Economic

Research

Service

Economic

Research 

Report 

Number 227

April 2017

The Potential Effects of Increased 

Demand for U.S. Agricultural Exports 

on Metro and Nonmetro Employment

Steven Zahniser, Tom Hertz, Peter Dixon, 

and Maureen Rimmer

United States Department of Agriculture
Economic Research Service 

www.ers.usda.gov

United States Department of Agriculture

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Recommended citation format for this publication:

Zahniser, Steven, Tom Hertz, Peter Dixon, and Maureen Rimmer. The Potential Effects of 

Increased Demand for U.S. Agricultural Exports on Metro and Nonmetro Employment, 

ERR-227, U.S. Department of Agriculture, Economic Research Service, April 2017.
United States Department of Agriculture

Economic 

Research 

Service

Economic

Research

Report

Number 227

April 2017

Abstract

This report uses a computable general equilibrium (CGE) model to explore the 

economic effects of a hypothetical 10-percent increase in foreign demand for U.S. agricultural exports. This demand shift was found to result in a 6.7-percent increase in the 

volume of such exports, worth $9.7 billion at 2013 prices, and a net increase in total U.S. 

employment (all economic sectors) of about 41,500 jobs—above and beyond the nearly 

1.1 million full-time civilian jobs that U.S. agricultural exports currently support. Some 

40 percent of these new jobs are created in rural (nonmetropolitan) counties. Most parts 

of the agri-food sector (i.e., production agriculture plus food and beverage manufacturing) would see an increase in employment, while employment in other trade-exposed 

industries—most notably non-food-and-beverage manufacturing and mining—would 

decrease. The agri-food sector’s share of regional employment is the main determinant of the percentage change in total regional employment in our simulation. Since 

the agri-food sector accounts for a larger share of nonmetro employment than of metro 

employment, growth in U.S. agricultural exports is of greater relative importance to the 

economic prosperity of nonmetro communities.

Keywords: Employment, trade, CGE, computable general equilibrium model.

Acknowledgments

We thank Jeffrey Reimer, Oregon State University; Fahran Robb, USDA, Foreign 

Agricultural Service (FAS); Timothy Wojan, USDA, Economic Research Service 

(ERS); and a fourth individual for their peer review comments. We also thank Andrew 

Muhammad, John Pender, and Suresh Persaud (USDA/ERS) and Mike Dwyer (formerly 

USDA/FAS) for their comments and suggestions and Timothy Parker (USDA/ERS) for 

constructing the maps used in the report. Special thanks go to Dale Simms and 

Cynthia A. Ray (USDA/ERS) for their expert editing and design of the report.

Steven Zahniser, Tom Hertz, Peter Dixon, 

and Maureen Rimmer

The Potential Effects of Increased 

Demand for U.S. Agricultural Exports 

on Metro and Nonmetro Employment
About the authors

Steven Zahniser is an agricultural economist and Tom Hertz is an economist at USDA’s 

Economic Research Service. Peter Dixon is a professor and principal researcher, and 

Maureen Rimmer is a professor, both at the Centre of Policy Studies at Victoria 

University in Melbourne, Australia.
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Contents

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

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

Modeling Approach..............................................................3

Model Structure and Macroeconomic Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Sectoral Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Regional Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Research Caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Appendix Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
United States Department of Agriculture

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of economic research and 

analysis from the U.S. 

Department of Agriculture, 

providing timely information on economic and policy 

issues related to agriculture, 

food, the environment, and 

rural America. www.ers.usda.gov

Economic

Research

Service

Economic

Research 

Report 

Number 227

March 2017

The Potential Effects of Increased 

Demand for U.S. Agricultural Exports 

on Metro and Nonmetro Employment

Steven Zahniser, Tom Hertz, Peter Dixon, 

and Maureen Rimmer

United States Department of Agriculture

April 2017

Find the full report at 

www.ers.usda.gov/

publications

Steven Zahniser, Tom Hertz, Peter Dixon, and Maureen Rimmer

The Potential Effects of Increased 

Demand for U.S. Agricultural Exports 

on Metro and Nonmetro Employment 

What Is the Issue?

ERS estimates that U.S. agricultural exports supported about 1.1 million full-time, civilian jobs 

in 2015. Yet, the economic linkages between U.S. agricultural exports and rural employment are 

not fully understood, particularly in the metro and nonmetro regions of each State and among 

production agriculture, food and beverage manufacturing, and other sectors of the economy. In 

this report, ERS researchers use a model of the U.S. economy to explore the possible economic 

effects of a 10-percent increase in foreign demand for U.S. agricultural exports, including in 

those various regions and economic sectors. 

What Did the Study Find?

A hypothetical 10-percent increase in foreign demand for U.S. agricultural exports results in a 

6.7-percent increase in the volume of U.S. agricultural exports, worth $9.7 billion at 2013 prices. 

(This increase in export volume (6.7 percent) is smaller than the increase in export demand (10 

percent) because the demand stimulus is partially offset by an increase in the prices of agricultural exports.) 

In addition, total employment in all sectors of the U.S. economy (agricultural and nonagricultural) increases by about 41,500 jobs, above and beyond the approximately 1.1 million jobs 

currently supported by U.S. agricultural exports.

At the regional level, our analysis shows employment increases in 32 of the 50 States and in the 

District of Columbia. The proportionate increase in nonmetro employment is about four times 

larger than the corresponding increase in metro employment (0.09 percent versus 0.02 percent). 

Regions whose share of total employment is greater in the agri-food sector exhibit a stronger 

positive change in total employment due to the hypothetical 10-percent increase in foreign 

demand for U.S. agricultural exports. However, regions with a greater share of both mining and 

non-food-and-beverage manufacturing exhibit a strong negative change in total employment. A 

region’s commodity mix in agri-food production and the presence of an international port also 

influence the regional employment results, but to a far lesser extent.

A report summary from the Economic Research Service

Summary
In our simulation, the expansion of export demand also leads to an increase in the exchange rate, which makes 

U.S. goods more expensive and reduces the competitiveness of products in trade-exposed industries. While 

employment would increase in some sectors, especially in export-oriented agricultural production and food and 

beverage industries, it would decrease in other trade-exposed industries, such as mining and manufacturing. On 

the other hand, an increase in the price of exports relative to the price of imports stimulates U.S. employment 

overall due to the increase in gross national expenditures. But, even within the agri-food sector, not all industries would expand. For example, land-intensive, non-export-oriented industries such as sugarcane and sugarbeet production would contract slightly. 

How Was the Study Conducted?

This report relies upon the U.S. Applied General Equilibrium (USAGE) model, a computable general equilibrium (CGE) model of the U.S. economy. The study focuses on employment effects at the economic sector and 

metro and nonmetro regional levels—a type of analysis for which the USAGE model is well suited. The model 

provides detail on over 500 economic sectors, both agricultural and nonagricultural. However, we concentrate 

on the agricultural and food and beverage sectors that produce agricultural exports, as defined by USDA.

The model includes a regional extension that allows the national results to be disaggregated into State- and 

county-level results. We then reaggregate the county-level results into groups of nonmetro and metro counties within each State in order to form a model with 98 regions—94 regions for the 47 States that have both 

metro and nonmetro counties, 3 regions for the States with only metro counties, and 1 region for the District 

of Columbia. 

www.ers.usda.gov
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The Potential Effects of Increased 

Demand for U.S. Agricultural Exports 

on Metro and Nonmetro Employment 

Introduction

The U.S. agri-food sector—defined in this report to encompass production agriculture and food and 

beverage manufacturing—accounted for 4.2 million jobs in 2016, or about 3 percent of total U.S. 

employment (U.S. Department of Labor, Bureau of Labor Statistics, 2017a). The agri-food sector is 

responsible for a larger share of employment in nonmetropolitan (nonmetro) than in metropolitan 

(metro) counties; in 2015, the agri-food sector accounted for 4 percent of the compensation of 

nonmetro employees, versus 1 percent for metro employees (U.S. Department of Commerce, Bureau 

of Economic Analysis, 2016a). 

Exports, in turn, are an important component of U.S. agricultural sales. In 2015, U.S. agricultural 

exports to all countries (including bulk, high-value intermediate, and consumer-oriented agricultural products) totaled just over $133 billion (USDA/FAS, 2017), compared with $394 billion in 

value added to the U.S. economy by the agri-food sector (U.S. Department of Commerce, Bureau of 

Economic Analysis, 2016b).1

Economists have a pretty good idea of the number of jobs that U.S. agricultural exports currently 

support. For some years, USDA’s Economic Research Service (ERS) has used input-output (I-O) 

modeling to construct agricultural trade multipliers. That research indicates that the number of jobs 

supported by U.S. agricultural exports rose from just over 800,000 full-time, civilian jobs in 2009 

to about 1.1 million jobs in 2015 (Persaud, 2017). Rasmussen (2016) arrives at a similar result using 

similar methods. Paggi and colleagues (2011) use the IMPLAN I-O model to conclude that agricultural exports supported a total of 1.6 million jobs nationwide in 2009. These I-O models measure 

the total amount of labor required to produce the observed level of agricultural exports, counting 

those employed directly on farms and in food processing operations, as well as those employed in the 

industries that supply the necessary inputs to agriculture and food processing operations.

What we seek to estimate in this report is not the total level of employment supported by agri-food 

exports, but how this total would change in response to an increase in foreign demand. Such stimulus 

could arise from an increase in foreign population or income, a contraction in foreign supply of 

agricultural products, free-trade agreements that improve the access of U.S. agricultural products to 

foreign markets, or a successful U.S. promotion campaign for such products. Whatever the underlying 

cause, we have in mind a shock that uniformly moves the foreign demand curve for each type of U.S. 

agricultural product horizontally 10 percent to the right, without changing the position of foreign 

1The statistics in this paragraph for employee compensation and value added cover tobacco manufacturing as well as the 

agri-food sector. USDA’s definition of agricultural trade does not include tobacco manufactures, but it does include food 

manufactures and many types of beverages, such as soft drinks, beer, and wine, as well as milk and juice. In 2014, U.S. 

agricultural exports totaled $150 billion. The year-to-year reduction in exports between 2014 and 2015 was due in part to 

lower commodity prices. Also, one should be careful when comparing U.S. agricultural exports to the agri-food sector’s 

value added, since exports are not a measure of value added.
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demand curves for other U.S. products. This means that at a given price, the quantity demanded by 

foreign buyers of a specific agricultural product will rise by 10 percent.

