Multinational Activity and Information and Communication ... Files/ICT... · Multinational Activity...

Multinational Activity and Information and Communication

Technology�

Laura Alfaroy

Harvard Business School and NBERMaggie X. Chenz

George Washington University

May 2015

Abstract

In this paper we examine the relationship between information and communi-cation technology (ICT) adoption and multinational activity. Using a rich cross-country �rm-level panel dataset, we present a description of how the location pat-terns of multinational �rms vary across countries depending on their levels of ICTadoption. Controlling for a number of host- and headquarter-country characteristicsthat have traditionally been used to explain multinationals�location choices, we �nda positive relation between ICT adoption levels and multinational entry. The ICTcharacteristics of host countries play a signi�cant role in a countries�ability to at-tract multinational entry. When exploring how the e¤ect of ICT might vary acrossindustries depending on industries�requirement for ICT, we �nd that the e¤ect ofbusiness computer and internet uses is larger for less routine and more communica-tion intensive industries. These results are robust to controlling for all country andindustry speci�c factors.

Key Words: ICT, Multinational activity, ICT intensity

�This paper is a background note prepared for the 2016 World Development Report: Internet for Development.We thank Shawn Tan for valuable comments and suggestions and Elizabeth Meyer for excellent research assistance.

yEmail: [email protected]: [email protected].

1 Introduction

In this paper, we examine the relationship between multinational activity and information and

communication technology (ICT). Multinational activity, characterized by its high intensity of

embedded capital, knowledge, and technology, has been essential for boosting countries� de-

velopment. Potential gains from multinational activity involve knowledge spillovers, backward

and forward linkages, technology transfers, improved managerial skills, new processes, employee

training, and access to international production networks and markets.1 Similarly, the ICT

revolution has transformed societies allowing di¤erent and new ways to produce, sell, buy, and

organize economic activities. There is, however, little existing analysis on the role of ICT in

fostering multinational activity.

A �rm-level cross-country dataset provided by Orbis enables us to present a comprehensive

and nuanced picture of global multinational activity and its relationship to ICT. The dataset in-

cludes comprehensive �nancial and ownership information for more than one million public and

private manufacturing companies for the 2002-2007 period (we restrict the analysis to the pre

Global Great Recession period). The database exhibits two strengths central to our analysis.

First, Orbis reports detailed ownership information that covers over 30 million shareholder-

subsidiary links collected from a variety of sources including o¢ cial registers, annual reports,

research, and newswires. We explore the shareholder, ultimate owner, and subsidiary informa-

tion to identify MNC activities across countries. Second, Orbis o¤ers a broad country coverage

that includes an extensive set of industrial and emerging economies displaying di¤erent levels of

ICT adoption rates. We use di¤erent indicators of ICT adoption rates, including ITU ICT De-

velopment Indicators containing information on internet access, �xed broadband subscription,

and telephone subscription and UNCTAD�s dataset on business uses of ICT.

We �rst present a description of how the location patterns of foreign �rms vary across regions

or di¤erent country groups based on income levels and ICT adoption rates. The data shows

that most foreign subsidiaries are located in countries with high business usages of internet

and computers while entry of multinational companies into foreign markets is also positively

correlated with businesses�ICT adoption measure.

To account for patterns of multinational entry more formally, we then control for a num-

ber of host- and headquarter-country characteristics that have traditionally been used to explain

multinationals�location choices such as market potential, endowments, distance, and tari¤. ICT

characteristics of host countries (e.g. proportions of businesses using computers and internet

or mobile subscriptions) play a signi�cant role in a countries�ability to attract multinational

entry. We further explore how the e¤ect of ICT varies across industries depending on industries�

requirement for communications by interacting the di¤erent ICT variables with an industry�s

1The academic literature on foreign direct investment is vast and has been surveyed many times. See Antràs andYeaple (2014) and Alfaro (2015) for recent overviews of the theoretical and empirical literature on multinational�rms.

2

routineness or communication intensity. We �nd that the e¤ect of business computer and inter-

net uses are larger for less routine industries and more communication intensive sectors. This

result holds when controlling for all country and industry speci�c factors using a vector of

country and industry dummies.

We perform a series of additional robustness tests such as looking at alternative measures

of ICT (internet tra¢ c volumes and other internet access and price indicators), analyzing dif-

ferential e¤ects across rich and developing countries, and regional complementarities on ICT

adoption.

The rest of the paper is organized as follows. In Section 2, we �rst overview related research

on patterns of FDI and ICT. Our work complements the existing literature and extends in

important ways by analyzing patterns across a wide range of emerging and developed nations.

We then describe the data and the general stylized facts in Section 3. We present the empirical

evidence in Section 4. Section 5 concludes.

2 Related Literature

Our paper builds on an extensive theoretical and empirical literature in international trade

that examines MNCs�decision to invest abroad. Two main motives of foreign investment have

been stressed by previous studies. First, �rms may choose to produce overseas to avoid trade

costs. This strategy, referred to as the market access (or tari¤ jumping) motive, leads �rms to

duplicate production processes in countries (Markusen and Venables, 2000). Second, �rms may

choose to locate di¤erent stage of productions in countries where the factor used intensively is

abundant. This strategy is referred to as the comparative advantage motive (Helpman, 1984).

These two motives, leading to horizontal and vertical FDI respectively, have been synchronized

in the knowledge-capital model developed by Markusen and Venables (1998) and Markusen

(2002) and examined in a number of empirical studies.2

Within the broad literature of FDI, an emerging strand of studies emphasizes the role of

communications in determining the patterns of multinational activity. Keller and Yeaple (2013),

for example, examine how the spatial costs of transferring knowledge might a¤ect multinationals�

operation overseas. The authors �nd that both the level of the a¢ liate�s sales and its imports

are a¤ected by the ease to which knowledge can be transferred across space. A¢ liate sales fall

as trade costs rise, and the e¤ect of trade costs is strongest for knowledge-intensive good as the

scope for o¤shoring is most limited by costly disembodied knowledge transfer. Further, as trade

costs increase, multinational a¢ liates substitute away from importing inputs but their ability

to do so is constrained by how high disembodied knowledge transfer costs are. Therefore, trade

costs have the weakest in�uence on a¢ liate imports in relatively knowledge-intensive industries.

