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Are all trade agreements equal? The role of distance in shaping the effect of economic integration agreements on trade flows

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Abstract

How does geographic distance affect the impact of trade agreements on bilateral exports, and through what channels? This paper examines these questions in a gravity model context for different types of goods for 185 countries over the period 1965–2010. Three stylized facts emerge. First, although economic integration agreements have a positive impact on trade flows, geographic distance significantly decreases their effect. Second, this phenomenon is in large part explained by the impact of economic integration agreements on intermediate goods. These results hold when controlling for trade agreement depth, measured by the type of agreement and content of provisions, and economic similarity among trading partners. Third, this paper finds either a smaller negative effect or no effect on the interaction between distance and economic integration agreements for non-intermediates. This underscores that while economic integration agreements promote trade in all goods, there is an additional benefit to intermediates which diminishes faster with distance.

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Notes

  1. To be sure, Bergstrand et al. (2015) use intra-country and between-country trade and find a decline in the effect of distance in recent years.

  2. Recent theoretical contributions in the international trade literature also provide micro-foundations for the aforementioned relationship between trade flows and distance. Hillberry and Hummels (2008) show that distance (along with other natural trade frictions) decreases trade flows, primarily through the variety (as opposed to the volume) of exported products. Chaney (2008); Bernard et al. (2007); and Lawless (2010) use industry-level gravity equations to show that distance dampens trade flows. They do not explore how policy variables interact with distance.

  3. On the theoretical determinants of gravity models and appropriate estimation strategies, see: Anderson and Van Wincoop (2003), Baldwin and Taglioni (2006), Baier and Bergstrand (2009), and references therein.

  4. The following presentation is heavily based on Baldwin and Taglioni (2006). Readers are referred to their paper for a more in-depth breakdown.

  5. In their model, BBC account for aggregate goods and break down trade flows into extensive and intensive goods margins, and compute comparative statistics for these margins. The following explanation and Eqs. (7)–(11) are based heavily on BBC, who draw inspiration from the mixed linear models section in Cameron and Trivedi (2005). Readers are referred to BBC (and the corresponding appendices) for a comprehensive explanation of the theoretical model.

  6. To be sure, BBC build upon previous extensions to the Melitz model in their treatment of trade costs. In particular, see Redding (2011) for a breakout of exogenous export fixed costs and Krautheim (2012) for endogenous export fixed costs.

  7. One of BBC’s main contributions is to explore the distance-EIA relationship across extensive and intensive goods margins. Their theoretical model yields a negative prediction of the interaction for both margins, which they verify empirically. While trade margins are not the focus of our analysis, we also verify a negative impact of EIAs and distance at intensive and extensive trade margins, defined per the Hummels and Klenow (2005) methodology. Results are available upon request.

  8. We repeat our analysis using five year differenced data for robustness, as in BBF. See Appendix 1.

  9. See Appendix 3 for a list of countries used in this analysis.

  10. See Appendix 2 for further details about the BEC classification system and how SITC Rev. 2 4-digit products were mapped to BEC categories.

  11. The types of agreements referred to here are partial scope agreements; free trade agreements; customs unions; and services agreements.

  12. The DESTA dataset includes two depth measures: a depth index and a depth variable based on explanatory factor analysis. Here, we rely on the depth index although both variables are highly correlated.

  13. The first provision category covers whether the agreement substantially reduces tariffs. The remaining six categories of substantive provisions encompass: services; investments; standards; public procurement; competition; and intellectual property rights. Additional information can be found in Dür et al. (2014).

  14. For a detailed description see both Mayer and Zignago (2011) and Head and Mayer (2002). Regression results are not sensitive to the distance measure used. The insensitivity of results to distance measures was also reported by Johnson and Noguera (2012) in the working paper version of their study on proximity and fragmentation, prepared for the 2012 AEA meetings (page 5, footnote 9). We report results using the simple distance measure, \(dist_{ij}\). Results using alternative distance measures are available upon request.

  15. Data on GDP per capita is not available for all countries for which we have data on exports and EIAs. This is particularly the case for developing and Middle Eastern countries in early years of our sample. As such, Tables 7 and 8 have fewer observations than tables which do not control for GDP per capita.

  16. See equation 10 and corresponding Column 1, Table 5 in Baier and Bergstrand (2007).

  17. To be sure, BBF examine the effect of trade agreements based on their depth. They find the average contemporaneous effect of agreements deeper than FTAs to be 39%.

  18. In their 2014 paper, BBF convincingly argue that the marginal impact of EIAs on bilateral gross exports is the highest for deepest agreements, both at intensive and extensive trade margins.

  19. Economic proximity is defined as the absolute value of the difference in log GDP per capita between bilateral trading partners.

  20. As noted in Sect. 3, in Table 4 our binary EIA variable is equal to unity if two countries have a two-way preferential trade agreement (TWPTA) or any deeper agreement, and zero otherwise. We are interested in TWPTAs and above, as these agreements encompass bilateral (versus unilateral) preferences. Excluding one-way PTAs aligns with excluding framework agreements when using the DESTA dataset.