To estimate the effects of this demand stimulus, we move beyond basic I-O modeling to a full 

computable general equilibrium (CGE) model. A CGE model is “a system of equations that describe 

an economy as a whole and the interactions of its parts” (Burfisher, 2011: 3); this system of equations includes an I-O matrix that relates inputs and outputs across industries. Unlike I-O models, 

however, CGE models can also capture the effects of competition for scarce resources, such as labor 

in different skill categories, capital, land, foreign currency, and public sector budget capacity. CGE 

models can show how the stimulation of one industry affects other industries by changing the availability of workers in different occupations, the availability and cost of financing for investment projects, the availability and rental of land for different uses, the exchange rate, the availability of public 

funds, and the relative prices of all goods and services, including most importantly the agricultural 

exports themselves. The CGE model also accounts for the effects of the re-spending of the additional 

income that accrues to the producers of agricultural exports and their suppliers, sometimes known as 

“induced demand.”

Each of the model’s 20 agri-food sectors produces a unique agricultural output—fruit or dairy farm 

products, for instance. The additional details of our CGE model make it possible to understand how 

the demand shift would result in both higher prices for exports and an increase in their volume, to 

quantify the resulting competition for resources between the agri-food sector and other industries, 

and to allocate economic changes resulting from the demand stimulus across States and between the 

nonmetro and metro regions of each State. Thus, the primary contribution of this report is to extend 

ERS’s agricultural trade multiplier model by adding CGE dynamics and by disaggregating the 

results regionally.

The 10-percent shift in the foreign demand curves is not intended to represent the effects of any 

specific policy or policy proposal. Instead, it provides an opportunity to consider the economic 

impacts of a marginal change in export demand. To be sure, a 10-percent increase in the quantity demanded of U.S. agricultural exports by foreign buyers would be a sizable increase, but it is 

not outside the realm of possibility. Sometimes, trade agreements can contribute to an even larger 

increase in demand. For instance, with the elimination of Mexico’s restrictions on corn imports from 

the United States as part of the North American Free Trade Agreement (NAFTA), the volume of 

U.S. corn exports to Mexico increased from an annual average of 27 kilograms per Mexican inhabitant during 1984-93 to 79 kilograms per inhabitant during 2013-15—an increase of 188 percent.2

Moreover, given the model’s fundamentally linear structure, the effects of smaller demand shocks 

can be estimated by assuming that they would be proportional to the effects reported here.

2The per capita consumption figures in this sentence were calculated by dividing average trade volumes for the periods 

in question (using trade data from USDA/FAS, 2016) by Mexico’s population during the middle year of each period (using population estimates from U.S. Department of Commerce, Bureau of the Census, 2015). For more detail on NAFTA’s 

impact on North American agriculture, see Zahniser et al. (2015).
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Modeling Approach

To estimate the effects of stimulating U.S. agricultural exports, we use the U.S. Applied General 

Equilibrium (USAGE) model—a 534-industry, CGE model of the U.S. economy. This model was 

developed at the Centre of Policy Studies at Victoria University in Melbourne, Australia, in collaboration with the U.S. International Trade Commission (USITC). The theoretical structure of the 

USAGE model is similar to that of the MONASH CGE model of Australia. Dixon and Rimmer 

(2002) provide complete technical documentation of the Australia model, and Victoria University, 

Centre of Policy Studies (2016) offers a reference library of technical information and working 

papers concerning the U.S. model. As is the case for all CGE models, at the heart of the USAGE 

model is a social accounting matrix (SAM), which describes the monetary flows within an economy 

between buyers and sellers—including industries that buy intermediate inputs from each other—and 

quantifies the public, private, and foreign sources of demand for national output. 

The USAGE model explicitly represents the markets for labor, capital, farmland, and output as 

endogenous variables, and we make several simplifying assumptions (discussed below) that allow 

us to focus on the economic linkages between agricultural exports and employment. Of particular 

interest are linkages between the markets for various factors of production, between different 

economic sectors (production agriculture, food manufacturing, and other industries), and between 

the U.S. economy and the rest of the world. On the input side, farmers, growers, and ranchers affect 

the rest of the economy by purchasing or renting fuel, fertilizer, equipment, land, and the services 

provided by hired farmworkers and other agricultural professionals. The sale, transportation, 

processing, and marketing of agricultural production—some of which is destined for export—generates further demand for employment in downstream industries, including the food manufacturing 

sector. Labor is assumed to be mobile across sectors, while land is assumed to be an input only in 

agricultural production but not in other sectors of the economy, including food processing. Land 

prices are flexible, and land can be reallocated from one agricultural sector to another.

The USAGE model has been applied by the USITC and the U.S. Departments of Commerce, 

Agriculture, Homeland Security, Energy, and Transportation in studies concerned with international 

trade policy (Dixon et al., 2007; Fox et al., 2008; USITC, 2004, 2007, 2009, 2011, 2013), bioenergy 

(Dixon et al., 2007b; Gehlhar et al., 2010), immigration (Dixon et al., 2011a; Dixon et al., 2011b; 

Zahniser et al., 2012; Dixon et al., 2014), the Great Recession of 2007-09 (Dixon and Rimmer, 

2011), the National Export Initiative (NEI) (Dixon and Rimmer, 2013a), climate change policies 

(Dixon and Rimmer, 2015), the economic dimensions of national security threats (Dixon et al., 

2010; Giesecke, 2011; Giesecke et al., 2012), and highway infrastructure (Dixon and Rimmer, 2014).

Among these works, Dixon and Rimmer (2013a) use the USAGE model to explore the likely impacts 

of four types of export promotion policies implemented under the National Export Initiative and 

other Government initiatives: (1) policies that shift the foreign demand curves for U.S. exports, (2) 

policies that shift the supply curves for U.S. exports, (3) efforts to pursue trade-expanding policies 

in international forums, and (4) efforts to improve the macro competitiveness of the U.S. economy 

by cutting public expenditure. The authors find that these policies, if successfully implemented, 

would lower the job losses associated with the Great Recession for the period 2008-20 from about 

70 million to 45 million job-years. This research, however, does not provide specific analysis for the 

agri-food sector, nor does it allow for an examination of separate impacts in nonmetro and metro 

parts of the country. The version of the USAGE model employed here addresses these limitations.
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The agricultural products we identify correspond to the definition used by the U.S. Department of 

Agriculture (USDA) in its Foreign Agricultural Trade of the United States (FATUS) data system 

(USDA/FAS, 2017). USDA has maintained FATUS in accordance with a congressional directive 

since about 1926. FATUS is an aggregation of the several thousand 10-digit U.S. Harmonized 

Tariff Schedule (HTS) codes that USDA considers to be “agricultural” into usable groupings 

of agricultural products that encompass both farm and nonfarm products (Cooke, 2015). These 

products consist of both agricultural commodities and food and beverages manufactured using such 

commodities as inputs.

To facilitate the presentation of our modeling results, we combine a number of industries into larger 

categories in order to focus on 20 agricultural or food and beverage manufacturing sectors, and 

we use the term “agri-food” to refer to these 20 sectors as a group. Appendix table 1 lists these 20 

sectors and the industries from the USAGE model that make up these sectors. The agri-food sector 

encompasses all the industries whose output falls into the category of products considered to be 

agricultural in USDA’s definition of agricultural trade. According to the model’s base data for 2013, 

the agri-food sector generated $144 billion in exports (6.4 percent of U.S. exports), contributed $381 

billion in value added (2.4 percent of U.S. gross domestic product, GDP), and provided employment 

for 4.9 million people (3.4 percent of total U.S. employment).3 Although both the USAGE model and 

the ERS agricultural trade multipliers rely upon USDA’s official definition of agricultural trade, the 

20 agri-food sectors analyzed in this report do not conform precisely to the individual commodity 

groups examined using the ERS multipliers.

The USAGE model generates results for a wide range of variables at different levels of analysis. In 

this study, we employ a variant of the model that provides results not only at the macroeconomic 

and industry levels, but also at the regional level. For our regional analysis, we use the distinction 

between metropolitan and nonmetropolitan counties, as defined by the Office of Management and 

Budget (OMB) in February 2013, as our guide for disaggregating the United States into regions.4

We divide each State into two regions—metro counties and nonmetro counties (fig. 1). Three States 

(Delaware, New Jersey, and Rhode Island) and the District of Columbia do not have any nonmetro 

counties, so our model contains a total of 98 regions (47 States, each with a metro and nonmetro 

region, plus Delaware, New Jersey, Rhode Island, and the District of Columbia). 

The distinction between metro and nonmetro regions is important to our study for two reasons. First, 

this analytical framework provides a means to consider the impact of agricultural exports on rural 

communities, as represented in our CGE model by nonmetro regions. Second, the agri-food sector 

accounts for a larger share of total employment in nonmetro counties than in metro counties—9 

percent versus 2 percent—according to our model’s base data. Thus, the effects of stimulating 

agricultural exports are likely to be larger in nonmetro than metro regions. This is not to say that 

the agri-food sector consistently plays a small role in the economic activities of metro regions. In 

absolute terms, some metro regions have large levels of agri-food employment—particularly in 

populous States. For instance, agri-food employment in metro California (about 904,000 jobs) is 

larger than total employment in 14 individual States and the District of Columbia. Moreover, some 

metro regions are distinguished by the agri-food sector’s large share of total employment. In metro 

Arkansas, for instance, this share is 7 percent; in metro Washington and metro California, it is 6 

3By region, the agri-food sector’s share of the number of jobs tends to be higher than its share of compensation (as 

described on page 1) because earnings in the agri-food sector are relatively low.

4U.S. Department of Commerce, Census Bureau (2013) describes the OMB’s delineation of metropolitan and micropolitan areas.
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percent. But the largest shares of agri-food employment are, not surprisingly, located in nonmetro 

regions, such as nonmetro Washington (26 percent), nonmetro Idaho (18 percent), and nonmetro 

Hawaii (18 percent).

Figure 1

Metro and nonmetro counties of the United States, 2013

Source: USDA, Economic Research Service.

Metropolitan

Nonmetropolitan
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Model Structure and Macroeconomic Results

The USAGE model can be run under different sets of assumptions. For this study, we assume that 

real wage rates in each U.S. industry are unaffected by the stimulation of agricultural exports. 