2The analysis by Carr, Markusen, and Maskus (2001), Yeaple (2003a), and Alfaro and Charlton (2010), forexample, o¤ers empirical support for both types of motives. See also Alfaro and Chen (2012 a, b).

3

Oldenski (2012) looks also at the role of communication and shows that because services

require more interaction with consumers than manufactures, the di¤erence in the importance

of communication can explain much of the di¤erence in export-to-FDI ratios between the two

sectors. Controlling for standard determinants of trade and investment, the author also shows

that the level of complexity of production has a negative e¤ect on o¤shoring. Because non-

routine activities are less codi�able and it is more di¢ cult to successfully transfer these processes

to teams in another country and to specify clear quality standards for these more abstract tasks,

their production is less likely to be o¤shored to foreign a¢ liates.

In related work, Abramovsky and Gri¢ th (2000) study the role of ICT on �rm�s decision

to outsource or o¤shore services, that is, decisions on whether to produce in-house or outsource

services, and the decision over the location of activity. The authors use UK census micro level

data at the establishment level and �nd that more ICT-intensive �rms purchase a greater amount

of services on the market and they are more likely to purchase o¤shore than less ICT-intensive

�rms. Other work in the literature has studied the role of management and organization in

adoption of ICT and its impact.3

Bahar (2014) shows that multinational corporations are signi�cantly less likely to horizontally

expand in relatively knowledge-intensive sectors. The evidence shows that when �rms do expand

their knowledge-intensive activities, they tend to do so at shorter geographic distances. Locating

a foreign subsidiary in the same time zone as its headquarters reduces barriers to knowledge

transmission by easing communication and e¤ectively reducing the distance between them by,

on average, 3500 km.

The work by Alfaro and Chen (2014) investigates the patterns and determinants of the global

economic geography of multinational �rms. The authors� analysis shows that the emerging

o¤shore clusters of multinationals are not a simple re�ection of domestic industrial clusters.

That is, within a host country, multinationals follow di¤erent agglomeration patterns than

their domestic counterparts do. The location decisions of MNEs re�ect location fundamentals,

including market access (to avoid trade costs) and comparative advantage (to seek abundant

factors with lower costs) but also re�ect agglomeration economies. Agglomeration economies

stress the bene�ts of geographic proximity between �rms, including lower transport costs between

input suppliers and �nal good producers (vertical linkages), labor-market and capital-good-

market externalities re�ecting MNEs� high capital- and innovation-intensity, and technology

di¤usion. These bene�ts may also relate to communication limitations/opportunities across

countries.

An emerging literature studies the role of management practices in inter-�rm and inter-

country productivity di¤erences. Researches have explored the slow di¤usion of technology, on

the assumption that di¤erentials are due to technological innovations as embodied in patents

or to the adoption of new advanced equipment. Alternatively, research has focused on the mis-

3See Bloom et al. (2013) for an overview of the role of ICT in �rm activity.

4

allocation of resources across plants (Alfaro, Charlton, and Kanczuk, 2009; Hsieh and Klenow,

2009). That is, di¤erences are a matter not only of the level of factor accumulation, but also

of how these factors are allocated across production units. Echoing these results, the work by

Alfaro and Chen (2015) suggests that reallocation of capital and labor as a result of increased

multinational production could lead to important productivity gains.

Yet another explanation of productivity di¤erences, studied more recently using �rm-level

data, is that they re�ect variations in management practices. Bloom and van Reenen (2010)

�nd that management practices vary widely across countries, industries, and �rms. They �nd

not only that multinationals are generally better managed in every country but also that multi-

nationals transplant their management styles abroad. In a related work, Bloom, Sadun, and van

Reenen (2012) examine the di¤erences in IT-related productivity between establishments owned

by U.S. multinationals and establishments that either are owned by non-U.S. multinationals or

are purely (non-U.S.) domestic. The authors �nd that foreign a¢ liates of U.S. multinationals

appear to obtain higher productivity from their IT capital than domestic �rms and a¢ liates of

non-U.S. multinationals do and are also more IT-intensive.4 The authors also show that Ameri-

can �rms have higher scores on "people management" practices, de�ned in terms of promotion,

reward, hiring, and �ring practices.

3 Data

3.1 Multinational Activity: The Orbis Database

We use a cross-country �rm-level panel dataset, drawn from Orbis, that contains comprehensive

�nancial, operation, and ownership information for public and private companies in 60 countries.

(See Alfaro and Chen, 2015, for more details of the data).

Orbis is published by Bureau van Dijk a leading source of company information and business

intelligence. For each company, the dataset reports: a) detailed 10-year �nancial information

including 26 balance sheet and 25 income sheet items, b) industries and activities including pri-

mary and secondary industry codes in both local and international classi�cations, c) corporate

structure including board members and management, and d) ownership information, including

shareholders and subsidiaries, direct and indirect ownership, ultimate owner, independence indi-

cator, corporate group, and all companies with the same ultimate owner as the subject company.

Orbis provides a broad country coverage, including a wide range of both industrial and emerging

economies.

We explore the shareholder, ultimate owner, and subsidiary information to identify (majority-

and wholly owned) MNC activities across countries. We use non-consolidated data in our analy-

sis. Second, the �nancial data in Orbis consist of a rich array of time-series information enabling

us to measure and compare a �rm�s total factor productivity over time.

4This is true in both a UK establishment-level dataset and a European �rm-level dataset.

5

Our analysis focuses on manufacturing industries and covers over 1.2 million companies in

60 countries. We use four categories of information for each �rm: (a) industry information

including the 4-digit NAICS code of the primary industry in which each establishment operates,

(b) ownership information including each �rm�s domestic and global parents and domestic and

foreign subsidiaries, (c) location information, and (d) �nancial information including revenue,

employment, assets, investment, and material cost. A �rm is considered foreign-owned if it is

majority- or wholly owned by a foreign multinational �rm. There are about 36,000 foreign-owned

subsidiaries in the �nal sample.

