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CAGE distance framework and
bilateral trade flows: case of India
CAGE
distance
framework
Kalpana Tokas and Arnab Kumar Deb
Department of Economics, International Management Institute, New Delhi, India
Abstract
Purpose – The paper is in the area of international business and international trade. Specifically, this paper
aims to focus on cross-border trade flows of goods and services between India and its partner nations.
Design/methodology/approach – Using the Cultural, Administrative, Geographic and Economic
(CAGE) distance framework (Ghemawat, 2001), this paper provides empirical support for the impact these
distance factors exert on the volume of trade in goods and services between countries. The sample used for
empirical analysis consists of a set of 62 OECD countries which are involved in trade in goods and services
with India over the period 2005 through 2015. This paper estimates a fixed-effects model to provide a
comprehensive examination of all the distance factors impacting the bilateral cross-border trade flows of
India.
Findings – The empirical findings in this paper show that different dimensions of the CAGE distances have
varied influence on volume of trade flows between India and its trading partners. Also, the extent of this
influence is guided by the nature of industries – manufacturing or services.
Originality/value – Departing from the common practice in the literature, using the trade flow data for
both Indian manufacturing and service sectors separately, this paper examines to what extent is the impact of
these distance factors industry driven.
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Received 3 September 2019
Revised 23 December 2019
16 March 2020
Accepted 24 March 2020
Keywords International business, Globalization, Cultural distance, Manufacturing sector,
Administrative distance, Cross-border trade flows, Economic distance, Geographic distance,
Services sector
Paper type Research paper
1. Introduction
In the globalizing world, evaluating the potential of cross-border integration in benefiting
the partnering countries and the world economy has been a thought-provoking area of
research in the domain of international business and international trade. Cross-border
integration has been the main driver of globalization. The term globalization has been
defined as a process of closer integration and increased interdependence between nations
through increased and unrestricted flow of goods, services, capital, labour and knowledge
across the borders (Stiglitz, 2003; IMF, 2008). The advances in modes of transportation,
telecommunications and information technology and their reduced costs over the past
decades have enhanced the pace of globalization and have been considered as the
fundamental shapers of the claims of a borderless world.
At the same time, business innovation has led the multinational enterprises (MNEs) to
evolve new modes of global integration such as foreign direct investment (FDI), global value
chains (GVC), international production networks and off-shoring (Venables, 2006). Further,
the market-oriented reforms in major emerging economies coupled with more liberal trade
regimes through reduction of tariffs – unilaterally or multilaterally – under the World Trade
The authors gratefully acknowledge and express appreciation for the helpful comments on earlier
versions of this paper by MRR reviewers.
Management Research Review
Vol. 43 No. 10, 2020
pp. 1157-1181
© Emerald Publishing Limited
2040-8269
DOI 10.1108/MRR-09-2019-0386
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Organization (WTO) framework have been the primary “institutional” stimuli to the process
of cross-border integration and thus globalization. Present trends of protectionism and
stricter immigration policies prevailing in developed countries, however, have impaired the
strong sentiment about globalization and an evident preference towards anti-globalization
(at a macro level) reignites the debate on the worth of rapid and colossal cross-border
integration that took place over the past two decades.
Nevertheless, while evaluating the success or future potential of cross-border integration,
it is imperative to explore the nature of cross-border integration first. Continuous
technological changes coupled with global shift in political institutions play a crucial role in
determining the nature and extent of cross-border integration. It has been postulated in
literature (Ghemawat, 2003) that though there has been a significant rise in levels of crossborder integration in product, labour and capital (through FDI and Foreign Institutional
Investment) markets, this integration is far from complete. The literature refers to the
outcome of this incomplete integration as semi-globalization, which is a state in between the
extremes of complete insulation and complete integration, with existing economic and
institutional parameters that prevent either. As per Ghemawat (2003).
[. . .] incomplete cross-border integration is conditioned on apparently broad as well as complex
range of situations in which neither the barriers to enter a foreign market nor the existing links
among markets in different countries can be neglected.
Multiple facets of distances – cultural, economic, political, administrative and geographic –
between partner countries strongly characterize and support the concept of semiglobalization. One major implication of such cross-border integration is that higher costs are
naturally inflicted upon firms engaging with host countries which are relatively more
“distant”. Thus, for a firm, an analysis of the cost implications arising out of various
dimensions of “distance” between home and host countries is an imperative before getting
integrated.
In the literature of cross-border integration, country portfolio analysis (CPA) has been a
popular tool in evaluating a country’s decision to enter into a foreign market. CPA is
a measure of attractiveness of a foreign market and thus used by the firms to choose a
potential overseas market for global expansion. However, CPA focusses only on market size
and potential sales through variables such as gross domestic product (GDP), propensity to
consume and the levels of wealth while being quite oblivious to the costs and risk posed by
the “distance” between the nations partner nations. Ghemawat (2001) cites an example of
how using only CPA and not distances to rank the attractiveness of an overseas market
could yield flawed results. In 1998, Tricon Restaurants International (TRI) – a fast-food
giant based out of Dallas, USA – decided to cut down its global operations from some
markets. To choose which markets, they mapped 20 overseas markets based on per capita
income, per capita fast food consumption and size of the market (GDP) and as a result
Mexico was ranked 16th. Based on this analysis, Mexico did not come up as an attractive
prospect for TRI. However, incorporating the influence of various distances changed these
rankings significantly. By adjusting for geographical distance from Dallas, Mexico’s
ranking jumped to sixth position. Further, accounting for a common border and the trade
agreement North American Free Trade Agreement between Mexico and USA, Mexico stood
at second most attractive market for TRI, second only to Canada. Therefore, CPA fails to
provide a holistic approach for strategizing internationalization by MNEs by ignoring the
adjustments for distance factors.
Against this backdrop, the main objective of this empirical paper is to answer the
question that which distances are influential for cross-border integration between countries.
This study specifically focusses on India as this emerging economy has been traversing a
high growth trajectory over past years and has been viewed as an important market by the
global players. Despite the radically transforming global landscape over the past years,
the place held by the emerging economies and especially India has remained critical for the
growth and expansion of MNEs. Following a bold set of economic reforms implemented in
1991 and onwards, India embarked on a much higher growth trajectory over the post-reform
years. Subsequent set of reforms carried out in the areas of starting a business, dealing with
construction permits, getting electricity, getting credit, paying taxes, and enhancing
efficiency in trading across borders (India’s National Trade Facilitation Action Plan 20172020) resulted in an improvement in Doing Business score to 67.23 over the past year (Doing
Business, The World Bank, 2019b). This in turn enabled the nation to sustain the growth
pace with projected growth rate of over 7 per cent over the period of 2016 to 2021 (Global
Economic Prospects, The World Bank, 2019a). Naturally, India has emerged as the most
preferred destination for foreign investors.