This assumption, which is also a feature of I-O models, is appropriate for showing the effects of an 

export-demand shock amid slack labor markets (relatively high rates of unemployment). As the U.S. 

economy recovered from the Great Recession of 2007-09, the U.S. unemployment rate (seasonally 

adjusted) gradually declined from a high of 10.0 percent in October 2009 to 4.8 percent in January 

2017 (U.S. Department of Labor, Bureau of Labor Statistics, 2017b). Estimates of the full-employment level of unemployment—the minimum unemployment level that the economy can sustain 

without generating additional inflation—vary between 4.2 percent and 5.3 percent. 

More comprehensive measures of labor underutilization that address both unemployment and 

underemployment indicate the persistence of considerable slack in the U.S. labor market. When the 

calculation includes people who are either marginally attached to the labor force or employed parttime for economic reasons, the rate of labor underutilization rises to 9.4 percent in January 2017, 

compared with 17.1 percent in October 2009 (U.S. Department of Labor, Bureau of Labor Statistics, 

2017b).5 Given this current context, our assumption of fixed real wages allows us to answer the 

following question: How many extra jobs could be sustained at current real wage rates if foreign 

demand curves for U.S. agricultural exports shifted to the right by 10 percent? 6

The model also contains several simplifying assumptions that focus our analysis on the linkage 

between increased demand for exports and employment. First, we assume that the increase in 

export demand does not affect the U.S. balance of trade, which is maintained via adjustment of 

the U.S. exchange rate. This assumption has two important implications for our modeling results. 

First, any increase in U.S. exports of agricultural products resulting from the increase in foreign 

demand will lead to an appreciation of the U.S. dollar, causing either an increase in U.S. imports, 

a decrease in U.S. exports of other products (agricultural or nonagricultural), or some combination 

of the two. This assumption is equivalent to assuming that the demand shift has no effect on the 

U.S. capital account, which is assumed to be primarily determined by factors in financial markets. 

This constraint virtually ensures a decrease in U.S. exports and/or increased U.S. imports of tradeexposed, nonagricultural products as a result of the demand shift.

Second, the stimulation to export demand is assumed not to affect the total size of the U.S. capital 

stock. This is a shortrun assumption, as is the assumption that industry-specific real wage rates do 

not change. A final simplifying macroeconomic assumption is that export stimulation does not affect 

the broad composition of gross national expenditure (GNE). GNE is defined as the sum of public 

5The marginally attached are defined as people “who currently are neither working nor looking for work but indicate 

that they want and are available for a job and have looked for work sometime in the past 12 months” (U.S. Department of 

Labor, Bureau of Labor Statistics, 2015). Some researchers have found that the Great Recession increased the percentage 

of people who are marginally detached from the labor force, although this increase is challenging to disentangle from the 

long-term decline in the labor-force participation rate resulting from such structural factors as the aging of the population 

and increased schooling among younger adults. See Jacobs (2015) for a discussion of this issue.

6Many CGE models rely on an alternative assumption, namely that full employment is continually maintained via 

adjustments in real wage rates. Under that assumption, no demand shocks or policies have any effect on total employment 

in the short run: any employment gains in one sector must be exactly offset by losses in another. Our opposite assumption 

(fixed real wages) may be closer to reality given the current state of the economy but may result in a slight overstatement 

of the employment effects.
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consumption, private consumption, and investment, and we assume equal percentage movements in 

these three components.

With respect to price elasticities in international trade, the USAGE model has the capacity to allow 

the price elasticity of export demand (and import demand) to vary by sector and by trading partner. 

In this application of the model, however, we allow the elasticities to vary only by sector and not 

by trading partner. For all sectors in production agriculture except fruit, vegetable, and greenhouse 

and nursery production, the price elasticity of export demand is assumed to equal -2. For all 

other industries—including fruit, vegetable, and greenhouse and nursery production, all the food 

manufacturing sectors, and all non-agri-food sectors—this elasticity is assumed to equal -3. The 

assumption that the price elasticity of export demand is around -2 to -3 is broadly consistent with 

trade elasticities estimated by other researchers over the past decade.7

Macroeconomic Results

The projected effect of a 10-percent increase in demand for U.S. agricultural exports on the number 

of people employed in all U.S. industries is an increase of 0.029 percent (about 41,500 jobs). 

However, the projected effect on labor input—the total amount of wages paid by employers (also 

known as the U.S. wage bill)—is just 0.006 percent (table 1). The projected effect on labor input 

weights the percentage effects on employment in each industry by that industry’s share of the total 

labor input. This means that if people in one industry have wage rates that are twice those of people 

in another industry, then the employment of an extra person in the first industry contributes twice as 

much to the increase in labor input as the employment of an extra person in the second industry. The 

discrepancy between the two employment measures in table 1 reflects the relatively low earnings of 

workers in the agri-food sectors. The shock (agricultural export stimulation) creates lower paying 

jobs and therefore stimulates labor input less than total employment.

7To make comparisons between the existing empirical estimates and our assumptions, see Athukorala and Khan 

(2016), Kee et al. (2008), and Raza et al. (2016).

Table 1

Macroeconomic effects of a 10-percent increase in demand for U.S. agricultural exports, 

in percent

Employment (people) 0.029

Labor input (total wage bill) 0.006

Real GDP 0.004

Capital stock 0.000

Gross national expenditure 0.042

Real exports (all industries) -0.010

Real imports (all industries) 0.231

Terms of trade 0.240

Exchange rate 0.232

Agricultural exports, volume 6.717

Agricultural exports, price in foreign currency 1.245

Agricultural exports, value in U.S. currency 7.796

Source: Simulation results using the USAGE model to evaluate the economic effects of a 10-percent increase in foreign 

demand for U.S. agricultural exports.
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The modest GDP effect—an increase of 0.004 percent (about $640 million)—accords with the 

result for labor input and the assumption of no change in aggregate capital. With labor contributing 

about two-thirds of GDP, the increase in labor input of 0.006 percent generates an increase in real 

GDP of 0.004 percent.

Unlike with GDP, there is a significant increase in GNE (0.042 percent, or about $6.7 billion). The 

increase in GNE is a measure of the total welfare effect of the assumed export stimulation. GNE 

provides a broad measure of domestic spending in the economy (consumption, investment, and 

government) without accounting for the trade balance, which we assume to be constant. The increase 

in GNE relative to GDP is made possible by an increase in the price of U.S. exports relative to the 

price of imports—that is, an improvement in the terms of trade (0.240 percent). With an improvement in the terms of trade, the United States receives more imports for a given volume of exports 

and is therefore able to increase its consumption and investment (GNE) relative to its output (GDP) 

without deterioration in its balance of trade.

The source of the improvement in terms of trade can be seen in figure 2. The original demand 

curve is D0D0, and the original supply curve is S0S0, with the intersection of the two curves (Point 

O) indicating the initial equilibrium S0S0. Under the assumption of a fixed exchange rate, the 

demand curve shifts to D1D1, which moves the equilibrium to point A, the intersection of D1D1, 

and S0S0. However, the exchange rate also moves. With more agricultural exports and no increase 

in the reliance of the United States on foreign borrowing (fixed balance of trade), the exchange 

Figure 2

Foreign demand and export supply curves for a U.S. agricultural product

Source: USDA, Economic Research Service.

Price in foreign currency

1 1.1 Quantity of U.S. export

D0

D1

P0

S0

S1

S0

S1

D0

D1

O

A

D1

B
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rate appreciates by 0.232 percent (table 1).8 In figure 2, this moves the export supply curve for 

agricultural products (S1S1), measured in foreign currency, to the left. The final equilibrium is 

at point B, the intersection of D1D1, and S1S1, indicating that the foreign currency prices of U.S. 

agricultural exports have increased. By contrast, there is no direct effect on the foreign currency 

prices of imports. Consequently, there is improvement in the terms of trade. The improvement 

emanating from the agricultural sector is reinforced by exchange-rate-induced movements in the 

export-supply curves for nonagricultural exports. While the exchange rate movement reduces 

nonagricultural exports, it increases their foreign-currency prices.

The upward-sloping supply curve for agricultural products and its exchange-rate-induced shift 

explain why the increase in the volume of agricultural exports is 6.72 percent, not 10 percent, and 

why the foreign-currency price of these exports increases by 1.25 percent (table 1). In combination, 

the foreign-currency price increase, the volume increase, and the exchange rate increase imply an 

increase in the domestic currency value of agricultural exports of 7.8 percent [≈ 100 x (1.01245 x 

1.06717 ÷ 1.00232 – 1)], or $11.24 billion. Thus, our model indicates that a $1-billion increase in 

the nominal value of agricultural exports generates about 3,700 additional jobs in the U.S. economy 

(41,493 ÷ 11.24 = 3,692). This equates to an increase of about 4,600 jobs per $1 billion of additional 

exports measured in real (volume) terms, at 2015 prices.9

This employment effect is smaller than ERS’s most recent estimate of the average number of U.S. 

full-time, civilian jobs currently supported by $1 billion of U.S. agricultural exports (Persaud, 2017). 

That research, obtained through I-O analysis, calculates an agricultural trade multiplier of about 

8,000 jobs per $1 billion in real agricultural exports, using data for 2015. The main reason for this 

difference is that our scenario involves an increase in foreign demand, which leads to an appreciation of the U.S. dollar, which in turn leads to a reduction in net exports in trade-exposed industries 

outside the agri-food sector. By contrast, the I-O analysis looks only at the number of jobs currently 

supported by U.S. agricultural exports and does not consider the macroeconomic effects of hypothetical changes in foreign demand or any other alternative scenario.

Despite the increase in the volume of agricultural exports, total U.S. exports (agricultural and 

nonagricultural) decline by 0.01 percent. The exchange-rate effect reduces nonagricultural exports 

sufficiently to cause this result. To understand how this can happen, we need to consider imports. 

With an increase in GNE of 0.042 percent, we might expect a proportionate increase in imports. 

However, the exchange-rate appreciation stimulates imports (an increase of 0.231 percent, table 1). 

With terms of trade improved by 0.24 percent, this increase in imports can be paid for (recall the 

assumption of a fixed trade balance) with only a slight reduction in real exports.

8This is not only the nominal appreciation but also the real appreciation. We assume that the export stimulation has no 

effect on U.S. inflation relative to that of its trading partners.

