United States 

Department of 

Agriculture

Economic

Research

Service

Economic

Research

Report

Number 138

August 2012

Reciprocal Trade Agreements

Impacts on U.S. and Foreign 

Suppliers in Commodity and 

Manufactured Food Markets

Thomas Vollrath

Jason Grant

Charles Hallahan
www.ers.usda.gov

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Recommended citation format for this publication:

Vollrath, Thomas, Jason Grant, and Charles Hallahan. Reciprocal Trade 

Agreements: Impacts on U.S. and Foreign Suppliers in Commodity and 

Manufactured Food Markets, ERR-138, U.S. Department of Agriculture, 

Economic Research Service, August 2012.
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

Reciprocal trade agreements (RTAs), which grant special preferences to members, affect 

the pattern and volume of bilateral trade in global markets. This study uses the gravity 

framework and panel data depicting annual trade between 69 countries over 31 years 

to examine how 11 RTAs have shaped U.S. and other suppliers’ exports of commodity 

and manufactured foods. Empirical results show that joint RTA membership enabled 

exporters to increase their trade with member country importers in the two food markets. 

The few agreements that failed to have a positive effect on member trade in either 

commodity food or manufactured food involve developing countries that typically grant 

very limited cross-border trade preference to member countries. Interestingly, model 

results indicate that RTAs can be a vehicle to increase trade externally. Nine of the 11 

RTAs also expanded exports externally to nonmember countries, albeit to a lesser degree 

than with member importers. In some cases, however, nonmember exporters of food 

bore the cost of the RTA-induced expansion of trade. Five RTAs lowered food imports 

from nonmember suppliers. The adverse effects on nonmember suppliers were more 

pronounced for the United States than for other competitors.

Keywords: Trade policy, reciprocal trade agreements, agricultural and food trade, 

gravity models 

Acknowledgments

The authors thank Anna D’Souza, Mark Gehlhar, Carol Goodloe, Maurice Landes, 

William Liefert, Andrew Muhammad, Daniel Pick, Donna Roberts, Ralph Seeley, and 

Steven Zahniser of USDA’s Economic Research Service and anonymous reviewers for 

their insights, critiques, and comments. 

Thomas Vollrath, Jason Grant, 

and Charles Hallahan

Reciprocal Trade Agreements: 

Impacts on U.S. and Foreign 

Suppliers in Commodity and 

Manufactured Food Markets

Economic

Research 

Report

Number 138 

August 2012
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About the authors

Thomas Vollrath is an economist, formerly with USDA’s Economic 

Research Service (ERS); Jason Grant is an assistant professor at Virginia 

Polytechnic Institute and State University; and Charles Hallahan is a research 

analyst with ERS.
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Contents

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

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

Theoretical and Applied Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

The Analytical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

The Benchmark and Generalized Food Models . . . . . . . . . . . . . . . . . . . . 7

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Empirical Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Trade Impacts of RTAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Summary and Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

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

Appendix Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
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Summary

What Is the Issue?

Reciprocal trade agreements (RTAs) between two or more countries offer 

an alternative approach to lower trade barriers than that provided through 

multilateral negotiations of the World Trade Organization (WTO). But 

because RTAs grant preferential cross-border treatment to member countries, 

they may distort markets and divert trade from nonmember exporters. This 

study focuses on two agricultural subsectors—commodity foods and manufactured foods—and quantifies the extent to which RTAs have expanded 

trade between member countries and altered trade between member and 

nonmember countries.

Most applied research on RTAs has examined the effects on intra-bloc 

trade, or trade between partners belonging to the same trading bloc. This 

study examines the impacts of 11 RTAs on both intra-bloc trade and extrabloc trade, or trade between member and nonmember countries. The study 

also differentiates between the effects on the United States as a nonmember 

exporter and the effects on other nonmember competitors.

What Did the Study Find?

Given that international policy dynamics make negotiating a multilateral 

trade agreement difficult, countries have used RTAs as a practical and 

feasible route to gain greater access to foreign markets. These agreements 

have affected the pattern and volume of bilateral trade. 

•	RTA membership increased trade in most commodity and manufactured

food markets during the 1975-2005 period. However, in some cases, the 

increase in intra-bloc trade came at the expense of a shift in imports away 

from nonmember food exporters.

•	Not surprisingly, the rise in exports to partner markets was particularly

pronounced when the exporter and the importer were members of the 

same RTA. For example, the formation of the European Union (EU) 

induced an expansion of intra-EU trade in manufactured foods and 

commodity foods of 93 and 95 percent, respectively, between 1975 and 

2005. This growth equates to an average annual increase in food trade 

between EU countries of 3 percent per year.

•	The rise in intra-bloc trade within the North American Free Trade

Agreement (NAFTA) and the Closer Economic Relations (CER) agreement was, in contrast to that within the EU, substantially greater for 

manufactured foods than for commodity foods. Between 1989 and 

2005, trade between NAFTA members expanded 6 percent per year, on 

average, in manufactured foods and 3 percent per year in commodity 

foods. Over the same period, trade between CER members Australia and 

New Zealand increased 14 percent per year in manufactured foods and 3 

percent per year in commodity foods.
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•	Empirical results also show that bloc-member exports often expanded to

nonmember markets following the start of the agreement. This finding 

supports the view that RTA liberalization often spills over into other 

markets, increasing member exports to nonmember importers. Given that 

RTAs grant preferential treatment to members, the rise in extra-bloc trade 

was understandably less than the increase in intra-bloc trade (with the 

exception being extra-bloc trade involving members of the Association of 

Southeast Asian Nations Free Trade Agreement, or ASEAN).

•	In some markets, nonmember exporters of food bore the cost of RTA

formation. Members of ASEAN, the EU, the Common Market for 

Eastern and Southern Africa (COMESA), the Greater Arab Free Trade 

Area (GAFTA), and the South Asian Preferential Trade Arrangement 

(SAPTA) were found to have lowered food imports from extra-bloc 

exporters following implementation of the agreements. The adverse 

effects of four of these RTAs were more pronounced for the United 

States than for other nonmember exporters. COMESA diverted trade in 

commodity foods away from non-U.S. exporters, but it did not curtail 

imports of food from U.S. suppliers.

How Was the Study Conducted?

ERS adopted the gravity framework and estimated benchmark and generalized equations to quantify the effects of 11 specific RTAs on intra- and 

extra-bloc trade in commodity and manufactured food markets. Data used 

in the applied analysis include 31 years of bilateral trade flows and selected 

socioeconomic characteristics of 69 countries and all partner pairs covering 

the 1975-to-2005 timeframe. To isolate the dimensions of trade creation 

and trade diversion attributable to each of the RTAs, the benchmark models 

accounted for such factors underlying trade as bilateral exchange rates, relative factor endowments, transportation costs, and membership/nonmembership in the various RTAs. The generalized models incorporated country-pair 

effects. By fixing these effects, the generalized equations embody nonobservable factors affecting trade. Both theory and statistical criteria were used to 

differentiate and identify the preferred analytical approach. 
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Introduction

Trade agreements are policy instruments used to reduce cross-border protection and to open markets. The primary goal of the World Trade Organization 

(WTO) and its predecessor organization, the Generalized Agreement on 

Tariffs and Trade (GATT), is to contribute to economic development and 

growth by reducing the obstacles to international trade and helping to ensure 

a level playing field for all member countries. The GATT/WTO provides a 

legal and institutional framework for governing cross-border trade between 

member nations. The “most-favored-nation” (MFN) principle in Article 1 is 

the centerpiece of the GATT/WTO. According to the MFN provision, each 

WTO member is to grant all members the same advantage, privilege, favor, 

or immunity that it grants to any other country. In other words, the provision 

implies that member countries are not to discriminate in their trade policy 

against other members. 

A challenge facing the WTO and its member countries is reaching consensus 

about the rules governing international commerce, given the diversity of 

national sovereignty issues and vested interests. The current stalemate in 

the Doha Development Agenda (DDA) of the WTO reflects the difficulty 

of achieving agreement about trade policy reforms at the multilateral level. 

A former U.S. Trade Representative recently noted that the DDA may be 

doomed, but reciprocal trade agreements (RTAs) involving bilateral and 

regional groupings of countries are not (Schwab, 2011). According to Pascal 

Lamy, the Director-General of the WTO, these preferential agreements “will 

form part of the long-term tapestry of international trade relations” (World 

Bank, 2011).

RTAs offer an alternative approach to bargaining for mutual reduction of 

trade barriers within the framework of the WTO. It is easier for policymakers 

to reach agreement when there are relatively few countries engaged in negotiations. Still, RTAs may distort markets and divert trade away from efficient 

nonmember suppliers due to preferential cross-border treatment granted to 

member countries. 

RTAs (i.e., free-trade agreements, customs unions, and common markets) 

are not consistent with Article I of the MFN provision. They are, however, 

allowed under Article XXIV. The key requirement of Article XXIV is that the 

exchange of preferences not be partial (i.e., the removal of duties and other 

restrictive regulations of commerce are to apply to “substantially all trade” 

in products between members). The meaning and interpretation of the word 

“substantial,” however, is not clear (Crawford and Laird, 2000; WTO, 2000).

Economists began examining the implications of RTAs for the global 

trading system during the first wave of countries joining trading blocs in 

the 1990s. Some observers saw RTAs as “stumbling blocs” to freer trade 

RTAs, while others saw them as “building blocs.” Bhagwati (1991; 1994; 

1995), an ardent supporter of the GATT/WTO, repeatedly expresses concern 

that expansion in the number of RTAs was generating a “spaghetti bowl” 

of tariffs whereby countries subject the same product to different tariff 

rates depending on its ostensible origin. Bhagwati believes that RTAs are 

likely to spawn a complex web of protection across products and countries, 

reproducing the chaos of the 1930s protectionism. Bhagwati and Panagariya 
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(1996) are skeptical of the ability of RTAs to generate net welfare gains. 

Both clearly consider adherence to the MFN principle critical to the success 

of trade policy.

Bergsten (1996, 1998) questions whether the GATT/WTO provides the only, 

or even the most effective, way to open markets and promote trade liberalization. Bergsten coins the phrase “competitive liberalization” to describe “the 

overarching force” behind the proliferation of RTAs. According to Bergsten, 

the increase of global interdependence has induced countries to create RTAs 

to compete for footloose international investments. He argues that countries 

belonging to RTAs are better able and more willing to adopt bold trade liberalization reforms than are possible via multilateral trade negotiations within 

the GATT/WTO. Estevadeordal et al. (2008) show that some RTAs lower 

tariffs on imports from both member partners and nonmember partners.

Clearly, RTAs have become an increasingly prominent feature in the global 

marketplace in recent years. Vicard (2009) observes that 14 percent of 

country pairs trading in 2005 were members of RTAs, up from 4 percent in 

1980. But trade flows between RTA partners represented one-third of world 

trade. This implies that countries belonging to RTAs trade intensively with 

each other. Crawford and Fiorentino (2005) note that the world has entered 

into one of the most prolific periods of RTA formations in history. Moreover, 

the post-1990 wave of RTA formation shows no sign of abating. As of July 

2010, the WTO is monitoring 283 agreements, up from 180 agreements in 

2003, less than 100 agreements in 1995, and just 40 agreements in 1990. 