Departing from the common practice of using CPA and drawing from Ghemawat’s (2001)
Cultural – Administrative – Geographic – Economic (CAGE) Distance Framework, this
paper attempts to empirically assess which of these dimensions of distance has an influence
on a country’s decision to involve in cross – border integration with India. Use of CAGE
framework in existing literature suggests its superiority over the traditional CPA to identify
the factors influencing various dimensions of cross-border integration. Analysis of the
influence of each of these CAGE distance factors individually is the most popular practice in
the existent literature. However, such analysis leads to under-estimation or over-estimation
of the impact of certain types of distances on cross-border integration because the degree of
opportunities and challenges posed by each of these distance factors could be different for
the countries engaging in cross-border integration. Furthermore, the specific features of
industries – manufacturing and services – also play a significant role in determination of the
impact of distances on cross-border integration between nations. Thus, estimation of a
multi-variable regression model which incorporates all distance factors in a single empirical
framework, attempts to fill this gap in the literature.
The rest of the paper is organized as follows. Section 2 presents the literature review and
discusses the prime contribution of this paper to the existing literature. Section 3 presents
the hypotheses developed to study which dimensions of “distance” influence the crossborder trade flows (CBTF) of India. This section also provides the conceptual research
framework that emerges from hypothesis development. Section 4 describes the econometric
specification, construction of dependent and explanatory variables proxying for various
dimensions of distances and nature of data. Section 5 reports the empirical findings of this
study. Section 6 contains the analysis and managerial implications of the empirical findings.
This section also identifies the limitations and possible future extensions of this study.
Finally, Section 7 concludes.
2. Literature review
The significance of the impact of various facets of distances on the decision of cross-border
integration has been a very pertinent question in the domain of international business.
Existing research in this area have looked at this multi-dimensional nature of cross-national
distance through a variety of lenses to emphasize the significance of varied impacts these
factors exert on a firm’s operations in foreign country.
Ghemawat (2001, 2007) highlighted that in 1991, the US media giant Star TV decided to
enter the Asian market. Their prime target was top 5 per cent of the population as they
would be receptive to English language TV programmes, hence saving Star from investing
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in local language programmes. The technology of satellite TV was trusted upon to diminish
the geographical distances. However, Star did not fare well, and News Corporation bought it
out in 1995, but it was not before 2006 that Star started making operating profits making
this buy-out to be a “high-profile” disaster. The reasons for Star’s fate was primarily the
oblivion to preference for local-content and the government’s role in monitoring political
content on television. This incident clearly shows that business operation in foreign country
is strongly influenced by political and cultural features of the host country.
Ghemawat (2007) undertook a country-level exercise to compare India and China from
the perspective of USA using the CAGE framework. The study concludes that on
geographic and economic distances front, China turns out to be a more preferred business
destination relative to India. Geographically, Indian ports are further as well as logistically
inferior as compared to the Chinese ports making China a more attractive prospect. Also, the
broader network of trade between China and other East and South East Asian countries
make China a preferred trading partner. Higher per capita GDP, higher labour productivity
and higher integration into GVCs relative to India move China higher on the economic
proximity ground. However, India attracts higher proportion of business from the USA as
far as cultural and administrative distances were concerned. The higher proportion of
English-speaking population in India, political harmony and the commonality in the legal
system with the USA due to a common colonizer stood out as the prime reasons for cultural
and administrative proximity between India and the USA.
Malhotra et al. (2009) used the CAGE distance framework to study the role of “distance”
in cross-border acquisitions (CBAs) by MNEs from 18 developing nations over the period
1990-2006. Further, they emphasized on the moderating impact of target market’s potential
in determining the destinations for expansion based on distances. Their study highlights
that administrative and economic distance had positive relation with CBAs by firms from
developing countries while the impact of cultural and geographical distance was negative.
Their results indicate that for the case of MNEs from developing nations, the risks
pertaining to distance are diluted by the market potential of the target nation.
Campbell et al. (2012) empirically analysed the question defining the role played by
distance in the CSR activities undertaken by foreign MNEs in host countries to cope with the
issues of liability of foreignness (LoF) using the CAGE distance framework. They carried
out the empirical analysis for the banks operational in the USA for the period 1990-2007.
The empirical findings suggest that the foreign banks which belong to more “distant” home
countries engage in lesser CSR initiatives in host country, despite the relatively larger gains
to be realized in terms of overcoming “LoF” and improvement in “social legitimacy”.
Kuo and Fang (2009) used the CAGE model to determine the impact of perceived
distances on the dichotomous variable of location choice of FDI from Taiwanese firms into
30 locations in China, for a sample size of 258 firms. They postulated that the concept of
“psychic distance” that has been widely used in the international business literature, focuses
only upon the cultural dimension of distance and shall be renewed. Therefore, they aim to
examine the correlation between location attractiveness and the various dimensions of
distance as formulated by the CAGE framework. Their findings suggested a negative and
significant relationship between location choice and administrative as well as geographic
distance. The impact of cultural distance and economic distance on location, however,
proved to be insignificant.
The most recent work by Antunes et al. (2019) uses a case-study approach for six firms to
qualitatively examine which CAGE distances between home and host countries impact the
business strategy of a firm’s international subsidiary. They postulated that the managers
choose a strategy for an international subsidiary based upon the distances that are
dominant between the home and the host country. Their results suggested that the cultural
and economic distances come out to be predominant, while the administrative and
geographic dimensions had a smaller impact on the business strategy.
As evident from the survey of relevant literature above, the CAGE (CulturalAdministrative- Geographic-Economic) distance framework (Ghemawat, 2001) has been
applied only limitedly to examine the impact of each of these distances individually on a
variety of phenomena such as trade flows, location choice, mode of entry and export
development. The studies using the holistic approach offered by the CAGE framework to
analyze which distances impact the internationalization process are very few. Also, it should
be noted that cross-border integration is characterized by multiple dimensions. The most
prominent dimensions analysed in the literature are CBA and FDI. CBTF measured by the
total value of trade between partner nations can be considered as another important
dimension to characterize the cross-border integration for a variety of reasons. First, total
value of trade flows between two countries reveals the extent of integration between two
countries through a direct access to each other’s markets. Higher value of trade flows –
either exports or imports – between partner nations reflects a stronger integration of two
nations’ markets. Second, the notion of participation in the GVC emerged out of cross-border
integration through nations’ trade in goods and services. With an objective of integrating in
the GVCs, the countries tend to participate in more bilateral trade through exports and
imports in intermediate goods and services. In either of the cases, the volume of total trade
flows increases between partner nations and thus strengthens cross-border integration
between them. Hence, this paper attempts to assess the impact of these distances on
internationalization measured by the value of trade flow between India and partner nations.