9We adjust for changing agricultural prices using the USDA National Agricultural Statistics Service (NASS) index for 

prices received by crop and livestock producers (USDA/NASS, 2017).
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Sectoral Results

Table 2 shows the change in export values for each agri-food sector associated with a 10-percent 

increase in foreign demand for U.S. agricultural exports. In our model, the base level of U.S. agricultural exports (in the base year of 2013) is $144.15 billion, compared with $144.36 billion in the latest 

official tally of U.S. agricultural exports for that year (USDA/FAS, 2017). Across agri-food sectors, 

the changes in export values in absolute terms range from a mere $1 million (sugar crop production, 

where output is usually refined in the source country and thus is rarely traded internationally) to $4.0 

billion (field crop production, the agri-food sector with the most exports by far in the model). Among 

agri-food sectors, the animal slaughtering and processing sector shows the second largest absolute 

change in exports, with an increase in export sales of $1.6 billion.

The percentage increases in the export values of the agri-food sector range from 5.6 percent (sugar 

crop production) to 9.1 percent (greenhouse and nursery production). Interestingly, the three agri-food 

Table 2

Nominal change in U.S. agricultural exports in response to a 10-percent increase in foreign 

demand for those exports, by agri-food sector

Base year 

export 

value

Change due to 10-percent 

Agri-food sector increase in export demand

$ Million $ Million Percent

Dairy farm products 96 8 8.71

Poultry and egg production 768 59 7.73

Livestock production 2,674 226 8.45

Field crop production 51,321 4,028 7.85

Fruit production 6,558 450 6.87

Tree nut production 2,560 191 7.47

Vegetable production 6,129 374 6.10

Sugar crop production 11 1 5.61

Greenhouse and nursery production 1,289 118 9.15

Animal slaughtering and processing 20,338 1,573 7.74

Dairy product manufacturing 4,297 362 8.43

Fruit and vegetable preserving and specialty food manufacturing 9,642 842 8.73

Grain and oilseed milling 14,284 1,003 7.02

Animal food manufacturing 3,970 327 8.24

Bakeries and tortilla manufacturing 1,439 131 9.10

Sugar manufacturing 908 52 5.74

Chocolate and confectionery manufacturing 2,704 228 8.42

Nuts and snack food manufacturing 4,606 326 7.07

Beverage manufacturing 2,973 270 9.09

Other food manufacturing 7,584 668 8.80

Total 144,150 11,238 7.80

Source: Simulation results using the USAGE model.
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sectors that see the largest proportionate change in export values—greenhouse and nursery production, 

bakeries and tortilla manufacturing, and beverage manufacturing—account for just 4 percent of total 

U.S. agri-food exports, using the base values in the model. Sugar crop production and sugar manufacturing experience the two smallest percentage increases in exports—a result that reflects the comparative advantages of the U.S. economy in other sectors. Overall, the United States is a large net importer 

of sugar (about $1.4 billion in 2013).

Table 3 shows the employment effects of our hypothetical 10-percent increase in foreign demand 

for U.S. agricultural exports. The additional demand leads to a 0.029-percent increase in total U.S. 

employment (table 1), or about 41,500 additional jobs. Some labor is drawn into agri-food employment from other sectors of the economy in response to the export stimulus. Total agri-food employment increases by 0.969 percent (about 47,200 jobs), while employment in other sectors decreases by 

0.004 percent (about 5,700 jobs). While all sectors of the economy are stimulated by the increase in 

GNE, non-agri-food employment generally declines due to the decrease in total exports and increase 

Table 3

Change in employment in response to a 10-percent increase in foreign demand for 

U.S. agricultural exports: by sector and by nonmetro/metro counties

Sector Nonmetro Metro Total Nonmetro Metro Total

Number Percent

Total 17,539 23,954 41,493 0.09 0.02 0.03

Sectors other than agriculture and food 

manufacturing -690 -5,035 -5,725 -0.004 -0.004 -0.004

Agriculture and food manufacturing 18,229 28,989 47,218 1.00 0.95 0.97

Dairy farm products 561 925 1,486 0.32 0.44 0.39

Poultry and egg production 1,423 992 2,415 0.46 0.57 0.50

Livestock production 3,127 1,822 4,949 0.84 1.01 0.90

Field crop production 10,362 11,329 21,691 2.57 3.04 2.80

Fruit production 619 4,306 4,925 0.87 1.01 0.99

Tree nut production 773 3,565 4,338 3.23 3.26 3.26

Vegetable production -230 -93 -322 -0.19 -0.03 -0.08

Sugar crop production -185 -249 -434 -1.45 -1.43 -1.44

Greenhouse and nursery production 69 581 651 0.38 0.59 0.55

Animal slaughtering and processing 675 1,327 2,002 0.58 0.71 0.66

Dairy product manufacturing 132 362 495 0.32 0.33 0.33

Fruit and vegetable preserving and 

specialty food manufacturing 233 1,258 1,491 0.66 0.84 0.81

Grain and oilseed milling 302 802 1,104 1.20 0.94 1.00

Animal food manufacturing 230 240 470 1.17 1.36 1.26

Bakeries and tortilla manufacturing 10 284 295 0.03 0.12 0.11

Sugar refining -70 -74 -144 -0.72 -0.62 -0.66

Chocolate and confectionery manufacturing 26 200 227 0.25 0.42 0.39

Nuts and snack food manufacturing 86 353 439 0.83 0.74 0.76

Beverage manufacturing 32 294 327 0.16 0.18 0.18

Other food manufacturing 51 764 816 0.47 0.85 0.81

Source: Simulation results using the USAGE model.
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in total imports induced by the appreciation of the exchange rate. A few non-agri-food sectors—such 

as agricultural services, pesticides, and farm machinery—benefit from increased sales of inputs to 

the agri-food sector. For almost all of the trade-exposed sectors outside of agri-food, however, the 

negative exchange-rate effects dominate the positive effects emanating from the increase in GNE 

and linkages to the agri-food sector.

The effect on employment is positive for 17 of the 20 agri-food sectors in our analysis. Of these, the 

three with the largest proportionate increase in employment are tree nuts (3.26 percent), field crops 

(2.80 percent), and animal food (food for dogs, cats, and other animals) processing (1.26 percent), 

while the three sectors with the largest absolute increase in employment are field crops (about 

22,000 jobs), livestock (5,000 jobs), and fruit (5,000 jobs). Field crops account for 46 percent of the 

total increase in agri-food employment. 

Employment in three agri-food sectors decreases: sugar crops (-1.44 percent), sugar refining (-0.66 

percent), and vegetables (-0.08 percent). In the cases of sugar crops and sugar refining, the negative 

effect is not surprising since the United States is a net importer of sugar, with imports accounting 

for about one-fourth of total use (USDA/FAS, 2015). Moreover, land accounts for a high share of the 

cost of production for sugarcane and sugarbeet cultivation. With the agri-food sector’s expansion in 

response to export demand, land prices rise, causing significant increases in the cost of production. 

This land-price effect, together with the appreciation of the dollar, causes imported raw and refined 

sugar to displace a portion of U.S. sugarcane and sugarbeet production. Currency appreciation also 

increases imports and reduces employment in sugar refining. In the model’s base year (2013), refined 

sugar accounted for a small portion of total U.S. sugar imports: 4 percent in 2012/13 and 9 percent 

in 2013/14 (USDA/FAS, 2015).10

The vegetable sector faces strong competition from imports, even though the sector has a high 

export share (26 percent). Although the United States is a major exporter of fresh vegetables and 

a net exporter of some fresh vegetables such as onions, it imports far more fresh vegetables than it 

exports. In the model’s base year, U.S. net imports of vegetables equaled $4.3 billion (USDA/FAS, 

2017). Households constitute the main customer for vegetables, with a 59-percent share of total sales. 

Although demand from households expands when export demand is stimulated, this expansion is 

small (recall that private consumption rises by only 0.042 percent) and competition from imported 

vegetables is sufficient to offset the gains to output and employment in the vegetable sector from 

expanded exports.

10To facilitate our discussion of the results, we aggregate the sugar-related sectors in the USAGE model into two sectors: sugar crops (i.e., sugarcane and sugar beets) and sugar refining. While the model used in this report does not include 

a representation of U.S. sugar policy, Winston (2005) uses the USAGE model to evaluate the tariff-rate quotas (TRQs) 

that regulate the U.S. supply of sugar.
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Regional Results

To obtain detailed results for metro and nonmetro regions, we first apportion the national results 

from our CGE model across the 50 States and the District of Columbia; then, we apportion the Statelevel results across individual counties, as described below. Finally, we aggregate the county-level 

results into groups of metro and nonmetro counties within each State, which we call regions. This 

aggregation means that any errors in the allocation of economic activity to particular counties within 

a given region will not affect the allocation at the regional level.

This State and county extension of the model takes three factors into account.11 The most 

important factor is the sectoral composition of economic activity in each region. If a region’s 

employment is heavily concentrated in sectors that are favored by the economic shock under 

consideration (in our case, the stimulation of agricultural exports), then the model will assign 

larger positive results to that region.

The second factor is interstate and international trade. If a region relies heavily on sales either to 

other regions or to international markets where expenditures grow strongly due to the shock under 

consideration, then the model will again generate positive effects for that region. These sales include 

both intermediate goods used as inputs in the production of other goods and final goods directly 

consumed by households.

Finally, the model encompasses local multiplier effects. If employment in the traded-goods 

industries12 of a particular region benefits from the first two factors (composition and trade), then 

employment in non-traded-goods industries such as retail trade will also benefit.

Both metro and nonmetro counties experience an increase in total agri-food employment as a result 

of the 10-percent increase in foreign demand for U.S. agricultural exports (table 3). In percentage 

terms, the increases in agri-food employment in nonmetro and metro counties are comparable (1.00 

percent versus 0.95 percent, respectively), but in absolute terms, the increase is much larger in metro 

counties than in nonmetro counties (about 29,000 jobs versus 18,000) because more agri-food jobs 

are located in metro than in nonmetro counties (3.0 million versus 1.8 million in 2013). However, 

because agri-food employment makes up a larger share of total employment in nonmetro counties, 

the proportionate increase in total employment is larger in nonmetro counties (0.09 percent) than in 

metro counties (0.02 percent). Thus, the promotion of agricultural exports would disproportionately 

benefit rural areas.

