Economic

Research

Service

Economic

Research 

Report 

Number 198

November 2015

United States Department of Agriculture

Agriculture in the Transatlantic 

Trade and Investment Partnership: 

Tariffs, Tariff-Rate Quotas, 

and Non-Tariff Measures

Jayson Beckman, Shawn Arita, Lorraine Mitchell, 

and Mary Burfisher
Economic Research Service 

www.ers.usda.gov

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Beckman, Jayson, Shawn Arita, Lorraine Mitchell, and Mary Burfisher. Agriculture in the 

Transatlantic Trade and Investment Partnership: Tariffs, Tariff-Rate Quotas, and Non-Tariff 

Measures, ERR-198, U.S. Department of Agriculture, Economic Research Service, 

November 2015.

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United States Department of Agriculture

Economic 

Research 

Service

Economic

Research

Report

Number 198

November 2015

Abstract

The proposed Transatlantic Trade and Investment Partnership (T-TIP) between the 

United States and the European Union (EU) aims to address several important barriers 

facing agricultural trade, including tariffs, tariff-rate quotas (TRQs), and non-tariff 

measures (NTMs). Estimated ad valorem tariff equivalents of tariffs/TRQs and NTMs 

currently in place are as high as 120 percent, significantly limiting trade between the 

two regions. This study uses model simulations to assess the effects of T-TIP on agriculture under three broad scenarios: complete removal of tariffs and TRQs; elimination of select NTMs along with tariffs and TRQs; and a lowering of the willingness 

of consumers to purchase imported goods previously limited by NTMs. Results of all 

scenarios suggest an increase in U.S.-EU agricultural trade from T-TIP, benefiting both 

regions. While the United States realizes a relative increase in agricultural exports, the 

EU benefits from lower import prices and larger macroeconomic gains than the United 

States. The estimated annual increase in U.S.-EU agricultural trade ranges from $6.3 

billion to $11.6 billion when compared with the 2011 base year.

Keywords: Agricultural trade, trade agreement, Transatlantic Trade and Investment 

Partnership, T-TIP, computable general equilibrium (CGE) model, non-tariff measures 

(NTMs), gravity model, United States, European Union, tariff rate quotas (TRQs)

Acknowledgments

The authors thank Joseph Cooper, John Dyck, and Andrew Muhammad (USDA, 

Economic Research Service (ERS)); Alan Matthews (Trinity College, Ireland); Nelson 

Villoria (Purdue University); Sharon Sydow (USDA, Office of the Chief Economist); 

Jason Hafemeister and Daniel Whitley (USDA, Foreign Agricultural Service); and three 

anonymous reviewers for their peer reviews of this report. John Wainio and Steven 

Zahniser of ERS provided valuable data for this report. The authors also thank John 

Weber and Cynthia A. Ray of ERS for editorial and design services.

Jayson Beckman, Shawn Arita, Lorraine Mitchell, 

and Mary Burfisher

Agriculture in the Transatlantic Trade 

and Investment Partnership: 

Tariffs, Tariff-Rate Quotas, 

and Non-Tariff Measures
ii

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Economic Research Service/USDA

Contents

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

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

Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

U.S. and EU Agricultural Tariffs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Non-Tariff Measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

CGE Model Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Market Access – Removal of all Tariffs and TRQs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Market Access With NTM Removal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Market Access With NTM Removal, Lingering Adverse Demand Effects. . . . . . . . . . . . . . . . . 23

Comparison Across Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28

Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Appendix 1—GTAP Data for T-TIP CGE Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Appendix 2—The CGE Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Appendix 3—Allocating NTM Costs for the CGE Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Appendix 4—NTM Removal and Demand Sensitivities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
United States Department of Agriculture

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rural America. www.ers.usda.gov

Economic

Research

Service

Economic

Research 

Report 

Number 198

November 2015

United States Department of Agriculture

Agriculture in the Transatlantic 

Trade and Investment Partnership: 

Tariffs, Tariff-Rate Quotas, 

and Non-Tariff Measures

Jayson Beckman, Shawn Arita, Lorraine Mitchell, 

and Mary Burfisher

November 2015

Find the full report 

at www.ers.usda.

gov/publications/erreconomic-researchreport/err198

Jayson Beckman, Shawn Arita, Lorraine Mitchell, and Mary Burfisher

Agriculture in the Transatlantic Trade and 

Investment Partnership: Tariffs, Tariff-Rate 

Quotas, and Non-Tariff Measures

What Is the Issue?

The Transatlantic Trade and Investment Partnership (T-TIP) is being negotiated between the 

United States and the European Union (EU). The two regions accounted for almost half of 

global gross domestic product (GDP) in 2013 and $35 billion in bilateral agricultural trade. 

While overall tariffs in the United States and EU are generally low, they are still relatively 

high for food and agricultural goods, most often in the form of tariff-rate quotas (TRQs). 

Additionally, U.S.-EU trade is restricted by other significant barriers, such as non-tariff 

measures (NTMs) that are especially prevalent for many agricultural commodities. NTMs are 

usually not considered in trade policy analysis because they are not easily quantified, leading to 

several data, methodological, and conceptual challenges. These issues have been prominent in 

the T-TIP negotiations, and an analytical approach to help understand their impacts on food and 

agricultural trade may benefit all stakeholders. 

What Did the Study Find?

A T-TIP agreement could address several barriers facing agricultural trade, including tariffs, 

TRQs, and NTMs. This report considers potential impacts of an agreement under three broad 

scenarios:

Scenario one (removal of tariffs and TRQs). In the first scenario, U.S. agricultural exports to the 

EU increase by $5.5 billion from base year (2011) levels, while EU agricultural exports to the 

United States increase by $0.8 billion. Overall, U.S. agricultural exports increase by 2 percent and 

agricultural imports by 1 percent. EU agricultural exports decrease by 0.25 percent, and agricultural imports rise by 0.5 percent. Among major U.S. agricultural export commodities, beef and 

dairy exports to the EU increase the most in percentage terms. The EU exports more vegetable 

oil and cheese to the United States and also produces more of these commodities, although the 

percentage increases in production are modest. The EU imposes higher tariffs on imports than 

does the United States, which accounts for the larger U.S. export gains in the scenario.

Scenario two (removal of select NTMs, in addition to tariffs and TRQs). NTMs commonly 

imposed in agricultural trade comprise sanitary and phytosanitary measures that help to 

ensure food safety but also create technical barriers to trade that require imports to have 

specific product characteristics. In the second scenario, the additional removal of select 

NTMs (e.g., meats, field crops, and fruits and vegetables) results in an increase in U.S. 

exports to the EU by an additional $4.1 billion over gains in the first scenario. For the EU, 

A report summary from the Economic Research Service

Summary
the removal of NTMs generates an additional gain of $1.2 billion in exports to the United States. U.S pork 

exports to the EU increase by $2.4 billion, and EU exports of fruits and vegetables to the United States 

increase by $495 million and $613 million, respectively. U.S. exports of poultry to the EU increase by a high 

percentage but the level change is only $18 million due to small base trade. Increases in bilateral U.S.-EU 

exports of individual commodities do not all lead to production increases, as commodities with modest 

increases in exports may lose resources to commodities with large increases. Overall, agricultural imports 

and exports each increase for the United States by about double the percentage in scenario one, while EU 

agricultural imports increase by 1 percent and agricultural exports decline.

Scenario three (effects of removal of NTMs on consumer demand). The removal of select NTMs could lead 

to consumers preferring domestically produced products versus the importer equivalent. Thus, in the third 

scenario, export gains are smaller for both the United States and the EU. Potentially, these demand-side effects 

could erase any gains from the removal of specific NTMs.

Overall, gains in bilateral and net exports due to T-TIP lead to production increases in many U.S. agricultural 

commodities. Some U.S. agricultural commodities have a decrease in production due to increased competition 

for resources. The increase in agricultural exports also leads to increases in almost all U.S. agricultural prices. 

For the EU, the increase in imports results in a decline in agricultural prices. The GDP of both the United 

States and the EU increases as a result of T-TIP, though the rate of increase is higher for the EU, due largely to 

export gains in nonagricultural products and lower prices on imports. GDP changes are uniformly modest, onethird of a percent or less.

How Was the Study Conducted?

The study uses the Global Trade Analysis Project’s (GTAP) static computable general equilibrium (CGE) model 

with the GTAP v.9 2011 database (the latest GTAP data available). To allow for more precise analysis of the 

agricultural sector, this study disaggregates agriculture into 38 commodities, including 24 unique agricultural 

and biofuel commodities beyond the standard GTAP database. In addition, the model uses the detailed land-use 

module (GTAP-AEZ) that captures heterogeneous land quality and allows for a more realistic representation of 

agriculture production. Estimates of the NTM tariff-equivalent measures used for the analysis were taken from 

a complementary 2015 ERS report Estimating the Effects of Selected Sanitary and Phytosanitary Measures 

and Technical Barriers to Trade on U.S.-E.U. Agricultural Trade. To account for demand-side effects on the 

analysis, country-specific (Armington) parameters were modified to reflect potential changes in consumer preferences due to removal of NTMs.

www.ers.usda.gov
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Agriculture in the Transatlantic Trade and 

Investment Partnership: Tariffs, Tariff-Rate 

Quotas, and Non-Tariff Measures

Introduction

The Transatlantic Trade and Investment Partnership (T-TIP) seeks to eliminate trade barriers 

between the United States and the European Union (EU).1 The United States and the EU have 

a history of cooperation in international trade policy, including the Transatlantic Economic 

Partnership of 1998 that attempted to address many of the current issues facing T-TIP negotiators (McKinney, 2014). T-TIP is drawing much attention, as the two member regions are major 

economies and markets for all goods, including agriculture. Together, the United States and the EU 

accounted for almost half of global gross domestic product (GDP) in 2013. 

Increased market access through elimination of tariffs and tariff-rate quotas (TRQ) is a main goal 

of T-TIP. Negotiations also center on reconciling differences in regulatory issues and intellectual 

property rights, strengthening rules-based investment, and promoting the global competitiveness of 

small- and medium-sized enterprises (USTR, 2015). For agriculture, non-tariff measures (NTMs) 

such as sanitary and phytosanitary (SPS) and technical barriers to trade (TBT) feature prominently 

in the negotiations.2 While SPS and TBT measures are intended to ensure that exporters meet 

food safety, animal or plant health, and technical regulations, they can also restrict trade by adding 

compliance, inspection, and operational costs. In some instances, the costs are prohibitive and 

prevent trade altogether. Exporters have raised concerns over SPS/TBT measures that have requirements disproportionate to the actual levels of risk, that excessively impede trade, and/or that are 

viewed as disguised barriers to trade (Anderson et al., 2012).

Recent studies examining the economic gains from T-TIP suggest that reducing NTMs could bring 

about benefits equal to or greater than those associated with tariff removal (table 1). For example, 

CEPR (2013) concludes that the reduction of NTMs may account for as much as 80 percent of the 

potential economic gains from T-TIP. The impacts of NTM reduction across products, however, can 

vary. Unfortunately, these T-TIP-based studies have generally aggregated agricultural commodities 

into one overall sector (sometimes adding forestry and fisheries). Thus, they are unable to provide 

estimates of the impacts to specific commodities. 

This study uses an agriculture-focused, multicountry, computable general equilibrium (CGE) model 

to examine the effect of market access and NTM reforms from T-TIP. A particular focus is placed on 

the role of NTMs, which are explicitly modeled across specific agricultural commodities. Moreover, 

the study considers the possibility that the removal of certain NTMs may not ensure that consumers 

will be willing to buy more cheaper imported goods relative to their domestically produced equiva1The first round of negotiations was in July 2013 in Washington, DC. As of November 2015, there have been 11 negotiation rounds.

2 NTMs are defined as policy measures other than tariffs that can potentially have a negative economic effect on 

international trade (UNCTAD, 2010).
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lents. Three hypothetical scenarios are simulated to estimate the range of possible outcomes from 

T-TIP: a market-access scenario, where tariffs and TRQs are removed on all agricultural and nonagricultural products; a more expanded market-access scenario, where selected NTMs are removed in 

addition to tariffs and TRQs; and a scenario that accounts for adjustments in consumer preferences 

for products with NTMs. The other North American Free Trade Agreement (NAFTA) countries 

(Canada and Mexico) and other major agricultural markets (Brazil, China, and India) are also 

included in the model. 

Table 1

CGE-based quantitative analyses of T-TIP

Authors

Type of CGE 

model

Food and agriculture coverage NTM estimation T-TIP scenariosRange of 

impacts 

General 

results

This study Static GTAP 

model, with 

GTAP-E and 

AEZs, V9 

(2011) database

Comprises 38 of 

47 sectors

Gravity model 

for selected 

NTMs

(1) Tariffs and 

TRQs removed 

(2) Select NTMs 

also removed 

(3) Demand 

sensitivity

% Δ in exports 

value: U.S. 

[0.43,0.52], EU 

[-.09,-.02]

GDP gains are 

larger for tariff 

removal scenario, compared 

with select NTM 

removal

ECORYS 

(2009)

Limited 

information 

provided, beyond a 10-year 

analysis used

Food and beverages sector onlyGravity model 

using business 

survey and 

literature

Two main 

scenarios: 

- Limited (25% 

NTMs eliminated); 

- Ambitious (50% 

NTMs eliminated)

% Δ in exports 

value: U.S. 

[1.74,3.97], EU 

[0.74,2.07]

GDP gains from 

ambitious scenario more than 

double those of 

limited 

CEPR (2013) Imperfect competition GTAP 

model, V8 

(2007) projected to 2027

Agriculture, 

forestry, fisheries aggregated, 

processed foods

NTM estimates 

from ECORYS 

(2009)

Two comprehensive scenarios: 

-10% NTMs, 98% 

tariffs eliminated 

- 25% NTMs, 

100% tariffs eliminated

% Δ in GDP: 

U.S. [0.04,0.39], 

EU [0.10,0.48]

NTMs account 

for as much as 

80% of gains 

from TTIP

EP (2014) MIRAGE 

model, with 

GTAP data

Comprises 17 of 

31 sectors

Gravity model 

for notified 

NTMs

Five scenarios 

with tariff removal 

and various NTM 

cuts and harmonization spillovers

% Δ in ag. 

exports: 

U.S. [30,120] 

EU [19,63]

Gains to 

agriculture are 

largest; greatest 

impacts: beef, 

sugar, dairy

Erixon and 

Bauer (2010)

GTAP model, 

V7 (2004) 

extrapolated to 

2010, projected 

to 2015

Comprises 8 of 

32 sectors

None Tariff removal 

with various trade 

facilitation and 

productivity assumptions

% Δ in GDP: 

U.S. [0.15,1.33] 

EU [0.01,0.47]

Potential gains 

are larger than 

most FTAs, 

including a 

potential Doha 

agreement

Fontage et al. 

(2013)

MIRAGE model, projected to 

2025

Comprises 6 of 

34 sectors

Gravity model 

for notified 

NTMs

(1) Tariff removal 

(2) 25% NTMs 

(3) Harmonization 

spillovers

(4) ECORYS 

NTMs

% Δ in exports: 

U.S. [2.1, 10.4] 

EU [0.4,3.4]

TTIP will boost 

bilateral trade 

with no trade 

diversion; similar impacts on 

GDP

Egger et al. 

(2014)

Monopolistic 

competition

Comprises 2 of 

22 sectors

Gravity model (1) Tariff removal 

(2) NTMs-goods 

(3) NTMs-services

% Δ in real 

income: U.S. 

[0.10,0.59], 

EU [0.10,1.14]

GDP gains from 

NTM removal 

greater than 

tariff removal

CGE = computable general equilibrium. T-TIP = Transatlantic Trade and Investment Partnership. NTM = non-tariff measure. 

GDP = gross domestic product. FTA = free-trade agreement. GTAP = Global Trade Analysis Project.

Source: USDA, Economic Research Service.
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Background

The United States and the EU are major producers of most agricultural goods and account for a 

significant share of global agricultural trade. While overall trade between the United States and the 

EU has increased over time, the relative importance of agricultural trade between these markets 

vis-a-vis other trading partners has decreased. This decline is due in part to the relatively high trade 

barriers facing U.S. agricultural exports to the EU, the proliferation of regional trade agreements 

(e.g., NAFTA) in which the United States and the EU partner with other countries, and the emergence of new countries as major players in global agricultural trade (e.g., Brazil).

U.S./EU Agricultural Production

Based on data from the Global Trade Analysis Project (GTAP), the U.S.-EU share of global agricultural production (including processed food and biofuels) was 31 percent in 2011. GTAP data 

also indicate that the United States had almost $1.3 trillion in agricultural production in 2011 and 

the EU had $2.25 trillion (see appendix 1 for agricultural sector breakdown). Among individual 

commodities, beverages and tobacco accounted for the largest share of U.S. agricultural production (12 percent of total value) in 2011, and beef accounted for the largest share of commodities 

other than processed food share (table 2).3 Similarly, beverages and tobacco accounted for the 

largest share of EU agricultural production (18 percent of total value) in 2011. For the EU, other 

dairy products was the largest commodity other than processed foods, based on value of production. For the other countries examined, processed food products also accounted for large shares of 

their total value of agricultural production. 