Apart from analysing the impact of distance factors on CBTFs, this study contributes to
existing literature in the following manner. First, the study focusses only on India and its
trade partners while developing the conceptual and the empirical models. Despite the
radically transforming global landscape over the past years, the place held by the emerging
economies and especially India, has remained critical for the growth and expansion of
MNEs. The emerging economies have been projected to grow at 4.2 per cent in 2019 (Global
Economic Prospects, The World Bank, 2019a), which is more than double the projected
growth rates for the advanced economies at 2 per cent. India provides an interesting case
study as it is one of the largest emerging economies that implemented a bold set of reforms
in 1991 and exhibited a steady shift to an open economy through privatization, liberalization
and finally globalization. Furthermore, due to these set of reforms India embarked on a
much higher growth trajectory as compared to pre-reform era. To sustain the growth pace,
subsequent set of reforms carried out in the doing business areas of starting a business,
dealing with construction permits, getting electricity, getting credit, paying taxes and
trading across borders resulted in an improvement in Doing Business score to 67.23 over the
past year (Doing Business, The World Bank, 2019b). Especially under India’s National
Trade Facilitation Action Plan 2017-2020, implementation of several initiatives not only
reduced border and documentary compliance time for both exports and imports but also
enhanced the efficiency of cross-border trade. As a result, projected average growth rate of
India is more than 7 per cent over the period of 2016 to 2021 exceeding the same for Asian
super power China (little over 6 per cent) and other BRICS nations Brazil, Russia and South
Africa (less than 2 per cent) (Global Economic Prospects, The World Bank, 2019a). Along
with being one of the fastest growing economies, India has emerged as the most preferred
destination for foreign investors. Considering the present state of global economy, India is
expected to be the prime destination of foreign countries not only in near future, as well as
for years to come. In 2017, India had a share of 1.68 and 2.48 per cent in the total global
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exports and imports of merchandise, respectively. The share of India in total world exports
of services stood at 3.47 per cent, while it was 3.02 per cent of the total world imports, in
2017. India’s rank in total world services exports was eight, and it ranked tenth in total
world services imports (Source: WTO Country Profile).
Departing from the common practice of estimating the econometric model consisting of
all possible combinations of countries partnering in trade, this paper particularly focuses on
India. Accordingly, the econometric specification has been modified to study the impact of
various distances on the cross-border integration with India. The extent of cross-border
integration has been measured by the value of CBTF as the former is an obvious outcome of
the latter. Using the data set constructed from multiple sources – OECD TiVA (trade in
value added) Database (December 2018 edition), CEPII Database – (Mayer and Zignago,
2011) and CIA World Factbook, World Bank Database) over the sample period 2005 through
2015, a panel data regression model has been estimated to establish the link between CBTF
and relevant distance factors for the case of India.
Second, given the differences in the role played by various dimensions of distances for
different industries depending upon their characteristics, this study carries out the analysis
for the manufacturing and services sector separately and highlights the key differences in
the results and the possible reasons behind them. Since a long period, the focus of
international trade and international business literature has primarily been on only crossborder flows of goods. However, in recent past, there has been a significant rise in the
volume and significance of trade in services. The intangible nature of services and their
inter-relation with the FDI flows is often responsible for issues related to the unavailability
of accurate data on trade in services. There is a dearth of studies exploring the relationship
between various dimensions of distances and CBTF for services as most of the studies focus
only on merchandise trade flows, and this study aims to fill that void.
Results from this study are expected to help MNEs of a partnering country in analysing
what are the primary costs that they are likely to face when engaging in cross-border
integration with India, specific to the manufacturing and services industries. Relevant
dimensions of distances especially CAGE distances between the home country and the
destination country inflict distinct monetary as well as non-monetary costs on the on the
operations of MNEs engaging in cross-border integration. The cultural distance between
two trading nations influences the cost of doing business with one another arising from
absence of a common language. Administrative distance between nations is primarily
known to increase the transaction costs involved in handling the bureaucratic issues by the
MNEs. Further, geographic distance is associated with higher transportation and
communication costs. The types of cost borne by MNEs also depends upon the type of
industry they are engaged in – manufacturing or services. Though services are traded
through multiple modes which often might not require physical proximity, there are costs
involved which can be attributed to coordination and knowledge transfer. As economic
distance captures the differences in per capita income, endowments, natural resources,
labour productivity, wage differential, etc., between two nations, it highlights the factors
that MNEs keep in mind while engaging with a partner nation.
3. Hypothesis development
3.1 Cultural distance
The concept of psychic distance was first introduced to the international economics
literature by Beckerman (1956) through the evaluation of impact of economic “distance”
(Transportation costs, physical distance, etc.) on Intra-European trade. Thereafter, it has
been widely used in the international business and international marketing literature where
it was initially defined as factors preventing or disturbing the flow of information between
potential and actual suppliers and customers (Vahlne and Wiedersheim-Paul, 1973;
Johanson and Vahlne, 1977; Vahlne and Nordström, 1993; O’Grady and Lane, 1996;
Stöttinger and Schlegelmilch, 1998; Evans and Mavondo, 2002; Sousa and Bradley, 2006;
Brewer, 2007). Cultural distance is another fundamental dimension of “distance” between
nations that has been studied in detail in the literature. The seminal work of Hofstede (1980)
introduced the concept of cultural distance between nations by questioning the universal
validity of management theories developed in one country, as a change in “cultural
environment” might render the policies of an organization ineffective. Many studies have
investigated the impact of common language on trade in goods between nations to measure
the cost imposed by cultural factors on business engagement and found a robust evidence
for the same (Frankel et al.,1997; Boisso and Ferrantino, 1997; Frankel and Rose, 2002;
Hutchinson, 2002; Guiso, et al., 2009; Melitz, 2008; Ku and Zussman, 2010; Lohmann, 2011).
The existence of a common official language and the proficiency in understanding it across
trade partners leads to a better communication and hence more trade in manufactured goods
as well as services. Hejazi and Ma (2011) highlighted that the countries having common
official language communicate more easily and enhances business relations between them
measured in terms of FDI. They further concluded, that this impact is highest when the
common language is English.