Figures 3-6 show the total and agri-food employment effects of a 10-percent increase in demand for 

U.S. agricultural exports in each State’s nonmetro and metro regions. (The full set of State and regional 

results is reported in appendix table 2.) For total nonmetro employment, 39 of the 47 States with 

nonmetro counties experience nonmetro job growth in the simulation (fig. 3). Nonmetro job growth 

above 0.05 percent occurs in 26 States, mostly along the Pacific Ocean, in the Great Plains, and in 

the Southeast. For total metro employment, 28 States plus the District of Colombia see an increase 

in metro employment from greater agricultural exports (fig. 4). The largest proportionate metro 

job growth (again, in excess of 0.05 percent) occurs in 13 States. Some States that see high rates of 

nonmetro job growth see more modest increases (or even decreases) in total metro employment. These 

11Dixon et al. (2007) explain the theory underlying the regional extension in greater detail.

12These are sectors that produce goods traded across regional, State, or international boundaries.
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Figure 3

Change in nonmetro employment from a 10-percent increase in demand for 

U.S. agricultural exports

Source: Simulation results using the USAGE model to evaluate the economic effects of a 10-percent increase in 

foreign demand for U.S. agricultural exports, as allocated among the nonmetro counties of each State.

No nonmetro areas in State

Loss of 0.025% or more

Loss of less than 0.025%

Gain of up to 0.025%

Gain of between 0.025% 

and 0.05%

Gain of more than 0.05%

Figure 4

Change in metro employment from a 10-percent increase in demand for 

U.S. agricultural exports

Source: Simulation results using the USAGE model to evaluate the economic effects of a 10-percent increase in 

foreign demand for U.S. agricultural exports, as allocated among the metro counties of each State.

Loss of 0.025% or more

Loss of less than 0.025%

Gain of up to 0.025%

Gain of between 0.025% 

and 0.05%

Gain of more than 0.05%
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States tend to be located in the southern Great Plains and Southeast. Eight States see a decrease in total 

nonmetro employment, and 22 States experience a decrease in total metro employment. 

Figures 5 and 6 pertain to changes in agri-food employment, using a different scale than in figures 

3 and 4 to reflect the larger proportionate effects. All regions, except for the District of Colombia, 

experience growth in agri-food employment. Eight nonmetro regions (Washington, Oregon, 

California, Hawaii, Montana, South Dakota, South Carolina, and Connecticut) and five metro 

regions (Washington, Oregon, Hawaii, South Carolina, and Connecticut) experience agri-food job 

growth in excess of 1.5 percent.

The range of effects on total employment across all 98 regions is from -0.07 percent (metro 

Michigan) to 0.81 percent (nonmetro Washington, see appendix table 2). The regional effects for 

agri-food employment range from -0.09 percent (District of Columbia) to 2.79 percent (nonmetro 

Washington State). In our regression analysis of the regional modeling results, we evaluate the 

importance of several possible determinants of the total employment effect. 

Regression Analysis

Simple regression analysis can be used to explore the heterogeneity of the regional results—in 

particular, which regional attributes are most strongly associated with positive employment 

outcomes from the export stimulus. For example, metro Michigan has several attributes that are 

negative determinants of employment growth: a low share of employment in the agri-food sector, a 

high share of employment in mining and non-food-and-beverage manufacturing, and the presence of 

international ports engaged in imports. In contrast, nonmetro Washington has several attributes that 

Figure 5

Change in nonmetro agri-food employment from a 10-percent increase in demand for 

U.S. agricultural exports

Source: Simulation results using the USAGE model to evaluate the economic effects of a 10-percent increase in 

foreign demand for U.S. agricultural exports, as allocated among the nonmetro counties of each State.

Gain of less than 0.5%

Gain of between 0.5% 

and 0.75%

Gain of between 0.75%

and 1%

Gain of between 1% 

and 1.5%

Gain of more than 1.5%

No nonmetro areas in State
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are positive determinants: a high share of employment in the agri-food sector, a favorable composition of agri-food employment, and international ports engaged in agricultural exports.

To examine these relationships, we estimate a series of regressions in which a region’s percentage 

change in total employment (PCT_CH_EMPr, where r indexes regions) is posited to be a function of 

a region’s sectoral composition of employment and the presence of one or more international ports in 

a region:

PCT CH EMP AG SHARE AG COMP MM SHARE

AGX PO

r r r r _ _ _ _ _

_

= + β β + + β β +

β

0 1 2 3

4 RT INDEX M PORT INDEX u r r r _ _ + + β _ 5

We consider the impact of three explanatory variables describing the sectoral composition of a 

region’s employment:

• AG_SHAREr —the agri-food sector’s share of employment in region r;

• AG_COMPr —the extent to which the sectoral composition of agri-food employment in region r

is weighted toward sectors that benefit most strongly from the export demand shock; and

• MM_SHAREr —the mining and non-food-and-beverage manufacturing sectors’ combined share 

of employment in region r; calculated as defined below.

The first variable indicates the relative size of a region’s agri-food sector, according to the USAGE 

model’s base data. In our simulation results for the country as a whole, agri-food employment 

expanded as a result of increased foreign demand for U.S. agricultural exports. Thus, a region with a 

Figure 6

Change in metro agri-food employment from a 10-percent increase in demand for 

U.S. agricultural exports

Source: Simulation results using the USAGE model to evaluate the economic effects of a 10-percent increase in 

foreign demand for U.S. agricultural exports, as allocated among the metro counties of each State.

Gain of less than 0.5%

Gain of between 0.5% 

and 0.75%

Gain of between 0.75%

and 1%

Gain of between 1% 

and 1.5%

Gain of more than 1.5%

No nonmetro areas in State
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larger proportion of agri-food employment is also expected to experience a higher percentage change 

in total employment as a result of the simulation.

The second variable describes a region’s composition of employment, AG_COMPr. This variable is 

defined as the sum of the products of each agri-food sector’s share of total employment in the region, 

SHs,r, times the percentage employment effect in the simulation for that sector at the national level, 

NAT_PCT_CHs, where s identifies sectors:

AG COMPr sSHs r NAT PCT CHs _ _ = × Σ , _ 

The employment effects in our export simulation vary from one agri-food sector to another. For 

any given share of agri-food employment, a region’s percentage change in employment is likely 

to depend on whether its agri-food employment is concentrated in activities with strongly positive 

effects—such as tree nuts, field crops, animal food manufacturing, grain and oilseed milling, and 

fruit—or in activities with negative or weakly positive effects, such as sugar crops, sugar refining, 

vegetables, bakeries and tortilla manufacturing, and beverage manufacturing (table 3). Thus, a 

region where export-oriented, agri-food sectors account for a larger share of employment is expected 

to have a higher percentage change in total employment in the simulation. Similarly, a region where 

agri-food sectors subject to heightened competition from imports account for a larger share of 

employment is expected to have a lower percentage change in total employment.

The mining and non-food-and-beverage manufacturing sectors are of interest because of their 

pronounced exposure to import competition and/or because exports account for a high share of 

their output. In addition to competing with the agri-food sector in input markets, the mining and 

non-food-and-beverage manufacturing sectors are under further pressure to contract due to the real 

appreciation of the exchange rate that occurs in the simulation. With this in mind, a region with a 

larger proportion of employment in these sectors is expected to see a lower percentage change in 

total employment.

We also consider the impact of two explanatory variables measuring the relative level of international port activity in a given region:

• AGX_PORT_ INDEXr —the ratio of the share of total agricultural exports departing the United 

States through the international ports located in region r and region r’s share of total U.S. 

employment; and

• M_PORT_ INDEXr —the ratio of the share of total imports entering the United States through the 

international ports located in region r and region r’s share of total U.S. employment.13

These variables capture both the direct employment effect of international port activity occurring in a region and the indirect employment effect of such activity on firms located in that region 

in sectors not directly involved in port activity. In our export simulation, we would expect the 

percentage change in total employment for a region with a strong specialization in export-oriented 

agricultural products to be higher if that region has a port through which agricultural exports 

depart the country. Proximity to a port reduces transport costs and gives the region a competitive advantage in export markets. This is the indirect employment effect that the agricultural 

13The port indexes are weighted in the denominator by the region’s share of total U.S. employment so that the variables 

measure the proportionate impact of port activity on a region’s total employment.
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export port index (AGX_PORT_ INDEXr) aims to capture. Conversely, we would expect that the 

percentage change in employment for a region with a strong specialization in the production of 

trade-exposed, non-agricultural goods (i.e., mining and non-food-and-beverage manufacturing) 

will be lower due to the presence of a port. This is the indirect employment effect that the import 

port index (M_PORT_ INDEXr) is intended to measure.

Ports of entry include seaports, border-crossing points for vehicular traffic and railroads, and 

airports. The vast majority of U.S. agricultural trade enters or departs the United States by land 

(truck or rail) or by sea. International ports engaged in the export of agricultural products are located 

in both metro and nonmetro regions. The five regions with the highest values for the agricultural 

export port index are:

• metro Louisiana (14.61), with the Ports of New Orleans and Gramercy, near where the Mississippi 

River enters the Gulf of Mexico;

• nonmetro Arizona (10.50), with the Port of Nogales on the U.S.-Mexico border;

• nonmetro North Dakota (9.88), with the Ports of Portal and Pembina on the U.S.-Canada border;

• metro Washington (6.15), with the Ports of Seattle, Tacoma, Kalama, and Longview on variousbodies of water leading to the Pacific Ocean and the Port of Blaine along the U.S.-Canada border; 

and

• nonmetro Montana (3.74), with the Port of Sweetgrass on the U.S.-Canada border.

Thirty of the CGE model’s 98 regions have no international ports where agricultural exports leave 

the United States.14

Ports of entry where the importation of products occurs are also located in both metro and nonmetro 

regions. The five regions with the highest values for the import port index are:

• metro Alaska (12.92), with the Port of Anchorage;

• nonmetro Arizona (11.57);

• nonmetro Louisiana (6.50), with the Port of Morgan City, near the Atchafalaya River and the 

Gulf of Mexico;

• nonmetro North Dakota (5.78); and

• metro Vermont (3.66), with the Port of Highgate Springs-Alburg on the U.S.-Canada border and 

the Port of Burlington on Lake Champlain.

Twenty-two of the CGE model’s regions lack an international port where imports enter the 

United States.