U.S./EU Trade 

GTAP data indicate that the United States had an overall trade deficit of $95 billion with the EU in 

2011. In the same period, the EU was the second-largest source (following China) of U.S. imports 

and the second-largest destination (following Canada) for U.S. exports. For EU imports, China was 

also the largest source (the United States was third). Overall, the EU accounted for 16.9 percent of 

U.S. trade (imports plus exports) in 2011, and the United States accounted for 14.2 percent of EU 

trade (EC, 2014). 

Total U.S.-EU agricultural trade was $35 billion in 2011 (table 3); U.S. agricultural exports to the EU 

totaled $13.85 billion, while EU exports to the United States totaled $21.19 billion.4 The importance 

of agricultural trade between the two countries has changed over time. For example, in 1992, the 

United States accounted for 21 percent of EU agricultural imports; by 2012, the share had decreased 

to 8 percent (EC, 2014). This change is partly due to trade barriers in place, the emergence of trade 

from other countries (e.g., Brazil), enlargement of the EU, and an increase in free-trade agreements 

(FTA) between the United States or the EU and other countries (e.g., EU-Mediterranean countries). 

Note that the United States has had a trade deficit in agricultural products with the EU since 2000, 

with the gap reaching $12 billion in 2012. The share of total EU agricultural exports that went to the 

United States was 13 percent in 2012, an all-time low over the last 20 years (EP, 2014). 

Among all products, beverages and tobacco had the highest value for U.S. agricultural exports to 

the EU in 2011 (table 3). The next highest products by value were other foods, nuts, and soybeans. 

3See appendix 1 for those products typically aggregated as processed food in the standard GTAP database. 

4Agriculture is defined as food, fiber, and bulk agricultural products, excluding fish and natural resources. 
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Table 2

GTAP agricultural production values, 2011

Sector U.S. EU All other countries

Million dollars

Paddy rice 2,881.61 1,446.79 242,983.35 

Wheat 18,231.86 43,766.98 174,913.54 

Coarse grains 63,140.31 38,833.09 213,422.34 

Fruits 25,004.78 38,051.03 359,284.79 

Vegetables 35,173.40 48,140.02 576,342.97 

Nuts 12,797.74 6,759.63 66,163.07 

Soybeans 37,541.59 3,675.95 114,850.04 

Rapeseed 648.89 15,985.56 23,334.55 

Other oilseeds 4,651.39 6,815.01 65,197.95 

Sugarcane/beet 2,229.85 5,599.84 73,647.92 

Other crops 30,444.93 114,781.33 367,090.75 

Bovine 45,926.09 46,882.94 238,193.06 

Hogs 18,189.68 50,865.06 327,820.73 

Poultry and eggs 30,394.17 29,937.75 324,220.26 

Other animals 2,051.21 4,748.40 29,620.05 

Raw milk 38,351.55 71,897.76 283,348.78 

Beef 114,878.21 69,832.39 329,223.07 

Pork 43,401.39 111,201.97 166,936.95 

Poultry meat 48,610.04 61,820.50 148,271.02 

Other meats 5,349.60 10,179.17 24,255.61 

Vegetable oil 21,652.29 49,354.48 364,881.58 

Whey 3,200.76 10,172.69 37,514.83 

Powdered milk 5,270.47 10,519.47 115,800.89 

Butter 5,625.71 21,300.44 39,614.11 

Cheese 25,941.35 88,204.20 32,321.49 

Other dairy products 62,687.41 201,507.56 193,209.74 

Processed sugar 14,617.30 26,296.48 163,159.98 

Processed rice 3,775.93 3,473.70 273,273.75 

Prepared f_v 55,546.07 95,160.91 311,795.89 

Cereal preparations 113,772.12 194,352.02 470,335.74 

Sugar preparations 15,196.13 32,616.56 152,475.31 

Processed feed 22,206.94 21,698.95 117,578.17 

Other foods 135,001.78 301,494.88 703,140.17 

Beverages and tobacco 157,613.06 404,555.13 726,238.06 

Ethanol1 41,907.27 67.94 3,373.23 

Ethanol2 1.80 1,413.22 17,831.30 

DDGS 9,481.31 500.79 2,410.47 

Biodiesel 4,040.26 12,959.83 10,799.73 

Total ag. 1,277,436.25 2,256,870.42 7,884,875.24

GTAP = Global Trade Analysis Project. DDGS = distillers’ dried grains with solubles. f_v = fruits and 

vegetables.

Source: USDA, Economic Research Service using data from GTAP v.9. 
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Table 3

Agricultural trade values, 2011

Bilateral trade Total exports Total imports

Sector U.S. to EU EU to U.S. U.S. EU U.S. EU

Million dollars

Paddy rice 22.06 0.90 834.04 293.92 86.27 885.81

Wheat 490.62 22.20 13,610.00 14,708.52 692.20 9,866.35

Coarse grains 519.20 23.43 16,363.49 9,827.02 1,022.03 10,098.72

Fruits 316.80 109.99 4,520.72 19,547.32 8,272.23 27,076.34

Vegetables 354.64 188.95 3,400.38 15,475.43 7,368.95 18,817.80

Nuts 1,733.85 27.20 4,709.38 3,061.57 1,984.34 6,312.93

Soybeans 1,261.50 0.05 20,889.57 903.05 568.91 6,323.68

Rapeseed 0.00 0.39 144.47 3,855.43 113.73 5,532.26

Other oilseeds 235.54 9.71 1,247.39 2,404.20 386.40 3,330.84

Sugarcane/beet 0.08 0.15 0.37 105.73 27.09 118.61

Other crops 856.77 755.18 13,316.82 21,633.70 12,952.93 38,908.58

Bovine 137.94 217.62 882.60 5,728.52 1,823.19 4,293.78

Hogs 249.28 95.05 2,561.55 6,223.94 730.93 6,702.70

Poultry and eggs 0.04 0.16 818.95 5,189.89 564.51 4,181.50

Other animals 1.61 32.21 833.38 2,963.24 776.98 3,245.39

Raw milk 0.07 2.67 0.26 63.28 45.33 152.81

Beef 217.18 44.67 6,753.54 18,688.85 4,101.81 19,477.42

Pork 177.93 468.18 6,589.04 14,233.45 1,456.04 10,034.30

Poultry meat 0.82 0.00 4,152.95 22,277.33 623.42 22,234.19

Other meats 44.16 15.79 1,311.39 7,588.15 390.82 7,665.94

Vegetable oil 389.18 877.79 7,132.84 20,239.27 7,341.98 28,835.96

Whey 7.82 1.61 1,092.73 2,493.31 79.46 1,197.32

Powdered milk 2.40 6.00 1,565.25 1,916.72 93.14 900.23

Butter 73.17 34.12 371.04 5,193.16 80.35 4,497.43

Cheese 7.65 1,068.37 511.01 20,508.14 1,186.21 16,841.68

Other dairy products 25.84 98.35 968.53 21,199.23 957.56 17,281.30

Processed sugar 33.00 18.29 301.97 6,104.51 2,714.64 7,805.47

Processed rice 54.16 23.07 1,490.90 1,218.35 682.19 1,853.20

Prepared f_v 612.26 565.98 5,821.14 25,101.55 8,142.90 29,628.26

Cereal preparations 1,004.53 2,112.44 6,060.85 31,557.11 10,659.83 24,360.70

Sugar preparations 32.74 81.06 1,859.60 4,211.97 733.86 2,720.65

Processed feed 145.37 128.11 1,734.12 8,598.88 832.12 7,531.70

Other foods 1,884.18 2,047.63 16,135.03 90,878.12 24,794.04 98,062.90

Beverages and tobacco 1,989.50 12,007.77 10,850.72 84,236.35 20,724.63 56,589.88

Ethanol1 0.00 0.00 0.00 36.94 412.75 163.59

Ethanol2 762.58 0.00 3,163.39 37.23 58.95 763.52

DDGS 129.40 0.00 1,820.37 0.00 89.73 129.40

Biodiesel 78.86 100.39 158.75 510.18 123.37 948.76

Total ag. 13,852.74 21,185.48 163,978.54 498,813.58 123,695.84 505,371.91

DDGS = distillers' dried grains with solubles. f_v = fruits and vegetables. 

Source: USDA, Economic Research Service using data from GTAP v.9. 
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Beverages and tobacco also had the highest value for EU agricultural exports to the United States in 

2011. The EU exported a large amount of other foods, cereal preparations, and cheese to the United 

States ($1 billion total in 2011). 
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U.S. and EU Agricultural Tariffs

Tariffs between the United States and the EU are relatively low by global standards.5 The simple 

average applied tariff for all goods is estimated at 3.5 percent for EU exports to the United States 

and 5.5 percent for U.S. exports to the EU (Akhtar and Jones, 2014). In addition, 37 percent of 

all tariff lines in the United States and 25 percent in the EU are already zero (Seshadri, 2014). 

Agricultural commodities, however, tend to have larger tariffs than nonagricultural products 

(Akhtar and Jones, 2014). That is, for agricultural goods specifically, the simple average tariffs are 

4.7 percent for EU exports to the United States and 13.7 percent for U.S. exports to the EU (Akhtar 

and Jones, 2014). In addition, TRQs are prevalent in agricultural commodities. Calculating an ad 

valorem equivalent (AVE)6 for a specific tariff is straightforward—the AVE is simply the tariff 

divided by the import unit value. However, constructing an AVE for a TRQ is more of a challenge. 

In addition, other types of tariffs that also involve additional calculations may also be in place, such 

as compound or mixed tariffs (see box “AVE Calculations for Nonspecific Tariffs”). 

5The Global Trade Analysis Project (GTAP) database provides an ad valorem equivalent (AVE) estimate for regular 

tariffs, specific tariffs, compound tariffs, and tariff-rate quotas. This total tariff allows for complete market liberalization 

experiments. The tariff estimates from the GTAP database are examined and corrected for any inconsistencies with other 

data sources (see appendix 1, tariffs).

6An AVE is a tool to bring all trade barriers into one universal measure, as a percentage of the price of the product. 

AVE Calculations for Nonspecific Tariffs

The Uruguay Round Agreement on Agriculture set about converting (or eliminating) non-tariff barriers in the 

form of quantitative import restrictions, variable import levies, discretionary import licensing, and voluntary 

export restraints to tariffs (Normile and Simone, 2001). However, high rates in some products effectively prohibited imports. Thus, a system of tariff-rate quotas (TRQs) was created to provide market access. 

TRQs feature two distinct components: an in-quota rate and an out-of-quota rate; the rate applied depends on the 

fill rate for the quota amount. To calculate an ad valorem equivalent (AVE) for TRQs, we apply the following rule: 

- Fill rate < 90 percent In-quota rate

- 99 percent < Fill rate > 90 percent Average in- and out-of-quota rate

- Fill rate > 100 percent Out-of-quota rate

That is, if a commodity has a fill rate less than 90 percent, the in-quota rate is used as the duty; if the fill rate is 

between 90 and 99 percent, an average of the two rates is used as the duty; and if the fill rate is greater than 100 

percent, the out-of-quota rate is used. For example, U.S. almond exports to the EU are allocated a quota of 90,000 

metric tons. Exports below that amount are subject to a 2-percent duty (in-quota rate), and exports above the quota 

face an out-of-quota rate of 5.6 percent. The fill rate is actually greater than 100 percent. Thus, the out-of-quota 

rate is used and the AVE is 5.6 percent. 

Compound or mixed tariffs are also commonly used in the agricultural sector. Calculations for those tariffs involve 

multiple steps to account for the joint nature of those tariffs. For example, U.S. beef exports to the EU (Harmonized 

System code 01022910) face a compound tariff of 10.2 percent plus 93.10 euros per 100 kilograms. Based on an 

import value of 73,000 euros and a subsequent unit value of 313 euros per 100 kilograms, the AVE for this product is: 

10.2 percent + (93.10 euros/(313 euros)*100) = 40 percent.
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Based on all the information for individual tariffs and TRQs, we compute a total AVE for tariffs 

(see appendix 1 “tariffs” for information on our need to calculate and validate the GTAP tariffs). 

U.S. and EU agricultural tariffs by category are presented in figure 1 (see appendix 1, table A1-2, 

for the sector aggregation). The tariffs reported in figure 1 were used for the analysis. Note that 

the AVE estimates for several commodities are quite large. For example, the EU tariff on U.S. beef 

is 61 percent. Overall, the EU has higher tariffs on agriculture than the United States does. Our 

tariffs tend to be slightly larger than those in the GTAP database. For example, U.S. agricultural 

and biofuel exports to the EU face a 12.19-percent simple-weighted tariff in the GTAP database; our 

calculations indicate that it is 14.57 percent. 

*Includes ad valorem equivalents of non-ad valorem tariffs and tariff-rate quotas. DDGS = distillers' dried grains with solubles. 

f_v = fruits and vegetables.

Source: USDA, Economic Research Service, authors’ calculations.

Figure 1

Total tariffs on agricultural trade between the United States and the EU*

Ad valorem equivalent (percent)

0

10

20

30

40

50

60

70

80

Paddy rice

Wheat

Coarse grains

Fruits

Vegetables

Nuts

Soybeans

Rapeseed

Other oilseeds

Sugarcane/beet

Other crops

Bovine

Hogs

Poultry & eggs

Other animals

Raw milk

BeefPork

Poultry meat

Other meats

Vegetable oil

Whey

Powdered milk

Butter

Cheese

Other dairy products

Processed sugar

Processed rice

Prepared f_v

Cereal preparations

Sugar preparations

Processed feed

Other foods

Beverages & tobacco

Ethanol1Ethanol2

DDGS

Biodiesel

U.S. tariffs on imports from EU-28 EU-28 tariffs on imports from United States
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Non-Tariff Measures 

The removal of NTMs is frequently described as being the largest source of potential trade gains for 

T-TIP (see table 1) and is a key focus of negotiations (ECORYS, 2009; Fontagne et al., 2013; Josling 

and Tangermann, 2014). For agriculture, sanitary and phytosanitary (SPS) measures and technical 

barriers to trade (TBT) stand out in particular as costly NTMs (Cadot et al., 2015; ITC, 2011).7

However, NTMs are not easily quantified, leading to several data, methodological, and conceptual 

challenges in assessing the potential economic gains from their removal (see Arita et al., 2015, for an 

overview of these issues). 

Given that concerns over NTMs are a key element in the T-TIP negotiations, there is growing 

interest in estimating the potential effects on bilateral trade from the removal of NTMs. However, 

analysis to date has largely applied broad-based approaches aimed at covering the widest scope of 

NTMs. For example, European Parliament (2014) used World Trade Organization (WTO) notifications in a gravity model framework to estimate the effects of NTMs on EU-U.S. trade (see box 

“Gravity Models and NTMs”). Although WTO notifications provide a comprehensive inventory of 

NTMs, they do not distinguish between different types of measures in an empirical framework (e.g., 

measures such as maximum pesticide residue tolerances are not differentiated from bans on beef 

hormones). Because there are many SPS and TBT measures that have not been raised as concerns, 

broad-based estimates of all NTMs likely overstate their cost in the context of FTA negotiations.8

A further limitation of the broad-based approach is that the resulting estimates are based on the 

assumption that all NTMs affect each country equally. Thus, this approach may lead to imprecise 

measures of NTM effects, which vary across exporting countries. For example, although one would 

expect the EU beef hormone restriction to significantly affect U.S. and Canadian beef exporters 

(because both countries use hormones extensively), one would not expect the SPS restriction to 

7Cadot et al. (2015) estimate that SPS/TBT measures account for the largest portion of NTM costs for agricultural 

commodities. Using data from business surveys on NTMs, ITC (2011) finds that 68 percent of exporters of agricultural 

products report SPS/TBT measures as the most burdensome type of NTM.

8Studies employing broad-based estimates typically assume only a certain percentage of the NTMs are removed in 

their simulations. The European Parliament study assumed 25 percent of NTMs were removed; ECORYS simulated a 25- 

to 50-percent removal scenario.

Gravity Models and NTMs

Gravity models have been employed to econometrically estimate the effects of non-tariff measures 

(NTMs) (Disdier and Marette, 2010; EP, 2014; ECORYS, 2009). In its basic form, the gravity 

model predicts that bilateral trade flows increase as the sizes of the economies of the trading 

partners increase and that trade flows decrease as trade costs increase. In current empirical applications, the model’s theoretical foundation is most frequently guided by the work of Anderson 

and van Wincoop (2003), who explicitly model multilateral resistance terms. Silva and Tenreyro 

(2006) have contributed further innovation using a poisson estimator. In applications to NTMs, 

the standard gravity equation is augmented to include NTMs as an additional variable to explain 

bilateral trade. Estimation of the model allows for the prediction of trade without the NTMs in 

place. A comparison of actual trade levels with predicted trade levels following the removal of 

NTMs can be used to estimate forgone levels of trade. From this, an ad valorem tariff equivalent 

(AVE) of NTM costs can be estimated. 
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affect South American exporters to the EU because South American producers are largely banned 

from using hormones.