Currently, India is the second largest English-speaking country in the world. Telecom,
computer, information, business and financial services account for 65 per cent of India’s
total services exports (Source: Reserve Bank of India). The nature of these services
enables the higher proximity in “cultural distance” and “common language” in positively
impacting the trade flows with partner nations. This is further supported by the fact that the
USA and the UK are the primary destinations of Indian services exports (62 and 17 per cent,
respectively, in 2017 (NASSCOM, 2017, Strategic Review), which have a majority Englishspeaking population. Thus, following the existing literature and relevance of cultural
distance in affecting the value of trade flows this paper hypothesizes the following:
H1. (S). The lower the cultural distance between India and the partner country, higher is
the bilateral trade in services industry between them.
H2. (M). The lower the cultural distance between Indian and the partner country, higher
is the bilateral trade in manufacturing industry between them.
3.2 Administrative distance
Administrative distance between nations can be captured through existence of colonial ties
between them and through the difference and similarities in their policies, institutions and
legal systems. The signing of preferential trade agreements amongst countries and being a
member of regional blocks as well as multilateral organizations like WTO are also
encompassed in this dimension, as these are the tools of commitment used by signatory
nations to align their institutions for freer trade engagements (Ghemawat, 2001). The
international trade literature tries to capture administrative distance through variables such
as existence of a preferential trade agreement between the nations, common currency,
common religion or common colonial history (colony-colonizer, common colonizer, etc.).
With a rapid rise in the number of trade agreements being signed across the world, the role
of trade agreements in enhancing CBTF in goods, between partner nations has been studied
in considerable depth in international trade and business literature. The literature highlights
that the Preferential Trade Agreements play a significant role in increasing trade flows
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between nations (Clausing, 2003; Kenichi, 2003; Magee, 2003; Trefler, 2004; Baier and
Bergstrand, 2007, 2009; Egger et al., 2011). Ceglowski (2006) concluded that:
[. . .] to the extent that preferential trading arrangements raise members’ bilateral goods trade,
they should have a similar, though smaller impact on bilateral services trade.
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Many other studies in the literature indicate a positive and significant impact of a trade
agreement on services trade (Kimura and Lee, 2006; Shingal, 2013). Further, the existence of
colonial history between countries often causes a reduction in “administrative distance” by
establishment of harmonized institutions, laws, property rights, etc., thus decreasing the
associated costs (Linders et al., 2005; Head et al., 2010). Therefore, this study proposes the
following hypothesis on the relationship between administrative distance and bilateral trade
flows:
H2. (S). The lower the administrative distance between India and the partner country,
higher is the bilateral trade in services industry between them.
H2. (M). The lower the administrative distance between India and the partner country,
higher is the bilateral trade in manufacturing industry between them.
3.3 Geographic distance
Geographic distance deals with the physical distance between two countries and has direct
implications for transportation costs and communication. The attributes considered
significant for accounting for geographic distance further include physical remoteness,
under-developed transport and communication channels, lack of waterways, contiguity and
sometimes, difference in climates and time-zones (Ghemawat, 2001). The negative role of
geographic distance in impacting trade flows has been captured in many studies
(McCallum,1995; Srivastava and Green, 1986; Anderson and Van Wincoop, 2003; Batra,
2006; Disdier and Head, 2008). They associated lower magnitudes of trade flows and hence
business engagement with geographically distant partner countries to increased economic
cost as well as coordination and management costs. Despite the intangible nature of
services, geographic distance does impact the trade flows in the services industry.
Ghemawat (2001) attributed this to difficulty arising in management of business operations
and local supervision in a target market through distant locations, even for the case of
services industry. Therefore, this study hypothesizes the following:
H3. (S). The lower the geographic distance between India and the partner country,
higher is the bilateral trade in services industry between them.
H3. (M). The lower the geographic distance between India and the partner country,
higher is the bilateral trade in manufacturing industry between them.
3.4 Economic distance
The differences across the wealth of individuals and income of consumers across countries
is the source of “economic distance” between them and is one of the major determinants of
magnitude of business engagement with other countries. Literature highlights that this
difference is crucial in determining the volume of CBTF between partnering countries.
However, the literature presents a mixed opinion about impact of “economic distance” on
determining the kind of trade partners a country engages with. The earliest works that
emphasized on “economic distance” include the Heckscher–Ohlin Theory and Linder’s
hypothesis. Linder’s hypothesis (1961) suggested that the countries with similar per capita
income levels and thus a lower “economic distance” have a similarity in the preferences and
hence demand patterns, thus engaging in a higher value of trade. Linder’s hypothesis
primarily highlighted a higher intra-industry trade between countries having similar per
capita incomes due to identical demand structures. The Heckscher–Ohlin theory, however,
contrasts with Linder’s hypothesis as it remarks that countries with differential resources
trade more with each other at inter-industry level. Some studies in the literature point out
that firms based in high-income countries may engage more and perform well in middleincome and low-income countries, as they have the “pioneering advantage” in these
economies (Evans and Mavondo, 2002). On the contrary, some studies suggest that a
similarity in the levels of per capita income and economic development allows one country
to engage more with another as similar business models can be replicated in the host
country (Mitra and Golder, 2002).
India is a primary exporter of IT, business and financial services to high-income
countries. One of the main advantages for choosing India as an off-shoring destination is the
lower cost of skilled professionals in India relative to their counterparts in developed nations
(Chandrasekhar and Ghosh, 2006). Firms belonging to industries where the differences in
costs are high across geographies, tend to engage more with countries with a greater
economic distance (Ghemawat, 2001). Therefore, India’s CBTF in services will be greater
with countries that have a higher per capita income than at home.
Within India’s goods export basket, there has been an evident increase in the share of
manufactured goods, but there is a continued reliance on primary goods and resource-based
exports. Though there has been a rise in the share of medium-tech manufactured goods in
India’s export basket, the share is much lower than other developing nations such as China,
Brazil and Russia (Anand et al., 2015). The major exports from the manufacturing industry
in India involve goods that lack technological sophistication and its exports of high-tech
manufacturing exports is still far lower than that of other countries (Kumar and Pradhan,
2007; Meyer, 2007). Agarwal (2001) suggested that India’s competitiveness lies in low-tech
manufacturing sectors. The competitive advantage of India lies primarily in unskilled
labour-intensive goods as dictated by their factor endowments (Veeramani, 2003). Therefore,
based on this information, it is hypothesized:
H4. (S). The higher the economic distance between India and the partner country, higher
is the bilateral trade in services industry between them.