The rapid expansion of RTAs raises questions. U.S. policymakers have 

expressed concern, for example, that the large number of RTAs to which 

the United States does not belong may be eroding U.S. presence in foreign 

markets. Economists acknowledge that RTAs can provide “stepping stones” 

toward a more open world market or can, alternatively, be instruments that 

undermine global free trade and support for multilateral free trade (Krueger, 

2000; Andriamananjara, 2003). Quantitative analysis is needed to provide 

empirical evidence that clarifies the benefits and costs of these agreements. 

Econometric studies based on the gravity framework have been used to 

gauge the impact of RTAs on the volume of trade.1 A principal objective 

of the majority of such gravity studies is to estimate intra-bloc trade expansion, often referred to as trade creation, of these agreements.2 Intra-bloc trade 

involves trade between members of the same agreement. Perhaps the focus 

on intra-bloc trade creation stems from policymaker requests for information 

on the most tangible gains from RTAs. 

Panagariya (2000) notes that there is a tendency to ignore possible welfare 

losses derived from RTAs, especially with respect to nonmember countries. He posits that the concepts of trade creation and trade diversion3 are 

central to the policy debates about RTAs and that empirical analysis of these 

concepts are a challenging, albeit important, area for research. To infer the 

welfare effects of RTAs, Panagariya advocates applied analysis of trade 

creation and trade diversion by sector.

“Trade creation” occurs when products from low-cost producers in member 

countries displace high-cost domestic production. To date, relatively little 

1The majority of gravity models focus 

on total merchandise trade. However, 

a growing number of applied studies 

published in recent years examine the 

impact of RTAs on agricultural trade 

(e.g., Zahniser et al., 2004; Clarete et 

al., 2003; Koo et al., 2006; Lambert 

and McKoy, 2009; Grant and Lambert, 

2008; Jayasinghe and Sarker, 2008; and 

Vollrath et al., 2009).

2See Aitken, 1973; Sapir, 2001; 

Rose and van Wincoop, 2001; Cheng 

and Wall, 2005;Vollrath et al., 2006; 

Sandberg et al., 2006; Eicher and Henn, 

2009; Subramanian and Wei, 2007; 

Baier and Bergstrand, 2007; and Baier et 

al., 2008.

3Viner (1950) was the first to define 

these terms.
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attention has been given to the impact of RTAs on the reduction of trade 

between member and nonmember countries, often described as “trade

diversion.”4 Trade diversion takes place when bloc countries reorient their 

trade away from low-cost, nonmember countries toward higher cost, bloc 

suppliers. Applied research that restricts attention to the intra-bloc impact 

of RTAs without also examining the extra-bloc trade effects (also known as 

third-country effects) does not fully examine the role of these agreements in 

shaping international trade patterns. 

A recent ERS study deploys generic trade-creation and trade-diversion 

indexes in gravity equations to identify the impacts on agricultural trade of 

mutual as well as asymmetric RTA membership (Vollrath and Hallahan, 

2011). Mutual RTA membership refers to cases where both the exporter and 

the importer belong to the same bloc agreement. Asymmetric membership is 

consigned to cases when the exporter is not a member of an RTA to which 

the importer belongs. Vollrath and Hallahan find that mutual RTA membership typically expands trade while asymmetric membership reduces partner 

trade in the world agricultural marketplace. 

The current study uses gravity models to quantify the intra- and extra-bloc 

effects of individual RTAs on trade in agriculture. Intra-bloc trade refers 

to cases when trading partners belong to the same agreement. Extra-bloc 

trade is consigned to cases when either exporting partners or importing partners, but not both, belong to a common RTA. Unlike previously published 

research, this study controls for source-specific extra-bloc trade diversion. 

Moreover, it differentiates between the impacts of individual RTAs on U.S. 

and other competing suppliers who are not members of individual RTAs 

under consideration. 

More specifically, this study quantifies the impacts of 11 RTAs on trade in 

the commodity and manufactured food markets. The 11 agreements examined are the Andean Free Trade Agreement (ANDE), the Association of 

Southeast Nations Free Trade Agreement (ASEAN), the Central American 

Common Market (CACM), the Closer Economic Relations (CER), the 

Common Market for Eastern and Southern Africa (COMESA), the European 

Union (EU), the Greater Arab Free Trade Area (GAFTA), the Southern 

Common Market (MERCOSUR), the Canada-U.S. Free Trade Agreement/

North American Free Trade Agreement (NAFTA), the South African 

Development Community (SADC), and the South Asian Preferential Trade 

Arrangement (SAPTA).

Collectively, the 11 agreements captured 78 to 92 percent of world agricultural trade during the 1975-2005 period. These agreements are widely distributed throughout the globe. The CER, EU, and NAFTA consist mostly of 

high-income, industrialized countries. The eight other agreements comprise 

developing countries. (See table 1 for more detailed information about the set 

of RTAs selected for this study.) 

Commodity foods include raw ingredients (e.g., grains, nuts, fruit, and 

vegetables) as well as semi-processed products (e.g., preserved fruit and 

vegetables, meats, and dairy products). They are widely traded throughout 

the world because of the global distribution of relatively fixed land and 

labor supply (Regmi et al., 2005). Manufactured foods are highly processed 

4Studies addressing trade diversion 

include those that have deployed 

generic indexes from which 

generalizations can be drawn about 

the RTA phenomenon (e.g., Koo et 

al. 2006; Magee, 2008; and Vollrath 

and Hallahan, 2011) as well as those 

that have focused on specific RTAs, 

enabling distinctions to be made among 

individual agreements with different 

trade-policy orientations (Frankel, 

1997; Frankel and Wei, 1997; Wei and 

Frankel, 1997; Endoh, 1999; Cernat, 

2001; Tang, 2005; Grant and Lambert, 

2005; and Sarker and Jayasinghe, 

2007). 
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products (e.g., breakfast cereal, bakery products, pasta, candy, beer, soft 

drinks, soups, infant formula, and other food preparations). They are 

also extensively traded throughout the world even though the location of 

production of these foods is less dependent on the domestic availability of 

natural resources. 

In today’s globalized economy, manufacturing firms produce finished 

consumer foods in almost every country because raw agricultural inputs, 

such as grains, flour, and vegetables oils, can be easily sourced. If inputs 

are not available domestically, they can be readily imported from foreign 

suppliers—unless raw-material imports are constrained by cross-border 

trade barriers. 

Table 1

Reciprocal Trade Agreements

Acronym Agreement name Initial

date Country membership (1975-2005)*

ANDE Andean Free Trade Agreement 1996 Bolivia, Colombia, Ecuador, Peru, Venezuela

ASEAN Association of Southeast Asian 

Nations Free Trade Agreement 

1993 Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, 

Philippines, Singapore, Venezuela, Thailand

CACM Central American Common 

Market 

1991 Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua

CER Closer Economic Relations 1983 Australia, New Zealand

COMESA Common Market for Eastern & 

Southern Africa

1995 Angola, Burundi, Comoros, Democratic Republic of the Congo, 

Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, 

Mauritius, Namibia, Rwanda, Seychelles, Sudan, Swaziland, 

Uganda, Zambia, Zimbabwe

EU European Union 1975 Austria, Belgium, Czech Republic, Denmark, Finland, France, Malta, 

Netherlands, Germany, Greece, Hungary, Ireland, Italy, Latvia, 

Lithuania, Luxembourg, Poland, Portugal, Slovakia, Slovenia, Spain, 

Sweden, United Kingdom

GAFTA Greater Arab Free Trade Area 1998 Egypt, Jordan, Kuwait, Morocco, Saudi Arabia, Tunisia

MERC Mercosur Free Trade Agreement 1995 Argentina, Brazil, Paraguay, Uruguay

NAFTA North American Free Trade 

Agreement

1989 Canada, Mexico, United States 

SADC South African Development 

Community

2001 Angola, Botswana, Lesotho, Malawi, Mauritius, Mozambique, 

Namibia, South Africa, Swaziland, Zambia, Zimbabwe 

SAPTA South Asian Preferential Trade 

Arrangement

1996 Bhutan, Bangladesh, India, Pakistan, Moldova, Nepal, Sri Lanka

*Country membership coded dynamically in the regression models as countries entered/exited from the agreements. 

Note: Data on the 11 RTAs and their date of entry into force were compiled by scrutinizing previously published gravity studies and multiple 

websites, including the WTO homepage.
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Theoretical and Applied Models

A recent study at the Organisation for Economic Co-operation and 

Development (OECD) examined a large number of RTAs and found 

evidence that they had contributed to agricultural trade liberalization (OECD, 

2010). The empirical methodology adopted by OECD focused on the share 

of tariff lines that became zero as a result of RTAs.5 It did not evaluate other 

modes of protection (e.g., regulatory mechanisms, technical barriers to trade, 

sanitary and phytosanitary measures). A methodology that does not quantify 

nontariff barriers is inherently limited, especially in view of the post-1995 

shift toward such barriers following implementation of the Uruguay Round 

Agreement on Agriculture. 

Gravity models provide a means to identify the extent to which RTAs affect 

trade and trade liberalization that go beyond the narrow focus on tariffs 

alone. Parameter estimates derived from gravity equations quantify positive/

negative deviations in trade that characterize RTAs from hypothesized counterfactual levels in their absence—conditional on the other regressors in the 

estimating equations. These econometric models are, therefore, capable of 

isolating the impact of RTAs on trade flows, an issue that the authors of the 

OECD study explicitly acknowledge as one that merits further research. 

In this study, the gravity framework is used in conjunction with panel data 

spanning trade between 69 countries from 1975 through 2005 to quantify 

how 11 RTAs have shaped the pattern of world trade in commodity and 

manufactured foods.6,7 Benchmark and generalized models are estimated to 

measure the “treatment effects” of 11 RTAs on intra- and extra-bloc trade in 

the two food markets.8

The Analytical Framework

Gravity models have been widely deployed to gain a better understanding of 

the forces underlying trade. Tinbergen (1962) was the first to estimate such 

a model. He posited that the volume of trade between two countries was a 

function of their “masses” (proxied by Gross Domestic Product or population) and the distance separating them. Linnemann (1966) provided the first 

economic framework underlying the gravity model of international trade. He 

showed that the basic model could be derived from a four-equation partial 

equilibrium model of export supply and import demand.9 Linnemann was 

also the first to include RTA membership as an explanatory variable.

Theoretical advances and improvements in model specification of gravity 

equations have been made since the pioneering work of Tinbergen and 

Linnemann. (See Anderson, 1979; Helpman and Krugman, 1985; Bergstrand, 

1985, 1989, and 1990; Deardorff, 1998; Evenett and Keller, 2002; Anderson 

and van Wincoop, 2003 and 2004; Feenstra, 2002 and 2004; Baldwin, 2006; 

and Baier and Bergstrand (2007).)