The NTM effects used in this study are from a complementary ERS study, Estimating the Effects 

of Selected Sanitary and Phytosanitary Measures and Technical Barriers to Trade on U.S.-EU 

Agricultural Trade (Arita et al., 2015). Unlike broad-based approaches to investigating the effects of 

NTMs, Arita et al. (2015) applied a more tailored strategy, focusing on specific SPS/TBT concerns 

raised by EU and U.S. exporters and reported to the U.S. Trade Representative, the European 

Commission, and the WTO (table 4).9 For each of these specific measures, Arita et al. combined 

commodity analysis with econometric tools to estimate individual gravity models and predict the 

levels of forgone trade had the NTMs not been in place. This empirical approach is tailored to examining many of the major NTM issues frequently discussed as barriers to trade, which tend to focus 

on specific SPS/TBT concerns (e.g., biotech restrictions, beef hormones). A summary of the results 

from Arita et al. (2015) is reported in table 5. Note that the second of three hypothetical T-TIP 

9Due to data and modeling limitations, the study did not econometrically analyze SPS/TBT concerns on animal 

byproducts, seeds, and dairy. The study did not examine other NTM issues, such as price control measures, licensing, 

administrative and customs requirements, geographic indicators, rules-of-origin issues, taxes discriminatory on 

exporters, State-specific requirements, and Government procurement policies that may also be addressed through a T-TIP 

agreement (see Arita et al., 2014 for a discussion of several of these NTMs).

Table 4

EU- and U.S.-specific SPS/TBT trade concerns

Specific trade concern* Products affected

Assessed in 

this study

Concerns raised by U.S. exporters about EU SPS/TBT measures:

Restrictions on the use of pathogen-reduction treatments (PRTs)1,3 Poultry and beef Yes for poultry and beef

Restrictions on the importation and use of agricultural commodities 

derived from agricultural biotechnology1,3

Various products Yes for soybeans and corn

Prohibition on beef and beef products raised with growth-promoting 

hormones1,3

Beef and beef products Yes

Low level of maximum-residue limit (MRL) tolerances; MRLs not 

established for some products1

Fruits, vegetables, nuts Yes for fruits, vegetables, 

and nuts

Restrictions on pork and other livestock produced with beta agonists1 Pork and other meat 

products

Yes for pork

Testing requirements for karnal bunt spores1 Wheat Yes

Restrictions on tallow over bovine spongiform encephalopathy (BSE) 

concerns1,3

Animal byproducts No

Limits on the number of somatic cells permitted in raw milk1 Milk and milk products No

Phytosanitary restrictions on seed re-export1 Seeds No

Concerns raised by EU exporters about U.S. SPS/TBT measures:

Restrictions on beef products due to BSE concerns2,3 Beef and beef products Yes**

Lengthy import approval procedures for new types of plant products2,3 Plants and plant products Yes for fruits and 

vegetables

U.S. standards on dairy products that differ from EU requirements2,3 Dairy products No

Notes: *Specific concerns on wine, distilled spirit, and seafood products were not included. **Only forgone losses of trade estimated (ad 

valorem equivalents not estimated). SPS = sanitary and phytosanitary. TBT = technical barrier to trade.

Sources: 1U.S. Trade Representative, 2014 Report on Sanitary and Phytosanitary Measures; 2European Commission, 

Market Access Databases; 3World Trade Organization (formal complaints and specific trade concerns).
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scenarios (market access with NTM removal) simulated later in this study removes this NTM AVE 

on exports in addition to removing the tariff. That is, the tariffs presented in figure 1 represent the 

price wedge between the exported and the imported price. The AVEs in table 5 are in addition to the 

AVEs from tariffs. Thus, for the agricultural commodities in table 5, the price wedge is larger than 

that with tariffs alone.10 

The EU’s NTMs on U.S. exports of poultry, pork, and corn were found to have the largest tradeimpeding effects, with estimated AVEs of 102, 81, and 79 percent, respectively. Estimated AVE 

effects for EU NTMs on U.S. vegetable and fruit exports were 53 and 35 percent, respectively. 

Almost all of these AVE effects of NTMs are larger than the estimated tariffs (see fig. 1), although 

both the AVEs and the tariffs are removed in this study’s second T-TIP scenario. EU NTMs on U.S. 

beef exports were found to be equivalent to a 23-percent tariff, a lower trade barrier than the tariff. 

The AVE effect of EU NTMs on U.S. exports of soybeans was estimated to be 17 percent. The EU 

does not impose a tariff on U.S. soy and soy products. Thus, all effects on U.S. soy exports can be 

attributed to the removal of NTMs. U.S. NTMs on EU exports of fruits and vegetables were estimated to be equivalent to tariffs of 45 and 37 percent, respectively. 

10The implementation of NTMs in the CGE model effectively resolves the model introducing the AVE distortion in 

table 5. This is because NTMs are not present in the base data. For example, for beef, the model will be resolved such 

that a 23-percent tax will be applied to U.S. exports to the EU. The second scenario will then remove the tariff price 

wedge plus the new NTM price wedge. 

Table 5

Estimated AVE cost of NTMs on U.S.-EU agricultural trade

Sector Example of NTM in sectors AVE 

EU sectors with NTM concerns 

raised by U.S. exporters Percent

Beef Growth hormones, PRTs 23

Poultry PRTs 102 

Pork Ractopamine, trichanae, PRTs 81

Corn Biotech restrictions 79

Soy Biotech restrictions 17

Fruits Maximum residue limits 35

Vegetables Maximum residue limits 53

U.S. sectors with NTM concerns 

raised by EU exporters

Beef Bovine spongiform encephalopathy (BSE) N/A

Fruits Import approval process 45

Vegetables Import approval process 37

AVE = ad valorem equivalent. NTM = non-tariff measure. PRT = pathogen-reduction treatment. N/A = not available.

Source: USDA, Economic Research Service estimates are from Arita et al. (2015), using a gravity model. 

NTMs not found to be statistically significant in the study are not reported.
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CGE Model Results

To analyze the effects of T-TIP on agricultural production, prices, and trade, as well as countryspecific macroeconomic impacts, such as GDP, we simulate three scenarios using a multi-product 

global CGE model (see appendix 2). In the first scenario, tariffs and TRQs are completely removed. 

The second scenario builds on the first scenario by also removing select NTMs, using AVE estimates from Arita et al. (2015) (see appendix 3 for information on incorporating these costs in the 

model). Finally, a third scenario considers demand sensitivities that may arise from changes in the 

NTM regime. Detailed commodity-level impacts are presented for the United States and the EU 

only. All other countries are grouped into one aggregate region. When possible, specific results for 

other countries are discussed.11

Market Access – Removal of all Tariffs and TRQs

Trade and Production Impacts – United States 

The EU has relatively higher tariffs on U.S. agricultural exports than the United States has on 

EU agricultural exports (see fig. 1). Consequently, the removal of all tariffs and TRQs results in a 

greater increase in U.S. exports to the EU for the broad agricultural commodities reported in table 6. 

In particular, U.S. exports of beef to the EU increase 685 percent annually in this scenario. This is 

not surprising, as this commodity had the highest tariffs in place (see fig. 1). With the removal of all 

tariffs, U.S. exports decrease for some agricultural commodities, such as distillers’ dried grains with 

solubles and soybeans. This effect stems from relatively low tariffs on these commodities, compared 

with other commodities with higher tariffs (see fig. 1). Resources are reallocated to those products 

with larger tariff removal. In the same scenario, U.S. agricultural export values to the rest of the 

world (all other countries) decline because of the increase in exports to the EU. 

Although U.S. exports to the EU increase for almost all agricultural commodities examined, changes 

in U.S. overall export values are mixed (table 7). Note that the United States (and all other regions) 

has limited resources (land, labor, and capital) to meet demand changes. The CGE model captures 

the movement of resources into commodities where the United States is most competitive with 

respect to EU markets, and vice versa. Some U.S. commodities have overall, but small, decreases 

in export values, notably, wheat, coarse grains, soybeans, vegetable oil, and poultry meat. This 

is due to supply constraints against other commodities that had higher initial tariffs. Many other 

commodities, however, see increases in overall exports in the scenario. In fact, U.S. export values of 

beef, butter, cheese, and processed sugar to the EU all increase by double-digit amounts. Other U.S. 

commodity exports with increases include various processed foods products (e.g., prepared fruits 

and vegetables, cereal preparations, sugar preparations, and other foods) pork, fruits, and vegetables. A higher share of U.S.-produced oilseeds, such as soybeans, and coarse grains is allocated to 

domestic use as feed, given the increase in beef exports in the scenario. This also contributes to the 

reduction in certain U.S. exports to the EU (e.g., soybeans). Similarly, overall values of processed 

feed exports decrease for the United States. Net U.S. imports of agricultural goods increase across 

the board with tariffs and TRQs removed (except for the ethanol commodities), as the United States 

has increases in bilateral imports from the EU, and more overall imports are needed for domestic 

use to replace the large increase in exports. The largest increase in U.S. imports is for cheese, which 

11Detailed results for the other five disaggregated regions are available upon request from the authors.
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Table 6

Changes in bilateral trade values from market access

Sector U.S. to EU U.S. to all others All others to U.S. EU to U.S. EU to all others All others to EU

Percent Million $ Percent Million $ Percent Million $ Percent Million $ Percent Million $ Percent Million $

Paddy rice 444.39 98.06 -4.52 -36.74 4.98 4.25 13.33 0.12 1.73 0.51 -15.68 -94.12

Wheat 23.22 113.94 -1.72 -225.16 0.97 6.51 5.47 1.21 0.42 29.27 -5.69 -91.22

Coarse grains 1.64 8.51 -0.58 -92.59 0.84 8.35 1.52 0.35 0.65 13.33 -0.56 -10.22

Fruits 29.67 94.00 -1.00 -42.12 0.59 48.44 5.32 5.85 0.47 9.31 -0.75 -69.69

Vegetables 15.79 56.01 -0.56 -17.18 0.50 35.98 8.29 15.67 0.33 12.04 -0.61 -41.75

Nuts 4.23 73.30 -1.12 -33.43 0.82 16.12 8.09 2.20 1.42 2.75 -2.96 -51.47

Soybeans -1.01 -12.73 -0.66 -129.46 0.51 2.88 1.03 0.00 0.73 0.22 0.22 9.05

Rapeseed -1.65 0.00 -0.71 -1.03 0.48 0.54 1.97 0.01 0.46 0.84 -0.10 -1.88

Other oilseeds -1.36 -3.21 -1.03 -10.45 0.52 1.95 1.85 0.18 0.33 2.05 0.09 1.21

Sugarcane/ 

beet 32.19 0.03 -3.45 -0.01 0.63 0.17 1.83 0.00 0.81 0.03 -0.48 -0.08

Other crops 35.38 303.10 -1.95 -242.47 0.26 31.72 9.35 70.59 0.80 44.40 -1.15 -260.34

Bovine 5.42 7.48 -1.92 -14.32 2.08 33.33 2.32 5.05 -0.18 -2.75 -4.64 -7.82

Hogs 59.15 147.44 -1.48 -34.19 0.94 5.95 1.24 1.18 0.09 0.86 -10.56 -135.30

Poultry and 

eggs 12.14 0.00 -1.51 -12.33 0.94 5.33 1.18 0.00 0.22 5.96 -0.01 -0.14

Other animals 26.91 0.43 -1.12 -9.32 0.50 3.74 0.75 0.25 0.17 0.85 -0.08 -0.68

Raw milk -3.68 -0.01 0.00 0.00 1.57 0.67 2.62 0.07 0.66 0.11 -0.09 -0.10

Beef 684.88 1,487.41 -1.36 -89.21 1.59 64.46 1.73 0.78 -0.01 -0.25 -26.32 -1,026.13

Pork 181.13 322.30 -1.46 -93.75 1.00 9.85 1.88 8.79 0.09 4.82 -18.57 -261.64

Poultry meat 197.25 1.62 -1.41 -58.62 1.22 7.61 7.88 0.00 -0.09 -1.81 -0.00 -0.01

Other meats 31.77 14.02 -1.33 -16.80 1.06 3.96 2.14 0.34 -0.06 -0.36 -1.76 -11.27

Vegetable oil 17.35 67.54 -1.32 -89.16 -0.36 -23.36 10.81 94.85 0.11 4.50 -0.37 -48.46

Whey 435.08 34.01 -2.70 -29.31 0.71 0.55 6.92 0.11 0.69 9.23 -74.93 -25.55

Powdered milk 908.72 21.83 -2.05 -32.11 0.36 0.31 4.54 0.28 0.04 0.44 -29.59 -16.75

Butter 206.06 150.78 -4.30 -12.80 -6.03 -2.79 11.71 3.99 2.66 22.62 -92.78 -106.21

Cheese 997.09 76.31 -2.17 -10.94 -24.30 -28.64 30.44 325.25 0.23 7.05 -12.55 -58.29

Other dairy 

products 125.83 32.50 -2.42 -22.84 0.11 0.93 4.21 4.14 0.07 3.49 -2.92 -26.55

Processed 

sugar 154.11 50.87 -1.19 -3.19 0.79 21.22 38.46 7.04 -0.21 -2.68 -0.82 -24.49

Processed rice 145.70 78.92 -1.41 -20.30 0.46 3.04 7.95 1.83 1.36 2.15 -7.13 -54.34

Prepared f_v 60.85 372.54 -0.94 -48.74 -0.21 -16.11 4.22 23.90 -0.03 -1.38 -3.14 -309.95

Cereal 

preparations 43.49 436.82 -0.86 -43.58 -0.14 -11.75 2.12 44.77 -0.01 -1.48 -6.50 -364.95

Sugar 

preparations 171.21 56.04 -0.87 -15.83 -0.21 -1.38 3.84 3.11 -0.07 -1.34 -9.22 -49.74

Processed 

feed 3.38 4.92 -0.98 -15.57 0.87 6.11 1.29 1.65 -0.14 -2.85 -0.97 -9.68

Other foods 60.02 1,130.96 -0.88 -125.53 0.03 6.78 2.55 52.13 0.00 0.35 -4.29 -966.14

Beverages 

and tobacco 10.41 207.18 -0.53 -47.24 -0.02 -1.89 0.76 91.34 0.02 3.88 -2.54 -155.25

Ethanol1 4.46 34.05 -0.49 -11.84 -0.14 -0.08 -0.53 0.00 0.38 0.14 -5.38 -0.05

Ethanol2 24.11 0.00 - - 0.21 0.88 0.10 0.00 0.19 0.07 -0.15 -0.25

DDGS -1.80 -2.33 -1.11 -18.78 0.86 0.77 1.44 0.00 - - - -

Biodiesel 14.06 11.09 0.06 0.05 -16.57 -3.81 30.70 30.81 -0.02 -0.08 -0.65 -5.66

Total ag. 5,475.73 -1,706.89 242.59 797.84 166.29 -4,275.91

DDGS = distillers' dried grains with solubles. f_v = fruits and vegetables. - = not applicable.

Source: USDA, Economic Research Service analysis using Transatlantic Trade and Investment Partnership model.
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Table 7

Changes in net trade values from market access

Exports Imports

Sector U.S. EU All others U.S. EU All others

Percent change

Paddy rice 7.35 -4.16 -4.28 5.22 -0.31 -1.21

Wheat -0.82 -0.37 -0.19 1.35 0.57 -0.17

Coarse grains -0.51 0.05 0.17 0.85 -0.10 -0.22

Fruits 1.15 -0.11 -0.04 0.64 0.06 -0.10

Vegetables 1.14 0.07 -0.05 0.67 0.08 -0.06

Nuts 0.85 -1.45 -0.32 0.91 0.33 -0.27

Soybeans -0.68 0.67 0.35 0.51 -0.04 -0.12

Rapeseed -0.72 0.35 -0.08 0.48 -0.10 -0.08

Other oilseeds -1.09 0.46 0.06 0.55 -0.11 -0.10

Sugarcane/beet 4.74 0.17 0.10 0.60 -0.38 0.05

Other crops 0.46 0.20 -0.20 0.79 0.19 -0.20

Bovine -0.78 -0.75 0.35 2.16 -0.04 -0.27

Hogs 4.42 -1.34 -1.12 1.05 1.25 -0.10

Poultry and eggs -1.50 0.22 0.02 0.94 -0.01 0.05

Other animals -1.07 0.15 0.15 0.54 -0.03 -0.14

Raw milk -3.68 0.84 0.17 1.67 -0.09 -0.02

Beef 20.70 -3.00 -3.63 1.60 2.86 -0.22

Pork 3.47 -1.11 -3.32 1.61 2.34 -0.21

Poultry meat -1.37 0.12 0.09 1.22 0.07 -0.28

Other meats -0.21 -0.08 -0.07 1.24 0.26 -0.21

Vegetable oil -0.30 0.27 -0.05 0.93 0.11 -0.07

Whey 0.43 -2.00 -3.24 1.86 6.48 0.33

Powdered milk -0.66 -0.54 -0.10 2.27 1.60 -0.09

Butter 37.19 -7.20 -7.00 9.86 12.40 1.55

Cheese 12.79 1.41 -3.36 29.91 0.79 0.14

Other dairy products 1.00 0.03 -0.45 2.49 0.19 0.00

Processed sugar 15.79 -0.72 -0.05 0.98 0.61 -0.07

Processed rice 3.93 -4.64 -0.27 0.72 1.81 -0.12

Prepared f_v 5.56 -0.05 -0.84 1.27 0.33 -0.08

Cereal preparations 6.49 -0.12 -0.94 1.27 0.62 -0.06

Sugar preparations 2.16 -0.34 -1.11 1.64 0.83 -0.18

Processed feed -0.61 -0.44 -0.03 1.05 -0.43 -0.17

Other foods 6.23 -0.11 -0.90 0.98 0.46 -0.07

Beverages and tobacco 1.47 -0.05 -0.43 0.63 0.49 -0.02

Ethanol1 0.43 -0.08 -0.65 -0.53 -0.64 -0.60

Ethanol2 0.70 0.17 -0.07 -0.15 -0.17 0.00

DDGS -1.35 -0.10 0.97 0.08 -0.01 -0.06

Biodiesel -0.91 0.02 0.50 0.07 0.04 0.05

DDGS = distillers' dried grains with solubles. f_v = fruits and vegetables.