H4. (M). The higher the economic distance between India and the partner country,
higher is the bilateral trade in manufacturing industry between them.
3.5 Market potential
In addition to testing the hypothesis developed for different dimensions of distance, this
study also examines the role of market potential in explaining the bi-lateral trade flows
between trading partners. The role of market potential has been studied intensively in the
area of international business and international trade. Malhotra et al. (2009) have studied the
role of market potential in target countries in influencing the incidents of CBAs. The study
particularly identifies that market potential plays an important role in moderating the
influence of various dimensions of distance on target market selection. Other studies (Ellis,
2008; Kobrin, 1976; Mitra and Golder, 2002; Robertson and Wood, 2001; Terpstra and Yu,
1988; Wood and Robertson, 2000; Wood and Goolsby, 1987) have also found market
potential as one of the important factors to be considered for evaluating the attractiveness of
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a destination country. However, those studies mostly aim at assessing the potential of FDI
and other type of capital investments and number of acquisitions in the destination country.
However, market potential should have a direct impact on cross border integration when it
is captured through CBTF. A common practice in the literature is to measure the market
potential of a country by the GDP of that nation. The volume of CBTF flows has direct link
to the total value of production in a country. Higher the value of GDP the higher is the
volume of import. This in turn increase the volume of total trade flows between trading
countries. Based on this, this study hypothesizes the following:
H5. (S). The higher the market potential of India, higher is the bilateral trade in services
between India and partner nation.
H5. (M). The higher the market potential of India, higher is the bilateral trade in
manufactured goods between India and partner nation.
Figure 1 presents the hypotheses developed on the relation between CAGE distance factors
and CBTF. The figure depicts the fact that like other dimensions of cross-border integration,
CBTF are also influenced by CAGE distance factors (see the section hypothesis
development for elaborate explanation and literature support) and market potential of the
host country.
4. Methodology
Intensive use of CAGE framework in existing literature suggests that it is an improvement
over the traditional CPA for analysing the factors influencing various dimensions of crossborder integration. The CAGE framework has come out to be a seminal and powerful tool
for a firm to analyse the significance of the costs associated with relevant multi-dimensional
facets of distance. However, barring a small set of literature most of the studies in this area
of research have studied the impact of each of these distances individually. This is a serious
limitation as this exercise fails to present an exhaustive analysis and in turn could lead to
under-estimation or over-estimation of the impact of certain types of distances on crossborder integration. A closer examination of each of these distances while incorporating all of
them into a single analytical framework warrants interest due to the difference in
Market
Potential
Dimensions of
Distance
Figure 1.
CAGE distances and
cross-border trade
flows
H5
Cultural Distance
H1
Administrative
Distance
H2
Geographic Distance
H3
Economic Distance
H4
Cross Border Trade Flows
opportunities and challenges posed by them for countries engaging in cross-border
integration.
Thus, empirical model used in this study incorporates all distance factors in a multivariable regression equation. The econometric analysis carried out in this study uses a panel
data set with 682 country–pair observations for both the manufacturing and services
industries. Total value of trade in goods and services measures the extent of CBTF of Indian
manufacturing and services sector with partnering countries, respectively. The dependent
SER
variable, TTIjt
, measures the total value of exports of services from India (I) to partner
nation j and the imports of services to India (I) from partner nation j in the year t (in US
MFG
measures the same for manufacturing sector.
dollars). Likewise, TTIjt
The variable common language (DCIjt Þproxies for cultural distance between India and
trading partner j in year t. A common official language between two trade partners
highlights that those countries are not culturally distant. The variable DCIjt assumes a value 1
if both the countries in the country-pair have the same official language and 0 otherwise. A
common practice in literature is to use Hofstede index to measure the cultural distance
between the partnering nations. The components of this index are individualism,
uncertainty avoidance, power distance, masculinity, long-term orientation and indulgence
(Hofstede, 1980). However, this study does not use Hofstede index as a measure of cultural
distance, as the components of this index are not particularly associated with the value of
trade flows between countries.
Trade agreement (DAIjt1 Þ, and Common Colony (DAIjt2 Þ together represent the administrative
distance. Both these variables are categorical in nature. If the two countries in a country-pair
are a part of a preferential trade agreement or a regional block in a year, the variable “Trade
Agreement” takes a value 1, else 0. The variable assumes a value of zero in the periods
before the year in which the agreement was signed by the member nations. Likewise, the
variable “Common Colony” takes a value 1 if the country-pair shared a common colonizer
and 0 otherwise.
G
The variables
Physical Geographic Distance xIjt and “Common Border” or
“Contiguity” DGIjt together constitute for the geographic distance in the empirical model.
To account for the physical geographic distance xGIjt , the physical distance (measured in
KMs) between the capital city of India and that of its partner
trading nation is used. The
categorical variable “Common Border” or “Contiguity” DGIjt takes a value 1 if the two
countries in the country-pairshare
a border and 0 otherwise.
The economic distance xEIjt is measured as the logged ratio of per capita income of the
partner country to the per capita income of India.
The empirical model incorporates another explanatory variable market potential xM
It to
examine the extent to which market potential of the host country affects trade flows along
with the influence of various dimensions of distance on the same.The market potential
variable is measured by logged value of GDP of India (Malhotra et al., 2009; Mitra and
Golder, 2002).
Table I provides the summary of the variables used for empirical estimation.
Following empirical models (1) and (2) are estimated to study the impact of various
dimensions of distance (CAGE) on bilateral goods and services trade flows between India
and partnering countries:
CAGE
distance
framework
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43,10
1168
Variables
Dependent variables
Total value of trade in services (TT SER
Ijt Þ
Total value of trade in goods TT MFG
Ijt
Explanatory variables
Cultural distance
Common language ( DCIjt Þ
Explanatory variables
Trade agreement (DAI jt1 Þ
Common colony (DAI jt2 Þ
Geographic distance
Physical geographic distance (xGIjt Þ
Table I.