  21. Results from the same exercise using the Baier and Bergstrand trade agreement dataset are similar. All output tables are available upon request to the authors.

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Author information

Authors and Affiliations

  1. The Graduate Institute, Geneva (IHEID), Chemin Eugene-Rigot 2, 1202, Geneva, Switzerland

    Rebecca Freeman

  2. World Bank and CIEC-ESPOL, 1818 H Street NW, Washington, DC, 20433, USA

    Samuel Pienknagura

Authors
  1. Rebecca Freeman
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  2. Samuel Pienknagura
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Corresponding author

Correspondence to Rebecca Freeman.

Additional information

This paper benefited from useful comments from Richard Baldwin, Jeffrey Bergstrand, Nicolas Berman, Chad Bown, Peter Egger, Aksel Erbahar, David Michael Gould, Russell Hillberry, Daniel Lederman, Peter Neary, Marcelo Olarreaga, Raymond Robertson, Angelos Theodorakopoulos, and participants in the Authors’ Workshop of the World Bank’s Regional Flagship Report on Regional Economic Integration and the IHEID Economics Department BBL Series. The authors are particularly grateful to Jeffrey Bergstrand for his comments on the working paper version of this study and to Richard Pomfret for his feedback on a more advanced stage of the paper. The authors acknowledge financial support from the World Bank’s Office of the Chief Economist for Latin America and the Caribbean.

Appendices

Appendix 1: Heterogeneous EIA effects on the growth rate of trade

As a further extension, we explore the effect of EIAs on the growth rate of trade flows by estimating our specifications of interest in first differences. While the main aim of our paper is to examine the heterogeneous effects of EIAs on trade volumes, estimating our specifications of interest in first differences has a few merits. As highlighted by Baier, Bergstrand, and Feng (2014), first differencing panel data can resolve the inefficiency of fixed effects estimators in the event of a long T and serially correlated error terms due to unobserved factors which influence the likelihood of an EIA. Moreover, fixed effects estimators in panel datasets with a long T might suffer from spurious regression if the trade data follow unit-root processes. Using first differences takes care of this issue by allowing data to deviate from the previous period (5-years in our case), getting closer to a unit-root process.

In light of the above, Baier, Bergstrand and Feng (2014) thus apply a random-growth first-difference model (RGFD). With this, they estimate their equations in first differences while still including the bilateral fixed effect term, \(\gamma _{ij}\), to account for changes over time in pair-specific unobservables. We follow this approach and estimate Eqs. (17)–(21) below, where \(\Delta _{5}\) represents the first differenced data over 5-year intervals and \(\delta _{5,it}\) and \(\xi _{5,jt}\) are changes over time in exporter-specific and importer-specific unobservables. All other variables are the same as in specifications (12)–(16) in the main text.

$$\Delta _{5}ln\varvec{X_{ijt}}= \alpha + \beta _1(\Delta _{5}EIA_{ijt})+\beta _2(\Delta _{5}EIA_{ijt}\text{*}lndist_{ij})+\delta _{5,it}+\xi _{5,jt}+\gamma _{ij}+\varepsilon _{5,ijt}$$
(17)
$$\Delta _{5}ln\varvec{X_{ijt}}=\alpha + \beta _1(\Delta _{5}EIA_{ijt}\text{*}lndist_{ij})+\beta _2(\Delta _{5}\varvec{D_{ijt}})+\delta _{5,it}+\xi _{5,jt}+\gamma _{ij}+\varepsilon _{5,ijt}$$
(18)
$$\Delta _{5}ln\varvec{X_{ijt}}= \alpha +\beta _1(\Delta _{5}\varvec{D_{ijt}})+\beta _2(\Delta _{5}\varvec{D_{ijt}}\text{*}lndist_{ij}) + \delta _{5,it}+\xi _{5,jt}+\gamma _{ij}+\varepsilon _{5,ijt}$$
(19)
$$\begin{aligned} \Delta _{5}ln\varvec{X_{ijt}}&= \alpha +\beta _1(\Delta _{5}EIA_{ijt})+\beta _2(\Delta _{5}EIA_{ijt}\text{*}lndist_{ij})+\beta _3(\Delta _{5}{} \textit{adiff}\_ \textit{lnGDPcap}_{ijt}) \\&\quad+\,\delta _{5,it}+\xi _{5,jt} +\gamma _{ij}+\varepsilon _{5,ijt} \end{aligned}$$
(20)
$$\begin{aligned} \Delta _{5}ln\varvec{X_{ijt}}& = \alpha + \beta _1(\Delta _{5}EIA_{ijt})+\beta _2(EIA_{ijt}\text{*}lndist_{ij})+\beta _3(\Delta _{5}{} \textit{adiff}\_ \textit{lnGDPcap}_{ijt}) \\&\quad +\beta 4\left( \Delta _{5}EIA_{ijt}\text{*}{} \textit{adiff}\_lnGDPcap_{ijt}\right) +\delta _{5,it}+\xi _{5,jt}+\gamma _{ij}+\varepsilon _{5,ijt}\end{aligned}$$
(21)

Tables 9, 10, 11 and 12 present results when using the DESTA dataset for EIAs.Footnote 21 As can be seen, our results hold in all cases. The interaction between trade agreements and distance is both negative and statistically significant for total and intermediate goods, whereas this is not the case for other goods categories (Table 9). This holds when controlling for the depth of agreement (Table 10) and when disaggregating trade agreements by depth (Table 11). Lastly, our main results hold when controlling for economic similarity, as shown in Table 12.