We imposed a number of requirements in cleaning the data. First, we dropped all records

that lack revenue, employment, asset, and industry information. Second, we focused on manu-

facturing industries only. Third, we excluded countries with fewer than 100 observations.

The countries in the analysis include: Algeria, Argentina, Australia, Austria, Belarus, Bel-

gium, Bermuda, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Czech, Denmark,

Egypt, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, Iceland, India, In-

donesia, Ireland, Israel, Italy, Japan, Kazakhastan, Latvia, Lithuania, Macedonia, Malaysia,

Mexico, Morocco, Netherlands, New Zealand, Norway, Poland, Portugal, Korea, Romania, Rus-

sia, Serbia, Slovakia, Slovenia, South Africa, Spain, Sweden, Switzerland, Taiwan, Tunisia,

Turkey, Ukraine, United Arab Emirates, United Kingdom, and United States.

In the empirical analysis, we examine the entry of multinational �rms in 2005-2007 and use

the number of new subsidiaries established in a host country and an industry as the dependent

variable.

3.2 Determinants of Multinational Activity

To account for patterns of multinational entry, we control for a number of host- and headquarter-

country characteristics that have traditionally been used to explain multinationals� location

choices. First, we follow Head and Mayer (2004) and Blonigen et al. (2007) and include a

measure of market potential, obtained from the CEPII databases, to control for the impact

of the size of both the domestic and potential export markets on the MNCs� choice of host

countries. We also control for the income level, measured by the GDP per capita taken from the

World Development Indicators (WDI), of the headquarter country. Second, we take into account

the di¤erence between host- and headquarter-country in skilled labor endowment, measured by

average years of schooling obtained from the WDI and each industry�s skilled labor intensity,

measured by the share of non-production workers computed using the U.S. Annual Survey

of Manufactures. Third, we include various measures of trade costs, including host-country

tari¤ rates on headquarters obtained from the UNCTAD TRAINS database, bilateral country

distance, and indicators of contiguity, colonial relationship, and sharing common languages

between the host and the headquarter countries, all of which are taken from the CEPII Gravity

database.

6

We measure host countries�ICT characteristics with data from two main sources, namely, the

ITU ICT Development Indicators with information on businesses with internet access, internet

bandwidth, mobile phone subscriptions, phone call prices and etc., and the UNCTAD�s business

usages of ICT. The ITU ICT data in general tends to have a better coverage than the UNCTAD

measures. In the main analysis, we focus on the following variables: international Internet

bandwidth in Mbit/s (ITU, I4214); mobile-cellular telephone subscriptions (ITU, I271); price of

a three-minute local call to a �xed-telephone line (peak rate) (ITU, I153_USD); the proportion

of businesses using computers (UCTAD_B1) and proportion of business placing orders over the

internet (UNCTAC_B8). In the robustness analysis, we also consider additional ICT measures

(tra¢ c and cost).5

In addition to the host-country ICT characteristics, various industry communication intensity

measures are also used. First, we obtain information capital data, speci�cally communication

capital, from the Bureau of Labor Statistics (BLS) Three-Digit Database. We normalize the data

using total asset, which include capital service �ows of equipment, structures, intellectual prop-

erty products, inventories, and land. The BLS measures of capital service inputs are prepared

using NIPA data on real gross investment in depreciable assets and inventories. Using these

data, we construct a measure of communication intensity, (communication capital/assets)*100.

Second, we use an industry-level average routineness, taken from Costinot, Oldenski and Rauch

(2011) who computed the variable using occupational level data on the intensity of "making

decisions and solving problems" from the US Department of Labor�s Occupational Information

Network (O*NET) and data on occupational shares at the sector level from the BLS. More

routine industries are considered to have a lower communication intensity. These industry com-

munication intensity measures enable us to examine how the e¤ects of country ICT levels might

vary across industries, depending on industries�communication requirements.

Table 1 describes all the main variables including their de�nitions and sources. Table 2

presents the descriptive statistics.

3.3 Stylized Facts

Now we highlight a number of stylized facts emerging from the data. First, we show in Figure 1

that the vast majority of foreign-owned subsidiaries are located in high-income countries while

middle-income countries also attract a substantial amount. This is consistent with the common

view that FDI is concentrated between developed countries. Across industries, we show in Figure

2 that most foreign subsidiaries are in manufacturing (SIC20-40), with the remainder in basic

services (SIC 40-50), trade (SIC 50-60), �nance (SIC 60-70), business and professional services

(SIC 70-80), and agricultural (SIC<20).

Second, in terms of the relationship between multinational activity and countries�ICT char-

5We focus on variables related to business activity. The coverage of the ICT variables varies substantially,especially for the UNCTAD measures. See Table 2 for the descriptive statistics.

7

Figure 1: Country Income and Multinational Activity

Figure 2: Multinational Entry across Industries

8

Figure 3: ICT (Proportion of Businesses Using Internet) and Multinational Activity

Figure 4: ICT (Proportion of Businesses Using Computers) and Multinational Activity

9

Figure 5: ICT (Proportion of Businesses Using Internet) and Multinational Entry

Figure 6: ICT (Proportion of Businesses Using Computers) and Multinational Entry

10

acteristics, we show in Figures 3-4 that most foreign subsidiaries are located in countries with

high business usages of internet and computers. A similar observation is found when examining

multinational entry. The number of entries by multinational companies into a foreign country

(opening up a foreign subsidiary) is positively correlated with businesses�ICT adoption measures

in that country (Figures 5-6 and Table 2).6

4 Empirical Evidence

4.1 Baseline Results

In this section, we present the empirical analysis investigating the e¤ect of ICT on multinational

entry. To proceed, we estimate the following equation:

entryik = f(�+Xik� + ICTi + �k + "ik)

where entryik is the number of multinational entries in country i and industry k, Xik is a vector

of country and industry factors including, for example, host-country market potential, host- and

headquarter-country di¤erences in skill endowment, and various measures of trade costs, ICTiis measures of host-country ICT characteristics, �k is a vector of industry dummies, and "ik is

the error term. Given the count nature of the dependent variable, Poisson estimations are used

in the analysis.

