food stage; âj

 represents the estimated pass-through rate for energy 

input j from processed food to retail food (separate estimates for food at 

home and food away from home); and E(ΔPI,j) represents a price change for 

energy input j. 

Estimated pass-through rates for energy inputs are shown in table C1, with 

the following correspondence to parameters and terms in equations C3 and C4:

•	Farm to processed foods, ĉj

•	Farm to processed foods, passed through to FAH CPI, ĉj x êFAH

•	Farm to processed foods, passed through to FAFH CPI, ĉj x êFAFH

•	Processed foods to FAH CPI, âFAH,j

•	Processed foods to FAFH CPI, âFAFH,j.

A number of assumptions should be noted with the predicted impacts on 

retail food prices. The pass-through models use variables that are thought 

to be analogous to the descriptions provided for the predicted energy price 

changes, and the model setup focuses only on direct connections between 

the price series. The pass-through relationships in the future are assumed to 

be consistent with what was found using historical data, and the estimated 

historical pass-through relationships are subject to the limitations inherent in 

using CPI and PPI data. Also, long-term substitutability in factor inputs has 

not been considered in the analysis.

Table C1

Estimated historical pass-through rates for energy inputs in the 

food marketing system

Production level Electricity Diesel fuel Natural gas

Percent

Farm to processed foods 4.942 1.296 0.885

 Passed through to FAH CPI 1.738 0.456 0.311

 Passed through to FAFH CPI 0.466 0.122 0.084

Processed foods to FAH CPI 6.865 1.246 -

Processed foods to FAFH CPI 0.672 NA 0.186

Note: All pass-through rates were statistically significant at the 10% level except for the case of 

diesel fuel in the processed foods to FAH CPI production level.
48

Impacts of Higher Energy Prices on Agriculture and Rural Economies / ERR-123

Economic Research Service / USDA

The estimated historical energy pass-through rates from the models are 

presented in table C1. These rates are then applied to energy price changes from 

table 3.1 to obtain the energy-related impacts on the CPI for FAH and FAFH in 

the food marketing chain that are part of results shown in table 4.8. Impacts for 

total food are constructed with weights of 0.558 for food at home and 0.442 for 

food away from home.

The majority of the effect on retail food prices appears to be from the predicted 

increases in electricity prices in the FAH production chain. The large impact 

from electricity price changes is likely due to the high values of the predicted 

changes and the relatively high estimated pass-through rate. The high estimated 

pass-through rate for electricity price changes could indicate that this variable 

may be serving as a proxy for other macroeconomic variables as the trend of 

this series is observed to be very similar to that of the FAH and FAFH CPIs.