Description of
variables
Common border or contiguity (DGIjt Þ
Economic distance xEIjt )
Market potential xM
It
Type of the variable
Data source
Non-categorical
OECD TiVA (Trade in Value Added)
Database (December 2018)
OECD TiVA Database (December 2018)
Non-categorical
Categorical
CEPII Database (Mayer and Zignago,
2011)
Categorical
CEPII Database (Mayer and Zignago,
2011)
CIA World Factbook
Categorical
Non-categorical
Non-categorical
CEPII Database (Mayer and Zignago,
2011)
CEPII Database (Mayer and Zignago,
2011)
World Bank Database
Non-categorical
World Bank Database
Categorical
SER
lnTTIjt
¼ b 0 þ b 1 DCIjt þ b 2 DAIjt1 þ b 3 DAIjt2 þ b 4 xGIjt þ b 5 DGIjt þ b 6 xEIjt
þ b 7 xM
Ijt þ þu Ij þ f t þ uIjt
(1)
MFG
lnTTIjt
¼ b 0 þ b 1 DCIjt þ b 2 DAIjt1 þ b 3 DAIjt2 þ b 4 xGIjt þ b 5 DGIjt
þ b 6 xEIjt þ b 7 xM
Ijt þ þu Ij þ f t þ uIjt
(2)
For j = 1,2,3,.
. .. . .62 and t = 2005, 2006. . .. . ..,2015 and the disturbance term
uIjt IID 0; s 2u .
A simple cross-section or a pooled ordinary least square (OLS) estimation
technique leads to a biased estimate of the effect of individual distance on bi-lateral
trade flows as the specified explanatory variables fail to capture the unobserved
heterogeneity for country-pairs. Therefore, the estimation incorporates country-pair
fixed effects denoted by u ij (Egger, 2000) in the model. To account for the
heterogeneity in the country-pair trade flows attributable to year-specific shocks, if
any, fixed effects f t are added to the model. Further, the “Administrative Distance”
variable proxied by a preferential trade agreement can be endogenous as countries
often “self-select” into these agreements. Thus, u ij and f t circumvent the problem of
possible self-selection bias and prevents the underestimation of the coefficients (Baier
and Bergstrand (2007, 2009).
OECD TiVA (Trade in Value Added) Database (December 2018) has been used in this
study for measuring India’s total value of goods and services trade with partner countries
for the years 2005-2015. This edition of data provides the trade flow data for 63 economies
(See the list of countries in Appendix) including India. For the data on common official
language, physical geographical distance, contiguity and existence of a Free Trade
Agreement (FTA) between a country pair, the paper uses the CEPII Database (Mayer and
Zignago, 2011). The data on the colonial linkage or common colonizer is derived from CIA
World Factbook. Nominal GDP (in US dollars) and population of the selected countries to
measure the economic distance come fromWorld Bank Database.
CAGE
distance
framework
5. Results
Table II and Table III show the correlation matrices for variables used for the study for the
case of services and manufacturing industries, respectively. The correlation matrices rule
out the issue of multicollinearity amongst the explanatory variables representing various
dimensions of distances.
Table IV represents the results for testing the hypotheses for the services sector. H1(S)
proposed the existence of a positive relationship between cultural distance and bilateral
trade in the services industry. Results shows that the coefficient of the variable “Common
Language” is positive and highly significant, thus supporting H1 (S). H2 stated that
administrative distance between India and partner nations reduces the trade in the
services industry. “Common Colony” and “Trade Agreement” dummies proxy for
“Administrative Distance”. Both these variables turn out to be insignificant in the
regression results.
Therefore, the results turn out to be contrary to H2 (S), as they do not support the
existence of a negative relationship between “Administrative Distance” and the trade
volume in services industry between India and its partner nations. Further, the results show
a positive and significant coefficient for the “Geographic Distance” variable, which supports
H3 (S). Estimated coefficient suggests that for a 1 per cent increase in the geographic
distance (measured in KMs) results in 2.89 per cent decrease in the value of trade flow in
services between India and partner country. The positive and significant coefficient for the
variable “Common Border” provides further support to H3 (S). Lastly, the coefficient for the
“Economic Distance” variable is positive and significant, providing support for H4 (S). An
increase in the per capita income ratio implies increase in the economic distance between
India and a trading partner. For 1 per cent increase in this distance value of trade flow in
services increases by around 0.6 per cent. The coefficient of the “Market Potential” variable
is positive and significant, supporting H5 (S) and estimated result shows that for 1 per cent
increases in the GDP of India increases the value of trade flow in services by around 1.3 per
cent.
Table V shows the results for the manufacturing industry. The coefficient for the
“Common Language” variable accounting for the cultural distance is positive and
significant supporting H1 (M). For the proxies used for administrative distance – “Common
Colony” and “Trade Agreement”, the coefficient is insignificant for the “common colony”
variable, while it is positive and significant for the “Trade Agreement” variable. This result
differs from the results derived for the service industry. The value of trade in manufacturing
between India and partner country bound by trade agreement is around 11 per cent higher
than otherwise. The coefficients of the variables measuring “Geographic Distance” are
positive and significant supporting H3 (M). One percent increase in geographic distance
leads to around 3.4 per cent decrease in value of trade in manufacturing. The impact of
geographic distance is more prominent for manufacturing industry as compared to that for
service sector. As trade in manufacturing goods is more dependent on transportation costs
and logistics management, higher distance between India and partner country should lead
to lower value of trade flow. The “Economic Distance” turns out to be positive and
significant supporting H4 (M). Similar to the case of service industry, 1 per cent increase in
1169
Table II.
Correlation matrix:
services industry
1.00
0.34**
0.17**
0.14**
0.01
0.17**
0.32**
0.17**
Notes: * p < 0.10; ** p < 0.01
Total trade
Common language
Common colony
Trade agreement
Distance
Common border
Per capita income ratio
Market potential
1170
1.00
0.69**
0.05
0.13**
0.06
0.17**
0.00
1.00
0.12**
0.09**
0.06
0.15**
0.00**
1.00
0.27**
0.04
0.14**
0.22**
1.00
0.13**
0.06*
0.00
1.00
0.12*
0.00
1.00
0.20**
1.00
Administrative
Geographical
Cultural distance
distance
distance
Economic distance
Total trade Common language Common colony Trade agreement Distance Common border Per capita income ratio Market potential
MRR
43,10
1.00
0.20**
0.05
0.15**
0.02
0.24**
0.04
0.18**
1.00
0.69**
0.05
0.13**
0.06
0.17**
0.00
Notes: *p < 0.10; ** p < 0.05; *** p < 0.01
Total trade
Common language
Common colony
Trade agreement
Distance
Common border
Per capita income ratio
Market potential
1.00
0.12**
0.09**
0.06
0.15**
0.00
1.00
0.27**
0.04
0.14**
0.22**
1.00
0.13**
0.06*
0.00
1.00
0.12**
0.00
1.00
0.20**
1.00
Administrative
Geographical
Cultural distance
distance
distance
Economic distance
Total trade Common language Common colony Trade agreement Distance Common border Per capita income ratio Market potential
CAGE
distance
framework
1171
Table III.