Table 3 reports the baseline results. We �nd that most conventional determinants of multi-

national activity exert a signi�cant and expected e¤ect on multinational entry. For example,

distance is shown to adversely a¤ect multinational entry while contiguity and formal colony

relationships are associated with greater multinational entry. Host-country tari¤ is found to

exert a positive and signi�cant e¤ect on multinational entry, consistent with the tari¤-jumping

motive of multinational �rms.

The ICT characteristics of host countries also play a signi�cant role in countries� ability

to attract multinational entry. For example, we �nd that countries with a greater proportion

of businesses using the computer or placing orders on the internet attract a signi�cantly larger

number of multinational entries. Host countries�mobile coverage also matters. A greater volume

of mobile subscription is positively associated with multinational entry and a higher telephone

call charge is negatively related to multinational entry. Speci�cally, a 10-percent increase in the

volume of mobile subscription is shown to raise the number of multinational entry by 0.1.

4.2 The Heterogeneous E¤ects of ICT across Industries

One issue that might arise in examining the e¤ect of ICT is that countries� ICT levels could

be correlated with omitted country characteristics� such as general technology conditions and

6The coe¢ cient is statistically signi�cant even when excluding the outlier (Russia).

11

infrastructure� which could similarly in�uence multinationals�entry decisions. To address this

concern, we explore next how the e¤ect of ICT might vary across industries depending on

industries� requirement for communications. This exercise enables us to better identify the

e¤ect of ICT as it is less likely that the role of other country characteristics would also vary

systematically with industries�communication requirement.

To investigate this, we interact the country ICT variables with industries� ICT intensity

measures, namely, an industry�s task routineness� which is assumed to be inversely related to

an industry�s ICT requirement� and communication intensity. Table 4 reports the estimation

results of the following equation:

entryik = f(�+Xik� + ICTi + �ICTi � ICT intensityk + �k + "ik): (1)

We �nd that the positive e¤ects of greater business computer and internet uses are larger

for less routine industries, suggesting that host countries�ICT characteristics play a particularly

important role in industries with more requirements on "making decisions and solving problems"

and inter-personal interactions. Table 5 shows a similar result: greater internet coverage and

faster internet access exert a particularly greater e¤ect on multinational entry in communication-

intensive industries.

In Table 6, we control for all country speci�c factors using a vector of country dummies. We

�nd that the results remain robust: the role of host-country ICT levels in multinational entry

is particularly important in less routine and more communication intensive industries.

4.3 The Heterogeneous E¤ects of ICT across Countries

In this sub-section, we examine how the e¤ect of ICT could vary between developed and devel-

oping host countries. We estimate equation (1) for North-North and North-South multinational

activity, respectively, and report the results in Tables 7 and 8.

We �nd that ICT plays a signi�cant role in the ability to attract multinational entry in both

developed and developing host countries. However, the marginal e¤ect of ICT appears smaller in

magnitude in developing countries compared to developed countries. One possible explanation

is the di¤erent composition of tasks o¤shored to these two groups of countries. It is conceivable

that multinationals tend to o¤shore relatively routine and less communication intensive tasks to

developing countries and are thus less dependent on these countries�ICT conditions, in which

case improvements in ICT could motivate multinational �rms to o¤shore more sophisticated,

ICT intensive tasks to developing countries.

4.4 Alternative Roles of ICT

Next we examine the robustness of the results by considering a range of other measures of

ICT, including internet tra¢ c volumes and other internet access, mobile, and phone-call price

12

indicators. See Table 9 for a description of the variables.

The results are summarized in Tables 10 and 11. We �nd that internet tra¢ c volumes are in

general positively associated with the number of multinational entries. Similar to the previous

tables, higher phone-call prices tend to lower the number of multinational entries.

However, we notice that evidence on the e¤ect of internet access is mixed. In particular, a

greater proportion of businesses receiving orders on the internet can be negatively associated

with multinational entry.7 This �nding is in contrast to the earlier results and suggests that

internet coverage might exert opposing e¤ects on multinational activity. While on the one

hand better internet infrastructure might attract multinational �rms, especially those with high

communication intensity, local businesses�internet coverage might raise foreign �rms�incentives

to engage in other international activities such as outsourcing and (arm�s length) imports instead

of multinational entry.

4.5 ICT Network

So far we have examined how host-country ICT conditions a¤ect the ability to attract multi-

national entry. It is plausible, however, that the ICT conditions of neighboring countries also

play a role. On the one hand, the production and the sales networks of multinational �rms,

often distributed regionally, could require extensive communications and interactions between

countries in the same region and could therefore bene�t from better ICT adoption in not only

the host country but also its surrounding region. In this case, the ICT levels of neighboring

countries are expected to play a positive and complementary role. On the other hand, countries

in the same region often compete with one another for multinational �rms. The ICT levels of

neighboring countries, in this case, could exert a negative e¤ect on a given host country�s receipt

of multinational activity and result in a substitution e¤ect.

To evaluate these hypotheses, we construct a distance-weighted ICT network variable, namely,Pj 6=i (ICTj=Distij), and incorporate it into equation (1). The results are reported in Table 12.

We �nd that the e¤ects of the computer and internet network variables are opposite to the ef-

fects of host countries�own computer and internet adoptions. This result is consistent with the

substitution hypothesis. A host country�s ability to attract multinational �rms is mitigated by

other countries�ICT conditions. The mobile network variable, however, exerts the same e¤ect

as the host country�s own mobile variable, in alignment with the complementarity hypothesis.

5 Conclusion

In this paper we examine the relationship between information and communication technology

(ICT) adoption and multinational activity. We use a detailed a cross-country �rm-level panel

7As mentioned, the coverage is di¤erent between the datasets and among the di¤erent indicators. Our studyis silent on the quality of the data.