In this study, all of the applied models are estimated using longitudinal rather 

than cross-sectional data. The employment of panel data creates greater 

efficiency, more variability, and less collinearity among the independent 

variables and is, therefore, generally preferred (Baltagi, 2001). Moreover, 

the use of panel data is particularly advantageous in estimation of econo5To be more precise, data are needed 

on duties collected under most-favorednation (MFN) status and under preferential tariffs on imported goods from 

partner countries to assess the impact of 

RTAs on bilateral trade. Most countries 

simply do not provide public access to 

data on preferential tariffs.

6Panel data, unlike cross-sectional 

data, can disentangle time-specific from 

country- and partner-pair-specific effects.

7The 69 countries include Algeria, Argentina, Australia, Austria, Bangladesh, 

Belgium-Luxembourg, Brazil, Cameroon, 

Canada, Chile, China (mainland), Colombia, Costa Rica, Denmark, Dominican 

Republic, Egypt, El Salvador, Finland, 

France, Germany, Ghana, Greece, Guatemala, Honduras, Hong Kong, India, Indonesia, Ireland, Israel, Italy, Ivory Coast, 

Jamaica, Japan, Jordan, Kenya, Malaysia, 

Mexico, Morocco, Netherlands, New 

Zealand, Nicaragua, Nigeria, Norway, 

Pakistan, Panama, Paraguay, Philippines, 

Poland, Portugal, Saudi Arabia, Senegal, 

Singapore, South African Customs Union 

(Botswana, Lesotho, Namibia, South Africa, and Swaziland), South Korea, Spain, 

Sri Lanka, Sweden, Switzerland, Taiwan, 

Thailand, Trinidad-Tobago, Tunisia, 

Turkey, United Kingdom, United States, 

Uruguay, Venezuela, and Zambia.

8RTA-treatment effects relate to the 

partial effects of RTA-based binary 

variables on the continuous dependent 

variable, unidirectional trade flow. 

See Heckman (2001) and Wooldridge 

(2002). Countries signing an RTA are, 

for example, classified as the “treatment group,” while country pairs that 

do not belong to the specific agreement 

are categorized as the “control group.” 

Comparisons can then be drawn 

between the existing levels of partner 

trade that characterize the member 

countries trading with each other in 

the various RTAs to the hypothesized 

counterfactual levels in the absence of 

these agreements.

9In Linnemann’s framework, markets 

cleared in response to the interaction 

between supply and demand forces, 

eliminating the need to include prices 

in applied analyses. His gravity model 

took the following reduced form: 

1 2

3 0

()( )

( )

β β

β = β i j

ij

ij

s D

X

R

where Xij denotes the volume of trade 

from country i and country j, si represents factors affecting the potential 
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metric models that incorporate RTA dummy variables. Sohn and Lee (2006) 

contend that any empirical examination of the impacts of RTAs should incorporate longitudinal data rather than just a single point in time to allow for the 

phaseout of trade barriers. Baier et al. (2008) observe that “panel data offer 

an opportunity to better identify unbiased effects” of RTAs on trade flow 

than cross-section models because implementation of RTAs is typically slow 

moving, whereas changes in trade flows occur rapidly. Importantly, panelbased models enable the researcher to enrich model specification with fixed 

effects that potentially mitigate omitted-variable and heterogeneity bias. The 

statistical framework encompassing conventional gravity models using panel 

data is characterized as follows: 

0 ln( ) X z ijt =+ + ijt ijt αβ ε ′ (1)

where Xijt denotes trade from country i to country j in year t, z’ is the vector 

of variables representing the export supply and import demand factors 

affecting partner trade, and εijt is the idiosyncratic error assumed to have a 

distribution characterized by a mean of zero and a constant variance. The 

independent variables include country-specific characteristics, such as the 

GDP of each country in year t, and other factors common to both countries 

that affect bilateral trade, such as symmetric or asymmetric RTA memberships in time t. 

Mátyás (1997) points out that model (1) imposes “unnecessary” restriction, 

namely that αi = αj = αt = 0 for all i, j, and t. He contends that the correct 

econometric specification takes the form of a triple-indexed model, with αt

capturing cyclic influences shared by all countries and αi and αj capturing 

exporter and importer fixed effects, respectively:

0 ln( ) X z ijt = ++++ + α αααβ ε i j t ijt ijt ′ (2)

Mátyás’s three fixed-effect approach has been criticized for being a restricted 

version of a more general model. Egger and Pfaffermayr (2003) note that model 

(2) does not span the whole vector space explaining variations in bilateral 

exports. They posit that the proper specification of gravity equations includes 

exporter-to-importer fixed effects (aij), in addition to Mátyás’s three-way effects:

0 ln( ) X z ijt = ++++ + + α αααα β ε i j t ij ijt ijt ′ (3)

To circumvent perfect collinearity in the estimation of the above model, one 

αi and one αj must be set to zero. The removal of an exporter-fixed and an 

importer-fixed effect imposes a series of cross-pair restrictions on αij’s. Due 

to orthogonality, model (3) is equivalent to the following two-way model 

with time and country-pair fixed effects: 

0 ln( ) X z ijt = ++++ + + α αααα β ε i j t ij ijt ijt ′ (3')

The inclusion of αij in model (3`) mitigates heterogeneity bias by relaxing 

the restriction that the intercept of the gravity equation must be the same for 

all trading pairs. The αij vector accounts for the many variables commonly 

specified in gravity equations, such as distance, as well as the many difficultto-quantify and/or unobservable variables that are specific to cross-sectional 

units over time.

supply of the exporting country, Dj

denotes factors affecting the potential 

demand of the importing country, and 

Rij consists of “resistance” factors that 

affect trade between the two countries, 

including impediments (such as transportation costs) as well as inducements 

(such as mutual RTA memberships).
7

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The Benchmark and Generalized Food Models

Our “benchmark gravity model” is designed to capture the impact of specific 

economic factors commonly believed to drive commodity and manufactured 

food trade between countries. This model, which is consistent with the traditional gravity framework, contains (1) time-varying covariates (i.e., exporter 

and importer incomes, relative factor endowments, bilateral exchange-rate 

misalignments, and indicators of mutual and asymmetric RTA membership) and (2) time-invariant dyadic variables denoting transaction costs (i.e., 

distance, contiguity, common language, colonial heritage, and indicators of 

partner countries being landlocked). The basic model is expressed as follows:

01 2 3 4

5 6 789 1

21 2

ln( ) ln( ) ln( ) ln( ) ln( )

ln( ) ( ) ( ) ( ) ( ) ( )

() ( ) ( )

ijt i it jt ijt ijt

r r

ij ij ij ij ij ijt

r

rr r r r r

ijt USjt OTHjt ijt

rr r

X Y Y EM FE

DT CB LL LS CH ITC

XTC MTD MTD

ααβ β β β

β β βββ δ

δγ γ μ

= ++ + + +

+ + +++ +

++ + +

∑

∑∑ ∑

(4)

Xijt is the value of trade between exporter i and importer j in year t. GDP 

quantifies market size, with Yit representing the potential supply of goods 

from exporter i in year t and Yjt gauging the potential demand of goods being 

traded by importer j in year t. EMijt is an indicator of bilateral-exchangerate misalignment. It is modeled after Perée and Steinherr’s (1989) measure 

of exchange-rate uncertainty and Rosenberg’s (2003) measure of the 

purchasing-power equilibrium exchange rate.10,11 Relative factor endowments are captured by FEijt, a measure of exporter-to-importer arable-land/

total-labor ratios in year t. The indicators for transportation costs include 

physical distance DTij and two indicator variables to reflect geographical 

characteristics, CBij, which equals 1 when i and j share a contiguous border 

and 0 otherwise and a landlocked variable LLij, which equals 1 when either i 

and/or j are landlocked and 0 otherwise. The measures for cultural proximity 

include language similarity LSij, an indicator variable which equals 1 whenever 9 percent or more of the population in both countries share a common 

language and 0 otherwise;12 and colonial heritage CHij, an indicator variable 

that equals 1 if two countries have established colonial ties since 1945 and 0 

otherwise. Time dummies (αt) account for factors common to all countries, 

including worldwide inflation, increases in trade attributable to globalization, and general shocks affecting the world economy,13 and μijt is an idiosyncratic error.

The RTA bivariate variables in equation (4) enable different dimensions of 

trade creation/diversion to be quantified for each of the 11 RTAs.14 They 

include (1) r

ijt ITC , a dummy variable that equals 1 when i and j are both 

members of agreement r in year t and 0 when either i or j do not belong to 

r in year t; (2) r XTCijt , a dummy variable that equals 1 when i is a member 

of agreement r and j is not a member of agreement r in year t and 0 otherwise; (3) r MTDUSjt , a dummy variable that equals 1 when the United States, 

as an exporter, is not a member of agreement r to which j belongs in year t

and 0 otherwise; and (4) r MTDOTHjt , a dummy variable that equals 1 when the 

non-U.S. exporter is not a member of agreement r to which j belongs in year 

t and 0 otherwise.

10EMijt controls for the influence of 

current and accumulated experience: 

t t t p

ij ij t k ij t k ij t k

ij t t t p

ij t k ij

Z Z Z Z EM V V Z Z

2

1 2 ,, ,

,

max min 1 min

−− −

−

  −   − = + = + +          

where max (min) Zij,t-k is the maximum 

(minimum) value of the exchange 

rate index over time interval of size k

past period and p Zij is the mean of the 

exchange rate between 1975 and 2005. 

The central notion underlying Vt

1 is 

that traders’ uncertainty is conditioned 

by their memory of the high and low 

exchange rate over 10 years. Vt

2 puts 

the contemporaneous exchange rate 

into historical perspective. It captures 

nonlinear responses to departures of the 

current exchange rate from its longrun 

equilibrium rate. 

11A reviewer suggested that we use 

an indicator of exchange-rate volatility 

rather than the exchange-rate misalignment variable. Others have noted that 

there is a certain amount of arbitrariness involved in choosing the appropriate exchange-rate variable to include in 

gravity models (Cho et al., 2002). We 

estimated the gravity models using Cho 

et al.’s measure of exchange-rate uncertainty in lieu of EMijt. The empirical results using our exchange-rate misalignment indicator and Cho et al.’s variable 

(calculated as a moving standard 

deviation of the first differences in the 

bilateral exchange rate over the prior 

10 years) were very similar. All the 

RTA parameter estimates carried the 

same signs and were nearly identical 

in magnitude. Both types of exchangerate variables carried the same sign 

and were statistically significant at the 

0.01 level. Our comparative results are 

available upon request.

12The 9-percent threshold serves to 

denote the level at which the ability to 

communicate is viewed as not imposing 

substantial transaction costs.

13αt

 = 1 when year is equal to t and 0 

otherwise.

14The RTAs are coded dynamically. 

Hence, the NAFTA-related dummies 

take the value of 1 for the United States 

and Canada between 1989 and 2005 and 

for the United States and Mexico and 

Canada and Mexico between 1994 and 

2005. Moreover, each RTA dummy was 

constructed using “full-year” enforcement, with full year referring to at least 

8 months of agreement implementation.
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The variables of particular interest in this study are the intra-bloc ( ) r

ijt ITC and 

three extra-bloc RTA-dummies (, , ) rr r

ijt USjt OTHjt ITC MTD MTD . The hypothesized 

signs and descriptions of these covariates are as follows:

•	 r

1 δ > 0 denotes intra-bloc trade creation for agreement r. 

