Do deeper regional trade agreements enhance international technology spillovers?

• Author: Naoto Jinji,Xingyuan Zhang,Shoji Haruna
• DOI: http://doi.org/10.1111/twec.12797
• Date: 01 August 2019
• Published date: 01 August 2019

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wileyonlinelibrary.com/journal/twec World Econ. 2019;42:2326–2363.

© 2019 John Wiley & Sons Ltd

Received: 4 June 2018

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Revised: 10 December 2018

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Accepted: 30 January 2019

DOI: 10.1111/twec.12797

ORIGINAL ARTICLE

Do deeper regional trade agreements enhance

international technology spillovers?

NaotoJinji1

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XingyuanZhang2

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ShojiHaruna3

1Faculty of Economics,Kyoto University, Kyoto, Japan

2Faculty of Economics,Okayama University, Okayama, Japan

3Faculty of Economics,Fukuyama University, Fukuyama, Japan

KEYWORDS

deep integration, patent citations, regional trade agreements, technology spillovers

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INTRODUCTION

The last two decades have witnessed a rapid proliferation of regional trade agreements (RTAs).

Although early studies on the economic effects of RTAs focused on their static effects, such as

trade creation and trade diversion (Viner, 1950), more recent studies have addressed the dynamic

effects of RTAs, including technology adoption and technology spillovers (Bustos, 2011; Ederington

& McCalman, 2008; Schiff & Wang, 2003). For example, Bustos (2011) estimates the effects of

Mercosur on the adoption of technology by Argentinean firms. Her results show that a tariff reduction

by Brazil induced statistically significant increases in Argentinean firms' technology spending and

innovation indexes.

RTAs may also enhance technology spillovers across countries (Das & Andriamananjara, 2006).

Although RTAs are primarily aimed at expanding trade in goods by reducing tariffs on imports, many

recent RTAs pursue a deeper integration (Baldwin, 2011). For example, many RTAs now include

liberalisation of investment and harmonisation of intellectual property rights (IPR) protection poli-

cies. Thus, it is expected that RTAs affect the flow of knowledge across countries. In this study, we

investigate this issue empirically.

One approach to measuring technology spillovers, pioneered by Jaffe, Trajtenberg, and Henderson

(1993), is to employ patent citation data (Branstetter, 2006; Hall, Jaffe, & Trajtenberg, 2001; Jaffe &

Trajtenberg, 1999; MacGarvie, 2006; Maurseth & Verspagen, 2002). Patent citations are references to

existing patents included in patent documents. Although the main role of these citations is to delimit

the scope of contributions by the present patent and by previous patents, they can be interpreted to

indicate that the knowledge in the cited patents was “in some way useful for developing the new

knowledge described in the citing patent” (Maurseth & Verspagen, 2002, p. 534). In other words,

patent citations represent a “paper trail” of knowledge flows from the cited patent to the citing patent

(Jaffe et al., 1993). Thus, patent citations can be used as a direct measure of knowledge flows (Hall

et al., 2001). In particular, patent applicants at the United States Patent and Trademark Office (USPTO)

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have a legal duty to list the existing patents at the USPTO they cite on the front page of the application

document. Given its effectiveness, we follow this approach to measuring technology spillovers.1 Note

that, in this study, by the term “technology spillovers” we refer to “the process by which one inventor

learns from the research outcomes of others' research projects and is able to enhance her own research

productivity with this knowledge without fully compensating the other inventors for the value of this

learning” (Branstetter, 2006, pp. 327–328). Thus, we distinguish technology spillovers from imitation

or technology adoption.

To the best of our knowledge, only Peri (2005) and Jinji, Zhang, and Haruna (2013) have investi-

gated the effects of RTAs on technology spillovers. Using a sample of 18 countries with 147 subna-

tional regions in Western Europe and North America for the period of 1975–96, Peri (2005) estimates

a gravity‐like model to examine the effects of several resistance factors on patent citations. He finds

that regional, national and linguistic borders have a negative and significant effect on technology

spillovers, but that the effect of “trade blocs” on technology spillovers is statistically insignificant.

However, his study is not comprehensive with regard to analysing the effects of RTAs on technology

spillovers, because he only includes the European Economic Community (EEC)/the European

Community (EC)/the European Union (EU) and North American Free Trade Agreement (NAFTA) as

“trade blocs.” Given that NAFTA came into force on 1 January 1994, and that his sample period ends

in 1996, the trade‐bloc dummy in his analysis may mainly capture the effects of the EEC/EC/EU. On

the other hand, Jinji et al. (2013) find a positive and significant effect of RTAs on technology spill-

overs based on a sample of 103 countries for the period 1990–99. However, the analysis in that study

is still limited, because it includes only nine RTAs.2 In contrast to these studies, we conduct a more

comprehensive analysis of the relationship between RTAs and international technology spillovers by

extending the sample to 114 countries and 125 RTAs. All RTAs notified to the World Trade

Organization (WTO) that came into force by the final year of our sample period are included, as long

as at least two countries in our sample are signatories of the RTA.

The main contribution of this study goes beyond a simple estimation of the average effect of RTAs

on technology spillovers. We focus on the effect of the depth of integration on technology spillovers.