13

dataset to present a description of how the location patterns of foreign �rms vary across countries

depending on countries� ICT conditions. We control for a number of host- and headquarter-

country characteristics that have traditionally been used to explain multinationals� location

choices.

Our results indicate a positive relation between foreign entry and ICT adoption rates. ICT

characteristics of host countries play a signi�cant role in a countries�ability to attract multi-

national entry. When exploring how the e¤ect of ICT varies across industries depending on

industries�requirement for communications, we �nd that the e¤ect of business computer and

internet uses are larger for less routine industries and more communication intensive sectors.

This result holds when controlling for all country and industry speci�c factors using vectors of

country and industry dummies. We also perform a series of additional robustness tests such

as looking at alternative measures of ICT (internet tra¢ c volumes and other internet access

and price indicators), analyzing di¤erential e¤ects across rich and developing countries, and

exploring potential regional complementarities on ICT adoption.

The �ndings of this paper convey useful implications for policy makers that aim to attract

multinational �rms. The analysis presented in this paper suggests that ICT plays an important

role in the patterns of multinational activity and countries�ability to attract foreign multina-

tional �rms. However, there is still little evidence on the role of ICT in determining countries�

ability to extract bene�ts from foreign multinational activity. The gains from multinational

activity depend crucially on the technology composition of multinational activity and domes-

tic �rms�ability to respond to new competition and absorb new technologies. Both of these

aspects are likely a¤ected by local country characteristics such as the level of human capital,

the development of local �nancial markets (see Alfaro et al., 2004, 2010), and the adoption of

ICT. Weaknesses in these areas may discourage technology intensive multinational entry as well

as reduce the capacity of domestic industries to absorb foreign knowhow and respond to the

challenges and the opportunities presented by foreign entrants. How ICT could in�uence the

gains from multinational activity hence represents a promising line of research.

14

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16

Table 1: List of Variables

Variable Description Sourceentry Number of entries Orbis

Host- and headquarter-country characteristicslnRMP-host Host-country real market potential (in log) CEPIIlngdpcap-hq Headquarter-country GDP per capita (in log) CEPIIskill-di¤ Host-headquarter-country di¤erence in ave. years of schooling WDIskill intensity Share of non-production workers AMStari¤ Host-country tari¤ rate WITSlndist Host-headquarter-country distance (in log) CEPIIcontig Contiguity CEPIIcomlang Common o¢ cial language CEPIIcolony Colony CEPII

Industry-level ICT intensityroutineness Industry task routineness O*NETinformation intensity The ratio of information capital to total asset BLScomputer intensity The ratio of computer capital to total asset BLScommunication intensity The ratio of communication capital to total asset BLS

Host-country ICT characteristicscomputer-busiuse Proportion of businesses using computers UNCTADinternet-busiuse Proportion of businesses placing orders on the Internet UNCTADinternet-bandwidth ITU International Internet bandwidth, in Mbit/s (in log) ITUmobile-subscription ITU Mobile-cellular telephone subscriptions (in log) ITUphone-price ITU Price of a three-minute local call to a �xed-telephone line ITU

Notes: This table summarizes the description and source of the main variables.

17

Table 2: Summary Statistics

Variable Obs Mean Std. Dev. Min Maxentry 949,375 0.00 0.07 0.00 13.00

Host- and headquarter-country characteristicslnRMP-host 910,625 19.99 1.56 17.07 23.07lngdpcap-hq 873,425 8.96 1.50 5.22 11.88skill-di¤ 833,280 -0.86 3.07 -10.77 8.64skill-intensity 810,167 0.28 0.13 0.58 0.86tari¤ 427,553 0.06 0.10 0.00 3.50lndist 900,240 8.45 1.00 1.81 9.88contig 900,240 0.03 0.17 0.00 1.00comlang 900,240 0.08 0.27 0.00 1.00colony 900,240 0.03 0.16 0.00 1.00

Industry-level ICT intensityroutineness 888,125 -54.96 3.33 -65.67 -44.70information intensity 900,375 5.14 1.62 1.85 10.96computer intensity 900,375 1.65 0.73 0.34 4.88communication intensity 900,375 1.05 0.30 0.34 2.16

Host-country ICT characteristicscomputer-businessuse 639,375 90.11 13.53 44.03 99.97internet-businessuse 697,500 25.42 17.41 3.36 62.63internet-bandwidth 930,000 10.78 2.20 3.53 14.65mobile-subscription 949,375 16.51 1.48 11.42 19.96phone-price 949,375 0.13 0.08 0.00 0.30

Notes: This table reports the summary statistics of the main variables.

18

Table 3: The E¤ect of ICT: Baseline Results

Dependent var: entry (1) (2) (3) (4) (5)lnRMP-host 0.0794*** 0.0238 -0.269*** -0.408*** 0.0384**

(0.0168) (0.0145) (0.0180) (0.0264) (0.0162)lngdpcap-hq 0.874*** 0.867*** 0.924*** 1.021*** 0.972***

(0.0341) (0.0319) (0.0285) (0.0268) (0.0278)skill-di¤ 0.0501*** 0.0782*** 0.0266** -0.117*** -0.0383***

(0.0114) (0.0133) (0.0112) (0.0102) (0.0106)tari¤ 0.513*** 0.433*** 0.400*** 0.0229 0.218

(0.0959) (0.1030) (0.0941) (0.3820) (0.1810)lndist -0.202*** -0.215*** -0.171*** -0.514*** -0.385***

(0.0309) (0.0274) (0.0314) (0.0389) (0.0357)contig 0.706*** 0.867*** 0.828*** 0.139 0.766***

(0.0933) (0.0828) (0.0846) (0.0930) (0.0829)comlang -0.312*** 0.277*** 0.287*** 0.788*** 0.413***

(0.1030) (0.0747) (0.0764) (0.0747) (0.0725)colony 0.950*** 0.669*** 0.569*** 0.587*** 0.653***

(0.0890) (0.0839) (0.0768) (0.0817) (0.0750)computer-businessuse 0.0168***

(0.0021)internet-businessuse 0.00593***

(0.0019)internet-bandwidth 0.414***

(0.0271)mobile-subscription 0.977***

(0.0322)phone-price -8.019***

(0.5240)Industry FE Yes Yes Yes Yes YesObs 236,787 285,626 352,524 352,524 352,524Log-likelihood -8931 -10409 -11969 -10928 -11694

Notes: This table reports the baseline estimation results. Standard errors are reported in theparentheses. ***, **, and * denote signi�cance at 1, 5, and 10 percent, respectively.