•	 r

2 δ > 0 denotes extra-bloc trade creation for the set of countries belonging 

to agreement r exporting to partners not members of bloc r. 

•	 r

1 γ < 0 denotes import trade diversion adversely affecting the United 

States as a foreign supplier that lowers U.S. exports to trading bloc r.

•	 r

2 γ < 0 denotes import trade diversion that lowers non-U.S.-foreign 

suppliers’ exports to agreement r.

Clearly, factors other than those specified in the benchmark models are 

likely to influence both the level of partner trade as well as motivations for 

countries to enter into mutual RTAs. Examples include nontariff barriers, 

shipping regulations, antitrust rules, product standards, and other behind-theborder constraints.15 Despite their potential importance, covariates denoting 

such factors are often not included in gravity equations as they are either not 

observable or are nearly impossible to quantify. Should the omitted factors 

be, in fact, correlated with countries’ decisions to forge trading agreements, 

the generated RTA coefficients capture more than just the effects of these 

agreements. Hence, parameter estimates obtained from benchmark models 

may be biased and/or inconsistent due to omitted variable bias.

Another statistical issue of potential concern is endogeneity bias. Baier and 

Bergstrand (2007) posit that the primary source of such bias in the majority 

of applied econometric trade models is unobserved heterogeneity. The intuition behind their concern can be illustrated in the case of “natural trading 

partners.” Consider the United States and Canada. Both countries are viewed 

as being natural trading partners. They share a common culture, speak the 

same language, have comparable institutional structures, and possess other 

affinities (e.g., similar values, tastes, and preferences). As a result, the United 

States and Canada are more likely to reach bilateral or regional trade agreements for reasons other than those captured by the variables specified in 

conventional gravity equations. Endogeneity bias is, therefore, likely to 

occur in such equations as the RTA dummy variables that denote joint or 

asymmetric RTA membership involving the United States and/or Canada 

and may be correlated with the error term. Hence, the absence of variables 

denoting affinity factors in estimating equations can lead to omitted-variable 

bias should they be systematically correlated with both bilateral trade and the 

RTA covariates.

The second type of gravity model used in this study mitigates both omittedvariable and endogeneity bias. It deploys exporter-to-importer fixed effects 

in lieu of the time-invariant variables in the benchmark model. Our “generalized gravity model” is expressed as follows:

ijt t ij it jt ijt ijt

rr r r r r r r

ijt ijt USjt OTHjt ijt

rr r r

X Y Y EM FE

ITC XTC MTD MTD

0 12 3 4

12 1 2

ln( ) ln( ) ln( ) ln( ) ln( )

() ( ) ( ) ( )

ααα β β β β

δδ γ γ ε

= ++ + + + + +

∑∑ ∑ ∑ ++ + +

(5)

15Lawrence (1996) notes that RTAs 

provide a vehicle for achieving deeper 

integration precisely because of their 

ability to remove and/or mitigate 

domestic barriers that inhibit crossborder trade.
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The exporter-to-importer fixed effects (αij) generate country-pair intercepts. 

They isolate the trade creation/diversion impacts of each agreement r from 

the influence of other dyadic determinants, whether the latter are observable or nonobservable, by virtue of their ability to allow each unidirectional 

bilateral trade flow to have its own intercept. These country-pair fixed effects 

embody many difficult, if not impossible, to quantify factors, such as the 

natural-trading-partner phenomenon characterizing two countries (e.g., the 

United States and Canada) that induce in some partners trading proportionally more with each other than with others even in the absence of trade agreements (e.g., NAFTA). Hence, equation (5) accounts for heterogeneity more 

thoroughly than the ad hoc set of time-invariant variables (i.e., distance, 

physical adjacency, common language) in equation (4). Thus, the generalized 

gravity model enables the trade impacts of the individual RTAs to be separated from the unique bilateral relationships characterizing each country pair.

Moreover, the inclusion of the exporter-to-importer fixed effects allays 

concerns expressed by Bayoumi and Eichengreen (1997) that RTAs serve 

as “catch basins” for omitted variables in conventional econometric trade 

models. As Baldwin and Taglioni (2006) put it, the omission of country-pair 

fixed effects in gravity models, constructed to calibrate the integration effects 

of RTAs, is a classic example of the “gold medal” error. The country-pair 

fixed effects account for a range of socio/political/institutional factors that 

characterize each partner pair. Hence, the inclusion of these effects in estimating equations controls for the correlation between unobservable dyadic 

effects and the RTA binary variables.

We conclude that the generalized framework, with its exporter-to-importer 

fixed effects, is conceptually more appropriate than the benchmark framework. We also hypothesize that our generalized gravity equations for 

commodity and manufactured foods are likely to generate more reliable RTA 

parameter estimates than the corresponding benchmark equations.

Data

Annual bilateral trade data were obtained from the United Nations (UN) 

Commodity Trade Statistics (UN Statistical Office, 2007) database, 

UNComtrade, the primary international trade data source. The ERS panel 

dataset used in this study was compiled using UN-reported partner trade 

flows of commodity and manufactured foods among 69 countries from 1975 

through 2005. Distances between capital cities and/or the major commercial center closest to partner countries were calculated using the great circle 

method obtained from USDA’s Agricultural Research Service (2005). Data 

on common borders, language, and landlocked countries were taken from the 

website at the Centre d’Etudes Prospectives et d’Informations Internationales 

(2007). Information about arable land was obtained from the Food and 

Agriculture Organization of the United Nations (2008) FAOSTAT database. 

Data on reciprocal trade agreements and their date of entry into force were 

compiled by scrutinizing previously published gravity studies and multiple 

websites, including the WTO homepage (2011). All other data were obtained 

from World Development Indicators (see World Bank, 2008). Summary 

statistics for the variables in the panel dataset are shown in the appendix table.
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Empirical Model Selection

In this study, the benchmark and generalized gravity equations are estimated 

for the commodity- and manufactured-food markets using alternative estimators and functional forms. The econometric results along with cluster-robust 

standard errors are presented in tables 2 and 3.16

Population-average (PA) and random-effects (RE) algorithms are deployed 

to estimate the benchmark equations, and the fixed-effects (FE) algorithm is 

used to estimate the generalized equations.17A statistical advantage of the PA 

and RE models is that all coefficients, including those for the time-invariant 

variables, can be estimated. A disadvantage is that their parameter estimates 

are inconsistent if the fixed-effects (FE) estimator is found to be appropriate.

All of our log-linear gravity models are estimated using ordinary-leastsquares (OLS) techniques. A shortcoming of this approach is that OLS 

omits trade observations that are zero because the log of zero is undefined. 

Truncation of datasets used in empirical analysis may cause problems by 

deleting information that leads to bias estimates.18 To address possible selection bias that may occur when not using the full panel dataset that includes 

both positive and zero trade flows, we estimate gravity equations using the 

Poisson-pseudo-maximum-likelihood (PPML) technique proposed by Santos 

Silva and Tenreyro (2006).

Diagnostic statistical tests are used to help identify the most appropriate 

empirical models to best gauge the RTA impacts on food trade. To evaluate 

the appropriateness of the RE estimator for our commodity and manufactured 

food trade models, we followed Egger and deployed the Hausman test, using 

the method of Wooldridge (2002).19 The null hypothesis under the Hausman 

test is that the RE estimator is preferred to the PA alternative. This hypothesis is rejected in all cases at the .01 level of significance using Wald tests 

(see, column 2 in tables 2 and 3). The Hausman test results are consistent 

with Egger’s empirical findings for total merchandise trade. We conclude 

that the PA estimator is the preferred approach to estimate the benchmark 

food equations.

The generalized gravity equations are estimated using the OLS and PPML 

techniques. The conventional practice is to use OLS. A shortcoming of this 

procedure is that information is lost whenever the trade matrix used does not 

include zero observations and is, therefore, unbalanced.

We used Stata’s areg procedure, which absorbs the country-pair fixed 

effects via centering, to estimate the log-linear models and its xtpqml procedure to estimate the corresponding Poisson models. The PPML model for 

commodity foods did not converge.20 Statistical criteria show that OLS is 

the most appropriate technique for estimating the generalized equation for 

processed food. Goodness-of-fit statistics clearly favor the OLS estimator. 

Witness the considerably larger Aikake information-criterion (AIC) penalty 

for the Poisson model vis-à-vis the AIC for the OLS model.

As a further check on the adequacy of the OLS in comparison with the PPML 

estimator, we performed heteroskedasticity-robust, regression-equationspecification-error tests. The null hypothesis for the Ramsey RESET test is 

16Note, cluster-robust standard errors 

are reported in parentheses in these 

tables using White’s (1980) corrective method, rather than the default 

standard errors. Default standard errors, 

which assume i.i.d. errors, are likely 

to be underestimated because gravity 

models using panel data are subject 

to (1) positive autocorrelation within 

each bilateral pair over time as each 

additional observation is not providing 

information independently, and (2) heteroskedasticity because the composite 

error is likely to be correlated over time 

for a given country pair.

17Both the PA and FE estimators assume that the covariates are exogenous. 

They use between (cross-partner pairs) 

and within (time series) variation in the 

data. The RE model, by contrast, makes 

the strong assumption that the intercept 

is uncorrelated with the regressors. In 

contrast to the PA and RE models, the 

FE framework uses the within transformation. The transformation eliminates 

the αij’s via centering. It permits 

arbitrary correlations between the 

omitted and the explanatory variables. 

This feature explains why the intercept 

varies over country pairs and over time 

in our generalized gravity equations.

18If the zeros are not randomly 

distributed, truncating the sample may 

generate a selection bias.

19Egger (2000) rejects the use of 

the RE estimator on both conceptual 

and empirical grounds in gravity 

models that are designed to gauge 

RTA treatment effects. He notes that 

in such models, “one is not interested 

in the estimation of typical trade flows 

between a randomly drawn sample 

of countries but between an ex ante

predetermined selection of nations.” 

Egger used the Hausman test to 

compare the RE estimator with the FE 

estimator for a gravity model focused 

on total merchandise trade flows. 

His empirical test results provided 

overwhelming evidence for the 

rejection of the RE gravity model.