14The weighting of agricultural exports by employment in the agricultural export port index is crucial to capturing the 

relative importance of ports located in less populated regions, as the two regions with the highest values for this variable illustrate. In 2013, $28.1 billion in agricultural exports departed the United States through the international ports in 

metro Louisiana, where total employment equaled about 1.9 million. By contrast, $872 million in agricultural exports 

departed through the ports in nonmetro Arizona, where employment totaled about 82,000.
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Regression Results

Table 4 reports the parameter estimates and their standard errors from four alternative specifications 

of our regression model, all of which are estimated using ordinary least squares. To compare the 

relative importance of the explanatory variables, we report the standardized coefficients in addition 

to the regular coefficients. The standardized coefficients measure the estimated impact of one standard deviation in an explanatory variable in terms of standard deviations of the dependent variable.

Our first specification is a simple model in which the percentage change in total employment (PCT_

CH_EMPr) is regressed on the agri-food sector’s share of employment (AG_ SHAREr). The results 

of this regression reveal that the agri-food sector’s share not only has a strong, positive influence 

on the total employment effect, but that it is also a major determinant of that effect. The coefficient 

of determination (R2) measures how well the model fits the data—that is, how close the data are 

to the fitted regression line. The value of R2 for our first specification (0.62) indicates that about 62 

percent of the variance across regions in the total employment effect is explained by the variance in 

a region’s direct dependence on agri-food employment.

One way of further emphasizing this relationship is to look at the employment gains in regions 

where the agri-food sector accounts for more than 10 percent of employment. Figure 7 identifies the 

16 regions where this is the case, using a plot of the data points and fitted regression line from our 

first specification. The 16 regions with this characteristic are all nonmetro, ranging from nonmetro 

Iowa (11 percent) to nonmetro Washington State (26 percent). As a group, these regions account for 

just 5 percent of U.S. employment but 32 percent of the total employment gain for the United States 

resulting from the simulated increase in agricultural exports.

Table 4

Regression analysis of regional results from the export demand simulation

Model 1 Model 2 Model 3 Model 4

Dependent variable = 

Percentage change in 

employment Coefficient

Standardized 

coefficient Coefficient

Standardized 

coefficient Coefficient

Standardized 

coefficient Coefficient

Standardized 

coefficient

Agri-food sector’s share 

of employment

0.0190*** 0.786 0.0170*** 0.705 0.0172*** 0.711 0.0170*** 0.701

(0.00348) (0.00311) (0.00277) (0.00275)

Composition of 

agri-food industries

0.0712*** 0.202 0.0770*** 0.218 0.0760*** 0.216

(0.0254) (0.0185) (0.0179)

Non-food manufacturing and mining’s share 

of employment

-0.00770*** -0.387 -0.00777*** -0.391

(0.000805) (0.000820)

Port index: imports

-4.59e-05** -0.077

(2.05e-05)

Port index: agri-food 

exports

5.44e-05** 0.100

(2.32e-05)

Constant

-0.000438*** -0.00103*** 4.03e-06 3.63e-05

(0.000162) (0.000292) (0.000189) (0.000197)

Observations 98 98 98 98 98

R-squared 0.62 0.65 0.80 0.81 0.81

Robust standard errors in parentheses

*** p<0.01, ** p<0.05

Source: Authors’ regression results.
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Our second specification incorporates the extent to which a region’s sectoral composition of agrifood employment is weighted toward the sectors that expand most rapidly at the national level 

(AG_COMPr). As was hypothesized, this new variable exerts a positive influence on the percentage 

change in total employment. Inclusion of this variable marginally improves the regression model’s 

explanatory power, as indicated by the slightly higher value for R2 (0.65 versus 0.62). Still, the 

standardized coefficient for the variable (0.202) indicates that the sectoral composition of agri-food 

employment has an appreciable impact on total employment.

Our third specification incorporates the mining and non-food-and-beverage manufacturing sectors’ 

combined share of total employment (MM_SHAREr) as an additional regressor. This new variable has 

a strong, negative effect on the percentage change in total employment, and its inclusion in the regression model considerably improves the regression’s explanatory power, raising the value of R2 from 0.65 

to 0.80. Thus, the variance across regions in the three variables describing the sectoral composition of 

employment explains 80 percent of the variance in the percentage change in total employment.

Our fourth and final specification incorporates the two port indices, AGX_PORT_ INDEXr and 

M_PORT_ INDEXr, as additional regressors. Both of these variables have the hypothesized direction 

of influence on the percentage change in employment—positive in the case of AGX_PORT_ INDEXr

and negative in the case of M_PORT_ INDEXr—but again, they only slightly improve the model’s 

Figure 7

A region’s employment outcomes following the export demand increase depend strongly 

on its agri-food employment share

Note: Each of the 16 nonmetro regions where the agri-food sector accounts for more than 10 percent of total employment 

is labeled with the postal abbreviation of the State to which it belongs. These sixteen regions account for nearly one-third 

of the total employment gain.

Source: USDA, Economic Research Service.

xxxx

x

x

x

xx

x

x

x

x

x

x

x

x

x

xx

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

x

x

x

x

x

x

x

x

x

x

x

IA

OK

WI

GA

ND

MN

AL

OR

NE

AR

CA

TXFL

HI

ID

WA

y = −0.0004 + 0.019*x

R−squared = .62

0.000

0.002

0.004

0.006

0.008

0.00 0.05 0.10 0.15 0.20 0.25

Agri−food employment as a share of total employment

Percentage change in total employment
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explanatory power, raising the value of R2 to 0.81. Of the five explanatory variables, the two port 

indices have the smallest standardized coefficients in absolute value: 0.0999 for AGX_PORT_

INDEXr and -0.0771 for M_PORT_ INDEXr. The limited impact of the port indices may be linked 

to how we construct the regions in our CGE model. While the direct employment effects of international port activity are likely to be focused on the communities where ports are located, the regions 

in our model cover far more extensive geographic areas. Thus, even though the direct employment 

effect on a port community may be large, this effect becomes diffused at a regional level. The fact 

that the overall impact of the port indices is small suggests that both the direct and indirect employment effects of port activity resulting from our simulation are small at the regional level.

The absolute values of the standardized coefficients from the fourth specification indicate the relative importance of the five explanatory variables. In order of importance from highest to lowest, 

the variables are: (1) the agri-food sector’s share of employment, (2) the mining and non-food-andbeverage manufacturing sectors’ share of employment, (3) the index comparing the composition of 

agri-food employment at the regional and national levels, (4) the agricultural export port index, and 

(5) the import port index.
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Research Caveats

The USAGE model accounts for many of the important linkages between agricultural exports and 

agri-food employment; however, there are some aspects of the U.S. economy and the relationship 

between exports and employment that the model does not fully capture:

• First, the simulation focuses exclusively on the impact of an increase in foreign demand for a 

very specific set of products: U.S. agricultural exports. This narrow focus allows us to evaluate 

the economic effects of such an increase. In reality, such an increase would almost certainly not 

occur in isolation, and the precise effects of the increase on employment would be difficult to 

distinguish from other changes in the economy.

• Second, the USAGE model is not intended to provide a complete representation of the foreign 

exchange market, even though this market plays an important role in the model by linking the 

simulated increase in foreign demand to the U.S. economy. Within the model’s framework, the 

purchase of foreign exchange is an indispenable requirement for the exportation of goods, just as 

land, labor, and capital are necessary inputs for the production of those goods. In real life, foreign 

exchange serves the additional purposes of being stores of value and instruments of speculation—

functions that are not represented by the USAGE model.

• Third, while the extension of the national CGE results to the county level takes many important 

local economic attributes into account, this mapping cannot capture the full heterogeneity of 

U.S. counties. Aggregating to the State metro/nonmetro level mitigates this concern and should 

provide a fair approximation of the rural versus urban outcomes in each State.
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Conclusions

This report explores the linkages between U.S. agricultural exports and employment at the macroeconomic, sectoral, and regional levels. Using a CGE model, we evaluate the economic effects of a 

hypothetical scenario in which foreign demand for U.S. agricultural exports increases by 10 percent. 

Such a stimulus could arise from free-trade agreements that improve the access of U.S. agricultural products to foreign markets or from a successful U.S. promotion campaign for its agricultural 

products. As a result of this demand shift, agricultural exports expand by about $11.24 billion (7.8 

percent in 2013), and the U.S. economy gains an additional 41,493 jobs (full-time equivalents)—

above and beyond the approximately 1.1 million full-time, civilian jobs currently supported by U.S. 

agriculture. Thus, our model indicates that a $1-billion increase in the nominal value of agricultural 

exports generates about 3,700 jobs. This equates to an increase of about 4,600 jobs per $1 billion of 

additional exports measured in real (volume) terms, at 2015 prices.

This employment effect is smaller than the estimate obtained from ERS’s most recent research on 

this subject using I-O analysis. That research calculates an agricultural trade multiplier of about 

8,000 jobs per $1 billion in real agricultural exports, using data for 2015. We obtain smaller estimates of employment effects relative to the I-O analysis because we model the effects of an increase 

in foreign demand, which in our CGE model leads to a stronger U.S. dollar and a consequent reduction in net exports and employment in trade-exposed industries outside the agri-food sector, such as 

mining and non-food-and-beverage manufacturing. On the other hand, an employment-stimulating 

factor captured by our CGE model and not the I-O analysis is the effect of improved terms of trade 

on employment via expansion of gross national expenditure, as well as the employment effects 

resulting from the re-spending of additional export earnings.

The demand stimulus has a positive impact on employment in 17 of the 20 agri-food sectors in our 

analysis, with the largest proportionate increases in jobs occurring in tree nuts, field crops, and 

animal food processing. In percentage terms, the increases in agri-food employment in nonmetro 

and metro counties are comparable (roughly 1 percent each), but in absolute terms, the increase is 

much larger in metro counties than in nonmetro counties (about 29,000 jobs versus 18,000). This 

latter outcome is due to the fact that more agri-food jobs are located in metro counties than in 

nonmetro counties.

At the regional level, the simulation’s effects on total employment range from -0.073 percent (metro 

Michigan) to 0.815 percent (nonmetro Washington), compared with an employment effect of 0.029 

percent for the United States as a whole. Of the model’s 98 regions, 68 regions (29 metro and 39 

nonmetro) see positive effects for total employment, and 30 regions (22 metro and 8 nonmetro) see 

negative effects.

Further analysis of the regional results indicates that the agri-food sector’s share of employment 

is the main determinant of the percentage change in total employment in our export simulation. 

Indeed, a group of 16 nonmetro regions accounting for just 5 percent of U.S. employment obtain 32 

percent of the job gains resulting from our hypothetical increase in foreign demand for U.S. agricultural exports. These regions are distinguished by high shares of agri-food employment, with such 

jobs accounting for over 10 percent of regional employment.
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Agri-food jobs are present in both metro and nonmetro counties, but the agri-food sector accounts 

for a larger share of nonmetro employment than of metro employment (9 percent versus 2 percent). 