Source: USDA, Economic Research Service using Transatlantic Trade and Investment Partnership model.
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Table 8

Changes in production values and prices from market access

Production Market price

Sector U.S. EU All others U.S. EU

Percent change

Paddy rice 2.27 -2.32 -0.03 0.81 -0.69

Wheat -0.55 -0.28 -0.02 0.09 -0.33

Coarse grains 0.08 -0.06 0.01 0.24 -0.32

Fruits 0.11 -0.08 -0.01 0.29 -0.30

Vegetables 0.08 0.02 -0.02 0.28 -0.27

Nuts 0.30 -1.03 -0.02 0.34 -0.50

Soybeans -0.36 0.42 0.11 0.15 -0.21

Rapeseed -0.25 0.20 -0.02 0.18 -0.25

Other oilseeds -0.30 0.30 0.00 0.17 -0.23

Sugarcane/beet 0.38 -0.18 -0.02 0.42 -0.32

Other crops -0.11 -0.06 -0.04 0.24 -0.28

Bovine 1.17 -0.64 -0.15 0.40 -0.24

Hogs 0.91 -0.31 -0.04 0.33 -0.34

Poultry and eggs -0.09 0.13 0.03 0.32 -0.33

Other animals -0.55 0.12 0.04 0.31 -0.33

Raw milk -0.14 0.11 -0.04 0.37 -0.26

Beef 1.33 -1.11 -0.30 0.28 -0.25

Pork 0.52 -0.01 -0.17 0.26 -0.32

Poultry meat -0.13 0.11 0.05 0.27 -0.19

Other meats -0.12 -0.04 0.03 0.25 -0.18

Vegetable oil -0.11 0.09 -0.02 0.16 -0.11

Whey 0.11 -0.06 -0.12 0.26 -0.68

Powdered milk -0.26 0.02 -0.01 0.26 -0.27

Butter 2.72 -0.86 -0.59 0.25 -2.33

Cheese -1.11 0.52 -0.36 0.18 -0.30

Other dairy products -0.01 0.09 -0.03 0.26 -0.20

Processed sugar 0.42 -0.31 -0.01 0.18 -0.07

Processed rice 1.46 -2.72 -0.01 0.20 -0.42

Prepared f_v 0.52 0.05 -0.09 0.18 -0.15

Cereal preparations 0.31 0.05 -0.09 0.19 -0.15

Sugar preparations 0.28 0.00 -0.03 0.23 -0.09

Processed feed 0.27 -0.51 -0.03 0.23 -0.08

Other foods 0.71 0.06 -0.15 0.18 -0.19

Beverages and tobacco 0.09 0.07 -0.02 0.18 -0.14

Ethanol1 -0.04 0.04 0.02 0.20 -0.06

Ethanol2 0.00 -0.02 0.00 0.15 -0.10

DDGS -0.04 0.04 0.05 0.41 -0.52

Biodiesel 1.08 0.18 -0.08 -0.11 -0.10

DDGS = distillers' dried grains with solubles. f_v = fruits and vegetables.

Source: USDA, Economic Research Service using Transatlantic Trade and Investment Partnership model.
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at 30 percent is triple the next largest increase for a commodity (butter). This increase for cheese 

is entirely due to the diversion of domestic production after the 30-percent increase in U.S. cheese 

imports from the EU (see table 6). 

The increase in exports for most U.S. agricultural commodities in the first scenario leads to similar 

increases in production (table 8); however, the increase in coarse grain production is small due to 

the decline in net exports. There are decreases in production for wheat, soybeans, and cheese, which 

is often due to falling exports. U.S. net exports of cheese increase significantly (12.79 percent), but 

the offsetting increase in imports (29.91 percent) negates the need for increased U.S. production. 

Consequently, the fall in U.S. cheese production in the scenario is the largest percentage decrease 

among all U.S. commodities examined. The expansion of U.S. exports to the EU leads to a small 

(under 1 percent) increase in U.S. prices for all agricultural commodities except biodiesel.

Trade and Production Impacts - EU

For the EU, exports to the United States increase for all agricultural commodities (except for 

ethanol1 (ethanol produced from grains), but the gains are mostly smaller than those for U.S. exports 

to the EU (table 6). As mentioned, this difference is due to relatively smaller U.S. tariffs on agriculture (see fig. 1). According to the GTAP baseline, the largest EU exports to the United States other 

than processed foods are vegetable oil and cheese (see table 3). Among all commodities, these two 

have some of the largest gains in exports to the United States in the scenario. 

EU exports to all other countries examined increase for most agricultural commodities in the 

scenario, which offsets some of the decline in U.S. exports to these countries. The increase in EU 

imports from the United States leads to an increase in overall EU imports for most agricultural 

commodities. Only a few EU agricultural commodities have overall gains in exports (see table 7). 

EU production declines for many agricultural commodities in the scenario, but there are increases 

for oilseeds (e.g., rapeseed and vegetable oil) and cheese due to the large expansion in exports. 

The commodity perhaps affected the most in the market access scenario is beef. Both the United 

States and the EU are large producers of beef (combined production value of $185 billion in 2011). 

The removal of tariffs and TRQs leads to an increase in U.S. beef production of 1.33 percent and a 

decrease in EU beef production of 1.11 percent. These changes are due to the removal of the large 

tariff/TRQ on U.S. beef exports to the EU. EU prices for all agricultural commodities decrease by a 

small amount for most products due to the increase in imports from the United States. 

Macro Impacts

With the removal of all tariffs and TRQs, net U.S. agricultural exports and imports increase and 

total EU agricultural imports increase (table 9). U.S. imports increase by just over 1 percent, largely 

due to an increase in dairy imports. EU imports increase by about 0.6 percent, largely due to 

increases in dairy products and pork imports. Given the sector-level results, total agricultural exports 

increase by over 2 percent for the United States and decrease by 0.25 percent for the EU. For all 

other countries examined, agricultural imports and exports decrease. Agricultural exports decline 

the most for the countries without trade agreements with the United States or the EU (China, India, 

and Brazil). 

The removal of tariffs and TRQs leads to an increase in GDP for the United States and the EU but 

no change in GDP for the non-NAFTA countries examined (table 9). The GDPs of the other NAFTA 

countries (Canada and Mexico) decrease slightly due to the decrease in trade with the United States. 
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As shown in other studies (see table 1), the percentage change in GDP is larger for the EU (0.23 

percent) than for the United States (0.09). This finding largely reflects an increase in EU exports of 

manufactured products and services and the benefit of lower priced EU imports. 

Land-use changes in the market-access scenario are largely the result of changes in total production 

(table 9). For example, U.S. primary livestock production and exports increase with the removal of 

tariffs and TRQs. Consequently, some land-use shifts from forestry and crops to livestock. The EU 

has a similar increase in land allocated to livestock production at the expense of cropland. All other 

countries examined except Brazil also have a movement toward increased land for livestock. Note 

that part of the movement toward livestock is attributed to the Keeney and Hertel (2009) component 

of the CGE model, which explicitly considers intensification of crop yields. Thus, less land is needed 

for crop production. 

Market Access With NTM Removal

In the second scenario, we consider the effects of removal of selected NTMs in addition to removal 

of tariffs and TRQs. The allocation of NTM costs in the CGE model is discussed in appendix 3. 

Although a final T-TIP agreement may draw from other scenarios, such as partial reductions of 

NTMs, we consider their elimination and their impact an upper bound. 

Trade and Production Impacts – United States

Given the removal of select NTMs in addition to tariffs and TRQs, the percentage change in U.S. 

exports to the EU increases in most agricultural commodities, especially those in which NTMs were 

analyzed (table 10). Some of the changes are considerably larger than those in the first scenario. 

For example, U.S. poultry exports increased 197 percent in the market-access scenario, but when 

the additional removal of NTMs is considered, the increase is 33,505 percent. However, base 

poultry exports were small, such that the level change in exports is only $18 million. U.S. beef and 

pork exports also increase considerably in the second scenario. The removal of NTMs leads to an 

increase in U.S. soybean exports to the EU (76.81 percent), whereas the removal of tariffs and TRQs 

in the first scenario resulted in a drop in U.S. soy exports. Similarly, U.S. agricultural exports to 

Table 9

Macro impacts from market access

Value of agriculture Land-use change

Region GDP Imports Exports Forestry Crops Livestock

Percent change

U.S. 0.09 1.19 2.30 -0.06 -0.06 0.10

EU 0.23 0.59 -0.25 0.05 -0.08 0.04

Canada -0.02 -0.28 -0.09 -0.01 -0.02 0.07

Mexico -0.03 -0.30 0.00 -0.02 0.00 0.01

China 0.00 -0.07 -0.42 -0.02 -0.01 0.02

India 0.00 0.00 -0.39 0.06 -0.01 0.12

Brazil 0.00 -0.09 -0.55 0.02 0.00 -0.02

ROW 0.00 -0.10 -0.62 0.01 0.01 -0.01

GDP = gross domestic product. ROW = rest of world.

Source: USDA, Economic Research Service using Transatlantic Trade and Investment Partnership model.
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Table 10

Change in bilateral trade values from tariff and non-tariff measure removal 

Sector U.S. to EU U.S. to all others All others to U.S. EU to U.S. EU to all others All others to EU

Percent Million $ Percent Million $ Percent Million $ Percent Million $ Percent Million $ Percent Million $

Paddy rice 432.84 95.51 -5.76 -46.79 5.79 4.94 15.56 0.14 2.37 0.70 -15.71 -94.33

Wheat 21.66 106.25 -2.61 -342.96 1.51 10.10 6.82 1.52 1.23 84.97 -5.91 -94.64

Coarse grains 95.85 361.02 -1.33 -210.02 1.51 15.04 5.21 1.22 3.95 81.44 -15.97 -293.42

Fruits 62.07 188.60 -1.37 -57.58 -4.74 -386.56 708.78 494.71 0.81 16.17 -1.53 -142.49

Vegetables 152.98 466.74 -1.48 -44.92 -4.78 -342.98 459.26 613.35 1.52 55.45 -5.32 -363.49

Nuts 3.70 64.15 -1.59 -47.41 1.34 26.20 9.03 2.45 2.38 4.60 -2.86 -49.74

Soybeans 76.81 861.26 -2.66 -521.40 2.00 11.36 8.31 0.01 7.91 2.40 -17.60 -737.32

Rapeseed -1.66 0.00 -1.52 -2.19 0.54 0.61 2.06 0.01 0.61 1.11 0.72 13.44

Other oilseeds -1.23 -2.90 -1.80 -18.19 0.42 1.57 1.99 0.19 0.82 5.12 1.06 14.02

Sugarcane/ 

beet 29.53 0.03 -6.90 -0.02 1.00 0.27 2.43 0.00 1.88 0.07 -1.26 -0.21

Other crops 33.28 285.11 -2.78 -346.03 0.46 55.68 10.87 82.12 2.25 124.48 -1.67 -379.07

Bovine 3.82 5.27 -3.33 -24.80 3.30 52.94 3.86 8.40 0.06 0.95 -4.75 -8.02

Hogs 54.75 136.49 -2.57 -59.32 1.68 10.66 2.74 2.60 0.62 5.96 -11.96 -153.28

Poultry and 

eggs 10.89 0.00 -2.83 -23.16 1.35 7.60 2.21 0.00 1.12 30.04 -0.28 -4.70

Other animals 25.68 0.41 -2.00 -16.62 0.59 4.40 1.54 0.50 0.87 4.44 -0.08 -0.62

Raw milk -6.70 -0.01 -5.26 -0.01 2.46 1.05 7.49 0.20 4.03 0.67 -1.05 -1.14

Beef 965.71 1,861.06 -1.97 -128.65 0.55 22.25 113.41 16.19 0.10 3.24 -36.09 -1,406.74

Pork 3,982.89 2,394.00 -2.16 -138.22 1.46 14.46 2.86 13.37 0.43 23.09 -97.44 -1,372.37

Poultry meat 33,505.16 18.03 -2.16 -89.72 1.66 10.34 8.57 0.00 0.14 2.88 -5.32 -104.65

Other meats 30.96 13.67 -1.89 -23.98 1.48 5.55 2.70 0.43 0.04 0.23 -1.64 -10.49

Vegetable oil 14.23 55.38 -2.93 -197.51 -0.28 -17.86 14.96 131.30 3.42 143.13 -0.96 -127.41

Whey 431.55 33.73 -3.60 -39.07 1.13 0.88 7.45 0.12 0.78 10.47 -74.81 -25.51

Powdered milk 900.94 21.65 -2.89 -45.21 0.85 0.74 5.16 0.31 0.11 1.19 -29.38 -16.63

Butter 206.25 150.92 -5.25 -15.64 -5.75 -2.66 12.48 4.26 2.77 23.53 -92.85 -106.29

Cheese 987.82 75.60 -3.26 -16.39 -24.19 -28.50 31.49 336.38 0.39 11.95 -12.38 -57.51

Other dairy 

products 123.70 31.96 -3.50 -33.02 0.58 4.95 4.84 4.76 0.24 11.60 -2.82 -25.61

Processed 

sugar 152.61 50.37 -1.87 -5.03 1.12 30.15 38.11 6.97 -0.48 -6.17 -0.72 -21.46

Processed rice 144.56 78.30 -2.02 -29.01 0.69 4.57 7.76 1.79 0.95 1.51 -6.94 -52.86

Prepared f_v 62.48 382.55 -0.12 -6.20 -0.41 -30.88 4.06 22.97 0.06 3.26 -3.23 -318.72

Cereal 

preparations 43.04 432.33 -1.10 -55.63 0.03 2.18 2.34 49.45 0.01 0.64 -6.46 -362.24

Sugar 

preparations 170.52 55.82 -1.02 -18.58 -0.07 -0.45 4.07 3.30 0.00 -0.06 -9.26 -49.92

Processed 

feed 1.76 2.56 -1.50 -23.82 1.18 8.33 1.74 2.23 -0.15 -3.18 -1.96 -19.61

Other foods 59.47 1,120.56 -1.28 -182.17 0.16 36.39 2.97 60.77 0.24 36.56 -4.22 -949.27

Beverages 

and tobacco 10.36 206.02 -0.71 -62.72 0.00 -0.05 0.94 113.11 0.12 28.61 -2.45 -149.86

Ethanol1 4.42 33.69 -0.66 -15.93 0.07 0.04 -0.83 0.00 -0.08 -0.03 -5.38 -0.05

Ethanol2 23.09 0.00 - - 0.90 3.71 -0.31 0.00 0.68 0.25 0.15 0.24

DDGS -8.44 -10.92 -2.14 -36.26 1.83 1.64 1.82 0.00 - - - 0.00

Biodiesel 14.03 11.07 0.13 0.10 -16.49 -3.79 31.01 31.13 0.07 0.30 -0.36 -3.17

Total ag. 9,586.28 -2,924.08 -465.13 2,006.26 711.57 -7,475.14

DDGS = distillers' dried grains with solubles. f_v = fruits and vegetables. - = not applicable.

Source: USDA, Economic Research Service analysis using Transatlantic Trade and Investment Partnership model.
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the rest of the world decrease in the second scenario as exports are diverted to the EU (except for 

biodiesel, which has a small increase). Because tariffs, TRQs, or NTMs do not change for all other 

regions, the change in U.S. exports to the rest of the world is never really greater than 5 percent, 

except for some commodities with small base exports. 