RTAs impose various legal obligations on member countries. As a result, we can evaluate the depth

and nature of the economic integration of RTAs by examining the policy areas covered by the provi-

sions of the agreements, as well as the extent to which these obligations are legally enforceable. To

deal with this issue, Horn, Mavroidis, and Sapir (2010) identify 52 policy areas covered by RTAs of

which either the United States (US) or EC is a member, classifying them into two groups: WTO‐plus

(WTO+) and WTO‐extra (WTO‐X). The WTO+ group includes provisions that fall under the current

mandate of the WTO, but go beyond the commitments at the multilateral level. In contrast, WTO‐X

provisions include issues that fall outside the current WTO mandate. In our analysis, we focus on the

WTO‐X provisions as a measure of the depth of integration of an RTA, mainly because the WTO‐X

areas include both technology‐related provisions, such as IPR and innovation policies, and non‐tech-

nology‐related provisions, such as cultural cooperation and economic policy dialogue. We then con-

struct variables to measure the depth of RTAs from the WTO‐X provisions data and estimate the

relationship between these variables and technology spillovers.

1 We explain the reason why we use patent citations as a measure of technology spillovers in more detail in Section3.

2 The nine RTAs are those included in the dataset provided by Andrew K. Rose: EEC/EC/EU, United States (US)–Israel FTA,

NAFTA, the Caribbean Community (CARICOM), the Agreement on Trade and Commercial Relations between Australia and

Papua New Guinea (PATCRA), Australia New Zealand Closer Economic Agreement (ANZCERTA), Central American

Common Market (CACM), Mercado Común del Sur (Mercosur) and the Association of Southeast Asian Nations (ASEAN).

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Using patent application and citation data at the USPTO, we construct a panel of 11,666 pairs of

citing and cited countries/regions from the sample of 114 countries/regions for the 17 years from 1991

to 2007.3 The reason why we use the USPTO data is that the share of non‐domestic residents in all

applications is much higher at the USPTO than at other major patent offices, such as the European

Patent Office (EPO) and the Japanese Patent Office (JPO). Thus, the USPTO data are the best avail-

able to capture cross‐country technology spillovers by patent citations.

Our empirical strategy is as follows. We employ the number of backward patent citations per pair of

countries as a proxy for technology spillovers between the two countries and use this as the dependent

variable in our regressions. With regard to explanatory variables, we include an RTA dummy and an

index of WTO‐X provisions. The RTA dummy captures the average effect of the common RTA mem-

bership on technology spillovers, and the WTO‐X index captures the impact of the deep regional inte-

gration on technology spillovers.4 We first estimate the empirical model for technology spillovers using

the ordinary least squares (OLS). We also estimate the model by a Poisson pseudo‐maximum‐likeli-

hood (PPML) estimator, mainly because our dependent variable (i.e., the number of patent citations) is

count data and there are many zeros in the dependent variable. Moreover, to account for possible het-

erogeneous effects of RTAs and deep integration, we examine heterogeneity in three dimensions: (a)

across country groups (i.e. North–North, North–South and South–South country pairs); (b) across the

nature of RTAs (i.e. free trade agreements [FTAs] and customs unions [CUs]); and (c) the phased‐in

effects with lagged terms.5 We also account for possible endogeneity in the RTA dummy and the

WTO‐X index by employing the instrumental variable (IV) approach. With regard to the impact of a

deep integration, we further investigate whether the technology‐related WTO‐X provisions are more

strongly related to technology spillovers than other WTO‐X provisions. To address this issue, we first

conduct a factor analysis on the WTO‐X provisions data to extract factors that can be used to select

subsets of the WTO‐X provisions. Then, based on the result of the factor analysis, we categorise the

WTO‐X provisions into three subsets. We finally estimate the impact of the WTO‐X indexes that rep-

resent the different WTO‐X provision subsets.

This is the first study of the effects of the depth of RTAs on international technology spillovers.

Our main findings are as follows. First, the estimated coefficient of the simple RTA dummy is positive

and highly significant. This finding is quite robust for different estimation techniques and is consistent

with the result of Jinji et al. (2013), but is different from that of Peri (2005). We do not observe any

significant difference between FTAs and CUs. The estimate of the RTA dummy is also positive and

significant, regardless of the country pairs with different country groups, though it is larger for the

North–North country pairs than for other combinations. In addition, we find the phased‐in effect of the

RTA formation on technology spillovers. The RTA dummies with two‐ and three‐period lags as well

3 Our analysis also differs from those in previous studies (Jinji etal., 2013; Peri, 2005) in terms of the sample period. Although

Peri (2005) covers until 1996, it is important to include the late 1990s and 2000s, because the number of RTAs increased rapidly

in these periods. Jinji etal. (2013) do not cover the 2000s. Note that our sample period ends in 2007 because of the unavailabil-

ity of reliable patent data after 2007.

4 Note that a formation of an RTA by itself does not necessarily facilitate patent applications among member countries in our

data because we use data at the USPTO rather than patent offices in individual countries. Thus, we do not need to worry about

the possibility of such a “facilitation effect” being included in the RTA dummy.

5 The issue of heterogeneous effects of RTAs has recently attracted considerable attention in the empirical studies on the trade

effects of RTAs. For example, Behar and Cirera‐i‐Crivillé (2013) have analysed how the impacts of RTAs on trade differ, de-

pending on whether signatories are developed or developing countries. The heterogeneity in the effects of RTAs on bilateral

trade by the nature of integrations has been addressed by Baier, Bergstrand, and Feng (2014) and Roy (2010). Moreover, the

phased‐in effects of RTAs on trade flows have been analysed by a number of studies, including Baier and Bergstrand (2007)

and Kohl (2014).