Thus, while growth in U.S. agricultural exports is important to people throughout the country, it 

takes on greater relative importance to the economic prosperity of nonmetro communities. The 

economic base of these communities, however, varies from one county to the next—with some 

engaged in crop or livestock production, some engaged in food processing, and some engaged in 

both—and their geographic location also differs—with some nonmetro communities located proximate to international ports and others relying on the Nation’s transportation system to get their 

product to the international market.
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Continued—

Appendix table 1 

The 20 agricultural production and food manufacturing sectors defined as agri-food and the 

industries from the USAGE Model that make up those sectors—continued

Agri-food sector USAGE Model industry

Dairy farm products Dairy farming

Poultry and egg production Poultry and egg farming

Livestock production Meat-oriented livestock farming

Miscellaneous livestock farming

Field crop production Cotton farming

Food grain farming

Feed grain farming

Grass seed farming

Tobacco farming

Miscellaneous crop farming

Oil-bearing crop farming

Fruit production Fruit farming

Tree nut production Tree nut farming

Vegetable production Vegetable farming

Sugar crop production Sugar crop farming

Greenhouse and nursery production Greenhouse and nursery production

Animal slaughtering and processing Meat processing

Sausage production

Poultry processing

Animal fats and oils

Dairy product manufacturing Creamery butter manufacturing

Cheese manufacturing

Dry, condensed, and evaporated dairy product manufacturing

Ice cream and frozen dessert manufacturing

Fluid milk manufacturing

Fruit and vegetable preserving and specialty 

food manufacturing

Specialty canning

Fruit and vegetable canning

Dried and dehydrated fruit manufacturing

Fruit and vegetable pickling

Frozen fruit, juice, and vegetable manufacturing

Frozen specialty food manufacturing

Grain and oilseed milling Flour milling

Breakfast cereal manufacturing

Prepared dough manufacturing

Rice milling

Wet corn milling

Malt manufacturing

Cottonseed milling

Soybean milling

Fats and oils refining and blending

Edible fats and oils
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Appendix table 1 

The 20 agricultural production and food manufacturing sectors defined as agri-food and the 

industries from the USAGE Model that make up those sectors—continued

Agri-food sector USAGE Model industry

Animal food manufacturing Dog and cat food manufacturing

Other animal food manufacturing

Bakeries and tortilla manufacturing Retail and commercial bakeries

Cookie and cracker manufacturing

Frozen cakes, pies, and other pastries manufacturing

Dry pasta, dough, and flour mixes manufacturing from purchased flour

Sugar manufacturing Sugar manufacturing

Chocolate and confectionery manufacturing Chocolate and confectionery manufacturing

Nonchocolate confectionery manufacturing

Nuts and snack food manufacturing Roasted nuts and peanut butter manufacturing

Other snack food manufacturing

Beverage manufacturing Breweries

Wineries

Soft drink manufacturing

Other food manufacturing Flavoring syrup and concentrate manufacturing

Coffee and tea manufacturing

All other food manufacturing

Source: USDA, Economic Research Service.
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Appendix table 2

Change in employment in response to a 10-percent increase in foreign demand for U.S. agricultural 

exports: by State and by nonmetro/metro counties—continued

State/

Nonmetro or metro

Agri-food Non-agri-food Total

Base Change Pcnt Chg Base Change Pcnt Chg Base Change Pcnt Chg

United States 4,871,797 47,218 0.97% 137,360,793 -5,725 -0.00% 142,290,600 41,493 0.03%

Nonmetro 1,829,807 18,229 1.00% 17,660,793 -690 -0.00% 19,490,600 17,539 0.09%

Metro 3,041,989 28,989 0.95% 119,700,000 -5,035 -0.00% 122,800,000 23,954 0.02%

Alaska 851 2 0.23% 358,662 22 0.01% 359,513 24 0.01%

Nonmetro 209 0 0.14% 129,208 9 0.01% 129,418 9 0.01%

Metro 642 2 0.27% 229,453 13 0.01% 230,095 15 0.01%

Alabama 106,963 567 0.53% 1,968,741 -155 -0.01% 2,075,704 413 0.02%

Nonmetro 61,427 324 0.53% 412,773 -59 -0.01% 474,200 265 0.06%

Metro 45,536 243 0.53% 1,555,968 -95 -0.01% 1,601,504 148 0.01%

Arkansas 120,558 812 0.67% 1,137,718 406 0.04% 1,258,276 1,218 0.10%

Nonmetro 66,638 518 0.78% 407,839 124 0.03% 474,478 641 0.14%

Metro 53,919 294 0.55% 729,879 283 0.04% 783,798 577 0.07%

Arizona 44,474 207 0.47% 2,254,956 -356 -0.02% 2,299,430 -149 -0.01%

Nonmetro 2,294 21 0.91% 80,177 15 0.02% 82,470 36 0.04%

Metro 42,181 186 0.44% 2,174,779 -371 -0.02% 2,216,960 -185 -0.01%

California 946,080 12,155 1.28% 15,627,078 5,501 0.04% 16,573,158 17,655 0.11%

Nonmetro 41,716 707 1.69% 235,158 146 0.06% 276,874 853 0.31%

Metro 904,364 11,448 1.27% 15,391,920 5,354 0.03% 16,296,284 16,802 0.10%

Colorado 63,959 384 0.60% 2,405,446 111 0.00% 2,469,405 495 0.02%

Nonmetro 28,343 233 0.82% 289,764 38 0.01% 318,107 271 0.09%

Metro 35,616 151 0.42% 2,115,682 73 0.00% 2,151,298 224 0.01%

Connecticut 35,526 604 1.70% 1,868,725 -1,071 -0.06% 1,904,252 -467 -0.02%

Nonmetro 2,292 44 1.92% 68,063 -45 -0.07% 70,355 -0 -0.00%

Metro 33,235 560 1.68% 1,800,663 -1,026 -0.06% 1,833,897 -466 -0.03%

District of Columbia 363 -0 -0.09% 716,684 76 0.01% 717,047 76 0.01%

Delaware 15,291 114 0.74% 431,590 -229 -0.05% 446,880 -115 -0.03%

Florida 207,441 2,154 1.04% 7,036,509 1,593 0.02% 7,243,950 3,748 0.05%

Nonmetro 38,389 379 0.99% 198,029 44 0.02% 236,418 423 0.18%

Metro 169,052 1,775 1.05% 6,838,480 1,549 0.02% 7,007,532 3,325 0.05%

Georgia 159,867 1,697 1.06% 3,892,073 1,204 0.03% 4,051,940 2,900 0.07%

Nonmetro 78,624 945 1.20% 596,177 81 0.01% 674,801 1,026 0.15%

Metro 81,243 751 0.92% 3,295,896 1,123 0.03% 3,377,139 1,874 0.06%

Hawaii 51,350 947 1.84% 655,285 310 0.05% 706,635 1,257 0.18%

Nonmetro 22,814 436 1.91% 105,659 46 0.04% 128,473 482 0.38%

Metro 28,536 510 1.79% 549,626 264 0.05% 578,162 775 0.13%

Iowa 98,896 803 0.81% 1,458,078 1,152 0.08% 1,556,974 1,955 0.13%

Nonmetro 71,167 594 0.83% 596,189 461 0.08% 667,355 1,055 0.16%

Metro 27,729 210 0.76% 861,890 691 0.08% 889,619 900 0.10%

Idaho 61,416 617 1.00% 569,686 490 0.09% 631,102 1,107 0.18%

Nonmetro 42,747 410 0.96% 194,651 171 0.09% 237,398 580 0.24%

Metro 18,669 208 1.11% 375,034 320 0.09% 393,704 527 0.13%

Continued—
32

The Potential Effects of Increased Demand for U.S. Agricultural Exports on Metro and Nonmetro Employment, ERR-227

Economic Research Service/USDA

Appendix table 2

Change in employment in response to a 10-percent increase in foreign demand for U.S. agricultural 