Bilateral imports for the United States in this scenario generally increase across agricultural 

commodities as more imports are needed to satisfy domestic demand. The increase in U.S. imports 

from the EU are much larger than those from other countries, as tariffs/TRQs and NTMs are 

removed on EU imports only. The largest percentage increases in U.S. agricultural imports from 

the EU are in commodities in which U.S. NTMs were removed: fruits (708.78 percent), vegetables 

(459.26), and beef (113.41). In addition, U.S. imports of cheese, paddy rice, other crops, vegetable 

oil, butter, and processed sugar from the EU increase by double-digit amounts due to the marketaccess portion of this scenario. 

Supply constraints in the United States dictate that overall exports decrease for some commodities. 

For example, total exports of wheat decrease in this scenario at an even greater rate than in the first 

scenario (table 11). Without the removal of NTMs, total U.S. wheat exports decrease by 0.82 percent 

(see table 7); with the removal of NTMs, they decrease by 1.74 percent (table 11). Eliminating 

NTMs in coarse grains, soybeans, and poultry meat results in an increase in overall U.S. exports 

for these sectors, even though net exports in each commodity decrease when only tariffs and TRQs 

are removed. The largest increases in overall U.S. exports in the second scenario are attributed to 

removal of either tariffs/TRQs or NTMs. U.S. commodities with increases in exports due to tariff/

TRQ removal include butter, cheese, and processed sugar. Commodities with changes due largely 

to NTM removal include vegetables and pork. Increases in U.S. beef exports are attributed to the 

removal of both types of measures. 

Changes in production for U.S. agricultural commodities in the second scenario largely follow the 

changes in exports (table 12). The U.S. commodities with the largest decreases in production are 

wheat, cheese, and fruits. The decline for wheat is larger in the second scenario (1.27 percent) than 

in the first (0.55) because of the reallocation of resources toward those commodities in which NTMs 

are removed. Interestingly, U.S. poultry meat production decreases despite the increase in exports to 

the EU when NTMs are removed. This effect stems from a decrease in domestic demand for poultry 

meat, a result of higher prices. U.S. production of fruits and vegetables also decreases in the second 

scenario, despite the removal of NTMs in these commodities. This is due to the rise in U.S. imports 

of fruits and vegetables from the EU and the fact that the EU only has NTMs removed on three 

commodities (versus seven for the United States). Thus, the EU is better able to use resources for the 

few commodities with NTMs removed. The U.S. commodities with the largest production increases 

in the second scenario are pork, butter, paddy rice, and beef. 

U.S. prices increase for all agricultural commodities in the scenario (except wheat, prepared fruits 

and vegetables, and biodiesel) due to increased competition for goods from the export market (table 

12). A comparison of prices across the two scenarios shows mixed results: some commodities have 

higher price increases in the second scenario and others do not. Clearly, however, price increases are 

greater for those commodities in which NTMs are removed.

Trade and Production Impacts - EU

Along with increases in EU agricultural exports to the United States in the second scenario, EU 

exports to all other countries increase for most agricultural commodities (except processed sugar, 

processed feed, and biodiesel) (see table 10). However, a large reduction in intra-EU trade leads to 
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Table 11

Changes in net trade values from tariff and non-tariff measure removal 

Exports Imports

Sector U.S. EU All others U.S. EU All others

Percent change

Paddy rice 5.84 -3.45 -4.26 6.03 -0.72 -1.76

Wheat -1.74 0.17 -0.25 1.95 0.05 -0.12

Coarse grains 1.41 -9.47 -1.03 1.59 8.97 -0.36

Fruits 2.95 2.84 -1.47 3.96 0.22 -0.13

Vegetables 13.14 2.42 -2.02 6.16 1.52 -0.02

Nuts 0.35 -0.33 -0.11 1.49 0.14 -0.39

Soybeans 1.91 -11.39 -1.16 1.99 3.30 -0.31

Rapeseed -1.51 1.81 0.25 0.54 0.71 -0.03

Other oilseeds -1.69 1.76 0.19 0.46 0.74 -0.22

Sugarcane/beet 2.87 0.89 0.03 0.95 -1.02 0.15

Other crops -0.46 1.26 -0.31 1.06 -0.39 -0.22

Bovine -2.21 -0.87 0.65 3.49 -0.83 -0.24

Hogs 3.01 -2.23 -1.15 2.07 -0.64 -0.04

Poultry and eggs -2.83 0.87 -0.34 1.35 -0.28 0.55

Other animals -1.95 0.78 0.14 0.73 -0.03 -0.01

Raw milk -6.29 3.84 -0.22 2.78 -1.04 0.15

Beef 24.57 -4.13 -5.16 6.62 4.28 -0.24

Pork 53.88 -48.38 -17.94 2.40 26.29 -0.11

Poultry meat 0.92 -0.24 -0.44 1.66 0.76 -0.20

Other meats -0.79 0.09 0.07 1.68 0.33 -0.24

Vegetable oil -1.99 3.16 -0.13 1.48 -0.56 -0.03

Whey -0.48 -2.08 -3.22 2.31 6.05 0.32

Powdered milk -1.50 -0.47 -0.08 2.83 1.60 -0.11

Butter 36.46 -7.09 -7.05 10.58 12.35 1.60

Cheese 11.58 1.59 -3.50 30.94 0.85 0.24

Other dairy products -0.11 0.16 -0.48 3.05 0.25 0.12

Processed sugar 15.01 -0.93 0.09 1.23 0.74 0.03

Processed rice 3.31 -4.86 -0.30 0.94 1.93 -0.24

Prepared f_v 6.47 -0.01 -0.95 1.09 0.33 0.01

Cereal preparations 6.22 -0.09 -0.90 1.47 0.60 -0.06

Sugar preparations 2.00 -0.31 -1.13 1.82 0.77 -0.17

Processed feed -1.23 -1.15 -0.20 1.42 -1.50 -0.25

Other foods 5.82 0.13 -0.91 1.22 0.49 0.00

Beverages and tobacco 1.32 0.09 -0.43 0.79 0.55 0.03

Ethanol1 0.31 -0.08 -0.64 -0.50 -0.58 -0.02

Ethanol2 0.56 0.67 -0.41 -0.49 -0.41 0.47

DDGS -1.57 -0.12 1.10 0.09 0.04 0.52

Biodiesel -1.07 0.03 0.54 0.08 0.08 0.42

DDGS = distillers' dried grains with solubles. f_v = fruits and vegetables. 

Source: USDA, Economic Research Service using Transatlantic Trade and Investment Partnership model.
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Table 12

Changes in production values and prices from tariff and non-tariff measure removal

Production Market price

Sector U.S. EU All others U.S. EU

Percent change

Paddy rice 1.78 -2.04 0.00 0.77 -1.20

Wheat -1.27 -0.16 -0.04 -0.07 -0.86

Coarse grains 0.48 -4.09 -0.06 0.41 -1.78

Fruits -0.87 1.13 -0.14 0.07 -0.72

Vegetables -0.25 -0.04 -0.13 0.24 -0.96

Nuts 0.12 -0.22 0.03 0.34 -0.98

Soybeans 0.80 -6.11 -0.17 0.56 -1.93

Rapeseed -0.72 1.51 0.03 0.14 -0.62

Other oilseeds -0.83 1.62 0.03 0.11 -0.60

Sugarcane/beet 0.29 -0.14 0.02 0.43 -0.88

Other crops -0.65 0.29 -0.04 0.12 -0.79

Bovine 1.59 -1.08 -0.20 0.58 -0.69

Hogs 3.28 -1.92 -0.18 0.47 -1.11

Poultry and eggs -0.13 0.29 0.00 0.44 -1.09

Other animals -1.00 0.63 0.05 0.42 -1.08

Raw milk -0.21 0.19 -0.03 0.53 -0.89

Beef 1.76 -1.52 -0.41 0.37 -0.44

Pork 5.45 -2.67 -1.00 0.33 -0.66

Poultry meat -0.17 0.08 0.00 0.34 -0.46

Other meats -0.25 0.12 0.05 0.31 -0.36

Vegetable oil -0.82 1.84 -0.04 0.34 -0.76

Whey -0.25 -0.34 -0.10 0.36 -0.84

Powdered milk -0.56 0.07 0.01 0.36 -0.44

Butter 2.64 -0.80 -0.58 0.35 -2.48

Cheese -1.19 0.64 -0.37 0.28 -0.46

Other dairy products -0.06 0.18 -0.02 0.36 -0.36

Processed sugar 0.33 -0.37 0.01 0.18 -0.14

Processed rice 1.20 -2.81 0.01 0.16 -0.51

Prepared f_v 0.94 0.08 -0.09 -0.22 -0.33

Cereal preparations 0.31 0.07 -0.07 0.23 -0.24

Sugar preparations 0.39 -0.02 -0.03 0.26 -0.21

Processed feed 0.77 -1.47 -0.14 0.29 -0.26

Other foods 0.64 0.23 -0.15 0.23 -0.59

Beverages and tobacco 0.09 0.18 -0.01 0.20 -0.31

Ethanol1 -0.10 -0.23 0.11 0.28 0.10

Ethanol2 -0.02 0.34 0.12 0.19 -0.26

DDGS -0.10 -0.25 0.18 0.62 -3.60

Biodiesel 1.28 0.32 0.06 -0.13 -0.13

DDGS = distillers' dried grains with solubles. f_v = fruits and vegetables.

Source: USDA, Economic Research Service using Transatlantic Trade and Investment Partnership model.
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a decrease in net EU exports for most agricultural commodities (table 11). The only EU agricultural 

commodities with large increases in total exports are those in which NTMs are removed and cheese 

(from tariff/TRQ removal). Total imports for most EU agricultural commodities increase due to 

rising exports from the United States. 

For the EU, production declines in many agricultural commodities in the scenario but increases 

in the cheese, rapeseed and vegetable oil, vegetables, and fruit commodities (table 12). EU fruit 

production in the second scenario benefits from NTM removal and increases by 1.13 percent 

compared to the first scenario, where EU fruit production decreases by 0.08 percent. Note that the 

elimination of EU NTMs on U.S. soybeans results in a large increase in EU production of oilseeds 

(i.e., rapeseed). This is despite the fact that the U.S. imposes no NTM on EU oilseeds. The following 

may help account for the EU increase in oilseed production under these circumstances. When the 

NTMs are removed, the EU increases its imports of U.S. oilseeds. Consequently, U.S. exports of 

oilseeds to non-EU countries decrease due to supply limitations. The increase in EU imports of 

oilseeds from the United States results in increased EU oilseed exports to those countries for which 

oilseeds from the United States have declined. The demand for imported oilseeds in these other 

countries leads to a production increase for EU oilseeds. The projected decrease in U.S. oilseed (e.g., 

soybean) exports to countries other than the EU is quite significant when tariffs and NTMs were 

removed (2.66). When only tariffs are eliminated, the decrease is only 0.66 percent. Prices for all 

EU agricultural commodities decrease in the second scenario (except for ethanol) due to the overall 

increase in EU imports.

Macro Impacts

The removal of NTMs would lead to an increase in real GDP for the United States and the EU (table 

13) but a small decrease for the other NAFTA countries (Canada and Mexico). GDP changes in 

the second scenario are similar to those in the first scenario for all countries examined. However, 

the increase in GDP in the second scenario is slightly larger than in the first scenario for the United 

States (0.10 percent versus 0.09 percent) and the EU (0.29 percent versus 0.23 percent). 

Table 13

Macro impacts from tariff and non-tariff measure removal

Value of agriculture Land-use change

Region GDP Imports Exports Forestry Crops Livestock

Percent change

U.S. 0.10 2.23 4.96 -0.08 -0.10 0.16

EU 0.29 1.33 -1.40 0.24 -0.21 -0.20

Canada -0.03 -0.33 -0.29 0.04 -0.23 0.21

Mexico -0.04 -0.57 -0.63 0.00 -0.06 0.04

China 0.00 0.03 -0.82 -0.01 -0.03 0.02

India 0.00 0.11 -0.80 0.16 -0.03 0.25

Brazil 0.00 -0.35 -1.56 0.16 -0.05 -0.12

ROW 0.00 -0.10 -1.08 0.04 -0.03 0.00

GDP = gross domestic product. ROW = rest of world.

Source: USDA, Economic Research Service using Transatlantic Trade and Investment Partnership model.
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For aggregate agricultural trade, total agricultural imports increase for both the United States and 

EU more in the second scenario than in the first. U.S. imports increase by 2.23 percent, which is 

largely the result of an increase in cheese and beef imports. EU imports increase by 1.33 percent, 

largely due to rising pork and butter imports. When NTMs are eliminated, U.S. agricultural exports 

increase at twice the rate as when NTMs are in place. Although results show a decrease in total EU 

agricultural exports in the second scenario, this change accounts for intra-EU and extra-EU trade. 

The change due to extra-EU trade only is an increase of 1.99 percent. The decrease in overall EU 

agricultural exports in the first scenario is also largely attributable to a reduction in intra-EU trade.

For all other countries examined, imports and exports decline for all but a handful of agricultural 

commodities in the second scenario. Changes in market prices for all other countries examined in 

the second scenario are not reported due to aggregation issues. However, changes in production 

are available. These changes are relatively small, except for large decreases in production of pork, 

nuts, and butter. The decreases for these three products are triggered by a decrease in exports, as 

U.S. exports displace all other-country exports, lessening the need to raise production. Overall 

trade decreases for all other countries examined except China and India, where agricultural imports 

increase by a small amount. 

When NTMs are removed in addition to tariffs and TRQs, the increase in land shifting to livestock 

use in the United States is larger than when only tariffs and TRQs are removed. That is, livestock 

land use increases 0.16 percent in the second scenario, up from 0.10 percent in the first scenario. 

Interestingly, the EU has a decrease in both crop and livestock land use in the second scenario. Two 

factors account for this effect: the earlier increase in EU imports of U.S. crops from tariff and TRQ 

removal is now coupled with a large increase in meat imports, and forestry land is valued more 

highly in the EU than in the United States in the GTAP model. Again, livestock land increases 

considerably in most other countries examined, except Brazil. 

Market Access With NTM Removal, Lingering Adverse 

Demand Effects

Several studies have documented the role of consumer preferences in relation to NTMs (e.g., Lusk 

et al., 2003; Costa-Font et al., 2008). If NTMs are removed for select agricultural products, export 

gains from their removal could be overstated if the NTMs had changed consumers’ preferences or 

if the NTMs in fact reflected consumer preferences. Either way, as NTMs could suggest or induce 

a negative consumer reaction to the associated imported goods, the removal of certain NTMs does 

not increase the likelihood that consumer preferences will switch from domestically produced 

goods to their imported equivalents. In the final scenario, we consider removal of tariffs/TRQs 

and select NTMs, as well as decreases in consumer demand (or preference changes). To account 

for demand changes, we modify the GTAP Armington parameter to reflect lower consumer 

demand for imported versions of the good (see appendix 4 for more details on this approach). The 

reduction in parameters is made for all commodities where NTMs are removed, both on the U.S. 

side and the EU side. 