Correlation matrix:
manufacturing
industry
MRR
43,10
1172
Table IV.
Regression analysis
results: services
industry
Variables
Coefficient
t-value
Cultural
Common language
4.01***
24.63
Administrative
Common colony
trade agreement
0.049
0.07
Geographic
Log (Distance)
common border
2.89***
2.71***
15.28
33.38
0.61***
1.27***
Yes
Yes
682
0.9846
9.08
21.01
Coefficient
t-value
Cultural
Common language
2.43***
11.05
Administrative
Common colony
trade agreement
0.024
0.11**
0.52
2.25
Geographic
Log (distance)
Common border
3.37***
3.11***
22.66
34.74
0.65***
1.29***
Yes
Yes
682
0.983
9.14
18.01
Economic
Log (per capita income ratio)
Market potential
Year effect
Country-pair effect
N
Adjusted R square
Notes: *p < 0.10; ** p < 0.05; *** p < 0.01
Variables
Table V.
Regression analysis
results:
manufacturing
industry
1.12
1.58
Economic
Log (per capita income ratio)
Market potential
Year effect
Country-pair effect
N
Adjusted R square
Notes: *p < 0.10; ** p < 0.05; *** p < 0.01
economic distance leads to around 0.65 per cent increase in the value of trade in
manufacturing between India and partner nation. Finally, “Market Potential” variable also
shows a positive and significant coefficient, providing support for H5 (M). For a 1 per cent
improvement of the market potential in India the value of trade in manufacturing with
partner country increases by around 1.29 per cent.
6. Discussion
6.1 Analysis of results
This study aims to ask a pertinent question that whether and which distances matter for
international integration for the case of Indian services and manufacturing industries.
Ghemawat (2001) highlighted the novelty of the CAGE framework in helping managers in
identification and assessment of impact of various distances on different industries. For
instance, the paper cites that the industries with high linguistic content like television
content are more impacted by cultural distance between trade partners, while industries
with perishable goods like fruits are more sensitive to geographical distance.
The empirical exercise in the current study uses various proxies for measuring multiple
dimensions of distance under the CAGE Framework. The empirical results of this study
make a significant contribution to the international business literature by demonstrating an
empirical exercise to study the differential impact of various distance factors on different
industries using Ghemawat’s (2001) CAGE framework using the case of India. The study
further contributes by providing insights about the possible reasons for the observed results
based on the inherent features of these sectors and the country under consideration. A
comprehensive empirical framework involving various dimensions of distances as well as
market potential of the host, provides a thorough roadmap for the choice of trade partners
and industries for the internationalizing MNEs.
First, it is observed that cultural distance plays a positive and important role in promoting
cross-border integration for the services and manufacturing industries, both. This study shows
that a common official language between India and a partner nation increases the trade flows
between them in the services and manufacturing industry. These results support the findings
from existent studies about the role of common language in the determination of trade and
business relations amongst countries (Ku and Zussman, 2010; Hejazi and Ma, 2011; Egger and
Lassmann, 2015). However, the results also suggest that for the Indian case, “cultural distance”
matters more for CBTF for the services industry relative to the manufacturing industry.
Existing structural differences across these two industries may offer possible explanations for
observed variation in the impact of cultural distance on bi-lateral trade flows. Services trade
requires frequent and higher amount of communication between the people across countries
(OECD, 2005). According to WTO nomenclature, the mode of trade in services with a partner
country can be: cross-border supply (tele-medicine, online learning etc.), consumption abroad
by consumers (tourism, students traveling to foreign country), commercial presence (local
affiliate/subsidiary or a representative office such as banks, hotels and restaurants) and
movement of natural persons across borders to supply a service (e.g. Doctors, Professors,
software engineers going to an off-site location, etc.). Therefore, the “common language”
dummy captures the higher impact of cultural proximity on services industry trade requiring a
higher people to people interaction rather than manufacturing industry which always requires
physical movement of goods across borders. Thus, differences in modes of international
transactions by these two sectors also support the observed variation in the impact of cultural
distance on trade flows of goods and services between India and partner countries.
Second, the impact of ‘administrative distance’ as captured by the ‘Trade Agreement’
dummy on bi-lateral trade flows is significant and positive for Indian manufacturing sector
while it comes out as insignificant for the services industry. The results for the impact of
administrative distance on the CBTF between nations support the wide and varied existent
literature (Clausing, 2003; Magee, 2003; Egger et al., 2011). The difference in results for the
case of services sector can have few possible explanations. Grünfeld and Moxnes (2003)
suggested a lack of a ‘stable and significant’ impact of free trade agreements on services
exports can be explained by the lack of services provisions in these agreements and lack of
CAGE
distance
framework
1173
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43,10
1174
liberalization initiatives regarding regulations etc. The reason for this lies in the distinctive
characteristics of trade in these two industries as well as the provisions and content of
standard preferential trade agreements that are signed between nations. The Preferential
Trade Agreements, throughout the course of history (including the multilateral organization
WTO), have focused their liberalization initiatives on trade in manufactures first followed
by services trade (Limão, 2016). However, over time more and more of these agreements
have been including services provisions. A trade agreement signed by countries has
traditionally been used to lower tariffs and simplify custom procedures for cross-border
flows of goods between them. The trade barriers faced by the services industry are “nontariff” in nature as they are usually related to legal and regulatory procedures (Walsh, 2008).
Therefore, achieving administrative ease for trade in services requires a different set of
liberalization initiatives such as licensing, certifications, mutual recognition of
qualifications, etc. Particularly for the case of India, the value of STRI (Services Trade
Restrictiveness Index measured by OECD) has been higher than the average of all 46
countries under study for all 22 service sectors which highlights the high barriers faced by
trade in services. Therefore, the business engagement in the services industry is not
strengthened due to the existence of lower administrative distance proxied by the existence
of a trade agreement between the partner countries as the “behind-the-border” barriers are
not adequately addressed (OECD, 2019).