19

Table 4: The Heterogeneous E¤ect of ICT across Industries: Routineness

Dependent var: entry (1) (2) (3) (4) (5)lnRMP-host 0.0793*** 0.0238* -0.269*** -0.408*** 0.0384**

(0.0168) (0.0145) (0.0180) (0.0264) (0.0162)lngdpcap-hq 0.874*** 0.866*** 0.923*** 1.020*** 0.971***

(0.0341) (0.0319) (0.0284) (0.0268) (0.0278)skill-di¤ 0.0501*** 0.0777*** 0.0263** -0.117*** -0.0386***

(0.0114) (0.0133) (0.0112) (0.0103) (0.0106)tari¤ 0.511*** 0.432*** 0.403*** 0.0228 0.221

(0.0958) (0.1030) (0.0947) (0.3830) (0.1810)lndist -0.202*** -0.216*** -0.172*** -0.514*** -0.385***

(0.0309) (0.0275) (0.0314) (0.0390) (0.0357)contig 0.706*** 0.865*** 0.827*** 0.137 0.764***

(0.0933) (0.0829) (0.0847) (0.0931) (0.0830)comlang -0.312*** 0.275*** 0.285*** 0.787*** 0.412***

(0.1030) (0.0747) (0.0764) (0.0747) (0.0725)colony 0.950*** 0.671*** 0.570*** 0.589*** 0.655***

(0.0890) (0.0838) (0.0769) (0.0817) (0.0751)routinenessx computer-businessuse -0.00153*

(0.0009)x internet-businessuse -0.00212*

(0.0012)x internet-bandwidth -0.0175

(0.0120)x mobile-subscription -0.0129

(0.0107)x phone-price 0.195

(0.2460)computer-businessuse -0.0675

(0.0484)internet-businessuse -0.111*

(0.0632)internet-bandwidth -0.555

(0.6590)mobile-subscription 0.261

(0.5880)phone-price 2.768


Notes: This table reports the heterogeneous e¤ect of ICT across industries. Standard errorsare reported in the parentheses. ***, **, and * denote signi�cance at 1, 5, and 10 percent,respectively.

20

Table 5: The Heterogeneous E¤ect of ICT across Industries: Communication Intensity

Dependent var: entry (1) (2) (3) (4) (5)lnRMP-host 0.0794*** 0.0235 -0.270*** -0.408*** 0.0382**

(0.0168) (0.0145) (0.0181) (0.0264) (0.0162)lngdpcap-hq 0.874*** 0.866*** 0.923*** 1.020*** 0.971***

(0.0341) (0.0319) (0.0284) (0.0268) (0.0278)skill-di¤ 0.0501*** 0.0779*** 0.0265** -0.117*** -0.0386***

(0.0114) (0.0133) (0.0112) (0.0102) (0.0106)tari¤ 0.513*** 0.436*** 0.399*** 0.0218 0.218

(0.0962) (0.1020) (0.0935) (0.3820) (0.1810)lndist -0.202*** -0.216*** -0.172*** -0.514*** -0.385***

(0.0309) (0.0274) (0.0314) (0.0390) (0.0357)contig 0.706*** 0.866*** 0.828*** 0.139 0.765***

(0.0933) (0.0828) (0.0845) (0.0930) (0.0829)comlang -0.312*** 0.277*** 0.287*** 0.789*** 0.413***

(0.1030) (0.0746) (0.0764) (0.0747) (0.0725)colony 0.950*** 0.669*** 0.568*** 0.587*** 0.653***

(0.0890) (0.0839) (0.0770) (0.0817) (0.0751)communication intensityx computer-businessuse -0.00144

(0.0023)x internet-businessuse 0.00419

(0.0027)x internet-bandwidth 0.0529*

(0.0278)x mobile-subscription -0.00425

(0.0229)x phone-price -0.0793

(0.5340)computer-businessuse 0.0237**

(0.0104)internet-businessuse -0.0143

(0.0127)internet-bandwidth 0.158

(0.1290)mobile-subscription 0.997***

(0.1140)phone-price -7.633***



21

Table 6: The Heterogeneous E¤ect of ICT across Industries: Country FE

Dependent var: entry (1) (2) (3) (4) (5)routineness 0.166** 0.007 0.0769*** 0.125 -0.0027

(0.0724) (0.0074) (0.0275) (0.0794) (0.0078)x computer-businessprop -0.002**

(0.0008)x internet-businessprop -0.0006***

(0.0001)x internet-bandwidth -0.007***

(0.0020)x mobile-subscription -0.007*

(0.0043)x phone-price -0.0285

(0.0492)skill intensity 1.854*** 1.878*** 1.926*** 1.929*** 1.928***

(0.3090) (0.2810) (0.2600) (0.2590) (0.2590)Country FE Yes Yes Yes Yes YesObs. 519,746 566,995 755,995 771,744 771,744Log-likelihood -11741 -13834 -16849 -16938 -16940

communication intensity -0.007 -0.232*** -0.353*** -0.054 -0.162***(0.2160) (0.0244) (0.1210) (0.2590) (0.0364)

x computer-businessprop -0.002(0.0023)

x internet-businessprop 0.0013**(0.0006)

x internet-bandwidth 0.015(0.0104)

x mobile-subscription -0.007(0.0151)

x phone-price -0.133(0.2310)

skill-intensity 1.444*** 1.468*** 1.492*** 1.487*** 1.487***(0.2610) (0.2410) (0.2190) (0.2170) (0.2180)