Trade and Production Impacts - United States 

In the third scenario, U.S exports to the EU increase for most agricultural commodities (table 

14). To be expected, the increase is lower than that in the second scenario for those commodities 

where demand decreases if NTMs are eliminated. In the third scenario, U.S. exports of coarse 
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Table 14

Changes in bilateral trade values from demand sensitivity

Sector U.S. to EU U.S. to all others All others to U.S. EU to U.S. EU to all others All others to EU

Percent Million $ Percent Million $ Percent Million $ Percent Million $ Percent Million $ Percent Million $

Paddy rice 437.51 96.54 -5.33 -43.30 5.36 4.58 14.44 0.13 1.96 0.58 -15.71 -94.31

Wheat 22.20 108.91 -2.24 -293.22 1.38 9.26 6.21 1.38 0.88 60.79 -5.80 -92.97

Coarse grains 89.19 335.93 -1.10 -173.73 1.11 11.05 2.16 0.51 1.03 21.25 -19.09 -350.82

Fruits 24.54 74.56 -0.81 -33.95 -3.00 -244.81 142.88 99.72 0.35 6.96 -1.43 -133.51

Vegetables 49.31 150.43 -0.72 -21.95 -4.12 -295.67 107.71 143.85 0.50 18.41 -4.61 -315.09

Nuts 3.94 68.26 -1.40 -41.63 1.13 22.04 8.51 2.31 1.97 3.80 -2.92 -50.81

Soybeans 30.75 344.73 -1.15 -226.32 1.57 8.90 5.66 0.01 5.41 1.64 -16.69 -699.11

Rapeseed -1.25 0.00 -1.37 -1.98 0.54 0.61 1.51 0.01 0.13 0.24 0.71 13.15

Other oilseeds -1.00 -2.36 -1.55 -15.73 0.49 1.84 1.58 0.15 0.40 2.51 0.96 12.79

Sugarcane/ 

beet 30.68 0.03 -3.45 -0.01 0.85 0.23 1.46 0.00 1.34 0.05 -0.96 -0.16

Other crops 34.14 292.54 -2.44 -304.33 0.39 47.45 10.07 76.04 1.56 86.29 -1.41 -319.85

Bovine 5.22 7.20 -2.24 -16.69 1.48 23.79 1.85 4.03 0.13 2.01 -4.64 -7.83

Hogs 56.63 141.18 -1.85 -42.86 1.22 7.73 2.03 1.93 0.51 4.87 -11.69 -149.78

Poultry and 

eggs 11.96 0.00 -2.08 -17.06 1.05 5.91 1.72 0.00 0.93 24.95 -0.12 -2.05

Other animals 26.71 0.42 -1.45 -12.10 0.46 3.42 1.24 0.40 0.73 3.69 -0.07 -0.61

Raw milk -4.57 -0.01 0.00 0.00 1.59 0.68 5.35 0.14 2.95 0.49 -0.74 -0.80

Beef 181.45 349.67 -0.79 -51.60 -2.97 -120.66 105.77 15.10 0.08 2.59 -18.05 -703.65

Pork 1,994.90 1,199.08 -0.84 -53.86 1.18 11.65 2.36 11.05 0.39 21.00 -85.18 -1,199.69

Poultry meat 841.66 0.46 -0.95 -39.54 1.33 8.31 8.14 0.00 0.11 2.28 -0.30 -5.87

Other meats 11.88 5.25 -0.77 -9.75 1.15 4.30 2.33 0.37 0.04 0.23 -1.56 -9.98

Vegetable oil 15.39 59.89 -2.25 -152.05 -0.42 -27.44 14.04 123.22 2.79 116.85 -0.86 -113.77

Whey 433.42 33.88 -2.89 -31.35 0.76 0.59 7.03 0.11 0.83 11.09 -74.99 -25.57

Powdered milk 907.90 21.81 -2.20 -34.35 0.48 0.42 4.73 0.29 0.11 1.17 -29.57 -16.74

Butter 206.52 151.12 -4.47 -13.32 -6.10 -2.82 11.93 4.07 2.76 23.43 -92.92 -106.36

Cheese 996.82 76.29 -2.44 -12.28 -24.38 -28.73 30.80 329.07 0.34 10.48 -12.50 -58.06

Other dairy 

products 125.74 32.48 -2.65 -25.02 0.15 1.29 4.35 4.28 0.21 9.80 -2.88 -26.17

Processed 

sugar 153.37 50.62 -1.63 -4.38 1.00 27.00 37.96 6.95 -0.50 -6.53 -0.71 -21.29

Processed rice 145.02 78.55 -1.82 -26.20 0.61 4.02 7.71 1.77 0.97 1.54 -6.98 -53.16

Prepared f_v 61.83 378.60 -0.45 -23.67 -0.35 -26.70 4.10 23.19 0.03 1.53 -3.18 -313.48

Cereal 

preparations 43.31 435.11 -0.96 -48.71 -0.09 -7.71 2.20 46.41 0.00 -0.01 -6.47 -363.27

Sugar 

preparations 171.03 55.99 -0.91 -16.64 -0.20 -1.30 3.89 3.16 -0.02 -0.47 -9.25 -49.87

Processed 

feed 2.39 3.47 -1.30 -20.60 0.79 5.59 1.31 1.68 -0.14 -2.96 -1.61 -16.09

Other foods 59.83 1,127.42 -1.07 -151.99 0.06 12.66 2.76 56.61 0.17 25.68 -4.24 -953.86

Beverages 

and tobacco 10.43 207.46 -0.61 -54.28 -0.03 -2.73 0.86 103.43 0.10 22.95 -2.48 -151.88

Ethanol1 4.43 33.76 -0.62 -14.85 0.02 0.01 -0.79 0.00 -0.43 -0.16 -5.38 -0.05

Ethanol2 23.33 0.00 - - 0.73 3.01 -0.26 0.00 0.41 0.15 0.12 0.19

DDGS -6.78 -8.76 -1.36 -23.00 1.27 1.14 0.63 0.00 - - - 0.00

Biodiesel 13.32 10.51 -0.38 -0.30 -16.83 -3.87 30.54 30.65 0.09 0.38 -0.38 -3.28

Total ag. 5,921.02 -2,052.60 -534.96 1,092.02 479.55 -6,383.66

DDGS = distillers' dried grains with solubles. f_v = fruits and vegetables. - = not applicable.

Source: USDA, Economic Research Service analysis using Transatlantic Trade and Investment Partnership model.
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grains, soybeans, fruits, vegetables, beef, pork, and poultry meat increase by less than in the second 

scenario, when NTM removal was considered without any shocks to import demand. For example, 

U.S. fruit exports to the EU increase by 62.22 percent in the second scenario. In the third scenario, 

the impact of decreased consumer demand causes exports of the same commodity to increase by 

only 24.54 percent. U.S. exports of fruits (and all other commodities with NTMs) to other countries 

do not decrease by as much if demand sensitivities are considered, as some of the decreased exports 

to the EU are redirected to the rest of the world. Overall, model results suggest that for U.S. products like beef and fruits, the benefits from removing NTMs could be offset by the negative demand 

shocks if enough strong consumer preferences are reflected in the standards and regulations that 

make up certain NTMs.

In the third scenario, bilateral imports increase for the United States for most agricultural commodities, especially from the EU. The increases in U.S. imports from the EU in the third scenario are 

much larger than those from other countries. The only U.S. commodity with a decrease in imports 

from the EU is ethanol. U.S. imports from EU commodities with NTMs are also smaller in the third 

scenario than in previous scenarios. For example, vegetable exports were 459.72 percent higher when 

NTMs were eliminated but are only 107.71 percent higher when consumer demand decreases. U.S. 

overall imports also increase when changes in consumer preferences are considered. This effect 

holds for all agricultural commodities, much as it did in the previous two scenarios (table 15). 

Similar to results in the other two scenarios, changes in production for U.S. agricultural commodities in the third scenario largely follow changes in exports (table 16). Although U.S. production 

of wheat, fruits, and cheese again declines more than that of any other commodity, the effect is 

mitigated somewhat by the reduction in demand for resources from commodities with NTMs. U.S. 

market prices increase for most agricultural commodities but decrease for wheat, prepared fruits and 

vegetables, and biodiesel.

Trade and Production Impacts - EU

Similar to U.S. exports, EU exports of commodities with demand sensitivities do not increase as 

much as they do in the second scenario. EU exports of fruits, vegetables, and beef exports to the 

United States increase by 709.51, 459.73, and 112.75 percent, respectively, under the NTM-removal 

scenario. When demand sensitivities are considered, the increases are smaller at 142.88, 107.71, and 

105.77 percent, respectively. Overall, the results for EU bilateral trade and total trade are similar 

to those from the other two scenarios. However, in the third scenario, resources are allocated away 

from the NTM commodities to other commodities. 

For the EU, production declines for many agricultural commodities but increases for cheese, rapeseed, vegetables, and fruits. Under this demand-sensitivity scenario, EU vegetable production 

declines (versus the increase in the NTM-removal scenario). Market prices for all EU agricultural 

commodities except ethanol decline in the third scenario.

Macro Impacts

Similar to results in the other two scenarios, the GDPs for the United States and the EU increase in 

the third scenario by 0.10 and 0.28 percent, respectively (table 17). Changes in GDP for the other 

countries examined are similar across scenarios as well. 
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Table 15

Changes in total trade values from demand sensitivity 

Exports Imports

Sector U.S. EU All others U.S. EU All others

Percent change

Paddy rice 6.38 -3.81 -4.32 5.59 -0.57 -1.62

Wheat -1.35 -0.10 -0.19 1.79 0.25 -0.13

Coarse grains 1.45 -5.24 -1.37 1.15 5.03 -0.33

Fruits 0.92 0.58 -1.04 1.33 0.09 -0.13

Vegetables 4.02 0.26 -1.73 2.13 0.60 -0.03

Nuts 0.57 -0.81 -0.21 1.26 0.22 -0.34

Soybeans 0.68 -12.35 -1.36 1.56 2.03 -0.24

Rapeseed -1.37 1.28 0.27 0.54 0.70 -0.02

Other oilseeds -1.45 1.23 0.19 0.51 0.69 -0.20

Sugarcane/beet 3.61 0.51 0.07 0.82 -0.74 0.10

Other crops -0.09 0.73 -0.24 0.95 -0.11 -0.21

Bovine -1.08 -0.60 0.21 1.61 -0.14 -0.10

Hogs 3.84 -1.99 -1.21 1.52 -0.11 -0.01

Poultry and eggs -2.08 0.77 -0.23 1.05 -0.12 0.48

Other animals -1.40 0.64 0.09 0.57 -0.02 0.02

Raw milk -4.32 2.88 -0.19 1.84 -0.74 0.09

Beef 4.25 -1.69 -3.07 2.88 1.94 -0.19

Pork 23.49 -34.68 -15.68 1.97 13.78 -0.05

Poultry meat -0.87 0.25 0.21 1.34 0.19 -0.16

Other meats -0.34 0.06 0.00 1.35 0.30 -0.19

Vegetable oil -1.29 2.62 -0.14 1.24 -0.43 -0.02

Whey 0.23 -2.03 -3.32 1.93 6.18 0.42

Powdered milk -0.80 -0.48 -0.12 2.44 1.61 -0.08

Butter 37.14 -7.14 -7.08 10.05 12.39 1.61

Cheese 12.52 1.51 -3.50 30.27 0.83 0.22

Other dairy products 0.77 0.12 -0.54 2.59 0.24 0.10

Processed sugar 15.32 -0.95 0.07 1.12 0.74 0.02

Processed rice 3.51 -4.87 -0.30 0.85 1.92 -0.21

Prepared f_v 6.10 -0.01 -0.90 1.14 0.35 -0.02

Cereal preparations 6.38 -0.10 -0.93 1.34 0.62 -0.05

Sugar preparations 2.12 -0.32 -1.14 1.67 0.81 -0.16

Processed feed -0.99 -0.89 -0.18 1.01 -1.12 -0.22

Other foods 6.05 0.06 -0.92 1.07 0.49 -0.02

Beverages and tobacco 1.41 0.05 -0.44 0.72 0.54 0.02

Ethanol1 0.43 -0.09 -0.62 -0.55 -0.63 -0.19

Ethanol2 0.60 0.40 -0.33 -0.43 -0.37 0.37

DDGS -1.38 -0.12 1.00 0.06 -0.01 0.34

Biodiesel -0.92 0.00 0.52 0.05 0.03 0.30

DDGS = distillers' dried grains with solubles. f_v = fruits and vegetables.

Source: USDA, Economic Research Service using Transatlantic Trade and Investment Partnership model.
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Table 16

Changes in production values and prices from demand sensitivity

Production Market price

Sector U.S. EU All others U.S. EU

Percent change

Paddy rice 1.94 -2.19 0.00 0.73 -1.04

Wheat -0.98 -0.26 -0.02 -0.06 -0.68

Coarse grains 0.36 -1.99 -0.10 0.31 -1.16

Fruits -0.17 0.23 -0.09 0.19 -0.66

Vegetables -0.01 -0.19 -0.11 0.24 -0.76

Nuts 0.19 -0.56 0.02 0.29 -0.83

Soybeans 0.24 -4.40 -0.26 0.32 -1.46

Rapeseed -0.61 1.16 0.03 0.09 -0.47

Other oilseeds -0.64 1.22 0.02 0.08 -0.46

Sugarcane/beet 0.32 -0.18 0.02 0.37 -0.69

Other crops -0.43 0.12 -0.03 0.11 -0.62

Bovine 0.33 -0.54 -0.11 0.32 -0.57

Hogs 1.99 -1.47 -0.16 0.28 -0.91

Poultry and eggs -0.07 0.35 0.02 0.27 -0.90

Other animals -0.70 0.52 0.04 0.26 -0.90

Raw milk -0.14 0.17 -0.03 0.31 -0.69

Beef 0.39 -0.58 -0.24 0.24 -0.35

Pork 2.76 -1.93 -0.88 0.23 -0.56

Poultry meat -0.08 0.24 0.06 0.24 -0.38

Other meats -0.13 0.09 0.03 0.22 -0.31

Vegetable oil -0.57 1.52 -0.05 0.21 -0.66

Whey 0.05 -0.26 -0.12 0.24 -0.79

Powdered milk -0.30 0.05 0.00 0.24 -0.38

Butter 2.73 -0.82 -0.59 0.23 -2.44

Cheese -1.11 0.60 -0.37 0.16 -0.41

Other dairy products 0.00 0.16 -0.02 0.24 -0.31

Processed sugar 0.36 -0.38 0.01 0.15 -0.11

Processed rice 1.28 -2.83 0.01 0.15 -0.48

Prepared f_v 0.75 0.08 -0.09 -0.07 -0.28

Cereal preparations 0.31 0.07 -0.08 0.19 -0.21

Sugar preparations 0.31 0.00 -0.03 0.23 -0.16

Processed feed 0.32 -1.11 -0.11 0.24 -0.22

Other foods 0.68 0.19 -0.15 0.18 -0.46

Beverages and tobacco 0.09 0.15 -0.01 0.17 -0.26

Ethanol1 -0.09 -0.44 0.09 0.26 0.18

Ethanol2 0.01 0.21 0.09 0.16 -0.19

DDGS -0.09 -0.45 0.16 0.29 -2.95

Biodiesel 0.60 0.29 0.06 0.03 -0.12

DDGS = distillers' dried grains with solubles. f_v = fruits and vegetables.

Source: USDA, Economic Research Service using Transatlantic Trade and Investment Partnership model.
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For aggregate agricultural trade, U.S. agricultural imports increase in the third scenario, but changes 

for both imports and exports are less than those in the second scenario. For the EU, aggregate 

imports increase slightly (0.88 percent) in the third scenario. For all regions, changes in exports in 

the third scenario are similar to those in the second scenario. 

Comparison Across Scenarios 

CGE results are often presented in percentage changes as the base data sometimes differ. Otherwise, 

one would not need to show the base levels. However, level changes can provide extra information for model results. Level changes from the first scenario (tariff and TRQ removal) indicate that annual U.S. agricultural exports to the EU would increase by $5.5 billion. The largest 

annual increases are in processed foods ($2.2 billion)—the largest of which is to the other-foods 

commodity, beef ($1.5 billion), and pork ($0.3 billion). In the second scenario, U.S. exports increase 

by an additional $4.1 billion. The largest additional gains are to pork ($2 billion), soybeans ($0.9 

billion), and beef ($0.4 billion). As resources are moved to commodities where NTMs are removed, 

the value of all other U.S. agricultural exports to the EU decreases from $14.5 billion to $14.4 

billion. For the EU, agricultural commodities with the largest gains in the first scenario are cheese 

($0.3 billion) and processed foods ($0.2 billion). In total, EU exports in the first scenario expand by 

$0.8 billion. The removal of select NTMs in the second scenario generates an additional gain of $1.2 

billion. Results in the two scenarios also suggest that T-TIP would decrease the U.S. agricultural 

trade deficit with the EU. When tariffs and TRQs are removed, the U.S. agricultural deficit declines 

from $7.3 billion in the base year (2011) to $2.6 billion. When NTMs are also removed, the deficit 

falls to $0.1 billion. Bilateral trade increases and GDP gains are moderated when accounting for 

lingering adverse demand effects after NTM removal.

Table 17

Macro impacts from demand sensitivity 

Value of agriculture Land-use change

Region GDP Imports Exports Forestry Crops Livestock

Percent change

U.S. 0.10 1.48 2.66 -0.08 -0.10 0.16

EU 0.28 0.88 -1.05 0.24 -0.21 -0.20

Canada -0.02 -0.31 -0.36 0.04 -0.23 0.21

Mexico -0.03 -0.49 -0.57 0.00 -0.06 0.04

China -0.01 0.03 -0.74 -0.01 -0.03 0.02

India 0.00 0.11 -0.75 0.16 -0.03 0.25

Brazil 0.00 -0.31 -1.33 0.16 -0.05 -0.12

ROW 0.00 -0.09 -0.93 0.04 -0.03 0.00

GDP = gross domestic product. ROW = rest of world.

Source: USDA, Economic Research Service using Transatlantic Trade and Investment Partnership model.
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Finally, figure 2 presents the percentage change in U.S. exports to the EU of commodities with 

NTMs in place. This figure brings together all the information from tables 6, 10, and 14. As shown, 

pork and poultry have the largest percentage gains in exports when NTMs are removed in addition 

to tariff removal, but those results are somewhat mitigated when demand sensitivity is considered. 

Source USDA, Economic Research Service. 

Figure 2

U.S. exports to the EU for commodities with non-tariff measures (NTMs) in place

Percent change

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

Tariffs

Tariffs + NTMs

Demand sensitivity

33,505

Coarse grains Fruits Vegetables Beef Pork Poultry Soy
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Conclusions

This study provides a quantitative assessment of the potential effects of a T-TIP agreement through 

a disaggregated analysis of agriculture, including detailed sector-specific changes for important 

commodities. In addition to the economic impacts of tariff and TRQ removal, the analysis considers 

the effects of select NTM removal and changes in consumer preferences. Thus, alternative hypothetical scenarios for a T-TIP agreement include reducing tariffs, TRQs, and quantified NTMs to zero. 