exports: by State and by nonmetro/metro counties—continued

State/

Nonmetro or metro

Agri-food Non-agri-food Total

Base Change Pcnt Chg Base Change Pcnt Chg Base Change Pcnt Chg

Illinois 136,767 648 0.47% 6,221,677 -1,139 -0.02% 6,358,444 -491 -0.01%

Nonmetro 52,758 392 0.74% 633,618 -138 -0.02% 686,376 254 0.04%

Metro 84,009 256 0.30% 5,588,059 -1,001 -0.02% 5,672,068 -745 -0.01%

Indiana 95,247 732 0.77% 2,972,812 -1,345 -0.05% 3,068,058 -612 -0.02%

Nonmetro 47,630 382 0.80% 637,373 -346 -0.05% 685,003 37 0.01%

Metro 47,617 350 0.73% 2,335,439 -999 -0.04% 2,383,055 -649 -0.03%

Kansas 64,190 732 1.14% 1,481,453 650 0.04% 1,545,643 1,381 0.09%

Nonmetro 49,603 618 1.25% 471,893 237 0.05% 521,496 855 0.16%

Metro 14,586 114 0.78% 1,009,561 413 0.04% 1,024,147 527 0.05%

Kentucky 81,410 483 0.59% 1,876,319 -375 -0.02% 1,957,729 109 0.01%

Nonmetro 44,628 250 0.56% 685,160 -110 -0.02% 729,787 140 0.02%

Metro 36,783 234 0.64% 1,191,159 -265 -0.02% 1,227,942 -31 -0.00%

Louisiana 55,106 501 0.91% 2,186,134 15 0.00% 2,241,240 516 0.02%

Nonmetro 23,119 342 1.48% 309,947 3 0.00% 333,066 345 0.10%

Metro 31,987 160 0.50% 1,876,187 12 0.00% 1,908,174 171 0.01%

Massachusetts 42,905 208 0.49% 3,571,026 -1,857 -0.05% 3,613,931 -1,649 -0.05%

Nonmetro 3,726 24 0.64% 43,916 -17 -0.04% 47,642 6 0.01%

Metro 39,180 184 0.47% 3,527,110 -1,839 -0.05% 3,566,289 -1,655 -0.05%

Maryland 40,841 344 0.84% 2,611,873 409 0.02% 2,652,714 752 0.03%

Nonmetro 7,679 73 0.96% 71,725 6 0.01% 79,404 80 0.10%

Metro 33,162 270 0.81% 2,540,148 402 0.02% 2,573,310 672 0.03%

Maine 20,288 97 0.48% 618,970 -88 -0.01% 639,259 9 0.00%

Nonmetro 13,700 57 0.42% 218,562 -22 -0.01% 232,263 35 0.02%

Metro 6,588 40 0.60% 400,408 -66 -0.02% 406,996 -26 -0.01%

Michigan 104,744 123 0.12% 4,728,967 -3,553 -0.08% 4,833,710 -3,430 -0.07%

Nonmetro 38,225 66 0.17% 681,191 -481 -0.07% 719,416 -415 -0.06%

Metro 66,519 57 0.09% 4,047,775 -3,072 -0.08% 4,114,294 -3,015 -0.07%

Minnesota 123,723 587 0.47% 2,682,343 -57 -0.00% 2,806,066 530 0.02%

Nonmetro 72,536 395 0.54% 488,312 43 0.01% 560,848 438 0.08%

Metro 51,187 192 0.38% 2,194,031 -100 -0.00% 2,245,218 92 0.00%

Missouri 114,465 663 0.58% 2,836,250 353 0.01% 2,950,714 1,016 0.03%

Nonmetro 52,708 368 0.70% 556,882 68 0.01% 609,590 436 0.07%

Metro 61,757 295 0.48% 2,279,368 285 0.01% 2,341,125 580 0.02%

Mississippi 78,192 733 0.94% 1,205,710 263 0.02% 1,283,903 996 0.08%

Nonmetro 62,319 609 0.98% 641,419 101 0.02% 703,737 710 0.10%

Metro 15,874 124 0.78% 564,292 162 0.03% 580,165 286 0.05%

Montana 18,759 278 1.48% 431,703 298 0.07% 450,462 575 0.13%

Nonmetro 15,477 249 1.61% 276,481 175 0.06% 291,959 424 0.15%

Metro 3,282 29 0.89% 155,222 123 0.08% 158,504 152 0.10%

Continued—
33

The Potential Effects of Increased Demand for U.S. Agricultural Exports on Metro and Nonmetro Employment, ERR-227

Economic Research Service/USDA

Appendix table 2

Change in employment in response to a 10-percent increase in foreign demand for U.S. agricultural 

exports: by State and by nonmetro/metro counties—continued

State/

Nonmetro or metro

Agri-food Non-agri-food Total

Base Change Pcnt Chg Base Change Pcnt Chg Base Change Pcnt Chg

North Carolina 126,052 683 0.54% 3,921,485 -1,389 -0.04% 4,047,537 -706 -0.02%

Nonmetro 56,317 353 0.63% 754,078 -252 -0.03% 810,395 100 0.01%

Metro 69,735 331 0.47% 3,167,407 -1,137 -0.04% 3,237,142 -806 -0.02%

North Dakota 29,530 343 1.16% 345,248 397 0.12% 374,778 740 0.20%

Nonmetro 23,052 290 1.26% 171,234 197 0.11% 194,286 486 0.25%

Metro 6,478 53 0.82% 174,014 201 0.12% 180,492 254 0.14%

Nebraska 69,206 720 1.04% 925,330 869 0.09% 994,536 1,589 0.16%

Nonmetro 46,589 542 1.16% 286,906 287 0.10% 333,494 829 0.25%

Metro 22,617 178 0.79% 638,425 582 0.09% 661,041 760 0.11%

New Hampshire 10,334 70 0.67% 629,699 -357 -0.06% 640,033 -288 -0.04%

Nonmetro 5,766 50 0.86% 232,571 -128 -0.06% 238,337 -78 -0.03%

Metro 4,568 20 0.43% 397,128 -229 -0.06% 401,696 -209 -0.05%

New Jersey 55,733 220 0.39% 4,131,727 -1,415 -0.03% 4,187,460 -1,195 -0.03%

New Mexico 34,300 296 0.86% 811,468 55 0.01% 845,768 351 0.04%

Nonmetro 23,934 233 0.97% 264,349 29 0.01% 288,283 262 0.09%

Metro 10,366 63 0.61% 547,119 26 0.00% 557,485 89 0.02%

Nevada 7,587 38 0.51% 907,174 -97 -0.01% 914,761 -58 -0.01%

Nonmetro 3,759 27 0.71% 95,794 -72 -0.07% 99,554 -45 -0.05%

Metro 3,828 12 0.31% 811,380 -25 -0.00% 815,207 -13 -0.00%

New York 115,456 571 0.49% 9,213,843 -2,049 -0.02% 9,329,300 -1,478 -0.02%

Nonmetro 34,785 202 0.58% 452,124 -197 -0.04% 486,909 5 0.00%

Metro 80,671 369 0.46% 8,761,720 -1,853 -0.02% 8,842,391 -1,484 -0.02%

Ohio 131,796 709 0.54% 5,683,877 -3,266 -0.06% 5,815,673 -2,557 -0.04%

Nonmetro 65,695 410 0.62% 1,003,597 -711 -0.07% 1,069,293 -301 -0.03%

Metro 66,101 298 0.45% 4,680,279 -2,555 -0.05% 4,746,380 -2,257 -0.05%

Oklahoma 87,192 759 0.87% 1,695,842 -38 -0.00% 1,783,034 721 0.04%

Nonmetro 62,422 620 0.99% 499,824 45 0.01% 562,246 664 0.12%

Metro 24,770 140 0.56% 1,196,018 -82 -0.01% 1,220,788 57 0.00%

Oregon 100,575 1,950 1.94% 1,690,892 1,051 0.06% 1,791,467 3,000 0.17%

Nonmetro 36,633 713 1.95% 231,310 172 0.07% 267,943 885 0.33%

Metro 63,942 1,237 1.93% 1,459,582 879 0.06% 1,523,524 2,116 0.14%

Pennsylvania 150,145 519 0.35% 5,753,924 -2,523 -0.04% 5,904,069 -2,004 -0.03%

Nonmetro 27,114 122 0.45% 532,472 -275 -0.05% 559,586 -153 -0.03%

Metro 123,031 397 0.32% 5,221,451 -2,248 -0.04% 5,344,483 -1,851 -0.03%

Rhode Island 4,129 1 0.03% 484,657 -253 -0.05% 488,786 -252 -0.05%

South Carolina 52,050 851 1.63% 1,883,714 -396 -0.02% 1,935,763 455 0.02%

Nonmetro 18,619 317 1.70% 294,289 -156 -0.05% 312,908 162 0.05%

Metro 33,431 533 1.60% 1,589,424 -240 -0.02% 1,622,855 293 0.02%

South Dakota 24,120 351 1.45% 371,367 287 0.08% 395,487 637 0.16%

Nonmetro 19,547 307 1.57% 187,899 139 0.07% 207,446 446 0.21%

Metro 4,573 44 0.96% 183,468 148 0.08% 188,041 192 0.10%

Continued—
34

The Potential Effects of Increased Demand for U.S. Agricultural Exports on Metro and Nonmetro Employment, ERR-227

Economic Research Service/USDA

Appendix table 2

Change in employment in response to a 10-percent increase in foreign demand for U.S. agricultural 

exports: by State and by nonmetro/metro counties—continued

State/

Nonmetro or metro

Agri-food Non-agri-food Total

Base Change Pcnt Chg Base Change Pcnt Chg Base Change Pcnt Chg

Tennessee 108,562 530 0.49% 2,728,073 -573 -0.02% 2,836,636 -43 -0.00%

Nonmetro 53,249 307 0.58% 520,415 -196 -0.04% 573,664 111 0.02%

Metro 55,313 223 0.40% 2,207,659 -377 -0.02% 2,262,972 -154 -0.01%

Texas 326,915 3,481 1.06% 10,455,671 -1,220 -0.01% 10,782,585 2,261 0.02%

Nonmetro 190,008 2,308 1.21% 1,020,690 -17 -0.00% 1,210,699 2,291 0.19%

Metro 136,906 1,173 0.86% 9,434,981 -1,203 -0.01% 9,571,887 -30 -0.00%

Utah 28,369 151 0.53% 1,118,271 -177 -0.02% 1,146,640 -26 -0.00%

Nonmetro 10,763 76 0.71% 123,399 -21 -0.02% 134,162 55 0.04%

Metro 17,606 74 0.42% 994,873 -156 -0.02% 1,012,479 -82 -0.01%

Virginia 79,863 1,007 1.26% 3,824,505 766 0.02% 3,904,368 1,773 0.05%

Nonmetro 23,716 311 1.31% 454,470 37 0.01% 478,186 347 0.07%

Metro 56,147 696 1.24% 3,370,035 729 0.02% 3,426,182 1,426 0.04%

Vermont 13,939 62 0.45% 322,470 -130 -0.04% 336,409 -68 -0.02%

Nonmetro 9,569 44 0.46% 202,704 -73 -0.04% 212,273 -29 -0.01%

Metro 4,370 19 0.43% 119,766 -58 -0.05% 124,136 -39 -0.03%

Washington 247,998 6,093 2.46% 2,976,469 3,238 0.11% 3,224,467 9,330 0.29%

Nonmetro 79,817 2,230 2.79% 222,913 236 0.11% 302,730 2,466 0.81%

Metro 168,181 3,863 2.30% 2,753,556 3,001 0.11% 2,921,737 6,864 0.23%

Wisconsin 144,937 336 0.23% 2,679,232 -950 -0.04% 2,824,169 -614 -0.02%

Nonmetro 74,562 153 0.20% 594,016 -161 -0.03% 668,578 -9 -0.00%

Metro 70,375 183 0.26% 2,085,216 -788 -0.04% 2,155,591 -606 -0.03%

West Virginia 24,632 230 0.93% 776,627 -208 -0.03% 801,259 22 0.00%

Nonmetro 15,157 131 0.86% 294,518 -130 -0.04% 309,675 1 0.00%

Metro 9,475 99 1.04% 482,109 -78 -0.02% 491,584 21 0.00%

Wyoming 8,703 58 0.66% 270,776 25 0.01% 279,479 82 0.03%

Nonmetro 7,966 51 0.64% 191,055 9 0.00% 199,021 60 0.03%

Metro 737 7 0.95% 79,721 16 0.02% 80,458 23 0.03%

Source: Simulation results using the USAGE Model to evaluate the economic effects of a 10-percent increase in foreign demand for 

U.S. agricultural exports.