Country FE Yes Yes Yes Yes YesObs. 527,996 575,995 767,995 783,994 783,994Log-likelihood -11736 -13840 -16868 -16959 -16959


22

Table 7: The Heterogeneous E¤ect of ICT across Countries: North-North

Dependent var: entry (1) (2) (3) (4) (5)lnRMP-hq 0.609*** 0.542*** -0.233*** -0.0454+ 0.144***

(0.0349) (0.0319) (0.0405) (0.0305) (0.0321)lngdpcap-hq 0.814*** 0.509*** 0.648*** 0.755*** 0.810***

(0.0955) (0.0784) (0.0796) (0.0773) (0.0820)school-di¤ 0.182*** 0.611*** 0.200*** 0.118*** 0.0587***

(0.0233) (0.0338) (0.0208) (0.0187) (0.0217)tari¤ 0.151 0.102 0.171 0.196 -0.0601

(0.242) (0.200) (0.185) (0.215) (0.334)lndist -0.129** -0.107*** -0.0691+ -0.228*** -0.286***

(0.0552) (0.0384) (0.0452) (0.0444) (0.0470)contig 0.951*** 1.391*** 1.307*** 0.922*** 1.072***

(0.148) (0.123) (0.121) (0.125) (0.128)comlang -0.0999 0.144 0.569*** 0.708*** 0.651***

(0.152) (0.101) (0.0992) (0.0979) (0.0953)colony 1.497*** 0.512*** 0.137 0.721*** 0.922***

(0.117) (0.116) (0.107) (0.0976) (0.0941)computer-businessprop 0.0534***

(0.00916)internet-businessprop 0.0752***

(0.00442)internet-bandwidth 1.30e-06***

(8.41e-08)mobile-subscription 1.05e-08***

(5.55e-10)phone-price -7.207***

(0.713)Industry FE Yes Yes Yes Yes YesObs. 44,573 56,894 65,037 65,037 65,037Log-likelihood -2600 -3361 -4138 -4194 -4194

Notes: This table reports the heterogeneous e¤ect of ICT across countries. Standard errorsare reported in the parentheses. ***, **, and * denote signi�cance at 1, 5, and 10 percent,respectively.

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Table 8: The Heterogeneous E¤ect of ICT across Countries: North-South

Dependent var: entry (1) (2) (3) (4) (5)lnRMP-hq 1.431*** 0.792*** 0.797*** 0.469*** 0.677***

(0.0702) (0.0311) (0.0629) (0.0492) (0.0234)lngdpcap-hq 0.585*** 0.788*** 0.982*** 1.005*** 1.012***

(0.0916) (0.0957) (0.0950) (0.0975) (0.0985)school-di¤ 0.172*** -0.0233 -0.110*** -0.132*** -0.144***

(0.0210) (0.0193) (0.0128) (0.0155) (0.0167)tari¤ 0.520*** 0.291 0.532*** 0.333+ 0.129

(0.140) (0.246) (0.151) (0.218) (0.356)lndist -0.414*** -0.511*** -0.380*** -0.420*** -0.491***

(0.0584) (0.0500) (0.0528) (0.0548) (0.0601)contig -0.439*** -0.0515 0.118 0.111 0.0973

(0.164) (0.130) (0.147) (0.147) (0.143)comlang -2.067* -1.432*** -0.920*** -1.033*** -1.068***

(1.065) (0.501) (0.348) (0.353) (0.354)colony 1.172*** 0.775*** 0.775*** 0.831*** 0.708***

(0.190) (0.157) (0.152) (0.148) (0.144)computer-businessprop -0.00418

(0.00264)internet-businessprop 0.0295***

(0.00231)internet-bandwidth -1.51E-06


(3.66e-10)phone-price -4.839***


Notes: This table reports the heterogeneous e¤ect of ICT across countries. Standard errorsare reported in the parentheses. ***, **, and * denote signi�cance at 1, 5, and 10 percent,respectively.

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Table 9: Additional ICT Measures

Variable Description SourceHost-Country internet tra¢ c characteristics

tra¢ c 1 SMS sent, millions (in log) ITUtra¢ c 2 MMS sent, millions (in log) ITUtra¢ c 3 Incoming international tra¢ c to mobile network, in millions of minutes (in log) ITUtra¢ c 4 Outgoing mobile tra¢ c to �xed networks, in millions of minutes (in log) ITUtra¢ c 5 Outgoing mobile tra¢ c to other mobile networks, in millions of minutes (in log) ITUtra¢ c 6 Outgoing mobile tra¢ c to same mobile network, in millions of minutes (in log) ITU

Host-Country mobile call price characteristicsprice 1 Mobile-cellular prepaid price of SMS (on-net), in USD ITUprice 2 Mobile-cellular prepaid price of a one-minute local call (o¤-peak, on-net), in USD ITUprice 3 Mobile-cellular prepaid price of a one-minute local call (peak, on-net), in USD ITUprice 4 Price of a three-minute local call to a �xed-telephone line (o¤-peak rate), in USD ITUprice 5 Mobile-cellular prepaid price of a three-minute local call (peak, on-net), in USD ITUprice 6 Mobile-cellular prepaid price of a three-minute local call (o¤-peak, on-net), in USD ITU

Notes: This table reports additional ICT measures.