Table 9 EIAs and bilateral distance: baseline RGFD
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Table 10 EIAs and bilateral distance: controlling for depth RGFD
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Table 11 EIAs and bilateral distance: disaggregating depth RGFD
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Table 12 EIAs and bilateral distance: controlling for economic similarity RGFD
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Appendix 2: Broad economic categories classification system

Annual 4-digit SITC Rev. 2 data on bilateral trade flows—from NBER-UN and described in Feenstra et al. (2005)—were used in the aggregation of total trade statistics. In order to classify goods as intermediate, consumption, or final, we used a concordance (available from the United Nations website) to map SITC goods into BEC categories.

In the event that there was not a one-to-one match between SITC and BEC classification systems (i.e. if one SITC good mapped to two or multiple BEC categories), we resolved the problem by attributing a proportionate share of the SITC recorded flow to each BEC category. For instance, if the same SITC category (e.g. processed sunflower seed oil) maps to one BEC code for intermediate goods (121) and one BEC code for consumption goods (122), we map one half of the bilateral trade flow to intermediate goods and one half to consumption goods. We chose to use this approach so that the sum of disaggregated goods would equal total trade.

Table 13 below contains the BEC breakdown by category of good used in this analysis.

Table 13 BEC breakdown
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Appendix 3: Countries used in analysis

Afghanistan

Georgia

Nigeria

Albania

Germany

Norway

Algeria

Ghana

Oman

Andorra

Greece

Pakistan

Angola

Greenland

Panama

Antigua and Barbuda

Grenada

Papua New Guinea

Argentina

Guatemala

Paraguay

Armenia

Guinea

Peru

Aruba

Guinea-Bissau

Philippines

Australia

Guyana

Poland

Austria

Haiti

Portugal

Azerbaijan

Honduras

Qatar

Bahamas

Hong Kong

Romania

Bahrain

Hungary

Russian Federation

Bangladesh

Iceland

Rwanda

Barbados

India

Samoa

Belarus

Indonesia

São Tomé and Príncipe

Belgium

Iran

Saudi Arabia

Belize

Iraq

Senegal

Benin

Ireland

Seychelles

Bermuda

Israel

Sierra Leone

Bhutan

Italy

Singapore

Bolivia

Jamaica

Slovak Republic

Bosnia and Herzegovina

Japan

Slovenia

Botswana

Jordan

Solomon Islands

Brazil

Kazakhstan

Somalia

Bulgaria

Kenya

South Africa

Burkina Faso

Kiribati

Spain

Burundi

Korea, DPR

Sri Lanka

Cambodia

Korea, Rep.

Saint Kitts and Nevis

Cameroon

Kuwait

Saint Lucia

Canada

Kyrgyz Republic

Saint Vincent and The Grenadines

Cape Verde

Lao PDR

Sudan

Central African Rep.

Latvia

Suriname

Chad

Lebanon

Swaziland

Chile

Liberia

Sweden

China

Libya

Switzerland

Colombia

Lithuania

Syrian Arab Republic

Comoros

Luxembourg

Taiwan

Congo, DRC

Macao

Tajikistan

Congo, Rep.

Macedonia

Tanzania

Costa Rica

Madagascar

Thailand

C^ote d'Ivoire

Malawi

Togo

Croatia

Malaysia

Tonga

Cuba

Maldives

Trinidad and Tobago

Cyprus

Mali

Tunisia

Czech Republic

Malta

Turkey

Denmark

Mauritania

Turkmenistan

Djibouti

Mauritius

Uganda

Dominica

Mexico

Ukraine

Dominican Republic

Moldova

United Arab Emirates

Ecuador

Mongolia

United Kingdom

Egypt

Morocco

United States

El Salvador

Mozambique

Uruguay

Estonia

Myanmar

Uzbekistan

Ethiopia

Namibia

Vanuatu

Faeroe Islands

Nepal

Venezuela

Fiji

Netherlands

Vietnam

Finland

New Caledonia

Yemen

France

New Zealand

Zambia

Gabon

Nicaragua

Zimbabwe

Gambia

Niger

 

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Freeman, R., Pienknagura, S. Are all trade agreements equal? The role of distance in shaping the effect of economic integration agreements on trade flows. Rev World Econ 155, 257–285 (2019). https://doi.org/10.1007/s10290-018-0327-3

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