20Convergence does not always 

occur with Poisson models because of 

their “sensitivity to small numerical 

problems,” according to personal 

communication from Santos Silva.
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Table 2

Benchmark and generalized models results for commodity foods—

continued

Variables

Benchmark models Generalized models

PA model RE model FE models

LICs & 

HICs (1)

LICs & 

HICs (2)

LICs & 

HICs (3) LICs (4) HICs (5)

ln(Yi) 0.58** 0.45** 0.01 0.02 0.01

(0.02) (0.02) (0.04) (0.04) (0.04)

ln(Yj) 0.76** 0.84** 0.81** 0.81** 0.80**

(0.02) (0.02) (0.04) (0.04) (0.04)

ln(EMijt) -0.06* -0.09** -0.09** -0.09** -0.11**

(0.03) (0.02) (0.02) (0.02) (0.02)

ln(FEijt) 0.18** 0.16** 0.04 0.06 0.05

(0.01) (0.02) (0.04) (0.04) (0.04)

ln(DTij) -0.83** -0.95**

(0.04) (0.04)

CBij 0.47** 0.78**

(0.16) (0.19)

LLij -0.36** -0.55**

(0.08) (0.09)

LSij 0.48** 0.29**

(0.07) (0.09)

CHij 1.68** 2.00**

(0.19) (0.23)

ITCANDE 3.01** 1.47** 1.40** 1.38**

(0.49) (0.09) (0.09) (0.09)

XTCANDE -0.08 0.11 0.08 0.04

(0.21) (0.12) (0.13) (0.13)

MTDANDE_US 1.76** -0.33 -0.34 -0.34

(0.24) (0.27) (0.29) (0.29)

MTDANDE_non-US -0.91** 0.01 0.11 0.11

(0.18) (0.12) (0.13) (0.13)

ITCASEAN 2.17** 0.24 0.21 0.19

(0.25) (0.24) (0.24) (0.24)

XTCASEAN 1.99** 0.31** 0.27** 0.23**

(0.11) (0.08) (0.08) (0.08)

MTDANSEAN_US 1.20* 0.04 -0.02 -0.01

(0.49) (0.26) (0.24) (0.24)

MTDASEAN_non-US 0.10 0.01 0.04 0.04

(0.12) (0.09) (0.10) (0.10)

ITCCACM 3.18** 1.09** 0.95** 0.93**

(0.22) (0.25) (0.29) (0.29)

XTCCACM 1.17** -0.10 -0.17 -0.21*

(0.13) (0.09) (0.09) (0.09)

continued—
12

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Table 2

Benchmark and generalized models results for commodity foods—

continued

Variables

Benchmark models Generalized models

PA model RE model FE models

LICs & 

HICs (1)

LICs & 

HICs (2)

LICs & 

HICs (3) LICs (4) HICs (5)

MTDCACM_US 2.05** 0.28 0.14 0.16

(0.21) (0.16) (0.16) (0.16)

MTDCACM_non-US -0.56** 0.43** 0.58** 0.58**

(0.12) (0.08) (0.09) (0.09)

ITCCER 2.90** 1.24** 0.63** 0.58**

(0.18) (0.05) (0.08) (0.08)

XTCCER 2.38** 0.98** 0.47** 0.43**

(0.16) (0.13) (0.16) (0.16)

MTDCER_US 0.35* 0.01 -0.42 -0.41

(0.18) (0.40) (0.51) (0.51)

MTDCER_non-US 0.56** 0.30** 0.24 0.22

(0.16) (0.11) (0.14) (0.14)

ITCCOMESA 1.98** 1.77** 1.75** 1.74**

(0.57) (0.32) (0.37) (0.37)

XTCCOMESA 0.30 0.26** 0.27* 0.23*

(0.16) (0.10) (0.11) (0.11)

MTDCOMESA_US 0.96 -0.22 -0.30 -0.29

(0.56) (0.28) (0.28) (0.28)

MTDCOMESA_non-US -0.25 -0.12 -0.13 -0.13

(0.15) (0.11) (0.12) (0.12)

ITCEU 1.21** 0.86** 0.67** 0.56**

(0.12) (0.08) (0.09) (0.09)

XTCEU 0.66** 0.20** 0.08 0.02

(0.07) (0.06) (0.07) (0.07)

MTDEU_US 0.29 -0.63** -1.08** -1.10**

(0.23) (0.24) (0.26) (0.25)

MTDEU_non-US 0.50** -0.18** -0.34** -0.35**

(0.09) (0.06) (0.07) (0.07)

ITCGAFTA 0.21 0.51 0.48 0.47

(0.36) (0.27) (0.29) (0.29)

XTCGAFTA -1.63** -0.07 0.04 0.00

(0.13) (0.09) (0.09) (0.09)

MTDGAFTA_US 0.91* -0.69** -0.74** -0.74**

(0.41) (0.12) (0.12) (0.12)

MTDGAFTA_non-US 0.35** -0.34** -0.36** -0.36**

(0.11) (0.07) (0.07) (0.07)

ITCMERC 3.43** 0.79** 0.62** 0.61**

(0.33) (0.12) (0.12) (0.12)

continued—
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Table 2

Benchmark and generalized models results for commodity foods—

continued

Variables

Benchmark models Generalized models

PA model RE model FE models

LICs & 

HICs (1)

LICs & 

HICs (2)

LICs & 

HICs (3) LICs (4) HICs (5)

XTCMERC 1.90** 0.45** 0.31** 0.27**

(0.11) (0.09) (0.10) (0.10)

MTDMERC_US -0.06 -0.26 -0.29 -0.28

(0.26) (0.27) (0.29) (0.29)

MTDMERC_non-US -0.59** 0.39** 0.46** 0.46**

(0.13) (0.10) (0.10) (0.10)

ITCNAFTA 2.17** 0.63** 0.50** 0.48** 0.40**

(0.42) (0.13) (0.12) (0.12) (0.11)

XTCNAFTA 1.21** 0.47** 0.35** 0.30** 0.26**

(0.12) (0.08) (0.09) (0.09) (0.09)

MTDNAFTA_non-US 0.57** 0.15 0.12 0.12 0.09

(0.13) (0.08) (0.09) (0.09) (0.09)

ITCSADC 4.16** 0.89** 0.71* 0.71**

(1.16) (0.34) (0.28) (0.27)

XTCSADC 0.75** 0.75** 0.68** 0.65**

(0.20) (0.13) (0.13) (0.13)

MTDSADC_US 0.11 0.16 0.16 0.16

(0.37) (0.53) (0.57) (0.57)

MTDSADC_non-US -0.07 0.15 0.18 0.18

(0.18) (0.14) (0.14) (0.14)

ITCSAPTA 2.15** 0.39 0.34 0.32

(0.63) (0.31) (0.33) (0.33)

XTCSAPTA 0.43** 0.31** 0.31** 0.27*

(0.15) (0.10) (0.11) (0.11)

MTDSAPTA_US 1.04** -1.21** -1.28** -1.27**

(0.39) (0.20) (0.22) (0.22)

MTDSAPTA_non-US -0.55** -0.13 -0.10 -0.10

(0.15) (0.09) (0.10) (0.10)

C 15.91** 16.98** 10.85** 10.82** 11.06**

(0.37) (0.38) (0.32) (0.32) (0.32)

Observations 89,723 89,723 89,723 89,723 89,723

Number of xm pair 4529

1.84

1.26

2.23

ρ 0.681

0.864

0.115

continued—

2 Rw

u σˆ

e σˆ

σˆ

50 Θˆ
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Table 2

Benchmark and generalized models results for commodity foods—

continued

Variables

Benchmark models Generalized models

PA model RE model FE models

LICs & 

HICs (1)

LICs & 

HICs (2)

LICs & 

HICs (3) LICs (4) HICs (5)

0.495

χ2 10357

0.47 0.83 0.83 0.83

AIC 3.99E+05 2.92E+05

RESET F = 27.33 F = 1.32

RESET test p-values 0.0000 0.2512

F (Hausman) F = 128.38

Prob> F 0.0000

Notes: PA = population-average effects. RE = random effects. FE = fixed effects. LIC = lowincome countries. HIC = high-income countries. The dependent variables and all time-varying 

independent variables are expressed in logarithmic terms. All regressions are estimated using 

annual panel data from 1975 through 2005. Time- and country-pair-dummy parameter estimates are not reported to save space. Standard errors are in parentheses. They are calculated 

using White’s heteroskedastic robust-standard errors. * and ** denote that the null hypothesis 

is rejected at the 0.05 and 0.01 level, respectively.

Table 3

Benchmark and generalized models results for manufactured foods—

continued

Variables

Benchmark models Generalized models

LICs & HICs FE (OLS)

FE 

(PPML)

PA 

(OLS)

(1)

RE (OLS) 

(2)

LICs & 

HICs

(3)

LICs

(4)

HICs

(5)

LICs & 

HICs

(6)

ln(Yi) 0.83** 0.79** 0.29** 0.29** 0.30** 0.27*

(0.02) (0.02) (0.05) (0.05) (0.05) (0.11)

ln(Yj) 0.61** 0.67** 0.94** 0.95** 0.96** 0.82**

(0.02) (0.02) (0.05) (0.05) (0.05) (0.09)

ln(EMijt) -0.25** -0.13** -0.11** 0.10** 0.11** -0.07*

(0.03) (0.02) (0.02) (0.02) (0.02) (0.03)

ln(FEijt) 0.04** 0.04* -0.13* -0.13* -0.15** -0.05

(0.01) (0.02) (0.05) (0.05) (0.05) (0.05)

ln(DTij) -1.11** -1.19**

(0.04) (0.04)

CBij 0.28 0.55**

(0.16) (0.19)

LLij -0.11 -0.18*

(0.08) (0.09)

LSij 0.83** 0.70**

(0.07) (0.08)

CHij 1.26** 1.54**

continued—

2 Rb

2 Radj
15

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Table 3

Benchmark and generalized models results for manufactured foods—

continued

Variables

Benchmark models Generalized models

LICs & HICs FE (OLS)

FE 

(PPML)

PA 

(OLS)

(1)

RE (OLS) 

(2)

LICs & 

HICs

(3)

LICs

(4)

HICs

(5)

LICs & 

HICs

(6)

(0.21) (0.26)

ITCANDE 2.87** 2.52** 2.45** 2.40** 1.08**

(0.39) (0.07) (0.06) (0.06) (0.28)

XTCANDE 0.46** 0.67** 0.62** 0.60** 0.04

(0.17) (0.15) (0.16) (0.16) (0.18)

MTDANDE_US 0.14 -0.30 -0.23 -0.27 -0.30

(0.24) (0.16) (0.16) (0.16) (0.24)

MTDANDE_non-US -0.44* 0.20 0.36* 0.33 0.27

(0.17) (0.16) (0.17) (0.17) (0.19)

ITCASEAN 1.59** 0.90** 0.80* 0.74* 0.05

(0.32) (0.33) (0.34) (0.34) (0.22)

XTCASEAN 1.09** 0.54** 0.50** 0.47** -0.15

(0.10) (0.09) (0.10) (0.10) (0.18)

MTDANSEAN_US 0.95** -0.21 -0.36* -0.39* -0.19

(0.36) (0.17) (0.17) (0.17) (0.14)

MTDASEAN_non-US 0.62** 0.36** 0.32** 0.28** -0.08

(0.12) (0.09) (0.10) (0.10) (0.11)

ITCCACM 3.20** 1.11** 0.79* 0.74* 0.55*

(0.25) (0.27) (0.31) (0.31) (0.22)

XTCCACM 0.24 -0.18 -0.32 -0.36 -0.60**

(0.16) (0.15) (0.19) (0.19) (0.23)