25

Table 10: Additional ICT Measures: Internet Tra¢ c

Dependent var: entry (1) (2) (3) (4) (5) (6)lnRMP-hq 0.049 0.386*** 0.229*** -0.684*** -0.104*** 0.137***

(0.0345) (0.0445) (0.0556) (0.0424) (0.0339) (0.0452)lngdpcap-hq 1.073*** 1.110*** 1.171*** 0.944*** 1.133*** 1.163***

(0.0509) (0.0559) (0.0696) (0.0311) (0.0446) (0.0490)ave-schooling-di¤ -0.0228** 0.0121 -0.108*** -0.0828*** -0.0390*** -0.0378**

(0.0113) (0.0162) (0.0218) (0.0096) (0.0118) (0.0154)tari¤ 0.214 0.282 0.619** 0.196 0.221 0.206

(0.3160) (0.2390) (0.2500) (0.3590) (0.3090) (0.2690)lndist -0.439*** -0.419*** -0.552*** -0.619*** -0.354*** -0.350***

(0.0367) (0.0337) (0.0589) (0.0370) (0.0392) (0.0400)contig 0.521*** 0.384*** 0.716*** -0.155 0.589*** 0.455***

(0.1370) (0.1230) (0.1460) (0.1070) (0.1280) (0.1220)comlang -0.131 -0.206 -0.484*** 0.0552 -0.0914 -0.185

(0.1280) (0.1410) (0.1670) (0.1080) (0.1340) (0.1270)colony 1.081*** 1.225*** 0.251 0.763*** 1.049*** 1.160***

(0.1040) (0.0907) (0.2240) (0.1090) (0.0930) (0.0953)tra¢ c 1 0.377***

(0.0288)tra¢ c 2 -0.00355

(0.0505)tra¢ c 3 0.0763

(0.0812)tra¢ c 4 0.813***

(0.0316)tra¢ c 5 0.514***

(0.0338)tra¢ c 6 0.304***

(0.0465)

Industry FE Yes Yes Yes Yes Yes YesObservations 228,204 166,726 92,224 217,764 187,071 173,104Log-likelihood -5745 -4800 -2325 -7858 -5540 -5359

Notes: This table reports the estimated e¤ects of additional ICT measures. Standard errorsare reported in the parentheses. ***, **, and * denote signi�cance at 1, 5, and 10 percent,respectively.

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Table 11: Additional ICT Measures: Mobile Call Prices

Dependent var: entry (1) (2) (3) (4) (5) (6)lnRMP-hq 0.104*** 0.136*** 0.118*** 0.0689*** 0.114*** 0.132***

(0.0162) (0.0162) (0.0162) (0.0136) (0.0162) (0.0163)lngdpcap-hq 0.968*** 0.991*** 0.989*** 0.925*** 0.987*** 0.986***

(0.0283) (0.0287) (0.0287) (0.0317) (0.0288) (0.0288)ave-schooling-di¤ -0.0244** -0.0338*** -0.0305*** -0.00885 -0.0284*** -0.0300***

(0.0100) (0.0095) (0.0099) (0.0108) (0.0099) (0.0096)tari¤ 0.362*** 0.357*** 0.385*** 0.417*** 0.391*** 0.368***

(0.1190) (0.1080) (0.1050) (0.1280) (0.1030) (0.1060)lndist -0.218*** -0.152*** -0.150*** -0.324*** -0.151*** -0.158***

(0.0322) (0.0333) (0.0326) (0.0318) (0.0326) (0.0329)contig 0.936*** 0.908*** 0.916*** 0.636*** 0.907*** 0.903***

(0.0867) (0.0863) (0.0863) (0.0981) (0.0861) (0.0859)comlang 0.401*** 0.343*** 0.343*** -0.213** 0.338*** 0.336***

(0.0806) (0.0773) (0.0771) (0.0970) (0.0772) (0.0772)colony 0.572*** 0.689*** 0.690*** 0.776*** 0.700*** 0.694***

(0.0788) (0.0755) (0.0762) (0.0902) (0.0761) (0.0752)price 1 -4.250***

(0.3780)price 2 -1.079***

(0.1210)price 3 -0.257**

(0.1070)price 4 -5.955***

(0.6710)price 5 -0.0284

(0.0399)price 6 -0.275***

(0.0431)

Industry FE Yes Yes Yes Yes Yes YesObservations 352,524 341,555 341,555 304,741 341,555 341,555Log-likelihood -12051 -12025 -12051 -10106 -12053 -12039

Notes: This table reports the estimated e¤ects of additional ICT measures. Standard errorsare reported in the parentheses. ***, **, and * denote signi�cance at 1, 5, and 10 percent,respectively.

27

Table 12: ICT Network

Dependent var: entry (1) (2) (3) (4) (5)lnRMP-host 0.0755*** 0.0258* -0.0902*** -0.0891*** 0.0419***

(0.0162) (0.0151) (0.0164) (0.0183) (0.0146)lngdpcap-hq 0.882*** 0.941*** 0.900*** 1.020*** 1.047***

(0.0349) (0.0344) (0.0328) (0.0315) (0.0312)skill-di¤ 0.0601*** 0.101*** 0.0189* -0.109*** -0.0285***

(0.0135) (0.0150) (0.0110) (0.0120) (0.0102)tari¤ 0.467*** 0.400*** 0.415*** 0.353** 0.206

(0.0910) (0.0845) (0.0934) (0.141) (0.147)lndist -0.287*** -0.367*** -0.152*** -0.318*** -0.506***

(0.0429) (0.0398) (0.0290) (0.0317) (0.0305)contig 0.664*** 0.776*** 0.929*** 0.628*** 0.668***

(0.111) (0.0984) (0.0966) (0.0993) (0.0900)comlang -0.322*** 0.222*** 0.328*** 0.496*** 0.276***

(0.105) (0.0786) (0.0840) (0.0770) (0.0709)colony 0.918*** 0.634*** 0.539*** 0.748*** 0.701***

(0.105) (0.108) (0.0971) (0.0934) (0.0891)computer-businessuse 0.00642*** 1.927*** 1.924*** 1.928***

(0.00185) (0.182) (0.182) (0.181)computer-businessuse (network) -0.0249***

(0.00495)internet-businessuse 0.00642***

(0.00185)internet-businessuse (network) -0.0249***

(0.00495)internet-bandwidth 6.35e-07***

(4.72e-08)internet-bandwidth (network) 4.03e-07


(2.15e-10)mobile-subscription (network) 2.64e-09

(3.50e-09)phone-price -8.049***

(0.451)phone-price (network) -3.276***


Notes: This table reports the estimated e¤ects of ICT network variables. Standard errorsare reported in the parentheses. ***, **, and * denote signi�cance at 1, 5, and 10 percent,respectively.

28

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