Moreover, the analysis considers a scenario that assesses the effects of consumer preferences on any 

benefits from the removal of NTMs. A detailed examination of specific agricultural NTMs in a CGE 

model has not been attempted in the literature thus far, nor has any analysis considered the potential 

demand impacts of NTM removal.

Findings suggest the potential for expanded agricultural trade between the United States and the EU 

of $6.3 billion if tariffs and TRQs are eliminated, despite tariffs already being relatively low. Based 

on model results, market access (scenario 1) would generate increases in GDP for both the United 

States (0.09 percent) and the EU (0.23 percent). U.S.-EU agricultural trade expands in the scenario, 

with larger increases for U.S. exports. As a result, U.S. agricultural prices increase (by less than half 

a percent), while EU agricultural prices fall. 

When select NTM removal is considered in addition to tariff and TRQ removal (scenario 2), agricultural trade between the EU and the United States expands even more than in the first scenario. Based 

on baseline year (2011) data, the EU agricultural trade surplus with the United States of $7.3 billion 

would fall to $2.6 billion in the market-access scenario and $0.1 billion when NTMs are removed. 

When accounting for lingering adverse demand effects after the removal of NTMs (scenario three), 

bilateral trade increases and GDP gains are moderated.

This study does not evaluate all NTMs affecting agricultural trade between the United States and 

the EU, nor does it consider NTM barriers for nonagricultural products. If liberalization of NTMs 

extends to nonagricultural products, those products would likely attract additional resources, which 

would lower agricultural trade and production changes. In addition, if T-TIP eliminates an NTM that 

is not quantified in this analysis, this could affect trade and production of other commodities. Lastly, 

the analysis does not consider possible spillover effects for other countries also affected by NTMs, 

although several studies have considered this issue (e.g., EP, 2014). 

The scope of the T-TIP negotiations goes beyond cutting tariffs, TRQs, and NTMs. The outcome of 

any negotiations on investment policy and trade in services could also affect agricultural trade. This 

analysis does not account for the gains that might be achieved in other areas of the negotiations, nor 

does it account for domestic farm policy, market responses (e.g., structural or efficiency changes 

in industries that lose their trade protections), or productivity gains that may result from increased 

trade opportunities.
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Appendix 1—GTAP Data for T-TIP CGE Model

We use the most recent GTAP database for our T-TIP experiments, version 9, which has a base year 

of 2011. The disaggregated GTAP base data contain over 130 regions and 57 sectors; researchers 

often aggregate these to make the results easier to comprehend and interpret. For T-TIP, we aggregate the regions to eight markets: Brazil, Canada, China, EU, India, Mexico, the United States, 

and a rest of the world (ROW) region that contains all other regions (app. table A1-1). Our regional 

aggregation includes those most likely to be impacted by T-TIP: the other NAFTA regions and other 

major agricultural trade markets. Indeed, five of our regions were the top agricultural exporters in 

2013 (EU, United States, Brazil, China, and Canada). Our aggregation set includes the top six agricultural importers, except for Japan and Russia.

Our sector aggregation scheme is heavily weighted toward agricultural commodities (app. table 

A1-2). To that end, we keep any GTAP base data agricultural commodity disaggregated (e.g., wheat 

and processed rice are treated as distinct commodities). Unfortunately, there are only 20 commodities that can be considered as agricultural commodities in the base data; thus, we use the SplitCom 

utility to create several commodities of interest in T-TIP. As a result, our final aggregation is 38 agricultural and biofuel commodities, with 47 total sectors. Agricultural commodities 1-16 are raw products in agricultural production, commodities 21-38 are the processed products of these raw products, 

and commodities 41, 42, 46, and 47 are biofuels and their co-products. This aggregation scheme 

allows analysis for meat products, which are a focus of NTMs; nuts, a large source of U.S. exports 

to the EU; and processed food commodities (33-37), which, in aggregate, are the largest agricultural 

sector by a wide margin. Energy products (18-20, 39, 43) keep their original disaggregation as is 

required for use of the specific GTAP model we employ.

Appendix table A1-1

Region aggregation scheme

No. Country/region Included GTAP country/regions

1 Brazil bra

2 Canada can 

3 China chn, hkg

4 European Union 

(EU)

aut, bel, cyp, cze, dnk, est, fin, fra, deu, grc, hun, irl, ita, lva, ltu, lux, mlt, nld, 

pol, prt, svk, svn, esp, swe, gbr, bgr, hrv, rou 

5 India ind

6 Mexico mex 

7 United States usa 

8 Rest of world (ROW)

aus, nzl, xoc, jpn, kor, mng, twn, xea, brn, khm, idn, lao, mys, phl, sgp, tha, 

vnm, xse, bgd, npl, pak, lka, xsa, xna, arg, bol, chl, col, ecu, pry, per, ury, ven, 

xsm, cri, gtm, hnd, nic, pan, slv, xca, dom, jam, pri, tto, xcb, che, nor, xef, alb, 

blr, rus, ukr, xee, xer, kaz, kgz, xus, arm, aze, geo, bhr, irn, isr, jor, kwt, omn, 

qat, sau, tur, are, xws, egy, mar, tun, xnf, ben, bfa, cmr, civ, gha, gin, nga, sen, 

tgo, xwf, wcf, xac, eth, ken, mdg, mwi, mus, moz, rwa, tza, uga, zmb, xec, 

bwa, nam, zaf, xsc, xtw

Source: USDA, Economic Research Service. 
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Appendix table A1-2

Sector aggregation scheme 

No. Name Description GTAP sector code

1 Paddy rice Paddy rice pdr

2 Wheat Wheat wht 

3 Coarse grains Barley, corn, oats, and sorghum gro 

4 Fruits* Fruits v_f 

5 Vegetables* Vegetables v_f 

6 Nuts* Tree nuts v_f 

7 Soybeans* Raw soybeans osd 

8 Rapeseed* Raw rapeseed osd 

9 Other oilseeds* All other oilseeds osd 

10 Sugarcane/beet Raw sugarcane and beet c_b

11 Other crops Plant-based fibers and other crops pfb, ocr

12 Bovine Cattle, sheep, goats, horses ctl

13 Hogs* Hogs oap

14 Poultry and eggs* Poultry oap

15 Other animals* Wool and all other animals oap

16 Raw milk Raw milk rmk

17 Natural resources Fishery, forestry frs, fsh 

18 Coal Coal coa

19 Oil Oil oil

20 Gas Natural gas gas, gdt

21 Beef Beef cmt

22 Pork* Pork and pork products omt

23 Poultry meat* Poultry meats and products omt

24 Other meats* Other meat products omt

25 Vegetable oil Vegetable oils and fats vol 

26 Whey* Whey mil 

27 Powdered milk* Powder milks mil 

28 Butter * Butter, fats, oils, and substitutes mil 

29 Cheese* Cheese mil 

30 Other dairy products* Fluid milk and products mil 

31 Processed sugar Processed sugars sgr

32 Processed rice Processed rice pcr

33 Prepared f_v* Processed fruits and vegetables ofd

34 Cereal preparations* Processed cereal products ofd

35 Sugar preparations* Processed sugar products ofd

36 Processed feed* Livestock feed products ofd

37 Other foods* Other food products ofd

38 Beverages and tobacco Beverages and tobacco products b_t

39 P_C Petroleum, coal products p_c

40 L_Mfg Labor-intensive manufacturing tex, wap, lea, lum, ppp, fmp, mvh, 

otn, omf 

41 Ethanol1* Corn-based ethanol p_c

42 Ethanol2* Sugar-based ethanol crp

43 H_Mfg Capital-intensive manufacturing crp, nmm, i_s, nfm, ele, ome 

44 Ely Electricity ely 

45 Other services All other servicesw wtr, cns, trd, otp, wtp, atp, cmn, 

ofi, isr, obs, ros, osg, dwe 

46 DDGS* Distillers' dried grains with solubles ofd

47 Biodiesel* Biodiesel crp

Note: * represents a commodity split using SplitCom. DDGS = distillers’ dried grains with solubles. 

GTAP = Global Trade Analysis Project.

Source: USDA, Economic Research Service. 
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SplitCom

We completely disaggregate six of the GTAP-defined commodities into subsectors using the 

SplitCom utility developed by Horridge (2008). In addition, we break out the respective amounts 

for biofuels from their previous aggregate commodity (e.g., Ethanol1 is split from commodity 

p_c, but the p_c commodity remains). SplitCom is a matrix-balancing program that enables the 

user to subdivide the rows and columns of a commodity from a balanced social accounting matrix 

(SAM). The user provides data to disaggregate a GTAP sector’s input demands, uses in intermediate and final demand and trade, and tax and tariff payments. SplitCom then uses methods 

similar to maximum entropy to balance the disaggregated SAM and to satisfy accounting identities. The utility manipulates only the disaggregated sectors, which can be re-aggregated to restore 

the original values in the GTAP SAM. We ultimately use SplitCom to disaggregate 24 grain, 

meat, dairy, and biofuel subcommodities. Those with an asterisk in appendix table A1-2 are split; 

the original aggregated commodity is represented in the fourth column. For example, the original 

GTAP database has a commodity referred to as: f_v. This commodity is split into three components: fruits, vegetables, nuts. 

Data for the SplitCom procedure are drawn from multiple sources. Bilateral trade and tariff data are 

disaggregated using TASTE (Tariff Analytical and Simulation Tool for Economists), a software developed by Horridge and Laborde (2010) and based on the Market Access Maps (MacMap) HS-6 trade 

and tariff database (Guimbard et al., 2012). We use the version from October 2012, which is compatible with the GTAP v. 9 database, with some adjustments to tariff rates based on multiple sources. 

TASTE disaggregates the GTAP sectors into HS-6 data for trade and tariffs. These disaggregated 

data are then re-aggregated into the sectors defined in the ERS T-TIP CGE model, using the HS2002 

concordance developed by Hutcheson (2006). Data for the disaggregation of subsectors’ inputs and 

demands for their output are drawn from multiple sources, including the FAOSTAT database compiled 

by the Food and Agriculture Organization of the United Nations; USDA’s Production, Supply and 

Distribution (PS&D) database; USDA’s Global Agricultural Information Network (GAIN) reports; 

U.S. Energy Information Administration energy statistics; and national statistics.

Tariffs

We use external data from multiple sources and ERS expert reviews to validate the remaining tariff 

rates, to estimate a tariff rate for commodities that were disaggregated using SplitCom, and to review 

and update country tariffs. Note that the SplitCom program will allocate the original tariff value to all 

newly split commodities (e.g., if the tariff for the f_v commodity is 20, the new fruits, vegetables, and 

nuts commodities will all have a tariff of 20). We use GTAP’s Altertax utility to update the model to 

redefine tariffs on split commodities and to correct or update various tariff rates. 
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Appendix 2—The CGE Model

Given the complex links and interactions between agricultural commodities, competition among 

these commodities for limited economic resources, and interactions between the production, 

consumption, and trade activities, an economy-wide computational general equilibrium (CGE) 

modeling approach provides an appropriate framework to analyze the impacts of T-TIP. The value of 

a global CGE approach in analyzing the impacts of trade policy has previously been demonstrated in 

the work of several T-TIP studies (e.g., CEPR, 2013; ECORYS, 2009; EP, 2014). For both the CGE 

data and model, we rely on GTAP resources. 

In the standard static GTAP model, producers are described as perfectly competitive cost minimizers, with technology defined as a nested production function. Producers’ demand for intermediate inputs responds to prices for inputs and outputs, subject to a Leontief intermediates production 

function. A Constant Elasticity of Substitution (CES) production function over value added enables 

producers to substitute among primary factors as their relative prices change. Consumer demand is 

described by a Constant Difference of Elasticity (CDE) demand system, a nonhomogeneous function 

that allows income growth to affect consumer preferences. Cobb-Douglas functions describe government and investment demand, which imply constant budget shares in total expenditure. Import 

demand is described by nested Armington functions, in which demand is first allocated between 

the domestic good and the composite import, and then among national sourcing of the composite 

import. Countries (or regions) are linked through their bilateral trade flows, which explicitly account 

for transportation and marketing costs in moving goods from port to port. Factors are assumed to be 

fixed in national supply, fully employed, and mobile across commodities, except for land, which is 

assumed to have limited substitutability across crops. 

We use a version of the GTAP model specified in Beckman et al. (2012), which encompasses all the 

standard features mentioned above, along with some critical updates for agricultural commodities. In 

particular, the model incorporates biofuels and biofuel co-products into GTAP-E model (Beckman et 

al., 2011) and also incorporates the livestock/feed nesting structure from Keeney and Hertel (2009). 

In addition, the model uses the detailed land-use module (GTAP-AEZ), which captures heterogeneous land quality and allows a more realistic representation of agricultural production. GTAP-AEZ 

disaggregates land into 18 agro-ecological zones (AEZs) that share common climate, precipitation, 

and moisture conditions (Hertel et al., 2008). Alternative agricultural and forestry land uses then 

compete for lands with heterogeneous quality. Land use competition is modeled in the AEZ module 

with a nested constant-elasticity-of-transformation (CET) function. By imposing homothetic separability on the revenue function, the land allocation decision can be split into two sequential stages. In 

the first stage, the land-owner decides on land cover (i.e., whether a given parcel of land will be in 

crops, forestry, or pasture). In the second stage, crop land is allocated across different uses. 

With the base model established, we turn to modeling tariffs, TRQs, and NTMs for T-TIP. 

Implementing a tariff/TRQ-removal scenario is relatively straightforward in the GTAP model; one 

only has to specify the size of the shock on the variable “tms,” the tax on imports into a country. 

This variable is defined as the tax introduced by the importer on the exporter for a given commodity. 

Conducting an NTM-removal scenario is also straightforward, as the variables used are all exogenous price shocks: tms, txs (the tax on exports from a country), and ams (a tax on production efficiency). More details are provided in appendix 3. 
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Appendix 3—Allocating NTM Costs for the CGE Model

NTMs can affect trade by influencing the economic decisions of multiple agents, including 

exporters, importers, and consumers, in exporting and importing countries. While the gravity model 

provides an aggregate estimate of NTM costs, it does not provide information regarding how these 

costs should be allocated across agents in our CGE model (see box “Allocating NTMs in GTAP”). 

Based on the literature, we examine the incidence of costs using a detailed supply-chain, price-gap 

approach. As described by Ferrantino (2012), this method can decompose the costs of the NTM, 

providing appropriate allocations for our CGE model. Following the supply-chain analysis for beef 

from Arita et al. (2015), we conduct a similar exercise for biotech crops, using corn as our example. 

The production of biotech-free crops entails higher costs of export production and higher “rents,” 

or price markups, by exporters and/or importers as a result of the scarcity of a product. These costs 

and markups cumulatively lead to price premiums observed at the retail level. The supply-chain 

approach works effectively if a commodity is defined to include few individual products (e.g., beef 

or corn). However, for commodities that consist of many individual products (e.g., fruits and vegetables), conducting a supply-chain analysis would be time intensive. Thus, we avoid the supply-chain 

approach and instead allocate NTM costs equally across the different mechanisms. 

Supply-Chain Analysis

Estimates of added costs of export production for agricultural producers begin with the cost of 

preserving seed purity. They also include the cost of cleaning planters, foregone benefits of not using 

biotech varieties (including increased pesticide use), and monitoring. Bullock et al. (2000) estimate 

the monitoring costs for corn at an average of $0.09 per bushel. Exporters must clean containers, but 

much of this is done as a matter of course, so the cost is not additional. They also do quite a bit of 

testing to make sure that varieties are nonbiotech. Bullock et al. (2000) estimate this cost at $.056 

per bushel. Maltsberger and Kalaitzandonakes (2000) estimate total identity preservation (IP) costs 

at $0.265 per bushel for high-oil corn, which, they note, has lower level testing than that required 

for genetically modified foods. Total per-bushel testing needed to meet IP requirements (including 

testing for biotech) is estimated to cost $0.32, which is about a 14-percent increase over conventional 

corn production. 

Premiums paid for nonbiotech corn are very low in the EU, as the region is largely self-sufficient in 

corn and can get any excess needed from Ukraine and India (Varacca et al., 2014). The premiums 

offered are generally 1 to 3 percent (Brookes et al., 2005). At a price of $88 per ton in 2000, this 

equates to about $.04 per bushel. This does not exceed the increased costs of nonbiotech corn, in dollar 

or percentage terms. In total, we assume that production inefficiencies (ams) represent one-third of the 

NTM and export taxes (txs) represent two-thirds. No NTM costs are allocated to the importer.