MTDCACM_US 0.67* -0.03 -0.11 -0.13 0.05

(0.27) (0.09) (0.11) (0.11) (0.09)

MTDCACM_non-US -0.10 0.24* 0.29* 0.25 0.27

(0.11) (0.11) (0.13) (0.13) (0.21)

ITCCER 2.63** 1.96** 1.29** 1.24** 1.66**

(0.18) (0.18) (0.24) (0.24) (0.25)

XTCCER 0.67** 0.36** 0.05 -0.02 -0.60

(0.17) (0.12) (0.17) (0.17) (0.34)

MTDCER_US 0.26 0.71** 0.50* 0.50* 0.60**

(0.27) (0.13) (0.21) (0.21) (0.14)

MTDCER_non-US 0.83** 0.69** 0.70** 0.70** 0.87**

(0.14) (0.13) (0.16) (0.16) (0.16)

ITCCOMESA 1.84** 1.56** 1.71** 1.67** 3.30**

(0.51) (0.51) (0.55) (0.54) (0.88)

XTCCOMESA -0.01 0.01 0.04 0.02 0.19

(0.16) (0.16) (0.18) (0.18) (0.21)

continued—
16

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Table 3

Benchmark and generalized models results for manufactured foods—

continued

Variables

Benchmark models Generalized models

LICs & HICs FE (OLS)

FE 

(PPML)

PA 

(OLS)

(1)

RE (OLS) 

(2)

LICs & 

HICs

(3)

LICs

(4)

HICs

(5)

LICs & 

HICs

(6)

MTDCOMESA_US -0.53 -0.40 -0.36 -0.39 -0.87**

(0.58) (0.31) (0.41) (0.41) (0.21)

MTDCOMESA_nonUS -0.71** -0.41** -0.42** -0.44** -0.56**

(0.16) (0.13) (0.14) (0.14) (0.21)

ITCEU 1.41** 0.83** 0.66** 0.56** 0.52**

(0.13) (0.09) (0.10) (0.10) (0.09)

XTCEU 1.14** 0.44** 0.18* 0.12 0.06

(0.08) (0.06) (0.07) (0.08) (0.07)

MTDEU_US -0.08 -0.20 -0.28 -0.31 -0.05

(0.27) (0.24) (0.30) (0.29) (0.14)

MTDEU_non-US -0.25** 0.04 0.15 0.15 0.04

(0.08) (0.07) (0.09) (0.09) (0.09)

ITCGAFTA 0.69* 0.39 0.34 0.30 0.38

(0.32) (0.23) (0.25) (0.25) (0.25)

XTCGAFTA -1.56** -0.19* -0.03 -0.05 0.17

(0.13) (0.10) (0.10) (0.10) (0.24)

MTDGAFTA_US 0.03 -0.66** -0.67** 0.69** -0.42**

(0.61) (0.15) (0.15) (0.15) (0.08)

MTDGAFTA_non-US -0.18 -0.25** -0.24** -0.26** -0.37**

(0.11) (0.08) (0.09) (0.09) (0.09)

ITCMERC 2.81** 0.81** 0.67* 0.62 0.27

(0.40) (0.30) (0.33) (0.33) (0.20)

XTCMERC 1.00** 0.35** 0.20 0.16 -0.34

(0.12) (0.10) (0.11) (0.11) (0.18)

MTDMERC_US 0.81** 0.48 0.50 0.47 0.59**

(0.24) (0.27) (0.27) (0.26) (0.18)

MTDMERC_non-US -0.41** 0.09 0.22 0.19 -0.08

(0.13) (0.11) (0.11) (0.11) (0.20)

ITCNAFTA 1.46** 1.46** 1.37** 1.31** 1.28** 0.72**

(0.41) (0.36) (0.37) (0.37) (0.36) (0.07)

XTCNAFTA 0.29* 0.48** 0.37** 0.34** 0.29** 0.21**

(0.11) (0.09) (0.10) (0.09) (0.09) (0.08)

MTDNAFTA_non-US 0.47** 0.08 0.04 0.01 0.02 -0.52**

(0.12) (0.10) (0.11) (0.11) (0.11) (0.13)

ITCSADC 2.84* 0.21** 0.03 0.03 0.72**

(1.30) (0.05) (0.14) (0.14) (0.16)

XTCSADC 0.25 0.30 0.20 0.18 0.35

continued—
17

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Table 3

Benchmark and generalized models results for manufactured foods—

continued

Variables

Benchmark models Generalized models

LICs & HICs FE (OLS)

FE 

(PPML)

PA 

(OLS)

(1)

RE (OLS) 

(2)

LICs & 

HICs

(3)

LICs

(4)

HICs

(5)

LICs & 

HICs

(6)

(0.19) (0.16) (0.17) (0.17) (0.28)

MTDSADC_US 0.00 0.58 0.68 0.66 -0.05

(0.39) (0.59) (0.65) (0.66) (0.05)

MTDSADC_non-US -0.27 0.13 0.21 0.20 0.26

(0.20) (0.16) (0.17) (0.17) (0.21)

ITCSAPTA -0.05 0.88* 0.94* 0.89* 0.87*

(0.67) (0.41) (0.44) (0.44) (0.43)

XTCSAPTA -0.15 0.29** 0.31** 0.28* 0.01

(0.14) (0.11) (0.12) (0.12) (0.14)

MTDSAPTA_US -0.42 -1.01** -1.04** -1.08** -1.25**

(0.36) (0.23) (0.27) (0.27) (0.05)

MTDSAPTA_non-US -0.40** -0.34** -0.34** -0.37** -0.60**

(0.13) (0.10) (0.10) (0.10) (0.12)

C 16.01** 16.07** 7.44** 7.35** 7.19**

(0.34) (0.37) (0.39) (0.29) (0.29)

Observations 70,954 70,954 70,954 70,954 70,954 102,299

Number of xm pair 4,189

1.62

1.27

2.06

ρ 0.62

0.83

0.24

0.54

χ2 17,542

0.52 0.81 0.81 0.81

AIC 3.01E+05 2.31E+05 1.96E+11

RESET F = 15.63 F = 0.48

chi2(1)=

9.20

RESET test 

p-values 0.0001 0.4888 0.0024

F (Hausman) F=187.63

Prob> F 0.0000

Notes: PA = population-average effects. RE = random effects. FE = fixed effects. LIC = lowincome countries. HIC = high-income countries. All time-varying independent variables are expressed in logarithmic terms. All regressions are estimated using annual panel data from 1975 

through 2005. Time- and country-pair-dummy parameter estimates are not reported to save 

space. Standard errors are in parentheses. They are calculated using White’s heteroskedastic 

robust-standard errors. * and ** denote that the null hypothesis is rejected at the 0.05 and 0.01 

level, respectively.

2 Rw

2 Rb

2 Radj

u σˆ

e σˆ

σˆ

50 Θˆ
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clearly rejected for the PPML estimator for processed foods but not for the 

OLS estimator in either the commodity food or the processed food equations 

(see column 6 in table 2 and columns 3 and 6 in table 3).21 These findings 

suggest that omitted variables are a source of errors-in-variable bias in the 

PPML estimation, a problem that is not apparent in the OLS equation.

Summary statistics in tables 2 and 3 indicate that the OLS generalized equations are preferred to the benchmark models. Adjusted R2s show that the 

generalized gravity models explain more variation than do the corresponding 

benchmark equations for both the commodity and manufactured food 

sectors. The overall measures of “goodness of fit” increased from 0.47 to 

0.83 [0.52 to 0.81] when comparing the PA-model with the FE-model results 

for commodity [manufactured] foods (see column 1 and 3 in tables 2 and 

3). Likelihood ratio and Akaike-information-criterion tests do not support 

the more restricted benchmark models in either food sector, adding further 

justification favoring the generalized model framework (see columns 1 and 

3 in tables 2 and 3). Finally, RESET tests point to omitted-variable bias in 

the benchmark but not in the generalized equations. This finding addresses 

Bayoumi and Eichengreen’s concern about errors-in-variable bias and 

Baldwin and Taglioni’s intuition of the appropriateness of using country-pair 

effects when examining the effects of RTAs on trade. 

Summary measures of overall statistical fit, information-criterion penalties, 

and both Ramsey RESET and Hausman tests provide support for the generalized gravity models vis-à-vis the benchmark models for both commodity and 

processed foods. Given our primary interest in this study to quantify the trade 

impacts of RTAs22 as well as theoretical considerations and the results of the 

aforementioned diagnostic tests, we limit discussion in the next section to the 

RTA parameter estimates using the preferred analytical framework. 

21The Ramsey RESET results for 

commodity and manufactured foods are 

not consistent with what Sun and Reed 

(2011) found when comparing OLS and 

Poisson models in their applied model 

for total agricultural trade.

22Our preferred PA benchmark 

models for food, though limited in their 

ability to diagnose the trade impacts of 

RTA, generate information about the 

fixed dyadic determinants that is not 

possible to obtain from the generalized 

models.
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Trade Impacts of RTAs

The intra-bloc trade creation (ITC) parameters in table 4 from our preferred 

generalized gravity equations show that since the time of their inception, 10 

of the 11 RTAs expanded trade in either the commodity and/or the manufactured food markets of member countries. The three RTAs that comprise 

mostly developed countries (i.e., the EU, NAFTA, and CER) increased 

member-country trade in both the commodity and manufactured food sectors. 

The formation of the EU increased intra-EU trade in the two food sectors 

during 1975-2005 between 93 and 95 percent, on average.23 This translates 

into a fixed annual increase in food trade between EU countries of 3 percent 

(table 5).24 The rise in intra-bloc trade within NAFTA and the CER was, in 

contrast to that within the EU, substantially greater for manufactured foods 

than for commodity foods. NAFTA caused trade between member countries 

to expand 8 percent per year on average in manufactured foods and 3 percent 

per year in commodity foods between 1989 and 2005. Results for the CER 

were similar in that the 6-percent annual increase of trade between Australia 

and New Zealand in manufactured foods was considerably greater than the 

3-percent expansion of intra-CER trade in commodity foods.

23The percentage change in trade attributable to RTA formation is r

( 1)x100 e

δ − .

24The formula to convert RTA coefficients into average annual percentage 

change is ( ) n e

1

1 δ − , where n = the number 

of years the RTA is in force.