Third, it is observed that “geographical distance” though having a negative and
significant impact on both industries, matters relatively more for the manufacturing
industry. Thus, higher geographic distance proves to be a greater hindrance for trade in
manufactures as compared to trade in services with India. The results are supportive of the
earlier findings in the literature which imply a negative relationship between CBTF and
geographic distance from partner nations (McCallum, 1995; Srivastava and Green, 1986;
Anderson and Van Wincoop, 2003; Batra, 2006). The reason for this might stem from the
fact that cross-border trade for goods requires physical movement of goods and a greater
distance signifies higher transportation costs. On the other hand, one of the important
modes of services trade – “cross-border supply” does not involve the movement of either the
consumer or supplier, thus significantly pruning the costs associated with transportation
across larger distances and logistics. Similarly, the trade in manufacturing industry is
impacted relatively more when countries share a common border as compared to the trade in
services industry. Thus, MNEs tend to involve with countries geographically closer to them
for both manufacturing and services industry albeit the extent differs.
Fourth, economic distance is observed to have a positive impact on bilateral trade flows
in service sector and manufacturing sector. These results provide support for the
Heckscher–Ohlin theory of trade, wherein countries having different resource endowments
trade more with one another.
Finally, the results observe a positive and significant role of the market potential of the
host country on CBTF. This confirms that the MNEs wish to engage in cross-border
integration with countries providing a sizeable market for the products. This is supportive
of the theory and past findings that concluded the existence of a direct relation between the
market size and the value of trade engagement between nations (Kobrin, 1976; Robertson
and Wood, 2001; Mitra and Golder, 2002; Ellis, 2008).
6.2 Managerial implications
This study has relevant implications for managers and researchers alike. The empirical
analysis undertaken using the CAGE distance framework (Ghemawat, 2001) provides a
comprehensive examination of all the distance factors impacting the bilateral CBTF of India.
This study moves beyond the practice of earlier works of analysing the impact of only one of
the distances on cross-border integration. Over the past years, the growth story of India has
projected it as one of the most important market for MNEs and it continues to hold this
position. Therefore, this study provides a roadmap for the MNEs looking to engage with
India with respect to the various kinds of distances between their home country and India.
The results of the study suggest that though market potential of a partner nation matters,
the internationalization strategies of firms need to focus upon similarity in language,
administrative ease, geographic proximity and economic disparities while choosing a
trading partner to reap the benefits of cross-border integration. The CAGE framework thus
provides a holistic framework regarding the influence of distances for decisions pertaining
to international markets. Further, the individual analysis of the manufacturing industry and
services industry carried out in this study indicates which distances are relevant for firms
targeting business engagement with India in distinct industries based on their differential
nature. The management of MNEs should consider lower administrative distance as a
motivation for them engage in cross-border integration with India in the manufacturing
industry, while lower cultural distance with India and their home country can make India an
attractive destination for business relations in the services industry.
6.3 Study limitations and future scope of research
This study opens the gates for several opportunities for future research in this domain. For
the case of India, based on availability of data, this study examined the role of cultural,
administrative, geographic and economic distance on the trade flows in the services and
manufacturing industry under the CAGE framework.
Further work can focus upon alternative measures of the four dimensions of distance.
Using psychic distance, institutional distance and government effectiveness index to
capture the relevant distances empirically will allow a more nuanced understanding of the
impact of each of the distances for the case of business engagements with India.
This study has a seminal contribution to the literature as there are not any studies
quantitatively estimating the role of distances under the CAGE distance framework
(Ghemawat,2001) for the services and manufacturing industries separately. Another
interesting extension of this study involves looking into the differential impact of various
types of distances for the case of different industries in India, empirically. For instance,
industries such as health services and pharmaceuticals may be more impacted by
administrative distance, while geographical distance may matter more in the case of certain
perishable food products.
Further, this study looks at only one pillar of international business engagement and
globalization – CBTF. Going further, this study can be extended to gain insights about the
role of distances for other pillars such as FDI, flow of information and flow of people across
countries, subject to the availability of data for the case of India. Also, this study can be
expanded to examine the same issue in the context of other relevant emerging economies.
7. Conclusion
In the current times of rapidly increasing globalization, various modes of cross-border
integration between nations have garnered the attention of academics and managers alike. In
this context, the role of multiple dimensions of distances – cultural, economic, political,
administrative and geographic – between the partner nations has come out to be particularly
significant. Existing literature in this area has established the significance of the superiority of
a holistic framework such as CAGE distance framework over traditional frameworks CPA to
determine the impact of distances on cross-border integration. In this transforming global
CAGE
distance
framework
1175
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43,10
1176
landscape, developing economies including India have held a crucial place for the
internationalization strategies of MNEs. This study uses the CAGE distance framework to
focus on the case of India to determine which distances matter for CBTF with partner nations.
Further, it contributes to the literature by carrying out this analysis for the manufacturing and
services industries separately. Using a sample of 682 country-pair observations, this study
finds out that cultural distance, geographic distance and economic distance impacts the crossborder integration with India in the services industry. While for the manufacturing sector, all
CAGE distances prove to be significant, however by varying magnitudes.
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Appendix
List of OECD countries
1180
Tabel AI.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
AUS: Australia
AUT: Austria
BEL: Belgium
CAN: Canada
CHL: Chile
CZE: Czech Republic
DNK: Denmark
EST: Estonia
FIN: Finland
FRA: France
DEU: Germany
GRC: Greece
HUN: Hungary
ISL: Iceland
IRL: Ireland
ISR: Israel
ITA: Italy
JPN: Japan
KOR: Korea
LVA: Latvia
LTU: Lithuania
LUX: Luxembourg
MEX: Mexico
NLD: The Netherlands
NZL: New Zealand
NOR: Norway
POL: Poland
PRT: Portugal
SVK: Slovak Republic
SVN: Slovenia
ESP: Spain
SWE: Sweden
CHE: Switzerland
TUR: Turkey
GBR: UK
USA: USA
ARG: Argentina
BRA: Brazil
BRN: Brunei Darussalam
BGR: Bulgaria
KHM: Cambodia
CHN: China (People’s Republic of China)
COL: Colombia
CRI: Costa Rica
HRV: Croatia
CYP: Cyprus
HKG: Hong Kong, China
IND: India
IDN: Indonesia
(continued)
List of OECD countries
50
51
52
53
54
55
56
57
58
59
60
61
62
63
KAZ: Kazakhstan
MYS: Malaysia
MLT: Malta
MAR: Morocco
PER: Peru
PHL: Philippines
ROU: Romania
RUS: Russian Federation
SAU: Saudi Arabia
SGP: Singapore
ZAF: South Africa
THA: Thailand
TUN: Tunisia
VNM: Vietnam
Corresponding author
Arnab Kumar Deb can be contacted at: arnab.deb04@gmail.com
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CAGE
distance
framework
1181
Tabel AI.