Once the supply-side calculations are complete, we can implement the costs of NTMs in our CGE 

model. To do so, we use the econometrically estimated AVEs and appropriate them according to 

the supply-side estimates. This amount is then built into the GTAP model based on the rent/cost 

literature approach. That is, if an NTM cost is attributable to an import or export tax, the model is 

rebased to build in the tax. The model does not need to be rebased if the NTM cost is due to the 

production inefficiencies. NTM costs for meats are assumed to follow the beef supply-chain structure of Arita et al. (2015), while field crops are assumed to follow the corn breakout.
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Allocating NTMs in GTAP

There is a large literature base on how to specify non-tariff measure (NTM) costs in computable general equilibrium (CGE) models. That is, CGE-based analyses simulate the effects of removing NTMs after drawing on 

external studies for estimates of the NTMs’ trade impacts. In these studies, the price or quantity gaps are converted 

into ad valorem equivalents (AVE) of surcharges on import tariffs that would have the same effect on prices or 

trade volumes as the NTM. The AVEs are then allocated in a CGE model across three mechanisms: (1) surcharges 

to import tariffs (Global Trade Analysis Project (GTAP) variable coded tms), which are appropriate when the 

NTM directly affects the price of the good in the import region; (2) export taxes (GTAP variable coded txs), 

assuming that the NTM restricts the ability of exporters to ship their products; or (3) production inefficiencies 

(GTAP variable coded ams), those extra costs needed to produce the product to comply with NTM standards. 

Approaches to allocating NTM costs (table) range from assigning all NTM costs to one of three mechanisms (e.g., 

Andriamananjara et al., 2003) to differentiating between rents versus costs (e.g., ECORYS, 2009) where rents are 

modeled, in effect, like export and import taxes (depending on where the price markup occurs) and any actual 

costs in production are allocated to production inefficiencies. Our NTM cost decomposition follows this rent/cost 

approach, assigning rents or price markups to mechanisms (1) and (2), import or export taxes, and production cost 

increases to mechanism (3), production inefficiencies. 

Literature review of CGE approaches to modeling NTMs

Author Case

Source of NTM 

meaure

Allocation across mechanisms

Export tax Import tax Efficiency Intrepretation

Andriamananjara 

et al. (2003)

Footwear Price gaps 100 Allocation based on implementation of specific NTM. Footwear quantitative import restrictions create quota rents. Apparel voluntary 

export restrictions create quota rents. NTMs 

have a "sand-in-the-wheels" type of effect.

Apparel 100

Processed 

food

100

ECORYS 

(2009)

TTIP Gravity model 26.6 13.3 60 Resource-using “waste” is modeled as an 

efficiency loss, rent-seeking distortions are 

modeled as import or export taxes. Allocation 

is estimated.

EP (2014) TTIP Gravity model 33 33 33 NTMs represented either as a pure efficiency 

loss (sand in the wheels) or as a tax, which 

may affect the importer and/or exporter. 

Fox et al. 

(2003)

U.S. 

Mexico 

trucking 

case

Secondary 

source: Empirical 

study of time and 

costs for crossborder shipments

40-66 33-60 Indirect trade costs are time spent and costs 

paid by shippers for nonessential bordercrossing services, divided by efficiency (time 

lost) effect and import tariff equivalent (border 

costs for which rent can be captured).

Fugazza 

(2008)

Global Secondary 

source: Kee et al. 

(2009)

0 <25 >75 Allocation depends upon sector. Rent-creating NTMs modeled as import tariffs; technical 

NTBs modeled as trade productivity shocks.

Hertel et al. 

(2001)

Japan and 

Singapore

Multiple secondary sources0 0 100 Productivity shock includes reduction in 

direct border costs, time saved, and wholesale-retail margins due to customs automation and increased business-to-business 

activities.

Minor and 

Tsigas 

(2008)

Global Secondary 

source: Hummels 

et al. (2007) 0 0 100

Hummels et al.’s tariff equivalents of trade 

delays counted time in three stages of transit: 

inland transport, customs processing, and 

port handling.

Winchester 

(2009)

AustraliaNew 

Zealand

Gravity model 0 0 100 AVE of the NTM includes all border costs, 

excluding transport costsand trade taxes.

NTM = non-tariff measure. AVE = ad valorem equivalent. T-TIP = Transatlantic Trade and Investment Partnership. NTB = non-tariff barrier.

Source: USDA, Economic Research Service. 
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During the course of this analysis, results indicate that the allocation across the three mechanisms 

does not necessarily affect trade or production but does have an impact on welfare calculations. 
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Appendix 4—NTM Removal and Demand Sensitivities

Non-tariff measures (NTMs) frequently arise when countries use different production processes 

or regulate them differently. Many studies suggest that consumers’ willingness to pay for goods12

may vary with production processes (Lusk et al., 2003; Tonsor and Schoroeder, 2003; Alfnes, 2004; 

Alfnes and Rickertsen, 2003, 2004 for beef). Studies also find that consumers are willing to pay a 

positive premium to avoid consuming biotech food (Burton et al., 2001; Moon et al., 2007; Hartl 

and Herrmann, 2009; Curtis and Moellner, 2007; Carlsson et al., 2007), although other studies 

find some willingness to purchase GE foods (Mather et al., 2011). This is particularly noticeable 

when European consumers are contrasted against consumers from the United States (Lusk et al., 

2003; Lusk, 2003; Moon and Balasubramian, 2001). In some cases, consumers are unwilling to buy 

biotech foods produced from animals fed with biotech feed (Curtis and Moellner, 2007).

Consumer concerns can translate into effects on imports. If retailers choose to respond to consumer 

preferences for goods produced with particular technologies, the concentration of retail in the EU 

and the high proportion of retailer-branded products in some EU countries can result in large negative effects on demand for imported goods affected by NTMs. For example, consumer concerns 

about GE foods have resulted in rejection of GE ingredients by many EU retailers, which has had 

implications for importers (See Arita et al., 2015; Varacca et al., 2014).

We note that the gravity estimates of NTM coefficients capture anything that is unique to the trade 

relationship between the United States or EU as importers and the set of exporters affected by the 

NTM. This can include the importer(s)’ demand parameters for the exported good. If the coefficient 

includes demand effects and not simply regulatory barriers, then we may not be able to assume that 

the full effect of the estimated NTM will disappear with a change in statute resulting from T-TIP 

negotiations.

To address potential demand-side effects of removing NTMs, we carry out a sensitivity analysis of 

the second scenario, which includes tariff/TRQ removal and an elimination of select NTMs. This 

analysis considers a lack of equalization of consumer demand preferences (willingness to pay) for 

domestic versus imported products even with the removal of NTMs. This is based on the Armington 

elasticity (see box “Armington Elasticity”). To do so, an ex-post sensitivity analysis is conducted, 

where the market access with NTM removal scenario is considered; however, Armington parameters are varied to mimic demand preferences. Here, we make no judgment on the degree to which 

consumers might avoid imported products; rather, we run a sensitivity analysis that considers a 

reduction in the Armington elasticities by one-half. This simulates a situation in which consumers 

do not regard imported goods as perfect substitutes for domestic goods, a situation that can arise 

when consumers regard two versions of a particular good produced with two different technologies 

as separate goods.

Appendix table A4-1 presents the Armington parameter for ESUBD used in the first two scenarios. 

Note that the GTAP model assumes that the domestic/imported elasticity is half of the estimated 

trade elasticities (ESUBM), an assumption that is used in our work. In addition, note that, in general, 

12These studies can be regarded as an upper bound as consumers frequently overestimate what they would be willing 

to spend on a particular food characteristic in an unconscious attempt to please the interviewer (Lusk, 2003). Even 

experimental auctions may not represent true consumer behavior because consumers are rarely spending their own 

money in a true market setting.
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the EU elasticities are smaller for agricultural commodities than those of all other countries. This is 

to prevent intra-EU trade from completely collapsing.13 Appendix table A4-2 indicates the ESUBD 

parameters used for the demand sensitivity scenario; note only the parameters for commodities with 

NTM removal were altered. 

13The regional aggregation scheme aggregates all the EU individual countries into one total EU region, thus keeping 

the Armington parameters the same as all other countries would assume that each individual country views another EU 

country as a non-EU country. Lowering the Armingtons reflects the FTA or no-borders aspect of the EU. 

Armington Elasticity

Import demand in the standard Global Trade Analysis Project (GTAP) model is described by nested Armington 

functions, in which demand is first allocated between the domestic good and the composite import, and then 

among national sourcing of the composite import. This structure is represented in the figure, where aggregate 

demand is based on the nesting with domestic demand and aggregate import demand. The substitution among 

these two sources is governed by the elasticity of substitution, noted as parameter σESUBD. Aggregate import 

demand is then further distributed among the regions that the domestic country imports from; this is governed 

by the parameter σESUBM. These parameters are very influential in computable general equilibrium (CGE)-based 

trade policy experiments (Gallaway et al., 2003) and have been shown to be important even in CGE experiments 

that do not conduct a trade policy experiment (Plevin et al., 2015). The Armington elasticities can be estimated 

econometrically. McDaniel and Balistreri (2003) provide findings from the literature. Among these, they note that 

longrun estimates are higher than shortrun estimates; more disaggregate analyses estimate higher elasticities; and 

time-series analyses generally find smaller elasticities than cross-section estimates. Estimating these elasticities is 

time-consuming and beyond the scope of this work; thus, we rely on those estimated by Hertel et al. (2007).

Source USDA, Economic Research Service. 

Appendix figure 4-1

GTAP Armington structure

Aggregate demand

Domestic demand Aggregate import demand

σESUBD

U.S. Canada Rest of world

σESUBM
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Appendix table A4-1

ESUBD parameters used in the market-access and non-tariff measure-removal scenarios—continued

ESUBD U.S. EU Canada Mexico China India Brazil ROW

Paddy rice 5.05 2 5.05 5.05 5.05 5.05 5.05 5.05

Wheat 4.45 1.5 4.45 4.45 4.45 4.45 4.45 4.45

Coarse grains 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3

Fruits 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85

Vegetables 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85

Nuts 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85

Soybeans 2.45 2.45 2.45 2.45 2.45 2.45 2.45 2.45

Rapeseed 2.45 2.45 2.45 2.45 2.45 2.45 2.45 2.45

Other oilseeds 2.45 2.45 2.45 2.45 2.45 2.45 2.45 2.45

Sugarcane/beet 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7

Other crops 3.11 3.11 3.11 3.11 3.11 3.11 3.11 3.11

Bovine 2 0.5 2 2 2 2 2 2

Hogs 2 0.5 2 2 2 2 2 2

Poultry and eggs 2 0.5 2 2 2 2 2 2

Other animals 2 0.5 2 2 2 2 2 2

Raw milk 3.65 3.65 3.65 3.65 3.65 3.65 3.65 3.65

Natural resources 1.28 1.28 1.28 1.28 1.28 1.28 1.28 1.28

Coal 3.05 3.05 3.05 3.05 3.05 3.05 3.05 3.05

Oil 5.2 5.2 5.2 5.2 5.2 5.2 5.2 5.2

Gas 10.96 10.96 10.96 10.96 10.96 10.96 10.96 10.96

Beef 3.5 1 3.5 3.5 3.5 3.5 3.5 3.5

Pork 3.5 1 3.5 3.5 3.5 3.5 3.5 3.5

Poultry meat 3.5 1 3.5 3.5 3.5 3.5 3.5 3.5

Other meats 3.5 1 3.5 3.5 3.5 3.5 3.5 3.5

Vegetable oil 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3

Whey 3.65 0.3 3.65 3.65 3.65 3.65 3.65 3.65

Powdered milk 3.65 0.3 3.65 3.65 3.65 3.65 3.65 3.65

Butter 3.65 0.3 3.65 3.65 3.65 3.65 3.65 3.65

Cheese 3.65 0.3 3.65 3.65 3.65 3.65 3.65 3.65

Other dairy products 3.65 0.3 3.65 3.65 3.65 3.65 3.65 3.65

Processed sugar 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7

Processed rice 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6

Prepared f_v 2 0.3 2 2 2 2 2 2

Cereal preparations 2 0.3 2 2 2 2 2 2

Sugar preparations 2 0.3 2 2 2 2 2 2

Processed feed 2 0.5 2 2 2 2 2 2

Other foods 2 0.3 2 2 2 2 2 2

Beverages and tobacco 1.15 0.3 1.15 1.15 1.15 1.15 1.15 1.15

P_C 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1

L_Mfg 1.75 1.75 3.5 3.5 3.5 3.5 3.5 3.5

Ethanol1 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7

Continued—
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Appendix table A4-1

ESUBD parameters used in the market-access and non-tariff measure-removal scenarios—continued

ESUBD U.S. EU Canada Mexico China India Brazil ROW

Ethanol2 1.75 1.75 3.5 3.5 3.5 3.5 3.5 3.5

H_Mfg 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8

Ely 1.91 1.91 1.91 1.91 1.91 1.91 1.91 1.91

Other services 2 2 2 2 2 2 2 2

DDGS 2 2 2 2 2 2 2 2

Biodiesel 2 2 2 2 2 2 2 2

ESUBD = estimated trade elasticities. f_v = fruits and vegetables. DDGS = distillers’ dried grains with solubles. ROW = rest of world. 

P_C = petroleum and coal products. L_Mfg = labor-intensive manufacturing. H_Mfg = capital-intensive manufacturing. Ely = electricity.

Source: USDA, Economic Research Service.
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Appendix table A4-2

ESUBD parameters used in the demand-sensitivity scenario—continued

ESUBD U.S. EU Canada Mexico China India Brazil ROW

Paddy rice 5.05 2 5.05 5.05 5.05 5.05 5.05 5.05

Wheat 4.45 1.5 4.45 4.45 4.45 4.45 4.45 4.45

Coarse grains 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3

Fruits 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85

Vegetables 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85

Nuts 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85

Soybeans 2.45 2.45 2.45 2.45 2.45 2.45 2.45 2.45

Rapeseed 2.45 2.45 2.45 2.45 2.45 2.45 2.45 2.45

Other oilseeds 2.45 2.45 2.45 2.45 2.45 2.45 2.45 2.45

Sugarcane/beet 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7

Other crops 3.11 3.11 3.11 3.11 3.11 3.11 3.11 3.11

Bovine 2 0.5 2 2 2 2 2 2

Hogs 2 0.5 2 2 2 2 2 2

Poultry and eggs 2 0.5 2 2 2 2 2 2

Other animals 2 0.5 2 2 2 2 2 2

Raw milk 3.65 3.65 3.65 3.65 3.65 3.65 3.65 3.65

Natural resources 1.28 1.28 1.28 1.28 1.28 1.28 1.28 1.28

Coal 3.05 3.05 3.05 3.05 3.05 3.05 3.05 3.05

Oil 5.2 5.2 5.2 5.2 5.2 5.2 5.2 5.2

Gas 10.96 10.96 10.96 10.96 10.96 10.96 10.96 10.96

Beef 3.5 1 3.5 3.5 3.5 3.5 3.5 3.5

Pork 3.5 1 3.5 3.5 3.5 3.5 3.5 3.5

Poultry meat 3.5 1 3.5 3.5 3.5 3.5 3.5 3.5

Other meats 3.5 1 3.5 3.5 3.5 3.5 3.5 3.5

Vegetable oil 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3

Whey 3.65 0.3 3.65 3.65 3.65 3.65 3.65 3.65

Powdered milk 3.65 0.3 3.65 3.65 3.65 3.65 3.65 3.65

Butter 3.65 0.3 3.65 3.65 3.65 3.65 3.65 3.65

Cheese 3.65 0.3 3.65 3.65 3.65 3.65 3.65 3.65

Other dairy products 3.65 0.3 3.65 3.65 3.65 3.65 3.65 3.65

Processed sugar 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7

Processed rice 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6

Prepared f_v 2 0.3 2 2 2 2 2 2

Cereal preparations 2 0.3 2 2 2 2 2 2

Sugar preparations 2 0.3 2 2 2 2 2 2

Processed feed 2 0.5 2 2 2 2 2 2

Other foods 2 0.3 2 2 2 2 2 2

Beverages and tobacco 1.15 0.3 1.15 1.15 1.15 1.15 1.15 1.15

P_C 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1

L_Mfg 1.75 1.75 3.5 3.5 3.5 3.5 3.5 3.5

Ethanol1 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7
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Appendix table A4-2

ESUBD parameters used in the demand sensitivity scenario—continued

ESUBD U.S. EU Canada Mexico China India Brazil ROW

Ethanol2 1.75 1.75 3.5 3.5 3.5 3.5 3.5 3.5

H_Mfg 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8

Ely 1.91 1.91 1.91 1.91 1.91 1.91 1.91 1.91

Other services 2 2 2 2 2 2 2 2

DDGS 2 2 2 2 2 2 2 2

Biodiesel 2 2 2 2 2 2 2 2

ESUBD = estimated trade elasticities. f_v = fruits and vegetables. DDGS = distillers’ dried grains with solubles. ROW = rest of world. 

P_C = petroleum and coal products. L_Mfg = labor-intensive manufacturing. H_Mfg = capital-intensive manufacturing. Ely = electricity.

Source: USDA, Economic Research Service.