Table 4

Trade creation and trade diversion parameter estimates for intra- and extra-RTA blocs 

from the preferred gravity model

Commodity foods Manufactured foods

Agreement ITC (1) XTC (2) MTDUS (3) MTDnon-US (4) ITC (5) XTC (6) MTDUS (7) MTDnon-US (8)

ANDE 1.40** 0.08 -0.34 0.11 2.45** 0.62** -0.23 0.36*

(0.09) (0.13) (0.29) (0.13) (0.06) (0.16) (0.16) (0.17)

ASEAN 0.21 0.27** -0.02 0.04 0.80* 0.50** -0.36* 0.32**

(0.24) (0.08) (0.24) (0.10) (0.34) (0.10) (0.17) (0.10)

CACM 0.95** -0.17 0.14 0.58** 0.79* -0.32 -0.11 0.29*

(0.29) (0.09) (0.16) (0.09) (0.31) (0.19) (0.11) (0.13)

CER 0.63** 0.47** -0.42 0.24 1.29** 0.05 0.50* 0.70**

(0.08) (0.16) (0.51) (0.14) (0.24) (0.17) (0.21) (0.16)

COMESA 1.75** 0.27* -0.30 -0.13 1.71** 0.04 -0.36 -0.42**

(0.37) (0.11) (0.28) (0.12) (0.55) (0.18) (0.41) (0.14)

EU 0.67** 0.08 -1.08** -0.34** 0.66** 0.18* -0.28 0.15

(0.09) (0.07) (0.26) (0.07) (0.10) (0.07) (0.30) (0.09)

GAFTA 0.48 0.04 -0.74** -0.36** 0.34 -0.03 -0.67** -0.24**

(0.29) (0.09) (0.12) (0.07) (0.25) (0.10) (0.15) (0.09)

MERCOSUR 0.62** 0.31** -0.29 0.46** 0.67* 0.20 0.50 0.22

(0.12) (0.10) (0.29) (0.10) (0.33) (0.11) (0.27) (0.11)

NAFTA 0.50** 0.35** 0.12 1.37** 0.37** 0.04

(0.12) (0.09) (0.09) (0.37) (0.10) (0.11)

SADC 0.71* 0.68** 0.16 0.18 0.03 0.20 0.68 0.21

(0.28) (0.13) (0.57) (0.14) (0.14) (0.17) (0.65) (0.17)

SAPTA 0.34 0.31** -1.28** -0.10 0.94* 0.31** -1.04** -0.34**

(0.33) (0.11) (0.22) (0.10) (0.44) (0.12) (0.27) (0.10)

Note: ITC (intra-bloc trade creation), XTC (extra-bloc trade creation), and MTD (import trade diversion) are variables. RTA-parameter estimates and 

robust standard errors replicated from the ordinary least squares generalized model for commodity (manufactured) foods in tables 2 and 3.
20

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ANDE, CACM, COMESA, and MERC expanded trade with member countries in both food sectors. By contrast, GAFTA had no effect on internal 

bloc trade in either sector. However, SADC increased intra-bloc trade in 

commodity foods, and ASEAN and SAPTA augmented trade internally 

in the manufactured food sector. The finding that some RTAs have not 

increased cross-border trade with member countries in one or both food 

markets is not altogether surprising. Many countries, especially those in the 

developing world, grant limited tariff preferences in agriculture. The lack 

of statistical significance in commodity-based trade in ASEAN, GAFTA, 

and SAPTA could reflect the relatively small land bases from which to 

produce land-intensive commodities. The absence of empirical support for 

RTA-induced intra-bloc trade-creation could also indicate that food markets 

are not always well integrated within trading blocs. 

Interestingly, model results reveal that not only did most of the RTAs 

create trade among member countries, but they also expanded trade externally to nonmember countries. Note the positive and statistically significant 

extra-bloc trade creation (XTCs) coefficients for the agreements that also 

augmented intra-bloc trade. These results may support Baldwin’s (2002) 

hypothesis that trade liberalization, whether it originates from regional or 

multilateral agreements, “begets more liberalization.” The results may reflect 

nondiscriminatory reduction in trade barriers to nonmember partners that 

took place as some of the RTA blocs were lowering barriers to member 

countries. They may also stem from increases in bloc-country competitiveness arising from spillover efficiencies caused by RTAs that enabled 

members to increase export sales to nonpartner importers. 

Not surprisingly, the RTA-induced trade expansions with external partners 

were considerably smaller than sales to partner importers when the agreeTable 5

RTA-induced average annual percentage change in trade induced by each agreement since inception

Agreement

Commodity foods Manufactured foods

Trade creation Trade diversion Trade creation Trade diversion

Intra-bloc

(ITC)

Extra-bloc

(XTC)

U.S.

(MTDUS)

Non-U.S. 

(MTDnon-US)

Intra-bloc

(ITC)

Extra-bloc

(XTC)

U.S.

(MTDUS)

Non-U.S. 

(MTDnon-US)

ANDE 0.15 0.28 0.06 0.04

ASEAN 0.02 0.06 0.04 -0.03 0.02

CACM 0.07 0.04 0.05 0.02

CER 0.03 0.02 0.06 0.02 0.03

COMESA 0.19 0.03 0.19 -0.04

EU 0.03 -0.03 -0.01 0.03 0.01

GAFTA -0.09 -0.04 -0.08 -0.03

MERCOCUR 0.04 0.02 0.03 0.05

NAFTA 0.03 0.02 0.08 0.02

SADC 0.15 0.15

SAPTA 0.03 -0.12 0.10 0.03 -0.10 -0.03

Note:ITC (intra-bloc trade creation), XTC (extra-bloc trade creation), and MTD (import trade diversion) are variables. Percentage average annual 

change derived from parameter estimates in table 4 that are statistically significant at either the 0.01 or the 0.05 level of significance using the 

formula ( ) n e

1

1 δ − , where n = the number of years the RTA is in force.
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ments also augmented intra-bloc trade, except for trade in manufactured 

foods by ASEAN exporters. The ASEAN countries have little incentive to 

trade with each other in commodity and manufactured foods given similarities in their members’ comparative advantages. 

Overall trade creation induced by RTAs, denoted by positive and significant 

ITC and/or XTC coefficients, depicts most but not all of the agreements in 

the commodity and manufactured food sectors. Exceptions include nonsignificant parameter estimates for SADC in manufactured foods and for GAFTA 

in both the commodity and manufactured food sectors. The absence of any 

statistical significance of trade creation suggests that the set of GAFTA countries is not internationally competitive in either food sector and that the group 

of SADC countries is not competitive in manufactured foods. 

The import-trade-diversion parameter estimates (MTDUS and MTDnon-US) 

reveal that 6 of 11 RTAs (i.e., ANDE, CACM, CER, MERCOSUR, NAFTA, 

and SADC) did not discriminate against outside suppliers.25 However, 

both U.S. exporters and the composite foreign supplier in commodity and/

or manufactured food markets were adversely affected by the formation of 

the EU, GAFTA, and SAPTA. The empirical results show, for example, 

that the EU reoriented imports of commodity foods away from both 

outside suppliers. The EU agreement effected a 66-percent decline in U.S. 

commodity food exports to EU members between 1975 and 2005. This drop 

is equivalent to an average annual proportional decline in U.S. exports to the 

EU of 3 percent. The EU had less of an impact on non-U.S. foreign suppliers 

of commodity foods, with exports falling 29 percent over the entire period, 

or 1 percent annually, on average. There is no statistical evidence that the EU 

discriminated against outsider suppliers of manufactured foods. 

Results show that countries belonging to GAFTA reoriented imports from 

the United States and other foreign suppliers in both the commodity and 

manufactured food sectors. Parameter estimates show that the GAFTA countries lowered food imports from the United States by 8 to 9 percent per year, 

on average, since GAFTA’s inception in 1998. In contrast, GAFTA curtailed 

imports from non-U.S. suppliers of commodity foods and manufactured 

foods at average annual rates of 4 percent and 3 percent, respectively. 

SAPTA exhibited discriminatory patterns against both U.S. exporters and 

non-U.S. exporters of manufactured foods. In the manufactured food sector, 

SAPTA’s imports from the United States fell by an annual average of 10 

percent from 1996 to 2005, while its imports from the other foreign suppliers 

declined by 3 percent. In the commodity food sector, SAPTA curtailed its 

imports from the United States by 12 percent per year, on average. It did not, 

however, discriminate against non-U.S. foreign suppliers in this sector. 

The ASEAN bloc also discriminated against the United States. On average, 

the ASEAN countries purchased 3 percent less manufactured food from the 

United States per year during 1993-2005 than would have been the case in 

the absence of this agreement. In sharp contrast to U.S. exporters, non-U.S. 

suppliers of manufactured foods to ASEAN actually increased their exports 

of manufactured foods to countries belonging to this bloc. This pattern 

suggests that ASEAN is a relatively open agreement overall, but not with 

respect to U.S. manufactured foods.

25There are a few instances where the 

gravity models generate negative MTD 

coefficients, but they are not statistically different from 0.
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The United States also lost export market share to competitors due to the 

formation of CACM and MERCOSUR. While these two RTA blocs did 

not directly affect the volume of food imports from the United States, they 

adversely affected U.S. competitiveness vis-à-vis other suppliers.26 In each 

case, bloc-member exporters and non-U.S. foreign suppliers increased sales 

of commodity and/or manufactured foods in the international marketplace.

COMESA is the only agreement that diverted trade away from non-U.S.-

foreign suppliers but not against U.S. exporters. Since its inception in 1995, 

COMESA curtailed imports of manufactured foods from non-U.S. exporters 

by 3.5 percent annually. In contrast, imports from the United States did not 

drop below levels expected in the absence of this agreement.

U.S. food exports to the 10 foreign trading blocs did not increase as a result 

of RTA formation, except in the case of manufactured food exports to CER, 

which rose 2 percent per year on average. However, the 6-percent annual 

increase in trade between Australia and New Zealand and the 3-percent 

annual increase in non-U.S. suppliers’ exports to CER exceeded the rise in 

U.S. exports of manufactured foods to the CER. 

To examine the robustness of model specifications, ERS derived estimates 

using different combinations of RTAs (see columns 4 and 5 in tables 2 and 

3). RTA parameter estimates using the OLS generalized gravity model for 

both bulk and manufactured foods are virtually identical when comparing 

food equations containing all 11 RTAs with corresponding regression models 

that included (1) the subset of RTAs comprising low-income members 

(i.e., ANDE, ASEAN, CACM, COMESA, GAFTA, MERCOSUR, SADC, 

and SAPTA) and (2) the subset of high-income RTAs (i.e., CER, EU, and 

NAFTA). Moreover, the statistical significance of 83 of the 86 parameter 

comparisons is the same.27 The empirical results underscore model robustness to different combinations of RTAs. 

To obtain information about how RTAs affect trade through time, comparisons are made of model estimations covering a 21-year period (1975-to1994) with those spanning a 31-year period (1975-2005).28 Not surprisingly, 

the intra-bloc trade creation coefficients for CACM, ASEAN, and NAFTA 

(i.e., ITCCACM, ITCASEAN, and ITCNAFTA) are not significantly different 

from zero for the 1975-94 period for either food sector. These three agreements entered into force near the end of the early period, in 1991, 1993, and 

1994, respectively. Insufficient time elapsed for them to have had a discernable impact on food trade during the 1974-94 period.

As another example of estimated impacts over time, the intra-bloc tradecreation parameter estimates for the CER are positive and significantly 

different from zero for both the 21- and 31-year time spans. When the twoperiod ITC coefficients for commodity and manufactured foods (which, 

understandably, are smaller in magnitude for the shorter time frame) are 

converted into annual average percentage changes, they reveal that the CER 

induced greater change in intra-bloc trade creation in the early period—4 

percent for commodity foods and 8 percent for manufactured foods—than for 

the 31-year period as a whole—3 and 6 percent for commodity and manufactured foods, respectively. These results are consistent with findings elsewhere 

in the literature that show that the impacts of RTA on trade are typically 

26As an example, one reviewer 

pointed out that MERCOSUR increased 

Argentine exports of wheat to Brazil 

while the United States lost share as 

Argentina now had the advantage of 

not paying the 10-percent “common 

external tariff.”

27 The three exceptions include the

XTCCACM coefficient that is significant 

at the .05-level in the bulk equation, 

which excludes the high-income 

RTAs and the MTDANDE_non-US and 

MTDCACM_non-US coefficients that are 

insignificant in the manufactured food 

equation, which also excludes the highincome RTAs.

28Empirical results are available from 

the authors upon request.
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more pronounced during the first 10 to 15 years than afterward (Baier and 

Bergstrand, 2007; Grant and Lambert, 2008).

To examine more closely the diminishing effect of RTAs on trade through 

time, generalized gravity models are run covering the first 10 years of four 

agreements—ASEAN, CACM, CER, and NAFTA—and the 9-year period 

separating the EU12 from its expansion to the EU15 (following the accession of Austria, Finland, and Sweden). Results are compared with ITC 

parameter estimates taken from the 1975-2005 model runs. Table 6 shows 

RTA-induced effects on intra-bloc trade over two timespans for each of the 

aforementioned RTAs by converting ITC parameter estimates into average 

annual percentage changes. 

In all cases, the empirical results depict larger impacts during the first decade 

than over the “life” of these agreements. For example, the average annual 

change of intra-NAFTA trade during the first 10 years of the agreement 

is 4 percent for commodity foods and 12 percent for manufactured foods. 

Corresponding figures for the 1975-2005 period as a whole are 3 percent 

for commodity foods and 8 percent for manufactured foods. In the case of 

the EU, the average annual percentage change of intra-EU trade through 

the EU12 period is 7 percent for commodity foods and 6 percent for manufactured foods. The corresponding EU figure for the 1975-2005 period is 3 

percent for both commodity foods and manufactured foods.

Table 6

RTA-induced effects on intra-bloc trade over two time periods

Agreement Commodity foods Manufactured foods

First decade Through 2005 First decade Through 2005

ASEAN 0.08 0.06

CACM 0.08 0.07 0.07 0.05

CER 0.04 0.03 0.09 0.06

EU 0.07 0.03 0.06 0.03

NAFTA 0.04 0.03 0.12 0.08

Note: Percentage average annual change derived from parameter estimates that are statistically significant at either the 0.05 or 0.01 level using the formula ( ) n e

1

1 δ − , where n = the number of years the RTA is in force.
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Summary and Policy Implications

A primary motivation for countries to participate in trade agreements is to 

increase exports by reducing impediments to trade. Given that the international policy dynamics make negotiating a multilateral trade agreement 

extremely difficult, countries have resorted to the use of preferential agreements as a practical and feasible route to gain greater access to foreign 

markets. There are, however, tradeoffs in selecting this policy option. RTAs 

may become “stepping stones” that lead to more open world markets or 

“stumbling blocs” that discriminate against efficient suppliers and undermine 

support for multilateral trade liberalization. Empirical analysis can clarify 

these effects by gauging the actual impacts of individual agreements.

In this study, gravity models are estimated to identify the effects of 11 RTAs 

on intra- and extra-bloc trade in commodity and manufactured food markets. 

Empirical evidence reveals that most RTAs increased food trade in the international marketplace, but some also diverted trade away from more efficient 

suppliers. Not surprisingly, the rise in exports to partner markets was particularly pronounced when importers belonged to the same RTA as the exporter. 

The empirical findings also show that bloc-member exports often expanded 

to nonmember markets. Given the preferential treatment characteristic of 

RTAs, the rise in this extra-bloc trade was understandably less than the 

increase in intra-bloc trade, with ASEAN being the notable exception.

Empirical results show that since the time of its inception, the EU curtailed 

imports of commodity foods from the United States and from non-U.S. 

suppliers by an annual average of 4 percent and 1 percent, respectively. 

Such declines accumulate over time. For the 1975-2005 period overall, U.S. 

commodity food exports to the EU fell 66 percent while exports by non-U.S. 

suppliers declined 29 percent.29

The study also found that U.S. food exporters were more adversely affected 

than non-U.S. foreign suppliers by three other agreements; namely—ASEAN, 

GAFTA, and SAPTA. COMESA is the only agreement that diverted food 

trade from non-U.S. outside suppliers but not from U.S. exporters. 

The majority of the 11 trade agreements examined in this study created trade 

in the commodity and manufactured markets while diverting comparatively 

little trade away from outside suppliers, with the exception of commodity 

food imports by the EU. This finding suggests that the benefits of RTAs 

generally outweigh costs in the international food marketplace. This inference is similar to the conclusion drawn by Vollrath and Hallahan (2011), a 

study of agricultural trade using generic RTA indexes. 

29The United States promoted 

multilateral negotiations via the GATT 

during the 1960s and 1970s to counter 

discrimination inherent by the formation and expansion of the European 

Community (Bergsten, 1996). It is 

interesting that the concern about the 

erosion of exports by foreign suppliers 

due to the agreement uniting Europe 

proved to be prescient.
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Appendix table 1

Summary statistics for variables in the panel dataset—continued

Variable Mean Std. deviation Description

Xij_CF 53 (263) Bulk commodity food trade between i and j (millions of U.S. dollars)

Xij_MF 131 (74) Manufactured food exports by i to j (millions of U.S. dollars)

Yi 368 (1053) Gross domestic product of importer j (billions of U.S. dollars)

Yj 362 (1055) GDP of exporter i (billions of U.S. dollars)

EMij 5.19 (26) Exchange-rate misalignment between i and j 

FEij 23.85 (217) Exporter-to-importer 0.1 hectare of arable land per worker in domestic labor 

force

DTij 7854 (4670) Kilometers between the exporter i and importer j

CBij 0.0296 (0.1695) Dummy: both countries share a common border

LLij 0.1243 (0.3299) Dummy: If either or both countries are landlocked

LSij 0.2067 (0.4049) Dummy: 9 percent or more of population in both countries sharing a common 

language

CHij 0.0144 (0.1192) Dummy: existence of a colonial linkage after 1945

ITCANDE 0.0002 (0.0134) Dummy: i and j in ANDE 

XTCANDE 0.0091 (0.0947) Dummy: i in ANDE, j outside of ANDE 

MTDANDE_US 0.0002 (0.0133) Dummy: U.S. as an exporter outside ANDE, j as an importer inside ANDE

MTDANDE_non-US 0.0094 (0.0966) Dummy: non-U.S. exporter outside ANDE, j as an importer inside ANDE

ITCASEAN 0.0014 (0.0373) Dummy: i and j in ASEAN 

XTCASEAN 0.0285 (0.1663) Dummy: i in ASEAN, j outside of ASEAN 

MTDANSEAN_US 0.0005 (0.0215) Dummy: U.S. as an exporter outside ASEAN, j as an importer inside ASEAN 

MTDASEAN_non-US 0.0240 (0.1531) Dummy: non-U.S. exporter outside ASEAN, j as an importer inside ASEAN 

ITCCACM 0.0027 (0.0517) Dummy: i and j in CACM 

XTCCACM 0.0291 (0.1681) Dummy: i in CACM, j outside of CACM 

MTDCACM_US 0.0007 (0.0259) Dummy: U.S. as an exporter outside CACM, j as an importer inside CACM

MTDCACM_non-US 0.0271 (0.1623) Dummy: non-U.S. exporter outside CACM, j as an importer inside CACM

ITCCER 0.0004 (0.0202) Dummy: i and j in CER 

XTCCER 0.0260 (0.1592) Dummy: i in CER, j outside of CER 

MTDCER_US 0.0004 (0.0202) Dummy: U.S. as an exporter outside CER, j as an importer inside CER

MTDCER_non-US 0.0234 (0.1512) Dummy: non-U.S. exporter outside CER, j as an importer inside CER

ITCCOMESA 0.0005 (0.0225) Dummy: i and j in COMESA 

XTCCOMESA 0.0132 (0.1142) Dummy: i in COMESA, j outside of COMESA 

MTDCOMESA_US 0.0003 (0.0166) Dummy: U.S. as an exporter outside COMESA, j as an importer inside COMESA

MTDCOMESA_non-US 0.0130 (0.1133) Dummy: non-U.S. exporter outside COMESA, j as an importer inside COMESA

ITCEU 0.0279 (0.1645) Dummy: i and j in EU 

XTCEU 0.1615 (0.3679) Dummy: i in EU, j outside of EU 

MTDEU_US 0.0028 (0.0531) Dummy: U.S. as an exporter outside EU, j as an importer inside EU

MTDEU_non-US 0.1555 (0.3623) Dummy: non-U.S. exporter outside EU, j as an importer inside EU

ITCGAFTA 0.0019 (0.0435) Dummy: i and j in GAFTA 

XTCGAFTA 0.0171 (0.1297) Dummy: i in GAFTA, j outside of GAFTA 

MTDGAFTA_US 0.0004 (0.0207) Dummy: U.S. as an exporter outside GAFTA, j as an importer inside GAFTA

MTDGAFTA_non-US 0.0217 (0.1457) Dummy: non-U.S. exporter outside GAFTA, j as an importer inside GAFTA

ITCMERC 0.0012 (0.0343) Dummy: i and j in MERC 

continued—
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Appendix table 1

Summary statistics for variables in the panel dataset—continued

Variable Mean Std. deviation Description

XTCMERC 0.0216 (0.1455) Dummy: i in MERC, j outside of MERC 

MTDMERC_US 0.0004 (0.0198) Dummy: U.S. as an exporter outside MERC, j as an importer inside MERC

MTDMERC_non-US 0.0181 (0.1333) Dummy: non-U.S. exporter outside MERC, j as an importer inside MERC

ITCNAFTA 0.0007 (0.0270) Dummy: i and j in NAFTA 

XTCNAFTA 0.0267 (0.1613) Dummy: i in NAFTA, j outside of NAFTA 

MTDNAFTA_non-US 0.0260 (0.1592) Dummy: i outside of NAFTA, i inside NAFTA 

ITCSADC 0.0001 (0.0094) Dummy: i and j in SADC 

XTCSADC 0.0042 (0.0644) Dummy: i in SADC, j outside of SADC 

MTDSADC_US 0.0001 (0.0094) Dummy: U.S. as an exporter outside SADC, j as an importer inside SADC

MTDSADC_non-US 0.0044 (0.0659) Dummy: non-U.S. exporter outside SADC, j as an importer inside SADC

ITCSAPTA 0.0011 (0.0325) Dummy: i and j in SAPTA 

XTCSAPTA 0.0189 (0.1360) Dummy: i in SAPTA, j outside of SAPTA 

MTDSAPTA_US 0.0004 (0.0189) Dummy: U.S. as an exporter outside SAPTA, j as an importer inside SAPTA

MTDSAPTA_non-US 0.0166 (0.1278) Dummy: non-U.S. exporter outside SAPTA, j as an importer inside SAPTA